Scientific output

This page gives an overview of scientific papers that have been produced based on work performed on the Hábrók, Peregrine and Millipede compute clusters of the University of Groningen, or computing work that has been supported by staff of the HPC team of the Center of Information Technology of the university.

If you want to acknowledge the use of the Hábrók cluster and the support of the CIT in your papers you can use the following acknowledgement:

We thank the Center for Information Technology of the University of Groningen for their support and for providing access to the Hábrók high performance computing cluster.

This list has been first constructed in February 2016, many older papers are therefore not reported. It also relies on input from the individual researchers and will therefore also not be complete. If you want to have your paper(s) added to this list, please send the references (preferably DOI) to hpc@rug.nl.

2024

  1. Adupa, V., Ustyantseva, E., Kampinga, H. H., & Onck, P. R. (2024). Tertiary structure and conformational dynamics of the anti-amyloidogenic chaperone DNAJB6b at atomistic resolution. Nature Communications, 15(1), 3285 [ DOI | http ]
  2. Aguayo, J., Bertoglio, C., & Osses, A. (2024). Distributed parameter identification for the Navier–Stokes equations for obstacle detection. Inverse Problems, 40(1), 015012 [ DOI | http ]
  3. Arrowsmith-Kron, G., Athanasakis-Kaklamanakis, M., Au, M., et al. (2024). Opportunities for fundamental physics research with radioactive molecules. Reports on Progress in Physics, 87(8), 084301. Publisher: IOP Publishing [ DOI | http ]
  4. Castro‐Alvarez, S., Sinharay, S., Bringmann, L. F., Meijer, R. R., & Tendeiro, J. N. (2024). Assessment of fit of the time‐varying dynamic partial credit model using the posterior predictive model checking method. British Journal of Mathematical and Statistical Psychology, page bmsp.12339 [ DOI | http ]
  5. Cotteret, M., Greatorex, H., Ziegler, M., & Chicca, E. (2024). Vector Symbolic Finite State Machines in Attractor Neural Networks. Neural Computation, 36(4), 549–595 [ DOI | http ]
  6. Edman, L., Sarti, G., Toral, A., Noord, G. V., & Bisazza, A. (2024). Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation. Transactions of the Association for Computational Linguistics, 12, 392–410 [ DOI | http ]
  7. Erić, V., Li, X., Dsouza, L., et al. (2024). Observation of Dark States in Two-Dimensional Electronic Spectra of Chlorosomes. The Journal of Physical Chemistry B, 128(15), 3575–3584 [ DOI | http ]
  8. Es Sayed, J., Mukherjee, A., El Aani, S., et al. (2024). Structure–Property Relationships of Granular Hybrid Hydrogels Formed through Polyelectrolyte Complexation. Macromolecules, 57(7), 3190–3201 [ DOI | http ]
  9. Ferreira, P., Neves, R. P. P., Miranda, F. P., et al. (2024). DszA Catalyzes C–S Bond Cleavage through N 5 –Hydroperoxyl Formation. Journal of Chemical Information and Modeling, 64(10), 4218–4230 [ DOI | http ]
  10. Gürel, U., Keten, S., & Giuntoli, A. (2024). Bidispersity Improves the Toughness and Impact Resistance of Star-Polymer Thin Films. ACS Macro Letters, 13(3), 302–307 [ DOI | http ]
  11. Jafari, H., Barts, E., Przybysz, P., et al. (2024). Robust Zeeman-type band splitting in sliding ferroelectrics. Physical Review Materials, 8(2), 024005 [ DOI | http ]
  12. Kasaei, H., Kasaei, M., Tziafas, G., Luo, S., & Sasso, R. (2024). Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains. Journal of Intelligent & Robotic Systems, 110(2), 62 [ DOI | http ]
  13. Kleibergen, F. & Kong, L. (2024). Identification robust inference for the risk premium in term structure models. Journal of Econometrics, page 105728 [ DOI | http ]
  14. Koopmans, L., Dhali, M. A., & Schomaker, L. (2024). Performance Analysis of Handwritten Text Augmentation on Style-Based Dating of Historical Documents. SN Computer Science, 5(4), 397 [ DOI | http ]
  15. König, K., Berengut, J. C., Borschevsky, A., et al. (2024). Nuclear Charge Radii of Silicon Isotopes. Physical Review Letters, 132(16), 162502 [ DOI | http ]
  16. Lan, L., Van Doorn, J. M. H., Wouda, N. A., Rijal, A., & Bhulai, S. (2024). An Iterative Sample Scenario Approach for the Dynamic Dispatch Waves Problem. Transportation Science, page trsc.2023.0111 [ DOI | http ]
  17. Markovitch, O., Wu, J., & Otto, S. (2024). Binding of Precursors to Replicator Assemblies Can Improve Replication Fidelity and Mediate Error Correction. Angewandte Chemie International Edition, 63(14), e202317997 [ DOI | http ]
  18. Marx, A. C., Jafari, H., Tekelenburg, E. K., et al. (2024). Nonlinear magnetotransport in MoTe 2. Physical Review B, 109(12), 125408 [ DOI | http ]
  19. Maurits De Roo, C., Sardjan, A. S., Postmus, R., et al. (2024). Reaction of (N4Py)Fe with H 2 O 2 and the relevance of its Fe(IV)=O species during and after H 2 O 2 disproportionation. ChemCatChem, page e202301594 [ DOI | http ]
  20. Ndung’u, S., Grobler, T., Wijnholds, S. J., Karastoyanova, D., & Azzopardi, G. (2024). Classification of radio galaxies with trainable COSFIRE filters. Monthly Notices of the Royal Astronomical Society, 530(1), 783–794 [ DOI | http ]
  21. Nelemans, S. (2024). Asset Pricing with Economic Inequality: a Macrofinancial Approach. Ph.D. thesis, University of Groningen [ DOI | http ]
  22. Nguyen, H. L., Do, T. N., Zhong, K., et al. (2024). Inter-subunit energy transfer processes in a minimal plant photosystem II supercomplex. Science Advances, 10(8), eadh0911 [ DOI | http ]
  23. Oldenburg, V., Cardenas-Cartagena, J., & Valdenegro-Toro, M. (2024). Forecasting Smog Clouds With Deep Learning: A Proof-Of-Concept [ http ]
  24. P. Neme, N., Jansen, T. L. C., & Havenith, R. W. A. (2024). Cyclopentene ring effects in cyanine dyes: a handle to fine-tune photophysical properties. Physical Chemistry Chemical Physics, 26(7), 6235–6241 [ DOI | http ]
  25. Sabo, E., Riveni, M., & Karastoyanova, D. (2024). Decentralized Networks Growth Analysis: Instance Dynamics on Mastodon. In H. Cherifi, L. M. Rocha, C. Cherifi, & M. Donduran, editors, Complex Networks & Their Applications XII, volume 1144, pages 366–377. Springer Nature Switzerland, Cham. ISBN 978-3-031-53502-4 978-3-031-53503-1. Series Title: Studies in Computational Intelligence [ DOI | http ]
  26. Soh, J. H., Jansen, T. L. C., & Palacino-González, E. (2024). Controlling the nonadiabatic dynamics of the charge-transfer process with chirped pulses: Insights from a double-pump time-resolved fluorescence spectroscopy scheme. The Journal of Chemical Physics, 160(2), 024110 [ DOI | http ]
  27. Tittes, C., Nijland, J., Schoentag, A. M. C., et al. (2024). Development of a genetic system for Haloferax gibbonsii LR2-5, model host for haloarchaeal viruses. Applied and Environmental Microbiology, 90(4), e00129–24 [ DOI | http ]
  28. Truong, H., Tello, A., Lazovik, A., & Degeler, V. (2024). Graph Neural Networks for Pressure Estimation in Water Distribution Systems. Water Resources Research, 60(7), e2023WR036741 [ DOI | http ]
  29. Wouda, N. A., Lan, L., & Kool, W. (2024). PyVRP: A High-Performance VRP Solver Package. INFORMS Journal on Computing, page ijoc.2023.0055 [ DOI | http ]
  30. Yi, Y., Liang, L., De Jong, A., & Kuipers, O. P. (2024). A systematic comparison of natural product potential, with an emphasis on RiPPs, by mining of bacteria of three large ecosystems. Genomics, 116(4), 110880 [ DOI | http ]
  31. Zhang, L., Csányi, G., Van Der Giessen, E., & Maresca, F. (2024). Efficiency, accuracy, and transferability of machine learning potentials: Application to dislocations and cracks in iron. Acta Materialia, 270, 119788 [ DOI | http ]
  32. Zhang, X., Nijland, J. G., & Driessen, A. J. M. (2024). Maltose accumulation-induced cell death in Saccharomyces cerevisiae. FEMS Yeast Research, 24, foae012 [ DOI | http ]
  33. Zhang, Z. & Krushynska, A. O. (2024). Shape Morphing of Tubular Structures with Tailorable Mechanical Properties. Advanced Engineering Materials, 26(2), 2300383 [ DOI | http ]

2023

  1. Alshehri, M. M., Danazumi, A. U., Alshammari, M. K., et al. (2023). Repurposing the inhibitors of MMP-9 and SGLT-2 against ubiquitin specific protease 30 in Parkinson’s disease: computational modelling studies. Journal of Biomolecular Structure and Dynamics, pages 1–12 [ DOI | http ]
  2. Anteghini, M., Haja, A., Martins Dos Santos, V. A., Schomaker, L., & Saccenti, E. (2023). OrganelX web server for sub-peroxisomal and sub-mitochondrial protein localization and peroxisomal target signal detection. Computational and Structural Biotechnology Journal, 21, 128–133 [ DOI | http ]
  3. Barkai, J. A., Verheijen, M. A. W., Talavera, E., & Wilkinson, M. H. F. (2023). A comparative study of source-finding techniques in H I emission line cubes using SoFiA, MTObjects, and supervised deep learning. Astronomy & Astrophysics, 670, A55 [ DOI | http ]
  4. Bechlenberg, A., Wei, Y., Jayawardhana, B., & Vakis, A. I. (2023). Analysing the influence of power take-off adaptability on the power extraction of dense wave energy converter arrays. Renewable Energy, 211, 1–12 [ DOI | http ]
  5. Bellas, C., Hackl, T., Plakolb, M.-S., et al. (2023). Large-scale invasion of unicellular eukaryotic genomes by integrating DNA viruses. Proceedings of the National Academy of Sciences, 120(16), e2300465120 [ DOI | http ]
  6. Biçer, A., Koelewijn, T., & Başkent, D. (2023). Short Implicit Voice Training Affects Listening Effort During a Voice Cue Sensitivity Task With Vocoder-Degraded Speech. Ear & Hearing, 44(4), 900–916 [ DOI | http ]
  7. Boer, D. & Setyadi, C. (2023). Probing gluon GTMDs through exclusive coherent diffractive processes. The European Physical Journal C, 83(10), 890 [ DOI | http ]
  8. Boot, T. (2023). Joint inference based on Stein-type averaging estimators in the linear regression model. Journal of Econometrics, 235(2), 1542–1563 [ DOI | http ]
  9. Boot, T., Niccodemi, G., & Wansbeek, T. (2023). Unbiased estimation of the OLS covariance matrix when the errors are clustered. Empirical Economics [ DOI | http ]
  10. Castro-Alvarez, S., Bringmann, L. F., Meijer, R. R., & Tendeiro, J. N. (2023). A Time-Varying Dynamic Partial Credit Model to Analyze Polytomous and Multivariate Time Series Data. Multivariate Behavioral Research, pages 1–20 [ DOI | http ]
  11. Chang, Y.-C. & Jian, J.-C. (2023). Biomaterial-based nonvolatile photonic memory. Carbon, 202, 167–172 [ DOI | http ]
  12. Chiariello, M. G., Grünewald, F., Zarmiento-Garcia, R., & Marrink, S. J. (2023). pH-Dependent Conformational Switch Impacts Stability of the PsbS Dimer. The Journal of Physical Chemistry Letters, 14(4), 905–911 [ DOI | http ]
  13. Chu, H., De La O Arévalo, L. R., Tang, W., et al. (2023). Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach. In V. Andrearczyk, V. Oreiller, M. Hatt, & A. Depeursinge, editors, Head and Neck Tumor Segmentation and Outcome Prediction, volume 13626, pages 114–120. Springer Nature Switzerland, Cham. ISBN 978-3-031-27419-0 978-3-031-27420-6. Series Title: Lecture Notes in Computer Science [ DOI | http ]
  14. De Biase, A., Sijtsema, N. M., Van Dijk, L. V., Langendijk, J. A., & Van Ooijen, P. M. A. (2023). Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images. Physics in Medicine & Biology, 68(5), 055013 [ DOI | http ]
  15. De Klein, N., Tsai, E. A., Vochteloo, M., et al. (2023). Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases. Nature Genetics, 55(3), 377–388 [ DOI | http ]
  16. Dekker, M., Van Der Giessen, E., & Onck, P. R. (2023). Phase separation of intrinsically disordered FG-Nups is driven by highly dynamic FG motifs. Proceedings of the National Academy of Sciences, 120(25), e2221804120 [ DOI | http ]
  17. Do, P., Coler, M., Dijkstra, J., & Klabbers, E. (2023). Strategies in Transfer Learning for Low-Resource Speech Synthesis: Phone Mapping, Features Input, and Source Language Selection. In 12th ISCA Speech Synthesis Workshop (SSW2023), pages 21–26. ISCA [ DOI | .html ]
  18. Do, P., Coler, M., Dijkstra, J., & Klabbers, E. (2023). The Effects of Input Type and Pronunciation Dictionary Usage in Transfer Learning for Low-Resource Text-to-Speech. In INTERSPEECH 2023, pages 5461–5465. ISCA [ DOI | .html ]
  19. Do, P., Coler, M., Dijkstra, J., & Klabbers, E. (2023). Resource-Efficient Fine-Tuning Strategies for Automatic MOS Prediction in Text-to-Speech for Low-Resource Languages. In INTERSPEECH 2023, pages 5466–5470. ISCA [ DOI | .html ]
  20. Eduardus, Shagam, Y., Landau, A., et al. (2023). Large vibrationally induced parity violation effects in CHDBrI+. Chemical Communications, 59(98), 14579–14582. Publisher: The Royal Society of Chemistry [ DOI | http ]
  21. Erić, V., Li, X., Dsouza, L., et al. (2023). Manifestation of Hydrogen Bonding and Exciton Delocalization on the Absorption and Two-Dimensional Electronic Spectra of Chlorosomes. The Journal of Physical Chemistry B, 127(5), 1097–1109 [ DOI | http ]
  22. Etienne, R. S., Haegeman, B., Dugo-Cota, �., et al. (2023). The Phylogenetic Limits to Diversity-Dependent Diversification. Systematic Biology, 72(2), 433–445 [ DOI | http ]
  23. Faber, T., Filipovic, L., & Koster, L. J. A. (2023). The Role of Thermalization in the Cooling Dynamics of Hot Carrier Solar Cells. Solar RRL, page 2300140 [ DOI | http ]
  24. Fazi, S., Choudhary, S. K., & Dong, J.-X. (2023). The multi-trip container drayage problem with synchronization for efficient empty containers re-usage. European Journal of Operational Research, 310(1), 343–359 [ DOI | http ]
  25. Feliciani, T., Tolsma, J., & Flache, A. (2023). Ethnic segregation and spatial patterns of attitudes: studying the link using register data and social simulation. Journal of Computational Social Science [ DOI | http ]
  26. Florez, E., Zapata-Escobar, A. D., Ferraro, F., et al. (2023). Coordination of Mercury(II) in Water Promoted over Hydrolysis in Solvated Clusters [Hg(H 2 O) 1–6 ] (aq) 2+ : Insights from Relativistic Effects and Free Energy Analysis. The Journal of Physical Chemistry A, 127(39), 8032–8049 [ DOI | http ]
  27. Flöss, T., Biagetti, M., & Meerburg, P. D. (2023). Primordial non-Gaussianity and non-Gaussian covariance. Physical Review D, 107(2), 023528 [ DOI | http ]
  28. Frans, N., Braeken, J., Veldkamp, B. P., & Paap, M. C. S. (2023). Empirical Priors in Polytomous Computerized Adaptive Tests: Risks and Rewards in Clinical Settings. Applied Psychological Measurement, 47(1), 48–63 [ DOI | http ]
  29. Ghara, S., Barts, E., Vasin, K., et al. (2023). Magnetization reversal through an antiferromagnetic state. Nature Communications, 14(1), 5174 [ DOI | http ]
  30. Grasso, S., Dabene, V., Hendriks, M. M. W. B., et al. (2023). Signal Peptide Efficiency: From High-Throughput Data to Prediction and Explanation. ACS Synthetic Biology, 12(2), 390–404 [ DOI | http ]
  31. Gurung, K., Vink, S. N., Salles, J. F., & Wertheim, B. (2023). More persistent bacterial than fungal associations in the microbiota of a pest insect. Journal of Pest Science, 96(2), 785–796 [ DOI | http ]
  32. Gürel, U. & Giuntoli, A. (2023). Shear Thinning from Bond Orientation in Model Unentangled Bottlebrush Polymer Melts. Macromolecules, 56(15), 5708–5717 [ DOI | http ]
  33. Haja, A., Horcas-Nieto, J. M., Bakker, B. M., & Schomaker, L. (2023). Towards automatization of organoid analysis: A deep learning approach to localize and quantify organoid images. Computer Methods and Programs in Biomedicine Update, 3, 100101 [ DOI | http ]
  34. Haja, A., Van Der Woude, B., & Schomaker, L. (2023). Organoids Segmentation using Self-Supervised Learning: How Complex Should the Pretext Task Be? In Proceedings of the 2023 10th International Conference on Biomedical and Bioinformatics Engineering, pages 17–27. ACM, Kyoto Japan. ISBN 9798400708343 [ DOI | http ]
  35. Haja, A., Zhelev, I., & Schomaker, L. (2023). Segmentation Of Organoid Cultures Images Using Diffusion Networks with Triplet Loss. In Proceedings of the 2023 10th International Conference on Biomedical and Bioinformatics Engineering, pages 1–10. ACM, Kyoto Japan. ISBN 9798400708343 [ DOI | http ]
  36. van Hengel, C. D. N., van Adrichem, K. E., & Jansen, T. L. C. (2023). Simulation of two-dimensional infrared Raman spectroscopy with application to proteins. The Journal of Chemical Physics, 158(6), 064106 [ DOI | http ]
  37. Huo, Y., Espinoza Cangahuala, M. K., Zamudio-Bayer, V., et al. (2023). An X-ray spectroscopy study of structural stability of superhydrogenated pyrene derivatives. Monthly Notices of the Royal Astronomical Society, 523(1), 865–875 [ DOI | http ]
  38. Kasaei, H. & Kasaei, M. (2023). MVGrasp: Real-time multi-view 3D object grasping in highly cluttered environments. Robotics and Autonomous Systems, 160, 104313 [ DOI | http ]
  39. Kind, L., Driver, M., Raasakka, A., et al. (2023). Structural properties of the HNF-1A transactivation domain. Frontiers in Molecular Biosciences, 10, 1249939 [ DOI | http ]
  40. Landau, A., Eduardus, Behar, D., et al. (2023). Chiral molecule candidates for trapped ion spectroscopy by ab initio calculations: From state preparation to parity violation. The Journal of Chemical Physics, 159(11), 114307 [ DOI | http ]
  41. Leach, I. F., Sorbelli, D., Belpassi, L., et al. (2023). How reduced are nucleophilic gold complexes? Dalton Transactions, 52(1), 11–15 [ DOI | http ]
  42. Leach, I. F., Speelman, T., Somsen, C., Klein, J. E. M. N., & Havenith, R. W. A. (2023). Revisiting sp 2 Dilithio Methandiides: From Geometric Curiosity to Simple Bonding Description. Chemistry – A European Journal, 29(56), e202301911 [ DOI | http ]
  43. Lefebvre, M. J. M., Daron, J., Legrand, E., et al. (2023). Population Genomic Evidence of Adaptive Response during the Invasion History of Plasmodium falciparum in the Americas. Molecular Biology and Evolution, 40(5), msad082 [ DOI | http ]
  44. Lepage, M. L., Alachouzos, G., Hermens, J. G. H., et al. (2023). Electron-Poor Butenolides: The Missing Link between Acrylates and Maleic Anhydride in Radical Polymerization. Journal of the American Chemical Society, 145(31), 17211–17219 [ DOI | http ]
  45. Leurs, G., Verkuil, Y. I., Hijner, N., et al. (2023). Addressing data-deficiency of threatened sharks and rays in a highly dynamic coastal ecosystem using environmental DNA. Ecological Indicators, 154, 110795 [ DOI | http ]
  46. Leyva-Vallina, M., Strisciuglio, N., & Petkov, N. (2023). Data-efficient large scale place recognition with graded similarity supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23487–23496
  47. Liu, T., Roy, A., Hidding, J., et al. (2023). Crystallographically dependent bilinear magnetoelectric resistance in a thin WTe 2 layer. Physical Review B, 108(16), 165407 [ DOI | http ]
  48. Luo, S. & Schomaker, L. (2023). Reinforcement learning in robotic motion planning by combined experience-based planning and self-imitation learning. Robotics and Autonomous Systems, 170, 104545 [ DOI | http ]
  49. Marinho, J. P. N., Neme, N. P., Matos, M. J. D. S., et al. (2023). Nanostructured system based on hydroxyapatite and curcumin: A promising candidate for osteosarcoma therapy. Ceramics International, 49(12), 19932–19949 [ DOI | http ]
  50. Marinus, N., Eisink, N. N. H. M., Reintjens, N. R. M., et al. (2023). A Predictive Model for the Pd‐Catalyzed Site‐Selective Oxidation of Diols. Chemistry – A European Journal, page e202300318 [ DOI | http ]
  51. Merekalov, A. S., Derikov, Y. I., Ezhov, A. A., et al. (2023). Orientation control of the hexagonal and lamellar phases in thin block copolymer films using in-plane AC electric field. Polymer, 264, 125544 [ DOI | http ]
  52. Mposhi, A., Cortés-Mancera, F., Heegsma, J., et al. (2023). Mitochondrial DNA methylation in metabolic associated fatty liver disease. Frontiers in Nutrition, 10, 964337 [ DOI | http ]
  53. Mulder, T. E., Baars, S., Wubs, F. W., et al. (2023). Symbiotic Ocean Modeling Using Physics‐Controlled Echo State Networks. Journal of Advances in Modeling Earth Systems, 15(12), e2023MS003631 [ DOI | http ]
  54. Nagam, B. C., Koopmans, L. V. E., Valentijn, E. A., et al. (2023). DenseLens – Using DenseNet ensembles and information criteria for finding and rank-ordering strong gravitational lenses. Monthly Notices of the Royal Astronomical Society, 523(3), 4188–4201 [ DOI | http ]
  55. Ndung’u, S., Grobler, T., Wijnholds, S. J., Karastoyanova, D., & Azzopardi, G. (2023). Deep supervised hashing for fast retrieval of radio image cubes. In 2023 XXXVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), pages 1–4. IEEE, Sapporo, Japan. ISBN 978-94-6396-809-6 [ DOI | http ]
  56. Nichols, M., Athanasakis-Kaklamanakis, M., Borschevsky, A., et al. (2023). Investigating radioactive negative ion production via double electron capture. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 541, 264–267 [ DOI | http ]
  57. Norrgard, E. B., Chamorro, Y., Cooksey, C. C., et al. (2023). Radiative decay rate and branching fractions of MgF. Physical Review A, 108(3), 032809 [ DOI | http ]
  58. Palacino-González, E. & Jansen, T. L. C. (2023). Modeling the Effect of Disorder in the Two-Dimensional Electronic Spectroscopy of Poly-3-hexyltiophene in an Organic Photovoltaic Blend: A Combined Quantum/Classical Approach. The Journal of Physical Chemistry C, 127(14), 6793–6801 [ DOI | http ]
  59. Parisotto, T., Mukherjee, S., & Kasaei, H. (2023). MORE: simultaneous multi-view 3D object recognition and pose estimation. Intelligent Service Robotics [ DOI | http ]
  60. Pase, G., Brinkhuis, E., De Vries, T., et al. (2023). A parametric geometry model of the aortic valve for subject-specific blood flow simulations using a resistive approach. Biomechanics and Modeling in Mechanobiology, 22(3), 987–1002 [ DOI | http ]
  61. Pramanik, R., Verstappen, R. W. C. P., & Onck, P. R. (2023). Magnetic-field-induced propulsion of jellyfish-inspired soft robotic swimmers. Physical Review E, 107(1), 014607 [ DOI | http ]
  62. Ramanantoanina, H., Borschevsky, A., Block, M., Viehland, L., & Laatiaoui, M. (2023). State-specific ion mobilities of Lr + ( Z = 103 ) in helium. Physical Review A, 108(1), 012802 [ DOI | http ]
  63. Sasso, R., Sabatelli, M., & Wiering, M. A. (2023). Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning. Transactions on Machine Learning Research [ http ]
  64. Saxena, A., Cole, A., Gazagnes, S., et al. (2023). Constraining the X-ray heating and reionization using 21-cm power spectra with Marginal Neural Ratio Estimation. Monthly Notices of the Royal Astronomical Society, 525(4), 6097–6111 [ DOI | http ]
  65. Saxena, A., Meerburg, P. D., de Lera Acedo, E., Handley, W., & Koopmans, L. V. E. (2023). Sky-averaged 21-cm signal extraction using multiple antennas with an SVD framework: the REACH case. Monthly Notices of the Royal Astronomical Society, 522(1), 1022–1032 [ DOI | http ]
  66. Schenkel, M. A., Billeter, J.-C., Beukeboom, L. W., & Pen, I. (2023). Divergent evolution of genetic sex determination mechanisms along environmental gradients. Evolution Letters, 7(3), 132–147 [ DOI | http ]
  67. Shomalzadeh, K., Scherpen, J. M., & Camlibel, M. K. (2023). A real-time balancing market optimization with personalized prices: From bilevel to convex. Operations Research Perspectives, 10, 100276 [ DOI | http ]
  68. Siebe, H. S., Sardjan, A. S., Maßmann, S. C., et al. (2023). Formation of substituted dioxanes in the oxidation of gum arabic with periodate. Green Chemistry, 25(10), 4058–4066 [ DOI | http ]
  69. Soni, S., Werner, I., Aidi, M., et al. (2023). Influence of Polyoxovanadate and Phthalocyanine on 4f Electron Transfer in Gold-Confined Monolayers Probed with EGaIn Top Contacts. ACS Applied Nano Materials, 6(24), 22643–22650 [ DOI | http ]
  70. Suárez-Menéndez, M., Bérubé, M., Furni, F., et al. (2023). Wild pedigrees inform mutation rates and historic abundance in baleen whales. Science, 381(6661), 990–995 [ DOI | http ]
  71. Tegegne, B. S., Said, M. A., Ani, A., et al. (2023). Phenotypic but not genetically predicted heart rate variability associated with all-cause mortality. Communications Biology, 6(1), 1013 [ DOI | http ]
  72. Tenzin, K., Roy, A., Cerasoli, F. T., et al. (2023). Collinear Rashba-Edelstein effect in nonmagnetic chiral materials. Physical Review B, 108(24), 245203 [ DOI | http ]
  73. Tenzin, K., Roy, A., Jafari, H., et al. (2023). Analogs of Rashba-Edelstein effect from density functional theory. Physical Review B, 107(16), 165140 [ DOI | http ]
  74. Ter Harmsel, M., Maguire, O. R., Runikhina, S. A., et al. (2023). A catalytically active oscillator made from small organic molecules. Nature, 621(7977), 87–93 [ DOI | http ]
  75. Trifonova, E. A., Leach, I. F., de Haas, W. B., et al. (2023). Spectroscopic Manifestations and Implications for Catalysis of Quasi‐d 10 Configurations in Formal Gold(III) Complexes. Angewandte Chemie International Edition, 62(3) [ DOI | http ]
  76. Vakili, M., Hoekstra, H., Bilicki, M., et al. (2023). Clustering of red sequence galaxies in the fourth data release of the Kilo-Degree Survey. Astronomy & Astrophysics [ DOI | http ]
  77. Valle, N. & Verstappen, R. (2023). Conservation of energy in the direct numerical simulation of interface-resolved multiphase flows. Experimental and Computational Multiphase Flow, 5(4), 333–343 [ DOI | http ]
  78. Veltmaat, N., Zhong, Y., De Jesus, F. M., et al. (2023). Genomic profiling of post-transplant lymphoproliferative disorders using cell-free DNA. Journal of Hematology & Oncology, 16(1), 104 [ DOI | http ]
  79. Vrijen, C., Nolte, I. M., Oldehinkel, A. J., Veenstra, R., & Kretschmer, T. (2023). Genetic confounding in bullying research: Causal claims revisited. Development and Psychopathology, pages 1–12 [ DOI | http ]
  80. Wang, Y., Jiménez, D. J., Zhang, Z., & Van Elsas, J. D. (2023). Functioning of a tripartite lignocellulolytic microbial consortium cultivated under two shaking conditions: a metatranscriptomic study. Biotechnology for Biofuels and Bioproducts, 16(1), 54 [ DOI | http ]
  81. Wouda, N. A., Aslan, A., & Vis, I. F. (2023). An adaptive large neighbourhood search metaheuristic for hourly learning activity planning in personalised learning. Computers & Operations Research, 151, 106089 [ DOI | http ]
  82. Xie, S., Valente, L., & Etienne, R. S. (2023). Can we ignore trait-dependent colonization and diversification in island biogeography? Evolution, 77(3), 670–681 [ DOI | http ]
  83. Xiong, S., Tziafas, G., & Kasaei, H. (2023). Enhancing Fine-Grained 3D Object Recognition Using Hybrid Multi-Modal Vision Transformer-CNN Models. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5751–5757. IEEE, Detroit, MI, USA. ISBN 978-1-66549-190-7 [ DOI | http ]
  84. Young, E. A., Chesterton, E., Lummaa, V., Postma, E., & Dugdale, H. L. (2023). The long-lasting legacy of reproduction: lifetime reproductive success shapes expected genetic contributions of humans after 10 generations. Proceedings of the Royal Society B: Biological Sciences, 290(1998), 20230287 [ DOI | http ]
  85. Yu, B., Kasaei, H., & Cao, M. (2023). Frontier Semantic Exploration for Visual Target Navigation. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 4099–4105. IEEE, London, United Kingdom. ISBN 9798350323658 [ DOI | http ]
  86. Yu, B., Kasaei, H., & Cao, M. (2023). L3MVN: Leveraging Large Language Models for Visual Target Navigation. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3554–3560. IEEE, Detroit, MI, USA. ISBN 978-1-66549-190-7 [ DOI | http ]
  87. Zhang, L., Csányi, G., Van Der Giessen, E., & Maresca, F. (2023). Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential. npj Computational Materials, 9(1), 217 [ DOI | http ]
  88. Zheng, X., Kamat, A. M., Cao, M., & Kottapalli, A. G. P. (2023). Wavy Whiskers in Wakes: Explaining the Trail‐Tracking Capabilities of Whisker Arrays on Seal Muzzles. Advanced Science, 10(2), 2203062 [ DOI | http ]
  89. Zhong, K., Nguyen, H. L., Do, T. N., et al. (2023). An efficient time-domain implementation of the multichromophoric Förster resonant energy transfer method. The Journal of Chemical Physics, 158(6), 064103 [ DOI | http ]

2022

  1. Acunzo, D. J., Low, D. M., & Fairhall, S. L. (2022). Deep neural networks reveal topic-level representations of sentences in medial prefrontal cortex, lateral anterior temporal lobe, precuneus, and angular gyrus. NeuroImage, 251, 119005 [ DOI | http ]
  2. van Adrichem, K. E. & Jansen, T. L. C. (2022). AIM: A Mapping Program for Infrared Spectroscopy of Proteins. Journal of Chemical Theory and Computation, 18(5), 3089–3098 [ DOI | http ]
  3. Alachouzos, G., Schulte, A. M., Mondal, A., Szymanski, W., & Feringa, B. L. (2022). Computational Design, Synthesis, and Photochemistry of Cy7‐PPG, an Efficient NIR‐Activated Photolabile Protecting Group for Therapeutic Applications. Angewandte Chemie International Edition [ DOI ]
  4. Ardévol Martínez, F., Min, M., Kamp, I., & Palmer, P. I. (2022). Convolutional neural networks as an alternative to Bayesian retrievals for interpreting exoplanet transmission spectra. Astronomy & Astrophysics, 662, A108 [ DOI | http ]
  5. Asefa, N. G., Kamali, Z., Pereira, S., et al. (2022). Bioinformatic Prioritization and Functional Annotation of GWAS-Based Candidate Genes for Primary Open-Angle Glaucoma. Genes, 13(6), 1055 [ DOI | http ]
  6. Asefa, N. G., Neustaeter, A., Vehof, J., et al. (2022). Development and validation of a questionnaire-based myopia proxy in adults: the LifeLines Cohort Study. British Journal of Ophthalmology, pages bjophthalmol–2021–319166 [ DOI | http ]
  7. Ashworth, M. A., Bombino, E., De Jong, R. M., et al. (2022). Computation-Aided Engineering of Cytochrome P450 for the Production of Pravastatin. ACS Catalysis, 12(24), 15028–15044 [ DOI | http ]
  8. Aucar, I. A., Chamorro, Y., & Borschevsky, A. (2022). Parity-violating contributions to nuclear spin-rotation interactions and to NMR shielding constants in tetrahedral molecules. Physical Review A, 106(6), 062802 [ DOI | http ]
  9. Awasthi, A., Leach, I. F., Engbers, S., et al. (2022). Formation and Reactivity of a Fleeting Ni iii Bisphenoxyl Diradical Species. Angewandte Chemie International Edition, 61(41) [ DOI | http ]
  10. Azadi-Chegeni, F., Thallmair, S., Ward, M. E., et al. (2022). Protein dynamics and lipid affinity of monomeric, zeaxanthin-binding LHCII in thylakoid membranes. Biophysical Journal, 121(3), 396–409 [ DOI | http ]
  11. Bartelds, M., de Vries, W., Sanal, F., et al. (2022). Neural representations for modeling variation in speech. Journal of Phonetics, 92, 101137 [ DOI | http ]
  12. Benjamins, J. W., Yeung, M. W., van de Vegte, Y. J., et al. (2022). Genomic insights in ascending aortic size and distensibility. eBioMedicine, 75, 103783 [ DOI | http ]
  13. Bennabhaktula, G. S., Alegre, E., Karastoyanova, D., & Azzopardi, G. (2022). Camera model identification based on forensic traces extracted from homogeneous patches. Expert Systems with Applications, 206, 117769 [ DOI | http ]
  14. Bennabhaktula, G. S., Timmerman, D., Alegre, E., & Azzopardi, G. (2022). Source Camera Device Identification from Videos. SN Computer Science, 3(4), 316 [ DOI | http ]
  15. Bernhardt, R., Manrho, M., Zablocki, J., et al. (2022). Structural Disorder as the Origin of Optical Properties and Spectral Dynamics in Squaraine Nano-Aggregates. Journal of the American Chemical Society, 144(42), 19372–19381 [ DOI | http ]
  16. Bhatt, D. K., Janzen, T., Daemen, T., & Weissing, F. J. (2022). Modelling the spatial dynamics of oncolytic virotherapy in the presence of virus-resistant tumour cells. PLOS Computational Biology, 18(12), e1010076 [ DOI | http ]
  17. Bhole, A., Udmale, S. S., Falzon, O., & Azzopardi, G. (2022). CORF3D contour maps with application to Holstein cattle recognition from RGB and thermal images. Expert Systems with Applications, 192, 116354 [ DOI | http ]
  18. Bilderbeek, R. J. C., Baranov, M. V., van den Bogaart, G., & Bianchi, F. (2022). Transmembrane Helices Are an Over-Presented and Evolutionarily Conserved Source of Major Histocompatibility Complex Class I and II Epitopes. Frontiers in Immunology, 12 [ DOI | http ]
  19. Bombelli, A. & Fazi, S. (2022). The ground handler dock capacitated pickup and delivery problem with time windows: A collaborative framework for air cargo operations. Transportation Research Part E: Logistics and Transportation Review, 159, 102603 [ DOI | http ]
  20. Brinker, P., Chen, F., Chehida, Y. B., et al. (2022). Microbiome composition is shaped by geography and population structure in the parasitic wasp Asobara japonica , but not in the presence of the endosymbiont Wolbachia. Molecular Ecology, page mec.16699 [ DOI | http ]
  21. Busana, M., Childs, D. Z., Burke, T. A., et al. (2022). Population level consequences of facultatively cooperative behaviour in a stochastic environment. Journal of Animal Ecology, 91(1), 224–240 [ DOI | http ]
  22. Carlotti, M., Soni, S., Kovalchuk, A., et al. (2022). Empirical Parameter to Compare Molecule–Electrode Interfaces in Large-Area Molecular Junctions. ACS Physical Chemistry Au, page acsphyschemau.1c00029 [ DOI | http ]
  23. Castro-Alvarez, S., Tendeiro, J. N., De Jonge, P., Meijer, R. R., & Bringmann, L. F. (2022). Mixed-Effects Trait-State-Occasion Model: Studying the Psychometric Properties and the Person–Situation Interactions of Psychological Dynamics. Structural Equation Modeling: A Multidisciplinary Journal, 29(3), 438–451 [ DOI | http ]
  24. Castro-Alvarez, S., Tendeiro, J. N., Meijer, R. R., & Bringmann, L. F. (2022). Using structural equation modeling to study traits and states in intensive longitudinal data. Psychological Methods, 27(1), 17–43 [ DOI | http ]
  25. Chamorro, Y., Borschevsky, A., Eliav, E., et al. (2022). Molecular enhancement factors for the P , T -violating electric dipole moment of the electron in BaCH 3 and YbCH 3 symmetric top molecules. Physical Review A, 106(5), 052811 [ DOI | http ]
  26. Conklin, J. R., Verkuil, Y. I., Battley, P. F., et al. (2022). Global flyway evolution in red knots Calidris canutus and genetic evidence for a Nearctic refugium. Molecular Ecology, 31(7), 2124–2139 [ DOI | http ]
  27. Danazumi, A. U. & Balogun, E. O. (2022). Microsecond-long simulation reveals the molecular mechanism for the dual inhibition of falcipain-2 and falcipain-3 by antimalarial lead compounds. Frontiers in Molecular Biosciences, 9, 1070080 [ DOI | http ]
  28. De Biase, A., Tang, W., Sourlos, N., et al. (2022). Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images. pages 109–120 [ DOI | http ]
  29. Denis, M., Haase, P. A. B., Mooij, M. C., et al. (2022). Benchmarking of the Fock-space coupled-cluster method and uncertainty estimation: Magnetic hyperfine interaction in the excited state of BaF. Physical Review A, 105(5), 052811 [ DOI | http ]
  30. Dietz, M. W., Matson, K. D., Versteegh, M. A., et al. (2022). Gut microbiota of homing pigeons shows summer–winter variation under constant diet indicating a substantial effect of temperature. Animal Microbiome, 4(1), 64 [ DOI | http ]
  31. Do, P., Coler, M., Dijkstra, J., & Klabbers, E. (2022). Text-to-Speech for Under-Resourced Languages: Phoneme Mapping and Source Language Selection in Transfer Learning. In Proceedings of SIGUL2022 @LREC2022, pages 16–22. European Language Resources Association (ELRA), Marseille
  32. Do, T. N., Nguyen, H. L., Akhtar, P., et al. (2022). Ultrafast Excitation Energy Transfer Dynamics in the LHCII–CP29–CP24 Subdomain of Plant Photosystem II. The Journal of Physical Chemistry Letters, 13(19), 4263–4271 [ DOI | http ]
  33. Eijsink, L. E., Sardjan, A. S., Sinnema, E. G., et al. (2022). In situ EPR and Raman spectroscopy in the curing of bis-methacrylate–styrene resins. RSC Advances, 12(5), 2537–2548 [ DOI | http ]
  34. Engbers, S., Leach, I. F., Havenith, R. W. A., & Klein, J. E. M. N. (2022). Homolytic X‐H Bond Cleavage at a Gold(III) Hydroxide: Insights into One‐Electron Events at Gold. Chemistry – A European Journal [ DOI ]
  35. Engbers, S., Trifonova, E. A., van der Geest, K. M., et al. (2022). Gold(III) Catalyzed Overman Rearrangements: Controlling Steric Interactions using Pincer‐Type Ligands. ChemCatChem, 14(24) [ DOI | http ]
  36. Fernandez-Chaves, D., Ruiz-Sarmiento, J.-R., Jaenal, A., Petkov, N., & Gonzalez-Jimenez, J. (2022). Robot@VirtualHome, an ecosystem of virtual environments and tools for realistic indoor robotic simulation. Expert Systems with Applications, 208, 117970 [ DOI | http ]
  37. Fiechter, M. R., Haase, P. A. B., Saleh, N., et al. (2022). Toward Detection of the Molecular Parity Violation in Chiral Ru(acac) 3 and Os(acac) 3. The Journal of Physical Chemistry Letters, 13(42), 10011–10017 [ DOI | http ]
  38. Fiorini, L. & Aiello, M. (2022). Automatic optimal multi-energy management of smart homes. Energy Informatics, 5(1), 68 [ DOI | http ]
  39. Flöss, T., de Wild, T., Meerburg, P. D., & Koopmans, L. V. (2022). The Dark Ages' 21-cm trispectrum. Journal of Cosmology and Astroparticle Physics, 2022(06), 020 [ DOI ]
  40. Fragasso, A., de Vries, H. W., Andersson, J., et al. (2022). Transport receptor occupancy in nuclear pore complex mimics. Nano Research [ DOI | http ]
  41. Frallicciardi, J., Melcr, J., Siginou, P., Marrink, S. J., & Poolman, B. (2022). Membrane thickness, lipid phase and sterol type are determining factors in the permeability of membranes to small solutes. Nature Communications, 13(1), 1605 [ DOI | http ]
  42. Frantseva, K., Nesvorný, D., Mueller, M., et al. (2022). Exogenous delivery of water to Mercury. Icarus, page 114980 [ DOI | http ]
  43. Gacesa, R., Kurilshikov, A., Vich Vila, A., et al. (2022). Environmental factors shaping the gut microbiome in a Dutch population. Nature [ DOI | http ]
  44. Gallop, N. P., Ye, J., Greetham, G. M., et al. (2022). The effect of caesium alloying on the ultrafast structural dynamics of hybrid organic–inorganic halide perovskites. Journal of Materials Chemistry A, 10(42), 22408–22418 [ DOI | http ]
  45. Garay, J., Nolte, D., Löcke, M., & Bertoglio, C. (2022). Parameter estimation in fluid flow models from aliased velocity measurements. Inverse Problems, 38(9), 095002 [ DOI | http ]
  46. Gerrits, E., Giannini, L. A. A., Brouwer, N., et al. (2022). Neurovascular dysfunction in GRN-associated frontotemporal dementia identified by single-nucleus RNA sequencing of human cerebral cortex. Nature Neuroscience, 25(8), 1034–1048 [ DOI | http ]
  47. Glavan, A. & Talavera, E. (2022). InstaIndoor and multi-modal deep learning for indoor scene recognition. Neural Computing and Applications, 34(9), 6861–6877 [ DOI | http ]
  48. Grünewald, F., Alessandri, R., Kroon, P. C., et al. (2022). Polyply; a python suite for facilitating simulations of macromolecules and nanomaterials. Nature Communications, 13(1), 68 [ DOI | http ]
  49. Grünewald, F., Punt, M. H., Jefferys, E. E., et al. (2022). Martini 3 Coarse-Grained Force Field for Carbohydrates. Journal of Chemical Theory and Computation, 18(12), 7555–7569 [ DOI | http ]
  50. Gulyaeva, A., Garmaeva, S., Kurilshikov, A., et al. (2022). Diversity and Ecology of Caudoviricetes Phages with Genome Terminal Repeats in Fecal Metagenomes from Four Dutch Cohorts. Viruses, 14(10), 2305 [ DOI | http ]
  51. Gulyaeva, A., Garmaeva, S., Ruigrok, R. A., et al. (2022). Discovery, diversity, and functional associations of crAss-like phages in human gut metagenomes from four Dutch cohorts. Cell Reports, 38(2), 110204 [ DOI | http ]
  52. Guo, Y., Borschevsky, A., Eliav, E., & Pašteka, L. F. (2022). Relativistic coupled cluster calculations of the electron affinity and ionization potential of Nh(113). Journal of Physics B: Atomic, Molecular and Optical Physics, 55(15), 155003 [ DOI | http ]
  53. Gupte, P. R., Albery, G. F., Gismann, J., Sweeny, A. R., & Weissing, F. J. (2022). Novel pathogen introduction rapidly alters evolved movement strategies, restructuring animal societies. preprint, Ecology [ DOI | http ]
  54. Haja, A., Radu, S., & Schomaker, L. (2022). A Comparison of Different U-Net Models for Segmentation of Overlapping Organoids. In Proceedings of the 2022 9th International Conference on Biomedical and Bioinformatics Engineering, pages 1–10. ACM, Kyoto Japan. ISBN 978-1-4503-9722-3 [ DOI | http ]
  55. Jafari, H., Roy, A., & Sławińska, J. (2022). Ferroelectric control of charge-to-spin conversion in WTe 2. Physical Review Materials, 6(9), L091404 [ DOI | http ]
  56. Janzen, T. & Miró Pina, V. (2022). Estimating the time since admixture from phased and unphased molecular data. Molecular Ecology Resources, 22(3), 908–926 [ DOI | http ]
  57. Jia, X., Dini-Andreote, F., & Salles, J. F. (2022). Unravelling the interplay of ecological processes structuring the bacterial rare biosphere. ISME Communications, 2(1), 96 [ DOI | http ]
  58. Jim, K. K., Aprianto, R., Koning, R., et al. (2022). Pneumolysin promotes host cell necroptosis and bacterial competence during pneumococcal meningitis as shown by whole-animal dual RNA-seq. Cell Reports, 41(12), 111851 [ DOI | http ]
  59. Jo, T., Taschinski, S., Leach, I. F., et al. (2022). On the Role of Noncovalent Ligand-Substrate Interactions in Au(I) Catalysis: An Experimental and Computational Study of Protodeauration. ACS Catalysis, 12(21), 13158–13163 [ DOI | http ]
  60. Kamphorst, E., Cantell, M., Minnaert, A., Houwen, S., & Cox, R. (2022). In Tune? Multimodal Mother-Child Interpersonal Coordination During Early Childhood’. Early Education and Development, pages 1–27 [ DOI | http ]
  61. Kathan, M., Crespi, S., Troncossi, A., et al. (2022). The Influence of Strain on the Rotation of an Artificial Molecular Motor. Angewandte Chemie International Edition, 61(34) [ DOI | http ]
  62. Kobauri, P., Galenkamp, N. S., Schulte, A. M., et al. (2022). Hypothesis-Driven, Structure-Based Design in Photopharmacology: The Case of eDHFR Inhibitors. Journal of Medicinal Chemistry, 65(6), 4798–4817 [ DOI | http ]
  63. Kramer, B. H., Van Doorn, G. S., Arani, B. M. S., & Pen, I. (2022). Eusociality and the Evolution of Aging in Superorganisms. The American Naturalist, 200(1), 63–80 [ DOI | http ]
  64. Krause, S., Evans, J. D., Bon, V., et al. (2022). Cooperative light-induced breathing of soft porous crystals via azobenzene buckling. Nature Communications, 13(1), 1951 [ DOI | http ]
  65. Kreider, J. J., Janzen, T., Bernadou, A., et al. (2022). Resource sharing is sufficient for the emergence of division of labour. Nature Communications, 13(1), 7232 [ DOI | http ]
  66. Kreider, J. J., Kramer, B. H., Komdeur, J., & Pen, I. (2022). The evolution of ageing in cooperative breeders. Evolution Letters, 6(6), 450–459 [ DOI | http ]
  67. Kretschmer, T., Vrijen, C., Nolte, I. M., Wertz, J., & Hartman, C. A. (2022). Gene–environment interplay in externalizing behavior from childhood through adulthood. Journal of Child Psychology and Psychiatry, 63(10), 1206–1213 [ DOI | http ]
  68. Kriksin, Y. A. & Tishkin, V. F. (2022). A High-Accuracy Algorithm for Solving Problems of Electrostatics in a Nonhomogeneous Spatially Periodic Dielectric Medium. Doklady Mathematics, 106(3), 440–444 [ DOI | http ]
  69. Lai, H., Mao, J., Toral, A., & Nissim, M. (2022). Human Judgement as a Compass to Navigate Automatic Metrics for Formality Transfer. In Proceedings of the 2nd Workshop on Human Evaluation of NLP Systems. Association for Computational Linguistics
  70. Lai, H., Toral, A., & Nissim, M. (2022). Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, ACL Anthology, Dublin
  71. Leach, I. F., Havenith, R. W. A., & Klein, J. E. M. N. (2022). Revisiting Formal Copper(III) Complexes: Bridging Perspectives with Quasi ‐ d 10 Configurations. European Journal of Inorganic Chemistry, 2022(27) [ DOI | http ]
  72. Lesci, G. F., Marulli, F., Moscardini, L., et al. (2022). AMICO galaxy clusters in KiDS-DR3: Cosmological constraints from counts and stacked weak lensing. Astronomy & Astrophysics, 659, A88 [ DOI | http ]
  73. Long, X., Liu, Y., Liker, A., et al. (2022). Does ecology and life history predict parental cooperation in birds? A comparative analysis. Behavioral Ecology and Sociobiology, 76(7), 92 [ DOI | http ]
  74. Ma, B., Guo, J., De Biase, A., et al. (2022). Self-supervised Multi-modality Image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer. pages 308–317 [ DOI | http ]
  75. Machado, N., Bruininks, B. M. H., Singh, P., et al. (2022). Complex nanoemulsion for vitamin delivery: droplet organization and interaction with skin membranes. Nanoscale, 14(2), 506–514 [ DOI | http ]
  76. Manrho, M., Jansen, T. L. C., & Knoester, J. (2022). Optical signatures of the coupling between excitons and charge transfer states in linear molecular aggregates. The Journal of Chemical Physics, 156(22), 224112 [ DOI | http ]
  77. Maurin, D., Ferronato Bueno, E., & Derome, L. (2022). A simple determination of the halo size from 10 Be/ 9 Be data. Astronomy & Astrophysics, 667, A25 [ DOI | http ]
  78. Maurin, D., Ferronato Bueno, E., Génolini, Y., Derome, L., & Vecchi, M. (2022). The importance of Fe fragmentation for LiBeB analyses: Is a Li primary source needed to explain AMS-02 data? Astronomy & Astrophysics, 668, A7 [ DOI | http ]
  79. Medema, M. & Lazovik, A. (2022). Correlating the Community Structure of Constraint Satisfaction Problems with Search Time. International Journal on Artificial Intelligence Tools, 31(07), 2260004 [ DOI | http ]
  80. Meijer, K. J., Franken, O., van der Heide, T., et al. (2022). Characterizing bedforms in shallow seas as an integrative predictor of seafloor stability and the occurrence of macrozoobenthic species. Remote Sensing in Ecology and Conservation, page rse2.312 [ DOI | http ]
  81. Mir Mohammadi Kooshknow, S., Herber, R., & Ruzzenenti, F. (2022). Are electricity storage systems in the Netherlands indispensable or doable? Testing single-application electricity storage business models with exploratory agent-based modeling. Journal of Energy Storage, 48, 104008 [ DOI | http ]
  82. Mohammadi, M., Tino, P., & Bunte, K. (2022). Manifold Alignment Aware Ants: A Markovian Process for Manifold Extraction. Neural Computation, 34(3), 595–641 [ DOI | http ]
  83. Müller-Cleve, S. F., Fra, V., Khacef, L., et al. (2022). Braille letter reading: A benchmark for spatio-temporal pattern recognition on neuromorphic hardware. Frontiers in Neuroscience, 16, 951164 [ DOI | http ]
  84. van Noord, R., García-Romero, C., Esplà-Gomis, M., Pla Sempere, L., & Toral, A. (2022). Building Domain-specific Corpora from the Web: the Case of European Digital Service Infrastructures. In Proceedings of the BUCC Workshop within LREC 2022, pages 23–32. European Language Resources Association, Marseille, France [ http ]
  85. van den Oever, L. B., Spoor, D. S., Crijns, A. P. G., et al. (2022). Automatic Cardiac Structure Contouring for Small Datasets with Cascaded Deep Learning Models. Journal of Medical Systems, 46(5), 22 [ DOI | http ]
  86. van den Oever, L. B., van Veldhuizen, W. A., Cornelissen, L. J., et al. (2022). Qualitative Evaluation of Common Quantitative Metrics for Clinical Acceptance of Automatic Segmentation: a Case Study on Heart Contouring from CT Images by Deep Learning Algorithms. Journal of Digital Imaging, 35(2), 240–247 [ DOI | http ]
  87. Penning, E., Verkuil, Y. I., Klunder, L., & Reneerkens, J. (2022). Sanderlings Feed on a Diverse Spectrum of Prey Worldwide but Primarily Rely on Brown Shrimp in the Wadden Sea. Ardea, 110(2) [ DOI | http ]
  88. Pfaehler, E., Euba, D., Rinscheid, A., et al. (2022). Convolutional neural networks for automatic image quality control and EARL compliance of PET images. EJNMMI Physics, 9(1), 53 [ DOI | http ]
  89. Pfeifer, L., Crespi, S., van der Meulen, P., et al. (2022). Controlling forward and backward rotary molecular motion on demand. Nature Communications, 13(1), 2124 [ DOI | http ]
  90. Pijning, T., te Poele, E. M., de Leeuw, T. C., Guskov, A., & Dijkhuizen, L. (2022). Crystal Structure of 4,6-α-Glucanotransferase GtfC-ΔC from Thermophilic Geobacillus 12AMOR1: Starch Transglycosylation in Non-Permuted GH70 Enzymes. Journal of Agricultural and Food Chemistry, 70(48), 15283–15295 [ DOI | http ]
  91. Pimenta Martins, L. G., Carvalho, B. R., Occhialini, C. A., et al. (2022). Electronic Band Tuning and Multivalley Raman Scattering in Monolayer Transition Metal Dichalcogenides at High Pressures. ACS Nano, 16(5), 8064–8075 [ DOI ]
  92. Poelman, W., Danoe, G., Ploeger, E., et al. (2022). RUG-1-Pegasussers at SemEval-2022 Task 3: Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word Relations. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 247–254. Association for Computational Linguistics, Seattle, United States [ DOI | http ]
  93. Poelman, W., van Noord, R., & Bos, J. (2022). Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4186–4192. International Committee on Computational Linguistics, Gyeongju, Republic of Korea [ http ]
  94. Pooler, D. R. S., Doellerer, D., Crespi, S., & Feringa, B. L. (2022). Controlling rotary motion of molecular motors based on oxindole. Organic Chemistry Frontiers, 9(8), 2084–2092 [ DOI | http ]
  95. Portoles, O., Qin, Y., Hadida, J., et al. (2022). Modulations of local synchrony over time lead to resting-state functional connectivity in a parsimonious large-scale brain model. PLOS ONE, 17(10), e0275819 [ DOI | http ]
  96. Rakhimberdiev, E., Karagicheva, J., Saveliev, A., et al. (2022). Misidentification errors in reencounters result in biased estimates of survival probability from CJS models: Evidence and a solution using the robust design. Methods in Ecology and Evolution [ DOI | http ]
  97. Ramanantoanina, H., Borschevsky, A., Block, M., & Laatiaoui, M. (2022). Electronic Structure of Lr+ (Z = 103) from Ab Initio Calculations. Atoms, 10(2), 48 [ DOI | http ]
  98. Remmerswaal, R. A. & Veldman, A. E. P. (2022). On simulating variability of sloshing loads in LNG tanks. In Proceedings of the ASME 41th International Conference on Ocean, Offshore and Arctic Engineering OMAE2022. Hamburg
  99. Rezaei, S., McKean, J. P., Biehl, M., & Javadpour, A. (2022). DECORAS: detection and characterization of radio-astronomical sources using deep learning. Monthly Notices of the Royal Astronomical Society, 510(4), 5891–5907 [ DOI | http ]
  100. Roy, A., Cerasoli, F. T., Jayaraj, A., et al. (2022). Long-range current-induced spin accumulation in chiral crystals. npj Computational Materials, 8(1), 243 [ DOI | http ]
  101. Roy, A., Guimarães, M. H. D., & Sławińska, J. (2022). Unconventional spin Hall effects in nonmagnetic solids. Physical Review Materials, 6(4), 045004 [ DOI | http ]
  102. Sangiacomo, A., Tanasescu, R., Donker, S., & Hogenbirk, H. (2022). Mapping the evolution of early modern natural philosophy: corpus collection and authority acknowledgement. Annals of Science, 79(1), 1–39 [ DOI | http ]
  103. Santhakumar, K. & Kasaei, H. (2022). Lifelong 3D object recognition and grasp synthesis using dual memory recurrent self-organization networks. Neural Networks, 150, 167–180 [ DOI | http ]
  104. Santos Neves, P., Lambert, J. W., Valente, L., & Etienne, R. S. (2022). The robustness of a simple dynamic model of island biodiversity to geological and sea‐level change. Journal of Biogeography, 49(11), 2091–2104 [ DOI | http ]
  105. Sas, D., Avgeriou, P., Pigazzini, I., & Arcelli Fontana, F. (2022). On the relation between architectural smells and source code changes. Journal of Software: Evolution and Process, 34(1) [ DOI | http ]
  106. Sas, D., Avgeriou, P., & Uyumaz, U. (2022). On the evolution and impact of architectural smells—an industrial case study. Empirical Software Engineering, 27(4), 86 [ DOI | http ]
  107. Saxena, V., Steendam, R., & Jansen, T. L. C. (2022). Distinguishing islet amyloid polypeptide fibril structures with infrared isotope-label spectroscopy. The Journal of Chemical Physics, 156(5), 055101 [ DOI | http ]
  108. Schleimer, A., Richart, L., Drygala, F., et al. (2022). Introgressive hybridisation between domestic pigs (Sus scrofa domesticus) and endemic Corsican wild boars (S. s. meridionalis): effects of human-mediated interventions. Heredity, 128(4), 279–290 [ DOI | http ]
  109. Schulte, A. M., Alachouzos, G., Szymański, W., & Feringa, B. L. (2022). Strategy for Engineering High Photolysis Efficiency of Photocleavable Protecting Groups through Cation Stabilization. Journal of the American Chemical Society, 144(27), 12421–12430 [ DOI | http ]
  110. Sorbelli, D., Rossi, E., Havenith, R. W., et al. (2022). Gold-Aluminyl and Gold-Diarylboryl Complexes: Bonding and Reactivity with Carbon Dioxide. Inorganic Chemistry, 61(19), 7327–7337 [ DOI ]
  111. Stadel, M. & Stulp, G. (2022). Balancing bias and burden in personal network studies. Social Networks, 70, 16–24 [ DOI | http ]
  112. Steen, J. D., Volker, A., Duijnstee, D. R., Sardjan, A. S., & Browne, W. R. (2022). pH-Induced Changes in the SERS Spectrum of Thiophenol at Gold Electrodes during Cyclic Voltammetry. The Journal of Physical Chemistry C, 126(17), 7680–7687 [ DOI ]
  113. Stähler, C., Pooler, D. R. S., Costil, R., et al. (2022). Coupled Rocking Motion in a Light-Driven Rotary Molecular Motor. The Journal of Organic Chemistry, page acs.joc.2c01830 [ DOI | http ]
  114. Swarte, J. C., Li, Y., Hu, S., et al. (2022). Gut microbiome dysbiosis is associated with increased mortality after solid organ transplantation. Science Translational Medicine, 14(660), eabn7566 [ DOI | http ]
  115. Taghribi, A., Bunte, K., Smith, R., et al. (2022). LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying density. IEEE Transactions on Knowledge and Data Engineering, pages 1–1 [ DOI | http ]
  116. Talsma, W., Ye, G., Liu, Y., et al. (2022). Efficient Selective Sorting of Semiconducting Carbon Nanotubes Using Ultra-Narrow-Band-Gap Polymers. ACS Applied Materials & Interfaces, 14(33), 38056–38066 [ DOI | http ]
  117. Thallmair, V., Schultz, L., Zhao, W., et al. (2022). Two cooperative binding sites sensitize PI(4,5)P 2 recognition by the tubby domain. Science Advances, 8(36), eabp9471 [ DOI | http ]
  118. Valle, N., Trias, F., & Verstappen, R. (2022). On the conservation of primary and secondary properties in the simulation of multiphase flows. In 8th European Congress on Computational Methods in Applied Sciences and Engineering. CIMNE [ DOI | http ]
  119. Van Der Goot, E., Vink, S. N., Van Vliet, D., et al. (2022). Gut-Microbiome Composition in Response to Phenylketonuria Depends on Dietary Phenylalanine in BTBR Pahenu2 Mice. Frontiers in Nutrition, 8, 735366 [ DOI | http ]
  120. Van Heck, J. I., Gacesa, R., Stienstra, R., et al. (2022). The Gut Microbiome Composition Is Altered in Long-standing Type 1 Diabetes and Associates With Glycemic Control and Disease-Related Complications. Diabetes Care, 45(9), 2084–2094 [ DOI | http ]
  121. Van Vliet, S., Alachouzos, G., De Vries, F., Pfeifer, L., & Feringa, B. L. (2022). Visible light activated BINOL-derived chiroptical switches based on boron integrated hydrazone complexes. Chemical Science, 13(33), 9713–9718 [ DOI | http ]
  122. van Veelen, H. P. J., Salles, J. F., Matson, K. D., et al. (2022). The microbial environment modulates non-genetic maternal effects on egg immunity. Animal Microbiome, 4(1), 44 [ DOI | http ]
  123. Verkuil, Y. I., Nicolaus, M., Ubels, R., et al. (2022). DNA metabarcoding quantifies the relative biomass of arthropod taxa in songbird diets: Validation with camera‐recorded diets. Ecology and Evolution, 12(5) [ DOI ]
  124. Versloot, R. C. A., Lucas, F. L. R., Yakovlieva, L., et al. (2022). Quantification of Protein Glycosylation Using Nanopores. Nano Letters, 22(13), 5357–5364 [ DOI | http ]
  125. Vink, S. N., Aldrich-Wolfe, L., Huerd, S. C., et al. (2022). Belowground mutualisms to support prairie reconstruction—Improving prairie habitat using mycorrhizal inoculum. Technical report, Reston, VA. Publication Title: Open-File Report ISBN: 2022-1055 [ DOI | http ]
  126. van der Weij, T., Soancatl Aguilar, V., & Solorio-Fernández, S. (2022). Runtime Prediction of Filter Unsupervised Feature Selection Methods. Research in Computing Science, 150(8), 138–150. Publisher: National Polytechnic Institute of Mexico
  127. van der Werff, T., van Noord, R., & Toral, A. (2022). Automatic Discrimination of Human and Neural Machine Translation: A Study with Multiple Pre-Trained Models and Longer Context. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, pages 161–170. European Association for Machine Translation, Ghent, Belgium [ http ]
  128. Ye, G., Talsma, W., Tran, K., et al. (2022). Polar Side Chains Enhance Selection of Semiconducting Single-Walled Carbon Nanotubes by Polymer Wrapping. Macromolecules, 55(4), 1386–1397 [ DOI | http ]
  129. Yeung, M. W., Wang, S., van de Vegte, Y. J., et al. (2022). Twenty-Five Novel Loci for Carotid Intima-Media Thickness: A Genome-Wide Association Study in >45 000 Individuals and Meta-Analysis of >100 000 Individuals. Arteriosclerosis, Thrombosis, and Vascular Biology, 42(4), 484–501 [ DOI | http ]
  130. van der Zee, J. P., Christianen, M. J., Bérubé, M., et al. (2022). Demographic changes in Pleistocene sea turtles were driven by past sea level fluctuations affecting feeding habitat availability. Molecular Ecology, 31(4), 1044–1056 [ DOI | http ]
  131. Zhang, Z. & Schomaker, L. (2022). DiverGAN: An Efficient and Effective Single-Stage Framework for Diverse Text-to-Image Generation. Neurocomputing, 473, 182–198 [ DOI | http ]
  132. Zhang, Z. & Schomaker, L. (2022). Optimized latent-code selection for explainable conditional text-to-image GANs. In 2022 International Joint Conference on Neural Networks (IJCNN), pages 1–9. IEEE, Padua, Italy. ISBN 978-1-72818-671-9 [ DOI | http ]
  133. Zwerwer, L. & den Hartigh, R. (2022). The dynamics underlying the rise of star performers. Nonlinear dynamics, psychology, and life sciences, 26(2), 163–186. Publisher: SOC CHAOS THEORY PSYCHOLOGY & LIFE SCIENCES
  134. Üstün, A., Bisazza, A., Bouma, G., & van Noord, G. (2022). UDapter: Typology-based Language Adapters for Multilingual Dependency Parsing and Sequence Labeling. Computational Linguistics, pages 1–37 [ DOI | http ]

2021

  1. Afruni, A., Fraternali, F., & Pezzulli, G. (2021). Most of the cool CGM of star-forming galaxies is not produced by supernova feedback. Monthly Notices of the Royal Astronomical Society, 501(4), 5575–5596 [ DOI | http ]
  2. Afruni, A., Pezzulli, G., & Fraternali, F. (2021). Inflow of low-metallicity cool gas in the halo of the Andromeda galaxy. Monthly Notices of the Royal Astronomical Society, 509(4), 4849–4864 [ DOI | http ]
  3. Aggarwal, P., Yin, Y., Esajas, K., et al. (2021). Deceleration and Trapping of SrF Molecules. Physical Review Letters, 127(17), 173201 [ DOI | http ]
  4. Amaro, V., Cavuoti, S., Brescia, M., et al. (2021). Rejection Criteria Based on Outliers in the KiDS Photometric Redshifts and PDF Distributions Derived by Machine Learning. In I. Zelinka, M. Brescia, & D. Baron, editors, Intelligent Astrophysics, volume 39, pages 245–264. Springer International Publishing, Cham. ISBN 978-3-030-65866-3 978-3-030-65867-0. Series Title: Emergence, Complexity and Computation [ DOI | http ]
  5. Ameryan, M. & Schomaker, L. (2021). A high-performance word recognition system for the biological fieldnotes of the Natuurkundige Commissie. In A. Weber, M. Heerlien, E. G. Miracle, & K. Wolstencroft, editors, CEUR Workshop Proceedings, volume 2810, pages 92–103. CEUR-WS.org. Series Title: {CEUR} Workshop Proceedings ISSN: 16130073
  6. Ani, A., van der Most, P. J., Snieder, H., Vaez, A., & Nolte, I. M. (2021). GWASinspector: comprehensive quality control of genome-wide association study results. Bioinformatics, 37(1), 129–130 [ DOI | http ]
  7. Arshad, R., Calvaruso, C., Boekema, E. J., Buchel, C., & Kouril, R. (2021). Revealing the architecture of the photosynthetic apparatus in the diatom Thalassiosira pseudonana. Plant Physiology, 186(4), 2124–2136 [ DOI | http ]
  8. Aschersleben, J., Peletier, R., Vecchi, M., & Wilkinson, M. (2021). Application of Pattern Spectra and Convolutional Neural Networks to the Analysis of Simulated Cherenkov Telescope Array Data. In Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021), page 697. Sissa Medialab, Trieste, Italy [ DOI | http ]
  9. Asgari, M., Lin, C. A., Joachimi, B., et al. (2021). KiDS-1000 cosmology: Cosmic shear constraints and comparison between two point statistics. Astronomy and Astrophysics, 645, A104 [ DOI ]
  10. Aucar, I. A. & Borschevsky, A. (2021). Relativistic study of parity-violating nuclear spin-rotation tensors. Journal of Chemical Physics, 155(13), 134307 [ DOI | http ]
  11. Baars, S., Castellana, D., Wubs, F. W., & Dijkstra, H. A. (2021). Application of adaptive multilevel splitting to high-dimensional dynamical systems. Journal of Computational Physics, 424, 109876 [ DOI | http ]
  12. Baars, S., van der Klok, M., Thies, J., & Wubs, F. W. (2021). A staggered-grid multilevel incomplete LU for steady incompressible flows. International Journal for Numerical Methods in Fluids, 93(4), 909–926 [ DOI | http ]
  13. Bachmann, L., Cabrera, A. A., Heide-Jørgensen, M. P., et al. (2021). Mitogenomics and the genetic differentiation of contemporary Balaena mysticetus (Cetacea) from Svalbard. Zoological Journal of the Linnean Society, 191(4), 1192–1203 [ DOI | http ]
  14. Bacle, A., Buslaev, P., Garcia-Fandino, R., et al. (2021). Inverse Conformational Selection in Lipid–Protein Binding. Journal of the American Chemical Society, 143(34), 13701–13709 [ DOI | http ]
  15. Barts, E. & Mostovoy, M. (2021). Magnetic particles and strings in iron langasite. npj Quantum Materials, 6(1), 104 [ DOI | http ]
  16. Barzakh, A., Andreyev, A. N., Raison, C., et al. (2021). Large Shape Staggering in Neutron-Deficient Bi Isotopes. Physical Review Letters, 127(19), 192501 [ DOI | http ]
  17. Ben Chehida, Y., Loughnane, R., Thumloup, J., et al. (2021). No leading‐edge effect in North Atlantic harbor porpoises: Evolutionary and conservation implications. Evolutionary Applications, 14(6), 1588–1611 [ DOI | http ]
  18. Bennabhaktula, G. S., Antonisse, J., & Azzopardi, G. (2021). On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 13052 LNCS, pages 434–444. ISBN 978-3-030-89127-5. ISSN: 16113349 [ DOI | http ]
  19. Bhushan, N., Steg, L., Jans, L., & Albers, C. (2021). Does installing photovoltaic panels affect daily electricity usage patterns? A generalized additive model approach. Energy and Climate Change, 2, 100052 [ DOI | http ]
  20. Bilderbeek, R. J., Laudanno, G., & Etienne, R. S. (2021). Quantifying the impact of an inference model in Bayesian phylogenetics. Methods in Ecology and Evolution, 12(2), 351–358 [ DOI | http ]
  21. Bilicki, M., Dvornik, A., Hoekstra, H., et al. (2021). Bright galaxy sample in the Kilo-Degree Survey Data Release 4: Selection, photometric redshifts, and physical properties. Astronomy & Astrophysics, 653, A82 [ DOI | http ]
  22. Bisazza, A., Üstün, A., & Sportel, S. (2021). On the Difficulty of Translating Free-Order Case-Marking Languages. Transactions of the Association for Computational Linguistics, 9, 1233–1248 [ DOI | http ]
  23. Bot, D. M., Wolf, B. J., & van Netten, S. M. (2021). The Quadrature Method: A Novel Dipole Localisation Algorithm for Artificial Lateral Lines Compared to State of the Art. Sensors, 21(13), 4558 [ DOI | http ]
  24. Bracco, P., Wijma, H. J., Nicolai, B., et al. (2021). CYP154C5 Regioselectivity in Steroid Hydroxylation Explored by Substrate Modifications and Protein Engineering. ChemBioChem, 22(6), 1099–1110 [ DOI | http ]
  25. Bragin, M. D., Kriksin, Y. A., & Tishkin, V. F. (2021). Entropy-Stable Discontinuous Galerkin Method for Two-Dimensional Euler Equations. Mathematical Models and Computer Simulations, 13(5), 897–906 [ DOI | http ]
  26. uit het Broek, M. A., Teunter, R. H., de Jonge, B., & Veldman, J. (2021). Joint condition-based maintenance and condition-based production optimization. Reliability Engineering and System Safety, 214, 107743 [ DOI | http ]
  27. Busana, M., Weissing, F. J., Hammers, M., et al. (2021). Structural equation modeling reveals determinants of fitness in a cooperatively breeding bird. Behavioral Ecology [ DOI | http ]
  28. Chen, F., Schenkel, M., Geuverink, E., van de Zande, L., & Beukeboom, L. W. (2021). Absence of complementary sex determination in two Leptopilina species (Figitidae, Hymenoptera) and a reconsideration of its incompatibility with endosymbiont-induced thelytoky. Insect Science [ DOI | http ]
  29. Chen, Y., Schomaker, L., & Wiering, M. (2021). An investigation into the effect of the learning rate on overestimation bias of connectionist q-learning. In ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence, volume 2, pages 107–118. SCITEPRESS - Science and Technology Publications. ISBN 978-989-758-484-8 [ DOI | http ]
  30. Crespi, S., Simeth, N. A., Di Donato, M., et al. (2021). Phenylimino Indolinone: A Green‐Light‐Responsive T‐Type Photoswitch Exhibiting Negative Photochromism. Angewandte Chemie International Edition, 60(48), 25290–25295 [ DOI | http ]
  31. De Mattei, L., Lai, H., Dell'Orletta, F., & Nissim, M. (2021). Human Perception in Natural Language Generation. In Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), pages 15–23. Association for Computational Linguistics, Online [ DOI | http ]
  32. Dhar, P. & Bisazza, A. (2021). Understanding Cross-Lingual Syntactic Transfer in Multilingual Recurrent Neural Networks. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa), pages 74–85. Linköping University Electronic Press, Sweden, Reykjavik [ http ]
  33. Dhar, P., Bisazza, A., & van Noord, G. (2021). Optimal Word Segmentation for Neural Machine Translation into Dravidian Languages. In Proceedings of the 8th Workshop on Asian Translation (WAT2021), pages 181–190. Association for Computational Linguistics, Stroudsburg, PA, USA [ DOI | http ]
  34. Dobbelaar, E., Rauber, C., Bonck, T., et al. (2021). Combining Structural with Functional Model Properties in Iron Synthetic Analogue Complexes for the Active Site in Rabbit Lipoxygenase. Journal of the American Chemical Society, 143(33), 13145–13155 [ DOI | http ]
  35. Dong, J., Sami, S., Balazs, D. M., et al. (2021). Fullerene derivatives with oligoethylene–glycol side chains: an investigation on the origin of their outstanding transport properties. Journal of Materials Chemistry C, 9(45), 16217–16225 [ DOI ]
  36. D’Addona, M., Riccio, G., Cavuoti, S., Tortora, C., & Brescia, M. (2021). Anomaly Detection in Astrophysics: A Comparison Between Unsupervised Deep and Machine Learning on KiDS Data. In I. Zelinka, M. Brescia, & D. Baron, editors, Intelligent Astrophysics, volume 39, pages 225–244. Springer International Publishing, Cham. ISBN 978-3-030-65866-3 978-3-030-65867-0. Series Title: Emergence, Complexity and Computation [ DOI | http ]
  37. Edman, L., Toral, A., & van Noord, G. (2021). Data Selection for Unsupervised Translation of German-Upper Sorbian. In 5th Conference on Machine Translation, WMT 2020 - Proceedings, pages 1099–1103. Association for Computational Linguistics, ACL Anthology. ISBN 978-1-948087-81-0
  38. Edman, L., Üstün, A., Toral, A., & van Noord, G. (2021). Unsupervised Translation of {G}erman{–}{L}ower {S}orbian: Exploring Training and Novel Transfer Methods on a Low-Resource Language. In Proceedings of the Sixth Conference on Machine Translation, pages 982–988. Association for Computational Linguistics, Online [ http ]
  39. El Khoury, Y., Le Breton, G., Cunha, A. V., et al. (2021). Lessons from combined experimental and theoretical examination of the FTIR and 2D-IR spectroelectrochemistry of the amide I region of cytochrome c. The Journal of Chemical Physics, 154(12), 124201 [ DOI | http ]
  40. van Els, P., Herrera-Alsina, L., Pigot, A. L., & Etienne, R. S. (2021). Evolutionary dynamics of the elevational diversity gradient in passerine birds. Nature Ecology & Evolution, 5(9), 1259–1265 [ DOI | http ]
  41. Feliciani, T., Flache, A., & Mäs, M. (2021). Persuasion without polarization? Modelling persuasive argument communication in teams with strong faultlines. Computational and Mathematical Organization Theory, 27(1), 61–92 [ DOI | http ]
  42. Fontaine, M. C., Labbé, F., Dussert, Y., et al. (2021). Europe as a bridgehead in the worldwide invasion history of grapevine downy mildew, Plasmopara viticola. Current Biology, 31(10), 2155–2166.e4 [ DOI | http ]
  43. Fowler, P. W. & Havenith, R. W. (2021). Per iodoannulenes: A Generalized Annulene-within-an-Annulene Paradigm for Combined σ and π Ring Currents. The Journal of Physical Chemistry A, 125(29), 6374–6383 [ DOI | http ]
  44. Fragasso, A., de Vries, H. W., Andersson, J., et al. (2021). A designer FG-Nup that reconstitutes the selective transport barrier of the nuclear pore complex. Nature Communications, 12(1), 2010 [ DOI | http ]
  45. Gacesa, R., Vich Vila, A., Collij, V., et al. (2021). A combination of fecal calprotectin and human beta-defensin 2 facilitates diagnosis and monitoring of inflammatory bowel disease. Gut Microbes, 13(1) [ DOI | http ]
  46. Gao, J. & Efstathiou, K. (2021). Synchronized clusters in globally connected networks of second-order oscillators: Uncovering the role of inertia. Chaos, 31(9), 093137 [ DOI | http ]
  47. García Hernández, E., Berg, M. P., Van Oosten, A. R., Smit, C., & Falcão Salles, J. (2021). Linking Bacterial Communities Associated with the Environment and the Ecosystem Engineer Orchestia gammarellus at Contrasting Salt Marsh Elevations. Microbial Ecology, 82(2), 537–548 [ DOI | http ]
  48. Gazagnes, S., Koopmans, L. V., & Wilkinson, M. H. (2021). Inferring the properties of the sources of reionization using the morphological spectra of the ionized regions. Monthly Notices of the Royal Astronomical Society, 502(2), 1816–1842 [ DOI | http ]
  49. Gazagnes, S. & Wilkinson, M. H. (2021). Distributed Connected Component Filtering and Analysis in 2D and 3D Tera-Scale Data Sets. IEEE Transactions on Image Processing, 30, 3664–3675 [ DOI | http ]
  50. Georgiou, C., Hoekstra, H., Kuijken, K., et al. (2021). Halo shapes constrained from a pure sample of central galaxies in KiDS-1000. Astronomy and Astrophysics, 647, A185 [ DOI ]
  51. Giblin, B., Heymans, C., Asgari, M., et al. (2021). KiDS-1000 catalogue: Weak gravitational lensing shear measurements. Astronomy and Astrophysics, 645, A105 [ DOI ]
  52. Giocoli, C., Marulli, F., Moscardini, L., et al. (2021). AMICO galaxy clusters in KiDS-DR3: Cosmological constraints from large-scale stacked weak lensing profiles. Astronomy & Astrophysics, 653, A19 [ DOI | http ]
  53. van der Goot, R., Üstün, A., & Plank, B. (2021). On the effectiveness of dataset embeddings in mono-lingual, multi-lingual and zero-shot conditions. Adapt-NLP 2021 - 2nd Workshop on Domain Adaptation for NLP, Proceedings, pages 183–194 [ http ]
  54. van der Graaf, A., Zorro, M. M., Claringbould, A., et al. (2021). Systematic Prioritization of Candidate Genes in Disease Loci Identifies TRAFD1 as a Master Regulator of IFNγ Signaling in Celiac Disease. Frontiers in Genetics, 11 [ DOI | http ]
  55. Grasso, S., Van Rij, T., & Van Dijl, J. M. (2021). GP4: An integrated Gram-Positive Protein Prediction Pipeline for subcellular localization mimicking bacterial sorting. Briefings in Bioinformatics, 22(4) [ DOI | http ]
  56. Groneck, M. & Wallenius, J. (2021). It Sucks to Be Single! Marital Status and Redistribution of Social Security. The Economic Journal, 131(633), 327–371 [ DOI | http ]
  57. Grønnow, A., Tepper-García, T., Bland-Hawthorn, J., & Fraternali, F. (2021). The role of the halo magnetic field on accretion through high-velocity clouds. Monthly Notices of the Royal Astronomical Society, 509(4), 5756–5770 [ DOI | http ]
  58. Guo, Y., Pašteka, L. F., Eliav, E., & Borschevsky, A. (2021). Ionization potentials and electron affinity of oganesson with relativistic coupled cluster method. In Advances in Quantum Chemistry, volume 83, pages 107–123. ISBN 978-0-12-823546-1. ISSN: 00653276 [ DOI | http ]
  59. Haase, P. A., Doeglas, D. J., Boeschoten, A., et al. (2021). Systematic study and uncertainty evaluation of P, T-odd molecular enhancement factors in BaF. Journal of Chemical Physics, 155(3), 034309 [ DOI | http ]
  60. Havenith, R. W. A., Cunha, A. V., Klein, J. E. M. N., Perolari, F., & Feng, X. (2021). The electronic structure of carbones revealed: Insights from valence bond theory. Physical Chemistry Chemical Physics, 23(5), 3327–3334 [ DOI | http ]
  61. Hendriks, K. P., Bisschop, K., Kortenbosch, H. H., et al. (2021). Microbiome and environment explain the absence of correlations between consumers and their diet in Bornean microsnails. Ecology, 102(2) [ DOI | http ]
  62. Heymans, C., Tröster, T., Asgari, M., et al. (2021). KiDS-1000 Cosmology: Multi-probe weak gravitational lensing and spectroscopic galaxy clustering constraints. Astronomy and Astrophysics, 646, A140 [ DOI ]
  63. Hildebrandt, H., Van Den Busch, J. L., Wright, A. H., et al. (2021). KiDS-1000 catalogue: Redshift distributions and their calibration. Astronomy and Astrophysics, 647, A124 [ DOI ]
  64. Holzheimer, M., Sinninghe Damsté, J. S., Schouten, S., et al. (2021). Total Synthesis of the Alleged Structure of Crenarchaeol Enables Structure Revision**. Angewandte Chemie International Edition, 60(32), 17504–17513 [ DOI ]
  65. Jain, S. & Kasaei, H. (2021). 3D_DEN: Open-ended 3D Object Recognition using Dynamically Expandable Networks. IEEE Transactions on Cognitive and Developmental Systems, pages 1–1 [ DOI | http ]
  66. Jansen, T. L. C. (2021). Computational spectroscopy of complex systems. The Journal of Chemical Physics, 155(17), 170901 [ DOI | http ]
  67. Janssen, F., Bardoutsos, A., El Gewily, S., & De Beer, J. (2021). Future life expectancy in Europe taking into account the impact of smoking, obesity, and alcohol. eLife, 10, e66590 [ DOI | http ]
  68. Janssen, F., El Gewily, S., & Bardoutsos, A. (2021). Smoking epidemic in Europe in the 21st century. Tobacco Control, 30(5), 523–529 [ DOI | http ]
  69. Janulevicius, A. & van Doorn, G. S. (2021). Selection for rapid uptake of scarce or fluctuating resource explains vulnerability of glycolysis to imbalance. PLoS Computational Biology, 17(1), e1008547 [ DOI | http ]
  70. Janz, J., Salo, H., Su, A. H., & Venhola, A. (2021). Signatures of quenching in dwarf galaxies in local galaxy clusters: A comparison of the galaxy populations in the Virgo and Fornax clusters. Astronomy and Astrophysics, 647, A80 [ DOI ]
  71. Janzen, T., Bokma, F., & Etienne, R. S. (2021). Nucleotide Substitutions during Speciation may Explain Substitution Rate Variation. Systematic Biology [ DOI | http ]
  72. Jargalsaikhan, B., Romeijnders, W., & Roodbergen, K. J. (2021). A compact arc-based ilp formulation for the pickup and delivery problem with divisible pickups and deliveries. Transportation Science, 55(2), 336–352 [ DOI | http ]
  73. Jo, T. G. & Klein, J. E. M. N. (2021). Gold‐Catalyzed Direct C(sp 3 )−H Acetoxylation of Saturated Hydrocarbons. ChemCatChem, 13(19), 4087–4091 [ DOI | http ]
  74. Joachimi, B., Lin, C. A., Asgari, M., et al. (2021). KiDS-1000 methodology: Modelling and inference for joint weak gravitational lensing and spectroscopic galaxy clustering analysis. Astronomy and Astrophysics, 646, A129 [ DOI ]
  75. Johnston, H., Wright, A. H., Joachimi, B., et al. (2021). Organised randoms: Learning and correcting for systematic galaxy clustering patterns in KiDS using self-organising maps. Astronomy and Astrophysics, 648, A98 [ DOI ]
  76. Kahl, E. V., Raeder, S., Eliav, E., Borschevsky, A., & Berengut, J. C. (2021). Ab initio calculations of the spectrum of lawrencium. Physical Review A, 104(5), 052810 [ DOI | http ]
  77. Kalaja, A., Meerburg, P. D., Pimentel, G. L., & Coulton, W. R. (2021). Fundamental limits on constraining primordial non-Gaussianity. Journal of Cosmology and Astroparticle Physics, 2021(4), 050 [ DOI | http ]
  78. Kaplan, E. C., Wagner, A. E., Toffanin, P., & Başkent, D. (2021). Do Musicians and Non-musicians Differ in Speech-on-Speech Processing? Frontiers in Psychology, 12 [ DOI | http ]
  79. Kasaei, S. H. (2021). OrthographicNet: A Deep Transfer Learning Approach for 3-D Object Recognition in Open-Ended Domains. IEEE/ASME Transactions on Mechatronics, 26(6), 2910–2921 [ DOI | http ]
  80. Kasaei, S. H., Ghorbani, M., Schilperoort, J., & van der Rest, W. (2021). Investigating the importance of shape features, color constancy, color spaces, and similarity measures in open-ended 3D object recognition. Intelligent Service Robotics, 14(3), 329–344 [ DOI | http ]
  81. Keijzer, M. A. & Mäs, M. (2021). The strength of weak bots. Online Social Networks and Media, 21, 100106 [ DOI | http ]
  82. Kiss, F. L., Corbet, B. P., Simeth, N. A., Feringa, B. L., & Crespi, S. (2021). Predicting the substituent effects in the optical and electrochemical properties of N,N′-substituted isoindigos. Photochemical & Photobiological Sciences, 20(7), 927–938 [ DOI | http ]
  83. Kobauri, P., Szymanski, W., Cao, F., et al. (2021). Biaryl sulfonamides as: Cisoid azosteres for photopharmacology. Chemical Communications, 57(34), 4126–4129 [ DOI | http ]
  84. Kolarski, D., Miller, S., Oshima, T., et al. (2021). Photopharmacological Manipulation of Mammalian CRY1 for Regulation of the Circadian Clock. Journal of the American Chemical Society, 143(4), 2078–2087 [ DOI | http ]
  85. Kooij, R., Grønnow, A., & Fraternali, F. (2021). Efficiency of thermal conduction in a magnetized circumgalactic medium. Monthly Notices of the Royal Astronomical Society, 502(1), 1263–1278 [ DOI | http ]
  86. Koopmans, M. & Koster, L. J. A. (2021). Carrier–carrier Coulomb interactions reduce power factor in organic thermoelectrics. Applied Physics Letters, 119(14), 143301 [ DOI | http ]
  87. Korb, J., Meusemann, K., Aumer, D., et al. (2021). Comparative transcriptomic analysis of the mechanisms underpinning ageing and fecundity in social insects. Philosophical Transactions of the Royal Society B: Biological Sciences, 376(1823), 20190728 [ DOI | http ]
  88. Kreider, J. J., Pen, I., & Kramer, B. H. (2021). Antagonistic pleiotropy and the evolution of extraordinary lifespans in eusocial organisms. Evolution Letters, 5(3), 178–186 [ DOI | http ]
  89. Kriksin, Y. (2021). 3D bicontinuous diamond-like morphologies in thin films of soluted block copolymers. Journal of Chemical Physics, 155(5), 054906 [ DOI | http ]
  90. Kriksin, Y. A. & Tishkin, V. F. (2021). Entropy-Stable Discontinuous Galerkin Method for Euler Equations Using Nonconservative Variables. Mathematical Models and Computer Simulations, 13(3), 416–425 [ DOI | http ]
  91. Kruijne, W., Bohte, S. M., Roelfsema, P. R., & Olivers, C. N. (2021). Flexible working memory through selective gating and attentional tagging. Neural Computation, 33(1), 1–40 [ DOI | http ]
  92. Krushynska, A. O., Anerao, N., Badillo-Ávila, M. A., Stokroos, M., & Acuautla, M. (2021). Arbitrary-curved waveguiding and broadband attenuation in additively manufactured lattice phononic media. Materials & Design, 205, 109714 [ DOI | http ]
  93. Kundu, K., Land, M. J., Portioli-Staudacher, A., & Bokhorst, J. A. (2021). Order review and release in make-to-order flow shops: analysis and design of new methods. Flexible Services and Manufacturing Journal, 33(3), 750–782 [ DOI | http ]
  94. Kunsel, T., Günther, L. M., Köhler, J., Jansen, T. L. C., & Knoester, J. (2021). Probing size variations of molecular aggregates inside chlorosomes using single-object spectroscopy. The Journal of Chemical Physics, 155(12), 124310 [ DOI | http ]
  95. Kunsel, T., Jansen, T. L. C., & Knoester, J. (2021). Scaling relations of exciton diffusion in linear aggregates with static and dynamic disorder. The Journal of Chemical Physics, 155(13), 134305 [ DOI | http ]
  96. Kuranova, A., Wigman, J. T. W., Menne-Lothmann, C., et al. (2021). Network dynamics of momentary affect states and future course of psychopathology in adolescents. PLoS ONE, 16(3 March), e0247458 [ DOI | http ]
  97. van der Laan, N. & Romeijnders, W. (2021). A loose Benders decomposition algorithm for approximating two-stage mixed-integer recourse models. Technical report, University of Groningen, SOM research school. Publication Title: Mathematical Programming Volume: 190 Issue: 1-2 ISSN: 14364646 [ DOI | http ]
  98. Lai, H., Toral, A., & Nissim, M. (2021). Generic resources are what you need: Style transfer tasks without task-specific parallel training data. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4241–4254. Association for Computational Linguistics, Stroudsburg, PA, USA [ DOI | http ]
  99. Lai, H., Toral, A., & Nissim, M. (2021). Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer. In ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference, volume 2, pages 484–494. Association for Computational Linguistics, Stroudsburg, PA, USA. ISBN 978-1-954085-52-7 [ DOI ]
  100. Larabi, D. I., Marsman, J.-B. C. B. C., Aleman, A., et al. (2021). Insight does not come at random: Individual gray matter networks relate to clinical and cognitive insight in schizophrenia. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 109, 110251 [ DOI | http ]
  101. Laudanno, G., Haegeman, B., Rabosky, D. L., & Etienne, R. S. (2021). Detecting Lineage-Specific Shifts in Diversification: A Proper Likelihood Approach. Systematic Biology, 70(2), 389–407 [ DOI | http ]
  102. Leach, I. F., Belpassi, L., Belanzoni, P., Havenith, R. W. A., & Klein, J. E. M. N. (2021). Efficient Computation of Geometries for Gold Complexes. ChemPhysChem, 22(12), 1262–1268 [ DOI | http ]
  103. Li, S. S., Kuijken, K., Hoekstra, H., et al. (2021). KiDS+VIKING-450: An internal-consistency test for cosmic shear tomography with a colour-based split of source galaxies. Astronomy and Astrophysics, 646, A175 [ DOI ]
  104. Linde, M. & van Ravenzwaaij, D. (2021). Bayes Factor Model Comparisons Across Parameter Values for Mixed Models. Computational Brain and Behavior [ DOI | http ]
  105. Linde, M., Tendeiro, J. N., Selker, R., Wagenmakers, E. J., & van Ravenzwaaij, D. (2021). Decisions About Equivalence: A Comparison of TOST, HDI-ROPE, and the Bayes Factor. Psychological Methods [ DOI | http ]
  106. Liu, J., Ye, G., Potgieser, H. G. O., et al. (2021). Amphipathic Side Chain of a Conjugated Polymer Optimizes Dopant Location toward Efficient N-Type Organic Thermoelectrics. Advanced Materials, 33(4), 2006694 [ DOI | http ]
  107. Majumdar, A. & Jansen, T. L. C. (2021). Quantum-Classical Simulation of Molecular Motors Driven Only by Light. The Journal of Physical Chemistry Letters, 12(23), 5512–5518 [ DOI | http ]
  108. Marjanovic, A., Rozeboom, H. J., de Vries, M. S., et al. (2021). Catalytic and structural properties of ATP-dependent caprolactamase from Pseudomonas jessenii. Proteins: Structure, Function and Bioinformatics, 89(9), 1079–1098 [ DOI | http ]
  109. Marsden, S. R., Mestrom, L., Wijma, H. J., et al. (2021). Thermodynamics Determine the Diastereochemical Outcome of Catalytic Reactions. ChemCatChem, 13(10), 2530–2536 [ DOI | http ]
  110. Meng, Q., Ramírez-Palacios, C., Capra, N., et al. (2021). Computational Redesign of an ω-Transaminase fromPseudomonas jesseniifor Asymmetric Synthesis of Enantiopure Bulky Amines. ACS Catalysis, 11(17), 10733–10747 [ DOI | http ]
  111. Milocco, F., de Vries, F., Siebe, H. S., et al. (2021). Widening the Window of Spin-Crossover Temperatures in Bis(formazanate)iron(II) Complexes via Steric and Noncovalent Interactions. Inorganic Chemistry, 60(3), 2045–2055 [ DOI | http ]
  112. Morales, D., Talavera, E., & Remeseiro, B. (2021). Playing to distraction: towards a robust training of CNN classifiers through visual explanation techniques. Neural Computing and Applications, 33(24), 16937–16949 [ DOI ]
  113. Nakoneczny, S. J., Bilicki, M., Pollo, A., et al. (2021). Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4. Astronomy and Astrophysics, 649, A81 [ DOI ]
  114. Nemchinova, M., Melcr, J., Wassenaar, T. A., Marrink, S. J., & Guskov, A. (2021). Asymmetric CorA Gating Mechanism as Observed by Molecular Dynamics Simulations. Journal of Chemical Information and Modeling, 61(5), 2407–2417 [ DOI | http ]
  115. Nepal, S., MaaΒ, S., Grasso, S., et al. (2021). Proteomic charting of imipenem adaptive responses in a highly carbapenem resistant clinical Enterobacter roggenkampii isolate. Antibiotics, 10(5), 501 [ DOI | http ]
  116. Neves-Moreira, F., Veldman, J., & Teunter, R. H. (2021). Service operation vessels for offshore wind farm maintenance: Optimal stock levels. Renewable and Sustainable Energy Reviews, 146, 111158 [ DOI | http ]
  117. Owomugisha, G., Melchert, F., Mwebaze, E., Quinn, J. A., & Biehl, M. (2021). Matrix Relevance Learning From Spectral Data for Diagnosing Cassava Diseases. IEEE Access, 9, 83355–83363 [ DOI | http ]
  118. Pannetier, T., Martinez, C., Bunnefeld, L., & Etienne, R. S. (2021). Branching patterns in phylogenies cannot distinguish diversity-dependent diversification from time-dependent diversification. Evolution, 75(1), 25–38 [ DOI | http ]
  119. Pearson, W. J., Wang, L., Brough, S., et al. (2021). Galaxy and Mass Assembly: Group and field galaxy morphologies in the star-formation rate - Stellar mass plane. Astronomy and Astrophysics, 646, A151 [ DOI ]
  120. Pentaliotis, A. & Wiering, M. (2021). Variation-resistant Q-learning: Controlling and utilizing estimation bias in reinforcement learning for better performance. In ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence, volume 2, pages 17–28. SCITEPRESS - Science and Technology Publications. ISBN 978-989-758-484-8 [ DOI | http ]
  121. Pfaehler, E., Mesotten, L., Kramer, G., et al. (2021). Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET. EJNMMI Research, 11(1), 4 [ DOI | http ]
  122. Pfaehler, E., Mesotten, L., Zhovannik, I., et al. (2021). Plausibility and redundancy analysis to select FDG-PET textural features in non-small cell lung cancer. Medical Physics, 48(3), 1226–1238 [ DOI | http ]
  123. Pittelkow, M. M., De Vries, Y. A., Monden, R., Bastiaansen, J. A., & Van Ravenzwaaij, D. (2021). Comparing the evidential strength for psychotropic drugs: A Bayesian meta-analysis. Psychological Medicine, 51(16), 2752–2761 [ DOI | http ]
  124. Pooler, D. R. S., Pierron, R., Crespi, S., et al. (2021). Effect of charge-transfer enhancement on the efficiency and rotary mechanism of an oxindole-based molecular motor. Chemical Science, 12(21), 7486–7497 [ DOI | http ]
  125. Popović, M., Dhali, M. A., & Schomaker, L. (2021). Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa). PLoS ONE, 16(4 April), e0249769 [ DOI | http ]
  126. Puddu, E., Radovich, M., Sereno, M., et al. (2021). AMICO galaxy clusters in KiDS-DR3: Evolution of the luminosity function between z = 0.1 and z = 0.8. Astronomy & Astrophysics, 645, A9 [ DOI | http ]
  127. Qiu, B., Guo, J., Kraeima, J., et al. (2021). Recurrent Convolutional Neural Networks for 3D Mandible Segmentation in Computed Tomography. Journal of Personalized Medicine, 11(6), 492 [ DOI | http ]
  128. Qiu, B., van der Wel, H., Kraeima, J., et al. (2021). Mandible Segmentation of Dental CBCT Scans Affected by Metal Artifacts Using Coarse-to-Fine Learning Model. Journal of Personalized Medicine, 11(6), 560 [ DOI | http ]
  129. Qiu, B., van der Wel, H., Kraeima, J., et al. (2021). Robust and Accurate Mandible Segmentation on Dental CBCT Scans Affected by Metal Artifacts Using a Prior Shape Model. Journal of Personalized Medicine, 11(5), 364 [ DOI | http ]
  130. Ramanantoanina, H., Borschevsky, A., Block, M., & Laatiaoui, M. (2021). Electronic structure of Rf+ (Z=104) from ab initio calculations ELECTRONIC STRUCTURE of Rf+ (Z=104) … HARRY RAMANANTOANINA et al. Physical Review A, 104(2), 022813 [ DOI | http ]
  131. Ramos Padilla, A. F., Wang, L., Ploeckinger, S., Van Der Tak, F. F., & Trager, S. C. (2021). Diagnosing the interstellar medium of galaxies with far-infrared emission lines: I. The [C II] 158 μ m line at z ∼0. Astronomy and Astrophysics, 645 [ DOI | http ]
  132. Ramos Padilla, A. F., Wang, L., Małek, K., Efstathiou, A., & Yang (杨光), G. (2021). The viewing angle in AGN SED models: a data-driven analysis. Monthly Notices of the Royal Astronomical Society, 510(1), 687–707 [ DOI | http ]
  133. Ramírez-Palacios, C., Wijma, H. J., Thallmair, S., Marrink, S. J., & Janssen, D. B. (2021). Computational Prediction of ω-Transaminase Specificity by a Combination of Docking and Molecular Dynamics Simulations. Journal of Chemical Information and Modeling, 61(11), 5569–5580 [ DOI | http ]
  134. van der Reijden, K. J., Govers, L. L., Koop, L., et al. (2021). Beyond connecting the dots: A multi-scale, multi-resolution approach to marine habitat mapping. Ecological Indicators, 128, 107849 [ DOI | http ]
  135. Robertson, N. C., Alonso, D., Harnois-Déraps, J., et al. (2021). Strong detection of the CMB lensing and galaxy weak lensing cross-correlation from ACT-DR4, Planck Legacy, and KiDS-1000. Astronomy and Astrophysics, 649, A146 [ DOI ]
  136. Said, M. A., Yeung, M. W., Van De Vegte, Y. J., et al. (2021). Genome-Wide Association Study and Identification of a Protective Missense Variant on Lipoprotein(a) Concentration: Protective Missense Variant on Lipoprotein(a) Concentration - Brief Report. Arteriosclerosis, Thrombosis, and Vascular Biology, 41(5), 1792–1800 [ DOI | http ]
  137. Saifollahi, T., Janz, J., Peletier, R. F., et al. (2021). Ultra-compact dwarfs beyond the centre of the Fornax galaxy cluster: Hints of UCD formation in low-density environments. Monthly Notices of the Royal Astronomical Society, 504(3), 3580–3609 [ DOI ]
  138. Sami, S., Menger, M. F., Faraji, S., Broer, R., & Havenith, R. W. A. (2021). Q-Force: Quantum Mechanically Augmented Molecular Force Fields. Journal of Chemical Theory and Computation, 17(8), 4946–4960 [ DOI ]
  139. Sangiacomo, A., Tanasescu, R., Donker, S., & Hogenbirk, H. (2021). Expanding the Corpus of Early Modern Natural Philosophy. Journal of Early Modern Studies, 10(1), 107–115 [ DOI | http ]
  140. Sas, D., Avgeriou, P., Kruizinga, R., & Scheedler, R. (2021). Exploring the Relation Between Co-changes and Architectural Smells. SN Computer Science, 2(1), 13 [ DOI | http ]
  141. Schenkel, M. (2021). Evolutionary genetics and dynamics of transitions in sex determination. Ph.D. thesis, University of Groningen [ DOI | http ]
  142. Schenkel, M. A., Beukeboom, L. W., & Pen, I. (2021). Epistatic interactions between sex chromosomes and autosomes can affect the stability of sex determination systems. Journal of Evolutionary Biology, 34(11), 1666–1677 [ DOI | http ]
  143. Schmerbauch, A. E., Vakis, A. I., & Jayawardhana, B. (2021). Electrical coupling analysis of 2D time-multiplexing memory actuators exhibiting asymmetric butterfly hysteresis. Journal of Applied Physics, 130(13), 134502 [ DOI | http ]
  144. Simeth, N. A., Kobayashi, S., Kobauri, P., et al. (2021). Rational design of a photoswitchable DNA glue enabling high regulatory function and supramolecular chirality transfer. Chemical Science, 12(26), 9207–9220 [ DOI | http ]
  145. Skuczynska, B., Shaar, S., Spenader, J., & Nakov, P. (2021). BeaSku at CheckThat! 2021: CLEF 2021 – Conference and Labs of the Evaluation Forum, September 21–24, 2021, Bucharest, Romania. Proceedings of the Working Notes of CLEF 2021, pages 639–647. Publisher: CEUR-WS.org [ http ]
  146. Souza, P. C., Alessandri, R., Barnoud, J., et al. (2021). Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nature Methods, 18(4), 382–388 [ DOI | http ]
  147. Spavone, M., Krajnović, D., Emsellem, E., Iodice, E., & Den Brok, M. (2021). Assembly history of massive galaxies: A pilot project with VEGAS deep imaging and M3G integral field spectroscopy. Astronomy and Astrophysics, 649, A161 [ DOI ]
  148. Speelman, T., Cunha, A. V., Kathir, R. K., & Havenith, R. W. A. (2021). Electronic couplings for singlet fission: Orbital choice and extrapolation to the complete basis set limit. Journal of Computational Chemistry, 42(5), 326–333 [ DOI ]
  149. Spierdijk, L., IJtsma, P., & Shaffer, S. (2021). U.S. banking deregulation and local economic growth: A spatial analysis. Spatial Statistics, 43, 100506 [ DOI | http ]
  150. Streher, L. B., Silvis, M. H., Cifani, P., & Verstappen, R. W. (2021). Mixed modeling for large-eddy simulation: The single-layer and two-layer minimum-dissipation-Bardina models. AIP Advances, 11(1), 015002 [ DOI | http ]
  151. Stölzner, B., Joachimi, B., Korn, A., Hildebrandt, H., & Wright, A. H. (2021). Self-calibration and robust propagation of photometric redshift distribution uncertainties in weak gravitational lensing. Astronomy and Astrophysics, 650, A148 [ DOI ]
  152. Su, A., Salo, H., Janz, J., et al. (2021). Galaxy preprocessing: A Fornax perspective. In American Astronomical Society Meeting Abstracts, volume 53, page 215.01D. Series Title: American Astronomical Society Meeting Abstracts
  153. Su, A., Salo, H., Janz, J., et al. (2021). VizieR Online Data Catalog: Fornax Deep Survey with VST. XI. (Su+, 2021). VizieR Online Data Catalog, pages J/A+A/647/A100
  154. Su, A. H., Salo, H., Janz, J., et al. (2021). The Fornax Deep Survey (FDS) with the VST: XI. The search for signs of preprocessing between the Fornax main cluster and Fornax A group. Astronomy and Astrophysics, 647, A100 [ DOI ]
  155. Thallmair, S., Javanainen, M., Fábián, B., Martinez-Seara, H., & Marrink, S. J. (2021). Nonconverged Constraints Cause Artificial Temperature Gradients in Lipid Bilayer Simulations. The Journal of Physical Chemistry B, 125(33), 9537–9546 [ DOI | http ]
  156. Timmerman, D., Bennabhaktula, G. S., Alegre, E., & Azzopardi, G. (2021). Video camera identification from sensor pattern noise with a constrained convnet. In ICPRAM 2021 - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods, pages 417–425. SCITEPRESS - Science and Technology Publications. ISBN 978-989-758-486-2 [ DOI | http ]
  157. Toplak, A., Teixeira de Oliveira, E. F., Schmidt, M., et al. (2021). From thiol-subtilisin to omniligase: Design and structure of a broadly applicable peptide ligase. Computational and Structural Biotechnology Journal, 19, 1277–1287 [ DOI | http ]
  158. Tröster, T., Asgari, M., Blake, C., et al. (2021). KiDS-1000 Cosmology: Constraints beyond flat ΛcDM. Astronomy and Astrophysics, 649, A88 [ DOI ]
  159. Tziafas, G. & Kasaei, H. (2021). Few-Shot Visual Grounding for Natural Human-Robot Interaction. In 2021 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2021, pages 50–55. IEEE. ISBN 978-1-66543-198-9 [ DOI | http ]
  160. Tziafas, G., Kogkalidis, K., & Caselli, T. (2021). Fighting the COVID-19 Infodemic with a Holistic BERT Ensemble. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 119–124. Association for Computational Linguistics, Stroudsburg, PA, USA [ DOI | http ]
  161. Uit Het Broek, M. A., Schrotenboer, A. H., Jargalsaikhan, B., Roodbergen, K. J., & Coelhoa, L. C. (2021). Asymmetric multidepot vehicle routing problems: Valid inequalities and a branch-and-cut algorithm. Operations Research, 69(2), 380–409 [ DOI | http ]
  162. Uit Het Broek, M. A., Teunter, R. H., de Jonge, B., & Veldman, J. (2021). Joint condition-based maintenance and load-sharing optimization for two-unit systems with economic dependency. European Journal of Operational Research, 295(3), 1119–1131 [ DOI | http ]
  163. Urzúa-Traslaviña, C. G., Leeuwenburgh, V. C., Bhattacharya, A., et al. (2021). Improving gene function predictions using independent transcriptional components. Nature Communications, 12(1), 1464 [ DOI | http ]
  164. van de Vegte, Y. J., Siland, J. E., Rienstra, M., & van der Harst, P. (2021). Atrial fibrillation and left atrial size and function: a Mendelian randomization study. Scientific Reports, 11(1), 8431 [ DOI | http ]
  165. Vink, S. N., Chrysargyris, A., Tzortzakis, N., & Salles, J. F. (2021). Bacterial community dynamics varies with soil management and irrigation practices in grapevines (Vitis vinifera L.). Applied Soil Ecology, 158, 103807 [ DOI | http ]
  166. Vink, S. N., Dini-Andreote, F., Höfle, R., Kicherer, A., & Salles, J. F. (2021). Interactive effects of scion and rootstock genotypes on the root microbiome of grapevines (Vitis spp. l.). Applied Sciences (Switzerland), 11(4), 1–11 [ DOI | http ]
  167. Wang, C., van Noord, R., Bisazza, A., & Bos, J. (2021). Evaluating Text Generation from Discourse Representation Structures. In Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), pages 73–83. Association for Computational Linguistics, Stroudsburg, PA, USA [ DOI ]
  168. Wang, C., van Noord, R., Bisazza, A., & Bos, J. (2021). Input Representations for Parsing Discourse Representation Structures: Comparing English with Chinese. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 767–775. Association for Computational Linguistics, Stroudsburg, PA, USA [ DOI ]
  169. Wang, L., Gao, F., Best, P. N., et al. (2021). The bright end of the infrared luminosity functions and the abundance of hyperluminous infrared galaxies. Astronomy & Astrophysics, 648, A8 [ DOI | http ]
  170. de Wijs, G. A., Kresse, G., Havenith, R. W. A., & Marsman, M. (2021). Comparing GIPAW with numerically exact chemical shieldings: The role of two-center contributions to the induced current. The Journal of Chemical Physics, 155(23), 234101 [ DOI | http ]
  171. Wonink, M. B. S., Corbet, B. P., Kulago, A. A., et al. (2021). Three-State Switching of an Anthracene Extended Bis-thiaxanthylidene with a Highly Stable Diradical State. Journal of the American Chemical Society, 143(43), 18020–18028 [ DOI | http ]
  172. Wullich, S. C., Wijma, H. J., Janssen, D. B., & Fetzner, S. (2021). Stabilizing AqdC, a Pseudomonas Quinolone Signal-Cleaving Dioxygenase from Mycobacteria, by FRESCO-Based Protein Engineering. ChemBioChem, 22(4), 733–742 [ DOI | http ]
  173. Xu, L., Van Doorn, S., Hildenbrandt, H., & Etienne, R. S. (2021). Inferring the Effect of Species Interactions on Trait Evolution. Systematic biology, 70(3), 463–479 [ DOI | http ]
  174. Yakovlieva, L., Ramírez-Palacios, C., Marrink, S. J., & Walvoort, M. T. (2021). Semiprocessive Hyperglycosylation of Adhesin by Bacterial Protein N-Glycosyltransferases. ACS Chemical Biology, 16(1), 165–175 [ DOI | http ]
  175. Yan, Z., Van Waerbeke, L., Tröster, T., et al. (2021). Probing galaxy bias and intergalactic gas pressure with KiDS Galaxies-tSZ-CMB lensing cross-correlations. Astronomy & Astrophysics, 651, A76 [ DOI | http ]
  176. Yoon, M. & Jee, M. J. (2021). Baryonic Feedback Measurement From KV450 Cosmic Shear Analysis. The Astrophysical Journal, 908(1), 13 [ DOI ]
  177. Yuan, Q., van de Bovenkamp, H. H., Zhang, Z., et al. (2021). Mechanistic Investigations into the Catalytic Levulinic Acid Hydrogenation, Insight in H/D Exchange Pathways, and a Synthetic Route to d 8 -γ-Valerolactone. ACS Catalysis, 11(16), 10467–10477 [ DOI ]
  178. van der Zee, J. P., Christianen, M. J., Bérubé, M., et al. (2021). The population genomic structure of green turtles (Chelonia mydas) suggests a warm-water corridor for tropical marine fauna between the Atlantic and Indian oceans during the last interglacial. Heredity, 127(6), 510–521 [ DOI | http ]
  179. Zhang, Z. & Schomaker, L. (2021). DTGAN: Dual Attention Generative Adversarial Networks for Text-to-Image Generation. In Proceedings of the International Joint Conference on Neural Networks, volume 2021-July, pages 1–8. IEEE. ISBN 978-0-7381-3366-9 [ DOI | http ]

2020

  1. Abdullah Said, M., van de Vegte, Y. J., Verweij, N., & van der Harst, P. (2020). Associations of observational and genetically determined caffeine intake with coronary artery disease and diabetes mellitus. Journal of the American Heart Association, 9(24) [ DOI | http ]
  2. Abzianidze, L. (2020). Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference. In Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics, pages 20–31. Association for Computational Linguistics, Barcelona, Spain (Online)
  3. Abzianidze, L., Bos, J., & Oepen, S. (2020). DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs. In Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing, pages 23–32. Association for Computational Linguistics, Online [ DOI | http ]
  4. Aguirre-Gamboa, R., De Klein, N., Di Tommaso, J., et al. (2020). Deconvolution of bulk blood eQTL effects into immune cell subpopulations. BMC Bioinformatics, 21(1), 243 [ DOI | http ]
  5. Albers, C. J. & Bringmann, L. F. (2020). Inspecting Gradual and Abrupt Changes in Emotion Dynamics with the Time-Varying Change Point Autoregressive Model. European Journal of Psychological Assessment, 36(3), 492–499 [ DOI | http ]
  6. Alessandri, R., Sami, S., Barnoud, J., et al. (2020). Resolving Donor–Acceptor Interfaces and Charge Carrier Energy Levels of Organic Semiconductors with Polar Side Chains. Advanced Functional Materials, 30(46), 2004799 [ DOI ]
  7. Ameryan, M. & Schomaker, L. (2020). Improving the robustness of LSTMs for word classification using stressed word endings in dual-state word-beam search. In Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR, volume 2020-Septe, pages 13–18. IEEE. ISBN 978-1-72819-966-5. ISSN: 21676453 [ DOI | http ]
  8. Arabnejad, H., Bombino, E., Colpa, D. I., et al. (2020). Computational Design of Enantiocomplementary Epoxide Hydrolases for Asymmetric Synthesis of Aliphatic and Aromatic Diols. ChemBioChem, 21(13), 1893–1904 [ DOI | http ]
  9. Asgari, M., Tröster, T., Heymans, C., et al. (2020). KiDS+VIKING-450 and DES-Y1 combined: Mitigating baryon feedback uncertainty with COSEBIs. Astronomy and Astrophysics, 634, A127 [ DOI ]
  10. Bartelds, M., Richter, C., Liberman, M., & Wieling, M. (2020). A New Acoustic-Based Pronunciation Distance Measure. Frontiers in Artificial Intelligence, 3, 39 [ DOI | http ]
  11. Beijers, L., Van Loo, H. M., Romeijn, J. W., et al. (2020). Investigating data-driven biological subtypes of sychiatric disorders using specification-curve analysis. Psychological Medicine, pages 1–12 [ DOI | http ]
  12. Bellstedt, S., Driver, S. P., Robotham, A. S., et al. (2020). Galaxy and mass assembly (GAMA): Assimilation of KiDS into the GAMA database. Monthly Notices of the Royal Astronomical Society, 496(3), 3235–3256 [ DOI ]
  13. Ben Chehida, Y., Thumloup, J., Schumacher, C., et al. (2020). Mitochondrial genomics reveals the evolutionary history of the porpoises (Phocoenidae) across the speciation continuum. Scientific Reports, 10(1), 15190 [ DOI | http ]
  14. Ben Chehida, Y., Thumloup, J., Vishnyakova, K., Gol’din, P., & Fontaine, M. C. (2020). Genetic homogeneity in the face of morphological heterogeneity in the harbor porpoise from the Black Sea and adjacent waters (Phocoena phocoena relicta). Heredity, 124(3), 469–484 [ DOI | http ]
  15. Bennabhaktula, G. S., Alegre, E., Karastoyanova, D., & Azzopardi, G. (2020). Device-based Image Matching with Similarity Learning by Convolutional Neural Networks that Exploit the Underlying Camera Sensor Pattern Noise. In ICPRAM 2020 - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods, pages 578–584. SCITEPRESS - Science and Technology Publications. ISBN 978-989-758-397-1 [ DOI | http ]
  16. Berrocal, J. A., Heideman, G. H., de Waal, B. F. M., et al. (2020). Engineering Long-Range Order in Supramolecular Assemblies on Surfaces: The Paramount Role of Internal Double Bonds in Discrete Long-Chain Naphthalenediimides. Journal of the American Chemical Society, 142(8), 4070–4078 [ DOI ]
  17. Blake, C., Amon, A., Asgari, M., et al. (2020). Testing gravity using galaxy-galaxy lensing and clustering amplitudes in KiDS-1000, BOSS, and 2dFLenS. Astronomy and Astrophysics, 642, A158 [ DOI ]
  18. Bondarenko, A. S., Jansen, T. L., & Knoester, J. (2020). Exciton localization in tubular molecular aggregates: Size effects and optical response. Journal of Chemical Physics, 152(19), 194302 [ DOI | http ]
  19. Bondarenko, A. S., Knoester, J., & Jansen, T. L. (2020). Comparison of methods to study excitation energy transfer in molecular multichromophoric systems. Chemical Physics, 529, 110478 [ DOI | http ]
  20. Bootsma, J., Guo, B., De Vries, J. G., & Otten, E. (2020). Ruthenium Complexes with PNN Pincer Ligands Based on (Chiral) Pyrrolidines: Synthesis, Structure, and Dynamic Stereochemistry. Organometallics, 39(4), 544–555 [ DOI | http ]
  21. Boursalian, G. B., Nijboer, E. R., Dorel, R., et al. (2020). All-photochemical rotation of molecular motors with a phosphorus stereoelement. Journal of the American Chemical Society, 142(39), 16868–16876 [ DOI | http ]
  22. Bragin, M. D., Kriksin, Y. A., & Tishkin, V. F. (2020). Discontinuous Galerkin Method with an Entropic Slope Limiter for Euler Equations. Mathematical Models and Computer Simulations, 12(5), 824–833 [ DOI | http ]
  23. Maillette de Buy Wenniger, G., van Dongen, T., Aedmaa, E., et al. (2020). Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction. In Proceedings of the First Workshop on Scholarly Document Processing, pages 158–167. Association for Computational Linguistics, Stroudsburg, PA, USA [ DOI | http ]
  24. Böhmer, V. I., Szymanski, W., van den Berg, K. O., et al. (2020). Modular Medical Imaging Agents Based on Azide–Alkyne Huisgen Cycloadditions: Synthesis and Pre-Clinical Evaluation of 18F-Labeled PSMA-Tracers for Prostate Cancer Imaging. Chemistry - A European Journal, 26(47), 10871–10881 [ DOI | http ]
  25. Cantiello, M., Venhola, A., Grado, A., et al. (2020). The Fornax Deep Survey with VST: IX. Catalog of sources in the FDS area with an example study for globular clusters and background galaxies. Astronomy and Astrophysics, 639, A136 [ DOI ]
  26. Cantiello, M., Venhola, A., Grado, A., et al. (2020). VizieR Online Data Catalog: The Fornax Deep Survey with the VST. IX. (Cantiello+, 2020). VizieR Online Data Catalog, pages J/A+A/639/A136
  27. Capela, F. (2020). Exploring architectural design decisions in issue tracking systems - PACITO - PAttern Changes Identifier TOol. Ph.D. thesis [ http ]
  28. Colla, D., Caselli, T., Basile, V., Mitrović, J., & Granitzer, M. (2020). GruPaTo at SemEval-2020 Task 12: Retraining mBERT on Social Media and Fine-tuned Offensive Language Models. In A. Herbelot, X. Zhu, A. Palmer, N. Schneider, J. May, & E. Shutova, editors, 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings, pages 1546–1554. Association for Computational Linguistics (ACL). ISBN 978-1-952148-31-6 [ DOI ]
  29. Costil, R., Crespi, S., Pfeifer, L., & Feringa, B. L. (2020). Modulation of a Supramolecular Figure-of-Eight Strip Based on a Photoswitchable Stiff-Stilbene. Chemistry - A European Journal, 26(35), 7783–7787 [ DOI | http ]
  30. Denis, M., Hao, Y., Eliav, E., et al. (2020). Enhanced P, T -violating nuclear magnetic quadrupole moment effects in laser-coolable molecules. Journal of Chemical Physics, 152(8), 084303 [ DOI | http ]
  31. Dhar, P., Bisazza, A., & van Noord, G. (2020). Linguistically Motivated Subwords for English-Tamil Translation: University of Groningen's Submission to WMT-2020. In 5th Conference on Machine Translation, WMT 2020 - Proceedings, pages 126–133. ISBN 978-1-948087-81-0
  32. Dietz, M. W., Salles, J. F., Hsu, B. Y., et al. (2020). Prenatal transfer of gut bacteria in rock pigeon. Microorganisms, 8(1), 61 [ DOI | http ]
  33. van Dongen, T., Maillette de Buy Wenniger, G., & Schomaker, L. (2020). SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction. In Proceedings of the First Workshop on Scholarly Document Processing, pages 148–157. Association for Computational Linguistics, Stroudsburg, PA, USA [ DOI | http ]
  34. Dvornik, A., Hoekstra, H., Kuijken, K., et al. (2020). KiDS+GAMA: The weak lensing calibrated stellar-to-halo mass relation of central and satellite galaxies. Astronomy and Astrophysics, 642, A83 [ DOI ]
  35. Edman, L., Toral, A., & Noord, G. V. (2020). Low-Resource Unsupervised NMT: Diagnosing the Problem and Providing a Linguistically Motivated Solution. In A. Martins, H. Moniz, S. Fumega, B. Martins, F. Batista, L. Coheur, C. Parra, I. Trancoso, M. Turchi, A. Bisazza, J. Moorkens, A. Guerberof, M. Nurminen, L. Marg, & M. L. Forcada, editors, Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, EAMT 2020, pages 81–90. ISBN 978-989-33-0589-8 [ http ]
  36. van Eldijk, T. J., Bisschop, K., & Etienne, R. S. (2020). Uniting Community Ecology and Evolutionary Rescue Theory: Community-Wide Rescue Leads to a Rapid Loss of Rare Species. Frontiers in Ecology and Evolution, 8 [ DOI | http ]
  37. Enthoven, D. L., Jargalsaikhan, B., Roodbergen, K. J., uit het Broek, M. A., & Schrotenboer, A. H. (2020). The two-echelon vehicle routing problem with covering options: City logistics with cargo bikes and parcel lockers. Computers and Operations Research, 118, 104919 [ DOI | http ]
  38. Ernst, A. F., Albers, C. J., Jeronimus, B. F., & Timmerman, M. E. (2020). Inter-Individual Differences in Multivariate Time-Series: Latent Class Vector-Autoregressive Modeling. European Journal of Psychological Assessment, 36(3), 482–491 [ DOI | http ]
  39. Farrugia, S., Ellul, J., & Azzopardi, G. (2020). Detection of illicit accounts over the Ethereum blockchain. Expert Systems with Applications, 150, 113318 [ DOI | http ]
  40. Fazi, S., Fransoo, J. C., Van Woensel, T., & Dong, J. X. (2020). A variant of the split vehicle routing problem with simultaneous deliveries and pickups for inland container shipping in dry-port based systems. Transportation Research Part E: Logistics and Transportation Review, 142, 102057 [ DOI | http ]
  41. Fokkema, J. E., Land, M. J., Coelho, L. C., Wortmann, H., & Huitema, G. B. (2020). A continuous-time supply-driven inventory-constrained routing problem. Omega (United Kingdom), 92, 102151 [ DOI | http ]
  42. Francese, T., Vela, S., Deumal, M., et al. (2020). Two different mechanisms of stabilization of regular π-stacks of radicals in switchable dithiazolyl-based materials. Journal of Materials Chemistry C, 8(16), 5437–5448 [ DOI ]
  43. Frantseva, K., Mueller, M., Pokorný, P., Van Der Tak, F. F., & Ten Kate, I. L. (2020). Enrichment of the HR 8799 planets by minor bodies and dust. Astronomy and Astrophysics, 638, A50 [ DOI | http ]
  44. Gabarrini, G., Grasso, S., van Winkelhoff, A. J., & van Dijl, J. M. (2020). Gingimaps: Protein Localization in the Oral Pathogen Porphyromonas gingivalis. Microbiology and Molecular Biology Reviews, 84(1) [ DOI | http ]
  45. Ghosh, S., Tino, P., & Bunte, K. (2020). Visualisation and knowledge discovery from interpretable models. In Proceedings of the International Joint Conference on Neural Networks, pages 1–8. IEEE. ISBN 978-1-72816-926-2 [ DOI | http ]
  46. Gomez Pech, C., Haase, P. A., Sergentu, D. C., et al. (2020). Quantum chemical topology at the spin–orbit configuration interaction level: Application to astatine compounds. Journal of Computational Chemistry, 41(23), 2055–2065 [ DOI | http ]
  47. van der Graaf, A., Claringbould, A., Rimbert, A., et al. (2020). Mendelian randomization while jointly modeling cis genetics identifies causal relationships between gene expression and lipids. Nature Communications, 11(1), 4930 [ DOI | http ]
  48. Groot, H. E., van Blokland, I. V., Lipsic, E., Karper, J. C., & van der Harst, P. (2020). Leukocyte profiles across the cardiovascular disease continuum: A population-based cohort study. Journal of Molecular and Cellular Cardiology, 138, 158–164 [ DOI | http ]
  49. Groot, H. E., van de Vegte, Y. J., Verweij, N., et al. (2020). Human genetic determinants of the gut microbiome and their associations with health and disease: a phenome-wide association study. Scientific Reports, 10(1), 14771 [ DOI | http ]
  50. Grünewald, F., Souza, P. C., Abdizadeh, H., et al. (2020). Titratable Martini model for constant pH simulations. Journal of Chemical Physics, 153(2), 024118 [ DOI | http ]
  51. Gustafsson, F. P., Ricketts, C. M., Reitsma, M. L., et al. (2020). Tin resonance-ionization schemes for atomic- And nuclear-structure studies. Physical Review A, 102(5), 052812 [ DOI | http ]
  52. Haase, P. A., Eliav, E., Iliaš, M., & Borschevsky, A. (2020). Hyperfine Structure Constants on the Relativistic Coupled Cluster Level with Associated Uncertainties. Journal of Physical Chemistry A, 124(16), 3157–3169 [ DOI | http ]
  53. Hammond, T., Schaap, D. J., Sabatelli, M., & Wiering, M. A. (2020). Forest Fire Control with Learning from Demonstration and Reinforcement Learning. In Proceedings of the International Joint Conference on Neural Networks, pages 1–8. IEEE. ISBN 978-1-72816-926-2 [ DOI | http ]
  54. Hao, Y., Navrátil, P., Norrgard, E. B., et al. (2020). Nuclear spin-dependent parity-violating effects in light polyatomic molecules. Physical Review A, 102(5), 052828 [ DOI | http ]
  55. He, Z., Er, X., Long, Q., et al. (2020). Deep learning for strong lensing search: Tests of the convolutional neural networks and new candidates from KiDS DR3. Monthly Notices of the Royal Astronomical Society, 497(1), 556–571 [ DOI ]
  56. Hernández, E. G., Baraza, E., Smit, C., Berg, M. P., & Salles, J. F. (2020). Salt marsh elevation drives root microbial composition of the native invasive grass elytrigia atherica. Microorganisms, 8(10), 1–19 [ DOI | http ]
  57. Heydenreich, S., Schneider, P., Hildebrandt, H., et al. (2020). The effects of varying depth in cosmic shear surveys. Astronomy and Astrophysics, 634, A104 [ DOI ]
  58. Hildebrandt, H., Köhlinger, F., Van Den Busch, J. L., et al. (2020). KiDS+VIKING-450: Cosmic shear tomography with optical and infrared data. Astronomy and Astrophysics, 633, A69 [ DOI ]
  59. Huitsing, G., Lodder, G. M., Browne, W. J., et al. (2020). A Large-Scale Replication of the Effectiveness of the KiVa Antibullying Program: a Randomized Controlled Trial in the Netherlands. Prevention Science, 21(5), 627–638 [ DOI | http ]
  60. Iodice, E. (2020). VEGAS-LSS: Mapping the large scale structure of the Fornax-Eridanus & Hydra I-Centaurus superclusters. In VST Beyond 2021, page 8 [ DOI ]
  61. Iodice, E., Cantiello, M., Hilker, M., et al. (2020). The first detection of ultra-diffuse galaxies in the Hydra i cluster from the VEGAS survey. Astronomy and Astrophysics, 642, A48 [ DOI ]
  62. Iodice, E., Spavone, M., Cattapan, A., et al. (2020). VEGAS: A VST Early-type GAlaxy Survey: V. IC 1459 group: Mass assembly history in low-density environments. Astronomy and Astrophysics, 635, A3 [ DOI ]
  63. Izquierdo, M. A., Tarantelli, F., Broer, R., et al. (2020). Orbital Decomposition of the Carbon Chemical Shielding Tensor in Gold(I) N‐Heterocyclic Carbene Complexes. European Journal of Inorganic Chemistry, 2020(13), 1177–1183 [ DOI ]
  64. Jafarinia, H., van der Giessen, E., & Onck, P. R. (2020). Phase Separation of Toxic Dipeptide Repeat Proteins Related to C9orf72 ALS/FTD. Biophysical Journal, 119(4), 843–851 [ DOI | http ]
  65. Janssen, F., Bardoutsos, A., & Vidra, N. (2020). Obesity Prevalence in the Long-Term Future in 18 European Countries and in the USA. Obesity Facts, 13(5), 514–527 [ DOI | http ]
  66. Janssen, F., Gewily, S. E., Bardoutsos, A., & Trias-Llimós, S. (2020). Past and future alcohol-attributable mortality in Europe. International Journal of Environmental Research and Public Health, 17(23), 1–21 [ DOI | http ]
  67. Janzen, T., Zotz, G., & Etienne, R. S. (2020). Community structure of vascular epiphytes: a neutral perspective. Oikos, 129(6), 853–867 [ DOI | http ]
  68. Jia, X., Dini-Andreote, F., Salles, J. F., & Falcão Salles, J. (2020). Comparing the influence of assembly processes governing bacterial community succession based on DNA and RNA data. Microorganisms, 8(6), 798 [ DOI | http ]
  69. Joudaki, S., Hildebrandt, H., Traykova, D., et al. (2020). KiDS+VIKING-450 and DES-Y1 combined: Cosmology with cosmic shear. Astronomy and Astrophysics, 638, L1 [ DOI ]
  70. Kanellakopoulos, A., Yang, X. F., Bissell, M. L., et al. (2020). Nuclear moments of germanium isotopes near N=40. Physical Review C, 102(5), 054331 [ DOI | http ]
  71. Kathir, R. K., de Graaf, C., Broer, R., & Havenith, R. W. A. (2020). Reduced Common Molecular Orbital Basis for Nonorthogonal Configuration Interaction. Journal of Chemical Theory and Computation, 16(5), 2941–2951 [ DOI ]
  72. Khan, H. M., Souza, P. C., Thallmair, S., et al. (2020). Capturing Choline-Aromatics Cation-πInteractions in the MARTINI Force Field. Journal of Chemical Theory and Computation, 16(4), 2550–2560 [ DOI | http ]
  73. Knabel, S., Steele, R. L., Holwerda, B. W., et al. (2020). Galaxy and Mass Assembly: A Comparison between Galaxy–Galaxy Lens Searches in KiDS/GAMA. The Astronomical Journal, 160(5), 223 [ DOI | http ]
  74. Koopmans, M., Leiviskä, M. A., Liu, J., et al. (2020). Electrical Conductivity of Doped Organic Semiconductors Limited by Carrier-Carrier Interactions. ACS Applied Materials and Interfaces, 12(50), 56222–56230 [ DOI | http ]
  75. Kriete, B., Bondarenko, A. S., Alessandri, R., et al. (2020). Molecular versus excitonic disorder in individual artificial light-harvesting systems. Journal of the American Chemical Society, 142(42), 18073–18085 [ DOI | http ]
  76. Kriksin, Y. & Erukhimovich, I. (2020). Macrophase versus Microphase Separation in Solutions of Block Copolymers: Lifshitz Line in the Energetic Parameter's Space. Macromolecular Theory and Simulations, 29(6), 2000044 [ DOI | http ]
  77. Kulish, K., Boldrini, C., Castiñeira Reis, M., Pérez, J. M., & Harutyunyan, S. R. (2020). Lewis Acid Promoted Dearomatization of Naphthols. Chemistry - A European Journal, 26(68), 15843–15846 [ DOI | http ]
  78. Kumar, S., Soni, S., Danowski, W., et al. (2020). Correlating the influence of disulfides in monolayers across photoelectron spectroscopy wettability and tunneling charge-transport. Journal of the American Chemical Society, 142(35), 15075–15083 [ DOI | http ]
  79. Kunsel, T., Löhner, A., Mayo, J. J., et al. (2020). Unraveling intra-aggregate structural disorder using single-molecule spectroscopy. The Journal of Chemical Physics, 153(13), 134304 [ DOI | http ]
  80. Larabi, D. I., Renken, R. J., Cabral, J., et al. (2020). Trait self-reflectiveness relates to time-varying dynamics of resting state functional connectivity and underlying structural connectomes: Role of the default mode network. NeuroImage, 219, 116896 [ DOI | http ]
  81. Laureijs, R. J., Racca, G., Mellier, Y., et al. (2020). Euclid mission status after mission critical design. In Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, volume 11443, page 7. ISBN 978-1-5106-3673-6. Series Title: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series ISSN: 1996756X [ DOI ]
  82. Leimbach, D., Karls, J., Guo, Y., et al. (2020). The electron affinity of astatine. Nature Communications, 11(1), 3824 [ DOI | http ]
  83. Li, R., Napolitano, N. R., Tortora, C., et al. (2020). New High-quality Strong Lens Candidates with Deep Learning in the Kilo-Degree Survey. The Astrophysical Journal, 899(1), 30 [ DOI ]
  84. Li, S., Van Der Velde, K. J., De Ridder, D., et al. (2020). CAPICE: A computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations. Genome Medicine, 12(1), 75 [ DOI | http ]
  85. Linke, L., Simon, P., Schneider, P., et al. (2020). KiDS+VIKING+GAMA: Testing semi-analytic models of galaxy evolution with galaxy-galaxy-galaxy lensing. Astronomy and Astrophysics, 640, A59 [ DOI ]
  86. Liu, J., van der Zee, B., Alessandri, R., et al. (2020). N-type organic thermoelectrics: demonstration of ZT > 0.3. Nature Communications, 11(1), 5694 [ DOI ]
  87. Liu, Y., De Vries, A. H., Barnoud, J., et al. (2020). Dual Resolution Membrane Simulations Using Virtual Sites. Journal of Physical Chemistry B, 124(19), 3944–3953 [ DOI | http ]
  88. Liu, Y., Ornago, L., Carlotti, M., et al. (2020). Intermolecular Effects on Tunneling through Acenes in Large-Area and Single-Molecule Junctions. Journal of Physical Chemistry C, 124(41), 22776–22783 [ DOI | http ]
  89. Liu, Y., Pezeshkian, W., Barnoud, J., De Vries, A. H., & Marrink, S. J. (2020). Coupling Coarse-Grained to Fine-Grained Models via Hamiltonian Replica Exchange. Journal of Chemical Theory and Computation, 16(8), 5313–5322 [ DOI | http ]
  90. Lopera Maya, E. A., van der Graaf, A., Lanting, P., et al. (2020). Lack of Association Between Genetic Variants at ACE2 and TMPRSS2 Genes Involved in SARS-CoV-2 Infection and Human Quantitative Phenotypes. Frontiers in Genetics, 11 [ DOI | http ]
  91. Luo, L., P. Straatsma, T., Suarez, L. E. A., et al. (2020). Pre-exascale accelerated application development: The ORNL Summit experience. IBM Journal of Research and Development, 64(3/4), 11:1–11:21 [ DOI ]
  92. Luo, S., Kasaei, H., & Schomaker, L. (2020). Accelerating Reinforcement Learning for Reaching Using Continuous Curriculum Learning. In Proceedings of the International Joint Conference on Neural Networks, pages 1–8. IEEE. ISBN 978-1-72816-926-2 [ DOI | http ]
  93. Maenpuen, S., Pongsupasa, V., Pensook, W., et al. (2020). Creating Flavin Reductase Variants with Thermostable and Solvent-Tolerant Properties by Rational-Design Engineering. ChemBioChem, 21(10), 1481–1491 [ DOI | http ]
  94. Maity, S., Ottelé, J., Santiago, G. M., et al. (2020). Caught in the Act: Mechanistic Insight into Supramolecular Polymerization-Driven Self-Replication from Real-Time Visualization. Journal of the American Chemical Society, 142(32), 13709–13717 [ DOI | http ]
  95. Marinov, B., Spenader, J., & Caselli, T. (2020). Topic and Emotion Development among {D}utch {COVID}-19 {T}witter Communities in the early Pandemic. In M. Nissim, V. Patti, B. Plank, & E. Durmus, editors, Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media, pages 87–98. Association for Computational Linguistics (ACL) [ http ]
  96. Markovitch, O., Kramer, B. H., Weissing, F. J., van Doorn, G. S., & Otto, S. (2020). Competition Dynamics in a Chemical System of Self-replicating Macrocycles. In The 2020 Conference on Artificial Life, pages 462–464. MIT Press, Cambridge, MA [ DOI | http ]
  97. Medema, M. & Lazovik, A. (2020). The Community Structure of Constraint Satisfaction Problems and Its Correlation with Search Time. In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), pages 153–160. IEEE, Baltimore, MD, USA. ISBN 978-1-72819-228-4 [ DOI | http ]
  98. Meng, Q., Capra, N., Palacio, C. M., et al. (2020). Robust ω-Transaminases by Computational Stabilization of the Subunit Interface. ACS Catalysis, 10(5), 2915–2928 [ DOI | http ]
  99. Milocco, F., de Vries, F., Bartels, I. M. A., et al. (2020). Electronic Control of Spin-Crossover Properties in Four-Coordinate Bis(formazanate) Iron(II) Complexes. Journal of the American Chemical Society, 142(47), 20170–20181 [ DOI | http ]
  100. Minhas, V., Aprianto, R., McAllister, L. J., et al. (2020). In vivo dual RNA-seq reveals that neutrophil recruitment underlies differential tissue tropism of Streptococcus pneumoniae. Communications Biology, 3(1), 293 [ DOI | http ]
  101. Mulder, W., de Jong, J., Verdoes Kleijn, G., et al. (2020). The Astrometric Calibration Software System for Euclid's External Surveys. In R. Pizzo, E. Deul, J. Mol, J. de Plaa, & H. Verkouter, editors, Astronomical Society of the Pacific Conference Series, volume 527, page 615. Series Title: Astronomical Society of the Pacific Conference Series
  102. Nagels, L., Bastiaanse, R., Başkent, D., & Wagner, A. (2020). Individual differences in lexical access among cochlear implant users. Journal of Speech, Language, and Hearing Research, 63(1), 286–304 [ DOI | http ]
  103. Napolitano, N. R., Li, R., Spiniello, C., et al. (2020). Discovery of Two Einstein Crosses from Massive Post-blue Nugget Galaxies at z 1 in KiDS. The Astrophysical Journal, 904(2), L31 [ DOI ]
  104. Noel-Storr, J., Mulder, W., Vogelaar, M., & Verdoes Kleijn, G. (2020). The 2019 ADASS/Kapteyn Youth Hackathon. In R. Pizzo, E. Deul, J. Mol, J. de Plaa, & H. Verkouter, editors, Astronomical Society of the Pacific Conference Series, volume 527, page 791. Series Title: Astronomical Society of the Pacific Conference Series
  105. van Noord, R., Toral, A., & Bos, J. (2020). Character-level representations improve drs-based semantic parsing even in the age of BERT. In EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, pages 4587–4603. Association for Computational Linguistics (ACL). ISBN 978-1-952148-60-6 [ DOI ]
  106. van den Oever, L. B., Cornelissen, L., Vonder, M., et al. (2020). Deep learning for automated exclusion of cardiac CT examinations negative for coronary artery calcium. European Journal of Radiology, 129, 109114 [ DOI | http ]
  107. Pawara, P., Boshchenko, A., Schomaker, L. R., & Wiering, M. A. (2020). Deep Learning with Data Augmentation for Fruit Counting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 12415 LNAI, pages 203–214. ISBN 978-3-030-61400-3. ISSN: 16113349 [ DOI | http ]
  108. Pawara, P., Okafor, E., Groefsema, M., et al. (2020). One-vs-One classification for deep neural networks. Pattern Recognition, 108, 107528 [ DOI | http ]
  109. Pezeshkian, W., König, M., Wassenaar, T. A., & Marrink, S. J. (2020). Backmapping triangulated surfaces to coarse-grained membrane models. Nature Communications, 11(1), 2296 [ DOI | http ]
  110. Pfaehler, E., Mesotten, L., Kramer, G., et al. (2020). Textural Feature Based Segmentation: A Repeatable and Accurate Segmentation Approach for Tumors in PET Images. In Communications in Computer and Information Science, volume 1248 CCIS, pages 3–14. ISBN 978-3-030-52790-7. ISSN: 18650937 [ DOI | http ]
  111. van der Ploeg, R., Steglich, C., & Veenstra, R. (2020). The way bullying works: How new ties facilitate the mutual reinforcement of status and bullying in elementary schools. Social Networks, 60, 71–82 [ DOI | http ]
  112. Poot, C. & van Cranenburgh, A. (2020). A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News. In Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference, pages 79–90. Association for Computational Linguistics, Barcelona, Spain (online) [ http ]
  113. Rab, C., Kamp, I., Dominik, C., et al. (2020). Interpreting high spatial resolution line observations of planet-forming disks with gaps and rings: The case of HD 163296. Astronomy and Astrophysics, 642, A165 [ DOI | http ]
  114. Radovich, M., Tortora, C., Bellagamba, F., et al. (2020). AMICO galaxy clusters in KiDS-DR3: galaxy population properties and their redshift dependence. Monthly Notices of the Royal Astronomical Society, 498(3), 4303–4315 [ DOI | http ]
  115. Rahbar Niazi, M., Sanjabi, S., Jamali, Y., & Miresmaeili, R. (2020). Martensitic transformation in stoichiometric NiMn and Ni–Mn-X alloys: A first principles study. Journal of Alloys and Compounds, 845, 155978 [ DOI | http ]
  116. Raj, M. A., Iodice, E., Napolitano, N. R., et al. (2020). The Fornax Deep Survey with VST: X. The assembly history of the bright galaxies and intra-group light in the Fornax A subgroup. Astronomy and Astrophysics, 640, A137 [ DOI ]
  117. Roest, C., Edman, L., Minnema, G., et al. (2020). Machine Translation for English-Inuktitut with Segmentation, Data Acquisition and Pre-Training. In 5th Conference on Machine Translation, WMT 2020 - Proceedings, pages 274–281. Association for Computational Linguistics (ACL). ISBN 978-1-948087-81-0
  118. la Roi, C., Dijkstra, J. K., Kretschmer, T., Savickaitė, R., & Veenstra, R. (2020). Peers and Homophobic Attitudes in Adolescence: Examining Selection and Influence Processes in Friendships and Antipathies. Journal of Youth and Adolescence, 49(11), 2229–2245 [ DOI | http ]
  119. Romeijnders, W. & van der Laan, N. (2020). Pseudo-valid cutting planes for two-stage mixed-integer stochastic programs with right-hand-side uncertainty. Operations Research, 68(4), 1199–1217 [ DOI | http ]
  120. Sami, S., Alessandri, R., Broer, R., & Havenith, R. W. A. (2020). How Ethylene Glycol Chains Enhance the Dielectric Constant of Organic Semiconductors: Molecular Origin and Frequency Dependence. ACS Applied Materials & Interfaces, 12(15), 17783–17789 [ DOI ]
  121. Sardjan, A. S., Roy, P., Danowski, W., et al. (2020). Ultrafast Excited State Dynamics in a First Generation Photomolecular Motor. ChemPhysChem, 21(7), 594–599 [ DOI | http ]
  122. Sardjan, A. S., Westerman, F. P., Ogilvie, J. P., & Jansen, T. L. (2020). Observation of Ultrafast Coherence Transfer and Degenerate States with Polarization-Controlled Two-Dimensional Electronic Spectroscopy. Journal of Physical Chemistry B, 124(42), 9420–9427 [ DOI | http ]
  123. Schmerbauch, A. E. M., Vasquez-Beltran, M. A., Vakis, A. I., Huisman, R., & Jayawardhana, B. (2020). Influence functions for a hysteretic deformable mirror with a high-density 2D array of actuators. Applied Optics, 59(27), 8077 [ DOI | http ]
  124. Schubert, M., Colomé-Tatché, M., & Foijer, F. (2020). Gene networks in cancer are biased by aneuploidies and sample impurities. Biochimica et Biophysica Acta - Gene Regulatory Mechanisms, 1863(6), 194444 [ DOI | http ]
  125. Scognamiglio, D., Tortora, C., Spavone, M., et al. (2020). Building the Largest Spectroscopic Sample of Ultracompact Massive Galaxies with the Kilo Degree Survey. The Astrophysical Journal, 893(1), 4 [ DOI ]
  126. Sereno, M., Ettori, S., Lesci, G. F., et al. (2020). CoMaLit – VI. Intrinsic scatter in stacked relations. The weak lensing AMICO galaxy clusters in KiDS-DR3. Monthly Notices of the Royal Astronomical Society, 497(1), 894–905 [ DOI | http ]
  127. Shaffer, S. & Spierdijk, L. (2020). Measuring multi-product banks’ market power using the Lerner index. Journal of Banking and Finance, 117, 105859 [ DOI | http ]
  128. Singh, S., Valentijn, E., Belikov, A., Buddelmeijer, H., & Roerdink, J. (2020). Scientific Visualisation of Extremely Large Distributed Astronomical Surveys. In R. Pizzo, E. Deul, J. Mol, J. de Plaa, & H. Verkouter, editors, Astronomical Data Analysis Software and Systems XXIX, volume 527, page 201. Series Title: Astronomical Society of the Pacific Conference Series
  129. Soni, S., Ye, G., Zheng, J., et al. (2020). Understanding the Role of Parallel Pathways via In-Situ Switching of Quantum Interference in Molecular Tunneling Junctions. Angewandte Chemie - International Edition, 59(34), 14308–14312 [ DOI | http ]
  130. Spavone, M., Iodice, E., Van De Ven, G., et al. (2020). The Fornax Deep Survey with VST: VIII. Connecting the accretion history with the cluster density. Astronomy and Astrophysics, 639, A14 [ DOI ]
  131. Straatsma, T. P., Broer, R., Faraji, S., et al. (2020). GronOR: Massively parallel and GPU-accelerated non-orthogonal configuration interaction for large molecular systems. The Journal of Chemical Physics, 152(6), 064111 [ DOI ]
  132. Taylor, E. N., Cluver, M. E., Duffy, A., et al. (2020). GAMA + KiDS: Empirical correlations between halo mass and other galaxy properties near the knee of the stellar-to-halo mass relation. Monthly Notices of the Royal Astronomical Society, 499(2), 2896–2911 [ DOI ]
  133. Tegegne, B. S., Man, T., Van Roon, A. M., et al. (2020). Heritability and the genetic correlation of heart rate variability and blood pressure in 29 000 families: The lifelines cohort study. Hypertension, 76(4), 1256–1262 [ DOI | http ]
  134. Toral, A., Oliver, A., & Ballestín, P. R. (2020). Machine Translation of Novels in the Age of Transformer. In J. Porsiel, editor, Maschinelle Übersetzung für Übersetzungsprofis, pages 276–295. BDÜ Fachverlag. ISBN 978-3-946702-09-2 [ http ]
  135. Tortora, C., Napolitano, N. R., Radovich, M., et al. (2020). Nature versus nurture: Relic nature and environment of the most massive passive galaxies at z 0.5. Astronomy and Astrophysics, 638, L11 [ DOI ]
  136. Tröster, T., Sánchez, A. G., Asgari, M., et al. (2020). Cosmology from large-scale structure: Constraining ΛcDM with BOSS. Astronomy and Astrophysics, 633, L10 [ DOI ]
  137. Uithet Broek, M. A., Teunter, R. H., de Jonge, B., Veldman, J., & van Foreest, N. D. (2020). Condition-based production planning: Adjusting production rates to balance output and failure risk. Manufacturing and Service Operations Management, 22(4), 792–811 [ DOI | http ]
  138. Valente, L., Phillimore, A. B., Melo, M., et al. (2020). A simple dynamic model explains the diversity of island birds worldwide. Nature, 579(7797), 92–96 [ DOI | http ]
  139. Van Den Busch, J. L., Hildebrandt, H., Wright, A. H., et al. (2020). Testing KiDS cross-correlation redshifts with simulations. Astronomy and Astrophysics, 642, A200 [ DOI ]
  140. Vazquez-Salazar, L. I., Selle, M., De Vries, A. H., Marrink, S. J., & Souza, P. C. (2020). Martini coarse-grained models of imidazolium-based ionic liquids: From nanostructural organization to liquid-liquid extraction. Green Chemistry, 22(21), 7376–7386 [ DOI | http ]
  141. van Veelen, H. P. J., Falcão Salles, J., Matson, K. D., van der Velde, M., & Tieleman, B. I. (2020). Microbial environment shapes immune function and cloacal microbiota dynamics in zebra finches Taeniopygia guttata. Animal Microbiome, 2(1), 21 [ DOI | http ]
  142. van de Vegte, Y. J., Said, M. A., Rienstra, M., van der Harst, P., & Verweij, N. (2020). Genome-wide association studies and Mendelian randomization analyses for leisure sedentary behaviours. Nature Communications, 11(1), 1770 [ DOI | http ]
  143. Venhola, A., Peletier, R., Laurikainen, E., et al. (2020). Erratum: The Fornax Deep Survey (FDS) with VST: VI. Optical properties of the dwarf galaxies in the Fornax cluster(A&A (2019) 625 (A143) DOI: 10.1051/0004-6361/201935231). Astronomy and Astrophysics, 633, C2 [ DOI ]
  144. Venhola, A., Peletier, R., Laurikainen, E., et al. (2020). Erratum: The Fornax Deep Survey with the VST: IV. A size and magnitude limited catalog of dwarf galaxies in the area of the Fornax cluster (A&A (2018) 620 A165 DOI: 10.1051/0004-6361/201833933). Astronomy and Astrophysics, 638, C5 [ DOI | http ]
  145. Verschoor, M., Albers, C., Poortinga, W., Böhm, G., & Steg, L. (2020). Exploring relationships between climate change beliefs and energy preferences: A network analysis of the European Social Survey. Journal of Environmental Psychology, 70, 101435 [ DOI | http ]
  146. Verweij, N., Benjamins, J. W., Morley, M. P., et al. (2020). The Genetic Makeup of the Electrocardiogram. Cell Systems, 11(3), 229–238.e5 [ DOI | http ]
  147. de Vries, Y., Verdoes Kleijn, G., Frantseva, K., & Mahlke, M. (2020). Data Mining the Kilo{textendash}Degree Survey for Solar System Objects. In R. Pizzo, E. Deul, J. Mol, J. de Plaa, & H. Verkouter, editors, Astronomical Society of the Pacific Conference Series, volume 527, page 651. Series Title: Astronomical Society of the Pacific Conference Series
  148. Wang, L., Pearson, W. J., & Rodriguez-Gomez, V. (2020). Towards a consistent framework of comparing galaxy mergers in observations and simulations. Astronomy and Astrophysics, 644, A87 [ DOI | http ]
  149. Williams, O., Begeman, K., Boxhoorn, D., et al. (2020). The Euclid Archive Processing and Data Distribution Systems: a Distributed Infrastructure for Euclid and Associated Data. In R. Pizzo, E. Deul, J. Mol, J. de Plaa, & H. Verkouter, editors, Astronomical Society of the Pacific Conference Series, volume 527, page 291. Series Title: Astronomical Society of the Pacific Conference Series
  150. Wright, A. H., Hildebrandt, H., Van Den Busch, J. L., et al. (2020). KiDS+VIKING-450: Improved cosmological parameter constraints from redshift calibration with self-organising maps. Astronomy and Astrophysics, 640, L14 [ DOI ]
  151. Xia, Q., Robertson, N., Heymans, C., et al. (2020). A gravitational lensing detection of filamentary structures connecting luminous red galaxies. Astronomy and Astrophysics, 633, A89 [ DOI ]
  152. Ye, G., Liu, Y., Abdu-Aguye, M., Loi, M. A., & Chiechi, R. C. (2020). Synthesis, optical and electrochemical properties of high-quality cross-conjugated aromatic polyketones. ACS Omega, 5(9), 4689–4696 [ DOI | http ]
  153. Ye, G., Liu, Y., Liu, J., et al. (2020). Protonic acid doping of low band-gap conjugated polyions. Materials Chemistry Frontiers, 4(12), 3585–3593 [ DOI | http ]
  154. Zhang, P., Das, A., Barts, E., et al. (2020). Robust skyrmion-bubble textures in SrRu O3 thin films stabilized by magnetic anisotropy. Physical Review Research, 2(3), 032026 [ DOI | http ]
  155. Üstün, A., Bisazza, A., Bouma, G., & van Noord, G. (2020). UDapter: Language adaptation for truly universal dependency parsing. EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, pages 2302–2315 [ DOI | http ]

2019

  1. Acke, G., Van Damme, S., Havenith, R. W. A., & Bultinck, P. (2019). Quantifying the conceptual problems associated with the isotropic NICS through analyses of its underlying density. Physical Chemistry Chemical Physics, 21(6), 3145–3153 [ DOI | http ]
  2. Aggarwal, P., Marshall, V. R., Bethlem, H. L., et al. (2019). Lifetime measurements of the A 2 Π1/2 and A 2 Π3/2 states in BaF. Physical Review A, 100(5), 052503 [ DOI | http ]
  3. Aguilar Suarez, L. E., Kathir, R., Siagri, E., Havenith, R. W., & Faraji, S. (2019). Determination of electronic couplings in the singlet fission process using a nonorthogonal configuration interaction approach. pages 263–287 [ DOI ]
  4. Alessandri, R., Souza, P. C., Thallmair, S., et al. (2019). Pitfalls of the Martini Model. Journal of Chemical Theory and Computation, 15(10), 5448–5460 [ DOI | http ]
  5. Alzate, A., Janzen, T., Bonte, D., Rosindell, J., & Etienne, R. S. (2019). A simple spatially explicit neutral model explains the range size distribution of reef fishes. Global Ecology and Biogeography, 28(7), 875–890 [ DOI | http ]
  6. Amaro, V., Cavuoti, S., Brescia, M., et al. (2019). Statistical analysis of probability density functions for photometric redshifts through the KiDS-ESO-DR3 galaxies. Monthly Notices of the Royal Astronomical Society, 482(3), 3116–3134 [ DOI ]
  7. Angora, G., Brescia, M., Cavuoti, S., et al. (2019). Astroinformatics-based search for globular clusters in the Fornax Deep Survey. Monthly Notices of the Royal Astronomical Society, 490(3), 4080–4106 [ DOI ]
  8. Ansó, N. S., Wiehe, A. O., Drugan, M. M., & Wiering, M. A. (2019). Deep reinforcement learning for pellet eating in agar.iO. In ICAART 2019 - Proceedings of the 11th International Conference on Agents and Artificial Intelligence, volume 2, pages 123–133. SCITEPRESS - Science and Technology Publications. ISBN 978-989-758-350-6 [ DOI | http ]
  9. Asgari, M., Heymans, C., Hildebrandt, H., et al. (2019). Consistent cosmic shear in the face of systematics: A B -mode analysis of KiDS-450, DES-SV and CFHTLenS. Astronomy and Astrophysics, 624, A134 [ DOI ]
  10. Baars, S. (2019). Numerical methods for studying transition probabilities in stochastic ocean-climate models. Ph.D. thesis, University of Groningen. ISBN: 978-94-034-1709-7
  11. Beijers, L., Wardenaar, K. J., Bosker, F. J., et al. (2019). Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study. Psychological Medicine, 49(4), 617–627 [ DOI | http ]
  12. Bekker, H., Borschevsky, A., Harman, Z., et al. (2019). Detection of the 5p – 4f orbital crossing and its optical clock transition in Pr9+. Nature Communications, 10(1), 5651 [ DOI | http ]
  13. Bellagamba, F., Sereno, M., Roncarelli, M., et al. (2019). AMICO galaxy clusters in KiDS-DR3: weak lensing mass calibration. Monthly Notices of the Royal Astronomical Society, 484(2), 1598–1615 [ DOI | http ]
  14. Bhole, A., Falzon, O., Biehl, M., & Azzopardi, G. (2019). A Computer Vision Pipeline that Uses Thermal and RGB Images for the Recognition of Holstein Cattle. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 11679 LNCS, pages 108–119. ISBN 978-3-030-29890-6. ISSN: 16113349 [ DOI | http ]
  15. Boulogne, L., Dijkstra, K., & Wiering, M. (2019). Extra Domain Data Generation with Generative Adversarial Nets. In Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, pages 1403–1410. IEEE. ISBN 978-1-5386-9276-9 [ DOI | http ]
  16. uit het Broek, M. A., Veldman, J., Fazi, S., & Greijdanus, R. (2019). Evaluating resource sharing for offshore wind farm maintenance: The case of jack-up vessels. Renewable and Sustainable Energy Reviews, 109, 619–632 [ DOI | http ]
  17. Bruininks, B. M., Souza, P. C., & Marrink, S. J. (2019). A Practical View of the Martini Force Field. In Methods in Molecular Biology, volume 2022, pages 105–127. ISSN: 19406029 [ DOI | http ]
  18. Cabrera, A. A., Hoekendijk, J. P., Aguilar, A., et al. (2019). Fin whale (Balaenoptera physalus) mitogenomics: A cautionary tale of defining sub-species from mitochondrial sequence monophyly. Molecular Phylogenetics and Evolution, 135, 86–97 [ DOI | http ]
  19. Cafagna, M., de Mattei, L., Bacciu, D., & Nissim, M. (2019). Suitable doesn’t mean attractive. Human-based evaluation of automatically generated headlines. In CEUR Workshop Proceedings, volume 2481. ISSN: 16130073
  20. Cantiello, M., Venhola, A., Paolillo, M., et al. (2019). Globular clusters in the Fornax cluster: A report from the FDS survey. In A. Bragaglia, M. Davies, A. Sills, & E. Vesperini, editors, Proceedings of the International Astronomical Union, volume 14, pages 68–71. Issue: 351 ISSN: 17439221 [ DOI ]
  21. Carlotti, M., Soni, S., Qiu, X., et al. (2019). Systematic experimental study of quantum interference effects in anthraquinoid molecular wires. Nanoscale Advances, 1(5), 2018–2028 [ DOI | http ]
  22. Cattapan, A., Spavone, M., Iodice, E., et al. (2019). VEGAS: A VST Early-type GAlaxy Survey. IV. NGC 1533, IC 2038, and IC 2039: An Interacting Triplet in the Dorado Group. The Astrophysical Journal, 874(2), 130 [ DOI ]
  23. Denis, M., Haase, P. A., Timmermans, R. G., et al. (2019). Enhancement factor for the electric dipole moment of the electron in the BaOH and YbOH molecules. Physical Review A, 99(4), 42512 [ DOI | http ]
  24. Erukhimovich, I. & Kriksin, Y. (2019). Thermodynamics of 3D diamond-like epitaxial (film) morphologies on 1D modulated substrate: Weak crystallization theory. Journal of Chemical Physics, 150(22), 224701 [ DOI | http ]
  25. Fazi, S. (2019). A decision-support framework for the stowage of maritime containers in inland shipping. Transportation Research Part E: Logistics and Transportation Review, 131, 1–23 [ DOI | http ]
  26. Fiorini, L. & Aiello, M. (2019). Predictive CO2-efficient scheduling of hybrid electric and thermal loads. In Proceedings - IEEE International Conference on Energy Internet, ICEI 2019, pages 392–397. IEEE. ISBN 978-1-72811-493-4 [ DOI | http ]
  27. Fluri, J., Kacprzak, T., Lucchi, A., et al. (2019). Cosmological constraints with deep learning from KiDS-450 weak lensing maps. Physical Review D, 100(6), 386.06 [ DOI | http ]
  28. Frailis, M., Belikov, A., Benson, K., et al. (2019). The Euclid Science Ground Segment Distributed Infrastructure: System Integration and Challenges. In M. Molinaro, K. Shortridge, & F. Pasian, editors, Astronomical Data Analysis Software and Systems Xxvi, volume 521, pages 612–615. ISBN 978-1-58381-929-6. Series Title: Astronomical Society of the Pacific Conference Series ISSN: 1050-3390
  29. Francese, T., Mota, F., Deumal, M., et al. (2019). Reorganization of Intermolecular Interactions in the Polymorphic Phase Transition of a Prototypical Dithiazolyl-Based Bistable Material. Crystal Growth and Design, 19(4), 2329–2339 [ DOI | http ]
  30. Frantseva, K. (2019). Enrichment of planetary surfaces by asteroid and comet impacts. Ph.D. thesis, University of Groningen. ISBN: 9789403420189 [ DOI | http ]
  31. Fürst, M. J., Boonstra, M., Bandstra, S., & Fraaije, M. W. (2019). Stabilization of cyclohexanone monooxygenase by computational and experimental library design. Biotechnology and Bioengineering, 116(9), 2167–2177 [ DOI | http ]
  32. Gazagnes, S. & Wilkinson, M. H. (2019). Distributed Component Forests in 2-D: Hierarchical Image Representations Suitable for Tera-Scale Images. International Journal of Pattern Recognition and Artificial Intelligence, 33(11), 1940012 [ DOI | http ]
  33. Georgiou, C., Chisari, N. E., Fortuna, M. C., et al. (2019). GAMA+KiDS: Alignment of galaxies in galaxy groups and its dependence on galaxy scale. Astronomy and Astrophysics, 628, A31 [ DOI ]
  34. Georgiou, C., Johnston, H., Hoekstra, H., et al. (2019). The dependence of intrinsic alignment of galaxies on wavelength using KiDS and GAMA. Astronomy and Astrophysics, 622, A90 [ DOI ]
  35. Graumas, L., David, R., & Caselli, T. (2019). Twitter-based Polarised Embeddings for Abusive Language Detection. ISBN 978-1-72813-891-6. Publication Title: 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2019 [ DOI ]
  36. Greenwood, A. J., Kamp, I., Waters, L. B., Woitke, P., & Thi, W. F. (2019). Effects of dust evolution on protoplanetary disks in the mid-infrared. Astronomy and Astrophysics, 626, A6 [ DOI | http ]
  37. Greenwood, A. J., Kamp, I., Waters, L. B., Woitke, P., & Thi, W. F. (2019). The infrared line-emitting regions of T Tauri protoplanetary disks. Astronomy and Astrophysics, 631, A81 [ DOI | http ]
  38. Guo, B. & De Vries, J. G. (2019). Hydration of nitriles using a metal-ligand cooperative ruthenium pincer catalyst. Chemical Science, 10(45), 10647–10652 [ DOI | http ]
  39. Hamraz, E., Peletier, R., Khosroshahi, H., et al. (2019). VizieR Online Data Catalog: YSOs in early-type dwarf galaxies (Hamraz+, 2019). VizieR Online Data Catalog, pages J/A+A/625/A94
  40. Hao, Y., Iliaš, M., & Borschevsky, A. (2019). Correlation trends in the nuclear anapole moment interaction of the Be atom in BeCl. In Proceedings of Science, volume 353, page 057. Sissa Medialab, Trieste, Italy. ISSN: 18248039 [ DOI | http ]
  41. Hao, Y., Pašteka, L. F., Visscher, L., et al. (2019). High accuracy theoretical investigations of CaF, SrF, and BaF and implications for laser-cooling. Journal of Chemical Physics, 151(3), 034302 [ DOI | http ]
  42. Hendriks, K. P., Alciatore, G., Schilthuizen, M., & Etienne, R. S. (2019). Phylogeography of Bornean land snails suggests long-distance dispersal as a cause of endemism. Journal of Biogeography, 46(5), 932–944 [ DOI | http ]
  43. Herrera-Alsina, L., Van Els, P., & Etienne, R. S. (2019). Detecting the Dependence of Diversification on Multiple Traits from Phylogenetic Trees and Trait Data. Systematic Biology, 68(2), 317–328 [ DOI | http ]
  44. Holwerda, B. W., Kelvin, L., Baldry, I., et al. (2019). The Frequency of Dust Lanes in Edge-on Spiral Galaxies Identified by Galaxy Zoo in KiDS Imaging of GAMA Targets. The Astronomical Journal, 158(3), 103 [ DOI ]
  45. Iodice, E. (2019). Deep (photometric & spectroscopic) Surveys of the Fornax cluster. In Light in the Suburbs: Structure and Chemodynamics of Galaxy Halos, page 8 [ DOI ]
  46. Iodice, E., Spavone, M., Capaccioli, M., et al. (2019). The Fornax Deep Survey with the VST: V. Exploring the faintest regions of the bright early-type galaxies inside the virial radius. Astronomy and Astrophysics, 623, A1 [ DOI ]
  47. Iodice, E., Spavone, M., Capaccioli, M., et al. (2019). VizieR Online Data Catalog: Fornax Deep Survey with VST. V. Isophote fit (Iodice+, 2019). VizieR Online Data Catalog, pages J/A+A/623/A1
  48. Izquierdo, M. A., Broer, R., & Havenith, R. W. A. (2019). Theoretical Study of the Charge Transfer Exciton Binding Energy in Semiconductor Materials for Polymer:Fullerene-Based Bulk Heterojunction Solar Cells. Journal of Physical Chemistry A, 123(6), 1233–1242 [ DOI | http ]
  49. Janssen, F., El-Gewily, S., & Bardoutsos, A. (2019). Future mortality in selected European countries, taking into account the impact of lifestyle epidemics. Technical report, Economic Commission for Europe. Issue: 15
  50. Jefferys, E. E., Bruininks, B., Souza, P. C. T., Marrink, S.-J., & Sansom, M. S. (2019). Towards Molecular Simulations of Juxtaposed Biomembranes. Biophysical Journal, 116(3), 176a [ DOI | http ]
  51. Johnston, H., Georgiou, C., Joachimi, B., et al. (2019). KiDS+GAMA: Intrinsic alignment model constraints for current and future weak lensing cosmology. Astronomy and Astrophysics, 624, A30 [ DOI ]
  52. Joudaki, S. & KiDS Collaboration Team (2019). Cosmological Constraints from the Kilo Degree Survey. In APS April Meeting Abstracts, volume 2019, page X04.003. Series Title: APS Meeting Abstracts
  53. Kahl, E. V., Berengut, J. C., Laatiaoui, M., Eliav, E., & Borschevsky, A. (2019). High-precision ab initio calculations of the spectrum of Lr+. Physical Review A, 100(6), 062505 [ DOI | http ]
  54. Kannawadi, A., Hoekstra, H., Miller, L., et al. (2019). Towards emulating cosmic shear data: Revisiting the calibration of the shear measurements for the Kilo-Degree Survey. Astronomy and Astrophysics, 624, A92 [ DOI ]
  55. Khramtsov, V., Sergeyev, A., Spiniello, C., et al. (2019). KiDS-SQuaD: II. Machine learning selection of bright extragalactic objects to search for new gravitationally lensed quasars. Astronomy and Astrophysics, 632, A56 [ DOI ]
  56. Kilpeläinen, T. O., Bentley, A. R., Noordam, R., et al. (2019). Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity. Nature Communications, 10(1), 376 [ DOI | http ]
  57. Klein, J. E., Knizia, G., & Rzepa, H. S. (2019). Epoxidation of Alkenes by Peracids: From Textbook Mechanisms to a Quantum Mechanically Derived Curly-Arrow Depiction. ChemistryOpen, 8(10), 1244–1250 [ DOI | http ]
  58. Krause, R. W. & Caimo, A. (2019). Missing data augmentation for bayesian exponential random multi-graph models. In Springer Proceedings in Complexity, pages 63–72. ISSN: 22138692 [ DOI | http ]
  59. Kriete, B., Lüttig, J., Kunsel, T., et al. (2019). Interplay between structural hierarchy and exciton diffusion in artificial light harvesting. Nature Communications, 10(1), 4615 [ DOI | http ]
  60. Kriksin, Y. A. & Tishkin, V. F. (2019). Hybrid Approach to Solve Single-Dimensional Gas Dynamics Equations. Mathematical Models and Computer Simulations, 11(2), 256–265 [ DOI | http ]
  61. Kuijken, K., Heymans, C., Dvornik, A., et al. (2019). The fourth data release of the Kilo-Degree Survey: Ugri imaging and nine-band optical-IR photometry over 1000 square degrees. Astronomy and Astrophysics, 625, A2 [ DOI ]
  62. Kumar, S., Soni, S., Danowski, W., et al. (2019). Eliminating Fatigue in Surface-Bound Spiropyrans. Journal of Physical Chemistry C, 123(42), 25908–25914 [ DOI | http ]
  63. Kunsel, T., Tiwari, V., Matutes, Y. A., et al. (2019). Simulating Fluorescence-Detected Two-Dimensional Electronic Spectroscopy of Multichromophoric Systems. Journal of Physical Chemistry B, 123(2), 394–406 [ DOI | http ]
  64. Leyva-Vallina, M., Strisciuglio, N., Lopez Antequera, M., et al. (2019). TB-places: A data set for visual place recognition in garden environments. IEEE Access, 7, 52277–52287 [ DOI | http ]
  65. Leyva-Vallina, M., Strisciuglio, N., & Petkov, N. (2019). Place Recognition in Gardens by Learning Visual Representations: Data Set and Benchmark Analysis. In M. Vento & G. Percannella, editors, Computer Analysis of Images and Patterns, volume 11678, pages 324–335. Springer International Publishing, Cham. ISBN 978-3-030-29887-6 978-3-030-29888-3. Series Title: Lecture Notes in Computer Science [ DOI | http ]
  66. Li, J., Solianyk, L., Schmidt, N., et al. (2019). Low-Dimensional Metal-Organic Coordination Structures on Graphene. Journal of Physical Chemistry C, 123(20), 12730–12735 [ DOI | http ]
  67. Liu, C. T., Merino, J., Rybin, D., et al. (2019). Genome-wide Association Study of Change in Fasting Glucose over time in 13,807 non-diabetic European Ancestry Individuals. Scientific Reports, 9(1), 9439 [ DOI | http ]
  68. Liu, Y., Barnoud, J., & Marrink, S. J. (2019). Gangliosides Destabilize Lipid Phase Separation in Multicomponent Membranes. Biophysical Journal, 117(7), 1215–1223 [ DOI | http ]
  69. Löhner, A., Kunsel, T., Röhr, M. I., et al. (2019). Spectral and Structural Variations of Biomimetic Light-Harvesting Nanotubes. Journal of Physical Chemistry Letters, 10(11), 2715–2724 [ DOI | http ]
  70. Mancera Pina, P. E., Aguerri, J. A., Peletier, R. F., et al. (2019). The evolution of ultra-diffuse galaxies in nearby galaxy clusters from the Kapteyn IAC WEAVE INT Clusters Survey. Monthly Notices of the Royal Astronomical Society, 485(1), 1036–1052 [ DOI ]
  71. Manzanera, O. M., Meles, S. K., Leenders, K. L., et al. (2019). Scaled Subprofile Modeling and Convolutional Neural Networks for the Identification of Parkinson's Disease in 3D Nuclear Imaging Data. International Journal of Neural Systems, 29(09), 1950010 [ DOI | http ]
  72. Maturi, M., Bellagamba, F., Radovich, M., et al. (2019). span style=“font-variant:small-caps;”amico/span galaxy clusters in KiDS-DR3: sample properties and selection function. Monthly Notices of the Royal Astronomical Society, 485(1), 498–512 [ DOI | http ]
  73. Moghaddam, K. G., de Vries, A. H., Marrink, S. J., & Faraji, S. (2019). Binding of quinazolinones to c-KIT G-quadruplex; an interplay between hydrogen bonding and π-π stacking. Biophysical Chemistry, 253, 106220 [ DOI | http ]
  74. Mohammadi, M., Petkov, N., Bunte, K., Peletier, R. F., & Schleif, F. M. (2019). Globular cluster detection in the GAIA survey. Neurocomputing, 342, 164–171 [ DOI | http ]
  75. Mondol, R. & Otten, E. (2019). Aluminum Complexes with Redox-Active Formazanate Ligand: Synthesis, Characterization, and Reduction Chemistry. Inorganic Chemistry, 58(9), 6344–6355 [ DOI | http ]
  76. Mondol, R. & Otten, E. (2019). Structure and bonding in reduced boron and aluminium complexes with formazanate ligands. Dalton Transactions, 48(37), 13981–13988 [ DOI | http ]
  77. Nakoneczny, S., Bilicki, M., Solarz, A., et al. (2019). Catalog of quasars from the Kilo-Degree Survey Data Release 3. Astronomy and Astrophysics, 624, A13 [ DOI ]
  78. Nolte, D. & Bertoglio, C. (2019). Reducing the impact of geometric errors in flow computations using velocity measurements. International Journal for Numerical Methods in Biomedical Engineering, 35(6), e3203 [ DOI | http ]
  79. Patrick Bos, E. G., Kitaura, F. S., & van de Weygaert, R. (2019). Bayesian cosmic density field inference from redshift space dark matter maps. Monthly Notices of the Royal Astronomical Society, 488(2), 2573–2604 [ DOI ]
  80. Pašteka, L. F., Hao, Y., Borschevsky, A., Flambaum, V. V., & Schwerdtfeger, P. (2019). Material Size Dependence on Fundamental Constants. Physical Review Letters, 122(16), 160801 [ DOI | http ]
  81. Pearson, W. J., Wang, L., Alpaslan, M., et al. (2019). Effect of galaxy mergers on star-formation rates. Astronomy and Astrophysics, 631, A51 [ DOI | http ]
  82. Pech, C. G., Haase, P. A., Galland, N., Borschevsky, A., & Maurice, R. (2019). Relevance of effective bond orders in heterodiatomic molecules and role of the spin-orbit coupling in the AtX (X=At-F) series. Physical Review A, 100(3), 032518 [ DOI | http ]
  83. Petrillo, C. E. (2019). Discovering Gravitational Lenses with Artificial Intelligence door. Ph.D. thesis, University of Groningen. Publication Title: Ph.D Thesis Issue: September
  84. Petrillo, C. E., Tortora, C., Chatterjee, S., et al. (2019). Testing convolutional neural networks for finding strong gravitational lenses in KiDS. Monthly Notices of the Royal Astronomical Society, 482(1), 807–820 [ DOI ]
  85. Petrillo, C. E., Tortora, C., Vernardos, G., et al. (2019). LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using convolutional neural networks. Monthly Notices of the Royal Astronomical Society, 484(3), 3879–3896 [ DOI ]
  86. Pfeifer, L., Scherübl, M., Fellert, M., et al. (2019). Photoefficient 2nd generation molecular motors responsive to visible light. Chemical Science, 10(38), 8768–8773 [ DOI | http ]
  87. Postek, K., Romeijnders, W., den Hertog, D., & van der Vlerk, M. H. (2019). An approximation framework for two-stage ambiguous stochastic integer programs under mean-MAD information. European Journal of Operational Research, 274(2), 432–444 [ DOI | http ]
  88. Prak, D. & Teunter, R. (2019). A general method for addressing forecasting uncertainty in inventory models. International Journal of Forecasting, 35(1), 224–238 [ DOI | http ]
  89. Prakash, J., Sheng, Y., Draksharapu, A., et al. (2019). Facile Conversion of syn-[Fe IV (O)(TMC)] 2+ into the anti Isomer via Meunier's Oxo–Hydroxo Tautomerism Mechanism. Angewandte Chemie - International Edition, 58(7), 1995–1999 [ DOI | http ]
  90. Prins, F. M., Said, M. A., van de Vegte, Y. J., et al. (2019). Genetically determined physical activity and its association with circulating blood cells. Genes, 10(11), 908 [ DOI | http ]
  91. Prole, D. J., van der Burg, R. F., Hilker, M., & Davies, J. I. (2019). Observational properties of ultra-diffuse galaxies in low-density environments: Field UDGs are predominantly blue and star forming. Monthly Notices of the Royal Astronomical Society, 488(2), 2143–2157 [ DOI ]
  92. Prole, D. J., Hilker, M., Van Der Burg, R. F., et al. (2019). Halo mass estimates from the globular cluster populations of 175 low surface brightness galaxies in the Fornax cluster. Monthly Notices of the Royal Astronomical Society, 484(4), 4865–4880 [ DOI ]
  93. Rab, C., Kamp, I., Ginski, C., et al. (2019). Observing the gas component of circumplanetary disks around wide-orbit planet-mass companions in the (sub)mm regime. Astronomy and Astrophysics, 624, A16 [ DOI | http ]
  94. Raj, M. A., Iodice, E., Napolitano, N. R., et al. (2019). The Fornax Deep Survey with the VST: VII. Evolution and structure of late type galaxies inside the virial radius of the Fornax cluster. Astronomy and Astrophysics, 628, A4 [ DOI ]
  95. van Ravenzwaaij, D., Brown, S. D., Marley, A. A., & Heathcote, A. (2019). Accumulating Advantages: A New Conceptualization of Rapid Multiple Choice. Psychological Review, 127(2), 186–215 [ DOI | http ]
  96. Riesmeijer, S. A., Werker, P. M., & Nolte, I. M. (2019). Ethnic differences in prevalence of Dupuytren disease can partly be explained by known genetic risk variants. European Journal of Human Genetics, 27(12), 1876–1884 [ DOI | http ]
  97. Rivera-León, V. E., Urbán, J., Mizroch, S., et al. (2019). Long-term isolation at a low effective population size greatly reduced genetic diversity in Gulf of California fin whales. Scientific Reports, 9(1), 12391 [ DOI | http ]
  98. Roke, D., Feringa, B. L., & Wezenberg, S. J. (2019). A Visible-Light-Driven Molecular Motor Based on Pyrene. Helvetica Chimica Acta, 102(2), e1800221 [ DOI | http ]
  99. Roke, D., Sen, M., Danowski, W., Wezenberg, S. J., & Feringa, B. L. (2019). Visible-Light-Driven Tunable Molecular Motors Based on Oxindole. Journal of the American Chemical Society, 141(18), 7622–7627 [ DOI | http ]
  100. Ryerson, J. L., Zaykov, A., Aguilar Suarez, L. E., et al. (2019). Structure and photophysics of indigoids for singlet fission: Cibalackrot. The Journal of Chemical Physics, 151(18), 184903 [ DOI ]
  101. Saggese, A., Strisciuglio, N., Vento, M., & Petkov, N. (2019). Learning skeleton representations for human action recognition. Pattern Recognition Letters, 118, 23–31 [ DOI | http ]
  102. Sas, D., Avgeriou, P., & Arcelli Fontana, F. (2019). Investigating Instability Architectural Smells Evolution: An Exploratory Case Study. In Proceedings - 2019 IEEE International Conference on Software Maintenance and Evolution, ICSME 2019, pages 557–567. ISBN 978-1-72813-094-1 [ DOI ]
  103. Schmidt, M., Huang, Y. H., Texeira de Oliveira, E. F., et al. (2019). Efficient Enzymatic Cyclization of Disulfide-Rich Peptides by Using Peptide Ligases. ChemBioChem, 20(12), 1524–1529 [ DOI | http ]
  104. Schmidt, N., Enache, M., Maggini, L., et al. (2019). Coverage-Controlled Polymorphism of H-Bonded Networks on Au(111). Journal of Physical Chemistry C, 123(12), 7151–7157 [ DOI | http ]
  105. Schmidt, N., Li, J., Gottardi, S., et al. (2019). Comparing the Self-Assembly of Sexiphenyl-Dicarbonitrile on Graphite and Graphene on Cu(111). Chemistry - A European Journal, 25(19), 5065–5070 [ DOI | http ]
  106. Schrotenboer, A. H., Ursavas, E., & Vis, I. F. (2019). A branch-and-price-and-cut algorithm for resource-constrained pickup and delivery problems. Transportation Science, 53(4), 1001–1022 [ DOI | http ]
  107. Song, W. (2019). Matrix-based techniques for (flow-)transition studies. Ph.D. thesis, University of Groningen. ISBN: 978-94-034-1353-2
  108. Sorbelli, D., Nunes dos Santos Comprido, L., Knizia, G., et al. (2019). Cationic Gold(I) Diarylallenylidene Complexes: Bonding Features and Ligand Effects. ChemPhysChem, 20(13), 1671–1679 [ DOI | http ]
  109. Spurio Mancini, A., Köhlinger, F., Joachimi, B., et al. (2019). Kids + Gama: Constraints on Horndeski gravity from combined large-scale structure probes. Monthly Notices of the Royal Astronomical Society, 490(2), 2155–2177 [ DOI ]
  110. Sriman, B. & Schomaker, L. (2019). Multi-script text versus non-text classification of regions in scene images. Journal of Visual Communication and Image Representation, 62, 23–42 [ DOI | http ]
  111. Steen, J. D., Stepanovic, S., Parvizian, M., et al. (2019). Lewis versus Brønsted Acid Activation of a Mn(IV) Catalyst for Alkene Oxidation. Inorganic Chemistry, 58(21), 14924–14930 [ DOI | http ]
  112. Steen, J. S., Knizia, G., & Klein, J. E. (2019). σ-Noninnocence: Masked Phenyl-Cation Transfer at Formal NiIV. Angewandte Chemie - International Edition, 58(37), 13133–13139 [ DOI | http ]
  113. Strisciuglio, N., Azzopardi, G., & Petkov, N. (2019). Brain-Inspired Robust Delineation Operator. In L. Leal-Taixé & S. Roth, editors, Computer Vision – ECCV 2018 Workshops, volume 11131, pages 555–565. Springer International Publishing, Cham. ISBN 978-3-030-11014-7 978-3-030-11015-4. Series Title: Lecture Notes in Computer Science [ DOI | http ]
  114. Strisciuglio, N., Azzopardi, G., & Petkov, N. (2019). Robust Inhibition-Augmented Operator for Delineation of Curvilinear Structures. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 28(12), 5852–5866 [ DOI | http ]
  115. Strisciuglio, N., Vento, M., & Petkov, N. (2019). Learning representations of sound using trainable COPE feature extractors. Pattern Recognition, 92, 25–36 [ DOI | http ]
  116. Stuckhardt, C., Roke, D., Danowski, W., et al. (2019). A chiral self-sorting photoresponsive coordination cage based on overcrowded alkenes. Beilstein Journal of Organic Chemistry, 15, 2767–2773 [ DOI | http ]
  117. Tan Pham, H., Rizzo, H. G., Havenith, R. W. A., & Nguyen, M. T. (2019). B x Ge 12 0/+ Clusters with x = 1–4: Germanium Tubes Stabilized by Three and Four Boron Dopants. The Journal of Physical Chemistry C, 123(40), 24676–24684 [ DOI ]
  118. Toral, A., Edman, L., Yeshmagambetova, G., & Spenader, J. (2019). Neural machine translation for English-Kazakh with morphological segmentation and synthetic data. In WMT 2019 - 4th Conference on Machine Translation, Proceedings of the Conference, volume 2, pages 386–392. Association for Computational Linguistics (ACL). ISBN 978-1-950737-27-7 [ DOI ]
  119. Vakili, M., Bilicki, M., Hoekstra, H., et al. (2019). Luminous red galaxies in the Kilo-Degree Survey: Selection with broad-band photometry and weak lensing measurements. Monthly Notices of the Royal Astronomical Society, 487(3), 3715–3733 [ DOI ]
  120. Valente, L., Etienne, R. S., & Garcia-R., J. C. (2019). Deep Macroevolutionary Impact of Humans on New Zealand's Unique Avifauna. Current Biology, 29(15), 2563–2569.e4 [ DOI | http ]
  121. Van Beers, F., Lindström, A., Okafor, E., & Wiering, M. A. (2019). Deep neural networks with intersection over union loss for binary image segmentation. In ICPRAM 2019 - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods, pages 438–445. SCITEPRESS - Science and Technology Publications. ISBN 978-989-758-351-3 [ DOI | http ]
  122. Venhola, A., Peletier, R., Laurikainen, E., et al. (2019). The Fornax Deep Survey (FDS) with VST VI. Optical properties of the dwarf galaxies in the Fornax cluster. Astronomy and Astrophysics, 625, A143 [ DOI ]
  123. Voncken, L., Albers, C. J., & Timmerman, M. E. (2019). Improving confidence intervals for normed test scores: Include uncertainty due to sampling variability. Behavior Research Methods, 51(2), 826–839 [ DOI | http ]
  124. Voncken, L., Albers, C. J., & Timmerman, M. E. (2019). Model Selection in Continuous Test Norming With GAMLSS. Assessment, 26(7), 1329–1346 [ DOI | http ]
  125. Wagner, A. E., Nagels, L., Toffanin, P., Opie, J. M., & Başkent, D. (2019). Individual Variations in Effort: Assessing Pupillometry for the Hearing Impaired. Trends in Hearing, 23, 233121651984559 [ DOI | http ]
  126. White, J. A., Kóspál, �., Rab, C., et al. (2019). APEX Observations of the CO Envelope around the Young FUor-type Star V883 Ori. The Astrophysical Journal, 877(1), 21 [ DOI | http ]
  127. Williams, O. R., Begeman, K., Boxhoorn, D., et al. (2019). The Role of the Euclid Archive System in the Processing of Euclid and External Data. In M. Molinaro, K. Shortridge, & F. Pasian, editors, Astronomical Data Analysis Software and Systems XXVI, pages 120–123. Astronomical Society of the Pacific. ISBN 978-1-58381-929-6. Series Title: ASP Conference Series
  128. Wright, A., Hildebrandt, H., Kuijken, K., et al. (2019). VizieR Online Data Catalog: KiDS+VIKING-450 opt+NIR dataset (Wright+, 2019). VizieR Online Data Catalog, pages J/A+A/632/A34
  129. Wright, A. H., Hildebrandt, H., Kuijken, K., et al. (2019). KiDS+VIKING-450: A new combined optical and near-infrared dataset for cosmology and astrophysics. Astronomy and Astrophysics, 632, A34 [ DOI ]
  130. Zaykov, A., Felkel, P., Buchanan, E. A., et al. (2019). Singlet Fission Rate: Optimized Packing of a Molecular Pair. Ethylene as a Model. Journal of the American Chemical Society, 141(44), 17729–17743 [ DOI ]
  131. Zhang, Q., Solanki, A., Parida, K., et al. (2019). Tunable Ferroelectricity in Ruddlesden-Popper Halide Perovskites. ACS Applied Materials and Interfaces, 11(14), 13523–13532 [ DOI | http ]

2018

  1. Abdullah Said, M., Eppinga, R. N., Lipsic, E., Verweij, N., & van der Harst, P. (2018). Relationship of arterial stiffness index and pulse pressure with cardiovascular disease and mortality. Journal of the American Heart Association, 7(2) [ DOI | http ]
  2. Abdullah Said, M., Verweij, N., & Van Der Harst, P. (2018). Associations of combined genetic and lifestyle risks with incident cardiovascular disease and diabetes in the UK biobank study. JAMA Cardiology, 3(8), 693–702 [ DOI | http ]
  3. Accomasso, D., Granucci, G., Havenith, R. W., & Persico, M. (2018). Testing new chromophores for singlet fission: A computational protocol applied to 2,3-diamino-1,4-benzoquinone. Chemical Physics, 515, 635–642 [ DOI | http ]
  4. Acke, G., Van Damme, S., Havenith, R. W. A., & Bultinck, P. (2018). Interpreting the behavior of the NICSzz by resolving in orbitals, sign, and positions. Journal of Computational Chemistry, 39(9), 511–519 [ DOI | http ]
  5. Aggarwal, P., Bethlem, H. L., Borschevsky, A., et al. (2018). Measuring the electric dipole moment of the electron in BaF. European Physical Journal D, 72(11), 197 [ DOI | http ]
  6. Ai, Y., Kovalchuk, A., Qiu, X., et al. (2018). In-Place Modulation of Rectification in Tunneling Junctions Comprising Self-Assembled Monolayers. Nano Letters, 18(12), 7552–7559 [ DOI | http ]
  7. Amon, A., Blake, C., Heymans, C., et al. (2018). KiDS+2dFLenS+GAMA: Testing the cosmological model with the EG statistic. Monthly Notices of the Royal Astronomical Society, 479(3), 3422–3437 [ DOI ]
  8. Amon, A., Heymans, C., Klaes, D., et al. (2018). KiDS-i-800: Comparing weak gravitational lensing measurements from same-sky surveys. Monthly Notices of the Royal Astronomical Society, 477(4), 4285–4307 [ DOI ]
  9. An, R., Feng, C., & Wang, B. (2018). Relieving the tension between weak lensing and cosmic microwave background with interacting dark matter and dark energy models. Journal of Cosmology and Astroparticle Physics, 2018(2), 38 [ DOI ]
  10. Andrei, D., Nagy, R. A., van Montfoort, A., et al. (2018). Differential miRNA Expression Profiles in Cumulus and Mural Granulosa Cells from Human Pre-ovulatory Follicles. MicroRNA, 8(1), 61–67 [ DOI | http ]
  11. Aprianto, R., Slager, J., Holsappel, S., & Veening, J. W. (2018). High-resolution analysis of the pneumococcal transcriptome under a wide range of infection-relevant conditions. Nucleic Acids Research, 46(19), 9990–10006 [ DOI | http ]
  12. Arvanitidis, A. G., Lim, K. Z., Havenith, R. W. A., & Ceulemans, A. (2018). Valence bonds in elongated boron clusters. International Journal of Quantum Chemistry, 118(13), e25575 [ DOI | http ]
  13. Bardoutsos, A., de Beer, J., & Janssen, F. (2018). Projecting delay and compression of mortality. Genus, 74(1), 17 [ DOI | http ]
  14. Bari, S., Egorov, D., Jansen, T. L., et al. (2018). Soft X-ray Spectroscopy as a Probe for Gas-Phase Protein Structure: Electron Impact Ionization from Within. Chemistry - A European Journal, 24(30), 7631–7636 [ DOI | http ]
  15. Bihlmeyer, N. A., Brody, J. A., Smith, A. V., et al. (2018). ExomeChip-Wide Analysis of 95 626 Individuals Identifies 10 Novel Loci Associated With QT and JT Intervals. Circulation. Genomic and precision medicine, 11(1), e001758 [ DOI | http ]
  16. Bilderbeek, R. J. & Etienne, R. S. (2018). babette: BEAUti 2, BEAST2 and Tracer for R. Methods in Ecology and Evolution, 9(9), 2034–2040 [ DOI | http ]
  17. Bilicki, M., Hoekstra, H., Brown, M. J., et al. (2018). Photometric redshifts for the Kilo-Degree Survey: Machine-learning analysis with artificial neural networks. Astronomy and Astrophysics, 616, A69 [ DOI ]
  18. Bosma, T., Lof, G. J. J., Gilardoni, C. M., et al. (2018). Identification and tunable optical coherent control of transition-metal spins in silicon carbide. npj Quantum Information, 4(1), 48 [ DOI ]
  19. Brouwer, M. M., Demchenko, V., Harnois-Déraps, J., et al. (2018). Studying galaxy troughs and ridges using weak gravitational lensing with the Kilo-Degree Survey. Monthly Notices of the Royal Astronomical Society, 481(4), 5189–5209 [ DOI ]
  20. Bruininks, B. M., Telles de Souza, P. C., & Jan Marrink, S. (2018). Microscopic View on Non-viral Mediated Transfection. Biophysical Journal, 114(3), 602a [ DOI | http ]
  21. Buring, R., Kiselev, A. V., & Rutten, N. (2018). Infinitesimal deformations of Poisson bi-vectors using the Kontsevich graph calculus. Journal of Physics: Conference Series, 965(1), 012010 [ DOI | http ]
  22. Cantiello, M., D'Abrusco, R., Spavone, M., et al. (2018). VEGAS-SSS. II. Comparing the globular cluster systems in NGC 3115 and NGC 1399 using VEGAS and FDS survey data: The quest for a common genetic heritage of globular cluster systems ? Astronomy and Astrophysics, 611, A93 [ DOI ]
  23. Cantiello, M., Fds Collaboration, & Vegas Collaboration (2018). Globular Clusters (& Other Compact Stellar Systems) in the VST FDS and VEGAS Surveys. In VST in the Era of the Large Sky Surveys, page 36 [ DOI ]
  24. Carlotti, M., Soni, S., Kumar, S., et al. (2018). Two-Terminal Molecular Memory through Reversible Switching of Quantum Interference Features in Tunneling Junctions. Angewandte Chemie - International Edition, 57(48), 15681–15685 [ DOI | http ]
  25. Carton, D., Brinchmann, J., Contini, T., et al. (2018). First gase-phase metallicity gradients of 0.1 ≲ z ≲ 0.8 galaxies with MUSE. Monthly Notices of the Royal Astronomical Society, 478(4), 4293–4316 [ DOI ]
  26. Chaib De Mares, M., Jiménez, D. J., Palladino, G., et al. (2018). Expressed protein profile of a Tectomicrobium and other microbial symbionts in the marine sponge Aplysina aerophoba as evidenced by metaproteomics. Scientific Reports, 8(1), 11795 [ DOI | http ]
  27. Chen, J., Draksharapu, A., Angelone, D., et al. (2018). H 2 O 2 Oxidation by Fe iii –OOH Intermediates and Its Effect on Catalytic Efficiency. ACS Catalysis, 8(10), 9665–9674 [ DOI | http ]
  28. Chen, J., Stepanovic, S., Draksharapu, A., Gruden, M., & Browne, W. R. (2018). A Non-Heme Iron Photocatalyst for Light-Driven Aerobic Oxidation of Methanol. Angewandte Chemie - International Edition, 57(12), 3207–3211 [ DOI | http ]
  29. Costa-Duarte, M. V., Viola, M., Molino, A., et al. (2018). The galaxy environment in GAMA G3C groups using the Kilo Degree Survey Data Release 3. Monthly Notices of the Royal Astronomical Society, 478(2), 1968–1979 [ DOI ]
  30. Dobra, A. & Mohammadi, R. (2018). Loglinear model selection and human mobility. Annals of Applied Statistics, 12(2), 815–845 [ DOI | http ]
  31. Dvornik, A., Hoekstra, H., Kuijken, K., et al. (2018). Unveiling galaxy bias via the halo model, KiDS, and GAMA. Monthly Notices of the Royal Astronomical Society, 479(1), 1240–1259 [ DOI ]
  32. Efstathiou, G. & Lemos, P. (2018). Statistical inconsistencies in the KiDS-450 data set. Monthly Notices of the Royal Astronomical Society, 476(1), 151–157 [ DOI ]
  33. Epinat, B., Contini, T., Finley, H., et al. (2018). Ionised gas structure of 100 kpc in an over-dense region of the galaxy group COSMOS-Gr30 at z ∼ 0.7. Astronomy and Astrophysics, 609, A40 [ DOI ]
  34. Evangelou, E., Warren, H. R., Mosen-Ansorena, D., et al. (2018). Erratum to: Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (Nature Genetics, (2018), 50, 10, (1412-1425), 10.1038/s41588-018-0205-x). Nature Genetics, 50(12), 1755 [ DOI | http ]
  35. Evangelou, E., Warren, H. R., Mosen-Ansorena, D., et al. (2018). Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nature Genetics, 50(10), 1412–1425 [ DOI | http ]
  36. Farag, M. H., Jansen, T. L., & Knoester, J. (2018). The origin of absorptive features in the two-dimensional electronic spectra of rhodopsin. Physical Chemistry Chemical Physics, 20(18), 12746–12754 [ DOI | http ]
  37. Fountain, E. D., Kang, J. k., Tempel, D. J., et al. (2018). Genomics meets applied ecology: Characterizing habitat quality for sloths in a tropical agroecosystem. Molecular Ecology, 27(1), 41–53 [ DOI | http ]
  38. Francese, T., Ribas-Arino, J., Novoa, J. J., et al. (2018). The magnetic fingerprint of dithiazolyl-based molecule magnets. Physical Chemistry Chemical Physics, 20(31), 20406–20416 [ DOI | http ]
  39. Frantseva, K., Mueller, M., ten Kate, I. L., van der Tak, F. F., & Greenstreet, S. (2018). Delivery of organics to Mars through asteroid and comet impacts. Icarus, 309, 125–133 [ DOI | http ]
  40. Gallego, S. G., Cantalupo, S., Lilly, S., et al. (2018). Stacking the Cosmic Web in fluorescent Ly α emission with MUSE. Monthly Notices of the Royal Astronomical Society, 475(3), 3854–3869 [ DOI ]
  41. Gallop, N. P., Selig, O., Giubertoni, G., et al. (2018). Rotational Cation Dynamics in Metal Halide Perovskites: Effect on Phonons and Material Properties. Journal of Physical Chemistry Letters, 9(20), 5987–5997 [ DOI | http ]
  42. Geddes, A. J., Skripnikov, L. V., Borschevsky, A., et al. (2018). Enhanced nuclear-spin-dependent parity-violation effects using the HgH 199 molecule. Physical Review A, 98(2), 022508 [ DOI | http ]
  43. Giblin, B., Heymans, C., Harnois-Déraps, J., et al. (2018). KiDS-450: Enhancing cosmic shear with clipping transformations. Monthly Notices of the Royal Astronomical Society, 480(4), 5529–5549 [ DOI ]
  44. Grunewald, F., Rossi, G., De Vries, A. H., Marrink, S. J., & Monticelli, L. (2018). Transferable MARTINI Model of Poly(ethylene Oxide). Journal of Physical Chemistry B, 122(29), 7436–7449 [ DOI | http ]
  45. Hao, Y., Iliaš, M., Eliav, E., et al. (2018). Nuclear anapole moment interaction in BaF from relativistic coupled-cluster theory. Physical Review A, 98(3), 032510 [ DOI | http ]
  46. Harnois-Déraps, J., Amon, A., Choi, A., et al. (2018). Cosmological simulations for combined-probe analyses: Covariance and neighbour-exclusion bias. Monthly Notices of the Royal Astronomical Society, 481(1), 1337–1367 [ DOI ]
  47. Helgadottir, A., Thorleifsson, G., Gretarsdottir, S., et al. (2018). Genome-wide analysis yields new loci associating with aortic valve stenosis. Nature Communications, 9(1), 987 [ DOI | http ]
  48. Herrera-Alsina, L., Pigot, A. L., Hildenbrandt, H., & Etienne, R. S. (2018). The influence of ecological and geographic limits on the evolution of species distributions and diversity. Evolution, 72(10), 1978–1991 [ DOI | http ]
  49. van Hoogmoed, A. H. & Kroesbergen, E. H. (2018). On the difference between numerosity processing and number processing. Frontiers in Psychology, 9(SEP) [ DOI | http ]
  50. Huijser, L. A., Bérubé, M., Cabrera, A. A., et al. (2018). Population structure of North Atlantic and North Pacific sei whales (Balaenoptera borealis) inferred from mitochondrial control region DNA sequences and microsatellite genotypes. Conservation Genetics, 19(4), 1007–1024 [ DOI | http ]
  51. Iodice, E. (2018). VST Early-Type Galaxy Survey. In VST in the Era of the Large Sky Surveys, page 42 [ DOI ]
  52. Jakobs, A., Viola, M., McCarthy, I., et al. (2018). Multiwavelength scaling relations in galaxy groups: A detailed comparison of GAMA and KiDS observations to BAHAMAS simulations. Monthly Notices of the Royal Astronomical Society, 480(3), 3338–3355 [ DOI ]
  53. Jansen, T. L. (2018). Simple Quantum Dynamics with Thermalization. Journal of Physical Chemistry A, 122(1), 172–183 [ DOI | http ]
  54. Joudaki, S., Blake, C., Johnson, A., et al. (2018). KiDS-450 + 2dFLenS: Cosmological parameter constraints from weak gravitational lensing tomography and overlapping redshift-space galaxy clustering. Monthly Notices of the Royal Astronomical Society, 474(4), 4894–4924 [ DOI | http ]
  55. Keijzer, M. A., Mäs, M., & Flache, A. (2018). Communication in Online Social Networks Fosters Cultural Isolation. Complexity, 2018, 1–18 [ DOI | http ]
  56. Kelvin, L. S., Bremer, M. N., Phillipps, S., et al. (2018). Galaxy and Mass Assembly (GAMA): Variation in galaxy structure across the green valley. Monthly Notices of the Royal Astronomical Society, 477(3), 4116–4130 [ DOI ]
  57. Klein, J. E., Draksharapu, A., Shokri, A., Cramer, C. J., & Que, L. (2018). On the Lewis Acidity of the Oxoiron(IV) Unit in a Tetramethylcyclam Complex. Chemistry - A European Journal, 24(20), 5373–5378 [ DOI | http ]
  58. Klein, J. E. & Knizia, G. (2018). cPCET versus HAT: A Direct Theoretical Method for Distinguishing X–H Bond-Activation Mechanisms. Angewandte Chemie - International Edition, 57(37), 11913–11917 [ DOI | http ]
  59. Klein, J. E. M. N., Havenith, R. W. A., & Knizia, G. (2018). The Pentagonal-Pyramidal Hexamethylbenzene Dication: Many Shades of Coordination Chemistry at Carbon. Chemistry - A European Journal, 24(47), 12340–12345 [ DOI | http ]
  60. Kriksin, Y. A., Potemkin, I. I., & Khalatur, P. G. (2018). Chirality in Self-Assembling Rod-Coil Copolymers: Macroscopic Homochirality Versus Local Chirality. Высокомолекулярные Соединения С, 60(2), 236–248 [ DOI | http ]
  61. Kuijper, M., van Lenthe, M., & van Noord, R. (2018). UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish. In NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop, pages 279–285. Association for Computational Linguistics (ACL). ISBN 978-1-948087-20-9 [ DOI ]
  62. Ligthart, S., Vaez, A., Võsa, U., et al. (2018). Genome Analyses of 200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders. American Journal of Human Genetics, 103(5), 691–706 [ DOI | http ]
  63. Lin, H., van Setten, J., Smith, A. V., et al. (2018). Common and Rare Coding Genetic Variation Underlying the Electrocardiographic PR Interval. Circulation. Genomic and precision medicine, 11(5), e002037 [ DOI | http ]
  64. Mahler, G., Richard, J., Clément, B., et al. (2018). Strong-lensing analysis of A2744 with MUSE and Hubble Frontier Fields images. Monthly Notices of the Royal Astronomical Society, 473(1), 663–692 [ DOI ]
  65. Mahlke, M., Bouy, H., Altieri, B., et al. (2018). Mining the Kilo-Degree survey for solar system objects. Astronomy and Astrophysics, 610, A21 [ DOI ]
  66. Martin, C., Ovalle Maqueo, A., Wijma, H. J., & Fraaije, M. W. (2018). Creating a more robust 5-hydroxymethylfurfural oxidase by combining computational predictions with a novel effective library design. Biotechnology for Biofuels, 11(1), 56 [ DOI | http ]
  67. Martinet, N., Schneider, P., Hildebrandt, H., et al. (2018). KiDS-450: Cosmological constraints from weak-lensing peak statistics - II: Inference from shear peaks using N-body simulations. Monthly Notices of the Royal Astronomical Society, 474(1), 712–730 [ DOI ]
  68. Milocco, F., De Vries, F., Dall'Anese, A., et al. (2018). Palladium alkyl complexes with a formazanate ligand: Synthesis, structure and reactivity. Dalton Transactions, 47(41), 14445–14451 [ DOI | http ]
  69. Negrello, M., Amber, S., Amvrosiadis, A., et al. (2018). VizieR Online Data Catalog: HATLAS candidate lensed galaxies (Negrello+, 2017). VizieR Online Data Catalog, page J/MNRAS/465/3558
  70. Nepal, S., Bonn, F., Grasso, S., et al. (2018). An ancient family of mobile genomic islands introducing cephalosporinase and carbapenemase genes in enterobacteriaceae. Virulence, 9(1), 1377–1389 [ DOI | http ]
  71. van Noord, R. & Bos, J. (2018). The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), volume 11, pages 929–933. Association for Computational Linguistics (ACL) [ DOI ]
  72. Owomugisha, G., Melchert, F., Mwebaze, E., Quinn, J. A., & Biehl, M. (2018). Machine learning for diagnosis of disease in plants using spectral data. In 2018 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2018 - Proceedings of the 2018 International Conference on Artificial Intelligence, ICAI 2018, pages 9–15. ISBN 1-60132-480-4
  73. Parmar, P., Lowry, E., Cugliari, G., et al. (2018). Association of maternal prenatal smoking GFI1-locus and cardio-metabolic phenotypes in 18,212 adults. EBioMedicine, 38, 206–216 [ DOI | http ]
  74. Pearson, W. J., Wang, L., Hurley, P. D., et al. (2018). Main sequence of star forming galaxies beyond the Herschel* confusion limit. Astronomy and Astrophysics, 615, A146 [ DOI | http ]
  75. Peletier, R. & FDS Team (2018). FDS - The Fornax Ultra-Deep Imaging Survey: Evolution of Dwarf Galaxies. In VST in the Era of the Large Sky Surveys, page 40 [ DOI ]
  76. Pieters, M. & Wiering, M. (2018). Comparison of machine learning techniques for multi-label genre classification. In B. Verheij & M. Wiering, editors, Communications in Computer and Information Science, volume 823, pages 131–144. Springer International Publishing, Cham. ISBN 978-3-319-76891-5. Series Title: Communications in Computer and Information Science ISSN: 18650929 [ DOI | http ]
  77. Portoles, O., Borst, J. P., & van Vugt, M. K. (2018). Characterizing synchrony patterns across cognitive task stages of associative recognition memory. European Journal of Neuroscience, 48(8), 2759–2769 [ DOI | http ]
  78. Post, R. M., Buijs, P., uit het Broek, M. A., et al. (2018). A solution approach for deriving alternative fuel station infrastructure requirements. Flexible Services and Manufacturing Journal, 30(3), 592–607 [ DOI | http ]
  79. Prak, D., Teunter, R. H., Babai, M. Z., Syntetos, A. A., & Boylan, J. E. (2018). Forecasting and Inventory Control with Compound Poisson Demand Using Periodic Demand Data. Technical report, University of Groningen. Publication Title: SOM Research Reports Volume: 2018 Issue: 010 [ http ]
  80. Prins, B. P., Mead, T. J., Brody, J. A., et al. (2018). Exome-chip meta-analysis identifies novel loci associated with cardiac conduction, including ADAMTS6. Genome Biology, 19(1), 87 [ DOI | http ]
  81. Prole, D. J., Davies, J., Hilker, M., & van der Burg, R. (2018). Automated Searches for Low Surface Brightness Galaxies in VST Data. In VST in the Era of the Large Sky Surveys, page 41 [ DOI ]
  82. Raeder, S., Ackermann, D., Backe, H., et al. (2018). Probing Sizes and Shapes of Nobelium Isotopes by Laser Spectroscopy. Physical Review Letters, 120(23), 232503 [ DOI | http ]
  83. Raj, M. A., Iodice, E., & Napolitano, N. R. (2018). The Fornax Deep Survey with VST: Surface Photometry of LTGS Inside the Virial Radius of the Fornax Cluster. In VST in the Era of the Large Sky Surveys, page 39 [ DOI ]
  84. Ramirez-Moreta, P., Verdes-Montenegro, L., Blasco-Herrera, J., et al. (2018). VizieR Online Data Catalog: CIG 96 deep optical and HI images (Ramirez-Moreta+, 2018). VizieR Online Data Catalog, pages J/A+A/619/A163
  85. Ramírez-Moreta, P., Verdes-Montenegro, L., Blasco-Herrera, J., et al. (2018). Unveiling the environment and faint features of the isolated galaxy CIG 96 with deep optical and HI observations. Astronomy and Astrophysics, 619, A163 [ DOI ]
  86. Randolph, C., Lahive, C. W., Sami, S., et al. (2018). Biobased Chemicals: 1,2,4-Benzenetriol, Selective Deuteration and Dimerization to Bifunctional Aromatic Compounds. Organic Process Research and Development, 22(12), 1663–1671 [ DOI | http ]
  87. Ripepi, V. (2018). STEP@VST and YMCA@VST, A Fresh Look into Magellanic System Stellar Populations. In VST in the Era of the Large Sky Surveys, page 45 [ DOI ]
  88. Roselli, C., Chaffin, M. D., Weng, L. C., et al. (2018). Multi-ethnic genome-wide association study for atrial fibrillation. Nature Genetics, 50(9), 1225–1233 [ DOI | http ]
  89. Roy, N., Napolitano, N. R., La Barbera, F., et al. (2018). Evolution of galaxy size-stellar mass relation from the Kilo-Degree Survey. Monthly Notices of the Royal Astronomical Society, 480(1), 1057–1080 [ DOI ]
  90. Sabatelli, M., Bidoia, F., Codreanu, V., & Wiering, M. (2018). Learning to evaluate chess positions with deep neural networks and limited lookahead. In ICPRAM 2018 - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods, volume 2018-Janua, pages 276–283. ISBN 978-989-758-276-9 [ DOI ]
  91. Salamatova, E., Cunha, A. V., Bloem, R., et al. (2018). Hydrophobic Collapse in N -Methylacetamide-Water Mixtures. Journal of Physical Chemistry A, 122(9), 2468–2478 [ DOI | http ]
  92. Sami, S., Haase, P. A., Alessandri, R., Broer, R., & Havenith, R. W. (2018). Can the Dielectric Constant of Fullerene Derivatives Be Enhanced by Side-Chain Manipulation? A Predictive First-Principles Computational Study. Journal of Physical Chemistry A, 122(15), 3919–3926 [ DOI | http ]
  93. Sato, T. K., Asai, M., Borschevsky, A., et al. (2018). First Ionization Potentials of Fm, Md, No, and Lr: Verification of Filling-Up of 5f Electrons and Confirmation of the Actinide Series. Journal of the American Chemical Society, 140(44), 14609–14613 [ DOI | http ]
  94. Scheers, J., Ryabtsev, A., Borschevsky, A., et al. (2018). Energy-level structure of Sn3+ ions. Physical Review A, 98(6), 062503 [ DOI | http ]
  95. Schrotenboer, A. H., uit het Broek, M. A., Jargalsaikhan, B., & Roodbergen, K. J. (2018). Coordinating technician allocation and maintenance routing for offshore wind farms. Computers and Operations Research, 98, 185–197 [ DOI | http ]
  96. Selig, O., Cunha, A. V., Van Eldijk, M. B., et al. (2018). Temperature-Induced Collapse of Elastin-like Peptides Studied by 2DIR Spectroscopy. Journal of Physical Chemistry B, 122(34), 8243–8254 [ DOI | http ]
  97. van Setten, J., Brody, J. A., Jamshidi, Y., et al. (2018). PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity. Nature Communications, 9(1), 2904 [ DOI | http ]
  98. Shan, H. Y., Liu, X., Hildebrandt, H., et al. (2018). KiDS-450: Cosmological constraints from weak lensing peak statistics - I. Inference from analytical prediction of high signal-to-noise ratio convergence peaks. Monthly Notices of the Royal Astronomical Society, 474(1), 1116–1134 [ DOI ]
  99. Signorelli, M. & Wit, E. C. (2018). A penalized inference approach to stochastic block modelling of community structure in the Italian Parliament. Journal of the Royal Statistical Society. Series C: Applied Statistics, 67(2), 355–369 [ DOI | http ]
  100. Simonet, C., Scherrer, R., Rego-Costa, A., & Etienne, R. S. (2018). Robustness of the approximate likelihood of the protracted speciation model. Journal of Evolutionary Biology, 31(3), 469–479 [ DOI | http ]
  101. Souza, P. C., Wassenaar, T. A., Skaf, M. S., & Marrink, S. J. (2018). TLR4 Recruitment into Lipid Rafts Studied by Coarse Grained MD Simulation. Biophysical Journal, 114(3), 72a [ DOI | http ]
  102. Spavone, M., Iodice, E., Capaccioli, M., et al. (2018). VEGAS: A VST Early-type Galaxy Survey. III. Mapping the Galaxy Structure, Interactions, and Intragroup Light in the NGC 5018 Group. The Astrophysical Journal, 864(2), 149 [ DOI ]
  103. Spavone, M. & Vegas Team (2018). Study of Galaxy Stellar Halos and Faint Structures with VEGAS@VST. In VST in the Era of the Large Sky Surveys, page 38 [ DOI ]
  104. Spierdijka, L. & Zaourasa, M. (2018). Measuring banks’ market power in the presence of economies of scale: A scale-corrected Lerner index. Journal of Banking and Finance, 87, 40–48 [ DOI | http ]
  105. Spiniello, C., Agnello, A., Napolitano, N. R., et al. (2018). KiDS-SQuaD: The KiDS Strongly lensed QUAsar Detection project. Monthly Notices of the Royal Astronomical Society, 480(1), 1163–1173 [ DOI ]
  106. Straatsma, T. P., Broer, R., Faraji, S., & Havenith, R. W. (2018). GronOR Nonorthogonal Configuration Interaction Calculations at Exascale. In Annual Reports in Computational Chemistry, volume 14, pages 77–91. ISBN 978-0-444-64116-8. ISSN: 18755232 [ DOI | http ]
  107. Teteloshvili, N., Dekkema, G., Boots, A. M., et al. (2018). Involvement of microRNAs in the aging-related decline of CD28 expression by human T cells. Frontiers in Immunology, 9(JUN) [ DOI | http ]
  108. Thyrhaug, E., Tempelaar, R., Alcocer, M. J., et al. (2018). Identification and characterization of diverse coherences in the Fenna-Matthews-Olson complex. Nature Chemistry, 10(7), 780–786 [ DOI | http ]
  109. Tiwari, V., Matutes, Y. A., Gardiner, A. T., et al. (2018). Spatially-resolved fluorescence-detected two-dimensional electronic spectroscopy probes varying excitonic structure in photosynthetic bacteria. Nature Communications, 9(1), 4219 [ DOI | http ]
  110. Toral, A. & Way, A. (2018). What Level of Quality Can Neural Machine Translation Attain on Literary Text? In Translation Quality Assessment: From Principles to Practice, pages 263–287 [ http ]
  111. Tortora, C., Napolitano, N. R., Roy, N., et al. (2018). The last 6 Gyr of dark matter assembly in massive galaxies from the Kilo Degree Survey. Monthly Notices of the Royal Astronomical Society, 473(1), 969–983 [ DOI ]
  112. Tortora, C., Napolitano, N. R., Spavone, M., et al. (2018). The first sample of spectroscopically confirmed ultra-compact massive galaxies in the Kilo Degree Survey. Monthly Notices of the Royal Astronomical Society, 481(4), 4728–4752 [ DOI ]
  113. van Uitert, E., Joachimi, B., Joudaki, S., et al. (2018). KiDS+GAMA: Cosmology constraints from a joint analysis of cosmic shear, galaxy-galaxy lensing, and angular clustering. Monthly Notices of the Royal Astronomical Society, 476(4), 4662–4689 [ DOI ]
  114. Umar, Z. & Spierdijk, L. (2018). The eligibility of emerging-market bonds for pension fund portfolios. In J. A. Bikker, editor, Pension Fund Economics and Finance, pages 91–117. Routledge. ISBN 978-1-138-65680-2. Series Title: Routledge International Studies in Money and Banking [ DOI ]
  115. Us, D., Brzezinski, K., Buitenhuis, T., Dendooven, P., & Ruotsalainen, U. (2018). Evaluation of Median Root Prior for Robust In-Beam PET Reconstruction. IEEE Transactions on Radiation and Plasma Medical Sciences, 2(5), 490–498 [ DOI | http ]
  116. Valente, L., Phillimore, A. B., & Etienne, R. S. (2018). Using molecular phylogenies in island biogeography: it's about time. Ecography, 41(10), 1684–1686 [ DOI | http ]
  117. Valentijn, E. A. (2018). Data Validation Beyond Big Data. In VST in the Era of the Large Sky Surveys, page 17 [ DOI ]
  118. Van Der Ende, M. Y., Hendriks, T., Van Veldhuisen, D. J., et al. (2018). Causal Pathways from Blood Pressure to Larger Qrs Amplitudes a Mendelian Randomization Study. Scientific Reports, 8(1), 5817 [ DOI | http ]
  119. Van Der Ende, M. Y., Said, M. A., Van Veldhuisen, D. J., Verweij, N., & Van Der Harst, P. (2018). Genome-wide studies of heart failure and endophenotypes: Lessons learned and future directions. Cardiovascular Research, 114(9), 1209–1225 [ DOI | http ]
  120. Van Der Harst, P. & Verweij, N. (2018). Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circulation Research, 122(3), 433–443 [ DOI | http ]
  121. Veenhoven, R., Snijders, S., Van Der Hall, D., & Van Noord, R. (2018). Using translated data to improve deep learning author profiling models: Notebook for PAN at CLEF 2018. In CEUR Workshop Proceedings, volume 2125, page 12. ISSN: 16130073
  122. van de Vegte, Y. J., van der Harst, P., & Verweij, N. (2018). Heart rate recovery 10 seconds after cessation of exercise predicts death. Journal of the American Heart Association, 7(8) [ DOI | http ]
  123. Venhola, A., Peletier, R., Laurikainen, E., et al. (2018). The Fornax Deep Survey with the VST: IV. A size and magnitude limited catalog of dwarf galaxies in the area of the Fornax cluster. Astronomy and Astrophysics, 620, A165 [ DOI | http ]
  124. Venhola, A., Peletier, R., Laurikainen, E., et al. (2018). VizieR Online Data Catalog: Fornax Deep Survey with VST. IV. dwarf galaxies (Venhola+, 2018). VizieR Online Data Catalog, pages J/A+A/620/A165
  125. Verweij, N., Van De Vegte, Y. J., & Van Der Harst, P. (2018). Genetic study links components of the autonomous nervous system to heart-rate profile during exercise. Nature Communications, 9(1), 898 [ DOI | http ]
  126. Wei, C., Li, G., Kang, X., et al. (2018). Full-sky Ray-tracing Simulation of Weak Lensing Using ELUCID Simulations: Exploring Galaxy Intrinsic Alignment and Cosmic Shear Correlations. The Astrophysical Journal, 853(1), 25 [ DOI ]
  127. Wilkinson, M. H. F. & Gazagnes, S. (2018). Distributed Component Forests: ImagesHierarchical Image Representations Suitable for Tera-Scale. In Proceedings of the International Conference on Pattern Recognition and Artificial Intelligence, pages 96–101
  128. Wolfshaar, J. v. d., Wiering, M., & Schomaker, L. (2018). Deep learning policy quantization. In ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence, volume 2, pages 122–130. ISBN 978-989-758-275-2 [ DOI ]
  129. Xu, L. & Etienne, R. S. (2018). Detecting local diversity-dependence in diversification. Evolution, 72(6), 1294–1305 [ DOI | http ]
  130. Zhang, Y., Soni, S., Krijger, T. L., et al. (2018). Tunneling Probability Increases with Distance in Junctions Comprising Self-Assembled Monolayers of Oligothiophenes. Journal of the American Chemical Society, 140(44), 15048–15055 [ DOI | http ]
  131. Zhang, Y., Ye, G., Soni, S., et al. (2018). Controlling destructive quantum interference in tunneling junctions comprising self-assembled monolayers: Via bond topology and functional groups. Chemical Science, 9(19), 4414–4423 [ DOI | http ]

2017

  1. Alessandri, R., Uusitalo, J. J., De Vries, A. H., Havenith, R. W., & Marrink, S. J. (2017). Bulk Heterojunction Morphologies with Atomistic Resolution from Coarse-Grain Solvent Evaporation Simulations. Journal of the American Chemical Society, 139(10), 3697–3705 [ DOI | http ]
  2. Alonso, H. M. & Plank, B. (2017). When is multitask learning effective? Semantic sequence prediction under varying data conditions. 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference, 1, 44–53 [ DOI | http ]
  3. Arabnejad, H., Lago, M. D., Jekel, P. A., et al. (2017). A robust cosolvent-compatible halohydrin dehalogenase by computational library design. Protein Engineering, Design and Selection, 30(3), 175–189 [ DOI | http ]
  4. Azhar, M. & Mostovoy, M. (2017). Incommensurate Spiral Order from Double-Exchange Interactions. Physical Review Letters, 118(2), 027203 [ DOI | http ]
  5. Baars, S., Viebahn, J. P., Mulder, T. E., et al. (2017). Continuation of probability density functions using a generalized Lyapunov approach. Journal of Computational Physics, 336, 627–643 [ DOI | http ]
  6. Bacon, R., Conseil, S., Mary, D., et al. (2017). The MUSE Hubble Ultra Deep Field Survey: I. Survey description, data reduction, and source detection ? Astronomy and Astrophysics, 608, A1 [ DOI ]
  7. Biegun, A. K., van Goethem, M. J., van der Graaf, E. R., et al. (2017). The optimal balance between quality and efficiency in proton radiography imaging technique at various proton beam energies: A Monte Carlo study. Physica Medica, 41, 141–146 [ DOI | http ]
  8. Bjerva, J. (2017). Will my auxiliary tagging task help? Estimating Auxiliary Tasks Effectivity in Multi-Task Learning. In J. Tiedemann, editor, NoDaLiDa 2017 - 21st Nordic Conference of Computational Linguistics, Proceedings of the Conference, pages 216–220. Linkoping UP. ISBN 978-91-7685-601-7. Series Title: Linköping Electronic Conference Proceedings [ http ]
  9. Bouisaghouane, A., Buring, R., & Kiselev, A. (2017). The Kontsevich tetrahedral flow revisited. Journal of Geometry and Physics, 119, 272–285 [ DOI | http ]
  10. Brinchmann, J., Inami, H., Bacon, R., et al. (2017). The MUSE Hubble Ultra Deep Field Survey: III. Testing photometric redshifts to 30th magnitude. Astronomy and Astrophysics, 608, A3 [ DOI ]
  11. Brouwer, M. M., Visser, M. R., Dvornik, A., et al. (2017). First test of Verlinde's theory of emergent gravity using weak gravitational lensing measurements. Monthly Notices of the Royal Astronomical Society, 466(3), 2547–2559 [ DOI ]
  12. Cabrera, A. A. & Palsbøll, P. J. (2017). Inferring past demographic changes from contemporary genetic data: A simulation-based evaluation of the ABC methods implemented in diyabc. Molecular Ecology Resources, 17(6), e94–e110 [ DOI | http ]
  13. Cantiello, M., D'Abrusco, R., Spavone, M., et al. (2017). VizieR Online Data Catalog: NGC3115 & NGC1399 VEGAS-SSS globular clusters (Cantiello+, 2018). VizieR Online Data Catalog, pages J/A+A/611/A93
  14. Carton, D., Carton, D., Brinchmann, J., et al. (2017). Inferring gas-phase metallicity gradients of galaxies at the seeing limit: A forward modelling approach. Monthly Notices of the Royal Astronomical Society, 468(2), 2140–2163 [ DOI ]
  15. Cavuoti, S., Tortora, C., Brescia, M., et al. (2017). A cooperative approach among methods for photometric redshifts estimation: An application to KiDS data. Monthly Notices of the Royal Astronomical Society, 466(2), 2039–2053 [ DOI ]
  16. Chatterjee, N. A., Giulianini, F., Geelhoed, B., et al. (2017). Genetic Obesity and the Risk of Atrial Fibrillation: Causal Estimates from Mendelian Randomization. Circulation, 135(8), 741–754 [ DOI | http ]
  17. Christophersen, I. E., Rienstra, M., Roselli, C., et al. (2017). Erratum: Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation (Nature Genetics DOI: 10.1038/ng.3843). Nature Genetics, 49(8), 1286 [ DOI | http ]
  18. Christophersen, I. E., Rienstra, M., Roselli, C., et al. (2017). Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation. Nature Genetics, 49(6), 946–952 [ DOI | http ]
  19. De Jong, J. T., Verdoes Kleijn, G. A., Erben, T., et al. (2017). The third data release of the Kilo-Degree Survey and associated data products. Astronomy and Astrophysics, 604, A134 [ DOI ]
  20. De Mares, M. C., Sipkema, D., Huang, S., et al. (2017). Host specificity for bacterial, archaeal and fungal communities determined for high- and low-microbial abundance sponge species in two genera. Frontiers in Microbiology, 8(DEC) [ DOI | http ]
  21. De Wijs, G. A., Laskowski, R., Blaha, P., et al. (2017). NMR shieldings from density functional perturbation theory: GIPAW versus all-electron calculations. Journal of Chemical Physics, 146(6), 064115 [ DOI | http ]
  22. Diener, C., Wisotzki, L., Schmidt, K. B., et al. (2017). The MUSE-Wide survey: Detection of a clustering signal from Lyman a emitters in the range 3 z 6. Monthly Notices of the Royal Astronomical Society, 471(3), 3186–3192 [ DOI ]
  23. Drake, A. B., Garel, T., Wisotzki, L., et al. (2017). The MUSE Hubble Ultra Deep Field Survey: VI. the faint-end of the Ly α luminosity function at 2.91 z 6.64 and implications for reionisation. Astronomy and Astrophysics, 608, A6 [ DOI ]
  24. Drake, A. B., Guiderdoni, B., Blaizot, J., et al. (2017). MUSE deep-fields: The Ly α luminosity function in the Hubble Deep Field-South at 2.91 z 6.64. Monthly Notices of the Royal Astronomical Society, 471(1), 267–278 [ DOI ]
  25. Dvornik, A., Cacciato, M., Kuijken, K., et al. (2017). A KiDS weak lensing analysis of assembly bias in GAMA galaxy groups. Monthly Notices of the Royal Astronomical Society, 468(3), 3251–3265 [ DOI ]
  26. Eppinga, R. N., Hartman, M. H., Verweij, N., et al. (2017). Refining Thromboembolic Risk in the General Population. Journal of the American College of Cardiology, 70(3), 403–404 [ DOI | http ]
  27. Erukhimovich, I., Kriksin, Y., & Ten Brinke, G. (2017). Ordering Lamellar-Forming Copolymer Thin Films in 3D Bicontinuous Morphologies via Lamellar Patterned Substrate. Macromolecules, 50(10), 3922–3932 [ DOI | http ]
  28. Fenech Conti, I., Herbonnet, R., Hoekstra, H., et al. (2017). Calibration of weak-lensing shear in the Kilo-Degree Survey. Monthly Notices of the Royal Astronomical Society, 467(2), 1627–1651 [ DOI ]
  29. Finley, H., Bouché, N., Contini, T., et al. (2017). Galactic winds with MUSE: A direct detection of Fe II ∗ emission from a z = 1.29 galaxy. Astronomy and Astrophysics, 605, A118 [ DOI ]
  30. Finley, H., Bouché, N., Contini, T., et al. (2017). The MUSE Hubble Ultra Deep Field Survey: VII. Fe II ∗ emission in star-forming galaxies. Astronomy and Astrophysics, 608, A7 [ DOI ]
  31. Franken, L. E., Oostergetel, G. T., Pijning, T., et al. (2017). A general mechanism of ribosome dimerization revealed by single-particle cryo-electron microscopy. Nature Communications, 8(1), 722 [ DOI | http ]
  32. Frederix, P. W., Idé, J., Altay, Y., et al. (2017). Structural and Spectroscopic Properties of Assemblies of Self-Replicating Peptide Macrocycles. ACS Nano, 11(8), 7858–7868 [ DOI | http ]
  33. Goot, R. V. D., Plank, B., & Nissim, M. (2017). To Normalize, or Not to Normalize: The Impact of Normalization on Part-of-Speech Tagging. In 3rd Workshop on Noisy User-Generated Text, W-NUT 2017 - Proceedings of the Workshop, pages 31–39. Association for Computational Linguistics (ACL). ISBN 978-1-945626-94-4 [ DOI ]
  34. Greenwood, A. J., Kamp, I., Waters, L. B., et al. (2017). Thermochemical modelling of brown dwarf discs. Astronomy and Astrophysics, 601, A44 [ DOI | http ]
  35. Guérou, A., Krajnović, D., Epinat, B., et al. (2017). The MUSE Hubble Ultra Deep Field Survey: V. Spatially resolved stellar kinematics of galaxies at redshift 0.2 ≲ z ≲ 0.8. Astronomy and Astrophysics, 608, A5 [ DOI ]
  36. Harnois-Déraps, J., Tröster, T., Chisari, N. E., et al. (2017). KiDS-450: Tomographic cross-correlation of galaxy shear with Planck lensing. Monthly Notices of the Royal Astronomical Society, 471(2), 1619–1633 [ DOI ]
  37. Hartley, P., Flamary, R., Jackson, N., Tagore, A. S., & Metcalf, R. B. (2017). Support vector machine classification of strong gravitational lenses. Monthly Notices of the Royal Astronomical Society, 471(3), 3378–3397 [ DOI ]
  38. Hashimoto, T., Garel, T., Guiderdoni, B., et al. (2017). The MUSE Hubble Ultra Deep Field Survey: X. Ly α equivalent widths at 2.9 z 6.6. Astronomy and Astrophysics, 608, A10 [ DOI ]
  39. Herenz, E. C., Urrutia, T., Wisotzki, L., et al. (2017). The MUSE-Wide survey: A first catalogue of 831 emission line galaxies. Astronomy and Astrophysics, 606, A12 [ DOI ]
  40. Hildebrandt, H., Viola, M., Heymans, C., et al. (2017). KiDS-450: Cosmological parameter constraints from tomographic weak gravitational lensing. Monthly Notices of the Royal Astronomical Society, 465(2), 1454–1498 [ DOI ]
  41. Hsu, P. C., Bruininks, B. M., Jefferies, D., et al. (2017). Charmm-gui martini maker for modeling and simulation of complex bacterial membranes with lipopolysaccharides. Journal of Computational Chemistry, 38(27), 2354–2363 [ DOI | http ]
  42. IJtsma, P., Spierdijk, L., & Shaffer, S. (2017). The concentration–stability controversy in banking: New evidence from the EU-25. Journal of Financial Stability, 33, 273–284 [ DOI | http ]
  43. Inami, H., Bacon, R., Brinchmann, J., et al. (2017). The MUSE Hubble Ultra Deep Field Survey: II. Spectroscopic redshifts and comparisons to color selections of high-redshift galaxies. Astronomy and Astrophysics, 608, A2 [ DOI ]
  44. Iodice, E., Spavone, M., Capaccioli, M., et al. (2017). The Fornax Deep Survey with VST. II. Fornax A: A Two-phase Assembly Caught in the Act. The Astrophysical Journal, 839(1), 21 [ DOI ]
  45. Janzen, T., Alzate, A., Muschick, M., et al. (2017). Community assembly in Lake Tanganyika cichlid fish: quantifying the contributions of both niche-based and neutral processes. Ecology and Evolution, 7(4), 1057–1067 [ DOI | http ]
  46. Johnson, A., Blake, C., Amon, A., et al. (2017). 2dFLenS and KiDS: Determining source redshift distributions with cross-correlations. Monthly Notices of the Royal Astronomical Society, 465(4), 4118–4132 [ DOI ]
  47. Jonathan Hogervorst, Emmanuel Okafor, & Marco Wiering (2017). Deep Colorization with Facial Gender Recognition. In Preproceedings of the 29th Benelux Conference on Artificial Intelligence (BNAIC'2017), pages 317–325
  48. Joudaki, S., Mead, A., Blake, C., et al. (2017). KiDS-450: Testing extensions to the standard cosmological model. Monthly Notices of the Royal Astronomical Society, 471(2), 1259–1279 [ DOI ]
  49. Justice, A. E., Winkler, T. W., Feitosa, M. F., et al. (2017). Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits. Nature Communications, 8(1), 14977 [ DOI | http ]
  50. Kamminga, M. E., De Wijs, G. A., Havenith, R. W., Blake, G. R., & Palstra, T. T. (2017). The Role of Connectivity on Electronic Properties of Lead Iodide Perovskite-Derived Compounds. Inorganic Chemistry, 56(14), 8408–8414 [ DOI | http ]
  51. Kazemier, J. J., Ouzounis, G. K., & Wilkinson, M. H. (2017). Connected morphological attribute filters on distributed memory parallel machines. In J. Angulo, S. Velasco-Forero, & F. Meyer, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10225 LNCS, pages 357–368. Springer International Publishing, Cham. ISBN 978-3-319-57239-0. ISSN: 16113349 [ DOI ]
  52. Kriete, B., Bondarenko, A. S., Jumde, V. R., et al. (2017). Steering Self-Assembly of Amphiphilic Molecular Nanostructures via Halogen Exchange. Journal of Physical Chemistry Letters, 8(13), 2895–2901 [ DOI | http ]
  53. Kupfer, T., Ramsay, G., Roestel, J. v., et al. (2017). The OmegaWhite Survey for Short-period Variable Stars. V. Discovery of an Ultracompact Hot Subdwarf Binary with a Compact Companion in a 44-minute Orbit. The Astrophysical Journal, 851(1), 28 [ DOI ]
  54. Köhlinger, F., Viola, M., Joachimi, B., et al. (2017). KiDS-450: The tomographic weak lensing power spectrum and constraints on cosmological parameters. Monthly Notices of the Royal Astronomical Society, 471(4), 4412–4435 [ DOI ]
  55. Leclercq, F., Bacon, R., Wisotzki, L., et al. (2017). The MUSE Hubble Ultra Deep Field Survey: VIII. Extended Lyman- α haloes around high- z star-forming galaxies. Astronomy and Astrophysics, 608, A8 [ DOI ]
  56. Lopez-Antequera, M., Petkov, N., & Gonzalez-Jimenez, J. (2017). City-scale continuous visual localization. In 2017 European Conference on Mobile Robots, ECMR 2017, pages 1–6. IEEE. ISBN 978-1-5386-1096-1 [ DOI | http ]
  57. Lubitz, S. A., Yin, X., Lin, H. J., et al. (2017). Genetic risk prediction of atrial fibrillation. Circulation, 135(14), 1311–1320 [ DOI | http ]
  58. Macfarlane, S. A., Woudt, P. A., Dufour, P., et al. (2017). The Omega White Survey for short-period variable stars - IV. Discovery of the warm DQ white dwarf OW J175358.85-310728.9. Monthly Notices of the Royal Astronomical Society, 470(1), 732–741 [ DOI ]
  59. Macfarlane, S. A., Woudt, P. A., Groot, P. J., et al. (2017). The OmegaWhite survey for short-period variable stars - III: Follow-up photometric and spectroscopic observations. Monthly Notices of the Royal Astronomical Society, 465(1), 434–459 [ DOI ]
  60. Mahlke, M., Bouy, H., Altieri, B., et al. (2017). VizieR Online Data Catalog: KiDS Survey for solar system objects mining (Mahlke+, 2018). VizieR Online Data Catalog, pages J/A+A/610/A21
  61. Marashdeh, A., Tiesma, T., van Velzen, N. J., et al. (2017). The rational design of a Au(I) precursor for focused electron beam induced deposition. Beilstein Journal of Nanotechnology, 8(1), 2753–2765 [ DOI | http ]
  62. Marchione, D., Izquierdo, M. A., Bistoni, G., et al. (2017). 13C NMR Spectroscopy of N-Heterocyclic Carbenes Can Selectively Probe σ Donation in Gold(I) Complexes. Chemistry - A European Journal, 23(11), 2722–2728 [ DOI | http ]
  63. Maseda, M. V., Brinchmann, J., Franx, M., et al. (2017). The MUSE Hubble Ultra Deep Field Survey: IV. Global properties of C III] emitters. Astronomy and Astrophysics, 608, A4 [ DOI ]
  64. Meijer, E., Spierdijk, L., & Wansbeek, T. (2017). Consistent estimation of linear panel data models with measurement error. Journal of Econometrics, 200(2), 169–180 [ DOI | http ]
  65. Mekonnen, S. A., Palma Medina, L. M., Glasner, C., et al. (2017). Signatures of cytoplasmic proteins in the exoproteome distinguish community- and hospital-associated methicillin-resistant staphylococcus aureus USA300 lineages. Virulence, 8(6), 891–907 [ DOI | http ]
  66. Mondol, R., Snoeken, D. A., Chang, M. C., & Otten, E. (2017). Stable, crystalline boron complexes with mono-, di- and trianionic formazanate ligands. Chemical Communications, 53(3), 513–516 [ DOI | http ]
  67. Morrison, C. (2017). the-wizz: Clustering redshift estimation code. Pages: ascl:1706.008
  68. Morrison, C. B., Hildebrandt, H., Schmidt, S. J., et al. (2017). THE-WIZZ: Clustering redshift estimation for everyone. Monthly Notices of the Royal Astronomical Society, 467(3), 3576–3589 [ DOI ]
  69. Negrello, M., Amber, S., Amvrosiadis, A., et al. (2017). The Herschel-ATLAS: A sample of 500 μm-selected lensed galaxies over 600 deg2. Monthly Notices of the Royal Astronomical Society, 465(3), 3558–3580 [ DOI ]
  70. Nijland, J. G., Shin, H. Y., Boender, L. G., et al. (2017). Improved xylose metabolism by a CYC8 mutant of Saccharomyces cerevisiae. Applied and Environmental Microbiology, 83(11) [ DOI | http ]
  71. Nolte, I. M., Munoz, M. L., Tragante, V., et al. (2017). Genetic loci associated with heart rate variability and their effects on cardiac disease risk. Nature Communications, 8(1), 15805 [ DOI | http ]
  72. Noord, R. V. & Bos, J. (2017). Dealing with Co-reference in Neural Semantic Parsing. In D. Gromann, T. Declerck, & G. Heigl, editors, Proceedings of the 2nd Workshop on Semantic Deep Learning, SemDeep 2017, volume 2, pages 49–57. Association for Computational Linguistics (ACL)
  73. Owers, M. S., Allen, J. T., Baldry, I., et al. (2017). The SAMI Galaxy Survey: The cluster redshift survey, target selection and cluster properties. Monthly Notices of the Royal Astronomical Society, 468(2), 1824–1849 [ DOI ]
  74. Padamati, S. K., Angelone, D., Draksharapu, A., et al. (2017). Transient Formation and Reactivity of a High-Valent Nickel(IV) Oxido Complex. Journal of the American Chemical Society, 139(25), 8718–8724 [ DOI | http ]
  75. Pawara, P., Okafor, E., Schomaker, L., & Wiering, M. (2017). Data augmentation for plant classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10617 LNCS, pages 615–626. ISBN 978-3-319-70352-7. ISSN: 16113349 [ DOI ]
  76. Pawara, P., Okafor, E., Surinta, O., Schomaker, L., & Wiering, M. (2017). Comparing local descriptors and bags of visualwords to deep convolutional neural networks for plant recognition. In ICPRAM 2017 - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, volume 2017-Janua, pages 479–486. ICPRAM. ISBN 978-989-758-222-6 [ DOI ]
  77. Pašteka, L. F., Eliav, E., Borschevsky, A., Kaldor, U., & Schwerdtfeger, P. (2017). Relativistic Coupled Cluster Calculations with Variational Quantum Electrodynamics Resolve the Discrepancy between Experiment and Theory Concerning the Electron Affinity and Ionization Potential of Gold. Physical Review Letters, 118(2), 023002 [ DOI | http ]
  78. Pearson, W. J., Wang, L., Van Der Tak, F. F., et al. (2017). De-blending deep Herschel surveys: A multi-wavelength approach. Astronomy and Astrophysics, 603, A102 [ DOI | http ]
  79. Petrillo, C. E., Tortora, C., Chatterjee, S., et al. (2017). Finding strong gravitational lenses in the Kilo Degree Survey with Convolutional Neural Networks. Monthly Notices of the Royal Astronomical Society, 472(1), 1129–1150 [ DOI ]
  80. Prak, D., Saccani, N., Syntetos, A., Teunter, R., & Visintin, F. (2017). The Repair Kit Problem with positive replenishment lead times and fixed ordering costs. European Journal of Operational Research, 261(3), 893–902 [ DOI | http ]
  81. Prak, D., Teunter, R., & Syntetos, A. (2017). On the calculation of safety stocks when demand is forecasted. European Journal of Operational Research, 256(2), 454–461 [ DOI | http ]
  82. Pratama, A. A., Haq, I. U., Nazir, R., Chaib De Mares, M., & van Elsas, J. D. (2017). Draft genome sequences of three fungal-interactive Paraburkholderia terrae strains, BS007, BS110 and BS437. Standards in Genomic Sciences, 12(1), 81 [ DOI | http ]
  83. Qiu, L., Liu, J., Alessandri, R., et al. (2017). Enhancing doping efficiency by improving host-dopant miscibility for fullerene-based n-type thermoelectrics. Journal of Materials Chemistry A, 5(40), 21234–21241 [ DOI | http ]
  84. Qiu, L., Zhang, Y., Krijger, T. L., et al. (2017). Rectification of current responds to incorporation of fullerenes into mixed-monolayers of alkanethiolates in tunneling junctions. Chemical Science, 8(3), 2365–2372 [ DOI | http ]
  85. Radovich, M., Puddu, E., Bellagamba, F., et al. (2017). Searching for galaxy clusters in the Kilo-Degree Survey. Astronomy and Astrophysics, 598, A107 [ DOI | http ]
  86. Rashid, Z., van Lenthe, J. H., & Havenith, R. W. (2017). Bonding in B2 and B2+: Insights from full configuration interaction and valence bond studies. Computational and Theoretical Chemistry, 1116, 92–95 [ DOI | http ]
  87. Ruder, S. & Plank, B. (2017). Learning to select data for transfer learning with Bayesian Optimization. In EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings, pages 372–382. Association for Computational Linguistics (ACL). ISBN 978-1-945626-83-8 [ DOI ]
  88. Said, M. A., Eppinga, R. N., Hagemeijer, Y., Verweij, N., & van der Harst, P. (2017). Telomere Length and Risk of Cardiovascular Disease and Cancer. Journal of the American College of Cardiology, 70(4), 506–507 [ DOI | http ]
  89. Schmidt, A. F., Swerdlow, D. I., Holmes, M. V., et al. (2017). PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study. The Lancet Diabetes and Endocrinology, 5(2), 97–105 [ DOI | http ]
  90. Selig, O., Sadhanala, A., Müller, C., et al. (2017). Organic Cation Rotation and Immobilization in Pure and Mixed Methylammonium Lead-Halide Perovskites. Journal of the American Chemical Society, 139(11), 4068–4074 [ DOI | http ]
  91. Shaffer, S. & Spierdijk, L. (2017). The Panzar-Rosse revenue test and market power in banking: An empirical illustration. In J. A. Bikker & L. Spierdijk, editors, Handbook of Competition in Banking and Finance, pages 27–45. Edward Elgar Publishing. ISBN 978-1-78536-330-6 [ DOI ]
  92. Silvis, M. H., Remmerswaal, R. A., & Verstappen, R. (2017). A framework for the assessment and creation of subgrid-scale models for large-eddy simulation. In Springer Proceedings in Physics, volume 196, pages 133–139. ISBN 978-3-319-57933-7. ISSN: 18674941 [ DOI | http ]
  93. Silvis, M. H., Remmerswaal, R. A., & Verstappen, R. (2017). Physical consistency of subgrid-scale models for large-eddy simulation of incompressible turbulent flows. Physics of Fluids, 29(1), 015105 [ DOI | http ]
  94. Spavone, M., Capaccioli, M., Napolitano, N. R., et al. (2017). VEGAS: A VST Early-type GAlaxy Survey: II. Photometric study of giant ellipticals and their stellar halos. Astronomy and Astrophysics, 603, A38 [ DOI ]
  95. Spierdijk, L., Shaffer, S., & Considine, T. (2017). How do banks adjust to changing input prices? A dynamic analysis of U.S. commercial banks before and after the crisis. Journal of Banking and Finance, 85, 1–14 [ DOI | http ]
  96. Strisciuglio, N. (2017). Learning audio and image representations with bio-inspired trainable feature extractors. Electronic Letters on Computer Vision and Image Analysis, 16(2), 17–20 [ DOI | http ]
  97. Strisciuglio, N., Azzopardi, G., & Petkov, N. (2017). Detection of curved lines with B-COSFIRE filters: A case study on crack delineation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10424 LNCS, pages 108–120. ISBN 978-3-319-64688-6 [ http ]
  98. Tröster, T., Camera, S., Fornasa, M., et al. (2017). Cross-correlation of weak lensing and gamma rays: Implications for the nature of dark matter. Monthly Notices of the Royal Astronomical Society, 467(3), 2706–2722 [ DOI ]
  99. Uitert, E. v., Hoekstra, H., Joachimi, B., et al. (2017). Halo ellipticity of GAMA galaxy groups from KiDS weak lensing. Monthly Notices of the Royal Astronomical Society, 467(4), 4131–4149 [ DOI ]
  100. Unjaroen, D., Swart, M., & Browne, W. R. (2017). Electrochemical polymerization of iron(III) polypyridyl complexes through C-C coupling of redox non-innocent phenolato ligands. Inorganic Chemistry, 56(1), 470–479 [ DOI | http ]
  101. Valente, L., Etienne, R. S., & Dávalos, L. M. (2017). Recent extinctions disturb path to equilibrium diversity in Caribbean bats. Nature Ecology and Evolution, 1(2), 0026 [ DOI | http ]
  102. Valente, L., Illera, J. C., Havenstein, K., et al. (2017). Equilibrium Bird Species Diversity in Atlantic Islands. Current Biology, 27(11), 1660–1666.e5 [ DOI | http ]
  103. Van Den Berg, M. E., Warren, H. R., Cabrera, C. P., et al. (2017). Discovery of novel heart rate-associated loci using the Exome Chip. Human Molecular Genetics, 26(12), 2346–2363 [ DOI | http ]
  104. Van Der Burg, R. F., Hoekstra, H., Muzzin, A., et al. (2017). The abundance of ultra-diffuse galaxies from groups to clusters UDGs are relatively more common in more massive haloes. Astronomy and Astrophysics, 607, A79 [ DOI ]
  105. Van Der Horn, H. J., Kok, J. G., De Koning, M. E., et al. (2017). Altered Wiring of the Human Structural Connectome in Adults with Mild Traumatic Brain Injury. Journal of Neurotrauma, 34(5), 1035–1044 [ DOI | http ]
  106. Van Eerden, F. J., Melo, M. N., Frederix, P. W., & Marrink, S. J. (2017). Prediction of Thylakoid Lipid Binding Sites on Photosystem II. Biophysical Journal, 113(12), 2669–2681 [ DOI | http ]
  107. Van Noord, R. & Bos, J. (2017). Neural semantic parsing by character-based translation: Experiments with abstract meaning representations. Computational Linguistics in the Netherlands Journal, 7, 93–108
  108. Velliscig, M., Cacciato, M., Hoekstra, H., et al. (2017). Galaxy-galaxy lensing in EAGLE: Comparison with data from 180 deg2 of the KiDS and GAMA surveys. Monthly Notices of the Royal Astronomical Society, 471(3), 2856–2870 [ DOI ]
  109. Venhola, A., Peletier, R., Laurikainen, E., et al. (2017). The fornax deep survey with VST III. Low surface brightness dwarfs and ultra diffuse galaxies in the center of the Fornax cluster. Astronomy and Astrophysics, 608, A142 [ DOI ]
  110. Ventou, E., Contini, T., Bouché, N., et al. (2017). The MUSE Hubble Ultra Deep Field Survey: IX. Evolution of galaxy merger fraction since z π 6. Astronomy and Astrophysics, 608, A9 [ DOI ]
  111. Verweij, N., Eppinga, R. N., Hagemeijer, Y., & Van Der Harst, P. (2017). Identification of 15 novel risk loci for coronary artery disease and genetic risk of recurrent events, atrial fibrillation and heart failure. Scientific Reports, 7(1), 2761 [ DOI | http ]
  112. Warren, H. R., Evangelou, E., Cabrera, C. P., et al. (2017). Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nature Genetics, 49(3), 403–415 [ DOI | http ]
  113. Weng, L. C., Lunetta, K. L., Müller-Nurasyid, M., et al. (2017). Genetic Interactions with Age, Sex, Body Mass Index, and Hypertension in Relation to Atrial Fibrillation: The AFGen Consortium. Scientific Reports, 7(1), 11303 [ DOI | http ]
  114. Wibowo, M., Broer, R., & Havenith, R. W. (2017). A rigorous nonorthogonal configuration interaction approach for the calculation of electronic couplings between diabatic states applied to singlet fission. Computational and Theoretical Chemistry, 1116, 190–194 [ DOI | http ]
  115. Wilkinson, M. H. (2017). A guided tour of connective morphology: Concepts, algorithms, and applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10502 LNCS, pages 9–18. ISBN 978-3-319-66271-8. ISSN: 16113349 [ DOI | http ]
  116. Wolf, C., Johnson, A. S., Bilicki, M., et al. (2017). The 2-degree Field Lensing Survey: Photometric redshifts from a large new training sample to r 19.5. Monthly Notices of the Royal Astronomical Society, 466(2), 1582–159 [ DOI ]
  117. Zelovich, T., Borschevsky, A., Eliav, E., & Kaldor, U. (2017). Relativistic coupled cluster calculation of Mössbauer isomer shifts of iodine compounds. Molecular Physics, 115(1-2), 138–143 [ DOI | http ]
  118. Zhang, Y., Qiu, X., Gordiichuk, P., et al. (2017). Mechanically and Electrically Robust Self-Assembled Monolayers for Large-Area Tunneling Junctions. Journal of Physical Chemistry C, 121(27), 14920–14928 [ DOI | http ]

2016

  1. Belikov, A., Williams, O., Altieri, B., et al. (2016). The Euclid Archive System: A Datacentric Approach to Big Data. In M. Molinaro, K. Shortridge, & F. Pasian, editors, Proceedings of 2016 conference on Big Data from Space (BiDS'16), volume JRC100655, pages 212–215. Series Title: Astronomical Society of the Pacific Conference Series
  2. Biegun, A. K., Takatsu, J., Nakaji, T., et al. (2016). Proton energy and scattering angle radiographs to improve proton treatment planning: A Monte Carlo study. Journal of Instrumentation, 11(12), C12015–C12015 [ DOI | http ]
  3. Biegun, A. K., Takatsu, J., Van Goethem, M. J., et al. (2016). Proton radiography to improve proton radiotherapy: Simulation study at different proton beam energies. Acta Physica Polonica B, 47(2), 329–334 [ DOI | http ]
  4. Bina, D., Pelló, R., Richard, J., et al. (2016). MUSE observations of the lensing cluster Abell 1689. Astronomy and Astrophysics, 590, A14 [ DOI ]
  5. Bjerva, J. (2016). Byte-based Language Identification with Deep Convolutional Networks. In Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3), pages 119–125. The COLING 2016 Organizing Committee, Osaka, Japan [ http ]
  6. Bjerva, J., Plank, B., & Bos, J. (2016). Semantic tagging with deep residual networks. In COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers, pages 3531–3541. Association for Computational Linguistics (ACL). ISBN 978-4-87974-702-0
  7. Blake, C., Amon, A., Childress, M., et al. (2016). The 2-degree Field Lensing Survey: Design and clustering measurements. Monthly Notices of the Royal Astronomical Society, 462(4), 4240–4265 [ DOI ]
  8. Blijleven, J. S., Boonstra, S., Onck, P. R., van der Giessen, E., & van Oijen, A. M. (2016). Mechanisms of influenza viral membrane fusion. Seminars in Cell and Developmental Biology, 60, 78–88 [ DOI | http ]
  9. Boonstra, S., Onck, P. R., & Van Der Giessen, E. (2016). CHARMM TIP3P Water Model Suppresses Peptide Folding by Solvating the Unfolded State. Journal of Physical Chemistry B, 120(15), 3692–3698 [ DOI | http ]
  10. Borisova, E., Cantalupo, S., Lilly, S. J., et al. (2016). UBIQUITOUS GIANT Ly α NEBULAE AROUND THE BRIGHTEST QUASARS AT z ∼ 3.5 REVEALED WITH MUSE. The Astrophysical Journal, 831(1), 39 [ DOI ]
  11. Bouché, N., Finley, H., Schroetter, I., et al. (2016). POSSIBLE SIGNATURES OF A COLD-FLOW DISK FROM MUSE USING A z ∼ 1 GALAXY–QUASAR PAIR TOWARD SDSS J1422−0001. The Astrophysical Journal, 820(2), 121 [ DOI ]
  12. Brouwer, M. M., Cacciato, M., Dvornik, A., et al. (2016). Dependence of GAMA galaxy halo masses on the cosmic web environment from 100 deg2 of KiDS weak lensing data. Monthly Notices of the Royal Astronomical Society, 462(4), 4451–4463 [ DOI ]
  13. Cantiello, M., Capaccioli, M., Brocato, E., et al. (2016). VEGAS-SSS: A VST early-type galaxy survey: Analysis of small stellar system. In N. R. Napolitano, G. Longo, M. Marconi, M. Paolillo, & E. Iodice, editors, Astrophysics and Space Science Proceedings, volume 42, pages 157–163. ISBN 978-3-319-19329-8. ISSN: 15706605 [ DOI ]
  14. Carlotti, M., Kovalchuk, A., Wächter, T., et al. (2016). Conformation-driven quantum interference effects mediated by through-space conjugation in self-assembled monolayers. Nature Communications, 7(1), 13904 [ DOI | http ]
  15. Chang, M. C. & Otten, E. (2016). Intramolecular Hydride Transfer Reactions in (Formazanate)Boron Dihydride Complexes. Organometallics, 35(4), 534–542 [ DOI | http ]
  16. Conklin, J. R., Reneerkens, J., Verkuil, Y. I., et al. (2016). Low genetic differentiation between greenlandic and siberian sanderling populations implies a different phylogeographic history than found in red knots. Journal of Ornithology, 157(1), 325–332 [ DOI | http ]
  17. Contini, T., Epinat, B., Bouché, N., et al. (2016). Deep MUSE observations in the HDFS: Morpho-kinematics of distant star-forming galaxies down to 10 8M⊙. Astronomy and Astrophysics, 591, A49 [ DOI ]
  18. Daams, M. N., Sijtsma, F. J., & van der Vlist, A. J. (2016). The effect of natural space on nearby property prices: Accounting for perceived attractiveness. Land Economics, 92(3), 389–410 [ DOI | http ]
  19. De Gier, H. D., Jahani, F., Broer, R., Hummelen, J. C., & Havenith, R. W. (2016). Promising Strategy to Improve Charge Separation in Organic Photovoltaics: Installing Permanent Dipoles in PCBM Analogues. Journal of Physical Chemistry A, 120(27), 4664–4671 [ DOI | http ]
  20. De Jong, D. H., Baoukina, S., Ingólfsson, H. I., & Marrink, S. J. (2016). Martini straight: Boosting performance using a shorter cutoff and GPUs. Computer Physics Communications, 199, 1–7 [ DOI | http ]
  21. Demetrescu, M. & Hanck, C. (2016). Robust Inference for Near-Unit Root Processes with Time-Varying Error Variances. Econometric Reviews, 35(5), 751–781 [ DOI | http ]
  22. Dubath, P., Apostolakos, N., Bonchi, A., et al. (2016). The Euclid Data Processing Challenges. In M. Brescia, S. Djorgovski, E. D. Feigelson, G. Longo, & S. Cavuoti, editors, Proceedings of the International Astronomical Union, volume 12, pages 73–82 [ DOI ]
  23. Duke, B. J. & Havenith, R. W. (2016). Implications of the complete basis set limit in valence bond theory: a case study of molecular hydrogen. Theoretical Chemistry Accounts, 135(3), 82 [ DOI | http ]
  24. D’Abrusco, R., Cantiello, M., Paolillo, M., et al. (2016). the Extended Spatial Distribution of Globular Clusters in the Core of the Fornax Cluster. The Astrophysical Journal, 819(2), L31 [ DOI ]
  25. Eijsink, L. E., Perdriau, S. C., De Vries, J. G., & Otten, E. (2016). Metal-ligand cooperative activation of nitriles by a ruthenium complex with a de-aromatized PNN pincer ligand. Dalton Transactions, 45(40), 16033–16039 [ DOI | http ]
  26. Eppinga, R. N., Hagemeijer, Y., Burgess, S., et al. (2016). Identification of genomic loci associated with resting heart rate and shared genetic predictors with all-cause mortality. Nature Genetics, 48(12), 1557–1563 [ DOI | http ]
  27. Foggia, P., Petkov, N., Saggese, A., Strisciuglio, N., & Vento, M. (2016). Audio surveillance of roads: A system for detecting anomalous sounds. IEEE Transactions on Intelligent Transportation Systems, 17(1), 279–288 [ DOI | http ]
  28. Galesloot, T. E., Verweij, N., Traglia, M., et al. (2016). Meta-GWAS and meta-analysis of exome array studies do not reveal genetic determinants of serum hepcidin. PLoS ONE, 11(11), e0166628 [ DOI | http ]
  29. Ghavami, A., Veenhoff, L. M., der Giessen, E. V., & Onck, P. R. (2016). Intrinsically Disordered Proteins: Gatekeepers of the Nuclear Pore Complex. Biophysical Journal, 110(3), 358a [ DOI | http ]
  30. van der Harst, P., van Setten, J., Verweij, N., et al. (2016). 52 Genetic Loci Influencing Myocardial Mass. Journal of the American College of Cardiology, 68(13), 1435–1448 [ DOI | http ]
  31. He, S., Samara, P., Burgers, J., & Schomaker, L. (2016). Historical Document Dating Using Unsupervised Attribute Learning. In Proceedings - 12th IAPR International Workshop on Document Analysis Systems, DAS 2016, pages 36–41. IEEE. ISBN 978-1-5090-1792-8 [ DOI | http ]
  32. He, S., Samara, P., Burgers, J., & Schomaker, L. (2016). Image-based historical manuscript dating using contour and stroke fragments. Pattern Recognition, 58, 159–171 [ DOI | http ]
  33. He, S. & Schomaker, L. (2016). General Pattern Run-Length Transform for Writer Identification. In Proceedings - 12th IAPR International Workshop on Document Analysis Systems, DAS 2016, pages 60–65. IEEE. ISBN 978-1-5090-1792-8 [ DOI | http ]
  34. Iodice, E., Capaccioli, M., Grado, A., et al. (2016). THE FORNAX DEEP SURVEY WITH VST. I. THE EXTENDED AND DIFFUSE STELLAR HALO OF NGC 1399 OUT TO 192 kpc. The Astrophysical Journal, 820(1), 42 [ DOI ]
  35. de Jong, J., Verdoes Kleijn, G., Boxhoorn, D., et al. (2016). VizieR Online Data Catalog: KiDS-ESO-DR2 multi-band source catalog (de Jong+, 2015). VizieR Online Data Catalog, page II/344
  36. Kamann, S. & Husser, T. O. (2016). The MUSE view on the dynamics of globular clusters. Memorie della Societa Astronomica Italiana - Journal of the Italian Astronomical Society, 87(4), 600–604
  37. Kamann, S., Husser, T. O., Brinchmann, J., et al. (2016). MUSE crowded field 3D spectroscopy of over 12 000 stars in the globular cluster NGC 6397: II. Probing the internal dynamics and the presence of a central black hole. Astronomy and Astrophysics, 588, A149 [ DOI ]
  38. Kamann, S., Husser, T. O., Wendt, M., et al. (2016). A Stellar Census in NGC 6397 with MUSE. The Messenger, 164, 18–22
  39. Kelz, A., Kamann, S., Urrutia, T., Weilbacher, P., & Bacon, R. (2016). Multi-Object Spectroscopy with MUSE. 507, 323. Conference Name: Multi-Object Spectroscopy in the Next Decade: Big Questions, Large Surveys, and Wide Fields ADS Bibcode: 2016ASPC..507..323K [ http ]
  40. Kistemaker, J. C., Lubbe, A. S., Bloemsma, E. A., & Feringa, B. L. (2016). On the Role of Viscosity in the Eyring Equation. ChemPhysChem, 17(12), 1819–1822 [ DOI | http ]
  41. Kriksin, Y., Erukhimovich, I., & ten Brinke, G. (2016). Self-Consistent Field Theory within Hildebrand Approximation: Microphase Separation in Gradient Copolymers. Macromolecular Theory and Simulations, 25(5), 466–474 [ DOI | http ]
  42. Kumar, S., Van Herpt, J. T., Gengler, R. Y., et al. (2016). Mixed Monolayers of Spiropyrans Maximize Tunneling Conductance Switching by Photoisomerization at the Molecule-Electrode Interface in EGaIn Junctions. Journal of the American Chemical Society, 138(38), 12519–12526 [ DOI | http ]
  43. Labun, A., Wittek, R., & Steglich, C. (2016). The co-evolution of power and friendship networks in an organization. Network Science, 4(3), 364–384 [ DOI | http ]
  44. van Leeuwen, E. M., Sabo, A., Bis, J. C., et al. (2016). Meta-analysis of 49 549 individuals imputed with the 1000 Genomes Project reveals an exonic damaging variant in ANGPTL4 determining fasting TG levels. Journal of Medical Genetics, 53(7), 441–449 [ DOI | http ]
  45. Lin, H., Mueller-Nurasyid, M., Smith, A. V., et al. (2016). Gene-gene Interaction Analyses for Atrial Fibrillation. Scientific Reports, 6(1), 35371 [ DOI | http ]
  46. Lopez-Antequera, M., Petkov, N., & Gonzalez-Jimenez, J. (2016). Image-based localization using Gaussian Processes. In 2016 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2016, pages 1–7. IEEE. ISBN 978-1-5090-2425-4 [ DOI | http ]
  47. Lu, Y., Day, F. R., Gustafsson, S., et al. (2016). New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Nature Communications, 7(1), 10495 [ DOI | http ]
  48. Macfarlane, S. (2016). The Search for Galactic Short-period Variable Stars in the OmegaWhite Survey. Ph.D. thesis, -
  49. Matías, A. S., Havenith, R. W., Alcamí, M., & Ceulemans, A. (2016). Is C50 a superaromat? Evidence from electronic structure and ring current calculations. Physical Chemistry Chemical Physics, 18(17), 11653–11660 [ DOI | http ]
  50. Minnema, A., Bijmolt, T. H., Gensler, S., & Wiesel, T. (2016). To Keep or Not to Keep: Effects of Online Customer Reviews on Product Returns. Journal of Retailing, 92(3), 253–267 [ DOI | http ]
  51. Namdeo, S. & Onck, P. R. (2016). Emergence of flagellar beating from the collective behavior of individual ATP-powered dyneins. Physical Review E, 94(4), 042406 [ DOI | http ]
  52. Napolitano, N., Covone, G., Roy, N., et al. (2016). Strong Lens Search in the ESO Public Survey KiDS. In N. R. Napolitano, G. Longo, M. Marconi, M. Paolillo, & E. Iodice, editors, The Universe of Digital Sky Surveys, volume 42, page 129 [ DOI ]
  53. Okbay, A., Beauchamp, J. P., Fontana, M. A., et al. (2016). Genome-wide association study identifies 74 loci associated with educational attainment. Nature, 533(7604), 539–542 [ DOI | http ]
  54. Patrício, V., Richard, J., Verhamme, A., et al. (2016). A young star-forming galaxy at z = 3.5 with an extended Lyman α halo seen with MUSE. Monthly Notices of the Royal Astronomical Society, 456(4), 4191–4208 [ DOI ]
  55. Pattaro, C., Teumer, A., Gorski, M., et al. (2016). Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nature Communications, 7(1), 10023 [ DOI | http ]
  56. Paulechka, E., Wassenaar, T. A., Kroenlein, K., Kazakov, A., & Smolyanitsky, A. (2016). Nucleobase-functionalized graphene nanoribbons for accurate high-speed DNA sequencing. Nanoscale, 8(4), 1861–1867 [ DOI | http ]
  57. Pham, H. T., Lim, K. Z., Havenith, R. W., & Nguyen, M. T. (2016). Aromatic character of planar boron-based clusters revisited by ring current calculations. Physical Chemistry Chemical Physics, 18(17), 11919–11931 [ DOI | http ]
  58. Plank, B. (2016). Keystroke dynamics as signal for shallow syntactic parsing. In COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers, pages 609–619. The COLING 2016 Organizing Committee, Osaka, Japan. ISBN 978-4-87974-702-0 [ http ]
  59. Pourhossein, P., Vijayaraghavan, R. K., Meskers, S. C., & Chiechi, R. C. (2016). Optical modulation of nano-gap tunnelling junctions comprising self-Assembled monolayers of hemicyanine dyes. Nature Communications, 7(1), 11749 [ DOI | http ]
  60. Radovich, M., Puddu, E., Bellagamba, F., et al. (2016). VizieR Online Data Catalog: Candidate galaxy clusters in KiDS-DR2 (Radovich+, 2017). VizieR Online Data Catalog, pages J/A+A/598/A107
  61. Ried, J. S., Jeff, J. M., Chu, A. Y., et al. (2016). A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape. Nature Communications, 7(1), 13357 [ DOI | http ]
  62. van Rijsewijk, L., Dijkstra, J. K., Pattiselanno, K., Steglich, C., & Veenstra, R. (2016). Who helps whom? Investigating the development of adolescent prosocial relationships. Developmental Psychology, 52(6), 894–908 [ DOI | http ]
  63. Saggese, A., Strisciuglio, N., Vento, M., & Petkov, N. (2016). Time-frequency analysis for audio event detection in real scenarios. In 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2016, pages 438–443. IEEE. ISBN 978-1-5090-3811-4 [ DOI | http ]
  64. Schomaker, L. (2016). Design considerations for a large-scale image-based text search engine in historical manuscript collections. IT - Information Technology, 58(2), 80–88 [ DOI | http ]
  65. Schroetter, I., Bouché, N., Wendt, M., et al. (2016). Muse Gas Flow and Wind (Megaflow). I. First Muse Results on Background Quasars. The Astrophysical Journal, 833(1), 39 [ DOI ]
  66. Schumann, G., Liu, C., O'Reilly, P., et al. (2016). KLB is associated with alcohol drinking, and its gene product β-Klotho is necessary for FGF21 regulation of alcohol preference. Proceedings of the National Academy of Sciences of the United States of America, 113(50), 14372–14377 [ DOI | http ]
  67. Sherkar, T. S. & Jan Anton Koster, L. (2016). Can ferroelectric polarization explain the high performance of hybrid halide perovskite solar cells? Physical Chemistry Chemical Physics, 18(1), 331–338 [ DOI | http ]
  68. Signorelli, M., Vinciotti, V., & Wit, E. C. (2016). NEAT: An efficient network enrichment analysis test. BMC Bioinformatics, 17(1), 352 [ DOI | http ]
  69. Silvis, M. H., Trias, F. X., Abkar, M., et al. (2016). Exploring nonlinear subgrid-scale models and new characteristic length scales for large-eddy simulation. In P. Moin & J. Urzay, editors, Center for Turbulence Research Proceedings of the Summer Program 2016, pages 265–273. Center for Turbulence Research, Stanford University
  70. Song, H., Van der Giessen, E., & Liu, X. (2016). Strain gradient plasticity analysis of elasto-plastic contact between rough surfaces. Journal of the Mechanics and Physics of Solids, 96, 18–28 [ DOI | http ]
  71. Spavone, M., Capaccioli, M., Cantiello, M., et al. (2016). Deep photometry of galaxies in the VEGAS survey: The case of NGC 4472. In N. R. Napolitano, G. Longo, M. Marconi, M. Paolillo, & E. Iodice, editors, Astrophysics and Space Science Proceedings, volume 42, pages 165–171. ISBN 978-3-319-19329-8 [ DOI ]
  72. Spierdijk, L. (2016). Confidence intervals for ARMA–GARCH Value-at-Risk: The case of heavy tails and skewness. Computational Statistics and Data Analysis, 100, 545–559 [ DOI | http ]
  73. Sriman, B. & Schomaker, L. (2016). Explicit foreground and background modeling in the classification of text blocks in scene images. In Proceedings - 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015, volume 1, pages 755–759. IEEE. ISBN 978-1-4799-6100-9 [ DOI ]
  74. Sriman, B., Schomaker, L., & Pruksasri, P. (2016). General Text-Chunk Localization in Scene Images using a Codebook-based Classifier. In Proceedings of the 4th IIAE International Conference on Intelligent Systems and Image Processing 2016, pages 134–141. IEEE [ DOI ]
  75. Stelder, D. (2016). Regional Accessibility Trends in Europe: Road Infrastructure, 1957–2012. Regional Studies, 50(6), 983–995 [ DOI | http ]
  76. Strisciuglio, N., Azzopardi, G., Vento, M., & Petkov, N. (2016). Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters. Machine Vision and Applications, 27(8), 1137–1149 [ DOI | http ]
  77. Strisciuglio, N., Vento, M., & Petkov, N. (2016). Bio-inspired filters for audio analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10087 LNCS, pages 101–115. ISBN 978-3-319-50861-0. ISSN: 16113349 [ DOI | http ]
  78. Surendran, P., Drenos, F., Young, R., et al. (2016). Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nature Genetics, 48(10), 1151–1161 [ DOI | http ]
  79. Takatsu, J., Van Der Graaf, E. R., Van Goethem, M. J., et al. (2016). Proton radiography to improve proton therapy treatment. Journal of Instrumentation, 11(1), C01004–C01004 [ DOI | http ]
  80. Teumer, A., Tin, A., Sorice, R., et al. (2016). Genome-wide association studies identify genetic loci associated with Albuminuria in diabetes. Diabetes, 65(3), 803–817 [ DOI | http ]
  81. Tiede, D., Baraldi, A., Sudmanns, M., Belgiu, M., & Lang, S. (2016). ImageQuerying – Earth Observation Image Content Extraction & Querying across Time and Space. In P. Soille & P. G. Marchetti, editors, Proceedings of the 2016 conference on Big Data from Space (BiDS’16) : 15th-17th March 2016, volume JRC100655, pages 192–195. Publications Office of the European Union. Issue: March [ DOI | http ]
  82. Toma, R., Ramsay, G., Macfarlane, S., et al. (2016). The OmegaWhite Survey for short period variable stars - II. An overview of results from the first four years. Monthly Notices of the Royal Astronomical Society, 463(1), 1099–1116 [ DOI ]
  83. Tortora, C., La Barbera, F., Napolitano, N. R., et al. (2016). Towards a census of supercompact massive galaxies in the Kilo Degree Survey. Monthly Notices of the Royal Astronomical Society, 457(3), 2845–2854 [ DOI ]
  84. Travieso-Puente, R., Broekman, J. O., Chang, M. C., et al. (2016). Spin-Crossover in a pseudo-tetrahedral bis(formazanate) iron complex. Journal of the American Chemical Society, 138(17), 5503–5506 [ DOI | http ]
  85. Valentijn, E. A., Begeman, K., Belikov, A., et al. (2016). Target and (Astro-)WISE technologies Data federations and its applications. In M. Brescia, S. Djorgovski, E. D. Feigelson, G. Longo, & S. Cavuoti, editors, Proceedings of the International Astronomical Union, volume 12, pages 333–340 [ DOI ]
  86. Van Damme, S., Acke, G., Havenith, R. W., & Bultinck, P. (2016). Can the current density map topology be extracted from the nucleus independent chemical shifts? Physical Chemistry Chemical Physics, 18(17), 11746–11755 [ DOI | http ]
  87. Van Den Berg, F., Postema, B. F., & Haverkort, B. R. (2016). Evaluating load balancing policies for performance and energy-efficiency. Electronic Proceedings in Theoretical Computer Science, EPTCS, 227, 98–117 [ DOI | http ]
  88. Van Uitert, E., Cacciato, M., Hoekstra, H., et al. (2016). The stellar-to-halo mass relation of GAMA galaxies from 100 deg2 of KiDS weak lensing data. Monthly Notices of the Royal Astronomical Society, 459(3), 3251–3270 [ DOI ]
  89. Van Uitert, E. & Schneider, P. (2016). Systematic tests for position-dependent additive shear bias. Astronomy and Astrophysics, 595, A93 [ DOI ]
  90. Verweij, N., Leach, I. M., Isaacs, A., et al. (2016). Twenty-eight genetic loci associated with ST-T-wave amplitudes of the electrocardiogram. Human Molecular Genetics, 25(10), 2093–2103 [ DOI | http ]
  91. Wang, M., Yang, P., & Falcão Salles, J. (2016). Distribution of root-associated bacterial communities along a Salt-Marsh primary succession. Frontiers in Plant Science, 6(JAN2016) [ DOI | http ]
  92. Warren, H. R., van den Berg, M. E., van der Harst, P., et al. (2016). Discovery of Novel Loci Associated with Heart Rate from Exome Chip Analysis. Genetic epidemiology, 40(7), 670. Publisher: Wiley
  93. Windberger, A., Torretti, F., Borschevsky, A., et al. (2016). Analysis of the fine structure of Sn11+-Sn14+ ions by optical spectroscopy in an electron-beam ion trap. Physical Review A, 94(1), 012506 [ DOI | http ]
  94. Winkler, T. W., Justice, A. E., Graff, M., et al. (2016). Correction: The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study. PLOS Genetics, 12(6), e1006166 [ DOI | http ]
  95. Wisotzki, L., Bacon, R., Blaizot, J., et al. (2016). Extended Lyman α haloes around individual high-redshift galaxies revealed by MUSE. Astronomy and Astrophysics, 587, A98 [ DOI ]
  96. Wu, B., Wijma, H. J., Song, L., et al. (2016). Versatile Peptide C-Terminal Functionalization via a Computationally Engineered Peptide Amidase. ACS Catalysis, 6(8), 5405–5414 [ DOI | http ]
  97. Zheng, S., Reintjens, N. R., Siegler, M. A., et al. (2016). Stabilization of the Low-Spin State in a Mononuclear Iron(II) Complex and High-Temperature Cooperative Spin Crossover Mediated by Hydrogen Bonding. Chemistry - A European Journal, 22(1), 331–339 [ DOI | http ]

2015

  1. Agnihotri, P. K. & Van Der Giessen, E. (2015). On the rate sensitivity in discrete dislocation plasticity. Mechanics of Materials, 90, 37–46 [ DOI | http ]
  2. Angelone, D., Abdolahzadeh, S., De Boer, J. W., & Browne, W. R. (2015). Mechanistic Links in the in-situ Formation of Dinuclear Manganese Catalysts, H2O2 Disproportionation, and Alkene Oxidation. European Journal of Inorganic Chemistry, 2015(21), 3532–3542 [ DOI | http ]
  3. Angelone, D., Draksharapu, A., Browne, W. R., et al. (2015). Dinuclear compounds without a metal-metal bond. Dirhodium(III,III) carboxamidates. Inorganica Chimica Acta, 424, 235–240 [ DOI | http ]
  4. Arnarez, C., Uusitalo, J. J., Masman, M. F., et al. (2015). Dry martini, a coarse-grained force field for lipid membrane simulations with implicit solvent. Journal of Chemical Theory and Computation, 11(1), 260–275 [ DOI | http ]
  5. Azzopardi, G., Strisciuglio, N., Vento, M., & Petkov, N. (2015). Trainable COSFIRE filters for vessel delineation with application to retinal images. Medical Image Analysis, 19(1), 46–57 [ DOI | http ]
  6. Bacon, R., Brinchmann, J., Richard, J., et al. (2015). The MUSE 3D view of the Hubble Deep Field South. Astronomy and Astrophysics, 575, A75 [ DOI ]
  7. Bacon, R., Brinchmann, J., Richard, J., et al. (2015). VizieR Online Data Catalog: MUSE 3D view of HDF-S (Bacon+, 2015). VizieR Online Data Catalog, pages J/A+A/575/A75
  8. Bakulin, A. A., Selig, O., Bakker, H. J., et al. (2015). Real-Time Observation of Organic Cation Reorientation in Methylammonium Lead Iodide Perovskites. Journal of Physical Chemistry Letters, 6(18), 3663–3669 [ DOI | http ]
  9. Beverborg, N. G., Verweij, N., Klip, I. T., et al. (2015). Erythropoietin in the general population: Reference ranges and clinical, biochemical and genetic correlates. PLoS ONE, 10(4), e0125215 [ DOI | http ]
  10. Bondarenko, A. S. & Jansen, T. L. (2015). Application of two-dimensional infrared spectroscopy to benchmark models for the amide i band of proteins. Journal of Chemical Physics, 142(21), 212437 [ DOI | http ]
  11. Borschevsky, A., Pašteka, L. F., Pershina, V., Eliav, E., & Kaldor, U. (2015). Ionization potentials and electron affinities of the superheavy elements 115-117 and their sixth-row homologues Bi, Po, and at. Physical Review A - Atomic, Molecular, and Optical Physics, 91(2), 020501 [ DOI | http ]
  12. den Brok, M., Peletier, R., Seth, A., et al. (2015). VizieR Online Data Catalog: HST/ACS Coma Cluster Survey. X. (den Brok+, 2014). VizieR Online Data Catalog, page J/MNRAS/445/2385
  13. Buddelmeijer, H., Noorishad, P., Williams, D., et al. (2015). Analyzing Living Surveys: Visualization Beyond the Data Release. In A. Taylor & E. Rosolowsky, editors, Astronomical Data Analysis Software an Systems XXIV (ADASS XXIV), volume 495, page 149. Series Title: Astronomical Society of the Pacific Conference Series
  14. Cantiello, M., Capaccioli, M., Napolitano, N., et al. (2015). VEGAS-SSS. A VST early-type galaxy survey: Analysis of small stellar systems - Testing the methodology on the globular cluster system in NGC3115. Astronomy and Astrophysics, 576, A14 [ DOI ]
  15. Cantiello, M., Capaccioli, M., Napolitano, N., et al. (2015). VEGAS-SSS: A VST Programme to Study the Satellite Stellar Systems around Bright Early-type Galaxies. The Messenger, 159, 46–50
  16. Cantiello, M., Capaccioli, M., Napolitano, N., et al. (2015). VizieR Online Data Catalog: VEGAS-SSS photometry of NGC3115 (Cantiello+, 2015). VizieR Online Data Catalog, pages J/A+A/576/A14
  17. Capaccioli, M., Spavone, M., Grado, A., et al. (2015). VEGAS: A VST Early-type GAlaxy Survey: I. Presentation, wide-field surface photometry, and substructures in NGC 4472. Astronomy and Astrophysics, 581, A10 [ DOI ]
  18. Capaccioli, M., Spavone, M., Grado, A., et al. (2015). VizieR Online Data Catalog: VEGAS: A VST Early-type GAlaxy Survey (Capaccioli+, 2015). VizieR Online Data Catalog, pages J/A+A/581/A10
  19. Caretta, A., Donker, M. C., Perdok, D. W., et al. (2015). Measurement of the acoustic-to-optical phonon coupling in multicomponent systems. Physical Review B - Condensed Matter and Materials Physics, 91(5), 054111 [ DOI | http ]
  20. Cavuoti, S., Brescia, M., Tortora, C., et al. (2015). Machine-learning-based photometric redshifts for galaxies of the ESO Kilo-Degree Survey data release 2. Monthly Notices of the Royal Astronomical Society, 452(3), 3100–3105 [ DOI ]
  21. Chang, M. C. & Otten, E. (2015). Reduction of (Formazanate)boron Difluoride Provides Evidence for an N-Heterocyclic B(I) Carbenoid Intermediate. Inorganic Chemistry, 54(17), 8656–8664 [ DOI | http ]
  22. De Jong, J. T., Verdoes Kleijn, G. A., Boxhoorn, D. R., et al. (2015). The first and second data releases of the Kilo-Degree Survey. Astronomy and Astrophysics, 582, A62 [ DOI ]
  23. De Jonge, B., Klingenberg, W., Teunter, R., & Tinga, T. (2015). Optimum maintenance strategy under uncertainty in the lifetime distribution. Reliability Engineering and System Safety, 133, 59–67 [ DOI | http ]
  24. Dendooven, P., Buitenhuis, H. J., Diblen, F., et al. (2015). Short-lived positron emitters in beam-on PET imaging during proton therapy. Physics in Medicine and Biology, 60(23), 8923–8947 [ DOI | http ]
  25. Draksharapu, A., Angelone, D., Quesne, M. G., et al. (2015). Identification and spectroscopic characterization of nonheme iron(III) hypochlorite intermediates. Angewandte Chemie - International Edition, 54(14), 4357–4361 [ DOI | http ]
  26. Droge, B., van der Meulen, L., & Schoof, G. (2015). How far do Dutch people live from attractive nature?: An assessment using parallel computing with Python and FOSS4G libraries. In M. A. Brovelli, M. Minghini, & M. Negretti, editors, Geomatics Workbooks, volume 12, pages 51–53. Laboratorio di Geomatica - Politecnico di Milano
  27. Erukhimovich, I., Kriksin, Y., & Ten Brinke, G. (2015). Diamond-forming block copolymers and diamond-like morphologies: A new route toward efficient block copolymer membranes. Macromolecules, 48(21), 7909–7922 [ DOI | http ]
  28. Floor, R. J., Wijma, H. J., Jekel, P. A., et al. (2015). X-ray crystallographic validation of structure predictions used in computational design for protein stabilization. Proteins: Structure, Function and Bioinformatics, 83(5), 940–951 [ DOI | http ]
  29. Foggia, P., Petkov, N., Saggese, A., Strisciuglio, N., & Vento, M. (2015). Reliable detection of audio events in highly noisy environments. Pattern Recognition Letters, 65, 22–28 [ DOI | http ]
  30. Gibson, C. M., Havenith, R. W., Fowler, P. W., & Jenneskens, L. W. (2015). Planar homotropenylium cation: A transition state with reversed aromaticity. Journal of Organic Chemistry, 80(3), 1395–1401 [ DOI | http ]
  31. de Gier, H. D., Broer, R., & Havenith, R. W. A. (2015). On the relation between local and charge-transfer exciton binding energies in organic photovoltaic materials. In Z. H. Kafafi, P. A. Lane, & I. D. W. Samuel, editors, Organic Photovoltaics XVI, volume 9567, page 95670N. ISBN 978-1-62841-733-3. ISSN: 1996756X [ DOI | http ]
  32. Guo, J., Shi, C., Azzopardi, G., & Petkov, N. (2015). Recognition of architectural and electrical symbols by cosfire filters with inhibition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 9257, pages 348–358. ISBN 978-3-319-23116-7. ISSN: 16113349 [ DOI | http ]
  33. Guo, J., Shi, C., Jansonius, N. M., & Petkov, N. (2015). Automatic Optic Disk Localization and Diameter Estimation in Retinal Fundus Images. In Autonomous Systems 2015 - Proceedings of the 8th GI Conference, pages 70–79. VDI Verlag. ISBN 978-3-18-384210-0. Series Title: Fortschritt-Berichte VDI. Informatik/Kommunikationstechnik
  34. He, S., Wiering, M., & Schomaker, L. (2015). Junction detection in handwritten documents and its application to writer identification. Pattern Recognition, 48(12), 4036–4048 [ DOI | http ]
  35. Holwerda, B. W., Baldry, I. K., Alpaslan, M., et al. (2015). Galaxy And Mass Assembly (GAMA) blended spectra catalogue: Strong galaxy-galaxy lens and occulting galaxy pair candidates. Monthly Notices of the Royal Astronomical Society, 449(4), 4277–4287 [ DOI ]
  36. Janzen, T., Haegeman, B., & Etienne, R. S. (2015). Erratum to A sampling formula for ecological communities with multiple dispersal syndromes [J. Theor. Biol., 374, (2015), 94-106] DOI:10.1016/j.jtbi.2015.03.018. Journal of Theoretical Biology, 387, 258–261 [ DOI | http ]
  37. Janzen, T., Haegeman, B., & Etienne, R. S. (2015). A sampling formula for ecological communities with multiple dispersal syndromes. Journal of Theoretical Biology, 374, 94–106 [ DOI | http ]
  38. Janzen, T., Höhna, S., & Etienne, R. S. (2015). Approximate Bayesian Computation of diversification rates from molecular phylogenies: Introducing a new efficient summary statistic, the nLTT. Methods in Ecology and Evolution, 6(5), 566–575 [ DOI | http ]
  39. Joshi, P. K., Esko, T., Mattsson, H., et al. (2015). Directional dominance on stature and cognition in diverse human populations. Nature, 523(7561), 459–462 [ DOI | http ]
  40. Kato, N., Loh, M., Takeuchi, F., et al. (2015). Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation. Nature Genetics, 47(11), 1282–1293 [ DOI | http ]
  41. Khaderi, S. N., den Toonder, J. M., & Onck, P. R. (2015). Magnetic artificial cilia for microfluidic propulsion. In Advances in Applied Mechanics, volume 48, pages 1–78. ISSN: 00652156 [ DOI | http ]
  42. Kistemaker, J. C., Štacko, P., Visser, J., & Feringa, B. L. (2015). Unidirectional rotary motion in achiral molecular motors. Nature Chemistry, 7(11), 890–896 [ DOI | http ]
  43. Koning, R. & Achterhof, J. (2015). Live football demand. In The Economics of Competitive Sports, pages 93–105. Edward Elgar Publishing. ISBN 978-1-78347-476-9 [ DOI | http ]
  44. Kopps, A. M., Kang, J., Sherwin, W. B., & Palsbøll, P. J. (2015). How well do molecular and pedigree relatedness correspond, in populations with diverse mating systems, and various types and quantities of molecular and demographic data? G3: Genes, Genomes, Genetics, 5(9), 1815–1826 [ DOI | http ]
  45. Krajnović, D., Weilbacher, P. M., Urrutia, T., et al. (2015). Unveiling the counter-rotating nature of the kinematically distinct core in NGC 5813 with MUSE. Monthly Notices of the Royal Astronomical Society, 452(1), 2–18 [ DOI ]
  46. Kuijken, K., Heymans, C., Hildebrandt, H., et al. (2015). Gravitational lensing analysis of the Kilo-Degree Survey. Monthly Notices of the Royal Astronomical Society, 454(4), 3500–3532 [ DOI ]
  47. van Leeuwen, E. M., Huffman, J. E., Bis, J. C., et al. (2015). Fine mapping the CETP region reveals a common intronic insertion associated to HDL-C. npj Aging and Mechanisms of Disease, 1(1), 15011 [ DOI | http ]
  48. Li, J., Nowak, P., & Otto, S. (2015). An allosteric receptor by simultaneous “casting” and “molding” in a dynamic combinatorial library. Angewandte Chemie - International Edition, 54(3), 833–837 [ DOI | http ]
  49. Liu, D., Liu, L., Onck, P. R., Broer, D. J., & Palffy-Muhoray, P. (2015). Reverse switching of surface roughness in a self-organized polydomain liquid crystal coating. Proceedings of the National Academy of Sciences of the United States of America, 112(13), 3880–3885 [ DOI | http ]
  50. Liu, J. Z., Van Sommeren, S., Huang, H., et al. (2015). Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nature Genetics, 47(9), 979–986 [ DOI | http ]
  51. Locke, A. E., Kahali, B., Berndt, S. I., et al. (2015). Genetic studies of body mass index yield new insights for obesity biology. Nature, 518(7538), 197–206 [ DOI | http ]
  52. Macfarlane, S. A., Toma, R., Ramsay, G., et al. (2015). The Omega White survey for short-period variable stars - I. Overview and first results. Monthly Notices of the Royal Astronomical Society, 454(1), 507–530 [ DOI ]
  53. Melo, M. N., Ingólfsson, H. I., & Marrink, S. J. (2015). Parameters for Martini sterols and hopanoids based on a virtual-site description. Journal of Chemical Physics, 143(24), 243152 [ DOI | http ]
  54. Monreal-Ibero, A., Weilbacher, P. M., Wendt, M., et al. (2015). Towards DIB mapping in galaxies beyond 100 Mpc: A radial profile of the λ5780.5 diffuse interstellar band in AM 1353-272 B. Astronomy and Astrophysics, 576, L3 [ DOI ]
  55. Nowak, P., Colomb-Delsuc, M., Otto, S., & Li, J. (2015). Template-Triggered Emergence of a Self-Replicator from a Dynamic Combinatorial Library. Journal of the American Chemical Society, 137(34), 10965–10969 [ DOI | http ]
  56. Patrício, V., Richard, J., & Verhamme, A. (2015). A young star-forming galaxy at z = 3.5 with an extended Lyman α halo seen with MUSE. Proceedings of the International Astronomical Union, 11(A29B), 820–821 [ DOI ]
  57. Pašteka, L. F., Borschevsky, A., Flambaum, V. V., & Schwerdtfeger, P. (2015). Search for the variation of fundamental constants: Strong enhancements in X Π 2 cations of dihalogens and hydrogen halides. Physical Review A - Atomic, Molecular, and Optical Physics, 92(1), 012103 [ DOI | http ]
  58. Perdriau, S., Zijlstra, D. S., Heeres, H. J., De Vries, J. G., & Otten, E. (2015). A metal-ligand cooperative pathway for intermolecular oxa-michael additions to unsaturated nitriles. Angewandte Chemie - International Edition, 54(14), 4236–4240 [ DOI | http ]
  59. Pigot, A. L. & Etienne, R. S. (2015). A new dynamic null model for phylogenetic community structure. Ecology Letters, 18(2), 153–163 [ DOI | http ]
  60. Podigachoski, P., Barthel, P., Haas, M., et al. (2015). VizieR Online Data Catalog: FIR photometry of 3CR galaxies (Podigachoski+, 2015). VizieR Online Data Catalog, pages J/A+A/575/A80
  61. Popken, P., Ghavami, A., Onck, P. R., Poolman, B., & Veenhoff, L. M. (2015). Size-dependent leak of soluble and membrane proteins through the yeast nuclear pore complex. Molecular Biology of the Cell, 26(7), 1386–1394 [ DOI | http ]
  62. Qi, Y., Ingólfsson, H. I., Cheng, X., et al. (2015). CHARMM-GUI Martini Maker for Coarse-Grained Simulations with the Martini Force Field. Journal of Chemical Theory and Computation, 11(9), 4486–4494 [ DOI | http ]
  63. Radenković, S., Tošović, J., Havenith, R. W., & Bultinck, P. (2015). Ring currents in benzo- And benzocyclobutadieno-annelated biphenylene derivatives. ChemPhysChem, 16(1), 216–222 [ DOI | http ]
  64. Richard, J., Mahler, G., Clément, B., et al. (2015). The MUSE view of the Frontier Field clusters. In Proceedings of the International Astronomical Union, volume 11, pages 764–767. Issue: A29B ISSN: 17439221 [ DOI ]
  65. Richard, J., Patricio, V., Martinez, J., et al. (2015). MUSE observations of the lensing cluster SMACSJ2031.8-4036: New constraints on the mass distribution in the cluster core. Monthly Notices of the Royal Astronomical Society: Letters, 446(1), L16–L20 [ DOI ]
  66. Rodríguez, P., Késenne, S., & Koning, R. (2015). The economics of competitive sports. Edward Elgar Publishing. ISBN 978-1-78347-476-9. Publication Title: The Economics of Competitive Sports [ DOI ]
  67. Sato, T. K., Asai, M., Borschevsky, A., et al. (2015). Measurement of the first ionization potential of lawrencium, element 103. Nature, 520(7546), 209–211 [ DOI | http ]
  68. Shantia, A., Timmers, R., Schomaker, L., & Wiering, M. (2015). Indoor localization by denoising autoencoders and semi-supervised learning in 3D simulated environment. In Proceedings of the International Joint Conference on Neural Networks, volume 2015-Septe, pages 1–7. IEEE. ISBN 978-1-4799-1960-4 [ DOI | http ]
  69. Shi, C., Guo, J., Azzopardi, G., et al. (2015). Automatic differentiation of u- and n-serrated patterns in direct immunofluorescence images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 9256, pages 513–521. ISBN 978-3-319-23191-4. ISSN: 16113349 [ DOI | http ]
  70. Shi, C., Meijer, J., Guo, J., et al. (2015). Automatic Classification of Serrated Patterns in Direct Immunofluorescence Images. In Autonomous Systems 2015 - Proceedings of the 8th GI Conference, pages 61–69. VDI Verlag. ISBN 978-3-18-384210-0. Series Title: Fortschritt-Berichte VDI. Informatik/Kommunikationstechnik
  71. Shinokita, K., Cunha, A. V., Jansen, T. L., & Pshenichnikov, M. S. (2015). Hydrogen bond dynamics in bulk alcohols. Journal of Chemical Physics, 142(21), 212450 [ DOI | http ]
  72. Shungin, D., Winkler, T., Croteau-Chonka, D. C., et al. (2015). New genetic loci link adipose and insulin biology to body fat distribution. Nature, 518(7538), 187–196 [ DOI | http ]
  73. Sifón, C., Cacciato, M., Hoekstra, H., et al. (2015). The masses of satellites in GAMA galaxy groups from 100 square degrees of KiDS weak lensing data. Monthly Notices of the Royal Astronomical Society, 454(4), 3938–3951 [ DOI ]
  74. Sifón, C., Hoekstra, H., Cacciato, M., et al. (2015). Constraints on the alignment of galaxies in galaxy clusters from 14 000 spectroscopic members. Astronomy and Astrophysics, 575, A48 [ DOI ]
  75. Song, H., Dikken, R. J., Nicola, L., & Van Der Giessen, E. (2015). Plastic ploughing of a sinusoidal asperity on a rough surface. Journal of Applied Mechanics, Transactions ASME, 82(7) [ DOI | http ]
  76. Spierdijk, L. & Umar, Z. (2015). Stocks, bonds, T-bills and inflation hedging: From great moderation to great recession. Journal of Economics and Business, 79, 1–37 [ DOI | http ]
  77. Sriman, B. & Schomaker, L. (2015). Object attention patches for text detection and recognition in scene images using SIFT. In ICPRAM 2015 - 4th International Conference on Pattern Recognition Applications and Methods, Proceedings, volume 1, pages 304–311. SCITEPRESS - Science and and Technology Publications. ISBN 978-989-758-076-5 [ DOI | http ]
  78. Surinta, O., Karaaba, M. F., Mishra, T. K., Schomaker, L. R., & Wiering, M. A. (2015). Recognizing handwritten characters with local descriptors and bags of visual words. In Communications in Computer and Information Science, volume 517, pages 255–264. ISBN 978-3-319-23981-1. ISSN: 18650929 [ DOI | http ]
  79. Surinta, O., Karaaba, M. F., Schomaker, L. R., & Wiering, M. A. (2015). Recognition of handwritten characters using local gradient feature descriptors. Engineering Applications of Artificial Intelligence, 45, 405–414 [ DOI | http ]
  80. Swerdlow, D. I., Preiss, D., Kuchenbaecker, K. B., et al. (2015). HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: Evidence from genetic analysis and randomised trials. The Lancet, 385(9965), 351–361 [ DOI | http ]
  81. Sánchez-Ramírez, S., Etienne, R. S., & Moncalvo, J. M. (2015). High speciation rate at temperate latitudes explains unusual diversity gradients in a clade of ectomycorrhizal fungi. Evolution, 69(8), 2196–2209 [ DOI | http ]
  82. Torabi, S., Jahani, F., Van Severen, I., et al. (2015). Strategy for enhancing the dielectric constant of organic semiconductors without sacrificing charge carrier mobility and solubility. Advanced Functional Materials, 25(1), 150–157 [ DOI | http ]
  83. Uusitalo, J. J., Ingólfsson, H. I., Akhshi, P., Tieleman, D. P., & Marrink, S. J. (2015). Martini Coarse-Grained Force Field: Extension to DNA. Journal of Chemical Theory and Computation, 11(8), 3932–3945 [ DOI | http ]
  84. Valente, L. M., Phillimore, A. B., & Etienne, R. S. (2015). Equilibrium and non-equilibrium dynamics simultaneously operate in the Galápagos islands. Ecology Letters, 18(8), 844–852 [ DOI | http ]
  85. Valentijn, E. A. (2015). Data federations: AstroWise and its applications. In Science Operations 2015: Science Data Management, page 1 [ DOI ]
  86. Van Der Plas, F., Janzen, T., Ordonez, A., et al. (2015). A new modeling approach estimates the relative importance of different community assembly processes. Ecology, 96(6), 1502–1515 [ DOI | http ]
  87. Van Der Vegte, C. P., Prajapati, J. D., Kleinekathöfer, U., Knoester, J., & Jansen, T. L. (2015). Atomistic modeling of two-dimensional electronic spectra and excited-state dynamics for a light harvesting 2 complex. Journal of Physical Chemistry B, 119(4), 1302–1313 [ DOI | http ]
  88. Van Eerden, F. J., De Jong, D. H., De Vries, A. H., Wassenaar, T. A., & Marrink, S. J. (2015). Characterization of thylakoid lipid membranes from cyanobacteria and higher plants by molecular dynamics simulations. Biochimica et Biophysica Acta - Biomembranes, 1848(6), 1319–1330 [ DOI | http ]
  89. Van Leeuwen, E. M., Karssen, L. C., Deelen, J., et al. (2015). Genome of the Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels. Nature Communications, 6(1), 6065 [ DOI | http ]
  90. Venemans, B. P., Verdoes Kleijn, G. A., Mwebaze, J., et al. (2015). First discoveries of z 6 quasars with the Kilo-Degree Survey and VISTA Kilo-Degree Infrared Galaxy survey. Monthly Notices of the Royal Astronomical Society, 453(3), 2259–2266 [ DOI ]
  91. Vermond, R. A., Geelhoed, B., Verweij, N., et al. (2015). Incidence of Atrial Fibrillation and Relationship With Cardiovascular Events, Heart Failure, and Mortality A Community-Based Study From the Netherlands. Journal of the American College of Cardiology, 66(9), 1000–1007 [ DOI | http ]
  92. Verweij, N., Brouwers, F. P., De Boer, R. A., et al. (2015). the Prognostic Significance of Electrocardiographic Parameters for New Onset Heart Failure in the General Population. Journal of the American College of Cardiology, 65(10), A1040 [ DOI | http ]
  93. Vimaleswaran, K. S., Cavadino, A., Verweij, N., et al. (2015). Interactions between uncoupling protein 2 gene polymorphisms, obesity and alcohol intake on liver function: A large meta-analysed population-based study. European Journal of Endocrinology, 173(6), 863–872 [ DOI | http ]
  94. Viola, M., Cacciato, M., Brouwer, M., et al. (2015). Dark matter halo properties of GAMA galaxy groups from 100 square degrees of KiDS weak lensing data. Monthly Notices of the Royal Astronomical Society, 452(4), 3529–3550 [ DOI ]
  95. Vriend, W.-J. (2015). Porting Big Data technology across domains. WISE for MUSE. In Science Operations 2015: Science Data Management, page 1 [ DOI ]
  96. Wassenaar, T. A., Ingólfsson, H. I., Böckmann, R. A., Tieleman, D. P., & Marrink, S. J. (2015). Computational lipidomics with insane: A versatile tool for generating custom membranes for molecular simulations. Journal of Chemical Theory and Computation, 11(5), 2144–2155 [ DOI | http ]
  97. Weilbacher, P. M., Monreal-Ibero, A., Kollatschny, W., et al. (2015). A MUSE map of the central Orion Nebula (M 42). Astronomy and Astrophysics, 582, A114 [ DOI ]
  98. Weilbacher, P. M., Monreal-Ibero, A., Leod, A. F. M., et al. (2015). The Central Orion Nebula (M42) as seen by MUSE. ESO The Messenger, 162, 37–41
  99. Wijma, H. J., Floor, R. J., Bjelic, S., et al. (2015). Enantioselective enzymes by computational design and in silico screening. Angewandte Chemie - International Edition, 54(12), 3726–3730 [ DOI | http ]
  100. Winkler, T. W., Justice, A. E., Graff, M., et al. (2015). The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study. PLoS Genetics, 11(10), 1–42 [ DOI | http ]
  101. Zimmermann, E., Ängquist, L. H., Mirza, S. S., et al. (2015). Is the adiposity-associated FTO gene variant related to all-cause mortality independent of adiposity? Meta-analysis of data from 169,551 Caucasian adults. Obesity Reviews, 16(4), 327–340 [ DOI | http ]
  102. Žagar, G., Onck, P. R., & Van Der Giessen, E. (2015). Two fundamental mechanisms govern the stiffening of cross-linked networks. Biophysical Journal, 108(6), 1470–1479 [ DOI | http ]

2014

  1. Bauer, J., Hou, L., Kistemaker, J. C., & Feringa, B. L. (2014). Tuning the rotation rate of light-driven molecular motors. Journal of Organic Chemistry, 79(10), 4446–4455 [ DOI | http ]
  2. Benyamin, B., Esko, T., Ried, J. S., et al. (2014). Novel loci affecting iron homeostasis and their effects in individuals at risk for hemochromatosis. Nature Communications, 5(1), 4926 [ DOI | http ]
  3. Bolton, J. L., Hayward, C., Direk, N., et al. (2014). Genome Wide Association Identifies Common Variants at the SERPINA6/SERPINA1 Locus Influencing Plasma Cortisol and Corticosteroid Binding Globulin. PLoS Genetics, 10(7), e1004474 [ DOI | http ]
  4. Bos, E. G., Van De Weygaert, R., Kitaura, F., & Cautun, M. (2014). Bayesian cosmic web reconstruction: BARCODE for clusters. Proceedings of the International Astronomical Union, 11(S308), 271–288 [ DOI | http ]
  5. den Brok, M., Peletier, R. F., Seth, A., et al. (2014). The HST/ACS Coma Cluster Survey - X. Nuclear star clusters in low-mass early-type galaxies: Scaling relations. Monthly Notices of the Royal Astronomical Society, 445(3), 2385–2403 [ DOI ]
  6. Caretta, A., Miranti, R., Havenith, R. W., et al. (2014). Low-frequency Raman study of the ferroelectric phase transition in a layered CuCl 4 -based organic-inorganic hybrid. Physical Review B - Condensed Matter and Materials Physics, 89(2), 024301 [ DOI | http ]
  7. Chang, M. C., Dann, T., Day, D. P., et al. (2014). The formazanate ligand as an electron reservoir: Bis(formazanate) zinc complexes isolated in three redox states. Angewandte Chemie - International Edition, 53(16), 4118–4122 [ DOI | http ]
  8. Chen, J., Kistemaker, J. C., Robertus, J., & Feringa, B. L. (2014). Molecular stirrers in action. Journal of the American Chemical Society, 136(42), 14924–14932 [ DOI | http ]
  9. Chen, K. Y., Ivashenko, O., Carroll, G. T., et al. (2014). Control of surface wettability using tripodal light-activated molecular motors. Journal of the American Chemical Society, 136(8), 3219–3224 [ DOI | http ]
  10. Chen, K. Y., Wezenberg, S. J., Carroll, G. T., et al. (2014). Tetrapodal molecular switches and motors: Synthesis and photochemistry. Journal of Organic Chemistry, 79(15), 7032–7040 [ DOI | http ]
  11. Cnossen, A., Kistemaker, J. C., Kojima, T., & Feringa, B. L. (2014). Structural dynamics of overcrowded alkene-based molecular motors during thermal isomerization. Journal of Organic Chemistry, 79(3), 927–935 [ DOI | http ]
  12. De Gier, H. D., Broer, R., & Havenith, R. W. (2014). Non-innocent side-chains with dipole moments in organic solar cells improve charge separation. Physical Chemistry Chemical Physics, 16(24), 12454–12461 [ DOI | http ]
  13. Deckers, T. & Hanck, C. (2014). Multiple testing for output convergence. Macroeconomic Dynamics, 18(1), 199–214 [ DOI | http ]
  14. Deckers, T. & Hanck, C. (2014). Variable Selection in Cross-Section Regressions: Comparisons and Extensions. Oxford Bulletin of Economics and Statistics, 76(6), 841–873 [ DOI | http ]
  15. Demetrescu, M., Hanck, C., & Tarcolea, A. I. (2014). Iv-Based Cointegration Testing in Dependent Panels With Time-Varying Variance. Journal of Time Series Analysis, 35(5), 393–406 [ DOI | http ]
  16. Dijkstra, P., Angelone, D., Talnishnikh, E., et al. (2014). Pyridyl-1,2,4-triazole diphenyl boron complexes as efficient tuneable blue emitters. Dalton Transactions, 43(47), 17740–17745 [ DOI | http ]
  17. Dini-Andreote, F., De Cássia Pereira E Silva, M., Triadó-Margarit, X., et al. (2014). Dynamics of bacterial community succession in a salt marsh chronosequence: Evidences for temporal niche partitioning. ISME Journal, 8(10), 1989–2001 [ DOI | http ]
  18. Flores-Johnson, E. A., Shen, L., Annabattula, R. K., et al. (2014). The effect of interface adhesion on buckling and cracking of hard thin films. Applied Physics Letters, 105(16), 161912 [ DOI | http ]
  19. Ghavami, A., Veenhoff, L. M., Van Der Giessen, E., & Onck, P. R. (2014). Probing the disordered domain of the nuclear pore complex through coarse-grained molecular dynamics simulations. Biophysical Journal, 107(6), 1393–1402 [ DOI | http ]
  20. de Gier, H. D., Rietberg, B. J., Broer, R., & Havenith, R. W. (2014). Influence of push-pull group substitution patterns on excited state properties of donor-acceptor co-monomers and their trimers. Computational and Theoretical Chemistry, 1040-1041, 202–211 [ DOI | http ]
  21. Haver, V. G., Verweij, N., Kjekshus, J., et al. (2014). The impact of coronary artery disease risk loci on ischemic heart failure severity and prognosis: Association analysis in the COntrolled ROsuvastatin multiNAtional trial in heart failure (CORONA). BMC Medical Genetics, 15(1), 140 [ DOI | http ]
  22. Holmes, M. V., Dale, C. E., Zuccolo, L., et al. (2014). Association between alcohol and cardiovascular disease: Mendelian randomisation analysis based on individual participant data. BMJ (Online), 349(jul10 6), g4164–g4164 [ DOI | http ]
  23. Li, J., Nowak, P., Fanlo-Virgós, H., & Otto, S. (2014). Catenanes from catenanes: Quantitative assessment of cooperativity in dynamic combinatorial catenation. Chemical Science, 5(12), 4968–4974 [ DOI | http ]
  24. Lillington, M., Havenith, R. W., Fowler, P. W., Baker, J., & Jenneskens, L. W. (2014). The homotropenylium cation: A system with a pinched π ring current. Physical Chemistry Chemical Physics, 16(23), 11566–11572 [ DOI | http ]
  25. Namdeo, S., Khaderi, S. N., & Onck, P. R. (2014). Numerical modelling of chirality-Induced bi-Directional swimming of artificial flagella. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 470(2162), 20130547 [ DOI | http ]
  26. Nijland, J. G., Shin, H. Y., De Jong, R. M., et al. (2014). Engineering of an endogenous hexose transporter into a specific D-xylose transporter facilitates glucose-xylose co-consumption in Saccharomyces cerevisiae. Biotechnology for Biofuels, 7(1), 168 [ DOI | http ]
  27. Riese, H., Muñoz, L. M., Hartman, C. A., et al. (2014). Identifying genetic variants for heart rate variability in the acetylcholine pathway. PLoS ONE, 9(11), e112476 [ DOI | http ]
  28. Rijnbeek, P. R., Van Herpen, G., Bots, M. L., et al. (2014). Normal values of the electrocardiogram for ages 16-90 years. Journal of Electrocardiology, 47(6), 914–921 [ DOI | http ]
  29. Rudavskyi, A., Sousa, C., De Graaf, C., Havenith, R. W., & Broer, R. (2014). Computational approach to the study of thermal spin crossover phenomena. Journal of Chemical Physics, 140(18), 184318 [ DOI | http ]
  30. Saane, S. S., Mangipudi, K. R., Loos, K. U., De Hosson, J. T. M., & Onck, P. R. (2014). Multiscale modeling of charge-induced deformation of nanoporous gold structures. Journal of the Mechanics and Physics of Solids, 66(1), 1–15 [ DOI | http ]
  31. Sergentu, D. C., Maurice, R., Havenith, R. W., Broer, R., & Roca-Sanjuán, D. (2014). Computational determination of the dominant triplet population mechanism in photoexcited benzophenone. Physical Chemistry Chemical Physics, 16(46), 25393–25403 [ DOI | http ]
  32. Simino, J., Shi, G., Bis, J. C., et al. (2014). Gene-age interactions in blood pressure regulation: A large-scale investigation with the CHARGE, global BPgen, and ICBP consortia. American Journal of Human Genetics, 95(1), 24–38 [ DOI | http ]
  33. Spierdijk, L. & Koning, R. H. (2014). Estimating Outstanding Claim Liabilities: The Role of Unobserved Risk Factors. Journal of Risk and Insurance, 81(4), 803–830 [ DOI | http ]
  34. Spierdijk, L. & Umar, Z. (2014). Are commodity futures a good hedge against inflation? The Journal of Investment Strategies, 3(2), 35–57 [ DOI | http ]
  35. Spierdijk, L. & Umar, Z. (2014). Stocks for the long run? Evidence from emerging markets. Journal of International Money and Finance, 47, 217–238 [ DOI | http ]
  36. Steglich, C. & Knecht, A. (2014). Über Beurteilungsfehler aufgrund verzerrt wahrgenommener Schülerfreundschaften. Zeitschrift fur Erziehungswissenschaft, 17(5), 153–170 [ DOI | http ]
  37. Taatgen, N. A. (2014). Between architecture and model: Strategies for cognitive control. Biologically Inspired Cognitive Architectures, 8, 132–139 [ DOI | http ]
  38. Van Duijnen, P. T., De Gier, H. D., Broer, R., & Havenith, R. W. (2014). The behaviour of charge distributions in dielectric media. Chemical Physics Letters, 615, 83–88 [ DOI | http ]
  39. Verkuil, Y. I., Juillet, C., Lank, D. B., Widemo, F., & Piersma, T. (2014). Genetic variation in nuclear and mitochondrial markers supports a large sex difference in lifetime reproductive skew in a lekking species. Ecology and Evolution, 4(18), 3626–3632 [ DOI | http ]
  40. Verweij, N., Leach, I. M., Van Den Boogaard, M., et al. (2014). Genetic Determinants of P Wave Duration and PR Segment. Circulation: Cardiovascular Genetics, 7(4), 475–481 [ DOI | http ]
  41. Voortman, T. P., De Gier, H. D., Havenith, R. W., & Chiechi, R. C. (2014). Stabilizing cations in the backbones of conjugated polymers. Journal of Materials Chemistry C, 2(17), 3407–3415 [ DOI | http ]
  42. Wezenberg, S. J., Vlatković, M., Kistemaker, J. C., & Feringa, B. L. (2014). Multi-state regulation of the dihydrogen phosphate binding affinity to a light- and heat-responsive bis-urea receptor. Journal of the American Chemical Society, 136(48), 16784–16787 [ DOI | http ]

2013

  1. Codd, V., Nelson, C. P., Albrecht, E., et al. (2013). Identification of seven loci affecting mean telomere length and their association with disease. Nature Genetics, 45(4), 422–427 [ DOI | http ]
  2. Cvejic, A., Haer-Wigman, L., Stephens, J. C., et al. (2013). SMIM1 underlies the Vel blood group and influences red blood cell traits. Nature Genetics, 45(5), 542–545 [ DOI | http ]
  3. Demetrescu, M. & Hanck, C. (2013). Nonlinear IV panel unit root testing under structural breaks in the error variance. Statistical Papers, 54(4), 1043–1066 [ DOI | http ]
  4. Markov, V., Kriksin, Y., Erukhimovich, I., & Ten Brinke, G. (2013). Perpendicular lamellar-in-lamellar and other planar morphologies in A-b-(B-b-A)2-b-C and (B-b-A)2-b-C ternary multiblock copolymer melts. Journal of Chemical Physics, 139(8), 084906 [ DOI | http ]
  5. Pijper, T. C., Kudernac, T., Browne, W. R., & Feringa, B. L. (2013). Effect of immobilization on gold on the temperature dependence of photochromic switching of dithienylethenes. Journal of Physical Chemistry C, 117(34), 17623–17632 [ DOI | http ]
  6. Surinta, O., Schomaker, L., & Wiering, M. (2013). A comparison of feature and pixel-based methods for recognizing handwritten bangla digits. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, pages 165–169. IEEE. ISBN 978-0-7695-4999-6. ISSN: 15205363 [ DOI | http ]
  7. Taatgen, N. a. (2013). Diminishing Return in Transfer: A PRIM Model of the Frensch (1991) Arithmetic Experiment. In Proceedings of the International Conference on Cognitive Modeling, pages 29–34. Issue: 1991
  8. Taatgen, N. A. (2013). The nature and transfer of cognitive skills. Psychological review, 120(3), 439–471 [ DOI | http ]
  9. Verweij, N., Mahmud, H., Leach, I. M., et al. (2013). Genome-wide association study on plasma levels of midregional- proadrenomedullin and C-terminal-pro-endothelin-1. Hypertension, 61(3), 602–608 [ DOI | http ]

2012

  1. Asselbergs, F. W., Guo, Y., Van Iperen, E. P., et al. (2012). Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci. American Journal of Human Genetics, 91(5), 823–838 [ DOI | http ]
  2. ĆurČić Blake, B., Swart, M., Ter Horst, G. J., et al. (2012). Variation of the gene coding for DARPP-32 (PPP1R1B) and brain connectivity during associative emotional learning. NeuroImage, 59(2), 1540–1550 [ DOI | http ]
  3. de Boer, R. A., Verweij, N., van Veldhuisen, D. J., et al. (2012). A Genome-Wide Association Study of Circulating Galectin-3. PLoS ONE, 7(10), e47385 [ DOI | http ]
  4. Erukhimovich, I., Kriksin, Y., & Ten Brinke, G. (2012). The diamond and other non-conventional morphologies in two-scale multiblock AB copolymers. Soft Matter, 8(7), 2159–2169 [ DOI | http ]
  5. Kathiresan, S. & Srivastava, D. (2012). Genetics of human cardiovascular disease. Ph.D. thesis, University of Groningen. Publication Title: Cell Volume: 148 Issue: 6 ISBN: 9789037778724 ISSN: 10974172 [ DOI ]
  6. Saxena, R., Elbers, C. C., Guo, Y., et al. (2012). Erratum: Large-scale gene-centric meta-analysis across 39 studies identifies type 2 diabetes loci (American Journal of Human Genetics (2012) 90 (410-425)). American Journal of Human Genetics, 90(4), 753 [ DOI | http ]
  7. Saxena, R., Elbers, C. C., Guo, Y., et al. (2012). Large-scale gene-centric meta-analysis across 39 studies identifies type 2 diabetes loci. American Journal of Human Genetics, 90(3), 410–425 [ DOI | http ]
  8. Scott, R. A., Lagou, V., Welch, R. P., et al. (2012). Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nature Genetics, 44(9), 991–1005 [ DOI | http ]
  9. Van Der Harst, P., Zhang, W., Mateo Leach, I., et al. (2012). Seventy-five genetic loci influencing the human red blood cell. Nature, 492(7429), 369–375 [ DOI | http ]
  10. Yang, J., Loos, R. J., Powell, J. E., et al. (2012). FTO genotype is associated with phenotypic variability of body mass index. Nature, 490(7419), 267–273 [ DOI | http ]