2024
Z. Liu, E. De Schutter, Y. Li: GABA-induced seizure like events caused by multi-ionic interactive dynamics. eNeuro in press.
G. Cirtala and E. De Schutter: Branch-specific clustered parallel fiber input controls dendritic computation in Purkinje cells. iScience 27: 9110756. MODEL
R.J. Nakatani and E. De Schutter: Active enhancement of synapse driven depolarization of perisynaptic astrocytic processes. Preprint.
J. Kwon, S. Kim, J. Woo, E. De Schutter, S. Hong and C.J. Lee: Cerebellar tonic inhibition orchestrates the maturation of information processing and motor coordination. Preprint.
I. Hepburn, J. Lallouette, W. Chen, A.R. Gallimore, S.Y. Nagasawa-Soeda and E. De Schutter: Vesicle and reaction-diffusion hybrid modeling with STEPS. Communications Biology 7: 573. SOFTWARE
G. Cirtala and E. De Schutter: Branch-specific clustered parallel fiber input controls dendritic computation in Purkinje cells. Preprint published in IScience.
2023
S. Kim, J. Jeon, D. Ganbat, T. Kim, K. Shin, S. Hong and J. Hong: Alteration of Neural Network and Hippocampal Slice Activation through Exosomes Derived from 5XFAD Nasal Lavage Fluid. International Journal of Molecular Sciences 24: 14064.
J. Myung, S. Hong, C. Schmal, H. Vitel and M.-Y. Wu: Weak synchronization can alter circadian period length: Implications for aging and disease conditions. Frontiers in Neuroscience 17: 1242800.
M.-Y. Kim, M. J. Kim, C. Lee, J. Lee, S.S. Kim, S. Hong, H.T. Kim, J. Seo, K.-J. Yoon and S. Han: Trametinib activates endogenous neurogenesis and recovers neuropathology in a model of Alzheimer’s disease. Experimental & Molecular Medicine 55: 2177–2189.
A.R. Gallimore, I. Hepburn, S. Rizzoli and E. De Schutter: Dynamic Regulation of Vesicle Pools in a Detailed Spatial Model of the Complete Synaptic Vesicle Cycle. Preprint.
Y. Zang and E. De Schutter: Recent data on the cerebellum require new models and theories. Current Opinion in Neurobiology 82: 102765.
M. Kato and E. De Schutter: Models of Purkinje cell dendritic tree selection during early cerebellar development. PLoS Computational Biology 19: e1011320. MODEL
E. De Schutter: Efficient simulation of neural development using shared memory parallelization. Frontiers in Neuroinformatics 17:1212384. SOFTWARE
I. Hepburn, J. Lallouette, W. Chen, A.R. Gallimore, S.Y. Nagasawa and E. De Schutter: Hybrid vesicle and reaction-diffusion modeling with STEPS. Preprint published in 2024 in Communications Biology.
A. Markanday*, S. Hong*, J. Inoue, E. De Schutter and P. Thier: Multidimensional cerebellar computations for flexible kinematic control of movements. Nature Communications 14: 2548.
2022
S. Verduzco-Flores and E. De Schutter: Self-configuring feedback loops for sensorimotor control. eLife: 77216. MODEL
A. Denizot, M. Arizono, V.U. Nägerl, H. Berry and E. De Schutter: Control of Ca2+ signals by astrocyte nanoscale morphology at tripartite synapses. Glia 70: 2378-2391. MODEL
W. Chen, I. Hepburn, A. Martyushev and E. De Schutter: Modeling Neurons in 3D at the Nanoscale. In: Giugliano, M., Negrello, M., Linaro, D. (eds) Computational Modelling of the Brain. Advances in Experimental Medicine and Biology 1359: 3-24. Springer, Cham.
W. Chen, T. Carel, O. Awile, N. Cantarutti, G. Castiglioni, A. Cattabiani, B. Del Marmol, I. Hepburn, J.G. King, C. Kotsalos, P. Kumbhar, J. Lallouette, S. Melchior, F Schürmann and E. De Schutter: STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale. Frontiers in Neuroinformatics 16: 883742. SOFTWARE
A. Denizot, M.F. Veloz Castillo, P. Puchenkov, C. Calì and E. De Schutter: The endoplasmic reticulum in perisynaptic astrocytic processes: shape, distribution and effect on calcium activity. Preprint.
S. Verduzco-Flores, W. Dorrell and E. De Schutter: A differential Hebbian framework for biologically-plausible motor control. Neural Networks 150: 237–258. MODEL
A. Covelo , A. Badoual and A. Denizot: Reinforcing Interdisciplinary Collaborations To Unravel The Astrocyte "Calcium Code". Journal of Molecular Neuroscience 72: 1443–1455.
L. Medlock, K. Sekiguchi, S. Hong, S. Dura-Bernal, W. Lytton and S. Prescott: Multiscale computer model of the spinal dorsal horn reveals changes in network processing associated with chronic pain. Journal of Neuroscience 42: 3133-3149. MODEL
2021
G.A. Ascoli, D.N. Kennedy and E. De Schutter: Farewell, Neuroinformatics! Neuroinformatics 19: 551-552.
A. Denizot, C. Cali, H. Berry and E. De Schutter: Stochastic Spatially-Extended Simulations Predict the Effect of ER Distribution on Astrocytic Microdomain Ca2+ Activity. The Eight Annual ACM International Conference on Nanoscale Computing and Communication (NANOCOM ’21) doi.org/10.1145/3477206.3477456
Y. Zang and E. De Schutter: The Cellular Electrophysiological Properties Underlying Multiplexed Coding in Purkinje Cells. Journal of Neuroscience 41: 1850-1863. MODEL
S. Lindeman*, S. Hong*, L. Kros*, J.F. Meijas, V. Romano, R. Oostenveld, M. Negrello, L.W. Bosman and C.I. De Zeeuw: Cerebellar Purkinje cells can differentially modulate coherence between sensory and motor cortex depending on region and behavior. Proceedings of the National Academy of Sciences USA 118: e2015292118.
2020
E. De Schutter: Comment on “The growth of cognition: Free energy minimization and the embryogenesis of cortical computation". Physics of Life Reviews 36:1-2.
S. Verduzco-Flores, W. Dorrell and E. De Schutter: An approach to synaptic learning for autonomous motor control. Preprint published in 2022 in Neural Networks.
D. Han, E. De Schutter and S. Hong: Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks. Advances in Neural Information Processing Systems 33 (NeurIPS 2020) 1828.
Y. Zang, S. Hong and E. De Schutter: Firing rate-dependent phase responses of Purkinje cells support transient oscillations. eLife 9: 60692. MODEL
I. Wichert, S. Jee, E. De Schutter and S. Hong: Pycabnn: Efficient and extensible software to construct an anatomical basis for a physiologically realistic neural network model. Frontiers in Neuroinformatics 14: 31. SOFTWARE
2019
Y. Zang and E. De Schutter: Climbing Fibers Provide Graded Error Signals in Cerebellar Learning. Frontiers in Systems Neuroscience 13: 46.
E. De Schutter: Fallacies of Mice Experiments. Neuroinformatics 17: 181-183.
N. Vrieler, S. Loyola, Y. Yarden-Rabinowitz, J. Hoogendorp, N. Medvedev, T.M. Hoogland, C.I. De Zeeuw, E. De Schutter, Y. Yarom, M. Negrello, B. Torben-Nielsen, and M.Y. Uusisaari: Variability and directionality of inferior olive neuron dendrites revealed by detailed 3D characterization of an extensive morphological library. Brain Structure and Function 224: 1677–1695.
S.O. Verduzco Flores and E. De Schutter: Draculab: A Python simulator for firing rate neural networks with delayed adaptive connections. Frontiers in Neuroinformatics 13: 18. SOFTWARE
2018
C.G. Zamora Chimal and E. De Schutter: Ca2+ requirements for Long-Term Depression are frequency sensitive in Purkinje Cells. Frontiers in Molecular Neuroscience 11: 438. MODEL
Y. Zang, S. Dieudonné and E. De Schutter Voltage- and Branch-specific Climbing Fiber Responses in Purkinje Cells. Cell Reports 24: 1536–1549. MODEL
J. Myung*, C. Schmal*, S. Hong*, Y. Tsukizawa, P. Rose, Y. Zhang, M. Holtzman, E. De Schutter, H. Herzel, G. Bordyugov and T. Takumi: The Choroid Plexus is an Important Circadian Clock Component. Nature Communications 9: 1062. MODEL
A.R. Gallimore, T. Kim, K. Tanaka-Yamamoto and E. De Schutter: Switching on depression and potentiation in the cerebellum. Cell Reports 22: 722-733. MODEL
E. De Schutter:Deep learning and computational neuroscience.Neuroinformatics 16: 1-2.
S. Nanda, H. Chen, R. Das, S. Bhattacharjee, H. Cuntz, B. Torben-Nielsen, H. Peng, D. Cox, E. De Schutter, and G. Ascoli: Design and implementation of multi-signal and time-varying neural reconstructions. Scientific Data 5: 170207.
2017
J. Myung and S.D. Pauls: Encoding seasonal information in a two-oscillator model of the multi-oscillator circadian clock. European Journal of Neuroscience 48: 2718-2727 (2018).
S.K. Sudhakar*, S. Hong*, I. Raikov, R. Publio, C. Lang, T. Close, D. Guo, M. Negrello& and E. De Schutter: Spatiotemporal network coding of physiological mossy fiber inputs by the cerebellar granular layer. PLoS Computational Biology 13: e1005754. MODEL
C. Schmal, J. Myung, H. Herzel and G. Bordyugov: Moran’s I quantifies spatio-temporal pattern formation in neural imaging data. Bioinformatics 33: 3072-3079.
W. Chen and E. De Schutter: Parallel STEPS: Large Scale Stochastic Spatial Reaction-Diffusion Simulation with High Performance Computers. Frontiers in Neuroinformatics 11: 13. MODEL. SOFTWARE
I. Hepburn, A. Jain, H. Gangal, Y. Yamamoto, K. Tanaka-Yamamoto and E. De Schutter: A model of induction of cerebellar Long-Term Depression including RKIP inactivation of Raf and MEK. Frontiers in Molecular Neuroscience 10: 19. MODEL
W. Chen and E. De Schutter: Time to bring single neuron modeling into 3D. Neuroinformatics 15: 1-3.
2016
I. Hepburn, W. Chen and E. De Schutter: Accurate reaction-diffusion operator splitting on tetrahedral meshes for parallel stochastic molecular simulations. Journal of Chemical Physics 145: SOFTWARE
A.R. Gallimore and R.J. Strassman: A model for the application of target-controlled intravenous infusion for a prolonged immersive DMT psychedelic experience. Frontiers in Pharmacology 7: 211.
S. Hong, M. Negrello, M. Junker, A. Smilgin, P. Thier and E. De Schutter: Multiplexed coding by cerebellar Purkinje neurons. eLife 5: e13810.
M. Negrello and E. De Schutter: Models of the Cortico-cerebellar System. Neuroscience in the 21st Century, D.W. Pfaff and N.D. Volkow, editors, 24 p.
A.R. Gallimore, A.R. Aricescu, M. Yuzaki, R. Calinescu: A computational model for the AMPA receptor phosphorylation master switch regulating cerebellar long-term depression. PLoS Computational Biology 12: e1004664.
E. De Schutter: Neuroinformatics for degenerate brains. Neuroinformatics 14: 1-3.
2015
S.K. Sudhakar, B. Torben-Nielsen, E. De Schutter: Cerebellar nuclear neurons use time and rate coding to transmit Purkinje neuron pauses. PLoS Computational Biology 11: e1004641. MODEL
S. Huang, S. Hong and E. De Schutter: Non-linear leak currents affect mammalian neuron physiology. Frontiers in Cellular Neuroscience 9: 432. MODEL
C. Yalgin, S. Ebrahimi, C. Delandre, L. Foong Yoong, S. Akimoto, H. Tran, R. Amikura, R. Spokony, B. Torben-Nielsen, K.P. White and A.W. Moore: Centrosomin represses dendrite branching by orienting microtubule nucleation. Nature Neuroscience 18: 1437-1446.
W. Wybo, D. Boccalini, B. Torben-Nielsen and M-O. Gewaltig: A sparse reformulation of the Green's function formalism allows efficient simulations of morphological neuron models.Neural Computation 27: 2587-2622.
J. Myung, S. Hong, D. DeWoskin, E De Schutter, D.B. Forger and T. Takumi: GABA-mediated repulsive coupling between circadian clock neurons in the SCN encodes seasonal time. Proceedings of the National Academy of Sciences USA 112: E3920-3929.
A.R. Gallimore: Restructuring consciousness – the psychedelic state in light of integrated information theory. Frontiers in Human Neuroscience 9: 346.
E. De Schutter: The Missing Piece of the Puzzle: Neuroinformatics at the Bench. Neuroinformatics 13: 131-132.
P. Warnaar, J. Couto, M. Negrello, M. Junker, A. Smilgin, A. Ignashchenkova, M. Giugliano, P. Thier and E. De Schutter: Duration of Purkinje cell complex spikes increases with their firing frequency. Frontiers in Cellular Neuroscience 9: 122.
J. Couto, D. Linaro, E. De Schutter and M. Giugliano: On the firing rate dependency of the phase response curve of rat Purkinje neurons in vitro. PLoS Computational Biology 11: e1004112. MODEL
2014
M. Negrello Valentino Braitenberg: From neuroanatomy to behavior and back. Biological Cybernetics 108: 527-539.
B. Torben-Nielsen and E. De Schutter: Context-aware modeling of neuronal morphologies. Frontiers in Neuroanatomy 8: 92. SOFTWARE
J. Laudanski*, B. Torben-Nielsen*, I. Segev, S. Shamma: Spatially distributed dendritic resonance selectively filters synaptic input. PLoS Computational Biology 10: e1003775.
H. Anwar, C. Roome, H. Nedelescu, W. Chen, B. Kuhn and E. De Schutter: Dendritic diameters affect the spatial variability of intracellular calcium dynamics in computer models. Frontiers in Cellular Neuroscience 8: 168. MODEL
B. Torben-Nielsen: An efficient and extendable Python library to analyze neuronal morphologies. Neuroinformatics 12: 619-622.
W. Chen and E. De Schutter: Python-based geometry preparation and simulation visualization toolkits for STEPS. Frontiers in Neuroinformatics 8: 37. SOFTWARE
E. De Schutter: The dangers of plug-and-play simulation using shared models. Neuroinformatics 12: 227-228.
2013
K. Veys, D. J. Snyders and E. De Schutter: Kv3.3b expression defines the shape of the complex spike in the Purkinje cell. Frontiers in Cellular Neuroscience 7: 205.
C. Simon, W. Chen, I. Hepburn and E. De Schutter: The role of dendritic spine morphology in the compartmentalization and delivery of surface receptors. Journal of Computational Neuroscience 36: 483-497.
I. Hepburn, R. Cannon and E. De Schutter: Efficient calculation of the quasi-static electrical potential on a tetrahedral mesh and its implementation in STEPS. Frontiers in Computational Neuroscience 7: 129. SOFTWARE
H. Anwar*, I. Hepburn*, H. Nedelescu, W. Chen and E. De Schutter: Stochastic calcium mechanisms cause dendritic calcium spike variability. Journal of Neuroscience 33: 15848-15867. MODEL
S. Ratté, S. Hong, E. De Schutter and S.A. Prescott: Impact of neuronal properties on network coding: Roles of spike initiation dynamics and robust synchrony transfer. Neuron 78: 758-772.
S. Huang and M.Y. Uusisaari: Physiological temperature during brain slicing enhances the quality of acute slice preparations. Frontiers in Cellular Neuroscience 7: 48.
E. De Schutter: Collaborative modeling in neuroscience: time to go open model? Neuroinformatics 11: 135-136.
E. De Schutter: The importance of stochastic signaling processes in the induction of long-term synaptic plasticity. Neural Networks 47: 3-10.
M.R. Diaz, A. Wadleigh, S. Kumar, E. De Schutter and C.F. Valenzuela: Na+/K+-ATPase inhibition partially mimics the ethanol-induced increase of the Golgi cell-dependent component of the tonic GABAergic current in rat cerebellar granule cells. PLoS One 8:e55673.
2012
E. De Schutter, G.A. Ascoli and D.N. Kennedy: Ten years of Neuroinformatics. Neuroinformatics 10: 329-330.
S. Hong, Q. Robberechts, E. De Schutter: Efficient estimation of phase response curves via compressive sensing. Journal of Neurophysiology 108: 2069-2081. SOFTWARE
K. Veys, A. J. Labro, E. De Schutter* and D. J. Snyders*: Quantitative single-cell ion-channel gene expression profiling through an improved qRT-PCR technique combined with whole cell patch clamp. Journal of Neuroscience Methods 209: 227–234.
M. Uzuntarla, M. Ozer and D. Q. Guo: Controlling the first-spike latency response of a single neuron via unreliable synaptic transmission. European Physical Journal B 85: 282-289.
G. Antunes and E. De Schutter: A stochastic signaling network mediates the probabilistic induction of cerebellar long-term depression. Journal of Neuroscience 32: 9288–9300. MODEL
J. Myung, S. Hong, F. Hatanaka, Y. Nakajima, E. De Schutter and T. Takumi: Period coding of Bmal1 oscillators in the suprachiasmatic nucleus. Journal of Neuroscience 32: 8900-8918.
I. Raikov and E. De Schutter: The layer-oriented approach to biological modeling languages. PLoS Computational Biology 8: e1002521. SOFTWARE
Y. Kim*, R. Sinclair*, N. Chindapol, J. A. Kaandorp and E. De Schutter: The geometry of dendritic trees: minimal wiring cost bifurcations are flat. PLoS Computational Biology 8: e1002474.
I. Hepburn, S. Wils, W. Chen and E. De Schutter: STEPS: Efficient simulation of stochastic reaction-diffusion models in realistic morphologies. BMC Systems Biology 6: 36. SOFTWARE
P. Botta, F. M. Simões de Souza, T. Sangrey, E. De Schutter and F. Valenzuela: Excitation of rat cerebellar Golgi cells by ethanol: Further characterization of the mechanism. Alcoholism: Clinical and Experimental Research 36: 616-624.
I. Raikov and E. De Schutter: The promise and shortcomings of XML as an interchange format for computational models of biology. Neuroinformatics 10: 1-3.
D. Guo, Q. Wang and M. Perc: Complex synchronous behavior in interneuronal networks with delayed inhibitory and fast electrical synapses. Physical Review E 85: 061905.
S. Hong, S. Ratté, S. Prescott* and E. De Schutter*: Single neuron firing properties impact correlation-based population coding. Journal of Neuroscience 32:1413–1428.
2011
F. Santamaria, S. Wils, E. De Schutter and G. J. Augustine: The diffusional properties of dendrites depend on the density of dendritic spines. European Journal of Neuroscience 34: 561-568. Featured article.
Y. Le Franc, M. Giugliano, and E. De Schutter, E. The Belgian node: a community platform toward the future. Acta Neurologica Belgica 11.
K. Tahon, M. Wijnants and E. De Schutter: The RAT-ROTADRUM: a reaction time task depending on a continuous stream of tactile sensory information to the rat. Journal of Neuroscience Methods 200: 153-163.
D.N. Kennedy, G.A. Ascoli, E. De Schutter: Next steps in data publishing. Neuroinformatics 9: 317-320.
K. Pfeiffer, M. Negrello and U. Homberg: Conditional Perception Under Stimulus Ambiguity: Polarization- and Azimuth-Sensitive Neurons in the Locust Brain Are Inhibited by Low Degrees of Polarization. Journal of Neurophysiology 105: 28-35.
W. Chen, R. Maex, R. Adams, V. Steuber, L. Calcraft and N. Davey: Clustering predicts memory performance in networks of spiking and non-spiking neurons. Frontiers in Computational Neuroscience 5: 14.
M. Uusisaari and E. De Schutter: The mysterious microcircuitry of the cerebellar nuclei. Journal of Physiology (London) 589: 3441-3457.
V. Steuber, N. Schultheiss, A. Silver, E. De Schutter and D. Jaeger: Determinants of synaptic integration and heterogeneity in rebound firing explored with date-driven models of deep cerebellar nuclei cells. Journal of Computational Neuroscience 30: 633-658. MODEL
K. Tahon, M. Wijnants, E. De Schutter and R. Maex: Current source density correlates of cerebellar Golgi and Purkinje cell responses to tactile input. Journal of Neurophysiology 105: 1327-1341.
F. M. Simões de Souza and E. De Schutter: Robustness effect of gap junctions between Golgi cells on cerebellar cortex oscillations. Neural Systems & Circuits 1: 7. MODEL
2010
H. Anwar, S. Hong, E. De Schutter: Controlling Ca2+-activated K2 channels with models of Ca2+ buffering in Purkinje cells. Cerebellum 11: 681-693 (2012). MODEL
Q. Robberechts, M. Wijnants, M. Giugliano, and E. De Schutter: Long-term depression at parallel fiber to Golgi cell synapses. Journal of Neurophysiology104: 3413–3423.
E. De Schutter: Data publishing and scientific journals: the future of the scientific paper in a world of shared data. Neuroinformatics 8: 151-153.
P. Botta, F. M. Simões de Souza, T. Sangrey, E. De Schutter and F. Valenzuela: Alcohol excites cerebellar Golgi cells by inhibiting the Na+/K+-ATPase. Neuropsychopharmacology 35: 1984-1996. MODEL
2009
E. De Schutter (editor): Computational Modeling Methods for Neuroscientists. MIT Press, Cambridge, MA, USA, 432 pages.
E. De Schutter, G.A. Ascoli and D.N. Kennedy: Review of papers describing neuroinformatics software. Neuroinformatics 7: 211–212.
E. De Schutter and V. Steuber: Patterns and pauses in Purkinje cell simple spike trains: experiments, modeling and theory. Neuroscience 162: 816-826.
R. Publio, R. F. Oliveira and A.C. Roque: A Computational Study on the Role of Gap Junctions and Rod I-h Conductance in the Enhancement of the Dynamic Range of the Retina. PLoS One 4: e6970.
S. Wils and E. De Schutter: STEPS: Modeling and simulating complex reaction-diffusion systems with Python. Frontiers in Neuroinformatics 3: 15. SOFTWARE
F. Gheysen, W. Gevers, E. De Schutter, H. Van Waelvelde and W. Fias: Disentangling perceptual from motor implicit sequence learning with a serial color matching task. Experimental Brain Research 197:163–174.
E. De Schutter: The International Neuroinformatics Coordinating Facility: Evaluating the first years. Neuroinformatics 7: 161–163.
2008
S. Hong and E. De Schutter: Purkinje Neurons: What is the signal for complex spikes? Current Biology 18: R969-R971.
E. De Schutter: Reviewing multi-disciplinary papers: a challenge in neuroscience? Neuroinformatics 6: 253–255.
P. Achard and E. De Schutter: Calcium, synaptic plasticity and intrinsic homeostasis in Purkinje neuron models. Frontiers in Computational Neuroscience 2: 8.
W. Van Geit, E. De Schutter and P. Achard: Automated neuron model optimization techniques: a review. Biological Cybernetics 99: 241–251.
E. De Schutter: Why are Computational neuroscience and Systems biology so separate? PLoS Computational Biology 4: e1000078.
2007
E. De Schutter: Neuroscience leading the way: reviews cascade by the INCF. Neuroinformatics 5: 205-206.
V. Steuber, W. Mittmann, F.E. Hoebeek, R.A. Silver, C.I. De Zeeuw, M. Häusser and E. De Schutter: Cerebellar LTD and pattern recognition by Purkinje cells. Neuron 54: 121–136. MODEL
S. Solinas, L. Forti, E. Cesana, J. Mapelli and E. De Schutter, E. D’Angelo: Fast-reset of pacemaking and theta-frequency resonance patterns in cerebellar Golgi cells: Simulations of their impact in vivo. Frontiers in Cellular Neuroscience 1: 4. MODEL
S. Solinas, L. Forti, E. Cesana, J. Mapelli and E. De Schutter, E. D’Angelo: Computational reconstruction of pacemaking and intrinsic electroresponsiveness in cerebellar Golgi cells. Frontiers in Cellular Neuroscience 1: 2. MODEL
S.-L. Shin, F.E. Hoebeek, M. Schonewille, C.I. De Zeeuw, A. Aertsen and E. De Schutter: Regular temporal patterns in cerebellar Purkinje cell simple spike trains. PLoS One 2: e485.
W. Van Geit, P. Achard and E. De Schutter: Neurofitter: a parameter tuning package for a wide range of electrophysiological neuron models. Frontiers in Neuroinformatics 1: 1. SOFTWARE
Note: this page lists only the Unit's publications in international journals, conference proceedings and books. We do not list abstracts or PhD theses. A complete list of Erik De Schutter's publications can be found at Orcid.
* these authors contributed equally to this work