John Hongyu Meng (14002826), Hermann Riecke (177161) · Figshare · 2022
<p>(A) Sketch of the network. All spines provide reciprocal synapses that excite GCs (green arrow) and inhibit MCs (red bar). (B) Spine formation is controlled by <i>R</i> = <i>Mϕ</i>(<i>G</i>). The figure shows <i>ϕ</i>/<i>τ</i><sub><i>f</i></sub> for <i>ϕ</i> > 0 and <i>ϕ</i>/<i>τ</i><sub><i>r</i></sub> for <i>ϕ</i> < 0 (cf. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010338#pcbi.1010338.e010" target="_blank">Eq 5</a>). (C) Disynaptic recurrent inhibition of MCs via GCs. The numbers in the right panel indicate the strength of the effective inhibition. (D) Simplified training stimuli. (E, H) MC activity before and after training, respectively. (F, I) Connectivity between MCs and GCs before and after training, respectively. Each white dot represents a connection between an MC and a GC. (G, J) Effective recurrent connectivity among MCs. The color represents the number of GCs that mediate the mutual disynaptic inhibition of MCs via GCs (cf. C).</p>
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Frederic Grabowski (6381851), Marek Kochańczyk (30349), Zbigniew Korwek (614479) et al. · Figshare · 2023
<p><b>A.</b> Host cell–virus interactions and adjusted kinetic rate parameter values. Black arrows indicate state transitions of seven variables characterizing the cell state: v, vRNA, vProteins, pIRF3, IFNi, pSTATs, ISGs. All forward transitions (thin straight arrows pointing right) are induced by other variables (as indicated by blue arrows), while the reverse transitions (thin straight arrows pointing left) have constant rates. The corresponding rate coefficients are given in gray. For forward reactions to occur, the inducing agent variable has to be maximally active, with the exception of activation of pSTATs (Michaelis–Menten kinetics) and activation of ISGs (by partially active pSTATs); see model reactions in Table A in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1011597#ppat.1011597.s001" target="_blank">S1 Appendix</a>. Red dashed hammer-headed arrows are inhibitory interactions: three of them emanate from vProteins, two emanate from ISGs. The expressions in red indicate how vProteins or ISGs modify the (forward) transition propensities. When an arrow has three roots (all inhibitions and ISGs activation), the strength of the interaction depends on the stage of activation of the inducing variable (additionally indicated by the hue of the inducing variable). The extracellular interferon IFNe is produced at the given rate (molecules / hour) when the state of the intracellular interferon is IFNi = 3. IFNe diffuses (see panel B) and, when present in the lower subcompartment above a given cell, regulates the pSTATs transition rate with Michaelis–Menten kinetics. An infected cell with vProteins = 3 infects adjacent (non-infected) cells (v = 0 → v = 1) at the given rate (the infecting virions are implicit). <b>B.</b> Processes related to the transport of extracellular IFN (IFNe) and related parameter values. IFNe diffuses between the lower and the upper subcompartment above each cell and between respective subcompartments above neighboring cells.</p>
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Briony Banks, Louise Connell, Cai Wingfield · OSF Preprints (OSF Preprints) · 2021
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Cai Wingfield, Louise Connell, Briony Banks · OSF Preprints (OSF Preprints) · 2022
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Marc D. Binder, Nobutaka Hirokawa, Uwe Windhorst · 2008
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Wayne Adams (6250928), James N. Graham (6250931), Xuchen Han (6250934) et al. · Figshare · 2019
<p>(A) Network structure emerging after learning 2 training stimuli. The modeled neuronal populations are indicated by circles marked OSNs (sensory neurons), MCs, GCs, and CCs. Excitatory connections in green with arrows, inhibitory ones in red with squares. Connectivity matrices indicate the connectivities between the individual neurons of the populations (black = connection, white = no connection; cf. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006611#pcbi.1006611.g002" target="_blank">Fig 2</a>). For the intra-cortical connectivity <i>W</i><sup>(<i>CC</i>)</sup> the colors indicate the synaptic strength of the connections. (B) The GCs mediated disynaptic mutual inhibition of MCs with effective connectivity matrix <i>W</i><sup>(<i>MM</i>)</sup> and disynaptic inhibition of MCs by CCs with effective connectivity matrix <i>W</i><sup>(<i>MC</i>)</sup>. The color indicates the number of GCs contributing to the respective connections. (C) Idealized schematic of the network structure emerging after learning two odors: CCs disynaptically inhibit predominantly those MCs that respond to the same odor as the CCs. Line thickness indicates the number of connections.</p>
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Sofia Kanwal (12256565), Sohail Asghar (11017743), Akhtar Hussain (83444) et al. · Figshare · 2022
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Joel Dokmegang (11198153), Moi Hoon Yap (7168892), Liangxiu Han (4261252) et al. · Figshare · 2021
<p><b>A</b>. 3D representation of a cell: The cell is abstracted by an agglomeration of particles (small white spheres, 34 in the picture), whose triangulation (white edges) forms the membrane, and by an intracellular particle (big white sphere). Interactions between the intracellular and membrane particles (blue lines) mimic the cytoskeleton. <b>B</b>. 3D rendering of a cell without its sub-cellular elements. <b>C</b>. Forces acting within a cell: , are the forces that membrane particles <i>j</i>, <i>k</i> exert on another membrane particle <i>i</i>. is the force that the intracellular particle <i>χ</i> exerts on <i>i</i>. <b>D</b>. External forces acting on a cell via its particles. Here, . <b>E</b>. Plots of the magnitude of Morse forces under different values of <i>J</i>, with <i>ρ</i> = 1 and <i>r</i><sub><i>eq</i></sub> = 0.5. <b>F</b>. Apical constriction of an epithelial cell with original radius <i>R</i> shrinking by <i>d</i>. <b>G</b>. Formulas of the new equilibrium lengths in an apically constricted cell.</p>
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Yariv Z. Levy, Dino Levy, Jerrold S. Meyer et al. · 2012
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Nicola Kuczewski, Zbili Mickael · OSF Preprints (OSF Preprints) · 2021
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Pavel Sanda (812154), Jaroslav Hlinka (6795635), Monica van den Berg (13937259) et al. · Figshare · 2024
<p>A. Structural connectome of a rat’s DMLN (imported from neuroVIISAS project). B. Model connectivity between two distinct DMN areas. Each source neuron has a p = 15% probability of connection to each neuron in the target area. The connectivity strength of AMPA connection between excitatory neurons was derived from the structural connectome in A. C: Model connectivity of excitatory and inhibitory neurons within a single module (area). D: Example traces of Na<sup>+</sup>and K<sup>+</sup> extracellular levels, the activity of Na<sup>+</sup>/K<sup>+</sup>pump, average dendritic excitatory synaptic input (average across 50 excitatory neurons in a single area and 100 ms time window) and resulting BOLD trace (in arbitrary units). E. Spectral amplitude of the same variables as in E (single trial, average across all areas).</p>
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Philip Slingerland · 2011
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Vinil T. Chackochan (8119643), Vittorio Sanguineti (4993244) · Figshare · 2019
<p><b>(a)</b> Two players jointly control dyad dynamics (approximated as two mass-points connected by a spring). Each player has a separate sensory system which provides information about dyad state. The controller includes a state observer, a feedback controller and an estimate of the partner’s control action (partner model). <b>(b)</b> From left to right: Interaction force and minimum distance from VP<sub>1</sub> (<i>MD</i><sub>21</sub>) and VP<sub>2</sub> (<i>MD</i><sub>12</sub>), for all three H, VH and PV scenarios. <b>(c)</b> Leadership index for player 1 and player 2 at VP<sub>1</sub> (left) and VP<sub>2</sub> (right) for all three H, VH and PV scenarios.</p>
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Maedeh Lotfi (15458908), Hui Zhou (13228), Janny Piñeiro Llanes (21453459) et al. · Figshare · 2025
In the computational model, alignment of engineered tissue was determined based on predicted deformation of an embedded circle (r = 0.05 mm) at a material point. (A) FE model used to predict deformation of sample when a shrinkage strain was applied. Red lines = free boundaries; black lines = fixed boundaries. (B) Predicted shape and magnitude of displacement contour ( after 12 days incubation; (C) Process to calculate stretch (<i>U</i>) and rotation (<i>R</i>) of each circle with embedded “fibers.” <i>a</i> and <i>b</i> are the minor and major ellipse axes. (E-F) Equivalent alignment index, <i>S</i>, was determined by introducing discrete, embedded “fibers” to each material point circle; (E) before and (F) after deformation. The predicted redistribution of embedded fibers was used to calculate and <i>S</i> and , the angle between each fiber and the axis of symmetry (<i>θ</i> = 0).
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Adrian M. Haith (714766), David M. Huberdeau (714767), John W. Krakauer (714768) · Figshare · 2015
<p>A) Subjects are uncertain as to the true goal location among two possibilities and expect their beliefs to vary over time. In this case, the subject marginally favors the leftward target at movement onset (<i>p</i><sub>0</sub> > ½). During movement, this belief may either strengthen (solid line) or reverse (dashed line). B) When targets are nearby, the optimal course of action is to bias movement only slightly towards the more likely target, allowing greater flexibility later in the movement. C) When targets are widely separated, an intermediate movement is less advantageous. Instead, the optimal course of action is to commit to the more likely target from the outset. D) Predicted initial reach direction (normalized so that 0 corresponds to initial target, and 1 corresponds to the post-jump target) is plotted for a variety of target separations as a function of the initial belief state <i>r</i><sub>0</sub>. E) As D), but for a model that uses an asymmetric cost function (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004171#sec011" target="_blank">Methods</a>).</p>
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Alessandro Taloni (548392), Alexander A. Alemi (548393), Emilio Ciusani (191588) et al. · Figshare · 2015
<p>a) A two dimensional section of the skin (left image courtesy of Dr. Claudio Clemente) is simulated as a non-linear elastic layered material (right). The top layer is the stratum corneum (pink cells), resting on the epidermis (blue cell). The epidermis is separated from the dermis (green cells) by a basal membrane (grey). In a typical simulation we consider a melanocyte (yellow) dupicating inside the skin, either in the epidermis, or in the dermis. b) The connecting fiber have a non-linear elastic behavior with a fracture strength set at . c) When in contact, the cells interact by a finite-thickness Hertz law. The corresponding zero-thickness law is reported for comparison.</p>
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Gharagozloo, Patricia E. · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2018
Abstract not provided.
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Alevtina Glovackaya · 2020
The textbook covers the basics of classical numerical methods of computational mathematics used for solving linear and nonlinear equations and systems; interpolation and approximation of functions; numerical integration and differentiation; solutions of ordinary differential equations by methods of one-dimensional and multidimensional optimization. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is intended for students of higher educational institutions studying in the discipline "Numerical methods".
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Omar A. Guerrero, Gonzalo Castañeda · Cambridge University Press eBooks · 2024
This chapter introduces a model in which a government allocates financial resources across several policy issues (development dimensions), and a set of public servants (or agencies) that, through government programmes, transform public spending into policy outcomes. We start by describing the macro-level dynamics and the relevant equations involved. Then, we introduce a political economy game between the government and its officials (or public servants). First, we describe the public servants' decision making in an environment of uncertainty through reinforcement learning. Second, we elaborate on the problem of the government (or central authority) and how we can specify its heuristic strategy. Finally, we provide an overview of the entire structure of the model.
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Yoram Zarai (3352892), Tamir Tuller (123492) · Figshare · 2018
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