New thermal decomposition pathway for TATB by Keith D. Morrison, Ana Racoveanu, Jason S. Moore, Alan K. Burnham, Batikan Koroglu, Keith R. Coffee, Adele F. Panasci-Nott, Gregory L. Klunder, Bradley A. Steele, M. A. McClelland, John G. Reynolds

“…This finding has a significant bearing on decomposition computational models for prediction of energy release and deflagration to detonation transitions, with respect to conditions which currently do not recognize this oxidation step.…”
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Deep Learning Assisted Medical Insurance Data Analytics With Multimedia System by Cheng Zhang, B. Vinodhini, Bala Anand Muthu

“…The abstractions of data are represented using the multiple layers of deep learning for building computational models. Clinician performance is augmented by the prospective of deep learning models in medical imaging interpretation, and automated segmentation is used to reduce the time for the diagnosis. …”
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Legal and medical aspects of regular firearms us age by law enforcementagencies in Ukraine by Aleksandr Gurov, Vitaliy Shcherbak, Victor Sapielkin

“…The study of the bullets physical properties and microelement structure, digital computer modeling of shock and contact interaction with a target and laboratory researches of products distribution of a shot when firing from various distances in biological and non-biological simulators of flesh, study of the influence of various samples of clothes material on the volume and nature of damages, the morphological nature of skin wounds and of clothes material injuries, weight of a fire wound depending on the speed of bullets and distance of a shot have been made. …”
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Multiscale Simulations for Defect-Controlled Processing of Group IV Materials by Gaetano Calogero, Ioannis Deretzis, Giuseppe Fisicaro, Manuel Kollmuß, Francesco La Via, Salvatore F. Lombardo, Michael Schöler, Peter J. Wellmann, Antonino La Magna

“…In this paper, we propose a general paradigm for the industrially driven computational modeling of materials by deploying a multiscale methodology based on physical–chemical simulations bridging macro, meso and atomic scale. …”
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Stimulating human prefrontal cortex increases reward learning by Margot Juliëtte Overman, Verena Sarrazin, Michael Browning, Jacinta O'Shea

“…Learning rates on the tasks were quantified using computational models. Stimulation over dorsolateral prefrontal cortex (DLPFC) but not motor cortex (M1) increased learning rates specifically for reward outcomes. …”
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When Computer Vision Gazes at Cognition by Gao, Tao, Harari, Daniel, Tenenbaum, Joshua, Ullman, Shimon

“…The synthesis of behavioral study and computer vision yields several interesting discoveries. (1) Human accuracy of discriminating targets 8{\deg}-10{\deg} of visual angle apart is around 40% in a free looking gaze task; (2) The ability to interpret gaze of different lookers vary dramatically; (3) This variance can be captured by the computational model; (4) Human outperforms the current model significantly. …”
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Targeting autocrine HB-EGF signaling with specific ADAM12 inhibition using recombinant ADAM12 prodomain by Miller, Miles Aaron, Moss, Marcia L., Powell, Gary, Petrovich, Robert, Edwards, Lori, Griffith, Linda G., Lauffenburger, Douglas A., Meyer, Aaron Samuel

“…Here, we hypothesized that this strategy depends on properties such as ligand-receptor binding affinity, which varies widely across the known ErbB-family ligands. Guided by computational modeling, we found that high-affinity ligands such as HB-EGF are more difficult to target with decoy antibodies compared to low-affinity ligands such as amphiregulin (AREG). …”
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Modelling of Atherosclerotic Plaque for Use in a Computational Test-Bed for Stent Angioplasty by McGarry, J. P., McHugh, P. E., Conway, Claire

“…The constitutive representation of atherosclerotic tissue is of great interest to researchers and engineers using computational models to analyse stents, as it is this in silico environment that allows extensive exploration of tissue response to device implantation. …”
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Functionally Distinct Dopamine Signals in Nucleus Accumbens Core and Shell in the Freely Moving Rat by Dreyer, Jakob K., Lovic, Vedran, Aragona, Brandon J., Vander Weele, Caitlin Miya

“…Because this is not yet possible solely by experimentation in vivo, we combine computational modeling and fast-scan cyclic voltammetry data to reconstruct the functionally relevant DA signal in DA neuron subpopulations projecting to the NAc core and shell in freely moving rats. …”
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Network-level effects of kinase inhibitors modulate TNF-α-induced apoptosis in the intestinal epithelium by Gierut, J. J., Lau, K. S., Lin, Y.-J., Genetti, C., Samatar, A. A., Haigis, K. M., Wood, Levi Benjamin, Lauffenburger, Douglas A

“…Furthermore, even different MEK inhibitors exerted distinct effects; one, CH5126766, potentiated TNF-α–induced apoptosis, and the others reduced cell death. Computational modeling and experimental perturbation identified the kinase Akt as the primary signaling node that enhanced apoptosis in the context of TNF-α signaling in the presence of CH5126766. …”
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Thalamocortical synchronization during induction and emergence from propofol-induced unconsciousness by Flores, Francisco J., Hartnack, Katharine E., Fath, Amanda B., Purdon, Patrick L., Kim, Seong-Eun, Brown, Emery Neal, Wilson, Matthew A.

“…Sedation and unconsciousness under GA are associated with stereotyped electrophysiological oscillations that are thought to reflect profound disruptions of activity in neuronal circuits that mediate awareness and cognition. Computational models make specific predictions about the role of the cortex and thalamus in these oscillations. …”
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Mercer Kernels and Integrated Variance Experimental Design: Connections Between Gaussian Process Regression and Polynomial Approximation by Gorodetsky, Alex Arkady, Marzouk, Youssef M

“…This paper examines experimental design procedures used to develop surrogates of computational models, exploring the interplay between experimental designs and approximation algorithms. …”
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Model Order Reduction for Stochastic Optimal Control by Nguyen, Ngoc-Hien, Willcox, Karen, Khoo, Boo Cheong

“…Application of the optimal control strategy shows the potential effectiveness of this computational modeling approach for managing flow quality. …”
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Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery by Yao, Monica, Hanson, Gunnar J., Wolfe, Justin, Fadzen, Colin M., Choo, Zi-Ning, Holden, Rebecca Lynn, Pentelute, Bradley L.

“…As a result, we envisioned that our PMO-CPP library would be a useful training set for a computational model to predict CPPs for PMO delivery. We used the PMO activity data to fit a random decision forest classifier to predict whether or not covalent attachment of a given peptide would enhance PMO activity at least 3-fold. …”
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Representation of Vegetation in Two-Dimensional Hydrodynamic Models by Shields, F. Douglas, Coulton, Kevin G., Nepf, Heidi

“…Previous approaches to predicting water surface elevations along vegetated channels and floodplains consisted of running computer models that typically allowed little temporal variation in flow resistance coefficients during the course of a flow event. …”
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Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics by Du, Jinyan, Jiao, Yang, Sawyer, Andrew, Drummond, Daryl C., Raue, Andreas, Kumar, Manu Prajapati, Lagoudas, Georgia K, Lauffenburger, Douglas A

“…To better understand how all cell types in a tumor cooperate to facilitate malignant growth, Kumar et al. studied communication between cells via ligand and receptor interactions using single-cell data and computational modeling. Keywords: computational analysis; single-cell RNA sequencing; cell-cell communication; ligand-receptor interaction; tumor microenvironment; syngeneic mouse models; cancer patient samples…”
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Multi-cell ECM compaction is predictable via superposition of nonlinear cell dynamics linearized in augmented state space by Kim, Min-Cheol, Asada, H. Harry

“…The key enabling technique is superposition of single cell computational models to predict multicellular behaviors. …”
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Prediction of the binding interface between monoclonal antibody m102.4 and Nipah attachment glycoprotein using structure-guided alanine scanning and computational docking by Tit-oon, Phanthakarn, Tharakaraman, Kannan, Artpradit, Charlermchai, Godavarthi, Abhinav, Sungkeeree, Pareenart, Sasisekharan, Varun, Kerdwong, Jarunee, Miller, Nathaniel Loren, Mahajan, Bhuvna, Khongmanee, Amnart, Ruchirawat, Mathuros, Sasisekharan, Ram, Fuangthong, Mayuree

“…In summary, our study provides insight into the m102.4-NiV interaction, demonstrating that structure-guided alanine-scanning and computational modeling can serve as the starting point for additional antibody reengineering (e.g. affinity maturation) to generate potential therapeutic candidates.…”
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Task-evoked activity quenches neural correlations and variability across cortical areas by Ito, Takuya, Brincat, Scott L, Siegel, Markus, Mill, Ravi D, He, Biyu J, Miller, Earl K, Rotstein, Horacio G, Cole, Michael W

“…We further found in both spiking and neural mass computational models that task-evoked activity increased the stability around a stable attractor, globally quenching neural variability and correlations. …”
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Multimodal Representation Learning for Medical Image Analysis by Liao, Ruizhi

“…Medical images capture rich information of a patient’s physiological and disease status, central in clinical practice and research. Computational models, such as artificial neural networks, enable automatic and quantitative medical image analysis, which may offer timely diagnosis in low-resource settings, advance precision medicine, and facilitate large-scale clinical research. …”
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