RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks. by Souvik Seal, Qunhua Li, Elle Butler Basner, Laura M Saba, Katerina Kechris

“…Inferring gene co-expression networks is a useful process for understanding gene regulation and pathway activity. …”
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Structural comparison of homologous protein-RNA interfaces reveals widespread overall conservation contrasted with versatility in polar contacts. by Ikram Mahmoudi, Chloé Quignot, Carla Martins, Jessica Andreani

“…Protein-RNA interactions play a critical role in many cellular processes and pathologies. However, experimental determination of protein-RNA structures is still challenging, therefore computational tools are needed for the prediction of protein-RNA interfaces. …”
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Advancing simulations of coupled electron and phonon nonequilibrium dynamics using adaptive and multirate time integration by Jia Yao, Ivan Maliyov, David J. Gardner, Carol S. Woodward, Marco Bernardi

“…The real-time Boltzmann equation (rt-BTE), used in conjunction with accurate interactions computed from first principles, has enabled reliable predictions of coupled electron and lattice dynamics. …”
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A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses. by William T Harvey, Vinny Davies, Rodney S Daniels, Lynne Whittaker, Victoria Gregory, Alan J Hay, Dirk Husmeier, John W McCauley, Richard Reeve

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Impact of ligand binding on VEGFR1, VEGFR2, and NRP1 localization in human endothelial cells. by Sarvenaz Sarabipour, Karina Kinghorn, Kaitlyn M Quigley, Anita Kovacs-Kasa, Brian H Annex, Victoria L Bautch, Feilim Mac Gabhann

“…We show that VEGFR2 trafficking is altered in response to ligand binding, but that trafficking of VEGFR1 is not; we also show that the altered trafficking can be explained by a single mechanistic process, increased internalization of the VEGFR2 receptor when bound to ligand; other processes are unaffected. …”
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Comparison of three one-dimensional time-domain electromagnetic forward algorithms by Frederik Alexander Falk, Anders Vest Christiansen, Thomas Mejer Hansen

“…Accurate, efficient, and accessible forward modeling of geophysical processes is essential for understanding them and for inversion of geophysical data. …”
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Quantitative analysis of immune response and erythropoiesis during rodent malarial infection. by Martin R Miller, Lars Råberg, Andrew F Read, Nicholas J Savill

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FLC-Net: Innovative fraud classification in loan applications with dual channel graph advanced neural network by P. Pedda Sadhu Naik, Kalleda Appanolla Architha, Maddela Mahesh, Chitkula Anand, Gaddam Shriya Reddy, Patlola Suraj Reddy

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A disordered encounter complex is central to the yeast Abp1p SH3 domain binding pathway. by Gabriella J Gerlach, Rachel Carrock, Robyn Stix, Elliott J Stollar, K Aurelia Ball

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Exon level machine learning analyses elucidate novel candidate miRNA targets in an avian model of fetal alcohol spectrum disorder. by Abrar E Al-Shaer, George R Flentke, Mark E Berres, Ana Garic, Susan M Smith

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Stress-induced impairment of a working memory task: role of spiking rate and spiking history predicted discharge. by David M Devilbiss, Rick L Jenison, Craig W Berridge

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A Deep Learning Approach to Unveil Types of Mental Illness by Analyzing Social Media Posts by Rajashree Dash, Spandan Udgata, Rupesh K. Mohapatra, Vishanka Dash, Ashrita Das

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Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma. by Yusuke Suita, Hardy Bright, Yuan Pu, Merih Deniz Toruner, Jordan Idehen, Nikos Tapinos, Ritambhara Singh

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Rational inattention and tonic dopamine. by John G Mikhael, Lucy Lai, Samuel J Gershman

“…Slow-timescale (tonic) changes in dopamine (DA) contribute to a wide variety of processes in reinforcement learning, interval timing, and other domains. …”
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How math shapes the world of life science animation by Rafael Oliveira, Evellyn Araujo Dias, Evellyn Araujo Dias, Ricardo Santos, Ricardo Santos, Vinicius Cotta-De-Almeida, José Aguiar Coelho Nt, José Aguiar Coelho Nt, José Aguiar Coelho Nt, Luiz Anastácio Alves

“…We synthesized findings from various studies to underscore the proven advantages of this new teaching tool, and by reviewing them, we reaffirm that animations have a demonstrable impact on improving the learning experience, making complex physiological processes more comprehensible and engaging. This highlights the critical role of animations as a pedagogical tool in science classrooms, and emphasizes the importance of understanding the mathematical and computational principles that support their creation. …”
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AUGUR, a flexible and efficient optimization algorithm for identification of optimal adsorption sites by Ioannis Kouroudis, Poonam, Neel Misciasci, Felix Mayr, Leon Müller, Zhaosu Gu, Alessio Gagliardi

“…Additionally, the pooling properties of graphs allow for the processing of molecules of different sizes by the same model. …”
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Unraveling the obstacles to transform agro-wastes into value-added bioproducts: Implications for sustainability by Khandakar Shamit Raja, Ayesha Maliha, Sourav Paul, Md Shihab Shakur, A.B.M. Mainul Bari

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Feasibility of proton dosimetry overriding planning CT with daily CBCT elaborated through generative artificial intelligence tools by Matteo Rossi, Gabriele Belotti, Luca Mainardi, Guido Baroni, Pietro Cerveri

“…Radiotherapy commonly utilizes cone beam computed tomography (CBCT) for patient positioning and treatment monitoring. …”
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Phasic attractors for flexible and adaptive working memory in spiking recurrent neural networks by Willian S Girão, Thomas F Tiotto, Jelmer P Borst, Niels A Taatgen, Elisabetta Chicca

“…The core innovation of the model lies in its ability to leverage phasic attractors for state-dependent computation in a probabilistic manner. By modulating the activity of attractors via synaptic delays, the model demonstrates context-sensitive information processing. …”
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Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning by Rajib Maity, Aman Srivastava, Subharthi Sarkar, Mohd Imran Khan

“…The review also addresses persistent challenges, including data limitations, computational demands, and model interpretability, proposing solutions that integrate emerging technologies like quantum computing, the Internet of Things (IoT), and interdisciplinary collaboration. …”
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