Development of adaptive and emotionally intelligent educational assistants based on conversational AI by Rommel Gutierrez, William Villegas-Ch, Jaime Govea

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Unveiling hydrogen chemical states in supersaturated amorphous alumina via machine learning-driven atomistic modeling by Simon Gramatte, Olivier Politano, Noel Jakse, Claudia Cancellieri, Ivo Utke, Lars P. H. Jeurgens, Vladyslav Turlo

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Electrodiffusion dynamics in the cardiomyocyte dyad at nano-scale resolution using the Poisson-Nernst-Planck (PNP) equations. by Karoline Horgmo Jæ Ger, Aslak Tveito

“…Here, we present a nano-scale computational model, representing dyad dynamics using the PNP system. …”
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A SuperLearner-based pipeline for the development of DNA methylation-derived predictors of phenotypic traits. by Dennis Khodasevich, Nina Holland, Lars van der Laan, Andres Cardenas

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Research on fusion generation algorithm of visual communication and product design based on AIGC technology by Guoying Chen, Xiaofeng Lan, Kai Liu, Can Cheng

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Intelligent Innovation Dataset on Scientific Research Outcomes and Patents by Xinran Wu, Hui Zou, Yidan Xing, Jingjing Qu, Weiwei Guo, Xiaoming Fu

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A new look at TFPI inhibition of factor X activation. by Fabian Santiago, Amandeep Kaur, Shannon Bride, Dougald Monroe, Karin Leiderman, Suzanne Sindi

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Low-latency neuromorphic air hockey player by Juan P Romero B, Dimitrios Korakovounis, Jens E Pedersen, Jorg Conradt

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Canalization of gene expression and domain shifts in the Drosophila blastoderm by dynamical attractors. by Manu, Svetlana Surkova, Alexander V Spirov, Vitaly V Gursky, Hilde Janssens, Ah-Ram Kim, Ovidiu Radulescu, Carlos E Vanario-Alonso, David H Sharp, Maria Samsonova, John Reinitz

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Simulated synapse loss induces depression-like behaviors in deep reinforcement learning by Eric Chalmers, Santina Duarte, Xena Al-Hejji, Daniel Devoe, Aaron Gruber, Robert J. McDonald

“…These results support a conceptual model of MDD as a reduction of brain connectivity (and thus information-processing capacity) rather than an imbalance in monoamines—though the computational model suggests a possible explanation for the dysfunction of dopamine systems in MDD. …”
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Combinatorial mathematical modelling approaches to interrogate rear retraction dynamics in 3D cell migration. by Joseph H R Hetmanski, Matthew C Jones, Fatima Chunara, Jean-Marc Schwartz, Patrick T Caswell

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Enhancing medical image privacy in IoT with bit-plane level encryption using chaotic map by Fatima Asiri, Wajdan Al Malwi, Tamara Zhukabayeva, Ibtehal Nafea, Abdullah Aziz, Nadhmi A. Gazem, Abdullah Qayyum

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SkinHealthMate app: An AI-powered digital platform for skin disease diagnosis by Amina Aboulmira, Mohamed Lachgar, Hamid Hrimech, Aboudramane Camara, Charafeddine Elbahja, Amine Elmansouri, Yassine Hassini

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CLASSIFICATION AND PREDICTION OF BENTHIC HABITAT FROM SCIENTIFIC ECHOSOUNDER DATA: APPLICATION OF MACHINE LEARNING ALGORITHMS by Baigo HAMUNA, Sri PUJIYATI, Jonson Lumban GAOL, Totok HESTIRIANOTO

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Comment on: Toward an optimal contraception dosing strategy. by Giuseppe Benagiano, Andrea R Genazzani, Giovanni Grandi, Sun-Wei Guo, Marwan Habiba

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Limits on reliable information flows through stochastic populations. by Lucas Boczkowski, Emanuele Natale, Ofer Feinerman, Amos Korman

“…Biological systems can share and collectively process information to yield emergent effects, despite inherent noise in communication. …”
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Adamic–Adar similarity indexed Wald boost data classification for diabetic disease diagnosis with big data by Ravichandran M, Saravanan Srinivasan, Sandeep Kumar Mathivanan, Hariharan Rajadurai, Benjula Anbu Malar M.B, Saurav Mallik, Hong Qin

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Task-dependent changes in cross-level coupling between single neurons and oscillatory activity in multiscale networks. by Ryan T Canolty, Karunesh Ganguly, Jose M Carmena

“…While a complete mechanistic account of this process remains elusive, evidence suggests that neuronal oscillations may play a key role in this process, with different rhythms influencing both local computation and long-range communication. …”
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miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts. by Albert Pla, Xiangfu Zhong, Simon Rayner

“…MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to partially complementary regions within the 3'UTR of their target genes. Computational methods play an important role in target prediction and assume that the miRNA "seed region" (nt 2 to 8) is required for functional targeting, but typically only identify ∼80% of known bindings. …”
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A peptide filtering relation quantifies MHC class I peptide optimization. by Neil Dalchau, Andrew Phillips, Leonard D Goldstein, Mark Howarth, Luca Cardelli, Stephen Emmott, Tim Elliott, Joern M Werner

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