Decoding event-related potentials: single-dose energy dietary supplement acts on earlier brain processes than we thought by Karina J. Maciejewska

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Flexible Adaptive Marine Predator Algorithm and Its Application in Fault Detection for Wind Turbines by WANG Wen, YI Jiabiao

“…In experiments involving nine benchmark functions, FAMPA outperformed traditional MPA in convergence rate, optimization accuracy, and robustness. …”
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Remimazolam Combined with Andrographolide Improve Postoperative Cognitive Dysfunction in Rats after Cardiopulmonary Bypass through the AMPK/SIRT1 Signaling Pathway by Chong Chen, Lixia Lan, Kai Xu

“…Introduction: The effects of remimazolam (Re) in combination with andrographolide (AP) on learning, memory, and motor abilities in rats following cardiopulmonary bypass (CPB) surgery were studied. …”
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Machine learning cluster analysis identifies increased 12-month mortality risk in transcatheter aortic valve replacement recipients by Thomas Meredith, Thomas Meredith, Thomas Meredith, Farhan Mohammed, Amy Pomeroy, Sebastiano Barbieri, Sebastiano Barbieri, Erik Meijering, Louisa Jorm, David Roy, Jason Kovacic, Jason Kovacic, Jason Kovacic, Jason Kovacic, Michael Feneley, Michael Feneley, Michael Feneley, Christopher Hayward, Christopher Hayward, Christopher Hayward, David Muller, David Muller, David Muller, Mayooran Namasivayam, Mayooran Namasivayam, Mayooran Namasivayam

“…Our results highlight the utility of machine learning applications for clinical risk prediction and scope to improve patient surveillance.…”
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Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning by Bin He, Yan Cheng, Juan Wang, Ya Zhan, YanQun Liu

“…Ion channel-related genes were extracted from microarray datasets and analyzed for differential expression and functional relevance to AF pathology. Machine learning algorithms (LASSO and SVM) were used to identify signature genes from ion channels in AF, followed by drug-enrichment analysis to explore potential therapeutic options.ResultsIn the structural remodeling investigation, single-cell analysis was employed to identify five distinct cell subtypes, including embryonic fibroblasts (EF), actively proliferating fibroblasts (APF), smooth muscle cells (SMC), endothelial cells (EC), and leukocytes (LBCs). …”
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Buffering brain aging: education moderates language impairment in Parkinson's disease by Minchul Kim, Kwangsun Yoo, Kwangsun Yoo, Kwangsun Yoo

“…The primary outcome was cognitive performance across six domains: attention, executive function, language, learning and memory, visuospatial ability, and global cognition.ResultsAmong the six domains, a significant moderation effect of education was found only for language ability (β = 0.01, p = 0.013, R2 = 0.20). …”
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Development and Validation of a Virtual Version of the Box and Block Test to Assess Manual Dexterity at Home for Adults with Stroke and Children with Cerebral Palsy by Zélie Rosselli, Merlin Somville, Edouard Ducoffre, Carlyne Arnould, Geoffroy Saussez, Yannick Bleyenheuft

“…The REAtouch<sup data-eusoft-scrollable-element="1">®</sup> Lite device was recently developed to support motor skill learning-based interventions, integrating both games/activities and assessment tools to enable home-based telerehabilitation. …”
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Learning Transversus Abdominis Activation in Older Adults with Chronic Low Back Pain Using an Ultrasound-Based Wearable: A Randomized Controlled Pilot Study by Luis Perotti, Oskar Stamm, Hannah Strohm, Jürgen Jenne, Marc Fournelle, Nils Lahmann, Ursula Müller-Werdan

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Effects and mechanisms of Tieguanyin on the behavior of Alzheimer’s disease APP/PS1 mice by KANG Hyunuk, ZHOU Hui, YE Yushan, LIU Zhonghua, WEI Yuede, YANG Jiangfan, KIM Eunhye, HE Puming, LI Bo, WU Yuanyuan, TU Youying

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THE COLLABORATIVE DESIGNING OF A PERSONALIZED HYBRID LMS USING A VIRTUAL MACHINE IN A CLOUD ENVIRONMENT by Lina Čeponienė, Péter Galambos, Tamás Haidegger, Dariusz Mikulowski, Branislav Mišota, Oliver Moravcik, Enikő Nagy, Mariya P. Shyshkina, Pavel Smolka, Stefan Svetsky

“…Part of current and future research is the development of the PIKS internet application, which operates as a multi-functional communication channel for e-Learning purposes (including use for the visually impaired). . …”
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Using independent component analysis to extract a cross-modality and individual-specific brain baseline pattern by Wei Liu, Xuemin Zhang

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Scalable Mixed-Criticality Safety Concepts for a Stationary Battery Management System (BMS) With Integrated Machine Learning (ML) by Xabier Arraztoa-Lazkanotegi, David Marcos, Maitane Garmendia, Enaut Muxika Olasagasti, Jon Perez-Cerrolaza

“…The integration of mixed-criticality functions (combining safety functions of different criticality and non-safety-related functions) into these electronic embedded systems offers significant potential benefits but also introduces technical and certification challenges. …”
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Cognitive Dysfunction in the Addictions (CDiA): protocol for a neuron-to-neighbourhood collaborative research program by Yuliya S. Nikolova, Yuliya S. Nikolova, Yuliya S. Nikolova, Anthony C. Ruocco, Anthony C. Ruocco, Anthony C. Ruocco, Anthony C. Ruocco, Daniel Felsky, Daniel Felsky, Daniel Felsky, Shannon Lange, Shannon Lange, Shannon Lange, Shannon Lange, Thomas D. Prevot, Thomas D. Prevot, Thomas D. Prevot, Erica Vieira, Erica Vieira, Daphne Voineskos, Daphne Voineskos, Jeffrey D. Wardell, Jeffrey D. Wardell, Jeffrey D. Wardell, Jeffrey D. Wardell, Daniel M. Blumberger, Daniel M. Blumberger, Kevan Clifford, Kevan Clifford, Ravinder Naik Dharavath, Philip Gerretsen, Philip Gerretsen, Ahmed N. Hassan, Ahmed N. Hassan, Ingrid M. Hope, Samantha H. Irwin, Sheila K. Jennings, Sheila K. Jennings, Bernard Le Foll, Bernard Le Foll, Osnat Melamed, Osnat Melamed, Josh Orson, Peter Pangarov, Leanne Quigley, Cayley Russell, Kevin Shield, Kevin Shield, Kevin Shield, Matthew E. Sloan, Matthew E. Sloan, Matthew E. Sloan, Matthew E. Sloan, Matthew E. Sloan, Ashley Smoke, Ashley Smoke, Victor Tang, Victor Tang, Victor Tang, Diana Valdes Cabrera, Diana Valdes Cabrera, Wei Wang, Samantha Wells, Samantha Wells, Samantha Wells, Rajith Wickramatunga, Etienne Sibille, Etienne Sibille, Etienne Sibille, Lena C. Quilty, Lena C. Quilty

“…Substance use disorders (SUDs), including Alcohol Use Disorder, are pressing global public health problems. Executive functions (EFs) are prominently featured in mechanistic models of addiction. …”
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Selection of target-binding proteins from the information of weakly enriched phage display libraries by deep sequencing and machine learning by Tomoyuki Ito, Thuy Duong Nguyen, Yutaka Saito, Yoichi Kurumida, Hikaru Nakazawa, Sakiya Kawada, Hafumi Nishi, Koji Tsuda, Tomoshi Kameda, Mitsuo Umetsu

“…In summary, the use of appropriate deep sequencing data and machine learning with the sequence data has the possibility of finding sequence space where functional variants are enriched.…”
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Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis by Raul Sanchez-Munoz, Thomas Depaepe, Marketa Samalova, Jan Hejatko, Isiah Zaplana, Dominique Van Der Straeten

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THE SYSTEM OF MOTIVES OF EDUCATIONAL ACTIVITY AS A PROGNOSTIC SYSTEM SUCCESS FACTORS IN LEARNING: VIEW THROUGH THE PRISM OF UNIFICATIONNOW APPROACH by A. Egorova, S. Bobryshov

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Different MRI-based radiomics machine learning models to predict CD3+ tumor-infiltrating lymphocytes in rectal cancer by Weili Ma, Chuanling Hou, Minxia Yang, Yuguo Wei, Jiwei Mao, Le Guan, Zhenhua Zhao

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SUMOylation-related genes define prognostic subtypes in stomach adenocarcinoma: integrating single-cell analysis and machine learning analyses by Kaiping Luo, Kaiping Luo, Donghui Xing, Donghui Xing, Xiang He, Yixin Zhai, Yanan Jiang, Hongjie Zhan, Zhigang Zhao

“…A SUMOylation Risk Score (SRS) model was developed using 69 machine learning models across 10 algorithms, with performance evaluated by C-index and AUC. …”
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Learning selection-based augmented reality interactions across different training modalities: uncovering sex-specific neural strategies by John Hayes, Joseph L. Gabbard, Ranjana K. Mehta

“…We analyzed brain activation and functional connectivity using functional near-infrared spectroscopy during the evaluation phase. …”
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Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury by Chen Lin, Meng Zheng, Wensi Wu, Zhishan Wang, Guofeng Lu, Shaodan Feng, Xinlan Zhang

“…We assessed 113 combinations of 12 different algorithms to develop an internally and externally validated machine-learning model for diagnosing AKI. Finally, we used functional enrichment analysis to identify potential therapeutic agents for AKI.ResultsWe identified 556 and 725 DEGs associated with sepsis and AKI, respectively, with 28 overlapping genes suggesting shared pathways. …”
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