Bioinformatic Methodologies in Assessing Gut Microbiota by James Douglas Fox, Austin Sims, Morgan Ross, Jeffery Bettag, Alexandra Wilder, Dylan Natrop, Alison Borsotti, Sree Kolli, Shaurya Mehta, Hema Verma, Kento Kurashima, Chandrashekhara Manithody, Arun Verma, Ajay Jain

“…Bioinformatics tools and algorithms process these data to identify microbial taxa and quantify and elucidate their roles within the microbiome. …”
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The PLSR-ML fusion strategy for high-accuracy leaf potassium inversion in karst region of Southwest China by Zhihao Song, Zhihao Song, Wen He, Yuefeng Yao, Ling Yu, Jinjun Huang, Yong Xu, Haoyu Wang

“…Validation coefficient of determination (R²) values reached 0.89, 0.94, and 0.96, respectively—representing improvements of 206%, 147%, and 108% over standalone algorithms. This performance gain was attributed to rigorous overfitting control: PLSR’s dimensionality reduction synergized with ensemble machine learning (RF, XGBoost, MLP) to eliminate redundant spectral features while retaining predictive signals. …”
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The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients by Zhou Liu, Guijun Jiang, Liang Zhang, Palpasa Shrestha, Yugang Hu, Yi Zhu, Guang Li, Yuanguo Xiong, Liying Zhan

“…Most importantly, two website prognostic prediction platforms were developed to enhance clinical accessibility: the ET model for AVGIB patients available at https://10zr656do5281.vicp.fun while the GB model for ANGIB patients accessible at http://10zr656do5281.vicp.fun.ConclusionThe ET model provides a reliable prognostic tool for AVGIB patients, while the GB model serves as a robust tool for ANGIB patients in predicting in-hospital mortality. …”
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Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking by Nikolaos Pavlidis, Andreas Sendros, Theodoros Tsiolakis, Periklis Kostamis, Christos Karasoulas, Eleni Briola, Christos Chrysanthos Nikolaidis, Vasilis Perifanis, George Drosatos, Eleftheria Katsiri, Despoina Elisavet Filippidou, Anastasios Manos, Pavlos S. Efraimidis

“…This paper presents the architecture of FLORA, a privacy-first ovulation-tracking application that leverages federated learning (FL), privacy-enhancing technologies (PETs), and blockchain to protect user data while delivering accurate and personalized health insights. …”
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FPGA-Accelerated Sparse Subset Segmentation Using ADMM for High-Resolution Imagery by Rupali Karthikeyan, Deep Amit Lodaya, Rama Muni Reddy Yanamala, Rayappa David Amar Raj, K. Krishna Prakasha, T. Subeesh, V. Anandkumar, Archana Pallakonda

“…This approach simplifies hardware integration and maintains accuracy while reducing latency and improving energy efficiency. …”
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Review of Self-Powered Wireless Sensors by Triboelectric Breakdown Discharge by Shuzhe Liu, Jixin Yi, Guyu Jiang, Jiaxun Hou, Yin Yang, Guangli Li, Xuhui Sun, Zhen Wen

“…Key research directions are synthesized to enhance TENG-based sensing capabilities, including novel device architectures, the optimization of RLC circuit models, the integration of machine learning algorithms, and power management strategies. While current breakdown discharge sensors face challenges such as energy dissipation, multimodal coupling complexity, and signal interpretation barriers, future breakthroughs in material engineering and structural design are anticipated to drive advancements in efficiency, miniaturization, and intelligent functionality in this field.…”
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MCOA: A Comprehensive Multimodal Dataset for Advancing Deep Learning in Corneal Opacity Assessment by Xinyu Ma, Jianxia Fang, Yaqi Wang, Zhichao Hu, Zhe Xu, Sha Zhu, Weijia Yan, Mengqi Chu, Jingwei Xu, Siting Sheng, Chujun Liu, Mingxuan Zhang, Ce Shi, Gangyong Jia, Wen Xu

“…However, the absence of large-scale, high-quality datasets hinders the development of deep learning algorithms for this purpose. To bridge this gap, we established the most extensive corneal opacity dataset available. …”
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Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment by Fabio Stagno, Giuseppe Mirabile, Patricia Rizzotti, Adele Bottaro, Antonio Pagana, Sebastiano Gangemi, Alessandro Allegra

“…In this review, we seek to identify the primary mechanisms and uses of artificial intelligence in myelodysplastic syndrome, pointing out its advantages and disadvantages while discussing the possible benefits of using AI pipelines in a therapeutic setting.…”
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Investigating the Use of Machine Learning Models to Understand the Drugs Permeability Across Placenta by Vaisali Chandrasekar, Mohammed Yusuf Ansari, Ajay Vikram Singh, Shahab Uddin, Kirthi S. Prabhu, Sagnika Dash, Souhaila Al Khodor, Annalisa Terranegra, Matteo Avella, Sarada Prasad Dakua

“…Owing to limited drug testing possibilities in pregnant population, the development of computational algorithms is crucial to predict the fate of drugs in the placental barrier; it could serve as an alternative to animal testing. …”
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Research Progress on Atmospheric Turbulence Perception and Correction Based on Adaptive Optics and Deep Learning by Qinghui Liu, Yihang Di, Mengmeng Zhang, Zhenbo Ren, Jianglei Di, Jianlin Zhao

“…Particular emphasis is placed on deep learning‐enabled intelligent correction paradigms, while critical analysis is provided regarding prospective research trajectories and implementation challenges.…”
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Unsupervised detection of rare events in liquid biopsy assays by Javier Murgoitio-Esandi, Dean Tessone, Amin Naghdloo, Stephanie N. Shishido, Brian Zhang, Haofeng Xu, Agnimitra Dasgupta, Jeremy Mason, Rajiv M. Nagaraju, George Courcoubetis, James Hicks, Peter Kuhn, Assad A. Oberai

“…This is done with the help of deep generative algorithms in an unsupervised manner, meaning without a-priori knowledge of the rare event associated with an analyte. …”
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EcoNicheS: enhancing ecological niche modeling, niche overlap and connectivity analysis using the shiny dashboard and R package by Armando Sunny, Clere Marmolejo, Rodrigo Vidal-López, Fredy A. Falconi-Briones, Ángela P. Cuervo-Robayo, René Bolom-Huet

“…EcoNicheS integrates innovative workflows with dynamic output visualizations while emphasizing reproducibility and comparability across statistical methods. …”
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Image-based maturity detection for selective cauliflower harvesting in field condition by Ajay Kushwah, P.K. Sharma, H.L. Kushwaha, Brij Bihari Sharma, Naseeb Singh, Ramineni Harsha Nag, Manojit Chowdhury, Nrusingh Charan Pradhan, Gopal Carpenter, Silpa Mandal, Ali Salem

“…To achieve optimal segmentation performance with the developed algorithms, threshold values were optimized using the response surface methodology technique. …”
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Investigating imperceptibility of adversarial attacks on tabular data: An empirical analysis by Zhipeng He, Chun Ouyang, Laith Alzubaidi, Alistair Barros, Catarina Moreira

“…The study also identifies limitations in current attack algorithms, offering insights that can guide future research in the area. …”
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Data transfers for nuclear reactor multiphysics studies using the MOOSE framework by Guillaume L. Giudicelli, Guillaume L. Giudicelli, Fande Kong, Roy Stogner, Logan Harbour, Derek Gaston, Alexander Lindsay, Zachary Prince, Lise Charlot, Stefano Terlizzi, Mahmoud Eltawila, April Novak

“…Advanced nuclear reactor cores are governed by multiple physical phenomena which should be all be resolved, and the coupling of these physics would also need to be resolved spatially in a high-fidelity approach, while lower fidelity may leverage integrated quantities for the coupling instead. …”
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Machine learning for predicting strength properties of waste iron slag concrete by Matiur Rahman Raju, Syed Ishtiaq Ahmad, Md Mehedi Hasan, Noor Md. Sadiqul Hasan, Md Monirul Islam, Md. Abdul Basit, Ishraq Tasnim Hossain, Saif Ahmed Santo, Md Shahrior Alam, Mahfuzur Rahman

“…Among the tested ML algorithms, Decision Tree (DT) and XGBoost showed the highest accuracy (R2 = 0.95135) in predicting concrete strength properties, while models like SVM and Symbolic Regression underperformed. …”
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Multi-time scale analysis of human activity patterns on the Qinghai–Tibet Plateau using location request data by Minglu Che, Yanyun Nian, Chengyao Wang, Wenhui Zhang, Jie Chen, Bingzhi Liao, Tao Pei

“…By applying clustering algorithms, we classified daily patterns into three main types. …”
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Identification of anaerobic bacterial strains by pyrolysis-gas chromatography-ion mobility spectrometry by Tim Kobelt, Jonas Klose, Rumjhum Mukherjee, Rumjhum Mukherjee, Martin Lippmann, Szymon P. Szafranski, Szymon P. Szafranski, Meike Stiesch, Meike Stiesch, Stefan Zimmermann

“…Preliminary experiments have demonstrated that pattern recognition algorithms can predict the genus of isolated bacteria with a precision of up to 97%.…”
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A machine learning approach to population pharmacokinetic modelling automation by Sam Richardson, Itziar Irurzun Arana, Andrzej Nowojewski, Diansong Zhou, Jacob Leander, Weifeng Tang, Richard Dearden, Megan Gibbs

“…This study demonstrates an automated, out-of-the-box approach for popPK model development, leveraging optimisation algorithms implemented using pyDarwin to efficiently handle a diverse range of extravascular drugs. …”
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Detection of carotid artery calcifications using artificial intelligence in dental radiographs: a systematic review and meta-analysis by Sarah Arzani, Parisa Soltani, Ali Karimi, Maryam Yazdi, Ashraf Ayoub, Zohaib Khurshid, Domenico Galderisi, Hugh Devlin

“…Results Nine studies were suitable for qualitative analysis, while five provided data for quantitative analysis. …”
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