Inflammation-Mediated Regulation of MicroRNA Expression in Transplanted Pancreatic Islets by Valia Bravo-Egana, Samuel Rosero, Dagmar Klein, Zhijie Jiang, Nancy Vargas, Nicholas Tsinoremas, Marco Doni, Michele Podetta, Camillo Ricordi, R. Damaris Molano, Antonello Pileggi, Ricardo L. Pastori

“…The association between these miRNAs and mRNA target-predicting algorithms in genome-wide RNA studies of β-cell inflammation identified 238 potential miRNA gene targets. …”
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Using Neural Networks to Automate the Identification of Brightest Cluster Galaxies in Large Surveys by Patrick Janulewicz, Tracy M. A. Webb, Laurence Perreault-Levasseur

“…Though some cluster finding techniques identify the position of the BCG and use it as the cluster center, other techniques may not automatically include these coordinates. …”
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Investigating the truncated fractional telegraph equation in engineering: Solitary wave solutions, chaotic and sensitivity analysis by Usman Younas, Jan Muhammad, Muhammad Amin S. Murad, D.K. Almutairi, Aziz Khan, Thabet Abdeljawad

“…The results obtained may enhance the understanding of nonlinear system dynamics and validate current methods, significantly contributing to the fields of nonlinear science and wave phenomena. …”
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Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review by Natalie S Blencowe, Neil J Smart, George E Fowler, Rhiannon C Macefield, Conor Hardacre, Mark P Callaway

“…Benefits include applications to improve health systems, such as rapid and accurate interpretation of medical images. This may improve the performance of diagnostic, prognostic and management decisions. …”
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In silico analysis of atrial fibrillation and hypertension mechanism of action secondary to ibrutinib/acalabrutinib in chronic lymphocytic leukemia by Raúl Córdoba, Antoni Bayés-Genís, Ana Muntañola, Dolors Colomer, Jorge Castro, Carolina Leiva, Esther Álvarez, Eduardo Zatarain-Nicolas

“…These adverse events seem to be derived from off-targets rather than BTK inhibition. Machine learning algorithms were applied to identify targets likely to trigger AF and hypertension in simulated CLL patients receiving acalabrutinib or ibrutinib. …”
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Human-based metaheuristics and non-parametric learning for groundwater-prone area mapping by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Seyedeh Zeinab Shogrkhodaei, Biswajeet Pradhan, Soo-Mi Choi

“…Existing GPM techniques often depend on parametric models, which may fail to capture the intricate patterns of groundwater distribution or adapt to varying data complexities. …”
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Harnessing Artificial Intelligence for the Diagnosis, Treatment and Research of Multiple Sclerosis by Manisha S. Patil, Linda Y. Lin, Rachel K. Ford, Elizaveta J. James, Stella Morton, Felix Marsh-Wakefield, Simon Hawke, Georges E. Grau

“…AI can help identify subtle changes in the brain and spinal cord that may be missed by human clinicians, leading to earlier diagnosis and more personalised treatment plans. …”
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An Exploration on Aperiodic Activities and Transient Oscillations During Semantic Processing: A Study With Wearable MEG by Xiaoyu Liang, Yuyu Ma, Ruonan Wang, Huanqi Wu, Changzeng Liu, Fuzhi Cao, Nan An, Min Xiang, Yueyang Zhai, Xiaolin Ning

“…In this study, recently developed algorithms were employed to parameterize the time-frequency characteristics of neural activities captured with optically pumped magnetometers-based wearable Magnetoencephalography from participants engaged in a Chinese semantic-based task. …”
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Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases by Huai-wen Zhang, Yi-ren Wang, Bo Hu, Bo Song, Zhong-jian Wen, Lei Su, Xiao-man Chen, Xi Wang, Ping Zhou, Xiao-ming Zhong, Hao-wen Pang, You-hua Wang

“…While machine learning shows significant promise in medical image analysis, relying solely on a single model may lead to suboptimal results. By combining the strengths of various algorithms, the stacking ensemble model offers a better solution for the classification of brain metastases based on radiomic features.…”
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Theory of an Automatic Seepage Meter and Ramifications for Applications by Vitaly A. Zlotnik, D. Kip Solomon, David P. Genereux, Troy E. Gilmore, C. Eric Humphrey, Aaron R. Mittelstet, Anatoly V. Zlotnik

“…On this basis, changing the ASM geometry by increasing the radius and decreasing tube insertion depth may enable ASM field test protocols that estimate interface flux and hydraulic conductivity faster while maintaining desired accuracy. …”
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Detection of Wormhole Attack in Vehicular Ad-hoc Network over Real Map using Machine Learning Approach with Preventive Scheme by Shahjahan Ali, Parma Nand, Shailesh Tiwari

“…The collected data is pre-processed and the k-NN & Random Forest algorithms are applied on this data, to make the model such type so that it can memorize the wormhole attack. …”
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Masked Feature Modeling for Generative Self-Supervised Representation Learning of High-Resolution Remote Sensing Images by Shiyan Pang, Hanchun Hu, Zhiqi Zuo, Jia Chen, Xiangyun Hu

“…However, current masked image models typically employ a pure Transformer architecture, which may not fully capitalize on low-level features. …”
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Diagnostic Utility of ¹⁸F-FDG PET/CT in Infective Endocarditis by Corina-Ioana Anton, Alice-Elena Munteanu, Mihaela Raluca Mititelu, Militaru Alexandru Ștefan, Cosmin-Alexandru Buzilă, Adrian Streinu-Cercel

“…Its integration into diagnostic algorithms may improve clinical management and outcomes in complex IE cases.…”
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Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma by Yi Li, Rui Zeng, Rui Zeng, Yuhua Huang, Yumin Zhuo, Jun Huang

“…Subsequently, the LASSO and SVM algorithms were employed to identify shared hub genes between the two diseases. …”
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Utilizing machine learning techniques to identify severe sleep disturbances in Chinese adolescents: an analysis of lifestyle, physical activity, and psychological factors by Lirong Zhang, Shaocong Zhao, Wei Yang, Zhongbing Yang, Zhi’an Wu, Hua Zheng, Mingxing Lei, Mingxing Lei, Mingxing Lei

“…Additionally, the XGBM model had the best accuracy (0.792), precision (0.780), F1 score (0.796), Brier score (0.143), and log loss (0.444).ConclusionsThe XGBM model may be a useful tool to estimate the risk of experiencing severe sleep disturbance among adolescents.…”
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Unsupervised Machine Learning for Classifying CHIME Fast Radio Bursts and Investigating Empirical Relations by Da-Chun Qiang, Jie Zheng, Zhi-Qiang You, Sheng Yang

“…However, observational limitations may result in misclassifications, potentially leading to a higher proportion of repeaters than currently identified. …”
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Accurate and rapid single nucleotide variation detection in PCSK9 gene using nanopore sequencing by Ilaria Massaiu, Vincenza Valerio, Valentina Rusconi, Valentina Rusconi, Francesca Bertolini, Donato De Giorgi, Veronika A. Myasoedova, Paolo Poggio, Paolo Poggio

“…Twelve subjects were analyzed using different sequencing flow cells, basecalling models, and SNV calling algorithms. Sanger sequencing served as the reference for performance validation. …”
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Effects of respiratory viruses on febrile neutropenia attacks in children by Bilge Aldemir-Kocabaş, Ergin Çiftçi, Gülsan Yavuz, Zümrüt Uysal, Elif İnce, İştar Dolapçı, Zeynep Ceren Karahan, Esra Pekpak, Adem Karbuz, Erdal İnce

“…Identifying viral agents may help to constitute individualized infection-management algorithms in these patients. …”
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Gut and respiratory microbiota landscapes in IgA nephropathy: a cross-sectional study by Xiaoli Yuan, Jianbo Qing, Wenqiang Zhi, Feng Wu, Yan Yan, Yafeng Li

“…We applied 16SrRNA sequencing to identify differential microbial populations. ML algorithms were then used to classify IgAN based on these microbial differences. …”
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Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared Images by Lili Zhang, Zihan Jin, Yibo Wang, Ziyi Wang, Zeyu Duan, Taoran Qi, Rui Shi

“…Concrete dams are prone to various hidden dangers after long-term operation and may lead to significant risk if failed to be detected in time. …”
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