Lysophospholipid metabolism, clinical characteristics, and artificial intelligence-based quantitative assessments of chest CT in patients with stable COPD and healthy smokers by Qiqiang Zhou, Lvxinhe Xing, Mi Ma, Bianba Qiongda, Deyuan Li, Ping Wang, Yahong Chen, Ying Liang, Meilang ChuTso, Yongchang Sun

“…LPC (18:0) and (18:2) correlated with higher post-bronchodilator FEV1, and the latter also correlated with FEV1% predicted, forced vital capacity (FVC), and FEV1/FVC ratio. …”
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Forecasting reference crop evapotranspiration using deep learning model and online training by DENG Xuanying, LYU Xinwei, ZHENG Wenyan, ZHENG Shizong, ZHANG Yadong, LUO Tongyuan, CUI Yuanlai, LUO Yufeng

“…Errors were observed in forecasting weather types and wind scales. The ET0 predicted by the model closely matched those calculated using real-time data, demonstrating high forecasting accuracy. …”
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Transcriptional fingerprinting of regulatory T cells: ensuring quality in cell therapy applications by Zhang Cheng, Li-Jie Wang, Yuchi Honaker, Steven A. Cincotta, Claire E. Page, Sydney Vollhardt, Victor Yuan, S. Alice Long, Yuanyuan Xiao, Joshua N. Beilke, Joseph R. Arron, Jeffrey A. Bluestone

“…We also showed that the identity fingerprint predicts Treg stability in in vitro settings and can be used to illustrate differences in drug products generated using distinct strategies. …”
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Altered diversity and composition of gut microbiota in Korean children with food allergy by Minyoung Jung, Ji Young Lee, Sukyung Kim, Jeongmin Song, Sehun Jang, Sanghee Shin, Min Hee Lee, Mi Jin Kim, Jiwon Kim, Han Byul Lee, Yeonghee Kim, Kangmo Ahn, Minji Kim, Jihyun Kim

“…Functional profiles were predicted using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) and the Minimal Set of Pathways (MinPath) algorithm. …”
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CD38 contributes to tumor progression and tumor microenvironment reshaping in epithelial ovarian cancer by Wei Wang, Xiangnan Liu, Shengjie Xu, Enci Dai, Yingying Li, Yinping Liu, Liyun Shan, Yanli Li

“…Immune-related functions of CD38 were explored using GO, KEGG analysis and TIP database. TIDE algorithm was employed to predict the correlation between CD38 and immune checkpoint blocking responsiveness. …”
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Risk factors and an interpretability tool of in-hospital mortality in critically ill patients with acute myocardial infarction by Rui Yang, Tao Huang, Renqi Yao, Di Wang, Yang Hu, Longbing Ren, Shaojie Li, Yali Zhao, Zhijun Dai

“…The balance random forest (BRF) model had the best performance in predicting in-hospital mortality. The compact model did not differ from the full variable model in performance (AUC: 0.891 vs 0.885, P = 0.06). …”
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A lightweight YOLO network using temporal features for high-resolution sonar segmentation by Sen Gao, Sen Gao, Wei Guo, Wei Guo, Gaofei Xu, Gaofei Xu, Ben Liu, Ben Liu, Yu Sun, Bo Yuan

“…The system processes non-keyframes by predicting semantic vectors through the trained BiLSTM model, selectively skipping computational layers to enhance efficiency. …”
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Enhancing environmental models with a new downscaling method for global radiation in complex terrain by A. Druel, J. Ruffault, H. Davi, A. Chanzy, O. Marloie, M. De Cáceres, A. Olioso, F. Mouillot, C. François, K. Soudani, N. K. Martin-StPaul

“…Finally, by using two different PBMs (CASTANEA, a PBM simulating tree growth, and SurEau, a plant hydraulic model simulating hydraulic failure risk), we showed that accounting for fine-resolution radiation can have a great impact on predictions of forest functions.</p>…”
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Hybrid Long Short-Term Memory Wavelet Transform Models for Short-Term Electricity Load Forecasting by Agbassou Guenoukpati, Akuété Pierre Agbessi, Adekunlé Akim Salami, Yawo Amen Bakpo

“…This proposed approach significantly narrows the gap between actual and predicted loads, reducing discrepancies from 10–50 MW to 0.5–3 MW. …”
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An Enhanced Deep Neural Network Approach for WiFi Fingerprinting-Based Multi-Floor Indoor Localization by Shehu Lukman Ayinla, Azrina Abd Aziz, Micheal Drieberg, Misfa Susanto, Afidalina Tumian, Mazlaini Yahya

“…To address these concerns, Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL) have been used to classify and predict indoor locations. While these techniques have shown promising results, they face two significant challenges. …”
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Management and edaphoclimatic factors that determine soybean top-performing farmers in Brazil by Victor Delino Barasuol Scarton, Ivan Ricardo Carvalho, Christiane de Fátima Colet, Leonardo Cesar Pradebon, Willyan Júnior Adorian Bandeira, Jaqueline Piesanti Sangiovo, Murilo Vieira Loro, José Antonio Gonzalez da Silva

“…Machine learning models proved superior to traditional methods for predicting productivity, offering a strategic tool for agribusiness. …”
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Machine Learning Approach to Aerodynamic Analysis of NACA0005 Airfoil: ANN and CFD Integration by Taiba Kouser, Dilek Funda Kurtulus, Srikanth Goli, Abdulrahman Aliyu, Imil Hamda Imran, Luai M. Alhems, Azhar M. Memon

“…This study presents a machine learning approach to predict the unsteady aerodynamic performance of a NACA0005 airfoil. …”
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Ultrasound Imaging and Machine Learning to Detect Missing Hand Motions for Individuals Receiving Targeted Muscle Reinnervation for Nerve-Pain Prevention by Anna Rita E. Moukarzel, Justin Fitzgerald, Marcus Battraw, Clifford Pereira, Andrew Li, Paul Marasco, Wilsaan M. Joiner, Jonathon Schofield

“…We found that attempted missing hand movements resulted in unique patterns of deformation in the reinnervated muscles and applying a K-nearest neighbors machine learning algorithm, we could predict 4-10 hand movements for each participant with 83.3-99.4% accuracy. …”
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Detecting cognitive motor dissociation by functional near-infrared spectroscopy by Yan Wang, Yan Wang, Yan Wang, Wentao Zeng, Wentao Zeng, Wentao Zeng, Leyao Zou, Leyao Zou, Leyao Zou, Qijun Wang, Bingkai Ren, Bingkai Ren, Bingkai Ren, Qi Xiong, Yang Bai, Yang Bai, Yang Bai, Zhen Feng, Zhen Feng, Zhen Feng

“…The support vector machine combined with genetic algorithm was employed to classify and predict the brain's response to spoken commands and to identify CMD patients among prolonged DOC individuals.ResultsWe identified seven CMD patients using fNIRS, of whom four were in VS/UWS and three were in MCS–. …”
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Diagnostic performance of actigraphy in Alzheimer’s disease using a machine learning classifier – a cross-sectional memory clinic study by Mathias Holsey Gramkow, Andreas Brink-Kjær, Frederikke Kragh Clemmensen, Nikolai Sulkjær Sjælland, Gunhild Waldemar, Poul Jennum, Steen Gregers Hasselbalch, Kristian Steen Frederiksen

“…We evaluated the performance of our classifier by assessing the accuracy and precision of predictions. Results We found that movement patterns as well as the robustness and fragmentation of the circadian rhythm differed significantly between groups. …”
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Artificial intelligence enhanced electrochemical immunoassay for staphylococcal enterotoxin B by Yuliang Zhao, Tingting Sun, Huawei Zhang, Chao Lian, Zhongpeng Zhao, Yongqiang Jiang, Huiqi Duan, Yuhao Ren, Xuyang Sun, Zhikun Zhan, Mingyue Qu, Shaolong Chen

“…Lastly, a multivariate linear regression algorithm is employed to effectively train and fit the extracted feature data. …”
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Exploration of metastasis-related signatures in osteosarcoma based on tumor microenvironment by integrated bioinformatic analysis by Shiyao Liao, Xing Gao, Kai Zhou, Yao Kang, Lichen Ji, Xugang Zhong, Jun Lv

“…Conclusion: We identified three principal genes as promising signatures for predicting the survival the prognosis of OS patients. …”
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Pork-YOLO: Automated collection of pork quality traits by Jiacheng Wei, Xi Tang, Jinxiu Liu, Ting Luo, Yan Wu, Junhui Duan, Shijun Xiao, Zhiyan Zhang

“…For marbling scoring, an image classification task yielded an average accuracy of 98.9 %, with a strong correlation (R2 = 0.999) between predicted and actual values. This study presents an innovative method for rapid automated assessment of pork quality traits, offering valuable insights for future phenotypic measurement automation.…”
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Using Machine Learning and Feature Importance to Identify Risk Factors for Mortality in Pediatric Heart Surgery by Lorenz A. Kapsner, Manuel Feißt, Ariawan Purbojo, Hans-Ulrich Prokosch, Thomas Ganslandt, Sven Dittrich, Jonathan M. Mang, Wolfgang Wällisch

“…All models were then applied to predict the outcome in an independent holdout test dataset (40% of the cohort). …”
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New design paradigm for federated edge learning towards 6G:task-oriented resource management strategies by Zhiqin WANG, Jiamo JIANG, Peixi LIU, Xiaowen CAO, Yang LI, Kaifeng HAN, Ying DU, Guangxu ZHU

“…In addition, the proposed single-device scheduling algorithm is also extended to multi-device scheduling scenarios. …”
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