Predicting inhibitors of OATP1B1 via heterogeneous OATP-ligand interaction graph neural network (HOLIgraph) by Mehrsa Mardikoraem, Joelle N. Eaves, Theodore Belecciu, Nathaniel Pascual, Alexander Aljets, Bruno Hagenbuch, Erik M. Shapiro, Benjamin J. Orlando, Daniel R. Woldring

“…Beyond improving inhibition prediction, the data used to train HOLIgraph can enable the characterization of protein residues involved in inhibitory drug-OATP interactions. …”
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PRevention Intervention and Support in Mental health for people with aphasia (Aphasia PRISM): protocol and mixed methods analysis plan for two feasibility studies by C. Baker, M. L. Rose, D. Wong, B. Ryan, S. Thomas, D. Cadilhac, I. Kneebone

“…Descriptive statistics will be used to analyse quantitative data based on the type and distribution of data. …”
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Large Area Crops Mapping by Phenological Horizon Attention Transformer (PHAT) Method Using MODIS Time-Series Imagery by Quanshan Gao, Taixia Wu, Hongzhao Tang, JingYu Yang, Shudong Wang

“…The PHAT model was therefore trained using the phenological features of endmembers to obtain the spatial distribution of crops, and to resolve the issue of varying time-series curves for the same crop across large areas. …”
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Frequency analysis and calculation of transformer neutral DC caused by metro stray currents by Aimin Wang, Sheng Lin, Qi Zhou, GuoXing Wu, Xiaopeng Li, Jun Liu, Hongbo Cheng

“…Through the detailed frequency analysis of the test data, the relationship between the main frequencies of neutral DC and metro train time headway is found. …”
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Biological heart and brain ageing in subjects with cardiovascular diseases by Elizabeth Mcavoy, Elizabeth Mcavoy, Elizabeth Mcavoy, Matthias Wilms, Matthias Wilms, Matthias Wilms, Matthias Wilms, Matthias Wilms, Nils D. Forkert, Nils D. Forkert, Nils D. Forkert, Nils D. Forkert

“…For BAG computation, a convolutional neural network was trained based on the MRI data, while a CatBoost model was trained for HAG analyses based on the tabulated cardiac features. …”
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Learning from low precision samples by Ji In Choi, Madeleine Georges, Jung Ah Shin, Olivia Wang, Tiffany Zhu, Tapan Shah

“… • We propose 2 future research areas, • dynamic quantizer update where the model is trained using streaming data and the quantizer is updated after each batch and • precision re-allocation under budget constraints where different precision is used for different features.…”
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From community to science to community, enhancing remote sensing of water quality in Chesapeake Bay tributaries through participatory science by Min-Sun Lee, Maria Tzortziou, Ji-Eun Park, Tong Lin, Patrick Neale, Shelby Brown, Tara Sill, Alison Cawood

“…Our results highlight the significant benefits of engaging volunteers in estuarine water quality monitoring activities, particularly for participatory data collection, standardized data collection across coastal systems, and improvement of satellite biogeochemical retrievals in complex nearshore waters that directly impact coastal communities and economies.…”
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A novel machine-learning algorithm to screen for trisomy 21 in first-trimester singleton pregnancies by James Osborne, Chris Cockcroft, Carolyn Williams

“…Test case results were compared with pregnancy outcome data to assess performance.Results A machine-learning model was able to outperform current multivariate distribution models (McNemar’s p = .006, AUC 0.978 vs 0.974). …”
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Improving Bimonthly Landscape Monitoring in Morocco, North Africa, by Integrating Machine Learning with GRASS GIS by Polina Lemenkova

“…This study contributes to environmental monitoring in North Africa using ML algorithms of satellite image processing. Using RS data combined with the powerful functionality of the GRASS GIS and FAO-derived datasets, the topographic variability, moderate-scale habitat heterogeneity, and bimonthly distribution of land cover types of northern Morocco in 2023 have been assessed for the first time.…”
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Comparative Analysis of Explainable AI Methods for Manufacturing Defect Prediction: A Mathematical Perspective by Gabriel Marín Díaz

“…Using a dataset based on empirical industrial distributions, we train an XGBoost model to classify high- and low-defect scenarios from multidimensional production and quality metrics. …”
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Multispectral UAV Image Classification of Jimson Weed (<i>Datura stramonium</i> L.) in Common Bean (<i>Phaseolus vulgaris</i> L.) by Marlies Lauwers, Benny De Cauwer, David Nuyttens, Wouter H. Maes, Jan G. Pieters

“…Based on the results, it was concluded that common bean and <i>D. stramonium</i> are separable based on multispectral information. A model trained and tested on the data of one location obtained a validation true positive rate and true negative rate of 99% and 95%, respectively. …”
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High-resolution surface soil moisture retrieval: A hybrid machine learning framework integrating change detection and downscaling for precision water management by Zihao Wang, Qi Gao, Michele Crosetto, Maria Jose Escorihuela

“…The ML model was trained using in-situ SSM data collected from 2017 to 2021 and validated against independent in-situ measurement datasets. …”
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Accurate Spatial Heterogeneity Dissection and Gene Regulation Interpretation for Spatial Transcriptomics using Dual Graph Contrastive Learning by Zhuohan Yu, Yuning Yang, Xingjian Chen, Ka‐Chun Wong, Zhaolei Zhang, Yuming Zhao, Xiangtao Li

“…In addition, dual graph contrastive learning is proposed to train the model, ensuring that the latent embedding representation closely resembles the actual spatial distribution and exhibits cluster similarity. …”
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Explainable AI-driven assessment of hydro climatic interactions shaping river discharge dynamics in a monsoonal basin by Prashant Parasar, Akhouri Pramod Krishna

“…The main findings of this study are (1) KAN demonstrated high predictive performance with root mean squared error (RMSE) values ranging from 42.7 to 58.3 m3/s, Nash–Sutcliffe efficiency (NSE) between 0.80 and 0.87, mean absolute error (MAE) between 28.9 to 52.7 and R2 values between 0.84 and 0.90 across stations. (2) SHAP based feature contribution analysis identified Relative humidity (hurs), specific humidity (huss), and temperature (tas) as key predictors, while (pr) showed limited contribution due to spatial inherent inconsistencies in GCM precipitation data. (3) The bootstrapped SHAP distributions highlighted substantial variability in feature importance, particularly for humidity variables, revealing station specific uncertainty patterns in model interpretation. (4) The KAN framework results indicate strong temporal alignment and physical realism, confirming KAN’s robustness in capturing seasonal discharge dynamics and extreme flow events under monsoon influence environments. (5) In this study KAN with SHAP (SHapley additive exPlanations) is implemented for hydrological modeling under monsoon-influenced and data-limited regions such as SRB, offering improved accuracy, functional precision and efficiency compared to traditional models. …”
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UAV-Multispectral Based Maize Lodging Stress Assessment with Machine and Deep Learning Methods by Minghu Zhao, Dashuai Wang, Qing Yan, Zhuolin Li, Xiaoguang Liu

“…Additionally, 22 vegetation indices (VIs) were extracted from multispectral data, followed by spatial aggregation and image cropping. …”
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Assessment of the HIV Enhanced Access Testing in the Emergency Department (HEATED) program in Nairobi, Kenya: a quasi-experimental prospective study by Adam R. Aluisio, Joshua Smith-Sreen, Agatha Offorjebe, Wamutitu Maina, Sankei Pirirei, John Kinuthia, David Bukusi, Harriet Waweru, Rose Bosire, Daniel K. Ojuka, McKenna C. Eastment, David A. Katz, Michael J. Mello, Carey Farquhar

“…HTS data were evaluated as facility-based HIV testing (completed in the ED) and distribution of HIV self-tests independently, and aggregated as ED-HTS. …”
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多行星排齿轮变速机构构型及效率研究 by 许爱芬, 贾巨民, 温秉权, 李德发, 王驰

“…Planetary gear train is the core of the automatic transmission（AT）and is the key part to decide the performance of the AT.Its efficiency varies with different structure types.Based on the planetary gear train of CX31,the structure characteristic is analyzed,the transmission ratio and transmission efficiency are calculated.On the condition that the speed ratio distribution remained largely unaltered,the characteristics and transmission efficiency of another typical configuration are studied.And its alternative is analyzed.It is provided some theoretical and data support for automatic gearbox independent design of heavy vehicle.…”
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Bio-Inspired Fine-Tuning for Selective Transfer Learning in Image Classification by Ana Davila, Jacinto Colan, Yasuhisa Hasegawa

“…Transfer learning addresses this challenge by utilizing pre-trained models to tackle new tasks with limited labeled data. …”
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Overview of Effects of Motor Learning Strategies in Neurologic and Geriatric Populations: A Systematic Mapping Review by Li-Juan Jie, PhD, Melanie Kleynen, PhD, Guus Rothuizen, MSc, Elmar Kal, PhD, Andreas Rothgangel, PhD, Susy Braun, PhD

“…Two reviewers extracted descriptive data regarding the population, type of motor learning strategy/intervention, frequency and total duration intervention, task trained, movement performance measures, assessment time points, and between-group effects of the included studies. …”
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DK-Port: construction and validation of port autonomous driving simulation environment based on large language models and reinforcement learning by LOU Yunjie, AI Mingfei, ZHUANG Shujie, YU Hai, WANG Xin, TENG Chu, WANG Jiangcheng, SHEN Tianyu, HAO Kunkun, CUI Wen

“…Then, human-machine mixed-traffic scenarios are constructed using complex road data, and adversarial driver models are trained with the PPO reinforcement learning algorithm to identify safety vulnerabilities in autonomous driving systems. …”
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