A digital twin-enabled fog-edge-assisted IoAT framework for Oryza Sativa disease identification and classification by Goluguri N.V. Rajareddy, Kaushik Mishra, Satish Kumar Satti, Gurpreet Singh Chhabra, Kshira Sagar Sahoo, Amir H. Gandomi

“…To boost the model's predictive accuracy, the Chaotic Honey Badger Algorithm (CHBA) is employed to optimize the CNN hyperparameters, resulting in an impressive average accuracy of 93.5 %. …”
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AFF3 is a Prognostic Biomarker Correlated with Immune Infiltrates in Triple-Negative Breast Cancer by Jing Chen, Bing Tan, Wei Zhuang, Tenghua Yu, Jianglong Li, Chongwu He

“…Conclusions: In TNBC, low AFF3 expression might be predictive of poor survival. AFF3 might provide additional insight into therapeutics in TNBC.…”
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Least travel time ray tracer version 2 (LTT v2) adapted to the grid geometry of the OpenIFS atmospheric model by M. Vasiuta, A. Navarro Trastoy, S. Motlaghzadeh, L. Tuppi, T. Mayer-Gürr, H. Järvinen

“…The atmospheric states are generated using a global numerical weather prediction model, the Open Integrated Forecast System of the European Centre for Medium-Range Weather Forecasts. …”
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AN INDIVIDUALIZED RISK ASSESSMENT OF SUDDEN CARDIAC DEATH IN DILATION CARDIOMYOPATHY PATIENTS by T. G. Vaykhanskaya, L. N. Sivitskaya, T. V. Kurushko, N. G. Danilenko, O. P. Melnikova, A. V. Frolov

“…With binary logit-regression analysis of independent risk factors (VES, sVT, mATW, TCR, JTd and GLS LV) we built-up a model of binary regression (F=31,2; χ2=143,2; p=0,0000) and developed an algorithm of SCD risk evaluation that make it to classify with high prediction power up to 93,9%, cases of DCMP (OR 470; sensitivity 80,8%, specificity 99,1%).Conclusion. …”
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Local adaptation and validation of a transdiagnostic risk calculator for first episode psychosis using mental health patient records by Elizabeth Ford, James Stone, James Stone, James Stone, Dominic Oliver, Dominic Oliver, Dominic Oliver, Benjamin Fell, Gloria Roque, Sam Robertson, Paolo Fusar-Poli, Paolo Fusar-Poli, Paolo Fusar-Poli, Paolo Fusar-Poli, Kathryn Greenwood, Kathryn Greenwood

“…A transdiagnostic risk calculator, predicting psychosis using electronic health record (EHR) data, was developed in London, UK to identify patients at risk, using structured data and 14 natural language processing (NLP)-derived symptom and substance use concepts. …”
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YOLOv8n-WSE-Pest: A Lightweight Deep Learning Model Based on YOLOv8n for Pest Identification in Tea Gardens by Hongxu Li, Wenxia Yuan, Yuxin Xia, Zejun Wang, Junjie He, Qiaomei Wang, Shihao Zhang, Limei Li, Fang Yang, Baijuan Wang

“…To enable the intelligent monitoring of pests within tea plantations, this study introduces a novel image recognition algorithm, designated as YOLOv8n-WSE-pest. Taking into account the pest image data collected from organic tea gardens in Yunnan, this study utilizes the YOLOv8n network as a foundation and optimizes the original loss function using WIoU-v3 to achieve dynamic gradient allocation and improve the prediction accuracy. …”
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A wheat seedling counting method based on two-stage convolutional neural network by Menghan Li, Lijie Zhang, Chunshan Wang, Chunjiang Zhao, Libo Li, Dongxiao Li, Yaxuan Xu

“…As one of the world’s major food crops, accurate counting of the number of wheat seedlings is of great significance for subsequent yield prediction. In response to the problems in existing manual counting methods, such as time consuming, labor-intensive, and prone to subjective errors, we proposed a new method for automatically counting wheat seedlings based on laser-labeled Convolutional Neural Network (CNN). …”
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Human responses to the DNA prime/chimpanzee adenovirus (ChAd63) boost vaccine identify CSP, AMA1 and TRAP MHC Class I-restricted epitopes. by Harini Ganeshan, Jun Huang, Maria Belmonte, Arnel Belmonte, Sandra Inoue, Rachel Velasco, Santina Maiolatesi, Keith Limbach, Noelle Patterson, Marvin J Sklar, Lorraine Soisson, Judith E Epstein, Kimberly A Edgel, Bjoern Peters, Michael R Hollingdale, Eileen Villasante, Christopher A Duplessis, Martha Sedegah

“…Individual antigen-specific 15mers in the subpools with strong responses were then deconvoluted, evaluated for activities, and MHC Class I-restricted epitopes within the active 15mers were predicted using NetMHCpan algorithms. The predicted epitopes were synthesized and evaluated in the FluoroSpot IFN-γ and GzB assays.…”
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Cross-talk of m6A methylation modification and the tumor microenvironment composition in esophageal cancer by Pan Song, Jinmao Ye, Haiyang Zhang, Yishu Li, Ruizhi Cao, Yang Feng, Lei Zhang, Min Sun, Min Sun

“…The m6A score developed herein provides a novel quantitative tool for predicting tumor behavior and treatment efficacy, paving the way for more personalized immunotherapeutic strategies in clinical practice. …”
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microRNA-497-5p-based screening identifies a novel synthetic lethal-type interaction via PKMYT1 and WEE1 in pleural mesothelioma by Nathanael Pruett, Sierra Wilferd, Anand Singh, Agnes Y. Choi, Shivani Dixit, Vivek Singh, Charlize Nguyen, Olivia Lin, David S. Schrump, Christopher L. Plaisier, Chuong D. Hoang

“…Surprisingly, multiple identified targets were not predicted by in silico algorithms. Using patient samples, cell lines, murine xenograft models, and our localized nanoparticle miRNA delivery platform, we validated miR-497-5p anti-tumor mechanisms, which consisted of pro-apoptotic and anti-cell-cycle effects. …”
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Predictors of community-based health insurance enrollment among reproductive-age women in Ethiopia based on the EDHS 2019 dataset: a study using SHAP analysis technique, 2024 by Sisay Yitayih Kassie, Solomon Abuhay Abebe, Mekdes Wondirad, Samrawit Fantaw Muket, Ayantu Melke, Alex Ayenew Chereka, Adamu Ambachew Shibabaw, Abiy Tasew Dubale, Yitayish Damtie, Habtamu Setegn Ngusie, Agmasie Damtew Walle

“…Eight machine learning algorithm classifiers were applied to a total weighted sample of 9,013 reproductive-age women and evaluated using performance metrics to predict community-based health insurance enrollment with Python 3.12.2 software, utilizing the Anaconda extension. …”
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Non-Destructive Detection of Silage pH Based on Colorimetric Sensor Array Using Extended Color Components and Novel Sensitive Dye Screening Method by Kai Zhao, Haiqing Tian, Jue Zhang, Yang Yu, Lina Guo, Jianying Sun, Haijun Li

“…The variable combination population analysis–iteratively retains informative variables algorithm was iterated to optimize effective features. …”
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JAK2 Inhibitors and Emerging Therapies in Graft-Versus-Host Disease: Current Perspectives and Future Directions by Behzad Amoozgar, Ayrton Bangolo, Abdifitah Mohamed, Charlene Mansour, Daniel Elias, Christina Cho, Siddhartha Reddy

“…Recent advances in biomarker development, such as the MAGIC Algorithm Probability (MAP), are enabling early risk stratification and response prediction. …”
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Machine learning and response surface methodology forecasting comparison for improved spray dry scrubber performance with brine sludge-derived sorbent by B.J. Chepkonga, L. Koech, R.S. Makomere, H.L. Rutto

“…Three machine learning (ML) models, multilayer perceptron (MLP), support vector regressor (SVR), and light gradient boosting machine (LightGBM), were assessed for their output estimation accuracy and compared to the CCD prediction model. The computational framework utilized experimental variables structured by CCD software as input metadata. …”
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Multi-Omics Profiling Reveals Glycerolipid Metabolism-Associated Molecular Subtypes and Identifies ALDH2 as a Prognostic Biomarker in Pancreatic Cancer by Jifeng Liu, Shurong Ma, Dawei Deng, Yao Yang, Junchen Li, Yunshu Zhang, Peiyuan Yin, Dong Shang

“…These findings establish a novel avenue for studying prognostic prediction and precision medicine in PC patients.…”
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Radio Propagation Models Based on Machine Learning Using Geometric Parameters for a Mixed City-River Path by Allan Dos S. Braga, Hugo A. O. Da Cruz, Leslye E. C. Eras, Jasmine P. L. Araujo, Miercio C. A. Neto, Diego K. N. Silva, Gervasio P. S. Cavalcante

“…This work presents and evaluates the use of geometric parameters of the environment in the prediction of the electric field in mixed city-river type environments, employing two techniques of Machine Learning (ML) as Artificial Neural Networks (ANN) and Neuro-Fuzzy System (NFS). …”
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Use of artificial intelligence to support prehospital traumatic injury care: A scoping review by Jake Toy, Jonathan Warren, Kelsey Wilhelm, Brant Putnam, Denise Whitfield, Marianne Gausche‐Hill, Nichole Bosson, Ross Donaldson, Shira Schlesinger, Tabitha Cheng, Craig Goolsby

“…The most common study objectives were to predict the need for critical care and life‐saving interventions (29%), assist in triage (22%), and predict survival (20%). …”
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Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile by Lien Rodríguez-López, Lisandra Bravo Alvarez, Iongel Duran-Llacer, David E. Ruíz-Guirola, Samuel Montejo-Sánchez, Rebeca Martínez-Retureta, Ernesto López-Morales, Luc Bourrel, Frédéric Frappart, Roberto Urrutia

“…This study examines the dynamics of limnological parameters of a South American lake located in southern Chile with the objective of predicting chlorophyll-a levels, which are a key indicator of algal biomass and water quality, by integrating combined remote sensing and machine learning techniques. …”
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Cancer epigenetics: Moving forward. by Angela Nebbioso, Francesco Paolo Tambaro, Carmela Dell'Aversana, Lucia Altucci

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Multiple-omics analysis of aggrephagy-related cellular patterns and development of an aggrephagy-related signature for hepatocellular carcinoma by Jiafen Xie, Xiaoming Wang

“…Prognostic aggrephagy-related genes (AGGRGs) were identified through univariate Cox and LASSO regression analyses, followed by the construction of a risk prediction model. Patients were stratified into high- and low-risk groups based on the median risk score. …”
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