Adverse events following 9-valent human papillomavirus vaccine (GARDASIL® 9) reported to the Vaccine Adverse Event Reporting System (VAERS), 2015–2024 by Qiong Liu, Guojun Liang, Yang Song

“…Disproportionality analyses using four independent algorithms (ROR, PRR, IC, EBGM) were applied to identify positive safety signals. …”
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Wearing WHOOP More Frequently Is Associated with Better Biometrics and Healthier Sleep and Activity Patterns by Gregory J. Grosicki, Finnbarr Fielding, Jeongeun Kim, Christopher J. Chapman, Maria Olaru, William von Hippel, Kristen E. Holmes

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Analysis of potential molecular targets and mechanisms of brominated flame retardants in causing osteoarthritis using network toxicology, machine learning, SHAP analysis, and molec... by Yu Liu, Guohang Shen, Zidong Xia, Ruoyan Wang, Yupei Dai

“…Subsequently, a comprehensive machine learning workflow involving 113 different algorithms was used to identify 10 core target genes as potential mediators of BDE-47-induced osteoarthritis. …”
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Explore potential immune-related targets of leeches in the treatment of type 2 diabetes based on network pharmacology and machine learning by Tairan Hu, Zhaohui Fang

“…Finally, we employed LASSO regression, SVM-RFE, XGBoost, and random forest algorithms to further predict potential targets, followed by validation through molecular docking.ResultsLeeches may influence cellular immunity by modulating immune receptor activity, particularly through the activation of RGS10, CAPS2, and OPA1, thereby impacting the pathology of Type 2 Diabetes Mellitus (T2DM).DiscussionHowever, it is important to note that our results lack experimental validation; therefore, further research is warranted to substantiate these findings.…”
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Numerical Modeling Tools and S-derivatives by Anatoly Nikolaevich Morozov

“…For dynamical systems, this question may be related with a generalization of the concept of a derivative, which keeps the used constructions relevant. …”
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Face Anti-spoofing Detection Based on Novel Encoder Convolutional Neural Network and Texture’s Grayscale Structural Information by Marwa Radad, Amira E. Enab, Salah S. Elagooz, Nawal A. El-Fishawy, Mohamed A. El-Rashidy

“…Furthermore, in real-world situations, color disparities may be introduced by lighting, camera settings, and other elements. …”
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Dynamic Response Prediction of Railway Bridges Considering Train Load Duration Using the Deep LSTM Network by Sui Tan, Xiandong Ke, Zhenhao Pang, Jianxiao Mao

“…Therefore, this study develops a novel framework, combining the train–bridge coupling mechanism and deep learning algorithms to efficiently predict the train-induced bridge responses while considering train load duration. …”
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Identification of developmental and reproductive toxicity of biocides in consumer products using ToxCast bioassays data and machine learning models by Donghyeon Kim, Siyeol Ahn, Jinhee Choi

“…Based on the assumption that chemicals active in these ToxCast assays and their machine learning models may have DART potential, we prioritized active biocides in consumer products. …”
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Machine learning classification of consumption habits of creatine supplements in gym goers by Patrícia C. Magalhães, Samuel Encarnação, Andre C. Schneider, Pedro Forte, José Teixeira, Antonio Miguel Monteiro, Tiago M. Barbosa, Ana M. Pereira

“…The aim is to identify usage patterns and the main factors that influence creatine supplementation, providing a basis for future educational interventions and recommendations for safe and effective use. …”
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Age-related changes in upper limb motion during typical development. by Cristina Simon-Martinez, Gabriela Lopes Dos Santos, Ellen Jaspers, Ruth Vanderschueren, Lisa Mailleux, Katrijn Klingels, Els Ortibus, Kaat Desloovere, Hilde Feys

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Analysis of multiple faults in induction motor using machine learning techniques by Puja Pohakar, Ravi Gandhi, Surender Hans, Gulshan Sharma, Pitshou N. Bokoro

“…Due to their limits, machine learning algorithms outperform traditional methods in real-time fault diagnosis, predictive maintenance, and multi-fault categorization. …”
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Predicting adolescent psychopathology from early life factors: A machine learning tutorial by Faizaan Siddique, Brian K. Lee

“…The best performing algorithms were elastic net and gradient boosted trees. …”
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Aided Greenway Design Approach Based on Internet Big Data and AIGC Fine-Tuning Model by Yifan WU, Lu MENG, Liang LI

“…However, the approach also shows potential for adaptation in landscape design, which is mainly reflected in two aspects: relevance and extensibility. …”
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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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Multi-task aquatic toxicity prediction model based on multi-level features fusion by Xin Yang, Jianqiang Sun, Bingyu Jin, Yuer Lu, Jinyan Cheng, Jiaju Jiang, Qi Zhao, Jianwei Shuai

“…Furthermore, in comparison with previous algorithms, ATFPGT-multi outperforms comparative methods, emphasizing that our approach exhibits higher accuracy and reliability in predicting aquatic toxicity. …”
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Orchard-Wide Visual Perception and Autonomous Operation of Fruit Picking Robots: A Review by CHEN Mingyou, LUO Lufeng, LIU Wei, WEI Huiling, WANG Jinhai, LU Qinghua, LUO Shaoming

“…Each of these approaches incorporates advanced algorithms and sensor technologies for cluttered orchard environments. …”
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Development and multicenter validation of an AI driven model for quantitative meibomian gland evaluation by Li Li, Kunhong Xiao, Taichen Lai, Kunfeng Lai, Jiawen Lin, Zongyuan Ge, Lingyi Liang, Hao Huang, Xiaoshan Zhang, Li Liu, Yujie Wang, Xianwen Shang, Mingguang He, Ying Xue, Zhuoting Zhu

“…This AI tool offers a standardized, efficient, and objective method for meibography image analysis, which may improve diagnostic precision and assist in the clinical management of meibomian gland dysfunction across diverse populations.…”
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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

“…Artificial intelligence (AI) can improve risk assessment and diagnosis, as well as boost the precision of clinical outcome prediction and illness classification. Algorithms based on artificial intelligence may be potentially helpful in discovering new needs for myelodysplastic syndrome-affected patients, choosing treatment and assessing minimal residual disease. …”
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Age and Saving Lives in Crisis Standards of Care: A Multicenter Cohort Study of Triage Score Prognostic Accuracy by Michael Hermsen, MD, MS, Patrick G. Lyons, MD, MS, Govind Persad, JD, PhD, Alice F. Bewley, MS, Chengsheng Mao, PhD, Kaveri Chhikara, MS, Anoop Mayampurath, PhD, Matthew Churpek, MD, PhD, Monica E. Peek, MD, MPH, MSc, Yuan Luo, PhD, William F. Parker, MD, PhD

“…Incorporating age into prioritization algorithms could save more lives in a crisis scenario.…”
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Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach by Artur Krolik, Radosław Drelich, Michał Pakuła, Dariusz Mikołajewski, Izabela Rojek

“…Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. …”
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