600 meters to VO2max: Predicting Cardiorespiratory Fitness with an Uphill Run by Kübra Stoican, Regina Oeschger

“…Ashfaq, A., Cronin, N., & Müller, P. (2022). Recent advances in machine learning for maximal oxygen uptake (VO₂ max) prediction: A review. …”
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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots by Sukhpal Singh Gill, Minxian Xu, Panos Patros, Huaming Wu, Rupinder Kaur, Kamalpreet Kaur, Stephanie Fuller, Manmeet Singh, Priyansh Arora, Ajith Kumar Parlikad, Vlado Stankovski, Ajith Abraham, Soumya K. Ghosh, Hanan Lutfiyya, Salil S. Kanhere, Rami Bahsoon, Omer Rana, Schahram Dustdar, Rizos Sakellariou, Steve Uhlig, Rajkumar Buyya

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The value of MRI in differentiating ovarian clear cell carcinoma from other adnexal masses with O-RADS MRI scores of 4–5 by Lingling Lin, Le Fu, Huawei Wu, Saiming Cheng, Guangquan Chen, Lei Chen, Jun Zhu, Yu Wang, Jiejun Cheng

“…Analysis of variance and support vector machine were used to develop four CCC prediction models. …”
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Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data by Lixuan Li, Yuekong Hu, Zhicheng Yang, Zeruxin Luo, Jiachen Wang, Wenqing Wang, Xiaoli Liu, Yuqiang Wang, Yong Fan, Pengming Yu, Zhengbo Zhang

“…This study leverages wearable technology and machine learning algorithms to preoperatively identify high-risk individuals, thereby enhancing clinical decision-making for the mitigation of PPCs. …”
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Aux sources de la spécialisation hémisphérique cérébrale du langage : l'intérêt de l'IRM anatomique chez le babouin by Adrien Meguerditchian, Damien Marie, Muriel Roth, Romain Lacoste, Bruno Nazarian, Ivan Balansard, Jean-Luc Anton, Olivier Coulon, Alice Bertello, Jean-Noël Benoit, Scott A. Love, Thomas Brochier, Martine Meunier, Sandrine Melot‑Dusseau, Jean‑Christophe Marin, William D Hopkins

“…Ces méthodes incluent la préparation du singe, le protocole d’anesthésie, l’installation du sujet et son monitoring dans la machine IRM 3Tesla Bruker, ainsi que l'ensemble des étapes de traitement d'images pour étudier les corrélats neuroanatomiques de la communication gestuelle chez le babouin. …”
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Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy by Qi Wan, Clifford Lindsay, Chenxi Zhang, Jisoo Kim, Xin Chen, Jing Li, Raymond Y. Huang, David A. Reardon, Geoffrey S. Young, Lei Qin

“…The Radiomics-based OS predictions, generated using Support Vector Machine (SVM), were compared between the two segmentation approaches and against OS prediction by the CNN model adapted for classification. …”
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Hydra Radio Access Network (H-RAN): Multi-Functional Communications and Sensing Networks, Initial Access Implementation, and Task-2 Approach by Rafid I. Abd, Daniel J. Findley, Kwang Soon Kim

“…Furthermore, we employed a multi-sparse input and multi-task learning (SMTL) framework in the Hydra distributed unit (H-DU) artificial intelligence and machine learning (AI/ML) (AI/ML D-engine), where each task is tailored to be executed in a particular environment based on online feedback. …”
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Predictive role of SLC1A5 in neuroblastoma prognosis and immunotherapy by Jian Cheng, Miaomiao Sun, Xiao Dong, Yang Yang, Xiaohan Qin, Xing Zhou, Yongcheng Fu, Yuanyuan Wang, Jingyue Wang, Da Zhang

“…A prognostic signature, SRPS, was constructed and validated using machine-learning approaches. Immune infiltration analysis was performed to evaluate the tumor immune microenvironment. …”
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Framework for smartphone-based grape detection and vineyard management using UAV-trained AI by Sergio Vélez, Mar Ariza-Sentís, Mario Triviño, Antonio Carlos Cob-Parro, Miquel Mila, João Valente

“…Recent technological and machine learning advancements, particularly in deep learning, have provided the tools necessary to create more efficient, automated processes that significantly reduce the time and effort required for these tasks. …”
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Enabling high-throughput quantitative wood anatomy through a dedicated pipeline by Jan Van den Bulcke, Louis Verschuren, Ruben De Blaere, Simon Vansuyt, Maxime Dekegeleer, Pierre Kibleur, Olivier Pieters, Tom De Mil, Wannes Hubau, Hans Beeckman, Joris Van Acker, Francis wyffels

“…The frame of the robot is a CNC (Computer Numerical Control) machine to position a camera above the objects. Images are taken at different focus points, with a small overlap between consecutive images in the X-Y plane, and merged by mosaic stitching, into a gigapixel image. …”
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Fabrication of Nano-Silver Composite Using Amomum longiligulare Fruit Polysaccharides and Their Biological Activities by Lian Y, Ma N, Cheng Q, Luo M, Xu Z, He F, Zhou X, Zhang Y, Jin D, Kong Y, Wang Y, Wei N

“…Yong Lian,1,* Ning Ma,2,* Qianying Cheng,1,* Mingquan Luo,1 Zhen Xu,1 Fei He,1 Xiaomei Zhou,1 Ying Zhang,1 Dejun Jin,1 Yidan Kong,1 Yong Wang,1 Na Wei1 1Hainan Provincial Key Laboratory of R&D on Tropical Herbs, Engineering Research Center of Tropical Medicine Innovation and Transformation of Ministry of Education, International Joint Research Center of Human-Machine Intelligent Collaborative for Tumor Precision Diagnosis and Treatment of Hainan Province, School of Pharmacy, Hainan Medical University, Haikou, 571199, People’s Republic of China; 2Reproductive Medical Center, Hainan Women and Children’s Medical Center, Haikou, Hainan Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Na Wei; Yong Wang, Hainan Provincial Key Laboratory of R&D on Tropical Herbs, School of Pharmacy, Hainan Medical University, Haikou, 571199, People’s Republic of China, Email weina-0613@163.com; wangyong1982_2004@163.comPurpose: Study aims to optimize the synthesis conditions for silver nanoparticle composites [ALP(D)-AgNPs] using a rapid and environmentally friendly method and investigate the antioxidant, antibacterial, and anticancer activities of the fabricated composite.Methods: The polysaccharide component ALP-D was extracted and purified from the fruits of Amomum longiligulare and subsequently used for further experiments. …”
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Characterization of β-Actin Promoter from Nile Tilapia (Oreochromis niloticus) by . Alimuddin, A. Octavera, O.Z. Arifin, K. Sumantadinata

“…Sequencing was performed using ABI PRISM 3100 machine. Analysis of sequences was conducted using GENETYX version 7 and TFBind softwares. …”
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Magnesium ions regulate the Warburg effect to promote the differentiation of enteric neural crest cells into neurons by Qiongqian Xu, Xixi He, Yaru Mou, Dong Sun, Xintao Zhang, Jichang Han, Xiaoyang Liu, Xingjian Liu, Xue Ren, Dongming Wang, Jian Wang, Chuncan Ma, Qiangye Zhang, Aiwu Li

“…The elastic modulus of the hydrogel was measured using a universal testing machine, while pore size and porosity were assessed with scanning electron microscopy. …”
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Factors and Reasons Associated with Hesitating to Seek Care for Migraine: Results of the OVERCOME (US) Study by Robert E. Shapiro, Eva Jolanda Muenzel, Robert A. Nicholson, Anthony J. Zagar, Michael L. Reed, Dawn C. Buse, Susan Hutchinson, Sait Ashina, Eric M. Pearlman, Richard B. Lipton

“…Methods The web-based OVERCOME (US) survey study identified adults with active migraine in a demographically representative US sample who answered questions about hesitating to seek care from a healthcare provider for migraine and reasons for hesitating. Supervised machine learning (random forest, least absolute shrinkage and selection operator) identified factors associated with hesitation; logistic regression models assessed association of factors on hesitation. …”
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Loading of CAR‐T cells with magnetic nanoparticles for controlled targeting suppresses inflammatory cytokine release and switches tumor cell death mechanism by Felix Pfister, Lucas R. Carnell, Lisa Löffler, Philipp Boosz, Niels Schaft, Jan Dörrie, René Stein, Malte Lenz, Erdmann Spiecker, Christian M. Huber, Sami Haddadin, Carola Berking, Christoph Alexiou, Christina Janko

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Question–Answer Methodology for Vulnerable Source Code Review via Prototype-Based Model-Agnostic Meta-Learning by Pablo Corona-Fraga, Aldo Hernandez-Suarez, Gabriel Sanchez-Perez, Linda Karina Toscano-Medina, Hector Perez-Meana, Jose Portillo-Portillo, Jesus Olivares-Mercado, Luis Javier García Villalba

“…Traditional static and dynamic analysis techniques, although widely used, often exhibit high false-positive rates, elevated costs, and limited interpretability. Machine Learning (ML)-based approaches aim to overcome these limitations but encounter challenges related to scalability and adaptability due to their reliance on large labeled datasets and their limited alignment with the requirements of secure development teams. …”
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An Explainable Artificial Intelligence Text Classifier for Suicidality Prediction in Youth Crisis Text Line Users: Development and Validation Study by Julia Thomas, Antonia Lucht, Jacob Segler, Richard Wundrack, Marcel Miché, Roselind Lieb, Lars Kuchinke, Gunther Meinlschmidt

“… BackgroundSuicide represents a critical public health concern, and machine learning (ML) models offer the potential for identifying at-risk individuals. …”
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A New Risk Prediction Model for the Assessment of Myocardial Injury in Elderly Patients Undergoing Non-Elective Surgery by Vedat Cicek, Mert Babaoglu, Faysal Saylik, Samet Yavuz, Ahmet Furkan Mazlum, Mahmut Salih Genc, Hatice Altinisik, Mustafa Oguz, Berke Cenktug Korucu, Mert Ilker Hayiroglu, Tufan Cinar, Ulas Bagci

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Metal mixtures exposure with risk of elevated serum neurofilament light chain concentrations in U.S. general adults, NHANES 2013–2014 by Yan Wang, Keyi Zhang, Hao Li, Si Liu, Linyao Ying, Lu Xiang, Na Liang, Liangkai Chen, Lin Xiao, Gang Luo

“…We employed a two-pronged approach, combining weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR) models, to examine the association between the multiple-metals effect and sNfL concentrations. …”
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Mortality Prediction Using SaO2/FiO2 Ratio Based on eICU Database Analysis by Sharad Patel, Gurkeerat Singh, Samson Zarbiv, Kia Ghiassi, Jean-Sebastien Rachoin

“…We hypothesize that S/F is noninferior to P/F as a predictive feature for ICU mortality. Using a machine-learning approach, we hope to demonstrate the relative mortality predictive capacities of S/F and P/F. …”
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