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    Identification of Potential Diagnostic Biomarkers of Carotid Atherosclerosis in Obese Populations by Wu X, Pan J, Pan X, Kang J, Ren J, Huang Y, Gong L, Li Y

    Published 2025-02-01
    “…Xize Wu,1,* Jiaxiang Pan,2,* Xue Pan,1,3,* Jian Kang,1 Jiaqi Ren,1 Yuxi Huang,1 Lihong Gong,2,4 Yue Li2,4 1Graduate School, Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning, 110847, People’s Republic of China; 2Department of Cardiology, Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, Liaoning, 110032, People’s Republic of China; 3College of Traditional Chinese Medicine, Dazhou Vocational College of Chinese Medicine, Dazhou, Sichuan, 635000, People’s Republic of China; 4Liaoning Provincial Key Laboratory of TCM Geriatric Cardio-Cerebrovascular Diseases, Shenyang, Liaoning, 110032, People’s Republic of China*These authors contributed equally to this workCorrespondence: Lihong Gong; Yue Li, Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, No. 33, Beiling Street, Huanggu District, Shenyang, Liaoning, 110032, People’s Republic of China, Tel +86 024-82961105, Email Linda1795@sina.com; med_liyue@163.comObjective: This study aimed to investigate the potential mechanisms and biomarkers between Obesity (OB) and carotid atherosclerosis (CAS).Methods: The GSE12828, GSE125771, GSE43292, and GSE100927 datasets were combined and normalized to obtain CAS-related differentially expressed genes (DEGs), and OB-related DEGs were obtained from the GSE151839 dataset and the GeneCards database. …”
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  3. 6323

    Hydrogen and hydrogen sulphide in volcanic gases: abundance, processes, and atmospheric fluxes by Aiuppa, Alessandro, Moussallam, Yves

    Published 2023-09-01
    “…Finally, by using our volcanic gas dataset in tandem with recently published global volcanic SO$_{2}$ and CO$_{2}$ budgets, we provide refined estimates for total H$_{2}$S (median, 1.4 Tg/yr; range, 0.9–8.8 Tg/yr) and H$_{2}$ (median, 0.23 Tg/yr; range, 0.06–1 Tg/yr) fluxes from global subaerial volcanism.…”
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  4. 6324

    Environmental, socioeconomic, and sociocultural drivers of monkeypox transmission in the Democratic Republic of the Congo: a One Health perspective by Guangyu Lu, Zeyin Chong, Enyu Xu, Ce Na, Kaixuan Liu, Liying Chai, Pengpeng Xia, Kai Yang, Guoqiang Zhu, Jinkou Zhao, Olaf Müller

    Published 2025-02-01
    “…Methods Epidemiological, environmental, socioeconomic, and sociocultural data from the DRC from 2000 to 2015 were obtained from publicly available dataset. Using these data, we applied negative binomial regression model, least absolute shrinkage and selection operator regression model, and principal component analysis (PCA) to identify key environmental, socioeconomic, and sociocultural factors contributing to mpox transmission. …”
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    Mathematical modelling-based blockchain with attention deep learning model for cybersecurity in IoT-consumer electronics by Hayam Alamro, Mohammed Maray, Jawhara Aljabri, Saad Alahmari, Monir Abdullah, Jehad Saad Alqurni, Faiz Abdullah Alotaibi, Abdelmoneim Ali Mohamed

    Published 2025-02-01
    “…To highlight the enhanced attack detection results of the MGOADL-CS technique, a detailed simulation set is accomplished under the NSL dataset. The performance validation of the MGOADL-CS method portrayed a superior accuracy value of 99.71 % over existing approaches.…”
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    Research note: A low-density SNP genotyping panel for Chinese native chickens by Jie Wang, Siyuan Xing, Yan Zhou, Jie Liu, Yan Sun, Qiuxia Lei, Haixia Han, Wei Liu, Dapeng Li, Fuwei Li, Dingguo Cao

    Published 2025-01-01
    “…Additionally, the 11K panel was applied to a genomic prediction of Wenshang Barred chickens, the predictive ability and prediction accuracy between the WGS and chip dataset non-significantly differed. Therefore, the newly developed chicken low-density SNP is efficient, cost-effective, and well-suited for application in local Chinese chickens, which can accelerate chicken breeding improvements and enhance conservation efforts.…”
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  12. 6332

    Improving machine learning predictions to estimate fishing effort using vessel's tracking data by J. Samarão, A. Moreno, M.B. Gaspar, M.M. Rufino

    Published 2025-03-01
    “…The proportion of the training/test dataset, showed a minimal impact on accuracy, with changes of less than 8 % when varying the training data percentage between 10 % and 90 %, making 60 % a suitable compromise. …”
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    Exploration of linear and interpretable models for quantification of cell parameters via contactless short-wave infrared hyperspectral sensing by Anjana Hevaganinge, Eva Lowenstein, Anna Filatova, Mihir Modak, Nandi Thales Mogo, Bryana Rowley, Jenny Yarmowsky, Joshua Ehizibolo, Ravidu Hevaganinge, Amy Musser, Abbey Kim, Anthony Neri, Jessica Conway, Yiding Yuan, Maurizio Cattaneo, Sui Seng Tee, Yang Tao

    Published 2025-01-01
    “…However, using current optical probes requires the laborious collection of sufficiently large datasets that can be used to calibrate optical probe signals to true metabolite concentrations. …”
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    Improving Sleep Disorder Diagnosis Through Optimized Machine Learning Approaches by Md. Atiqur Rahman, Israt Jahan, Maheen Islam, Taskeed Jabid, Md Sawkat Ali, Mohammad Rifat Ahmmad Rashid, Mohammad Manzurul Islam, Md. Hasanul Ferdaus, Md Mostofa Kamal Rasel, Mahmuda Rawnak Jahan, Shayla Sharmin, Tanzina Afroz Rimi, Atia Sanjida Talukder, Md. Mafiul Hasan Matin, M. Ameer Ali

    Published 2025-01-01
    “…This paper compares the MLAs for sleep disorder classification, specifically targeting None, Sleep Apnea, and Insomnia, using the Sleep Health and Lifestyle Dataset. We conducted two experiments. In the first one, we selected five key features from the feature spaces using the Gradient Boosting Regressor based on the Mean Decrease Impurity (MDI) technique. …”
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