CPO-VMD Combined With Multiscale Permutation Entropy for Noise Reduction in GNSS Vertical Time Series in Mining Areas by Xu Yang, Xinxin Yao, Xinjian Fang, Xuexiang Yu, Yi Wu, Shicheng Xie

“…Taken together, the CPO-VMD-MPE method proposed in this paper significantly reduces the noise in the time series and provides a better theoretical and methodological reference for deformation analysis and prediction.…”
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Identification and validation of a prognostic risk model based on radiosensitivity-related genes in nasopharyngeal carcinoma by Yi Li, Xinyi Hong, Wenqian Xu, Jinhong Guo, Yongyuan Su, Haolan Li, Yingjie Xie, Xing Chen, Xiong Zheng, Sufang Qiu

“…This study aims to identify radiosensitivity-related genes in NPC and develop a prognostic model to predict patient outcomes. Methods: We analyzed 179 NPC samples from Fujian Cancer Hospital using RNA sequencing. …”
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The association between ozone exposure and blood pressure in a general Chinese middle-aged and older population: a large-scale repeated-measurement study by Chen Tang, Yiqin Zhang, Jingping Yi, Zhonghua Lu, Xianfa Xuan, Hanxiang Jiang, Dongbei Guo, Hanyu Xiang, Ting Wu, Jianhua Yan, Siyu Zhang, Yuxin Wang, Jie Zhang

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Comprehensive method for evaluating results of ultrasonic testing of parts and components of rolling stock when adjusting the sensitivity for different reflectors by A. N. KIREEV, V. V. BYKADOROV, M. A. KIREEVA

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Rehabilitation of patients after surgical treatment of pulmonary tuberculosis in a specialized sanatorium by R. V. Tarasov, E. V. Krasnikova, D. F. Gorlov, M. I. Chushkin, M. A. Bagirov

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TANS: A Tolerance-Aware Neighborhood Search Method for Workflow Scheduling with Uncertainties in Cloud Manufacturing by Haiyan Xu, Fanhao Ma, Long Chen

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Unveiling ac4C modification pattern: a prospective target for improving the response to immunotherapeutic strategies in melanoma by Jianlan Liu, Pengpeng Zhang, Chaoqin Wu, Binlin Luo, Xiaojian Cao, Jian Tang

“…We developed and confirmed an excellent acRG-related signature (acRGS) utilizing a comprehensive set of 101 algorithm combinations derived from 10 machine learning algorithms. …”
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Towards accurate L4 ocean colour products: Interpolating remote sensing reflectance via DINEOF by Christian Marchese, Simone Colella, Vittorio Ernesto Brando, Maria Laura Zoffoli, Gianluca Volpe

“…Our outcomes show that this “upstream interpolation” method can generate a consistent Rrs dataset, thereby improving the accuracy of L4 Chl predictions when used as input in algorithms for remote Chl estimation. …”
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Automatic Selection of Machine Learning Models for Armed People Identification by Alonso Javier Amado-Garfias, Santiago Enrique Conant-Pablos, Jose Carlos Ortiz-Bayliss, Hugo Terashima-Marin

“…These models use 20 predictors to make their predictions. These predictors are computed from the bounding box coordinates of the detected people and weapons, their distances, and areas of intersection. …”
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Development and Testing a New Online Dynamic Nomogram for Contrast-Induced Acute Kidney Injury in Elderly Patients with ST-Segment Elevation Myocardial Infarction by Jin J, Ding J, Zhang X, Wang L, Zhang X, Li W, Li S

“…Lasso regression selected predictors, and nine Machine Learning (ML) algorithms were evaluated via Receiver Operating Characteristic (ROC) analysis. …”
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Critical gene network and signaling pathway analysis of the extracellular signal-regulated kinase (ERK) pathway in ischemic stroke by Rui Mao, Lei Wang, Haitao Zhang, Jiaojiao Gong, Hua Liu

“…Four machine learning algorithms (Boruta, SVM, LASSO, random forest) corroborated hub gene robustness. …”
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Nasolacrimal Duct Obstruction Secondary to Radioactive Iodine-131 Therapy for Differentiated Thyroid Cancer by A. A. Trukhin, V. D. Yartsev, M. S. Sheremeta, D. V. Yudakov, M. O. Korchagina, R. Kh. Salimkhanov, S. V. Grishkov

“…The authors have developed a new method for predicting secondary NLDO by a combination of the patient’s individual parameters and treatment plan; the identified predictors help to personalise radioiodine therapy. …”
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MMLT: Efficient object tracking through machine learning-based meta-learning by Bibek Das, Asfak Ali, Suvojit Acharjee, Jaroslav Frnda, Sheli Sinha Chaudhuri

“…The proposed hybrid model refines predictions from traditional tracking methods using machine learning to enhance performance. …”
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Integrative Analysis of Neutrophil-Associated Genes Reveals Prognostic Significance and Immune Microenvironment Modulation in Cervical Cancer by Ting Hu, Haijing Wu, Xinghan Cheng, Haoyue Gao, Min Yang

“…High-risk patients exhibited an immunosuppressive tumor microenvironment, elevated TIDE scores, and lower predicted responsiveness to immunotherapy. SEMA6B was significantly downregulated in the tumour group but may be reactivated during metastasis. …”
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Host Circulating Immunometabolism-Associated Biomarkers for Early Diagnosis of Active Tuberculosis: Multi-Omics Screening with Experimental Validation by Yang Z, Dong Y, Shang Y, Li H, Ren W, Li S, Pang Y

“…A nomogram was constructed to comprehensively predict the risk of active TB. Mechanistically, protein–protein interactions and gene set enrichment analysis revealed that four hub genes affected pteridine and lipid metabolism and were associated with the innate immune pathways. …”
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Treatment of infections in young infants in low- and middle-income countries: a systematic review and meta-analysis of frontline health worker diagnosis and antibiotic access. by Anne C C Lee, Aruna Chandran, Hadley K Herbert, Naoko Kozuki, Perry Markell, Rashed Shah, Harry Campbell, Igor Rudan, Abdullah H Baqui

“…For study question 1, meta-analysis showed that clinical sign-based algorithms predicted bacterial infection in young infants with high sensitivity (87%, 95% CI 82%-91%) and lower specificity (62%, 95% CI 48%-75%) (six studies, n = 14,254). …”
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Machine learning analysis of FOSL2 and RHoBTB1 as central immunological regulators in knee osteoarthritis synovium by Kun Gao, Zhenyu Huang, Zhouwei Liao, Yanfei Wang, Dayu Chen

“…We employed several machine learning algorithms, including least absolute shrinkage and selection operator and support vector machine–recursive feature elimination, to screen for key genes. …”
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Integrated transcriptome analysis and combinatorial machine learning to construct a homeostatic model of acetylation for ccRCC and validate the key gene GCNT4 by Baohua Zhu, Ziyang Mo, Yi Bao, Xinxin Gan, Linhui Wang

“…The LASSO + RSF combination model performed best, and the model could accurately predict patient prognosis. The survival of patients in the high-risk group was significantly worse than that in the low-risk group. …”
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Wide-angle deep ultraviolet antireflective multilayers via discrete-to-continuous optimization by Kim Jae-Hyun, Kim Dong In, Lee Sun Sook, An Ki-Seok, Yim Soonmin, Lee Eungkyu, Kim Sun-Kyung

“…To date, various optimization algorithms have been used to design non-intuitive photonic structures with unconventional optical performance. …”
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Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis. by Shang-Ming Zhou, Fabiola Fernandez-Gutierrez, Jonathan Kennedy, Roxanne Cooksey, Mark Atkinson, Spiros Denaxas, Stefan Siebert, William G Dixon, Terence W O'Neill, Ernest Choy, Cathie Sudlow, UK Biobank Follow-up and Outcomes Group, Sinead Brophy

“…<h4>Objectives</h4>1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs.…”
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