Comprehensive Transcriptome Sequencing and Analysis of <i>Euspira gilva</i>: Insights into Aquaculture and Conservation by Zhixing Su, Jiayuan Xu, Xiaokang Lv, Xuefeng Song, Yanming Sui, Benjian Wang, Xiaoshan Wang, Bianbian Zhang, Baojun Tang, Liguo Yang

“…Further, 530 long non-coding RNAs (lncRNAs) were identified through the application of the CPC2, CNCI, Pfam, and PLEK algorithms. The highest overall sequence similarity in the NR database was observed with <i>Pomacea canaliculata</i>, a freshwater species, but with a similarity of only 36.6%, indicating a unique genetic makeup of <i>E. gilva</i>. …”
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Impact of a national HPV vaccination programme for preadolescent girls on cytology screening performance and CIN2+ incidence: five-year population-based cervical screening results... by Abyan Irzaldy, Tine Jerman, Inge M.C.M. de Kok, Jan A.C. Hontelez, Harry J. de Koning, Erik E.L. Jansen, Urška Ivanuš

“…This warrants an adaptation in screening algorithms in vaccinated cohorts. Funding: European Union Horizon 2020.…”
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JUN and ATF3 in Gout: Ferroptosis-related potential diagnostic biomarkers by Yang Li, ChengCheng Huang, Yuhan Xie, WenBin Liu, MengJuan Wei, Shudong Li, Zhenguo Yang, JingWu Wang, Gang Li

“…Methods: Utilizing both bioinformatics analysis and machine learning algorithms to systematically identify biomarkers for gout. …”
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Integrating deep learning in public health: a novel approach to PICC-RVT risk assessment by Yue Li, Yue Li, Shengxiao Nie, Lei Wang, Dongsheng Li, Shengmiao Ma, Ting Li, Hong Sun

“…Existing models often assess PICC-RVT risk as static and discrete outcomes, which may limit their practical application.ObjectivesThis study aims to evaluate the effectiveness of seven diverse machine learning algorithms, including three deep learning and four traditional machine learning models, that incorporate time-series data to assess PICC-RVT risk. …”
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DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure by Wenwu Tang, Wenwu Tang, Zhixin Wang, Xinzhu Yuan, Liping Chen, Haiyang Guo, Zhirui Qi, Ying Zhang, Xisheng Xie

“…In addition, we further explored potential mechanism and function of hub genes in HF of patients with MHD through GSEA, immune cell infiltration analysis, drug analysis and establishment of molecular regulatory network.ResultsTotally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. Through GSEA analysis, it was found that the four genes were closely related to ribosome, cell cycle, ubiquitin-mediated proteolysis. …”
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A prospective study of associations between accelerated biological aging and twenty musculoskeletal disorders by Wenming Wei, Xin Qi, Bolun Cheng, Na Zhang, Yijing Zhao, Xiaoyue Qin, Dan He, Xiaoge Chu, Sirong Shi, Qingqing Cai, Xuena Yang, Shiqiang Cheng, Peilin Meng, Jingni Hui, Chuyu Pan, Li Liu, Yan Wen, Huan Liu, Yumeng Jia, Feng Zhang

“…Biological age was calculated using the KDM-BA and PhenoAge algorithms based on blood biomarkers. Musculoskeletal disorders were diagnosed using the ICD-10 criteria, with sample sizes ranging from 1,182 to 23,668. …”
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Enhanced Metabolic Control in a Pediatric Population with Type 1 Diabetes Mellitus Using Hybrid Closed-Loop and Predictive Low-Glucose Suspend Insulin Pump Treatments by Irina Bojoga, Sorin Ioacara, Elisabeta Malinici, Victor Chiper, Olivia Georgescu, Anca Elena Sirbu, Simona Fica

“…Background: Insulin pumps coupled with continuous glucose monitoring sensors use algorithms to analyze real-time blood glucose levels. …”
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Epidemiological, molecular, and evolutionary characteristics of G1P[8] rotavirus in China on the eve of RotaTeq application by Rui Peng, Rui Peng, Mengxuan Wang, Saleha Shahar, Guangping Xiong, Qing Zhang, Lili Pang, Hong Wang, Xiangyu Kong, Dandi Li, Zhaojun Duan

“…Neutralizing epitope, amino acid selection pressure, and evolution dynamics analyses on VP7 and VP4 were performed using BioEdit v.7.0.9.0 and PyMOL v.2.5.2, four algorithms (MEME, SLAC, FEL, and FUBAR) in the Datamonkey online software, and the MCMC model in BEAST v. 1.10.4, respectively. …”
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Heat transfer with magnetic force and slip velocity on non-Newtonian fluid flow through a porous medium by Muhammad Ramzan, Muhammad Shahryar, Shajar Abbas, Muhammad Amir, Shaxnoza Ravshanbekovna Saydaxmetova, Rashid Jan, Afnan Al Agha, Hakim AL Garalleh

“…Model validation is achieved by comparing results obtained through algorithmic solutions, confirming the robustness of the fractional approach by taking the values of fractional parameter γ in the range 0.2≤γ≤1, whereas the values of slip parameter λ lies in the range 0≤λ≤0.8. …”
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Machine learning-derived natural killer cell signature predicts prognosis and therapeutic response in clear cell renal cell carcinoma by Jinchen Luo, Mingjie Lin, Minyu Chen, Jinwei Chen, Xinwei Zhou, Kezhi Liu, Yanping Liang, Jiajie Chen, Hui Liang, Zhu Wang, Qiong Deng, Jieyan Wang, Meiyu Jin, Junhang Luo, Wei Chen, Junjie Cen

“…A risk model was established by integrating ten different machine learning algorithms, which resulted in 101 combined models. The model with the highest average C-index was selected for further analysis, and was assessed using nomogram, time-dependent receiver operating characteristics (ROC) and Kaplan–Meier survival analysis. …”
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Neurochallenges in smart cities: state-of-the-art, perspectives, and research directions by Begüm Özkaynak, Necati Aras, İrem Daloğlu Çetinkaya, Cem Ersoy, Özlem Durmaz İncel, Mutlu Koca, İrem Nalça, Turgut Tüzün Onay, Sinan Öncü, Berivan Ülger Vatansever, Eda Yücesoy, Can A. Yücesoy

“…This paper aims to explore how neuroscientific and neurotechnological solutions can contribute to the development of smart cities, as experts in various fields underline that real-time sensing designs and control algorithms inspired by the brain could help build and plan urban systems that are healthy, safe, inclusive, and resilient. …”
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Monitoring multidimensional aspects of quality of life after cancer immunotherapy: protocol for the international multicentre, observational QUALITOP cohort study by Geertruida H De Bock, Delphine Maucort-Boulch, Clémence Leyrat, Amélie Anota, Stephane Dalle, Susana Puig, Marie Préau, Mohammad S Jalali, Marie-Jose Kersten, Martijn GH van Oijen, Mathieu Fauvernier, Petra C. Vinke, Marc Combalia, Anne Mea Spanjaart, Maria Gomes da Silva, Aurore Fouda Essongue, Aurélie Rabier, Myriam Pannard, Amal Elgammal, Mike Papazoglou, Mohand-Said Hacid, Catherine Rioufol, Erick Suazo-Zepeda, Ananya Malhotra, Emmanuel Coquery, Elsa Coz

“…Using advanced statistical methods, including causal inference methods, artificial intelligence algorithms and simulation modelling, we will use data from the QUALITOP cohort to improve the understanding of the complex relationships among treatment regimens, patient characteristics, irAEs and HRQoL.Ethics and dissemination All aspects of the QUALITOP project will be conducted in accordance with the Declaration of Helsinki and with ethical approval from a suitable local ethics committee, and all patients will provide signed informed consent. …”
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Monitoring poultry social dynamics using colored tags: Avian visual perception, behavioral effects, and artificial intelligence precision by Florencia B. Rossi, Nicola Rossi, Gabriel Orso, Lucas Barberis, Raul H. Marin, Jackelyn M. Kembro

“…However, maintaining the identity of individuals over time, especially in homogeneous poultry flocks, remains challenging for algorithms. We propose using differentially colored “backpack” tags (black, gray, white, orange, red, purple, and green) detectable with computer vision (eg. …”
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Effect of multi-unit and multi-type DG installation using integrated optimization technique in distribution power system planning by Azlina Abdullah, Ismail Musirin, Muhammad Murtadha Othman, Siti Rafidah Abdul Rahim, Mohd Helmi Mansor, Sharifah Azwa Shaaya, Norziana Aminuddin, Lalit Kumar Goel

“…This technique employs a hybrid approach that combines elements of immune algorithms, moth flame optimization, and evolutionary programming to achieve more accurate and efficient results. …”
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Predictors of Successful Testicular Sperm Extraction: A New Era for Men with Non-Obstructive Azoospermia by Aris Kaltsas, Sofoklis Stavros, Zisis Kratiras, Athanasios Zikopoulos, Nikolaos Machairiotis, Anastasios Potiris, Fotios Dimitriadis, Nikolaos Sofikitis, Michael Chrisofos, Athanasios Zachariou

“…Integrating molecular biomarkers with artificial intelligence and machine learning algorithms may enhance predictive accuracy. <b>Conclusions</b>: Predicting TESE outcomes in men with NOA remains challenging using conventional clinical and hormonal parameters. …”
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Integrating near-infrared hyperspectral imaging with machine learning and feature selection: Detecting adulteration of extra-virgin olive oil with lower-grade olive oils and hazeln... by Derick Malavi, Katleen Raes, Sam Van Haute

“…Classification was performed using Partial Least Squares-Discriminant Analysis (PLS-DA) and ML algorithms, including k-Nearest Neighbors (k-NN), Naïve Bayes, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). …”
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Study and Analysis of the Thunder Source Location Error Based on Acoustic Ray-Tracing by Jinyuan Guo, Caixia Wang, Jia Xu, Song Jia, Hui Yang, Zhuling Sun, Xiaobao Wang

“…Most of the thunder source location algorithms are based on the simplified model of the straight-line propagation of acoustic waves to determine the location of the thunder source; however, the acoustic wave is affected by the inhomogeneity of the atmosphere medium in the propagation process and its acoustic ray will be bent. …”
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Analysis of Synergistic Changes in PM_2.5 and O₃ Concentrations Based on Structural Equation Model Study by Zhangwen Su, Liming Yang, Yimin Chen, Rongyu Ni, Wenlong Wang, Honghao Hu, Bin Xiao, Sisheng Luo

“…We identified the regions where these pollutants exhibited synergistic changes and established the pathways between the pollutants and their potential drivers, using geographically weighted random forest algorithms and structural equation modeling. The study results revealed as follows: (1) Overall, the PM_2.5 concentrations show a decreasing trend, while the O₃ concentrations exhibit an increasing trend, in the YRD. …”
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The association of origin and environmental conditions with performance in professional IRONMAN triathletes by Beat Knechtle, Mabliny Thuany, David Valero, Elias Villiger, Pantelis T. Nikolaidis, Marilia S. Andrade, Ivan Cuk, Thomas Rosemann, Katja Weiss

“…Three different ML models were built and evaluated, based on three algorithms, in order of growing complexity and predictive power: Decision Tree Regressor, Random Forest Regressor, and XG Boost Regressor. …”
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Relative Radiometric Normalization for the PlanetScope Nanosatellite Constellation Based on Sentinel-2 Images by Rafael Luís Silva Dias, Ricardo Santos Silva Amorim, Demetrius David da Silva, Elpídio Inácio Fernandes-Filho, Gustavo Vieira Veloso, Ronam Henrique Fonseca Macedo

“…Five machine learning algorithms were then applied, incorporating both linear and nonlinear models based on rules and decision trees: Multiple Linear Regression (MLR), Model Averaged Neural Network (avNNet), Random Forest (RF), k-Nearest Neighbors (KKNN), and Support Vector Machine with Radial Basis Function (SVM-RBF). …”
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