Effects of mode of reproduction on genetic polymorphism and divergence in wild yams (Dioscoreaceae: Dioscorea) by Xin Wang, Qing-Hong Feng, Zhi-Hua Zeng, Zhi-Qiang Zhang, Jie Cai, Gao Chen, De-Zhu Li, Hong Wang, Wei Zhou

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Co-infection of SARS‐CoV‐2 and influenza A/B among patients with COVID-19: a systematic review and meta-analysis by Monireh Golpour, Hossein Jalali, Reza Alizadeh-Navaei, Masoumeh Rezaei Talarposhti, Tahoora Mousavi, Ali Asghar Nadi Ghara

“…A random effects model was used to determine prevalence rates, and a forest plot diagram was used to present results with 95% confidence intervals. …”
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Building resilient cattle through better management of buffalo health by Alfredo Sánchez, Merilio Montero

“… Livestock has been involved among the causes of environmental deterioration due to the felling of forests, soil degradation, the affectation of water resources, and greenhouse gas (GHG) emissions. …”
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Construction of a diagnostic model for chronic mountain sickness among young male migrants to high-altitude areas by ZHANG Quan, CHEN Jian, LIU Bao

“…Independent risk factors for CMS occurrence were screened out， through random forest variable importance ranking， univariate and multivariable logistic regression analysis， and a diagnostic model was constructed based on these factors. …”
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Advances in machine learning applications to resource technology for organic solid waste by Hongzhi MA, Yichan LIU, Jihua ZHAO, Fan FEI, Ming GAO, Qunhui WANG

“…This study explores a range of commonly used ML models, including artificial neural network (ANN), support vector machine (SVM), decision tree, random forest, and extreme gradient boosting (XGBoost). These models have been used to predict waste characteristics, classify diverse types of OSW, and optimize treatment parameters across various processes, such as thermochemical conversion, anaerobic digestion, and aerobic composting. …”
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Molecular epidemiology of extended-spectrum beta-lactamases and carbapenemases-producing Shigella in Africa: a systematic review and meta-analysis by Namwin Siourimè Somda, Rabbi Nyarkoh, Abel Tankoano, Ouindgueta Juste Isidore Bonkoungou, Patience B. Tetteh-Quarcoo, Eric S. Donkor

“…The meta-analysis and forest plots of Shigella species, ESBL and carbapenemases genes were done using the comprehensive Meta-Analysis software. …”
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Physio-Biochemical Indexes as Indicators of Cadmium Tolerance in <i>Brassica napus</i> L. Cultivars by Yu Qiu, Shuhe Wei, Jie Zhan, Brett H. Robinson, Lidia Skuza, Jianming Xue, Huiping Dai, Li Zhan, Ziyang Tang

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Allelopathic Properties of the Species Comprising Communities of Invasive <i>Impatiens</i> spp. and Antioxidant System of Invaders’ Populations by Eugenija Kupcinskiene, Ruta Budreviciute, Vaida Jasionyte, Laura Simanaviciute, Lina Jociene, Edvina Krokaite-Kudakiene, Tomas Rekasius, Vitas Marozas

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Quality Assessment of Systematic Review of the Bariatric Surgery for Diabetes Mellitus by Xinye Jin, Jinjing Wang, Xueqiong Li, Ping An, Haibin Wang, Wenfeng Mao, Qi Zhou, Yaolong Chen, Jie Wang, Kang Chen, Yiming Mu

“…Data were visualized using the forest plot in RevMan 5.3 software. Results. A total of 64 SRs were included. …”
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Microtubule inhibition as a proposed mechanism for the anthelmintic effect of phytochemicals isolated from Cicerbita alpina by Mark James Horgan, Ines Sigg, Ioanna Poulopoulou, Francisco J. Rodriguez-Mejias, Eva Albertini, Pietro Fusani, Florian Fischer, Eftychia Martinidou, Daniela Schuster, Stefan Martens, Pidder Jansen Dürr, Matthias Gauly, Hermann Stuppner, Alexander Weiss, Veronika Temml, Bianka Siewert

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Machine learning based models for predicting presentation delay risk among gastric cancer patients by Huali Zhou, Huali Zhou, Qiong Gu, Rong Bao, Liping Qiu, Yuhan Zhang, Fang Wang, Wenlian Liu, Lingling Wu, Li Li, Yihua Ren, Lei Qiu, Qian Wang, Gaomin Zhang, Xiaoqing Qiao, Wenjie Yuan, Juan Ren, Min Luo, Rong Huang, Qing Yang, Qing Yang

“…Prediction models were developed based on six machine learning algorithms, which are logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosted trees (GBDT), extremely gradient boosting (XGBoost) and muti-layer perceptron (MLP). …”
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Comparative Analysis of Carbon Density Simulation Methods in Grassland Ecosystems: A Case Study from Gansu Province, China by Luyao Wu, Jiaqiang Du, Xinying Liu, Lijuan Li, Xiaoqian Zhu, Xiya Chen, Yue Gong, Yushuo Li

“…Using remote sensing, topography, climate, and 490 measured sample data points, this study employs five representative inversion models from three model categories: parametric (single-factor model and stepwise multivariate linear regression), spatial (geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR)), and non-parametric (random forest (RF)). Inversion models were constructed for four components of the grassland ecosystem: aboveground (AGBC) and belowground biomass carbon density (BGBC), dead organic matter carbon density (DOMC), and soil organic carbon density (SOC). …”
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Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer by Pu Zhou, Pu Zhou, Hongyan Qian, Pengfei Zhu, Jiangyuan Ben, Jiangyuan Ben, Guifang Chen, Qiuyi Chen, Lingli Chen, Jia Chen, Ying He, Ying He

“…We compared 10 ML models based on radiomics features: support vector machine (SVM), logistic regression (LR), random forest, extra trees (ET), naïve Bayes (NB), k-nearest neighbor (KNN), multilayer perceptron (MLP), gradient boosting ML (GBM), light GBM (LGBM), and adaptive boost (AB). …”
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Reservoir water level decline accelerates ground subsidence: InSAR monitoring and machine learning prediction of surface deformation in the Three Gorges Reservoir area by Jiaer Yang, Jiaer Yang, Pinglang Kou, Pinglang Kou, Xu Dong, Xu Dong, Ying Xia, Qinchuan Gu, Yuxiang Tao, Jiangfan Feng, Qin Ji, Weizao Wang, Ram Avtar

“…Machine learning models—Random Forest (RF), Extremely Randomized Trees (ERT), Gradient Boosting Decision Tree (GBDT), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)—were evaluated using six metrics, including RMSE, R2, and SMAPE, to assess their predictive performance across diverse geological settings.ResultsDeformation rates for riverside urban ground, road slopes, and ancient landslides were −3.48 ± 2.91 mm/yr, −5.19 ± 3.62 mm/yr, and −6.02 ± 4.55 mm/yr, respectively, with ancient landslides exhibiting the most pronounced deformation. …”
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The association between metabolomic profiles of lifestyle and the latent phase of incident chronic kidney disease in the UK Population by Tingting Jin, Yunqi Wu, Siyi Zhang, Ya Peng, Yao Lin, Saijun Zhou, Hongyan Liu, Pei Yu

“…A total of 249 biological metabolites covering multiple categories were determined by the NMR Metabolomics Platform. Random forest algorithms and LASSO regression were employed to identify lifestyle-related metabolites. …”
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Flood susceptibility mapping based on watershed geomorphometric characteristics and land use/land cover on a small island by B.H. Narendra, O. Setiawan, R.A. Hasan, C.A. Siregar, , Pratiwi, N. Sari, A. Sukmana, I.W.S. Dharmawan, R. Nandini

“…Flooding still occurs despite good forest cover because the geomorphometric characteristics of the watershed also play a major role in flood events.…”
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Response mechanism of water status and photosynthetic characteristics of Cotoneaster multiflorus under drought stress and rehydrated conditions by Qiu-liang Huang, Miao-miao Zhang, Chang-shun Li, Chang-shun Li, Bo-yang Li, Sheng-lan Zhuo, Yu-shan Yang, Yu-da Chen, An-na Zhong, Hao-yang Liu, Wen-feng Lai, Zhen-bei Huang, Ming-hui Cao, Zong-sheng Yuan, Guo-fang Zhang

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Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study by Yanqi Kou, Shicai Ye, Yuan Tian, Ke Yang, Ling Qin, Zhe Huang, Botao Luo, Yanping Ha, Liping Zhan, Ruyin Ye, Yujie Huang, Qing Zhang, Kun He, Mouji Liang, Jieming Zheng, Haoyuan Huang, Chunyi Wu, Lei Ge, Yuping Yang

“…A total of 7 ML algorithms—logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and neural networks—were trained using 10-fold cross-validation. …”
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EIF1AX mutation in thyroid tumors: a retrospective analysis of cytology, histopathology and co-mutation profiles by Noha Elsherbini, Dong Hyun Kim, Richard J. Payne, Thomas Hudson, Véronique-Isabelle Forest, Michael P. Hier, Alexandra E. Payne, Marc P. Pusztaszeri

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The Metempiricism of Margins: Professor Anthony Aṣiwaju and the Circumference of Knowledge by Toyin Falola

“…The life of the author, Aṣiwaju, becomes the account of the aṣíwájú of the borderlands, the leader of the bold and courageous to discover the confines of space, like Ogún, the Yoruba god of iron, who forges new paths and abodes from the forest to the savanna for people to occupy. Strikingly, the inseparability of the man’s life from his career path appears to be synonymous with the bond between a snail and its shell: his origin and horizon live within the same shell. …”
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