High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds by S. Bovo, M. Bolner, G. Schiavo, G. Galimberti, F. Bertolini, S. Dall’Olio, A. Ribani, P. Zambonelli, M. Gallo, L. Fontanesi

“…The molecular data were analysed using a bioinformatics pipeline specifically designed for identifying differentially abundant metabolites between the two breeds in a robust and statistically significant manner, including the Boruta algorithm, which is a Random Forest wrapper, and sparse Partial Least Squares Discriminant Analysis (sPLS-DA) for feature selection. …”
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The distribution, phenology, host range and pathogen prevalence of Ixodes ricinus in France: a systematic map and narrative review by Perez, Grégoire, Bournez, Laure, Boulanger, Nathalie, Fite, Johanna, Livoreil, Barbara, McCoy, Karen D., Quillery, Elsa, René-Martellet, Magalie, Bonnet, Sarah I.

“…Abundance seems positively influenced by forest cover and host abundance. Rodents and ruminants are the most studied species groups, but the diversity of sampling protocols (e.g., location, season, exhaustivity of inspection) precluded direct comparisons between groups. …”
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Long-term effects of post-earthquake landslides on vegetation ecosystem net carbon by Wen He, Baofeng Di, Shaolin Wu, Jierui Li, Wen Zeng, Yajie Zeng, Ruowei Li, Joseph Kimuli Balikuddembe, Hongkai Chen, Bin Zhang, Gang Chen, Constantine A. Stamatopoulos, Ufuk Yazgan, Isaya Kisekka

“…Subsequently, the Random Forest Regression Model and Structural Equation Model were used to explore the environmental drivers of ENCL. …”
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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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Digital technologies for water use and management in agriculture: Recent applications and future outlook by Carlos Parra-López, Saker Ben Abdallah, Guillermo Garcia-Garcia, Abdo Hassoun, Hana Trollman, Sandeep Jagtap, Sumit Gupta, Abderrahmane Aït-Kaddour, Sureerat Makmuang, Carmen Carmona-Torres

“…UAV-mounted multispectral cameras) can accurately monitor soil moisture to optimise irrigation scheduling, while AI-driven models (e.g. random forest or neural networks) can predict groundwater recharge or forecast rainfall events. …”
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Effects of Different Vegetation Restoration Measures on the Disintegration Characteristics of Eroded Degraded Land of Pinus massoniana in Red Soil Region by XI Jie, WU Zehua, LIN Qiang, WU Jieling, ZHANG Liu, ZHOU Shiqi, ZHOU Qin, SUN Lili, ZHA Xuan

“…This measure is more effective for soil and water loss control in severely degraded P. massoniana forest in granite red soil area. Soil geometric mean diameter (GMD) is a direct control factor affecting soil disintegration characteristics, and can be used as a preferred index to evaluate soil disintegration characteristics.…”
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Deciphering key nano-bio interface descriptors to predict nanoparticle-induced lung fibrosis by Jiayu Cao, Yuhui Yang, Xi Liu, Yang Huang, Qianqian Xie, Aliaksei Kadushkin, Mikhail Nedelko, Di Wu, Noel J. Aquilina, Xuehua Li, Xiaoming Cai, Ruibin Li

“…The fibrogenic potential of MeONPs in mouse lungs was assessed by examining collagen deposition and growth factor release. Random forest classification was employed for analyzing in chemico, in vitro and in vivo data to identify predictive descriptors. …”
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Serological Survey of Hantavirus in Inhabitants from Tropical and Subtropical Areas of Brazil by Felipe Alves Morais, Alexandre Pereira, Aparecida Santo Pietro Pereira, Marcos Lazaro Moreli, Luís Marcelo Aranha Camargo, Marcello Schiavo Nardi, Cristina Farah Tófoli, Jansen Araujo, Lilia Mara Dutra, Tatiana Lopes Ometto, Renata Hurtado, Fábio Carmona de Jesus Maués, Tiene Zingano Hinke, Sati Jaber Mahmud, Monica Correia Lima, Luiz Tadeu Moraes Figueiredo, Edison Luiz Durigon

“…The aim of this study was to analyze hantavirus antibody levels in inhabitants from a tropical area (Amazon region) in Rondônia state and a subtropical (Atlantic Rain Forest) region in São Paulo state, Brazil. A total of 1,310 serum samples were obtained between 2003 and 2008 and tested by IgG-ELISA, and 82 samples (6.2%), of which 62 were from the tropical area (5.8%) and 20 from the subtropical area (8.3%), tested positive. …”
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Machine learning helps reveal key factors affecting tire wear particulate matter emissions by Zhenyu Jia, Jiawei Yin, Tiange Fang, Zhiwen Jiang, Chongzhi Zhong, Zeping Cao, Lin Wu, Ning Wei, Zhengyu Men, Lei Yang, Qijun Zhang, Hongjun Mao

“…The response relationship between TWP emissions (both PM2.5 and PM2.5-10) and factors (the radial force, the lateral force, the tangential force, speed, driving torque, tire contact area, total contour length and tire tread temperature) was obtained by machine learning (ML) method. The random forest (RF) model was developed and displayed good prediction performance with an R2 of 0.84 and 0.78 for PM2.5 and PM2.5-10 on the test set, respectively. …”
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Evaluation of linear, nonlinear and ensemble machine learning models for landslide susceptibility assessment in southwest China by Bingwei Wang, Qigen Lin, Tong Jiang, Huaxiang Yin, Jian Zhou, Jinhao Sun, Dongfang Wang, Ran Dai

“…Linear models represented by logistic regression (LR), nonlinear models represented by support vector machine (SVM), artificial neural network (ANN) and classification 5.0 decision tree (C5.0 DT), and ensemble models represented by random forest (RF) and categorical boosting (Catboost) were selected. …”
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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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Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective... by Seok Jun Park, Seungwon Yang, Suhyun Lee, Sung Hwan Joo, Taemin Park, Dong Hyun Kim, Hyeonji Kim, Soyun Park, Jung-Tae Kim, Won Gun Kwack, Sung Wook Kang, Yun-Kyoung Song, Jae Myung Cha, Sang Youl Rhee, Eun Kyoung Chung

“…Among the tested ML algorithms, random forest achieved the highest accuracy (area under the receiver operating characteristic curve [AUROC] 0.88 for both cohorts). …”
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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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Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (<i>Annona squamosa</i> L.) by Xiangtai Jiang, Lutao Gao, Xingang Xu, Wenbiao Wu, Guijun Yang, Yang Meng, Haikuan Feng, Yafeng Li, Hanyu Xue, Tianen Chen

“…A two-layer ensemble model is built to quantitatively estimate leaf nitrogen using the stacking ensemble learning (Stacking) principles. Random Forest (RF), Adaptive Boosting (ADA), Gradient Boosting Decision Trees (GBDT), Linear Regression (LR), and Extremely Randomized Trees (ERT) are among the basis estimators that are integrated in the first layer. …”
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Impacts of plant root traits and microbial functional attributes on soil respiration components in the desert-oasis ecotone by Jinlong Wang, Jinlong Wang, Jinlong Wang, Guanghui Lv, Guanghui Lv, Guanghui Lv, Jianjun Yang, Jianjun Yang, Jianjun Yang, Xuemin He, Xuemin He, Xuemin He, Hengfang Wang, Hengfang Wang, Hengfang Wang, Wenjing Li, Wenjing Li, Wenjing Li

“…Concomitantly, the Ra and Rh components exhibited a similar trend throughout the study period, with Rh emerging as the dominant driver of Rs. Utilizing random forest modeling, we unearthed significant associations between microbial taxonomic and functional features and Rs components. …”
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Identification of Inflammatory Biomarkers for Predicting Peripheral Arterial Disease Prognosis in Patients with Diabetes by Kian Draper, Ben Li, Muzammil Syed, Farah Shaikh, Abdelrahman Zamzam, Batool Jamal Abuhalimeh, Kharram Rasheed, Houssam K. Younes, Rawand Abdin, Mohammad Qadura

“…In the discovery phase the cohort was randomly split into a 70:30 ratio, and proteins with a higher mean level of expression in the DM PAD group compared to the DM non-PAD group were identified. Next, a random forest model was trained using (1) clinical characteristics, (2) a five-protein panel, and (3) clinical characteristics combined with the five-protein panel. …”
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