Study on Change of Landscape Pattern Characteristics of Comprehensive Land Improvement Based on Optimal Spatial Scale by Baoping Feng, Hui Yang, Yarong Ren, Shanshan Zheng, Genxiang Feng, Yuwei Huang

“…This scale can reflect the spatial variability of the landscape pattern in the study area and is the most suitable analysis range. (3) The fragmentation degree of paddy fields as landscape matrix decreased and the landscape dominance degree increased in the comprehensive land improvement; the degree of fragmentation of irrigated land and agricultural land for facilities increased, the aggregation of land for construction increased, the dominance degree of the pond surface decreased, and the overall landscape diversity of each mosaic decreased; the landscape heterogeneity of ditches, rural roads, forest and grassland corridors was weakened, and the ecosystem service function was weakened. (4) The trend of increased fragmentation, simplification of landscape types, and decreased diversity presented by the landscape pattern clearly indicates that the landscape pattern of the study area has been seriously damaged to some extent under the influence of human activities. …”
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Distinctive Gut Microbiota Alteration Is Associated with Poststroke Functional Recovery: Results from a Prospective Cohort Study by Yini Dang, Xintong Zhang, Yu Zheng, Binbin Yu, Dijia Pan, Xiaomin Jiang, Chengjie Yan, Qiuyu Yu, Xiao Lu

“…Microbial composition, diversity indices, and species cooccurrence were compared between groups. Random forest and receiver operating characteristic analysis were used to identify potential diagnostic biomarkers. …”
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Phenotypic plasticity and genetic variation of Prosopis juliflora (Sw.) DC. across diverse rangelands in northeastern Ethiopia by Minyahel Tilahun, Ayana Angassa, Zinabu Bora, Shimelis Mengistu, Jianshuang Wu

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Identification of mitophagy-related key genes and their correlation with immune cell infiltration in acute myocardial infarction via bioinformatics analysis by Zulong Sheng, Rui Zhang, Zhenjun Ji, Zhuyuan Liu, Yaqing Zhou

“…Next, the MRDEGs were screened using machine learning methods (logistic regression analysis, RandomForest, least absolute shrinkage and selection operator) to construct a diagnostic risk model and select the key genes in AMI. …”
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Quantitative Approach for Determining Reproductive Life‐History Strategies of Parasitic Plants: A Case Study in Balanophora by Trevor Padgett, Huei‐Jiun Su, Shu‐Hui Wu, Li‐yen Huang, Yiching Lin

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Morphotypes and genetic diversity of <i>Dendrobaena schmidti</i> (Lumbricidae, Annelida) by S. V. Shekhovtsov, I. B. Rapoport, T. V. Poluboyarova, A. P. Geraskina, E. V. Golovanova, S. E. Peltek

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Kinetic-pharmacodynamic model to predict post-rituximab B-cell repletion as a predictor of relapse in pediatric idiopathic nephrotic syndrome by Ziwei Li, Qian Shen, Hong Xu, Zhiping Li

“…This study aimed to identify factors that influence disease relapse and B-cell repletion to provide tailored treatment.MethodsLASSO and random survival forest were performed on 143 children to screen covariates which were then included in Cox regression model to determine the biomarkers of relapse and establish a nomogram. …”
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Control de niveles poblacionales endémicos de la avispa de los pinos sirex noctilio (hymenoptera: siricidae) mediante el raleo sanitario de hospederos atacados by J. M. Villacide, J. C. Corley

“…Durante las epidemias es precisamente cuando el daño sobre el recurso forestal puede ser muy importante. El manejo de la plaga se basa típicamente en el control biológico con enemigos naturales que sostengan sus poblaciones en niveles endémicos. …”
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Machine Learning Does Not Improve Humeral Torsion Prediction Compared to Regression in Baseball Pitchers by Garrett S Bullock, Charles A Thigpen, Gary S Collins, Nigel K Arden, Thomas K Noonan, Michael J Kissenberth, Ellen Shanley

“…Regression model RMSE was 12° and calibration was 1.00 (95% CI: 0.94, 1.06). Random Forest RMSE was 9° and calibration was 1.33 (95% CI: 1.29, 1.37). …”
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Symbiotic fungal inoculation promotes the growth of Pinus tabuliformis seedlings in relation to the applied nitrogen form by Lingjie Xu, Yanhui Li, Xiaoyu Dai, Xueyu Jin, Qiannai Zhao, Boyu Tian, Yong Zhou

“…However, relatively few studies have investigated the effects of different nitrogen sources on forest plant-microbial symbionts. In this study, the effects of four nitrogen sources, N free, NH4Cl, L-glutamic acid, and Na(NO3)2 (N-, NH4 +-N, Org-N, and NO3 --N) on four fungal species, Suillus granulatus (Sg), Pisolithus tinctorius (Pt), Pleotrichocladium opacum (Po), and Pseudopyrenochaeta sp. …”
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A new species of Astragalus (Fabaceae) from the Irano-Turanian biodiversity hotspot: an integrative approach by Zahra Karbalaei, Ali Bagheri, Ali Asghar Maassoumi, Twan Rutten, Frank R. Blattner

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Overwintering of Thrips (Thysanoptera) Under the Bark of the Plane Tree (<i>Platanus</i> x <i>hispanica</i> Mill. ex Münchh.) in Southeastern Poland by Halina Kucharczyk, Marek Kucharczyk, Tomasz Olbrycht

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Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing by Enas Al-khlifeh, Ahmad S. Tarawneh, Khalid Almohammadi, Malek Alrashidi, Ramadan Hassanat, Ahmad B. Hassanat

“…Prediction accuracy and precision ranged from 92% to 94.6% when employing decision tree classifiers including decision tree (DT), random forest (RF), XGBoost, AdaBoost, and gradient boosting (GB). …”
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The efficacy of hypothermia combined with thrombolysis or mechanical thrombectomy on acute ischemic stroke: a systematic review and meta-analysis by Dan Wang, Dan Yan, Mingmin Yan, Hao Tian, Haiwei Jiang, Bifeng Zhu, Yu Chen, Tao Peng, Yue Wan

“…In addition, subgroup analyses were performed focusing on the different hypothermia modalities and duration.ResultsAfter screening 2,265 articles, 10 studies were included in the present analysis with a total sample size of 785. Forest plots of clinical outcomes were as follows: modified Rankin Scale (mRS) ≤2 at 3 months (RR = 1.28, 95% CI 1.01–1.61, p = 0.04), mortality within 3 months (RR = 0.95, 95% CI 0.69–1.29, p = 0.73), total complications (RR = 1.02, 95% CI 0.89–1.16, p = 0.77) and pneumonia (RR = 1.35, 95% CI 0.76–2.40, p = 0.31). …”
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Effects of Film-Bottomed Treatment on Absorbability and Translocation of Nitrogen in Spring Wheat in Arid Area by Zizhen Li, Xiaolei Zhou, Qing Tian, Low Pak Sum, Yuee Yan, Xujiao Zhou

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Development of risk models for early detection and prediction of chronic kidney disease in clinical settings by Pegah Bahrami, Davoud Tanbakuchi, Monavar Afzalaghaee, Majid Ghayour-Mobarhan, Habibollah Esmaily

“…Four main algorithms and four algorithms using the stratified K-folds cross-validation technique, consisting of gender-specific Random Forest and feedforward Neural Networks were developed using the preprocessed data of 6855 participants. …”
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Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model. by Li Wang, Yu Zhang, Feng Li, Caiyun Li, Hongzeng Xu

“…Seven classical artificial intelligence methods of Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (ADB), Extra Tree (ET), and Gradient Boosting Decision Tree (GBDT) were selected as candidate models for the base model of the first layer of the model, and extreme gradient enhancement (XGBOOST) was selected as the meta-model for the second layer.…”
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Land use transition and its driving mechanisms in China’s human-elephant conflict areas by WANG Yuan, WANG Yahui, YANG Aoxi, FAN Hui, XIE Fei

“…[Results] (1) Land use transition in regions inhabited by Asian elephants is significant, with forest areas experiencing a decrease followed by a slow recovery, accompanied by a decrease in orchard area. …”
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Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection by Kuan-Yu Chen, Yen-Chun Huang, Chih-Kuang Liu, Shao-Jung Li, Mingchih Chen

“…The machine learning algorithms used included linear regression (LR), random forest (RF), support vector regression (SVR), generalized linear model boost (GLMBoost), Bayesian generalized linear model (BayesGLM), and extreme gradient boosting (eXGB). …”
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