Using Deep Learning to Identify High-Risk Patients with Heart Failure with Reduced Ejection Fraction by Zhibo Wang, Xin Chen, Xi Tan, Lingfeng Yang, Kartik Kannapur, Justin L. Vincent, Garin N. Kessler, Boshu Ru, Mei Yang

“…For comparison, we also tested multiple traditional machine learning models including logistic regression, random forest, and eXtreme Gradient Boosting (XGBoost). Model performance was assessed by area under the curve (AUC) values, precision, and recall on an independent testing dataset. …”
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Comparison of the efficacy of seven non-surgical methods combined with mechanical debridement in peri-implantitis and peri-implant mucositis: A network meta-analysis. by Yingjie Bai, Shengao Qin, Bingshuai Lu, Weiyi Wang, Guowu Ma

“…Network meta-analyses include network plots, paired comparison forest plots, league tables, funnel plots, surface under the cumulative ranking area (SUCRA) plots, and sensitivity analysis plots. …”
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Aroma profile of two commercial truffle species from Yunnan and Sichuan, China: inter- and intraspecific variability and shared key compounds by Bin Lu, Jesús Perez-Moreno, Fengming Zhang, Andrea C. Rinaldi, Fuqiang Yu

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Mood Detection from Physical and Neurophysical Data Using Deep Learning Models by Zeynep Hilal Kilimci, Aykut Güven, Mitat Uysal, Selim Akyokus

“…Multinomial Naïve Bayes (MNB), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and Decision Integration Strategy (DIS) are evaluated as conventional machine learning algorithms. …”
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Reproductive and persistence strategy of the liverwort Arnellia fennica after the last glaciation in the area of disjunction in Central Europe (Polish Tatra Mountains, carpathians) by Piotr Górski, Monika Szczecińska, Jakub Sawicki

“…Within the Carpathians, it is only known from the Tatra Mountains (in Poland), where so far only four occurrences have been documented in the forest belt of the limestone part of the Western Tatras. …”
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Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of... by Ronald P. Dillner, Maria A. Wimmer, Matthias Porten, Thomas Udelhoven, Rebecca Retzlaff

“…To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. …”
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Association of Methylenetetrahydrofolate Reductase C677T Gene Polymorphisms with Mild Cognitive Impairment Susceptibility: A Systematic Review and Meta-Analysis by Jiahui Sun, Xuefan Jiang, Ming Zhao, Lina Ma, Hui Pei, Nanyang Liu, Hao Li

“…We used fixed or random effects to examine the association between MTHFR C677T (rs1801133) gene variants and MCI susceptibility. Forest plots of pooled odds ratios (ORs) and 95% confidence intervals (CIs) were generated. …”
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Physiological and intestinal microbiota responses of sea cucumber Apostichopus japonicus to various stress and signatures of intestinal microbiota dysbiosis by Liang Cui, Yumeng Xie, Kai Luo, Mingyang Wang, Longzhen Liu, Longzhen Liu, Changlin Li, Xiangli Tian

“…The increased abundance of Verrucomicrobia species could also be identified as the sensitive indicator for diagnosing whether the host was under stressful pressure by random forest analysis.…”
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Habitat Quality Dynamics in Urumqi over the Last Two Decades: Evidence of Land Use and Land Cover Changes by Siying Chen, Ümüt Halik, Lei Shi, Wentao Fu, Lu Gan, Martin Welp

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Evaluation of Secondary Disaster Criteria after Disaster: An Application for Ordu Province by İrem Gaferoğlu, Sude Kaya, Y. Baran Kalemler, Emel Güven, Tamer Eren

“…Floods, earthquakes, forest fires, landslides, avalanches, snow and storms are the most common natural hazards in Türkiye. …”
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Seasonal investigation of ultrafine-particle organic composition in an eastern Amazonian rainforest by A. E. Thomas, H. S. Glicker, A. B. Guenther, R. Seco, O. Vega Bustillos, J. Tota, R. A. F. Souza, J. N. Smith

“…Here, we present measurements of the composition of ultrafine particles collected in the Tapajós National Forest (2.857° S, 54.959° W) during three different seasonal periods: 10–30 September 2016 (SEP), 18 November–23 December 2016 (DEC), and 22 May–21 June 2017 (JUN). …”
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Transplantation of fecal microbiota from low to high residual feed intake chickens: Impacts on RFI, microbial community and metabolites profiles by Chunlin Xie, Qiying Liang, Jiaheng Cheng, Yushan Yuan, Lu Xie, Jian Ji

“…Using 16S rDNA sequencing and RandomForest analysis, Slackia, Peptococcus, Blautia, and Dorea were identified as key microbial markers associated with feed efficiency. …”
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Combined Impact of Canada Goldenrod Invasion and Soil Microplastic Contamination on Seed Germination and Root Development of Wheat: Evaluating the Legacy of Toxicity by Guanlin Li, Yi Tang, Hongliang Xie, Babar Iqbal, Yanjiao Wang, Ke Dong, Xin Zhao, Hyun-Jun Kim, Daolin Du, Chunwang Xiao

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Effect of Yoga on Blood Pressure in Prehypertension: A Systematic Review and Meta-Analysis by Janhavi Sandeep Khandekar, Vanamala Lakshmi Vasavi, Vijay Pratap Singh, Stephen Rajan Samuel, S. G. Sudhan, Bidita Khandelwal

“…RevMan 5.4 by Cochrane was used for meta-analysis and forest plot construction. Risk of bias was determined using the Downs and Black checklist by three independent authors. …”
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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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Evaluation of spring barley cultivars released in Belarus under the environmental conditions of the Northern Trans-Urals by M. N. Fomina, Yu. S. Ivanova, M. V. Bragina, O. N. Kovaleva

“…The results of an 8-year trial (2016–2023) of 11 spring barley accessions of Belarusian origin in the northern forest-steppe zone of Tyumen Province are presented. …”
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Reconstruction of Araucaria araucana cone production reveals warming intensifies regionally synchronized masting by Andrew Hacket‐Pain, Fidel A. Roig, Davide Ascoli, Carlos LeQuesne, Martin Hadad

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Prevalence and pattern of rheumatic valvular heart disease in Africa: Systematic review and meta-analysis, 2015-2023, population based studies. by Seid Mohammed Abdu, Altaseb Beyene Kassaw, Amare Abera Tareke, Gosa Mankelkl, Mekonnen Belete, Mohammed Derso Bihonegn, Ahmed Juhar Temam, Gashaw Abebe, Ebrahim Msaye Assefa

“…The descriptive information for the study is presented in the table, and the quantitative results are presented in forest plots. The Cochrane Q test and I2 test statistic were used to test heterogeneity across studies. …”
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The Climate Change Impact on Agriculture Productivity in Karama Sector, Nyagatare District, Rwanda. by Odile, Twagiramariya

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Expert projections on the development and application of bioenergy with carbon capture and storage technologies by Tobias Heimann, Lara-Sophie Wähling, Tomke Honkomp, Ruth Delzeit, Alessandra Pirrone, Franziska Schier, Holger Weimar

“…BECCS can substitute fossil fuels in energy production and reduce CO _2 emissions, while using biomass for energy production can have feedback effects on land use, agricultural and forest products markets, as well as biodiversity and water resources. …”
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