Analysis of the CHS Gene Family Reveals Its Functional Responses to Hormones, Salinity, and Drought Stress in Moso Bamboo (<i>Phyllostachys edulis</i>) by Shiying Su, Xueyun Xuan, Jiaqi Tan, Zhen Yu, Yang Jiao, Zhijun Zhang, Muthusamy Ramakrishnan

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The Fabrication and Evaluation of a Potential Biomaterial Produced with Stem Cell Sheet Technology for Future Regenerative Medicine by Shukui Zhou, Ying Wang, Kaile Zhang, Nailong Cao, Ranxing Yang, Jianwen Huang, Weixin Zhao, Mahbubur Rahman, Hong Liao, Qiang Fu

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UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review by Zhen Zhang, Lehao Huang, Qingwang Wang, Linhuan Jiang, Yemao Qi, Shunyuan Wang, Tao Shen, Bo-Hui Tang, Yanfeng Gu

“…In recent years, significant advances in unmanned aerial vehicle (UAV) technology and hyperspectral remote sensing have spurred rapid and innovative developments in UAV-based hyperspectral image (HSI) classification across a range of fields, including environmental monitoring, precision agriculture, forest health assessment, and disaster management. …”
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Construction land transition in Qinghai-Tibet Plateau and its eco-environmental effects during 2000-2020 by ZHANG Zining, LUO Junqiang, ZHANG Peilei, RONG Han, LIU Haiyang, AN Shuang, CHANG Genying

“…Early expansion mainly occupied unused land, whereas later expansion predominantly encroached on ecological land such as cropland, forest, and grassland. Regional differences in land-use transitions were evident: the Northern Tibet Plateau and Qaidam Basin showed higher proportions of transitions to water bodies, water conservancy land, and unused land, while the expansion mainly occupied grasslands and unused land; in other regions, construction land predominantly shifted between cropland and grassland. (3) The <i>IRSEI</i> values of construction land in the regions of Qinghai-Tibet Plateau were ranked as follows: Sichuan-Tibet alpine canyon region &gt; Qilian Mountains region &gt; Southern Tibet valley region &gt; Qinghai Plateau &gt; Qaidam Basin &gt; Northern Tibet Plateau. (4) Overall, construction land transition demonstrated negative ecological effects. …”
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MRI-based deep learning and radiomics for predicting the efficacy of PD-1 inhibitor combined with induction chemotherapy in advanced nasopharyngeal carcinoma: A prospective cohort... by Yiru Wang, Fuli Chen, Zhechen Ouyang, Siyi He, Xinling Qin, Xian Liang, Weimei Huang, Rensheng Wang, Kai Hu

“…From MRI scans, DLFs and conventional radiomic characteristics were recovered. The random forest algorithm was employed to identify the most valuable features. …”
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Dengue dynamics, predictions, and future increase under changing monsoon climate in India by Yacob Sophia, Mathew Koll Roxy, Raghu Murtugudde, Anand Karipot, Amir Sapkota, Panini Dasgupta, Kalpana Baliwant, Sujata Saunik, Abhiyant Tiwari, Rajib Chattopadhyay, Revati K. Phalkey

“…Based on these weather-dengue associations, we developed a machine-learning model utilizing the random forest regression algorithm. The dengue model yields a skillful forecast, achieving a statistically significant correlation coefficient of r = 0.77 and a relatively low Normalized Root Mean Squared Error score of 0.52 between actual and predicted dengue mortalities, at a lead time of two months. …”
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Urban green infrastructure index: Assessing supply of regulating and cultural ecosystem services at a megacity scale by Yury Dvornikov, Valentina Grigorieva, Vyacheslav Vasenev, Mikhail Varentsov, Olga Romzaykina, Olga Maximova, Anastasia Konstantinova, Victor Matasov, Ekaterina Kozlova

“…., differences between central districts and suburbs or neighborhoods with higher and lower accessibility of the nearest urban forest) challenges the city’s environmental strategy, which aims to provide an equal access to green spaces for all citizens. …”
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Predictive value of preoperative pan-immune-inflammation value index in the prognosis of oral cancer patients undergoing radical resection by Weihai Huang, Yulan Lin, Enling Xu, Yanmei Ji, Jing Wang, Fengqiong Liu, Fa Chen, Yu Qiu, Bin Shi, Lisong Lin, Baochang He

“…Univariate and multivariate Cox regression was used to assess the prognostic value of PIV, and propensity score matching (PSM) analysis was used to adjust for potential confounders. Randomized survival forest (RSF) was used to assess the relative importance of preoperative PIV in prognostic prediction. …”
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External particle mixing influences hygroscopicity in a sub-urban area by S. Deshmukh, L. Poulain, B. Wehner, S. Henning, J.-E. Petit, P. Fombelle, P. Fombelle, O. Favez, H. Herrmann, M. Pöhlker, M. Pöhlker

“…During the ACROSS campaign (Atmospheric ChemistRy Of the Suburban foreSt, conducted in Paris in summer 2022), particles' hygroscopic growth rates at 90 % relative humidity (RH) and chemical composition were measured at the sub-urban site using a Hygroscopicity Tandem Differential Mobility Analyser (HTDMA, scanning at 100, 150, 200, and 250 nm) and an Aerodyne High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS). …”
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Clinical evidence of acupuncture for amnestic mild cognitive impairment: A systematic review and meta-analysis of randomized controlled trials by Guangyao Lin, Stella Lim Jin Yie, Shanshan Guo, Xuanling Li, Lianwei Xu

“…The results of this meta-analysis were exhibited with forest plots. Sensitivity analyses were conducted to determine the robustness of the pooled results, and publication bias was estimated by Egger's and Begg's tests. …”
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The contribution of different polyphenol compositions from chokeberry produced in China to cellular antioxidant and antiproliferative activities by Ningxuan Gao, Xu Si, Wenzhong Han, Ersheng Gong, Chi Shu, Jinlong Tian, Yuehua Wang, Jiyue Zhang, Binxu Li, Bin Li

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Unraveling the interplay between NDVI, soil moisture, and snowmelt: A comprehensive analysis of the Tibetan Plateau agroecosystem by Di Wei, Lin Yan, Ziqi Zhang, Jia Yu, Xue’er Luo, Yun Zhang, Bo Wang

“…Using geostatistical methods such as trend analysis, cross-correlation, random forest algorithm, and Granger causality, we explore the temporal dynamics and causal relationships among these ecological variables. …”
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Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniques by Mohamed Abdellatief, Leong Sing Wong, Norashidah Md Din, Ali Najah Ahmed, Abba Musa Hassan, Zainah Ibrahim, G. Murali, Kim Hung Mo, Ahmed El-Shafie

“…In this context, the current study proposes a novel approach by developing a thoughtful system for assessing performance and intelligent design utilizing ML models such as Gradient Boosting (GB), Random Forest (RF), Gaussian Process Regression (GPR), and Linear Regression (LR) to predict porosity and compressive strength (CS) of FG. …”
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Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions by Jin-Hyun Park, Yu-Bin Shin, Dooyoung Jung, Ji-Won Hur, Seung Pil Pack, Heon-Jeong Lee, Hwamin Lee, Chul-Hyun Cho, Chul-Hyun Cho

“…We developed ML models that predict the upper tertile group for various anxiety symptoms in SAD using Random Forest, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) models. …”
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Integrated analysis of bioinformatics, mendelian randomization, and experimental validation reveals novel diagnostic and therapeutic targets for osteoarthritis: progesterone as a p... by Ziyu Weng, Chenzhong Wang, Bo Liu, Yi Yang, Yueqi Zhang, Chi Zhang

“…Methods In this study, Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) machine learning techniques were employed to identify hub genes. …”
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Prediction model, risk factor score and ventilator-associated pneumonia: A two-stage case-control study by Hua Meng, Yuxin Shi, Kaming Xue, Di Liu, Xiongjing Cao, Yanyan Wu, Yunzhou Fan, Fang Gao, Ming Zhu, Lijuan Xiong

“…We constructed a prediction model by least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost) models in 363 patients and 363 controls, and weighted RFS was calculated based on significant predictors. …”
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Machine Learning Model Reveals Land Use and Climate’s Role in Caatinga Wildfires: Present and Future Scenarios by Rodrigo N. Vasconcelos, Mariana M. M. de Santana, Diego P. Costa, Soltan G. Duverger, Jefferson Ferreira-Ferreira, Mariana Oliveira, Leonardo da Silva Barbosa, Carlos Leandro Cordeiro, Washington J. S. Franca Rocha

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Photosynthetic characteristics of Paris polyphylla var. chinensis in response to different light intensities and soil water contents by Yaling Zhang, Yaling Zhang, Rong Xu, Rong Xu, Rong Xu, Zhaozhao Wang, Juan Zhang, Xinghao Tang, Yu Chen, Xiao-Li Yan, Lu-Ping Qu, Lu-Ping Qu

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Effects of feature selection and normalization on network intrusion detection by Mubarak Albarka Umar, Zhanfang Chen, Khaled Shuaib, Yan Liu

“…The models were evaluated using popular evaluation metrics in IDS modeling, intra- and inter-model comparisons were performed between models and with state-of-the-art works. Random forest (RF) models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86% and 96.01%, respectively, whereas artificial neural network (ANN) achieved the best accuracy of 95.43% on the CSE–CIC–IDS2018 dataset. …”
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Prevalence of Food Insecurity and Its Associated Factors among Adult People with Human Immunodeficiency Virus in Ethiopia: A Systematic Review and Meta-Analysis by Hagos Degefa Hidru, Haftay Gebremedhine, Alem Gebretsadik, Hirut Teame, Hadush Negash, Meresa Berwo Mengesha

“…Moreover, quality appraisal of the included studies, publication bias was checked using the funnel symmetry test, and heterogeneity was checked using forest plot and inverse variance square (I2). The searches were restricted to articles published in the English language only, and Medical Subject Headings (MeSH terms) was used to help expand the search in advanced PubMed search. …”
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