Revealing the fireworks-set-off pattern of spatial multi-function expansion across cities leveraging big geodata – a case of the Greater Bay Area, China by Ku Gao, Xiaomei Yang, Zhihua Wang, Yueming Liu, Huifang Zhang, Xiaoliang Liu, Qingyang Zhang

“…We found that (1) across-cities SMFE exhibited a fireworks-set-off pattern, including sprawling along river in the plains between coastal port cities and inland core cities and diffusing from inland core cities on the plains to inland node cities in the mountains; (2) social-living, business-trade, and industry-production functions were sequentially primary expanding functions; (3) paddy and forest were two major land cover types encroaching upon inland cities, while coastal cities primarily suffered from losses of water. …”
Get full text

Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement lo... by Qing Huang, Zihao Jiang, Bo Shi, Jiaxu Meng, Li Shu, Fuyong Hu, Jing Mi

“…Data preprocessing included missing value imputation via random forest. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (Lasso CV) method with cross-validation prior to model training. …”
Get full text

An investigation of machine learning methods applied to genomic prediction in yellow-feathered broilers by Bogong Liu, Huichao Liu, Junhao Tu, Jian Xiao, Jie Yang, Xi He, Haihan Zhang

“…In this study, seven different ML methods—support vector regression (SVR), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), kernel ridge regression (KRR) and multilayer perceptron (MLP) were employed to predict the genomic breeding values of laying traits, growth and carcass traits in a yellow-feathered broiler breeding population. …”
Get full text

Combining a Risk Factor Score Designed From Electronic Health Records With a Digital Cytology Image Scoring System to Improve Bladder Cancer Detection: Proof-of-Concept Study by Sandie Cabon, Sarra Brihi, Riadh Fezzani, Morgane Pierre-Jean, Marc Cuggia, Guillaume Bouzillé

“…MethodsThe first step relied on designing a predictive model based on clinical data (ie, risk factors identified in the literature) extracted from the clinical data warehouse of the Rennes Hospital and machine learning algorithms (logistic regression, random forest, and support vector machine). It provides a score corresponding to the risk of developing bladder cancer based on the patient’s clinical profile. …”
Get full text

Prognostic correlation analysis of multiple myeloma based on HALP score of peripheral blood before chemotherapy by CHEN Min, AN Liying, LIN Xiaojing, ZHAO Pan, ZOU Xingli, WEI Jin, NI Xun

“…Univariate and multivariate analyses were conducted using the Cox regression model, and a forest plot was generated using Graphpad Prism to illustrate factors that may impact patient prognosis. …”
Get full text

Genetic Rescue of the Dinaric Lynx Population: Insights for Conservation From Genetic Monitoring and Individual‐Based Modelling by Elena Pazhenkova, Matej Bartol, Barbara Boljte, Urša Fležar, Andrea Gazzola, Tomislav Gomerčić, Marjeta Konec, Ivan Kos, Miha Krofel, Jakub Kubala, Ladislav Paule, Mihai Pop, Hubert Potočnik, Barbara Promberger, Robin Rigg, Teodora Sin, Magda Sindičić, Vedran Slijepčević, Astrid Vik Stronen, Ira Topličanec, Tomaž Skrbinšek

Get full text

Evaluating novel approaches for estimating awake and sleep blood pressure: design of the Better BP Study – a randomised, crossover trial by Paul Muntner, Ligong Chen, Daichi Shimbo, Joseph E Schwartz, Maria Cepeda, Demetria Hubbard, Suzanne Oparil, Byron C Jaeger, Shakia T Hardy, Julia Medina

Get full text

Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings by Mosbeh R. Kaloop, Abidhan Bardhan, Pijush Samui, Jong Wan Hu, Mohamed Elsharawy

“…Two deep learning methods viz deep belief network (DBN) and deep neural network (DNN), and five machine learning methods namely feedforward neural network, extreme learning machine, weighted extreme learning machine, random forest, and gradient boosting machine were evaluated, and compared in predicting the design wind pressures on tall buildings. …”
Get full text

River-groundwater transformation and ecological effects in the Tuwei River watershed by Jinxuan WANG, Yi WANG, Fan GAO, Xuanming ZHANG, Zhitong MA, Fan YANG

“…Under the control of geological and geomorphological conditions and the three-water transformation, the watershed can be spatially divided into lakes-shrub-grass-tree wet environment ecosystem, grass-shrub-tree-sand dry environment ecosystem, dwarf sparse forest-grass dry environment ecosystem, farmland-tree wet environment ecosystem, and riparian wet environment ecosystem. …”
Get full text

Radiomic prediction for durable response to high‐dose methotrexate‐based chemotherapy in primary central nervous system lymphoma by Haoyi Li, Mingming Xiong, Ming Li, Caixia Sun, Dao Zheng, Leilei Yuan, Qian Chen, Song Lin, Zhenyu Liu, Xiaohui Ren

“…The radiomic‐clinical integrated models were developed using the random forest method. Model performance was externally validated to verify its clinical utility. …”
Get full text

Evaluation of Machine Learning Models for Stress Symptom Classification of Cucumber Seedlings Grown in a Controlled Environment by Kyu-Ho Lee, Samsuzzaman, Md Nasim Reza, Sumaiya Islam, Shahriar Ahmed, Yeon Jin Cho, Dong Hee Noh, Sun-Ok Chung

“…Four ML classifiers: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF), were trained to detect stress symptoms based on selected features, highlighting that stress symptoms were detectable after day 4. …”
Get full text

Rainwater harvesting in arid and semi-arid lands of Africa: challenges and opportunities by Etienne Umukiza, Romain Ntole, Sylvester R. Chikavumbwa, Erion Bwambale, Davis Sibale, Zechariah Jeremaih, Ciro Apollonio, Andrea Petroselli

Get full text

Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients by Liping Xu, Fang Cao, Lian Wang, Weihua Liu, Meizhu Gao, Li Zhang, Fuyuan Hong, Miao Lin

“…Introduction The study presented here aimed to establish a predictive model for heart failure (HF) and all-cause mortality in peritoneal dialysis (PD) patients with machine learning (ML) algorithm.Methods We retrospectively included 1006 patients who initiated PD from 2010 to 2016. XGBoost, random forest (RF), and AdaBoost were used to train models for assessing risk for 1-year and 5-year HF hospitalization and mortality. …”
Get full text

Widely Targeted Metabolomics Analysis Reveals Key Quality-Related Metabolites in Kernels of Sweet Corn by Ruichun Yang, Yunfeng Li, Yuanyuan Zhang, Jun Huang, Junjie Liu, Zimei Lin, Qinqin Yu, Aimin Wu, Bo Wang

Get full text

Depth dependence of soil organic carbon additional storage capacity in different soil types by the 2050 target for carbon neutrality by C. Chirol, C. Chirol, G. Séré, P.-O. Redon, C. Chenu, D. Derrien, D. Derrien

“…Maximum SOC accrual varies from 19 tC ha<span class="inline-formula">⁻¹</span> in forested Leptosols to 197 tC ha<span class="inline-formula">⁻¹</span> in grassland Gleysols. …”
Get full text

Risk of myocardial infarction and heart failure in gout patients: a systematic review and meta-analysis by Panpan Wang, Huanhuan Yang

“…Relevant data were extracted from the final screened literature, and a forest map was drawn using RevMan 5.3 software for meta-analysis. …”
Get full text

Predictive value of dendritic cell-related genes for prognosis and immunotherapy response in lung adenocarcinoma by Zihao Sun, Mengfei Hu, Xiaoning Huang, Minghan Song, Xiujing Chen, Jiaxin Bei, Yiguang Lin, Size Chen

“…Leveraging the Coxboost and random survival forest combination algorithm, we filtered out six DC-related genes on which a prognostic prediction model, DCRGS, was established. …”
Get full text

Role of Aging in Ulcerative Colitis Pathogenesis: A Focus on ETS1 as a Promising Biomarker by Ni M, Peng W, Wang X, Li J

“…Next, core module genes were screened using WGCNA and then the hub genes were characterized using LASSO and random forest methods. Besides, the associations between hub genes, immune cells, and key pathways were explored. …”
Get full text

The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: a prospective study from the NHANES by Xinghong Guo, Mingze Ma, Lipei Zhao, Jian Wu, Yan Lin, Fengyi Fei, Clifford Silver Tarimo, Saiyi Wang, Jingyi Zhang, Xinya Cheng, Beizhu Ye

“…Extreme gradient enhancement, random forest, support vector machine and other machine learning methods are used to build the prediction model. …”
Get full text

Responses of Typical Riparian Vegetation to Annual Variation of River Flow in a Semi-Arid Climate Region: Case Study of China’s Xiliao River by Xiangzhao Yan, Wei Yang, Zaohong Pu, Qilong Zhang, Yutong Chen, Jiaqi Chen, Weiqi Xiang, Hongyu Chen, Yuyang Cheng, Yanwei Zhao

“…To identify the main influencing factors of riparian vegetation changes, we extracted the river flow indicators, climate indicators, and riparian vegetation indicators of a Xiliao River typical section from 1985 to 2020 in spring and summer, and established a random forest model to screen the key driving factors of riparian vegetation. …”
Get full text