Neutrophil to Lymphocyte Ratio Predicts Adverse Cardiovascular Outcome in Peritoneal Dialysis Patients Younger than 60 Years Old by Yingsi Zeng, Zijun Chen, Qinkai Chen, Xiaojiang Zhan, Haibo Long, Fenfen Peng, Fengping Zhang, Xiaoran Feng, Qian Zhou, Lingling Liu, Xuan Peng, Evergreen Tree Nephrology Association, Guanhua Guo, Yujing Zhang, Zebin Wang, Yueqiang Wen, Jiao Li, Jianbo Liang

“…Kaplan-Meier cumulative incidence curve and multivariable COX regression analysis were used to determine the relationship between NLR and the incidence of adverse CV outcome, while a competitive risk model was applied to assess the effects of other outcomes on adverse CV prognosis. Besides, forest plot was investigated to analyze the adverse CV prognosis in different subgroups. …”
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Temperature Is a Key Factor Affecting Total Phosphorus and Total Nitrogen Concentrations in Northeastern Lakes Based on Sentinel-2 Images and Machine Learning Methods by Haoming Qin, Chong Fang, Ge Liu, Kaishan Song, Zhuoshi Li, Sijia Li, Hui Tao, Zhaojiang Yan

“…Results indicate that random forest (RF) and XGBoost regression algorithms perform better. …”
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Diagnostic Accuracy of Procalcitonin for Bacterial Infection in Liver Failure: A Meta-Analysis by Xinchun He, Liang Chen, Haiou Chen, Yuqing Feng, Baining Zhu, Caixia Yang

“…In addition, the threshold effect analysis showed that the threshold effect was 0.23 and the correlation coefficient was −0.48, indicating that there was no threshold effect. In the forest map, the DOR of each study and the combined DOR are not distributed along the same line, and Q = 2.2 × 1014, P≤0.001. …”
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Identification of heat tolerant lentil genotypes through stress tolerance indices by Rohit Sarkar, Gyan Prakash Mishra, Premakumar, Ankita Singh, Jenia Roy, Kumbarahally Murthigowda Shivaprasad, Shashidhar B. Reddappa, Sudhir Kumar, Ruchi Bansal, Uttarayan Dasgupta, Soma Gupta, Susheel Kumar Sarkar, Sanjeev Gupta, Shiv Kumar, Harsh K. Dikshit, Prachi Sripathrao Yadav, Muraleedhar S. Aski

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Characteristics of Soil Profile Greenhouse Gas Concentrations and Fluxes of Alpine Grassland as Affected by Livestock Grazing by Mingyuan Yin, Xiaopeng Gao, Yanyan Li, Yufeng Wu, Wennong Kuang, Fanjiang Zeng

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Challenges of Engineering Skillsets Essential for Driving Circularity of Smart Cities by Benjamin Neale, Sakdirat Kaewunruen, Dan Li, Ayfer Donmez Cavdar, Rand Askar, Tatjana Tambovceva, Diana Bajare

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Ratio-dependent competitions between a Wolbachia-uninfected bisexual strain and Wolbachia-infected thelytokous strain of the egg parasitoid, Trichogramma dendrolimi Matsumura (Hyme... by Qian-Jin Dong, Yue He, Yu-Zhe Dong, Wu-Nan Che, Jin-Cheng Zhou, Hui Dong

“…Abstract Background Wolbachia-infected thelytokous Trichogramma wasps have been considered as potential effective biocontrol agents against lepidopteran pests in agriculture and forests. However, intra-specific competition may arise when Wolbachia-infected thelytokous Trichogramma coexist with their uninfected bisexual counterparts in fields or during mass-rearing procedures. …”
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HPLC Analysis and Antioxidant Evaluation of Acteoside-Rich Osmanthus fragrans Extracts by Sung-Ho Lee, Seong-Wook Seo, Seung-Yub Song, Deuk-Sil Oh, Jung-Hyun Shim, Goo Yoon, Ye-Yong Choi, Do-Kyung Kim, Jae-Sung Kim, In-Soo Yoon, Seung-Sik Cho

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Diversity of Picea omorika (Pančić) Purk. Populations Based on Morpho-anatomical Needle Traits and Bioclimatic Parameters by Biljana Nikolić, Nemanja Rajčević, Zorica Mitić, Sanja Jovanović, Nevena Čule, Katarina Mladenović, Marija Marković, Petar Marin

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Predictive models for the implementation of targeted reproductive management in multiparous cows on automatic milking systems by Fergus P. Hannon, Martin J. Green, Luke O'Grady, Chris Hudson, Anneke Gouw, Laura V. Randall

“…Using data derived solely from the AMS (RBT dataset) a binary random forest classification model was constructed for both outcomes of interest. …”
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Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning... by Xiaobo Xu, Zhaofeng Wang, Erjie Lu, Tao Lin, Hengchao Du, Zhongfei Li, Jiahong Ma

“…This study was based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), and Extremely Randomized Trees (ERT) models were constructed to classify carbapenem-resistant Escherichia coli (CREC) and carbapenem-resistant Klebsiella pneumoniae (CRKP). …”
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Mitigating bias in AI mortality predictions for minority populations: a transfer learning approach by Tianshu Gu, Wensen Pan, Jing Yu, Guang Ji, Xia Meng, Yongjun Wang, Minghui Li

“…Results Decision Tree (DT) and Random Forest (RF) models consistently showed improvements in accuracy, precision, and ROC-AUC scores for Non-Hispanic Black, Hispanic/Latino, and Asian populations. …”
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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. …”
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A retrospective cohort study: do patients with graves’ disease need to be euthyroid prior to surgery? by Abrar Al Jassim, Tim Wallace, Sarah Bouhabel, Agnieszka Majdan, Michael Hier, Veronique-Isabelle Forest, Richard Payne

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EL ROL DE LOS ACTORES SOCIALES EN LA CONFIGURACIÓN TERRITORIAL DE MOQUEHUE by Celia Viviana Torrens, Elsie Marcela Jurio, Vanesa Yanina Cappelletti, Germán Gabriel Pérez, Daiana Elcira Cara Plá, Gimena Nathalie Cuevas, María Silvina Hauw, Pablo Alejandro Leyes

“…It aroses in the 40's, as a product of the development of cattle raising and forestation. Since these activities began, the permanent settlement and first commercial activities appeared, in order to cover the new demands. …”
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Conservation of wild western honey bees Apis Mellifera in the Polissia natural zone of Ukraine: history, sources of nectar and pollen by Sichenko O., Kryvyi M., Horchanok A., Kuzmenko O., Tytariova O.

“…Pollen of Potentilla erecta L, Frangula alnus L, Lamium purpureum L, as species with the longest flowering season, remain available almost throughout the honey collection season, and the anemophilic pollen-producing tree of the Fagaceae family Quercus robur L is a common source of pollen for honey bees in Polissia forests.…”
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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. …”
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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

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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

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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. …”
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