Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions by Qing Zhu, Daniel J Jacob, Kunxiaojia Yuan, Fa Li, Benjamin R K Runkle, Min Chen, A Anthony Bloom, Benjamin Poulter, James D East, William J Riley, Gavin McNicol, John Worden, Christian Frankenberg, Meghan Halabisky

“…WETCHIMP: 190 ± 39 TgCH _4 yr ^−1 ); and (3) data-driven machine learning approach (e.g. UpCH4: 146 ± 43 TgCH _4 yr ^−1 ). …”
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Dynamic monitoring and drivers of ecological environmental quality in the Three-North region, China: Insights based on remote sensing ecological index by Leyi Zhang, Xia Li, Xiuhua Liu, Zhiyang Lian, Guozhuang Zhang, Zuyu Liu, Shuangxian An, Yuexiao Ren, Yile Li, Shangdong Liu

“…Additionally, a SHapley Additive eXplanation (SHAP) interpretable machine learning model was employed to identify the dominant factors and thresholds influencing the EEQ in the TNEP and its sub-regions. …”
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Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation by Guiling Wu, Guiling Wu, Sihui Wu, Sihui Wu, Tian Xiong, Tian Xiong, Tian Xiong, You Yao, You Yao, Yu Qiu, Yu Qiu, Yu Qiu, Liheng Meng, Cuihong Chen, Xi Yang, Xi Yang, Xi Yang, Xinghuan Liang, Yingfen Qin

“…Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. …”
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The impact of war on people with type 2 diabetes in Ukraine: a survey studyResearch in context by Oksana Sulaieva, Viktoriia Yerokhovych, Sergii Zemskov, Iuliia Komisarenko, Vitalii Gurianov, Volodymyr Pankiv, Oleksandr Tovkai, Tetyana Yuzvenko, Violetta Yuzvenko, Andrii Tovkai, Zlatoslava Shaienko, Tetyana Falalyeyeva, Nadiya Skrypnyk, Taras Romaniv, Nadiya Pasyechko, Taras Krytskyy, Solomiia Danyliuk, Andrii Klantsa, Dmytro Krasnienkov, Oleksandr Gurbych, Nazarii Kobyliak

“…Next, the impact of intrinsic and war-related factors on T2D progression was assessed via logistic regression analysis and machine learning tools. Findings: Two years of war experience was associated with significant increase in the median HbA1c from 7.8% (7.0–8.93) to 8.4% (7.4–9.9; p < 0.001), with the highest value occurring in eastern and northern Ukraine. …”
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An atmospheric correction method for Himawari-8 imagery based on a multi-layer stacking algorithm by Menghui Wang, Donglin Fan, Hongchang He, You Zeng, Bolin Fu, Tianlong Liang, Xinyue Zhang, Wenhan Hu

“…For comparative analysis, a near-infrared–shortwave infrared AC method and a general machine learning AC method were also implemented. …”
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Identification of EARS2 as a Potential Biomarker with Diagnostic, Prognostic, and Therapeutic Implications in Colorectal Cancer by Wang L, Deng X, Tang J, Gong Y, Bu S, Li Z, Liao B, Ding Y, Dai T, Liao Y, Li Y

“…This study identifies key genes associated with lactic acid metabolism and explore their impact on CRC.Patients and Methods: This study utilized data from The Cancer Genome Atlas, Gene Expression Omnibus, other public databases, and our institutional resources. Machine learning identified key lactate metabolism-related genes. …”
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Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska by Pratima Khatri-Chhetri, Hans-Erik Andersen, Bruce Cook, Sean M. Hendryx, Liz van Wagtendonk, Van R. Kane

“…In this study, we present a framework for forest type classification combining field plots and high-resolution remote sensing data using machine learning models in the boreal forest of Interior Alaska. …”
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Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial. by Hee Yun Seol, Pragya Shrestha, Joy Fladager Muth, Chung-Il Wi, Sunghwan Sohn, Euijung Ryu, Miguel Park, Kathy Ihrke, Sungrim Moon, Katherine King, Philip Wheeler, Bijan Borah, James Moriarty, Jordan Rosedahl, Hongfang Liu, Deborah B McWilliams, Young J Juhn

“…<h4>Measurements</h4>Intervention was a quarterly A-GPS report to clinicians including relevant clinical information for asthma management from EHRs and machine learning-based prediction for risk of asthma exacerbation (AE). …”
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Consecutive prediction of adverse maternal outcomes of preeclampsia, using the PIERS-ML and fullPIERS models: A multicountry prospective observational study. by Guiyou Yang, Tünde Montgomery-Csobán, Wessel Ganzevoort, Sanne J Gordijn, Kimberley Kavanagh, Paul Murray, Laura A Magee, Henk Groen, Peter von Dadelszen

“…Among women whose pregnancies are complicated by preeclampsia, the Preeclampsia Integrated Estimate of RiSk (PIERS) models (i.e., the PIERS Machine Learning [PIERS-ML] model, and the logistic regression-based fullPIERS model) accurately identify individuals at greatest or least risk of adverse maternal outcomes within 48 h following admission. …”
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ECG‐based epileptic seizure prediction: Challenges of current data‐driven models by Sotirios Kalousios, Jens Müller, Hongliu Yang, Matthias Eberlein, Ortrud Uckermann, Gabriele Schackert, Witold H. Polanski, Georg Leonhardt

“…Two separate patient‐specific experiments were performed using a support vector machine (SVM). Firstly, the separability of training data was examined in a non‐causal trial. …”
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Disproportionate Distribution of HBV Genotypes A and D and the Recombinant Genotype D/E in the High and Low HBV Endemic Regions of Uganda: A Wake-Up Call for Regional Specific HBV... by Hussein Mukasa Kafeero, Dorothy Ndagire, Ponsiano Ocama, Charles Drago Kato, Eddie Wampande, Henry Kajumbula, David Kateete, Abdul Walusansa, Ali Kudamba, Kigozi Edgar, Fred Ashaba Katabazi, Maria Magdalene Namaganda, Jamilu E. Ssenku, Hakim Sendagire

“…The HBsAg Rapid Test Strip (Healgen Scientific Limited Liability Company, Houston, TX77047- USA) was used to screen for HBsAg while the Roche machine (Roche, Basel Switzerland/Abbot Technologies (USA)) was used to determine the viral load. …”
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Fatty Acids of Erythrocyte Membranes and Blood Serum in Differential Diagnosis of Inflammatory Bowel Diseases by M. V. Kruchinina, I. O. Svetlova, M. F. Osipenko, N. V. Abaltusova, A. A. Gromov, M. V. Shashkov, A. S. Sokolova, I. N. Yakovina, A. V. Borisova

“…The study of FA levels in groups with different nosological forms of IBDs using complex statistical analysis, including machine learning methods, made it possible to create diagnostic models that differentiate CD, UC and UCC in the acute stage with high accuracy. …”
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Technological dental sealants: in vitro evaluation of material properties and antibiofilm potential by Witalo Pereira de Jesus, Luiza de Almeida Queiroz Ferreira, Washington Henrique Themoteo da Silva, Fernanda Vieira Belém, Ana Paula Turrioni, Cláudia Silami de Magalhães, Mila Fernandes Moreira Madeira, Ivana Márcia Alves Diniz, Marco Aurelio Benini Paschoal

“…For the MBS test, specimens were made in bovine enamel with the addition of the experimental group FluroShield® (Dentsply) + Single Bond Universal® adhesive (3 M)(G5) and measured in a universal testing machine. Data were submitted to ANOVA and Tukey's post hoc tests (p < 0.05) using the SPSS software. …”
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New role of platelets in schizophrenia: predicting drug response by Tao Li, Wei Wei, Qiang Wang, Yamin Zhang, Wei Deng, Rui Xue, Wanjun Guo, Xiaojing Li, Hua Yu, Yanghao Zheng, Peiyan Ni, Sugai Liang, Xueyu Qi, Xueli Yu, Liansheng Zhao

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The Fate of the Interstellar Medium in Early-type Galaxies. V. Active Galactic Nucleus Feedback from Optical Spectral Classification by Oleh Ryzhov, Michał J. Michałowski, J. Nadolny, J. Hjorth, A. Leśniewska, M. Solar, P. Nowaczyk, C. Gall, T. T. Takeuchi

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Subtyping Social Determinants of Health in the "All of Us" Program: Network Analysis and Visualization Study by Suresh K Bhavnani, Weibin Zhang, Daniel Bao, Mukaila Raji, Veronica Ajewole, Rodney Hunter, Yong-Fang Kuo, Susanne Schmidt, Monique R Pappadis, Elise Smith, Alex Bokov, Timothy Reistetter, Shyam Visweswaran, Brian Downer

“…However, the high degree of systematic missingness requires repeating the analysis as the data become more complete by using our generalizable and scalable machine learning code available on the All of Us workbench.…”
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Convolutional neural networks for sea surface data assimilation in operational ocean models: test case in the Gulf of Mexico by O. Zavala-Romero, O. Zavala-Romero, A. Bozec, E. P. Chassignet, J. R. Miranda, J. R. Miranda

“…Recent advancements in ocean sciences, particularly in data assimilation (DA), suggest that machine learning can emulate dynamical models, replace traditional DA steps to expedite processes, or serve as hybrid surrogate models to enhance forecasts. …”
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Modeling Portfolio Optimization based on behavioral Preferences and Investor’s Memory by vahideh mousavi kakhki, Sanaz Khatabi

“…Additionally, researchers can investigate the application of other optimization techniques, such as machine learning algorithms, to portfolio optimization.…”
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Carbon stock dynamics of forest to oil palm plantation conversion for ecosystem rehabilitation planning by D. Frianto, E. Sutrisno, A. Wahyudi, E. Novriyanti, W.C. Adinugroho, A.S. Yunianto, H. Kurniawan, H. Khotimah, A. Windyoningrum, I.W.S. Dharmawan, H.L. Tata, S. Suharti, H.H. Rachmat, E.M. Lim

“…Data analysis was carried out using Classification and Regression Tree, a decision tree algorithm used in machine learning for guided classification. Furthermore, purposive sampling was utilized to gather socioeconomic data, followed by the implementation of a benefit-cost analysis.FINDINGS: The results revealed significant changes in the land cover within the Kepau Jaya specific purpose forest area over a 24-year period, with forested areas and open areas decreasing by 23.15 hectares per year and 16.94 hectares per year respectively, and oil palm plantation areas expanding by 40.10 hectares per year. …”
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A China dataset of soil properties for land surface modelling (version 2, CSDLv2) by G. Shi, W. Sun, W. Shangguan, Z. Wei, H. Yuan, L. Li, X. Sun, Y. Zhang, H. Liang, D. Li, F. Huang, Q. Li, Q. Li, Y. Dai

“…Using advanced ensemble machine learning and a high-performance parallel-computing strategy, we developed comprehensive maps of 23 soil physical and chemical properties at six standard depth layers from 0 to 2 m in China at a 90 m spatial resolution (China dataset of soil properties for land surface modelling version 2, CSDLv2). …”
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