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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Spinal cord demyelination predicts neurological deterioration in patients with mild degenerative cervical myelopathy by Jefferson R Wilson, David Anderson, Stephan Duplessis, Julien Cohen-Adad, David W Cadotte, Nathan Evaniew, Jacques Bouchard, Michael Craig, Abdul Al-Shawwa, Kalum Ost, Steve Casha, W Bradley Jacobs, Saswati Tripathy, Peter Lewkonia, Fred Nicholls, Alex Soroceanu, Ganesh Swamy, Kenneth C Thomas, Michael MH Yang, Nicholas Dea

“…Quantitative MRI (qMRI) metrics were derived above and below maximally compressed cervical levels (MCCLs). Various machine learning (ML) models were trained to predict 6 month neurological deterioration, followed by global and local model interpretation to assess feature importance.Results A total of 49 patients were followed for a maximum of 2 years, contributing 110 6-month data entries. …”
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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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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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MO-GCN: A multi-omics graph convolutional network for discriminative analysis of schizophrenia by Haiyuan Wang, Runlin Peng, Yuanyuan Huang, Liqin Liang, Wei Wang, Baoyuan Zhu, Chenyang Gao, Minxin Guo, Jing Zhou, Hehua Li, Xiaobo Li, Yuping Ning, Fengchun Wu, Kai Wu

“…The methodology of machine learning with multi-omics data has been widely adopted in the discriminative analyses of schizophrenia, but most of these studies ignored the cooperative interactions and topological attributes of multi-omics networks. …”
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Risk factors affecting polygenic score performance across diverse cohorts by Daniel Hui, Scott Dudek, Krzysztof Kiryluk, Theresa L Walunas, Iftikhar J Kullo, Wei-Qi Wei, Hemant Tiwari, Josh F Peterson, Wendy K Chung, Brittney H Davis, Atlas Khan, Leah C Kottyan, Nita A Limdi, Qiping Feng, Megan J Puckelwartz, Chunhua Weng, Johanna L Smith, Elizabeth W Karlson, Regeneron Genetics Center, Penn Medicine BioBank, Gail P Jarvik, Marylyn D Ritchie

“…Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. …”
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Osteopenia Metabolomic Biomarkers for Early Warning of Osteoporosis by Jie Wang, Dandan Yan, Suna Wang, Aihua Zhao, Xuhong Hou, Xiaojiao Zheng, Jingyi Guo, Li Shen, Yuqian Bao, Wei Jia, Xiangtian Yu, Cheng Hu, Zhenlin Zhang

“…A few metabolites were identified as candidate early-warning biomarkers by machine learning analysis, which could indicate bone loss and provide new prevention guidance for osteoporosis.…”
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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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Identification and susceptibility assessment of landslide disasters in the red bed formation along the Nanjian-Jingdong Expressway by Yifan Cao, Zhifang Zhao, Mingchun Wen, Xin Zhao, Dingyi Zhou, Jingyi Qin, Liu Ouyang, Jingyao Cao

“…In combination with optical imagery data, a total of 521 landslide disaster points were identified. (2) In comparison to individual machine learning models, the Stacking demonstrated superior performance, with prediction capabilities and accuracy that surpassed other models. …”
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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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Proteomic profiling of the local and systemic immune response to pediatric respiratory viral infections by Emily Lydon, Christina M. Osborne, Brandie D. Wagner, Lilliam Ambroggio, J. Kirk Harris, Ron Reeder, Todd C. Carpenter, Aline B. Maddux, Matthew K. Leroue, Nadir Yehya, Joseph L. DeRisi, Mark W. Hall, Athena F. Zuppa, Joseph Carcillo, Kathleen Meert, Anil Sapru, Murray M. Pollack, Patrick McQuillen, Daniel A. Notterman, Charles R. Langelier, Peter M. Mourani

“…From tracheal aspirate (TA), we defined a proteomic signature of vLRTI characterized by increased expression of interferon signaling proteins and decreased expression of proteins involved in immune modulation including FABP and MIP-5. Using machine learning, we developed a parsimonious diagnostic classifier that distinguished vLRTI from non-infectious respiratory failure with high accuracy. …”
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Bridging animal models and humans: neuroimaging as intermediate phenotypes linking genetic or stress factors to anhedonia by Huiling Guo, Yao Xiao, Shuai Dong, Jingyu Yang, Pengfei Zhao, Tongtong Zhao, Aoling Cai, Lili Tang, Juan Liu, Hui Wang, Ruifang Hua, Rongxun Liu, Yange Wei, Dandan Sun, Zhongchun Liu, Mingrui Xia, Yong He, Yankun Wu, Tianmei Si, Fay Y. Womer, Fuqiang Xu, Yanqing Tang, Jie Wang, Weixiong Zhang, Xizhe Zhang, Fei Wang

“…We then applied a machine-learning approach to cluster neuroimaging subtypes of depression. …”
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Adaptive anomaly detection disruption prediction starting from first discharge on tokamak by X.K. Ai, W. Zheng, M. Zhang, Y.H. Ding, D.L. Chen, Z.Y. Chen, B.H. Guo, C.S. Shen, N.C. Wang, Z.J. Yang, Z.P. Chen, Y. Pan, B. Shen, B.J. Xiao, J-TEXT team

“…While current data-driven machine learning methods perform well in disruption prediction, they require extensive discharge data for model training. …”
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Gaps in U.S. livestock data are a barrier to effective environmental and disease management by Rebecca Logsdon Muenich, Sanskriti Aryal, Amanda J Ashworth, Michelle L Bell, Melanie R Boudreau, Stephanie A Cunningham, K Colton Flynn, Kerry A Hamilton, Ting Liu, Michael L Mashtare, Natalie G Nelson, Barira Rashid, Arghajeet Saha, Danica Schaffer-Smith, Callie Showalter, Aureliane Tchamdja, Jada Thompson

“…We then feature some recent work to improve livestock data availability through remote-sensing and machine learning, ending with our takeaways to address these data needs for the future of environmental and public health management.…”
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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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The TB27 Transcriptomic Model for Predicting Mycobacterium tuberculosis Culture Conversion by Maja Reimann, Korkut Avsar, Andrew DiNardo, Torsten Goldmann, Gunar Günther, Michael Hoelscher, Elmira Ibraim, Barbara Kalsdorf, Stefan Kaufmann, Niklas Köhler, Anna Mandalakas, Florian Maurer, Marius Müller, Dörte Nitschkowski, Ioana Olaru, Cristina Popa, Andrea Rachow, Thierry Rolling, Helmut Salzer, Patricia Sanchez-Carballo, Maren Schuhmann, Dagmar Schaub, Victor Spinu, Elena Terhalle, Markus Unnewehr, Nika Zielinski, Jan Heyckendorf, Christoph Lange

“…Methods: Using a multi-step machine learning algorithm to identify an RNA-based algorithm to predict the remaining time to culture conversion at flexible time points during anti-tuberculosis therapy.  …”
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Development and Evaluation of an AI-based Exergame Training System for Ice-Hockey Players: a Randomized Controlled Trial by Nicole Sieber, Simon Walser, Thomas Weber, Raphael Gubler, Hannes Badertscher, Patrick Eggenberger

“…Artificial intelligence (i.e., machine learning) was applied to train and validate algorithms to accurately detect joint positions of the human body based on large open-source training and validation data sets. …”
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Feature Engineering to Embed Process Knowledge: Analyzing the Energy Efficiency of Electric Arc Furnace Steelmaking by Quantum Zhuo, Mansour N. Al-Harbi, Petrus C. Pistorius

“…Further improvement was obtained by applying the engineered features to a non-linear machine-learned model (based on XGBoost), yielding both physically reasonable trends and smaller prediction errors. …”
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