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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Retrieving the atmospheric concentrations of carbon dioxide and methane from the European Copernicus CO2M satellite mission using artificial neural networks by M. Reuter, M. Hilker, S. Noël, A. Di Noia, M. Weimer, O. Schneising, M. Buchwitz, H. Bovensmann, J. P. Burrows, H. Bösch, R. Lang

“…Conventional so-called full-physics algorithms for retrieving XCO<span class="inline-formula">₂</span> and/or XCH<span class="inline-formula">₄</span> from satellite-based measurements of reflected solar radiation are typically computationally intensive and still usually require empirical bias corrections based on supervised machine learning methods. Here we present the retrieval algorithm Neural networks for Remote sensing of Greenhouse gases from CO2M (NRG-CO2M), which derives XCO<span class="inline-formula">₂</span> and XCH<span class="inline-formula">₄</span> from CO2M radiance measurements with minimal computational effort using artificial neural networks (ANNs). …”
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Single-cell microbiota phenotyping reveals distinct disease and therapy-associated signatures in Crohn’s disease by Lisa Budzinski, Gi-Ung Kang, René Riedel, Toni Sempert, Leonie Lietz, René Maier, Janine Büttner, Bettina Bochow, Marcell T. Tordai, Aayushi Shah, Amro Abbas, Tanisha Momtaz, Jannike L. Krause, Robin Kempkens, Katrin Lehman, Gitta A. Heinz, Anne E. Benken, Stefanie Bartsch, Kathleen Necke, Ute Hoffmann, Mir-Farzin Mashreghi, Robert Biesen, Tilmann Kallinich, Tobias Alexander, Bosse Jessen, Carl Weidinger, Britta Siegmund, Andreas Radbruch, Anja Schirbel, Benjamin Moser, Hyun-Dong Chang

“…The data were analyzed by machine-learning to assess disease-specific marker patterns in the microbiota phenotype. mMFC captured detailed characteristics of CD microbiota and identified patterns to classify CD patients. …”
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Applied pharmacogenetics to predict response to treatment of first psychotic episode: study protocol by Sergi Mas, Sergi Mas, Sergi Mas, Laura Julià, Manuel J. Cuesta, Manuel J. Cuesta, Manuel J. Cuesta, Benedicto Crespo-Facorro, Benedicto Crespo-Facorro, Benedicto Crespo-Facorro, Benedicto Crespo-Facorro, Javier Vázquez-Bourgon, Javier Vázquez-Bourgon, Javier Vázquez-Bourgon, Carlos Spuch, Carlos Spuch, Ana Gonzalez-Pinto, Ana Gonzalez-Pinto, Angela Ibañez, Angela Ibañez, Judith Usall, Judith Usall, Cristina Romero-López-Alberca, Cristina Romero-López-Alberca, Ana Catalan, Ana Catalan, Ana Catalan, Ana Catalan, Ana Catalan, Anna Mané, Anna Mané, Anna Mané, Anna Mané, Miquel Bernardo, Miquel Bernardo, Miquel Bernardo, Miquel Bernardo

“…These predictors will integrate, through machine learning techniques, pharmacogenetic (measured as polygenic risk scores) and epigenetic data together with clinical, sociodemographic, environmental, and neuroanatomical data. …”
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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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CompSafeNano project: NanoInformatics approaches for safe-by-design nanomaterials by Dimitrios Zouraris, Angelos Mavrogiorgis, Andreas Tsoumanis, Laura Aliisa Saarimäki, Giusy del Giudice, Antonio Federico, Angela Serra, Dario Greco, Ian Rouse, Julia Subbotina, Vladimir Lobaskin, Karolina Jagiello, Krzesimir Ciura, Beata Judzinska, Alicja Mikolajczyk, Anita Sosnowska, Tomasz Puzyn, Mary Gulumian, Victor Wepener, Diego S.T. Martinez, Romana Petry, Naouale El Yamani, Elise Rundén-Pran, Sivakumar Murugadoss, Sergey Shaposhnikov, Vasileios Minadakis, Periklis Tsiros, Harry Sarimveis, Eleonora Marta Longhin, Tanima SenGupta, Ann-Karin Hardie Olsen, Viera Skakalova, Peter Hutar, Maria Dusinska, Anastasios G. Papadiamantis, L. Cristiana Gheorghe, Katie Reilly, Emilie Brun, Sami Ullah, Sebastien Cambier, Tommaso Serchi, Kaido Tämm, Candida Lorusso, Francesco Dondero, Evangelos Melagrakis, Muhammad Moazam Fraz, Georgia Melagraki, Iseult Lynch, Antreas Afantitis

“…By building on established nanoinformatics frameworks, such as those developed in the H2020-funded projects NanoSolveIT and NanoCommons, CompSafeNano addresses critical challenges in nanosafety through development and integration of innovative methodologies, including advanced in vitro models, in silico approaches including machine learning (ML) and artificial intelligence (AI)-driven predictive models and 1st-principles computational modelling of NMs properties, interactions and effects on living systems. …”
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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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BotCatcher:botnet detection system based on deep learning by Di WU, Binxing FANG, Xiang CUI, Qixu LIU

“…Machine learning technology has wide application in botnet detection.However,with the changes of the forms and command and control mechanisms of botnets,selecting features manually becomes increasingly difficult.To solve this problem,a botnet detection system called BotCatcher based on deep learning was proposed.It automatically extracted features from time and space dimension,and established classifier through multiple neural network constructions.BotCatcher does not depend on any prior knowledge which about the protocol and the topology,and works without manually selecting features.The experimental results show that the proposed model has good performance in botnet detection and has ability to accurately identify botnet traffic .…”
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Development of particle flow algorithm with GNN for Higgs factories by Murata Tatsuki, Suehara Taikan

“…It is a multi-step reconstruction algorithm consisting of clustering, track-cluster association, and various refinement processes. We have studied machine learned particle flow model using Graph Neural Network based algorithm developed in the context of CMS HGCAL clustering. …”
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Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019 by Jenberu Mekurianew Kelkay, Deje Sendek Anteneh, Henok Dessie Wubneh, Abraham Dessie Gessesse, Gebeyehu Fassil Gebeyehu, Kalkidan Kassahun Aweke, Mikiyas Birhanu Ejigu, Mathias Amare Sendeku, Kirubel Adrissie Barkneh, Hasset Girma Demissie, Wubshet D. Negash, Birku Getie Mihret

“…The dataset was then imported into a Jupyter notebook for further detailed analysis and visualization. An ensemble Machin learning algorithm using different classification models were implemented. …”
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