Pandemic one health clones of Escherichia coli and Klebsiella pneumoniae producing CTX-M-14, CTX-M-27, CTX-M-55 and CTX-M-65 ESβLs among companion animals in northern Ecuador by Fernando A. Gonzales-Zubiate, José Humberto M. Tambor, José Humberto M. Tambor, Juan Valencia-Bacca, María Fernanda Villota-Burbano, Adriana Cardenas-Arias, Adriana Cardenas-Arias, Fernanda Esposito, Fernanda Esposito, Quézia Moura, Bruna Fuga, Bruna Fuga, Bruna Fuga, Elder Sano, Jesus G. M. Pariona, Mishell Poleth Ortiz Jacome, Nilton Lincopan, Nilton Lincopan, Nilton Lincopan

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Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning by F. Bortolussi, H. Sandström, F. Partovi, F. Partovi, J. Mikkilä, P. Rinke, P. Rinke, P. Rinke, P. Rinke, M. Rissanen, M. Rissanen

“…We then trained two machine learning methods on these data: (1) random forest (RF) for classifying if a pesticide can be detected with CIMS and (2) kernel ridge regression (KRR) for predicting the expected CIMS signals. …”
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Recent centennial drought on the Tibetan Plateau is outstanding within the past 3500 years by Yu Liu, Huiming Song, Zhisheng An, Qiang Li, Steven W. Leavitt, Ulf Büntgen, Qiufang Cai, Ruoshi Liu, Congxi Fang, Changfeng Sun, Kerstin Treydte, Meng Ren, Lidong Mo, Yi Song, Wenju Cai, Quan Zhang, Weijian Zhou, Achim Bräuning, Jussi Grießinger, Deliang Chen, Hans W. Linderholm, Ashish Sinha, Hai Cheng, Lu Wang, Ying Lei, Junyan Sun, Wei Gong, Xuxiang Li, Linlin Cui, Liang Ning, Lingfeng Wan, Thomas W. Crowther, Constantin M. Zohner

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Influence of ESBL colonization status on gut microbiota composition during allogenic hematopoietic stem cell transplantation by Silvia Corcione, Ilario Ferrocino, Tommaso Lupia, Alessandro Busca, Gabriele Bianco, Chiara Dellacasa, Luisa Giaccone, Lucia Brunello, Sara Butera, Cristina Costa, Benedetto Bruno, Francesco Giuseppe De Rosa

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An artificial intelligence application to predict prolonged dependence on mechanical ventilation among patients with critical orthopaedic trauma: an establishment and validation st... by Weigang Jiang, Tao Liu, Baisheng Sun, Lixia Zhong, Zhencan Han, Minhua Lu, Mingxing Lei

“…Patients in the training cohort were used to establish models using machine learning techniques, including logistic regression (LR), extreme gradient boosting machine (eXGBM), decision tree (DT), random forest (RF), support vector machine (SVM), and light gradient boosting machine (LightGBM), whereas patients in the validation cohort were used to validate these models. …”
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DFT study of co-doping effects on the electronic, optical, transport, and thermodynamic properties of (5,5) SWCNTs for photovoltaic and photonic applications by I.A. Tabet Djeudi, G.W. Ejuh, P.F. Bissi Nyandou, Oumaima Douass, A. Teyou Ngoupo, C.C. Fonkem, Y. Tadjouteu Assatse, R.A. Yossa Kamsi, J.M.B. Ndjaka, Bilel Mehnen

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Eye Collateral Channel Characteristic Analysis and Identification Model Construction of Mild Cognitive Impairment by WU Tiecheng, CAO Lei, YIN Lianhua, HE Youze, LIU Zhizhen, YANG Minguang, XU Ying, WU Jinsong

“…Different MCI identification models were constructed using support vector machine, decision tree, artificial neural network and random forest algorithm, with MCI eye collateral channel characteristics and TCM syndrome elements as independent variables and onset of MCI as a dependent variable. …”
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Treatment efficacy for infantile epileptic spasms syndrome in children with trisomy 21 by Henry Chen, Henry Chen, Adam L. Numis, Adam L. Numis, Renée A. Shellhaas, John R. Mytinger, Debopam Samanta, Rani K. Singh, Rani K. Singh, Shaun A. Hussain, Danielle Takacs, Kelly G. Knupp, Li-Rong Shao, Carl E. Stafstrom

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Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning by Chenbo Yang, Chenbo Yang, Meichen Feng, Juan Bai, Hui Sun, Rutian Bi, Lifang Song, Chao Wang, Yu Zhao, Wude Yang, Lujie Xiao, Meijun Zhang, Xiaoyan Song

“…Hyperspectral monitoring models for winter wheat ChD were constructed using 8 machine learning algorithms, including partial least squares regression, support vector regression, multi-layer perceptron regression, random forest regression, extra-trees regression (ETsR), decision tree regression, K-nearest neighbors regression, and gaussian process regression, based on the full spectrum band and the band selected by competitive adaptive reweighted sampling (CARS). …”
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Persistent and emerging threats to Arctic biodiversity and ways to overcome them: a horizon scan by Tanya A. Lemieux, Jackson D.R. Coles, Anne L. Haley, Michelle L. LaFlamme, Sara K. Steel, Kara M. Scott, Jennifer F. Provencher, Courtney Price, Joseph R. Bennett, Isabel C. Barrio, Helen S. Findlay, Simon J. Goodman, Blake Matthews, Joacim Näslund, David. A. Pearce, Robert D. Hollister, Mark L. Mallory, Paul A. Smith, Gabriela Schaepman-Strub, Steven J. Cooke

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Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Develo... by Hui Li, Ruyi Zou, Hongxia Xin, Ping He, Bin Xi, Yaqiong Tian, Qi Zhao, Xin Yan, Xiaohua Qiu, Yujuan Gao, Yin Liu, Min Cao, Bi Chen, Qian Han, Juan Chen, Guochun Wang, Hourong Cai

“…Six ML algorithms (Extreme Gradient Boosting [XGBoost], logistic regression (LR), Light Gradient Boosting Machine [LightGBM], random forest [RF], support vector machine [SVM], and k-nearest neighbor [KNN]) were applied to construct and evaluate the model. …”
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Molecular testing for cytologically suspicious and malignant (Bethesda V and VI) thyroid nodules to optimize the extent of surgical intervention: a retrospective chart review by Jessica Hier, Galit Avior, Marc Pusztaszeri, Joshua R. Krasner, Noura Alyouha, Veronique-Isabelle Forest, Michael P. Hier, Alex Mlynarek, Keith Richardson, Nader Sadeghi, Michael Tamilia, Richard J. Payne

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To what extent does the CO₂ diurnal cycle impact flux estimates derived from global and regional inversions? by S. Munassar, S. Munassar, S. Munassar, C. Rödenbeck, M. Gałkowski, M. Gałkowski, F.-T. Koch, F.-T. Koch, K. U. Totsche, K. U. Totsche, S. Botía, C. Gerbig

“…Furthermore, the differences in NEE estimates calculated with CS increase the magnitude of the flux budgets for some regions such as North American temperate forests and northern Africa by a factor of about 1.5. …”
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Multimodal data deep learning method for predicting symptomatic pneumonitis caused by lung cancer radiotherapy combined with immunotherapy by Mingyu Yang, Jianli Ma, Chengcheng Zhang, Liming Zhang, Jianyu Xu, Shilong Liu, Jian Li, Jiabin Han, Songliu Hu

“…Comparatively, the radiomic feature model based on random forest (RF) yielded an AUC of 0.576, with a 95% confidence interval of 0.523-0.628. …”
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Work performance, physiological and hemato-biochemical responses in working buffalo with different load weights by Visoky Paján-Jiménez, Fernando D. Pazmiño-Rodríguez, Patricia Roldán-Santiago, Cristian Larrondo-Cornejo, Anna Dutro-Aceves, Luis A. de la Cruz-Cruz

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La datation dendrochronologique du coffrage de fondation d’une pile du pont-siphon de l’Yzeron à Beaunant (Sainte-Foy-lès-Lyon, Métropole de Lyon) by François Blondel, David Baldassari

“…This high correlation value (Student’s t = 5.25 and coefficient (r) = 0.61) most likely suggests a supply of fir from the west. This limits the forests exploited to the Pilat massif, Monts Lyonnais or the Forez foothills. …”
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Development of a clinical-radiological nomogram for predicting severe postoperative peritumoral brain edema following intracranial meningioma resection by Chen Bo, Geng Ao, Lu Siyuan, Lu Siyuan, Wu Ting, Wang Dianjun, Zhao Nan, Shan Xiuhong, Deng Yan, Sun Eryi

“…Based on these analyses, we developed five predictive models using R software: conventional logistic regression, XGBoost, random forest, support vector machine (SVM), and k-nearest neighbors (KNN). …”
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The Inventory of the Estate farm Senkoniai by Stasys Pamerneckis, Roberta Sakalauskaitė

“…., the quantity of the land and its quality (whether it is a humus, a humus with loam, a sandy loam), its purpose (whether it is arable, used as a hayfield, a pasture, grown with bushes, a forest, or it is barren land) is indicated in it. …”
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Eoldist, a Web Application for Estimating Cautionary Detection Distance of Birds by Automatic Detection Systems to Reduce Collisions With Wind Turbines by Julie Fluhr, Olivier Duriez, Constance Blary, Thierry Chambert, Bettina Almasi, Patrik Byholm, Nelleke H. Buitendijk, Jocelyn Champagnon, Mindaugas Dagys, Wolfgang Fiedler, Charlotte Francesiaz, Frédéric Jiguet, Simon Lee, Alexandre Millon, Flavio Monti, Lucile Morcelet, Ran Nathan, Bart A. Nolet, Rascha Nuijten, Philippe Pilard, Cécile Ponchon, Alexandre Roulin, Carlos D. Santos, Orr Spiegel, Kim Schalcher, Aurélie De Seynes, Geert Spanoghe, Martin Wikelski, Ramunas Žydelis, Aurélien Besnard

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Development and Validation of a Cost-Effective Machine Learning Model for Screening Potential Rheumatoid Arthritis in Primary Healthcare Clinics by Wu W, Hu X, Yan L, Li Z, Li B, Chen X, Lin Z, Zeng H, Li C, Mo Y, Wu Y, Wang Q

“…Subsequently, we retrained and validated our proposed model based on two primary healthcare validation cohorts.Results: In experiments, the algorithms achieved over 88% accuracy on training and test sets. Random Forest (RF) excelled with 96.20% (95% CI 95.39% to 97.02%) accuracy, 96.22% (95% CI 95.40% to 97.03%) specificity, 96.18% (95% CI 95.37% to 97.00%) sensitivity, and 96.20% (95% CI 95.39% to 97.02%) Areas Under Curves (AUC). …”
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