Interactions between NAD+ metabolism and immune cell infiltration in ulcerative colitis: subtype identification and development of novel diagnostic models by Linglin Tian, Huiyang Gao, Tian Yao, Tian Yao, Yuhao Chen, Linna Gao, Jingxiang Han, Lanqi Zhu, He Huang, He Huang, He Huang

“…GSEA and GSVA identified potential biological pathways active within these subtypes, while the CIBERSORT algorithm assessed differential immune cell infiltration. …”
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Cancer phylogenetic tree inference at scale from 1000s of single cell genomes by Salehi, Sohrab, Dorri, Fatemeh, Chern, Kevin, Kabeer, Farhia, Rusk, Nicole, Funnell, Tyler, Williams, Marc J., Lai, Daniel, Andronescu, Mirela, Campbell, Kieran R., McPherson, Andrew, Aparicio, Samuel, Roth, Andrew, Shah, Sohrab P., Bouchard-Côté, Alexandre

“…The sitka transformation allows us to design novel scalable Markov chain Monte Carlo (MCMC) algorithms. Moreover, we introduce a novel point mutation calling method that incorporates the CN data and the underlying phylogenetic tree to overcome the low per-cell coverage of scWGS. …”
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In-Season Automated Mapping of Xinjiang Cotton Based on Cumulative Spectral and Phenological Characteristics by Yongsheng Huang, Yaozhong Pan, Yu Zhu, Xiufang Zhu, Xingsheng Xia, Qiong Chen, Jufang Hu, Hongyan Che, Xuechang Zheng, Lingang Wang

“…Second, using the maximum between-class variance method (OTSU) and Sauvola algorithms, a new local adaptive threshold method (Otsu–Sauvola) was developed for the automatic determination of the classification threshold. …”
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Early Detection of Oncological Diseases of the Digestive System (Guidelines of the Russian Gastroenterological Association and the Russian Association of Oncologists for Primary Ca... by V. T. Ivashkin, I. V. Mayev, A. D. Kaprin, M. Yu. Agapov, D. N. Andreev, A. S. Vodoleev, M. Yu. Zharkova, M. P. Korolev, Yu. A. Kucheryavyi, T. L. Lapina, M. V. Mayevskaya, A. V. Okhlobystin, Ch. S. Pavlov, A. V. Paraskevova, S. S. Pirogov, E. A. Poluektova, D. E. Rumyantseva, A. S. Trukhmanov, P. V. Tsarkov, A. A. Sheptulin, O. S. Shifrin

“…Each section offers practical algorithms in cases of suspected esophageal adenocarcinoma, esophageal squamous cell carcinoma, gastric cancer, colorectal cancer, hepatocellular carcinoma and extrahepatic bile duct and gallbladder cancer, as well as pancreatic cancer.Conclusion. …”
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Advances in machine learning applications to resource technology for organic solid waste by Hongzhi MA, Yichan LIU, Jihua ZHAO, Fan FEI, Ming GAO, Qunhui WANG

“…A key focus of this work is the combination of ML models with optimization algorithms like Genetic Algorithm, which improves the performance of ML models by optimizing hyperparameters and enhancing prediction accuracy. …”
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Assessment of atmospheric correction methods in MSI imagery for deriving bathymetry and substrates of shallow-water coral reefs by Wei Huang, Wei Huang, Wei Huang, Wei Huang, Jun Zhao, Mingjie Li, Mingjie Li, Mingjie Li, Quansheng Lou, Quansheng Lou, Nanyang Yan, Nanyang Yan, Nanyang Yan, Shaojie Sun

“…These include short-wave infrared band Glint Correction (GC), DeGlint (DG), and near-infrared band intercept (DG865) algorithms to enhance glint correction after the DSF processor. …”
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Resveratrol contributes to NK cell-mediated breast cancer cytotoxicity by upregulating ULBP2 through miR-17-5p downmodulation and activation of MINK1/JNK/c-Jun signaling by Bisha Ding, Jie Li, Jia-Lin Yan, Chun-Yan Jiang, Ling-Bo Qian, Jie Pan

“…The target gene of miR-17-5p were predicted with different algorithms from five databases and further confirmed with dual-luciferase reporter assay. …”
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Single-cell profiling of SLC family transporters: uncovering the role of SLC7A1 in osteosarcoma by Yan Liao, Junkai Chen, Hao Yao, Ting Zheng, Jian Tu, Weidong Chen, ZeHao Guo, Yutong Zou, Lili Wen, Xianbiao Xie

“…Multiple analytical algorithms indicated that SLCs were associated with immune cell infiltration and immune checkpoint gene expression. …”
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On-Demand Gait-Synchronous Electrical Cueing in Parkinson's Disease Using Machine Learning and Edge Computing: A Pilot Study by Ardit Dvorani, Constantin Wiesener, Christina Salchow-Hommen, Magdalena Jochner, Lotta Spieker, Matej Skrobot, Hanno Voigt, Andrea Kuhn, Nikolaus Wenger, Thomas Schauer

“…<italic>Methods:</italic> We present a new technical solution, that runs detection and cueing algorithms directly on the sensing and cueing devices, bypassing the smartphone. …”
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Toward accurate prediction of carbon dioxide (CO2) compressibility factor using tree-based intelligent schemes (XGBoost and LightGBM) and equations of state by Behnam Amiri-Ramsheh, Aydin Larestani, Saeid Atashrouz, Elnaz Nasirzadeh, Meriem Essakhraoui, Ali Abedi, Mehdi Ostadhassan, Ahmad Mohaddespour, Abdolhossein Hemmati-Sarapardeh

“…In this study, two powerful and robust tree-based machine learning (ML) algorithms, namely light gradient boosted machine (LightGBM) and extreme gradient boosting (XGBoost) were utilized to precisely estimate CO2 Z-factor. …”
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Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence by Xiaoyin Wu, Xiaoyin Wu, Buyu Guo, Buyu Guo, Xingyu Chang, Xingyu Chang, Yuxuan Yang, Yuxuan Yang, Qianqian Liu, Qianqian Liu, Jiahui Liu, Jiahui Liu, Yichen Yang, Yichen Yang, Kang Zhang, Yumei Ma, Songbo Fu, Songbo Fu, Songbo Fu

“…The expression levels of diagnostic signatures were verified in vitro.ResultsThrough the 108 combinations of machine learning algorithms, we selected 12 diagnostic signatures, including CD163, CYBB, ELF3, FCN1, PROM1, GPR65, LCN2, LTF, S100A4, SOX4, TGFB1 and TNFAIP8. …”
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Clinical Practice Guidelines of the Russian Society for the Study of the Liver, the Russian Gastroenterological Association, the National Scientific Society of Infectious Disease S... by V. T. Ivashkin, V. P. Chulanov, N. A. Mamonova, M. V. Maevskaya, M. S. Zharkova, I. N. Tikhonov, P. O. Bogomolov, E. V. Volchkova, A. S. Dmitriev, O. O. Znojko, E. A. Klimova, K. V. Kozlov, I. E. Kravchenko, E. Yu. Malinnikova, R. V. Maslennikov, M. I. Mikhailov, K. E. Novak, I. G. Nikitin, V. E. Syutkin, E. V. Esaulenko, A. A. Sheptulin, E. N. Shirokova, N. D. Yushchuk

“…Аim: diagnosis and treatment algorithms in the clinical recommendations intended for general practitioners, gastroenterologists, infectious disease specialists, hepatologists on the of chronic hepatitis C are presented.Summary. …”
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Population Dynamics of the Crocodile Shark, <i>Pseudocarcharias kamoharai</i>, in the Tropical Equatorial Pacific Ocean, Ecuador by Marcos Douglas Calle-Morán, Eugenio Alberto Aragón-Noriega, Ana Rosa Hernández-Téllez, Emigdio Marín-Enríquez, Javier Tovar-Ávila, Juan Francisco Arzola-González, Jorge Payán-Alejo

“…These values were similar to the six algorithms designed for cartilaginous fish, ranging from 0.16 to 0.35; for this reason, these mortality rates were considered low. …”
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Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR Data by Jiannan He, Yongbin Zhang, Mingyue Liu, Lin Chen, Weidong Man, Hua Fang, Xiang Li, Xuan Yin, Jianping Liang, Wenke Bai, Fuping Li

“…We compared the predictive performance of these different machine learning algorithms to identify the most effective one. The results show that the following. 1) The prediction accuracy is improved by classifying the data into three types: unlodging <italic>S. alterniflora</italic> (ULSA), lodging <italic>S. alterniflora</italic> (LSA), and mudflats. 2) XGBoost outperformed RF and SVM in accurately predicting SOC content, with <italic>R</italic>²; values of 0.743 for ULSA, 0.731 for LSA, and 0.705 for mudflats; 3) In the XGBoost models constructed for ULSA, LSA, and mudflats, spectral features contributed 75.7&#x0025;, 73.1&#x0025;, and 63.1&#x0025;, respectively, with the normalized difference vegetation index emerging as the most critical spectral feature. …”
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Design of Command and Dispatch System for Automatic Reading of Meter Images in Substations by UAV Inspection Photos by Guanghong Deng, Liangpei Lin, Baihao Lin, Wenlong Jing, Xiaodan Zhao, Yong Li, Rui Yang, Shidun Xie, Pengfeng Wei

“…The backbone network of the Deeplabv3[Formula: see text] algorithm is improved by using the MobileNetv3 network, which not only effectively extracts pointers and scales, but also makes the model more lightweight. …”
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Applied machine learning for predicting the properties and carbon and phosphorus fate of pristine and engineered hydrochar by Shiyu Xie, Tao Zhang, Siming You, Santanu Mukherjee, Mingjun Pu, Qing Chen, Yaosheng Wang, Esmat F. Ali, Hamada Abdelrahman, Jörg Rinklebe, Sang Soo Lee, Sabry M. Shaheen

“…Also, different ML algorithms were used to model and predict the hydrochar solid yield, properties, and nutrients content. …”
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Protocol for an intervention development and pilot implementation evaluation study of an e-health solution to improve newborn care quality and survival in two low-resource settings... by Hassan Haghparast-Bidgoli, Michelle Heys, Caroline Crehan, Erin Kesler, Monica Lakhanpaul, Emma Wilson, Fabiana Lorencatto, Felicity Fitzgerald, Mario Cortina Borja, Msandeni Chiume, Hannah Gannon, Simbarashe Chimhuya, Gwendoline Chimhini, Tim Hull-Bailey, Tarisai Chiyaka, Yali Sassoon, Deliwe Nkhoma, Nushrat Khan, Alexander G. Stevenson, Kristina Curtis

“…Neotree clinical decision support algorithms will be optimised according to best available evidence and clinical validation studies.Ethics and dissemination This is a Wellcome Trust funded project (215742_Z_19_Z). …”
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A compilation of surface inherent optical properties and phytoplankton pigment concentrations from the Atlantic Meridional Transect by T. M. Jordan, G. Dall'Olmo, G. Tilstone, R. J. W. Brewin, F. Nencioli, R. Airs, C. S. Thomas, L. Schlüter

“…<p>In situ measurements of particulate inherent optical properties (IOPs) – absorption (<span class="inline-formula"><i>a</i>_p(<i>λ</i>)</span>), scattering (<span class="inline-formula"><i>b</i>_p(<i>λ</i>)</span>), and beam attenuation (<span class="inline-formula"><i>c</i>_p(<i>λ</i>)</span>) – are crucial for the development of optical algorithms that retrieve biogeochemical quantities such as chlorophyll <span class="inline-formula"><i>a</i></span>, particulate organic carbon (POC), and total suspended matter (TSM). …”
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Artificial intelligence-based prediction of second stage duration in labor: a multicenter retrospective cohort analysisResearch in context by Xiaoqing Huang, Xiaodan Di, Suiwen Lin, Minrong Yao, Suijin Zheng, Shuyi Liu, Wayan Lau, Zhixin Ye, Zilian Wang, Bin Liu

“…After the optimal features selected by recursive feature elimination (RFE) method, four ML algorithms were employed to build the models. The best model would be selected with the predictive performance and interpreted with Shapley Additive exPlanations method. …”
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Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study by Yanqi Kou, Shicai Ye, Yuan Tian, Ke Yang, Ling Qin, Zhe Huang, Botao Luo, Yanping Ha, Liping Zhan, Ruyin Ye, Yujie Huang, Qing Zhang, Kun He, Mouji Liang, Jieming Zheng, Haoyuan Huang, Chunyi Wu, Lei Ge, Yuping Yang

“…Propensity score matching was adjusted for demographics, and the Boruta algorithm identified key predictors. A total of 7 ML algorithms—logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest (RF), extreme gradient boosting, and neural networks—were trained using 10-fold cross-validation. …”
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