Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota by Polina V. Popova, Artem O. Isakov, Anastasiia N. Rusanova, Stanislav I. Sitkin, Anna D. Anopova, Elena A. Vasukova, Alexandra S. Tkachuk, Irina S. Nemikina, Elizaveta A. Stepanova, Angelina I. Eriskovskaya, Ekaterina A. Stepanova, Evgenii A. Pustozerov, Maria A. Kokina, Elena Y. Vasilieva, Lyudmila B. Vasilyeva, Soha Zgairy, Elad Rubin, Carmel Even, Sondra Turjeman, Tatiana M. Pervunina, Elena N. Grineva, Omry Koren, Evgeny V. Shlyakhto

“…The study involved 105 pregnant women (77 with GDM, 28 healthy), who underwent continuous glucose monitoring (CGM) for 7 days, provided food diaries, and gave stool samples for microbiome analysis. Machine learning models were created using CGM data, meal content, lifestyle factors, biochemical parameters, and microbiota data (16S rRNA gene sequence analysis). …”
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The study of high-performance generation methods for rural plan based on generative adversarial network by Xiao-Hu Liu, Peng-Cheng Miao, Xiao-Xiao Dong, Baghdad Esmail, Fei Ye, Dian Lei

“…This study employs CGAN to generate comprehensive village layouts based on archetypal traditional villages, while also exploring parameters and network architectures to enhance result quality. …”
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Estimating forest aboveground carbon sink based on landsat time series and its response to climate change by Kun Yang, Kai Luo, Jialong Zhang, Bo Qiu, Feiping Wang, Qinglin Xiao, Jun Cao, Yunrun He, Jian Yang

“…Fewer studies have used machine learning-based dynamic models to estimate forest carbon sink. …”
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Employing artificial intelligence for optimising antibiotic dosages in sepsis on intensive care unit: a study protocol for a prospective observational study (KI.SEP) by Tim Rahmel, Michael Adamzik, Hartmuth Nowak, Lars Bergmann, Björn Koos, Martin Eisenacher, Barbara Sitek, Britta Marko, Lars Palmowski, Andrea Witowski, Katharina Rump, Julia Bandow, Patrick Günther

“…This study addresses these challenges by developing machine learning (ML)-based ASC prediction models capable of handling variable data input and encompassing diverse clinical, laboratory, microbiological and proteomic parameters without the need for daily TDM.Methods This prospective observational study is conducted in a German university hospital intensive care unit. …”
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Genotyping Identification of Maize Based on Three-Dimensional Structural Phenotyping and Gaussian Fuzzy Clustering by Bo Xu, Chunjiang Zhao, Guijun Yang, Yuan Zhang, Changbin Liu, Haikuan Feng, Xiaodong Yang, Hao Yang

“…We then conducted a correlation analysis between the extracted phenotypic parameters of the maize tassel structure and the genotypes of the maize materials. …”
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Deep learning-based quantitative analysis of glomerular morphology in IgA nephropathy whole slide images and its prognostic implications by Seung Yeon Cho, Yisak Kim, Sehoon Park, Jin Ho Paik, Ho Jun Chin, Jeong Hwan Park, Jung Pyo Lee, Yong-Jin Kim, Sun-Hee Park, Ho-chang Lee, Hyunjeong Cho, Beom Jin Lim, Hyung Woo Kim, Seung Hyeok Han, Heounjeong Go, Chung Hee Baek, Hajeong Lee, Kyung Chul Moon, Young-Gon Kim

“…Subsequently, it was used for machine learning-based risk prediction of IgAN progression. …”
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Early cortical alterations and neuropsychological mechanisms in amyotrophic lateral sclerosis by Qianqian Zhang, Yu Ding, Yu Zhang, Qingyang Li, Shiyu Shi, Yaxi Liu, Sijie Chen, Qian Wu, Xiaoquan Xu, Feiyun Wu, Xi Cheng, Qi Niu

“…Mediation analyses were performed to examine how cortical alterations influence the relationship between emotional and cognitive functions. Support vector machine (SVM) classification models were constructed to assess the diagnostic utility of differential cortical parameters. …”
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Hypoxia alters the response of ovarian cancer cells to the mitomycin C drug by Aleksandra Gawrylak, Aleksandra Gawrylak, Klaudia Brodaczewska, Roksana Iwanicka-Nowicka, Roksana Iwanicka-Nowicka, Marta Koblowska, Marta Koblowska, Agnieszka Synowiec, Lubomir Bodnar, Lubomir Bodnar, Cezary Szczylik, Bogdan Lesyng, Rafał Stec, Claudine Kieda, Claudine Kieda

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Cyanobacteria Desertifilum tharense NIOF17/006 as a novel aquafeed additive: effect on the growth, immunity, digestive function, and gene expression of whiteleg shrimp postlarvae by Mohamed Ashour, Ahmed Said Al-Souti, Ahmed Mamoon, Fawzia S. Ali, Mostafa E. Elshobary, Mohamed M. Mabrouk, Ahmed I. A. Mansour, Abdallah Tageldein Mansour, Ehab El-Haroun, Ahmed F. Abdelhamid

“…Moreover, the polynomial regression machine learning model predicts that the ideal supplementation level of the probiotic cyanobacteria D. tharense NIOF17/006 ranges from 3.4 to 4.2 g/kg diet. …”
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Predictive Modelling of H2S Removal from Biogas Generated from Palm Oil Mill Effluent (POME) Using a Biological Scrubber in an Industrial Biogas Plant: Integration of Artificial Ne... by Joanna Lisa Clifford, Yi Jing Chan, Mohd Amran Bin Mohd Yusof, Timm Joyce Tiong, Siew Shee Lim, Chai Siah Lee, Woei-Yenn Tong

“…The H2S removal was then predicted using a machine learning algorithm, specifically ANN, based on two years of historical data obtained from the biogas plant. …”
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Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing Data by Jian Li, Junrui Kang, Ji Qi, Jian Lu, Hongkun Fu, Baoqi Liu, Xinglei Lin, Jiawei Zhao, Hengxu Guan, Jing Chang, Zhihan Liu

“…This framework synergistically integrates an optimized bidirectional hierarchical gated recurrent unit (BiHGRU), a Transformer encoder, and a novel Greenness and Water Content Composite Index, with critical parameters optimized by particle swarm optimization (PSO). …”
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Contribution to the research of Anthropometric measurements and comparison of body proportions in the student population in Bangladesh by Md Eanamul Haque Nizam, Emeritus Darko Ujevic, Ayub Nabi Khan

“…This integration of statistical and machine learning methods enhances garment fit, reduces fit loss, and improves consumer satisfaction. …”
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Fatty acids of erythrocyte membranes and blood serum as biomarkers for early detection of colorectal cancer by M. V. Kruchinina, V. N. Kruchinin, A. A. Gromov, M. V. Shashkov, A. S. Sokolova, I. N. Yakovina, A. A. Shestov

“…The aim of the study was to identify fatty acids (FA) of erythrocyte membranes and blood serum, which can serve as potential biomarkers for early detection of colorectal cancer.Material and methods. the study involved 65 patients with stage I-II CRC (mean age 63,3 ± 9,6 years (30 men, 35 women) and 35 people in the comparison group, matched by age and sex. the composition of fatty acids of erythrocyte membranes and blood serum was studied using a gc/ms system based on three agilent 7000B quadrupoles (USA). methods of the MATLAB software (R2019a, MathWorks) and the R programming language were used for statistical processing: t-test, unsupervised principal component analysis (PCA), Fold change, Volcano plot, machine learning method (Random Forest), Roc analysis, Heatmaps.Results. patients with stage I-II CRC showed a decrease in the level of saturated, individual monounsaturated fatty acids and a highly significant increase in the most of the polyunsaturated fatty acids with a predominance of omega-3. …”
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Modeling Wetland Biomass and Aboveground Carbon: Influence of Plot Size and Data Treatment Using Remote Sensing and Random Forest by Tássia Fraga Belloli, Diniz Carvalho de Arruda, Laurindo Antonio Guasselli, Christhian Santana Cunha, Carina Cristiane Korb

“…The contribution of this study is the finding that in addition to optimizing RF model parameters, optimizing the AGB and Corg dataset collected in the field, i.e., evaluating normalization and plot sizes, is crucial to obtain more accurate estimates with RS- and ML-based models. …”
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Hydra Radio Access Network (H-RAN): Multi-Functional Communications and Sensing Networks, Initial Access Implementation, and Task-2 Approach by Rafid I. Abd, Daniel J. Findley, Kwang Soon Kim

“…This means that networks can automatically detect issues, assess their impact, implement corrective measures, and modify operating parameters in response to variations in user behavior or environmental factors. …”
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Artificial intelligence-enabled non-invasive ubiquitous anemia screening: The HEMO-AI pilot study on pediatric population by Daniel Gordon, Jason Hoffman, Keren Gamrasni, Yotam Barlev, Alex Levine, Tamar Landau, Ronen Shpiegel, Avishai Lahad, Ariel Koren, Carina Levin, Osnat Naor, Hannah Lee, Xin Liu, Shwetak Patel, Gilad Chayen, Michael Brandwein

“…It identifies important sample collection parameters and design, provides critical algorithms for the pre-processing of fingernail data, and reports an initial capability to detect anemia with 87% sensitivity and 84% specificity. …”
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Algorithms of the Method of Statically Admissible Discontinuous Stress Fields (SADSF) – Part II: The Algorithms for Solving Limit Stress Fields Around Isolated Nodes of Stress Disc... by W. Bodaszewski

“…SUMMARY OF THE WHOLE PAPER: By now, the SADSF method is practically the only tool of shape design of complex machine elements that provides an effective solution even to the problems of 3D distribution of the material, and at the same time it is still enough user-friendly to be useful for engineers. …”
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The determination of optimal cells disintegration method of Candida albicans and Candida tropicalis fungals by M. V. Rybalkyn

“…Many researchers to highlight cytoplasm antigens and all the other layers of the cell use the following methods: grinding cells with quartz sand, destroying them in different machine disintegrating, freezing and thawing a multi others. …”
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Adaptive zero velocity correction method for fiber optic inertial navigation system in coal mining roadheader by Qinghua MAO, Qing ZHOU, Jianquan CHAI, Yanzhang CHEN, Wenjuan YANG, Xusheng XUE

“…Finally, the accuracy of zero-speed detection is improved by introducing the sandcat swarm optimization algorithm to optimize the kernel function and penalty parameters.Aiming at the problem of high cost and error accumulation with time of high-precision fiber-optic inertial navigation, an adaptive zero-speed correction method is proposed. …”
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Optimized design and performance testing of hydraulic electrostatic actuator by Yuze Ye, Qingsong He, Changli Yang, Xiaodong Xu, Lin Xie, Diyi Liu, Bo Zeng, Zhijian Leng

“…Actuation strain tests were conducted with optimized parameters under no-load. The experimental results of the actuation strain test show that the relative error between the test value and the model prediction value of 16.47% is less than 5%, and the optimization model results are reliable. …”
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