Exploring the relationship between sepsis and Golgi apparatus dysfunction: bioinformatics insights and diagnostic marker discovery by Wanli Ma, Xinyi Liu, Ran Yu, Jiannan Song, Lina Hou, Ying Guo, Hongwei Wu, Dandan Feng, Qi Zhou, Haibo Li

“…A diagnostic model constructed using five pivotal genes (B3GNT5, FUT11, MAN1C1, ST6GAL1, and C1GALT1C1) exhibited predictive accuracy, with AUC values exceeding 0.96 for all genes. …”
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Clinical and Epidemiological Manifestations of Ixodic Tick-Borne Borreliosis Foci in the Tomsk region by O. V. Voronkova, E. N. Ilyinskikh, A. A. Rudikov, T. N. Poltoratskaya, I. E. Esimova, L. V. Lukashova, M. R. Karpova

“…The study of the genotypic diversity of pathogenic borrelias in relation to the species diversity of vectors, the analysis of the clinical manifestations of different etiological variants of tick-borne borreliosis (mono- and mixed infections), as well as the development of an algorithm for differential diagnostic search and a model for predicting the outcomes of the infectious process in tick-borne borreliosis and mixed infections are priority directions of problem-oriented scientific research in Tomsk region.…”
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Deep reinforcement learning applications and prospects in industrial scenarios by JING TAN, Ligang YANG, Xiaorui LI, Zhaolin YUAN, Yunduan CUI, Chao YAO, Zongjie WANG, Xiaojuan BAN

“…Central to these systems are control algorithms, which enable the automation of operations, optimization of process parameters, and reduction of operational costs. …”
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Multigenerational effects of bisphenol A or ethinyl estradiol exposure on F2 California mice (Peromyscus californicus) pup vocalizations. by Sarah A Johnson, Michelle J Farrington, Claire R Murphy, Paul D Caldo, Leif A McAllister, Sarabjit Kaur, Catherine Chun, Madison T Ortega, Brittney L Marshall, Frauke Hoffmann, Mark R Ellersieck, A Katrin Schenk, Cheryl S Rosenfeld

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Advanced classification of optical water types and ensemble learning models for Chl-a inversion in Dongting and Poyang lakes using Sentinel-2 remote sensing: assessing the impact o... by Kai Xiong, Bin Deng, Jiang Liu, Zhixin Guan, Weizhi Lu, Changbo Jiang, Wei Luo, Han Rao, Longbin Yin, Kang Yang

“…The results demonstrated the superior stability and predictive accuracy of the Voting strategy under low Chl-a conditions in Dongting Lake, achieving a maximum MAPE reduction of 84.76 %. …”
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Prostate-specific membrane antigen in circulating tumor cells is a new poor prognostic marker for castration-resistant prostate cancer. by Naoya Nagaya, Masayoshi Nagata, Yan Lu, Mayuko Kanayama, Qi Hou, Zen-U Hotta, Toshiyuki China, Kosuke Kitamura, Kazuhito Matsushita, Shuji Isotani, Satoru Muto, Yoshiro Sakamoto, Shigeo Horie

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The role of intracellular signaling in the stripe formation in engineered Escherichia coli populations. by Xiaoru Xue, Chuan Xue, Min Tang

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Artificial intelligence-based radiogenomics reveals the potential immunoregulatory role of COL22A1 in glioma and its induced autoimmune encephalitis by Bingchao Yan, Bingchao Yan, Qian Chen, Dacheng Wang, Leili Ding, Leili Ding, Jingfeng Qu, Renfei Du, Renfei Du, Wenjie Shi, Ulf D. Kahlert, Zhengquan Yu

“…Immune infiltration was evaluated using five algorithms (MCP-counter, EPIC, TIMER, QUANT and IPS). …”
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Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease by Ruijie Zhao, Yun Wang, Jiaru Wang, Zixing Wang, Ran Xiao, Ying Ming, Sirong Piao, Jinhua Wang, Lan Song, Yinghao Xu, Zhuangfei Ma, Peilin Fan, Xin Sui, Wei Song

“…Conclusion With 63.7% reduction of radiation dose, the overall image quality of LDCT DLR was comparable to HRCT HIR in prone scanning for early ILD patients. …”
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In vivo and in silico dynamics of the development of Metabolic Syndrome. by Yvonne J W Rozendaal, Yanan Wang, Yared Paalvast, Lauren L Tambyrajah, Zhuang Li, Ko Willems van Dijk, Patrick C N Rensen, Jan A Kuivenhoven, Albert K Groen, Peter A J Hilbers, Natal A W van Riel

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Mapping global risk levels of Bemisia tabaci in areas of suitability for open field tomato cultivation under current and future climates. by Rodrigo Soares Ramos, Lalit Kumar, Farzin Shabani, Marcelo Coutinho Picanço

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Remote sensing inversion of nitrogen content in silage maize plants based on feature selection by Kejing Cheng, Kejing Cheng, Jixuan Yan, Jixuan Yan, Guang Li, Guang Li, Weiwei Ma, Weiwei Ma, Zichen Guo, Zichen Guo, Wenning Wang, Wenning Wang, Haolin Li, Qihong Da, Qihong Da, Xuchun Li, Xuchun Li, Yadong Yao, Yadong Yao

“…In studies on nitrogen content inversion in the maize canopy, the random forest (RF) algorithm, coupled with PLSR, demonstrated superior predictive performance. …”
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Fatness, fitness and the aging brain: A cross sectional study of the associations between a physiological estimate of brain age and physical fitness, activity, sleep, and body comp... by David Wing, Lisa T. Eyler, Eric J. Lenze, Julie Loebach Wetherell, Jeanne F. Nichols, Romain Meeusen, Job G. Godino, Joshua S. Shimony, Abraham Z. Snyder, Tomoyuki Nishino, Ginger E. Nicol, Guy Nagels, Bart Roelands

“…Methods: Using T1 weighted MRI images, we applied a novel algorithm to determine the physiological age of the brain (brain-predicted age) and the predicted age difference between this physiologically based estimate and chronological age (BrainPAD) to 551 sedentary adults aged 65 to 84 with self-reported cognitive complaint measured at baseline as part of a larger study. …”
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Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers by Wentong Li, Yiyi Zhang, Chuanhong Li, Mingyang Jiang, Dong Wang, Luomeng Chao, Luomeng Chao, Luomeng Chao, Yuxia Yang

“…Immune analysis revealed that this molecular chaperone axis modulates neuroinflammation by suppressing naive B cell differentiation (61% reduction) and activating Tregs (55.53% increase). …”
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Assessing reading fluency in elementary grades: A machine learning approach by Gabriel Candido da Silva, Rodrigo Lins Rodrigues, Américo N. Amorim, Lieny Jeon, Emilia X.S. Albuquerque, Vanessa C. Silva, Vinícius F. da Silva, André L.A. Pinheiro, João P.J.R. Nunes, Suzana X.M.G. de Souza, Maxsuel S. Silva, Igor Mauro, Alexandre Magno Andrade Maciel

“…The research objective was to determine which algorithm best predicts fluency, considering diverse evaluation setups including binary classification (fluent versus non-fluent), multiclass classification (differentiated fluency levels), and regression analysis to estimate continuous fluency scores. …”
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Changes in parasite traits, rather than intensity, affect the dynamics of infection under external perturbation. by Suma Ghosh, Matthew J Ferrari, Ashutosh K Pathak, Isabella M Cattadori

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Elementary Growth Modes provide a molecular description of cellular self-fabrication. by Daan H de Groot, Josephus Hulshof, Bas Teusink, Frank J Bruggeman, Robert Planqué

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Upper thermal limits differ among and within component species in a tritrophic host-parasitoid-hyperparasitoid system. by Salvatore J Agosta, Kanchan A Joshi, Karen M Kester

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Optimizing circadian drug infusion schedules towards personalized cancer chronotherapy. by Roger J W Hill, Pasquale F Innominato, Francis Lévi, Annabelle Ballesta

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Exploring the role of ferroptosis in pemphigus: identification of diagnostic markers and regulatory mechanisms by Jing Mao, Jianping Lan, Zheyu Zhuang, Ying Chen, Ying Chen, Yushan Ou, Xinhong Su, Xueting Zeng, Fuchen Huang, Zequn Tong, Xiaoqing Lv, Xiaoqing Lv, Xiaoqing Lv, Hui Ke, Zhenlan Wu, Ying Zou, Bo Cheng, Bo Cheng, Bo Cheng, Chao Ji, Chao Ji, Chao Ji, Ting Gong

“…Weighted Gene Co-expression Network Analysis (WGCNA) was employed to identify co-expressed gene modules related to pemphigus. Machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were used to select key ferroptosis-related genes. …”
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