Discrepancy in Metabolic Dysfunction–Associated Steatotic Liver Disease Prevalence in a Large Northern California Cohort by Luis A. Rodriguez, Lue-Yen S. Tucker, Varun Saxena, Theodore R. Levin

“…Annual MASLD prevalence was identified based on International Classification of Diseases, Ninth or Tenth Revision, Clinical Modification diagnosis codes, the application of natural language processing of all radiology imaging report text that included the liver, and the application of the Dallas Steatosis Index, a MASLD prediction algorithm. Results: Between 2009 and 2018, the estimated MASLD prevalence ranged from 0.37% to 0.95% using diagnosis codes, 0.88%–1.37% using imaging, and 6.14%–11.27% using the Dallas Steatosis Index. …”
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YOLOv9-GDV: A Power Pylon Detection Model for Remote Sensing Images by Ke Zhang, Ningxuan Zhang, Chaojun Shi, Qiaochu Lu, Xian Zheng, Yujie Cao, Xiaoyun Zhang, Jiyuan Yang

“…On the Satellite Remote Sensing Power Tower Dataset (SRSPTD), the YOLOv9-GDV algorithm achieves an mAP of 80.2%, representing a 4.7% improvement over the baseline algorithm. …”
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Expression Characteristics and Prognostic Value of KLRG2 in Endometrial Cancer: A Comprehensive Analysis Based on Multi-Omics Data by Xiaoyan Huang, Ailian Li, Dianbo Xu

“…High KLRG2 expression independently predicted worse overall survival (HR = 3.08, 95% CI = 1.92–4.96) and progression-free interval (HR = 1.98, 95% CI = 1.37–2.87). …”
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Impacts of Spatial Expansion of Urban and Rural Construction on Typhoon-Directed Economic Losses: Should Land Use Data Be Included in the Assessment? by Siyi Zhou, Zikai Zhao, Jiayue Hu, Fengbao Liu, Kunyuan Zheng

“…Results demonstrate three key findings: (1) By introducing prototype learning, a meta-learning approach, to guide model updates, we achieved precise assessments with small training samples, attaining an MAE of 1.02, representing 58.5–76.1% error reduction compared to conventional machine learning algorithms. This reveals that implicitly classifying typhoon disaster loss types through prototype learning can significantly improve assessment accuracy in data-scarce scenarios. (2) By designing a dual-path uncertainty quantification mechanism, we realized high-reliability risk assessment, with 95.45% of actual loss values falling within predicted confidence intervals (theoretical expectation: 95%). …”
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The UK Biobank mental health enhancement 2022: Methods and results. by Katrina A S Davis, Jonathan R I Coleman, Mark Adams, Gerome Breen, Na Cai, Helena L Davies, Kelly Davies, Alexandru Dregan, Thalia C Eley, Elaine Fox, Jo Holliday, Christopher Hübel, Ann John, Aliyah S Kassam, Matthew J Kempton, William Lee, Danyang Li, Jared Maina, Rose McCabe, Andrew M McIntosh, Sian Oram, Marcus Richards, Megan Skelton, Fenella Starkey, Abigail R Ter Kuile, Laura M Thornton, Rujia Wang, Zhaoying Yu, Johan Zvrskovec, Matthew Hotopf

“…This paper has just scratched the surface of the results from MHQ2 and how this can be combined with other tranches of UKB data, but we predict it will enable many future discoveries about mental health and health in general.…”
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Study on Lacticaseibacillus casei TCS fermentation kinetic models and high-density culture strategy by Chen Chen, Tianyu Guo, Di Wu, Jingyan Shu, Ningwei Huang, Huaixiang Tian, Haiyan Yu, Chang Ge

“…Fermentation kinetics were modeled using logistic growth and Luedeking–Piret models, which accurately predicted cell growth. Amberlite IRA 67, an anion exchange resin, effectively adsorbed lactic acid and maintained pH levels. …”
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Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma by Qingyan Kou, Zhichao Wu, Wenbin Zhao, Zhenyuan Liu, Shengxian Qiao, Qiang Mu, Xu Zhang

“…High-risk patients exhibited poorer prognosis and higher immune cell infiltration, particularly T cells and neutrophils. The model also predicted drug sensitivity, with high-risk patients showing greater sensitivity to chemotherapy agents like 5-Fluorouracil and Paclitaxel. …”
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Modeling of the Potential Effect of Revaccination against Whooping Cough in Children Aged 6–7 and 14 years within the Framework of the National of preventive vaccinations by N. I. Briko, A. Ya. Mindlina, I. V. Mikheeva, L. D. Popovich, A. V. Lomonosova

“…A simulation dynamic mathematical model is constructed that allows predicting the development of the epidemiological process of whooping cough on the basis of the dynamics of the main indicators of its prevalence in the population that developed in previous years. …”
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The photometry and kinematics studies of NGC 2509 derived from Gaia DR3 by Nasser M. Ahmed, A. L. Tadross

“…We employed the pyUPMASK Python package and HDBSCAN algorithms to identify the cluster member stars. The current analysis introduces a new method that connects the membership probability of stars in the cluster (using the pyUPMASK tool) with the number of stars predicted by the King model at different distances from the center of the cluster. …”
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EcoTaskSched: a hybrid machine learning approach for energy-efficient task scheduling in IoT-based fog-cloud environments by Asfandyar Khan, Faizan Ullah, Dilawar Shah, Muhammad Haris Khan, Shujaat Ali, Muhammad Tahir

“…The CNN model efficiently extracts workload features from tasks and resources, while the BiLSTM captures complex sequential information, predicting optimal task placement sequences. A real fog-cloud environment is implemented using the COSCO framework for the simulation setup together with four physical nodes from the Azure B2s plan to test the proposed model. …”
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Few-shot crop disease recognition using sequence- weighted ensemble model-agnostic meta-learning by Junlong Li, Quan Feng, Junqi Yang, Jianhua Zhang, Jianhua Zhang, Sen Yang

“…The SWE-MAML framework employs meta-learning to sequentially train a set of base learners, followed by a weighted sum of their predictions for classifying plant disease images. This method integrates ensemble learning with Model-Agnostic Meta-Learning (MAML), allowing the effective training of multiple classifiers within the MAML framework. …”
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Assessment of Risks of Voltage Quality Decline in Load Nodes of Power Systems by Pylyp Hovorov, Roman Trishch, Romualdas Ginevičius, Vladislavas Petraškevičius, Karel Šuhajda

“…The economic criterion is taken into account through the use of complex and inaccurate models that do not accurately predict the result. The emergence of market relations in the energy sector makes power systems economic entities in terms of production and satisfaction of demand for electricity by various economic entities (industry, households, businesses, etc.). …”
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Radiomic Analysis and Liquid Biopsy in Preoperative CT of NSCLC: An Explorative Experience by Maria Paola Belfiore, Mario Sansone, Giovanni Ciani, Vittorio Patanè, Carlotta Genco, Roberta Grassi, Giovanni Savarese, Marco Montella, Riccardo Monti, Salvatore Cappabianca, Alfonso Reginelli

“…Radiomic features were extracted from CT images, and circulating tumor DNA (ctDNA) was sequenced to identify genetic mutations. Machine learning algorithms were employed to assess the association between radiomic features and gene mutations. …”
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An Integrative Analysis of Transcriptome Combined with Machine Learning and Single-Cell RNA-Seq for the Common Biomarkers in Crohn’s Disease and Kidney Stone Disease by Zhu J, Du Y, Gao L, Wang J, Mei Q

“…Therefore, finding biomarkers that can predict CD with KD become increasingly important.Methods: We obtained three CD and one KSD dataset from GEO database. …”
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On the need of individually optimizing temporal interference stimulation of human brains due to inter-individual variability by Tapasi Brahma, Alexander Guillen, Jeffrey Moreno, Abhishek Datta, Yu Huang

“…Experimental recordings on a head phantom confirms the drop in TI stimulation strength when using unoptimized montages as predicted by our in silico models. Conclusion: This work demonstrates the need of individually optimizing TI to target deep brain areas, and advocates against using a common head model and montage for TI modeling and experimental studies.…”
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Identification of Diagnostic Biomarkers and Therapeutic Targets in Sepsis-Associated ARDS via Combining Bioinformatics with Machine Learning Analysis by Liu T, Gao L, Li X

“…Three machine learning algorithms were applied to refine the intersected genes. …”
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Desain Penilaian Risiko Privasi pada Aplikasi Seluler Melalui Model Machine Learning Berbasis Ensemble Learning dan Multiple Application Attributes by R. Ahmad Imanullah Zakariya, Kalamullah Ramli

“…The likelihood assessment is performed by combining ensemble learning predictions and information on multiple application attributes, while the severity assessment is performed by utilizing the number and characteristics of permissions. …”
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Teens and opioids postsurgery (TOPS): protocol for a prospective observational study describing associations between sleep deficiency and opioid use following outpatient surgery in... by Tonya Palermo, Jennifer A Rabbitts, Cornelius B Groenewald, Rebecca L Flack, Sophia L Kreider

“…We will apply modern machine learning algorithms to develop and validate models predicting adolescent prescription opioid misuse at 24 months from surgery.Ethics and dissemination This study was approved by Advarra’s Center for Institutional Review Board Intelligence (CIRBI) (Protocol 00072049), which serves as the single IRB of record for this multisite study.…”
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Stacking data analysis method for Langmuir multi-probe payload by Jin Wang, Jin Wang, Duan Zhang, Qinghe Zhang, Qinghe Zhang, Xinyao Xie, Fangye Zou, Qingfu Du, Qingfu Du, V. Manu, Yanjv Sun

“…This study uses a stacking algorithm to process m-NLP data and incorporates the International Reference Ionosphere (IRI) model to correct the predicted electron density (Ne) values. …”
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Enhancing wheat flour origin traceability by using laser-induced breakdown spectroscopy and Raman spectroscopy by Minghui Gu, Chao Liu, Hansong Huang, Xin Zhang, Jiguo Li, Qingbin Jiao, Liang Xu, Mingyu Yang, Xin Tan

“…The transfer model achieved a prediction accuracy of 97% on the remaining data, demonstrating the effectiveness of transfer learning.…”
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