Genetic Biomarkers and Circulating White Blood Cells in Osteoarthritis: A Bioinformatics and Mendelian Randomization Analysis by Yimin Pan, Xiaoshun Sun, Jun Tan, Chao Deng, Changwu Wu, Georg Osterhoff, Nikolas Schopow

“…The bioinformatics methods utilized include the Limma package, WGCNA, PPI network analysis, and machine learning algorithms. Genetic variants were used as instrumental variables to evaluate the potential causal impact of circulating white blood cell (WBC) counts on OA. …”
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Using Quantitative Trait Locus Mapping and Genomic Resources to Improve Breeding Precision in Peaches: Current Insights and Future Prospects by Umar Hayat, Cao Ke, Lirong Wang, Gengrui Zhu, Weichao Fang, Xinwei Wang, Changwen Chen, Yong Li, Jinlong Wu

“…This work shows how combining genome-wide association studies and machine learning can improve the synthesis of multi-omics data and result in faster breeding cycles while preserving genetic diversity. …”
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BenthicNet: A global compilation of seafloor images for deep learning applications by Scott C. Lowe, Benjamin Misiuk, Isaac Xu, Shakhboz Abdulazizov, Amit R. Baroi, Alex C. Bastos, Merlin Best, Vicki Ferrini, Ariell Friedman, Deborah Hart, Ove Hoegh-Guldberg, Daniel Ierodiaconou, Julia Mackin-McLaughlin, Kathryn Markey, Pedro S. Menandro, Jacquomo Monk, Shreya Nemani, John O’Brien, Elizabeth Oh, Luba Y. Reshitnyk, Katleen Robert, Chris M. Roelfsema, Jessica A. Sameoto, Alexandre C. G. Schimel, Jordan A. Thomson, Brittany R. Wilson, Melisa C. Wong, Craig J. Brown, Thomas Trappenberg

“…The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering mobilization of this crucial environmental information. Machine learning approaches provide opportunities to increase the efficiency with which seafloor imagery is analyzed, yet large and consistent datasets to support development of such approaches are scarce. …”
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Research Progress in Monitoring Technology of Cold Chain Logistics for Meat Products by Bin HAN, Dongmei LENG, Yuqian XU, Jianyang SHEN, Xin LI, Xiaochun ZHENG, Wei WANG, Dequan ZHANG, Chengli HOU

“…To analyze the future development of meat cold chain logistics monitoring technology combined with sensors, narrowband internet of things, machine learning, and blockchain, and to provide theoretical support for the research and application of China's cold chain logistics monitoring technology.…”
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The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency by Md. Faiyazuddin, Syed Jalal Q. Rahman, Gaurav Anand, Reyaz Kausar Siddiqui, Rachana Mehta, Mahalaqua Nazli Khatib, Shilpa Gaidhane, Quazi Syed Zahiruddin, Arif Hussain, Ranjit Sah

“…The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. …”
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New bitongling regulates gut microbiota to predict angiogenesis in rheumatoid arthritis via the gut-joint axis: a deep neural network approach by Yin Guan, Xiaoqian Zhao, Yun Lu, Yue Zhang, Yan Lu, Yue Wang

“…The study employed 16S ribosomal DNA (16S rDNA) sequencing to analyze gut microbiota composition, machine learning techniques to identify characteristic microbial taxa, and transcriptomic analysis (GSVA) to assess the impact on the VEGF signaling pathway. …”
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Cross-sectional design and protocol for Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) by Gerald McGwin, Linda M Zangwill, Nicholas Evans, Shannon McWeeney, Cecilia S Lee, Bhavesh Patel, Jeffrey C Edberg, Cynthia Owsley, Aaron Lee, Cecilia Lee, Sally L Baxter, Michael Snyder, Samantha Hurst, Nicole Ehrhardt, Christopher Chute, Dawn S Matthies, Julia P Owen, Amir Bahmani, Sally Baxter, Edward Boyko, Aaron Cohen, Jorge Contreras, Garrison Cottrell, Virginia de Sa, Jeffrey Edberg, Irl Hirsch, Michelle Hribar, T.Y. Alvin Liu, Bonnie Maldenado, Sara Singer, Bradley Voytek, Joseph Yracheta, Linda Zangwill

“…Introduction Artificial Intelligence Ready and Equitable for Diabetes Insights (AI-READI) is a data collection project on type 2 diabetes mellitus (T2DM) to facilitate the widespread use of artificial intelligence and machine learning (AI/ML) approaches to study salutogenesis (transitioning from T2DM to health resilience). …”
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600 meters to VO2max: Predicting Cardiorespiratory Fitness with an Uphill Run by Kübra Stoican, Regina Oeschger

“…Recent advances in machine learning for maximal oxygen uptake (VO₂ max) prediction: A review. …”
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Vision-based manipulation of transparent plastic bags in industrial setups by F. Adetunji, F. Adetunji, A. Karukayil, A. Karukayil, P. Samant, P. Samant, S. Shabana, S. Shabana, F. Varghese, F. Varghese, U. Upadhyay, U. Upadhyay, R. A. Yadav, R. A. Yadav, A. Partridge, E. Pendleton, R. Plant, Y. R. Petillot, Y. R. Petillot, M. Koskinopoulou, M. Koskinopoulou

“…Integrating autonomous systems, including collaborative robots (cobots), into industrial workflows is crucial for improving efficiency and safety.MethodsThe proposed system employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), for identifying transparent plastic bags under diverse lighting and background conditions. …”
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Fiber-optic system for monitoring stability of quarry slopes by P. Sh. Madi, А. D. Аlkina, A. V. Yurchenko, A. D. Mekhtiyev, R. Zh. Aimagambetova

“…A hardware-software control complex has also been developed with a wide range of elements that allows you to adjust sensitivity and has machine learning elements.…”
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Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data by Lixuan Li, Yuekong Hu, Zhicheng Yang, Zeruxin Luo, Jiachen Wang, Wenqing Wang, Xiaoli Liu, Yuqiang Wang, Yong Fan, Pengming Yu, Zhengbo Zhang

“…This study leverages wearable technology and machine learning algorithms to preoperatively identify high-risk individuals, thereby enhancing clinical decision-making for the mitigation of PPCs. …”
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Improving daily reference evapotranspiration forecasts: Designing AI-enabled recurrent neural networks based long short-term memory by Mumtaz Ali, Jesu Vedha Nayahi, Erfan Abdi, Mohammad Ali Ghorbani, Farzan Mohajeri, Aitazaz Ahsan Farooque, Salman Alamery

“…The main purpose of this investigation was to forecast the daily ETo trends at Melbourne and Sydney stations in Australia, where several cutting-edge machine learning methodologies were employed. The modeling approach encompassed the implementation of Neural Network (NN), Deep Learning (DL), Recurrent Neural Networks (RNN), RNN based Long Short-Term Memory (RNN-LSTM), and Convolutional Neural Network based LSTM (CNN-LSTM) to forecast daily ETo using historical meteorology data. …”
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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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Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients by Olawande Daramola, Tatenda Duncan Kavu, Maritha J. Kotze, Jeanine L. Marnewick, Oluwafemi A. Sarumi, Boniface Kabaso, Thomas Moser, Karl Stroetmann, Isaac Fwemba, Fisayo Daramola, Martha Nyirenda, Susan J. van Rensburg, Peter S. Nyasulu

“…Aside from clinical methods, artificial intelligence (AI)-based solutions such as machine learning (ML) models have been employed in treating COVID-19 cases. …”
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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

“…Furthermore, we employed a multi-sparse input and multi-task learning (SMTL) framework in the Hydra distributed unit (H-DU) artificial intelligence and machine learning (AI/ML) (AI/ML D-engine), where each task is tailored to be executed in a particular environment based on online feedback. …”
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Predictive role of SLC1A5 in neuroblastoma prognosis and immunotherapy by Jian Cheng, Miaomiao Sun, Xiao Dong, Yang Yang, Xiaohan Qin, Xing Zhou, Yongcheng Fu, Yuanyuan Wang, Jingyue Wang, Da Zhang

“…A prognostic signature, SRPS, was constructed and validated using machine-learning approaches. Immune infiltration analysis was performed to evaluate the tumor immune microenvironment. …”
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Framework for smartphone-based grape detection and vineyard management using UAV-trained AI by Sergio Vélez, Mar Ariza-Sentís, Mario Triviño, Antonio Carlos Cob-Parro, Miquel Mila, João Valente

“…Recent technological and machine learning advancements, particularly in deep learning, have provided the tools necessary to create more efficient, automated processes that significantly reduce the time and effort required for these tasks. …”
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Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triang... by Bankole Olatosi, Jiajia Zhang, Xiaoming Li, Chen Liang, Jihong Liu, Peiyin Hung, Shan Qiao, Berry A Campbell, Myriam E Torres, Neset Hikmet

“…Non-Hispanic black and Hispanic pregnant women appear to have disproportionate SARS-CoV-2 infection and death rates.Methods and analysis We will use the socioecological framework and employ a concurrent triangulation, mixed-methods study design to achieve three specific aims: (1) examine the impacts of the COVID-19 pandemic on racial/ethnic disparities in severe maternal morbidity and mortality (SMMM); (2) explore how social contexts (eg, racial/ethnic residential segregation) have contributed to the widening of racial/ethnic disparities in SMMM during the pandemic and identify distinct mediating pathways through maternity care and mental health; and (3) determine the role of social contextual factors on racial/ethnic disparities in pregnancy-related morbidities using machine learning algorithms. We will leverage an existing South Carolina COVID-19 Cohort by creating a pregnancy cohort that links COVID-19 testing data, electronic health records (EHRs), vital records data, healthcare utilisation data and billing data for all births in South Carolina (SC) between 2018 and 2021 (>200 000 births). …”
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ANN-based two hidden layers computational procedure for analysis of heat transport dynamics in polymer-based trihybrid Carreau nanofluid flow over needle geometry by Adil Darvesh, Fethi Mohamed Maiz, Basma Souayeh, Luis Jaime Collantes Santisteban, Hakim AL. Garalleh, Afnan Al Agha, Lucerito Katherine Ortiz García, Nicole Anarella Sánchez-Miranda

“…Their advantages in handling nonlinearities and modeling high-dimensional data through integrating physical laws make them far superior to simpler machine learning and other traditional techniques, despite requiring greater data and computational resources. …”
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Factors and Reasons Associated with Hesitating to Seek Care for Migraine: Results of the OVERCOME (US) Study by Robert E. Shapiro, Eva Jolanda Muenzel, Robert A. Nicholson, Anthony J. Zagar, Michael L. Reed, Dawn C. Buse, Susan Hutchinson, Sait Ashina, Eric M. Pearlman, Richard B. Lipton

“…Methods The web-based OVERCOME (US) survey study identified adults with active migraine in a demographically representative US sample who answered questions about hesitating to seek care from a healthcare provider for migraine and reasons for hesitating. Supervised machine learning (random forest, least absolute shrinkage and selection operator) identified factors associated with hesitation; logistic regression models assessed association of factors on hesitation. …”
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