Human responses to the DNA prime/chimpanzee adenovirus (ChAd63) boost vaccine identify CSP, AMA1 and TRAP MHC Class I-restricted epitopes. by Harini Ganeshan, Jun Huang, Maria Belmonte, Arnel Belmonte, Sandra Inoue, Rachel Velasco, Santina Maiolatesi, Keith Limbach, Noelle Patterson, Marvin J Sklar, Lorraine Soisson, Judith E Epstein, Kimberly A Edgel, Bjoern Peters, Michael R Hollingdale, Eileen Villasante, Christopher A Duplessis, Martha Sedegah

“…Individual antigen-specific 15mers in the subpools with strong responses were then deconvoluted, evaluated for activities, and MHC Class I-restricted epitopes within the active 15mers were predicted using NetMHCpan algorithms. The predicted epitopes were synthesized and evaluated in the FluoroSpot IFN-γ and GzB assays.…”
Get full text

Prognostic and therapeutic relevance of IL2RG-related LncRNAs in clear cell renal cell carcinoma by Weijing Hu, Bo Wu, Yongquan Chen, Xiaoling Guo, Xiaosong Wang, Dongwen Wang

“…The 6-IRLs model provides a robust tool for predicting prognosis and guiding therapeutic decisions.…”
Get full text

Round, just-below, or precise prices? Cultural differences in the prevalence of price endings in E-commerce by Alfio Ventura, Alfio Ventura, Alfio Ventura, Eve Sarah Troll, Meikel Soliman, David D. Loschelder

Get full text

Association of Obesity with Forearm Fractures, Bone Mineral Density and Fracture Risk (FRAX®) During Postmenopausal Period by Erkan Mesci, Nilgün Mesci, Afitap İçağasıoğlu, Ercan Madenci

“…It should be kept in mind that obesity may not necessarily be protective against fractures and treatment algorithms based solely on BMD might be inadequate to predict future fracture risk.…”
Get full text

Inferring Mechanical Properties of Wire Rods via Transfer Learning Using Pre-Trained Neural Networks by Adriany A. F. Eduardo, Gustavo A. S. Martinez, Ted W. Grant, Lucas B. S. Da Silva, Wei-Liang Qian

“…The primary objective of this study is to explore how machine learning techniques can be incorporated into the analysis of material deformation. Neural network algorithms are applied to the study of mechanical properties of wire rods subjected to cold plastic deformations. …”
Get full text

Exercise-related immune gene signature for hepatocellular carcinoma: machine learning and multi-omics analysis by Cheng Pu, Lei Pu, Xiaoyan Zhang, Qian He, Jiacheng Zhou, Jianyue Li

“…Univariate COX analysis and 101 combinations of 10 machine learning algorithms were used to construct EIG prognostic signature (EIGPS), and survival analyses were performed. …”
Get full text

Associations between anthropometric indices and biological age acceleration in American adults: insights from NHANES 2009–2018 data by Xinyun Chen, Xia Chen, Fangyu Shi, Wenhui Yu, Chang Gao, Shenju Gou, Ping Fu

“…Receiver Operating Characteristic (ROC) curve analysis assessed the predictive capabilities of the anthropometric indices. …”
Get full text

Assessment of sensor driven automatic smart soil and paddy seed metering mechanisms using artificial intelligence for paddy nurseries by Vinod Choudhary, Rajendra Machavaram, Prakhar Patidar, Gajendra Singh, Naseeb Singh, Lokesh Kumawat

“…The ANN-MOGA (multi-objective genetic algorithms) predicted higher experimental response values compared to the response surface methodology. …”
Get full text

Intelligent energy management of microgrids using machine learning: Leveraging random forest models for solar and wind power by Hasanur Zaman Anonto, Md Ismail Hossain, Abu Shufian, Md. Shaoran Sayem, S M Tanvir Hassan Shovon, Protik Parvez Sheikh, Sadman Shahriar Alam

“…The study at hand suggests dedicating a new type of energy management of the microgrid by using the machine-learning algorithms, namely the Random Forest (RF) regressor along with real-time forecasting the energy use and renewable-energy production. …”
Get full text

A Versatile, Machine-Learning-Enhanced RF Spectral Sensor for Developing a Trunk Hydration Monitoring System in Smart Agriculture by Oumaima Afif, Leonardo Franceschelli, Eleonora Iaccheri, Simone Trovarello, Alessandra Di Florio Di Renzo, Luigi Ragni, Alessandra Costanzo, Marco Tartagni

“…Thanks to the flexibility of the system’s architecture, which embeds a Linux operating system, we can easily embed machine learning (ML) algorithms and predictive models for information detection. …”
Get full text

AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity by Giulia Cerrato, Peng Liu, Liwei Zhao, Adriana Petrazzuolo, Juliette Humeau, Sophie Theresa Schmid, Mahmoud Abdellatif, Allan Sauvat, Guido Kroemer

“…Conclusions We developed AI-based algorithms for predicting CON-inducing drugs based on molecular descriptors and their validation using automated micrographs analysis, offering a new approach for screening ICD inducers with minimized adverse effects in cancer therapy.…”
Get full text

Big data and data science in global governance: anticipating future needs and applications in the UN and beyond by Lanxin Li, Jiarou Wang, Xi Wang, Peng Peng, Jiaying Shen, Haining Zhu, Ziyang Zhang

“…The research identifies the “4Vs” of big data (Volume, Velocity, Variety, and Veracity) as fundamental characteristics reshaping governance approaches while highlighting innovative applications like UN Global Pulse, SDG tracking systems, and AI-driven predictive analytics in crisis prevention. We assess technical, ethical, and organizational challenges, including data quality inconsistencies, interoperability issues, privacy concerns, algorithmic bias, and resource constraints that impede the full integration of big data into governance systems. …”
Get full text

Comprehensive evaluation of phosphoproteomic-based kinase activity inference by Sophia Müller-Dott, Eric J. Jaehnig, Khoi Pham Munchic, Wen Jiang, Tomer M. Yaron-Barir, Sara R. Savage, Martin Garrido-Rodriguez, Jared L. Johnson, Alessandro Lussana, Evangelia Petsalaki, Jonathan T. Lei, Aurelien Dugourd, Karsten Krug, Lewis C. Cantley, D. R. Mani, Bing Zhang, Julio Saez-Rodriguez

“…We used benchmarKIN to evaluate kinase-substrate libraries, inference algorithms and the potential of adding predicted kinase-substrate interactions to overcome the coverage limitations. …”
Get full text

Identifying potential biomarkers and molecular mechanisms related to arachidonic acid metabolism in vitiligo by Xiaoqing Li, Xiaoqing Li, Li Yang, Longfei Zhu, Jingying Sun, Jingying Sun, Cuixiang Xu, Cuixiang Xu, Lijun Sun, Lijun Sun

“…Machine-learning algorithms were used to identify six key genes as PTGDS, PNPLA8, FAAH, ABHD12, PTGS1, and MGLL. …”
Get full text

The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review by Saran Singh Gill, Hariharan Subbiah Ponniah, Sho Giersztein, Rishi Miriyala Anantharaj, Srikar Reddy Namireddy, Joshua Killilea, DanieleS.C. Ramsay, Ahmed Salih, Ahkash Thavarajasingam, Daniel Scurtu, Dragan Jankovic, Salvatore Russo, Andreas Kramer, Santhosh G. Thavarajasingam

“…Method: ology: The primary aim was to evaluate the performance of AI algorithms in diagnosing and prognosticating tSCI. Subsequent systematic searching of seven databases identified studies evaluating AI models. …”
Get full text

Impact of bridging the gap between Artificial Intelligence and nanomedicine in healthcare by Divyam Mishra, Bhavishya Chaturvedi, Vishal Soni, Dhairya Valecha, Megha Goel, Jamilur R. Ansari

“…We will also assess the long-term implications of lipid nanoparticles in drug delivery applications. Machine Learning algorithms are employed to create data-driven adaptive nanomaterials and paradigms, further advancing the field. …”
Get full text

Monitoring and modeling hydrologic conditions in Ukraine for hydropower generation by Matthew J. McCarthy, Jesus D. Gomez-Velez, David Hughes, Shannon Meade

“…To address this data gap, we developed a protocol that combined satellite-based time-series measurements of river width at seven locations throughout Ukraine from 2013 to 2023 with reanalysis data, climate-model predictions, and hydrologic models to both provide a means of monitoring a proxy for near-real-time discharge and also predict near-term (i.e., 2023–2030) hydrologic patterns for the region. …”
Get full text

Differences in parameter estimates derived from various methods for the ORYZA (v3) Model by Jun-wei TAN, Qing-yun DUAN, Wei GONG, Zhen-hua DI

“…The results showed that there were substantial differences between the parameter estimates derived by the different methods, and they had strong effects on model predictions. The parameter estimates given by the frequentist methods were obviously sensitive to initial values, and the extent of the sensitivity varied with algorithms and objective functions. …”
Get full text

Incorporating food plant distributions as important predictors in the habitat suitability model of sumatran orangutan (Pongo abelii) in Gunung Leuser National Park, Indonesia by Salmah Widyastuti, Wanda Kuswanda, M. Hadi Saputra, Hendra Helmanto, Nunu Anugrah, U. Mamat Rahmat, Rudianto Saragih Napitu, Andrinaldi Adnan, Iskandarrudin

“…This study enhances habitat suitability models (HSM) for Sumatran orangutans by incorporating the predictive distributions for 21 key orangutan food plants, which had not been previously explored. …”
Get full text

A Machine Learning-Based Diagnostic Nomogram for Moyamoya Disease: The Validation of Hypoxia-Immune Gene Signatures by Cunxin Tan, Xilong Wang, Zhenyu Zhou, Yutong Liu, Shihao He, Yuanli Zhao

“…These genes were further refined through machine learning algorithms. The diagnostic value was confirmed using an external dataset, and a diagnostic nomogram was constructed. …”
Get full text