Obesity arises from intertwined and reciprocal diet-microbiome-host pathways that reshape energy balance, insulin sensitivity, and inflammation. This review synthesizes mechanistic links between microbial functions and metabolic control, charts lifestyle-related lifecourse dynamics from birth to older age, examines how GLP-1-based therapies may perturb gut ecology and metabolite output and surveys AI/ML frameworks for multi-omics integration. Plant-based, fiber-rich dietary patterns generally enrich saccharolytic guilds, boost SCFAs production, and modulate bile acid signaling, whereas Westernized patterns favor bile-tolerant, amino acid-fermenting consortia and proinflammatory metabolites. Preclinical data suggest that incretin-based therapies remodel the microbiome-metabolome axis, but human causal mediation remains unproven and observed changes may partly reflect weight loss or metabolic improvement. Function-centered metrics outperform phylum-level ratios for translation. Harmonized longitudinal cohorts and explainable ML-derived microbial and metabolomic signatures are now pivotal to identify responder subtypes and actionable microbe-metabolite targets, enabling precision nutrition alongside pharmacotherapy across the lifespan.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. The post Offloaded inference for real-world physical AI robotics appeared first on Microsoft Research.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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