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research#medical imaging🔬 ResearchAnalyzed: Jan 4, 2026 06:49

Incorporating Tissue Composition Information in Total-Body PET Metabolic Quantification of Bone Marrow through Dual-Energy CT

Published:Dec 29, 2025 14:50
1 min read
ArXiv

Analysis

This article describes a research study focusing on improving the accuracy of Positron Emission Tomography (PET) scans, specifically for bone marrow analysis. The use of Dual-Energy Computed Tomography (CT) is highlighted as a method to incorporate tissue composition information, potentially leading to more precise metabolic quantification. The source being ArXiv suggests this is a pre-print or research paper.
Reference

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PermalinkArXiv
Research Paper#Computational Neuroscience, Spiking Neural Networks, Metabolic Modeling🔬 ResearchAnalyzed: Jan 4, 2026 00:19

Metabolic Constraints in Spiking Neural Networks

Published:Dec 25, 2025 12:57
1 min read
ArXiv

Analysis

This paper addresses a crucial limitation in standard Spiking Neural Network (SNN) models by incorporating metabolic constraints. It demonstrates how energy availability influences neuronal excitability, synaptic plasticity, and overall network dynamics. The findings suggest that metabolic regulation is essential for network stability and learning, highlighting the importance of considering biological realism in AI models.
Reference

“The paper defines an "inverted-U" relationship between bioenergetics and learning, demonstrating that metabolic constraints are necessary hardware regulators for network stability.”

PermalinkArXiv
Research#Imaging🔬 ResearchAnalyzed: Jan 10, 2026 10:37

Deep Learning Enhances Brain Imaging at Ultra-High Field

Published:Dec 16, 2025 21:41
1 min read
ArXiv

Analysis

This research explores the application of deep learning in Magnetic Resonance Spectroscopic Imaging (MRSI) at ultra-high field strengths, potentially improving the accuracy and efficiency of brain imaging. The paper's novelty likely lies in the combination of deep learning methods with the advanced MRSI techniques to achieve simultaneous quantitative metabolic, susceptibility, and myelin water imaging.
Reference

“Deep learning water-unsuppressed MRSI at ultra-high field for simultaneous quantitative metabolic, susceptibility and myelin water imaging.”

PermalinkArXiv
Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 11:45

AI-MASLD: Examining Metabolic Dysfunction and Information Overload in Large Language Models

Published:Dec 12, 2025 13:25
1 min read
ArXiv

Analysis

This ArXiv paper explores the potential for "information steatosis" – an overload of information – in Large Language Models (LLMs), drawing parallels to metabolic dysfunction. The study's focus on AI-MASLD is novel, potentially offering insights into model robustness and efficiency.
Reference

“The paper originates from ArXiv, suggesting it's a pre-print or research publication.”

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Dec 25, 2025 18:31

Too Much Screen Time Linked to Heart Problems in Children

Published:Nov 1, 2025 12:01
1 min read
ScienceDaily AI

Analysis

This article from ScienceDaily AI highlights a concerning link between excessive screen time in children and adolescents and increased cardiometabolic risks. The study, conducted by Danish researchers, provides evidence of a measurable rise in cardiometabolic risk scores and a distinct metabolic "fingerprint" associated with frequent screen use. The article rightly emphasizes the importance of sufficient sleep and balanced daily routines to mitigate these negative effects. While the article is concise and informative, it could benefit from specifying the types of screens considered (e.g., smartphones, tablets, TVs) and the duration of screen time that constitutes "excessive" use. Further context on the study's methodology and sample size would also enhance its credibility.
Reference

“Better sleep and balanced daily routines can help offset these effects and safeguard lifelong health.”

PermalinkScienceDaily AI
Research#AI in Biology📝 BlogAnalyzed: Dec 29, 2025 08:24

Predicting Metabolic Pathway Dynamics w/ Machine Learning with Zak Costello - TWiML Talk #163

Published:Jul 11, 2018 21:27
1 min read
Practical AI

Analysis

This article summarizes a podcast episode featuring Zak Costello, a post-doctoral fellow, discussing his research on using machine learning to predict metabolic pathway dynamics. The focus is on applying ML to optimize metabolic reactions for biofuel engineering within the context of synthetic biology. The article highlights the use of time-series multiomics data and the potential for scaling up biofuel production. The brevity of the article suggests it serves as a brief introduction or announcement of the podcast episode, directing readers to the show notes for more detailed information.
Reference

“Zak gives us an overview of synthetic biology and the use of ML techniques to optimize metabolic reactions for engineering biofuels at scale.”

PermalinkPractical AI