Modern disease classification often overlooks molecular commonalities hidden beneath divergent clinical presentations. This study introduces a transcriptomics-driven framework for discovering disease relationships by analyzing over 1300 disease-condition pairs using GenoMAS, a fully automated agentic AI system. Beyond identifying robust gene-level overlaps, we develop a novel pathway-based similarity framework that integrates multi-database enrichment analysis to quantify functional convergence across diseases. The resulting disease similarity network reveals both known comorbidities and previously undocumented cross-category links. By examining shared biological pathways, we explore potential molecular mechanisms underlying these connections-offering functional hypotheses that go beyond symptom-based taxonomies. We further show how background conditions such as obesity and hypertension modulate transcriptomic similarity, and identify therapeutic repurposing opportunities for rare diseases like autism spectrum disorder based on their molecular proximity to better-characterized conditions. In addition, this work demonstrates how biologically grounded agentic AI can scale transcriptomic analysis while enabling mechanistic interpretation across complex disease landscapes. All results are publicly accessible at github.com/KeeeeChen/Pathway_Similarity_Network.

Monitoring people's breathing could help diagnose, or even treat, various conditions Forget facial recognition – there could be a new way to identify you. Researchers have discovered that we all seem to have a "respiratory fingerprint", a unique way of breathing that could revolutionise how we diagnose and treat various health conditions, from obesity to depression. The breakthrough comes from Timna Soroka at the Weizmann Institute of Science in Israel and her colleagues, who have developed a wearable device that captures the subtle nuances of how we breathe. It addresses many longstanding questions about how respiratory signals relate to health and mental state – all in one body of work," says Torben Noto, who wasn't involved in the research, at Osmo in New York, an AI company aiming to give computers a sense of smell. The idea that breathing patterns contain health information isn't new – work dating back to the 1950s hints at this connection.

Exploring complex adaptive financial trading environments through multi-agent based simulation methods presents an innovative approach within the realm of quantitative finance. Despite the dominance of multi-agent reinforcement learning approaches in financial markets with observable data, there exists a set of systematically significant financial markets that pose challenges due to their partial or obscured data availability. We, therefore, devise a multi-agent simulation approach employing small-scale meta-heuristic methods. This approach aims to represent the opaque bilateral market for Australian government bond trading, capturing the bilateral nature of bank-to-bank trading, also referred to as "over-the-counter" (OTC) trading, and commonly occurring between "market makers". The uniqueness of the bilateral market, characterized by negotiated transactions and a limited number of agents, yields valuable insights for agent-based modelling and quantitative finance. The inherent rigidity of this market structure, which is at odds with the global proliferation of multilateral platforms and the decentralization of finance, underscores the unique insights offered by our agent-based model. We explore the implications of market rigidity on market structure and consider the element of stability, in market design. This extends the ongoing discourse on complex financial trading environments, providing an enhanced understanding of their dynamics and implications.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. It's been proven that what people eat can help prevent or slow Alzheimer's disease -- but what about when they eat? Participating in intermittent (time-restricted) fasting could lead to a reduced risk of cognitive deterioration, a recent study published in the journal Cell Metabolism suggests. Researchers at University of California San Diego School of Medicine adjusted the feeding schedule of certain groups of mice so that they only ate within six-hour windows each day.

The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a Least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including eXtreme Gradient Boosting (XGB), random forest, decision tree and logistic regression, by AUC-ROC curves, decision curves, calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley Additive exPlanations (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model, and can possibly be biomarkers for DN.

Crops are constantly challenged by different environmental conditions. Seed treatment by nanomaterials is a cost-effective and environmentally-friendly solution for environmental stress mitigation in crop plants. Here, 56 seed nanopriming treatments are used to alleviate environmental stresses in maize. Seven selected nanopriming treatments significantly increase the stress resistance index (SRI) by 13.9% and 12.6% under salinity stress and combined heat-drought stress, respectively. Metabolomics data reveals that ZnO nanopriming treatment, with the highest SRI value, mainly regulates the pathways of amino acid metabolism, secondary metabolite synthesis, carbohydrate metabolism, and translation. Understanding the mechanism of seed nanopriming is still difficult due to the variety of nanomaterials and the complexity of interactions between nanomaterials and plants. Using the nanopriming data, we present an interpretable structure-activity relationship (ISAR) approach based on interpretable machine learning for predicting and understanding its stress mitigation effects. The post hoc and model-based interpretation approaches of machine learning are combined to provide complementary benefits and give researchers or policymakers more illuminating or trustworthy results. The concentration, size, and zeta potential of nanoparticles are identified as dominant factors for correlating root dry weight under salinity stress, and their effects and interactions are explained. Additionally, a web-based interactive tool is developed for offering prediction-level interpretation and gathering more details about specific nanopriming treatments. This work offers a promising framework for accelerating the agricultural applications of nanomaterials and may profoundly contribute to nanosafety assessment.

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action's perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios.

Biological systems can be studied at multiple levels of information, including gene, protein, RNA and different interaction networks levels. The goal of this work is to explore how the fusion of systems' level information with temporal dynamics of gene expression can be used in combination with non-linear approximation power of deep neural networks to predict novel gene functions in a non-model organism potato \emph{Solanum tuberosum}. We propose DDeMON (Dynamic Deep learning from temporal Multiplex Ontology-annotated Networks), an approach for scalable, systems-level inference of function annotation using time-dependent multiscale biological information. The proposed method, which is capable of considering billions of potential links between the genes of interest, was applied on experimental gene expression data and the background knowledge network to reliably classify genes with unknown function into five different functional ontology categories, linked to the experimental data set. Predicted novel functions of genes were validated using extensive protein domain search approach.

According to a recent study from Karl Landsteiner University of Health Sciences (KL Krems), using radiomics and deep learning algorithms can quickly and accurately distinguish between glioblastoma (primary tumors) and brain metastases. The study, which was published in Metabolites, discovered that magnetic resonance-based radiological data of tumor oxygen metabolism provide a solid foundation for discrimination via neural networks. This combination of oxygen metabolic radiomics and AI analysis was discovered to be vastly superior to human expert evaluations in all critical criteria, even when essential oxygen values did not differ significantly between tumor types. The neural networks' ability to make clear distinctions based on these values demonstrates the method's potential. Glioblastoma (GB) and brain metastasis (BM) are the most commonly occurring types of brain tumors in adults.

Abstract: Objective: The cerebral network subserving repetition suppression (RS) of the P50 auditory evoked response as observed using paired-identical-stimulus (S1-S2) paradigms is not well-described. Methods: We analyzed S1-S2 data from electrodes placed on the cortices of 64 epilepsy patients. We identified regions with maximal amplitude responses to S1 (i.e., stimulus registration), regions with maximal suppression of responses to S2 relative to S1 (i.e., RS), and regions with no or minimal RS 30–80 ms post stimulation. Results: Several temporal, parietal and cingulate area regions were shown to have significant initial registration activity (i.e., strong P50 response to S1). Moreover, prefrontal, cingulate, and parietal lobe regions not previously proposed to be part of the P50 habituation neural circuitry were found to exhibit significant RS.