Ancient genomics support deep divergence between Eastern and Western Mediterranean Indo-European languages.

The Indo-European languages are among the most widely spoken in the world, yet their early diversification remains contentious. It is widely accepted that the spread of this language family across Europe from the 5th millennium BP correlates with the expansion and diversification of steppe-related genetic ancestry from the onset of the Bronze Age. However, multiple steppe-derived populations co-existed in Europe during this period, and it remains unclear how these populations diverged and which provided the demographic channels for the ancestral forms of the Italic, Celtic, Greek, and Armenian languages.

Monte Carlo modeling of the origin of contaminant electrons on a 0.5T bi-planar Linac-MR.

Introduction: In the last decade, hybrid linear accelerator magnetic resonance imaging (Linac-MR) devices have evolved into FDA-cleared clinical tools, facilitating magnetic resonance guided radiotherapy (MRgRT). The addition of a magnetic field to radiation therapy has previously demonstrated dosimetric and electron effects regardless of magnetic field orientation.

Purpose: This study uses Monte Carlo simulations to investigate the importance and efficacy of the magnetic field design in mitigating surface dose enhancement in the Aurora-RT, focusing specifically on contaminant electrons, their origin, and energy spectrum.

Improved survival prediction for kidney transplant outcomes using artificial intelligence-based models: development of the UK Deceased Donor Kidney Transplant Outcome Prediction (UK-DTOP) Tool.

The UK Deceased Donor Kidney Transplant Outcome Prediction (UK-DTOP) Tool, developed using advanced artificial intelligence (AI), significantly enhances the prediction of outcomes for deceased-donor kidney transplants in the UK. This study analyzed data from the UK Transplant Registry (UKTR), including 29,713 transplant cases between 2008 and 2022, to assess the predictive performance of three machine learning models: XGBoost, Random Survival Forest, and Optimal Decision Tree. Among these, XGBoost demonstrated exceptional performance with the highest concordance index of 0.

Extracellular vesicle transfer of miR-1 to adipose tissue modifies lipolytic pathways following resistance exercise.

Extracellular vesicles (EVs) have emerged as important mediators of intertissue signaling and exercise adaptations. In this human study, we provide evidence that muscle-specific microRNA-1 (miR-1) was transferred to adipose tissue via EVs following an acute bout of resistance exercise. Using a multimodel machine learning automation tool, we discovered muscle primary miR-1 transcript and CD63+ EV count in circulation as top explanatory features for changes in adipose miR-1 levels in response to resistance exercise.