Fate mapping in mouse demonstrates early secretory differentiation directly from Lgr5+ intestinal stem cells.

The intestinal epithelium has a remarkably high turnover in homeostasis. It remains unresolved how this is orchestrated at the cellular level and how the behavior of stem and progenitor cells ensures tissue maintenance. To address this, we combined quantitative fate mapping in three complementary mouse models with mathematical modeling and single-cell RNA sequencing.

Ensembl 2025.

Ensembl (www.ensembl.org) is an open platform integrating publicly available genomics data across the tree of life with a focus on eukaryotic species related to human health, agriculture and biodiversity.

A novel root hair mutant, , affects root hair elongation and reactive oxygen species levels in wheat.

Background: Root hairs are single-celled projections on root surfaces, critical for water and nutrient uptake. Here, we describe the first short root hair mutant in wheat ( L.), identified in a mutagenized population and termed here short root hair 1 ().

Diversifying Policies With Non-Markov Dispersion to Expand the Solution Space.

Policy diversity, encompassing the variety of policies an agent can adopt, enhances reinforcement learning (RL) success by fostering more robust, adaptable, and innovative problem-solving in the environment. The environment in which standard RL operates is usually modeled with a Markov Decision Process (MDP) as the theoretical foundation. However, in many real-world scenarios, the rewards depend on an agent's history of states and actions leading to a non-MDP.

An encoding framework for binarized images using hyperdimensional computing.

Introduction: Hyperdimensional Computing (HDC) is a brain-inspired and lightweight machine learning method. It has received significant attention in the literature as a candidate to be applied in the wearable Internet of Things, near-sensor artificial intelligence applications, and on-device processing. HDC is computationally less complex than traditional deep learning algorithms and typically achieves moderate to good classification performance.