The balance between IFN-γ and ERK/MAPK signaling activities ensures lifelong maintenance of intestinal stem cells.

While the intestinal epithelium has the highest cellular turnover rates in the mammalian body, it is also considered one of the tissues most resilient to aging-related disorders. Here, we reveal an innate protective mechanism that safeguards intestinal stem cells (ISCs) from environmental conditions in the aged intestine. Using in vivo phenotypic analysis, transcriptomics, and in vitro intestinal organoid studies, we show that age-dependent activation of interferon-γ (IFN-γ) signaling and inactivation of extracellular signal-regulated kinase/mitogen-activated protein kinase (ERK/MAPK) signaling are responsible for establishing an equilibrium of Lgr5 ISCs-between active and quiescent states-to preserve the ISC pool during aging.

scEGOT: single-cell trajectory inference framework based on entropic Gaussian mixture optimal transport.

Background: Time-series scRNA-seq data have opened a door to elucidate cell differentiation, and in this context, the optimal transport theory has been attracting much attention. However, there remain critical issues in interpretability and computational cost.

Results: We present scEGOT, a comprehensive framework for single-cell trajectory inference, as a generative model with high interpretability and low computational cost.

Resolution of the curse of dimensionality in single-cell RNA sequencing data analysis.

Single-cell RNA sequencing (scRNA-seq) can determine gene expression in numerous individual cells simultaneously, promoting progress in the biomedical sciences. However, scRNA-seq data are high-dimensional with substantial technical noise, including dropouts. During analysis of scRNA-seq data, such noise engenders a statistical problem known as the curse of dimensionality (COD).

Flow estimation solely from image data through persistent homology analysis.

Topological data analysis is an emerging concept of data analysis for characterizing shapes. A state-of-the-art tool in topological data analysis is persistent homology, which is expected to summarize quantified topological and geometric features. Although persistent homology is useful for revealing the topological and geometric information, it is difficult to interpret the parameters of persistent homology themselves and difficult to directly relate the parameters to physical properties.

Protein-Folding Analysis Using Features Obtained by Persistent Homology.

Understanding the protein-folding process is an outstanding issue in biophysics; recent developments in molecular dynamics simulation have provided insights into this phenomenon. However, the large freedom of atomic motion hinders the understanding of this process. In this study, we applied persistent homology, an emerging method to analyze topological features in a data set, to reveal protein-folding dynamics.

Hepatic tumor classification using texture and topology analysis of non-contrast-enhanced three-dimensional T1-weighted MR images with a radiomics approach.

The purpose of this study is to evaluate the accuracy for classification of hepatic tumors by characterization of T1-weighted magnetic resonance (MR) images using two radiomics approaches with machine learning models: texture analysis and topological data analysis using persistent homology. This study assessed non-contrast-enhanced fat-suppressed three-dimensional (3D) T1-weighted images of 150 hepatic tumors. The lesions included 50 hepatocellular carcinomas (HCCs), 50 metastatic tumors (MTs), and 50 hepatic hemangiomas (HHs) found respectively in 37, 23, and 33 patients.

Non-empirical identification of trigger sites in heterogeneous processes using persistent homology.

Macroscopic phenomena, such as fracture, corrosion, and degradation of materials, are associated with various reactions which progress heterogeneously. Thus, material properties are generally determined not by their averaged characteristics but by specific features in heterogeneity (or 'trigger sites') of phases, chemical states, etc., where the key reactions that dictate macroscopic properties initiate and propagate.

Persistent homology analysis of craze formation.

We apply a persistent homology analysis to investigate the behavior of nanovoids during the crazing process of glassy polymers. We carry out a coarse-grained molecular dynamics simulation of the uniaxial deformation of an amorphous polymer and analyze the results with persistent homology. Persistent homology reveals the void coalescence during craze formation, and the results suggest that the yielding process is regarded as the percolation of nanovoids created by deformation.

Hierarchical structures of amorphous solids characterized by persistent homology.

This article proposes a topological method that extracts hierarchical structures of various amorphous solids. The method is based on the persistence diagram (PD), a mathematical tool for capturing shapes of multiscale data. The input to the PDs is given by an atomic configuration and the output is expressed as 2D histograms.

Persistent homology and many-body atomic structure for medium-range order in the glass.

The characterization of the medium-range (MRO) order in amorphous materials and its relation to the short-range order is discussed. A new topological approach to extract a hierarchical structure of amorphous materials is presented, which is robust against small perturbations and allows us to distinguish it from periodic or random configurations. This method is called the persistence diagram (PD) and introduces scales to many-body atomic structures to facilitate size and shape characterization.