GLI1+ Cells Contribute to Vascular Remodeling in Pulmonary Hypertension.

Background: The precise origin of newly formed ACTA2+ (alpha smooth muscle actin-positive) cells appearing in nonmuscularized vessels in the context of pulmonary hypertension is still debatable although it is believed that they predominantly derive from preexisting vascular smooth muscle cells (VSMCs).

Methods: mice were used to lineage trace GLI1+ (glioma-associated oncogene homolog 1-positive) cells in the context of pulmonary hypertension using 2 independent models of vascular remodeling and reverse remodeling: hypoxia and cigarette smoke exposure. Hemodynamic measurements, right ventricular hypertrophy assessment, flow cytometry, and histological analysis of thick lung sections followed by state-of-the-art 3-dimensional reconstruction and quantification using Imaris software were used to investigate the contribution of GLI1+ cells to neomuscularization of the pulmonary vasculature.

Vitamin D constrains inflammation by modulating the expression of key genes on Chr17q12-21.1.

Vitamin D possesses immunomodulatory functions and vitamin D deficiency has been associated with the rise in chronic inflammatory diseases, including asthma (Litonjua and Weiss, 2007). Vitamin D supplementation studies do not provide insight into the molecular genetic mechanisms of vitamin D-mediated immunoregulation. Here, we provide evidence for vitamin D regulation of two human chromosomal loci, Chr17q12-21.

Impact of forest landscape restoration in combating soil erosion in the Lake Abaya catchment, Southern Ethiopia.

As an effect of forest degradation, soil erosion is among Ethiopia's most pressing environmental challenges and a major threat to food security where it could potentially compromise the ecosystem functions and services. As the effects of soil erosion intensify, the landscape's capacity to support ecosystem functions and services is compromised. Exploring the ecological implications of soil erosion is crucial.

Crystal search - feasibility study of a real-time deep learning process for crystallization well images.

To avoid the time-consuming and often monotonous task of manual inspection of crystallization plates, a Python-based program to automatically detect crystals in crystallization wells employing deep learning techniques was developed. The program uses manually scored crystallization trials deposited in a database of an in-house crystallization robot as a training set. Since the success rate of such a system is able to catch up with manual inspection by trained persons, it will become an important tool for crystallographers working on biological samples.