Cystatin F Ensures Eosinophil Survival by Regulating Granule Biogenesis

SummaryEosinophils are now recognized as multifunctional leukocytes that provide critical homeostatic signals to maintain other immune cells and aid tissue repair. Paradoxically, eosinophils also express an armory of granule-localized toxins and hydrolases believed to contribute to pathology in inflammatory disease. How eosinophils deliver their supporting functions while avoiding self-inflicted injury is poorly understood. We have demonstrated that cystatin F (CF) is a critical survival factor for eosinophils. Eosinophils from CF null mice had reduced lifespan, reduced granularity, and disturbed granule morphology. In vitro, cysteine protease inhibitors restored granularity, demonstrating that control of cysteine protease activity by CF is critical for normal eosinophil development. CF null mice showed reduced pulmonary pathology in a model of allergic lung inflammation but also reduced ability to combat infection by the nematode Brugia malayi. These data identify CF as a “cytoprotectant” that promotes eosinophil survival and function by ensuring granule integrity.Video Abstrac

Exploring data provenance in handwritten text recognition infrastructure:Sharing and reusing ground truth data, referencing models, and acknowledging contributions. Starting the conversation on how we could get it done

This paper discusses best practices for sharing and reusing Ground Truth in Handwritten Text Recognition infrastructures, and ways to reference and acknowledge contributions to the creation and enrichment of data within these Machine Learning systems. We discuss how one can publish Ground Truth data in a repository and, subsequently, inform others. Furthermore, we suggest appropriate citation methods for HTR data, models, and contributions made by volunteers. Moreover, when using digitised sources (digital facsimiles), it becomes increasingly important to distinguish between the physical object and the digital collection. These topics all relate to the proper acknowledgement of labour put into digitising, transcribing, and sharing Ground Truth HTR data. This also points to broader issues surrounding the use of Machine Learning in archival and library contexts, and how the community should begin toacknowledge and record both contributions and data provenance

Impact of Treadmill Running and Sex on Hippocampal Neurogenesis in the Mouse Model of Amyotrophic Lateral Sclerosis

Hippocampal neurogenesis in the subgranular zone (SGZ) of dentate gyrus (DG) occurs throughout life and is regulated by pathological and physiological processes. The role of oxidative stress in hippocampal neurogenesis and its response to exercise or neurodegenerative diseases remains controversial. The present study was designed to investigate the impact of oxidative stress, treadmill exercise and sex on hippocampal neurogenesis in a murine model of heightened oxidative stress (G93A mice). G93A and wild type (WT) mice were randomized to a treadmill running (EX) or a sedentary (SED) group for 1 or 4 wk. Immunohistochemistry was used to detect bromodeoxyuridine (BrdU) labeled proliferating cells, surviving cells, and their phenotype, as well as for determination of oxidative stress (3-NT; 8-OHdG). BDNF and IGF1 mRNA expression was assessed by in situ hybridization. Results showed that: (1) G93A-SED mice had greater hippocampal neurogenesis, BDNF mRNA, and 3-NT, as compared to WT-SED mice. (2) Treadmill running promoted hippocampal neurogenesis and BDNF mRNA content and lowered DNA oxidative damage (8-OHdG) in WT mice. (3) Male G93A mice showed significantly higher cell proliferation but a lower level of survival vs. female G93A mice. We conclude that G93A mice show higher hippocampal neurogenesis, in association with higher BDNF expression, yet running did not further enhance these phenomena in G93A mice, probably due to a ‘ceiling effect’ of an already heightened basal levels of hippocampal neurogenesis and BDNF expression

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages