Lateral cortical Cdca7 expression levels are regulated by Pax6 and influence the production of intermediate progenitors

Abstract Background We studied whether regulation of Cdca7 (Cell division cycle associated 7) expression by transcription factor Pax6 contributes to Pax6’s cellular actions during corticogenesis. The function of Cdca7 in mediating Pax6’s effects during corticogenesis has not been explored. Pax6 is expressed by radial glial progenitors in the ventricular zone of the embryonic cortical neuroepithelium, where it is required for the development of a normal complement of Tbr2-expressing intermediate progenitor cells in the subventricular zone. Pax6’s expression levels are graded across the ventricular zone, with highest levels laterally where Tbr2-expressing progenitors are generated in greatest numbers at early stages of corticogenesis. Methods We used in situ hybridization and immunohistochemistry to analyse patterns of Cdca7 and Pax6 expression in cortical tissue from wild-type and Pax6 −/− embryos. In each genotype we compared the graded expression of the two genes quantitatively at several ages. To test whether defects in Cdca7 expression in lateral cortical cells might contribute to the cellular defects in this region caused by Pax6 loss, we electroporated a Cdca7 expression vector into wild-type lateral cortex and examined the effect on the production of Tbr2-expressing cells. Results We found that Cdca7 is co-expressed with Pax6 in cortical progenitors, at levels opposite to those of Pax6. Lowest levels of Cdca7 are found in the radial glial progenitors of lateral cortex, where Pax6 levels are highest. Higher levels of Cdca7 are found in ventral telencephalon, where Pax6 levels are low. Loss of Pax6 causes Cdca7 expression to increase in the lateral cortex. Elevating Cdca7 in normal lateral cortical progenitors to levels close to those normally found in ventral telencephalon reduces their production of Tbr2-expressing cells early in lateral cortical formation. Conclusion Our results suggest that Pax6 normally represses Cdca7 expression in the lateral cortex and that repression of Cdca7 in cells of this region is required for their production of a normal complement of Tbr2-expressing intermediate progenitors

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

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

Moving Towards Automated Interstellar Boundary Explorer Data Selection with LOTUS

The Interstellar Boundary Explorer (IBEX) satellite collects data on energetic neutral atoms (ENAs) that provide insight into the heliosphere, the region surrounding our solar system and separating it from interstellar space. IBEX collects information on these particles and on extraneous ``background'' particles. While IBEX records how and when the different particles are observed, it does not distinguish between heliospheric ENA particles and incidental background particles. To address this issue, all IBEX data has historically been manually labeled as ``good'' ENA data, or ``bad'' background data. This manual culling process is incredibly time-intensive and contingent on subjective, manually-induced decision thresholds. In this paper, we develop a three-stage automated culling process, called LOTUS, that uses random forests to expedite and standardize the labelling process. In Stage 1, LOTUS uses random forests to obtain probabilities of observing true ENA particles on a per-observation basis. In Stage 2, LOTUS aggregates these probabilities to obtain predictions within small windows of time. In Stage 3, LOTUS refines these predictions. We compare the labels generated by LOTUS to those manually generated by the subject matter expert. We use various metrics to demonstrate that LOTUS is a useful automated process for supplementing and standardizing the manual culling process

Plasma irisin levels predict telomere length in healthy adults

The ageing process is strongly influenced by nutrient balance, such that modest calorie restriction (CR) extends lifespan in mammals. Irisin, a newly described hormone released from skeletal muscles after exercise, may induce CR-like effects by increasing adipose tissue energy expenditure. Using telomere length as a marker of ageing, this study investigates associations between body composition, plasma irisin levels and peripheral blood mononuclear cell telomere length in healthy, non-obese individuals. Segmental body composition (by bioimpedance), telomere length and plasma irisin levels were assessed in 81 healthy individuals (age 43±15.8 years, BMI 24.3±2.9 kg/m2). Data showed significant correlations between log-transformed relative telomere length and the following: age (p<0.001), height (p=0.045), total body fat percentage (p=0.031), abdominal fat percentage (p=0.038), visceral fat level (p<0.001), plasma leptin (p=0.029) and plasma irisin (p=0.011), respectively. Multiple regression analysis using backward elimination revealed that relative telomere length can be predicted by age (b=−0.00735, p= 0.001) and plasma irisin levels (b=0.04527, p=0.021). These data support the view that irisin may have a role in the modulation of both energy balance and the ageing process

Metabolic reprogramming of the tumor

Cancer is classically considered as a genetic and, more recently, epigenetic multistep disease. Despite seminal studies in the 1920s by Warburg showing a characteristic metabolic pattern for tumors, cancer bioenergetics has often been relegated to the backwaters of cancer biology. This review aims to provide a historical account on cancer metabolism research, and to try to integrate and systematize the metabolic strategies in which cancer cells engage to overcome selective pressures during their inception and evolution. Implications of this renovated view on some common concepts and in therapeutics are also discussed. © 2012 Macmillan Publishers Limited All rights reserved.SCOPUS: re.jinfo:eu-repo/semantics/publishe

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks