Transcription activity hot spot, is it real or an artifact?

Transcription activity 'hot spots', defined as chromosome regions that contain more expression quantitative trait loci than would have been expected by chance, have been frequently detected both in humans and in model organisms. It has been common to consider the existence of hot spots as evidence for master regulation of gene expression. However, hot spots could also simply be due to highly correlated gene expressions or linkage disequilibrium and do not truly represent master regulators. A recent simulation study using real human gene expression data but simulated random single-nucleotide polymorphism genotypes showed patterns of clustering of expression quantitative trait loci that resemble those in actual studies [Perez-Enciso: Genetics 2004, 166: 547–554.]. In this study, to assess the credibility of transcription activity hot spots, we conducted genetic analyses on gene expressions provided by Genetic Analysis Workshop 15 Problem 1

miR-152 Attenuates the Severity of Lupus Nephritis Through the Downregulation of Macrophage Migration Inhibitory Factor (MIF)-Induced Expression of COL1A1

Background: The role of miR-152 in lupus nephritis has not been elucidated. The aim of this study was to investigate the role of miR-152 in the pathogenesis of lupus nephritis (LN).Methods: miR-152 expression was detected using RT-PCR in LN tissue and normal controls. The miR-152 expression was compared with clinical parameters such as 24 h urine protein excretion level, serum creatinine, and serum complement level and SLEDAI score. The function of miR-152 was examined using human renal proximal tubular epithelial cells (HRPTE). miR-152 mimics and inhibitors were transfected to HRPTEs to ascertain the effects of miR-152.Results: miR-152 expression was downregulated in LN tissue. There was an inverse correlation between miR-152 expression in LN tissue and clinical parameters like 24 h urine protein excretion levels and serum creatinine, but not serum complement levels or SLEDAI. Further analysis showed that macrophage migration inhibitory factor (MIF) was a direct target of miR-152. Downregulation of MIF through complementary binding of miR-152 inhibited the renal expression of COL1A1.Conclusion: miR-152 expression was tapered in LN tissue and miR-152 expression was inversely correlated with chronicity index (CI), serum creatinine and severity of proteinuria. miR-152 may attenuate the severity of LN through the downregulation of MIF-induced expression of COL1A1. These findings suggest that miR-152 may be a potential target for the treatment of LN

Transcriptome Analysis on Maternal Separation Rats With Depression-Related Manifestations Ameliorated by Electroacupuncture

Maternal separation (MS), a stressful event in early life, has been linked to neuropsychiatric disorders later in life, especially depression. In this study we investigated whether treatment with electroacupuncture (EA) could ameliorate depression-related manifestations in adult animals that had adverse early life experiences. We demonstrated depression-like behavior deficiencies in a sucrose preference test and a forced swimming test in a rat model with neonatal MS. Repeated EA treatment at the acupoints Baihui (GV20) and Yintang (GV29) during adulthood was shown to be remarkably attenuated above behavioral deficits. Using unbiased genome-wide RNA sequencing to investigate alterations in the transcriptome of the prefrontal cortex (PFC), we explored the altered gene sets involved in circadian rhythm and neurotransmitter transporter activity in MS rats, and their expression tended to be reversed after EA treatment. In addition, we analyzed the interaction network of differentiated lncRNA– or circRNA–miRNA–mRNA by using the principle of competitive endogenous RNA (ceRNA). These results suggest that EA at GV20 and GV29 ameliorates depression-related manifestations by regulating the expression of multiple genes

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

Characterization and evaluation of fractal dimension of intersalt shale oil reservoirs in Qianjiang Depression

At present, studies of the shales in Qianjiang Formation from the Qianjiang Depression has been widely documented and significant progress has been made in the aspects of geochemical characteristics and reservoir physical properties.However, the complexity of pores, the main minerals forming pore throats and their influencing factors is still poorly understood. In this work, based on fractal theory and combined with nitrogen adsorption and high-pressure mercury injection experiments, Wells BX7 and BYY2 at different deposition locations were selected to evaluate the specific surface fractal characteristics, pore structure fractal characteristics and pore throat fractal characteristics of the Eq34-10 rhythmic shale of the Qianjiang Formation and analyses the influencing factors.The results show that the pore surface of shale in study are a is relatively flat, with the fractal dimension D1 being close to 2, and the roughness of the pore surface is mainly affected by the characteristics of the clay minerals. Compared with Well BX7 at the sedimentary edge, Well BYY2 at the sedimentary center has simpler fractal characteristics of reservoir pore structure. The larger the proportion of pore volume with a small pore size is, the more complex the fractal characteristics of the pore structure are. The fractal dimension D2 of the reservoir pore structure in Well BX7 is mainly affected by clay minerals and quartz, while dolomiteis the dominant controlling factor for the fractal dimension D2 of the reservoir pore structure in Well BYY2. Dolomite is the main mineral that constitutes the pore throat of the Well BX7 reservoir. With the increase in dolomite, the pore throat features are complex, and the pore connectivity becomes poor. The pore throat diameter formed by quartz in the Well BYY2 reservoir is small.The increase in quartz will worsen the pore connectivity, and the pore throat diameter formed by calcite is large. The occurrence of shale oil will reduce the fractal dimension of pores and have little influence on the fractal characteristics of pore throats. The presence of salt minerals can block the pores and make the pore connectivity poor