Monte Carlo simulations of microchannel plate detectors I: steady-state voltage bias results

X-ray detectors based on straight-channel microchannel plates (MCPs) are a powerful diagnostic tool for two-dimensional, time-resolved imaging and timeresolved x-ray spectroscopy in the fields of laser-driven inertial confinement fusion and fast z-pinch experiments. Understanding the behavior of microchannel plates as used in such detectors is critical to understanding the data obtained. The subject of this paper is a Monte Carlo computer code we have developed to simulate the electron cascade in a microchannel plate under a static applied voltage. Also included in the simulation is elastic reflection of low-energy electrons from the channel wall, which is important at lower voltages. When model results were compared to measured microchannel plate sensitivities, good agreement was found. Spatial resolution simulations of MCP-based detectors were also presented and found to agree with experimental measurements

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

Challenges of case identification and diagnosis of Autism Spectrum Disorders in China: A critical review of procedures, assessment, and diagnostic criteria

The estimated prevalence of Autism Spectrum Disorders (ASD) in China has been consistently lower than most of the studies in the West. The current article addressed several challenges in identifying and diagnosing ASD in mainland China. The underestimated prevalence may due to a variety of reasons, including inconsistencies in screening and diagnostic procedures, variations in translated instruments, and discrepancies between diagnostic criteria. This review provides insight into ASD assessment and diagnosis in the Chinese population and discusses strategies for the further advancement of ASD identification and intervention in mainland China

Construction of a miRNA-Based Nomogram Model to Predict the Prognosis of Endometrial Cancer

Objective: To investigate the differential expression of microRNA (miRNA) in patients with endometrial cancer and its relationship with prognosis and survival. Method: We used The Cancer Genome Atlas (TCGA) database to analyze differentially expressed miRNAs in endometrial cancer tissues and adjacent normal tissues. In addition, we successfully screened out key microRNAs to build nomogram models for predicting prognosis and we performed survival analysis on the key miRNAs as well. Result: We identified 187 differentially expressed miRNAs, which includes 134 up-regulated miRNAs and 53 down-regulated miRNAs. Further univariate Cox regression analysis screened out 47 significantly differentially expressed miRNAs and selected 12 miRNAs from which the prognostic nomogram model for ECA patients by LASSO analysis was constructed. Survival analysis showed that high expression of hsa-mir-138-2, hsa-mir-548f-1, hsa-mir-934, hsa-mir-940, and hsa-mir-4758 as well as low-expression of hsa-mir-146a, hsa-mir-3170, hsa-mir-3614, hsa-mir-3616, and hsa-mir-4687 are associated with poor prognosis in EC patients. However, significant correlations between the expressions levels of has-mir-876 and hsa-mir-1269a and patients’ prognosis are not found. Conclusion: Our study found that 12 significantly differentially expressed miRNAs might promote the proliferation, invasion, and metastasis of cancer cells by regulating the expression of upstream target genes, thereby affecting the prognosis of patients with endometrial cancer

Diagnosis and Treatment of Inguinal Hernias after Surgical Treatment of Prostate Cancer, Current State of the Problem

(1) Purpose: To compare and evaluate the immediate and long-term results of the use of various hernioplasties for the treatment of inguinal hernias after surgical treatment of prostate cancer; to determine the possibility of performing transabdominal preperitoneal (TAPP) hernioplasty and total extraperitoneal (eTEP) hernioplasty in patients with inguinal hernia during surgical treatment of prostate cancer. (2) Method: This study is a clinical analytical prospective study, without the use of randomization. The study included 220 patients with inguinal hernia, who were randomly divided into two groups (group A (n = 100) and group B (n = 120)). Patients in group A received eTEP, and those in group B received TAPP. The end points of the study were the results associated with the operation itself and the prognosis of the disease in the two groups. (3) Results: Group A: five patients had a scrotal hematoma, in 10 cases nosocomial pneumonia or infectious complications from the postoperative wound. The overall rate of early postoperative complications was 15%. In group B, the following postoperative complications were reported: one case of intestinal injury, six cases of acute urinary retention, eight cases of scrotal hematoma and 12 cases of nosocomial pneumonia or infectious complications from the postoperative wound were admitted. The overall incidence of early postoperative complications was 22.5%. There was no statistically significant difference in the incidence of postoperative complications between the two groups (χ2 (3) = 2.54, p > 0.05). (4) Conclusion: During the analysis of the obtained results, no statistically significant difference was found in the duration of hospitalization, the volume of blood loss, the severity of pain syndrome, postoperative complication incidence and recurrence incidence (p > 0.05); however, the comparison groups differed in the duration of the operation: the operation time in group A was much longer compared to group B (p < 0.05)

Construction of a miRNA-Based Nomogram Model to Predict the Prognosis of Endometrial Cancer

Objective: To investigate the differential expression of microRNA (miRNA) in patients with endometrial cancer and its relationship with prognosis and survival. Method: We used The Cancer Genome Atlas (TCGA) database to analyze differentially expressed miRNAs in endometrial cancer tissues and adjacent normal tissues. In addition, we successfully screened out key microRNAs to build nomogram models for predicting prognosis and we performed survival analysis on the key miRNAs as well. Result: We identified 187 differentially expressed miRNAs, which includes 134 up-regulated miRNAs and 53 down-regulated miRNAs. Further univariate Cox regression analysis screened out 47 significantly differentially expressed miRNAs and selected 12 miRNAs from which the prognostic nomogram model for ECA patients by LASSO analysis was constructed. Survival analysis showed that high expression of hsa-mir-138-2, hsa-mir-548f-1, hsa-mir-934, hsa-mir-940, and hsa-mir-4758 as well as low-expression of hsa-mir-146a, hsa-mir-3170, hsa-mir-3614, hsa-mir-3616, and hsa-mir-4687 are associated with poor prognosis in EC patients. However, significant correlations between the expressions levels of has-mir-876 and hsa-mir-1269a and patients’ prognosis are not found. Conclusion: Our study found that 12 significantly differentially expressed miRNAs might promote the proliferation, invasion, and metastasis of cancer cells by regulating the expression of upstream target genes, thereby affecting the prognosis of patients with endometrial cancer