Does Haze Removal Help CNN-based Image Classification?

Hazy images are common in real scenarios and many dehazing methods have been developed to automatically remove the haze from images. Typically, the goal of image dehazing is to produce clearer images from which human vision can better identify the object and structural details present in the images. When the ground-truth haze-free image is available for a hazy image, quantitative evaluation of image dehazing is usually based on objective metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). However, in many applications, large-scale images are collected not for visual examination by human. Instead, they are used for many high-level vision tasks, such as automatic classification, recognition and categorization. One fundamental problem here is whether various dehazing methods can produce clearer images that can help improve the performance of the high-level tasks. In this paper, we empirically study this problem in the important task of image classification by using both synthetic and real hazy image datasets. From the experimental results, we find that the existing image-dehazing methods cannot improve much the image-classification performance and sometimes even reduce the image-classification performance

Generation of integration-free neural progenitor cells from cells in human urine

Human neural stem cells hold great promise for research and therapy in neural disease. We describe the generation of integration-free and expandable human neural progenitor cells (NPCs). We combined an episomal system to deliver reprogramming factors with a chemically defined culture medium to reprogram epithelial-like cells from human urine into NPCs (hUiNPCs). These transgene-free hUiNPCs can self-renew and can differentiate into multiple functional neuronal subtypes and glial cells in vitro. Although functional in vivo analysis is still needed, we report that the cells survive and differentiate upon transplant into newborn rat brain.postprin

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

Hydrogen-Rich Saline Attenuates Chronic Allodynia after Bone Fractures via Reducing Spinal CXCL1/CXCR2-Mediated Iron Accumulation in Mice

Purpose: Neuroinflammation often initiates iron overload in the pathogenesis of neurological disorders. Chemokine-driven neuroinflammation is required for central sensitization and chronic allodynia following fractures, but specific molecular modulations are elusive. This present study explored whether hydrogen-rich saline, as one potent anti-inflammatory pharmaceutical, could alleviate fracture-caused allodynia by suppressing chemokine CXCL1 expression and iron overload. Methods: A mouse model of tibial fracture with intramedullary pinning was employed for establishing chronic allodynia. Three applications of hydrogen-rich saline (1, 5 or 10 mL/kg) were administrated intraperitoneally on a daily basis from days 4 to 6 following fractures. Spinal CXCL1 and its receptor CXCR2 levels, transferrin receptor 1 (TfR1) expression and iron concentration were examined. Recombinant CXCL1, a selective CXCR2 antagonist and an iron chelator were used for verification of mechanisms. Results: Repetitive injections of hydrogen-rich saline (5 and 10 mL/kg but not 1 mL/kg) prevent fracture-caused mechanical allodynia and cold allodynia in a dose-dependent manner. Single exposure to hydrogen-rich saline (10 mL/kg) on day 14 after orthopedic surgeries controls the established persistent fracture allodynia. Furthermore, hydrogen-rich saline therapy reduces spinal CXCL1/CXCR2 over-expression and TfR1-mediated iron accumulation in fracture mice. Spinal CXCR2 antagonism impairs allodynia and iron overload following fracture surgery. Intrathecal delivery of recombinant CXCL1 induces acute allodynia and spinal iron overload, which is reversed by hydrogen-rich saline. Moreover, iron chelation alleviates exogenous CXCL1-induced acute pain behaviors. Conclusions: These findings identify that hydrogen-rich saline confers protection against fracture-caused chronic allodynia via spinal down-modulation of CXCL1-dependent TfR1-mediated iron accumulation in mice

Generating a Non-Integrating Human Induced Pluripotent Stem Cell Bank from Urine-Derived Cells

<div><p>Induced pluripotent stem cell (iPS cell) holds great potential for applications in regenerative medicine, drug discovery, and disease modeling. We describe here a practical method to generate human iPS cells from urine-derived cells (UCs) under feeder-free, virus-free, serum-free condition and without oncogene <i>c-MYC</i>. We showed that this approach could be applied in a large population with different genetic backgrounds. UCs are easily accessible and exhibit high reprogramming efficiency, offering advantages over other cell types used for the purpose of iPS generation. Using the approach described in this study, we have generated 93 iPS cell lines from 20 donors with diverse genetic backgrounds. The non-viral iPS cell bank with these cell lines provides a valuable resource for iPS cells research, facilitating future applications of human iPS cells.</p></div