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

Promoting effect of nitrogen doping on carbon nanotube-supported RuO2 applied in the electrocatalytic oxygen evolution reaction

RuO2 nanoparticles supported on multi-walled carbon nanotubes (CNTs) functionalized with oxygen (OCNTs) and nitrogen (NCNTs) were employed for the oxygen evolution reaction (OER) in 0.1 M KOH. The catalysts were synthesized by metal-organic chemical vapor deposition using ruthenium carbonyl (Ru3(CO)12) as Ru precursor. The obtained RuO2/OCNT and RuO2/NCNT composites were characterized using TEM, H2-TPR, XRD and XPS in order probe structure-activity correlations, particularly, the effect of the different surface functional groups on the electrochemical OER performance. The electrocatalytic activity and stability of the catalysts with mean RuO2 particle sizes of 13-14 nm was evaluated by linear sweep voltammetry, cyclic voltammetry, and chronopotentiometry, showing that the generation of nitrogen-containing functional groups on CNTs was beneficial for both OER activity and stability. In the presence of RuO2, carbon corrosion was found to be significantly less severe

Co3O4–MnO2–CNT Hybrids Synthesized by HNO3 Vapor Oxidation of Catalytically Grown CNTs as OER Electrocatalysts

An efficient two-step gas-phase method was developed for the synthesis of Co3O4–MnO2–CNT hybrids used as electrocatalysts in the oxygen evolution reaction (OER). Spinel Co–Mn oxide was used for the catalytic growth of multiwalled carbon nanotubes (CNTs) and the amount of metal species remaining in the CNTs was adjusted by varying the growth time. Gas-phase treatment in HNO3 vapor at 200 \ub0C was performed to 1) open the CNTs, 2) oxidize encapsulated Co nanoparticles to Co3O4 as well as MnO nanoparticles to MnO2, and 3) to create oxygen functional groups on carbon. The hybrid demonstrated excellent OER activity and stability up to 37.5 h under alkaline conditions, with longer exposure to HNO3 vapor up to 72 h beneficial for improved electrocatalytic properties. The excellent OER performance can be assigned to the high oxidation states of the oxide nanoparticles, the strong electrical coupling between these oxides and the CNTs as well as favorable surface properties rendering the hybrids a promising alternative to noble metal based OER catalysts

Phosphatase and tensin homolog-β-catenin signaling modulates regulatory T cells and inflammatory responses in mouse liver ischemia/reperfusion injury.

The phosphatase and tensin homolog (PTEN) deleted on chromosome 10 plays an important role in regulating T cell activation during inflammatory response. Activation of β-catenin is crucial for maintaining immune homeostasis. This study investigates the functional roles and molecular mechanisms by which PTEN-β-catenin signaling promotes regulatory T cell (Treg) induction in a mouse model of liver ischemia/reperfusion injury (IRI). We found that mice with myeloid-specific phosphatase and tensin homolog knockout (PTENM-KO ) exhibited reduced liver damage as evidenced by decreased levels of serum alanine aminotransferase, intrahepatic macrophage trafficking, and proinflammatory mediators compared with the PTEN-proficient (floxed phosphatase and tensin homolog [PTENFL/FL ]) controls. Disruption of myeloid PTEN-activated b-catenin promoted peroxisome proliferator-activated receptor gamma (PPARγ)-mediated Jagged-1/Notch signaling and induced forkhead box P3 (FOXP3)1 Tregs while inhibiting T helper 17 cells. However, blocking of Notch signaling by inhibiting γ-secretase reversed myeloid PTEN deficiency-mediated protection in ischemia/reperfusion-triggered liver inflammation with reduced FOXP3+ and increased retinoid A receptor-related orphan receptor gamma t-mediated interleukin 17A expression in ischemic livers. Moreover, knockdown of β-catenin or PPARγ in PTEN-deficient macrophages inhibited Jagged-1/Notch activation and reduced FOXP3+ Treg induction, leading to increased proinflammatory mediators in macrophage/T cell cocultures. In conclusion, our findings demonstrate that PTEN-β-catenin signaling is a novel regulator involved in modulating Treg development and provides a potential therapeutic target in liver IRI. Liver Transplantation 23 813-825 2017 AASLD