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

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Abstracts from the 8th International Conference on cGMP Generators, Effectors and Therapeutic Implications

This work was supported by a restricted research grant of Bayer AG

Hong Kong vehicle emission changes from 2003 to 2015 in the Shing Mun Tunnel

This study characterized motor vehicle emission rates and compositions in Hong Kong's Shing Mun tunnel (SMT) during 2015 and compared them to similar measurements from the same tunnel in 2003. Average PM2.5 concentrations in the SMT decreased by approximate to 70% from 229.1 22.1 mu g/m(3) in 2003 to 74.2 +/- 2.1 mu g/m(3) in 2015. Both PM2.5 and sulfur dioxide (SO2) emission factors (EFD) were reduced by approximate to 80% and total non-methane (NMHC) hydrocarbons EFD were reduced by 44%. These reductions are consistent with long-term trends of roadside ambient concentrations and emission inventory estimates, indicating the effectiveness of emission control measures. EFD changes between 2003 and 2015 were not statistically significant for carbon monoxide (CO), ammonia (NH3), and nitrogen oxides (NOx). Tunnel nitrogen dioxide (NO2) concentrations and NO2/NOx volume ratios increased, indicating an increased NO2 fraction in the primary vehicle exhaust emissions. Elemental carbon (EC) and organic matter (OM) were the most abundant PM2.5 constituents, with EC and OM, respectively, contributing to 51 and 31% of PM2.5 in 2003, and 35 and 28% of PM2.5 in 2015. Average EC and OM EFD decreased by approximate to 80% from 2003 to 2015. The sulfate EFD decreased to a lesser degree (55%) and its contribution to PM2.5 increased from 10% in 2003 to 18% in 2015, due to influences from ambient background sulfate concentrations. The contribution of geological materials to PM2.5 increased from 2% in 2003 to 5% in 2015, signifying the importance of non-tailpipe emissions.(c) 2018 American Association for Aerosol Researc