Background: Nonpharmaceutical interventions (NPIs) are the primary tools to mitigate early spread of the coronavirus disease 2019 (COVID-19) pandemic; however, such policies are implemented variably at the federal, provincial or territorial, and municipal levels without centralized documentation. We describe the development of the comprehensive open Canadian Non-Pharmaceutical Intervention (CAN-NPI) data set, which identifies and classifies all NPIs implemented in regions across Canada in response to COVID-19, and provides an accompanying description of geographic and temporal heterogeneity.
Methods: We performed an environmental scan of government websites, news media and verified government social media accounts to identify NPIs implemented in Canada between Jan. 1 and Apr. 19, 2020. The CAN-NPI data set contains information about each intervention's timing, location, type, target population and alignment with a response stringency measure. We conducted descriptive analyses to characterize the temporal and geographic variation in early NPI implementation.
Results: We recorded 2517 NPIs grouped in 63 distinct categories during this period. The median date of NPI implementation in Canada was Mar. 24, 2020. Most jurisdictions heightened the stringency of their response following the World Health Organization's global pandemic declaration on Mar. 11, 2020. However, there was variation among provinces or territories in the timing and stringency of NPI implementation, with 8 out of 13 provinces or territories declaring a state of emergency by Mar. 18, and all by Mar. 22, 2020.
Interpretation: There was substantial geographic and temporal heterogeneity in NPI implementation across Canada, highlighting the importance of a subnational lens in evaluating the COVID-19 pandemic response. Our comprehensive open-access data set will enable researchers to conduct robust interjurisdictional analyses of NPI impact in curtailing COVID-19 transmission.
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http://dx.doi.org/10.9778/cmajo.20200100 | DOI Listing |
Sci Data
January 2025
Department of Crop Science, Chungnam National University, Daejeon, 34134, Republic of Korea.
Goosegrass, belonging to the genus Eleusine within the Chloridoideae subfamily, is often one of the problematic weeds with strong invasiveness, competing with crops for essential survival resources. Although a chromosome-level genome assembly of E. indica from China was published last year, the present research focuses on a population of E.
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January 2025
Chinese Academy of Sciences Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei, 230026, China.
Terrestrial geothermal springs, reminiscent of early Earth conditions, host diverse and abundant populations of Archaea. In this study, we reconstructed 2,949 metagenome-assembled genomes (MAGs) from 152 metagenomes collected over six years from 48 geothermal springs in Tengchong, China. Among these MAGs, 1,431 (49%) were classified as high-quality, while 1,518 (51%) were considered as medium-quality.
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January 2025
Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture (CAS), Hubei Hongshan Laboratory, Key Laboratory of Aquaculture Disease Control, Ministry of Agriculture and Rural Affairs, The Innovation Academy of Seed Design, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, 430072, China.
The large-scale loach (Paramisgurnus dabryanus; Cypriniformes: Cobitidae) is primarily distributed in East Asia. It is an important economic fish species characterized by fast growth, temperature-dependent sex determination and the ability to breathe air. Currently, molecular mechanism studies related to some aspects such as sex determination, toxicology, feed nutrition, growth and genetic evolution have been conducted.
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January 2025
School of Social Policy & Practice, University of Pennsylvania, Philadelphia, PA, United States.
Background: Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured EHR text into structured features, which can then be integrated into statistical prediction models, ensuring that the results are both clinically meaningful and interpretable.
Objective: This study aims to compare the classification decisions made by clinical experts with those generated by a state-of-the-art LLM, using terms extracted from a large EHR data set of individuals with mental health disorders seen in emergency departments (EDs).
Sci Data
January 2025
Key Laboratory of Humid Subtropical Eco-Geographical Process of Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China.
Deforestation-induced forest loss largely affects both the carbon budget and ecosystem services. Subsequent forest regrowth plays a crucial role in ecosystem restoration and carbon replenishment. However, there is an absence of comprehensive datasets explicitly delineating the forest regrowth following deforestation.
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