Background. Downs syndrome (DS) is the commonest of the congenital genetic defects. Its incidence has been rising in recent years for unknown reasons. Objective. Investigate the relationship of DS to substance- and cannabinoid- exposure; and causality.
Methods. Observational ecological population-based epidemiological study 1986-2016. Analysis performed January 2020. Geotemporospatial and causal inference analysis. Participants: Patients were diagnosed with DS and reported to state based registries; collated nationally. Data source: annual reports of National Birth Defects Prevention Network of Centres for Disease Control. Exposures: Drug exposure was taken from the National Survey of Drug Use and Health (NSDUH) conducted annually by Substance Abuse and Mental Health Services Administration. Nationally representative sample 67,000 participants annually. Drug exposures: cigarette consumption, alcohol abuse, analgesic/opioid abuse, cocaine use and last month cannabis use. Ethnicity and median household income: US Census Bureau. Maternal age of childbearing: CDC births registries. Cannabinoid concentrations: Drug Enforcement Agency seizures.
Results. NSDUH report 74.1% mean annual response rate. All other data was population-wide. DS rate (DSR) was noted to be rising over time, cannabis use, and cannabis-use quintile. In the optimal geospatial model lagged to four years terms including Δ9-tetrahydrocannabinol and cannabigerol were significant (from β-est.=4189.96 (95%C.I. 1924.74, 6455.17), P=2.9x10-4). Ethnicity, income, and maternal age covariates were not significant. DSR in states where cannabis was not illegal was higher than elsewhere (β-est.=2.160 (1.5, 2.82), R.R.=1.81 (1.51, 2.16), P=4.7x10-10). In inverse probability-weighted mixed models terms including cannabinoids were significant (from β-estimate=18.82 (16.82, 20.82), P<0.0001). EValues in geospatial models ranged up to infinity.
Conclusions. Our data show that the association between DSR and substance- and cannabinoid- exposure is robust to multivariable geotemporospatial adjustment, implicate particularly cannabigerol and Δ9-tetrahydrocannabinol, and fulfil causal crietria. Cannabis legalization was associated with elevated DSR’s. These findings are consistent with those from Hawaii, Colorado, Canada and Australia and concordant with several cellular mechanisms. Given that the cannabis industry is presently in a rapid growth-commercialization phase the present findings linking cannabis use with megabase scale genotoxicity suggest unrecognized DS risk factors, are of public health importance and suggest that re-focussing the cannabis debate on multigenerational and intergenerational health concerns is prudent.
The html documents presented here were developed for the experimental task in Rivas and Beier (2020). Relationship between episodic memory and the memory benefits of retrieval and restudy practice for related and unrelated words (submitted for publication).
The experimental task consists of two parts: The document "Learning Session" is the task where participants practice learning related and unrelated words through either retrieval or restudy. The document "Final Session" is the final task where participants try to recall all the words that they practiced during the "Learning Session". Researchers should feel free to run the experiment as is, or to change any of the parameters in order to fit their research design (e.g., change stimuli, stimuli presentation time, retention intervals, etc.).
Animal modelling for infectious diseases is critical to understand the biology of the pathogens including viruses and to develop therapeutic strategies against it. Herein, we present the sequence homology and expression data analysis of proteins found in Drosophila melanogaster that are orthologous to human proteins, reported as components of SARS-CoV-2/Human interactome. The dataset enlists sequence homology, query coverage, domain conservation, OrthoMCL and Ensembl Genome Browser support of 326 proteins in D.melanogaster that are potentially orthologous to 417 human proteins reported for their direct physical interactions with 28 proteins encoded by SARS-CoV-2 genome. Expression of these D.melanogaster orthologous genes in 26 anatomical positions are also plotted as heat maps in 27 sets, corresponding to the potential protein interactors for each viral protein. The data could be used to direct experiments and potentially predict their phenotypic and molecular outcome in order to dissect the biological roles and molecular functionality of SARS-CoV-2 proteins in a convenient animal model system like D.melanogaster.
Contributors:Sola Ortigosa Joaquin, Carlos Muñoz Santos, Guilabert-Vidal Antonio
Dataset of 636 patient swith 1000 keratotic lesions and diagnosis of three teledermatologists in three stages.
Stage 1: First evaluation of the 3 teledermatologists
Stage 2: Second evaluation of the 3 teledermatologists
Stage 3: Face-to-face diagnosis of the 3 teledermatologists (separately and final consensus or diagnosis by biopsy).
Contributors:KIM MIN JEE, Kim Iksoo, Park Jeong Sun
Phylogenetic tree for the subfamily Luciolinae. The maximum likelihood (ML) method was applied using randomized axelerated maximum likelihood (RAxML) ver. 8.0.24 (Stamatakis 2014), which was incorporated into the cyberinfrastructure for phylogenetic research (CIPRES) Portal ver. 3.1 (Miller et al. 2010).
Contributors:Jiang Weimin, Zang Leizhen, Cole Michael, Sun Jiajing
How regulation affects environmental protection has generated much attention amongst scholars. However, studies have produced inconsistent results, often derived from a narrow focus on one particular country/region and or inconsistent regulatory definitions. This study analysed panel data obtained from World bank and quality of government institute (QoG) from 1996 to 2014, using World Bank’s definition of regulatory quality. We contrasted different countries according to their stages of economic development and political regimes. The findings support the Environment Kuznets Curve (EKC) that economic growth facilitates carbon reduction. Regulatory quality had significant effects on reducing carbon emissions for democracies, with an inverted ‘U’ shaped curve for democracies and a ‘U’ shaped curve for dictatorships.
Our variables are obtained from the authoritative sources, for example World Development Indicators were taken from the World Bank and data on regimes from the Quality of Government (QoG) Institute. We measured regulatory quality through the World Bank’s Regulatory Quality Index, which captures perceptions of government capacity ‘to formulate and implement sound policies and regulations that permit and promote private sector development.’ Data (kg per 2010 US$ of GDP) from the World Bank was used as the main measure for carbon emissions.
Heterogeneity amongst countries necessitated control variables, choice reflecting usage in previous studies. First, we incorporated the GDP annual percentage growth rate, reflective of findings that economic growth raises carbon emissions. The stage of economic development, measured by per capita GDP, was also included, reflective of the EKC-concept that as countries shift from middle to high-income status economic growth starts to facilitate carbon emission reductions. Industrialization, measured by ‘Industry (including construction), value added (% of GDP)’ and urbanization, measured by ‘Urban population (% of total)’, were included to reflect consensus that industrialization and urbanization raise carbon emissions. The percentage of the land area allocated to forests, the percentage of electricity generated from fossil fuels and the percentage of renewable energy consumption were included to reflect the effect of energy structure on carbon emissions. Finally, an education measure, the number of years of secondary schooling, was incorporated to reflect observed connections between educational attainment and carbon emissions.
Contact Email: firstname.lastname@example.org
Compressive thermal creep of U3Si2 measured at the University of South Carolina. Folders are organized by Pellet ID as described by E. Mercado. Data processing scripts were originally written in python 2.x by R. Austin Freeman and modified by J.A. Yingling.