The authors studied bacterial contamination of the New York City transit system, as well as the effectiveness of hand washing and hand sanitizer.
Read More...Bacterial contamination on New York City transit and the effectiveness of hand hygiene
The authors studied bacterial contamination of the New York City transit system, as well as the effectiveness of hand washing and hand sanitizer.
Read More...School sustainability: The implications of implementing living walls at schools for air purification
The authors compare air quality in the presence and absence of a living wall in a high school hallway in Brooklyn, NY.
Read More...Demographic trends of alcohol and marijuana co-use: examining age, gender, and race/ethnicity trends
This study aims to examine the demographic factors that predict patterns of co-use of alcohol and marijuana in the United States. Significant findings were identified using data from the National Survey on Drug Use and Health (2012-2022), showing that there were significant differences in the prevalence of substance use among demographic groups, with young adults showing the highest co-use of alcohol and marijuana.
Read More...A five-year retrospective analysis of Tuberculosis risk factors and their variability in the United States
The main goal of this study is to determine what demographics are related to tuberculosis incidence in the United States populations, particularly if changing demographics are related to differences in tuberculosis risk over two discrete time periods. The major finding is that in the two studied time periods, tuberculosis risk factors were somewhat consistent and may be influenced by things such as immigration, healthcare access, and race or ethnicity, although the top predictor did change.
Read More...The sight of disparity: how social determinants shape visual impairment and blindness across the U.S.
This study examined how social determinants of health (SDH) relate to vision loss by analyzing publicly available data from 18 northern and southern U.S. states and using Bayesian correlation analysis.
Read More...Immunogenicity of Minhai 13-valent pneumococcal polysaccharide conjugate vaccine in experimental mice
The authors looked at the immunogenicity of a newly developed pneumococcal conjugate vaccine compared to a previously developed one. They found the newly developed vaccine did elicit an immune response.
Read More...The Role of Corresponding Race, Gender, and Species as Incentives for Charitable Giving
Inherent bias is often the unconscious driver of human behavior, and the first step towards overcoming these biases is our awareness of them. In this article the authors investigate whether race, gender or species affect the choice of charity by middle class Spaniards. Their conclusions serve as a starting point for further studies that could help charities refine their campaigns in light of these biases effectively transcending them or taking advantage of them to improve their fundraising attempts.
Read More...A Data-Centric Analysis of “Stop and Frisk” in New York City
The death of George Floyd has shed light on the disproportionate level of policing affecting non-Whites in the United States of America. To explore whether non-Whites were disproportionately targetted by New York City's "Stop and Frisk" policy, the authors analyze publicly available data on the practice between 2003-2019. Their results suggest African Americans were indeed more likely to be stopped by the police until 2012, after which there was some improvement.
Read More...Innovative fake health news detection: Integrating emotional features into graph neural networks
This manuscript tackles a major social issue in the health news sector, with social media being one of the primary sources of information and a prime spot to propagate fake news. The author proposes X-HND , which is a unique architecture that combines emotional and contextual analysis in a Graph Neural Network to accurately detect fake news. This was a multi-step process which involved the creation of a custom health news dataset (HNDataset), and an emotional variant that uses RoBERTa to extract emotion. These dataset were then used to prove the hypothesis that accuracy increases when the custom dataset is used to train the model and that with the integration of emotion capture, the detection accuracy increases further.
Read More...Environmental contributors of asthma via explainable AI: Green spaces, climate, traffic & air quality
This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.
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