SNPpy - Database Management for SNP Data from Genome Wide Association Studies

Background: We describe SNPpy, a hybrid script database system using the Python SQLAlchemy library coupled with the PostgreSQL database to manage genotype data from Genome-Wide Association Studies (GWAS). This system makes it possible to merge study data with HapMap data and merge across studies for meta-analyses, including data filtering based on the values of phenotype and Single-Nucleotide Polymorphism (SNP) data. SNPpy and its dependencies are open source software. Results: The current version of SNPpy offers utility functions to import genotype and annotation data from two commercial platforms. We use these to import data from two GWAS studies and the HapMap Project. We then export these individual datasets to standard data format files that can be imported into statistical software for downstream analyses. Conclusions: By leveraging the power of relational databases, SNPpy offers integrated management and manipulation of genotype and phenotype data from GWAS studies. The analysis of these studies requires merging across GWAS datasets as well as patient and marker selection. To this end, SNPpy enables the user to filter the data and output the results as standardized GWAS file formats. It does low level and flexible data validation, including validation of patient data. SNPpy is

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Teaching and Generative AI

With the rapid development of generative AI, teachers are experiencing a new pedagogical challenge, one that promises to forever change the way we approach teaching and learning. As a response to this unprecedented teaching context, this collection—Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions—provides interdisciplinary teachers, librarians, and instructional designers with practical and thoughtful pedagogical resources for navigating the possibilities and challenges of teaching in an AI era. Because our goal with this edited collection is to present nuanced discussions of AI technologies across disciplines, the chapters collectively acknowledge or explore both possibilities and tensions—including the strengths, limitations, ethical considerations, and disciplinary potential and challenges—of teaching in an AI era. As such, the authors in this collection do not simply praise or criticize AI, but thoughtfully acknowledge and explore its complexities within educational settings

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

Rural School Administrators\u27 Understanding of Play Therapy and Its Value

In 2019, 19% of students attended a rural school in the United States (National Center for Education Statistics, 2024). However, the CDC (2024) reports only 20% of children who need specialized mental health services receive them. The need for mental health services exists at similar rates across rural and urban communities; yet rural families face more barriers to mental health access (Lee, 2023; Morales et al., 2020). Schools are very influential in children’s lives given the amount of time they spend there. In addition, schools are a natural community gathering point, making them an optimal location for providing mental health services (Lee, 2023). However, even though school counselors see play therapy as valuable, they report encountering barriers such as training, time, resources, administration and support (Ebrahim & Steen, 2012). Due to the lack of specialized mental health services in rural communities, it is unlikely that children will have access to play therapy services in rural communities if they are not provided in the school setting. Implementing play therapy is a necessity if schools intend to provide effective, developmentally appropriate services, yet many schools fail to do so. Hindman et al. (2022) discovered that knowledge about play therapy significantly increased participants’ willingness to seek out play therapy services. Because school administrators help determine the direction of schools, their knowledge about and support of play therapy is imperative. In this study, researchers examined rural school administrators’ knowledge of play therapy to determine if information about play therapy increased willingness to support play therapy services.https://thekeep.eiu.edu/che_posters_college_student_affairs/1000/thumbnail.jp

Rural School Administrators\u27 Understanding of Play Therapy and Its Value

In 2019, 19% of students attended a rural school in the United States (National Center for Education Statistics, 2024). However, the CDC (2024) reports only 20% of children who need specialized mental health services receive them. The need for mental health services exists at similar rates across rural and urban communities; yet rural families face more barriers to mental health access (Lee, 2023; Morales et al., 2020). Schools are very influential in children’s lives given the amount of time they spend there. In addition, schools are a natural community gathering point, making them an optimal location for providing mental health services (Lee, 2023). However, even though school counselors see play therapy as valuable, they report encountering barriers such as training, time, resources, administration and support (Ebrahim & Steen, 2012). Due to the lack of specialized mental health services in rural communities, it is unlikely that children will have access to play therapy services in rural communities if they are not provided in the school setting. Implementing play therapy is a necessity if schools intend to provide effective, developmentally appropriate services, yet many schools fail to do so. Hindman et al. (2022) discovered that knowledge about play therapy significantly increased participants’ willingness to seek out play therapy services. Because school administrators help determine the direction of schools, their knowledge about and support of play therapy is imperative. In this study, researchers examined rural school administrators’ knowledge of play therapy to determine if information about play therapy increased willingness to support play therapy services.https://thekeep.eiu.edu/che_posters_faculty/1000/thumbnail.jp