Analysis of Post-Secondary Bound Graduation Rates in Pennsylvania Public Schools

High school graduation rates have been increasing statewide in Pennsylvania in recent years. However, the rate of these graduates attending any form of post-secondary education remains inconsistent across the state and even within districts. Access to post-secondary education is important to public health because significant reductions in negative health outcomes have been observed in those with post-secondary education levels. This thesis analyzes the relationship between a school’s post-secondary bound graduation rate and the distribution of the race and socioeconomic status of the student population. In order to quantify and test these relationships for statistical significance, we developed a mixed effects model relating demographic covariates and other school characteristics to multiple post-secondary bound graduation rates. In addition, we also utilized machine learning clustering techniques to categorize schools on student demographic data distributions and model the differences in post-secondary bound graduation rates between these groups. We observed that school-wide Title I status (as an indicator for socioeconomic status) had a negative effect on post-secondary bound rates. In addition, there a was positive relationship observed between the proportion of students from Historically Underserved Groups (HUGs) in a school’s student population and post-secondary bound graduation rates. Through our cluster analysis, we found that the race/ethnicity distribution of students in individual schools fell into four categories. Further analysis using the results from the cluster analysis showed the previous relationship between student HUG proportion and post-secondary graduation rates applied only toward schools with a majority Black student population and not schools with a majority Hispanic student population or schools with more diverse student populations. In conclusion, there is evidence the proportion of school’s student population that is historically underserved may affect the post-secondary bound graduation rates of that school, however, this trend may not be similar in schools serving different demographics of students. The results of this analysis justify further quantitative and qualitative research into understanding what school-level qualities influence a student’s access to post-secondary education

American Gut: an Open Platform for Citizen Science Microbiome Research

McDonald D, Hyde E, Debelius JW, et al. American Gut: an Open Platform for Citizen Science Microbiome Research. mSystems. 2018;3(3):e00031-18

Phylogenetic and ecological factors impact the gut microbiota of two Neotropical primate species

Recent studies suggest that variation in diet across time and space results in changes in the mammalian gut microbiota. This variation may ultimately impact host ecology by altering nutritional status and health. Wild animal populations provide an excellent opportunity for understanding these interactions. However, compared to clinical studies, microbial research targeting wild animals is currently limited, and many published studies focus only on a single population of a single host species. In this study we utilize fecal samples from two species of howler monkey (Alouatta pigra and A. palliata) collected at four sites to investigate factors influencing the gut microbiota at three scales: taxonomic (host species), ecosystemic (forest type), and local (habitat disturbance/season). The results demonstrate that the effect of host species on the gut microbiota is stronger than the effect of host forest type, which is stronger than the effect of habitat disturbance or seasonality. Nevertheless, within host species, gut microbiota composition differs in response to forest type, habitat disturbance, and season. Variations in the effect size of these factors are associated both with host species and environment. This information may be beneficial for understanding ecological and evolutionary questions associated with Mesoamerican howler monkeys, as well as determining conservation challenges facing each species. These mechanisms may also provide insight into the ecology of other species of howler monkeys, non-human primates, and mammals

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Placing changes in the microbiome in the context of the American Gut. We accumulated samples over sequencing runs to demonstrate the structural consistency in the data. We demonstrate that while the ICU dataset (https://www.ncbi.nlm.nih.gov/pubmed/27602409) falls within the American Gut samples, they do not fall close to most samples at any of the body sites. We then highlight samples from the United Kingdom, Australia, the United States and other countries to show that nationality does not overcome the variation in body site. We then highlight the utility of the American Gut in meta-analysis by reproducing results from (https://www.ncbi.nlm.nih.gov/pubmed/20668239) and (https://www.ncbi.nlm.nih.gov/pubmed/23861384), using the AGP dataset as the context for dynamic microbiome changes instead of the HMP dataset. We show rapid, complete recovery of C. diff patients following fecal material transplantation and also contextualized the change in an infant gut over time until it settles into an adult state. This demonstrates the power of the American Gut dataset, both as a cohesive study and as a context for other investigations

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The SEPP (Mirarab et al Pac Symp Biocomput 2012) fragment insertion tree used for phylogenetic analyses

American Gut Project fecal sOTU relative abundance table

The Deblur sOTU relative abundance table for the fecal samples used in the American Gut Project manuscript. The samples were trimmed to a common read length of 125nt, and processed by Deblur (Amir et al mSystems 2017). Blooms were removed (Amir et al mSystems 2017) and any sample with fewer than 1250 sequences was omitted. This table is not rarefied, and is normalized to 1

American Gut collated alpha diversities

Collated alpha diversity values for the 9511 fecal samples used in the American Gut Project manuscript

American Gut Project fecal sOTU counts table

The Deblur sOTU counts table for the fecal samples used in the American Gut Project manuscript. The samples were trimmed to a common read length of 125nt, and processed by Deblur (Amir et al mSystems 2017). Blooms were removed (Amir et al mSystems 2017) and any sample with fewer than 1250 sequences was omitted. This table is not rarefied,

Unweighted UniFrac distances

The unweighted UniFrac distance (Lozupone and Knight AEM 2005) matrix of the 9511 fecal samples used in the American Gut paper. UniFrac was computed using Striped UniFrac (https://github.com/biocore/unifrac). Prior to execution of UniFrac, Deblur (Amir et al mSystems 2017) was run on the samples, all bloom sOTUs were removed (Amir et al mSystems 2017), and samples were rarefied to a depth of 1250 reads (Weiss et al Microbiome 2017). For the phylogeny, fragments were inserted using SEPP (Mirarab et al Pac Symp Biocomput 2012) into the Greengenes 13_5 99% OTU tree (McDonald et al ISME 2012)