DNA samples were shipped to both LGC Genomics (http://www.lgcgroup.com) and the Genomic Diversity facility at Cornell University (http://www.biotech.cornell.edu/brc/genomic-diversity-facility).
GBS library preparation (Elshire et al., 2011), sequencing, and SNP calling were performed at the Genomic Diversity Facility (GDF) at Cornell University's Biotechnology Resource Center.
GBS data was generated by the Genomic Diversity Facility, Cornell University using ApeKI as restriction enzyme and 96-plex multiplexing.> The raw allele calls received from LGC Genomics consisted of several unassigned SNP calls.
A 2006 InfoUSA™ facility database, containing North American Industrial Classification System (NAICS) codes and longitude and latitude for each facility [ 48] was used to create the diversity of facilities and facility density variables within each buffer.
The majority of the built environment variables, including population density, intersection density, diversity of facilities, and most facility densities, were consistently higher compared to outside of clusters.
Eleven objective built environment variables were created using ArcGIS 9.3 software (ESRI, Redland, CA) and employed methods described more fully in earlier work [ 42]: population density, intersection density, diversity of facilities, and eight facility density variables.
In contrast, in two other higher physical activity clusters in California and Massachusetts, population density, diversity of facilities, and density of facilities were significantly lower than in areas outside of clusters.
In Pennsylvania, population density, intersection density, diversity of facilities, and certain types of facility density inside obesity clusters were significantly lower compared to areas outside the clusters.
2003 persons/km), intersection density (e.g., 6.08 vs. 4.01), and diversity of facilities (e.g., 0.77 vs. 0.52) and facility density (consistent with higher walkability), compared to outside of clusters.
In the higher obesity cluster 8, values for built environment characteristics, such as population density (i.e., 942 vs. 1,175 persons/km), intersection density (i.e., 3.90 vs. 4.07), diversity of facilities (i.e., 0.50 vs. 0.56) and most facility density variables were significantly lower than outside the cluster.
The idea behind this list is to highlight schools making "promising and innovative changes" across "academics, faculty, students, campus life, diversity and facilities".
