High density (HD) SNP genotyping arrays are an important tool for genetic analyses of animals and plants. Although the chicken is one of the most important farm animals, no HD array is yet available for high resolution genetic analysis of this species.

A Youth Compendium of Physical Activities (Youth Compendium) was developed to estimate the energy costs of physical activities using data on youth only. Based on a literature search and pooled data of energy expenditure measurements in youth, the energy costs of 196 activities were compiled in 16 activity categories to form a Youth Compendium of Physical Activities. To estimate the intensity of each activity, measured oxygen consumption (V[Combining Dot Above]O2) was divided by basal metabolic rate (Schofield age-, sex- and mass-specific equations) to produce a youth MET (METy). A mixed linear model was developed for each activity category to impute missing values for age ranges with no observations for a specific activity. This Youth Compendium consists of METy values for 196 specific activities classified into 16 major categories for four age groups, 6-9, 10-12, 13-15, and 16-18 years. METy values in this Youth Compendium were measured (51%) or imputed (49%) from youth data. This Youth Compendium of Physical Activities uses pediatric data exclusively, addresses the age-dependency of METy and imputes missing METy values and thus represents advancement in the physical activity research and practice. This Youth Compendium will be a valuable resource for stakeholders interested in evaluating interventions, programs, and policies designed to assess and encourage physical activity in youth. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Background:  Comprehensive school‐based physical activity programs consist of physical education and other physical activity opportunities including recess and other physical activity breaks, intramurals, interscholastic sports, and walk and bike to school initiatives. This article describes the characteristics of school physical education and physical activity policies and programs in the United States at the state, district, school, and classroom levels.Methods:  The Centers for Disease Control and Prevention conducts the School Health Policies and Programs Study every 6 years. In 2006, computer‐assisted telephone interviews or self‐administered mail questionnaires were completed by state education agency personnel in all 50 states plus the District of Columbia and among a nationally representative sample of districts (n = 453). Computer‐assisted personal interviews were conducted with personnel in a nationally representative sample of elementary, middle, and high schools (n = 988) and with a nationally representative sample of teachers of required physical education classes and courses (n = 1194).Results:  Most states and districts had adopted a policy stating that schools will teach physical education; however, few schools provided daily physical education. Additionally, many states, districts, and schools allowed students to be exempt from participating in physical education. Most schools provided some opportunities for students to be physically active outside physical education. Staff development for physical education was offered by states and districts, but physical education teachers generally did not receive staff development on a variety of important topics.Conclusions:  To enhance physical education and physical activity in schools, a comprehensive approach at the state, district, school, and classroom levels is necessary. Policies, practices, and comprehensive staff development at the state and district levels might enable schools to improve opportunities for students to become physically active adults.

The major histocompatibility complex (MHC) is present within the genomes of all jawed vertebrates. MHC genes are especially important in regulating immune responses, but even after over 80 years of research on the MHC, much remains to be learned about how it influences adaptive and innate immune responses. In most species, the MHC is highly polymorphic and polygenic. Strong and highly reproducible associations are established for chicken MHC-B haplotypes in a number of infectious diseases. Here, we report (1) the development of a high-density SNP (single nucleotide polymorphism) panel for MHC-B typing that encompasses a 209,296 bp region in which 45 MHC-B genes are located, (2) how this panel was used to define chicken MHC-B haplotypes within a large number of lines/breeds and (3) the detection of recombinants which contributes to the observed diversity. A SNP panel was developed for the MHC-B region between the BG2 and CD1A1 genes. To construct this panel, each SNP was tested in end-point read assays on more than 7500 DNA samples obtained from inbred and commercially used egg-layer lines that carry known and novel MHC-B haplotypes. One hundred and one SNPs were selected for the panel. Additional breeds and experimentally-derived lines, including lines that carry MHC-B recombinant haplotypes, were then genotyped. MHC-B haplotypes based on SNP genotyping were consistent with the MHC-B haplotypes that were assigned previously in experimental lines that carry B2, B5, B12, B13, B15, B19, B21, and B24 haplotypes. SNP genotyping resulted in the identification of 122 MHC-B haplotypes including a number of recombinant haplotypes, which indicate that crossing-over events at multiple locations within the region lead to the production of new MHC-B haplotypes. Furthermore, evidence of gene duplication and deletion was found. The chicken MHC-B region is highly polymorphic across the surveyed 209-kb region that contains 45 genes. Our results expand the number of identified haplotypes and provide insights into the contribution of recombination events to MHC-B diversity including the identification of recombination hotspots and an estimation of recombination frequency.