Demonstrable Evidence of Beneficial Physical Outcomes from University Physical Education Activity Courses

Introduction: Engagement in physical activity (PA) is often dramatically reduced during the transition from high school into college. There appears to be more stability in PA patterns during the transition from college into post-graduate life. Consequently, researchers have highlighted the years in higher education as pivotal for shaping lasting PA habits. Sadly, there is a widespread lack of evidence regarding the outcomes from physical education activity courses (PEAC) offered on campuses of higher education. Thus, their overall value lacks validation. The purpose of this work was to offer evidence of outcomes from engagement in a single, semester-long university PEAC class. Methods: Students were recruited from a variety of classes. There were no directions provided to the instructors of the courses. For grouping, classes were categorized as aerobic- (aerobics, jogging, and walking) or sport-activity (badminton, pickle ball, self-defense, strength training, and ultimate frisbee). Students in the aerobic-activity arm were randomized to aerobic testing where they underwent a submaximal treadmill protocol and grip strength (GS) testing or body composition testing (air displacement plethysmography) and GS. Those in the sport-activity arm underwent vertical jump and GS testing. Students reported to the human performance lab in the first two and final two weeks of the semester. Paired t-tests were conducted to identify differences in pre-post outcomes. Values were carried forward, not dropped, when a subject failed to return at post-test. Results: A total of 46 students (age = 21.7 ± 4.1) were randomized into the aerobic (n=25; m/f = 11/14) or body composition arms (n=21; m/f = 7/14). Additionally, 45 students (age = 20.8 ± 3.2; m/f = 23/22) from sport-activity classes were enrolled. Participation in aerobic-activity classes resulted in improvements in estimated maximal aerobic ability (p = 0.030; 42.9 ± 9.9 vs. 44.6 ± 10.1). Participation also resulted in increases in GS for those allocated to both the aerobic (p = 0.010; 56.4 ± 21.5 vs. 60.3 ± 22.3) and body composition (p = 0.022; 54.1 ± 22.1 vs. 58.1 ± 24.6) arms. Participation did not result in changes in body composition (p = 0.817; 24.7 ± 8.5 vs. 24.6 ± 7.4) despite a near-significant increase in weight (p = 0.057; 152.7 ± 38.5 vs. 154.5 ± 37.7). Participation in sport-activity classes resulted in an improvement in vertical jump (p = 0.007; 18.2 ± 6.1 vs. 18.9 ± 6.0) and GS (p = 0.002; 65.3 ± 25.6 vs. 70.0 ± 27.8). Discussion: An important first step in rebutting challenges about the credibility and worthiness of PEAC offerings is evidencing beneficial outcomes. These results represent simple, but important, markers of change. Additional demonstrable evidence is needed to ascertain elements such as what outcomes are achievable, what classes are most effective classes, and what components from class support lasting change

Survey Responses From “Wellness for Life” Classes: Overall Value and Barriers, Motivators, and Motives Towards Physical Activity

Introduction: Skill-based sport activity classes were the historical trend, but greater numbers of higher education institutions now offer courses that encompass “wellness for life” concepts. The goal of these offerings is to guide students in the development of knowledge, skills, and behaviors to adopt and maintain healthful behaviors. There is a need to amass evidence of the outcomes arising from engagement in these classes. Purpose: The purpose of this work was to document outcomes from participation in a single, semester-long, university wellness for life class. Methods: Students were recruited from courses at two universities. Survey responses were collected in the first two and final two weeks of class. The survey items included identification of: engagement in regular physical activity (PA), perceptions about PA (“view of self as an exerciser”, “contentment with current PA level”, among others), and barriers, motivators, and motives towards PA. There were no intervention suggestions provided to instructors. Results: A total of 173 students (m/f/not identified = 51/118/4; age 19.6 ± 1.4) participated. When questioned, many students identified as being an “exerciser.” Some perceived “no need to change their program” (n=37) but most “wanted more regular exercise” (n=88). A lesser number of students identified as being a “non-exerciser.” Most all “wanted more regular exercise” (n=46), but two had “no desire to start a program.” At post-test, the respective numbers were: 37, 95, 41, and 3. Numbers did not always align due to incomplete survey responses. The perceived value of the class to current and future health, rated on a scale from 0 (no impact)-100 (most influential), improved pre-post class (p \u3c 0.001) from 61.7 (±24.5) to 67.8 (±23.5). The top barrier, motivator, and motive at pre-test were: “I need to do better at managing my time to exercise more often,” “If I better organized my time or schedule I could exercise more,” and “I get pleasure or enjoy sports so I exercise,” respectively. There was shuffling among the top choices from pre- to post-test, but the top barrier remained the same. The top motivator became, “If I had more time I would exercise more,” and the top motive became, “I feel less stress after I exercise.” Discussion: Evidenced by the pre-post responses, students feel that wellness for life classes have some benefit and that perception improves after experiencing the class. There appears to be consistency in those who view themselves as “exercisers” and “non-exercisers,” which might represent a precarious situation. There is constancy in the primary barrier to exercise – the socially acceptable answer – time. It is obvious that time management is a critical element for inclusion in these classes. Students may also benefit more if instructors would offer insight on the use of motivators and motives in overcoming personal barriers

MuSiC: Identifying mutational significance in cancer genomes

Massively parallel sequencing technology and the associated rapidly decreasing sequencing costs have enabled systemic analyses of somatic mutations in large cohorts of cancer cases. Here we introduce a comprehensive mutational analysis pipeline that uses standardized sequence-based inputs along with multiple types of clinical data to establish correlations among mutation sites, affected genes and pathways, and to ultimately separate the commonly abundant passenger mutations from the truly significant events. In other words, we aim to determine the Mutational Significance in Cancer (MuSiC) for these large data sets. The integration of analytical operations in the MuSiC framework is widely applicable to a broad set of tumor types and offers the benefits of automation as well as standardization. Herein, we describe the computational structure and statistical underpinnings of the MuSiC pipeline and demonstrate its performance using 316 ovarian cancer samples from the TCGA ovarian cancer project. MuSiC correctly confirms many expected results, and identifies several potentially novel avenues for discovery

SciClone: Inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution

The sensitivity of massively-parallel sequencing has confirmed that most cancers are oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine resolution view of this clonal architecture provides insight into tumor heterogeneity, evolution, and treatment response, all of which may have clinical implications. Single tumor analysis already contributes to understanding these phenomena. However, cryptic subclones are frequently revealed by additional patient samples (e.g., collected at relapse or following treatment), indicating that accurately characterizing a tumor requires analyzing multiple samples from the same patient. To address this need, we present SciClone, a computational method that identifies the number and genetic composition of subclones by analyzing the variant allele frequencies of somatic mutations. We use it to detect subclones in acute myeloid leukemia and breast cancer samples that, though present at disease onset, are not evident from a single primary tumor sample. By doing so, we can track tumor evolution and identify the spatial origins of cells resisting therapy

Monte Carlo of Trapped Ultracold Neutrons in the UCNτ Trap

In the UCNτ experiment, ultracold neutrons (UCN) are confined by magnetic fields and the Earth’s gravitational field. Field-trapping mitigates the problem of UCN loss on material surfaces, which caused the largest correction in prior neutron experiments using material bottles. However, the neutron dynamics in field traps differ qualitatively from those in material bottles. In the latter case, neutrons bounce off material surfaces with significant diffusivity and the population quickly reaches a static spatial distribution with a density gradient induced by the gravitational potential. In contrast, the field-confined UCN—whose dynamics can be described by Hamiltonian mechanics—do not exhibit the stochastic behaviors typical of an ideal gas model as observed in material bottles. In this report, we will describe our efforts to simulate UCN trapping in the UCNτ magneto-gravitational trap. We compare the simulation output to the experimental results to determine the parameters of the neutron detector and the input neutron distribution. The tuned model is then used to understand the phase space evolution of neutrons observed in the UCNτ experiment. We will discuss the implications of chaotic dynamics on controlling the systematic effects, such as spectral cleaning and microphonic heating, for a successful UCN lifetime experiment to reach a 0.01% level of precision

Mutation Size Optimizes Speciation in an Evolutionary Model

The role of mutation rate in optimizing key features of evolutionary dynamics has recently been investigated in various computational models. Here, we address the related question of how maximum mutation size affects the formation of species in a simple computational evolutionary model. We find that the number of species is maximized for intermediate values of a mutation size parameter μ; the result is observed for evolving organisms on a randomly changing landscape as well as in a version of the model where negative feedback exists between the local population size and the fitness provided by the landscape. The same result is observed for various distributions of mutation values within the limits set by μ. When organisms with various values of μ compete against each other, those with intermediate μ values are found to survive. The surviving values of μ from these competition simulations, however, do not necessarily coincide with the values that maximize the number of species. These results suggest that various complex factors are involved in determining optimal mutation parameters for any population, and may also suggest approaches for building a computational bridge between the (micro) dynamics of mutations at the level of individual organisms and (macro) evolutionary dynamics at the species level

Genome modeling system: A knowledge management platform for genomics

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms

Genome remodelling in a basal-like breast cancer metastasis and xenograft

Massively parallel DNA sequencing technologies provide an unprecedented ability to screen entire genomes for genetic changes associated with tumour progression. Here we describe the genomic analyses of four DNA samples from an African-American patient with basal-like breast cancer: peripheral blood, the primary tumour, a brain metastasis and a xenograft derived from the primary tumour. The metastasis contained two de novo mutations and a large deletion not present in the primary tumour, and was significantly enriched for 20 shared mutations. The xenograft retained all primary tumour mutations and displayed a mutation enrichment pattern that resembled the metastasis. Two overlapping large deletions, encompassing CTNNA1, were present in all three tumour samples. The differential mutation frequencies and structural variation patterns in metastasis and xenograft compared with the primary tumour indicate that secondary tumours may arise from a minority of cells within the primary tumour

The Somatic Genomic Landscape of Glioblastoma

We describe the landscape of somatic genomic alterations based on multi-dimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer

Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

Recent genomic analyses of pathologically-defined tumor types identify “within-a-tissue” disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head & neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multi-platform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All datasets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies