Motivational Interviewing via Co-Active Life Coaching Intervention for Women Seeking a more Physically Active Lifestyle

The purpose of this 12-week pre-post design pilot study was to assess the impact of Motivational Interviewing via Co-Active Life Coaching (MI-via-CALC) on exercise-specific self-efficacy, barrier-specific self-efficacy, self-esteem, and 12-week study duration engagement in physical activity (PA) for 25 women between the ages of 30 and 55 years. Participants were assessed quantitatively using the previously validated McAuley Exercise-Specific Self-Efficacy Scale (EXSE), McAuley Barrier-Specific Self-Efficacy Scale (BARSE), Rosenberg Self-Esteem Scale (RSES), and International Physical Activity Questionnaire (IPAQ-SF) at pre-, mid-, and post-intervention. Four one-way repeated measures ANOVAs were completed for each scale, and statistically significant differences in barrier-specific self-efficacy were detected between pre- and post intervention, and statistically significant differences in self-esteem between pre- and post-intervention were found. No statistically significant differences were found in participants’ exercise-specific self-efficacy scores and IPAQ scores, although scores in both increased by the end of the study. Participants Body Mass Index (BMI), waist, and hip circumferences were also quantitatively measured at pre- and post-intervention. Paired t-tests were completed for each measure, and statistically significant decreases in weight, waist, and hip circumferences were detected. MI-via-CALC is an encouraging approach for women who are seeking a more physically active lifestyle, and additional research on a larger scale is recommended

Assessing the impact of Motivational-Interviewing via Co-active Life Coaching on engagement in physical activity

The purpose of this 12-week pre-post design study was to assess the impact of Motivational Interviewing via Co-Active Life Coaching (MI-via-CALC) on engagement in physical activity for 25 women between the ages of 30 and 55 years. Data on task self-efficacy, barrier-specific self-efficacy, self-esteem, physical activity (PA), body mass index (BMI), and waist-to-tip ratio and circumference were collected. Results indicated some positive, but not significant differences in barrier-specific self-efficacy, which were detected between pre- and post- intervention, and statistically significant differences in self-esteem between pre- and post-intervention were found. No statistically significant differences were found in participants’ task self-efficacy scores and PA scores. Statistically significant decreases were detected for BMI, and waist-to-hip ratios and circumference. MI-via-CALC is an encouraging approach for women who are seeking a more physically active lifestyle, and additional research with a larger sample size is recommended

Observations on questing activity of adult Gulf Coast ticks, Amblyomma maculatum

Abstract During August of 2008 and 2009, observations were made on the questing heights, behaviors, and spatial distribution of adult Gulf Coast ticks, Amblyomma maculatum, in a plot near Gautier, Jackson County, Mississippi, U.S.A. Ticks were not evenly distributed in the plot, being found mostly on torpedograss and/or wiregrass along and in a small dirt field road. Adult ticks were visually observed questing on three different plants: torpedograss, Panicum repens, wiregrass, Aristida stricta, and Johnsongrass, Sorghum halepense, all but the last of which have small-diameter stems and thin, pointed leaves. Ticks were located at or near the tips of the plants (2-tail binomial probability, p = 0.0074). Observed questing heights ranged from 20-75 cm, with an average of 36 cm. Nine of 15 ticks (60%) seen questing were oriented head upward, while 6 (40%) were headdown. Limited mark-release-recapture observations were made in the study site, using ticks collected from the field road. Of 27 ticks marked and released, 15 were recaptured in three samples spanning a 24-d period. Of these, 5 had moved closer to the dirt road where they were originally captured and 2 farther away

Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility

Fasting ​glucose and ​insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in ​GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=−0.09±0.01 mmol l^(−1), P=3.4 × 10^(−12)), T2D risk (OR[95%CI]=0.86[0.76–0.96], P=0.010), early ​insulin secretion (β=−0.07±0.035 pmol_(insulin) mmol_(glucose)^(−1), P=0.048), but higher 2-h ​glucose (β=0.16±0.05 mmol l^(−1), P=4.3 × 10^(−4). We identify a gene-based association with FG at ​G6PC2 (pSKAT=6.8 × 10−6) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ​ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol l^(−1), P=1.3 × 10^(−8)). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility

Thermal history modeling techniques and interpretation strategies: applications using HeFTy

Advances in low-temperature thermochronology, and the wide range of geologic problems that it is used to investigate, have prompted the routine use of thermal history (time-temperature, tT) models to quantitatively explore and evaluate rock cooling ages. As a result, studies that investigate topics ranging from Proterozoic tectonics to Pleistocene erosion now commonly require a substantial numerical modeling effort that combines the empirical understanding of chronometer thermochemical behavior (kinetics) with independent knowledge or hypotheses about a study area’s geologic history (geologic constraints). Although relatively user-friendly programs, such as HeFTy and QTQt, are available to facilitate thermal history modeling, there is a critical need to provide the geoscience community with more accessible entry points for using these tools. This contribution addresses this need by offering an explicit discussion of modeling strategies in the program HeFTy. Using both synthetic data and real examples, we illustrate the opportunities and limitations of thermal history modeling. We highlight the importance of testing the sensitivity of model results to model design choices and describe a strategy for classifying model results that we call the Path Family Approach. More broadly, we demonstrate how HeFTy can be used to build an intuitive understanding of the thermochronologic data types and model design strategies that are capable of discriminating among geologic hypotheses

Thermal history modeling techniques and interpretation strategies: applications using QTQt

Advances in low-temperature thermochronology have made it applicable to a plethora of geoscience investigations. The development of modeling programs (e.g., QTQt and HeFTy) that extract thermal histories from thermochronologic data has facilitated growth of this field. However, the increasingly wide range of scientists who apply these tools requires an accessible entry point to thermal history modeling and how these models develop our understanding of complex geological processes. This contribution offers a discussion of modeling strategies, using QTQt, including making decisions about model design, data input, kinetic parameters, and other factors that may influence the model output. We present a suite of synthetic data sets derived from known thermal histories with accompanying tutorial exercises in the Supplemental Material. These data sets illustrate the opportunities and limitations of thermal history modeling. Examining these synthetic data helps to develop intuition about which thermochronometric data are most sensitive to different thermal events and to what extent user decisions on data handling and model setup can control the recovery of the true solution. We also use real data to demonstrate the importance of incorporating sensitivity testing into thermal history modeling and suggest several best practices for exploring model sensitivity to factors including, but not limited to, the model design or inversion algorithm, geologic constraints, data trends, the spatial relationship between samples, or the choice of kinetics model. Finally, we provide a detailed and explicit workflow and an applied example for a method of interrogating vague model results or low observation-prediction fits that we call the “Path Structure Approach.” Our explicit examination of thermal history modeling practices is designed to guide modelers to identify the factors controlling model results and demonstrate reproducible approaches for the interpretation of thermal histories