1′-Ethyl­sulfanyl-1,1′-bicyclo­hexyl-2-one

There are two independent molecules in the asymmetric unit of the title cyclo­hexa­none derivative, C14H24OS, in which both cyclo­hexane rings exhibit chair conformations. They are also equatorial to each other, which permits the ethanethiol substituent to be in a syn conformation with the α-H atom of the parent attached cyclo­hexa­none

Design and descriptive results of the "Growth, Exercise and Nutrition Epidemiological Study In preSchoolers": The GENESIS Study

BACKGROUND: The Growth, Exercise and Nutrition Epidemiological Study in preSchoolers (GENESIS) attempts to evaluate the food and nutrient intakes, as well as growth and development of a representative sample of Greek toddlers and preschool children. In the current work the study design, data collection procedures and some preliminary data of the GENESIS study are presented. METHODS: From April 2003 to July 2004, 1218 males and 1156 females 1 to 5 years old, stratified by parental educational level (Census 1999), were examined from 105 nurseries in five counties. Approximately 300 demographic, lifestyle, physical activity, dietary, anthropometrical and DNA variables have been recorded from the study population (children and parents). RESULTS: Regarding anthropometrical indices, boys were found to be taller than girls at all ages (P < 0.05) and heavier only for the age period from 1 to 3 years old (P < 0.05). No significant differences were found between genders regarding the prevalence of at risk of overweight (16.5% to 18.6% for boys and 18.5 to 20.6 % for girls) and overweight (14.0% to 18.9% for boys and 12.6% to 20.0% for girls). Additionally, boys older than 2 years of age were found to have a higher energy intake compared to girls (P < 0.05). A similar tendency was observed regarding the mean dietary intake of fat, saturated fat, carbohydrates and protein with boys exhibiting a higher intake than girls in most age groups (P < 0.05). CONCLUSION: The prevalence of overweight in the current preschool population is considerably high. Future but more extensive analyses of the GENESIS data will be able to reveal the interactions of the parameters leading to this phenomenon

Neural crest derivatives in ocular development: Discerning the eye of the storm

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111961/1/bdrc21095.pd

Prediction of activity type in preschool children using machine learning techniques

Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3-6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Results Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Conclusions Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children

Girls’ physical activity levels during organized sports in Australia

Purpose: The primary aim of this study was to objectively examine physical activity (PA) levels of girls during organized sport (OS), and to compare the levels between games and practices for the same participants. Secondary aims of this study were to document lesson context and coach behavior during practices and games. Methods: Participants were 94 girls recruited from 10 teams in three OS (netball, basketball, soccer) from the Western Suburbs of Sydney. Each participant wore an ActiGraph GT3X monitor for the duration of one practice and one game. The SOFIT was concurrently used to document lesson context and coach behavior. Results: Girls spent a significantly higher percentage of time in moderate-to-vigorous PA (MVPA) during practices compared to games (33.8% vs. 30.6%; t = 2.94, P < 0.05). Girls spent about 20 min/hr in MVPA during practices and about 18 min/hr in MVPA during games. An average of 2,957 and 2,702 steps/hr were accumulated during practice and games, respectively. However, girls spent roughly two-thirds of their OS time in light PA or sedentary. Based on SOFIT findings, coaches spent a large proportion of practice time in management (15.0%) and knowledge delivery (18.5%). An average of 13.0 and 15.8 occurrences/hr were observed during games and practices where coaches promoted PA. Conclusion: For every hour of game play or practice time, girls accumulated approximately one-third of the recommended 60 minutes of MVPA time and about one-quarter of the 12,000 steps that girls are recommended to accumulate daily. For this population, OS appears to make a substantial contribution to recommended amounts of MVPA and steps for participating girls. OS alone, however, does not provide amounts of PA sufficient to meet daily recommendations for adolescent girls

Prediction of activity type in preschool children using machine learning techniques

Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3–6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Results Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Conclusions Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children

Autumn Landbird Communities in the Boise Foothills and Owyhee Mountains of Southwestern Idaho

Identifying important stopover areas is a critical step in conservation and management of migratory birds, and relatively little effort has been directed toward this task in Idaho or the Intermountain West. We used mist-net captures to describe the relative abundance, species richness, and community similarity of autumn migrant landbirds in the Boise Foothills and Owyhee Mountains of southwestern Idaho, two mountain ranges separated by the Snake River Plain. We captured birds at three mist-net sites from August to October 1998. Two sites were situated in the Boise Foothills, one in deciduous mountain shrubland, the other in an adjacent willow-dominated riparian draw; the third site was at a riparian spring in the Owyhee Mountains. Capture rates for resident species, temperate-zone migrants, and irruptive migrants were highest at the Boise Foothills riparian site, whereas the Boise Foothills mountain shrubland site had the highest abundance of neotropical migrants. Species richness was highest at the two Boise Foothills sites, but at all sites diversity and evenness were similar. Among the three sites, the two Boise Foothills sites (mountain shrubland and willow riparian) had the most similar bird communities. Capture rates were high (\u3e 1 bird per mist-net hour) at all three sites, and these results demonstrate that many species of autumn migrants occur frequently in montane deciduous habitats across southwestern Idaho