Central Hemodynamics Measured During 5 Repetition Maximum Free Weight Resistance Exercise

International Journal of Exercise Science 11(2): 342-354, 2018. The PhysioFlow™ is a piece of equipment that uses bioimpedance cardiography to measure central hemodynamics. The purpose of this research was to explore the novel approach of monitoring central hemodynamics during free weight resistance exercise using bioimpedance cardiography throughout a 5 repetition maximum (5RM). Thirty participants ranging from beginner to advanced lifters (16 males and 14 females) completed a 5RM for back squat, seated push press, and bicep curl while connected to the PhysioFlow™ to assess the response of heart rate (HR), stroke volume (SV), cardiac output (Q), and ejection fraction (EF). Participants were cued for form and to breathe normally throughout the lifts. The PhysioFlow™ detected an increase in HR and Q for all lifts between rest and each repetition (p \u3c 0.05). There was also an increase in HR and Q from repetition 1 to repetition 5 for all lifts (p \u3c 0.05). No changes in EF or SV were detected between resting measurements and each repetition for all lifts (p \u3e 0.05) and no changes in EF or SV were detected when all repetitions were compared to each other for all lifts (p \u3e 0.05). In conclusion, the PhysioFlow™ was able to detect changes in HR and Q during dynamic free weight resistance exercise. This novel approach may provide a mechanism for monitoring central hemodynamics during free weight resistance training. However, more research needs to be conducted as the exercise protocol for this investigation did not allow for a comparison to a reference method

Savanna Chimpanzees (Pan troglodytes schweinfurthii) Consume and Share Blue Duiker (Philantomba monticola) Meat in the Issa Valley, Ugalla, Western Tanzania

Meat eating is pervasive across chimpanzee populations in Africa, with red colobus monkey (Piliocolobus spp.) being the most common prey (Boesch & Boesch 1989; Stanford et al. 1994a; Watts et al. 2012, Hosaka 2015) if sympatric in the same habitat. Besides colobus monkeys, chimpanzees consume a variety of other primates, including olive and yellow baboons (Papio spp.) and bushbabies (Galago spp.). In the forest habitats of western Tanzania chimpanzees have been reported to consume numerous different mammalian species: 18 at Mahale Mountains National Park (Uehara 1997; Hosaka 2015) and eight at Gombe National Park, whilst in the miombo woodland dominated Ugalla Region no direct observations have been recorded to date (Table 1). In West Africa, chimpanzees from Taï Forest, Ivory Coast consume eight different mammal species, all primates (Boesch & Boesch 1989). Wherever chimpanzees consume meat, it is almost always via hunting, as they rarely scavenge (Watts 2008)

Dispelling the myths of online education: learning via the information superhighway

There continues to be a perception that online education is inferior to traditional education. In the U.S. online learning is more developed than in the U.K. This paper provides insights into a U.S. provision and takes a close look at what are perceived as weaknesses of on line learning and argues that these are not necessarily inherent weaknesses of this form of educational delivery. Then, results of two major studies, undertaken in the U.S. are provided comparing the effectiveness of online education to traditional education as perceived by current MBA students and past graduates. Results of these studies suggest that students of MBA modules and MBA graduates perceive the quality and effectiveness of online education to be similar to, if not higher than, the quality and effectiveness of traditional modules and programmes

Crowd-Driven Deep Learning Tracks Amazon Deforestation

The Amazon forests act as a global reserve for carbon, have very high biodiversity, and provide a variety of additional ecosystem services. These forests are, however, under increasing pressure, coming mainly from deforestation, despite the fact that accurate satellite monitoring is in place that produces annual deforestation maps and timely alerts. Here, we present a proof of concept for rapid deforestation monitoring that engages the global community directly in the monitoring process via crowdsourcing while subsequently leveraging the power of deep learning. Offering no tangible incentives, we were able to sustain participation from more than 5500 active contributors from 96 different nations over a 6-month period, resulting in the crowd classification of 43,108 satellite images (representing around 390,000 km2). Training a suite of AI models with results from the crowd, we achieved an accuracy greater than 90% in detecting new and existing deforestation. These findings demonstrate the potential of a crowd–AI approach to rapidly detect and validate deforestation events. Our method directly engages a large, enthusiastic, and increasingly digital global community who wish to participate in the stewardship of the global environment. Coupled with existing monitoring systems, this approach could offer an additional means of verification, increasing confidence in global deforestation monitoring

Nutritional Systems Biology Modeling: From Molecular Mechanisms to Physiology

The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a “middle-out” strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from “-omics” signatures are identified as key elements of a successful systems biology modeling approach in nutrition research—one that integrates physiological mechanisms and data at multiple space and time scales

Level of agreement between objectively determined body composition and perceived body image in 6- To 8-year-old South African children- To Body Composition-Isotope Technique study

To assess the level of agreement between body size self-perception and actual body size determined by body mass index (BMI) z-score and body fatness measured by the deuterium dilution method (DDM) in South African children aged 6-8 years. A cross-sectional sample of 202 children (83 boys and 119 girls) aged 6-8 years from the Body Composition-Isotope Technique study (BC-IT) was taken. Subjective measures of body image (silhouettes) were compared with the objective measures of BMI z-score and body fatness measured by the DDM. The World Health Organization BMI z-scores were used to classify the children as underweight, normal, overweight, or obese. DDM-measured fatness was classified based on the McCarthy centile curves set at 2nd, 85th and 95th in conjunction with fatness cut-off points of 25% in boys and 30% in girls. Data were analyzed using SPSS v26. Of 202 children, 32.2%, 55.1%, 8.8%, and 2.4% perceived their body size as underweight, normal, overweight, and obese, respectively. Based on BMI z-score, 18.8%, 72.8%, 6.9%, and 1.5% were classified as underweight, normal, overweight, and obese, respectively. Body fatness measurement showed that 2.5%, 48.0%, 21.8%, and 29.7% were underweight, normal weight, overweight, and obese, respectively

An Investigation of the Differences between Perceived vs. Measured Body Composition, Social Physique Anxiety, and Overall Appearance Satisfaction among College Students

International Journal of Exercise Science 11(5): 957-967, 2018. The purpose of this study was to examine the differences between body composition, social physique anxiety (SPA) and appearance satisfaction among college students by sex and BMI. A secondary purpose was to assess levels of appearance satisfaction before and after body composition testing and discover any differences between perceived body fat percentage (BF%) and weight versus actual measurements.Participants included 212 college students (93 males, 119 females), 22.0 ± 2.2 years. They answered a demographic questionnaire, the Social Physique Anxiety Scale (SPAS), post-assessment questions, and underwent body composition testing via dual energy x-ray absorptiometry (DXA). There was a significant correlation between measured BF% and SPAS scores (r = 0.531, P\u3c 0.001). Males scored lower on the SPAS compared to females (25.3 ± 9.4 vs. 33.1 ± 9.1, P\u3c 0.001). The sample underestimated BF% with females underestimating by more than males, 5.7 ± 7.3% vs. 3.7 ± 5.4%, P= 0.036. There was no difference between perceived and measured weight for women (P= 0.500) however, males overestimated their weight (P= 0.004). There was a difference in appearance satisfaction pre and post body composition for females (pre = 4.4 ±1.7, post = 4.2 ±1.9, P= 0.026) but not for males (pre = 5.3 ±1.5, post = 5.0 ±1.8, P= 0.063). Overall, males had lower levels of SPA and higher levels of appearance satisfaction than females and knowledge of BF% negatively affected appearance satisfaction scores for females. Finally, both males and females underestimated BF% compared to DXA

Letter from Dr. James S. McLester, Birmingham, Alabama, to Borden Burr, Birmingham, Alabama, April 11, 1941

This is part of the Hill Ferguson July 1961 loose scrapbook, whose items range from u0003_0000511_0000190 to u0003_0000511_0000312 in Box 106, Folders 22 and 23