Relationship of VO2 Peak, Body Fat Percentage, and Power Output Measured During Repeated Bouts of a Wingate Protocol

The principle of specificity would indicate that being aerobically trained would not necessarily enhance performance in events relying principally on oxygen-independent metabolic pathways (i.e. “anaerobic” exercise). Body fatness may be associated with aerobic and anaerobic performance. VO2 Peak was determined with a graded cycle ergometry and, in a separate session 4 consecutive Wingate power tests (3 min recovery) in 31 males. Pearson correlations were calculated for VO2 Peak and Body Fat Percentage with Peak Power, Mean Power, Minimum Power, Fatigue Index, Peak Heart Rate, and Recovery Heart Rate. No significant correlations were found for VO2 Peak or Body Fat Percentage with Peak Power on any bout (p\u3e0.05). Significant correlations were found for VO2 Peak and Body Fat Percentage with Mean Power, Minimum Power, and Fatigue Index. Significant correlations were found for VO2 Peak with delta values of power performance and heart rates (peak and 3 min recovery). Results indicate that VO2 Peak is associated with repeated anaerobic performance, possibly due to greater capacity to recover between bouts. Body Fat Percentage was correlated with measures of power performance (strongest relationships existing in the earlier bouts), but is not strongly correlated with either the heart rate response to power performance or the change in performance over successive bouts

Carbohydrate Rinse Fails to Enhance Cycling Performance or Alter Metabolic and Autonomic Recovery in Recreational Cyclists

The purpose of the study was to examine the effects of carbohydrate (CHO) mouth rinsing on autonomic and metabolic recovery as well as cycling performance. Ten male recreational cyclists (age = 30 ± 6 years, VO2peak = 54.5 ± 8.1 mL·kg-1·min-1) completed a randomized, double-blind, placebo-controlled, crossover designed study. A CHO or a placebo (PLA) rinse was administered every 12.5% of a work to completion trial (75%Wmax). Heart rate variability (lnRMSSD), the respiratory exchange ratio, and plasma epinephrine, norepinephrine, insulin, glucose, free fatty acids (FFA), and lactate were measured pre- and post-exercise. The CHO rinse did not improve time to completion of the test trial (CHO: 4108 ± 307 s, PLA: 4176 ± 374 s, p = 0.545). Further, the CHO rinse did not impact autonomic recovery, as measured by lnRMSSD (p = 0.787) and epinephrine (p = 0.132). Metabolic biomarkers were also unaffected by the CHO rinse, with no differences observed in responses of FFA (p = 0.064), lactate (p = 0.302), glucose (p = 0.113) or insulin (p = 0.408). Therefore, the CHO mouth rinse does not reduce the acute sympathetic response following strenuous exercise and does not result in improvements in cycling time to completion

Chimpanzees and their mammalian sympatriates in the Issa Valley, Tanzania

Chimpanzees have been studied for nearly 300 combined years across Africa, but aside from their roles as predators or prey, remarkably little is known about the diverse species with whom they share habitats. We calculated likely chimpanzee encounter rates with sympatric mammals in the Issa Valley, Tanzania, through modelling actual researcher encounter rates with all medium and large mammals. Compared to other long‐term chimpanzee study sites, Issa had a relatively high diversity in medium and large mammal species present, with 36 species documented. We encountered common duiker (Sylvicapra grimmia) most frequently, followed by yellow baboons (Papio cynocephalus) and bushbuck. Chimpanzees ranked fifth overall. Chimpanzees, on the other hand, were predicted to most frequently encounter bushbuck, klipspringer and hartebeest—all woodland species. We compare these results to published literature and contextualise them in light of reconstructing diverse mammalian communities in which hominins lived during the Plio‐Pleistocene and the use of chimpanzees as flagship species for conservation policy

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

The problem of assessing problem solving: can comparative judgement help?

This definitive version of this paper is available at Springerlink: http:dx.doi.org/10.1007/s10649-015-9607-1School mathematics examination papers are typically dominated by short, structured items that fail to assess sustained reasoning or problem solving. A contributory factor to this situation is the need for student work to be marked reliably by a large number of markers of varied experience and competence. We report a study that tested an alternative approach to assessment, called comparative judgement, which may represent a superior method for assessing open-ended questions that encourage a range of unpredictable responses. An innovative problem solving examination paper was specially designed by examiners, evaluated by mathematics teachers, and administered to 750 secondary school students of varied mathematical achievement. The students’ work was then assessed by mathematics education experts using comparative judgement as well as a specially designed, resourceintensive marking procedure. We report two main findings from the research. First, the examination paper writers, when freed from the traditional constraint of producing a mark scheme, designed questions that were less structured and more problem-based than is typical in current school mathematics examination papers. Second, the comparative judgement approach to assessing the student work proved successful by our measures of inter-rater reliability and validity. These findings open new avenues for how school mathematics, and indeed other areas of the curriculum, might be assessed in the future

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