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

Specificity of Training Modalities on Upper Body One Repetition Maximum Performance: Free Weights vs. Hammer Strength Equipment

The purpose of this study was to determine whether a relationship exists between 1-repetition maximum (1RM) performed on hammer strength (HS) machines compared to free weights (FWs) and also to develop regression equations that can accurately predict 1RM when switching from exercise modality to another. Thirty-one trained male subjects performed 1-RM lifts (1RM\u27s) on 3 HS externally loaded machines and 3 comparable FW exercises. Subjects performed 2 1RM tests during each laboratory session, with at least 48-72 hours of recovery between each. One repetition maximum data were used to (a) determine the relationship between 1RM performed on HS vs. FW and (b) to develop regression equations that can accurately predict 1RM\u27s when switching from 1 exercise modality to another. Statistics revealed significant differences (p \u3c 0.05) between 1RM\u27s performed on the HS equipment as compared to its corresponding (FW) exercise. For all exercises, 1RM\u27s were significantly greater on the HS equipment. Regression equations were developed for all exercises, except when predicting the HS shoulder press and the HS preacher curls from their free weight counterparts, where no variables existed that could significantly predict their respective 1RM\u27s. As 1 RMs were significantly greater when using the HS equipment compared to when using FWs, those transitioning from HS exercise to FW exercise should exercise caution