EFFECTS OF VARYING BODY FAT LEVELS ON FAT OXIDATION RATES IN EUMENORRHEIC FEMALES

BACKGROUND: Fatty acids (FA) are the predominate fuel source at exercise intensities \u3c 65% of maximal oxygen consumption (VO2max). Aerobically trained individuals tend to exhibit a greater ability to oxidize FA at higher relative intensities than untrained individuals. Eumenorrheic females have a greater ability to oxidize FA than men, partially due to hormonal differences. Interestingly, a recent review concluded that unlike males, body composition does not affect fat oxidation (Foxi) rates in women. However, no studies have yet matched subjects first for aerobic fitness status prior to testing Foxi rates. Therefore, the purpose of this study is to compare Foxi rates between lean and overweight females when matched prior for aerobic fitness status. METHODS: A between-subjects design will be used to compare Foxi between lean and overweight females with matched fitness statuses. Thirty healthy and eumenorrheic female participants between the ages of 18-39 will be recruited to complete two trials. Trial 1 will consist of descriptive data collection and a peak aerobic test (VO2peak) consisting of a walk phase (1.0-3.5 mph) and a run phase (4-8 mph) using a motorized treadmill set at a 3% grade. Participants will be matched for aerobic status (VO2peak between 35 ± 5 mL·kg-1·min-1) and stratified into 2 groups based on body fat percentage (BF%),(seca mBCA 515, Hamburg, Germany). A graded exercise test (GXT) for trial 2 will be completed during the menses phase of the participants’ menstrual cycle and will consist of 6 (25%, 35%, 45%, 55%, 65%, 75% of VO2peak), 3-minute stages at a 3% incline on a motorized treadmill. Throughout the GXT, metabolic data and heart rate will be collected via Parvo Metabolic cart and a sternal heart rate monitor, respectively. At each stage heart rate and cardiorespiratory measures will be recorded and later used to calculate Foxi rates across each stage. Data will be analyzed with a 2-way repeated measures analysis of variance to identify differences, if any, between lower-fat and higher-fat groups. ANTICIPATED RESULTS: It is hypothesized that, when matched for aerobic fitness, overweight females will demonstrate higher rates of Foxi compared to their lean counterparts at lower intensities

Automatic recognition of biological particles in microscopic images

A simple and general-purpose system to recognize biological particles is presented. It is composed of four stages: First (if necessary) promising locations in the image are detected and small regions containing interesting samples are extracted using a feature finder. Second, differential invariants of the brightness are computed at multiple scales of resolution. Third, after point-wise non-linear mappings to a higher dimensional feature space, this information is averaged over the whole region thus producing a vector of features for each sample that is invariant with respect to rotation and translation. Fourth, each sample is classified using a classifier obtained from a mixture-of-Gaussians generative model. This system was developed to classify 12 categories of particles found in human urine; it achieves a 93.2% correct classification rate in this application. It was subsequently trained and tested on a challenging set of images of airborne pollen grains where it achieved an 83% correct classification rate for the three categories found during one month of observation. Pollen classification is challenging even for human experts and this performance is considered good