Over 40% of adults in the United States are considered obese. Obesity is known to cause abnormal metabolic effects and lead to other negative health consequences. Interestingly, differences in metabolism and contractile performance between obese and healthy weight individuals are associated with differences in skeletal muscle fiber type composition between these groups. Each fiber type is characterized by unique metabolic and contractile properties, which are largely determined by the myosin heavy chain isoform (MHC) or isoform combination that the fiber expresses. In previous studies, SDS-PAGE single fiber analysis has been utilized as a method to determine MHC isoform distribution and single fiber type distribution in skeletal muscle. Herein, a methodological approach to analyze MHC isoform and fiber type distribution in skeletal muscle was fine-tuned for use in human and rodent studies. In the future, this revised methodology will be implemented to evaluate the effects of obesity and exercise on the phenotypic fiber type composition of skeletal muscle.

Seven human subjects with body mass indices (BMIs) ranging from 19.4 kg/ m2 to 26.7 kg/ m2 and six human subjects with BMIs ranging from 32.1 kg/ m2 to 37.6 kg/ m2 were recruited and subjected to 45-minute bouts of acute exercise to look at the changes in the plasma concentration of the dopamine metabolite homovanillic acid (HVA) in response to acute physical activity. Plasma HVA concentration was measured before exercise and during the last 10 minutes of the exercise bout via competitive ELISA. On average the optical density (OD) of the samples taken from lean subjects decreased from 0.203 before exercise to 0.192 during exercise, indicating increased plasma HVA concentration. In subjects with obesity OD increased from 0.210 before exercise to 0.219 during exercise, indicating reduced plasma HVA concentration. These differences in OD were not statistically significant. Between the lean group and the group with obesity no significant difference was observed between the OD of the plasma samples taken before exercise, but a significant difference (p = 0.0209) was observed between the ODs of the samples taken after exercise. This indicated that there was a significant difference between the percent changes in OD between the lean group and the group with obesity, which suggested that there may be a body weight-dependent difference in the amount of dopamine released in response to exercise. Because of the lack of significance in the changes in OD within the lean group and the group with obesity the results of this study were insufficient to conclude that this difference is not due to chance, but further investigation is warranted.

Critical flicker fusion thresholds (CFFTs) describe when quick amplitude modulations of a light source become undetectable as the frequency of the modulation increases and are thought to underlie a number of visual processing skills, including reading. Here, we compare the impact of two vision-training approaches, one involving contrast sensitivity training and the other directional dot-motion training, compared to an active control group trained on Sudoku. The three training paradigms were compared on their effectiveness for altering CFFT. Directional dot-motion and contrast sensitivity training resulted in significant improvement in CFFT, while the Sudoku group did not yield significant improvement. This finding indicates that dot-motion and contrast sensitivity training similarly transfer to effect changes in CFFT. The results, combined with prior research linking CFFT to high-order cognitive processes such as reading ability, and studies showing positive impact of both dot-motion and contrast sensitivity training in reading, provide a possible mechanistic link of how these different training approaches impact reading abilities.

Although autism spectrum disorder (ASD) is a serious lifelong condition, its underlying neural mechanism remains unclear. Recently, neuroimaging-based classifiers for ASD and typically developed (TD) individuals were developed to identify the abnormality of functional connections (FCs). Due to over-fitting and interferential effects of varying measurement conditions and demographic distributions, no classifiers have been strictly validated for independent cohorts. Here we overcome these difficulties by developing a novel machine-learning algorithm that identifies a small number of FCs that separates ASD versus TD. The classifier achieves high accuracy for a Japanese discovery cohort and demonstrates a remarkable degree of generalization for two independent validation cohorts in the USA and Japan. The developed ASD classifier does not distinguish individuals with major depressive disorder and attention-deficit hyperactivity disorder from their controls but moderately distinguishes patients with schizophrenia from their controls. The results leave open the viable possibility of exploring neuroimaging-based dimensions quantifying the multiple-disorder spectrum.