EFFECT OF PHYSICAL ACTIVITY ON SEVERAL LIPIDS, AMINO ACIDS, AND PEPTIDE-DERIVED HORMONES IN HEALTHY INDIVIDUALS

Physical activity induces many changes in the human body by increasing energy metabolism and resting energy expenditure and hormones play a major role in these changes. Hormones are chemical messengers that stimulate biochemical reactions that trigger cell activity and functions. Hormones are secreted from the glands of the endocrine system and communication between the endocrine system and nervous system regulates both internal and external changes and maintains homeostasis. Hormones are classified into lipid, amino acid, and peptide-derived hormones and they play major roles in the human body. Lipid-derived hormones perform many important functions i.e., muscle growth, neuromuscular adaptation, protein metabolism, carbohydrate metabolism, gluconeogenesis, fat oxidation, salt and water homeostasis, etc. Amino acid-derived hormones also perform many important functions like vasoconstriction, thermoregulation, tissue differentiation, fight or flight response, maintaining circadian rhythm and sleep-wake cycle, etc. Peptide-derived hormones play a major role in body fluid homeostasis, regulating appetite, gluconeogenesis, glucose production, and lipid metabolism, maintaining circadian rhythm, maintaining energy balance, reducing weight gain, delaying gastric emptying, etc. Physical activity regulates hormone levels in the body to provide major benefits and enhance the health status of healthy individuals. This review will provide a brief description of all lipid, amino acid, and peptide-derived hormones that perform many important functions and how their functions are influenced by physical activity.  Article visualizations

MASR: Metadata Aware Speech Representation

In the recent years, speech representation learning is constructed primarily as a self-supervised learning (SSL) task, using the raw audio signal alone, while ignoring the side-information that is often available for a given speech recording. In this paper, we propose MASR, a Metadata Aware Speech Representation learning framework, which addresses the aforementioned limitations. MASR enables the inclusion of multiple external knowledge sources to enhance the utilization of meta-data information. The external knowledge sources are incorporated in the form of sample-level pair-wise similarity matrices that are useful in a hard-mining loss. A key advantage of the MASR framework is that it can be combined with any choice of SSL method. Using MASR representations, we perform evaluations on several downstream tasks such as language identification, speech recognition and other non-semantic tasks such as speaker and emotion recognition. In these experiments, we illustrate significant performance improvements for the MASR over other established benchmarks. We perform a detailed analysis on the language identification task to provide insights on how the proposed loss function enables the representations to separate closely related languages