Georgia State University

About: Georgia State University is a education organization based out in Atlanta, Georgia, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 13988 authors who have published 35895 publications receiving 1164332 citations. The organization is also known as: GSU & Georgia State.

Central nervous system origins of the sympathetic nervous system outflow to white adipose tissue

Abstract: White adipose tissue (WAT) is innervated by postganglionic sympathetic nervous system (SNS) neurons, suggesting that lipid mobilization could be regulated by the SNS [T. G. Youngstrom and T. J. Bartness. Am. J. Physiol. 268 (Regulatory Integrative Comp. Physiol. 37): R744-R751, 1995]. A viral transsynaptic retrograde tract tracer, the pseudorabies virus (PRV), was used to identify the origins of the SNS outflow from the brain to WAT neuroanatomically. PRV was injected into epididymal or inguinal WAT (EWAT and IWAT, respectively) of Siberian hamsters and IWAT of rats. PRV-infected neurons were visualized by immunocytochemistry and found in the spinal cord, brain stem (medulla, nucleus of the solitary tract, caudal raphe nucleus, C1 and A5 regions), midbrain (central gray), and several areas within the forebrain. The general pattern of infection of WAT in both species was more similar than different and resembled that seen after PRV injections into the adrenal medulla in rats (A. M. Strack, W. B. Sawyer, J. H. Hughes, K. B. Platt, and A. D. Loewy. Brain Res. 491: 156-162, 1989). EWAT versus IWAT injected hamsters had relatively less labeling in the suprachiasmatic, dorsomedial, and arcuate nuclei. Overall, it appeared that the SNS innervation of WAT originates from the general SNS outflow of the central nervous system and therefore may play a significant role in lipid mobilization.

The effectiveness of intervention programs for perpetrators and victims of intimate partner violence.

Abstract: ion of Data Four of the authors (CE, CM, RS, & KW) abstracted all study data. All study information and results were checked and confirmed by a senior author (CE or CM).

An empirical study of machine learning techniques for affect recognition in human–robot interaction

Abstract: Given the importance of implicit communication in human interactions, it would be valuable to have this capability in robotic systems wherein a robot can detect the motivations and emotions of the person it is working with. Recognizing affective states from physiological cues is an effective way of implementing implicit human–robot interaction. Several machine learning techniques have been successfully employed in affect-recognition to predict the affective state of an individual given a set of physiological features. However, a systematic comparison of the strengths and weaknesses of these methods has not yet been done. In this paper, we present a comparative study of four machine learning methods—K-Nearest Neighbor, Regression Tree (RT), Bayesian Network and Support Vector Machine (SVM) as applied to the domain of affect recognition using physiological signals. The results showed that SVM gave the best classification accuracy even though all the methods performed competitively. RT gave the next best classification accuracy and was the most space and time efficient.

Measuring socioeconomic status: Reliability and preliminary validity for different approaches.

Abstract: This study investigated issues related to commonly used socioeconomic status (SES) measures in 140 participants from three cities (Atlanta, Boston, and Toronto) in two countries (United States and Canada). Measures of SES were two from the United States (four-factor Hollingshead scale, Nakao and Treas scale) and one from Canada (Blishen, Carroll, and Moore scale). Reliability was examined both within (interrater agreement) and across (intermeasure agreement) measures. Interrater reliability and classification agreement was high for the total sample (ranger = .86 to .91), as were intermeasure correlations and classification agreement (range r = .81 to .88). The weakest agreement across measures was found when families had one wage earner who was female. Validity data for these SES measures with academic and intellectual measures also were obtained. Some support for a simplified approach to measuring SES was found. Implications of these findings for the use of SES in social and behavioral science research are discussed.

The metabolic actions of glucagon revisited

Abstract: The initial identification of glucagon as a counter-regulatory hormone to insulin revealed this hormone to be of largely singular physiological and pharmacological purpose. Glucagon agonism, however, has also been shown to exert effects on lipid metabolism, energy balance, body adipose tissue mass and food intake. The ability of glucagon to stimulate energy expenditure, along with its hypolipidemic and satiating effects, in particular, make this hormone an attractive pharmaceutical agent for the treatment of dyslipidemia and obesity. Studies that describe novel preclinical applications of glucagon, alone and in concert with glucagon-like peptide 1 agonism, have revealed potential benefits of glucagon agonism in the treatment of the metabolic syndrome. Collectively, these observations challenge us to thoroughly investigate the physiology and therapeutic potential of insulin's long-known opponent.