University of North Carolina at Charlotte

About: University of North Carolina at Charlotte is a education organization based out in Charlotte, North Carolina, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 8772 authors who have published 22239 publications receiving 562529 citations. The organization is also known as: UNC Charlotte & UNCC.

Does the difference between physically active and couch potato lie in the dopamine system

Abstract: Obesity and other inactivity related diseases are increasing at an alarming rate especially in Western societies. Because of this, it is important to understand the regulating mechanisms involved in physical activity behavior. Much research has been done in regard to the psychological determinants of physical activity behavior; however, little is known about the underlying genetic and biological factors that may contribute to regulation of this complex trait. It is true that a significant portion of any trait is regulated by genetic and biological factors. In the case of voluntary physical activity behavior, these regulating mechanisms appear to be concentrated in the central nervous system. In particular, the dopamine system has been shown to regulate motor movement, as well as motivation and reward behavior. The pattern of regulation of voluntary physical activity by the dopamine system is yet to be fully elucidated. This review will summarize what is known about the dopamine system and regulation of physical activity, and will present a hypothesis of how this signaling pathway is mechanistically involved in regulating voluntary physical activity behavior. Future research in this area will aid in developing personalized strategies to prevent inactivity related diseases.

Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing

Abstract: Digital video now plays an important role in medical education, health care, telemedicine and other medical applications. Several content-based video retrieval (CBVR) systems have been proposed in the past, but they still suffer from the following challenging problems: semantic gap, semantic video concept modeling, semantic video classification, and concept-oriented video database indexing and access. In this paper, we propose a novel framework to make some advances toward the final goal to solve these problems. Specifically, the framework includes: 1) a semantic-sensitive video content representation framework by using principal video shots to enhance the quality of features; 2) semantic video concept interpretation by using flexible mixture model to bridge the semantic gap; 3) a novel semantic video-classifier training framework by integrating feature selection, parameter estimation, and model selection seamlessly in a single algorithm; and 4) a concept-oriented video database organization technique through a certain domain-dependent concept hierarchy to enable semantic-sensitive video retrieval and browsing.

Estrogens augment cell surface TLR4 expression on murine macrophages and regulate sepsis susceptibility in vivo.

Abstract: Gender-based differences exist in infectious disease susceptibility. In general, females generate more robust and potentially protective humoral and cell-mediated immune responses after antigenic challenge than their male counterparts. Furthermore, evidence is accumulating that sex may also influence the early perception of microbial challenges and the generation of inflammatory immune responses such as sepsis. These differences have previously been attributed to the actions of reproductive hormones. Whereas androgens have been shown to suppress acute host immune responses to bacterial endotoxin challenge, estrogens have been found to promote increased resistance to bacterial infections. However, the mechanisms by which estrogens exert immunoprotective effects have not been established. In this study, we investigated the in vivo effects of 17beta-estradiol on endotoxin susceptibility in mice. Importantly, we have examined the actions of this female reproductive hormone on the expression of pattern recognition receptors that recognize bacterial endotoxin by key innate immune sentinel cells. We show that removal of endogenous estrogens decreases both pro- and antiinflammatory cytokine production, with a concomitant reduction in circulating levels of lipopolysaccharide-binding protein and cell surface expression of Toll-like receptor 4 on murine macrophages. Exogenous in vivo replacement of 17beta-estradiol, but not progesterone, significantly elevates sera lipopolysaccharide-binding protein levels and cell surface expression of Toll-like receptor 4 and CD14 on macrophages. Furthermore, this effect corresponds with significantly higher inflammatory cytokine levels after in vivo lipopolysaccharide challenge and a marked increase in endotoxin-associated morbidity. Taken together, these data provide a potential mechanism underlying the immunoenhancing effects of estrogens.

Inference-based accuracy of metagenome prediction tools varies across sample types and functional categories

Abstract: Despite recent decreases in the cost of sequencing, shotgun metagenome sequencing remains more expensive compared with 16S rRNA amplicon sequencing. Methods have been developed to predict the functional profiles of microbial communities based on their taxonomic composition. In this study, we evaluated the performance of three commonly used metagenome prediction tools (PICRUSt, PICRUSt2, and Tax4Fun) by comparing the significance of the differential abundance of predicted functional gene profiles to those from shotgun metagenome sequencing across different environments. We selected 7 datasets of human, non-human animal, and environmental (soil) samples that have publicly available 16S rRNA and shotgun metagenome sequences. As we would expect based on previous literature, strong Spearman correlations were observed between predicted gene compositions and gene relative abundance measured with shotgun metagenome sequencing. However, these strong correlations were preserved even when the abundance of genes were permuted across samples. This suggests that simple correlation coefficient is a highly unreliable measure for the performance of metagenome prediction tools. As an alternative, we compared the performance of genes predicted with PICRUSt, PICRUSt2, and Tax4Fun to sequenced metagenome genes in inference models associated with metadata within each dataset. With this approach, we found reasonable performance for human datasets, with the metagenome prediction tools performing better for inference on genes related to “housekeeping” functions. However, their performance degraded sharply outside of human datasets when used for inference. We conclude that the utility of PICRUSt, PICRUSt2, and Tax4Fun for inference with the default database is likely limited outside of human samples and that development of tools for gene prediction specific to different non-human and environmental samples is warranted.

All-fiber Mach-Zehnder curvature sensor based on multimode interference combined with a long-period grating.

Abstract: A novel Mach-Zehnder interferometer based on a fiber multimode interference structure combined with a long-period fiber grating (LPG) is proposed. The multimode interference is achieved through the use of a MMF section spliced between two single-mode fibers, with a length adjusted to couple a fraction of light into the cladding modes. A LPG placed after the MMF couples light back into the fiber core, completing the Mach-Zehnder interferometer. This novel configuration was demonstrated as a bending sensor.