Advanced Mobile Robotics: Volume 3

Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective

BIOLOGICAL AND CLINICAL IMPLICATIONS OF OBESITY GENOMICS IN ANCESTRALLY DIVERSE POPULATIONS

Obesity, a major risk factor for numerous health outcomes, particularly cardiovascular diseases (CVD), is a highly polygenic trait. Thousands of obesity-associated genetic loci have been identified, facilitating more accurate risk prediction through polygenic risk scores (PRS). Nonetheless, significant research gaps in obesity genomics exist, notably regarding two key aspects: (1) Heterogeneities in PRS prediction across different PRS estimation methods, self-reported race/ethnicity, and different individual-level contexts, and (2) Heterogeneities in shared genetic underpinnings between obesity and dyslipidemia, a major contributor to CVD risk. This dissertation had two specific aims that addressed these research gaps as follows: to characterize the prediction performance of PRS for obesity traits across different PRS estimation methods and diverse settings, including self-reported race/ethnicity, demographic factors, lifestyle factors, and comorbidities (Aim 1); and to identify shared genetic underpinnings in obesity and lipid traits that increased the risk of obesity but were protective for dyslipidemia, as a means to understand why not all obese populations have high risk of CVD (Aim 2). To achieve these goals, we leveraged data from the Population Architecture Using Genomics and Epidemiology (PAGE) study. Our findings reveal notable differences in PRS prediction across different PRS estimation methods, self-reported racial/ethnic groups, age, gender, smoking status, hypertension, and type 2 diabetes. We also identified 966 genomic regions (among a total of 2,495 partitioned genomic regions) with shared genetic signals between obesity-related traits and lipid traits, with 16 genomic regions of these loci exhibiting counterintuitive directions (associated with increased body mass index (BMI) but decreased dyslipidemia). In PAGE, we observed significant associations of the PRS constructed from variants within these counterintuitive BMI-HDL bivariate loci with lower levels of CVD risk factors. These results enhance our understanding of the heterogeneous underpinnings of obesity susceptibility.Doctor of Philosoph

Genetic determinants of metabolic biomarkers and their associations with cardiometabolic traits in Hispanic/Latino adolescents

Background: Metabolic regulation plays a significant role in energy homeostasis, and adolescence is a crucial life stage for the development of cardiometabolic disease (CMD). This study aims to investigate the genetic determinants of metabolic biomarkers-adiponectin, leptin, ghrelin, and orexin-and their associations with CMD risk factors. Methods: We characterized the genetic determinants of the biomarkers among Hispanic/Latino adolescents of the Santiago Longitudinal Study (SLS) and identified the cumulative effects of genetic variants on adiponectin and leptin using biomarker polygenic risk scores (PRS). We further investigated the direct and indirect effect of the biomarker PRS on downstream body fat percent (BF%) and glycemic traits using structural equation modeling. Results: We identified putatively novel genetic variants associated with the metabolic biomarkers. A substantial amount of biomarker variance was explained by SLS-specific PRS, and the prediction was improved by including the putatively novel loci. Fasting blood insulin and insulin resistance were associated with PRS for adiponectin, leptin, and ghrelin, and BF% was associated with PRS for adiponectin and leptin. We found evidence of substantial mediation of these associations by the biomarker levels. Conclusions: The genetic underpinnings of metabolic biomarkers can affect the early development of CMD, partly mediated by the biomarkers. Impact: This study characterized the genetic underpinnings of four metabolic hormones and investigated their potential influence on adiposity and insulin biology among Hispanic/Latino adolescents. Fasting blood insulin and insulin resistance were associated with polygenic risk score (PRS) for adiponectin, leptin, and ghrelin, with evidence of some degree of mediation by the biomarker levels. Body fat percent (BF%) was also associated with PRS for adiponectin and leptin. This provides important insight on biological mechanisms underlying early metabolic dysfunction and reveals candidates for prevention efforts. Our findings also highlight the importance of ancestrally diverse populations to facilitate valid studies of the genetic architecture of metabolic biomarker levels