DEVELOPING ROBUST AUTONOMOUS VEHICLES WITH ROS

The demand for autonomous vehicles (AVs) is rising across both military and civilian sectors. These unmanned systems offer numerous advantages, such as improved efficiency, safety, and adaptability. Addressing this demand requires the development of resilient and versatile autonomous vehicles crucial for the transport and reconnaissance markets. The sensory perception of autonomous vehicles of any kind is paramount to their ability to navigate and localize in their environment. Factors such as sensor noise, erroneous readings, and deliberate attacks should all be considered when developing a robust autonomous system. This work aims to quantify the degradation of sensor data which causes mapping algorithms to fail and properly localize. In this thesis, we explore five different simulated LIDAR perturbation models and their effects on mapping indoor and outdoor locations. The noise models are categorized into two types: \emph{fake} and \emph{real} point returns. A similarity metric is utilized to quantify the degradation of the resulting point clouds. An advantage of this approach, over implementing perturbations in physical environments, is the ability to test challenging or impractical perturbations on a simulated system. Our findings confirm that increased levels of noise correlate with elevated errors in mapping. We discuss the process of cascading failures and the additional overlaid topography that is produced. We also discovered that certain types of sensor noise affect indoor mapping more than outdoor, particularly when the noise is localized. In future research, we plan to investigate methods to physically implement the noise models employed in this study and to develop strategies for mitigating their impact on autonomous navigation

Using Fluorescence Intensity of Enhanced Green Fluorescent Protein to Quantify Pseudomonas aeruginosa

A variety of direct and indirect methods have been used to quantify planktonic and biofilm bacterial cells. Direct counting methods to determine the total number of cells include plate counts, microscopic cell counts, Coulter cell counting, flow cytometry, and fluorescence microscopy. However, indirect methods are often used to supplement direct cell counting, as they are often more convenient, less time-consuming, and require less material, while providing a number that can be related to the direct cell count. Herein, an indirect method is presented that uses fluorescence emission intensity as a proxy marker for studying bacterial accumulation. A clinical strain of Pseudomonas aeruginosa was genetically modified to express a green fluorescent protein (PA14/EGFP). The fluorescence intensity of EGFP in live cells was used as an indirect measure of live cell density, and was compared with the traditional cell counting methods of optical density (OD600) and plate counting (colony-forming units (CFUs)). While both OD600 and CFUs are well-established methods, the use of fluorescence spectroscopy to quantify bacteria is less common. This study demonstrates that EGFP intensity is a convenient reporter for bacterial quantification. In addition, we demonstrate the potential for fluorescence spectroscopy to be used to measure the quantity of PA14/EGFP biofilms, which have important human health implications due to their antimicrobial resistance. Therefore, fluorescence spectroscopy could serve as an alternative or complementary quick assay to quantify bacteria in planktonic cultures and biofilms

Developing Robust Autonomous Vehicles with ROS

In the rapidly evolving landscape of military technology, the demand for autonomous vehicles (AVs) is increasing in both public and private sectors. These autonomous systems promise many benefits including enhanced efficiency, safety, and flexibility. To meet this demand, development of autonomous vehicles that are resilient and versatile are essential to the transport and reconnaissance market. The sensory perception of autonomous vehicles of any kind is paramount to their ability to navigate and localize in their environment. Typically, the sensors used for localization and mapping include LIDAR, IMU, GPS, and radar. Each of these has inherent weaknesses that must be accounted for in a robust system. This paper presents quantified results of simulated perturbations, artificial noise models, and other sensor challenges on autonomous vehicle platforms. The research aims to establish a foundation for robust autonomous systems, accounting for sensor limitations, environmental noise, and defense against nefarious attacks

Association of pre-pregnancy body mass index with offspring metabolic profile:analyses of 3 European prospective birth cohorts

Abstract Background: A high proportion of women start pregnancy overweight or obese. According to the developmental overnutrition hypothesis, this could lead offspring to have metabolic disruption throughout their lives and thus perpetuate the obesity epidemic across generations. Concerns about this hypothesis are influencing antenatal care. However, it is unknown whether maternal pregnancy adiposity is associated with long-term risk of adverse metabolic profiles in offspring, and if so, whether this association is causal, via intrauterine mechanisms, or explained by shared familial (genetic, lifestyle, socioeconomic) characteristics. We aimed to determine if associations between maternal body mass index (BMI) and offspring systemic cardio-metabolic profile are causal, via intrauterine mechanisms, or due to shared familial factors. Methods and findings: We used 1- and 2-stage individual participant data (IPD) meta-analysis, and a negative-control (paternal BMI) to examine the association between maternal pre-pregnancy BMI and offspring serum metabolome from 3 European birth cohorts (offspring age at blood collection: 16, 17, and 31 years). Circulating metabolic traits were quantified by high-throughput nuclear magnetic resonance metabolomics. Results from 1-stage IPD meta-analysis (N = 5327 to 5377 mother-father-offspring trios) showed that increasing maternal and paternal BMI was associated with an adverse cardio-metabolic profile in offspring. We observed strong positive associations with very-low-density lipoprotein (VLDL)-lipoproteins, VLDL-cholesterol (C), VLDL-triglycerides, VLDL-diameter, branched/aromatic amino acids, glycoprotein acetyls, and triglycerides, and strong negative associations with high-density lipoprotein (HDL), HDL-diameter, HDL-C, HDL₂-C, and HDL₃-C (all P < 0.003). Slightly stronger magnitudes of associations were present for maternal compared with paternal BMI across these associations; however, there was no strong statistical evidence for heterogeneity between them (all bootstrap P > 0.003, equivalent to P > 0.05 after accounting for multiple testing). Results were similar in each individual cohort, and in the 2-stage analysis. Offspring BMI showed similar patterns of cross-sectional association with metabolic profile as for parental pre-pregnancy BMI associations but with greater magnitudes. Adjustment of parental BMI–offspring metabolic traits associations for offspring BMI suggested the parental associations were largely due to the association of parental BMI with offspring BMI. Limitations of this study are that inferences cannot be drawn about the role of circulating maternal fetal fuels (i.e., glucose, lipids, fatty acids, and amino acids) on later offspring metabolic profile. In addition, BMI may not reflect potential effects of maternal pregnancy fat distribution. Conclusion: Our findings suggest that maternal BMI–offspring metabolome associations are likely to be largely due to shared genetic or familial lifestyle confounding rather than to intrauterine mechanisms

One-stage individual participant data meta-analysis.

<p>Offspring lipoprotein and lipid differences in means in SD units per 1-SD higher maternal (pink) or paternal (blue) body mass index (BMI), meta-analysed across Avon Longitudinal Study of Parents and Children (ALSPAC) and Northern Finland Birth Cohort of 1986 (NFBC86) cohorts. Associations were adjusted for parental age, smoking, education, head of household social class, maternal parity, offspring age at blood collection, sex, and cohort membership. Results are shown in SD-scaled concentration units of outcome; differences in absolute concentration units are listed in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002376#pmed.1002376.s010" target="_blank">S3 Table</a>. Error bars = 95% confidence intervals (CI). Abbreviations: C, cholesterol; HDL, high-density lipoprotein; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids.</p

Linear fit between paternal and maternal models (green dashed line).

<p>Each green dot represents a metabolic trait and the positions of the dots are determined by difference in mean offspring metabolite (in SD units) for each increase of 1-SD maternal body mass index (BMI) (x-axis) and difference in mean offspring metabolite (in SD units) for each increase in 1-SD paternal BMI (y-axis). The horizontal grey lines on each dot denote the confidence intervals (CI) for maternal associations and the vertical grey lines indicate the CI for paternal estimates. A linear fit of the overall correspondence summarises the similarity in magnitude between maternal and paternal associations (green dashed line). A slope of 1 with an intercept of 0 (dashed grey line), with all green dots sitting on that line (R² = 1), would indicate that maternal and paternal estimates had the same magnitude and direction. R² indicates goodness of linear fit and as such is a measure of the consistency between maternal and paternal associations. Results are shown in SD-scaled concentration units of outcome, difference in absolute concentration units are listed in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002376#pmed.1002376.s010" target="_blank">S3 Table</a>. Abbreviations: C, cholesterol; HDL, high-density lipoprotein; LDL, low-density lipoprotein; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids.</p

Hypothesised paths between maternal pre-pregnancy BMI and offspring future metabolic traits tested here.

<p>Abbreviation: BMI, body mass index.</p

One-stage individual participant data meta-analysis.

<p>Offspring lipoprotein and lipid differences in means in SD units per 1-SD higher maternal (pink) or paternal (blue) body mass index (BMI), meta-analysed across Avon Longitudinal Study of Parents and Children (ALSPAC) and Northern Finland Birth Cohort of 1986 (NFBC86) cohorts. Associations were adjusted for parental age, smoking, education, head of household social class, maternal parity, offspring age at blood collection, sex, and cohort membership. Results are shown in SD-scaled concentration units of outcome; differences in absolute concentration units are listed in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002376#pmed.1002376.s010" target="_blank">S3 Table</a>. Error bars = 95% confidence intervals (CI). Abbreviations: IDL, intermediate-density lipoprotein; LDL, low-density lipoprotein; VLDL, very-low-density lipoprotein.</p