The associations between sweet raste function, oral complex carbohydrate sensitivity, dietary intake patterns and body composition

&nbsp;The studies conducted as part of this thesis contribute to the growing knowledge surrounding sweet taste function and potential existence of complex carbohydrate taste in humans. The findings supports the existence and functionality of complex carbohydrate sensitivity and ad libitum consumption of complex carbohydrate foods, dietary intake and body composition.<br /

The role of sweet taste in satiation and satiety

Increased energy consumption, especially increased consumption of sweet energy-dense food, is thought to be one of the main contributors to the escalating rates in overweight individuals and obesity globally. The individual\u27s ability to detect or sense sweetness in the oral cavity is thought to be one of many factors influencing food acceptance, and therefore, taste may play an essential role in modulating food acceptance and/or energy intake. Emerging evidence now suggests that the sweet taste signaling mechanisms identified in the oral cavity also operate in the gastrointestinal system and may influence the development of satiety. Understanding the individual differences in detecting sweetness in both the oral and gastrointestinal system towards both caloric sugar and high intensity sweetener and the functional role of the sweet taste system may be important in understanding the reasons for excess energy intake. This review will summarize evidence of possible associations between the sweet taste mechanisms within the oral cavity, gastrointestinal tract and the brain systems towards both caloric sugar and high intensity sweetener and sweet taste function, which may influence satiation, satiety and, perhaps, predisposition to being overweight and obesity

Structures in a class of magnetized scale-free discs

We construct analytically stationary global configurations for both aligned and logarithmic spiral coplanar magnetohydrodynamic (MHD) perturbations in an axisymmetric background MHD disc with a power-law surface mass density $\Sigma_0\propto r^{-\alpha}$, a coplanar azimuthal magnetic field $B_0\propto r^{-\gamma}$, a consistent self-gravity and a power-law rotation curve $v_0\propto r^{-\beta}$ where $v_0$ is the linear azimuthal gas rotation speed. The barotropic equation of state $\Pi\propto\Sigma^{n}$ is adopted for both MHD background equilibrium and coplanar MHD perturbations where $\Pi$ is the vertically integrated pressure and $n$ is the barotropic index. For a scale-free background MHD equilibrium, a relation exists among $\alpha$, $\beta$, $\gamma$ and $n$ such that only one parameter (e.g., $\beta$) is independent. For a linear axisymmetric stability analysis, we provide global criteria in various parameter regimes. For nonaxisymmetric aligned and logarithmic spiral cases, two branches of perturbation modes (i.e., fast and slow MHD density waves) can be derived once $\beta$ is specified. To complement the magnetized singular isothermal disc (MSID) analysis of Lou, we extend the analysis to a wider range of $-1/4<\beta<1/2$. As an example of illustration, we discuss specifically the $\beta=1/4$ case when the background magnetic field is force-free. Angular momentum conservation for coplanar MHD perturbations and other relevant aspects of our approach are discussed.Comment: 25 page

Magnetized massive stars as magnetar progenitors

The origin of ultra-intense magnetic fields on magnetars is a mystery in modern astrophysics. We model the core collapse dynamics of massive progenitor stars with high surface magnetic fields in the theoretical framework of a self-similar general polytropic magnetofluid under the self-gravity with a quasi-spherical symmetry. With the specification of physical parameters such as mass density, temperature, magnetic field and wind mass loss rate on the progenitor stellar surface and the consideration of a rebound shock breaking through the stellar interior and envelope, we find a remnant compact object (i.e. neutron star) left behind at the centre with a radius of $\sim 10^6$ cm and a mass range of $\sim 1-3$ solar masses. Moreover, we find that surface magnetic fields of such kind of compact objects can be $\sim 10^{14}-10^{15}$ G, consistent with those inferred for magnetars which include soft gamma-ray repeaters (SGRs) and anomalous X-ray pulsars (AXPs). The magnetic field enhancement factor critically depends on the self-similar scaling index $n$, which also determines the initial density distribution of the massive progenitor. We propose that magnetized massive stars as magnetar progenitors based on the magnetohydrodynamic evolution of the gravitational core collapse and rebound shock. Our physical mechanism, which does not necessarily require ad hoc dynamo amplification within a fast spinning neutron star, favours the `fossil field' scenario of forming magnetars from the strongly magnetized core collapse inside massive progenitor stars. With a range of surface magnetic field strengths over massive progenitor stars, our scenario allows a continuum of magnetic field strengths from pulsars to magnetars.Comment: 10 pages, 4 figures, accepted for publication in Monthly Notices of the Royal Astronomical Societ

Genomewide association study of leprosy.

BACKGROUND: The narrow host range of Mycobacterium leprae and the fact that it is refractory to growth in culture has limited research on and the biologic understanding of leprosy. Host genetic factors are thought to influence susceptibility to infection as well as disease progression. METHODS: We performed a two-stage genomewide association study by genotyping 706 patients and 1225 controls using the Human610-Quad BeadChip (Illumina). We then tested three independent replication sets for an association between the presence of leprosy and 93 single-nucleotide polymorphisms (SNPs) that were most strongly associated with the disease in the genomewide association study. Together, these replication sets comprised 3254 patients and 5955 controls. We also carried out tests of heterogeneity of the associations (or lack thereof) between these 93 SNPs and disease, stratified according to clinical subtype (multibacillary vs. paucibacillary). RESULTS: We observed a significant association (P<1.00x10(-10)) between SNPs in the genes CCDC122, C13orf31, NOD2, TNFSF15, HLA-DR, and RIPK2 and a trend toward an association (P=5.10x10(-5)) with a SNP in LRRK2. The associations between the SNPs in C13orf31, LRRK2, NOD2, and RIPK2 and multibacillary leprosy were stronger than the associations between these SNPs and paucibacillary leprosy. CONCLUSIONS: Variants of genes in the NOD2-mediated signaling pathway (which regulates the innate immune response) are associated with susceptibility to infection with M. leprae

Chromosome 2p14 Is Linked to Susceptibility to Leprosy

BACKGROUND: A genetic component to the etiology of leprosy is well recognized but the mechanism of inheritance and the genes involved are yet to be fully established. METHODOLOGY: A genome-wide single nucleotide polymorphism (SNP) based linkage analysis was carried out using 23 pedigrees, each with 3 to 7 family members affected by leprosy. Multipoint parametric and non-parametric linkage analyses were performed using MERLIN 1.1.1. PRINCIPAL FINDINGS: Genome-wide significant evidence for linkage was identified on chromosome 2p14 with a heterogeneity logarithm of odds (HLOD) score of 3.51 (rs1106577) under a recessive model of inheritance, while suggestive evidence was identified on chr.4q22 (HLOD 2.92, rs1349350, dominant model), chr. 8q24 (HLOD 2.74, rs1618523, recessive model) and chr.16q24 (HLOD 1.93, rs276990 dominant model). Our study also provided moderate evidence for a linkage locus on chromosome 6q24-26 by non-parametric linkage analysis (rs6570858, LOD 1.54, p = 0.004), overlapping a previously reported linkage region on chromosome 6q25-26. CONCLUSION: A genome-wide linkage analysis has identified a new linkage locus on chromosome 2p14 for leprosy in Pedigrees from China

ESR1 and EGF genetic variation in relation to breast cancer risk and survival

The main purposes of this thesis were to analyse common genetic variation in candidate genes and candidate pathways in relation to breast cancer risk, prognosticators and survival, to develop statistical methods for genetic association analysis for evaluating the joint importance of genes, and to investigate the potential impact of adding genetic information to clinical risk factors for projecting individualised risk of developing breast cancer over specific time periods. In Paper I we studied genetic variation in the estrogen receptor α and epidermal growth factor genes in relation to breast cancer risk and survival. We located a region in the estrogen receptor α gene which showed a moderate signal for association with breast cancer risk but were unable to link common variation in the epidermal growth factor gene with breast cancer aetiology or prognosis. In Paper II we investigated whether suspected breast cancer risk SNPs within genes involved in androgen-to-estrogen conversion are associated with breast cancer prognosticators grade, lymph node status and tumour size. The strongest association was observed for a marker within the CYP19A1 gene with histological grade. We also found evidence that a second marker from the same gene is associated with histological grade and tumour size. In Paper III we developed a novel test of association which incorporates multivariate measures of categorical and continuous heterogeneity. In this work we described both a single-SNP and a global multi-SNP test and used simulated data to demonstrate the power of the tests when genetic effects differ across disease subtypes. In Paper IV we assessed the extent to which recently associated genetic risk variants improve breast cancer risk-assessment models. We investigated empirically the performance of eighteen breast cancer risk SNPs together with mammographic density and clinical risk factors in predicting absolute risk of breast cancer. We also examined the usefulness of various prediction models considered at a population level for a variety of individualised breast cancer screening approaches. The goal of a genetic association study is to establish statistical associations between genetic variants and disease states. Each variant linked to a disease can lead the way to a better understanding of the underlying biological mechanisms that govern the development of a disease. Increased knowledge of molecular variation provides the opportunity to stratify populations according to genetic makeup, which in turn has the potential to lead to improved disease prevention programs and improved patient care

Preliminary study on drone navigation in urban environments using visual odometry and partially observable Monte Carlo planning

Due to the recent technological development in drone technology, a drone is used in many applications like delivery, search and rescue, and safety inspection especially in low altitude airspace. However, the mass deployment of drones for commercial purposes is yet to be matured. Therefore, normally drone is used in time-critical applications like the delivery of essential medical supplies, these applications often require high reliability. Nowadays, drone normally relies on Global Positioning System (GPS) alone for outdoor navigation, but there is also the possibility that the GPS signal is lost due to GPS jamming in the area. This paper provides a solution for drone navigation in an unknown outdoor environment with no GPS signal. The drone’s surrounding environment is perceived via a camera and is constructed into a 3D occupancy grid map, followed by localization of its position. The navigation is formulated as a sequential decision-making problem and modeled using a partially observable Markov decision process (POMDP). The simulation shows the drone can navigate towards the goal by taking a local optimum decision iteratively based on its perceived surrounding environment at each step.Civil Aviation Authority of Singapore (CAAS)National Research Foundation (NRF)Submitted/Accepted versionThis research is supported by the National Research Foundation (NRF), Singapore, and the Civil Aviation Authority of Singapore (CAAS), under the Aviation Transformation Programme (ATP). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the National Research Foundation, Singapore, or the Civil Aviation Authority of Singapore

A self-supervised monocular depth estimation approach based on UAV aerial images

The Unmanned Aerial Vehicles (UAVs) have gained increasing attention recently, and depth estimation is one of the essential tasks for the safe operation of UAVs, especially for drones at low altitudes. Considering the limitations of UAVs’ size and payload, innovative methods combined with deep learning techniques have taken the place of traditional sensors to become the mainstream for predicting per-pixel depth information. Since supervised depth estimation methods require a massive amount of depth ground truth as the supervisory signal. This article proposes an unsupervised framework to tackle the issue of predicting the depth map given a sequence of monocular images. Our model can solve the problem of scale ambiguity by training the depth subnetwork jointly with the pose subnetwork. Moreover, we introduce a modified loss function that utilizes a weighted photometric loss combined with the edge-aware smoothness loss to optimize the training. The evaluation results are compared with the model without weighted loss and other unsupervised monocular depth estimation models (Monodepth and Monodepth2). Our model shows better performance than the others, indicating potential assistance in enhancing the capability of UAVs to estimate distance with the surrounding environment.Civil Aviation Authority of Singapore (CAAS)Submitted/Accepted versionThis research is supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme