Analysis Of Vitamin E Metabolites By Liquid Chromatography-Tandem Mass Spectrometry

Naturally occurring forms of Vitamin E are metabolized to various carboxychromanols and conjugated carboxychromanols. Recent studies showed that vitamin E metabolites, especially the long-chain carboxychromanols are more bioactive than unmetabolized vitamin E forms. It is necessary to quantify vitamin E metabolites in biological environment. Here a simple and effective extraction method was developed to achieve extraction efficacy of more than 90% of various forms of vitamin E and metabolites with less than 10% inter- or intra-day variation. An LC-MS/MS assay was developed and optimized to acquire best sensitivity for the detection of vitamin E and metabolites. This method allows simultaneous detection of all carboxychromanols and sulfated carboxychromanols. Using the optimized extraction and LC-MS/MS analysis conditions, vitamin E metabolites in plasma or feces of animals fed with γT, γTE, δT or δTE-13\u27 were analyzed. Results showed that, the major metabolites in the blood were conjugated g-CEHC and sulfated long chain carboxychromanols

Generative adversarial networks for sequential learning

Generative modelling aims to learn the data generating mechanism from observations without supervision. It is a desirable and natural approach for learning unlabelled data which is easily accessible. Deep generative models refer to a class of generative models combined with the usage of deep learning techniques, taking advantage of the intuitive principles of generative models as well as the expressiveness and flexibility of neural networks. The applications of generative modelling include image, audio, and video synthesis, text summarisation and translation, and so on. The methods developed in this thesis particularly emphasise on domains involving data of sequential nature, such as video generation and prediction, weather forecasting, and dynamic 3D reconstruction. Firstly, we introduce a new adversarial algorithm for training generative models suitable for sequential data. This algorithm is built on the theory of Causal Optimal Transport (COT) which constrains the transport plans to respect the temporal dependencies exhibited in the data. Secondly, the algorithm is extended to learn conditional sequences, that is, how a sequence is likely to evolve given the observation of its past evolution. Meanwhile, we work with the modified empirical measures to guarantee the convergence of the COT distance when the sequences do not overlap at any time step. Thirdly, we show that state-of-the-art results in the complex spatio-temporal modelling using GANs can be further improved by leveraging prior knowledge in the spatial-temporal correlation in the domain of weather forecasting. Finally, we demonstrate how deep generative models can be adopted to address a classical statistical problem of conditional independence testing. A class of classic approaches for such a task requires computing a test statistic using samples drawn from two unknown conditional distributions. We therefore present a double GANs framework to learn two generative models that approximate both conditional distributions. The success of this approach sheds light on how certain challenging statistical problems can benefit from the adequate learning results as well as the efficient sampling procedure of deep generative models

Dual – loop force – displacement mixed control strategy and its application on the quasi – static test

The Quasi-static test is a well-known powerful methodology to evaluate the seismic performance of structural components and systems. One of the most important challenges in the Quasi-static testing is to achieve precise boundary conditions, especially for the axial loading of vertical components. The requirement of synchronized displacement loading and target axial force formed a pair of contradiction. A dual-loop force-displacement mixed control strategy is proposed. The presented approach is successfully verified through the quasi-static testing for a full-scale concrete filled steel tube column. The control targets are achieved with an excellent control performance

Neural Image Compression with a Diffusion-Based Decoder

Diffusion probabilistic models have recently achieved remarkable success in generating high quality image and video data. In this work, we build on this class of generative models and introduce a method for lossy compression of high resolution images. The resulting codec, which we call DIffuson-based Residual Augmentation Codec (DIRAC),is the first neural codec to allow smooth traversal of the rate-distortion-perception tradeoff at test time, while obtaining competitive performance with GAN-based methods in perceptual quality. Furthermore, while sampling from diffusion probabilistic models is notoriously expensive, we show that in the compression setting the number of steps can be drastically reduced.Comment: v1: 26 pages, 13 figures v2: corrected typo in first author name in arxiv metadat

Evaluating the Dissemination and Implementation of a Community Health Worker-Based Community Wide Campaign to Improve Fruit and Vegetable Intake and Physical Activity among Latinos along the U.S.-Mexico Border

This study evaluated the dissemination and implementation of a culturally tailored community-wide campaign (CWC), Tu Salud ¡Si Cuenta! (TSSC), to augment fruit and vegetable (FV) consumption and physical activity (PA) engagement among low-income Latinos of Mexican descent living along the U.S.-Mexico Border in Texas. TSSC used longitudinal community health worker (CHW) home visits as a core vehicle to enact positive change across all socioecological levels to induce behavioral change. TSSC’s reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) was examined. A dietary questionnaire and the Godin-Shepherd Exercise Questionnaire measured program effectiveness on mean daily FV consumption and weekly PA engagement, respectively. Participants were classified based on CHW home visits into “low exposure” (2–3 visits) and “high exposure” (4–5 visits) groups. The TSSC program reached low-income Latinos (n = 5686) across twelve locations. TSSC demonstrated effectiveness as, compared to the low exposure group, the high exposure group had a greater FV intake (mean difference = +0.65 FV servings daily, 95% CI: 0.53–0.77) and an increased PA (mean difference = +185.6 MET-minutes weekly, 95% CI: 105.9–265.4) from baseline to the last follow-up on a multivariable linear regression analysis. Multivariable logistic regression revealed that the high exposure group had higher odds of meeting both FV guidelines (adjusted odds ratio (AOR) = 2.03, 95% CI: 1.65–2.47) and PA guidelines (AOR = 1.36, 95% CI: 1.10–1.68) at the last follow-up. The program had a 92.3% adoption rate, with 58.3% of adopting communities meeting implementation fidelity, and 91.7% of communities maintaining TSSC. TSSC improved FV consumption and PA engagement behaviors among low-income Latinos region wide. CHW delivery and implementation funding positively influenced reach, effectiveness, adoption, and maintenance, while lack of qualified CHWs negatively impacted fidelit

Scaling a Community-Wide Campaign Intervention to Manage Hypertension and Weight Loss

Public health impacts can be achieved when evidence-based interventions are implemented to those most in need. Too often implementation never or slowly occurs. The community-wide campaign intervention Tu Salud ¡Si Cuenta! has evidence of improving health outcomes related to chronic disease among low-income, Latinos. Using the RE-AIM Framework, this study examined if the scaled-up version of the intervention is associated with improvements in hypertension and obesity in 12 locations. Each element of the RE-AIM framework was examined. For Effectiveness, we examined outcomes overall and by implementing location. We used linear and logistic regression to assess if exposure in the intervention was associated with improvement in hypertension and weight loss. Participants were stratified into low exposure (2-3 outreach visits) vs. high exposure (4-5 outreach visits). Based on the RE-AIM Framework, the intervention reached its intended population of low-income Latinos, demonstrated effectiveness in improving hypertension and obesity, was adopted at a high level in all but one site, was implemented with fidelity to the intervention model with moderate success across locations, and showed high maintenance over time. For effectiveness specifically, we found that out of 5,019 participants, 2,508 (50%) had a baseline hypertensive blood pressure (BP) reading. Of the 2,508, 1,245 (49.9%) recovered to normal blood pressure or pre-hypertension stage by last follow-up. After adjusting for baseline BP and potential confounders in multivariable linear regression models, the high exposure group had significantly more reduction in systolic BP (adjusted mean difference in % change = -0.96; p = 0.002) and diastolic BP (adjusted mean difference in % change = -1.61; p \u3c 0.0001) compared to the low exposure group. After controlling for baseline weight and other confounders, the high exposure group had significantly greater decrease in weight compared to the low exposure group (adjusted mean difference in % change = -1.28; p \u3c 0.0001). Results from the multivariable logistic regression models indicated that compared to the low exposure group the high exposure group was more likely to achieve a clinically significant minimum 5% weight loss [adjusted odds ratio (OR) = 2.97; p \u3c 0.0001). This study contributes evidence that a Community-Wide Campaign model holds promise for addressing hypertension and obesity among low-income Latinos

Electroacupuncture Reduces Weight Gain Induced by Rosiglitazone through PPAR γ

We investigate the effect of electroacupuncture (EA) on protecting the weight gain side effect of rosiglitazone (RSG) in type 2 diabetes mellitus (T2DM) rats and its possible mechanism in central nervous system (CNS). Our study showed that RSG (5 mg/kg) significantly increased the body weight and food intake of the T2DM rats. After six-week treatment with RSG combined with EA, body weight, food intake, and the ratio of IWAT to body weight decreased significantly, whereas the ratio of BAT to body weight increased markedly. HE staining indicated that the T2DM-RSG rats had increased size of adipocytes in their IWAT, but EA treatment reduced the size of adipocytes. EA effectively reduced the lipid contents without affecting the antidiabetic effect of RSG. Furthermore, we noticed that the expression of PPARγ gene in hypothalamus was reduced by EA, while the expressions of leptin receptor and signal transducer and activator of transcription 3 (STAT3) were increased. Our results suggest that EA is an effective approach for inhibiting weight gain in T2DM rats treated by RSG. The possible mechanism might be through increased levels of leptin receptor and STAT3 and decreased PPARγ expression, by which food intake of the rats was reduced and RSG-induced weight gain was inhibited