White adipose tissue mitochondrial metabolism in health and in obesity

White adipose tissue is one of the largest organs of the body. It plays a key role in whole-body energy status and metabolism; it not only stores excess energy but also secretes various hormones and metabolites to regulate body energy balance. Healthy adipose tissue capable of expanding is needed for metabolic well-being and to prevent accumulation of triglycerides to other organs. Mitochondria govern several important functions in the adipose tissue. We review the derangements of mitochondrial function in white adipose tissue in the obese state. Downregulation of mitochondrial function or biogenesis in the white adipose tissue is a central driver for obesity-associated metabolic diseases. Mitochondrial functions compromised in obesity include oxidative functions and renewal and enlargement of the adipose tissue through recruitment and differentiation of adipocyte progenitor cells. These changes adversely affect whole-body metabolic health. Dysfunction of the white adipose tissue mitochondria in obesity has long-term consequences for the metabolism of adipose tissue and the whole body. Understanding the pathways behind mitochondrial dysfunction may help reveal targets for pharmacological or nutritional interventions that enhance mitochondrial biogenesis or function in adipose tissue.Peer reviewe

Intelligent Financial Fraud Detection Practices: An Investigation

Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour. This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered. Issues and challenges associated with the current practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and Privacy in Communication Networks (SecureComm 2014

Inkjet Printing Based Mono-layered Photonic Crystal Patterning for Anti-counterfeiting Structural Colors

Photonic crystal structures can be created to manipulate electromagnetic waves so that many studies have focused on designing photonic band-gaps for various applications including sensors, LEDs, lasers, and optical fibers. Here, we show that mono-layered, self-assembled photonic crystals (SAPCs) fabricated by using an inkjet printer exhibit extremely weak structural colors and multiple colorful holograms so that they can be utilized in anti-counterfeit measures. We demonstrate that SAPC patterns on a white background are covert under daylight, such that pattern detection can be avoided, but they become overt in a simple manner under strong illumination with smartphone flash light and/or on a black background, showing remarkable potential for anti-counterfeit techniques. Besides, we demonstrate that SAPCs yield different RGB histograms that depend on viewing angles and pattern densities, thus enhancing their cryptographic capabilities. Hence, the structural colorations designed by inkjet printers would not only produce optical holograms for the simple authentication of many items and products but also enable a high-secure anti-counterfeit techniqueope

Bridging Time Scales in Cellular Decision Making with a Stochastic Bistable Switch

Cellular transformations which involve a significant phenotypical change of the cell's state use bistable biochemical switches as underlying decision systems. In this work, we aim at linking cellular decisions taking place on a time scale of years to decades with the biochemical dynamics in signal transduction and gene regulation, occuring on a time scale of minutes to hours. We show that a stochastic bistable switch forms a viable biochemical mechanism to implement decision processes on long time scales. As a case study, the mechanism is applied to model the initiation of follicle growth in mammalian ovaries, where the physiological time scale of follicle pool depletion is on the order of the organism's lifespan. We construct a simple mathematical model for this process based on experimental evidence for the involved genetic mechanisms. Despite the underlying stochasticity, the proposed mechanism turns out to yield reliable behavior in large populations of cells subject to the considered decision process. Our model explains how the physiological time constant may emerge from the intrinsic stochasticity of the underlying gene regulatory network. Apart from ovarian follicles, the proposed mechanism may also be of relevance for other physiological systems where cells take binary decisions over a long time scale.Comment: 14 pages, 4 figure

Reconstructing ‘the Alcoholic’: Recovering from Alcohol Addiction and the Stigma this Entails

Public perception of alcohol addiction is frequently negative, whilst an important part of recovery is the construction of a positive sense of self. In order to explore how this might be achieved, we investigated how those who self-identify as in recovery from alcohol problems view themselves and their difficulties with alcohol and how they make sense of others’ responses to their addiction. Semi-structured interviews with six individuals who had been in recovery between 5 and 35 years and in contact with Alcoholics Anonymous were analysed using Interpretative Phenomenological Analysis. The participants were acutely aware of stigmatising images of ‘alcoholics’ and described having struggled with a considerable dilemma in accepting this identity themselves. However, to some extent they were able to resist stigma by conceiving of an ‘aware alcoholic self’ which was divorced from their previously unaware self and formed the basis for a new more knowing and valued identity

The Effects of Overexpression of Histamine Releasing Factor (HRF) in a Transgenic Mouse Model

Asthma is a disease that affects all ages, races and ethnic groups. Its incidence is increasing both in Westernized countries and underdeveloped countries. It involves inflammation, genetics and environment and therefore, proteins that exacerbate the asthmatic, allergic phenotype are important. Our laboratory purified and cloned a histamine releasing factor (HRF) that was a complete stimulus for histamine and IL-4 secretion from a subpopulation of allergic donors' basophils. Throughout the course of studying HRF, it was uncovered that HRF enhances or primes histamine release and IL-13 production from all anti-IgE antibody stimulated basophils. In order to further delineate the biology of HRF, we generated a mouse model.We constructed an inducible transgenic mouse model with HRF targeted to lung epithelial cells, via the Clara cells. In antigen naïve mice, overproduction of HRF yielded increases in BAL macrophages and statistical increases in mRNA levels for MCP-1 in the HRF transgenic mice compared to littermate controls. In addition to demonstrating intracellular HRF in the lung epithelial cells, we have also been able to document HRF's presence extracellularly in the BAL fluid of these transgenic mice. Furthermore, in the OVA challenged model, we show that HRF exacerbates the allergic, asthmatic responses. We found statistically significant increases in serum and BAL IgE, IL-4 protein and eosinophils in transgenic mice compared to controls.This mouse model demonstrates that HRF expression enhances allergic, asthmatic inflammation and can now be used as a tool to further dissect the biology of HRF

Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

Background: A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group. Methods: The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80 % of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20 % of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models. Conclusions: The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection

Physical, Psychological and Emotional Benefits of Green Physical Activity: An Ecological Dynamics Perspective

© 2015 Springer International Publishing Switzerland Increasing evidence supports the multiple benefits to physical, psychological and emotional wellbeing of green physical activity, a topic of increasing interest in the past decade. Research has revealed a synergistic benefit of green physical activity, which includes all aspects of exercise and physical activity in the presence of nature. Our theoretical analysis suggests there are three distinct levels of engagement in green physical activity, with each level reported to have a positive effect on human behaviours. However, the extent to which each level of green physical activity benefits health and wellbeing is assumed to differ, requiring confirmation in future research. This elucidation of understanding is needed because previous literature has tended to focus on recording empirical evidence rather than developing a sound theoretical framework to understand green physical activity effects. Here we propose an ecological dynamics rationale to explain how and why green physical activity might influence health and wellbeing of different population groups. This framework suggests a number of unexplored, interacting constraints related to types of environment and population groups, which shape reported levels of benefit of green physical activity. Further analysis is needed to clarify the explicit relationship between green physical activity and health and wellbeing, including levels of engagement, types of environmental constraints, levels of physical activity, adventure effects, skill effects and sampling of different populations