Chronic Consumption of Farmed Salmon Containing Persistent Organic Pollutants Causes Insulin Resistance and Obesity in Mice

Background: Dietary interventions are critical in the prevention of metabolic diseases. Yet, the effects of fatty fish consumption on type 2 diabetes remain unclear. The aim of this study was to investigate whether a diet containing farmed salmon prevents or contributes to insulin resistance in mice. Methodology/Principal Findings: Adult male C57BL/6J mice were fed control diet (C), a very high-fat diet without or with farmed Atlantic salmon fillet (VHF and VHF/S, respectively), and Western diet without or with farmed Atlantic salmon fillet (WD and WD/S, respectively). Other mice were fed VHF containing farmed salmon fillet with reduced concentrations of persistent organic pollutants (VHF/S-POPs). We assessed body weight gain, fat mass, insulin sensitivity, glucose tolerance, ex vivo muscle glucose uptake, performed histology and immunohistochemistry analysis, and investigated gene and protein expression. In comparison with animals fed VHF and WD, consumption of both VHF/S and WD/S exaggerated insulin resistance, visceral obesity, and glucose intolerance. In addition, the ability of insulin to stimulate Akt phosphorylation and muscle glucose uptake was impaired in mice fed farmed salmon. Relative to VHF/S-fed mice, animals fed VHF/S-POPs had less body burdens of POPs, accumulated less visceral fat, and had reduced mRNA levels of TNFa as well as macrophage infiltration in adipose tissue. VHF/S-POPs-fed mice further exhibited better insulin sensitivity and glucose tolerance than mice fed VHF/S. Conclusions/Significance: Our data indicate that intake of farmed salmon fillet contributes to several metabolic disorders linked to type 2 diabetes and obesity, and suggest a role of POPs in these deleterious effects. Overall, these findings may participate to improve nutritional strategies for the prevention and therapy of insulin resistance

Machine Learning and Neural Network for Maintenance Management

A novel Non-Destructive Test (NDT) is presented in this paper. It employs a radiometric sensor that measures the infrared emissivity of the solar panel surface embedded in an unmanned aerial vehicle. The measurements provided by the sensor will determine if the panel is healthy, damaged or dirty. A thermographic camera has been used to check the temperature variations and validate the results by the sensor. The study shows that the amount of dirt influences the temperature on the surface and the energy generated. Similarly, faults in photovoltaic cells influence the temperature of the panel. The NDT system is less expensive than traditional thermographic sensors or cameras. Early detection of these problems, together with an optimal maintenance strategy, allows to reduce costs and increase the competitiveness of this renewable energy source