New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Probabilistic condition monitoring of azimuth thrusters based on acceleration measurements

Abstract Drill ships and offshore rigs use azimuth thrusters for propulsion, maneuvering and steering, attitude control and dynamic positioning activities. The versatile operating modes and the challenging marine environment create demand for flexible and practical condition monitoring solutions onboard. This study introduces a condition monitoring algorithm using acceleration and shaft speed data to detect anomalies that give information on the defects in the driveline components of the thrusters. Statistical features of vibration are predicted with linear regression models and the residuals are then monitored relative to multivariate normal distributions. The method includes an automated shaft speed selection approach that identifies the normal distributed operational areas from the training data based on the residuals. During monitoring, the squared Mahalanobis distance to the identified distributions is calculated in the defined shaft speed ranges, providing information on the thruster condition. The performance of the method was validated based on data from two operating thrusters and compared with reference classifiers. The results suggest that the method could detect changes in the condition of the thrusters during online monitoring. Moreover, it had high accuracy in the bearing condition related binary classification tests. In conclusion, the algorithm has practical properties that exhibit suitability for online application

Whitening CNN-based rotor system fault diagnosis model features

Abstract Intelligent fault diagnosis (IFD) models have the potential to increase the level of automation and the diagnosis accuracy of machine condition monitoring systems. Many of the latest IFD models rely on convolutional layers for feature extraction from vibration data. The majority of these models employ batch normalisation (BN) for centring and scaling the input for each neuron. This study includes a novel examination of a competitive approach for layer input normalisation in the scope of fault diagnosis. Network deconvolution (ND) is a technique that further decorrelates the layer inputs reducing redundancy among the learned features. Both normalisation techniques are implemented on three common 1D-CNN-based fault diagnosis models. The models with ND mostly outperform the baseline models with BN in three experiments concerning fault datasets from two different rotor systems. Furthermore, the models with ND significantly outperform the baseline models with BN in the common CWRU bearing fault tests with load domain shifts, if the data from drive-end and fan-end sensors are employed. The results show that whitened features can improve the performance of CNN-based fault diagnosis models

Short-Term Overeating Induces Insulin Resistance in Fat Cells in Lean Human Subjects

Insulin resistance and type 2 diabetes (T2D) are closely linked to obesity. Numerous prospective studies have reported on weight gain, insulin resistance, and insulin signaling in experimental animals, but not in humans. We examined insulin signaling in adipocytes from lean volunteers, before and at the end of a 4-wk period of consuming a fast-food, high-calorie diet that led to weight gain. We also examined adipocytes from patients with T2D. During the high-calorie diet, subjects gained 10% body weight and 19% total body fat, but stayed lean (body mass index = 24.3 kg/m2) and developed moderate systemic insulin resistance. Similarly to the situation in T2D subjects, in subjects on the high-calorie diet, the amount of insulin receptors was reduced and phosphorylation of IRS1 at tyrosine and at serine-307 (human sequence, corresponding to murine serine-302) were impaired. The amount of insulin receptor substrate protein-1 (IRS1) and the phosphorylation of IRS1 at serine-312 (human sequence, corresponding to murine serine-307) were unaffected by the diet. Unlike the T2D subjects, in subjects on the high-calorie diet, likely owing to the ongoing weight-gain, phosphorylation of MAP-kinases ERK1/2 became hyperresponsive to insulin. To our knowledge this study is the first to investigate insulin signaling during overeating in humans, and it demonstrates that T2D effects on intracellular insulin signaling already occur after 4 wks of a high-calorie diet and that the effects in humans differ from those in laboratory animals

Genetic studies of body mass index yield new insights for obesity biology

Note: A full list of authors and affiliations appears at the end of the article. Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P 20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.</p