Postprandial Hypertriglyceridemia Predicts Development of Insulin Resistance Glucose Intolerance and Type 2 Diabetes

Insulin resistance (IR) and type 2 diabetes mellitus (T2DM) have been found to be associated with postprandial hypertriglyceridemia (PPHTg). However, whether PPHTg can cause IR and diabetes is not clear. We therefore investigated the role of PPHTg in development of T2DM in rat model of T2DM. 96 male Wistar rats were randomized into four groups (24 rats each). Control Group A, high sucrose diet (HSD) Group B, HSD+Pioglitazone (10mg/kg/day) Group C and HSD+Atorvastatin (20mg/kg/day) Group D. Fat and glucose tolerance tests were done at regular intervals in all groups besides insulin and body weight measurement. At 26 weeks, low dose streptozotocin (15mg/kg,i.p.) was given to half of the rats. All rats were followed up till 48 weeks. PPHTg developed as early as week 2 in Group B and stabilized by week 14. Group B displayed highest PPHTg compared to other groups. Atorvastatin treatment (Group D) abolished PPHTg which became comparable to controls, pioglitazone treatment partially blunted PPHTg resulting in intermediate PPHTg. Group B with highest PPHTg showed highest subsequent IR, glucose intolerance (GI) and highest incidence of prediabetes at week 26 and diabetes at week 34 and 46 compared to other groups. Group D rats displayed lower IR, GI, low incidence of prediabetes and diabetes at these time points compared to Groups B and C. ROC analysis showed that triglyceride area under the curve of each time point significantly predicts the risk of diabetes. Present study provides the evidence that PPHTg predicts the development of IR, GI and T2DM in rat model of diet induced T2DM.     

Inertia weight control strategies for particle swarm optimization

Particle swarm optimization (PSO) is a population-based, stochastic optimization technique inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control parameters, and thus, there has been a significant amount of research effort devoted to the dynamic adaptation of these parameters. The focus of the adaptive approaches has largely revolved around adapting the inertia weight as it exhibits the clearest relationship with the exploration/exploitation balance of the PSO algorithm. However, despite the significant amount of research efforts, many inertia weight control strategies have not been thoroughly examined analytically nor empirically. Thus, there are a plethora of choices when selecting an inertia weight control strategy, but no study has been comprehensive enough to definitively guide the selection. This paper addresses these issues by first providing an overview of 18 inertia weight control strategies. Secondly, conditions required for the strategies to exhibit convergent behaviour are derived. Finally, the inertia weight control strategies are empirically examined on a suite of 60 benchmark problems. Results of the empirical investigation show that none of the examined strategies, with the exception of a randomly selected inertia weight, even perform on par with a constant inertia weight.     

Turing learning: a metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for ‘tricking’ the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product—the classifiers—that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.