Inflammation of the Hypothalamus Leads to Defective Pancreatic Islet Function

Type 2 diabetes mellitus results from the complex association of insulin resistance and pancreatic beta-cell failure. Obesity is the main risk factor for type 2 diabetes mellitus, and recent studies have shown that, in diet-induced obesity, the hypothalamus becomes inflamed and dysfunctional, resulting in the loss of the perfect coupling between caloric intake and energy expenditure. Because pancreatic beta-cell function is, in part, under the control of the autonomic nervous system, we evaluated the role of hypothalamic inflammation in pancreatic islet function. In diet-induced obesity, the earliest markers of hypothalamic inflammation are present at 8 weeks after the beginning of the high fat diet; similarly, the loss of the first phase of insulin secretion is detected at the same time point and is restored following sympathectomy. Intracerebroventricular injection of a low dose of tumor necrosis factor a leads to a dysfunctional increase in insulin secretion and activates the expression of a number of markers of apoptosis in pancreatic islets. In addition, the injection of stearic acid intracerebroventricularly, which leads to hypothalamic inflammation through the activation of tau-like receptor-4 and endoplasmic reticulum stress, produces an impairment of insulin secretion, accompanied by increased expression of markers of apoptosis. The defective insulin secretion, in this case, is partially dependent on sympathetic signal-induced peroxisome proliferator receptor-gamma coactivator Delta a and uncoupling protein-2 expression and is restored after sympathectomy or following PGC1 alpha expression inhibition by an antisense oligonucleotide. Thus, the autonomic signals generated in concert with hypothalamic inflammation can impair pancreatic islet function, a phenomenon that may explain the early link between obesity and defective insulin secretion.

Aerobic Exercise Training Prevents Heart Failure-Induced Skeletal Muscle Atrophy by Anti-Catabolic, but Not Anabolic Actions

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Expression of genes related to mitochondrial function in Nellore cattle divergently ranked on residual feed intake

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Dichotomous effects of VEGF-A on adipose tissue dysfunction

Obese fat pads are frequently undervascularized and hypoxic, leading to increased fibrosis, inflammation, and ultimately insulin resistance. We hypothesized that VEGF-A-induced stimulation of angiogenesis enables sustained and sufficient oxygen and nutrient exchange during fat mass expansion, thereby improving adipose tissue function. Using a doxycycline (Dox)-inducible adipocyte-specific VEGF-A overexpression model, we demonstrate that the local up-regulation of VEGF-A in adipocytes improves vascularization and causes a "browning" of white adipose tissue (AT), with massive up-regulation of UCP1 and PGC1 alpha. This is associated with an increase in energy expenditure and resistance to high fat diet-mediated metabolic insults. Similarly, inhibition of VEGF-A-induced activation of VEGFR2 during the early phase of high fat diet-induced weight gain, causes aggravated systemic insulin resistance. However, the same VEGF-A-VEGFR2 blockade in ob/ob mice leads to a reduced body-weight gain, an improvement in insulin sensitivity, a decrease in inflammatory factors, and increased incidence of adipocyte death. The consequences of modulation of angiogenic activity are therefore context dependent. Proangiogenic activity during adipose tissue expansion is beneficial, associated with potent protective effects on metabolism, whereas antiangiogenic action in the context of preexisting adipose tissue dysfunction leads to improvements in metabolism, an effect likely mediated by the ablation of dysfunctional proinflammatory adipocytes.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.