Differential vascular adaptive response in spontaneously hypertensive and Wistar rats: Role of nitric oxide, and prehypertensive and hypertensive states

Stress-induced vascular adaptive response in SHR was investigated, focusing on the endothelium. Noradrenaline responses were studied in intact and denuded aortas from 6-week-old (prehypertensive) and 14-week-old (hypertensive) SHR and age-matched Wistar rats submitted or not to acute stress (20-min swimming and I-h immobilization 25 min apart), preceded or not by chronic stress (2 sessions 2 days apart of 1-h day immobilization for 5-consecutive days). Stress did not alter the reactivity of denuded aorta. Moreover, no alteration in the EC50 values was observed after stress exposure. In intact aortas, acute stress-induced hyporeactivity to noradrenaline similar between strains at both age. Chronic stress potentiated this adaptive response in 6- and 14-week-old Wistar but not in 6-week-old SHR, and did not alter the reactivity of 14-week-old SHR. Maximum response (g) in intact aortas [6-week-old: Wistar 3.25 +/- 0.12, Wistar/acute 1.95 +/- 0.12*, Wistar/chronic 1.36 +/- 0.21*(+), SHR 1.75 +/- 0.11, SHR/acute 0.88 +/- 0.08*, SHR/chronic 0.85 +/- 0.05*; 14-week-old: Wistar 3.83 +/- 0.13, Wistar/acute 2.72 +/- 0.13*, Wistar/chronic 1.91 +/- 0.19*', SHR 4.03 +/- 0.17, SHR/acute 2.26 +/- 0.12*, SHR/chronic 4.10 +/- 0.23; inside the same strain: *P < 0.05 relate to non-stressed rat, (+)P < 0.05 related to acute stressed rat; n = 6-18]. Independent of age and strain, L-NAME and endothelium removal abolished the stress-induced aorta hyporeactivity. Conclusion: the vascular adaptive response to stress is impaired in SHR, independently of the hypertensive state. Moreover, this vascular adaptive response is characterized by endothelial nitric oxide-system hyperactivity in both strains. (c) 2006 Elsevier B.V. All rights reserved.

A hierarchy of adaptive (X)over-bar control charts

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

5-Lipoxygenase Deficiency Impairs Innate and Adaptive Immune Responses during Fungal Infection

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

Comments, with reply on 'Continuous time relay-controlled model reference adaptive-system' by A. Abdulkareem and R. Nagarajam

An adaptive scheme is shown by the authors of the above paper (ibid. vol. 71, no. 2, pp. 275-276, Feb. 1983) for continuous time model reference adaptive systems (MRAS), where relays replace the usual multipliers in the existing MRAS. The commenter shows an error in the analysis of the hyperstability of the scheme, such that the validity of this configuration becomes an open question.

Another prefiltering algorithm for adaptive IIR filtering

An algorithm for adaptive IIR filtering that uses prefiltering structure in direct form is presented. This structure has an estimation error that is a linear function of the coefficients. This property greatly simplifies the derivation of gradient-based algorithms. Computer simulations show that the proposed structure improves convergence speed.

New approach of induction machine drive using an adaptive digital PID controller with gain planning based on artificial neural networks

The present work introduces a new strategy of induction machines speed adjustment using an adaptive PID (Proportional Integral Derivative) digital controller with gain planning based on the artificial neural networks. This digital controller uses an auxiliary variable to determine the ideal induction machine operating conditions and to establish the closed loop gain of the system. The auxiliary variable value can be estimated from the information stored in a general-purpose artificial neural network based on CMAC (Cerebellar Model Articulation Controller).

Adaptive control charts for attributes

We develop a general model for adaptive c, np, u and p control charts in which one, two or three design parameters (sample size, sampling interval and control limit width) switch between two values, according to the most recent process information. For a given in-control average sampling rate and a given false alarm rate, the adaptive chart detects changes in the process much faster than a chart with fixed parameters. Moreover, this study also offers general guidance on how to choose an effective design.

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

Strong selection against hybrids at a hybrid zone in the Ensatina ring species complex and its evolutionary implications

The analysis of interactions between lineages at varying levels of genetic divergence can provide insights into the process of speciation through the accumulation of incompatible mutations. Ring species, and especially the Ensatina eschscholtzii system exemplify this approach. The plethodontid salamanders E. eschscholtzii xanthoptica and E. eschscholtzii platensis hybridize in the central Sierran foothills of California. We compared the genetic structure across two transects (southern and northern Calaveras Co.), one of which was resampled over 20 years, and examined diagnostic molecular markers (eight allozyme loci and mitochondrial DNA) and a diagnostic quantitative trait (color pattern). Key results across all studies were: (1) cline centers for all markers were coincident and the zones were narrow, with width estimates of 730 m to 2000 m; (2) cline centers at the northern Calaveras transect were coincident between 1981 and 2001, demonstrating repeatability over five generations; (3) there were very few if any putative F1s, but a relatively high number of backcrossed individuals in the central portion of transects: and (4) we found substantial linkage disequilibrium in all three studies and strong heterozygote deficit both in northern Calaveras, in 2001, and southern Calaveras. Both linkage disequilibrium and heterozygote deficit showed maximum values near the center of the zones. Using estimates of cline width and dispersal, we infer strong selection against hybrids. This is sufficient to promote accumulation of differences at loci that are neutral or under divergent selection, but would still allow for introgression of adaptive alleles. The evidence for strong but incomplete isolation across this centrally located contact is consistent with theory suggesting a gradual increase in postzygotic incompatibility between allopatric populations subject to divergent selection and reinforces the value of Ensatina as a system for the study of divergence and speciation at multiple stages. © 2005 The Society for the Study of Evolution. All rights reserved.

Double Higgs production and quadratic divergence cancellation in little Higgs models with T-parity

We analyze double Higgs boson production at the Large Hadron Collider in the context of Little Higgs models. In double Higgs production, the diagrams involved are directly related to those that cause the cancellation of the quadratic divergence of the Higgs self-energy, providing a robust prediction for this class of models. We find that in extensions of this model with the inclusion of a so-called T-parity, there is a significant enhancement in the cross sections as compared to the Standard Model. © SISSA 2006.

Genetic divergence in Eucalyptus spp. clones by molecular RAPD markers

The genetic divergence in 20 Eucalyptus spp. clones was evaluated by multivariate techniques based on 167 RAPD markers, of which 155 were polymorphic and 12 monomorphic. The measures of genetic distances were obtained by the arithmetic complement of the coefficients of Jaccard and of Sorenso-Nei and Li and evaluated by the hierarchical methods of Single Linkage clustering and Unweighted Pair Group Method with Arithmetic Mean (UPGMA). Independent of the dissimilarity coefficient, the greatest divergence was found between clones 7 and 17 and the smallest between the clones 11 and 14. Clone clustering was little influenced by the applied procedure so that, adopting the same percentage of divergence, the UPGMA identified two groups less for the coefficient of Sorenso-Nei and Li. The clones evidenced considerable genetic divergence, which is partly associated to the origin of the study material. The clusters formed by the UPGMA clustering algorithm associated to the arithmetic complement of Jaccard were most consistent.

Genetic divergence in sesame based on morphological and agronomic traits

The evaluation of diversity in germplasm collections is important for both plant breeders and germplasm curators to optimize the use of the variability available. Diversity can be estimated by different genetic markers. The purpose of this study was to estimate the genetic divergence of 30 morphological and agronomic traits in 108 sesame genotypes by multivariate analysis. The Cole-Rodgers index was used to establish the dissimilarity matrices. The principal component analysis identified the traits that contributed most to the divergence and the genotypes were clustered by Tocher's optimization. Despite the narrow genetic basis, the markers were efficient to characterize the genotypes and identify the most similar groups or duplicate and divergent genotypes. Greatest variation was found for the traits number of capsules per plant and grain yield.

Applying a genetic neuro-model reference adaptive controller in drilling optimization

Motivated by rising drilling operation costs, the oil industry has shown a trend toward real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated with parameters modeling. One of the drillbit performance evaluators, the Rate Of Penetration (ROP), has been used as a drilling control parameter. However, relationships between operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on an auto-regressive with extra input signals, or ARX model and on a Genetic Algorithm (GA) to control the ROP. © [2006] IEEE.