Morphology and protein content of hepatocytes in type I diabetic rats submitted to physical exercises

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Correlation between the mobility of spin-labeled peptide chains and resin solvation: An approach to optimize the synthesis of aggregating sequences

Resin solvation properties affect the efficiency of the coupling reactions in solid-phase peptide synthesis. Here we report a novel approach to evaluate resin solvation properties, making use of spin label electron paramagnetic resonance (EPR) spectroscopy. The aggregating VVLGAAIV and ING sequences were assembled in benzhydrylamine-resin with different amino group contents (up to 2.6 mmol/g) to examine the extent of chain association within the beads. These model peptidyl-resins were first labeled at their N-terminus with the amino acid spin label 2,2,6,6-tetramethylpiperidine-N-oxyl-4-amino-4-carboxylic acid (Toac). Their solvation properties in different solvents were estimated, either by bead swelling measurement or by assessing the dynamics of their polymeric matrixes through the analysis of Toac EPR spectra, and were correlated with the yield of the acylation reaction. In most cases the coupling rate was found to depend on bead swelling. Comparatively, the EPR approach was more effective. Line shape analysis allowed the detection of more than one peptide chain population, which influenced the reaction. The results demonstrated the unique potential of EPR spectroscopy not only for improving the yield of peptide synthesis, even in challenging conditions, but also for other relevant polymer-supported methodologies in chemistry and biology.

A recursive approach to space resection using straight lines

An approach using straight lines as features to solve the photogrammetric space resection problem is presented. An explicit mathematical model relating straight lines, in both object and image space, is used. Based on this model, Kalman Filtering is applied to solve the space resection problem. The recursive property of the filter is used in an iterative process which uses the sequentially estimated camera location parameters to feedback to the feature extraction process in the image. This feedback process leads to a gradual reduction of the image space for feature searching, and consequently eliminates the bottleneck due to the high computational cost of the image segmentation phase. It also enables feature extraction and the determination of feature correspondence in image and object space in an automatic way, i.e., without operator interference. Results obtained from simulated and real data show that highly accurate space resection parameters are obtained as well as a progressive processing time reduction. The obtained accuracy, the automatic correspondence process, and the short related processing time show that the proposed approach can be used in many real-time machine vision systems, making possible the implementation of applications not feasible until now.

A social network approach to trust management in VANETs

In the past few years, vehicular ad hoc networks(VANETs) was studied extensively by researchers. VANETs is a type of P2P network, though it has some distinct characters (fast moving, short lived connection etc.). In this paper, we present several limitations of current trust management schemes in VANETs and propose ways to counter them. We first review several trust management techniques in VANETs and argue that the ephemeral nature of VANETs render them useless in practical situations. We identify that the problem of information cascading and oversampling, which commonly arise in social networks, also adversely affects trust management schemes in VANETs. To the best of our knowledge, we are the first to introduce information cascading and oversampling to VANETs. We show that simple voting for decision making leads to oversampling and gives incorrect results in VANETs. To overcome this problem, we propose a novel voting scheme. In our scheme, each vehicle has different voting weight according to its distance from the event. The vehicle which is more closer to the event possesses higher weight. Simulations show that our proposed algorithm performs better than simple voting, increasing the correctness of voting. © 2012 Springer Science + Business Media, LLC.

A New Approach to Reducing Search Space and Increasing Efficiency in Simulation Optimization Problems via the Fuzzy-DEA-BCC

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The Epstein-Glaser causal approach to the light-front QED(4). I: Free theory

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

An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.