Refolded dengue virus type 2 NS1 protein expressed in Escherichia coli preserves structural and immunological properties of the native protein

The dengue virus NS1 protein has been shown to be a protective antigen under different experimental conditions but the recombinant protein produced in bacterial expression systems is usually not soluble and loses structural and immunological features of the native viral protein In the present study, experimental conditions leading to purification and refolding of the recombinant dengue virus type 2 (DENV-2) NS1 protein expressed in Escherichia coil are described The refolded recombinant protein was recovered as heat-stable soluble dimers with preserved structural features, as demonstrated by spectroscopic methods In addition, antibodies against epitopes of the NS1 protein expressed in eukaryotic cells recognized the refolded protein expressed in E coli but not the denatured form or the same protein submitted to a different refolding condition Collectively, the results demonstrate that the recombinant NS1 protein preserved important conformation and antigenic determinants of the native virus protein and represents a valuable reagent either for the development of vaccines or for diagnostic methods. (C) 2010 Elsevier B V All rights reserved

ANALYSIS & STUDY OF AI TECHNIQUES FORAUTOMATIC CONDITION MONITORING OFRAILWAY TRACK INFRASTRUCTURE : Artificial Intelligence Techniques

Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.

Non-destructive evaluation tool for monitoring and detection of structural damage by using neural network.

This work studies the capability of generalization of Neural Network using vibration based measurement data aiming at operating condition and health monitoring of mechanical systems. The procedure uses the backpropagation algorithm to classify the input patters of a system with different stiffness ratios. It has been investigated a large set of input data, containing various stiffness ratios as well as a reduced set containing only the extreme ones in order to study generalizing capability of the network. This allows to definition of Neural Networks capable to use a reduced set of data during the training phase. Once it is successfully trained, it could identify intermediate failure condition. Several conditions and intensities of damages have been studied by using numerical data. The Neural Network demonstrated a good capacity of generalization for all case. Finally, the proposal was tested with experimental data.

Structural variations in root and stem wood of Styrax (Styracaceae) from Brazilian forest and cerrado

The genus Styrax L. (Styracaceae) has a wide distribution in Brazil, occurring in diverse ecosystems. To get a better insight into the ecological adaptations of wood structure, we studied two species, S. camporum and S.ferrugineus from the cerrado, and three species, S. latifolium, S. martii and S. leprosus from the Atlantic forest. For each species, the wood of root and stem was analyzed separately and observations included qualitative as well as quantitative wood characteristics. The results show that there were significant anatomical differences between the forest and cerrado species as well as between the root and stem wood within single species. Quantitatively, the most informative features in the root wood that separated the forest from the cerrado, species were diameter, length and number of vessels, length of fibres, and width and frequency of rays. In the stem wood, length and frequency of vessels, length of fibres, and width and frequency of rays were the most informative features. In contrast to the forest species, which had larger vessel diameters in their stem wood, the cerrado species had larger vessel diameters in their root wood. The calculated vulnerability index indicates that all Sryrax species have adaptations to mesic conditions. The cerrado species had the smallest index values, which could be related to the seasonally dry condition of this environment.

SnO2 nanocrystals synthesized by microwave-assisted hydrothermal method: towards a relationship between structural and optical properties

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Chondroitin sulfate proteoglycans are structural renewable constituents of the rabbit vitreous body

Purpose: To characterize the vitreous intrinsic proteoglycans, investigate their dynamics, and examine their role in the supramolecular organization of the vitreous. Methods: Vitreous from normal rabbits was collected and processed for observation with the transmission electron microscope after treatment with glycosidases. Also, rabbits were injected intravitreally with [S-35]-sodium sulfate and sacrificed at several time intervals after the injection. Proteoglycans (PGs) were assayed in the vitreous supernatant or in whole samples extracted with guanidine hydrochloride by polyacrylamide or agarose gel electrophoresis, followed respectively by fluorography or autoradiography, and ion-exchange chromatography and gel-filtration chromatography, combined with glycolytic treatment of the samples. The sulfated glycosaminoglycans (GAGs) were characterized by agarose gel electrophoresis after treating vitreous samples with protease and specific glycosidases. Results: the electron microscopic study revealed a network with hyaluronic acid ( HA) as thin threads coating and connecting collagen fibrils. The elimination of the HA coat showed chondroitin sulfate granules (8-25 nm) arranged at regular intervals on the fibril surface. The chondroitinase ABC digestion, besides removing the granules, also caused the formation of thicker bundles of the collagen fibrils. The PG and GAG analysis indicated that there are three renewable PGs in the vitreous ( e. g., one heparan-and two chondroitin-sulfate ones). Conclusions: At least one of the chondroitin sulfate PGs is involved in the interactions that occur in the vitreous structure, mainly by providing adequate spacing between the collagen fibrils, a condition that is probably required for the transparency of the vitreous.

Application of a model updating technique for health condition monitoring and damage detection of flexible structures

This paper discusses the application of a damage detection methodology to monitor the location and extent of partial structural damage. The methodology combines, in an iterative way, the model updating technique based on frequency response functions (FRF) with monitoring data aiming at identifying the damage area of the structure. After the updating procedure reaches a good correlation between the models, it compares the parameters of the damage structure with those of the undamaged one to find the deteriorated area. The influence of the FEM mesh size on the evaluation of the extent of the damage has also been discussed. The methodology is applied using real experimental data from a spatial frame structure.

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

Structural reforms, technological gaps and economic development: a Latin American perspective

Includes bibliography

Influence of microwave energy on structural and piezoelectric response of Bi 4Ti 3O 12 ceramics

Bismuth titanate ceramics (Bi 4Ti 3O 12) with 10 wt% in excess of bismuth (BIT10) were prepared by the polymeric precursor method and sinterized in microwave (MW) and conventional furnaces (CF). The effect of microwave energy on structural and electrical behavior of BIT10 ceramics was investigated by means of X-ray diffraction (XRD), Scanning electron microscopy (SEM) and electrical measurements. The results of the BIT10 ceramics processed in the microwave furnace (MW) showed a high structural organization compared to conventional treatment (CF). Size of grains and dieletrical properties are influenced by annealing conditions while coercitive field is not dependent on it. The maximum dielectric permittivity (12000) was obtained for the sample sintered in the microwave furnace. Piezoelectric force microscopy images reveals that in-plane response may not change its sign upon polarization switching, while the out-of-plane response does with the influence of microwave energy. Copyright © 2010 American Scientific Publishers All rights reserved.

Versatile and easy-to-assemble measurement system for impedance-based structural health monitoring

In this paper we present a versatile and easy-to-assemble measurement system for structural health monitoring (SHM) based on the electromechanical impedance (EMI) technique. The hardware of the proposed system consists only of a common data acquisition (DAQ) device with external resistors and allows real-time data acquisition from multiple sensors. Besides the low-cost compared to conventional impedance analyzers, the hardware and the software are simple and easier to implement than other measurement systems that have been recently proposed.

Artificial Immune Systems Applied to the Analysis of Structural Integrity of a Building

This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This methodology can assist professionals in the inspection of mechanical and civil structures, to identify and characterize flaws, in order to perform preventative maintenance to ensure the integrity of the structure and decision-making. In order to evaluate the methodology was made modeling a two-story building and several situations were simulated (base-line condition and improper conditions), yielding a database of signs, which were used as input data for the negative selection algorithm. The results obtained by the present method efficiency, robustness and accuracy.

Analysis of Structural Integrity Using an ARTMAP-Fuzzy Artificial Neural Network

This paper presents the application of artificial neural networks in the analysis of the structural integrity of a building. The main objective is to apply an artificial neural network based on adaptive resonance theory, called ARTMAP-Fuzzy neural network and apply it to the identification and characterization of structural failure. This methodology can help professionals in the inspection of structures, to identify and characterize flaws in order to conduct preventative maintenance to ensure the integrity of the structure and decision-making. In order to validate the methodology was modeled a building of two walk, and from this model were simulated various situations (base-line condition and improper conditions), resulting in a database of signs, which were used as input data for ARTMAP-Fuzzy network. The results show efficiency, robustness and accuracy.

Influence of structural nonlinearities in stall-induced aeroelastic response of pitching airfoils

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

Structural features of lignin obtained at different alkaline oxidation conditions from sugarcane bagasse

Lignin is a macromolecule frequently obtained as residue during technological processing of biomass. Modifications in chemical structure of lignin generate valuable products, some with particular and unique characteristics. One of the available methods for modification of industrial lignin is oxidation by hydrogen peroxide. In this work, we conducted systematic studies of the oxidation process that were carried out at various pHs and oxidizing agent concentrations. Biophysical, biochemical, structural properties of the oxidized lignin were analyzed by UV spectrophotometry, Fourier transform infrared spectroscopy, scanning electron microscopy and small angle X-ray scattering. Our results reveal that lignin oxidized with 9.1% H(2)O(2) (m/v) at pH 13.3 has the highest fragmentation, oxidation degree and stability. Although this processing condition might be considered quite severe, we have concluded that the stability of the obtained oxidized lignin was greatly increased. Therefore, the identified processing conditions of oxidation may be of practical interest for industrial applications. (C) 2011 Elsevier B.V. All rights reserved.