Expressed sequence tags from Atta laevigata and identification of candidate genes for the control of pest leaf-cutting ants.

Leafcutters are the highest evolved within Neotropical ants in the tribe Attini and model systems for studying caste formation, labor division and symbiosis with microorganisms. Some species of leafcutters are agricultural pests controlled by chemicals which affect other animals and accumulate in the environment. Aiming to provide genetic basis for the study of leafcutters and for the development of more specific and environmentally friendly methods for the control of pest leafcutters, we generated expressed sequence tag data from Atta laevigata, one of the pest ants with broad geographic distribution in South America. Results: The analysis of the expressed sequence tags allowed us to characterize 2,006 unique sequences in Atta laevigata. Sixteen of these genes had a high number of transcripts and are likely positively selected for high level of gene expression, being responsible for three basic biological functions: energy conservation through redox reactions in mitochondria; cytoskeleton and muscle structuring; regulation of gene expression and metabolism. Based on leafcutters lifestyle and reports of genes involved in key processes of other social insects, we identified 146 sequences potential targets for controlling pest leafcutters. The targets are responsible for antixenobiosis, development and longevity, immunity, resistance to pathogens, pheromone function, cell signaling, behavior, polysaccharide metabolism and arginine kynase activity. Conclusion: The generation and analysis of expressed sequence tags from Atta laevigata have provided important genetic basis for future studies on the biology of leaf-cutting ants and may contribute to the development of a more specific and environmentally friendly method for the control of agricultural pest leafcutters.

The diagnostic value of Gram stain for initial identification of the etiologic agent of peritonitis in CAPD patients

Objective: To evaluate the effectiveness of the Gram stain in the initial diagnosis of the etiologic agent of peritonitis in continuous ambulatory peritoneal dialysis (CAPD). Design: Retrospective study analyzing the sensitivity (S), specificity (SS), positive predictive value (+PV), and negative predictive value (-PV) of the Gram stain relating to the results of cultures in 149 episodes of peritonitis in CAPD. The data were analyzed in two studies. In the first, only the cases with detection of a single agent by Gram stain were taken (Study 1). In the second, only the cases with two agents in Gram stain were evaluated (Study 2). Setting: Dialysis Unit and Laboratory of Microbiology of a tertiary medical center. Patients: Sixty-three patients on regular CAPD who presented one or more episodes of peritonitis from May 1992 to May 1995. Results: The positivity of Gram stain was 93.2% and the sensitivity was 95.7%. The values of S, SS, +PV, and -PV were respectively: 94.9%, 53.5%, 68.3%, and 90.9% for gram-positive cocci and 83.3%, 98.8%, 95.2%, and 95.6% for gram-negative bacilli. The association of gram-positive cocci plus gram-negative bacilli were predictive of growth of both in 6.8%, growth of gram-positive cocci in 13.7%, and growth of gram-negative bacilli in 72.5%. Conclusions: The Gram stain is a method of great value in the initial diagnosis of the etiologic agent of peritonitis in CAPD, especially for gram-negative bacilli.

A barrier method for constrained nonlinear optimization using a modified Hopfield network

The ability of neural networks to realize some complex nonlinear function makes them attractive for system identification. This paper describes a novel barrier method using artificial neural networks to solve robust parameter estimation problems for nonlinear model with unknown-but-bounded errors and uncertainties. This problem can be represented by a typical constrained optimization problem. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the network convergence to the equilibrium points. A solution for the robust estimation problem with unknown-but-bounded error corresponds to an equilibrium point of the network. Simulation results are presented as an illustration of the proposed approach.

Investigations into the use of the finite element method and artificial neural networks in the non-destructive analysis of metallic tubes

This work presents an investigation into the use of the finite element method and artificial neural networks in the identification of defects in industrial plants metallic tubes, due to the aggressive actions of the fluids contained by them, and/or atmospheric agents. The methodology used in this study consists of simulating a very large number of defects in a metallic tube, using the finite element method. Both variations in width and height of the defects are considered. Then, the obtained results are used to generate a set of vectors for the training of a perceptron multilayer artificial neural network. Finally, the obtained neural network is used to classify a group of new defects, simulated by the finite element method, but that do not belong to the original dataset. The reached results demonstrate the efficiency of the proposed approach, and encourage future works on this subject.

Identification of synchronous machine parameters using load rejection test data

This work shows a computational methodology for the determination of synchronous machines parameters using load rejection test data. By machine modeling one can obtain the quadrature parameters through a load rejection under an arbitrary reference, reducing the present difficulties. The proposed method is applied to a real machine.

Comparison of a multiplex-PCR assay with mycolic acids analysis and conventional methods for the identification of Mycobacteria

A fast, sensitive and cost-effective multiplex-PCR assay for Mycobacterium tuberculosis complex (MTC) and Mycobacterium avium (M. avium) identification for routine diagnosis was evaluated. A total of 158 isolates of mycobacteria from 448 clinical specimens from patients with symptoms of mycobacterial disease were analyzed. By conventional biochemical methods 151 isolates were identified as M. tuberculosis, five as M. avium and two as Mycobacterium chelonae (M. chelonae). Mycolic acid patterns confirmed these results. Multiplex-PCR detected only IS6110 in isolates identified as MTC, and IS1245 was found only in the M. avium isolates. The method applied to isolates from two patients, identified by conventional methods and mycolic acid analysis, one as M. avium and other as M. chelonae, resulted positive for IS6110, suggesting co-infection with M. tuberculosis. These patients were successfully submitted to tuberculosis treatment. The multiplex-PCR method may offer expeditious identification of MTC and M. avium, which may minimize risks for active transmission of these organisms and provide useful treatment information.

Resistivity (DC) method applied to aouifer protection studies

Resistivity (DC) method using vertical electrical soundings (Schlumberger array) are conducted In the vicinity of Canoas/RS, applied to environmental studies with the objective of Investigating groundwater conditions, The present paper shows a geoelectrlcal Identification of the lithology and an estimate of the relationship between the resistivity and Dar Zarrouk parameters (transverse unit resistance and longitudinal unit conductance) with the properties such as aquifer transmlssivlty and protection of ground water resources, In the saturated sediments, resistivity values defined the following sequence: clay layers (resistivity < 40 ohm-m) and sand layers (resistivity > 40 ohm-m), Two sand layers were identified; one corresponding to the unconfined aquifer and another to the confined aquifer between two clay layers, In the map of the transverse unit resistance of the unconfined aquifer, the tendencies of high values can be associated with the zones of high transmissivity; hence, these zones are suggested for the installation of monitoring wells, The map of longitudinal conductance Illustrates the Impermeability of the confining clay layer, Values of S > 1.0 siemens would indicate zones in which the confined aquifer would be protected; In comparison, values of S < 1.0 siemens would indicate zones of probable risks of contamination. © 2006 Sociedade Brasileira de Geofísica.

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.

A simplified method for determining the high frequency induction motor equivalent electrical circuit parameters to be used in EMI effect

The aim of this paper is to present a simple method for determining the high frequency parameters of a three-phase induction motor to be used in studies involving variable speed drives with PWM three-phase inverters, in which it is necessary to check the effects caused to the motor by the electromagnetic interference, (EMI) in the differential mode, as well as in the common mode. The motor parameters determination is generally performed in adequate laboratories using accurate instruments, such as very expensive RLC bridges. The method proposed here consists in the identification of the motor equivalent electrical circuit parameters in rated frequency and in high frequency through characteristic tests in the laboratory, together with the use of characteristic equations and curves, shown in the references to be mentioned for determining the motor high frequency parasite capacitances and also through system simulations using dedicated software, like Pspice, determining the characteristic waveforms involved in the differential and common mode phenomena, comparing and validating the procedure through published papers [01].

Fusion of fingerprint recognition methods for robust human identification

Biometrics is one of the biggest tendencies in human identification. The fingerprint is the most widely used biometric. However considering the automatic fingerprint recognition a completely solved problem is a common mistake. The most popular and extensively used methods, the minutiae-based, do not perform well on poor-quality images and when just a small area of overlap between the template and the query images exists. The use of multibiometrics is considered one of the keys to overcome the weakness and improve the accuracy of biometrics systems. This paper presents the fusion of a minutiae-based and a ridge-based fingerprint recognition method at rank, decision and score level. The fusion techniques implemented leaded to a reduction of the Equal Error Rate by 31.78% (from 4.09% to 2.79%) and a decreasing of 6 positions in the rank to reach a Correct Retrieval (from rank 8 to 2) when assessed in the FVC2002-DB1A database. © 2008 IEEE.

Utilização do alcohol use disorders identification test (Audit) para identificação do consumo de álcool entre estudantes do ensino médio

Alcohol is one of the drugs most widely used among teenagers. Just recently, studies have been developed regarding the screening of use of alcohol by this population. This work aimed to investigate the use of AUDIT as a method for screening and evaluation of alcohol consumption among High School students. The sample was composed by 1227 students from two public schools, who answered to the Alcohol Use Disorders Identification Test (AUDIT) and informed their socioeconomic level, religion, and occurrence of relationship problems caused by drunkenness of family members. Using an 8 cut-off point, AUDIT has identified 17.8% of students with risk drinking. These results have revealed that AUDIT is easy to be applied and well accepted by the students. It was also evident the importance of this instrument in the follow-up programs of prevention and intervention of alcoholic beverages use.

Evaluation of a digital methodology for human identification using palatal rugoscopy

Palatal rugoscopy, or palatoscopy, is the process by which human identification can be obtained by inspecting the transverse palatal rugae inside the mouth. Aim: This study evaluated a digital method for human identification using palatoscopy, by comparing photographs of the palate against the images of cast models of the maxilla photographed with and without highlighting of the palatal rugae. Methods: Condensation silicone impressions were made from the upper arches of 30 adult subjects of both genders and their palates were then photographed. The first impression was made with heavy silicone, the second impression with light silicone, and then the models were cast in improved type IV dental stone. The casts were photographed, the palatal rugae of each one were highlighted with a pencil, and then the models were photographed again. Using a free image-editing software, the digital photographs were overlapped over the images of the palatal rugae of the models with and without highlighting of the palatal rugae, in order to identify the pairs. Results: The result of overlapping the digital photographs with the images of the models without highlighted palatal rugae resulted in 90% positive identification. For the overlapping of the digital photographs with the images of models with highlighted palatal rugae, there was 100% positive identification. Conclusions: The digital method evaluated in this study was proven effective for human identification.

What is the importance of selecting features for non-technical losses identification?

Although non-technical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy has not attracted much attention in this context. In this paper, we focus on this problem applying a novel feature selection algorithm based on Particle Swarm Optimization and Optimum-Path Forest. The results demonstrated that this method can improve the classification accuracy of possible frauds up to 49% in some datasets composed by industrial and commercial profiles. © 2011 IEEE.

Adaptive filter feature identification for structural health monitoring in aeronautical panel

This paper presents an approach for structural health monitoring (SHM) by using adaptive filters. The experimental signals from different structural conditions provided by piezoelectric actuators/sensors bonded in the test structure are modeled by a discrete-time recursive least square (RLS) filter. The biggest advantage to use a RLS filter is the clear possibility to perform an online SHM procedure since that the identification is also valid for non-stationary linear systems. An online damage-sensitive index feature is computed based on autoregressive (AR) portion of coefficients normalized by the square root of the sum of the square of them. The proposed method is then utilized in a laboratory test involving an aeronautical panel coupled with piezoelectric sensors/actuators (PZTs) in different positions. A hypothesis test employing the t-test is used to obtain the damage decision. The proposed algorithm was able to identify and localize the damages simulated in the structure. The results have shown the applicability and drawbacks the method and the paper concludes with suggestions to improve it. ©2010 Society for Experimental Mechanics Inc.

Identification of foliar diseases in cotton crop

The pathogens manifestation in plantations are the largest cause of damage in several cultivars, which may cause increase of prices and loss of crop quality. This paper presents a method for automatic classification of cotton diseases through feature extraction of leaf symptoms from digital images. Wavelet transform energy has been used for feature extraction while Support Vector Machine has been used for classification. Five situations have been diagnosed, namely: Healthy crop, Ramularia disease, Bacterial Blight, Ascochyta Blight, and unspecified disease. © 2012 Taylor & Francis Group.