Diagnóstico molecular da talassemia ALFA+ (DELEÇÃO -3.7) em indivíduos com microcitose e/ou hipocromia

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Diagnóstico molecular da talassemia ALFA em indivíduos com microcitose e/ou hipocromia

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Prevalência da infecção por Chlamydia Trachomatis em mulheres atendidas no Programa de rastreamento do câncer de colo do útero

Genital infection with Chlamydia trachomatis is now recognized as one of the most prevalent sexually transmitted infections (STDs). Despite major advances in laboratory diagnosis techniques, primarily the character of asymptomatic chlamydial infection in both men and in women constitutes the basis for the formation of reservoirs that perpetuate transmission and acquisition of this and other STDs. The asymptomatic in women favors the rise of infection to the upper genital tract, causing injuries that can result in infertility. An examination of population screening for early detection and treatment of asymptomatic infections is the key step in combating this major public health problem. The present study aimed to evaluate the prevalence of infection by C. trachomatis in sexually active women attended the screening program for cervical cancer of the uterus in health facilities in municipalities in different regions of the State of Rio Grande do Norte, and identify factors that may contribute to the spread of this pathogen and its relationship with the lesions of the uterine cervix. It is a cross-sectional study aimed at detecting the presence of genital tract infection by C. trachomatis either in isolated form or in association with human papilloma virus (HPV) infection in asymptomatic women. Were included in this study, a total sample of 1,134 women aged 13-76, mean 34.4 years, from March 2008 to September 2012. Specimens containing exfoliated cells of the epithelium of the uterine cervix were analyzed by examining Pap cytology for the detection of possible injuries, and the polymerase chain reaction (PCR) for detection of plasmid DNA from C. trachomatis and HPV. Infection with C. trachomatis was detected with overall prevalence rate of 8.1% in the isolated form and 2.8% in co-infection with HPV. The infection was detected in 7.4% of women with normal cytology 11.5% of those with atypical cells of undetermined significance (ASC-US) and 16.7% of those with low-grade squamous intraepithelial lesion (LSIL). We observed an association between C. trachomatis and incidence of low-grade squamous intraepithelial lesion (LSIL). The genital tract infection by C. trachomatis alone was associated with education level, ethnicity and parity, revealing that women with higher education, those of non-white ethnicity and those who had three or more pregnancies were more likely to acquire infection. Levels very close to statistical significance were observed for chronological age, age at first sexual intercourse and first pregnancy. There was no association with marital status, number of sexual partners. Co-infection with C. trachomatis and HPV was detected in 2.3% of women with normal cytology, who had 5.1% in ASC-US and 10.4% in those with LSIL. No association was found between infection C. trachomatis and increased risk of HPV infection, but women with simultaneous infection by both pathogens showed greater risk for LSIL. Co-infection was more prevalent among single women, who had in the first sexual intercourse under 18 years and those who had two or more sexual partners over a lifetime

Transporte eletrônico e propriedades termodinâmicas de nanobiomoléculas

We use a tight-binding formulation to investigate the transmissivity and the currentvoltage (I_V) characteristics of sequences of double-strand DNA molecules. In order to reveal the relevance of the underlying correlations in the nucleotides distribution, we compare theresults for the genomic DNA sequence with those of arti_cial sequences (the long-range correlated Fibonacci and RudinShapiro one) and a random sequence, which is a kind of prototype of a short-range correlated system. The random sequence is presented here with the same _rst neighbors pair correlations of the human DNA sequence. We found that the long-range character of the correlations is important to the transmissivity spectra, although the I_V curves seem to be mostly inuenced by the short-range correlations. We also analyze in this work the electronic and thermal properties along an _-helix sequence obtained from an _3 peptide which has the uni-dimensional sequence (Leu-Glu-Thr- Leu-Ala-Lys-Ala)3. An ab initio quantum chemical calculation procedure is used to obtain the highest occupied molecular orbital (HOMO) as well as their charge transfer integrals, when the _-helix sequence forms two di_erent variants with (the so-called 5Q variant) and without (the 7Q variant) _brous assemblies that can be observed by transmission electron microscopy. The di_erence between the two structures is that the 5Q (7Q) structure have Ala ! Gln substitution at the 5th (7th) position, respectively. We estimate theoretically the density of states as well as the electronic transmission spectra for the peptides using a tight-binding Hamiltonian model together with the Dyson's equation. Besides, we solve the time dependent Schrodinger equation to compute the spread of an initially localized wave-packet. We also compute the localization length in the _nite _-helix segment and the quantum especi_c heat. Keeping in mind that _brous protein can be associated with diseases, the important di_erences observed in the present vi electronic transport studies encourage us to suggest this method as a molecular diagnostic tool

Auditoria de inteligência: um método para o diagnóstico de sistemas de inteligência competitiva e organizacional

CARVALHO, Andréa Vasconcelos ; ESTEBAN NAVARRO, Miguel Ángel. . Auditoria de Inteligência: um método para o diagnóstico de sistemas de inteligência competitiva e organizacional. In: XI ENANCIB - Encontro Nacional de Pesquisa em Ciência da Informação, 2010, Rio de Janeiro. Anais do XI ENANCIB. Rio de Janeiro: ANCIB, 2010.

Diagnóstico da cultura organizacional em hospitais da rede privada de Natal/RN

As peculiaridades presentes no ser humano são observadas a partir da manifestação de sua cultura. Estas manifestações podem ser influenciadas pelo contexto social do indivíduo. A cultura nacional é uma destas influências. A brasileira, por exemplo, possui alguns traços nacionais peculiares ao seu povo que amenizam a sua complexidade e pluralidade. Estes traços podem se tornar influentes na cultura organizacional, através da manifestação das práticas e dos valores utilizados pela organização. Diante disso, o presente trabalho teve como objetivo diagnosticar a cultura organizacional dos hospitais, enfatizando as práticas e os valores organizacionais e a percepção dos colaboradores sobre a cultura organizacional. A metodologia utilizada na pesquisa foi de caráter descritivo e explicativo. A pesquisa ocorreu através da aplicação do Instrumento Brasileiro para a Avaliação da Cultura Organizacional IBACO, com 283 sujeitos distribuídos em três hospitais da rede privada de Natal/RN. As evidências encontradas pelo estudo foram analisadas através do software estatístico SPSS, com as técnicas multivariadas de análise fatorial, análise de aglomerados e análise discriminante. A partir dessas análises, foram obtidos cinco fatores distribuídos entre práticas e valores apresentados pelo IBACO, que foram: as práticas de recompensa e treinamento; integração externa e promoção do relacionamento interpessoal; e, os valores de satisfação, bem-estar e cooperação dos empregados e profissionalismo competitivo. Os grupos encontrados na análise apresentaram divergências de opinião quanto às práticas e os valores utilizados nos hospitais. Concluiu-se, portanto, que o Hospital A apresentou uma cultura mais proativa e voltada mais para a satisfação e bem-estar dos funcionários. Já os outros dois hospitais apresentaram culturas semelhantes, com limitações quanto à valorização do bem-estar e da cooperação entre os colaboradores, porém com uma boa prática do relacionamento interpessoal

Produção pesqueira artesanal: diagnóstico ergonômico e bases para um planejamento nutricional situado e a promoção da saúde dos jangadeiros

The traditional fishing with rafts is characterized by unpredictability, high stakes and inadequate work conditions. The extensive working hours, physical wear, inadequate nutrition, unsanitary conditions, lack of salvage equipment and instruments suitable working, added by the presence of changes in the nutritional status of fisherman, that contribute to the picture of insecurity in high seas, injuries and health. This study aimed to analyze the activity of the fisherman s from Ponta Negra, Natal / RN, and check the conditions of supply of these fishermen and their implications on health and development of their work. To this finality, was used a methodology based on the ergonomic work analysis employing techniques such as observational and interactional conversational action, listening to the answers, observation protocols, photographic and video records. The script conversational dynamic action was developed from literature searches about the artisanal fisheries, culture and food habits of this population, and analyzes the overall situation of focus and two reference situations. To collect data on the usual diet of fisherman as well as quantitative and qualitative analysis that was used for data analysis and 24h recall the Food Frequency Questionnaire (FFQ). The impact of this power to the health of fisherman was evaluated performing a nutritional assessment. The results revealed that the fishermen carry out their activities with poor working conditions, health and nutrition. Feeding practices of these fishermen undertake development work, making it even more stressful, as well contributing to the emergence of Chronic Noncommunicable Diseases. The management of the activity, as well as the current structure of the vessel, also contributes to the adoption of inappropriate feeding practices during the shipment of catch. The results of this indicate the need for adequate interventions in order to assist in recovery and / or maintenance of health of fisherman minimizing reflections of nutritional disorders for the development activity by improving the quality of life in this population

Sistema inteligente para diagnóstico de patologias na laringe utilizando máquinas de vetor de suporte

The human voice is an important communication tool and any disorder of the voice can have profound implications for social and professional life of an individual. Techniques of digital signal processing have been used by acoustic analysis of vocal disorders caused by pathologies in the larynx, due to its simplicity and noninvasive nature. This work deals with the acoustic analysis of voice signals affected by pathologies in the larynx, specifically, edema, and nodules on the vocal folds. The purpose of this work is to develop a classification system of voices to help pre-diagnosis of pathologies in the larynx, as well as monitoring pharmacological treatments and after surgery. Linear Prediction Coefficients (LPC), Mel Frequency cepstral coefficients (MFCC) and the coefficients obtained through the Wavelet Packet Transform (WPT) are applied to extract relevant characteristics of the voice signal. For the classification task is used the Support Vector Machine (SVM), which aims to build optimal hyperplanes that maximize the margin of separation between the classes involved. The hyperplane generated is determined by the support vectors, which are subsets of points in these classes. According to the database used in this work, the results showed a good performance, with a hit rate of 98.46% for classification of normal and pathological voices in general, and 98.75% in the classification of diseases together: edema and nodules

Método Fuzzy para auxílio ao diagnóstico de câncer de mama em ambiente inteligente de telediagnóstico colaborativo para apoio à tomada de decisão

Breast cancer, despite being one of the leading causes of death among women worldwide is a disease that can be cured if diagnosed early. One of the main techniques used in the detection of breast cancer is the Fine Needle Aspirate FNA (aspiration puncture by thin needle) which, depending on the clinical case, requires the analysis of several medical specialists for the diagnosis development. However, such diagnosis and second opinions have been hampered by geographical dispersion of physicians and/or the difficulty in reconciling time to undertake work together. Within this reality, this PhD thesis uses computational intelligence in medical decision-making support for remote diagnosis. For that purpose, it presents a fuzzy method to assist the diagnosis of breast cancer, able to process and sort data extracted from breast tissue obtained by FNA. This method is integrated into a virtual environment for collaborative remote diagnosis, whose model was developed providing for the incorporation of prerequisite Modules for Pre Diagnosis to support medical decision. On the fuzzy Method Development, the process of knowledge acquisition was carried out by extraction and analysis of numerical data in gold standard data base and by interviews and discussions with medical experts. The method has been tested and validated with real cases and, according to the sensitivity and specificity achieved (correct diagnosis of tumors, malignant and benign respectively), the results obtained were satisfactory, considering the opinions of doctors and the quality standards for diagnosis of breast cancer and comparing them with other studies involving breast cancer diagnosis by FNA.

Sistema híbrido para detecção e diagnóstico de falhas em sistemas dinâmicos

The industries are getting more and more rigorous, when security is in question, no matter is to avoid financial damages due to accidents and low productivity, or when it s related to the environment protection. It was thinking about great world accidents around the world involving aircrafts and industrial process (nuclear, petrochemical and so on) that we decided to invest in systems that could detect fault and diagnosis (FDD) them. The FDD systems can avoid eventual fault helping man on the maintenance and exchange of defective equipments. Nowadays, the issues that involve detection, isolation, diagnose and the controlling of tolerance fault are gathering strength in the academic and industrial environment. It is based on this fact, in this work, we discuss the importance of techniques that can assist in the development of systems for Fault Detection and Diagnosis (FDD) and propose a hybrid method for FDD in dynamic systems. We present a brief history to contextualize the techniques used in working environments. The detection of fault in the proposed system is based on state observers in conjunction with other statistical techniques. The principal idea is to use the observer himself, in addition to serving as an analytical redundancy, in allowing the creation of a residue. This residue is used in FDD. A signature database assists in the identification of system faults, which based on the signatures derived from trend analysis of the residue signal and its difference, performs the classification of the faults based purely on a decision tree. This FDD system is tested and validated in two plants: a simulated plant with coupled tanks and didactic plant with industrial instrumentation. All collected results of those tests will be discussed

Técnicas neuronais de agrupamentos aplicadas à detecção de anomalias em mamogramas digitais

This work proposes the development of an intelligent system for analysis of digital mammograms, capable to detect and to classify masses and microcalcifications. The digital mammograms will be pre-processed through techniques of digital processing of images with the purpose of adapting the image to the detection system and automatic classification of the existent calcifications in the suckles. The model adopted for the detection and classification of the mammograms uses the neural network of Kohonen by the algorithm Self Organization Map - SOM. The algorithm of Vector quantization, Kmeans it is also used with the same purpose of the SOM. An analysis of the performance of the two algorithms in the automatic classification of digital mammograms is developed. The developed system will aid the radiologist in the diagnosis and accompaniment of the development of abnormalities

Aplicação de sistemas multi-classificadores no diagnóstico de falhas em motores de indução trifásicos

Equipment maintenance is the major cost factor in industrial plants, it is very important the development of fault predict techniques. Three-phase induction motors are key electrical equipments used in industrial applications mainly because presents low cost and large robustness, however, it isn t protected from other fault types such as shorted winding and broken bars. Several acquisition ways, processing and signal analysis are applied to improve its diagnosis. More efficient techniques use current sensors and its signature analysis. In this dissertation, starting of these sensors, it is to make signal analysis through Park s vector that provides a good visualization capability. Faults data acquisition is an arduous task; in this way, it is developed a methodology for data base construction. Park s transformer is applied into stationary reference for machine modeling of the machine s differential equations solution. Faults detection needs a detailed analysis of variables and its influences that becomes the diagnosis more complex. The tasks of pattern recognition allow that systems are automatically generated, based in patterns and data concepts, in the majority cases undetectable for specialists, helping decision tasks. Classifiers algorithms with diverse learning paradigms: k-Neighborhood, Neural Networks, Decision Trees and Naïves Bayes are used to patterns recognition of machines faults. Multi-classifier systems are used to improve classification errors. It inspected the algorithms homogeneous: Bagging and Boosting and heterogeneous: Vote, Stacking and Stacking C. Results present the effectiveness of constructed model to faults modeling, such as the possibility of using multi-classifiers algorithm on faults classification

Predição de promotores de Bacillus subtilis usando técnicas de aprendizado de máquina

One of the most important goals of bioinformatics is the ability to identify genes in uncharacterized DNA sequences on world wide database. Gene expression on prokaryotes initiates when the RNA-polymerase enzyme interacts with DNA regions called promoters. In these regions are located the main regulatory elements of the transcription process. Despite the improvement of in vitro techniques for molecular biology analysis, characterizing and identifying a great number of promoters on a genome is a complex task. Nevertheless, the main drawback is the absence of a large set of promoters to identify conserved patterns among the species. Hence, a in silico method to predict them on any species is a challenge. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. In this work, we present an empirical comparison of Machine Learning (ML) techniques such as Na¨ýve Bayes, Decision Trees, Support Vector Machines and Neural Networks, Voted Perceptron, PART, k-NN and and ensemble approaches (Bagging and Boosting) to the task of predicting Bacillus subtilis. In order to do so, we first built two data set of promoter and nonpromoter sequences for B. subtilis and a hybrid one. In order to evaluate of ML methods a cross-validation procedure is applied. Good results were obtained with methods of ML like SVM and Naïve Bayes using B. subtilis. However, we have not reached good results on hybrid database

Aplicação de técnicas de aprendizado de máquina no reconhecimento de classes estruturais de proteínas

Nowadays, classifying proteins in structural classes, which concerns the inference of patterns in their 3D conformation, is one of the most important open problems in Molecular Biology. The main reason for this is that the function of a protein is intrinsically related to its spatial conformation. However, such conformations are very difficult to be obtained experimentally in laboratory. Thus, this problem has drawn the attention of many researchers in Bioinformatics. Considering the great difference between the number of protein sequences already known and the number of three-dimensional structures determined experimentally, the demand of automated techniques for structural classification of proteins is very high. In this context, computational tools, especially Machine Learning (ML) techniques, have become essential to deal with this problem. In this work, ML techniques are used in the recognition of protein structural classes: Decision Trees, k-Nearest Neighbor, Naive Bayes, Support Vector Machine and Neural Networks. These methods have been chosen because they represent different paradigms of learning and have been widely used in the Bioinfornmatics literature. Aiming to obtain an improvment in the performance of these techniques (individual classifiers), homogeneous (Bagging and Boosting) and heterogeneous (Voting, Stacking and StackingC) multiclassification systems are used. Moreover, since the protein database used in this work presents the problem of imbalanced classes, artificial techniques for class balance (Undersampling Random, Tomek Links, CNN, NCL and OSS) are used to minimize such a problem. In order to evaluate the ML methods, a cross-validation procedure is applied, where the accuracy of the classifiers is measured using the mean of classification error rate, on independent test sets. These means are compared, two by two, by the hypothesis test aiming to evaluate if there is, statistically, a significant difference between them. With respect to the results obtained with the individual classifiers, Support Vector Machine presented the best accuracy. In terms of the multi-classification systems (homogeneous and heterogeneous), they showed, in general, a superior or similar performance when compared to the one achieved by the individual classifiers used - especially Boosting with Decision Tree and the StackingC with Linear Regression as meta classifier. The Voting method, despite of its simplicity, has shown to be adequate for solving the problem presented in this work. The techniques for class balance, on the other hand, have not produced a significant improvement in the global classification error. Nevertheless, the use of such techniques did improve the classification error for the minority class. In this context, the NCL technique has shown to be more appropriated

Metodologia de análise e diagnóstico de fratura em um componente automotivo sujeito a cargas cíclicas alternadas

The competitiveness of the trade generated by the higher availability of products with lower quality and cost promoted a new reality of industrial production with small clearances. Track deviations at the production are not discarded, uncertainties can statistically occur. The world consumer and the Brazilian one are supported by the consumer protection code, in lawsuits against the products poor quality. An automobile is composed of various systems and thousands of constituent parts, increasing the likelihood of failure. The dynamic and security systems are critical in relation to the consequences of possible failures. The investigation of the failure gives us the possibility of learning and contributing to various improvements. Our main purpose in this work is to develop a systematic, specific methodology by investigating the root cause of the flaw occurred on an axle end of the front suspension of an automobile, and to perform comparative data analyses between the fractured part and the project information. Our research was based on a flaw generated in an automotive suspension system involved in a mechanical judicial cause, resulting in property and personal damages. In the investigations concerning the analysis of mechanical flaws, knowledge on materials engineering plays a crucial role in the process, since it enables applying techniques for characterizing materials, relating the technical attributes required from a respective part with its structure of manufacturing material, thus providing a greater scientific contribution to the work. The specific methodology developed follows its own flowchart. In the early phase, the data in the records and information on the involved ones were collected. The following laboratory analyses were performed: macrography of the fracture, micrography with SEM (Scanning Electron Microscope) of the initial and final fracture, phase analysis with optical microscopy, Brinell hardness and Vickers microhardness analyses, quantitative and qualitative chemical analysis, by using X-ray fluorescence and optical spectroscopy for carbon analysis, qualitative study on the state of tension was done. Field data were also collected. In the analyses data of the values resulting from the fractured stock parts and the design values were compared. After the investigation, one concluded that: the developed methodology systematized the investigation and enabled crossing data, thus minimizing diagnostic error probability, the morphology of the fracture indicates failure by the fatigue mechanism in a geometrically propitious location, a tension hub, the part was subjected to low tensions by the sectional area of the final fracture, the manufacturing material of the fractured part has low ductility, the component fractured in an earlier moment than the one recommended by the manufacturer, the percentages of C, Si, Mn and Cr of the fractured part present values which differ from the design ones, the hardness value of the superior limit of the fractured part is higher than that of the design, and there is no manufacturing uniformity between stock and fractured part. The work will contribute to optimizing the guidance of the actions in a mechanical engineering judicial expertise