Thermodynamic information system for diagnosis and prognosis of power plant operation condition

A thermodynamic information system for diagnosis and prognosis of an existing power plant was developed. The system is based on an analytic approach that informs the current thermodynamic condition of all cycle components, as well as the improvement that can be obtained in the cycle performance by the elimination of the discovered anomalies. The effects induced by components anomalies and repairs in other components efficiency, which have proven to be one of the main drawbacks in the diagnosis and prognosis analyses, are taken into consideration owing to the use of performance curves and corrected performance curves together with the thermodynamic data collected from the distributed control system. The approach used to develop the system is explained, the system implementation in a real gas turbine cogeneration combined cycle is described and the results are discussed. (C) 2011 Elsevier Ltd. All rights reserved.

PCR bias toward the wild-type k-ras and p53 sequences: Implications for PCR detection of mutations and cancer diagnosis

PCR-based cancer diagnosis requires detection of rare mutations in k-ras, p53 or other genes. The assumption has been that mutant and wild-type sequences amplify with near equal efficiency, so that they are eventually present in proportions representative of the starting material. Work factor IX suggests that this assumption is invalid for one case of near-sequence identity To test the generality of this phenomenon and its relevance to cancer diagnosis, primers distant from point mutations in p53 and k-ras were used to amplify, wild-type and mutant sequences from these genes. A substantial bias against PCR amplification of mutants was observed for two regions of the p53 gene and one region of k-ras. For kras and p53, bias was observed when the wild-type and mutant sequences were amplified separately or when mixed in equal proportions before PCR. Bias was present with proofreading and non-proofreading polymerases. Mutant and wild-type segments of the factor V cystic fibrosis transmembrane conductance regulator and prothrombin genes were amplified and did not exhibit PCR bias. Therefore, the assumption of equal PCR efficiency for point mutant and wild-type sequences is invalid in several systems. Quantitative or diagnostic PCR will require validation for each locus, and enrichment strategies may be needed to optimize detection of mutants.

Diagnosis of Organizational Soft Problems in a Peruvian Financial Institution by Systemic Thinking

Managing financial institutions in an underdeveloped economic context has become a real challenge nowadays. In order to reach the organization`s planned goals, they have to deal with structural, behavioral and informational problems. From the systemic point of view, this situation gets even worse when the company does not present organizational boundaries and a cohesive identification for their stakeholders. Thus, European countries have some special financial lines in order to help the development of micro credit in Latin communities in an attempt to help the local economy. However, institutions like Caixa dos Andes in Peru present management problems when dealing with this complexity. Based on this, how can the systemic eye help in the diagnosis of soft problems of a Peruvian financial company? This study aims to diagnose soft problems of a Peruvian financial company based on soft variables like identity, communication and autonomy and also intends to identify possible ways to redesign its basic framework. The (VSM--Viable System Model) method from Beer (1967), applied in this diagnostic study, was used in a practical way as a management tool for organizations` analysis and planning. By describing the VSM`s five systems, the creation of a systemic vision or a total vision is possible, showing the organization`s complexity from the inside. Some company`s soft problems like double control, inefficient use of physical and human resources, low information flows, slowness, etc. The VSM presented an organizational diagnosis indicating effective solutions that do integrate its five systems.

Systemic Model for Diagnosis of the Micro, Small and Medium Enterprises from Two Cities from the Countryside of the State of So Paulo in Brazil

This article presents a proposal of a systemic model composed for the micro and small companies (MSE) of the region of Ribeiro Preto and the agents which influenced their environment. The proposed model was based on Stafford Beer`s (Diagnosing the system for organizations. Chichester, Wiley, 1985) systemic methodologies VSM (Viable System Model) and on Werner Ulrich`s (1983) CSH (Critical Systems Heuristics). The VSM is a model for the diagnosis of the structure of an organization and of its flows of information through the application of the cybernetics concepts (Narvarte, In El Modelo del Sistema Viable-MSV: experiencias de su aplicacin en Chile. Proyecto Cerebro Colectivo del IAS, Santiago, 2001). On the other hand, CSH focus on the context of the social group applied to the systemic vision as a counterpoint to the organizational management view considered by the VSM. MSE of Ribeiro Preto and Sertozinho had been analyzed as organizations inserted in systems that relate and integrate with other systems concerning the public administration, entities of representation and promotion agencies. The research questions: which are the bonds of interaction among the subsystems in this process and who are the agents involved? The systemic approach not only diagnosed a social group, formed by MSE of Ribeiro Preto and Sertozinho, public authorities and support entities, but could also delineate answers that aimed the clarification of obscure questions generating financial assistance to the formularization of efficient actions for the development of this system.

Late Gadolinium Enhancement Magnetic Resonance Imaging in the Diagnosis and Prognosis of Endomyocardial Fibrosis Patients

Background-Endocardial fibrous tissue (FT) deposition is a hallmark of endomyocardial fibrosis (EMF). Echocardiography is a first-line and the standard technique for the diagnosis of this disease. Although late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) allows FT characterization, its role in the diagnosis and prognosis of EMF has not been investigated. Methods and Results-Thirty-six patients (29 women; age, 54 +/- 12 years) with EMF diagnosis after clinical evaluation and comprehensive 2-dimensional Doppler echocardiography underwent cine-CMR for assessing ventricular volumes, ejection fraction and mass, and LGE-CMR for FT characterization and quantification. Indexed FT volume (FT/body surface area) was calculated after planimetry of the 8 to 12 slices obtained in the short-axis view at end-diastole (mL/m(2)). Surgical resection of FT was performed in 16 patients. In all patients, areas of LGE were confined to the endocardium, frequently as a continuous streak from the inflow tract extending to the apex, where it was usually most prominent. There was a relation between increased FT/body surface area and worse New York Heart Association functional class and with increased probability of surgery (P<0.05). The histopathologic examination of resected FT showed typical features of EMF with extensive endocardial fibrous thickening, proliferation of small vessels, and scarce inflammatory infiltrate. In multivariate analysis, the patients with FT/body surface area >19 mL/m(2) had an increased mortality rate, with a relative risk of 10.8. Conclusions-Our study provides evidence that LGE-CMR is useful in the diagnosis and prognosis of EMF through quantification of the typical pattern of FT deposition. (Circ Cardiovasc Imaging. 2011;4:304-311.)

Model for Differential Nursing Diagnosis of Alterations in Urinary Elimination Based on Fuzzy Logic

Nursing diagnoses associated with alterations of urinary elimination require different interventions, Nurses, who are not specialists, require support to diagnose and manage patients with disturbances of urine elimination. The aim of this study was to present a model based on fuzzy logic for differential diagnosis of alterations in urinary elimination, considering nursing diagnosis approved by the North American Nursing Diagnosis Association, 2001-2002. Fuzzy relations and the maximum-minimum composition approach were used to develop the system. The model performance was evaluated with 195 cases from the database of a previous study, resulting in 79.0% of total concordance and 19.5% of partial concordance, when compared with the panel of experts. Total discordance was observed in only three cases (1.5%). The agreement between model and experts was excellent (kappa = 0.98, P < .0001) or substantial (kappa = 0.69, P < .0001) when considering the overestimative accordance (accordance was considered when at least one diagnosis was equal) and the underestimative discordance (discordance was considered when at least one diagnosis was different), respectively. The model herein presented showed good performance and a simple theoretical structure, therefore demanding few computational resources.

Quantitative myocardial contrast echocardiography during pharmacological stress for diagnosis of coronary artery disease: a systematic review and meta-analysis of diagnostic accuracy studies

Aims We conducted a meta-analysis to evaluate the accuracy of quantitative stress myocardial contrast echocardiography (MCE) in coronary artery disease (CAD). Methods and results Database search was performed through January 2008. We included studies evaluating accuracy of quantitative stress MCE for detection of CAD compared with coronary angiography or single-photon emission computed tomography (SPECT) and measuring reserve parameters of A, beta, and A beta. Data from studies were verified and supplemented by the authors of each study. Using random effects meta-analysis, we estimated weighted mean difference (WMD), likelihood ratios (LRs), diagnostic odds ratios (DORs), and summary area under curve (AUC), all with 95% confidence interval (0). Of 1443 studies, 13 including 627 patients (age range, 38-75 years) and comparing MCE with angiography (n = 10), SPECT (n = 1), or both (n = 2) were eligible. WMD (95% CI) were significantly less in CAD group than no-CAD group: 0.12 (0.06-0.18) (P < 0.001), 1.38 (1.28-1.52) (P < 0.001), and 1.47 (1.18-1.76) (P < 0.001) for A, beta, and A beta reserves, respectively. Pooled LRs for positive test were 1.33 (1.13-1.57), 3.76 (2.43-5.80), and 3.64 (2.87-4.78) and LRs for negative test were 0.68 (0.55-0.83), 0.30 (0.24-0.38), and 0.27 (0.22-0.34) for A, beta, and A beta reserves, respectively. Pooled DORs were 2.09 (1.42-3.07), 15.11 (7.90-28.91), and 14.73 (9.61-22.57) and AUCs were 0.637 (0.594-0.677), 0.851 (0.828-0.872), and 0.859 (0.842-0.750) for A, beta, and A beta reserves, respectively. Conclusion Evidence supports the use of quantitative MCE as a non-invasive test for detection of CAD. Standardizing MCE quantification analysis and adherence to reporting standards for diagnostic tests could enhance the quality of evidence in this field.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

Root Canal Area Increase Promoted by the EndoSequence and ProTaper Systems: Comparison by Computed Tomography

Introduction: The aim of this study was to compare the increase of the root canal area after instrumentation with EndoSequence or ProTaper rotary systems. Methods: Twenty-two mesial root canals from mandibular molars were instrumented. Teeth were mounted on a base, numbered, and divided into 2 groups; teeth from 1-11 (PT group) were instrumented by using the ProTaper system, and teeth from 12-22 (ES group) were instrumented by using the EndoSequence system. Cone beam computed tomography was performed on all teeth before and after instrumentation. Measurements at 3,5, and 7 mm as well as differences in instrument performance were statistically compared by the Student t test at 5% significance level. Results: Both systems increased significantly the root canal area (P < .05) at all levels. Comparison between the rotary systems showed significantly greater increase (P < .05) for EndoSequence at 3 mm, with no statistically significant difference (P < .05) at the other levels. Conclusions: Both rotary systems increased significantly the root canal area. (J Endod 2010;36:1179-1182)

Digital imaging systems for plain radiography

Advances in digital technology led to the development of digital x-ray detectors that are currently in wide use for projection radiography, including Computed Radiography (CR) and Digital Radiography (DR). Digital Imaging Systems for Plain Radiography addresses the current technological methods available to medical imaging professionals to ensure the optimization of the radiological process concerning image quality and reduction of patient exposure. Based on extensive research by the authors and reference to the current literature, the book addresses how exposure parameters influence the diagnostic quality in digital systems, what the current acceptable radiation doses are for useful diagnostic images, and at what level the dose could be reduced to maintain an accurate diagnosis. The book is a valuable resource for both students learning the field and for imaging professionals to apply to their own practice while performing radiological examinations with digital systems.

Avaliação da caracterização de lesões em mamografia com recurso a sistemas CAD (Computer-Aided Diagnosis)

Os sistemas Computer-Aided Diagnosis (CAD) auxiliam a deteção e diferenciação de lesões benignas e malignas, aumentando a performance no diagnóstico do cancro da mama. As lesões da mama estão fortemente correlacionadas com a forma do contorno: lesões benignas apresentam contornos regulares, enquanto as lesões malignas tendem a apresentar contornos irregulares. Desta forma, a utilização de medidas quantitativas, como a dimensão fractal (DF), pode ajudar na caracterização dos contornos regulares ou irregulares de uma lesão. O principal objetivo deste estudo é verificar se a utilização concomitante de 2 (ou mais) medidas de DF – uma tradicionalmente utilizada, a qual foi designada por “DF de contorno”; outra proposta por nós, designada por “DF de área” – e ainda 3 medidas obtidas a partir destas, por operações de dilatação/erosão e por normalização de uma das medidas anteriores, melhoram a capacidade de caracterização de acordo com a escala BIRADS (Breast Imaging Reporting and Data System) e o tipo de lesão. As medidas de DF (DF contorno e DF área) foram calculadas através da aplicação do método box-counting, diretamente em imagens de lesões segmentadas e após a aplicação de um algoritmo de dilatação/erosão. A última medida baseia-se na diferença normalizada entre as duas medidas DF de área antes e após a aplicação do algoritmo de dilatação/erosão. Os resultados demonstram que a medida DF de contorno é uma ferramenta útil na diferenciação de lesões, de acordo com a escala BIRADS e o tipo de lesão; no entanto, em algumas situações, ocorrem alguns erros. O uso combinado desta medida com as quatro medidas propostas pode melhorar a classificação das lesões.

CardioML: integrating personal cardiac information for ubiquous diagnosis and analysis

The latest medical diagnosis devices enable the performance of e-diagnosis making the access to these services easier, faster and available in remote areas. However this imposes new communications and data interchange challenges. In this paper a new XML based format for storing cardiac signals and related information is presented. The proposed structure encompasses data acquisition devices, patient information, data description, pathological diagnosis and waveform annotation. When compared with similar purpose formats several advantages arise. Besides the full integrated data model it may also be noted the available geographical references for e-diagnosis, the multi stream data description, the ability to handle several simultaneous devices, the possibility of independent waveform annotation and a HL7 compliant structure for common contents. These features represent an enhanced integration with existent systems and an improved flexibility for cardiac data representation.

Diagnosis of an EPS module

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e Computadores

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.