DITRANS - a multi-agent system for integrated diagnosis of power transformers

This paper describes the development and the implementation of a multi-agent system for integrated diagnosis of power transformers. The system is divided in layers which contain a number of agents performing different functions. The social ability and cooperation between the agents lead to the final diagnosis and to other relevant conclusions through integrating various monitoring technologies, diagnostic methods and data sources, such as the dissolved gas analysis.

Prevalência de Osteoporose numa amostra de mulheres Portuguesas

A osteoporose é uma doença crónica e progressiva. O diagnóstico baseia-se na avaliação de densidade mineral óssea (DMO), por absorciometria bifotónica por RX (DXA) e no conceito operacional da Organização Mundial de Saúde (OMS) indicando que a doença acontece para valores de DMO inferiores a 2,5 desvios-padrão do valor médio máximo para mulheres jovens, saudáveis de etnia caucasóide. Existe variabilidade nos valores de pico de massa óssea entre diferentes populações. Este estudo pretende avaliar a prevalência de osteoporose quando utilizada a base de referência do equipamento e quando utilizada a base de dados para score-T construída a partir da amostra.

Training control centers' operators in incident diagnosis and power restoration using intelligent tutoring systems

The activity of Control Center operators is important to guarantee the effective performance of Power Systems. Operators’ actions are crucial to deal with incidents, especially severe faults like blackouts. In this paper, we present an Intelligent Tutoring approach for training Portuguese Control Center operators in tasks like incident analysis and diagnosis, and service restoration of Power Systems. Intelligent Tutoring System (ITS) approach is used in the training of the operators, having into account context awareness and the unobtrusive integration in the working environment. Several Artificial Intelligence techniques were criteriously used and combined together to obtain an effective Intelligent Tutoring environment, namely Multiagent Systems, Neural Networks, Constraint-based Modeling, Intelligent Planning, Knowledge Representation, Expert Systems, User Modeling, and Intelligent User Interfaces.

Dual source/dual energy-renal applications

Development of Dual Source Computed Tomography (Definition, Siemens Medical Solutions, Erlanger, Germany) allowed advances in temporal resolution, with the addition of a second X-ray source and an array of detectors to the TCM 64 slices. The ability to run exams on Dual Energy, allows greater differentiation of tissues, showing differences between closer attenuation coefficients. In terms of renal applications, the distinction of kidney stones and masses become one of the main advantages of the use of dual-energy technology. This article pretends to demonstrate operating principles of this equipment, as its main renal applications.

Voltammetric immunosensor for the diagnosis of celiac disease based on the quantification of anti-gliadin antibodies

Antibodies against gliadin are used to detect celiac disease (CD) in patients. An electrochemical immunosensor for the voltammetric detection of human anti-gliadin antibodies (AGA) IgA and AGA IgG in real serum samples is proposed. The transducer surface consists of screen-printed carbon electrodes modified with a carbon nanotube/gold nanoparticle hybrid system, which provides a very useful surface for the amplification of the immunological interactions. The immunosensing strategy is based on the immobilization of gliadin, the antigen for the autoantibodies of interest, onto the nanostructured surface. The antigen–antibody interaction is recorded using alkaline phosphatase labeled anti-human antibodies and a mixture of 3-indoxyl phosphate with silver ions (3-IP/Ag+) was used as the substrate. The analytical signal is based on the anodic redissolution of the enzymatically generated silver by cyclic voltammetry. The electrochemical behavior of this immunosensor was carefully evaluated assessing aspects as sensitivity, non-specific binding and matrix effects, and repeatability and reproducibility. The results were supported with a commercial ELISA test.

Dual augmentation for aerobic bioremediation of MTBE and TCE pollution in heavy metal-contaminated soil

In this work we isolated from soil and characterized several bacterial strains capable of either resisting high concentrations of heavy metals (Cd2+ or Hg2+ or Pb2+) or degrading the common soil and groundwater pollutants MTBE (methyl-tertbutyl ether) or TCE (trichloroethylene). We then used soil microcosms exposed to MTBE (50 mg/l) or TCE (50 mg/l) in the presence of one heavy metal (Cd 10 ppm or Hg 5 ppm or Pb 50 or 100 ppm) and two bacterial isolates at a time, a degrader plus a metalresistant strain. Some of these two-membered consortia showed degradation efficiencies well higher (49–182% higher) than those expected under the conditions employed, demonstrating the occurrence of a synergetic relationship between the strains used. Our results show the efficacy of the dual augmentation strategy for MTBE and TCE bioremediation in the presence of heavy metals.

Celiac disease diagnosis and gluten-free food analytical control

Celiac disease (CD) is an autoimmune enteropathy, characterized by an inappropriate T-cell-mediated immune response to the ingestion of certain dietary cereal proteins in genetically susceptible individuals. This disorder presents environmental, genetic, and immunological components. CD presents a prevalence of up to 1% in populations of European ancestry, yet a high percentage of cases remain underdiagnosed. The diagnosis and treatment should be made early since untreated disease causes growth retardation and atypical symptoms, like infertility or neurological disorders. The diagnostic criteria for CD, which requires endoscopy with small bowel biopsy, have been changing over the last few decades, especially due to the advent of serological tests with higher sensitivity and specificity. The use of serological markers can be very useful to rule out clinical suspicious cases and also to help monitor the patients, after adherence to a gluten-free diet. Since the current treatment consists of a life-long glutenfree diet, which leads to significant clinical and histological improvement, the standardization of an assay to assess in an unequivocal way gluten in gluten-free foodstuff is of major importance.

An electrochemical deamidated gliadin antibody immunosensor for celiac disease clinical diagnosis

The first electrochemical immunosensor (EI) for the detection of antibodies against deamidated gliadin peptides (DGP) is described here. A disposable nanohybrid screen-printed carbon electrode modified with DGP was employed as the transducer's sensing surface. Real serumsampleswere successfully assayed and the results were corroborated with an ELISA kit. The presented EI is a promising analytical tool for celiac disease diagnosis.

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.

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.

MicroRNA-375 plays a dual role in prostate carcinogenesis

Background: Prostate cancer (PCa), a highly incident and heterogeneous malignancy, mostly affects men from developed countries. Increased knowledge of the biological mechanisms underlying PCa onset and progression are critical for improved clinical management. MicroRNAs (miRNAs) deregulation is common in human cancers, and understanding how it impacts in PCa is of major importance. MiRNAs are mostly downregulated in cancer, although some are overexpressed, playing a critical role in tumor initiation and progression. We aimed to identify miRNAs overexpressed in PCa and subsequently determine its impact in tumorigenesis. Results: MicroRNA expression profiling in primary PCa and morphological normal prostate (MNPT) tissues identified 17 miRNAs significantly overexpressed in PCa. Expression of three miRNAs, not previously associated with PCa, was subsequently assessed in large independent sets of primary tumors, in which miR-182 and miR-375 were validated, but not miR-32. Significantly higher expression levels of miR-375 were depicted in patients with higher Gleason score and more advanced pathological stage, as well as with regional lymph nodes metastases. Forced expression of miR-375 in PC-3 cells, which display the lowest miR-375 levels among PCa cell lines, increased apoptosis and reduced invasion ability and cell viability. Intriguingly, in 22Rv1 cells, which displayed the highest miR-375 expression, knockdown experiments also attenuated the malignant phenotype. Gene ontology analysis implicated miR-375 in several key pathways deregulated in PCa, including cell cycle and cell differentiation. Moreover, CCND2 was identified as putative miR-375 target in PCa, confirmed by luciferase assay. Conclusions: A dual role for miR-375 in prostate cancer progression is suggested, highlighting the importance of cellular context on microRNA targeting.