81 resultados para random search algorithms


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The main focus of this essay is the first American round-the-world scientific voyage, the U. S Exploring Expedition, which took place between 1838 and 1841 and was lead by Lieutenant Charles Wilkes. Here, I discuss the purposes of this expedition in the context of the voyages of circumnavigation accomplished by the various European powers during the same period.

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Chronic beryllium disease (CBD) is clinically similar to other granulomatous diseases such as sarcoidosis. It is often misdiagnosed if a thorough occupational history is not taken. When appropriate, a beryllium lymphocyte proliferation tests (BeLPT) need to be performed. We aimed to search for CBD among currently diagnosed pulmonary sarcoidosis patients and to identify the occupations and exposures in Ontario leading to CBD. Questionnaire items included work history and details of possible exposure to beryllium. Participants who provided a history of previous work with metals underwent BeLPTs and an ELISPOT on the basis of having a higher pretest probability of CBD. Among 121 sarcoid patients enrolled, 87 (72%) reported no known previous metal dust or fume exposure, while 34 (28%) had metal exposure, including 17 (14%) with beryllium exposure at work or home. However, none of these 34 who underwent testing had positive test results. Self-reported exposure to beryllium or metals was relatively common in these patients with clinical sarcoidosis, but CBD was not confirmed using blood assays in this population.

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Objective: The study we assessed how often patients who are manifesting a myocardial infarction (MI) would not be considered candidates for intensive lipid-lowering therapy based on the current guidelines. Methods: In 355 consecutive patients manifesting ST elevation MI (STEMI), admission plasma C-reactive protein (CRP) was measured and Framingham risk score (FRS), PROCAM risk score, Reynolds risk score, ASSIGN risk score, QRISK, and SCORE algorithms were applied. Cardiac computed tomography and carotid ultrasound were performed to assess the coronary artery calcium score (CAC), carotid intima-media thickness (cIMT) and the presence of carotid plaques. Results: Less than 50% of STEMI patients would be identified as having high risk before the event by any of these algorithms. With the exception of FRS (9%), all other algorithms would assign low risk to about half of the enrolled patients. Plasma CRP was <1.0 mg/L in 70% and >2 mg/L in 14% of the patients. The average cIMT was 0.8 +/- 0.2 mm and only in 24% of patients was >= 1.0 mm. Carotid plaques were found in 74% of patients. CAC > 100 was found in 66% of patients. Adding CAC >100 plus the presence of carotid plaque, a high-risk condition would be identified in 100% of the patients using any of the above mentioned algorithms. Conclusion: More than half of patients manifesting STEMI would not be considered as candidates for intensive preventive therapy by the current clinical algorithms. The addition of anatomical parameters such as CAC and the presence of carotid plaques can substantially reduce the CVD risk underestimation. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

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The present study was carried out to evaluate the effectiveness of a specific program regarding the occurrence of vocal attrition symptoms in telemarketers. A total of 71 subjects participated in this study: 28 completed the Vocal Symptoms questionnaire to test its reliability, and 43 were randomly assigned to two groups: an 8-week vocal training group (n = 14) and a no-training control group (n = 29), to evaluate the effectiveness of the training program with this tool. The voice training group also filled in the posttraining questionnaire `Benefits Obtained with Voice Training` (BVT). The vocal training program was not considered effective with regard to the occurrence of vocal symptoms. However, due to a probable increase in symptoms in untrained telemarketers, it can work as a protective factor. According to BVT answers, the vocal training contributed to an improvement in vocal use as a communication tool for telemarketers. Copyright (C) 2009 S. Karger AG, Basel

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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.

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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.