Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

New concept for lifting in onshore oil wells

This paper presents the development of a prototype of a tubular linear induction motor applied to onshore oil exploitation, named MAT AE OS (which is the Portuguese acronym for Tubular Asynchronous Motor for Onshore Oil Exploitation). The function of this motor is to directly drive the sucker-rod pump installed in the down hole of the oil well. Considering the drawbacks and operational costs of the conventional oil extraction method, which is based on the walking beam and rod, string system, the developed prototype is intended to become a feasible alternative from both technical and economic points of view. At the present time, the MAT AE OS prototype is installed in a test bench at the Applied Electromagnetism Laboratory at the Escola Politecnica da Universidade de Sao Paulo. The complete testing system is controlled and supervised by special software, enabling good flexibility in operation, data acquisition, and performance analysis. The test results indicate that the motor develops a constant lift force along the pumping cycle, as shown by the measured dynamometric charts. Also, the evaluated electromechanical performance seems to be superior to that obtained by the traditional method. The system utilizing the MAT AE OS prototype allows the complete elimination of the rod string sets required by the conventional equipment, indicating that the new system may advantageously replace the surface mechanical components presently utilized.

Adaptive Carrier Tracking for Mars to Earth Communications During Entry, Descent, and Landing

We propose a robust and low complexity scheme to estimate and track carrier frequency from signals traveling under low signal-to-noise ratio (SNR) conditions in highly nonstationary channels. These scenarios arise in planetary exploration missions subject to high dynamics, such as the Mars exploration rover missions. The method comprises a bank of adaptive linear predictors (ALP) supervised by a convex combiner that dynamically aggregates the individual predictors. The adaptive combination is able to outperform the best individual estimator in the set, which leads to a universal scheme for frequency estimation and tracking. A simple technique for bias compensation considerably improves the ALP performance. It is also shown that retrieval of frequency content by a fast Fourier transform (FFT)-search method, instead of only inspecting the angle of a particular root of the error predictor filter, enhances performance, particularly at very low SNR levels. Simple techniques that enforce frequency continuity improve further the overall performance. In summary we illustrate by extensive simulations that adaptive linear prediction methods render a robust and competitive frequency tracking technique.

Improving the tracking capability of adaptive filters via convex combination

As is well known, Hessian-based adaptive filters (such as the recursive-least squares algorithm (RLS) for supervised adaptive filtering, or the Shalvi-Weinstein algorithm (SWA) for blind equalization) converge much faster than gradient-based algorithms [such as the least-mean-squares algorithm (LMS) or the constant-modulus algorithm (CMA)]. However, when the problem is tracking a time-variant filter, the issue is not so clear-cut: there are environments for which each family presents better performance. Given this, we propose the use of a convex combination of algorithms of different families to obtain an algorithm with superior tracking capability. We show the potential of this combination and provide a unified theoretical model for the steady-state excess mean-square error for convex combinations of gradient- and Hessian-based algorithms, assuming a random-walk model for the parameter variations. The proposed model is valid for algorithms of the same or different families, and for supervised (LMS and RLS) or blind (CMA and SWA) algorithms.

Resource use pattern analysis for predicting resource availability in opportunistic grids

This work presents a method for predicting resource availability in opportunistic grids by means of use pattern analysis (UPA), a technique based on non-supervised learning methods. This prediction method is based on the assumption of the existence of several classes of computational resource use patterns, which can be used to predict the resource availability. Trace-driven simulations validate this basic assumptions, which also provide the parameter settings for the accurate learning of resource use patterns. Experiments made with an implementation of the UPA method show the feasibility of its use in the scheduling of grid tasks with very little overhead. The experiments also demonstrate the method`s superiority over other predictive and non-predictive methods. An adaptative prediction method is suggested to deal with the lack of training data at initialization. Further adaptative behaviour is motivated by experiments which show that, in some special environments, reliable resource use patterns may not always be detected. Copyright (C) 2009 John Wiley & Sons, Ltd.

Transcriptional responses of host peripheral blood cells to tuberculosis infection

Host responses following exposure to Mycobacterium tuberculosis (TB) are complex and can significantly affect clinical outcome. These responses, which are largely mediated by complex immune mechanisms involving peripheral blood cells (PBCs) such as T-lymphocytes, NK cells and monocyte-derived macrophages, have not been fully characterized. We hypothesize that different clinical outcome following TB exposure will be uniquely reflected in host gene expression profiles, and expression profiling of PBCs can be used to discriminate between different TB infectious outcomes. In this study, microarray analysis was performed on PBCs from three TB groups (BCG-vaccinated, latent TB infection, and active TB infection) and a control healthy group. Supervised learning algorithms were used to identify signature genomic responses that differentiate among group samples. Gene Set Enrichment Analysis was used to determine sets of genes that were co-regulated. Multivariate permutation analysis (p < 0.01) gave 645 genes differentially expressed among the four groups, with both distinct and common patterns of gene expression observed for each group. A 127-probeset, representing 77 known genes, capable of accurately classifying samples into their respective groups was identified. In addition, 13 insulin-sensitive genes were found to be differentially regulated in all three TB infected groups, underscoring the functional association between insulin signaling pathway and TB infection. Published by Elsevier Ltd.

A chemometric study on the analgesic activity of cannabinoid compounds using SDA, KNN and SIMCA methods

The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA-principal component analysis and HCA-hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R (3) (charge density on substituent at position C(3)), Q (1) (charge on atom C(1)), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active, moderately active, and inactive.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Effects of interventions in health care settings on physical activity or cardiorespiratory fitness

Introduction: This paper reviews studies of physical activity interventions in health care settings to determine effects on physical activity and/or fitness and characteristics of successful interventions. Methods: Studies testing interventions to promote physical activity in health care settings for primary prevention (patients without disease) and secondary prevention (patients with cardiovascular disease [CVD]) were identified by computerized search methods and reference lists of reviews and articles. Inclusion criteria included assignment to intervention and control groups, physical activity or cardiorespiratory fitness outcome measures, and, for the secondary prevention studies, measurement 12 or more months after randomization. The number of studies with statistically significant effects was determined overall as well as for studies testing interventions with various characteristics. Results: Twelve studies of primary prevention were identified, seven of which were randomized. Three of four randomized studies with short-term measurement (4 weeks to 3 months after randomization), and two of five randomized studies with long-term measurement (6 months after randomization) achieved significant effects on physical activity. Twenty-four randomized studies of CVD secondary prevention were identified; 13 achieved significant effects on activity and/or fitness at twelve or more months. Studies with measurement at two time points showed decaying effects over time, particularly if the intervention were discontinued. Successful interventions contained multiple contacts, behavioral approaches, supervised exercise, provision of equipment, and/or continuing intervention. Many studies had methodologic problems such as low follow-up rates. Conclusion: Interventions in health care settings can increase physical activity for both primary and secondary prevention. Long-term effects are more likely with continuing intervention and multiple intervention components such as supervised exercise, provision of equipment, and behavioral approaches. Recommendations for additional research are given.

Who fails to complete tuberculosis treatment? Temporal trends and risk factors for treatment interruption in a community-based directly observed therapy programme in a rural district of South Africa

SETTING: Hlabisa Tuberculosis Programme, Hlabisa, South Africa. OBJECTIVE: To determine trends in and risk factors for interruption of tuberculosis treatment. METHODS: Data were extracted from the control programme database starting in 1991. Temporal trends in treatment interruption are described; independent risk factors for treatment interruption were determined with a multiple logistic regression model, and Kaplan-Meier survival curves for treatment interruption were constructed for patients treated in 1994-1995. RESULTS: Overall 629 of 3610 surviving patients (17%) failed to complete treatment; this proportion increased from 11% (n = 79) in 1991/1992 to 22% (n = 201) in 1996. Independent risk factors for treatment interruption were diagnosis between 1994-1996 compared with 1991-1393 (odds ratio [OR] 1.9, 95% confidence interval [CT] 1.6-2.4); human immunodeficiency virus (HIV) positivity compared with HIV negativity (OR 1.8, 95% CI 1.4-2.4); supervised by village clinic compared with community health worker (OR 1.9, 95% CI 1.4-2.6); and male versus female sex (OR 1.3, 95% CI 1.1-1.6). Few patients interrupted treatment during the first 2 weeks, and the treatment interruption rate thereafter was constant at 1% per 14 days. CONCLUSIONS: Frequency of treatment interruption from this programme has increased recently. The strongest risk factor was year of diagnosis, perhaps reflecting the impact of an increased caseload on programme performance. Ensuring adherence to therapy in communities with a high level of migration remains a challenge even within community-based directly observed therapy programmes.

Evaluation of the Efficacy of a Dental Plaque Control Program in Autistic Patients

The aim of this study was to verify the efficacy of a programme for dental plaque control in autistics. Patients were evaluated on five occasions over a period of 180 days using the following instruments: OHI-S, DMF-T, the Fonnes brushing technique and diet questionnaire. Participants were divided into two groups according to level of co-operation on the programme: Group A (co-operative) and Group B (non-cooperative). A statistically significant improvement (p < 0.001) in Oral Hygiene was attained, with 84.2% showing regular or satisfactory hygiene at study end-point. Conclusion: Groups A and B both showed improvement in hygiene (p < 0.001 and p = 0.004), but improvement was significantly higher among co-operative patients (p < 0.001 at 180 days), who also had a higher mean age (p = 0.02).

Aerobic Exercise Improves Reverse Cholesterol Transport in Cholesteryl Ester Transfer Protein Transgenic Mice

We analyzed the effect of a 6-week aerobic exercise training program on the in vivo macrophage reverse cholesterol transport (RCT) in human cholesteryl ester transfer protein (CETP) transgenic (CETP-tg) mice. Male CETP-tg mice were randomly assigned to a sedentary group or a carefully supervised exercise training group (treadmill 15 m/min, 30 min sessions, five sessions per week). The levels of plasma lipids were determined by enzymatic methods, and the lipoprotein profile was determined by fast protein liquid chromatography (FPLC). CETP activity was determined by measuring the transfer rate of (14)C-cholesterol from HDL to apo-B containing lipoproteins, using plasma from CETP-tg mice as a source of CETP. The reverse cholesterol transport was determined in vivo by measuring the [(3)H]-cholesterol recovery in plasma and feces (24 and 48 h) and in the liver (48 h) following a peritoneal injection of [(3)H]-cholesterol labeled J774-macrophages into both sedentary and exercise trained mice. The protein levels of liver receptors were determined by immunoblot, and the mRNA levels for liver enzymes were measured using RT-PCR. Exercise training did not significantly affect the levels of plasma lipids or CETP activity. The HDL fraction assessed by FPLC was higher in exercise-trained compared to sedentary mice. In comparison to the sedentary group, a greater recovery of [(3)H]-cholesterol from the injected macrophages was found in the plasma, liver and feces of exercise-trained animals. The latter occurred even with a reduction in the liver CYP7A1 mRNA level in exercised trained animals. Exercise training increased the liver LDL receptor and ABCA-1 protein levels, although the SR-BI protein content was unchanged. The RCT benefit in CETP-tg mice elicited by exercise training helps to elucidate the role of exercise in the prevention of atherosclerosis in humans.

Effects of Exercise Training in Patients with Chronic Heart Failure and Sleep Apnea

Study Objectives: To test the effects of exercise training on sleep and neurovascular control in patients with systolic heart failure with and without sleep disordered breathing. Design: Prospective interventional study. Setting: Cardiac rehabilitation and exercise physiology unit and sleep laboratory. Patients: Twenty-five patients with heart failure, aged 42 to 70 years, and New York Heart Association Functional Class I-III were divided into 1 of 3 groups: obstructive sleep apnea (n = 8), central sleep apnea (n 9) and no sleep apnea (n = 7). Interventions: Four months of no-training (control) followed by 4 months of an exercise training program (three 60-minute, supervised, exercise sessions per week). Measures and Results: Sleep (polysomnography), microneurography, forearm blood flow (plethysmography), peak VO(2). and quality of life were evaluated at baseline and at the end of the control and trained periods. No significant changes occurred in the control period. Exercise training reduced muscle sympathetic nerve activity (P < 0.001) and increased forearm blood flow (P < 0.01), peak VO(2) (P < 0.01), and quality of life (P < 0.01) in all groups, independent of the presence of sleep apnea. Exercise training improved the apnea-hypopnea index, minimum O(2) saturation, and amount stage 3-4 sleep (P < 0.05) in patients with obstructive sleep apnea but had no significant effects in patients with central sleep apnea. Conclusions. The beneficial effects of exercise training on neurovascular function, functional capacity, and quality of life in patients with systolic dysfunction and heart failure occurs independently of sleep disordered breathing. Exercise training lessens the severity of obstructive sleep apnea but does not affect central sleep apnea in patients with heart failure and sleep disordered breathing.

Outpatient Radioiodine Therapy for Thyroid Cancer A Safe Nuclear Medicine Procedure

Purposes: To evaluate the dosimetric effect of outpatient radioiodine therapy for thyroid cancer in members of a patient`s family and their living environment, when using iodine-131 doses reaching 7.4 GBq. The following parameters were thus defined: (a) whole-body radiation doses to caregivers, (b) the production of contaminated solid waste, and (c) radiation potential and surface contamination within patients` living quarters. Methods: In total, 100 patients were treated on an outpatient basis, taking into consideration their acceptable living conditions, interests, and willingness to comply with medical and radiation safety guidelines. Both the caregivers and the radiation dose potentiality inside patients` residences were monitored by using thermoluminescent dosimeters. Surface contamination and contaminated solid wastes were identified and measured with a Geiger-Muller detector. Results: A total of 90 monitored individuals received a mean dose of 0.27 (+/- 0.28) mSv, and the maximum dose registered was 1.6 mSv. The mean value for the potential dose within all living quarters was 0.31(+/- 0.34) mSv, and the mean value per monitored surface was 5.58 Bq/cm(2) for all the 1659 points measured. The overall production of contaminated solid wastes was at a low level, being about 3 times less than the exemption level indicated by the International Atomic Energy Agency. Conclusions: This study indicates that the treatment of thyroid cancer by applying radioiodine activities up to 7.4 GBq, on an outpatient basis, is a safe procedure, especially when supervised by qualified professionals. This alternative therapy should be a topic for careful discussion considering the high potential for reducing costs in healthcare and improving patient acceptance.