887 resultados para path sampling


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The objective of the present study was to develop a sequential sampling plan for the decision-making process to control Tenuipalpus heveae Baker (Acari: Tenuipalpidae), an important pest of the rubber tree crop. The experimental area was represented by 1,000 plants of the RRIM 600 clone divided in 100 plots with 10 plants each. Leaves were collected and the number of mites determined under laboratory conditions. The sequential sampling plan was developed in accordance with the Sequential Test Likelihood Ratio. The value 0.10 was pre-established for α and β representing type I and type II errors, respectively. The level of control adopted was six mites per 12 cm2. The operating characteristic curve and the curve of maximum expected sample were determined. Two lines were generated: the upper one, when the condition for chemical control is recommended (S1 = 23.3080 + 2.1972); and the lower, when chemical control is not recommended (S0 = -23.3080 + 2.1972). Sample size for the decision-making process to control T. heveae requires 6 to 18 plants. © 2013 Sociedade Entomológica do Brasil.

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Twenty-five specimens of the freshwater red alga Compsopogon were collected from locations in North America, South America, Europe, Asia, Australasia and Oceania, and from an aquarium, with the goal of determining genetic diversity among specimens and ascertaining the number of phylogenetic species. Specimens were morphologically identified as having either the 'caeruleus' morphology, with regular polyhedral cortical cells, or the 'leptoclados' morphology, with irregular cortical cells with rhizoidal outgrowths. The 'leptoclados' morphology has been used by some researchers to distinguish the genus Compsopogonopsis from Compsopogon, or at least to distinguish C. leptoclados from other Compsopogon species. Sequence data for the rbcL gene and cox1 barcoding region were obtained for most specimens. In addition, SSU and partial LSU (barcode) rDNA were explored for a few specimens, but all sequences were identical. For the 25 newly generated and eight previously published rbcL gene data, there were seven unique haplotypes, but the sequence divergence was very low (≤7 bp, ≤ 0.7%). One haplotype was widespread, represented by 21 specimens from diverse locations in all regions sampled. Likewise, the 22 new and one previously published cox1 barcode region sequences yielded seven unique haplotypes with little sequence divergence (≤13 bp, ≤ 2.0%). One haplotype was widespread, being shared among 16 specimens from all regions. The combined molecular and morphological data showed no genetic differentiation between the 'caeruleus' and 'leptoclados' morphologies. The ubiquitous distribution of Compsopogon in tropical/subtropical regions and its low genetic variation are probably facilitated by the alga's ability to tolerate a wide range of stream conditions and its propagation via asexual spores. Given the findings of previous culture-based studies, morphometric research and field observations, coupled with the results of our study, we conclude there is only a single monospecific genus worldwide and that the species is correctly called C. caeruleus, since this is the oldest validly published name; all other previously described species of Compsopogon and Compsopogonopsis are synonyms. © 2013 British Phycological Society.

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The purpose of the current study was to investigate the role of visual information on gait control in people with Parkinson's disease as they crossed over obstacles. Twelve healthy individuals, and 12 patients with mild to moderate Parkinson's disease, walked at their preferred speeds along a walkway and stepped over obstacles of varying heights (ankle height or half-knee height), under three visual sampling conditions: dynamic (normal lighting), static (static visual samples, similar to stroboscopic lighting), and voluntary visual sampling. Subjects wore liquid crystal glasses for visual manipulation. In the static visual sampling condition only, the patients with Parkinson's disease made contact with the obstacle more often than did the control subjects. In the successful trials, the patients increased their crossing step width in the static visual sampling condition as compared to the dynamic and voluntary visual sampling conditions; the control group maintained the same step width for all visual sampling conditions. The patients showed lower horizontal mean velocity values during obstacle crossing than did the controls. The patients with Parkinson's disease were more dependent on optic flow information for successful task and postural stability than were the control subjects. Bradykinesia influenced obstacle crossing in the patients with Parkinson's disease. © 2013 Elsevier B.V.

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An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.

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Countries have different official programs and implement different sampling methods for the detection of Salmonella on poultry carcasses. In Brazil, a 25-g sample of skin and muscle excision (SME) from the wings, neck, and pericloacal parts is used; in the European Union (EU), a 25-g sample of neck skin (NSE) is used; and, in the United States, the whole carcass is rinsed with 400 ml of diluent (WCR). In the present study, these methods were evaluated to compare Salmonella occurrence and counts of hygiene indicator microorganisms (Escherichia coli, Enterobacteriaceae, and total viable count of aerobic mesophilic bacteria) using different carcasses from the same flock and also using different analytical units taken from the same carcass. Eighty flocks, with four broiler carcasses from each, were included in this study; three broilers were sampled according to protocols from Brazil, the EU, and the United States, and the last one by all three methods. SME, NSE, and WCR provided equivalent results (P > 0.05) for Salmonella detection on broiler carcasses when using different carcasses from the same flock and when using the same carcass. The predominant serovar was Salmonella Enteritidis. For the enumeration of hygiene indicator microorganisms, WRC provided higher counts than SME or NSE (P < 0.05), when using both the same or different carcasses. Therefore, it is possible to directly compare Salmonella results in poultry carcasses when using the methods recommended by the legislative bodies of Brazil, the United States, and the EU. However, WCR provides the best results for hygiene indicator microorganisms. Copyright © International Association for Food Protection.

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Some machine learning methods do not exploit contextual information in the process of discovering, describing and recognizing patterns. However, spatial/temporal neighboring samples are likely to have same behavior. Here, we propose an approach which unifies a supervised learning algorithm - namely Optimum-Path Forest - together with a Markov Random Field in order to build a prior model holding a spatial smoothness assumption, which takes into account the contextual information for classification purposes. We show its robustness for brain tissue classification over some images of the well-known dataset IBSR. © 2013 Springer-Verlag.

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Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. © 2013 Elsevier Ltd. All rights reserved.