80 resultados para hierarchical image analysis


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The objective of this work was to identify sources of resistance to dry root rot induced by Fusarium sp. in cassava accessions. A macroconidial suspension (20 µL) of 11 Fusarium sp. isolates was inoculated in cassava roots, from 353 acessions plus seven commercial varieties. Ten days after inoculation, the total area colonized by the pathogen on the root pulp was evaluated by digital image analysis. Cluster analysis revealed the presence of five groups regarding resistance. The root lesion areas ranged from 18.28 to 1,096.07 mm² for the accessions BGM 1518 and BGM 556, respectively. The genotypes BGM 1042, BGM 1552, BGM 1586, BGM 1598, and BGM 1692 present the best agronomical traits.

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An experiment was carried out to determine the root distribution of four grapevine rootstocks (Salt Creek, Dogridge, Courdec 1613, IAC 572) in a coarse texture soil of a commercial growing area in Petrolina County, São Francisco Valley, Brazil. Rootstocks were grafted to a seedless table grape cv. Festival, and irrigated by microsprinkler. Roots were quantified by the trench wall method aided by digital image analysis. Results indicated that roots reached 1 m depth, but few differences among rootstocks were found. All of them presented at least 90 % of the roots distributed until 0.6 m depth, with a greater root presence in the first 0.4 m. The upper 0.6 m can be taken into account as the effective rooting depth for soil and water management.

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In order to establish guidelines for irrigation water management of banana cv. Pacovan (AAB group, Prata sub-group) in Petrolina County, northeastern Brazil, the root distribution and activity were measured on an irrigated plantation, in a medium texture soil, with plants spaced in a 3 x 3 m grid. Root distribution was evaluated by the soil profile method aided by digital image analysis, while root activity was indirectly determined by the changing of soil water content and by the direction of soil water flux. Data were collected since planting in January 1999 to the 3rd harvest in September 2001. Effective rooting depth increased from 0.4 m at 91 days after planting (dap), to 0.6 m at 370, 510, and 903 dap, while water absorption by roots was predominantly in the top 0,6 m.

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Abstract Objective: To propose a protocol for pulmonary angiography using 64-slice multidetector computed tomography (64-MDCT) with 50 mL of iodinated contrast material, in an unselected patient population, as well as to evaluate vascular enhancement and image quality. Materials and Methods: We evaluated 29 patients (22-86 years of age). The body mass index ranged from 19.0 kg/m2 to 41.8 kg/m2. Patients underwent pulmonary CT angiography in a 64-MDCT scanner, receiving 50 mL of iodinated contrast material via venous access at a rate of 4.5 mL/s. Bolus tracking was applied in the superior vena cava. Two experienced radiologists assessed image quality and vascular enhancement. Results: The mean density was 382 Hounsfield units (HU) for the pulmonary trunk; 379 and 377 HU for the right and left main pulmonary arteries, respectively; and 346 and 364 HU for the right and left inferior pulmonary arteries, respectively. In all patients, subsegmental arteries were analyzed. There were streak artifacts from contrast material in the superior vena cava in all patients. However, those artifacts did not impair the image analysis. Conclusion: Our findings suggest that pulmonary angiography using 64-MDCT with 50 mL of iodinated contrast can produce high quality images in unselected patient populations.

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The modern technological ability to handle large amounts of information confronts the chemist with the necessity to re-evaluate the statistical tools he routinely uses. Multivariate statistics furnishes theoretical bases for analyzing systems involving large numbers of variables. The mathematical calculations required for these systems are no longer an obstacle due to the existence of statistical packages that furnish multivariate analysis options. Here basic concepts of two multivariate statistical techniques, principal component and hierarchical cluster analysis that have received broad acceptance for treating chemical data are discussed.

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The present paper has as objective to apply a sequential Cluster Analysis to the atmospheric particles: Hierarchical Cluster Analysis followed by Nonhierarchical Cluster Analysis. The hierarchical cluster analysis results were used as start point for the nonhierarchical cluster analysis as an agglomerative technique. These particles were taken from two areas of the metropolitan region of Porto Alegre, Charqueadas and Sapucaia do Sul., from may /97 to may/98, using a High Volume Sampler (Hi-Vol). Around 10,000 particles were analysed by Scanning Electron Microscope with Energy-Dispersive X-Ray microanalysis (SEM-EDS). The Hierarchical Cluster Analysis allowed the identification of five groups of particles, whose amounts were differentiated according to the summer and the winter campaigns. The abundance of each type of particles inside each group according to the different sections was verified by the Nonhierarchical Cluster Analysis, resulting in information about the emissions sources. The groups of particles of Si/Al and Si and of Fe/Zn and Fe for Charqueadas were more significant in section 2 and 3 (NW and W wind directions) and in section 1 (SE wind direction), evidencing the influence of the coal power plant and steel industry, respectively located in these quadrants. In Sapucaia do Sul the data were more heterogeneous, causing a certain difficulty to identify the source as anthropogenic. Nevertheless the group of particles containing Fe was found in sectors of NW/W wind directions which shows the influence of the steel plant.

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A model based on chemical structure was developed for the accurate prediction of octanol/water partition coefficient (K OW) of polychlorinated biphenyls (PCBs), which are molecules of environmental interest. Partial least squares (PLS) was used to build the regression model. Topological indices were used as molecular descriptors. Variable selection was performed by Hierarchical Cluster Analysis (HCA). In the modeling process, the experimental K OW measured for 30 PCBs by thin-layer chromatography - retention time (TLC-RT) has been used. The developed model (Q² = 0,990 and r² = 0,994) was used to estimate the log K OW values for the 179 PCB congeners whose K OW data have not yet been measured by TLC-RT method. The results showed that topological indices can be very useful to predict the K OW.

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A simple and didactic experiment was developed for image monitoring of the browning of fruit tissues caused by the enzyme polyphenol oxidase. The procedure, easy and inexpensive, is a valuable tool to teach and demonstrate the redox reaction between the enzyme and the natural polyphenols. To obtain the browning percentage for apple, pear and banana, digital photographs were employed, and the images were analyzed by means of Monte Carlo methods and digital analysis programs. The effects of several experimental conditions were studied, such as pH, light, temperature and the presence of oxygen or anti-oxidants. It was observed that each fruit presented a different condition that better minimized the oxidation process. The absence of oxygen and the application of a bissulphite solution were sufficient to keep the quality of all fruits tested.

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The concentrations of Cu, Pb, Zn, Cr, Ni, Al, Mn and Fe were measured by atomic absorption spectrometry, of 19 topsoil samples collected in the Teresina city urban area to discriminate natural and anthropic contributions and identify possible sources of pollution. The average concentrations of Cu, Zn, Pb and Cr of the urban soils were 6.11, 8.56, 32.12 and 7,17 mg/kg-1, respectively. Statistical analysis techniques, such as principal component analysis (PCA) and hierarchical cluster analysis (HCA), were used to analyze the data. Mn, Ni and Cr levels were interpreted as natural contributions, whereas Pb, Zn and, in part, Cu were accounted for mainly by anthropic activities. High Pb levels were observed in the ancient avenues.

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Inductively Coupled Plasma Optical Emission Spectrometry was used to determine Ca, Mg, Mn, Fe, Zn and Cu in samples of processed and natural coconut water. The sample preparation consisted in a filtration step followed by a dilution. The analysis was made employing optimized instrumental parameters and the results were evaluated using methods of Pattern Recognition. The data showed common concentration values for the analytes present in processed and natural samples. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) indicated that the samples of different kinds were statistically different when the concentrations of all the analytes were considered simultaneously.

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Soils play an important role in the biogeochemical cycle of mercury as a sink for and source of this metallic species to atmospheric and hydrological compartments. In the study reported here, various types of soil were evaluated to ascertain the influence of parameters such as pH, organic matter content, Fe, Al, sand, silt, clay, C/H, C/N, C/O atomic ratios, and cation exchange capacity on the distribution of Hg in Amazonia's mid-Negro River basin. The data obtained were interpreted by multivariate exploratory analyses (hierarchical cluster analysis and principal component analysis), which indicated that organic matter plays an important role in mercury uptake in the various soils studied. The soils in floodable areas were found to contain 1.5 to 2.8-fold higher Hg concentrations than those in non-floodable areas. Since these soils are flooded almost year-round, they are less available to participate in redox processes at the soil/atmosphere interface. Hence, floodable areas, which comprise humic-rich soils, accumulate more mercury than non-floodable soils, thus playing an important role in the biogeochemical cycle of Hg in Amazonia's mid-Negro River basin.

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An activity for introducing hierarchical cluster analysis (HCA) and principal component analysis (PCA) during the Instrumental Analytical Chemistry course is presented. The posed problem involves the discrimination of mineral water samples according to their geographical origin. Thirty-seven samples of 9 different brands were considered and the results from the determination of Na, K, Mg, Ca, Sr and Ba were taken into account. Non-supervised methods for pattern recognition were explored to construct a dendrogram, score and loading plots. The devised activity can be adopted for introducing Chemometrics devoted to data handling, stressing its importance in the context of modern Analytical Chemistry.

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The Brazilian legislation requires analysis of certain parameters to classify a wine and allow its commercialization. Some physico-chemical and some color parameters were determined in this work in samples of different red wines sold in the metropolitan area of Recife. Multivariate analysis comprising principal component analysis and hierarchical cluster analysis was employed to distinguish the analyzed wines. The results for pH, chloride concentration, color parameters and ammonium content were the most important variables for sample classification. It was also possible to classify the wines as soft or dry wines and amongst the soft wines we could determine two out of four winegrowing producers.

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This paper describes an approach for the colour-based classification of RGB (red-green-blue) images, acquired using a common scanner, of commercial carbonated soft drinks. Mean histograms of image colour channels were evaluated for the PCA classification of 29 brands of Guaraná, Cola, and orange flavors. Loadings for principal component axes resulted in different patterns for sample grouping on score plots according to RGB histograms. pH, sorbic acid and sucrose measurements were also correlated to the analyzed brands through PCA score plots of the digitalized images.

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The hedonic level of commercial cachaças, was evaluated by consumers and by a tasters. The results of sensorial methods analyzed trough Principal Components Analysis, Hierarchical Cluster Analysis and the Pearson linear correlation indicated that the best classified cachaças were produced in copper stills and aged in oak casks. By contrast the worst classified exhibited as the main features be not aged and high alcohol percentage. The index of preference is positively correlated with the intensity of yellow color, wood flavor, sweetness and fruit aroma. There is a negative preference correlation with the acidity, the taste of alcohol and bitterness.