804 resultados para Pixel-based Classification
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The elemental analysis of Spanish palm dates by inductively coupled plasma atomic emission spectrometry and inductively coupled plasma mass spectrometry is reported for the first time. To complete the information about the mineral composition of the samples, C, H, and N are determined by elemental analysis. Dates from Israel, Tunisia, Saudi Arabia, Algeria and Iran have also been analyzed. The elemental composition have been used in multivariate statistical analysis to discriminate the dates according to its geographical origin. A total of 23 elements (As, Ba, C, Ca, Cd, Co, Cr, Cu, Fe, H, In, K, Li, Mg, Mn, N, Na, Ni, Pb, Se, Sr, V, and Zn) at concentrations from major to ultra-trace levels have been determined in 13 date samples (flesh and seeds). A careful inspection of the results indicate that Spanish samples show higher concentrations of Cd, Co, Cr, and Ni than the remaining ones. Multivariate statistical analysis of the obtained results, both in flesh and seed, indicate that the proposed approach can be successfully applied to discriminate the Spanish date samples from the rest of the samples tested.
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Thaddeus Mason Harris, who served as interim librarian of the Harvard College Library in 1787 and as its librarian from 1791 through 1793, is believed to have created these notes while helping compile the library's first printed subject-based catalog. The catalog, Catalogus Bibliothecae Harvardianae Cantabrigiae Nov-Anglorum, was published in 1790 and represented a significant change in approach to the cataloging of the library's collections, which had formerly been cataloged alphabetically. These documents, many of them on small scraps of paper, contain the titles and bibliographic information of books on a range of topics, from "Anatomici" to "Rhetorica."
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The Maser thesis is devoted to developing a model to technical state of gas turbine engine estimation. The approaches to preparation data, especially to handle unbalanced data were presented in the thesis. In order to efficient estimation of model performance, the special metric was chosen. Goal of the master thesis is analyzing of monitoring parameters data and developing a model of technical state of GTE estimation based on the data.
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A valid assessment of selective aerobic degradation on organic matter (OM) and its impact on OM-based proxies is vital to produce accurate environmental reconstructions. However, most studies investigating these effects suffer from inherent environmental heterogeneities. In this study, we used surface samples collected along two meter-scale transects and one longer transect in the northeastern Arabian Sea to constrain initial OM heterogeneity, in order to evaluate selective aerobic degradation on temperature, productivity and alteration indices at the sediment-water interface. All of the studied alteration indices, the higher plant alkane index, alcohol preservation index, and diol oxidation index, demonstrated that they are sensitive indicators for changes in the oxygen regime. Several export production indices, a cholesterol-based stanol/stenol index and dinoflagellate lipid- and cyst-based ratios, showed significant (more than 20%) change only over the lateral oxygen gradients. Therefore, these compounds do not exclusively reflect surface water productivity, but are significantly altered after deposition. Two of the proxies, glycerol dibiphytanyl glycerol tetraether-based TEX86 sea surface temperature indices and indices based on phytol, phytane and pristane, did not show any trends related to oxygen. Nevertheless, unrealistic sea surface temperatures were obtained after application of the TEX86, TEX86L, and TEX86H proxies. The phytol-based ratios were likely affected by the sedimentary production of pristane. Our results demonstrate the selective impact of aerobic organic matter degradation on the lipid and palynomorph composition of surface sediments along a short lateral oxygen gradient and suggest that some of the investigated proxies may be useful tracers of changing redox conditions at the sediment-water interface.
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"Prepared by Theodore A. Janssen, chief of the Nosology section."--p.1.
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Thesis (Ph.D.)--University of Washington, 2016-04
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Thesis (Master's)--University of Washington, 2016-06
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Background: Many factors need to be considered in a food-based intervention. Vitamin A deficiency and chronic diseases, such as diabetes, heart disease and cancer, have become serious problems in the Federated States of Micronesia (FSM) following the decreased production and consumption of locally grown foods. However, agricultural and social conditions are still favourable for local food production. Aim: To identify key factors to consider in a Micronesian food-based intervention focusing on increased production and consumption of four major Micronesian staple foods: banana, breadfruit, giant swamp taro and pandanus. Methods: Ethnographic methods including key informant interviews and a literature review. Results: Pacific and Micronesian values, concepts of food and disease, and food classifications differ sharply from Western concepts. There are few FSM professionals with nutrition expertise. Traditional foods and food cultivars vary in nutrient content, consumption level, cost, availability, status, convenience in growing, storing and cooking, and organoleptic factors. Conclusions: A systematic consideration of the factors that relate to a food-based intervention is critical to its success. The evaluation of which food and cultivar of that food that might be most effectively promoted is also critical. Regional differences, for example FSM inter-island differences between the staple foods and cultivars, must be considered carefully. The evaluation framework presented here may be relevant to Pacific island and other countries with similar foods where food-based interventions are being planned. An ethnographic approach was found to be essential in understanding the cultural context and in data collection and analysis.
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We consider the problem of assessing the number of clusters in a limited number of tissue samples containing gene expressions for possibly several thousands of genes. It is proposed to use a normal mixture model-based approach to the clustering of the tissue samples. One advantage of this approach is that the question on the number of clusters in the data can be formulated in terms of a test on the smallest number of components in the mixture model compatible with the data. This test can be carried out on the basis of the likelihood ratio test statistic, using resampling to assess its null distribution. The effectiveness of this approach is demonstrated on simulated data and on some microarray datasets, as considered previously in the bioinformatics literature. (C) 2004 Elsevier Inc. All rights reserved.
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The development of chronic symptoms following whiplash injury is common and contributes substantially to costs associated with this condition. The currently used Quebec Task Force classification system of whiplash associated disorders is primarily based on the severity of signs and symptoms following injury and its usefulness has been questioned. Recent evidence is emerging that demonstrates differences in physical and psychological impairments between individuals who recover from the injury and those who develop persistent pain and disability. Motor dysfunction, local cervical mechanical hyperalgesia and psychological distress are present soon after injury in all whiplash injured persons irrespective of recovery. In contrast those individuals who develop persistent moderate/severe pain and disability show a more complex picture, characterized by additional impairments of widespread sensory hypersensitivity indicative of underlying disturbances in central pain processing as well as acute posttraumatic stress reaction, with these changes present from soon after injury. Based on this heterogeneity a new classification system is proposed that takes into account measurable disturbances in motor, sensory and psychological dysfunction. The implications for the management of this condition are discussed. (C) 2004 Elsevier Ltd. All rights reserved.
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Classifications of perinatal deaths have been undertaken for surveillance of causes of death, but also for auditing individual deaths to identify suboptimal care at any level, so that preventive strategies may be implemented. This paper describes the history and development of the paired obstetric and neonatal Perinatal Society of Australia and New Zealand (PSANZ) classifications in the context of other classifications. The PSANZ Perinatal Death Classification is based on obstetric antecedent factors that initiated the sequence of events leading to the death, and was developed largely from the Aberdeen and Whitfield classifications. The PSANZ Neonatal Death Classification is based on fetal and neonatal factors associated with the death. The classifications, accessible on the PSANZ website (http://www.psanz.org), have definitions and guidelines for use, a high level of agreement between classifiers, and are now being used in nearly all Australian states and New Zealand.
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The Wet Tropics World Heritage Area in Far North Queens- land, Australia consists predominantly of tropical rainforest and wet sclerophyll forest in areas of variable relief. Previous maps of vegetation communities in the area were produced by a labor-intensive combination of field survey and air-photo interpretation. Thus,. the aim of this work was to develop a new vegetation mapping method based on imaging radar that incorporates topographical corrections, which could be repeated frequently, and which would reduce the need for detailed field assessments and associated costs. The method employed G topographic correction and mapping procedure that was developed to enable vegetation structural classes to be mapped from satellite imaging radar. Eight JERS-1 scenes covering the Wet Tropics area for 1996 were acquired from NASDA under the auspices of the Global Rainforest Mapping Project. JERS scenes were geometrically corrected for topographic distortion using an 80 m DEM and a combination of polynomial warping and radar viewing geometry modeling. An image mosaic was created to cover the Wet Tropics region, and a new technique for image smoothing was applied to the JERS texture bonds and DEM before a Maximum Likelihood classification was applied to identify major land-cover and vegetation communities. Despite these efforts, dominant vegetation community classes could only be classified to low levels of accuracy (57.5 percent) which were partly explained by the significantly larger pixel size of the DEM in comparison to the JERS image (12.5 m). In addition, the spatial and floristic detail contained in the classes of the original validation maps were much finer than the JERS classification product was able to distinguish. In comparison to field and aerial photo-based approaches for mapping the vegetation of the Wet Tropics, appropriately corrected SAR data provides a more regional scale, all-weather mapping technique for broader vegetation classes. Further work is required to establish an appropriate combination of imaging radar with elevation data and other environmental surrogates to accurately map vegetation communities across the entire Wet Tropics.
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beta-turns are important topological motifs for biological recognition of proteins and peptides. Organic molecules that sample the side chain positions of beta-turns have shown broad binding capacity to multiple different receptors, for example benzodiazepines. beta-turns have traditionally been classified into various types based on the backbone dihedral angles (phi 2, psi 2, phi 3 and psi 3). Indeed, 57-68% of beta-turns are currently classified into 8 different backbone families (Type I, Type II, Type I', Type II', Type VIII, Type VIa1, Type VIa2 and Type VIb and Type IV which represents unclassified beta-turns). Although this classification of beta-turns has been useful, the resulting beta-turn types are not ideal for the design of beta-turn mimetics as they do not reflect topological features of the recognition elements, the side chains. To overcome this, we have extracted beta-turns from a data set of non-homologous and high-resolution protein crystal structures. The side chain positions, as defined by C-alpha-C-beta vectors, of these turns have been clustered using the kth nearest neighbor clustering and filtered nearest centroid sorting algorithms. Nine clusters were obtained that cluster 90% of the data, and the average intra-cluster RMSD of the four C-alpha-C-beta vectors is 0.36. The nine clusters therefore represent the topology of the side chain scaffold architecture of the vast majority of beta-turns. The mean structures of the nine clusters are useful for the development of beta-turn mimetics and as biological descriptors for focusing combinatorial chemistry towards biologically relevant topological space.
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Risk assessment systems for introduced species are being developed and applied globally, but methods for rigorously evaluating them are still in their infancy. We explore classification and regression tree models as an alternative to the current Australian Weed Risk Assessment system, and demonstrate how the performance of screening tests for unwanted alien species may be quantitatively compared using receiver operating characteristic (ROC) curve analysis. The optimal classification tree model for predicting weediness included just four out of a possible 44 attributes of introduced plants examined, namely: (i) intentional human dispersal of propagules; (ii) evidence of naturalization beyond native range; (iii) evidence of being a weed elsewhere; and (iv) a high level of domestication. Intentional human dispersal of propagules in combination with evidence of naturalization beyond a plants native range led to the strongest prediction of weediness. A high level of domestication in combination with no evidence of naturalization mitigated the likelihood of an introduced plant becoming a weed resulting from intentional human dispersal of propagules. Unlikely intentional human dispersal of propagules combined with no evidence of being a weed elsewhere led to the lowest predicted probability of weediness. The failure to include intrinsic plant attributes in the model suggests that either these attributes are not useful general predictors of weediness, or data and analysis were inadequate to elucidate the underlying relationship(s). This concurs with the historical pessimism that we will ever be able to accurately predict invasive plants. Given the apparent importance of propagule pressure (the number of individuals of an species released), future attempts at evaluating screening model performance for identifying unwanted plants need to account for propagule pressure when collating and/or analysing datasets. The classification tree had a cross-validated sensitivity of 93.6% and specificity of 36.7%. Based on the area under the ROC curve, the performance of the classification tree in correctly classifying plants as weeds or non-weeds was slightly inferior (Area under ROC curve = 0.83 +/- 0.021 (+/- SE)) to that of the current risk assessment system in use (Area under ROC curve = 0.89 +/- 0.018 (+/- SE)), although requires many fewer questions to be answered.
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We consider the statistical problem of catalogue matching from a machine learning perspective with the goal of producing probabilistic outputs, and using all available information. A framework is provided that unifies two existing approaches to producing probabilistic outputs in the literature, one based on combining distribution estimates and the other based on combining probabilistic classifiers. We apply both of these to the problem of matching the HI Parkes All Sky Survey radio catalogue with large positional uncertainties to the much denser SuperCOSMOS catalogue with much smaller positional uncertainties. We demonstrate the utility of probabilistic outputs by a controllable completeness and efficiency trade-off and by identifying objects that have high probability of being rare. Finally, possible biasing effects in the output of these classifiers are also highlighted and discussed.