922 resultados para principal component regression


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Background: Differences in the distribution of genotypes between individuals of the same ethnicity are an important confounder factor commonly undervalued in typical association studies conducted in radiogenomics. Objective: To evaluate the genotypic distribution of SNPs in a wide set of Spanish prostate cancer patients for determine the homogeneity of the population and to disclose potential bias. Design, Setting, and Participants: A total of 601 prostate cancer patients from Andalusia, Basque Country, Canary and Catalonia were genotyped for 10 SNPs located in 6 different genes associated to DNA repair: XRCC1 (rs25487, rs25489, rs1799782), ERCC2 (rs13181), ERCC1 (rs11615), LIG4 (rs1805388, rs1805386), ATM (rs17503908, rs1800057) and P53 (rs1042522). The SNP genotyping was made in a Biotrove OpenArrayH NT Cycler. Outcome Measurements and Statistical Analysis: Comparisons of genotypic and allelic frequencies among populations, as well as haplotype analyses were determined using the web-based environment SNPator. Principal component analysis was made using the SnpMatrix and XSnpMatrix classes and methods implemented as an R package. Non-supervised hierarchical cluster of SNP was made using MultiExperiment Viewer. Results and Limitations: We observed that genotype distribution of 4 out 10 SNPs was statistically different among the studied populations, showing the greatest differences between Andalusia and Catalonia. These observations were confirmed in cluster analysis, principal component analysis and in the differential distribution of haplotypes among the populations. Because tumor characteristics have not been taken into account, it is possible that some polymorphisms may influence tumor characteristics in the same way that it may pose a risk factor for other disease characteristics. Conclusion: Differences in distribution of genotypes within different populations of the same ethnicity could be an important confounding factor responsible for the lack of validation of SNPs associated with radiation-induced toxicity, especially when extensive meta-analysis with subjects from different countries are carried out.

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PURPOSE: Statistical shape and appearance models play an important role in reducing the segmentation processing time of a vertebra and in improving results for 3D model development. Here, we describe the different steps in generating a statistical shape model (SSM) of the second cervical vertebra (C2) and provide the shape model for general use by the scientific community. The main difficulties in its construction are the morphological complexity of the C2 and its variability in the population. METHODS: The input dataset is composed of manually segmented anonymized patient computerized tomography (CT) scans. The alignment of the different datasets is done with the procrustes alignment on surface models, and then, the registration is cast as a model-fitting problem using a Gaussian process. A principal component analysis (PCA)-based model is generated which includes the variability of the C2. RESULTS: The SSM was generated using 92 CT scans. The resulting SSM was evaluated for specificity, compactness and generalization ability. The SSM of the C2 is freely available to the scientific community in Slicer (an open source software for image analysis and scientific visualization) with a module created to visualize the SSM using Statismo, a framework for statistical shape modeling. CONCLUSION: The SSM of the vertebra allows the shape variability of the C2 to be represented. Moreover, the SSM will enable semi-automatic segmentation and 3D model generation of the vertebra, which would greatly benefit surgery planning.

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Macrofossil analysis of a composite 19 m long sediment core from Rano Raraku Lake (Easter Island)was related to litho-sedimentary and geochemical features of the sediment. Strong stratigraphical patterns are shown by indirect gradient analyses of the data. The good correspondence between the stratigraphical patterns derived from macrofossil (Correspondence Analysis) and sedimentary and geochemical data (Principal Component Analysis) shows that macrofossil associations provide sound palaeolimnological information in conjunction with sedimentary data. The main taphonomic factors in fluencing the macrofossil assemblages are run-off from the catchment, the littoral plant belt, and the depositional environment within the basin. Five main stages during the last 34,000 calibrated years BP (cal yr BP) are characterised from the lithological, geochemical, and macrofossil data. From 34 to 14.6 cal kyr BP (last glacial period) the sediments were largely derived from the catchment, indicating a high energy lake environment with much erosion and run-off bringing abundant plant trichomes, lichens, and mosses into the centre of Raraku Lake.

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In recent years there has been growing interest in composite indicators as an efficient tool of analysis and a method of prioritizing policies. This paper presents a composite index of intermediary determinants of child health using a multivariate statistical approach. The index shows how specific determinants of child health vary across Colombian departments (administrative subdivisions). We used data collected from the 2010 Colombian Demographic and Health Survey (DHS) for 32 departments and the capital city, Bogotá. Adapting the conceptual framework of Commission on Social Determinants of Health (CSDH), five dimensions related to child health are represented in the index: material circumstances, behavioural factors, psychosocial factors, biological factors and the health system. In order to generate the weight of the variables, and taking into account the discrete nature of the data, principal component analysis (PCA) using polychoric correlations was employed in constructing the index. From this method five principal components were selected. The index was estimated using a weighted average of the retained components. A hierarchical cluster analysis was also carried out. The results show that the biggest differences in intermediary determinants of child health are associated with health care before and during delivery.

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This paper presents a composite index of early childhood health using a multivariate statistical approach. The index shows how child health varies across Colombian departments, -administrative subdivisions-. In recent years there has been growing interest in composite indicators as an efficient analysis tool and a way of prioritizing policies. These indicators not only enable multi-dimensional phenomena to be simplified but also make it easier to measure, visualize, monitor and compare a country’s performance in particular issues. We used data collected from the Colombian Demographic and Health Survey, DHS, for 32 departments and the capital city, Bogotá, in 2005 and 2010. The variables included in the index provide a measure of three dimensions related to child health: health status, health determinants and the health system. In order to generate the weight of the variables and take into account the discrete nature of the data, we employed a principal component analysis, PCA, using polychoric correlation. From this method, five principal components were selected. The index was estimated using a weighted average of the components retained. A hierarchical cluster analysis was also carried out. We observed that the departments ranking in the lowest positions are located on the Colombian periphery. They are departments with low per capita incomes and they present critical social indicators. The results suggest that the regional disparities in child health may be associated with differences in parental characteristics, household conditions and economic development levels, which makes clear the importance of context in the study of child health in Colombia.

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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 input of heavy metals concentrations determinated by ICP-AES, in samples of the Cambé river basin, was evaluated by using the Principal Component Analysis. The results distinguishes clearly one site, which is strongly influenced by almost all elements studied. Special attention was given to Pb, because of the presence of one battery industry in this area. Some downstream samples were associated with the same characteristics of this site, showing residual action of contaminants along the basin. Other sites presented influence of soil elements, plus Cr near a tannery industry. This study allowed to distinguish different sites in the upper basin of the Cambé (Londrina-PR-BR), in accordance to elements input.

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This work analyzes sunshine duration variability in the western part of Europe (WEU) over the 1938– 2004 period. A principal component analysis is applied to cluster the original series from 79 sites into 6 regions, and then annual and seasonal mean series are constructed on regional and also for the whole WEU scales. Over the entire period studied here, the linear trend of annual sunshine duration is found to be nonsignificant. However, annual sunshine duration shows an overall decrease since the 1950s until the early 1980s, followed by a subsequent recovery during the last two decades. This behavior is in good agreement with the dimming and brightening phenomena described in previous literature. From the seasonal analysis, the most remarkable result is the similarity between spring and annual series, although the spring series has a negative trend; and the clear significant increase found for the whole WEU winter series, being especially large since the 1970s. The behavior of the major synoptic patterns for two seasons is investigated, resulting in some indications that sunshine duration evolution may be partially explained by changes in the frequency of some of them

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Hoitotyön laatu - lasten näkökulma Tämän kolmivaiheisen tutkimuksen tarkoituksena oli kuvailla lasten odotuksia ja arviointeja lasten hoitotyön laadusta sekä kehittää mittari kouluikäisille sairaalassa oleville lapsille laadun arviointiin. Perimmäisenä tavoitteena oli lasten hoitotyön laadun kehittäminen sairaalassa. Ensimmäisessä vaiheessa 20 alle kouluikäistä (4-6v) sekä 20 kouluikäistä (7-11v) lasta kuvailivat odotuksiaan lasten hoitotyön laadusta. Aineisto kerättiin haastattelulla ja lasten piirustusten avulla, sekä analysoitiin sisällön analyysilla. Lasten odotukset lasten hoitotyön laadusta kohdistuivat hoitajaan, hoitotyön toimintoihin ja ympäristöön, fyysinen ympäristö korostui piirustuksissa. Ensimmäisen vaiheen tulosten, aikaisemman kirjallisuuden sekä Leino-Kilven “HYVÄ HOITO” mittarin pohjalta kehitettiin “Lasten Hoidon Laatu Sairaalassa” (LHLS) mittari ja testattiin sen psykometrisiä ominaisuuksia tutkimuksen toisessa vaiheessa. Mittaria kehitettiin ja testattiin kolmen vaiheen kautta. Aluksi asiantuntijapaneeli (n=7) arvioi mittarin sisältöä. Seuraavaksi mittari esitestattiin kahdesti kouluikäisillä sairaalassa olevilla lapsilla (n=41 ja n=16), samassa vaiheessa myös viiden lastenosaston hoitajat (n=19) yhdessä arvioivat mittarin sisältöä sekä 8 lasta. Lopuksi mittaria testattiin kouluikäisillä lapsilla (n=388) sairaalassa sekä hoitajat (n=198) arvioivat mittarin sisällön validiteettia. Mittarin kehittämisen aikana päälaatuluokkien: hoitajan ominaisuudet, hoitotyön toiminnot ja hoitotyön ympäristö Cronbachin alfa kertoimet paranivat. Pääkomponentti analyysi tuki mittarin hoitotyön toimintojen ja ympäristön alaluokkien teoreettista rakennetta. Kolmannessa vaiheessa “Lasten Hoidon Laatu Sairaalassa” (LHLS III, versio neljä) mittarilla kerättiin aineisto Suomen yliopistosairaaloiden lastenosastoilta kouluikäisiltä 7-11 -vuotiailta lapsilta (n=388). Mittarin lopussa lapsia pyydettiin lisäksi kuvailemaan kivointa ja ikävintä kokemustaan sairaalahoidon aikana lauseen täydennystehtävänä. Aineisto analysoitiin tilastollisesti sekä sisällön analyysilla. Lapset arvioivat fyysisen hoitoympäristön, hoitajien inhimillisyyden ja luotettavuuden sekä huolenpidon ja vuorovaikutustoiminnot kiitettäviksi. Lapset arvioivat hoitajien viihdyttämistoiminnot kaikkein alhaisimmiksi. Lapsen ikä ja sairaalantulotapa olivat yhteydessä lasten saamaan tiedon määrään. Lasten kivoimmat kokemukset liittyivät ihmisiin ja heidän ominaisuuksiinsa, toimintoihin, ympäristöön sekä lopputuloksiin. Ikävimmät kokemukset liittyivät potilaana oloon, tuntemuksiin sairauden oireista sekä erossaoloon, hoitotyön fyysisiin toimintoihin sekä ympäristöön. Tutkimuksen tulokset osoittavat lasten olevan kykeneviä arvioimaan omaa hoitoaan ja heidän näkökulmansa tulisi nähdä osana koko laadun kehittämisprosessia parannettaessa laatua käytännössä todella lapsilähtöisemmällä lähestymistavalla. “Lasten Hoidon Laatu Sairaalassa” (LHLS) mittari on mahdollinen väline saada tietoa lasten arvioinneista lasten hoitotyön laadusta, mutta mittarin testaamista tulisi jatkaa tulevaisuudessa

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One of the major interests in soil analysis is the evaluation of its chemical, physical and biological parameters, which are indicators of soil quality (the most important is the organic matter). Besides there is a great interest in the study of humic substances and on the assessment of pollutants, such as pesticides and heavy metals, in soils. Chemometrics is a powerful tool to deal with these problems and can help soil researchers to extract much more information from their data. In spite of this, the presence of these kinds of strategies in the literature has obtained projection only recently. The utilization of chemometric methods in soil analysis is evaluated in this article. The applications will be divided in four parts (with emphasis in the first two): (i) descriptive and exploratory methods based on Principal Component Analysis (PCA); (ii) multivariate calibration methods (MLR, PCR and PLS); (iii) methods such as Evolving Factor Analysis and SIMPLISMA; and (iv) artificial intelligence methods, such as Artificial Neural Networks.

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The ¹H NMR data set of a series of 3-aryl (1,2,4)-oxadiazol-5-carbohydrazide benzylidene derivatives synthesized in our group was analyzed using the chemometric technique of principal component analysis (PCA). Using the original ¹H NMR data PCA allowed identifying some misassignments of the proton aromatic chemical shifts. As a consequence of this multivariate analysis, nuclear Overhauser difference experiments were performed to investigate the ambiguity of other assignments of the ortho and meta aromatic hydrogens for the compound with the bromine substituent. The effect of the 1,2,4-oxadiazol group as an electron acceptor, mainly for the hydrogens 12,13, has been highlighted.

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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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This study presents a catalogue of synoptic patterns of torrential rainfall in northeast of the Iberian Peninsula (IP). These circulation patterns were obtained by applying a T-mode Principal Component Analysis (PCA) to a daily data grid (NCEP/NCAR reanalysis) at sea level pressure (SLP). The analysis made use of 304 days which recorded >100 mm in one or more stations in provinces of Barcelona, Girona and Tarragona (coastland area of Catalonia) throughout the 1950-2005 period. The catalogue comprises 7 circulation patterns showing a great variety of atmospheric conditions and seasonal or monthly distribution. Likewise, we computed the mean index value of the Western Mediterranean Oscillation index (WeMOi) for the synoptic patterns obtained by averaging all days grouped in each pattern. The results showed a clear association between the negative values of this teleconnection index and torrential rainfall in northeast of the IP. We therefore put forward the WeMO as an essential tool for forecasting heavy rainfall in northeast of Spain

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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.