930 resultados para Compositional data analysis


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Compositional data (concentrations) are common in geosciences. Neglecting its character mey lead to erroneous conclusions. Spurious correlation (K. Pearson, 1897) has disastrous consequences. On the basis of the pioneering work by J. Aitchison in the 1980s, a methodology free of these drawbacks is now available. The geometry of the símplex allows the representation of compositions using orthogonal co-ordinares, to which usual statistical methods can be applied, thus facilating computation ans analysis. The use of (log) ratios precludes the interpretation of single concentrations disregarding their relative character. A hydro-chemical data set is used to illustrate the point

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We present a participant study that compares biological data exploration tasks using volume renderings of laser confocal microscopy data across three environments that vary in level of immersion: a desktop, fishtank, and cave system. For the tasks, data, and visualization approach used in our study, we found that subjects qualitatively preferred and quantitatively performed better in the cave compared with the fishtank and desktop. Subjects performed real-world biological data analysis tasks that emphasized understanding spatial relationships including characterizing the general features in a volume, identifying colocated features, and reporting geometric relationships such as whether clusters of cells were coplanar. After analyzing data in each environment, subjects were asked to choose which environment they wanted to analyze additional data sets in - subjects uniformly selected the cave environment.

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Recent years have produced great advances in the instrumentation technology. The amount of available data has been increasing due to the simplicity, speed and accuracy of current spectroscopic instruments. Most of these data are, however, meaningless without a proper analysis. This has been one of the reasons for the overgrowing success of multivariate handling of such data. Industrial data is commonly not designed data; in other words, there is no exact experimental design, but rather the data have been collected as a routine procedure during an industrial process. This makes certain demands on the multivariate modeling, as the selection of samples and variables can have an enormous effect. Common approaches in the modeling of industrial data are PCA (principal component analysis) and PLS (projection to latent structures or partial least squares) but there are also other methods that should be considered. The more advanced methods include multi block modeling and nonlinear modeling. In this thesis it is shown that the results of data analysis vary according to the modeling approach used, thus making the selection of the modeling approach dependent on the purpose of the model. If the model is intended to provide accurate predictions, the approach should be different than in the case where the purpose of modeling is mostly to obtain information about the variables and the process. For industrial applicability it is essential that the methods are robust and sufficiently simple to apply. In this way the methods and the results can be compared and an approach selected that is suitable for the intended purpose. Differences in data analysis methods are compared with data from different fields of industry in this thesis. In the first two papers, the multi block method is considered for data originating from the oil and fertilizer industries. The results are compared to those from PLS and priority PLS. The third paper considers applicability of multivariate models to process control for a reactive crystallization process. In the fourth paper, nonlinear modeling is examined with a data set from the oil industry. The response has a nonlinear relation to the descriptor matrix, and the results are compared between linear modeling, polynomial PLS and nonlinear modeling using nonlinear score vectors.

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A new analytical method was developed to non-destructively determine pH and degree of polymerisation (DP) of cellulose in fibres in 19th 20th century painting canvases, and to identify the fibre type: cotton, linen, hemp, ramie or jute. The method is based on NIR spectroscopy and multivariate data analysis, while for calibration and validation a reference collection of 199 historical canvas samples was used. The reference collection was analysed destructively using microscopy and chemical analytical methods. Partial least squares regression was used to build quantitative methods to determine pH and DP, and linear discriminant analysis was used to determine the fibre type. To interpret the obtained chemical information, an expert assessment panel developed a categorisation system to discriminate between canvases that may not be fit to withstand excessive mechanical stress, e.g. transportation. The limiting DP for this category was found to be 600. With the new method and categorisation system, canvases of 12 Dalí paintings from the Fundació Gala-Salvador Dalí (Figueres, Spain) were non-destructively analysed for pH, DP and fibre type, and their fitness determined, which informs conservation recommendations. The study demonstrates that collection-wide canvas condition surveys can be performed efficiently and non-destructively, which could significantly improve collection management.

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Raw measurement data does not always immediately convey useful information, but applying mathematical statistical analysis tools into measurement data can improve the situation. Data analysis can offer benefits like acquiring meaningful insight from the dataset, basing critical decisions on the findings, and ruling out human bias through proper statistical treatment. In this thesis we analyze data from an industrial mineral processing plant with the aim of studying the possibility of forecasting the quality of the final product, given by one variable, with a model based on the other variables. For the study mathematical tools like Qlucore Omics Explorer (QOE) and Sparse Bayesian regression (SB) are used. Later on, linear regression is used to build a model based on a subset of variables that seem to have most significant weights in the SB model. The results obtained from QOE show that the variable representing the desired final product does not correlate with other variables. For SB and linear regression, the results show that both SB and linear regression models built on 1-day averaged data seriously underestimate the variance of true data, whereas the two models built on 1-month averaged data are reliable and able to explain a larger proportion of variability in the available data, making them suitable for prediction purposes. However, it is concluded that no single model can fit well the whole available dataset and therefore, it is proposed for future work to make piecewise non linear regression models if the same available dataset is used, or the plant to provide another dataset that should be collected in a more systematic fashion than the present data for further analysis.

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Workshop at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

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Results of subgroup analysis (SA) reported in randomized clinical trials (RCT) cannot be adequately interpreted without information about the methods used in the study design and the data analysis. Our aim was to show how often inaccurate or incomplete reports occur. First, we selected eight methodological aspects of SA on the basis of their importance to a reader in determining the confidence that should be placed in the author's conclusions regarding such analysis. Then, we reviewed the current practice of reporting these methodological aspects of SA in clinical trials in four leading journals, i.e., the New England Journal of Medicine, the Journal of the American Medical Association, the Lancet, and the American Journal of Public Health. Eight consecutive reports from each journal published after July 1, 1998 were included. Of the 32 trials surveyed, 17 (53%) had at least one SA. Overall, the proportion of RCT reporting a particular methodological aspect ranged from 23 to 94%. Information on whether the SA preceded/followed the analysis was reported in only 7 (41%) of the studies. Of the total possible number of items to be reported, NEJM, JAMA, Lancet and AJPH clearly mentioned 59, 67, 58 and 72%, respectively. We conclude that current reporting of SA in RCT is incomplete and inaccurate. The results of such SA may have harmful effects on treatment recommendations if accepted without judicious scrutiny. We recommend that editors improve the reporting of SA in RCT by giving authors a list of the important items to be reported.

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The aim of the present study was to compare heart rate variability (HRV) at rest and during exercise using a temporal series obtained with the Polar S810i monitor and a signal from a LYNX® signal conditioner (BIO EMG 1000 model) with a channel configured for the acquisition of ECG signals. Fifteen healthy subjects aged 20.9 ± 1.4 years were analyzed. The subjects remained at rest for 20 min and performed exercise for another 20 min with the workload selected to achieve 60% of submaximal heart rate. RR series were obtained for each individual with a Polar S810i instrument and with an ECG analyzed with a biological signal conditioner. The HRV indices (rMSSD, pNN50, LFnu, HFnu, and LF/HF) were calculated after signal processing and analysis. The unpaired Student t-test and intraclass correlation coefficient were used for data analysis. No statistically significant differences were observed when comparing the values analyzed by means of the two devices for HRV at rest and during exercise. The intraclass correlation coefficient demonstrated satisfactory correlation between the values obtained by the devices at rest (pNN50 = 0.994; rMSSD = 0.995; LFnu = 0.978; HFnu = 0.978; LF/HF = 0.982) and during exercise (pNN50 = 0.869; rMSSD = 0.929; LFnu = 0.973; HFnu = 0.973; LF/HF = 0.942). The calculation of HRV values by means of temporal series obtained from the Polar S810i instrument appears to be as reliable as those obtained by processing the ECG signal captured with a signal conditioner.

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Studying testis is complex, because the tissue has a very heterogeneous cell composition and its structure changes dynamically during development. In reproductive field, the cell composition is traditionally studied by morphometric methods such as immunohistochemistry and immunofluorescence. These techniques provide accurate quantitative information about cell composition, cell-cell association and localization of the cells of interest. However, the sample preparation, processing, staining and data analysis are laborious and may take several working days. Flow cytometry protocols coupled with DNA stains have played an important role in providing quantitative information of testicular cells populations ex vivo and in vitro studies. Nevertheless, the addition of specific cells markers such as intracellular antibodies would allow the more specific identification of cells of crucial interest during spermatogenesis. For this study, adult rat Sprague-Dawley rats were used for optimization of the flow cytometry protocol. Specific steps within the protocol were optimized to obtain a singlecell suspension representative of the cell composition of the starting material. Fixation and permeabilization procedure were optimized to be compatible with DNA stains and fluorescent intracellular antibodies. Optimization was achieved by quantitative analysis of specific parameters such as recovery of meiotic cells, amount of debris and comparison of the proportions of the various cell populations with already published data. As a result, a new and fast flow cytometry method coupled with DNA stain and intracellular antigen detection was developed. This new technique is suitable for analysis of population behavior and specific cells during postnatal testis development and spermatogenesis in rodents. This rapid protocol recapitulated the known vimentin and γH2AX protein expression patterns during rodent testis ontogenesis. Moreover, the assay was applicable for phenotype characterization of SCRbKO and E2F1KO mouse models.

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In this thesis the process of building a software for transport accessibility analysis is described. The goal was to create a software which is easy to distribute and simple to use for the user without particular background in the field of the geographical data analysis. It was shown that existing tools do not suit for this particular task due to complex interface or significant rendering time. The goal was accomplished by applying modern approaches in the process of building web applications such as maps based on vector tiles, FLUX architecture design pattern and module bundling. It was discovered that vector tiles have considerable advantages over image-based tiles such as faster rendering and real-time styling.

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The main objective of this research was to examine the relationship between surface electromyographic (SEMG) spike activity and force. The secondary objective was to determine to what extent subcutaneous tissue impacts the high frequency component of the signal, as well as, examining the relationship between measures of SEMG spike shape and their traditional time and frequency analogues. A total of96 participants (46 males and 50 females) ranging in age (18-35 years), generated three 5-second isometric step contractions at each force level of 40, 60, 80, and 100 percent of maximal voluntary contraction (MVC). The presentation of the contractions was balanced across subjects. The right arm of the subject was positioned in the sagittal plane, with the shoulder and elbow flexed to 90 degrees. The elbow rested on a support in a neutral position (mid pronation/mid supination) and placed within a wrist cuff, fastened below the styloid process. The wrist cuff was attached to a load cell (JR3 Inc., Woodland, CA) recording the force produced. Biceps brachii activity was monitored with a pair of Ag/AgCI recording electrodes (Grass F-E9, Astro-Med Inc., West Warwick, RI) placed in a bipolar configuration, with an interelectrode distance (lED) of 2cm distal to the motor point. Data analysis was performed on a I second window of data in the middle of the 5-second contraction. The results indicated that all spike shape measures exhibited significant (p < 0.01) differences as force increase~ from 40 to 100% MVC. The spike shape measures suggest that increased motor unit (MU) recruitment was responsible for increasing force up to 80% MVC. The results suggested that further increases in force relied on MU III synchronization. The results also revealed that the subcutaneous tissue (skin fold thickness) had no relationship (r = 0.02; P > 0.05) with the mean number of peaks per spike (MNPPS), which was the high frequency component of the signal. Mean spike amplitude (MSA) and mean spike frequency (MSF) were highly correlated with their traditional measures root mean square (RMS) and mean power frequency (MPF), respectively (r = 0.99; r = 0.97; P < 0.01).

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The purpose ofthis study was to explore various types ofreflection and to explore reflection on action, reflection as a practice, and reflection as a process. In doing this, the intent was to discover the perceived benefits of reflection in the classroom and to provide guidelines for future use at the undergraduate and graduate level. The qualitative components in this study included the data collection strategy of semistructured interviews with 2 undergraduate students, 2 graduate students, 1 undergraduate studies professor, and 1 graduate studies professor. The data analysis strategies included a within-case analysis and a cross-case analysis. Through the interviews participants discussed their experiences with the use ofreflection in the classroom. Through the completion ofthis analysis the researcher expected to discover the benefits ofreflection at this level of education, as well as provide suggestions for future use. Both undergraduate and graduate students and professors were found to benefit from the use of reflection in the classroom. The use ofreflection in the undergraduate and graduate classroom was found to improve student/teacher and student/peer relationships, foster critical thinking, allow for connections between learned theory and life experience, and improve students' writing abilities. Based on the results ofthe study the implications ofreflection for the undergraduate and graduate classroom and for further research are provided.

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Behavioral researchers commonly use single subject designs to evaluate the effects of a given treatment. Several different methods of data analysis are used, each with their own set of methodological strengths and limitations. Visual inspection is commonly used as a method of analyzing data which assesses the variability, level, and trend both within and between conditions (Cooper, Heron, & Heward, 2007). In an attempt to quantify treatment outcomes, researchers developed two methods for analysing data called Percentage of Non-overlapping Data Points (PND) and Percentage of Data Points Exceeding the Median (PEM). The purpose of the present study is to compare and contrast the use of Hierarchical Linear Modelling (HLM), PND and PEM in single subject research. The present study used 39 behaviours, across 17 participants to compare treatment outcomes of a group cognitive behavioural therapy program, using PND, PEM, and HLM on three response classes of Obsessive Compulsive Behaviour in children with Autism Spectrum Disorder. Findings suggest that PEM and HLM complement each other and both add invaluable information to the overall treatment results. Future research should consider using both PEM and HLM when analysing single subject designs, specifically grouped data with variability.

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Affiliation: Département de biochimie, Faculté de médecine, Université de Montréal

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Triple quadrupole mass spectrometers coupled with high performance liquid chromatography are workhorses in quantitative bioanalyses. It provides substantial benefits including reproducibility, sensitivity and selectivity for trace analysis. Selected Reaction Monitoring allows targeted assay development but data sets generated contain very limited information. Data mining and analysis of non-targeted high-resolution mass spectrometry profiles of biological samples offer the opportunity to perform more exhaustive assessments, including quantitative and qualitative analysis. The objectives of this study was to test method precision and accuracy, statistically compare bupivacaine drug concentration in real study samples and verify if high resolution and accurate mass data collected in scan mode can actually permit retrospective data analysis, more specifically, extract metabolite related information. The precision and accuracy data presented using both instruments provided equivalent results. Overall, the accuracy was ranging from 106.2 to 113.2% and the precision observed was from 1.0 to 3.7%. Statistical comparisons using a linear regression between both methods reveal a coefficient of determination (R2) of 0.9996 and a slope of 1.02 demonstrating a very strong correlation between both methods. Individual sample comparison showed differences from -4.5% to 1.6% well within the accepted analytical error. Moreover, post acquisition extracted ion chromatograms at m/z 233.1648 ± 5 ppm (M-56) and m/z 305.2224 ± 5 ppm (M+16) revealed the presence of desbutyl-bupivacaine and three distinct hydroxylated bupivacaine metabolites. Post acquisition analysis allowed us to produce semiquantitative evaluations of the concentration-time profiles for bupicavaine metabolites.