data analysis methods in fMRI

Abstract. The gravitational wave detector VIRGO is in the final commissioning phase and some preliminary data has already been acquired. The aim of VIRGO is to directly detect gravitational waves emitted by compact objects at the time of coalescence and by other astrophysical sources. Here we report on the present sensitivity for detecting these sources and what we Will achieve in the near future. We also discuss some details of our data analysis methods and we show some preliminary results related to the fraction of the ...

Environmental microbiology studies commonly use terminal restriction fragment length polymorphism (T-RFLP) of 16S rRNA genes, for example, to analyze changes in community structure in relation to changing physicochemical and biological conditions over space and time. Although T-RFLP is most useful for comparing samples from different environments, a large number of samples makes effective analysis difficult using the Web-based tools that are currently available. To resolve this dilemma, we used a new approach for calculating data from multiple T-RFLP samples by estimating terminal fragment combinations, then applying a correlation analysis using two different fluorescent dyes generated simultaneously from all samples. This calculation was based on the expectation that the proportions of two terminal fragments from one full-length polymerase chain reaction fragment would be nearly the same in each analysis. Using this program, the oral microflora in 73 human saliva samples were analyzed, and 24 bacterial groups, with peak areas of at least 0.5% and correlation coefficients of 0.55 or greater, were identified from the T-RFs within 40 s.

Text Mining is the automatic discovery of new, previously unknown information, by automatic analysis of various textual resources. Text mining starts by extracting facts and events from textual sources and then enables forming new hypotheses that are further explored by traditional Data Mining and data analysis methods. In this chapter we will define text mining and describe the three main approaches for performing information extraction. In addition, we will describe how we can visually display and analyze the outcome of the information extraction process.

A large data set pertaining to water quality of an alluvial river was analyzed using multi-way data analysis methods with a view to extract the hidden information, spatial and temporal variation trends in the river water quality. Four-way data (8 monitoring sites × 22 water quality variables × 10 monitoring years × 12 sampling months) analysis was performed using PARAFAC and Tucker3 models. A two component PARAFAC model, although explained 35.1% of the data variance, could not fit to the data set. Tucker3 model of optimum complexity (2,3,1,3) explaining 39.7% of the data variance, allowed interpretation of the data information in four modes. The model explained spatial and temporal variation trends in terms of water quality variables during the study period and revealed that sampling sites in mid-stretch of the river were dominated mainly by the variables of anthropogenic origin. The results delineated the mid stretch of the river as critical from pollution point of view and also identified summer months as having high influence on river water quality in this stretch. The information regarding spatial and temporal variations in water quality generated by the four-way modeling of data would be useful in developing long-term water resources management strategies in the river basin.

Description/Abstract An Ecological Classification and Inventory (EC I) of the Manistee National Forest was conducted to provide the Forest with information needed as an ecological framework for integrated resource planning and management. A companion to ...

EXAFS spectroscopies extract structural information from a sample by analyzing its X-ray ab- sorption spectrum. They make it possible to determine the chemical environment of an element in terms of the number and type of its neighbors, inter-atomic distances and structural disor- ders. This determination is confined to a distance of 4 to 8 Å radius from the element. The

Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to three or more sets of variables. It constitutes a general framework for many multi-block data analysis methods. It combines the power of multi-block data analysis methods (maximization of well identified criteria) and the flexibility of PLS path modeling (the researcher decides which blocks are connected and which are not). Searching for a fixed point of the stationary equations related to RGCCA, a new monotonically convergent algorithm, very similar to the PLS algorithm proposed by Herman Wold, is obtained. Finally, a practical example is discussed.

: The decision to start up or to take over an enterprise is induced both by the social and personal situation of the individual and by an environment,which is more or less favourable to entrepreneurship. In this environment, we identify several constraints such as financial, technical, informational and entrepreneurial networking constraints. Data Analysis Methods allow us to build models,of