Scalable Group Level Probabilistic Sparse Factor Analysis

Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a group level scalable probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex noise models than the presently considered.Comment: 10 pages plus 5 pages appendix, Submitted to ICASSP 1

Group Factor Analysis

Factor analysis provides linear factors that describe relationships between individual variables of a data set. We extend this classical formulation into linear factors that describe relationships between groups of variables, where each group represents either a set of related variables or a data set. The model also naturally extends canonical correlation analysis to more than two sets, in a way that is more flexible than previous extensions. Our solution is formulated as variational inference of a latent variable model with structural sparsity, and it consists of two hierarchical levels: The higher level models the relationships between the groups, whereas the lower models the observed variables given the higher level. We show that the resulting solution solves the group factor analysis problem accurately, outperforming alternative factor analysis based solutions as well as more straightforward implementations of group factor analysis. The method is demonstrated on two life science data sets, one on brain activation and the other on systems biology, illustrating its applicability to the analysis of different types of high-dimensional data sources

Bayesian Group Factor Analysis

We introduce a factor analysis model that summarizes the dependencies between observed variable groups, instead of dependencies between individual variables as standard factor analysis does. A group may correspond to one view of the same set of objects, one of many data sets tied by co-occurrence, or a set of alternative variables collected from statistics tables to measure one property of interest. We show that by assuming group-wise sparse factors, active in a subset of the sets, the variation can be decomposed into factors explaining relationships between the sets and factors explaining away set-specific variation. We formulate the assumptions in a Bayesian model which provides the factors, and apply the model to two data analysis tasks, in neuroimaging and chemical systems biology.Comment: 9 pages, 5 figure

A Factor Analysis of Investment Behaviour for Small Investors in the Hong Kong Stock Market

Hon (2012) found that small investors were overconfident and bought more stock during the buoyant market in the Hong Kong stock market. Small investors also exhibited herd behaviour. In this paper we extend his paper to identify and analyse the important factors that capture the behaviour of small investors in the Hong Kong stock market,especially during the financial crisis.Exploratory factor analysis is employed to analyse the data, we found that monitor investments is the second important factor and reference group is the most important factor..Keywords. Factor analysis, Small investors, Stock market, Hong Kong.JEL. E22, G02, G10

PENGARUH KOMBINASI CYCLOPHOSPHAMIDE - TRANSFER FACTOR TERHADAP SKOR SEL T CD4+ PADA ADENOCARSINOMA MAMMAE MENCIT C3H

Background : Transfer factor are extracts from Colostrum, which is considered as an immunostimulant oligoribonukleotida for alternative therapies. Breast cancer patients will experience a decline in the immune system. CD4+ T cells is one of the cellular immune response to cancer. Is Tansfer factor may increase the number of CD4+ T cells. Objective : To analyze the effect of cyclophosphamid-transfer factor to the number of CD4+ T cells in C3H mice mammary Adenocarcinoma. Methods : The research laboratory experimental design with "Randomized posttest only control group." Using pure C3H female 20 mice amounted. Samples were divided into 4 groups : K as a control, P1 given cyclphosphamide, P2 and P3 were given transfer factor were given a combination of cyclophosphamidetransfer factor. As the dependent variable is the number of CD4+ T cells. Analysis of differences between the four groups using ANOVA with 95% confidence level. Results : The difference in the number of CD4+ T cells obtained value of p = <0.001 between the control group (K) to the treatment group (P1, P2, P3) and between treatment groups are given a combination of Cyclophosphamide-transfer factor to the treatment group who were given Cyclophosphamide alone or transfer factor alone. There was no significant difference in the number of CD4+ T cells in treatment groups that were given Cyclophosphamide alone compared with the treatment group who were given transfer factor only. Conclusion : There is increased number of CD4+ T cells significantly in C3H mice mammary adenocarcinoma who were given cyclophosphamide-transfer factor. Keywords: Transfer Factor, cyclophosphamide, CD4+ T cells, mammary adenocarcinom

Structural, item, and test generalizability of the psychopathology checklist - revised to offenders with intellectual disabilities

The Psychopathy Checklist–Revised (PCL-R) is the most widely used measure of psychopathy in forensic clinical practice, but the generalizability of the measure to offenders with intellectual disabilities (ID) has not been clearly established. This study examined the structural equivalence and scalar equivalence of the PCL-R in a sample of 185 male offenders with ID in forensic mental health settings, as compared with a sample of 1,212 male prisoners without ID. Three models of the PCL-R’s factor structure were evaluated with confirmatory factor analysis. The 3-factor hierarchical model of psychopathy was found to be a good fit to the ID PCL-R data, whereas neither the 4-factor model nor the traditional 2-factor model fitted. There were no cross-group differences in the factor structure, providing evidence of structural equivalence. However, item response theory analyses indicated metric differences in the ratings of psychopathy symptoms between the ID group and the comparison prisoner group. This finding has potential implications for the interpretation of PCL-R scores obtained with people with ID in forensic psychiatric settings

Digital morphometry of cytologic aspirate endometrial samples [Digitalna morfometrijska analiza citoloških uzoraka aspirata endometrija]

Unlike cervical cytology, morphological cytology criteria in the differential diagnosis of endometrium have not yet been clearly defined, and methods to allow for more precise evaluation of endometrium status have been searched for. The aim of the present study was to assess the value of morphometric nucleus analysis of cytologic aspirate endometrial samples in proliferative, hyperplastic and malignant endometrium by use of digital image analysis. Morphometric analysis was performed on archival cytologic aspirate endometrial samples (at least 10 per group) stained according to Papanicolaou (n=77) and May-Grünwald-Giemsa (MGG; n=80) with the following histopathologic diagnoses: proliferative endometrium, hyperplasia simplex, hyperplasia complex, hyperplasia complex atypica, and adenocarcinoma endometriodes endometrii (grade I, II and III). Interactive image analysis (nuclear area, convex area, perimeter, maximum and minimum radius, length and breadth, as well as nucleus form factor and elongation factor) was performed by use of the SFORM software (VAMSTEC, Zagreb) on at least 50 (Papanicolaou stain) and 100 (MGG stain) well preserved endometrial epithelial cell nuclei without overlapping, at magnification of ´1000. Statistical data analysis was done by use of the Statistica Ver. 6 statistical package. Multivariate analysis (ANOVA) distinguished malignant, hyperplastic and proliferative endometrium according to all morphometric variables with both staining methods (p0.05) from atypical hyperplasia, adenocarcinoma and proliferative endometrium only according to the nucleus form factor and elongation factor (Papanicolaou stain), whereas malignant and atypical hyperplastic endometrium (MGG stain) differed statistically significantly (p0.05). According to the cytologic staining method, morphometric parameters were considerably higher in MGG stained endometrial samples, reaching the level of statistical significance (p0.05) in the groups of hyperplasia simplex and complex, well differentiated adenocarcinoma (form factor) and atypical hyperplasia (elongation factor). A combination of cytomorphology and the morphometric variables assessed in this study can yield useful information on the cytologic state of endometrium, with special reference to the possible differentiation of the group of hyperplasia without atypia from the group of adenocarcinoma and atypical hyperplasia

Fermion Schwinger's function for the SU(2)-Thirring model

We study the Euclidean two-point function of Fermi fields in the SU(2)-Thirring model on the whole distance (energy) scale. We perform perturbative and renormalization group analyses to obtain the short-distance asymptotics, and numerically evaluate the long-distance behavior by using the form factor expansion. Our results illustrate the use of bosonization and conformal perturbation theory in the renormalization group analysis of a fermionic theory, and numerically confirm the validity of the form factor expansion in the case of the SU(2)-Thirring model.Comment: 27 pages, harvmac.tex, references added, typos correcte

A validity and reliability study of the Attitudes toward Sustainable Development scale

This article describes the development and validation of the Attitudes toward Sustainable Development scale, a quantitative 20-item scale that measures Italian university students\u2019 attitudes toward sustainable development. A total of 484 undergraduate students completed the questionnaire. The validity and reliability of the scale was statistically tested by computing the KMO and Bartlett tests and via an exploratory factor analysis, descriptive statistics, Cronbach\u2019s alpha, a confirmatory factor analysis and a multi-group invariance testing. The results of the principal components factor analysis show that the scale consists of the following four dimensions, with five items in each: environment, economy, society and education. The overall structure and measurement of the scale are confirmed by the confirmatory factor analysis and by the multi-group invariance testing. Internal reliability, which was found using Cronbach\u2019s alpha, varies between .660 and .854. The results show that the instrument meets the validity and reliability criteria. To demonstrate its utility, the scale was applied to detect differences in sustainable development attitudes among students pursuing degrees in psychology and in agriculture. Relevant differences were detected for the dimensions of environment and society. The Attitudes toward Sustainable Development scale could be useful for understanding the ways in which students think about sustainability issues and could be used to investigate the relationship between sustainability attitudes and other variables