An ica algorithm for analyzing multiple data sets

In this paper we derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model permits there to be components individual to the various data sets, and others that are common to all the sets. We explore the assumed time autocorrelation of independent signal components and base our algorithm on prediction analysis. We illustrate the algorithm using a simple image separation example. Our aim is to apply this method to functional brain mapping using functional magnetic resonance imaging (fMRI). 1

Correlations Between Incidence of Foot Pad Lesions and Body Weight of Broilers in Different Rearing Systems

The incidence of foot pad lesions of broilers of moderate growth was investigated in order to establish correlations with body weight. Broilers were reared until the age of 42 days in the floor system in the poultry house and then were divided into two groups. The first group continued growing in the poultry house until the age of 84 days and the second group was growing in the free range system until the same age. Individual measurements of body weight and evaluation of the incidence of foot pad lesions of broilers were carried out at the end of the experiment. In a correlation analysis of previously transformed data on the percentage of broilers with lesions and body weight within each weight group, data were obtained that showed an association between these traits depending on the rearing system. System of rearing had significant impact on the strength and direction of correlation between body weight and the incidence of foot pad lesions, in light of the determined correlation coefficient r = -0.95 at the significance level p=0.01 in the free range system, and r=0.56 (p>0.05) in chickens reared in the poultry house

IL-33/ST2 axis in innate and acquired immunity to tumors

Interleukin-33, a ligand for ST2/T1, has an important role in allergy, autoimmunity and inflammation. The role of IL-33/ST2 axis in cancer is not elucidated. Using metastatic breast cancer model we provide evidence that lack of ST2 signaling led to reduced tumor growth and metastasis and enhanced anti-tumor immunity

Pointwise consistency of the kriging predictor with known mean and covariance functions

This paper deals with several issues related to the pointwise consistency of the kriging predictor when the mean and the covariance functions are known. These questions are of general importance in the context of computer experiments. The analysis is based on the properties of approximations in reproducing kernel Hilbert spaces. We fix an erroneous claim of Yakowitz and Szidarovszky (J. Multivariate Analysis, 1985) that the kriging predictor is pointwise consistent for all continuous sample paths under some assumptions.Comment: Submitted to mODa9 (the Model-Oriented Data Analysis and Optimum Design Conference), 14th-19th June 2010, Bertinoro, Ital

A Spatially Robust ICA Algorithm for Multiple fMRI Data Sets

In this paper we derive an independent-component analysis (ICA) method for analyzing two or more data sets simultaneously. Our model extracts independent components common to all data sets and independent data-set-specific components. We use time-delayed autocorrelations to obtain independent signal components and base our algorithm on prediction analysis. We applied this method to functional brain mapping using functional magnetic resonance imaging (fMRI). The results of our 3-subject analysis demonstrate the robustness of the algorithm to the spatial misalignment intrinsic in multiple-subject fMRI data sets. 1

Zero-temperature behavior of the random-anisotropy model in the strong-anisotropy limit

We consider the random-anisotropy model on the square and on the cubic lattice in the strong-anisotropy limit. We compute exact ground-state configurations, and we use them to determine the stiffness exponent at zero temperature; we find $\theta = -0.275(5)$ and $\theta \approx 0.2$ respectively in two and three dimensions. These results show that the low-temperature phase of the model is the same as that of the usual Ising spin-glass model. We also show that no magnetic order occurs in two dimensions, since the expectation value of the magnetization is zero and spatial correlation functions decay exponentially. In three dimensions our data strongly support the absence of spontaneous magnetization in the infinite-volume limit

A Novel Approach of Determining the Risks for the Development of Hyperinsulinemia in the Children and Adolescent Population Using Radial Basis Function and Support Vector Machine Learning Algorithm

Hyperinsulinemia is a condition with extremely high levels of insulin in the blood. Various factors can lead to hyperinsulinemia in children and adolescents. Puberty is a period of significant change in children and adolescents. They do not have to have explicit symptoms for prediabetes, and certain health indicators may indicate a risk of developing this problem. The scientific study is designed as a cross-sectional study. In total, 674 children and adolescents of school age from 12 to 17 years old participated in the research. They received a recommendation from a pediatrician to do an OGTT (Oral Glucose Tolerance test) with insulinemia at a regular systematic examination. In addition to factor analysis, the study of the influence of individual factors was tested using RBF (Radial Basis Function) and SVM (Support Vector Machine) algorithm. The obtained results indicated statistically significant differences in the values of the monitored variables between the experimental and control groups. The obtained results showed that the number of adolescents at risk is increasing, and, in the presented research, it was 17.4%. Factor analysis and verification of the SVM algorithm changed the percentage of each risk factor. In addition, unlike previous research, three groups of children and adolescents at low, medium, and high risk were identified. The degree of risk can be of great diagnostic value for adopting corrective measures to prevent this problem and developing potential complications, primarily type 2 diabetes mellitus, cardiovascular disease, and other mass non-communicable diseases. The SVM algorithm is expected to determine the most accurate and reliable influence of risk factors. Using factor analysis and verification using the SVM algorithm, they significantly indicate an accurate, precise, and timely identification of children and adolescents at risk of hyperinsulinemia, which is of great importance for improving their health potential, and the health of society as a whole