This study aimed at assessing the effect of packaging materials and preservatives on nutrient composition fish was carried out using 864 live Clarias gariepinus with an average weight 226g acquired from earthen pond a farm in Omi-Adio, Ibadan, Nigeria. The experiment conducted 4 × 3 11 factorial (4 preservatives, storage periods). protein content decreased consistently 77.538 (0days after smoki...

This paper presents the application of the expectation-maximization/maximization of the posterior marginals (EM/MPM) algorithm to signal detection for functional MRI (fMRI). On basis of assumptions for fMRI 3-D image data, a novel analysis method is proposed and applied to synthetic data and human brain data. Synthetic data analysis is conducted using two statistical noise models (white and aut...

This study evaluates the effects of spatial smoothing on inter-subject correlation (ISC) analysis for FMRI data using the traditional model based analysis as a reference. So far within ISC analysis the effects of smoothing have not been studied systematically and linear Gaussian filters with varying kernel widths have been used without better knowledge about the effects of filtering. Instead, w...

The aim of the present paper is two-fold. First, it attempts to support previous findings on the role of some psychometric variables, such as, M-capacity, the degree of field dependence-independence, logical thinking and the mobilityfixity dimension, on students’ achievement in chemistry problem solving. Second, the paper aims to raise some methodological and epistemological issues concerning t...

Single-index model is a potentially tool for multivariate nonparametric regression, generalizes both the generalized linear models(GLM) and the missing-link function problem in GLM. In this paper, we extend Cook’s local influence analysis to the penalized Gaussian likelihood estimator based on P-spline for the partially linear single-index model. Some influence measures, based on the minor pert...

Conventional approaches to modeling classification image data can be described in terms of a standard linear model (LM). We show how the problem can be characterized as a Generalized Linear Model (GLM) with a Bernoulli distribution. We demonstrate via simulation that this approach is more accurate in estimating the underlying template in the absence of internal noise. With increasing internal n...

The thresholded t-map produced by the General Linear Model (GLM) gives an effective summary of activation patterns in functional brain images and is widely used for feature selection in fMRI related classification tasks. As part of a project to build content-based retrieval systems for fMRI images, we have investigated ways to make GLM more adaptive and more robust in dealing with fMRI data fro...

Using single-trial parameters as a regressor in the General Linear Model (GLM) is becoming an increasingly popular method for informing fMRI analysis. However, the parameter used to characterise or to differentiate brain regions involved in the response to a particular task varies across studies (e.g. ERP amplitude, ERP latency, reaction time). Furthermore, the way in which the single-trial inf...

The General Linear Model (GLM), has been used to analyse simultaneous EEG-fMRI to reveal BOLD changes linked to interictal epileptic discharges (IED) identified on scalp EEG. This approach is ineffective when IED are not evident in the EEG. Data-driven fMRI analysis techniques that do not require an EEG derived model may offer a solution in these circumstances. We compared the findings of indep...