Molecular basis of copper transport: cellular and physiological functions of Menkes and Wilson disease proteins (ATP7A and ATPB)

Eukaryotic cells prevent copper-induced, free radical damage to cell components by employing copper-binding proteins and transporters that minimize the likelihood of free copper ions existing in the cell. In the cell, copper is actively transported from the cytoplasm during the biosynthesis of secreted coppercontaining proteins and, as a protective measure, when there is an excess of copper. In humans, this is accomplished by two related copper-transporting ATPases (ATP7A and ATP7B), which are the affected genes in two distinct human genetic disorders of copper transport, Menkes disease (copper deficiency) and Wilson disease (copper toxicosis). The study of these ATPases has revealed their molecular mechanisms of copper transport and their roles in physiological copper homeostasis. Both ATP7A and ATP7B are expressed in specific brain regions and neurological abnormalities are important clinical features in Menkes and Wilson disease.

Comparison of Two Methods for Caluculating the Partition Functions of Various Spatial Statistical Models

Likelihood computation in spatial statistics requires accurate and efficient calculation of the normalizing constant (i.e. partition function) of the Gibbs distribution of the model. Two available methods to calculate the normalizing constant by Markov chain Monte Carlo methods are compared by simulation experiments for an Ising model, a Gaussian Markov field model and a pairwise interaction point field model.

Interpolation of Lipschitz functions

This paper describes a new computational approach to multivariate scattered data interpolation. It is assumed that the data is generated by a Lipschitz continuous function f. The proposed approach uses the central interpolation scheme, which produces an optimal interpolant in the worst case scenario. It provides best uniform error bounds on f, and thus translates into reliable learning of f. This paper develops a computationally efficient algorithm for evaluating the interpolant in the multivariate case. We compare the proposed method with the radial basis functions and natural neighbor interpolation, provide the details of the algorithm and illustrate it on numerical experiments. The efficiency of this method surpasses alternative interpolation methods for scattered data.

A comparison of search functions and other navigational tools provided on government websites

This paper reports the outcomes of a study that evaluated the effectiveness of search functions compared to other navigational tools available on government websites. The study used an observation exercise triangulated with a post observation interview. Results suggest that while there wasn't any significant difference in effectiveness between search functions and other navigational tools, the skill with which the search function is implemented and participants' familiarity with the website, are fundamental determinants of users' opinions. Implications of the findings for research and practice are discussed.

The eight variable homogeneous degree three bent functions

We determine the affine equivalence classes of the eight variable degree three homogeneous bent functions using a new algorithm. Our algorithm applies to general bent functions and can systematically determine the automorphism groups. We provide a partial verification of the computer enumeration of bent functions by Meng et al.

Determination of yield and erosion damage functions using subjectively elicited data: application to small holder tea in Sri Lanka

Tea has been Sri Lanka's major export earner for several decades. However, soil erosion on tea-producing land has had considerable on-site and off-site effects. This study quantifies soil erosion impacts for smallholder tea farms in Sri Lanka by estimating a yield damage function and an erosion damage function using a subjective elicitation technique. The Mitscherlich-Spillman type of function was found to yield acceptable results. The study indicates that high rates of soil erosion require earlier adoption of soil conservation measures than do low rates of erosion. Sensitivity analysis shows the optimum year to change to a conservation practice is very sensitive to the discount rate but less sensitive to the cost of production and price of tea.

Smoothing Lipschitz functions

This paper describes a new approach to multivariate scattered data smoothing. It is assumed that the data are generated by a Lipschitz continuous function f, and include random noise to be filtered out. The proposed approach uses known, or estimated value of the Lipschitz constant of f, and forces the data to be consistent with the Lipschitz properties of f. Depending on the assumptions about the distribution of the random noise, smoothing is reduced to a standard quadratic or a linear programming problem. We discuss an efficient algorithm which eliminates the redundant inequality constraints. Numerical experiments illustrate applicability and efficiency of the method. This approach provides an efficient new tool of multivariate scattered data approximation.

Standardizing anthropometric measures in children and adolescents with new functions for egen

A new function for egen has been developed to allow transformation of child anthropometric data to z-scores using the LMS method and the reference data available from the 1990 British Growth Reference and the 2000 US CDC Growth Reference. An additional function allows for children to be categorized according to body mass index (weight/height ²) using international cutoff points recommended by the Childhood Obesity Working Group of the International Obesity Taskforce. Copyright 2004 by StataCorp LP.

Multi-wavelets from B-spline super-functions with approximation order

Approximation order is an important feature of all wavelets. It implies that polynomials up to degree p−1 are in the space spanned by the scaling function(s). In the scalar case, the scalar sum rules determine the approximation order or the left eigenvectors of the infinite down-sampled convolution matrix H determine the combinations of scaling functions required to produce the desired polynomial. For multi-wavelets the condition for approximation order is similar to the conditions in the scalar case. Generalized left eigenvectors of the matrix H_f; a finite portion of H determines the combinations of scaling functions that produce the desired superfunction from which polynomials of desired degree can be reproduced. The superfunctions in this work are taken to be B-splines. However, any refinable function can serve as the superfunction. The condition of approximation order is derived and new, symmetric, compactly supported and orthogonal multi-wavelets with approximation orders one, two, three and four are constructed.

Estimating money demand functions for South Asian countries

In this paper, we estimate a money demand function for a panel of five South Asian countries. We find that the money demand and its determinants, namely real income, real exchange rate and short-term domestic and foreign interest rates are cointegrated both for individual countries as well as for the panel, and panel long-run elasticities provide robust evidence of statistically significant relationships between money demand and its determinants. Our test for panel Granger causality suggests short-run causality running from all variables, except foreign interest rate, to money demand, and we find evidence that except for Nepal money demand functions are stable.

Texture recognition by using GLCM and various aggregation functions

We discuss the problem of texture recognition based on the grey level co-occurrence matrix (GLCM). We performed a number of numerical experiments to establish whether the accuracy of classification is optimal when GLCM entries are aggregated into standard metrics like contrast, dissimilarity, homogeneity, entropy, etc., and compared these metrics to several alternative aggregation methods.We conclude that k nearest neighbors classification based on raw GLCM entries typically works better than classification based on the standard metrics for noiseless data, that metrics based on principal component analysis inprove classification, and that a simple change from the arithmetic to quadratic mean in calculating the standard metrics also improves classification.