Enhancing guideline-based decision support with distributed computation through local mobile application

We introduce the need for a distributed guideline-based decision support (DSS) process, describe its characteristics, and explain how we implemented this process within the European Union’s MobiGuide project. In particular, we have developed a mechanism of sequential, piecemeal projection, i.e., 'downloading' small portions of the guideline from the central DSS server, to the local DSS in the patient's mobile device, which then applies that portion, using the mobile device's local resources. The mobile device sends a callback to the central DSS when it encounters a triggering pattern predefined in the projected module, which leads to an appropriate predefined action by the central DSS, including sending a new projected module, or directly controlling the rest of the workflow. We suggest that such a distributed architecture that explicitly defines a dialog between a central DSS server and a local DSS module, better balances the computational load and exploits the relative advantages of the central server and of the local mobile device

Relationships among charges, fields, and potential on spherical surfaces boundary value problems

This paper suggests a new point of view for the Poisson equation and its solution for the potential and field on the d dimensional sphere, Sd, on which point charges are distributed. The available solutions for the potential on multidimensional spheres in the literature are purely mathematical, while the solution suggested here is motivated by physical intuition and requires minimal background; namely, basic laws of electrostatics and dimensional analysis. In this study, the modified Coulomb’s law is presented by means of “dimensional reduction” and the use of equivalence between a point charge on Sd and a charged ray in Rd+1.Besides formal detailed solutions and theorems, this paper presents concrete physical examples (unstudied or studied partly), such as distribution of charges/sources on a two-sphere; Dirichlet problem for currents on a truncated sphere; and fields and potentials created by “infinite” cones. Well-known statements about special charge distributions in Euclidean space must be reformulated and amended when dealing with the case of charges embedded in a spherical manifold

Radiation fields created by accelerated relativistic charges: A simple approach based on inertial moving charges

An electrical circuit is considered a polygon in that it has edges and nodes. The moving (let’s say, relativistic) charges that change their velocity directions (i.e., are accelerating), radiate, and we confront this radiation with the well-known fact that DC current does not radiate at all. This method of dealing with “kinks” (i.e. sharp changes of velocity) leads us to obtain (relatively cheaply): 1) The fields of stopping charges, 2) The Biot-Savart law, and 3) The radiation field of an accelerated charge.This procedure avoids the use of retarded sources and is based on actual time or “should be actual” sources. Simple elementary arguments based on special relativity consider only inertial moving charges; the acceleration effects are implicit and obtained by consistency relations

Function of Cancer Associated Genes Revealed by Modern Univariate and Multivariate Association Tests

<div><p>Copy number variation (CNV) plays a role in pathogenesis of many human diseases, especially cancer. Several whole genome CNV association studies have been performed for the purpose of identifying cancer associated CNVs. Here we undertook a novel approach to whole genome CNV analysis, with the goal being identification of associations between CNV of different genes (CNV-CNV) across 60 human cancer cell lines. We hypothesize that these associations point to the roles of the associated genes in cancer, and can be indicators of their position in gene networks of cancer-driving processes. Recent studies show that gene associations are often non-linear and non-monotone. In order to obtain a more complete picture of all CNV associations, we performed omnibus univariate analysis by utilizing dCov, MIC, and HHG association tests, which are capable of detecting any type of association, including non-monotone relationships. For comparison we used Spearman and Pearson association tests, which detect only linear or monotone relationships. Application of dCov, MIC and HHG tests resulted in identification of twice as many associations compared to those found by Spearman and Pearson alone. Interestingly, most of the new associations were detected by the HHG test. Next, we utilized dCov's and HHG's ability to perform multivariate analysis. We tested for association between genes of unknown function and known cancer-related pathways. Our results indicate that multivariate analysis is much more effective than univariate analysis for the purpose of ascribing biological roles to genes of unknown function. We conclude that a combination of multivariate and univariate omnibus association tests can reveal significant information about gene networks of disease-driving processes. These methods can be applied to any large gene or pathway dataset, allowing more comprehensive analysis of biological processes.</p></div

Bipartite graph displaying gene-to-pathway associations, as determined by HHG and dCov.

<p>In panels A and B, genes (on the left) and pathways (on the right) were analyzed for association by HHG and dCov. Significant associations (after adjusting for multiple testing) are linked by lines: dashed for HHG, dotted for dCov, and solid for both. A) Significant associations between genes with unknown function and cancer related pathways. Associations found by dCov and HHG are marked. B) Significant associations between genes with known function and cancer related pathways. Only associations found by dCov are shown as no significant associations were found by HHG.</p

Example of significant relationships.

<p>First line consists of three findings discovered only by Spearman or Pearson; second, only by HHG; third, only by dCov; and fourth, only by MIC. P-values (after adjusting for multiple testing) are denoted in each plot.</p

Euler diagram of the significant discoveries found by Pearson or Spearman, dCov and HHG.

<p>MIC was excluded due to the small number of significant findings provided by this method. The area of each oval represents the number of significant tests of each method, and intersections (emphasized by different colors) represent common discoveries. Evidently, Pearson or Spearman, dCov and HHG share 185 discoveries; 184 tests were significant by HHG but not by Pearson, Spearman or dCov; 10 tests were significant by dCov and not by Pearson, Spearman or HHG; 29 tests were significant by Pearson or Spearman but not by dCov or HHG; dCov and HHG share 26 discoveries; Pearson or Spearman and dCov share 35 discoveries; and Pearson or Spearman and HHG share only 5 discoveries.</p

Indication for the linear associations that explains the difference between dCov and HHG in the multivariate analysis with known genes.

<p>Scatter plots of PIK3CA versus HRAS (left panel) and MSH2 versus REL (right panel).</p