Integrative analysis of chemo-transcriptomic profiles for drug-feature specific gene expression signatures

One of the major challenges in systems biology is to understand the complex responses of a biological system to external perturbations or internal signalling depending on its biological conditions. Genome-wide transcriptomic profiling of cellular systems under various chemical perturbations allows the manifestation of certain features of the chemicals through their transcriptomic expression profiles. The insights obtained may help to establish the connections between human diseases, associated genes and therapeutic drugs. The main objective of this study was to systematically analyse cellular gene expression data under various drug treatments to elucidate drug-feature specific transcriptomic signatures. We first extracted drug-related information (drug features) from the collected textual description of DrugBank entries using text-mining techniques. A novel statistical method employing orthogonal least square learning was proposed to obtain drug-feature-specific signatures by integrating gene expression with DrugBank data. To obtain robust signatures from noisy input datasets, a stringent ensemble approach was applied with the combination of three techniques: resampling, leave-one-out cross validation, and aggregation. The validation experiments showed that the proposed method has the capacity of extracting biologically meaningful drug-feature-specific gene expression signatures. It was also shown that most of signature genes are connected with common hub genes by regulatory network analysis. The common hub genes were further shown to be related to general drug metabolism by Gene Ontology analysis. Each set of genes has relatively few interactions with other sets, indicating the modular nature of each signature and its drug-feature-specificity. Based on Gene Ontology analysis, we also found that each set of drug feature (DF)-specific genes were indeed enriched in biological processes related to the drug feature. The results of these experiments demonstrated the pot- ntial of the method for predicting certain features of new drugs using their transcriptomic profiles, providing a useful methodological framework and a valuable resource for drug development and characterization.

On the statistical memory architecture exploration and optimization

The worsening of process variations and the consequent increased spreads in circuit performance and consumed power hinder the satisfaction of the targeted budgets and lead to yield loss. Corner based design and adoption of design guardbands might limit the yield loss. However, in many cases such methods may not be able to capture the real effects which might be way better than the predicted ones leading to increasingly pessimistic designs. The situation is even more severe in memories which consist of substantially different individual building blocks, further complicating the accurate analysis of the impact of variations at the architecture level leaving many potential issues uncovered and opportunities unexploited. In this paper, we develop a framework for capturing non-trivial statistical interactions among all the components of a memory/cache. The developed tool is able to find the optimum memory/cache configuration under various constraints allowing the designers to make the right choices early in the design cycle and consequently improve performance, energy, and especially yield. Our, results indicate that the consideration of the architectural interactions between the memory components allow to relax the pessimistic access times that are predicted by existing techniques.

A Statistical Characterization of Shadowed Fading in Indoor Off-Body Communications Channels At 5.8 GHz

This paper investigates the characteristics of the shadowed fading observed in off-body communications channels at 5.8 GHz using the κ-μ / gamma composite fading model. Realistic measurements have been conducted considering four individual scenarios namely line of sight (LOS) and non-LOS (NLOS) walking, rotation and random movements within an indoor laboratory environment. It is shown that the κ-μ / gamma composite fading model provides a better fit to the fading observed in off-body communications channels compared to the conventional Nakagami-m and Rician fading models.

The International Regulatory Regime on Capital Flows

Capital controls and exchange restrictions are used to restrict international capital flows during economic crises. This paper looks at the legal implications of these restrictions and explores the current international regulatory framework applicable to international capital movements and current payments. It shows how international capital flows suffer from the lack of a comprehensive and coherent regulatory framework that would harmonize the patchwork of
multilateral, regional, and bilateral treaties that currently regulate this issue. These treaties include the Articles of Agreement of the International Monetary Fund (IMF Articles), the General Agreement on Trade in Services (GATS), free-trade agreements, the European Union treaty, bilateral investment treaties, and the Organization for Economic Co-operation and Development (OECD) Code of Liberalization of Capital Movements (OECD Code of Capital Movement). Each
of these instruments regulate differently capital movements with little coordination with other areas of law. This situation sometimes leads to regulatory overlaps and conflict between different sources of law. Given the strong links between capital movements and trade in services, this paper pays particular attention to the rules of the GATS on capital flows and discusses the policy space available in the GATS for restricting capital flows in times of crisis.

Statistical Tests for Joint Analysis of Performance Measures

Recently there has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and architectural complexity). Once one has learned a model based on their devised method, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Unfortunately, the standard tests used for this purpose are not able to jointly consider performance measures. The aim of this paper is to resolve this issue by developing statistical procedures that are able to account for multiple competing measures at the same time. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameter of such models, as usually the number of studied cases is very reduced in such comparisons. Real data from a comparison among general purpose classifiers is used to show a practical application of our tests.