Subhajit Dutta

Indian Statistical Institute, Calcutta, Stat-Math Unit, Alumnus

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Μιχαήλ Τσαγρής

University of Crete

Sumitra Purkayastha

Indian Statistical Institute, Calcutta

Rice University

Marmara University

Hacettepe University

The University of Newcastle

Yogendra Chaubey

Concordia University (Canada)

Rezzy Eko Caraka

University of Indonesia

Ahmed Z. Afify

Benha University

Raúl Jiménez

Universidad Carlos III de Madrid

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In vitro structure-toxicity relationship of chalcones in human hepatic stellate cells

Toxicology, 2015

Xanthohumol (XN), the major prenylated chalcone from hops (Humulus lupulus L.), has received much... more Xanthohumol (XN), the major prenylated chalcone from hops (Humulus lupulus L.), has received much attention within the last years, due to its multiple pharmacological activities including anti-proliferative, anti-inflammatory, antioxidant, pro-apoptotic, anti-bacterial and anti-adhesive effects. However, there exists a huge number of metabolites and structurally-related chalcones, which can be expected, or are already known, to exhibit various effects on cells. We have therefore analyzed the effects of XN and 18 other chalcones in a panel, consisting of multiple cell-based assays. Readouts of these assays addressed distinct aspects of cell-toxicity, like proliferation, mitochondrial health, cell cycle and other cellular features. Besides known active structural elements of chalcones, like the Michael system, we have identified several moieties that seem to have an impact on specific effects and toxicity in human liver cells in vitro. Based on these observations, we present a structure-toxicity model, which will be crucial to understand the molecular mechanisms of wanted effects and unwanted side-effects of chalcones.

Visuanimation in statistics

Stat, 2015

A non-Gaussian multivariate distribution with all lower-dimensional Gaussians and related families

Journal of Multivariate Analysis, 2014

ABSTRACT Several fascinating examples of non-Gaussian bivariate distributions which have marginal... more ABSTRACT Several fascinating examples of non-Gaussian bivariate distributions which have marginal distribution functions to be Gaussian have been proposed in the literature. These examples often clarify several properties associated with the normal distribution. In this paper, we generalize this result in the sense that we construct a pp-dimensional distribution for which any proper subset of its components has the Gaussian distribution. However, the jointpp-dimensional distribution is inconsistent with the distribution of these subsets because it is not Gaussian. We study the probabilistic properties of this non-Gaussian multivariate distribution in detail. Interestingly, several popular tests of multivariate normality fail to identify this pp-dimensional distribution as non-Gaussian. We further extend our construction to a class of elliptically contoured distributions as well as skewed distributions arising from selections, for instance the multivariate skew-normal distribution.

On some transformations of high dimension, low sample size data for nearest neighbor classification

Machine Learning, 2015

For data with more variables than the sample size, phenomena like concentration of pairwise dista... more For data with more variables than the sample size, phenomena like concentration of pairwise distances, violation of cluster assumptions and presence of hubness often have adverse effects on the performance of the classic nearest neighbor classifier. To cope with such problems, some dimension reduction techniques like those based on random linear projections and principal component directions have been proposed in the literature. In this article, we construct nonlinear transformations of the data based on inter-point distances, which also lead to reduction in data dimension. More importantly, for such high dimension low sample size data, they enhance separability among the competing classes in the transformed space. When the classic nearest neighbor classifier is used on the transformed data, it usually yields lower misclassification rates. Under appropriate regularity conditions, we derive asymptotic results on misclassification probabilities of nearest neighbor classifiers based on the l 2 norm and the l p norms (with p ∈ (0, 1]) in the transformed space, when the training sample size remains fixed and the dimension of the data grows to infinity. Strength of the proposed transformations in the classification context is demonstrated by analyzing several simulated and benchmark data sets.

Subhajit Dutta

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