Eugene Demchuk

Nearest Neighbor Estimates of Entropy

SYNOPTIC ABSTRACT Motivated by the problems in molecular sciences, we introduce new nonparametric estimators of entropy which are based on the kth nearest neighbor distances between the n sample points, where k (< n – 1) is a fixed positive integer. These provide competing estimators to an estimator proposed by Kozachenko and Leonenko (1987), which is based on the first nearest neighbor distances of the sample points. These estimators are helpful in the evaluation of entropies of random vectors. We establish the asymptotic unbiasedness and consistency of the proposed estimators. For some standard distributions, we also investigate their performance for finite sample sizes using Monte Carlo simulations. The proposed estimators are applied to estimate the entropy of internal rotation in the methanol molecule, which can be characterized by a one-dimensional random vector, and of diethyl ether, which is described by a four-dimensional random vector.

Impact of silanol surface density on the toxicity of silica aerosols measured by erythrocyte haemolysis.

Exposures to silica-containing dusts are associated with a risk of developing life-threatening lung diseases. However, the mechanism of silica toxicity is poorly understood. In this work the atomic structure of the surfaces of different silica polymorphs was determined, and a relationship with in vitro silica toxicity was examined. The density of geminal and single silanol groups was quantitatively estimated for different silica polymorphs using a novel molecular modeling method. An association was found between the reported haemolytic activity and modeled densities of surface geminal (but not single) silanol groups on several silica polymorphs. These findings suggest a new view of aerosol toxicity based on the estimation of surface site densities. The results can be used in the development of new toxicological assays for respirable particulates, including nanomaterials.

Impact of negatively charged patches on the surface of MHC class II antigen-presenting proteins on risk of chronic beryllium disease

Chronic beryllium disease (CBD) is a granulomatous lung disease that occurs primarily in workers who are exposed to beryllium dust or fumes. Although exposure to beryllium is a necessary factor in the pathobiology of CBD, alleles that code for a glutamic acid residue at the 69th position of the HLA-DPβ1 gene have previously been found to be associated with CBD. To date, 43 HLA-DPβ1 alleles that code for glutamic acid 69 (E69) have been described. Whether all of these E69 coding alleles convey equal risk of CBD is unknown. The present study demonstrates that, on the one hand, E69 alleloforms of major histocompatibility complex class II antigen-presenting proteins with the greatest negative surface charge convey the highest risk of CBD, and on the other hand, irrespective of allele, they convey equal risk of beryllium sensitization (BeS). In addition, the data suggest that the same alleles that cause the greatest risk of CBD are also important for the progression from BeS to CBD. Alleles convey the highest risk code for E26 in a constant region and for E69, aspartic acid 55 (D55), E56, D84 and E85 in hypervariable regions of the HLA-DPβ1 chain. Together with the calculated high binding affinities for beryllium, these results suggest that an adverse immune response, leading to CBD, is triggered by chemically specific metal–protein interactions.

Statistical thermodynamics of hindered rotation from computer simulations

Due to the nonlinearity of internal molecular coordinate space, comprehensive statistical mechanical treatment of polyatomic molecules presents a formidable theoretical problem where traditional statistics does not hold. Here we introduce a new theoretical approach to the hindered internal rotor problem, which relies on simulated statistics of microstates, rather than on first-principles calculations of the partition function. The concept links traditional statistical-mechanical thermodynamics to an authentic treatment of curvilinear descriptive statistics. The results are illustrated by classical molecular dynamics simulations.

Random sampling or ‘random’ model in skin flux measurements? [Commentary on “Investigation of the mechanism of flux across human skin in vitro by quantitative structure–permeability relationships”]

Transdermal therapy receives increasing attention as an attractive alternative to traditional drug delivery. Unfortunately the exact algorithm of transdermal permeation that could guide medicinal chemists towards delivery optimization at an early stage of the drug design process still remains to be decoded. This paper discusses some major hurdles on the way to full understanding of Quantitative Structure-Activity Relationships (QSAR) of skin permeation. From the statistical perspective, a recently published combined data set is found to be inappropriate with respect to the distribution of major molecular descriptors, and therefore should be approached cautiously as a source for QSAR model training and in modelling of occupational and environmental skin exposures.