Papers by Adam Fedorowicz
Military Medicine, 2015
Dental Disease and Non-Battle Injuries (D-DNBI) continue to be a problem among U.S. Army active d... more Dental Disease and Non-Battle Injuries (D-DNBI) continue to be a problem among U.S. Army active duty (AD), U.S. Army National Guard (ARNG), and U.S. Army Reserve (USAR) deployed soldiers to Operation Iraqi Freedom/Operation New Dawn in Iraq and Operation Enduring Freedom in Afghanistan. A previous study reported the annual rates to be 136 D-DNBI per 1,000 personnel for AD, 152 for ARNG, and 184 for USAR. The objectives of this study were to describe D-DNBI incidence and to determine risk factors for dental encounters and high severity diagnoses for deployed soldiers. The 78 diagnoses were classified into three categories based on severity. Poisson regression was used to compare D-DNBI rates and logistic regression was used to analyze the risk of high severity D-DNBI. In both campaigns, Reserve had a higher risk of D-DNBI than active duty. For Afghanistan, ARNG and USAR demonstrated over 50% increased risk of D-DNBI compared to AD. In Iraq, USAR had a 17% increased risk over AD. Females had a higher risk of D-DNBI (50%) compared to males in both campaigns. High severity D-DNBI made up 2.77% of all diagnoses. Within Afghanistan, there was a 4.6% increased risk of high severity D-DNBI for each additional deployment month.
International Journal of Molecular Sciences, 2004
Allergic Contact Dermatitis (ACD) is a common work-related skin disease that often develops as a ... more Allergic Contact Dermatitis (ACD) is a common work-related skin disease that often develops as a result of repetitive skin exposures to a sensitizing chemical agent. A variety of experimental tests have been suggested to assess the skin sensitization potential. We applied a method of Quantitative Structure-Activity Relationship (QSAR) to relate measured and calculated physical-chemical properties of chemical compounds to their sensitization potential. Using statistical methods, each of these properties, called molecular descriptors, was tested for its propensity to predict the sensitization potential. A few of the most informative descriptors were subsequently selected to build a model of skin sensitization. In this work sensitization data for the murine Local Lymph Node Assay (LLNA) were used. In principle, LLNA provides a standardized continuous scale suitable for quantitative assessment of skin sensitization. However, at present many LLNA results are still reported on a dichotomous scale, which is consistent with the scale of guinea pig tests, which were widely used in past years. Therefore, in this study only a dichotomous version of the LLNA data was used. To the statistical end, we relied on the logistic regression approach. This approach provides a statistical tool for investigating and predicting skin sensitization that is expressed only in categorical terms of activity and nonactivity. Based on the data of compounds used in this study, our results suggest a QSAR model of ACD that is based on the following descriptors: nDB (number of double bonds), C-003 (number of CHR3 molecular subfragments), GATS6M (autocorrelation coefficient) and HATS6m (GETAWAY descriptor), although the relevance of the identified descriptors to the continuous ACD QSAR has yet to be shown. The proposed QSAR model gives a percentage of positively predicted responses of 83% on the training set of compounds, and in cross validation it correctly identifies 79% of responses.
The Journal of Physical Chemistry B, 2014
For biomolecules in solution, changes in configurational entropy are thought to contribute substa... more For biomolecules in solution, changes in configurational entropy are thought to contribute substantially to the free energies of processes like binding and conformational change. In principle, the configurational entropy can be strongly affected by pairwise and higher-order correlations among conformational degrees of freedom. However, the literature offers mixed perspectives regarding the contributions that changes in correlations make to changes in configurational entropy for such processes. Here we take advantage of powerful techniques for simulation and entropy analysis to carry out rigorous in silico studies of correlation in binding and conformational changes. In particular, we apply information-theoretic expansions of the configurational entropy to wellsampled molecular dynamics simulations of a model host−guest system and the protein bovine pancreatic trypsin inhibitor. The results bear on the interpretation of NMR data, as they indicate that changes in correlation are important determinants of entropy changes for biologically relevant processes and that changes in correlation may either balance or reinforce changes in first-order entropy. The results also highlight the importance of main-chain torsions as contributors to changes in protein configurational entropy. As simulation techniques grow in power, the mathematical techniques used here will offer new opportunities to answer challenging questions about complex molecular systems.
Journal of Computational Chemistry, 2005
A method of statistical estimation is applied to the problem of evaluating the absolute entropy o... more A method of statistical estimation is applied to the problem of evaluating the absolute entropy of internal rotation in a molecule with two torsional degrees of freedom. The configurational part of the entropy is obtained as that of the joint probability density of an arbitrary form represented by a two-dimensional Fourier series, the coefficients of which are statistically estimated using a sample of the torsional angles of the molecule obtained by a stochastic simulation. The internal rotors in the molecule are assumed to be attached to a common frame, and their reduced moments of inertia are initially calculated as functions of the two torsional angles, but averaged over all the remaining internal degrees of freedom using the stochastic-simulation sample of the atomic configurations of the molecule. The torsional-angle dependence of the reduced moments of inertia can be also averaged out, and the absolute internalrotation entropy of the molecule is obtained in a good approximation as the sum of the configurational entropy and a kinetic contribution fully determined by the averaged reduced moments of inertia. The method is illustrated using Monte Carlo simulations of isomers of stilbene and halogenated derivatives of propane. The two torsional angles in cis-stilbene are found to be much more strongly correlated than those in trans-stilbene, while the degree of the angular correlation in propane increases strongly on substitution of hydrogen atoms with chlorine.
Journal of Computational Chemistry, 2003
A method of statistical estimation is applied to the problem of one-dimensional internal rotation... more A method of statistical estimation is applied to the problem of one-dimensional internal rotation in a hindering potential of mean force. The hindering potential, which may have a completely general shape, is expanded in a Fourier series, the coefficients of which are estimated by fitting an appropriate statistical-mechanical distribution to the random variable of internal rotation angle. The function of reduced moment of inertia of an internal rotation is averaged over the thermodynamic ensemble of atomic configurations of the molecule obtained in stochastic simulations. When quantum effects are not important, an accurate estimate of the absolute internal rotation entropy of a molecule with a single rotatable bond is obtained. When there is more than one rotatable bond, the "marginal" statisticalmechanical properties corresponding to a given internal rotational degree of freedom are educed. The method is illustrated using Monte Carlo simulations of two public health relevant halocarbon molecules, each having a single internal-rotation degree of freedom, and a molecular dynamics simulation of an immunologically relevant polypeptide, in which several dihedral angles are analyzed.
Journal of Computational Chemistry, 2006
A method for estimating the configurational (i.e., non-kinetic) part of the entropy of internal m... more A method for estimating the configurational (i.e., non-kinetic) part of the entropy of internal motion in complex molecules is introduced that does not assume any particular parametric form for the underlying probability density function. It is based on the nearest-neighbor (NN) distances of the points of a sample of internal molecular coordinates obtained by a computer simulation of a given molecule. As the method does not make any assumptions about the underlying potential energy function, it accounts fully for any anharmonicity of internal molecular motion. It provides an asymptotically unbiased and consistent estimate of the configurational part of the entropy of the internal degrees of freedom of the molecule. The NN method is illustrated by estimating the configurational entropy of internal rotation of capsaicin and two stereoisomers of tartaric acid, and by providing a much closer upper bound on the configurational entropy of internal rotation of a pentapeptide molecule than that obtained by the standard quasi-harmonic method. As a measure of dependence between any two internal molecular coordinates, a general coefficient of association based on the information-theoretic quantity of mutual information is proposed. Using NN estimates of this measure, statistical clustering procedures can be employed to group the coordinates into clusters of manageable dimensions and characterized by minimal dependence between coordinates belonging to different clusters.
Journal of Chemical Information and Modeling, 2005
The random forest and classification tree modeling methods are used to build predictive models of... more The random forest and classification tree modeling methods are used to build predictive models of the skin sensitization activity of a chemical. A new two-stage backward elimination algorithm for descriptor selection in the random forest method is introduced. The predictive performance of the random forest model was maximized by tuning voting thresholds to reflect the unbalanced size of classification groups in available data. Our results show that random forest with a proposed backward elimination procedure outperforms a single classification tree and the standard random forest method in predicting Local Lymph Node Assay based skin sensitization activity. The proximity measure obtained from the random forest is a natural similarity measure that can be used for clustering of chemicals. Based on this measure, the clustering analysis partitioned the chemicals into several groups sharing similar molecular patterns. The improved random forest method demonstrates the potential for future QSAR studies based on a large number of descriptors or when the number of available data points is limited.
Chemical Research in Toxicology, 2005
Allergic contact dermatitis (ACD) is a widespread cause of workers' disabilities. Although some s... more Allergic contact dermatitis (ACD) is a widespread cause of workers' disabilities. Although some substances found in the workplace are rigorously tested, the potential of the vast majority of chemicals to cause skin sensitization remains unknown. At the same time, exhaustive testing of all chemicals in workplaces is costly and raises ethical concerns. New approaches to developing information for risk assessment based on computational (quantitative) structureactivity relationship [(Q)SAR] methods may be complementary to and reduce the need for animal testing. Virtually any number of existing, de novo, and even preconceived compounds can be screened in silico at a fraction of the cost of animal testing. This work investigates the utility of ACD (Q)SAR modeling from the occupational health perspective using two leading software products, DEREK for Windows and TOPKAT, and an original method based on logistic regression methodology. It is found that the correct classification of (Q)SAR predictions for guinea pig data achieves values of 73.3, 82.9, and 87.6% for TOPKAT, DEREK for Windows, and the logistic regression model, respectively. The correct classification using LLNA data equals 73.0 and 83.2% for DEREK for Windows and the logistic regression model, respectively.
American Journal of Mathematical and Management Sciences, 2003
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Papers by Adam Fedorowicz