Entity

Time filter

Source Type

Vicksburg, MS, United States

Uchimiya M.,U.S. Army | Gorb L.,SpecPro Inc | Isayev O.,Case Western Reserve University | Qasim M.M.,U.S. Army | And 2 more authors.
Environmental Pollution | Year: 2010

Extensive studies have been conducted in the past decades to predict the environmental abiotic and biotic redox fate of nitroaromatic and nitramine explosives. However, surprisingly little information is available on one-electron standard reduction potentials (Eo(R-NO 2/R-NO2-)). The Eo(R-NO 2/R-NO2-) is an essential thermodynamic parameter for predicting the rate and extent of reductive transformation for energetic residues. In this study, experimental (linear free energy relationships) and theoretical (ab initio calculation) approaches were employed to determine Eo(R-NO2/R-NO2-) for nitroaromatic, (caged) cyclic nitramine, and nitroimino explosives that are found in military installations or are emerging contaminants. The results indicate a close agreement between experimental and theoretical E o(R-NO2/R-NO2-) and suggest a key trend: Eo(R-NO2/R-NO2-) value decreases from di- and tri-nitroaromatic (e.g., 2,4-dinitroanisole) to nitramine (e.g., RDX) to nitroimino compound (e.g., nitroguanidine). The observed trend in Eo(R-NO2/R-NO2-) agrees with reported rate trends for reductive degradation, suggesting a thermodynamic control on the reduction rate under anoxic/suboxic conditions. © 2010 Elsevier Ltd. All rights reserved. Source


Li Y.,University of Southern Mississippi | Wang N.,University of Southern Mississippi | Perkins E.J.,U.S. Army | Zhang C.,University of Southern Mississippi | Gong P.,SpecPro Inc
PLoS ONE | Year: 2010

Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 unique oligo probes and have used it to profile gene expression in 248 earthworms exposed to TNT, RDX or neither. We assembled a new machine learning pipeline consisting of several well-established feature filtering/selection and classification techniques to analyze the 248-array dataset in order to construct classifier models that can separate earthworm samples into three groups: control, TNT-treated, and RDX-treated. First, a total of 869 genes differentially expressed in response to TNT or RDX exposure were identified using a univariate statistical algorithm of class comparison. Then, decision tree-based algorithms were applied to select a subset of 354 classifier genes, which were ranked by their overall weight of significance. A multiclass support vector machine (MC-SVM) method and an unsupervised K-mean clustering method were applied to independently refine the classifier, producing a smaller subset of 39 and 30 classifier genes, separately, with 11 common genes being potential biomarkers. The combined 58 genes were considered the refine dsubset and used to build MC-SVM and clustering models with classification accuracy of 83.5% and 56.9%, respectively. This study demonstrates that the machine learning approach can be used to identify and optimize a small subset of classifier/ biomarker genes from high dimensional datasets and generate classification models of acceptable precision for multiple classes. Source


Chaitankar V.,University of Southern Mississippi | Ghosh P.,University of Southern Mississippi | Perkins E.J.,U.S. Army | Gong P.,SpecPro Inc | Zhang C.,University of Southern Mississippi
BMC Bioinformatics | Year: 2010

Background: A number of models and algorithms have been proposed in the past for gene regulatory network (GRN) inference; however, none of them address the effects of the size of time-series microarray expression data in terms of the number of time-points. In this paper, we study this problem by analyzing the behaviour of three algorithms based on information theory and dynamic Bayesian network (DBN) models. These algorithms were implemented on different sizes of data generated by synthetic networks. Experiments show that the inference accuracy of these algorithms reaches a saturation point after a specific data size brought about by a saturation in the pair-wise mutual information (MI) metric; hence there is a theoretical limit on the inference accuracy of information theory based schemes that depends on the number of time points of micro-array data used to infer GRNs. This illustrates the fact that MI might not be the best metric to use for GRN inference algorithms. To circumvent the limitations of the MI metric, we introduce a new method of computing time lags between any pair of genes and present the pair-wise time lagged Mutual Information (TLMI) and time lagged Conditional Mutual Information (TLCMI) metrics. Next we use these new metrics to propose novel GRN inference schemes which provides higher inference accuracy based on the precision and recall parameters.Results: It was observed that beyond a certain number of time-points (i.e., a specific size) of micro-array data, the performance of the algorithms measured in terms of the recall-to-precision ratio saturated due to the saturation in the calculated pair-wise MI metric with increasing data size. The proposed algorithms were compared to existing approaches on four different biological networks. The resulting networks were evaluated based on the benchmark precision and recall metrics and the results favour our approach.Conclusions: To alleviate the effects of data size on information theory based GRN inference algorithms, novel time lag based information theoretic approaches to infer gene regulatory networks have been proposed. The results show that the time lags of regulatory effects between any pair of genes play an important role in GRN inference schemes. © 2010 Zhang et al; licensee BioMed Central Ltd. Source


Tang J.,Alcorn State University | Guo S.,Alcorn State University | Sun Q.,University of Southern Mississippi | Deng Y.,SpecPro Inc | Zhou D.,The Fifth Hospital of Harbin
BMC Genomics | Year: 2010

Background: Ultrasound imaging technology has wide applications in cattle reproduction and has been used to monitor individual follicles and determine the patterns of follicular development. However, the speckles in ultrasound images affect the post-processing, such as follicle segmentation and finally affect the measurement of the follicles. In order to reduce the effect of speckles, a bilateral filter is developed in this paper.Results: We develop a new bilateral filter for speckle reduction in ultrasound images for follicle segmentation and measurement. Different from the previous bilateral filters, the proposed bilateral filter uses normalized difference in the computation of the Gaussian intensity difference. We also present the results of follicle segmentation after speckle reduction. Experimental results on both synthetic images and real ultrasound images demonstrate the effectiveness of the proposed filter.Conclusions: Compared with the previous bilateral filters, the proposed bilateral filter can reduce speckles in both high-intensity regions and low intensity regions in ultrasound images. The segmentation of the follicles in the speckle reduced images by the proposed method has higher performance than the segmentation in the original ultrasound image, and the images filtered by Gaussian filter, the conventional bilateral filter respectively. © 2010 Tang et al; licensee BioMed Central Ltd. Source


Johnson D.R.,U.S. Army | Ang C.,SpecPro Inc | Bednar A.J.,U.S. Army | Inouye L.S.,U.S. Army
Toxicological Sciences | Year: 2010

Tungsten, in the form of tungstate, polymerizes with phosphate, and as extensive polymerization occurs, cellular phosphorylation and dephosphorylation reactions may be disrupted, resulting in negative effects on cellular functions. A series of studies were conducted to evaluate the effect of tungsten on several phosphate-dependent intracellular functions, including energy cycling (ATP), regulation of enzyme activity (cytosolic protein tyrosine kinase [cytPTK] and tyrosine phosphatase), and intracellular secondary messengers (cyclic adenosine monophosphate [cAMP]). Rat non-cancerous hepatocyte (Clone-9), rat cancerous hepatocyte (H4IIE), and human cancerous hepatocyte (HepG2) cells were exposed to 1-1000 mg/l tungsten (in the form of sodium tungstate) for 24 h, lysed, and analyzed for the above biochemical parameters. Cellular ATP levels were not significantly affected in any cell line. After 4 h, tungsten significantly decreased cytPTK activity in Clone-9 cells at ≥ 18 mg/l, had no effect in H4IIE cells, and significantly increased cytPTK activity by 70% in HepG2 cells at ≥ 2 mg/l. CytPTK displayed a slight hormetic response to tungsten after 24-h exposure yet returned to normal after 48-h exposure. Tungsten significantly increased cAMP by over 60% in Clone-9 cells at ≥ 100 mg/l, significantly increased cAMP in H4IIE cells at only 100 mg/l, and significantly increased cAMP in HepG2 cells between 1-100 mg/l but at much more modest levels (8-20%). In conclusion, these data indicate that tungsten produces complex results that must be carefully interpreted in the context of their respective animal models, as well as the phenotype of the cell lines (i.e., normal vs. cancerous). © The Author 2010. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. Source

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