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At least 1,207 records · Page 67Linked to original sources
PubMed1997

Sequence data analysis reveals a relationship between LSR2, the recombinant fusion protein mimicing M. Leprae and VIF of bovine immunodeficiency virus (BIV).

In the course of computer simulation study looking for active sites for the interaction between MHC II and T7--a 12 residue long peptide of LSR2--a recombinant fusion protein mimicing the native bacillus M.Leprae--an interesting relationship between the antigenicity of LSR2 and VIF of BIF has come to light. Computer analysis study has revealed this stretch of residue from 36 to 48 of LSR2 is highly antigenic. The experimental observation seems to confirm the role of this 12 residue peptide in antibody response. In an effort to determine whether a significant sequence level relationship exists between this and any other known protein, the sequence homology of both protein and nucleic acid was studied. It is found that this 12 residue long peptide (T7) of LSR2 is homologous with Viral Infectivity Factor (VIF) of the Bovine Immunodeficiency Virus (BIV). Homology with translated nucleic acid sequence also indicate the same fact. The VIF gene which codes for this protein is known to be essential for ability of cell-free virus preparation to infect cells. These results lead to the question--whether this 12 residue long peptide which is common to both proteins play a role in their infectivity. Whether mutations in the peptide or elimination of this peptide from the protein and studying the effect of this on the diseases themselves may help in controlling them is another important question relevant to medical researchers.

Animals↗
PubMed2005

Sequence preference for BI/BII conformations in DNA: MD and crystal structure data analysis.

Deciphering sequence information from sugar-phosphate backbone is finely tuned through the conformational substates of DNA. BII conformation, one of the conformational substates of B-DNA, is known to play a key role in DNA-protein recognition. BI and BII are identified by the epsilon-zeta difference, which is negative in BI and positive in BII. Our analysis of MD and crystal structures shows that BII conformation is sequence specific and dinucleotides GC, CG, CA, TG, TA show high preference to take up BII conformation, while TT, TC, CT, CC dinucleotides rarely take up this conformation. Significant changes were observed in the dinucleotide parameters viz. twist, roll, and slide for the steps having BII conformation. Interestingly, the magnitude of variation in the dinucleotide parameters is seen to depend mainly on two factors, the magnitude of epsilon-zeta difference and the presence or absence of BII conformation in the second strand, across the WC base-paired dinucleotide step. Based on these two factors, the conformational substate of a dinucleotide step can be further classified as BI.BI (BI conformation in both strands), BI.BII (BI conformation in one strand and BII conformation in the other), and BII.BII (BII conformation in both strands). The occurrence of BII in both strands was found to be quite rare and thus, it can be concluded that BI.BI and BI.BII hybrid steps are more favorable than a BII.BII step. In conformity with the sequence preference seen for dinucleotides in each strand, BII.BII combination of backbone conformation was observed only for GC, CG, CA, and TG containing dinucleotide steps. We further classified BII.BII step as strong BII and weak BII depending on the magnitude of the average epsilon-zeta difference. The dinucleotide steps which belong to the category of strong BII, have large twist, high positive slide and negative roll values, while those in the weak BII group have roll, twist, and slide values similar to that of hybrid BI.BII steps. This conformational property could be contributing to the groove opening/closing and thus can modulate protein-DNA interaction.

Computer Simulation↗
PubMed1991

An air quality data analysis system for interrelating effects, standards, and needed source reductions: Part 11. A lognormal model relating human lung function decrease to O3 exposure.

Forced expiratory volume in 1 second (FEV1) was measured in 21 men exercising while exposed to four O3 concentrations (0.0, 0.08, 0.10, and 0.12 ppm). A lognormal multiple linear regression model was fitted to their mean FEV1 measurements to predict FEV1 percent decrease as a function of O3 concentration and exposure duration. The exercise level used was probably comparable to heavy manual labor. The longest O3 exposure studied was 6 h. Extrapolating cautiously to an 8-h workday of heavy manual labor, the model predicts that O3 concentrations of 0.08, 0.10, and 0.12 ppm would decrease FEV1 by 9, 15, and 20 percent, respectively.

Adult↗
PubMed1981

Titration of penicilloyl antibody by enzyme-immunoassay: method and data analysis to assign titer.

Antisera to the hapten, penicilloyl, were titered in an indirect enzyme immunoassay with the marker enzyme, horseradish peroxidase. A multivalent solid-phase antigen for stable solid-phase immunocomplex adsorption and a 10 min peroxidase light-assay with photolabile ortho-phenylenediamine were features of the assay. The penicilloyl antisera titration curves formed the basis for an evaluation of 4 methods to assign titer. Titer was expressed as either the antiserum dilution-1 that produced an assigned absorbance, exhibited 50% maximal antigen-antibody binding, and equaled zero absorbance by extrapolation, or as absorbance at 10(-3) antiserum dilution. The accuracy for extrapolated titer was improved with 2 or more solid-phase antigen concentrations. Absorbance at 10(-3) antiserum dilution was most useful as a screen for relative titer and for specific titer from a standard curve generated with many test sera. The antiserum dilution may vary with a particular enzyme immunoassay.

Animals↗
PubMed2004

Epidemiologic issues in study design and data analysis related to FoodNet activities.

The Foodborne Disease Active Surveillance Network (FoodNet) seeks to determine and to monitor the burden of foodborne diseases in the United States more precisely and to attribute these diseases to specific food vehicles or other exposures. These objectives present statistical and epidemiologic challenges. Estimates of the burden of foodborne diseases should include an estimate of the uncertainty in such calculations. Monitoring the burden of foodborne diseases should account for the expansion of the FoodNet population over time. Attributing foodborne diseases to specific vehicles is facilitated by FoodNet case-control studies of sporadic illness. This article discusses the strengths and limitations of the various studies aimed at addressing these objectives in this supplement. Furthermore, because the FoodNet surveillance areas were not chosen specifically to reflect the demographic composition of the US population, this article also discusses the generalizability of FoodNet results to the US population.

Adolescent↗
PubMed1999

Nonlinear curve fitting for bioelectrical impedance data analysis: a minimum ellipsoid volume method.

Bioelectrical impedance spectroscopy has become a widely used method with various applications in physiological and clinical investigations. Its measurement results are often analysed using the Cole-Cole model in an attempt to relate its parameters to structural or physiological characteristics of the studied object. The model parameter values are usually assessed by nonlinear curve fitting based on the least mean square method. A new approach is proposed, leading to better accuracy. It is based on the notion that the difference between measured and model parameter values is randomly distributed with zero mean. The values of the unknown parameters are then deduced by minimizing the covariance matrix determinant. This method can be considered as a minimization of the volume of an ellipsoid, or ajoint minimization of two regressions having the same argument and common parameters.

Electric Impedance↗
PubMed2003

Regression approaches for microarray data analysis.

A variety of new procedures have been devised to handle the two-sample comparison (e.g., tumor versus normal tissue) of gene expression values as measured with microarrays. Such new methods are required in part because of some defining characteristics of microarray-based studies: (i) the very large number of genes contributing expression measures which far exceeds the number of samples (observations) available and (ii) the fact that by virtue of pathway/network relationships, the gene expression measures tend to be highly correlated. These concerns are exacerbated in the regression setting, where the objective is to relate gene expression, simultaneously for multiple genes, to some external outcome or phenotype. Correspondingly, several methods have been recently proposed for addressing these issues. We briefly critique some of these methods prior to a detailed evaluation of gene harvesting. This reveals that gene harvesting, without additional constraints, can yield artifactual solutions. Results obtained employing such constraints motivate the use of regularized regression procedures such as the lasso, least angle regression, and support vector machines. Model selection and solution multiplicity issues are also discussed. The methods are evaluated using a microarray-based study of cardiomyopathy in transgenic mice.

Animals↗
PubMed2005

Phenotypical enrichment strategies for microarray data analysis applied in a type II diabetes study.

Combining results from gene microarrays, clinical chemistry, and quantitative tissue histomorphology in an integrated bioinformatics setting enables prioritization of gene families as well as individual genes in a type II diabetes animal study. This new methodology takes advantage of a time-controlled mouse study as the animals progress from a normal phenotype to that of type II diabetes. Profiles from different levels of the biological hierarchy of unpooled entities provide an encompassing, system-wide view of biological changes. Here, phenotypic changes on the tissue-structural and physiological level are used as statistical covariants to enrich the gene expression analysis, suggesting correlative processes between gene expression and phenotype unlocked by multi-sample comparisons. We apply correlative and gene set enrichment procedures and compare the results to differential analysis to identify molecular markers. Evaluation based on ontological classifications proves changes in prioritization of disease-related genes that would have been overlooked by conventional gene expression analyses strategies.

Animals↗