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Q2 Value (q2 + value)

Distribution by Scientific Domains

Chemistry	50%

Selected Abstracts

A multivariate biomarker-based model predicting population-level responses of Daphnia magna

ENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 9 2003
Wim M. De Coen
Abstract A multivariate model is proposed relating short-term biomarker measurements in Daphnia magna to chronic effects (21-d exposure) occurring at the population level (time to death, mean brood size, mean total young per female, intrinsic rate of natural increase, net reproductive rate, and growth). The results of the short-term exposure (48h-96 h) to eight model toxicants (cadmium, chromium, mercury, tributyl tin, linear alkylsulfonic acid, sodium pentachlorophenolate, lindane, and 2,4-dichloro-phenoxyacetic acid) on the following biomarkers were used for the multivariate model: digestive enzymes (amylase, cellulase, ,-galactosidase, trypsin, and esterase), enzymes of the intermediary metabolism (glycogen phosphorylase, glucose-6-phosphate de-hydrogenase, pyruvate kinase, lactate dehydrogenase, and isocitrate dehydrogenase), cellular energy allocation (CEA) (protein, carbohydrate, and lipid content and electron transport activity), and DNA damage and antioxidative stress activity. Using partial least squares to latent structures (PLS), a two-component model was obtained with R2 of 0.68 and a Q2 value of 0.60 based on the combined analysis of a limited number of the 48- and 96-h biomarker responses. For the individual population-level responses, the R2 values varied from 0.66 to 0.77 and the Q2 values from 0.52 to 0.69. Energy-related biomarkers (cellular energy allocation, lipid contents, anaerobic metabolic activity,pyruvate kinase, and lactate dehydrogenase), combined with parameters related to oxidative stress (catalase) and DNA damage measured after 48 and 96 h of exposure, were able to predict long-term effects at higher levels of biological organization. [source]

A Combinatorial Approach to the Variable Selection in Multiple Linear Regression: Analysis of Selwood et al.

MOLECULAR INFORMATICS, Issue 6 2003
A Case Study, Data Set
Abstract A combinatorial protocol (CP) is introduced here to interface it with the multiple linear regression (MLR) for variable selection. The efficiency of CP-MLR is primarily based on the restriction of entry of correlated variables to the model development stage. It has been used for the analysis of Selwood et al data set [16], and the obtained models are compared with those reported from GFA [8] and MUSEUM [9] approaches. For this data set CP-MLR could identify three highly independent models (27, 28 and 31) with Q2 value in the range of 0.632,0.518. Also, these models are divergent and unique. Even though, the present study does not share any models with GFA [8], and MUSEUM [9] results, there are several descriptors common to all these studies, including the present one. Also a simulation is carried out on the same data set to explain the model formation in CP-MLR. The results demonstrate that the proposed method should be able to offer solutions to data sets with 50 to 60 descriptors in reasonable time frame. By carefully selecting the inter-parameter correlation cutoff values in CP-MLR one can identify divergent models and handle data sets larger than the present one without involving excessive computer time. [source]

Comprehensive analysis of short peptides in sera from patients with IgA nephropathy

RAPID COMMUNICATIONS IN MASS SPECTROMETRY, Issue 23 2009
Nagayuki Kaneshiro
We analyzed serum short peptides comprehensively to know whether they were useful to characterize IgA nephropathy (IgAN). Serum samples from 26 patients with untreated IgAN and 25 healthy donors were tested. Short peptides with molecular weights of ,7,kDa, purified from the serum samples by magnetic-beads-based weak cation exchange, were detected by mass spectrometry. Then the peptide peaks detected were subjected to the multivariate data analysis by SIMCA-P+® containing principal component analysis (PCA) and orthogonal partial-least-squares-discriminate analysis (OPLS-DA). A total of 92 peptide peaks were detected in the tested serum samples. The OPLS-DA analysis revealed that the profile of all the peptide peak intensities discriminated the IgAN group and the healthy group completely with a high R2 value (0.919) and a high Q2 value (0.861). Further, the profile of only five peptide peaks was found to discriminate the two groups. By tandem mass spectrometry and database searching, three of the five peptides which increased in the IgAN group were identified as fragments of fibrinogen alpha chain, and the two peptides which increased in the healthy group were identified as fragments of complement C3f and kininogen-1 light chain. Taken together, the profile of the serum short peptides would be useful to discriminate IgAN and healthy conditions. Further, the five peptides may be candidate serum markers for IgAN and may be related to pathogenesis of IgA. Copyright © 2009 John Wiley & Sons, Ltd. [source]