bluuebunny/crossref_metadata_2025_split huggingface dataset:
Phenolic antioxidants – radical‐scavenging and chain‐breaking activity: A comparative study
Authors: Kancheva Vessela D. 
Published: November 2009

AbstractFifty phenolic antioxidants (AH) (42 individual compounds and 8 binary mixtures of two antioxidants) were chosen for a comparative analysis of their radical‐scavenging (H‐donating) and chain‐breaking (antioxidant) activity. Correlations between experimental (antiradical and antioxidant) and predictable (theoretical) activities of 15 flavonoids, 15 hydroxy cinnamic acid derivatives, 5 hydroxy chalcones, 4 dihydroxy coumarins and 3 standard antioxidants (butylated hydroxytoluene, hydroquinone, DL‐α‐tocopherol) were summarized and discussed. The following models were applied to explain the structure‐activity relationships of phenolic antioxidants of natural origin: (a) model 1, a DPPH assay used for the determination of the radical‐scavenging capacity (AH + DPPH• → A• + DPPH‐H); (b) model 2, chemiluminescence of a model substrate RH (cumene or diphenylmethane) used for the determination of the rate constant of a reaction with model peroxyl radicals (AH + RO2• → ROOH + A•); (c) model 3, lipid autoxidation used for the determination of the chain‐breaking antioxidant efficiency and reactivity (AH + LO2• → LOOH + A•; A• + LH (+O2) → AH + LO2•); and (d) model 4, theoretical methods used for predicting the activity (predictable activity). The highest lipid oxidation stability was found for antioxidants with a catecholic structure and for their binary mixtures with DL‐α‐tocopherol, as a result of synergism between them.
Efficient and Chirally Specific Synthesis of Phenanthro‐Indolizidine Alkaloids by Parham‐Type Cycloacylation
Authors: Wang Ziwen , Li Zheng , Wang Kailiang , Wang Qingmin 
Published: November 2009

AbstractA concise, efficient and modular route involving Parham‐type cycloacylation as the key step has been used to synthesize six enantiopure phenanthro‐indolizidine alkaloids 1a–c. The preparation of enantiomerically pure tylophora alkaloids and their seco analogues on a large‐scale is now feasible. The alcohol intermediates 8a–c, which are difficult to prepare by other synthetic methodologies, have been synthesized by a metallation–cyclization–reduction sequence in excellent yields.
A New Method for the Synthesis of Multisubstituted Pyrroles of Biological Interest by Double Nucleophilic Addition to α,β‐Unsaturated Imines
Authors: Takahashi Atsushi , Kawai Shiho , Hachiya Iwao , Shimizu Makoto 
Published: November 2009

AbstractDouble nucleophilic addition reactions of dialkoxy ketenesilyl acetals proceeded with α,β‐unsaturated imines to give 1,4‐ and 1,2‐double addition products, and their subsequent transformations afforded multisubstituted pyrroles in good yields. Application of this procedure to the synthesis of an imidazole glycerol phosphate dehydratase inhibitor (IGPDI), a physiologically active 2,3,5‐trisubstituted pyrrole, is also described.
High‐performance CE of Escherichia coli K4 cell surface polysaccharides
Authors: Restaino Odile F. , Cimini Donatella , De Rosa Mario , De Castro Cristina , Parrilli Michelangelo , Schiraldi Chiara 
Published: November 2009

AbstractA high‐performance CE application for a quick, reproducible, highly precise and sensitive determination of the lipopolysaccharide produced by Escherichia coli K4 (O5:K4:H4) and of its de‐lipid A form is described. The two species were separated within 30 min on an uncoated fused‐silica capillary, in normal polarity mode at 20 kV, using an SDS buffer. Detected at 190 nm, the de‐lipid A and the LPS species showed two peaks at distinctive migration times (10.45 and 16.10 min, respectively) and were quantified with high reproducibility and linearity (the correlation factors were 0.99 and 0.98, respectively) over the ranges from 60 to 600 ng (1–10 ng/nL) for de‐lipid A lipopolysaccharide and from 150 to 600 ng (2.5–10 ng/nL) for the LPS. The described method was also employed in the contemporary analysis and the determination of the two E. coli K4 cell surface polysaccharides, the LPS and the K4, and of their defructosylated and de‐lipid A species, respectively. The four molecules were detected and precisely quantified in complex matrices as fermentation broth supernatant or in samples withdrawn throughout the purification process, thus demonstrating the possibility to apply high‐performance CE as a reliable analytical tool in biotechnological processes.
Estimating the integrity of aged DNA samples by CE
Authors: Fattorini Paolo , Marrubini Giorgio , Ricci Ugo , Gerin Fabio , Grignani Pierangela , Sorçaburu Cigliero Solange , Xamin Andrea , Edalucci Elisabetta , La Marca Giancarlo , Previderé Carlo 
Published: November 2009

AbstractA CE/UV method was developed to separate by a micellar system the four DNA bases and other five purinic–pyrimidinic compounds (5‐methyl‐cytosine, uracil, xanthyne, hypoxanthyne and 5‐bromo‐uracil). Selectivity, precision, accuracy and sensitivity were assessed and proved to be suitable for the analysis of the primary structure of DNA. This method was adopted to study 16 aged samples including two Egyptian mummies, formaldehyde‐fixed paraffin‐embedded tissues and other forensic specimens. Lower relative values of the four canonical unmodified DNA bases (uDNAb) and more complex pherograms were found in the aged samples when compared with the modern controls. The results of the CE analysis, together with those obtained by classical molecular methods (agarose gel electrophoresis, DNase I and RNase A assays, and UV spectrophotometry), were finally evaluated for assessing the reliability of STR typing. Since samples with low uDNAb showed no amplification or unreliable STR profiles, the uDNAb value is discussed as a further quality criterion in the evaluation of the genetic data obtained from aged samples.
Is the general conclusion justified that higher applicable field strength results in shorter analysis time with organic solvents in CE?
Authors: Téllez Adolfo , Kenndler Ernst 
Published: November 2009

AbstractIn this paper, a widespread opinion in CE with organic solvents for the background electrolyte is critically questioned, namely that in general a shorter analysis time can be achieved due to the higher field strength applicable compared with aqueous electrolyte systems. This view, common in the literature, is based on the supposition that the conductance in organic solvents is lower than in water. Indeed in many organic solvents with higher viscosity than water lower ion mobility is observed, and higher fields can be applied in these cases. However, in this paper the problem is sharper defined and treated two‐fold: (i) in all solvents conditions are such that either the same electric power is generated, or (ii) the same temperature increase is taken into account. It was shown that for the same electric power the field strength in the organic solvent can be changed to a less extent than the ionic mobility changes. As a result, the migration velocity of the analytes is lower and the analysis time is longer in most organic solvents compared with water; acetonitrile (MeCN) is an exception (in this solvent the mobilities are higher than in water). The more stringent treatment of the problem takes an equal temperature increase due to Joule heating into account rather than equal electric power. The temperature increase in the capillary depends on the thermal conductivity of the solvent, which is only about one‐third of that of water for organic liquids. The consequence is that in none of the organic solvent systems a shorter analysis time can be achieved compared with water (given that the experimental conditions are comparable, e.g. zero EOF). The theoretical predictions were confirmed by measurements with water, methanol, propylenecarbonate, and MeCN as solvents.
Business failure prediction using decision trees
Authors: Gepp Adrian , Kumar Kuldeep , Bhattacharya Sukanto 
Published: November 2009

AbstractAccurate business failure prediction models would be extremely valuable to many industry sectors, particularly financial investment and lending. The potential value of such models is emphasised by the extremely costly failure of high‐profile companies in the recent past. Consequently, a significant interest has been generated in business failure prediction within academia as well as in the finance industry. Statistical business failure prediction models attempt to predict the failure or success of a business. Discriminant and logit analyses have traditionally been the most popular approaches, but there are also a range of promising non‐parametric techniques that can alternatively be applied. In this paper, the relatively new technique of decision trees is applied to business failure prediction. The numerical results suggest that decision trees could be superior predictors of business failure as compared to discriminant analysis. Copyright © 2009 John Wiley & Sons, Ltd.
Genetic Analysis Workshop 16: introduction to workshop summaries
Authors: MacCluer Jean W. , Amos Christopher I. , Gregersen Peter K. , Heard‐Costa Nancy , Lee Monica , Kraja Aldi T. , Borecki Ingrid B. , Cupples L. Adrienne , Almasy Laura 
Published: November 2009

AbstractGenetic Analysis Workshop 16 (GAW16) was held on September 17–20, 2008 in St. Louis, Missouri. The focus of GAW16 was on methods and challenges in analysis of single‐nucleotide polymorphism data from genome‐wide scans. GAW16 attracted 221 participants from 12 countries. The 168 contributions were organized into 17 discussion groups of 6–17 papers each. Three data sets were available for analysis. Two of these were data from ongoing studies, generously provided by the investigators. The North American Rheumatoid Arthritis Consortium provided case‐control data on rheumatoid arthritis, and the Framingham Heart Study (FHS) made available information on cardiovascular risk factors for participants in three generations of pedigree data. The third data set included simulated phenotypes for participants in the FHS, using actual pedigree structures and genotypes. This volume includes a paper for each of the 17 discussion groups, summarizing their main findings. Genet. Epidemiol. 33 (Suppl. 1):S1–S7, 2009. © 2009 Wiley‐Liss, Inc.
Genome‐wide association studies for discrete traits
Authors: Thomas Duncan C. 
Published: November 2009

AbstractGenome‐wide association studies of discrete traits generally use simple methods of analysis based on χ2 tests for contingency tables or logistic regression, at least for an initial scan of the entire genome. Nevertheless, more power might be obtained by using various methods that analyze multiple markers in combination. Methods based on sliding windows, wavelets, Bayesian shrinkage, or penalized likelihood methods, among others, were explored by various participants of Genetic Analysis Workshop 16 Group 1 to combine information across multiple markers within a region, while others used Bayesian variable selection methods for genome‐wide multivariate analyses of all markers simultaneously. Imputation can be used to fill in missing markers on individual subjects within a study or in a meta‐analysis of studies using different panels. Although multiple imputation theoretically should give more robust tests of association, one participant contribution found little difference between results of single and multiple imputation. Careful control of population stratification is essential, and two contributions found that previously reported associations with two genes disappeared after more precise control. Other issues considered by this group included subgroup analysis, gene‐gene interactions, and the use of biomarkers. Genet. Epidemiol. 33 (Suppl. 1):S8–S12, 2009. © 2009 Wiley‐Liss, Inc.
Genome‐wide association analyses of quantitative traits: the GAW16 experience
Authors: Ghosh Saurabh 
Published: November 2009

AbstractThe group that formed on the theme of genome‐wide association analyses of quantitative traits (Group 2) in the Genetic Analysis Workshop 16 comprised eight sets of investigators. Three data sets were available: one on autoantibodies related to rheumatoid arthritis provided by the North American Rheumatoid Arthritis Consortium; the second on anthropometric, lipid, and biochemical measures provided by the Framingham Heart Study (FHS); and the third a simulated data set modeled after FHS. The different investigators in the group addressed a large set of statistical challenges and applied a wide spectrum of association methods in analyzing quantitative traits at the genome‐wide level. While some previously reported genes were validated, some novel chromosomal regions provided significant evidence of association in multiple contributions in the group. In this report, we discuss the different strategies explored by the different investigators with the common goal of improving the power to detect association.Genet. Epidemiol. 33 (Suppl. 1):S13–S18, 2009. © 2009 Wiley‐Liss, Inc.
Haplotype‐based analysis: a summary of GAW16 Group 4 analysis
Authors: Hauser Elizabeth , Cremer Nadine , Hein Rebecca , Deshmukh Harshal 
Published: November 2009

AbstractIn this summary article, we describe the contributions included in the haplotype‐based analysis group (Group 4) at the Genetic Analysis Workshop 16, which was held in September 17–20, 2008. Our group applied a large number of haplotype‐based methods in the context of genome‐wide association studies. Two general approaches were applied: a two‐stage approach that selected significant single‐nucleotide polymorphisms (SNPs) in the first stage and then created haplotypes in the second stage and genome‐wide analysis of smaller sets of SNPs selected by sliding windows or estimating haplotype blocks. Genome‐wide haplotype analyses performed in these ways were feasible. The presence of the very strong chromosome 6 association in the North American Rheumatoid Arthritis Consortium data was detected by every method, and additional analyses attempted to control for this strong result to allow detection of additional haplotype associations. Genet. Epidemiol. 33 (Suppl. 1):S24–S28, 2009. © 2009 Wiley‐Liss, Inc.
Improving the Signal‐to‐Noise ratio in genome‐wide association studies
Authors: Martin Lisa J. , Gao Guimin , Kang Guolian , Fang Yixin , Woo Jessica G. 
Published: November 2009

AbstractGenome‐wide association studies employ hundreds of thousands of statistical tests to determine which regions of the genome may likely harbor disease‐causing alleles. Such large‐scale testing simultaneously requires stringent control over type I error and maintenance of sufficient power to detect true associations. These contradictory goals have led some researchers beyond Bonferroni correction of P‐values to an exploration of methods to improve the detection of a few true effects in the presence of many unassociated loci. This article reviews how Genetic Analysis Workshop 16 Group 5 investigators proposed to adjust for multiple tests while simultaneously using information about the structure of the genome to improve the detection of true positives. Genet. Epidemiol. 33 (Suppl. 1):S29–S32, 2009. © 2009 Wiley‐Liss, Inc.
Analysis of multiple phenotypes
Authors: Kent Jack W. 
Published: November 2009

AbstractThe complex etiology of common diseases like cardiovascular disease, diabetes, hypertension, and rheumatoid arthritis has led investigators to focus on the genetics of correlated phenotypes and risk factors. Joint analysis of multiple disease‐related phenotypes may reveal genes of pleiotropic effect and increase analytical power, but at the cost of increased analytical and computational complexity. All three data sets provided for analysis at the Genetic Analysis Workshop 16 offered multiple quantitative measures of phenotypes related to underlying disease processes as well as discrete measures of affection status. Participants in Group 6 addressed the challenges and possibilities of association analysis of these data sets on multiple levels, including phenotype definition and data reduction, multivariate approaches to gene discovery, analysis of causality and data structure, and development of predictive models. These approaches included combinations of continuous and discrete phenotypes, use of repeated measures in longitudinal data, and models that included multiple phenotypic measures and multiple single‐nucleotide polymorphism variants. Most research teams regarded the use of multiple related phenotypes as a tool for increasing analytical power, as well as for clarifying the underlying biology of complex diseases. Genet. Epidemiol. 33 (Suppl. 1):S33–S39, 2009. © 2009 Wiley‐Liss, Inc.
The challenge of detecting epistasis (G×G Interactions): Genetic Analysis Workshop 16
Authors: An Ping , Mukherjee Odity , Chanda Pritam , Yao Li , Engelman Corinne D. , Huang Chien‐Hsun , Zheng Tian , Kovac Ilija P. , Dubé Marie‐Pierre , Liang Xueying , Li Jia , de Andrade Mariza , Culverhouse Robert , Malzahn Doerthe , Manning Alisa K. , Clarke Geraldine M. , Jung Jeesun , Province Michael A. 
Published: November 2009

AbstractInterest is increasing in epistasis as a possible source of the unexplained variance missed by genome‐wide association studies. The Genetic Analysis Workshop 16 Group 9 participants evaluated a wide variety of classical and novel analytical methods for detecting epistasis, in both the statistical and machine learning paradigms, applied to both real and simulated data. Because the magnitude of epistasis is clearly relative to scale of penetrance, and therefore to some extent, to the choice of model framework, it is not surprising that strong interactions under one model might be minimized or even disappear entirely under a different modeling framework. Genet. Epidemiol. 33 (Suppl. 1):S58–S67, 2009. © 2009 Wiley‐Liss, Inc.
Detecting gene‐environment interactions in genome‐wide association data
Authors: Engelman Corinne D. , Baurley James W. , Chiu Yen‐Feng , Joubert Bonnie R. , Lewinger Juan P. , Maenner Matthew J. , Murcray Cassandra E. , Shi Gang , Gauderman W. James 
Published: November 2009

AbstractDespite the importance of gene‐environment (G×E) interactions in the etiology of common diseases, little work has been done to develop methods for detecting these types of interactions in genome‐wide association study data. This was the focus of Genetic Analysis Workshop 16 Group 10 contributions, which introduced a variety of new methods for the detection of G×E interactions in both case‐control and family‐based data using both cross‐sectional and longitudinal study designs. Many of these contributions detected significant G×E interactions. Although these interactions have not yet been confirmed, the results suggest the importance of testing for interactions. Issues of sample size, quantifying the environmental exposure, longitudinal data analysis, family‐based analysis, selection of the most powerful analysis method, population stratification, and computational expense with respect to testing G×E interactions are discussed. Genet. Epidemiol. 33 (Suppl. 1):S68–S73, 2009. © 2009 Wiley‐Liss, Inc.
Combining information from linkage and association methods
Authors: Marchani Elizabeth E. , Callegaro Andrea , Daw E. Warwick , Wijsman Ellen M. 
Published: November 2009

AbstractGroup 12 evaluated approaches to incorporate outside information or otherwise optimize traditional linkage and association analyses. The abundance of available data allowed exploration of identity‐by‐descent (IBD) estimation, score statistics, formal combination of linkage and association testing, significance estimation, and replication. We observed that IBD estimation can be optimized with a subset of marker data while estimation of inheritance vectors can provide both IBD estimates and a measure of their uncertainty. Score statistics incorporating covariates or combining association and linkage information performed at least as well as standard approaches while requiring less computation time. The formal combination of linkage and association methods may be fruitful, although the nature of the simulated data limited our conclusions. Estimation of significance may be improved through simulation, correction for cryptic relatedness, and the inclusion of prior information. Replication using real data provided consistent results, though the same was not true of simulated data replicates. Overall, we found that increasing the amount of available data limits analyses due to computational constraints and motivates the need to improve methods for the identification of complex‐trait genes. Genet. Epidemiol. 33 (Suppl. 1):S81–S87, 2009. © 2009 Wiley‐Liss, Inc.
Population stratification and patterns of linkage disequilibrium
Authors: Hinrichs Anthony L. , Larkin Emma K. , Suarez Brian K. 
Published: November 2009

AbstractAlthough the importance of selecting cases and controls from the same population has been recognized for decades, the recent advent of genome‐wide association studies has heightened awareness of this issue. Because these studies typically deal with large samples, small differences in allele frequencies between cases and controls can easily reach statistical significance. When, unbeknownst to a researcher, cases and controls have different substructures, the number of false‐positive findings is inflated. There have been three recent developments of purely statistical approaches to assessing the ancestral comparability of case and control samples: genomic control, structured association, and multivariate reduction analyses. The widespread use of high‐throughput technology has allowed the quick and accurate genotyping of the large number of markers required by these methods. Group 13 dealt with four population stratification issues: single‐nucleotide polymorphism marker selection, association testing, nonstandard methods, and linkage disequilibrium calculations in stratified or mixed ethnicity samples. We demonstrated that there are continuous axes of ethnic variation in both data sets of Genetic Analysis Workshop 16. Furthermore, ignoring this structure created P‐value inflation for a variety of phenotypes. Principal‐components analysis (or multidimensional scaling) can control inflation as covariates in a logistic regression. One can weigh for local ancestry estimation and allow the use of related individuals. Problems arise in the presence of extremely high association or unusually strong linkage disequilibrium (e.g., in chromosomal inversions). Our group also reported a method for performing an association test controlling for substructure, when genome‐wide markers are not available, to explicitly compute stratification Genet. Epidemiol. 33 (Suppl. 1):S88–S92, 2009. © 2009 Wiley‐Liss, Inc.