bluuebunny/crossref_metadata_2025_split huggingface dataset:
R2P's Missing Link, Culture
Authors: Mani Rama , Weiss Thomas G. 
Published: December 2011

AbstractCulture has been absent from analyses and debates about the responsibility to protect (R2P) norm. The use of the military to enforce a no-fly zone in Libya and to protect civilians took place with support from the local population and more widely across the Arab World even when the dominant 'culture' supposedly made outside interference unthinkable. As R2P enters its second decade, a deeper understanding of culture is desirable, as is the incorporation of cultural perspectives in framing responses to mass atrocities. UN debates and resolutions have helped dispel myths about R2P and reaffirmed its validity as a universal norm that is close to a 'tipping point'. Instead of an 'emerging' norm (the original contention in 2001 by the International Commission on Intervention and State Sovereignty), R2P has 'emerged' as consensus continues to widen and deepen across the North and the global South. This essay shares insights from research about cultural perspectives in the global South from local researchers who explore three themes (religion and spirituality, philosophy and ethics, and art and aesthetics) and three country cases (Rwanda, Kosovo, and Nepal).
Genome-wide case-control study in GAW17 using coalesced rare variants
Authors: Wang Libo , Pungpapong Vitara , Lin Yanzhu , Zhang Min , Zhang Dabao 
Published: December 2011

Abstract
Genome-wide association studies have successfully identified numerous loci at which common variants influence disease risks or quantitative traits of interest. Despite these successes, the variants identified by these studies have generally explained only a small fraction of the variations in the phenotype. One explanation may be that many rare variants that are not included in the common genotyping platforms may contribute substantially to the genetic variations of the diseases. Next-generation sequencing, which would better allow for the analysis of rare variants, is now becoming available and affordable; however, the presence of a large number of rare variants challenges the statistical endeavor to stably identify these disease-causing genetic variants. We conduct a genome-wide association study of Genetic Analysis Workshop 17 case-control data produced by the next-generation sequencing technique and propose that collapsing rare variants within each genetic region through a supervised dimension reduction algorithm leads to several macrovariants constructed for rare variants within each genetic region. A simultaneous association of the phenotype to all common variants and macrovariants is undertaken using a linear discriminant analysis using the penalized orthogonal-components regression algorithm. The results suggest that the proposed analysis strategy shows promise but needs further development.
Evaluating methods for the analysis of rare variants in sequence data
Authors: Luedtke Alexander , Powers Scott , Petersen Ashley , Sitarik Alexandra , Bekmetjev Airat , Tintle Nathan L 
Published: December 2011

Abstract
A number of rare variant statistical methods have been proposed for analysis of the impending wave of next-generation sequencing data. To date, there are few direct comparisons of these methods on real sequence data. Furthermore, there is a strong need for practical advice on the proper analytic strategies for rare variant analysis. We compare four recently proposed rare variant methods (combined multivariate and collapsing, weighted sum, proportion regression, and cumulative minor allele test) on simulated phenotype and next-generation sequencing data as part of Genetic Analysis Workshop 17. Overall, we find that all analyzed methods have serious practical limitations on identifying causal genes. Specifically, no method has more than a 5% true discovery rate (percentage of truly causal genes among all those identified as significantly associated with the phenotype). Further exploration shows that all methods suffer from inflated false-positive error rates (chance that a noncausal gene will be identified as associated with the phenotype) because of population stratification and gametic phase disequilibrium between noncausal SNPs and causal SNPs. Furthermore, observed true-positive rates (chance that a truly causal gene will be identified as significantly associated with the phenotype) for each of the four methods was very low (<19%). The combination of larger than anticipated false-positive rates, low true-positive rates, and only about 1% of all genes being causal yields poor discriminatory ability for all four methods. Gametic phase disequilibrium and population stratification are important areas for further research in the analysis of rare variant data.
Identifying rare disease variants in the Genetic Analysis Workshop 17 simulated data: a comparison of several statistical approaches
Authors: Fan Ruixue , Huang Chien-Hsun , Lo Shaw-Hwa , Zheng Tian , Ionita-Laza Iuliana 
Published: December 2011

Abstract
Genome-wide association studies have been successful at identifying common disease variants associated with complex diseases, but the common variants identified have small effect sizes and account for only a small fraction of the estimated heritability for common diseases. Theoretical and empirical studies suggest that rare variants, which are much less frequent in populations and are poorly captured by single-nucleotide polymorphism chips, could play a significant role in complex diseases. Several new statistical methods have been developed for the analysis of rare variants, for example, the combined multivariate and collapsing method, the weighted-sum method and a replication-based method. Here, we apply and compare these methods to the simulated data sets of Genetic Analysis Workshop 17 and thereby explore the contribution of rare variants to disease risk. In addition, we investigate the usefulness of extreme phenotypes in identifying rare risk variants when dealing with quantitative traits. Finally, we perform a pathway analysis and show the importance of the vascular endothelial growth factor pathway in explaining different phenotypes.
Testing gene-environment interactions in gene-based association studies
Authors: Wang Xuefeng , Qin Huaizhen , Morris Nathan J , Zhu Xiaofeng , Elston Robert C 
Published: December 2011

Abstract
Gene-based and single-nucleotide polymorphism (SNP) set association studies provide an important complement to SNP analysis. Kernel-based nonparametric regression has recently emerged as a powerful and flexible tool for this purpose. Our goal is to explore whether this approach can be extended to incorporate and test for interaction effects, especially for genes containing rare variant SNPs. Here, we construct nonparametric regression models that can be used to include a gene-environment interaction effect under the framework of the least-squares kernel machine and examine the performance of the proposed method on the Genetic Analysis Workshop 17 unrelated individuals data set. Two hundred simulated replicates were used to explore the power for detecting interaction. We demonstrate through a genome scan of the quantitative phenotype Q1 that the simulated gene-environment interaction effect in the data can be detected with reasonable power by using the least-squares kernel machine method.
Detecting functional rare variants by collapsing and incorporating functional annotation in Genetic Analysis Workshop 17 mini-exome data
Authors: Yan Xiting , Li Lun , Lee Joon Sang , Zheng Wei , Ferguson John , Zhao Hongyu 
Published: December 2011

Abstract
Association studies using tag SNPs have been successful in detecting disease-associated common variants. However, common variants, with rare exceptions, explain only at most 5–10% of the heritability resulting from genetic factors, which leads to the common disease/rare variants assumption. Indeed, recent studies using sequencing technologies have demonstrated that common diseases can be due to rare variants that could not be systematically studied earlier. Unfortunately, methods for common variants are not optimal if applied to rare variants. To identify rare variants that affect disease risk, several investigators have designed new approaches based on the idea of collapsing different rare variants inside the same genomic block (e.g., the same gene or pathway) to enrich the signal. Here, we consider three different collapsing methods in the multimarker regression model and compared their performance on the Genetic Analysis Workshop 17 data using the consistency of results across different simulations and the cross-validation prediction error rate. The comparison shows that the proportion collapsing method seems to outperform the other two methods and can find both truly associated rare and common variants. Moreover, we explore one way of incorporating the functional annotations for the variants in the data that collapses nonsynonymous and synonymous variants separately to allow for different penalties on them. The incorporation of functional annotations led to higher sensitivity and specificity levels when the detection results were compared with the answer sheet. The initial analysis was performed without knowledge of the simulating model.
Comparison of genetic association strategies in the presence of rare alleles
Authors: Mahachie John Jestinah M , Cattaert Tom , De Lobel Lizzy , Van Lishout François , Empain Alain , Van Steen Kristel 
Published: December 2011

Abstract
In the quest for the missing heritability of most complex diseases, rare variants have received increased attention. Advances in large-scale sequencing have led to a shift from the common disease/common variant hypothesis to the common disease/rare variant hypothesis or have at least reopened the debate about the relevance and importance of rare variants for gene discoveries. The investigation of modeling and testing approaches to identify significant disease/rare variant associations is in full motion. New methods to better deal with parameter estimation instabilities, convergence problems, or multiple testing corrections in the presence of rare variants or effect modifiers of rare variants are in their infancy. Using a recently developed semiparametric strategy to detect causal variants, we investigate the performance of the model-based multifactor dimensionality reduction (MB-MDR) technique in terms of power and family-wise error rate (FWER) control in the presence of rare variants, using population-based and family-based data (FAM-MDR). We compare family-based results obtained from MB-MDR analyses to screening findings from a quantitative trait Pedigree-based association test (PBAT). Population-based data were further examined using penalized regression models. We restrict attention to all available single-nucleotide polymorphisms on chromosome 4 and consider Q1 as the outcome of interest. The considered family-based methods identified marker C4S4935 in the VEGFC gene with estimated power not exceeding 0.35 (FAM-MDR), when FWER was kept under control. The considered population-based methods gave rise to highly inflated FWERs (up to 90% for PBAT screening).
Prioritizing single-nucleotide variations that potentially regulate alternative splicing
Authors: Teng Mingxiang , Wang Yadong , Wang Guohua , Jung Jeesun , Edenberg Howard J , Sanford Jeremy R , Liu Yunlong 
Published: December 2011

Abstract
Recent evidence suggests that many complex diseases are caused by genetic variations that play regulatory roles in controlling gene expression. Most genetic studies focus on nonsynonymous variations that can alter the amino acid composition of a protein and are therefore believed to have the highest impact on phenotype. Synonymous variations, however, can also play important roles in disease pathogenesis by regulating pre-mRNA processing and translational control. In this study, we systematically survey the effects of single-nucleotide variations (SNVs) on binding affinity of RNA-binding proteins (RBPs). Among the 10,113 synonymous SNVs identified in 697 individuals in the 1,000 Genomes Project and distributed by Genetic Analysis Workshop 17 (GAW17), we identified 182 variations located in alternatively spliced exons that can significantly change the binding affinity of nine RBPs whose binding preferences on 7-mer RNA sequences were previously reported. We found that the minor allele frequencies of these variations are similar to those of nonsynonymous SNVs, suggesting that they are in fact functional. We propose a workflow to identify phenotype-associated regulatory SNVs that might affect alternative splicing from exome-sequencing-derived genetic variations. Based on the affecting SNVs on the quantitative traits simulated in GAW17, we further identified two and four functional SNVs that are predicted to be involved in alternative splicing regulation in traits Q1 and Q2, respectively.
Application of Bayesian regression with singular value decomposition method in association studies for sequence data
Authors: Kwon Soonil , Yan Xiaofei , Cui Jinrui , Yao Jie , Yang Kai , Tsiang Donald , Li Xiaohui , Rotter Jerome I , Guo Xiuqing 
Published: December 2011

Abstract
Genetic association studies usually involve a large number of single-nucleotide polymorphisms (SNPs) (k) and a relative small sample size (n), which produces the situation that k is much greater than n. Because conventional statistical approaches are unable to deal with multiple SNPs simultaneously when k is much greater than n, single-SNP association studies have been used to identify genes involved in a disease’s pathophysiology, which causes a multiple testing problem. To evaluate the contribution of multiple SNPs simultaneously to disease traits when k is much greater than n, we developed the Bayesian regression with singular value decomposition (BRSVD) method. The method reduces the dimension of the design matrix from k to n by applying singular value decomposition to the design matrix. We evaluated the model using a Markov chain Monte Carlo simulation with Gibbs sampler constructed from the posterior densities driven by conjugate prior densities. Permutation was incorporated to generate empirical p-values. We applied the BRSVD method to the sequence data provided by Genetic Analysis Workshop 17 and found that the BRSVD method is a practical method that can be used to analyze sequence data in comparison to the single-SNP association test and the penalized regression method.
Integrating binary traits with quantitative phenotypes for association mapping of multivariate phenotypes
Authors: Mukhopadhyay Indranil , Saha Sujayam , Ghosh Saurabh 
Published: December 2011

AbstractClinical binary end-point traits are often governed by quantitative precursors. Hence it may be a prudent strategy to analyze a clinical end-point trait by considering a multivariate phenotype vector, possibly including both quantitative and qualitative phenotypes. A major statistical challenge lies in integrating the constituent phenotypes into a reduced univariate phenotype for association analyses. We assess the performances of certain reduced phenotypes using analysis of variance and a model-free quantile-based approach. We find that analysis of variance is more powerful than the quantile-based approach in detecting association, particularly for rare variants. We also find that using a principal component of the quantitative phenotypes and the residual of a logistic regression of the binary phenotype on the quantitative phenotypes may be an optimal method for integrating a binary phenotype with quantitative phenotypes to define a reduced univariate phenotype.
A pathway-based association analysis model using common and rare variants
Authors: Cheng Lu , Hu Pingzhao , Sykes Jenna , Pintilie Melania , Liu Geoffrey , Xu Wei 
Published: December 2011

AbstractHow various genetic effects in combination affect susceptibility to certain disease states continues to be a major area of methodological research. Various rare variant models have been proposed, in response to a common failure to either identify or validate biologically driven causal genetic variants in genome-wide association studies. Adopting the idea that multiple rare variants may effectively produce a combined effect equal to a single common variant effect through common linkage with this variant, we construct a pathway-based genetic association analysis model using both common and rare variants. This genetic model is applied to the disease status of unrelated individuals in replication 1 from Genetic Analysis Workshop 17. In this simulated example, we were able to identify several pathways that were potentially associated with the disease status and found that common variants showed stronger genetic effect than rare variants.
Rare variant collapsing in conjunction with mean log p-value and gradient boosting approaches applied to Genetic Analysis Workshop 17 data
Authors: Cherkas Yauheniya , Raghavan Nandini , Francke Stephan , DeFalco Frank , Wilcox Marsha A 
Published: December 2011

Abstract
In addition to methods that can identify common variants associated with susceptibility to common diseases, there has been increasing interest in approaches that can identify rare genetic variants. We use the simulated data provided to the participants of Genetic Analysis Workshop 17 (GAW17) to identify both rare and common single-nucleotide polymorphisms and pathways associated with disease status. We apply a rare variant collapsing approach and the usual association tests for common variants to identify candidates for further analysis using pathway-based and tree-based ensemble approaches. We use the mean log p-value approach to identify a top set of pathways and compare it to those used in simulation of GAW17 dataset. We conclude that the mean log p-value approach is able to identify those pathways in the top list and also related pathways. We also use the stochastic gradient boosting approach for the selected subset of single-nucleotide polymorphisms. When compared the result of this tree-based method with the list of single-nucleotide polymorphisms used in dataset simulation, in addition to correct SNPs we observe number of false positives.
Addition of multiple rare SNPs to known common variants improves the association between disease and gene in the Genetic Analysis Workshop 17 data
Authors: Sykes Jenna , Cheng Lu , Xu Wei , Tsao Ming-Sound , Liu Geoffrey , Pintilie Melania 
Published: December 2011

Abstract
The upcoming release of new whole-genome genotyping technologies will shed new light on whether there is an associative effect of previously immeasurable rare variants on incidence of disease. For Genetic Analysis Workshop 17, our team focused on a statistical method to detect associations between gene-based multiple rare variants and disease status. We added a combination of rare SNPs to a common variant shown to have an influence on disease status. This method provides us with an enhanced ability to detect the effect of these rare variants, which, modeled alone, would normally be undetectable. Adjusting for significant clinical parameters, several genes were found to have multiple rare variants that were significantly associated with disease outcome.
Ausführbare UML-Modelle multimodaler Interaktionsanwendungen
Authors: Dausend Marcel , Poguntke Mark 
Published: December 2011

Zusammenfassung
Komplexe interaktive Anwendungen stellen sowohl für die Nutzer als auch für die Entwickler eine große Herausforderung dar, z.B. das Infotainmentsystem moderner Fahrzeuge. Dem Nutzer wird Multimodalität (Sprachbedienung neben der grafisch-haptischen Bedienung) angeboten, um die Ablenkung während des Fahrens zu reduzieren. Da die Kombination der verschiedenen Interaktionsmöglichkeiten die Komplexität der Anwendung deutlich erhöht, ist es notwendig, schon die Entwicklung dieser Systeme möglichst optimal zu unterstützen. Wir stellen einen Ansatz zur Modellierung multimodaler interaktiver Anwendungen in Form einer domänenspezifischen Modellierungssprache vor. UML-Zustandsautomaten werden in erweiterter Form verwendet. Benutzerschnittstellen verschiedener Modalitäten werden zu ausführbaren Modellen kombiniert, so dass eine interaktive Simulation der Anwendung möglich wird, die die multimodale Benutzerschnittstelle einschließt.
Can Birth Order Affect Temperament, Anxiety and Behavior in 5 to 7-year-old Children in the Dental Setting?
Authors: Aminabadi Naser Asl , Sohrabi Azin , Oskouei Sina Ghertasi , Ajami Behjat Almolook 
Published: December 2011

ABSTRACTAimThe aim of this study was to evaluate the relationships between birth order and child's temperament, anxiety and behavior in the dental setting.Materials and methodsA total of 200 healthy children aged 5 to 7 years, were included in this double-blind randomized controlled trial. The study consisted of two sessions. In the initial appointment, parents were provided with instructions and asked to complete children's behavior questionnaire (CBQ). In the second appointment, identical dental treatments were rendered to all subjects. During treatment, Frankl scale for child's behavior, facial Image scale (FIS) for situational anxiety, and clinical anxiety rating scale for clinical anxiety were utilized. Analysis of data was done using U Mann-Whitney and Kruskal-Wallis tests.ResultsOnly children had higher clinical (p = 0.041) and situational (p < 0.001) anxiety, and more negative behavior (p = 0.013) compared to children with siblings. In children with siblings, first-born child was in increased risk of developing negative behavior (p = 0.008), clinical anxiety (p < 0.001) and situational anxiety (p = 0.006). With an exception (sadness, p < 0.001), no significant differences in temperament scale were observed among children with different birth orders.ConclusionAccording to the results, only children and laterborns are at higher risk of developing worse outcomes in the dental setting.Clinical significanceThe role of birth order has been ignored as a possible factor of behavior during routine dental treatment and these findings may shed light on our understanding of behavior management strategies in the dental setting. Considering the increasing pattern of family with an only child both in modern and developing countries, this is more likely that the dental team will face children with negative outcome during dental treatment.How to cite this articleAminabadi NA, Sohrabi A, Erfanparast L, Oskouei SG, Ajami BA. Can Birth Order Affect Temperament, Anxiety and Behavior in 5 to 7-year-old Children in the Dental Setting? J Contemp Dent Pract 2011;12(4):225-231.
Zhdanov in Finland
Authors: Rieber Alfred J. 
Published: December 2011

As the Soviet archives begin grudgingly to yield their secrets, it becomes possible to reconsider the origins of the Cold War from new perspectives. Up to this point most books on the subject have been written out of Western source materials. Even the best of the very few attempts to put Moscow at the center of things have had to rely heavily on published or indirect evidence culled from western diplomatic and intelligence reports in order to fathom Soviet intentions.