bluuebunny/crossref_metadata_2025_split huggingface dataset:
A microwell array device with integrated microfluidic components for enhanced single‐cell analysis
Authors: Lindström Sara , Mori Kiichiroh , Ohashi Toshiro , Andersson‐Svahn Helene 
Published: November 2009

AbstractIncreasing awareness of the importance of cell heterogeneity in many biological and medical contexts is prompting increasing interest in systems that allow single‐cell analysis rather than conventional bulk analysis (which provides average values for variables of interest from large numbers of cells). Recently, we presented a microwell chip for long‐term, high‐throughput single‐cell analysis. The chip has proved to be useful for purposes such as screening individual cancer and stem cells for protein/gene markers. However, liquids in the wells can only be added or changed by manually rinsing the chip, or parts of it. This procedure has several well‐known drawbacks – including risks of cross‐contamination, large dead volumes and laboriousness – but there have been few previous attempts to integrate liquid rinsing/switching channels in “ready‐to‐use” systems for single‐cell analysis. Here we present a microwell system designed (using flow simulations) for single‐cell analysis with integrated microfluidic components (microchannels, magnetically driven micropumps and reservoirs) for supplying the cell culture wells with reagents, or rinsing, thus facilitating controlled, directed liquid handling. It can be used totally independently, since tubing is not essential. The practical utility of the integrated system has been demonstrated by culturing endothelial cells in the microwells, and successfully applying live‐cell Calcein AM staining. Systems such as this combining high‐density microwell chips with microfluidic components have great potential in numerous screening applications, such as exploring the important, but frequently undetected, heterogeneity in drug responses among individual cells.
Traumatic events, posttraumatic stress symptomatology and somatoform symptoms in eating disorder patients
Authors: Tagay Sefik , Schlegl Sandra , Senf Wolfgang 
Published: November 2009

AbstractObjectiveThe central aim of the study was to assess the frequency of traumatic events and comorbid posttraumatic stress disorder (PTSD) in women with eating disorders (EDs). In addition, the frequency of somatoform complaints was investigated in patients with PTSD compared to those without PTSD.Method101 ED patients (26.4 years, SD = 7.4) from an outpatient department were investigated by means of standardized questionnaires.Results63.3% of the anorexic and 57.7% of the bulimic patients had experienced at least one trauma in their life. 10% of the anorexic and 14.1% of the bulimic patients fulfilled the study definition for a current diagnosis of PTSD. Patients with a comorbid PTSD reported somatoform symptoms more frequently than patients without PTSD (p < 0.001).DiscussionThese findings provide additional support for the association between somatization and PTSD in ED patients. In addition, clinical interventions for traumatized ED patients may benefit from a focus on posttraumatic stress symptomatology. Copyright © 2009 John Wiley & Sons, Ltd and Eating Disorders Association.
Multistage analysis strategies for genome‐wide association studies: summary of group 3 contributions to Genetic Analysis Workshop 16
Authors: Neuman Rosalind J. , Sung Yun Ju 
Published: November 2009

AbstractThis contribution summarizes the work done by six independent teams of investigators to identify the genetic and non‐genetic variants that work together or independently to predispose to disease. The theme addressed in these studies is multistage strategies in the context of genome‐wide association studies (GWAS). The work performed comes from Group 3 of the Genetic Analysis Workshop 16 held in St. Louis, Missouri in September 2008. These six studies represent a diversity of multistage methods of which five are applied to the North American Rheumatoid Arthritis Consortium rheumatoid arthritis case‐control data, and one method is applied to the low‐density lipoprotein phenotype in the Framingham Heart Study simulated data. In the first stage of analyses, the majority of studies used a variety of screening techniques to reduce the noise of single‐nucleotide polymorphisms purportedly not involved in the phenotype of interest. Three studies analyzed the data using penalized regression models, either LASSO or the elastic net. The main result was a reconfirmation of the involvement of variants in the HLA region on chromosome 6 with rheumatoid arthritis. The hope is that the intense computational methods highlighted in this group of papers will become useful tools in future GWAS.Genet. Epidemiol. 33 (Suppl. 1):S19–S23, 2009. © 2009 Wiley‐Liss, Inc.
Phenotype definition and development—contributions from Group 7
Authors: Wilcox Marsha A. , Paterson Andrew D. 
Published: November 2009

AbstractThe papers in Genetic Analysis Workshop 16 Group 7 covered a wide range of topics. The effects of confounder misclassification and selection bias on association results were examined by one group. Another focused on bias introduced by various methods of accounting for treatment effects. Two groups used related methods to derive phenotypic traits. They used different analytic strategies for genetic associations with non‐overlapping results (but because they used different sets of single‐nucleotide polymorphisms (SNPs) and significance criteria, this is not surprising). Another group relied on the well‐characterized definition of type 2 diabetes to show benefits of a novel predictive test. Transmission‐ratio distortion was the focus of another paper. The results were extended to show a potential secondary benefit of the test to identify potentially mis‐called SNPs. Genet. Epidemiol. 33 (Suppl. 1):S40–S44, 2009. © 2009 Wiley‐Liss, Inc.
Genome‐wide association studies: quality control and population‐based measures
Authors: Ziegler Andreas 
Published: November 2009

AbstractGenome‐wide association studies, using hundreds of thousands of single‐nucleotide polymorphism (SNP) markers, have become a standard approach for identifying disease susceptibility genes. The change in the technology poses substantial computational and statistical challenges that have been addressed in the quality control, imputation, and population‐based measure groups of the Genetic Analysis Workshop 16. The computational challenges pertain to efficient memory management and computational speed of the statistical procedures, and we discuss an approach for efficient SNP storage. Accuracy and computational speed is relevant for genotype calling, and the results from a comparison of three calling algorithms are discussed. The first statistical challenge is related to statistical quality control, and we discuss two novel quality control procedures. These low‐level analyses have an effect on subsequent preparatory steps for high‐level analyses, e.g., the quality of genotype imputation approaches. After the conduct of a genome‐wide association study with successful replication and/or validation, measures of diagnostic accuracy, including the area under the curve, are investigated. The area under the curve can be constructed from summary data in some situations. Finally, we discuss how the population‐attributable risk of a genetic variant that is only measured in a reference data set can be determined. Genet. Epidemiol. 33 (Suppl. 1):S45–S50, 2009. © 2009 Wiley‐Liss, Inc.
Machine learning in genome‐wide association studies
Authors: Szymczak Silke , Biernacka Joanna M. , Cordell Heather J. , González‐Recio Oscar , König Inke R. , Zhang Heping , Sun Yan V. 
Published: November 2009

AbstractRecently, genome‐wide association studies have substantially expanded our knowledge about genetic variants that influence the susceptibility to complex diseases. Although standard statistical tests for each single‐nucleotide polymorphism (SNP) separately are able to capture main genetic effects, different approaches are necessary to identify SNPs that influence disease risk jointly or in complex interactions. Experimental and simulated genome‐wide SNP data provided by the Genetic Analysis Workshop 16 afforded an opportunity to analyze the applicability and benefit of several machine learning methods. Penalized regression, ensemble methods, and network analyses resulted in several new findings while known and simulated genetic risk variants were also identified. In conclusion, machine learning approaches are promising complements to standard single‐and multi‐SNP analysis methods for understanding the overall genetic architecture of complex human diseases. However, because they are not optimized for genome‐wide SNP data, improved implementations and new variable selection procedures are required. Genet. Epidemiol. 33 (Suppl. 1):S51–S57, 2009. © 2009 Wiley‐Liss, Inc.
Inclusion of a priori information in genome‐wide association analysis
Authors: Tintle Nathan , Lantieri Francesca , Lebrec Jérémie , Sohns Melanie , Ballard David , Bickeböller Heike 
Published: November 2009

AbstractGenome‐wide association studies (GWAS) continue to gain in popularity. To utilize the wealth of data created more effectively, a variety of methods have recently been proposed to include a priori information (e.g., biologically interpretable sets of genes, candidate gene information, or gene expression) in GWAS analysis. Six contributions to Genetic Analysis Workshop 16 Group 11 applied novel or recently proposed methods to GWAS of rheumatoid arthritis and heart disease related phenotypes. The results of these analyses were a variety of novel candidate genes and sets of genes, in addition to the validation of well‐known genotype‐phenotype associations. However, because many methods are relatively new, they would benefit from further methodological research to ensure that they maintain type I error rates while increasing power to find additional associations. When methods have been adapted from other study types (e.g., gene expression data analysis or linkage analysis), the lessons learned there should be used to guide implementation of techniques. Lastly, many open research questions exist concerning the logistic details of the origin of the a priori information and the way to incorporate it. Overall, our group has demonstrated a strong potential for identifying novel genotype‐phenotype relationships by including a priori data in the analysis of GWAS, while also uncovering a series of questions requiring further research. Genet. Epidemiol. 33 (Suppl. 1):S74–S80, 2009. © 2009 Wiley‐Liss, Inc.
Use of longitudinal data in genetic studies in the genome‐wide association studies era: summary of Group 14
Authors: Kerner Berit , North Kari E. , Fallin M. Daniele 
Published: November 2009

AbstractParticipants analyzed actual and simulated longitudinal data from the Framingham Heart Study for various metabolic and cardiovascular traits. The genetic information incorporated into these investigations ranged from selected single‐nucleotide polymorphisms to genome‐wide association arrays. Genotypes were incorporated using a broad range of methodological approaches including conditional logistic regression, linear mixed models, generalized estimating equations, linear growth curve estimation, growth modeling, growth mixture modeling, population attributable risk fraction based on survival functions under the proportional hazards models, and multivariate adaptive splines for the analysis of longitudinal data. The specific scientific questions addressed by these different approaches also varied, ranging from a more precise definition of the phenotype, bias reduction in control selection, estimation of effect sizes and genotype associated risk, to direct incorporation of genetic data into longitudinal modeling approaches and the exploration of population heterogeneity with regard to longitudinal trajectories. The group reached several overall conclusions: (1) The additional information provided by longitudinal data may be useful in genetic analyses. (2) The precision of the phenotype definition as well as control selection in nested designs may be improved, especially if traits demonstrate a trend over time or have strong age‐of‐onset effects. (3) Analyzing genetic data stratified for high‐risk subgroups defined by a unique development over time could be useful for the detection of rare mutations in common multifactorial diseases. (4) Estimation of the population impact of genomic risk variants could be more precise. The challenges and computational complexity demanded by genome‐wide single‐nucleotide polymorphism data were also discussed. Genet. Epidemiol. 33 (Suppl. 1):S93–S98, 2009. © 2009 Wiley‐Liss, Inc.
Summary of contributions to GAW Group 15: family‐based samples are useful in identifying common polymorphisms associated with complex traits
Authors: Knight Stacey , Uh Hae‐Won , Martinez Maria 
Published: November 2009

AbstractTraditionally, family‐based samples have been used for genetic analyses of single‐gene traits caused by rare but highly penetrant risk variants. The utility of family‐based genetic data for analyzing common complex traits is unclear and contains numerous challenges. To assess the utility as well as to address these challenges, members of Genetic Analysis Workshop 16 Group 15 analyzed Framingham Heart Study data using family‐based designs ranging from parent—offspring trios to large pedigrees. We investigated different methods including traditional linkage tests, family‐based association tests, and population‐based tests that correct for relatedness between subjects, and tests to detect parent‐of‐origin effects. The analyses presented an assortment of positive findings. One contribution found increased power to detect epistatic effects through linkage using ascertainment of sibships based on extreme quantitative values or presence of disease associated with the quantitative value. Another contribution found four single‐nucleotide polymorphisms (SNPs) showing a maternal effect, two SNPs with an imprinting effect, and one SNP having both effects on a binary high blood pressure trait. Finally, three contributions illustrated the advantage of using population‐based methods to detect association to complex binary or quantitative traits. Our findings highlight the contribution of family‐based samples to the genetic dissection of complex traits. Genet. Epidemiol. 33 (Suppl. 1):S99–S104, 2009. © 2009 Wiley‐Liss, Inc.
Gene‐ or region‐based analysis of genome‐wide association studies
Authors: Beyene Joseph , Tritchler David , Asimit Jennifer L. , Hamid Jemila S. 
Published: November 2009

AbstractWith rapid advances in genotyping technologies in recent years and the growing number of available markers, genome‐wide association studies are emerging as promising approaches for the study of complex diseases and traits. However, there are several challenges with analysis and interpretation of such data. First, there is a massive multiple testing problem, due to the large number of markers that need to be analyzed, leading to an increased risk of false positives and decreased ability for association studies to detect truly associated markers. In particular, the ability to detect modest genetic effects can be severely compromised. Second, a genetic association of a given single‐nucleotide polymorphism as determined by univariate statistical analyses does not typically explain biologically interesting features, and often requires subsequent interpretation using a higher unit, such as a gene or region, for example, as defined by haplotype blocks. Third, missing genotypes in the data set and other data quality issues can pose challenges when comparisons across platforms and replications are planned. Finally, depending on the type of univariate analysis, computational burden can arise as the number of markers continues to grow into the millions. One way to deal with these and related challenges is to consider higher units for the analysis, such as genes or regions. This article summarizes analytical methods and strategies that have been proposed and applied by Group 16 to two genome‐wide association data sets made available through the Genetic Analysis Workshop 16. Genet. Epidemiol. 33 (Suppl. 1):S105–S110, 2009. © 2009 Wiley‐Liss, Inc.
Expression of the HGF receptor c‐met by macrophages in experimental autoimmune encephalomyelitis
Authors: Moransard Martijn , Sawitzky Mandy , Fontana Adriano , Suter Tobias 
Published: November 2009

AbstractHepatocyte growth factor (HGF) is a pleiotropic cytokine able to evoke a wide array of cellular responses including proliferation, migration, and survival through activation of its receptor c‐met. Various types of leukocytes have been described to express c‐met suggesting that HGF/c‐met signaling may directly influence leukocyte responses in inflammation. We have investigated the HGF/c‐met pathway in experimental autoimmune encephalomyelitis (EAE), a common mouse model of multiple sclerosis (MS), in which macrophages play a dual role, contributing directly to CNS damage at disease onset but promoting recovery during remission by removing myelin debris. Here we show that during EAE both HGF and c‐met are expressed in the CNS and that c‐met is activated. We subsequently demonstrate that c‐met is primarily expressed in inflammatory lesions by macrophages and a small number of dendritic cells (DCs) and oligodendrocyte progenitor cells (OPCs) but not by microglia or T cells. Complementary in vitro experiments show that only LPS and TNFα, but not IL‐6, IL‐10, or IL‐13, are able to induce c‐met expression in macrophages. In addition, using TNF signaling deficient macrophages we demonstrate that LPS and TNFα induce c‐met through distinct pathways. Furthermore, TNFα‐ and LPS‐induced c‐met is functional because treatment of macrophages with recombinant HGF results in rapid phosphorylation of c‐met. Interestingly, HGF/c‐met signaling does not modulate cytokine expression, phagocytosis, or antigen presentation but promotes proliferation of activated macrophages. Taken together, our data indicate a pro‐inflammatory role for the HGF/c‐met pathway in EAE rather than a role in the initiation of repair mechanisms. © 2009 Wiley‐Liss, Inc.
A novel type of glial cell in the retina is stimulated by insulin‐like growth factor 1 and may exacerbate damage to neurons and Müller glia
Authors: Fischer Andy J. , Scott Melissa A. , Zelinka Christopher , Sherwood Patrick 
Published: November 2009

AbstractRecent studies have demonstrated that insulin can have profound affects on the survival of neurons within the retina. The purpose of this study was to determine how insulin‐like growth factor 1 (IGF1) influences retinal cells; in particular, the glial cells. We identify a novel type of glial cell in the avian retina and provide evidence that these cells can respond to acute damage and IGF1. In normal retinas, we found a distinct cell‐type, scattered across the ganglion cell and inner plexiform layers that express Sox2, Sox9, Nkx2.2, vimentin, and transitin, the avian homologue of mammalian nestin. These glial cells have a unique immunohistochemical profile, morphology, and distribution that are distinct among other known types of retinal glia, including microglia, oligodendrocytes, astrocytes, and Muller glia. We termed these cells nonastrocytic inner retinal glia‐like (NIRG) cells. We found that the NIRG cells may express the IGF1 receptor and respond to IGF1 by proliferating, migrating distally into the retina, and upregulating transitin. In addition, IGF1 stimulated microglia to become reactive and upregulate lysosomal membrane glycoprotein and CD45. With microglia and NIRG cells stimulated by IGF1 there were elevated levels of cell death and numerous focal detachments across the retina in response to excitotoxic damage. Cell death was prominent within the areas of detachment coinciding with a stark loss of Müller glia and accumulation of NIRG cells. We conclude that NIRG cells are a novel type of retinal glia that is sensitive to IGF1 and whose activity may impact the survival of neurons and Müller glia. © 2009 Wiley‐Liss, Inc.
Flexible and stable optical parametric oscillator based laser system for coherent anti‐Stokes Raman scattering microscopy
Authors: Zhang Wei , Parsons Maddy , McConnell Gail 
Published: November 2009

AbstractThe characteristics of a stable and flexible laser system based on a synchronously pumped optical parametric oscillator (OPO) is presented. This OPO can offer very stable operation with both ∼1 ps and ∼300 fs outputs over a broad wavelength range, i.e., 920–1200 nm. Combining the pump tuning with the OPO tuning, a total Raman range of 1900–5500 cm−1 is accessible. For maximum spectral sensitivity, the CARS microsope based on the ps laser system is presented in detail. The lateral resolution of the microscope is diffraction limited to be about 390 nm. Fast wavelength switching (sub‐second) between two Raman vibrational frequencies, i.e., 2848 cm−1 for CH aliphatic vibrations and 3035 cm−1 for CH aromatic vibrations is presented as an example, although this also extends to other Raman frequencies. The possibility of obtaining a multimodal imaging system based on the fs laser system is also discussed. Microsc. Res. Tech., 2010. © 2009 Wiley‐Liss, Inc.
Transient detection of E1‐containing adenovirus in saliva after the delivery of a first‐generation adenoviral vector to human parotid gland
Authors: Zheng Changyu , Nikolov Nikolay P. , Alevizos Ilias , Cotrim Ana P. , Liu Shuying , McCullagh Linda , Chiorini John A. , Illei Gabor G. , Baum Bruce J. 
Published: November 2009

AbstractBackgroundRadiation‐induced salivary hypofunction is a common side‐effect of treatment for head and neck cancers. Patients suffer significant morbidity and there is no suitable conventional therapy. We are conducting a Phase I clinical trial, using a first‐generation serotype 5 adenoviral (Ad5) vector encoding human aquaporin‐1 (AdhAQP1) to treat such patients. One week after the administration of AdhAQP1 to an enrolled, generally healthy patient, E1‐containing adenovirus was detected in parotid saliva.MethodsThe real‐time quantitative polymerase chain reactuion (PCR) was used to measure the Ad5 E1 gene and AdhAQP1 in saliva and serum. PCR and sequencing were used to characterize viral/vector DNA extracted from saliva. The presence of infectious adenovirus was assessed by the inoculation of A549 cells with aliquots of saliva. Serum Ad5 neutralizing antibodies were measured by the inhibition of 293‐cell transduction with an Ad5 vector encoding luciferase. Multiple clinical evaluations were performed.ResultsOn day 7 after AdhAQP1 delivery, low levels of the Ad5 E1 gene were detected in parotid saliva (82 copies/µl). In addition, significant levels of AdhAQP1 were also detected (1.5 × 103 copies/µl). The patient was asymptomatic and subsequent analysis of parotid saliva samples prior to day 7 and after day 7 until day 42 was negative for both virus and vector. No virus or vector was detected in serum at any time. Detailed PCR analyses of DNA extracted from the day 7 parotid saliva sample suggested the absence of a recombination event, and no infectious virus was found.ConclusionsThe patient most likely had a latent Ad5 infection in the targeted parotid gland that was activated after gene transfer and was without clinical consequence. Published in 2009 by John Wiley & Sons, Ltd.