1,258 research outputs found

    Time-to-Arrival and Useful Field of View: Associations with Reported Driving Difficulties Among Older Adults

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    The purpose of this study was to determine self-reported driving difficulties related to time-to-arrival (TTA) and/or useful field of view (UFOV), to identify associations between TTA and UFOV, and to compare the TTA estimated accuracy between different vehicle speed approaches. Thirty-eight male and female active drivers ages 61 to 81 years (70,2 ± 5,0 years) participated in this study. The UFOV® test was applied. TTA was studied with a removal paradigm, using two vehicle speeds (50 and 70km/h) for data collection. A driving habits questionnaire was administered, including items concerning driving difficulties. Results showed that lower performances in divided attention (subtest 2 of UFOV®) were significantly associated with reported difficulties in hightraffic intersections, trouble seeing the signs in time to respond to them, and problems judging the speed or distance of an approaching vehicle. Concerning TTA, greater response bias and/or greater response inconsistency were significantly associated with difficulties in overtaking other cars, difficulties seeing the signs in time to react to them, difficulties in the speed or distance perception of an oncoming vehicle. Low associations were found between UFOV and TTA measures. TTA estimated accuracy increased when the vehicle traveled at higher speeds. We concluded that both TTA and UFOV seem to be important measures to identify driving difficulties in older adults. The TTA could be relevant in measuring specific aspects of visual information processing in addition to other instruments like UFOV®

    Hyaluronic acid hydrogel for controlled self-renewal and differentiation of human embryonic stem cells

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    Control of self-renewal and differentiation of human ES cells (hESCs) remains a challenge. This is largely due to the use of culture systems that involve poorly defined animal products and do not mimic the normal developmental milieu. Routine protocols involve the propagation of hESCs on mouse fibroblast or human feeder layers, enzymatic cell removal, and spontaneous differentiation in cultures of embryoid bodies, and each of these steps involves significant variability of culture conditions. We report that a completely synthetic hydrogel matrix can support (i) long-term self-renewal of hESCs in the presence of conditioned medium from mouse embryonic fibroblast feeder layers, and (ii) direct cell differentiation. Hyaluronic acid (HA) hydrogels were selected because of the role of HA in early development and feeder layer cultures of hESCs and the controllability of hydrogel architecture, mechanics, and degradation. When encapsulated in 3D HA hydrogels (but not within other hydrogels or in monolayer cultures on HA), hESCs maintained their undifferentiated state, preserved their normal karyotype, and maintained their full differentiation capacity as indicated by embryoid body formation. Differentiation could be induced within the same hydrogel by simply altering soluble factors. We therefore propose that HA hydrogels, with their developmentally relevant composition and tunable physical properties, provide a unique microenvironment for the selfrenewal and differentiation of hESCs

    Dissection of a Complex Disease Susceptibility Region Using a Bayesian Stochastic Search Approach to Fine Mapping.

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    Identification of candidate causal variants in regions associated with risk of common diseases is complicated by linkage disequilibrium (LD) and multiple association signals. Nonetheless, accurate maps of these variants are needed, both to fully exploit detailed cell specific chromatin annotation data to highlight disease causal mechanisms and cells, and for design of the functional studies that will ultimately be required to confirm causal mechanisms. We adapted a Bayesian evolutionary stochastic search algorithm to the fine mapping problem, and demonstrated its improved performance over conventional stepwise and regularised regression through simulation studies. We then applied it to fine map the established multiple sclerosis (MS) and type 1 diabetes (T1D) associations in the IL-2RA (CD25) gene region. For T1D, both stepwise and stochastic search approaches identified four T1D association signals, with the major effect tagged by the single nucleotide polymorphism, rs12722496. In contrast, for MS, the stochastic search found two distinct competing models: a single candidate causal variant, tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals, one of which was tagged by the major T1D associated rs12722496 and the other by rs56382813. There is low to moderate LD between rs2104286 and both rs12722496 and rs56382813 (r2 ≃ 0:3) and our two SNP model could not be recovered through a forward stepwise search after conditioning on rs2104286. Both signals in the two variant model for MS affect CD25 expression on distinct subpopulations of CD4+ T cells, which are key cells in the autoimmune process. The results support a shared causal variant for T1D and MS. Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data.We acknowledge use of DNA from The UK Blood Services collection of Common Controls (UKBS-CC collection), which is funded by the Wellcome Trust grant 076113/C/04/Z and by the USA National Institute for Health Research program grant to the National Health Service Blood and Transplant (RP-PG-0310-1002). We acknowledge the use of DNA from the British 1958 Birth Cohort collection, which is funded by the UK Medical Research Council grant G0000934 and the Wellcome Trust grant 068545/Z/02. This research utilized resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Allergy and Infectious Diseases, the National Human Genome Research Institute, the National Institute of Child Health and Human Development and the JDRF and is supported by the USA National Institutes of Health grant U01-DK062418. The JDRF/Wellcome Trust Diabetes and Inflammation Laboratory is funded by the JDRF (9-2011-253), the Wellcome Trust (091157) and the National Institute for Health Research Cambridge Biomedical Centre. The research leading to these results has received funding from the European Union's 7th Framework Programme (FP7/2007-2013) under grant agreement no.241447 (NAIMIT). The Cambridge Institute for Medical Research (CIMR) is in receipt of a Wellcome Trust Strategic Award (100140). CW is supported by the Wellcome Trust (089989). We acknowledge the National Institute for Health Research Cambridge Biomedical Research Centre for funding.This is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pgen.100527

    Pneumococcal Serotypes Colonise the Nasopharynx in Children at Different Densities.

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    Prevalence of pneumococcal serotypes in carriage and disease has been described but absolute serotype colonisation densities have not been reported. 515 paediatric nasal swab DNA extracts were subjected to lytA qPCR and molecular serotyping by microarray. Absolute serotype densities were derived from total pneumococcal density (qPCR cycle threshold and standard curve) and relative abundance (microarray) and varied widely. Compared to all serotype densities observed, the strongest evidence of differences was seen for serotypes 21 and 35B (higher) and 3, 38 and non-typeables (lower) (p<0.05) with a similar hierarchy when only a single serotype carriage was assessed. There was no evidence of any overall density differences between children with single or multiple serotypes detected but serotypes with mid-range densities were more prevalent. The hierarchy of distinct pneumococcal serotype carriage densities described here for the first time, may help explain the dynamics of transmission between children

    Gene expression patterns associated with p53 status in breast cancer

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    BACKGROUND: Breast cancer subtypes identified in genomic studies have different underlying genetic defects. Mutations in the tumor suppressor p53 occur more frequently in estrogen receptor (ER) negative, basal-like and HER2-amplified tumors than in luminal, ER positive tumors. Thus, because p53 mutation status is tightly linked to other characteristics of prognostic importance, it is difficult to identify p53's independent prognostic effects. The relation between p53 status and subtype can be better studied by combining data from primary tumors with data from isogenic cell line pairs (with and without p53 function). METHODS: The p53-dependent gene expression signatures of four cell lines (MCF-7, ZR-75-1, and two immortalized human mammary epithelial cell lines) were identified by comparing p53-RNAi transduced cell lines to their parent cell lines. Cell lines were treated with vehicle only or doxorubicin to identify p53 responses in both non-induced and induced states. The cell line signatures were compared with p53-mutation associated genes in breast tumors. RESULTS: Each cell line displayed distinct patterns of p53-dependent gene expression, but cell type specific (basal vs. luminal) commonalities were evident. Further, a common gene expression signature associated with p53 loss across all four cell lines was identified. This signature showed overlap with the signature of p53 loss/mutation status in primary breast tumors. Moreover, the common cell-line tumor signature excluded genes that were breast cancer subtype-associated, but not downstream of p53. To validate the biological relevance of the common signature, we demonstrated that this gene set predicted relapse-free, disease-specific, and overall survival in independent test data. CONCLUSION: In the presence of breast cancer heterogeneity, experimental and biologically-based methods for assessing gene expression in relation to p53 status provide prognostic and biologically-relevant gene lists. Our biologically-based refinements excluded genes that were associated with subtype but not downstream of p53 signaling, and identified a signature for p53 loss that is shared across breast cancer subtypes
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