McMaster University

About: McMaster University is a education organization based out in Hamilton, Ontario, Canada. It is known for research contribution in the topics: Population & Health care. The organization has 41361 authors who have published 101269 publications receiving 4251422 citations.

Measuring quality of life in the parents of children with asthma

Abstract: Parents and primary caregivers of children with asthma are limited in normal daily activities and experience anxieties and fears due to the child's illness. We have developed the Paediatric Asthma Caregiver's Quality of Life Questionnaire (PACQLQ) to measure these impairments. The objective of this study was to evaluate the measurement properties of the PACQLQ. A 9-week single cohort study was conducted with assessments at 1, 5 and 9 weeks. Participants in the study were primary caregivers of 52 children (age 7–17 years) with symptomatic asthma, recruited from notices in the local media and paediatric asthma clinics. Caregivers completed the PACQLQ, Impact-on-Family Scale and Global Rating of Change Questionnaires. Patients completed the Paediatric Asthma Quality of Life Questionnaire and an asthma control questionnaire. Spirornetry and β-agonist use were recorded. The PACQLQ was able to detect quality of life changes in those caregivers who changed (p<0.001) and to differentiate these from the caregivers whose quality of life remained stable (p<0.0001). The PACQLQ is reproducible in subjects who are stable (ICC=0.84), and showed acceptable levels of longitudinal and cross-sectional correlations with the child's asthma status and health-related quality of life and with other measures of caregiver health-related quality of life. The PACQLQ functions well as both an evaluative and a discriminative instrument.

How cigarette smoke skews immune responses to promote infection, lung disease and cancer

Abstract: A complex and multilayered immune defence system protects the host against harmful agents and maintains tissue homeostasis. Cigarette smoke exposure markedly impacts the immune system, compromising the host's ability to mount appropriate immune and inflammatory responses and contributing to smoking-related pathologies. These adverse effects on the immune system not only occur in active smokers, but also in those exposed to smoke passively in contaminated environments, and may persist for decades after exposure has ended.

Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation

Abstract: The challenge of image interpolation is to preserve spatial details. We propose a soft-decision interpolation technique that estimates missing pixels in groups rather than one at a time. The new technique learns and adapts to varying scene structures using a 2-D piecewise autoregressive model. The model parameters are estimated in a moving window in the input low-resolution image. The pixel structure dictated by the learnt model is enforced by the soft-decision estimation process onto a block of pixels, including both observed and estimated. The result is equivalent to that of a high-order adaptive nonseparable 2-D interpolation filter. This new image interpolation approach preserves spatial coherence of interpolated images better than the existing methods, and it produces the best results so far over a wide range of scenes in both PSNR measure and subjective visual quality. Edges and textures are well preserved, and common interpolation artifacts (blurring, ringing, jaggies, zippering, etc.) are greatly reduced.

How to make more out of community data? A conceptual framework and its implementation as models and software.

Abstract: Community ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology, we propose Hierarchical Modelling of Species Communities (HMSC) as a general, flexible framework for modern analysis of community data. While non-manipulative data allow for only correlative and not causal inference, this framework facilitates the formulation of data-driven hypotheses regarding the processes that structure communities. We model environmental filtering by variation and covariation in the responses of individual species to the characteristics of their environment, with potential contingencies on species traits and phylogenetic relationships. We capture biotic assembly rules by species-to-species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and implement it as R- and Matlab-packages which enable computationally efficient analyses of large data sets. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time-series data. We illustrate the use of this framework through a series of diverse ecological examples.

A genome-wide linkage and association scan reveals novel loci for autism

Abstract: Although autism is a highly heritable neurodevelopmental disorder, attempts to identify specific susceptibility genes have thus far met with limited success. Genome-wide association studies using half a million or more markers, particularly those with very large sample sizes achieved through meta-analysis, have shown great success in mapping genes for other complex genetic traits. Consequently, we initiated a linkage and association mapping study using half a million genome-wide single nucleotide polymorphisms (SNPs) in a common set of 1,031 multiplex autism families (1,553 affected offspring). We identified regions of suggestive and significant linkage on chromosomes 6q27 and 20p13, respectively. Initial analysis did not yield genome-wide significant associations; however, genotyping of top hits in additional families revealed an SNP on chromosome 5p15 (between SEMA5A and TAS2R1) that was significantly associated with autism (P = 2 x 10(-7)). We also demonstrated that expression of SEMA5A is reduced in brains from autistic patients, further implicating SEMA5A as an autism susceptibility gene. The linkage regions reported here provide targets for rare variation screening whereas the discovery of a single novel association demonstrates the action of common variants.