Noise sensitivity medical, psychological and genetic aspects

Noise can be defined as unwanted sound. It may adversely affect the health and well-being of individuals. Noise sensitivity is a personality trait covering attitudes towards noise in general and a predictor of noise annoyance. Noise sensitive individuals are more affected by noise than less sensitive individuals. The determinants and characteristics related to noise sensitivity are rather poorly known. The risk of health effects caused by noise can be hypothesized to be higher for noise sensitive individuals compared to those who are not noise sensitive. A cardiovascular disease may be an example of outcomes. The general aim of the present study was to investigate the association of noise sensitivity with specific somatic and psychological factors, including the genetic component of noise sensitivity, and the association of noise sensitivity with mortality. The study was based on the Finnish Twin Cohort of same-sex twin pairs born before 1958. In 1988 a questionnaire was sent to twin pairs discordant for hypertension. 1495 individuals (688 men, 807 women) aged 31 88 years replied, including 573 twin pairs. 218 of the subjects lived in the Helsinki Metropolitan Area. Self-reported noise sensitivity, lifetime noise exposure and hypertension were obtained from the questionnaire study in 1988 and other somatic and psychological factors from the questionnaire study in 1981 for the same individuals. In addition, noise map information (1988 1992) from the Helsinki Metropolitan Area and mortality follow-up 1989 2003 were used. To evaluate the stability and validity of noise sensitivity, a new questionnaire was sent in 2002 to a sample of the subjects who had replied to the 1988 questionnaire. Of all subjects who had answered the question on noise sensitivity, 38 % were noise sensitive. Noise sensitivity was independent of noise exposure levels indicated in noise maps. Subjects with high noise sensitivity reported more transportation noise exposure than subjects with low noise sensitivity. Noise sensitive subjects reported transportation noise exposure outside the environmental noise map areas almost twice as often as non-sensitive subjects. Noise sensitivity was associated with hypertension, emphysema, use of psychotropic drugs, smoking, stress and hostility, even when lifetime noise exposure was adjusted for. Monozygotic twin pairs were more similar with regards noise sensitivity than dizygotic twin pairs, and quantitative genetic modelling indicated significant familiality. The best fitting genetic model provided an estimate of heritability of 36 %. Follow-up of subjects in the case-control study showed that cardiovascular mortality was significantly increased among noise sensitive women, but not among men. For coronary heart mortality the interaction of noise sensitivity and lifetime noise exposure was statistically significant in women. In conclusion, noise sensitivity has both somatic and psychological components. It does aggregate in families and probably has a genetic component. Noise sensitivity may be a risk factor for cardiovascular mortality in women.

Methods to improve gene signal: Application to cDNA microarrays

Microarrays are high throughput biological assays that allow the screening of thousands of genes for their expression. The main idea behind microarrays is to compute for each gene a unique signal that is directly proportional to the quantity of mRNA that was hybridized on the chip. A large number of steps and errors associated with each step make the generated expression signal noisy. As a result, microarray data need to be carefully pre-processed before their analysis can be assumed to lead to reliable and biologically relevant conclusions. This thesis focuses on developing methods for improving gene signal and further utilizing this improved signal for higher level analysis. To achieve this, first, approaches for designing microarray experiments using various optimality criteria, considering both biological and technical replicates, are described. A carefully designed experiment leads to signal with low noise, as the effect of unwanted variations is minimized and the precision of the estimates of the parameters of interest are maximized. Second, a system for improving the gene signal by using three scans at varying scanner sensitivities is developed. A novel Bayesian latent intensity model is then applied on these three sets of expression values, corresponding to the three scans, to estimate the suitably calibrated true signal of genes. Third, a novel image segmentation approach that segregates the fluorescent signal from the undesired noise is developed using an additional dye, SYBR green RNA II. This technique helped in identifying signal only with respect to the hybridized DNA, and signal corresponding to dust, scratch, spilling of dye, and other noises, are avoided. Fourth, an integrated statistical model is developed, where signal correction, systematic array effects, dye effects, and differential expression, are modelled jointly as opposed to a sequential application of several methods of analysis. The methods described in here have been tested only for cDNA microarrays, but can also, with some modifications, be applied to other high-throughput technologies. Keywords: High-throughput technology, microarray, cDNA, multiple scans, Bayesian hierarchical models, image analysis, experimental design, MCMC, WinBUGS.

Residual noise covariance for Planck low-resolution data analysis

Aims: Develop and validate tools to estimate residual noise covariance in Planck frequency maps. Quantify signal error effects and compare different techniques to produce low-resolution maps. Methods: We derive analytical estimates of covariance of the residual noise contained in low-resolution maps produced using a number of map-making approaches. We test these analytical predictions using Monte Carlo simulations and their impact on angular power spectrum estimation. We use simulations to quantify the level of signal errors incurred in different resolution downgrading schemes considered in this work. Results: We find an excellent agreement between the optimal residual noise covariance matrices and Monte Carlo noise maps. For destriping map-makers, the extent of agreement is dictated by the knee frequency of the correlated noise component and the chosen baseline offset length. The significance of signal striping is shown to be insignificant when properly dealt with. In map resolution downgrading, we find that a carefully selected window function is required to reduce aliasing to the sub-percent level at multipoles, ell > 2Nside, where Nside is the HEALPix resolution parameter. We show that sufficient characterization of the residual noise is unavoidable if one is to draw reliable contraints on large scale anisotropy. Conclusions: We have described how to compute the low-resolution maps, with a controlled sky signal level, and a reliable estimate of covariance of the residual noise. We have also presented a method to smooth the residual noise covariance matrices to describe the noise correlations in smoothed, bandwidth limited maps.