Methods and background characteristics of the TOHNN study : a population-based study of oral health conditions in northern Norway

OBJECTIVES: The aim of the Tromstannen - Oral Health in Northern Norway (TOHNN) study was to investigate oral health and dental-related diseases in an adult population. This article provides an overview of the background of the study and a description of the sample characteristics and methods employed in data collection. STUDY DESIGN: Cross-sectional population-based study including a questionnaire and clinical dental examination. METHODS: A randomly selected sample of 2,909 individuals (20-79 years old) drawn from the population register was invited to participate in the study. The data were collected between October 2013 and November 2014 in Troms County in northern Norway. The questionnaire focused on oral health-related behaviours and attitudes, oral health-related quality of life, sense of coherence, dental anxiety and symptoms from the temporomandibular joint. The dental examinations, including radiographs, were conducted by 11 dental teams in 5 dental offices. The examination comprised of registration of dental caries, full mouth periodontal status, temporomandibular disorders, mucosal lesions and height and weight. The participants were grouped by age (20-34, 35-49, 50-64 and 65-79) and ethnicity (Norwegian, Sámi, other European and other world). RESULTS: From the original sample of 2,909 individuals, 1,986 (68.3%) people participated, of whom 1,019 (51.3%) were women. The highest attendance rate was among women 20-34 years old (80.3%) and the lowest in the oldest age group of women (55.4%). There was no difference in response rate between rural and urban areas. There was a positive correlation between population size and household gross income (p < 0.001) and education level (p < 0.001). The majority of Sámi resided in smaller municipalities. In larger cities, most participants used private dental health care services, whereas, in rural areas, most participants used the public dental health care service. CONCLUSION: The TOHNN study has the potential to generate new knowledge on a wide range of oral health conditions beneficial to the population in Troms County. Due to the high participation rate, generalization both nationally and to the circumpolar area ought to be possible.

Dynamic Fuzzy Logic Control of GeneticAlgorithm Probabilities

Genetic algorithms are commonly used to solve combinatorial optimizationproblems. The implementation evolves using genetic operators (crossover, mutation,selection, etc.). Anyway, genetic algorithms like some other methods have parameters(population size, probabilities of crossover and mutation) which need to be tune orchosen.In this paper, our project is based on an existing hybrid genetic algorithmworking on the multiprocessor scheduling problem. We propose a hybrid Fuzzy-Genetic Algorithm (FLGA) approach to solve the multiprocessor scheduling problem.The algorithm consists in adding a fuzzy logic controller to control and tunedynamically different parameters (probabilities of crossover and mutation), in anattempt to improve the algorithm performance. For this purpose, we will design afuzzy logic controller based on fuzzy rules to control the probabilities of crossoverand mutation. Compared with the Standard Genetic Algorithm (SGA), the resultsclearly demonstrate that the FLGA method performs significantly better.

Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability

A number of recent works have introduced statistical methods for detecting genetic loci that affect phenotypic variability, which we refer to as variability-controlling quantitative trait loci (vQTL). These are genetic variants whose allelic state predicts how much phenotype values will vary about their expected means. Such loci are of great potential interest in both human and non-human genetic studies, one reason being that a detected vQTL could represent a previously undetected interaction with other genes or environmental factors. The simultaneous publication of these new methods in different journals has in many cases precluded opportunity for comparison. We survey some of these methods, the respective trade-offs they imply, and the connections between them. The methods fall into three main groups: classical non-parametric, fully parametric, and semi-parametric two-stage approximations. Choosing between alternatives involves balancing the need for robustness, flexibility, and speed. For each method, we identify important assumptions and limitations, including those of practical importance, such as their scope for including covariates and random effects. We show in simulations that both parametric methods and their semi-parametric approximations can give elevated false positive rates when they ignore mean-variance relationships intrinsic to the data generation process. We conclude that choice of method depends on the trait distribution, the need to include non-genetic covariates, and the population size and structure, coupled with a critical evaluation of how these fit with the assumptions of the statistical model.

How do neighbouring populations affect local population change over time?

This study covers a period when society changed from a pre-industrial agricultural society to a post-industrial service-producing society. Parallel with this social transformation, major population changes took place. In this study, we analyse how local population changes are affected by neighbouring populations. To do so we use the last 200 years of local population change that redistributed population in Sweden. We use literature to identify several different processes and spatial dependencies in the redistribution between a parish and its surrounding parishes. The analysis is based on a unique unchanged historical parish division, and we use an index of local spatial correlation to describe different kinds of spatial dependencies that have influenced the redistribution of the population. To control inherent time dependencies, we introduce a non-separable spatial temporal correlation model into the analysis of population redistribution. Hereby, several different spatial dependencies can be observed simultaneously over time. The main conclusions are that while local population changes have been highly dependent on the neighbouring populations in the 19th century, this spatial dependence have become insignificant already when two parishes is separated by 5 kilometres in the late 20th century. Another conclusion is that the time dependency in the population change is higher when the population redistribution is weak, as it currently is and as it was during the 19th century until the start of industrial revolution.