Young Men s Sexual Behaviour in Finland and Estonia : Opportunities for prevention of sexually transmitted infections

Background: The incidence of sexually transmitted infections (STIs) in most EU states has gradually increased and the rate of newly diagnosed HIV cases has doubled since 1999. STIs differ in their clinical features, prognosis and transmission dynamics, though they do share a common factor in their mode of transmission −that is, human behaviour. The evolvement of STI epidemiology involves a joint action of biological, epidemiological and societal factors. Of the more immediate factors, besides timely diagnosis and appropriate treatment, STI incidence is influenced by population patterns of sexual risk behaviour, particularly the number of sexual partners and the frequency of unprotected intercourse. Assessment of sexual behaviour, its sociodemographic determinants and time-trends are important in understanding the distribution and dynamic of STI epidemiology. Additionally, in the light of the basic structural determinants, such as increased level of migration, changes in gender dynamics and impacts from globalization, with its increasing alignment of values and beliefs, can reveal future challenges related to STI epidemiology. STI case surveillance together with surveillance on sexual behaviour can guide the identification of preventive strategies, assess their effectiveness and predict emerging trends. The objective of this study was to provide base line data on sexual risk behaviour, self-reported STIs and their patterns by sociodemographic factors as well as associations of sexual risk behaviour with substance use among young men in Finland and Estonia. In Finland national population based data on adult men s sexual behaviour is limited. The findings are discussed in the context of STI epidemiology as well as their possible implications for public health policies and prevention strategies. Materials and Methods: Data from three different cross-sectional population-based surveys conducted in Finland and Estonia, during 1998 2005, were used. Sexual behaviour- and health-related questions were incorporated in two surveys in Finland; the Health 2000, a large scale general health survey, focussed on young adults, and the Military health behavioural survey on military conscripts participating in the mandatory military training. Through research collaboration with Estonia, similar questions to the Finnish surveys were introduced to the second Estonian HIV/AIDS survey, which was targeted at young adults. All surveys applied mail-returned, anonymous, self-administered questionnaires with multiple choice formatted answers. Results: In Finland, differences in sexual behaviour between young men and women were minor. An age-stratified analysis revealed that the sex-related difference observed in the youngest age group (18 19 years) levelled off in the age group 20 24 and almost disappeared among those aged 25 29. Marital status was the most important sociodemographic correlate for sexual behaviour for both sexes, singles reporting higher numbers of lifetime-partners and condom use. This effect was stronger for women than for men. However, of those who had sex with casual partners, 15% were married or co-habiting, with no difference between male and female respondents. According to the Military health behavioural survey, young men s sexual risk behaviour in Finland did not markedly change over a period of time between 1998 and 2005. Approximately 30−40% of young men had had multiple sex partners (more than five) in their lifetime, over 20% reported having had multiple sex partners (at least three) over the past year and 50% did not use a condom in their last sexual intercourse. Some 10% of men reported accumulation of risk factors, i.e. having had both, multiple sex partners and not used a condom in their last intercourse, over the past year of the survey. When differences and similarities were viewed within Finland and Estonia, a clear sociodemographic patterning of sexual risk behaviour and self-reported STIs was found in Finland, but a somewhat less consistent trend in Estonia. Generally, both, alcohol and drug use were strong correlates for sexual risk behaviour and self-reported STIs in Finland and Estonia, having a greater effect on engagement with multiple sex partners rather than unprotected intercourse or self-reported STIs. In Finland alcohol use, relative to drug use, was a stronger predictor of sexual risk behaviour and self-reported STIs, while in Estonia drug use predicted sexual risk behaviour and self-reported STIs stronger than alcohol use. Conclusions: The study results point to the importance for prevention of sexual risk behaviour, particularly strategies that integrate sexual risk with alcohol and drug use risks. The results point to the need to focus further research on sexual behaviour and STIs among young people; on tracking trends among general population as well as applying in-depth research to identify and learn from vulnerable and high-risk population groups for STIs who are exposed to a combination of risk factors.

Computational Techniques for Haplotype Inference and for Local Alignment Significance

This thesis which consists of an introduction and four peer-reviewed original publications studies the problems of haplotype inference (haplotyping) and local alignment significance. The problems studied here belong to the broad area of bioinformatics and computational biology. The presented solutions are computationally fast and accurate, which makes them practical in high-throughput sequence data analysis. Haplotype inference is a computational problem where the goal is to estimate haplotypes from a sample of genotypes as accurately as possible. This problem is important as the direct measurement of haplotypes is difficult, whereas the genotypes are easier to quantify. Haplotypes are the key-players when studying for example the genetic causes of diseases. In this thesis, three methods are presented for the haplotype inference problem referred to as HaploParser, HIT, and BACH. HaploParser is based on a combinatorial mosaic model and hierarchical parsing that together mimic recombinations and point-mutations in a biologically plausible way. In this mosaic model, the current population is assumed to be evolved from a small founder population. Thus, the haplotypes of the current population are recombinations of the (implicit) founder haplotypes with some point--mutations. HIT (Haplotype Inference Technique) uses a hidden Markov model for haplotypes and efficient algorithms are presented to learn this model from genotype data. The model structure of HIT is analogous to the mosaic model of HaploParser with founder haplotypes. Therefore, it can be seen as a probabilistic model of recombinations and point-mutations. BACH (Bayesian Context-based Haplotyping) utilizes a context tree weighting algorithm to efficiently sum over all variable-length Markov chains to evaluate the posterior probability of a haplotype configuration. Algorithms are presented that find haplotype configurations with high posterior probability. BACH is the most accurate method presented in this thesis and has comparable performance to the best available software for haplotype inference. Local alignment significance is a computational problem where one is interested in whether the local similarities in two sequences are due to the fact that the sequences are related or just by chance. Similarity of sequences is measured by their best local alignment score and from that, a p-value is computed. This p-value is the probability of picking two sequences from the null model that have as good or better best local alignment score. Local alignment significance is used routinely for example in homology searches. In this thesis, a general framework is sketched that allows one to compute a tight upper bound for the p-value of a local pairwise alignment score. Unlike the previous methods, the presented framework is not affeced by so-called edge-effects and can handle gaps (deletions and insertions) without troublesome sampling and curve fitting.

Methods for Answer Extraction in Textual Question Answering

In this thesis we present and evaluate two pattern matching based methods for answer extraction in textual question answering systems. A textual question answering system is a system that seeks answers to natural language questions from unstructured text. Textual question answering systems are an important research problem because as the amount of natural language text in digital format grows all the time, the need for novel methods for pinpointing important knowledge from the vast textual databases becomes more and more urgent. We concentrate on developing methods for the automatic creation of answer extraction patterns. A new type of extraction pattern is developed also. The pattern matching based approach chosen is interesting because of its language and application independence. The answer extraction methods are developed in the framework of our own question answering system. Publicly available datasets in English are used as training and evaluation data for the methods. The techniques developed are based on the well known methods of sequence alignment and hierarchical clustering. The similarity metric used is based on edit distance. The main conclusions of the research are that answer extraction patterns consisting of the most important words of the question and of the following information extracted from the answer context: plain words, part-of-speech tags, punctuation marks and capitalization patterns, can be used in the answer extraction module of a question answering system. This type of patterns and the two new methods for generating answer extraction patterns provide average results when compared to those produced by other systems using the same dataset. However, most answer extraction methods in the question answering systems tested with the same dataset are both hand crafted and based on a system-specific and fine-grained question classification. The the new methods developed in this thesis require no manual creation of answer extraction patterns. As a source of knowledge, they require a dataset of sample questions and answers, as well as a set of text documents that contain answers to most of the questions. The question classification used in the training data is a standard one and provided already in the publicly available data.

Alignment of the ALICE Inner Tracking System with cosmic-ray tracks

ALICE (A Large Ion Collider Experiment) is the LHC (Large Hadron Collider) experiment devoted to investigating the strongly interacting matter created in nucleus-nucleus collisions at the LHC energies. The ALICE ITS, Inner Tracking System, consists of six cylindrical layers of silicon detectors with three different technologies; in the outward direction: two layers of pixel detectors, two layers each of drift, and strip detectors. The number of parameters to be determined in the spatial alignment of the 2198 sensor modules of the ITS is about 13,000. The target alignment precision is well below 10 micron in some cases (pixels). The sources of alignment information include survey measurements, and the reconstructed tracks from cosmic rays and from proton-proton collisions. The main track-based alignment method uses the Millepede global approach. An iterative local method was developed and used as well. We present the results obtained for the ITS alignment using about 10^5 charged tracks from cosmic rays that have been collected during summer 2008, with the ALICE solenoidal magnet switched off.