Life events and social support among patients with major depressive disorder

The study is part of a research project of 269 psychiatric patients with major depression, Vantaa Depression Study, in the Department of Mental Health and Alcohol Research of the National Public Health Institute and the Department of Psychiatry of the Peijas Medical Care District. The aim was to study at the onset of MDE psychosocial differences in subgroups of patients and clustering of events into time before depression and its prodromal phase, to study whether more severe life events and less social support predict poorer outcome in all patients, but most among those currently in partial remission, whether social support declines as a consequence of time spent in MDE, is sensitive to improvement, and whether social support is influenced by neuroticism and extraversion. After screening, a semistructured interview (SCAN, version 2.0) was used for the presence of DSM-IV MDE, and other psychiatric diagnoses. Life events and social support were studied with semistructured methods (IRLE, Paykel 1983; IMSR, Brugha et al. 1987), perceived social support and neuroticism/extraversion with questionnaires (PSSS-R, Blumenthal et al. 1987; EPI, Eysenck and Eysenck 1964) at baseline, 6 and 18 months. At the onset of depression life events were common. No major differences between subgroups of patients were found; the younger had more events, whereas those with comorbid alcoholism and personality disorders perceived less support. Although events were distributed evenly between the time before depression, the prodromal phase and the index MDE, two thirds of the patients attributed their depression to some life event. Adversities and poor perceived support influenced the outcome of all psychiatric patients, most in the subgroup of full remission. In the partial remission group, the impact of severe events and in the MDE, perceived support was important. Low objective and subjective support were predicted by longer time spent in MDE. Along with improvement subjective support improved. Neuroticism and extraversion were associated with the size of social network and perceived support and predicted change of perceived support. In conclusion, adversities were common in all phases of depression. They may thus have many roles; before depression they may precipitate it, in the prodromal phase worsen symptoms, and during the MDE, the outcome of depression. Patients often attributed their depression to a life event. Psychosocial subgroup differences were quite small. Perceived support predicted the outcome of depression, and time spent in MDE objective and subjective support. Neuroticism and extraversion may modify the level and change particularly in perceived support, thereby indirectly effecting vulnerability to depression.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Data mining for telecommunications network log analysis

Telecommunications network management is based on huge amounts of data that are continuously collected from elements and devices from all around the network. The data is monitored and analysed to provide information for decision making in all operation functions. Knowledge discovery and data mining methods can support fast-pace decision making in network operations. In this thesis, I analyse decision making on different levels of network operations. I identify the requirements decision-making sets for knowledge discovery and data mining tools and methods, and I study resources that are available to them. I then propose two methods for augmenting and applying frequent sets to support everyday decision making. The proposed methods are Comprehensive Log Compression for log data summarisation and Queryable Log Compression for semantic compression of log data. Finally I suggest a model for a continuous knowledge discovery process and outline how it can be implemented and integrated to the existing network operations infrastructure.

Fine-tuning of the signalling network controlling morphogenesis and stem cell development in teeth

Tooth development is regulated by sequential and reciprocal interactions between epithelium and mesenchyme. The molecular mechanisms underlying this regulation are conserved and most of the participating molecules belong to several signalling families. Research focusing on mouse teeth has uncovered many aspects of tooth development, including molecular and evolutionary specifi cs, and in addition offered a valuable system to analyse the regulation of epithelial stem cells. In mice the spatial and temporal regulation of cell differentiation and the mechanisms of patterning during development can be analysed both in vivo and in vitro. Follistatin (Fst), a negative regulator of TGFβ superfamily signalling, is an important inhibitor during embryonic development. We showed the necessity of modulation of TGFβ signalling by Fst in three different regulatory steps during tooth development. First we showed that tinkering with the level of TGFβ signalling by Fst may cause variation in the molar cusp patterning and crown morphogenesis. Second, our results indicated that in the continuously growing mouse incisors asymmetric expression of Fst is responsible for the labial-lingual patterning of ameloblast differentiation and enamel formation. Two TGFβ superfamily signals, BMP and Activin, are required for proper ameloblast differentiation and Fst modulates their effects. Third, we identifi ed a complex signalling network regulating the maintenance and proliferation of epithelial stem cells in the incisor, and showed that Fst is an essential modulator of this regulation. FGF3 in cooperation with FGF10 stimulates proliferation of epithelial stem cells and transit amplifying cells in the labial cervical loop. BMP4 represses Fgf3 expression whereas Activin inhibits the repressive effect of BMP4 on the labial side. Thus, Fst inhibits Activin rather than BMP4 in the cervical loop area and limits the proliferation of lingual epithelium, thereby causing the asymmetric maintenance and proliferation of epithelial stem cells. In addition, we detected Lgr5, a Wnt target gene and an epithelial stem cell marker in the intestine, in the putative epithelial stem cells of the incisor, suggesting that Lgr5 is a marker of incisor stem cells but is not regulated by Wnt/β-catenin signalling in the incisor. Thus the epithelial stem cells in the incisor may not be directly regulated by Wnt/β-catenin signalling. In conclusion, we showed in the mouse incisors that modulating the balance between inductive and inhibitory signals constitutes a key mechanism regulating the epithelial stem cells and ameloblast differentiation. Furthermore, we found additional support for the location of the putative epithelial stem cells and for the stemness of these cells. In the mouse molar we showed the necessity of fi ne-tuning the signalling in the regulation of the crown morphogenesis, and that altering the levels of an inhibitor can cause variation in the crown patterning.