Associations of interleukin-1 (IL-1) gene variation and IL-1 receptor antagonist phenotypes with immunological responses, metabolic dysregulation and type 2 diabetes

The upstream proinflammatory interleukin-1 (IL-1) cytokines, together with a naturally occurring IL-1 receptor antagonist (IL-1Ra), play a significant role in several diseases and physiologic conditions. The IL-1 proteins affect glucose homeostasis at multiple levels contributing to vascular injuries and metabolic dysregulations that precede diabetes. An association between IL-1 gene variations and IL-1Ra levels has been suggested, and genetic studies have reported associations with metabolic dysregulation and altered inflammatory responses. The principal aims of this study were to: 1) examine the associations of IL-1 gene variation and IL-1Ra expression in the development and persistence of thyroid antibodies in subacute thyroiditis; 2) investigate the associations of common variants in the IL-1 gene family with plasma glucose and insulin concentrations, glucose homeostasis measures and prevalent diabetes in a representative population sample; 3) investigate genetic and non-genetic determinants of IL-1Ra phenotypes in a cross-sectional setting in three independent study populations; 4) investigate in a prospective setting (a) whether variants of the IL-1 gene family are predictors for clinically incident diabetes in two population-based observational cohort studies; and (b) whether the IL-1Ra levels predict the progression of metabolic syndrome to overt diabetes during the median follow-up of 10.8 and 7.1 years. Results from on patients with subacte thyroiditis showed that the systemic IL-1Ra levels are elevated during a specific proinflammatory response and they correlated with C-reactive protein (CRP) levels. Genetic variation in the IL-1 family seemed to have an association with the appearance of thyroid peroxidase antibodies and persisting local autoimmune responses during the follow-up. Analysis of patients suffering from diabetes and metabolic traits suggested that genetic IL-1 variation and IL-1Ra play a role in glucose homeostasis and in the development of type 2 diabetes. The coding IL-1 beta SNP rs1143634 was associated with traits related to insulin resistance in cross-sectional analyses. Two haplotype variants of the IL-1 beta gene were associated with prevalent diabetes or incident diabetes in a prospective setting and both of these haplotypes were tagged by rs1143634. Three variants of the IL-1Ra gene and one of the IL-1 beta gene were consistently identified as significant, independent determinants of the IL-1Ra phenotype in two or three populations. The proportion of the phenotypic variation explained by the genetic factors was modest however, while obesity and other metabolic traits explained a larger part. Body mass index was the strongest predictor of systemic IL-1Ra concentration overall. Furthermore, the age-adjusted IL-1Ra concentrations were elevated in individuals with metabolic syndrome or diabetes when compared to those free of metabolic dysregulation. In prospective analyses the systemic IL-1Ra levels were found as independent predictors for the development of diabetes in people with metabolic syndrome even after adjustment for multiple other factors, including plasma glucose and CRP levels. The predictive power of IL-1Ra was better than that of CRP. The prospective results also provided some evidence for a role of common IL-1 alpha promoter SNP rs1800587 in the development of type 2 diabetes among men and suggested that the role may be gender specific. Likewise, common variations in the IL-1 beta coding region may have a gender specific association with diabetes development. Further research on the potential benefits of IL-1Ra measurements in identifying individuals at high risk for diabetes, who then could be targeted for specific treatment interventions, is warranted. It has been reported in the recent literature that IL-1Ra secreted from adipose tissue has beneficial effects on glucose homeostasis. Furthermore, treatment with recombinant human IL-1Ra has been shown to have a substantial therapeutic potential. The genetic results from the prospective analyses performed in this study remain inconclusive, but together with the cross-sectional analyses they suggest gender-specific effects of the IL-1 variants on the risk of diabetes. Larger studies with more extensive genotyping and resequencing may help to pinpoint the exact variants responsible and to further elucidate the biological mechanisms for the observed associations. This would improve our understanding of the pathways linking inflammation and obesity with glucose and insulin metabolism.

Social networks for volunteered geographic information - A case study of OpenStreetMap

The world of mapping has changed. Earlier, only professional experts were responsible for map production, but today ordinary people without any training or experience can become map-makers. The number of online mapping sites, and the number of volunteer mappers has increased significantly. The development of the technology, such as satellite navigation systems, Web 2.0, broadband Internet connections, and smartphones, have had one of the key roles in enabling the rise of volunteered geographic information (VGI). As opening governmental data to public is a current topic in many countries, the opening of high quality geographical data has a central role in this study. The aim of this study is to investigate how is the quality of spatial data produced by volunteers by comparing it with the map data produced by public authorities, to follow what occurs when spatial data are opened for users, and to get acquainted with the user profile of these volunteer mappers. A central part of this study is OpenStreetMap project (OSM), which aim is to create a map of the entire world by volunteers. Anyone can become an OpenStreetMap contributor, and the data created by the volunteers are free to use for anyone without restricting copyrights or license charges. In this study OpenStreetMap is investigated from two viewpoints. In the first part of the study, the aim was to investigate the quality of volunteered geographic information. A pilot project was implemented by following what occurs when a high-resolution aerial imagery is released freely to the OpenStreetMap contributors. The quality of VGI was investigated by comparing the OSM datasets with the map data of The National Land Survey of Finland (NLS). The quality of OpenStreetMap data was investigated by inspecting the positional accuracy and the completeness of the road datasets, as well as the differences in the attribute datasets between the studied datasets. Also the OSM community was under analysis and the development of the map data of OpenStreetMap was investigated by visual analysis. The aim of the second part of the study was to analyse the user profile of OpenStreetMap contributors, and to investigate how the contributors act when collecting data and editing OpenStreetMap. The aim was also to investigate what motivates users to map and how is the quality of volunteered geographic information envisaged. The second part of the study was implemented by conducting a web inquiry to the OpenStreetMap contributors. The results of the study show that the quality of OpenStreetMap data compared with the data of National Land Survey of Finland can be defined as good. OpenStreetMap differs from the map of National Land Survey especially because of the amount of uncertainty, for example because of the completeness and uniformity of the map are not known. The results of the study reveal that opening spatial data increased notably the amount of the data in the study area, and both the positional accuracy and completeness improved significantly. The study confirms the earlier arguments that only few contributors have created the majority of the data in OpenStreetMap. The inquiry made for the OpenStreetMap users revealed that the data are most often collected by foot or by bicycle using GPS device, or by editing the map with the help of aerial imageries. According to the responses, the users take part to the OpenStreetMap project because they want to make maps better, and want to produce maps, which have information that is up-to-date and cannot be found from any other maps. Almost all of the users exploit the maps by themselves, most popular methods being downloading the map into a navigator or into a mobile device. The users regard the quality of OpenStreetMap as good, especially because of the up-to-dateness and the accuracy of the map.

HDL subspecies : association with low HDL-C, obesity, and metabolic syndrome

AIMS An independent, powerful coronary heart disease (CHD) predictor is a low level of high-density lipoprotein cholesterol (HDL-C). Discoidal preβ-HDL particles and large HDL2 particles are the primary cholesterol acceptors in reverse cholesterol transport, a key anti-atherogenic HDL mechanism. The quality of HDL subspecies may provide better markers of HDL functionality than does HDL-C alone. We aimed I) to study whether alterations in the HDL subspecies profile exist in low-HDL-C subjects II) to explore the relationship of any changes in HDL subspecies profile in relation to atherosclerosis and metabolic syndrome; III) to elucidate the impact of genetics and acquired obesity on HDL subspecies distribution. SUBJECTS The study consisted of 3 cohorts: A) Finnish families with low HDL-C and premature CHD (Study I: 67 subjects with familial low HDL-C and 64 controls; Study II: 83 subjects with familial low HDL-C, 65 family members with normal HDL-C, and 133 controls); B) a cohort of 113 low- and 133 high-HDL-C subjects from the Health 2000 Health Examination Survey carried out in Finland (Study III); and C) a Finnish cohort of healthy young adult twins (52 monozygotic and 89 dizygotic pairs) (Study IV). RESULTS AND CONCLUSIONS The subjects with familial low HDL-C had a lower preβ-HDL concentration than did controls, and the low-HDL-C subjects displayed a dramatic reduction (50-70%) in the proportion of large HDL2b particles. The subjects with familial low HDL-C had increased carotid atherosclerosis measured as intima-media-thickness (IMT), and HDL2b particles correlated negatively with IMT. The reduction in both key cholesterol acceptors, preβ-HDL and HDL2 particles, supports the concept of impaired reverse cholesterol transport contributing to the higher CHD risk in low-HDL-C subjects. The family members with normal HDL-C and the young adult twins with acquired obesity showed a reduction in large HDL2 particles and an increase in small HDL3 particles, which may be the first changes leading to the lowering of HDL-C. The low-HDL-C subjects had a higher serum apolipoprotein E (apoE) concentration, which correlated positively with the metabolic syndrome components (waist circumference, TG, and glucose), highlighting the need for a better understanding of apoE metabolism in human atherosclerosis. In the twin study, the increase in small HDL3b particles was associated with obesity independent of genetic effects. The heritability estimate, of 73% for HDL-C and 46 to 63% for HDL subspecies, however, demonstrated a strong genetic influence. These results suggest that the relationship between obesity and lipoproteins depends on different elements in each subject. Finally, instead of merely elevating HDL-C, large HDL2 particles and discoidal preβ-HDL particles may provide beneficial targets for HDL-targeted therapy.

Fast distributed approximation algorithms for vertex cover and set cover in anonymous networks

We present a distributed algorithm that finds a maximal edge packing in O(Δ + log* W) synchronous communication rounds in a weighted graph, independent of the number of nodes in the network; here Δ is the maximum degree of the graph and W is the maximum weight. As a direct application, we have a distributed 2-approximation algorithm for minimum-weight vertex cover, with the same running time. We also show how to find an f-approximation of minimum-weight set cover in O(f2k2 + fk log* W) rounds; here k is the maximum size of a subset in the set cover instance, f is the maximum frequency of an element, and W is the maximum weight of a subset. The algorithms are deterministic, and they can be applied in anonymous networks.

Optimisation problems in wireless sensor networks

This thesis studies optimisation problems related to modern large-scale distributed systems, such as wireless sensor networks and wireless ad-hoc networks. The concrete tasks that we use as motivating examples are the following: (i) maximising the lifetime of a battery-powered wireless sensor network, (ii) maximising the capacity of a wireless communication network, and (iii) minimising the number of sensors in a surveillance application. A sensor node consumes energy both when it is transmitting or forwarding data, and when it is performing measurements. Hence task (i), lifetime maximisation, can be approached from two different perspectives. First, we can seek for optimal data flows that make the most out of the energy resources available in the network; such optimisation problems are examples of so-called max-min linear programs. Second, we can conserve energy by putting redundant sensors into sleep mode; we arrive at the sleep scheduling problem, in which the objective is to find an optimal schedule that determines when each sensor node is asleep and when it is awake. In a wireless network simultaneous radio transmissions may interfere with each other. Task (ii), capacity maximisation, therefore gives rise to another scheduling problem, the activity scheduling problem, in which the objective is to find a minimum-length conflict-free schedule that satisfies the data transmission requirements of all wireless communication links. Task (iii), minimising the number of sensors, is related to the classical graph problem of finding a minimum dominating set. However, if we are not only interested in detecting an intruder but also locating the intruder, it is not sufficient to solve the dominating set problem; formulations such as minimum-size identifying codes and locating–dominating codes are more appropriate. This thesis presents approximation algorithms for each of these optimisation problems, i.e., for max-min linear programs, sleep scheduling, activity scheduling, identifying codes, and locating–dominating codes. Two complementary approaches are taken. The main focus is on local algorithms, which are constant-time distributed algorithms. The contributions include local approximation algorithms for max-min linear programs, sleep scheduling, and activity scheduling. In the case of max-min linear programs, tight upper and lower bounds are proved for the best possible approximation ratio that can be achieved by any local algorithm. The second approach is the study of centralised polynomial-time algorithms in local graphs – these are geometric graphs whose structure exhibits spatial locality. Among other contributions, it is shown that while identifying codes and locating–dominating codes are hard to approximate in general graphs, they admit a polynomial-time approximation scheme in local graphs.

Early Growth and Later Health, Focus on Metabolic Syndrome, Obesity and Physical Activity

The Developmental Origins of Health and Disease Hypothesis proposes that adverse health outcomes in adult life are in part programmed during fetal life and infancy. This means that e.g. restricted nutrition during pregnancy programmes the offspring to store fat more effectively, to develop faster and to reach puberty earlier. These adaptations are beneficial in terms of short term survival. However, in developed countries these adaptations often lead to an increased risk of obesity and metabolic disturbances in later life, due to a mismatch between the prenatal and postnatal environment. This thesis aimed to study the role of early growth in people who are obese as adults, but metabolically healthy as well as in those who are normal in weight but metabolically obese. Other study aims were to assess whether physical activity and cardiorespiratory fitness are programmed early in life. The role of socioeconomic status in the development of obesity from a life course setting was also studied. These studies included 2003 men and women born in Helsinki between 1934 and 1944 with detailed information of their prenatal and childhood growth as well as living conditions. They participated in the detailed clinical examination during the years 2001-2004. A sub-group of the subjects participated in the UKK Institute 2-kilometre walk test. Metabolic syndrome was defined according to the 2005 criteria of the International Diabetes Federation. Among the obese men and women 20 % were metabolically healthy. Those with metabolic syndrome did not differ in birth size compared to the healthy ones, but by two years of age, they were lighter and thinner, and remained so up to 11 years. The period when changes in BMIs were predictive of the metabolic syndrome was from birth to 7 years. Of the normal weight individuals 17 % were metabolically obese. Again, there were no differences in birth size. However, by the age 7 years, those men who later developed metabolic syndrome were thinner. Gains in BMI during the first two years of life were protective of the syndrome. Children who were heavier, and especially taller, were more physically active, exercised with higher intensity and had higher cardiorespiratory fitness in their adult life than those who were shorter and thinner as children. Lower educational attainment and lower adult social class were associated with obesity in both men and women. Childhood social class was inversely associated with body mass index only in men while lower household income was associated with higher BMI in women. These results support the role of early life factors in the development of metabolic syndrome and adult life style. Early detection of risk factors predisposing to these conditions is highly relevant from a public health point of view.

Discriminative learning of Bayesian networks via factorized conditional log-likelihood

We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (ˆfCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-theoretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-of-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show that ˆfCLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, using significantly less computational resources.

Identification of collagenolytic MMP networks as potential biomarkers in progressive chronic periodontitis subjects and MMP-8 null allele model

Chronic periodontitis results from a complex aetiology, including the formation of a subgingival biofilm and the elicitation of the host s immune and inflammatory response. The hallmark of chronic periodontitis is alveolar bone loss and soft periodontal tissue destruction. Evidence supports that periodontitis progresses in dynamic states of exacerbation and remission or quiescence. The major clinical approach to identify disease progression is the tolerance method, based on sequential probing. Collagen degradation is one of the key events in periodontal destructive lesions. Matrix metalloproteinase (MMP)-8 and MMP-13 are the primary collagenolytic MMPs that are associated with the severity of periodontal inflammation and disease, either by a direct breakdown of the collagenised matrix or by the processing of non-matrix bioactive substrates. Despite the numerous host mediators that have been proposed as potential biomarkers for chronic periodontitis, they reflect inflammation rather than the loss of periodontal attachment. The aim of the present study was to determine the key molecular MMP-8 and -13 interactions in gingival crevicular fluid (GCF) and gingival tissue from progressive periodontitis lesions and MMP-8 null allele mouse model. In study (I), GCF and gingival biopsies from active and inactive sites of chronic periodontitis patients, which were determined clinically by the tolerance method, and healthy GCF were analysed for MMP-13 and tissue inhibitor of matrix metalloproteinases (TIMP)-1. Chronic periodontitis was characterised by increased MMP-13 levels and the active sites showed a tendency of decreased TIMP-1 levels associated with increments of MMP-13 and total protein concentration compared to inactive sites. In study (II), we investigated whether MMP-13 activity was associated with TIMP-1, bone collagen breakdown through ICTP levels, as well as the activation rate of MMP-9 in destructive lesions. The active sites demonstrated increased GCF ICTP levels as well as lowered TIMP-1 detection along with elevated MMP-13 activity. MMP-9 activation rate was enhanced by MMP-13 in diseased gingival tissue. In study (III), we analysed the potential association between the levels, molecular forms, isoenzyme distribution and degree of activation of MMP-8, MMP-14, MPO and the inhibitor TIMP-1 in GCF from periodontitis progressive patients at baseline and after periodontal therapy. A positive correlation was found for MPO/MMP-8 and their levels associated with progression episodes and treatment response. Because MMP-8 is activated by hypochlorous acid in vitro, our results suggested an interaction between the MPO oxidative pathway and MMP-8 activation in GCF. Finally, in study (IV), on the basis of the previous finding that MMP-8-deficient mice showed impaired neutrophil responses and severe alveolar bone loss, we aimed to characterise the detection patterns of LIX/CXCL5, SDF-1/CXCL12 and RANKL in P. gingivalis-induced experimental periodontitis and in the MMP-8-/- murine model. The detection of neutrophil-chemoattractant LIX/CXCL5 was restricted to the oral-periodontal interface and its levels were reduced in infected MMP-8 null mice vs. wild type mice, whereas the detection of SDF-1/CXCL12 and RANKL in periodontal tissues increased in experimentally-induced periodontitis, irrespectively from the genotype. Accordingly, MMP-8 might regulate LIX/CXCL5 levels by undetermined mechanisms, and SDF-1/CXCL12 and RANKL might promote the development and/or progression of periodontitis.

Algorithms for Exact Structure Discovery in Bayesian Networks

Bayesian networks are compact, flexible, and interpretable representations of a joint distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure. This is called structure discovery. This thesis contributes to two areas of structure discovery in Bayesian networks: space--time tradeoffs and learning ancestor relations. The fastest exact algorithms for structure discovery in Bayesian networks are based on dynamic programming and use excessive amounts of space. Motivated by the space usage, several schemes for trading space against time are presented. These schemes are presented in a general setting for a class of computational problems called permutation problems; structure discovery in Bayesian networks is seen as a challenging variant of the permutation problems. The main contribution in the area of the space--time tradeoffs is the partial order approach, in which the standard dynamic programming algorithm is extended to run over partial orders. In particular, a certain family of partial orders called parallel bucket orders is considered. A partial order scheme that provably yields an optimal space--time tradeoff within parallel bucket orders is presented. Also practical issues concerning parallel bucket orders are discussed. Learning ancestor relations, that is, directed paths between nodes, is motivated by the need for robust summaries of the network structures when there are unobserved nodes at work. Ancestor relations are nonmodular features and hence learning them is more difficult than modular features. A dynamic programming algorithm is presented for computing posterior probabilities of ancestor relations exactly. Empirical tests suggest that ancestor relations can be learned from observational data almost as accurately as arcs even in the presence of unobserved nodes.