Novel conditional mouse models for targeted kidney research : Emphasis on the analysis of the biological function of nephrin

Nephrin is a transmembrane protein belonging to the immunoglobulin superfamily and is expressed primarily in the podocytes, which are highly differentiated epithelial cells needed for primary urine formation in the kidney. Mutations leading to nephrin loss abrogate podocyte morphology, and result in massive protein loss into urine and consequent early death in humans carrying specific mutations in this gene. The disease phenotype is closely replicated in respective mouse models. The purpose of this thesis was to generate novel inducible mouse-lines, which allow targeted gene deletion in a time and tissue-specific manner. A proof of principle model for succesful gene therapy for this disease was generated, which allowed podocyte specific transgene replacement to rescue gene deficient mice from perinatal lethality. Furthermore, the phenotypic consequences of nephrin restoration in the kidney and nephrin deficiency in the testis, brain and pancreas in rescued mice were investigated. A novel podocyte-specific construct was achieved by using standard cloning techniques to provide an inducible tool for in vitro and in vivo gene targeting. Using modified constructs and microinjection procedures two novel transgenic mouse-lines were generated. First, a mouse-line with doxycycline inducible expression of Cre recombinase that allows podocyte-specific gene deletion was generated. Second, a mouse-line with doxycycline inducible expression of rat nephrin, which allows podocyte-specific nephrin over-expression was made. Furthermore, it was possible to rescue nephrin deficient mice from perinatal lethality by cross-breeding them with a mouse-line with inducible rat nephrin expression that restored the missing endogenous nephrin only in the kidney after doxycycline treatment. The rescued mice were smaller, infertile, showed genital malformations and developed distinct histological abnormalities in the kidney with an altered molecular composition of the podocytes. Histological changes were also found in the testis, cerebellum and pancreas. The expression of another molecule with limited tissue expression, densin, was localized to the plasma membranes of Sertoli cells in the testis by immunofluorescence staining. Densin may be an essential adherens junction protein between Sertoli cells and developing germ cells and these junctions share similar protein assembly with kidney podocytes. This single, binary conditional construct serves as a cost- and time-efficient tool to increase the understanding of podocyte-specific key proteins in health and disease. The results verified a tightly controlled inducible podocyte-specific transgene expression in vitro and in vivo as expected. These novel mouse-lines with doxycycline inducible Cre recombinase and with rat nephrin expression will be useful for conditional gene targeting of essential podocyte proteins and to study in detail their functions in the adult mice. This is important for future diagnostic and pharmacologic development platforms.

Conditional Moments in Asset Pricing

A better understanding of stock price changes is important in guiding many economic activities. Since prices often do not change without good reasons, searching for related explanatory variables has involved many enthusiasts. This book seeks answers from prices per se by relating price changes to their conditional moments. This is based on the belief that prices are the products of a complex psychological and economic process and their conditional moments derive ultimately from these psychological and economic shocks. Utilizing information about conditional moments hence makes it an attractive alternative to using other selective financial variables in explaining price changes. The first paper examines the relation between the conditional mean and the conditional variance using information about moments in three types of conditional distributions; it finds that the significance of the estimated mean and variance ratio can be affected by the assumed distributions and the time variations in skewness. The second paper decomposes the conditional industry volatility into a concurrent market component and an industry specific component; it finds that market volatility is on average responsible for a rather small share of total industry volatility — 6 to 9 percent in UK and 2 to 3 percent in Germany. The third paper looks at the heteroskedasticity in stock returns through an ARCH process supplemented with a set of conditioning information variables; it finds that the heteroskedasticity in stock returns allows for several forms of heteroskedasticity that include deterministic changes in variances due to seasonal factors, random adjustments in variances due to market and macro factors, and ARCH processes with past information. The fourth paper examines the role of higher moments — especially skewness and kurtosis — in determining the expected returns; it finds that total skewness and total kurtosis are more relevant non-beta risk measures and that they are costly to be diversified due either to the possible eliminations of their desirable parts or to the unsustainability of diversification strategies based on them.

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.

Matkalla suomalaiseksi ja hoiva-alan ammattilaiseksi? : Venäläis- ja virolaistaustaisten naisten ja suomalaisuuden kohtaamiset kuulumista rakentamassa

The research examines the process by which a sense of belonging to Finnish society is constructed among women of Russian and Estonian background who are multiply marginalised in Finnish society. It does so by analysing the encounters between their nationality and 'being Finnis'. Attention is focused on the question of what kind of "journey" they take after moving to Finland, how a sense of belonging is constructed especially along the paths followed in education and at work, and what kind of agency is available to them. The thesis is connected with post-colonial research and also draws from studies on citizenship and nationality as well as the social structures of interaction, when analysing careers. As the educational system forms the most central context of the research, the work is also focused on educational sociology. The research methodology includes life history and a narrative approach. The raw data is from thematic interviews concerning the life experiences of women of immigrant backgrounds. They were studying in Finland to be practical nurses or to complete Bachelor of Social Service degree. According to the study, the women had been encountered as alien, strange, and carrying a shade of "otherness". The experience of inclusion in Finnish communities and society turned out to be conditional, an inclusion based on the notion of a citizen worker, which is defined by national needs. The person from abroad is placed in the position of someone who fills gaps in the services of the welfare state. The choice of education in the care sector and the overall necessity of obtaining Finnish education turned out to be socially directed. Gendered structures of education and working life were found to act as a frame in which the decisions of the immigrant women were made. Although national education policy emphasis as an orientation to global labour markets, the immigrant student is placed above all in the position of an object to be made suitable for the Finnish labour market. Citizenship, a goal of education, requires consent to being "socialised" into Finnish society as well as learning to be Finnish. One s only option to negotiate appearing suitable as a member is to construct oneself into someone who adopts Finnish and Western cultural values, values which favour individuality. However, Finnish education is a resource to Finnishness. Finnish education enables a sense of being Finnish, and empowers the job applicant for example, and in addition to providing cultural, human and social capital strengthen inclusion as well. The study confirms the view that the encounter of an immigrant is still characterised by its colonial nature. It shows that encounters with Finns and Finnish society place the person of immigrant background, even one receiving a Finnish education, in the position of "the other". The journey as an immigrant continues. The immigrant has access only to certain predefined subject positions, which limits agency. When categorised as an immigrant, one becomes a per-son who is different and "other", while the sense of belonging as a member of Finnish society without conditions appears to be somewhat unreachable. Yet, new arrivals are capable of acting change. An immigrant woman can challenge the positions offered to her and present herself as strong. Her life story has often included struggle, and she has the fortitude strength to change her circumstances. Key words: life story, post-colonial encounter, nationality, citizenship, the career of immi-grant, position, agency

Statistical analysis of the associations of hereditary factors with the risk of Type 1 diabetes and Diabetic Nephropathy based on the familial data collected through ascertaiment from population-based registers

In genetic epidemiology, population-based disease registries are commonly used to collect genotype or other risk factor information concerning affected subjects and their relatives. This work presents two new approaches for the statistical inference of ascertained data: a conditional and full likelihood approaches for the disease with variable age at onset phenotype using familial data obtained from population-based registry of incident cases. The aim is to obtain statistically reliable estimates of the general population parameters. The statistical analysis of familial data with variable age at onset becomes more complicated when some of the study subjects are non-susceptible, that is to say these subjects never get the disease. A statistical model for a variable age at onset with long-term survivors is proposed for studies of familial aggregation, using latent variable approach, as well as for prospective studies of genetic association studies with candidate genes. In addition, we explore the possibility of a genetic explanation of the observed increase in the incidence of Type 1 diabetes (T1D) in Finland in recent decades and the hypothesis of non-Mendelian transmission of T1D associated genes. Both classical and Bayesian statistical inference were used in the modelling and estimation. Despite the fact that this work contains five studies with different statistical models, they all concern data obtained from nationwide registries of T1D and genetics of T1D. In the analyses of T1D data, non-Mendelian transmission of T1D susceptibility alleles was not observed. In addition, non-Mendelian transmission of T1D susceptibility genes did not make a plausible explanation for the increase in T1D incidence in Finland. Instead, the Human Leucocyte Antigen associations with T1D were confirmed in the population-based analysis, which combines T1D registry information, reference sample of healthy subjects and birth cohort information of the Finnish population. Finally, a substantial familial variation in the susceptibility of T1D nephropathy was observed. The presented studies show the benefits of sophisticated statistical modelling to explore risk factors for complex diseases.

Oncolytic Adenoviruses for Gynecologic Cancer

Gene therapy is a promising novel approach for treating cancers resistant to or escaping currently available modalities. Treatment approaches are based on taking advantage of molecular differences between normal and tumor cells. Various strategies are currently in clinical development with adenoviruses as the most popular vehicle. Recent developments include improving targeting strategies for gene delivery to tumor cells with tumor specific promoters or infectivity enhancement. A rapidly developing field is as well replication competent agents, which allow improved tumor penetration and local amplification of the anti-tumor effect. Adenoviral cancer gene therapy approaches lack cross-resistance with other treatment options and therefore synergistic effects are possible. This study focused on development of adenoviral vectors suitable for treatment of various gynecologic cancer types, describing the development of the field from non-replicating adenoviral vectors to multiple-modified conditional replicating viruses. Transcriptional targeting of gynecologic cancer cells by the use of the promoter of vascular endothelial growth factor receptor type 1 (flt-1) was evaluated. Flt-1 is not expressed in the liver and thus an ideal promoter for transcriptional targeting of adenoviruses. Our studies implied that the flt-1 promoter is active in teratocarcinomas.and therefore a good candidate for development of oncolytic adenoviruses for treatment of this often problematic disease with then poor outcome. A tropism modified conditionally replicating adenovirus (CRAd), Ad5-Δ24RGD, was studied in gynecologic cancers. Ad5-Δ24RGD is an adenovirus selectively replication competent in cells defective in the p16/Rb pathway, including many or most tumor cells. The fiber of Ad5-Δ24RGD contains an integrin binding arginine-glycine-aspartic acid motif (RGD-4C), allowing coxackie-adenovirus receptor independent infection of cancer cells. This approach is attractive because expression levels of CAR are highly variable and often low on primary gynecological cancer cells. Oncolysis could be shown for a wide variety of ovarian and cervical cancer cell lines as well as primary ovarian cancer cell spheroids, a novel system developed for in vitro analysis of CRAds on primary tumor substrates. Biodistribution was evaluated and preclinical safety data was obtained by demonstrating lack of replication in human peripheral blood mononuclear cells. The efficicacy of Ad5-Δ24RGD was shown in different orthotopic murine models including a highly aggressive intraperitoneal model of disseminated ovarian cancer cells, where Ad5-Δ24RGD resulted in complete eradication of intraperitoneal disease in half of the mice. To further improve the selectivity and specificity of CRAds, triple-targeted oncolytic adenoviruses were cloned, featuring the cyclo-oxygenase-2 (cox-2) promoter, E1A transcomplementation and serotype chimerism. Those viruses were evaluated on ovarian cancer cells for specificity and oncolytic potency with regard to two different cox2 versions and three different variants of E1A (wild type, delta24 and delta2delta24). Ad5/3cox2Ld24 emerged as the best combination due to enhanced selectivity without potency lost in vitro or in an aggressive intraperitoneal orthotopic ovarian tumor model. In summary, the preclinical therapeutic efficacy of the CRAds tested in this study, taken together with promising biodistribution and safety data, suggest that these CRAds are interesting candidates for translation into clinical trials for gynecologic cancer.

On probability-based inference under data missing by design

Whether a statistician wants to complement a probability model for observed data with a prior distribution and carry out fully probabilistic inference, or base the inference only on the likelihood function, may be a fundamental question in theory, but in practice it may well be of less importance if the likelihood contains much more information than the prior. Maximum likelihood inference can be justified as a Gaussian approximation at the posterior mode, using flat priors. However, in situations where parametric assumptions in standard statistical models would be too rigid, more flexible model formulation, combined with fully probabilistic inference, can be achieved using hierarchical Bayesian parametrization. This work includes five articles, all of which apply probability modeling under various problems involving incomplete observation. Three of the papers apply maximum likelihood estimation and two of them hierarchical Bayesian modeling. Because maximum likelihood may be presented as a special case of Bayesian inference, but not the other way round, in the introductory part of this work we present a framework for probability-based inference using only Bayesian concepts. We also re-derive some results presented in the original articles using the toolbox equipped herein, to show that they are also justifiable under this more general framework. Here the assumption of exchangeability and de Finetti's representation theorem are applied repeatedly for justifying the use of standard parametric probability models with conditionally independent likelihood contributions. It is argued that this same reasoning can be applied also under sampling from a finite population. The main emphasis here is in probability-based inference under incomplete observation due to study design. This is illustrated using a generic two-phase cohort sampling design as an example. The alternative approaches presented for analysis of such a design are full likelihood, which utilizes all observed information, and conditional likelihood, which is restricted to a completely observed set, conditioning on the rule that generated that set. Conditional likelihood inference is also applied for a joint analysis of prevalence and incidence data, a situation subject to both left censoring and left truncation. Other topics covered are model uncertainty and causal inference using posterior predictive distributions. We formulate a non-parametric monotonic regression model for one or more covariates and a Bayesian estimation procedure, and apply the model in the context of optimal sequential treatment regimes, demonstrating that inference based on posterior predictive distributions is feasible also in this case.

Mathematical Aspects of Financial Markets with Frictions

Frictions are factors that hinder trading of securities in financial markets. Typical frictions include limited market depth, transaction costs, lack of infinite divisibility of securities, and taxes. Conventional models used in mathematical finance often gloss over these issues, which affect almost all financial markets, by arguing that the impact of frictions is negligible and, consequently, the frictionless models are valid approximations. This dissertation consists of three research papers, which are related to the study of the validity of such approximations in two distinct modeling problems. Models of price dynamics that are based on diffusion processes, i.e., continuous strong Markov processes, are widely used in the frictionless scenario. The first paper establishes that diffusion models can indeed be understood as approximations of price dynamics in markets with frictions. This is achieved by introducing an agent-based model of a financial market where finitely many agents trade a financial security, the price of which evolves according to price impacts generated by trades. It is shown that, if the number of agents is large, then under certain assumptions the price process of security, which is a pure-jump process, can be approximated by a one-dimensional diffusion process. In a slightly extended model, in which agents may exhibit herd behavior, the approximating diffusion model turns out to be a stochastic volatility model. Finally, it is shown that when agents' tendency to herd is strong, logarithmic returns in the approximating stochastic volatility model are heavy-tailed. The remaining papers are related to no-arbitrage criteria and superhedging in continuous-time option pricing models under small-transaction-cost asymptotics. Guasoni, Rásonyi, and Schachermayer have recently shown that, in such a setting, any financial security admits no arbitrage opportunities and there exist no feasible superhedging strategies for European call and put options written on it, as long as its price process is continuous and has the so-called conditional full support (CFS) property. Motivated by this result, CFS is established for certain stochastic integrals and a subclass of Brownian semistationary processes in the two papers. As a consequence, a wide range of possibly non-Markovian local and stochastic volatility models have the CFS property.

Modeling Asymmetries in Financial Data with Multiplicative Error Models

This thesis addresses modeling of financial time series, especially stock market returns and daily price ranges. Modeling data of this kind can be approached with so-called multiplicative error models (MEM). These models nest several well known time series models such as GARCH, ACD and CARR models. They are able to capture many well established features of financial time series including volatility clustering and leptokurtosis. In contrast to these phenomena, different kinds of asymmetries have received relatively little attention in the existing literature. In this thesis asymmetries arise from various sources. They are observed in both conditional and unconditional distributions, for variables with non-negative values and for variables that have values on the real line. In the multivariate context asymmetries can be observed in the marginal distributions as well as in the relationships of the variables modeled. New methods for all these cases are proposed. Chapter 2 considers GARCH models and modeling of returns of two stock market indices. The chapter introduces the so-called generalized hyperbolic (GH) GARCH model to account for asymmetries in both conditional and unconditional distribution. In particular, two special cases of the GARCH-GH model which describe the data most accurately are proposed. They are found to improve the fit of the model when compared to symmetric GARCH models. The advantages of accounting for asymmetries are also observed through Value-at-Risk applications. Both theoretical and empirical contributions are provided in Chapter 3 of the thesis. In this chapter the so-called mixture conditional autoregressive range (MCARR) model is introduced, examined and applied to daily price ranges of the Hang Seng Index. The conditions for the strict and weak stationarity of the model as well as an expression for the autocorrelation function are obtained by writing the MCARR model as a first order autoregressive process with random coefficients. The chapter also introduces inverse gamma (IG) distribution to CARR models. The advantages of CARR-IG and MCARR-IG specifications over conventional CARR models are found in the empirical application both in- and out-of-sample. Chapter 4 discusses the simultaneous modeling of absolute returns and daily price ranges. In this part of the thesis a vector multiplicative error model (VMEM) with asymmetric Gumbel copula is found to provide substantial benefits over the existing VMEM models based on elliptical copulas. The proposed specification is able to capture the highly asymmetric dependence of the modeled variables thereby improving the performance of the model considerably. The economic significance of the results obtained is established when the information content of the volatility forecasts derived is examined.

Stochastic Filtering

The stochastic filtering has been in general an estimation of indirectly observed states given observed data. This means that one is discussing conditional expected values as being one of the most accurate estimation, given the observations in the context of probability space. In my thesis, I have presented the theory of filtering using two different kind of observation process: the first one is a diffusion process which is discussed in the first chapter, while the third chapter introduces the latter which is a counting process. The majority of the fundamental results of the stochastic filtering is stated in form of interesting equations, such the unnormalized Zakai equation that leads to the Kushner-Stratonovich equation. The latter one which is known also by the normalized Zakai equation or equally by Fujisaki-Kallianpur-Kunita (FKK) equation, shows the divergence between the estimate using a diffusion process and a counting process. I have also introduced an example for the linear gaussian case, which is mainly the concept to build the so-called Kalman-Bucy filter. As the unnormalized and the normalized Zakai equations are in terms of the conditional distribution, a density of these distributions will be developed through these equations and stated by Kushner Theorem. However, Kushner Theorem has a form of a stochastic partial differential equation that needs to be verify in the sense of the existence and uniqueness of its solution, which is covered in the second chapter.

Aspects of Arminian Soteriology in Methodist-Lutheran Ecumenical Dialogues in 20th and 21st Century

The aim of this thesis is to analyse the key ecumenical dialogues between Methodists and Lutherans from the perspective of Arminian soteriology and Methodist theology in general. The primary research question is defined as: "To what extent do the dialogues under analysis relate to Arminian soteriology?" By seeking an answer to this question, new knowledge is sought on the current soteriological position of the Methodist-Lutheran dialogues, the contemporary Methodist theology and the commonalities between the Lutheran and Arminian understanding of soteriology. This way the soteriological picture of the Methodist-Lutheran discussions is clarified. The dialogues under analysis were selected on the basis of versatility. Firstly, the sole world organisation level dialogue was chosen: The Church – Community of Grace. Additionally, the document World Methodist Council and the Joint Declaration on the Doctrine of Justification is analysed as a supporting document. Secondly, a document concerning the discussions between two main-line churches in the United States of America was selected: Confessing Our Faith Together. Thirdly, two dialogues between non-main-line Methodist churches and main-line Lutheran national churches in Europe were chosen: Fellowship of Grace from Norway and Kristuksesta osalliset from Finland. The theoretical approach to the research conducted in this thesis is systematic analysis. The Remonstrant articles of Arminian soteriology are utilised as an analysis tool to examine the soteriological positions of the dialogues. New knowledge is sought by analysing the stances of the dialogues concerning the doctrines of partial depravity, conditional election, universal atonement, resistible grace and conditional perseverance of saints. This way information is also provided for approaching the Calvinist-Arminian controversy from new perspectives. The results of this thesis show that the current soteriological position of the Methodist-Lutheran dialogues is closer to Arminianism than Calvinism. The dialogues relate to Arminian soteriology especially concerning the doctrines of universal atonement, resistible grace and conditional perseverance of saints. The commonalities between the Lutheran and Arminian understanding of soteriology exist mainly in these three doctrines as they are uniformly favoured in the dialogues. The most discussed area of soteriology is human depravity, in which the largest diversity of stances occurs as well. On the other hand, divine election is the least discussed topic. The overall perspective, which the results of the analysis provide, indicates that the Lutherans could approach the Calvinist churches together with the Methodists with a wider theological perspective and understanding when the soteriological issues are considered as principal. Human depravity is discovered as the area of soteriology which requires most work in future ecumenical dialogues. However, the detected Lutheran hybrid notion on depravity (a Calvinist-Arminian mixture) appears to provide a useful new perspective for Calvinist-Arminian ecumenism and offers potentially fruitful considerations to future ecumenical dialogues.

Elements of Volatility at High Frequency

Volatility is central in options pricing and risk management. It reflects the uncertainty of investors and the inherent instability of the economy. Time series methods are among the most widely applied scientific methods to analyze and predict volatility. Very frequently sampled data contain much valuable information about the different elements of volatility and may ultimately reveal the reasons for time varying volatility. The use of such ultra-high-frequency data is common to all three essays of the dissertation. The dissertation belongs to the field of financial econometrics. The first essay uses wavelet methods to study the time-varying behavior of scaling laws and long-memory in the five-minute volatility series of Nokia on the Helsinki Stock Exchange around the burst of the IT-bubble. The essay is motivated by earlier findings which suggest that different scaling laws may apply to intraday time-scales and to larger time-scales, implying that the so-called annualized volatility depends on the data sampling frequency. The empirical results confirm the appearance of time varying long-memory and different scaling laws that, for a significant part, can be attributed to investor irrationality and to an intraday volatility periodicity called the New York effect. The findings have potentially important consequences for options pricing and risk management that commonly assume constant memory and scaling. The second essay investigates modelling the duration between trades in stock markets. Durations convoy information about investor intentions and provide an alternative view at volatility. Generalizations of standard autoregressive conditional duration (ACD) models are developed to meet needs observed in previous applications of the standard models. According to the empirical results based on data of actively traded stocks on the New York Stock Exchange and the Helsinki Stock Exchange the proposed generalization clearly outperforms the standard models and also performs well in comparison to another recently proposed alternative to the standard models. The distribution used to derive the generalization may also prove valuable in other areas of risk management. The third essay studies empirically the effect of decimalization on volatility and market microstructure noise. Decimalization refers to the change from fractional pricing to decimal pricing and it was carried out on the New York Stock Exchange in January, 2001. The methods used here are more accurate than in the earlier studies and put more weight on market microstructure. The main result is that decimalization decreased observed volatility by reducing noise variance especially for the highly active stocks. The results help risk management and market mechanism designing.

Essays on Russia's Economic Transition

This study comprises an introductory section and three essays analysing Russia's economic transition from the early 1990s up to the present. The papers present a combination of both theoretical and empirical analysis on some of the key issues Russia has faced during its somewhat troublesome transformation from state-controlled command economy to market-based economy. The first essay analyses fiscal competition for mobile capital between identical regions in a transition country. A standard tax competition framework is extended to account for two features of a transition economy: the presence of two sectors, old and new, which differ in productivity; and a non-benevolent regional decision-maker. It is shown that in very early phase of transition, when the old sector clearly dominates, consumers in a transition economy may be better off in a competitive equilibrium. Decision-makers, on the other hand, will prefer to coordinate their fiscal policies. The second essay uses annual data for 1992-2003 to examine income dispersion and convergence across 76 Russian regions. Wide disparities in income levels have indeed emerged during the transition period. Dispersion has increased most among the initially better-off regions, whereas for the initially poorer regions no clear trend of divergence or convergence could be established. Further, some - albeit not highly robust - evidence was found of both unconditional and conditional convergence, especially among the initially richer regions. Finally, it is observed that there is much less evidence of convergence after the economic crisis of 1998. The third essay analyses industrial firms' engagement in provision of infrastructure services, such as heating, electricity and road maintenance. Using a unique dataset of 404 large and medium-sized industrial enterprises in 40 regions of Russia, the essay examines public infrastructure provision by Russian industrial enterprises. It is found that to a large degree engagement in infrastructure provision, as proxied by district heating production, is a Soviet legacy. Secondly, firms providing district heating to users outside their plant area are more likely to have close and multidimensional relations with the local public sector.