Effect of long-wave UV radiation on mouse melanoma: an in vitro and in vivo study

The skin cancer incidence has increased substantially over the past decades and the role of ultraviolet (UV) radiation in the etiology of skin cancer is well established. Ultraviolet B radiation (280-320 nm) is commonly considered as the more harmful part of the UV-spectrum due to its DNA-damaging potential and well-known carcinogenic effects. Ultraviolet A radiation (320-400 nm) is still regarded as a relatively low health hazard. However, UVA radiation is the predominant component in sunlight, constituting more than 90% of the environmentally relevant solar ultraviolet radiation. In the light of the recent scientific evidence, UVA has been shown to have genotoxic and immunologic effects, and it has been proposed that UVA plays a significant role in the development of skin cancer. Due to the popularity of skin tanning lamps, which emit high intensity UVA radiation and because of the prolonged sun tanning periods with the help of effective UVB blockers, the potential deleterious effects of UVA has emerged as a source of concern for public health. The possibility that UV radiation may affect melanoma metastasis has not been addressed before. UVA radiation can modulate various cellular processes, some of which might affect the metastatic potential of melanoma cells. The aim of the present study was to investigate the possible role of UVA irradiation on the metastatic capacity of mouse melanoma both in vitro and in vivo. The in vitro part of the study dealt with the enhancement of the intercellular interactions occurring either between tumor cells or between tumor cells and endothelial cells after UVA irradiation. The use of the mouse melanoma/endothelium in vitro model showed that a single-dose of UVA to melanoma cells causes an increase in melanoma cell adhesiveness to non-irradiated endothelium after 24-h irradiation. Multiple-dose irradiation of melanoma cells already increased adhesion at a 1-h time-point, which suggests the possible cumulative effect of multiple doses of UVA irradiation. This enhancement of adhesiveness might lead to an increase in binding tumor cells to the endothelial lining of vasculature in various internal organs if occurring also in vivo. A further novel observation is that UVA induced both decline in the expression of E-cadherin adhesion molecule and increase in the expression of the N-cadherin adhesion molecule. In addition, a significant decline in homotypic melanoma-melanoma adhesion (clustering) was observed, which might result in the reduction of E-cadherin expression. The aim of the in vivo animal study was to confirm the physiological significance of previously obtained in vitro results and to determine whether UVA radiation might increase melanoma metastasis in vivo. The use of C57BL/6 mice and syngeneic melanoma cell lines B16-F1 and B16-F10 showed that mice, which were i.v. injected with B16-F1 melanoma cells and thereafter exposed to UVA developed significantly more lung metastases when compared with the non-UVA-exposed group. To study the mechanism behind this phenomenon, the direct effect of UVA-induced lung colonization capacity was examined by the in vitro exposure of B16-F1 cells. Alternatively, the UVA-induced immunosuppression, which might be involved in increased melanoma metastasis, was measured by standard contact hypersensitivity assay (CHS). It appears that the UVA-induced increase of metastasis in vivo might be caused by a combination of UVA-induced systemic immunosuppression, and to the lesser extent, it might be caused by the increased adhesiveness of UVA irradiated melanoma cells. Finally, the UVA effect on gene expression in mouse melanoma was determined by a cDNA array, which revealed UVA-induced changes in the 9 differentially expressed genes that are involved in angiogenesis, cell cycle, stress-response, and cell motility. These results suggest that observed genes might be involved in cellular response to UVA and a physiologically relevant UVA dose have previously unknown cellular implications. The novel results presented in this thesis offer evidence that UVA exposure might increase the metastatic potential of the melanoma cells present in blood circulation. Considering the wellknown UVA-induced deleterious effects on cellular level, this study further supports the notion that UVA radiation might have more potential impact on health than previously suggested. The possibility of the pro-metastatic effects of UVA exposure might not be of very high significance for daily exposures. However, UVA effects might gain physiological significance following extensive sunbathing or solaria tanning periods. Whether similar UVA-induced pro-metastatic effects occur in people sunbathing or using solaria remains to be determined. In the light of the results presented in this thesis, the avoidance of solaria use could be well justified.

The watershed simulation and forecasting system in the National Board of and the Environment

Yhteenveto: Vesistömalleihin perustuva vesistöjen seuranta- ja ennustejärjestelmä vesi- ja ympäristöhallinnossa

Modeling and Forecasting Implied Volatility: Implications for Trading, Pricing, and Risk Management

Modeling and forecasting of implied volatility (IV) is important to both practitioners and academics, especially in trading, pricing, hedging, and risk management activities, all of which require an accurate volatility. However, it has become challenging since the 1987 stock market crash, as implied volatilities (IVs) recovered from stock index options present two patterns: volatility smirk(skew) and volatility term-structure, if the two are examined at the same time, presents a rich implied volatility surface (IVS). This implies that the assumptions behind the Black-Scholes (1973) model do not hold empirically, as asset prices are mostly influenced by many underlying risk factors. This thesis, consists of four essays, is modeling and forecasting implied volatility in the presence of options markets’ empirical regularities. The first essay is modeling the dynamics IVS, it extends the Dumas, Fleming and Whaley (DFW) (1998) framework; for instance, using moneyness in the implied forward price and OTM put-call options on the FTSE100 index, a nonlinear optimization is used to estimate different models and thereby produce rich, smooth IVSs. Here, the constant-volatility model fails to explain the variations in the rich IVS. Next, it is found that three factors can explain about 69-88% of the variance in the IVS. Of this, on average, 56% is explained by the level factor, 15% by the term-structure factor, and the additional 7% by the jump-fear factor. The second essay proposes a quantile regression model for modeling contemporaneous asymmetric return-volatility relationship, which is the generalization of Hibbert et al. (2008) model. The results show strong negative asymmetric return-volatility relationship at various quantiles of IV distributions, it is monotonically increasing when moving from the median quantile to the uppermost quantile (i.e., 95%); therefore, OLS underestimates this relationship at upper quantiles. Additionally, the asymmetric relationship is more pronounced with the smirk (skew) adjusted volatility index measure in comparison to the old volatility index measure. Nonetheless, the volatility indices are ranked in terms of asymmetric volatility as follows: VIX, VSTOXX, VDAX, and VXN. The third essay examines the information content of the new-VDAX volatility index to forecast daily Value-at-Risk (VaR) estimates and compares its VaR forecasts with the forecasts of the Filtered Historical Simulation and RiskMetrics. All daily VaR models are then backtested from 1992-2009 using unconditional, independence, conditional coverage, and quadratic-score tests. It is found that the VDAX subsumes almost all information required for the volatility of daily VaR forecasts for a portfolio of the DAX30 index; implied-VaR models outperform all other VaR models. The fourth essay models the risk factors driving the swaption IVs. It is found that three factors can explain 94-97% of the variation in each of the EUR, USD, and GBP swaption IVs. There are significant linkages across factors, and bi-directional causality is at work between the factors implied by EUR and USD swaption IVs. Furthermore, the factors implied by EUR and USD IVs respond to each others’ shocks; however, surprisingly, GBP does not affect them. Second, the string market model calibration results show it can efficiently reproduce (or forecast) the volatility surface for each of the swaptions markets.

Modelling and Forecasting Property Rents and Returns (summary section only)

Recently, focus of real estate investment has expanded from the building-specific level to the aggregate portfolio level. The portfolio perspective requires investment analysis for real estate which is comparable with that of other asset classes, such as stocks and bonds. Thus, despite its distinctive features, such as heterogeneity, high unit value, illiquidity and the use of valuations to measure performance, real estate should not be considered in isolation. This means that techniques which are widely used for other assets classes can also be applied to real estate. An important part of investment strategies which support decisions on multi-asset portfolios is identifying the fundamentals of movements in property rents and returns, and predicting them on the basis of these fundamentals. The main objective of this thesis is to find the key drivers and the best methods for modelling and forecasting property rents and returns in markets which have experienced structural changes. The Finnish property market, which is a small European market with structural changes and limited property data, is used as a case study. The findings in the thesis show that is it possible to use modern econometric tools for modelling and forecasting property markets. The thesis consists of an introduction part and four essays. Essays 1 and 3 model Helsinki office rents and returns, and assess the suitability of alternative techniques for forecasting these series. Simple time series techniques are able to account for structural changes in the way markets operate, and thus provide the best forecasting tool. Theory-based econometric models, in particular error correction models, which are constrained by long-run information, are better for explaining past movements in rents and returns than for predicting their future movements. Essay 2 proceeds by examining the key drivers of rent movements for several property types in a number of Finnish property markets. The essay shows that commercial rents in local markets can be modelled using national macroeconomic variables and a panel approach. Finally, Essay 4 investigates whether forecasting models can be improved by accounting for asymmetric responses of office returns to the business cycle. The essay finds that the forecast performance of time series models can be improved by introducing asymmetries, and the improvement is sufficient to justify the extra computational time and effort associated with the application of these techniques.

Simulating and Forecasting the Demand for New Consumer Durables

A diffusion/replacement model for new consumer durables designed to be used as a long-term forecasting tool is developed. The model simulates new demand as well as replacement demand over time. The model is called DEMSIM and is built upon a counteractive adoption model specifying the basic forces affecting the adoption behaviour of individual consumers. These forces are the promoting forces and the resisting forces. The promoting forces are further divided into internal and external influences. These influences are operationalized within a multi-segmental diffusion model generating the adoption behaviour of the consumers in each segment as an expected value. This diffusion model is combined with a replacement model built upon the same segmental structure as the diffusion model. This model generates, in turn, the expected replacement behaviour in each segment. To be able to use DEMSIM as a forecasting tool in early stages of a diffusion process estimates of the model parameters are needed as soon as possible after product launch. However, traditional statistical techniques are not very helpful in estimating such parameters in early stages of a diffusion process. To enable early parameter calibration an optimization algorithm is developed by which the main parameters of the diffusion model can be estimated on the basis of very few sales observations. The optimization is carried out in iterative simulation runs. Empirical validations using the optimization algorithm reveal that the diffusion model performs well in early long-term sales forecasts, especially as it comes to the timing of future sales peaks.

Snow cover models in operational watershed forecasting

Yhteenveto: Lumimallit vesistöjen ennustemalleissa

Tsoi Yuen Resistance and Post-80s Generation: A new wave of domestic social movement

Hong Kong was once a British colony and has been under the sovereignty of People’s Republic of China (PRC) since 1997. However, some of the unjust practices and colonial legacies are infiltrated into the development ideology as well as the social structures. The construction of intercity express railway project announced in 2008 causing the demolishment of Tsoi Yuen Tsuen, a “non-indigenous” agricultural village in Hong Kong, was one of the current examples. Tsoi Yuen village was established under the former colonial sovereignty sixty years ago. Approximately 450 populations were affected that they had to relocate their homeland involuntarily. However, these villagers were very attached to their homelands and were unwilling to move, and meanwhile they found that they were absent in the government’s consultation and decision-making process. Soon they began their resistance and demanded for “No Move! No Demolish!”. Their movement was strongly supported by a group of “Post-80s generation” and turned into the most important social movement of the city in recent years. In fact, demolition of Tsoi Yuen Village for city development is not an isolated case in the city. Meanwhile the situation is getting worse in Mainland China. I chose the case study of Tsoi Yuen Resistance from 2008 to 2011 for revelation of the complicated colonial history and postcolonial era of Hong Kong. I focused on discussing the Tsoi Yuen Resistance and the Post-80s movement, and how they have exposed the tension between top-down urban planning and development and public movements fighting for a more democratic process in choosing their way of living. Through the study of a village movement which as well as the rationale behind the Post-80s’ support, I hoped to illustrate how this movement has awaken a different sense of living for the new generations in the midst of the high-sounding urban development. It is an opportunity to examine Hong Kong’s colonial epoch in a different perspective: through studying the Tsoi Yuen Village, let them (subalterns) speak for themselves. Furthermore, the significance of this resistance, taking place eleven years after the handover to the PRC, is an important fact that I shall not miss in later discussion. Last but not least, during the resistance, advanced technology and social networks such as Facebook, Twitter, iPhone were used by Post 80s generation to spread the latest information in order to attract public’s concern and participation. Therefore, apart from studying Tsoi Yuen Resistance as a local social movement, I also regard it as a part of the global movement in perusing ecological lifestyle and civil society. How Post 80s’ generation manipulates the global idea in a local context will also be examined.

Dynamic Factor Models and Forecasting Finnish Macroeconomic Variables

In recent years, thanks to developments in information technology, large-dimensional datasets have been increasingly available. Researchers now have access to thousands of economic series and the information contained in them can be used to create accurate forecasts and to test economic theories. To exploit this large amount of information, researchers and policymakers need an appropriate econometric model.Usual time series models, vector autoregression for example, cannot incorporate more than a few variables. There are two ways to solve this problem: use variable selection procedures or gather the information contained in the series to create an index model. This thesis focuses on one of the most widespread index model, the dynamic factor model (the theory behind this model, based on previous literature, is the core of the first part of this study), and its use in forecasting Finnish macroeconomic indicators (which is the focus of the second part of the thesis). In particular, I forecast economic activity indicators (e.g. GDP) and price indicators (e.g. consumer price index), from 3 large Finnish datasets. The first dataset contains a large series of aggregated data obtained from the Statistics Finland database. The second dataset is composed by economic indicators from Bank of Finland. The last dataset is formed by disaggregated data from Statistic Finland, which I call micro dataset. The forecasts are computed following a two steps procedure: in the first step I estimate a set of common factors from the original dataset. The second step consists in formulating forecasting equations including the factors extracted previously. The predictions are evaluated using relative mean squared forecast error, where the benchmark model is a univariate autoregressive model. The results are dataset-dependent. The forecasts based on factor models are very accurate for the first dataset (the Statistics Finland one), while they are considerably worse for the Bank of Finland dataset. The forecasts derived from the micro dataset are still good, but less accurate than the ones obtained in the first case. This work leads to multiple research developments. The results here obtained can be replicated for longer datasets. The non-aggregated data can be represented in an even more disaggregated form (firm level). Finally, the use of the micro data, one of the major contributions of this thesis, can be useful in the imputation of missing values and the creation of flash estimates of macroeconomic indicator (nowcasting).