Approaches for improving the safety and efficacy of adenoviral gene therapy

Cancer is a devastating disease with poor prognosis and no curative treatment, when widely metastatic. Conventional therapies, such as chemotherapy and radiotherapy, have efficacy but are not curative and systemic toxicity can be considerable. Almost all cancers are caused due to changes in the genetic material of the transformed cells. Cancer gene therapy has emerged as a new treatment option, and past decades brought new insights in developing new therapeutic drugs for curing cancer. Oncolytic viruses constitute a novel therapeutic approach given their capacity to replicate in and kill specifically tumor cells as well as reaching tumor distant metastasis. Adenoviral gene therapy has been suggested to cause liver toxicity. This study shows that new developed adenoviruses, in particular Ad5/19p-HIT, can be redirected towards kidney while adenovirus uptake by liver is minimal. Moreover, low liver transduction resulted in a favorable tumor to liver ratio of virus load. Further, we established a new immunocompetent animal model Syrian hamsters. Wild type adenovirus 5 was found to replicate in Hap-T1 hamster tumors and normal tissues. There are no antiviral drugs available to inhibit adenovirus replication. In our study, chlorpromazine and cidofovir efficiently abrogated virus replication in vitro and showed significant reduction in vivo in tumors and liver. Once safety concerns were addressed together with the new given antiviral treatment options, we further improved oncolytic adenoviruses for better tumor penetration, local amplification and host system modulation. Further, we created Ad5/3-9HIF-Δ24-VEGFR-1-Ig, oncolytic adenovirus for improved infectivity and antiangiogenic effect for treatment of renal cancer. This virus exhibited increased anti-tumor effect and specific replication in kidney cancer cells. The key player for good efficacy of oncolytic virotherapy is the host immune response. Thus, we engineered a triple targeted adenovirus Ad5/3-hTERT-E1A-hCD40L, which would lead to tumor elimination due to tumor-specific oncolysis and apoptosis together with an anti-tumor immune response prompted by the immunomodulatory molecule. In conclusion, the results presented in this thesis constitute advances in our understanding of oncolytic virotherapy by successful tumor targeting, antiviral treatment options as a safety switch in case of replication associated side-effects, and modulation of the host immune system towards tumor elimination.  

Essays on Risk Modeling. Applications to Portfolio and Risk Management (summary section only)

In this thesis we deal with the concept of risk. The objective is to bring together and conclude on some normative information regarding quantitative portfolio management and risk assessment. The first essay concentrates on return dependency. We propose an algorithm for classifying markets into rising and falling. Given the algorithm, we derive a statistic: the Trend Switch Probability, for detection of long-term return dependency in the first moment. The empirical results suggest that the Trend Switch Probability is robust over various volatility specifications. The serial dependency in bear and bull markets behaves however differently. It is strongly positive in rising market whereas in bear markets it is closer to a random walk. Realized volatility, a technique for estimating volatility from high frequency data, is investigated in essays two and three. In the second essay we find, when measuring realized variance on a set of German stocks, that the second moment dependency structure is highly unstable and changes randomly. Results also suggest that volatility is non-stationary from time to time. In the third essay we examine the impact from market microstructure on the error between estimated realized volatility and the volatility of the underlying process. With simulation-based techniques we show that autocorrelation in returns leads to biased variance estimates and that lower sampling frequency and non-constant volatility increases the error variation between the estimated variance and the variance of the underlying process. From these essays we can conclude that volatility is not easily estimated, even from high frequency data. It is neither very well behaved in terms of stability nor dependency over time. Based on these observations, we would recommend the use of simple, transparent methods that are likely to be more robust over differing volatility regimes than models with a complex parameter universe. In analyzing long-term return dependency in the first moment we find that the Trend Switch Probability is a robust estimator. This is an interesting area for further research, with important implications for active asset allocation.

Understanding Customer Loyalty and Disloyalty The Effect of Loyalty-Supporting and -Repressing Factors (first edition sold out, 'print on demand' 60 €)

Customer loyalty has been a central topic of both marketing theory and practice for several decades. Customer disloyalty, or relationship ending, has received much less attention. Despite the close relation between customer loyalty and disloyalty, they have rarely been addressed in the same study. The thesis bridges this gap by focusing on both loyal and disloyal customers and the factors characterising them. Based on a qualitative study of loyal and disloyal bank customers in the Finnish retail banking market, both factors that are common to the groups and factors that differentiate between them are identified. A conceptual framework of factors that affect customer loyalty or disloyalty is developed and used to analyse the empirical data. According to the framework, customers’ loyalty status (behavioural and attitudinal loyalty) is influenced by positive, loyalty-supporting, and negative, loyalty-repressing factors. Loyalty-supporting factors either promote customer dedication, making the customer want to remain loyal, or act as constraints, hindering the customer from switching. Among the loyalty-repressing factors it is especially important to identify those that act as triggers of disloyal behaviour, making customers switch service providers. The framework further suggests that by identifying the sources of loyalty-supporting and -repressing factors (the environment, the provider, the customer, the provider-customer interaction, or the core service) one can determine which factors are within the control of the service provider. Attitudinal loyalty is approached through a customer’s “feeling of loyalty”, as described by customers both orally and graphically. By combining the graphs with behavioural loyalty, seven customer groups are identified: Stable Loyals, Rescued Loyals, Loyals at Risk, Positive Disloyals, Healing Disloyals, Fading Disloyals, and Abrupt Disloyals. The framework and models of the thesis can be used to analyse factors that affect customer loyalty and disloyalty in different service contexts. Since the empirical study was carried out in a retail bank setting, the thesis has managerial relevance especially for banks. Christina Nordman is associated with CERS, Center for Relationship Marketing and Service Management at the Swedish School of Economics and Business Administration. The doctoral thesis is part of the Göran Collert Research Project in Customer Relationships and Retail Banking and has been funded by The Göran Collert Foundation.

Optimal Risk Adoption: A Real Options Approach

This study develops a real options approach for analyzing the optimal risk adoption policy in an environment where the adoption means a switch from one stochastic flow representation into another. We establish that increased volatility needs not decelerate investment, as predicted by the standard literature on real options, once the underlying volatility of the state is made endogenous. We prove that for a decision maker with a convex (concave) objective function, increased post-adoption volatility increases (decreases) the expected cumulative present value of the post-adoption profit flow, which consequently decreases (increases) the option value of waiting and, therefore, accelerates (decelerates) current investment.

Nonlinear mixture autoregressive model: economic applications

The aim of this dissertation is to model economic variables by a mixture autoregressive (MAR) model. The MAR model is a generalization of linear autoregressive (AR) model. The MAR -model consists of K linear autoregressive components. At any given point of time one of these autoregressive components is randomly selected to generate a new observation for the time series. The mixture probability can be constant over time or a direct function of a some observable variable. Many economic time series contain properties which cannot be described by linear and stationary time series models. A nonlinear autoregressive model such as MAR model can a plausible alternative in the case of these time series. In this dissertation the MAR model is used to model stock market bubbles and a relationship between inflation and the interest rate. In the case of the inflation rate we arrived at the MAR model where inflation process is less mean reverting in the case of high inflation than in the case of normal inflation. The interest rate move one-for-one with expected inflation. We use the data from the Livingston survey as a proxy for inflation expectations. We have found that survey inflation expectations are not perfectly rational. According to our results information stickiness play an important role in the expectation formation. We also found that survey participants have a tendency to underestimate inflation. A MAR model has also used to model stock market bubbles and crashes. This model has two regimes: the bubble regime and the error correction regime. In the error correction regime price depends on a fundamental factor, the price-dividend ratio, and in the bubble regime, price is independent of fundamentals. In this model a stock market crash is usually caused by a regime switch from a bubble regime to an error-correction regime. According to our empirical results bubbles are related to a low inflation. Our model also imply that bubbles have influences investment return distribution in both short and long run.

Investigating online reading with eye tracking and EEG : The influence of text format, reading task and parafoveal stimuli on reading processes

Research on reading has been successful in revealing how attention guides eye movements when people read single sentences or text paragraphs in simplified and strictly controlled experimental conditions. However, less is known about reading processes in more naturalistic and applied settings, such as reading Web pages. This thesis investigates online reading processes by recording participants eye movements. The thesis consists of four experimental studies that examine how location of stimuli presented outside the currently fixated region (Study I and III), text format (Study II), animation and abrupt onset of online advertisements (Study III), and phase of an online information search task (Study IV) affect written language processing. Furthermore, the studies investigate how the goal of the reading task affects attention allocation during reading by comparing reading for comprehension with free browsing, and by varying the difficulty of an information search task. The results show that text format affects the reading process, that is, vertical text (word/line) is read at a slower rate than a standard horizontal text, and the mean fixation durations are longer for vertical text than for horizontal text. Furthermore, animated online ads and abrupt ad onsets capture online readers attention and direct their gaze toward the ads, and distract the reading process. Compared to a reading-for-comprehension task, online ads are attended to more in a free browsing task. Moreover, in both tasks abrupt ad onsets result in rather immediate fixations toward the ads. This effect is enhanced when the ad is presented in the proximity of the text being read. In addition, the reading processes vary when Web users proceed in online information search tasks, for example when they are searching for a specific keyword, looking for an answer to a question, or trying to find a subjectively most interesting topic. A scanning type of behavior is typical at the beginning of the tasks, after which participants tend to switch to a more careful reading state before finishing the tasks in the states referred to as decision states. Furthermore, the results also provided evidence that left-to-right readers extract more parafoveal information to the right of the fixated word than to the left, suggesting that learning biases attentional orienting towards the reading direction.