This thesis focuses on the evaluation of biomarkers for radio-immunodiagnostics and radio-immunotherapy and on radiosensitization strategies after HSP90 inhibition, as a step towards more personalized cancer medicine. There is a need to develop new tracers that target cancer-specific biomarkers to improve diagnostic imaging, as well as to combine treatment strategies to potentiate synergistic effects. Special focus has been on the cell surface molecule CD44 and its oncogenic variants, which were found to exhibit unique expression patterns in head and neck squamous cell carcinoma (HNSCC). The variant CD44v6 seems to be a promising target, because it is overexpressed in this cancer type and is associated with radioresistance. Two new radioconjugates that target CD44v6, namely, the Fab fragment AbD15179 and the bivalent fragment AbD19384, were investigated with regard to specificity, biodistribution and imaging performance. Both conjugates were able to efficiently target CD44v6-positive tumors in vitro and in vivo. PET imaging of CD44v6 with 124I-AbD19384 revealed many advantages compared with the clinical standard 18F-FDG. Furthermore, the efficacy of the novel HSP90 inhibitor AT13387 and its potential use in combination with radiation treatment were evaluated. AT13387 proved to be a potent new cancer drug with favorable pharmacokinetics. Synergistic combination effects at clinically relevant drug and radiation doses are promising for both radiation dose reduction and minimization of side effects, or for an improved therapeutic response. The AT13387 investigation indicated that CD44v6 is not dependent on the molecular chaperone HSP90, and therefore, radio-immunotargeting of CD44v6 in combination with the HSP90 inhibitor AT13387 might potentiate treatment outcomes. However, EGFR expression levels did correlate with HSP90 inhibition, and therefore, molecular imaging of EGFR-positive tumors may be used to assess the treatment response to HSP90 inhibitors. In conclusion, these results demonstrate how tumor targeting with radiolabeled vectors and chemotherapeutic compounds can provide more specific and sensitive diagnostic tools and treatment options, which can lead to customized treatment decisions and a functional diagnosis that provides more precise and safer drug prescribing, as well as a more effective treatment for each patient.

We provide a systematic comparison of the out-of-sample forecasts based on multivariate macroeconomic models and forecast combinations for the euro against the US dollar, the British pound, the Swiss franc and the Japanese yen. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive accuracy and profit measures, data snooping bias free tests are used. The results indicate that forecast combinations help to improve over benchmark trading strategies for the exchange rate against the US dollar and the British pound, although the excess return per unit of deviation is limited. For the euro against the Swiss franc or the Japanese yen, no evidence of generalized improvement in profit measures over the benchmark is found. (authors' abstract) / Series: Department of Economics Working Paper Series

Most software programs that deal with mathematical objects require input expressions to be linearized using somewhat awkward and unfamiliar string-based syntax. It is natural to desire a method for inputting mathematics using the same two-dimensional syntax employed with pen and paper, and the increasing prevalence of pen- and touch-based interfaces causes this topic to be of practical as well as theoretical interest. Accurately recognizing two-dimensional mathematical notation is a difficult problem that requires not only theoretical advancement over the traditional theories of string-based languages, but also careful consideration of runtime efficiency, data organization, and other practical concerns that arise during system construction. This thesis describes the math recognizer used in the MathBrush pen-math system. At a high level, the two-dimensional syntax of mathematical writing is formalized using a relational grammar. Rather than reporting a single recognition result, all recognizable interpretations of the input are simultaneously represented in a data structure called a parse forest. Individual interpretations may be extracted from the forest and reported one by one as the user requests them. These parsing techniques necessitate robust tree scoring functions, which themselves rely on several lower-level recognition processes for stroke grouping, symbol recognition, and spatial relation classification. The thesis covers the recognition, parsing, and scoring aspects of the MathBrush recognizer, as well as the algorithms and assumptions necessary to combine those systems and formalisms together into a useful and efficient software system. The effectiveness of the resulting system is measured through two accuracy evaluations. One evaluation uses a novel metric based on user effort, while the other replicates the evaluation process of an international accuracy competition. The evaluations show that not only is the performance of the MathBrush recognizer improving over time, but it is also significantly more accurate than other academic recognition systems.

Nowell (1976) first proposed that unless cytotoxic cancer therapy eradicates all tumor cells, genetic or heritable variation within heterogeneous tumors will inevitably lead to the evolution of chemotherapeutic resistance through clonal selection. This evolutionary hypothesis was formalized by Goldie and Coldman (1979), who developed one of the earliest mathematical kinetic models of resistance evolution in neoplasms. Their model predicted that the likelihood of response and cure would be increased in combination vs single agent cytotoxic therapies. In a later study, Gardner (2002) developed a computational kinetic model to predict chemotherapeutic combinations, doses, and schedules most likely to result in patient response and prolonged life. This model predicts that combination therapy involving both cytotoxic and cytostatic drugs will be more effective than combination therapy involving only cytotoxic drugs. Thus far, no systematic evaluation of the Goldie and Coldman and Gardner hypotheses have been conducted in the metastatic clinical trial setting. Here I test these hypotheses using the results of over 700 phase II, III and II/III clinical trials. I show that, as predicted by Goldie and Coldman, both overall response rate and overall survival were greater in combination arms. Moreover, median duration of response – the key indicator of the rate of resistance evolution - was also greater in combination vs single agent arms. These results suggest that generally combination chemotherapy is more effective than single agent therapy for advanced solid tumors as predicted by Goldie and Coldman (1979) hypothesis and that, at least in the metastatic setting, the potential disadvantages of combination therapy with respect to accelerated resistance evolution are outweighed by the greater waiting times for resistance mutations to arise. By contrast, although combination cytotoxic and cytostatic therapy is associated with a greater average overall response rate than multi agent cytotoxic therapy, this is not the case for both median duration of response and overall survival. Hence, there is no evidence that, in contrast to the predictions of the Gardner (2002) model, combination cytotoxic and cytostatic therapy decreases the rate of resistance evolution relative to that obtaining under combination cytotoxic therapy.

The growing need for designing and implementing reliable voice-based human-machine interfaces has inspired intensive research work in the field of voice-enabled systems, and greater robustness and reliability are being sought for those systems. Speech recognition has become ubiquitous. Automated call centers, smart phones, dictation and transcription software are among the many systems currently being designed and involving speech recognition. The need for highly accurate and optimized recognizers has never been more crucial. The research community is very actively involved in developing powerful techniques to combine the existing feature extraction methods for a better and more reliable information capture from the analog signal, as well as enhancing the language and acoustic modeling procedures to better adapt for unseen or distorted speech signal patterns. Most researchers agree that one of the most promising approaches for the problem of reducing the Word Error Rate (WER) in large vocabulary speech transcription, is to combine two or more speech recognizers and then generate a new output, in the expectation that it provides a lower error rate. The research work proposed here aims at enhancing and boosting even further the performance of the well-known Recognizer Output Voting Error Reduction (ROVER) combination technique. This is done through its integration with an error filtering approach. The proposed system is referred to as cROVER, for context-augmented ROVER. The principal idea is to flag erroneous words following the combination of the word transition networks through a scanning process at each slot of the resulting network. This step aims at eliminating some transcription errors and thus facilitating the voting process within ROVER. The error detection technique consists of spotting semantic outliers in a given decoder's transcription output. Due to the fact that most error detection techniques suffer from a high false positive rate, we propose to combine the error filtering techniques to compensate for the poor performance of each of the individual error classifiers. Experimental results, have shown that the proposed cROVER approach is able to reduce the relative WER by almost 10% through adequate combination of speech decoders. The approaches proposed here are generic enough to be used by any number of speech decoders and with any type of error filtering technique. A novel voting mechanism has also been proposed. The new confidence-based voting scheme has been inspired from the cROVER approach. The main idea consists of using the confidence scores collected from the contextual analysis, during the scoring of each word in the transition network. The new voting scheme outperformed ROVER's original voting, by up to 16% in terms of relative WER reduction.

The thesis introduces a new technique that is an alternative method instead of applying deltas to literal file sequentially to retrieve a version from a repository. To my best knowledge / this is the first investigation about delta combination for copy/insert instruction type with many experimental results and conclusions. The thesis proves that the delta combination eliminates unnecessary I/O process for intermediate versions when delta application is considered, therefore reduces I/O time. Deltas are applied to literal sequentially to generate the required version in the classical way. Replace algorithm combines delta files which would be applied in delta application as combined delta, and applies it to literal to generate the required one. Apply runs in O (size (D)) time where D is the destination file and size (D) is its size. To retrieve nth version in a chain where 1st version is literal, it requires n-1 time apply. Replace algorithm runs in O (i + c * log2 n) time where i is the total length of all inserts, c is the total length of all copies in destination delta, and n is the number of instructions in source delta. To retrieve the same nth version, it requires n-2 time replace and one apply.

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