This thesis summarizes ways of representation of biological sequences and file formats used for sequence exchange and storage. Next part deals with techniques used for sequence pairwise alignment, followed by extension of these techniques to the problem of multiple sequence alignment. Additional methods are introduced, that are suboptimal, but on the other hand are able to compute results in reasonable time. Practical part of this thesis consists of implementing multiple sequence alignment application in Java programming language.

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Gender roles are a tool used by society to set acceptable boundaries and ideals upon the sexes, and during the early part of the twentieth century in America those gender boundaries began to blur. As a result of the 19th Amendment, men must have felt their decreasing importance because women were no longer solely dependent upon them, and gender roles shifted as woman began to occupy territory that was traditionally held by men. The “New Woman" entered the workforce, and refused to accept traditional female gender conventions. In response to the “New Woman," Theodore Roosevelt and other leading males sought to reinforce the ideal of the male as the protector and provider. As woman took on characteristics commonly associated with men, men now had to grapple with a changing gender identity that often left them confused and frustrated. Willa Sibert Cather's life reflects the fluctuating gender conventions of early twentieth century America as she struggled to define her gender identity. In her youth, Cather chopped her hair and dressed like a boy. She also spent time dissecting frogs and called herself “William Cather, M.D." Cather's cross-dressing reveals her unconventional core and her desire to define herself regardless of societal expectations. Cather also had many close relationships with woman, and these close relationships have led many scholars to label her a lesbian. Cather, however, left us a mystery surrounding her gender preference because she never openly called herself a lesbian. Cather's supposed lesbianism is useful because it reveals the ambiguity of her personality. Cather is paradox because she sought for self-definition, but she also suffered from an identity crisis. By using the shifting nature of gender roles in the America during the early decades of the twentieth century and Cather's confused and unconventional life as a backdrop, I would argue that My Ántonia (1918), The Professor's House (1925), and “Neighbor Rosicky (1932)" reveal the consequences of gender roles. Cather's novels and short story should be analyzed for her interest in exploring male reaction to prescribed gender roles which, ultimately, reveals Cather's attitude towards the existence of gender conventions. Cather advocated for a more fluid and balance way of defining male and female roles. Cather's novel My Ántonia and The Professor's House reveal the consequences of gender roles because both Jim and Professor St. Peter are frustrated, fearful, unsatisfied, ambiguous, and unhappy with the roles that they have been playing. In sharp contrast to these two novels is Cather's delightful short story entitled “Neighbor Rosicky." In this short story Cather presents a protagonist who is whole and balanced. “Neighbor Rosicky" is Cather's statement regarding the importance and beauty of self-definition. Ultimately, her literature can be viewed as a rejection of both male and female gender qualities which demonstrates that Cather and her fiction cannot be reduced to an identity agenda.

While Machine Learning is one of the most popular research areas in Computer Science, there are still only a few deployed applications intended for use by the general public. We have developed an exemplary application that can be directly applied to eBay trading. Our system predicts how much an item would sell for on eBay based on that item's attributes. We ran our experiments on the eBay laptop category, with prior trades used as training data. The system implements a feature-weighted k-Nearest Neighbor algorithm, using genetic algorithms to determine feature weights. Our results demonstrate an average prediction error of 16%; we have also shown that this application greatly reduces the time a reseller would need to spend on trading activities, since the bulk of market research is now done automatically with the help of the learned model.

Ground subsidence due to underground coal mining is a complex, narrowly-understood phenomenon. Due to the complicated physical processes involved and the lack of a complete knowledge of the characteristics of overlying strata, the reliability of current prediction techniques varies widely. Furthermore, the accuracy of any given prediction technique is largely dependent upon the accuracy of field measurements and surveys which provide input data for the technique. A valuable resource available for predicting and modeling subsidence is aerial survey technology. This technology produces yearly datasets with a high density of survey points. The following study introduces a method wherein these survey points are converted into elevation plots and subsidence plots using GIS. This study also presents a method, titled the Type-Xi Integration method (TXI method), which improves upon a previous subsidence prediction technique. This method differs from the previous technique in that it incorporates accurate surface topography and considers irregular mine geometry, as well as seam thickness and overburden variations in its predictions. The TXI method also involves comparing predicted subsidence directly to measured subsidence from subsidence plots. In summary, this study illustrates a method of combining data from aerial survey points and mine geometry with subsidence models in order to improve the accuracy of the models.

Water stored in snow is a critical contribution to the world’s available freshwater supply and is fundamental to the sustenance of natural ecosystems, agriculture and human societies. The importance of snow for the natural environment and for many socio-economic sectors in several mid‐ to high‐latitude mountain regions around the world, leads scientists to continuously develop new approaches to monitor and study snow and its properties. The need to develop new monitoring methods arises from the limitations of in situ measurements, which are pointwise, only possible in accessible and safe locations and do not allow for a continuous monitoring of the evolution of the snowpack and its characteristics. These limitations have been overcome by the increasingly used methods of remote monitoring with space-borne sensors that allow monitoring the wide spatial and temporal variability of the snowpack. Snow models, based on modeling the physical processes that occur in the snowpack, are an alternative to remote sensing for studying snow characteristics. However, from literature it is evident that both remote sensing and snow models suffer from limitations as well as have significant strengths that it would be worth jointly exploiting to achieve improved snow products. Accordingly, the main objective of this thesis is the development of novel methods for the estimation of snow parameters by exploiting the different properties of remote sensing and snow model data. In particular, the following specific novel contributions are presented in this thesis: i. A novel data fusion technique for improving the snow cover mapping. The proposed method is based on the exploitation of the snow cover maps derived from the AMUNDSEN snow model and the MODIS product together with their quality layer in a decision level fusion approach by mean of a machine learning technique, namely the Support Vector Machine (SVM). ii. A new approach has been developed for improving the snow water equivalent (SWE) product obtained from AMUNDSEN model simulations. The proposed method exploits some auxiliary information from optical remote sensing and from topographic characteristics of the study area in a new approach that differs from the classical data assimilation approaches and is based on the estimation of AMUNDSEN error with respect to the ground data through a k-NN algorithm. The new product has been validated with ground measurement data and by a comparison with MODIS snow cover maps. In a second step, the contribution of information derived from X-band SAR imagery acquired by COSMO-SkyMed constellation has been evaluated, by exploiting simulations from a theoretical model to enlarge the dataset.

Gait disorders are common in neurodegenerative diseases and distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge even for the experienced clinician. Ultimately, muscle activity underlies the generation of kinematic patterns. Therefore, one possible way to address this problem may be to differentiate gait disorders by analyzing intrinsic features of muscle activations patterns. Here, we examined whether it is possible to differentiate electromyography (EMG) gait patterns of healthy subjects and patients with different gait disorders using machine learning techniques. Nineteen healthy volunteers (9 male, 10 female, age 28.2 ± 6.2 years) and 18 patients with gait disorders (10 male, 8 female, age 66.2 ± 14.7 years) resulting from different neurological diseases walked down a hallway 10 times at a convenient pace while their muscle activity was recorded via surface EMG electrodes attached to 5 muscles of each leg (10 channels in total). Gait disorders were classified as predominantly hypokinetic (n = 12) or ataxic (n = 6) gait by two experienced raters based on video recordings. Three different classification methods (Convolutional Neural Network—CNN, Support Vector Machine—SVM, K-Nearest Neighbors—KNN) were used to automatically classify EMG patterns according to the underlying gait disorder and differentiate patients and healthy participants. Using a leave-one-out approach for training and evaluating the classifiers, the automatic classification of normal and abnormal EMG patterns during gait (2 classes: “healthy” and “patient”) was possible with a high degree of accuracy using CNN (accuracy 91.9%), but not SVM (accuracy 67.6%) or KNN (accuracy 48.7%). For classification of hypokinetic vs. ataxic vs. normal gait (3 classes) best results were again obtained for CNN (accuracy 83.8%) while SVM and KNN performed worse (accuracy SVM 51.4%, KNN 32.4%). These results suggest that machine learning methods are useful for distinguishing individuals with gait disorders from healthy controls and may help classification with respect to the underlying disorder even when classifiers are trained on comparably small cohorts. In our study, CNN achieved higher accuracy than SVM and KNN and may constitute a promising method for further investigation.

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