The human genome is predicted to contain ~20,500 protein-coding genes. The encoded proteins are the key players in the body, but the functions and localizations of most proteins are still unknown. Antibody-based proteomics has great potential for exploration of the protein complement of the human genome, but there are antibodies only to a very limited set of proteins. The Human Proteome Resource (HPR) project was launched in August 2003, with the aim to generate high-quality specific antibodies towards the human proteome, and to use these antibodies for large-scale protein profiling in human tissues and cells. The goal of the work presented in this thesis was to evaluate if antigens can be selected, in a high-throughput manner, to enable generation of specific antibodies towards one protein from every human gene. A computationally intensive analysis of potential epitopes in the human proteome was performed and showed that it should be possible to find unique epitopes for most human proteins. The result from this analysis was implemented in a new web-based visualization tool for antigen selection. Predicted protein features important for antigen selection, such as transmembrane regions and signal peptides, are also displayed in the tool. The antigens used in HPR are named protein epitope signature tags (PrESTs). A genome-wide analysis combining different protein features revealed that it should be possible to select unique, 50 amino acids long PrESTs for ~80% of the human protein-coding genes. The PrESTs are transferred from the computer to the laboratory by design of PrEST-specific PCR primers. A study of the success rate in PCR cloning of the selected fragments demonstrated the importance of controlled GC-content in the primers for specific amplification. The PrEST protein is produced in bacteria and used for immunization and subsequent affinity purification of the resulting sera to generate mono-specific antibodies. The antibodies are tested for specificity and approved antibodies are used for tissue profiling in normal and cancer tissues. A large-scale analysis of the success rates for different PrESTs in the experimental pipeline of the HPR project showed that the total success rate from PrEST selection to an approved antibody is 31%, and that this rate is dependent on PrEST length. A second PrEST on a target protein is somewhat less likely to succeed in the HPR pipeline if the first PrEST is unsuccessful, but the analysis shows that it is valuable to select several PrESTs for each protein, to enable generation of at least two antibodies, which can be used to validate each other. / QC 20100705

Living cells are controlled by proteins and genes that interact through complex molecular pathways to achieve a specific function. Therefore, determination of protein-protein interaction is fundamental for the understanding of the cell’s lifecycle and functions. The function of a protein is also largely determined by its interactions with other proteins. The amount of protein-protein interaction data available has multiplied by the emergence of large-scale technologies for detecting them, but the drawback of such measures is the relatively high amount of noise present in the data. It is time consuming to experimentally determine protein-protein interactions and therefore the aim of this project is to create a computational method that predicts interactions with high sensitivity and specificity. Semantic similarity measures were applied across the Gene Ontology terms assigned to proteins in S. cerevisiae to predict protein-protein interactions. Three semantic similarity measures were tested to see which one performs best in predicting such interactions. Based on the results, a method that predicts function of proteins in connection with connectivity was devised. The results show that semantic similarity is a useful measure for predicting protein-protein interactions.

It was hypothesized that in a decision-making situation, individuals will not only think of an optimal alternative but also as the most promising alternative, choose the alternative that is closest to their optimal alternative. Therefore, based on participants’ optimal alternative, five alternatives, each equal in terms of constant Multi-attribute Utility, were presented to participants. Two of the alternatives were constructed to be most similar to the participant’s optimal alternative, two alternatives were associated with two non-compensatory rules, and one alternative was not linked to any decision making rule. Results showed that participants thought not only of an optimal alternative in the given decision-making situation, they also chose the alternative that was most similar to their optimal. This alternative also got highest preference ratings. These findings present an optimal alternative. In addition, they demonstrate the influence that such an alternative have on the outcome in a decision-making situation.

This thesis focuses on video watermarking robust against geometric attacks and video compressions. In addition to the requirements for an image watermarking algorithm, a digital video watermarking algorithm has to be robust against advanced video compressions, frame loss, frame swapping, aspect ratio change, frame rate change, intra- and inter-frame filtering, etc. Video compression, especially, the most efficient compression standard, H.264, and geometric attacks, such as rotation and cropping, frame aspect ratio change, and translation, are considered the most challenging attacks for video watermarking algorithms. In this thesis, we first review typical watermarking algorithms robust against geometric attacks and video compressions, and point out their advantages and disadvantages. Then, we propose our robust video watermarking algorithms against Rotation, Scaling and Translation (RST) attacks and MPEG-2 compression based on the logpolar mapping and the phase-only filtering method. Rotation or scaling transformation in the spatial domain results in vertical or horizontal shift in the log-polar mapping (LPM) of the magnitude of the Fourier spectrum of the target frame. Translation has no effect in this domain. This method is very robust to RST attacks and MPEG-2 compression. We also demonstrate that this method can be used as a RST parameters detector to work with other watermarking algorithms to improve their robustness to RST attacks. Furthermore, we propose a new video watermarking algorithm based on the 1D DFT (one-dimensional Discrete Fourier Transform) and 1D projection. This algorithm enhances the robustness to video compression and is able to resist the most advanced video compression, H.264. The 1D DFT for a video sequence along the temporal domain generates an ideal domain, in which the spatial information is still kept and the temporal information is obtained. With detailed analysis and calculation, we choose the frames with highest temporal frequencies to embed the fence-shaped watermark pattern in the Radon transform domain of the selected frames. The performance of the proposed algorithm is evaluated by video compression standards MPEG-2 and H.264; geometric attacks such as rotation, translation, and aspect-ratio changes; and other video processing. The most important advantages of this video watermarking algorithm are its simplicity, practicality and robustness.

With the proliferation of mobile devices and wireless connectivity technologies, mobile social communities offer novel opportunities for targeted marketing by service or product providers. Unfortunately, marketers are still unable to realize the full potential of these markets due to their inability to effectively target right audiences. This thesis presents a novel middleware for identifying spontaneous social communities (SSCs) of mobile users in ad hoc networks in order to facilitate marketers' advertisements. The contributions of the presented work are two fold; the first is a novel model for SSCs that captures their unique dynamic nature, in terms of community structure and interest in different \textit{hot-topics} over time. These time-varying interests are represented through an inferred \textit{community profile prototype} that reflects dominant characteristics of community members. This prototype is then employed to facilitate the identification of new potential members. The selected community prototypes are also used by marketers to identify the right communities for their services or products promotions. The second contribution of this paper is novel distributed techniques for efficient calculation of the community prototypes and identification of potential community links. In contrast to traditional models of detecting fixed and mobile social networks that rely on pre-existing friendships among its members to predict new ones, the proposed model focuses on measuring the degree of similarity between the new user's profile and the profiles of members of each community in order to predict new users' relationships in the community. The adopted model of SSCs can foster many existing and new socially-aware applications such as recommender systems for social events and tools for collaborative work. It is also an ideal target for business-oriented applications such as short-message-service (SMS) advertisement messages, podcasting news feeds in addition to location/context-aware services. The performance of the proposed work was evaluated using the NetLogo platform where obtained experimental results demonstrate the achieved high degree of stability in the resulting communities in addition to the effectiveness of the proposed middleware in terms of the reduction in the number of routing messages required for advertisements.

Finding subgroups within social networks is important for understanding and possibly influencing the formation and evolution of online communities. This thesis addresses the problem of finding cohesive subgroups within social networks inferred from online interactions. The dissertation begins with a review of relevant literature and identifies existing methods for finding cohesive subgroups. This is followed by the introduction of the SCAN method for identifying subgroups in online interaction. The SCAN (Social Cohesion Analysis of Networks) methodology involves three steps: selecting the possible members (Select), collecting those members into possible subgroups (Collect) and choosing the cohesive subgroups over time (Choose). Social network analysis, clustering and partitioning, and similarity measurement are then used to implement each of the steps. Two further case studies are presented, one involving the TorCamp Google group and the other involving YouTube vaccination videos, to demonstrate how the methodology works in practice. Behavioural measures of Sense of Community and the Social Network Questionnaire are correlated with the SCAN method to demonstrate that the SCAN approach can find meaningful subgroups. Additional empirical findings are reported. Betweenness centrality appears to be a useful filter for screening potential subgroup members, and members of cohesive subgroups have stronger community membership and influence than others. Subgroups identified using weighted average hierarchical clustering are consistent with the subgroups identified using the more computationally expensive k-plex analysis. The value of similarity measurement in assessing subgroup cohesion over time is demonstrated, and possible problems with the use of Q modularity to identify cohesive subgroups are noted. Applications of this research to marketing, expertise location, and information search are also discussed.

I examined whether perceptions of intergroup similarity and conflict interact to predict prejudice and facilitation of an intergroup social interaction as a consequence of physiological linkage – a state correlated with successful social interactions wherein two people's autonomic nervous systems synch-up in time. Studies 1 and 2a, revealed an association between similarity, conflict and low prejudice. In Study 2b participants completed essays priming similarity and conflict in order to test the indirect effect of their interaction with participants' physiological reactivity on the success of a dyadic social interaction. Similarity, conflict and physiological reactivity interacted to predict physiological linkage, which in turn moderated the effects of conflict on the success of the social interaction. These results suggest that physiological and social cognitive processes play key roles in determining the important moment when an outgroup stranger becomes a potential friend.

I examined whether perceptions of intergroup similarity and conflict interact to predict prejudice and facilitation of an intergroup social interaction as a consequence of physiological linkage – a state correlated with successful social interactions wherein two people's autonomic nervous systems synch-up in time. Studies 1 and 2a, revealed an association between similarity, conflict and low prejudice. In Study 2b participants completed essays priming similarity and conflict in order to test the indirect effect of their interaction with participants' physiological reactivity on the success of a dyadic social interaction. Similarity, conflict and physiological reactivity interacted to predict physiological linkage, which in turn moderated the effects of conflict on the success of the social interaction. These results suggest that physiological and social cognitive processes play key roles in determining the important moment when an outgroup stranger becomes a potential friend.

This thesis documents the design and fabrication of an experimental facility that was built to produce a turbulent plane wall jet. The target flow was two-dimensional with a uniform profile of the mean streamwise velocity and a low turbulence level at the slot exit. The design requirements for a flow conditioning apparatus that could produce this flow were determined. The apparatus was then designed and constructed, and measurements of the fluid flow were obtained using particle image velocimetry (PIV). The first series of measurements was along the slot width, the second series was along the slot centerline and the third was at 46 slot heights off the centerline. The Reynolds number, based on the slot height and jet exit velocity, of the wall jet varied from 7594 to 8121. Data for the streamwise and transverse components of velocity and the three associated Reynolds stress components were analyzed and used to determine the characteristics of the wall jet.<p> This experimental facility was able to produce a profile of the mean streamwise velocity near the slot exit that was uniform over 71% of the slot height with a streamwise turbulence that was equal to 1.45% of the mean velocity. This initial velocity was maintained to 6 slot heights. The fully developed region for the centerline and the off-centerline measurements was determined to extend from 50 to 100 slot heights and 40 to 100 slot heights, respectively. This was based on self-similarity of the mean streamwise velocity profiles when scaled using the maximum streamwise velocity and the jet half-width. The off-centerline Reynolds stress profiles achieved a greater degree of collapse than did the centerline profiles.<p> The rate of spread of the wall jet along the centerline was 0.080 in the self-similar region from 50 to 100 slot heights, and the off-centerline growth rate was 0.077 in the self-similar region from 40 to 100 slot heights. The decay rate of the maximum streamwise velocity was -0.624 within the centerline self-similar region, and -0.562 within the off-centerline self-similar region. These results for the spread and decay of the wall jet compared well with recent similar studies.<p> The two-dimensionality was initially assessed by measuring the mean streamwise velocity at 1 slot height along the entire slot width. The two-dimensionality of this wall jet was further analyzed by comparing the centerline and off-centerline profiles of the mean streamwise velocity at 2/3, 4, 50, 80, and 100 slot heights, and by comparing the growth rates and decay rates. Although this facility was able to produce a wall jet that was initially two-dimensional, the two-dimensionality was compromised downstream of the slot, most likely due to the presence of return flow and spanwise spreading. Without further measurements, it is not yet clear exactly how the lack of complete two-dimensionality affects the flow characteristics noted above.

Finding subgroups within social networks is important for understanding and possibly influencing the formation and evolution of online communities. This thesis addresses the problem of finding cohesive subgroups within social networks inferred from online interactions. The dissertation begins with a review of relevant literature and identifies existing methods for finding cohesive subgroups. This is followed by the introduction of the SCAN method for identifying subgroups in online interaction. The SCAN (Social Cohesion Analysis of Networks) methodology involves three steps: selecting the possible members (Select), collecting those members into possible subgroups (Collect) and choosing the cohesive subgroups over time (Choose). Social network analysis, clustering and partitioning, and similarity measurement are then used to implement each of the steps. Two further case studies are presented, one involving the TorCamp Google group and the other involving YouTube vaccination videos, to demonstrate how the methodology works in practice. Behavioural measures of Sense of Community and the Social Network Questionnaire are correlated with the SCAN method to demonstrate that the SCAN approach can find meaningful subgroups. Additional empirical findings are reported. Betweenness centrality appears to be a useful filter for screening potential subgroup members, and members of cohesive subgroups have stronger community membership and influence than others. Subgroups identified using weighted average hierarchical clustering are consistent with the subgroups identified using the more computationally expensive k-plex analysis. The value of similarity measurement in assessing subgroup cohesion over time is demonstrated, and possible problems with the use of Q modularity to identify cohesive subgroups are noted. Applications of this research to marketing, expertise location, and information search are also discussed.