Ultrasonography is widely used in medical diagnosis with the advantages of being low cost, non-invasive and capable of real time imaging. When interpreting ultrasonographic images of mammalian ovaries, the structures of interest are follicles, corpora lutea (CL) and stroma. This thesis presents an approach to perform CL texture analysis, including detection and segmentation, based on the classiers trained by genetic programming (GP). The objective of CL detection is to determine whether there is a CL in the ovarian images, while the goal of segmentation is to localize the CL within the image. Genetic programming (GP) oers a solution through the evolution of computer programs by methods inspired by the mechanisms of natural selection. Herein, we use rotationally invariant local binary patterns (LBP) to encode the local texture features. These are used by the programs which are manipulated by GP to obtain highly t CL classiers. Grayscale standardization was performed on all images in our data set based on the reference grayscale in each image. CL classication programs were evolved by genetic programming and tested on ultrasonographic images of ovaries. On the bovine dataset, our CL detection algorithm is reliable and robust. The detection algorithm correctly determined the presence or absence of a CL in 93.3% of 60 test images. The segmentation algorithm achieved a mean ( standard deviation) sensitivity and specicity of 0.87 (0.14) and 0.91 (0.05), respectively, over the 30 CL images. Our CL segmentation algorithm is an improvement over the only previously published algorithm, since our method is fully automatic and does not require the placement of an initial contour. The success of these algorithms demonstrates that similar algorithms designed for analysis of in vivo human ovaries are likely viable.

Ultrasonography is widely used in medical diagnosis with the advantages of being low cost, non-invasive and capable of real time imaging. When interpreting ultrasonographic images of mammalian ovaries, the structures of interest are follicles, corpora lutea (CL) and stroma. This thesis presents an approach to perform CL texture analysis, including detection and segmentation, based on the classiers trained by genetic programming (GP). The objective of CL detection is to determine whether there is a CL in the ovarian images, while the goal of segmentation is to localize the CL within the image. Genetic programming (GP) oers a solution through the evolution of computer programs by methods inspired by the mechanisms of natural selection. Herein, we use rotationally invariant local binary patterns (LBP) to encode the local texture features. These are used by the programs which are manipulated by GP to obtain highly t CL classiers. Grayscale standardization was performed on all images in our data set based on the reference grayscale in each image. CL classication programs were evolved by genetic programming and tested on ultrasonographic images of ovaries. On the bovine dataset, our CL detection algorithm is reliable and robust. The detection algorithm correctly determined the presence or absence of a CL in 93.3% of 60 test images. The segmentation algorithm achieved a mean ( standard deviation) sensitivity and specicity of 0.87 (0.14) and 0.91 (0.05), respectively, over the 30 CL images. Our CL segmentation algorithm is an improvement over the only previously published algorithm, since our method is fully automatic and does not require the placement of an initial contour. The success of these algorithms demonstrates that similar algorithms designed for analysis of in vivo human ovaries are likely viable.

How patterns arise from an apparently uniform group of cells is one of the classical problems in developmental biology. The mechanism is complicated by the fact that patterning occurs on a growing medium. Therefore, changes in an organism’s size and shape affect the patterning processes. In turn, patterning itself may affect growth. This interaction between growth and patterning leads to the generation of complex shapes and structures from simpler ones. Studying such interactions requires the possibility to monitor both processes in vivo. To this end, we developed a new technique to monitor and quantify vein patterning in a growing leaf over time using the leaves of Arabidopsis thaliana as a model system. We used a transgenic line with fluorescent markers associated with the venation. Individual leaves are followed in many samples in vivo through time-lapse imaging. Custom-made software allowed us to extract the leaf surface and vein pattern from images of each leaf at each time point. Then average spatial maps from multiple samples that were generated revealed spatio-temporal gradients. Our quantitative description of wild type vein patterns during leaf development revealed that there is no constant size at which a part of tissue enclosed by vasculature will become irrigated by a new vein. Instead, it seemed that vein formation depends on the growth rate of the tissue. This is the first time that vein patterning in growing leaves was quantified. The techniques developed will later be used to explore the interaction between growth and patterning through a variety of approaches, including mutant analysis, pharmacological treatments and variation of environmental conditions.

Open source software is designed, developed and maintained by means of electronic media. These media include discussions on a variety of issues reflecting the evolution of a software system, such as reports on bugs and their fixes, new feature requests, design change, refactoring tasks, test plans, etc. Often this valuable information is simply buried as plain text in the mailing archives. We believe that email interactions collected prior to a product release are related to its source code modifications, or if they do not immediately correlate to change events of the current release, they might affect changes happening in future revisions. In this work, we propose a method to reason about the nature of software changes by mining and correlating electronic mailing list archives. Our approach is based on the assumption that developers use meaningful names and their domain knowledge in defining source code identifiers, such as classes and methods. We employ natural language processing techniques to find similarity between source code change history and history of public interactions surrounding these changes. Exact string matching is applied to find a set of common concepts between discussion vocabulary and changed code vocabulary. We apply our correlation method on two software systems, LSEdit and Apache Ant. The results of these exploratory case studies demonstrate the evidence of similarity between the content of free-form text emails among developers and the actual modifications in the code. We identify a set of correlation patterns between discussion and changed code vocabularies and discover that some releases referred to as minor should instead fall under the major category. These patterns can be used to give estimations about the type of a change and time needed to implement it.

We analyzed how land-use patterns and changes in urbanization influence positive raccoon rabies cases in an established enzootic area. County resolution was used and the study area included all 159 counties in Georgia. We obtained data on raccoons submitted from 2006 through 2010 for testing at the state public health labs due to exposure incidents with people or domesticated animals. The land-use patterns were extracted from the US Geological Survey’s National Land Cover Database from both 2001 and 2006. Odds ratios were calculated on 16 land-use variables that included natural topography, agricultural development, and urbanization. An additional variable, Submissions/Population density, was used to normalize counties and to account for population bias associated with rabies surveillance. The use of this demographic variable was substantiated by GIS clustering analysis. The outcome variable was heavily right skewed and over dispersed and therefore a negative binomial regression was used in this count statistics technique. The final analysis showed that low intensity residential development is associated with raccoon rabies cases while evergreen forest offers protection. This study supports the hypothesis that the raccoon rabies enzootic is maintained in those edge ecosystems of urbanization. It is advocated here that the public health animal rabies database to include GPS coordinates when reporting wildlife rabies submissions for testing to improve the resolution when studying the disease ecology of enzootic rabies.

Subject to Change is the culmination of a two-year investigation of the idea of home. This thesis discusses the journey of creating a home for my family and the work it spawned. Various cultural and artistic influences that shape the work include Grimm’s fairytales, my upbringing in Germany, Sigmund Freud, Bernd and Hilla Becher, and Josef Beuys. This work reflects a situation and transforms material that takes on symbolic meaning.

Feature-oriented requirements decompose a system's requirements into individual bundles of functionality called features, where each feature's behaviour can be expressed as a state-machine model. However, state machines are difficult to write; determining how to decompose behaviour into states is not obvious, different stakeholders will have different opinions on how to structure the state machine, and the state machines can easily become too complex. This thesis proposes a pattern for decomposing and structuring the model of a feature's behavioural requirements, based on modes of operation (e.g., Active, Inactive, Failed) that are common to features in multiple domains. Interestingly, the highest-level modes of the pattern can serve as a generic behavioural interface for all features that adhere to the pattern. The thesis proposes also several pattern extensions that provide guidance on how to structure the Active and Inactive behaviour of the feature. The pattern was applied to model the behavioural requirements of 21 automotive features that were specified in 7 production-grade requirements documents. The pattern was applicable to all 21 features, and the proposed generic feature interface was applicable to 50 out of 58 inter-feature references. A user study with 18 participants evaluated whether use of the pattern made it easier than otherwise to write state machines for features and whether feature state machines written with the help of the pattern are more readable than those written without the help of the pattern. The results of the study indicate that use of the pattern facilitates writing of feature state machines.

The purpose of this project is to help a growing company reform their static information system to a dynamic system compatible with growth. The solution consists of migrating the legacy system that they have in FileMaker to an open technology platform. To solve this specific problem “patterns” have been used and this project explains these general solution “patterns”. We understand patterns like a generic solution to persistent design problems. General solutions will not only be useful in this specific problem, but they will be useful in all kind of similar migration projects as well. This thesis gives a detailed explanation of how to apply these patterns into the AEMI specific problem and how they can be useful in the migration process. The solution of the problem then, is following the advice of “patterns” to achieve our goals; these goals are the requirements that are given from the company supervisor. After the migration process a redesign process must be done in order to organize the information. This redesign consists of organizing the migrated information as well as adding the new information in the correct place. This thesis focuses on the process of migrating from a legacy system to a MySQL system through the use of the generic solution called “patterns”.  The final result is a MySQL database with all the old and new information together in a more adaptable platform for the company’s scalability.

Android is a fast growing platform with a lot of users and applications on the market. In order to challenge the competition, a new software product should be designed carefully, conforming to the platform constraints and conveying to the user expectations. This research focuses on defining a suitable architecture design for the specific use case of interest, an Android application focused on location based data. The research process is backed up by a prototype application construction with features such as location based reminders and mobile communication with web services. Moreover, an analysis has been conducted on existing products with the proven quality, to extract information on current best practice implementations of several interesting features. Furthermore, the demand for targeting multiple platforms with the same application motivated a research on portability and reuse of code among different platforms. The constructed system is divided into a client-server pair. Opposite to the client (mobile) side, the server side analyzes the process of extending an existing architecture by integrating it with a web service project used for exchanging data with the mobile devices. Finally, the thesis is not strictly constrained to the given use case because it presents several general concepts of application design through architectural and design patterns.

Open source software is designed, developed and maintained by means of electronic media. These media include discussions on a variety of issues reflecting the evolution of a software system, such as reports on bugs and their fixes, new feature requests, design change, refactoring tasks, test plans, etc. Often this valuable information is simply buried as plain text in the mailing archives. We believe that email interactions collected prior to a product release are related to its source code modifications, or if they do not immediately correlate to change events of the current release, they might affect changes happening in future revisions. In this work, we propose a method to reason about the nature of software changes by mining and correlating electronic mailing list archives. Our approach is based on the assumption that developers use meaningful names and their domain knowledge in defining source code identifiers, such as classes and methods. We employ natural language processing techniques to find similarity between source code change history and history of public interactions surrounding these changes. Exact string matching is applied to find a set of common concepts between discussion vocabulary and changed code vocabulary. We apply our correlation method on two software systems, LSEdit and Apache Ant. The results of these exploratory case studies demonstrate the evidence of similarity between the content of free-form text emails among developers and the actual modifications in the code. We identify a set of correlation patterns between discussion and changed code vocabularies and discover that some releases referred to as minor should instead fall under the major category. These patterns can be used to give estimations about the type of a change and time needed to implement it.