Patterns of Learning Object Reuse in the Connexions Repository

Doctoral Dissertation abstract: Since the term learning object was first published, there has been either an explicit or implicit expectation of reuse. There has also been a lot of speculation about why learning objects are, or are not, reused. This study quantitatively examined the actual amount and type of learning object use, to include reuse, modification, and translation, within a single open educational resource repositoryâConnexions. The results indicate that about a quarter of used objects are subsequently reused, modified, or translated. While these results are repository specific, they represent an important first step in providing an empirical evaluation of the frequency and some reasons for reuse, as well as establishing metrics and terminology for future studies

Design and data analysis of kinome microarrays

Catalyzed by protein kinases, phosphorylation is the most important post-translational modification in eukaryotes and is involved in the regulation of almost all cellular processes. Investigating phosphorylation events and how they change in response to different biological conditions is integral to understanding cellular signaling processes in general, as well as to defining the role of phosphorylation in health and disease. A recently-developed technology for studying phosphorylation events is the kinome microarray, which consists of several hundred "spots" arranged in a grid-like pattern on a glass slide. Each spot contains many peptides of a particular amino acid sequence chemically fixed to the slide, with different spots containing peptides with different sequences. Each peptide is a subsequence of a full protein, containing an amino acid residue that is known or suspected to undergo phosphorylation in vivo, as well as several surrounding residues. When a kinome microarray is exposed to cell lysate, the protein kinases in the lysate catalyze the phosphorylation of the peptides on the array. By measuring the degree to which the peptides comprising each spot are phosphorylated, insight can be gained into the upregulation or downregulation of signaling pathways in response to different biological treatments or conditions. There are two main computational challenges associated with kinome microarrays. The first is array design, which involves selecting the peptides to be included on a given array. The level of difficulty of this task depends largely on the number of phosphorylation sites that have been experimentally identified in the proteome of the organism being studied. For instance, thousands of phosphorylation sites are known for human and mouse, allowing considerable freedom to select peptides that are relevant to the problem being examined. In contrast, few sites are known for, say, honeybee and soybean. For such organisms, it is useful to expand the set of possible peptides by using computational techniques to predict probable phosphorylation sites. In this thesis, existing techniques for the computational prediction of phosphorylation sites are reviewed. In addition, two novel methods are described for predicting phosphorylation events in organisms with few known sites, with each method using a fundamentally different approach. The first technique, called PHOSFER, uses a random forest-based machine-learning strategy, while the second, called DAPPLE, takes advantage of sequence homology between known sites and the proteome of interest. Both methods are shown to allow quicker or more accurate predictions in organisms with few known sites than comparable previous techniques. Therefore, the use of kinome microarrays is no longer limited to the study of organisms having many known phosphorylation sites; rather, this technology can potentially be applied to any organism having a sequenced genome. It is shown that PHOSFER and DAPPLE are suitable for identifying phosphorylation sites in a wide variety of organisms, including cow, honeybee, and soybean. The second computational challenge is data analysis, which involves the normalization, clustering, statistical analysis, and visualization of data resulting from the arrays. While software designed for the analysis of DNA microarrays has also been used for kinome arrays, differences between the two technologies prompted the development of PIIKA, a software package specifically designed for the analysis of kinome microarray data. By comparing with methods used for DNA microarrays, it is shown that PIIKA improves the ability to identify biological pathways that are differentially regulated in a treatment condition compared to a control condition. Also described is an updated version, PIIKA 2, which contains improvements and new features in the areas of clustering, statistical analysis, and data visualization. Given the previous absence of dedicated tools for analyzing kinome microarray data, as well as their wealth of features, PIIKA and PIIKA 2 represent an important step in maximizing the scientific value of this technology. In addition to the above techniques, this thesis presents three studies involving biological applications of kinome microarray analysis. The first study demonstrates the existence of "kinotypes" - species- or individual-specific kinome profiles - which has implications for personalized medicine and for the use of model organisms in the study of human disease. The second study uses kinome analysis to characterize how the calf immune system responds to infection by the bacterium Mycobacterium avium subsp. paratuberculosis. Finally, the third study uses kinome arrays to study parasitism of honeybees by the mite Varroa destructor, which is thought to be a major cause of colony collapse disorder. In order to make the methods described above readily available, a website called the SAskatchewan PHosphorylation Internet REsource (SAPHIRE) has been developed. Located at the URL http://saphire.usask.ca, SAPHIRE allows researchers to easily make use of PHOSFER, DAPPLE, and PIIKA 2. These resources facilitate both the design and data analysis of kinome microarrays, making them an even more effective technique for studying cellular signaling

Correctional Landscape Studies: Improving the Restorative Potential

The United States is the world’s leader in incarceration with 2.2 million people currently in the nation’s prisons and jails. On average, one-third of former offenders will return to prison for re-offence within three years of their release (Bureau of Justice Statistics 2018). This cycle is known as recidivism, and demonstrates a major reflection of the criminal justice system’s failure to provide rehabilitation that meets the needs of the incarcerated population. However, horticultural therapy in prison may offer a sliver of hope. Also referred to as Green Prison Programs (GPPs), studies indicate that participants in these programs gain valuable job skills and improved emotional well-being, reducing the likelihood of former offenders returning to criminal behavior after imprisonment (Jiler 2006, Khatib & Krasny 2015, Insight Garden Program 2019). Furthermore, growing interest in the application of thoughtfully designed landscapes integrating principles in restorative environments reveals similar benefits not only to offenders, but to correctional staff and visitors (Stevens, et al. 2018). This project synthesizes current trends at the intersection of landscape architecture, restorative environments, and prison reform, and offers a series of adaptable design recommendations aimed to benefit all who experience the correctional landscape. In collaboration with the Massachusetts Department of Corrections (MA DOC), the Massachusetts Correctional Institution of Framingham (MCI-F) serves as a demonstration site for the application of these recommendations at both a master plan scale and 3-acre focus area. Community engagement with MCI-F inmates and staff, coupled with analysis of case studies reveals normative design and trauma-informed design strategies as an added layer of consideration for designers. Foundational to this project is environmental justice and human dignity, which asserts the belief that despite the crimes of convicted offenders, people in jail deserve access to the benefits of spending time in nature. Although landscape architecture cannot solve the social and political issues that have led to mass incarceration in the U.S., this project demonstrates how landscape architects can play a pivotal role in helping inmates and correctional officers experience the personal and societal benefits of thoughtfully designed landscapes

Dissection of pleiotropic effects in genome-wide association studies of phenotypes related to cardiometabolic health

In the past seven years, Genome-Wide Association Studies (GWAS) have identified hundreds of variants associated with cardiometabolic quantitative traits and diseases. Many genetic loci appear to harbour variants associated with multiple phenotypes (cross-phenotype associations, CP). CP associations highlight that phenotypes may share common underlying genetic mechanisms that might, or might not, be consistent with epidemiological expectations and, therefore, add complexity to the relationships between human phenotypes. Pleiotropy occurs when the same genetic causal element affects more than one phenotype “in parallel” and can explain the presence of CP associations. It can appear at a single variant level, where a single causal variant is related to multiple phenotypes, or at a locus level, that is when multiple variants in the same gene or locus are associated with different phenotypes by affecting the same functional element. However, other potential genetic mechanisms, that can explain CP associations, exist. Among them, mediation occurs when a genetic variant is directly associated with a phenotype and that phenotype is itself causal for a second phenotype or more phenotypes; multi-phenotype allelic heterogeneity is a phenomenon which involves independent uncorrelated variants within the same locus which cause changes in multiple phenotypes, by affecting them through independent pathways related to distinct functional elements. The identification and characterisation of CP associations across the genome may help uncovering the mechanistic basis of physiological processes that underlie variability of cardiometabolic quantitative traits, and of pathogenetic processes leading to metabolic disorders. The definition of specific patterns of effect combinations on cardiometabolic phenotypes will highlight novel biological pathways, targets for translational research, for therapeutic intervention, and for the understanding of the pathophysiology of human metabolism. Based on this hypothesis, and in collaboration with the Cross-Consortia pleiotropy group and with the European Network for Genetic and Genomic Epidemiology (ENGAGE) consortium, my PhD project focused on dissection of CP effects, pleiotropy in particular, at common variants across the genome in association with cardiometabolic phenotypes. The objective was to improve our understanding of the extent of shared genetics between cardiometabolic phenotypes and of the influences of DNA sequence variation on risk of metabolic diseases, considering phenotypes as a range of inter-related manifestations of biological mechanisms rather than as isolated events. My research has been divided into three sub-projects: Project 1: Clustering and pathway analysis of univariate GWAS results for the detection of pleiotropic effects. We explored multi-phenotype effects at hundreds of established cardiometabolic genetic variants from published univariate GWAS meta-analyses on more than 20 respective phenotypes, by defining clusters of loci with similar multiple effects, comparing them to known epidemiological expectations, and identifying enriched biological networks within the most interesting groups of loci. Our results highlighted that many variants at cardiometabolic loci have multiple associations that characterise different aspects of metabolism. Cardiometabolic loci can be grouped according to their shared multi-phenotype effects and metabolic syndrome represents just one possible combination; in fact, several other unexpected combinations might be observed, for example healthy obesity/unhealthy leanness. We also highlighted that genetic loci with similar cardiometabolic effects are involved in shared biological pathways. Some of these may be expected, for instance, regulation of lipids metabolism or cholesterol transport for groups of loci with strong effects on lipids, and circulatory system processes for genes near blood pressure-association signals. Sometimes groups of loci affected fundamental cell functions, such as regulation of cellular processes, for the loci with effects on obesity and anthropometric traits. The enriched connectivity within pathway networks revealed new potential candidate genes and tissues of action that are more likely to have causal effect on phenotypes. Project 2: Validating pleiotropy and analysis of locus architecture in potential pleiotropic regions. We aimed to dissect the architecture of established cardiometabolic loci showing multiple associations for a better definition of the underlying mechanisms of multi-phenotype effects and for the discernment of potential pleiotropy from allelic heterogeneity. To this aim, we applied an approximate conditional analysis, based on observed linkage disequilibrium patterns, which led us to the discovery of multiple associations at adjacent variants that underlie the same genetic cause for variability of different phenotypes. Our results also highlighted that a substantial proportion of metabolic loci incorporate complex patterns of multi-phenotype allelic heterogeneity, thus suggesting an important contribution of this mechanism into cross-phenotype effects. Project 3: Application of a multivariate statistical approach for the study of pleiotropy within cardiometabolic phenotypes. We developed and applied a statistical strategy for joint multivariate analysis of multiple correlated phenotypes using individual genetic data from the ENGAGE consortium to discover new uncovered multiple associations and to follow-up GWAS meta-analysis at two loci, FTO and FADS1. Using this approach we were able to take into account correlation between phenotypes, and we achieved a boost in power; moreover, we improved precision of parameter estimates and of the identification of novel candidate genes. Our results allowed us to identify several variants jointly associated with multiple lipid traits and body mass index. Our approach was useful for the identification of mediation: we, in fact, confirmed mediation underlying causal relationship between adiposity and other cardiometabolic phenotypes at the FTO locus. Additionally, we demonstrated that multiple effects on cardiometabolic phenotypes attributable to the FADS1 locus are mediated by its independent, thus pleiotropic, effect on total cholesterol and triglycerides. In conclusion, we applied several statistical approaches which allowed dissecting suggestive CP effects and their mechanisms, including pleiotropy, mediation and allelic heterogeneity. Our analyses have demonstrated the complexity of the relationships between cardiometabolic phenotypes related to the variability of both, underlying genetic mechanisms and genetic loci architecture

Intégration d'instructions data-parallèles dans le langage PSC et compilation pour processeur SIMD (INTEL SSE)

II existe des instructions data-parallèles dans les processeurs modernes. Ces instructions permettent d'effectuer la même opération sur plusieurs données différentes en parallèle. Présentement il est difficile de programmer des logiciels qui utilisent ces instructions data-parallèles avec les solutions existantes, Nous avons donc exploré l'utilisation d'un langage destiné à la programmation des circuits parallèles comme les FPGA (Field Programmable Gate Array) pour fabriqué un logiciel qui permet d'utiliser ces instructions data-parallèles de manière simple et efficace. Un langage de haut niveau pour la programmation des FPGA. le langage psC- Parallel and Synchronous C- a été choisi, Sa syntaxe proche du C, son paradigme entièrement parallèle et la disponibilité du code source ont justifié ce choix, II y a plusieurs années, les gens pensaient qu'aujourd'hui l'optimisation ne serait plus aussi importante qu'elle l'était pour eux. Ils disaient que la quantité de mémoire et la puissance de calculs des processeurs ferait en sorte que le gain en temps ne vaudrait pas l'effort de programmation nécessaire pour programmer du code optimisé. Maintenant, nous savons que ce n'est pas le cas. Les processeurs ont certes eu un gain de performance important, mais les tâches qu'ils accomplissent nécessitent de plus en plus de puissance de calculs et de mémoire. Aujourd'hui, une bonne partie de la puissance de calculs s'obtient par l'utilisation des instructions data-parallèles disponibles dans les processeurs modernes. Pour inclure ces instructions data-parallèles dans un logicieL il n'y a pas beaucoup d'alternatives disponibles. Ce travail a consisté à réaliser un compilateur complet pour machine SIMD. Une nouvelle syntaxe permettant de supporter les instructions data-parallèles a été définie et intégrée à celle du langage psC. L'algorithme de génération de code assembleur pour les instructions data-parallèles de type SSE d'Intel a été implémenté et testé. Finalement, trois applications ont été programmées et les performances de rapidité d'exécution comparées à diverses méthodes classiques de programmation. Les résultats montrent que les performances obtenu par le langage psC est toujours situé entre celui obtenu par un expert codant en langage assembleur et celui obtenu par les compilateurs C et C++, Ceci correspond à ce qui était désiré. En conclusion, ce travail de recherche a démontré qu'il était possible d'utiliser un langage HL-HDL (High Level Hardware Description Language) pour générer du code qui bénéficie des instructions data-parallèles. Le gain en performance de F implementation psC est présenté pour tous les cas étudié, et se rapproche de F implementation assembleur qui est le maximum atteignable

Microarray image processing: A novel neural network framework

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods. Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.Aleppo University, Syri