What is the contribution of personal information management systems (PIMS) to the Working Model and personal work system of knowledge workers?

The thesis reports research into a phenomenon which it calls the personal working model of an individual knowledge worker. The principal conjecture addressed in this thesis is that each of us has a personal working model which is supported by a personal work system enabled by a personal information management system. For some people, these are well defined; for most they are not even explicit. By means of structured self-reflection aided by conceptual knowledge modelling within the context of a process of action learning they can be improved. That personal working model is predicted by Ashby's law of requisite variety and by the good regulator theorem of Conant and Ashby. The latter theorem states that the only good regulator of a system is a model of that system. The thesis and the work it reports result from a systemic approach to identifying the personal information management system and personal work system which together contribute to the personal working model. Starting with abductive conjecture, the author has sought to understand what models are and to explore ways in which those models can themselves be expressed. The thesis shows how a new approach to the conceptual modelling of aspects of the personal knowledge of knowledge worker was designed, built and then used. Similarly, the actual data used by a knowledge worker had to be stored, and for this purpose a personal information management system was also designed. Both these artefacts are evaluated in accordance with principles drawn from the literature of design science research. The research methodology adopted in the first phase of the research now ending also included a relatively novel approach in which the PhD student attempted to observe himself over the last five years of his PhD research – this approach is sometimes called autoethnography. This autoethnographic element is one of a number of methods used within an overall framework grounded by the philosophical approach called critical realism. The work reported in the thesis is initial exploratory research which, it is planned, will continue in empirical action research involving mentored action learning undertaken by professional knowledge workers

Taking aim at moving targets in computational cell migration

Cell migration is central to the development and maintenance of multicellular organisms. Fundamental understanding of cell migration can, for example, direct novel therapeutic strategies to control invasive tumor cells. However, the study of cell migration yields an overabundance of experimental data that require demanding processing and analysis for results extraction. Computational methods and tools have therefore become essential in the quantification and modeling of cell migration data. We review computational approaches for the key tasks in the quantification of in vitro cell migration: image pre-processing, motion estimation and feature extraction. Moreover, we summarize the current state-of-the-art for in silico modeling of cell migration. Finally, we provide a list of available software tools for cell migration to assist researchers in choosing the most appropriate solution for their needs

ICT for development reconsidered: a critical realist approach to the strategic context in Kenya's transition to e-governance

This study contributes to critical information systems research understanding of the broader strategic context of information systems initiatives in developing countries. It investigates contextual influences with structural impacts that may lead to instabilities and discontinuities in the immediate project context using a critical realist paradigm. It was informed by literature on development as discourse, ICT4D policy and technology transfer, E-Government adoption, and information systems research paradigms and applications in developing countries. A disconnection was observed between ICT4D policy practice that favors positivist technology diffusion models and research findings that suggest interpretive and critical contextual approaches. A theoretical framework was developed to reconsider ICT4D from a postcolonial country perspective by integrating critiques of modernity from Critical realism and postcolonial theory. An empirical case study investigation of change in Kenya‘s transition to E-Governance was then conducted and analyzed using a critical realist research framework, the Morphogenetic approach, supplemented by Q-methodology to study subjectivity. Finally ICT change was interpreted using critical realist concepts for structure, culture, and agency, with an overriding direction towards greater freedom. The main research contribution is a new approach to ICT4D where change is conceived within a dialectical framework that assumes people are moral and ethical beings possessing values. Research findings have implications for understanding the strategic context of E-Governance and ICT4D, time and temporality in contextual integrative frameworks, and suggest an alternative approach to strategy analysis in situations of rapid political and institutional change. They highlight the importance of political leaders and development agencies as mediators and interpreters of the strategic context. Development was conceived as a dialectical process towards transformative praxis, which together with the suggested approach to the strategic context, may require us to rethink the meaning of IS project success or failure in postcolonial developing countries

Evolution of soil solution chemistry in temperate forests under decreasing atmospheric deposition in Flanders

Elevated depositions of non-marine sulphate (SO42-) and inorganic nitrogen (N), as a consequence of air pollution, resulted in a progressive acidification and eutrophication of Flemish forests. Since the 1980s emission abatement reduced the acidifying and eutrophying emissions and depositions in Flanders. This thesis aimed to evaluate the impact of the evolution in depositions on soil solution chemistry, using long-term data collected in 5 plots of the ICP Forests monitoring (Level II) network in Flanders. The sharp decrease of SO42- and ammonium (NH4+) depositions made that abiotic N status started to improve and acidification slowed down during the past two decades. However, N depositions are still far above the critical loads for ectomycorrhiza and epiphytic lichens. Given the still very low soil pH (3.5‒4.5) unfavourable for microbial life, the generally observed tendency of increased dissolved organic carbon (DOC) and nitrogen (DON) mobility is likely a direct result of lowered ionic strength and partly rise in pH. Abiotic recovery is delayed by a simultaneous decrease in the deposition of base cations (Ca2+, K+ and Mg2+) and SO42- desorption. Biotic recovery is lagging behind on the changes in soil solution chemistry, as indicated by the stable but unbalanced tree mineral nutrition. Acidification and eutrophication will likely continue to produce after-effects for many decades. The results from this thesis indicate that the Programmatic Approach to Nitrogen (PAS) is partly missing its target for oxidized N compounds and that extra measures will be necessary to bring NOx emissions at an acceptable level

Computational Analysis of RNAi Screening Data to Identify Host Factors Involved in Viral Infection and to Characterize Protein-Protein Interactions

The study of gene functions in a variety of different treatments, cell lines and organisms has been facilitated by RNA interference (RNAi) technology that tracks the phenotype of cells after silencing of particular genes. In this thesis, I describe two computational approaches developed to analyze the image data from two different RNAi screens. Firstly, I developed an alternative approach to detect host factors (human proteins) that support virus growth and replication of cells infected with the Hepatitis C virus (HCV). To identify the human proteins that are crucial for the efficiency of viral infection, several RNAi experiments of viral-infected cells have been conducted. However, the target lists from different laboratories have shown only little overlap. This inconsistency might be caused not only by experimental discrepancies, but also by not fully explored possibilities of the data analysis. Observing only viral intensity readouts from the experiments might be insufficient. In this project, I describe our computational development as a new alternative approach to improve the reliability for the host factor identification. Our approach is based on characterizing the clustering of infected cells. The idea is that viral infection is spread by cell-cell contacts, or at least advantaged by the vicinity of cells. Therefore, clustering of the HCV infected cells is observed during spreading of the infection. We developed a clustering detection method basing on a distance-based point pattern analysis (K-function) to identify knockdown genes in which the clusters of HCV infected cells were reduced. The approach could significantly separate between positive and negative controls and found good correlations between the clustering score and intensity readouts from the experimental screens. In comparison to another clustering algorithm, the K-function method was superior to Quadrat analysis method. Statistical normalization approaches were exploited to identify protein targets from our clustering-based approach and the experimental screens. Integrating results from our clustering method, intensity readout analysis and secondary screen, we finally identified five promising host factors that are suitable candidate targets for drug therapy. Secondly, a machine learning based approach was developed to characterize protein-protein interactions (PPIs) in a signaling network. The characterization of each PPI is fundamental to our understanding of the complex signaling system of a human cell. Experiments for PPI identification, such as yeast two-hybrid and FRET analysis, are resource-intensive, and, therefore, computational approaches for analysing large-scale RNAi knockdown screens have become an important pursuit of inferring the functional similarities from the phenotypic similarities of the down-regulated proteins. However, these methods did not provide a more detailed characterization of the PPIs. In this project, I developed a new computational approach that is based on a machine learning technique which employs the mitotic phenotypes of an RNAi screen. It enables the identification of the nature of a PPI, i.e., if it is of rather activating or inhibiting nature. We established a systematic classification using Support Vector Machines (SVMs) that was based on the phenotypic descriptors and used it to classify the interactions that activate or inhibit signal transduction. The machines yielded promising results with good performance when integrating different sets of published descriptors and our own developed descriptors calculated from fractions of specific phenotypes, linear classification of phenotypes, and phenotypic distance to distinct proteins. A comprehensive model generated from the machines was used for further predictions. We investigated the nature of pairs of interacting proteins and generated a consistency score that enhanced the precisions of the classification results. We predicted the activating/inhibiting nature for 214 PPIs with high confidence in signaling pathways and enabled to identify a new subgroup of chemokine receptors. These findings might facilitate an enhanced understanding of the cellular mechanisms during inflammation and immunologic responses. In summary, two computational approaches were developed to analyze the image data of the different RNAi screens: 1) a clustering-based approach was used to identify the host factors that are crucial for HCV infection; and 2) a machine learning-based approach with various descriptors was employed to characterize PPI activities. The results from the host factor analysis revealed novel target proteins that are involved in the spread of the HCV. In addition, the results of the characterization of the PPIs lead to a better understanding of the signaling pathways. The two large-scale RNAi data were successfully analyzed by our established approaches to obtain new insights into virus biology and cellular signaling