A Theoretical Framework to Develop a Research Agenda for Information Systems Innovation

This article is a response to the assessment by IS scholars that there are significant research questions to be addressed in the important topic of information systems innovation. For example, Swanson concludes that current theory explains little about IS innovation; Avgerou describes it as a relatively unexplored subject, and Fichman identifies signs of exhaustion in the current research agenda. The result of our analysis is an adaptation of ecological systems theory (EST) in order to apply it to the IS innovation landscape. We then build on the theoretical framework to propose an agenda for future research in terms of research directions, research themes, and study designs. Finally, implications for researchers and practitioners are discussed

A new organisational ecology for open innovation: the Innovation Value Institute

In this paper a new theoretical framework for Innovation Eco-Systems is proposed and the application of the model in the Innovation Value Institute (IVI) is described. The IVI is engaged in the development of the IT-Capability Maturity Model which is a response to the need for a more systematic, comprehensive approach to managing IT in a manner that meets the requirements of practicing IT professionals

A Critical Role for Syk in Signal Transduction and Phagocytosis Mediated by Fcγ Receptors on Macrophages

Receptors on macrophages for the Fc region of IgG (FcγR) mediate a number of responses important for host immunity. Signaling events necessary for these responses are likely initiated by the activation of Src-family and Syk-family tyrosine kinases after FcγR cross-linking. Macrophages derived from Syk-deficient (Syk−) mice were defective in phagocytosis of particles bound by FcγRs, as well as in many FcγR-induced signaling events, including tyrosine phosphorylation of a number of cellular substrates and activation of MAP kinases. In contrast, Syk− macrophages exhibited normal responses to another potent macrophage stimulus, lipopolysaccharide. Phagocytosis of latex beads and Escherichia coli bacteria was also not affected. Syk− macrophages exhibited formation of polymerized actin structures opposing particles bound to the cells by FcγRs (actin cups), but failed to proceed to internalization. Interestingly, inhibitors of phosphatidylinositol 3-kinase also blocked FcγR-mediated phagocytosis at this stage. Thus, PI 3-kinase may participate in a Syk-dependent signaling pathway critical for FcγR-mediated phagocytosis. Macrophages derived from mice deficient for the three members of the Src-family of kinases expressed in these cells, Hck, Fgr, and Lyn, exhibited poor Syk activation upon FcγR engagement, accompanied by a delay in FcγR-mediated phagocytosis. These observations demonstrate that Syk is critical for FcγR-mediated phagocytosis, as well as for signal transduction in macrophages. Additionally, our findings provide evidence to support a model of sequential tyrosine kinase activation by FcγR's analogous to models of signaling by the B and T cell antigen receptors

CytoCensus, mapping cell identity and division in tissues and organs using machine learning.

A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends convenient 2D 'point-and-click' user training to 3D detection of cells in challenging datasets with ill-defined cell boundaries. In tests on such datasets, CytoCensus outperforms other freely available image analysis software in accuracy and speed of cell detection. We used CytoCensus to count stem cells and their progeny, and to quantify individual cell divisions from time-lapse movies of explanted Drosophila larval brains, comparing wild-type and mutant phenotypes. We further illustrate the general utility and future potential of CytoCensus by analysing the 3D organisation of multiple cell classes in Zebrafish retinal organoids and cell distributions in mouse embryos. CytoCensus opens the possibility of straightforward and robust automated analysis of developmental phenotypes in complex tissues

Re-structuring of marine communities exposed to environmental change

Species richness is the most commonly used but controversial biodiversity metric in studies on aspects of community stability such as structural composition or productivity. The apparent ambiguity of theoretical and experimental findings may in part be due to experimental shortcomings and/or heterogeneity of scales and methods in earlier studies. This has led to an urgent call for improved and more realistic experiments. In a series of experiments replicated at a global scale we translocated several hundred marine hard bottom communities to new environments simulating a rapid but moderate environmental change. Subsequently, we measured their rate of compositional change (re-structuring) which in the great majority of cases represented a compositional convergence towards local communities. Re-structuring is driven by mortality of community components (original species) and establishment of new species in the changed environmental context. The rate of this re-structuring was then related to various system properties. We show that availability of free substratum relates negatively while taxon richness relates positively to structural persistence (i.e., no or slow re-structuring). Thus, when faced with environmental change, taxon-rich communities retain their original composition longer than taxon-poor communities. The effect of taxon richness, however, interacts with another aspect of diversity, functional richness. Indeed, taxon richness relates positively to persistence in functionally depauperate communities, but not in functionally diverse communities. The interaction between taxonomic and functional diversity with regard to the behaviour of communities exposed to environmental stress may help understand some of the seemingly contrasting findings of past research

BayFlux: A Bayesian Method to Quantify Metabolic Fluxes and their Uncertainty at the Genome Scale.

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty

Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models

In combination with advanced mechanistic modeling and the generation of high-quality multi-dimensional data sets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can complement each other and be used in a combined approach to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets and produce a large combinatorial library of metabolic pathway designs with different promoters which, once phenotyped, provide the basis for machine learning algorithms to be trained and used for new design recommendations. The approach enables successful forward engineering of aromatic amino acid metabolism in yeast, with the new recommended designs improving tryptophan production by up to 17% compared to the best designs used for algorithm training, and ultimately producing a total increase of 106% in tryptophan accumulation compared to optimized reference designs. Based on a single high-throughput data-generation iteration, this study highlights the power of combining mechanistic and machine learning models to enhance their predictive power and effectively direct metabolic engineering efforts

Even-parity autoionizing states in the extreme-ultraviolet photoabsorption spectra of Mg, Al⁺, and Si²⁺

The dual-laser-produced plasma (DLP) photoabsorption technique has been used to study 2p→3s excitations in the isoelectronic species Mg, Al+, and Si2+ prepared in the excited configuration 2p63s3p. The autoionizing upper states belong to the 2p53s23p even-parity configuration. The versatility of the technique is demonstrated through a careful combination of space- and time-resolved photoabsorption scans. Plasma conditions optimized for the observation of the inaccessible parity regime were successfully reproduced along the isoelectronic sequence of interest. All the observed transitions were interpreted with the help of multiconfigurational atomic structure calculations. In the case of magnesium, the photoabsorption data are compared with the ejected-electron spectra excited by low-energy electron impact of Pejcev et al. [J. Phys. B 10, 2389 (1977)]

Recording problems and diagnoses in clinical care: developing guidance for healthcare professionals and system designers.

Funder: Professional Record Standards BodyFunder: Royal College of Physicians; FundRef: http://dx.doi.org/10.13039/501100000395BACKGROUND: Accurate recording of problems and diagnoses in health records is key to safe and effective patient care, yet it is often done poorly. Electronic health record systems vary in their functionality and ease of use, and are not optimally designed for easy recording and sharing of clinical information. There is a lack of professional consensus and guidance on how problems and diagnoses should be recorded. METHODS: The Professional Record Standards Body commissioned work led by the Royal College of Physicians Health Informatics Unit to carry out a literature review, draft guidance, carry out an online consultation and round table discussion, and produce a report including recommendations for systems. A patient workshop was held to explore patient preferences for mechanisms for sharing diagnosis information between primary and secondary care. RESULTS: Consensus was reached among medical specialties on key elements of diagnosis recording, and draft guidance was produced ready for piloting in a variety of care settings. Patients were keen for better ways for diagnosis information to be shared. DISCUSSION: Improving the recording of diagnoses and problems will require a major effort of which the new guidance is only a part. The guidance needs to be embedded in training, and clinical systems need to have improved, standardised functionality. Front-line clinicians, specialist societies, clinical informaticians and patients need to be engaged in developing information models for diagnoses to support care and research, accessible via user-friendly interfaces