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Changing Categorical Work in Healthcare: the Use of Patient-Generated Health Data in Cancer Rehabilitation: Cerna Katerina et al.
(2020) Cerna, Katerina; Grisot, Miria; Islind, Anna Sigridur; Lindroth, Tomas; Lundin, Johan; Steineck, Gunnar; Department of Computer Science
Verk
Algorithmic Work: The Impact of Algorithms on Work with Social Media
(2020) Norström, Livia; Islind, Anna Sigriður; Lundh Snis, Ulrika M; Department of Computer Science
Verk
Co-Designing a Digital Platform with Boundary Objects: Bringing Together Heterogeneous Users in Healthcare
(2019) Islind, Anna Sigridur; Lindroth, Tomas; Lundin, Johan; Steineck, Gunnar; Department of Computer Science
Verk
Computational analysis of the effects of nitrogen source and sin1 knockout on biosilica morphology in the model diatom Thalassiosira pseudonana
(2021-12) Horvát, Szabolcs; Fathima, Adeeba; Görlich, Stefan; Schlierf, Michael; Modes, Carl D.; Kröger, Nils; Department of Computer Science
Morphogenesis of the silica based cell walls of diatoms, a large group of microalgae, is a paradigm for the self-assembly of complex 3D nano- and microscale patterned inorganic materials. In recent years, loss-of-function studies using genetic manipulation were successfully applied for the identification of genes that guide silica morphogenesis in diatoms. These studies revealed that the loss of one gene can affect multiple morphological parameters, and the morphological changes can be rather subtle being blurred by natural variations in morphology even within the same clone. Both factors severely hamper the identification of morphological mutants using subjective by-eye inspection of electron micrographs. Here we have developed automated image analysis for objectively quantifying the morphology of ridge networks and pore densities from numerous electron micrographs of diatom biosilica. This study demonstrated differences in ridge network morphology and pore density in diatoms growing on ammonium rather than nitrate as the sole nitrogen source. Furthermore, it revealed shortcomings in previous by-eye evaluation of the biosilica phenotype of the silicanin-1 knockout mutant. We anticipate that the computational methods established in the present work will be invaluable for unraveling genotype–phenotype correlations in diatom biosilica morphogenesis.
Verk
Connectedness matters : construction and exact random sampling of connected networks
(2021-03) Horvát, Sz; Modes, Carl D.; Department of Computer Science
We describe a new method for the random sampling of connected networks with a specified degree sequence. We consider both the case of simple graphs and that of loopless multigraphs. The constraints of fixed degrees and of connectedness are two of the most commonly needed ones when constructing null models for the practical analysis of physical or biological networks. Yet handling these constraints, let alone combining them, is non-trivial. Our method builds on a recently introduced novel sampling approach that constructs graphs with given degrees independently (unlike edge-switching Markov chain Monte Carlo methods) and efficiently (unlike the configuration model), and extends it to incorporate the constraint of connectedness. Additionally, we present a simple and elegant algorithm for directly constructing a single connected realization of a degree sequence, either as a simple graph or a multigraph. Finally, we demonstrate our sampling method on a realistic scale-free example, as well as on degree sequences of connected real-world networks, and show that enforcing connectedness can significantly alter the properties of sampled networks.

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