Collective supervision of Master's thesis students: Experiences, expectations and new departures from the Security Risk Management programme

Collective supervision has become a common way to provide supervision at schools of higher education. This is also true for the supervision of master’s thesis students on the Master’s Programme, Security Risk Management at the University of Copenhagen. Based on experiences with collective supervision of master thesis students, this paper engages with the many understandings of feedback and learning in play in the teaching situation. In the scholarly literature, features such as multivoicedness, dialogue, process- and student-orientation are empha-sized when addressing collective supervision. Yet, our findings show a clash of expectations between a majority of the students (and supervisors) and these ide-als of collective supervision. Indeed, many students still believe feedback should be troubleshooting and product-oriented. In the final part of the paper we out-line a handful of ideas on how to improve future collective supervision to explicit-ly address the gap between expectations and conceptions of good feedback

Robotter i Folkeskolen: Begrundelser, visioner, faktisk brug og udfordringer i normalklasser

I denne rapport præsenter vi resultaterne af et mini-projekt koblet til Technucation-projektet  (2011-2015) med yderligere støtte fra DPU, Aarhus Universitet. I forbindelse med Technucations forskning, og vores øvrige forskning i robotter som fremtidsteknologi, bliver vi opmærksomme på, at mange skoler enten har lånt eller købt robotter i uddannelsesøjemed. Der er mange ambitioner bag robotters indtog i skolerne, ligesom der investeres store summer i det, og mange interessenter er involverede (såsom skoler, kommuner, udviklere, sælgere, fonde mv.).Projektet Robotter i Folkeskolen (RIF) blev igangsat i sommeren 2015 af forskere fra forskningsprogrammet Fremtidsteknologi, Kultur og Læreprocesser, samt en række studenterassistenter fra DPU, Aarhus Universitet. Hovedrapporten har to dele, der er blevet bearbejdet hver for sig: en del der omhandler robotter almenundervisningen og en del der omhandler robotter i specialundervisningen. I denne rapport præsenteres fortrinsvis robotter i almenundervisningen

Quantitative metric profiles capture three-dimensional temporospatial architecture to discriminate cellular functional states

<p>Abstract</p> <p>Background</p> <p>Computational analysis of tissue structure reveals sub-visual differences in tissue functional states by extracting quantitative signature features that establish a diagnostic profile. Incomplete and/or inaccurate profiles contribute to misdiagnosis.</p> <p>Methods</p> <p>In order to create more complete tissue structure profiles, we adapted our cell-graph method for extracting quantitative features from histopathology images to now capture temporospatial traits of three-dimensional collagen hydrogel cell cultures. Cell-graphs were proposed to characterize the spatial organization between the cells in tissues by exploiting graph theory wherein the nuclei of the cells constitute the <it>nodes </it>and the approximate adjacency of cells are represented with <it>edges</it>. We chose 11 different cell types representing non-tumorigenic, pre-cancerous, and malignant states from multiple tissue origins.</p> <p>Results</p> <p>We built cell-graphs from the cellular hydrogel images and computed a large set of features describing the structural characteristics captured by the graphs over time. Using three-mode tensor analysis, we identified the five most significant features (metrics) that capture the compactness, clustering, and spatial uniformity of the 3D architectural changes for each cell type throughout the time course. Importantly, four of these metrics are also the discriminative features for our histopathology data from our previous studies.</p> <p>Conclusions</p> <p>Together, these descriptive metrics provide rigorous quantitative representations of image information that other image analysis methods do not. Examining the changes in these five metrics allowed us to easily discriminate between all 11 cell types, whereas differences from visual examination of the images are not as apparent. These results demonstrate that application of the cell-graph technique to 3D image data yields discriminative metrics that have the potential to improve the accuracy of image-based tissue profiles, and thus improve the detection and diagnosis of disease.</p

Multiscale Feature Analysis of Salivary Gland Branching Morphogenesis

Pattern formation in developing tissues involves dynamic spatio-temporal changes in cellular organization and subsequent evolution of functional adult structures. Branching morphogenesis is a developmental mechanism by which patterns are generated in many developing organs, which is controlled by underlying molecular pathways. Understanding the relationship between molecular signaling, cellular behavior and resulting morphological change requires quantification and categorization of the cellular behavior. In this study, tissue-level and cellular changes in developing salivary gland in response to disruption of ROCK-mediated signaling by are modeled by building cell-graphs to compute mathematical features capturing structural properties at multiple scales. These features were used to generate multiscale cell-graph signatures of untreated and ROCK signaling disrupted salivary gland organ explants. From confocal images of mouse submandibular salivary gland organ explants in which epithelial and mesenchymal nuclei were marked, a multiscale feature set capturing global structural properties, local structural properties, spectral, and morphological properties of the tissues was derived. Six feature selection algorithms and multiway modeling of the data was performed to identify distinct subsets of cell graph features that can uniquely classify and differentiate between different cell populations. Multiscale cell-graph analysis was most effective in classification of the tissue state. Cellular and tissue organization, as defined by a multiscale subset of cell-graph features, are both quantitatively distinct in epithelial and mesenchymal cell types both in the presence and absence of ROCK inhibitors. Whereas tensor analysis demonstrate that epithelial tissue was affected the most by inhibition of ROCK signaling, significant multiscale changes in mesenchymal tissue organization were identified with this analysis that were not identified in previous biological studies. We here show how to define and calculate a multiscale feature set as an effective computational approach to identify and quantify changes at multiple biological scales and to distinguish between different states in developing tissues

Microbial diversity in bioaerosol samples causing ODTS compared to reference bioaerosol samples as measured using Illumina sequencing and MALDI-TOF

AbstractThe importance of the microbial diversity of bioaerosols in relation to occupational exposure and work related health symptoms is not known. The aim of this paper is to gain knowledge on the bacterial and fungal communities in dust causing organic dust toxic syndrome (ODTS) and in reference dust not causing ODTS. Bacterial and fungal communities were described in personal exposure samples from grass seed workers developing ODTS, in dust generated from grass seeds causing ODTS and in dust generated from reference seeds not causing ODTS. Amplicon sequencing of the bacterial 16S rRNA gene and the fungal ITS region, as well as matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) were used for identification of fungi and bacteria in personal exposure samples and in dust samples from grass seeds causing ODTS and in dust from reference grass seeds. Furthermore, activities of enzymes were measured in the same samples.The sequencing data revealed more than 150 bacterial and 25 fungal genera present in each sample. Streptomyces spp., Aspergillus fumigatus and Rhizopus microsporus were dominating in the dust causing ODTS but not in the reference dust. The dustiness in terms of Mucor sp. and R. microsporus were 100–1000 times higher for problematic seeds compared to reference seeds. The bacterial species in the dust causing ODTS included pathogenic species such as Klebsiella pneumonia and Streptomyces pneumonia, and it contained increased concentrations of total protein, serine protease, chitinase, and β-glucosidase. Twenty-three bacterial genera covered more than 50% of the total reads in the personal and problematic seed dust. These 23 genera accounted for less than 7% of the total reads in the reference seed dust. The microbial community of the dust from the problematic seeds showed great similarities to that from the personal air samples from the workers. In conclusion, we have shown for the first time a shift in the microbial community in aerosol samples that caused ODTS compared to the reference samples that did not cause the ODTS. Furthermore, elevated enzyme activities were found in the dust causing ODTS