Collaborative Teacher Educator Professional Development in Europe: Different Voices, One Goal

In this paper we present an embedded case study focussed on the learning activities provided for and by us through our involvement in an international forum focused on the professional development of teacher educators. The aim of this research was to get more insights into the complicated processes of professional learning across national borders. Data included personal narratives about learning and documentary analysis of written accounts of the forums’ activities. Following a collaborative self-study approach we utilised an interactive exploration of the data, using coding techniques derived from grounded theory. We conclude that our professional learning can be seen through two inter-related perspectives. The first perspective is the interplay between our own learning and the ways in which we want to support colleagues in their professional development. The second perspective is the reciprocal effect of working in national as well as in transnational contexts. By studying our professional learning processes we developed insights in how a shared communal international forum can be established without losing individual voices and national perspectives. Moreover, by our involvement in an international forum we also continue to develop our own self-understanding as ‘educators of teacher educators’

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment. Tumor-infiltrating lymphocytes (TILs) were identified from standard pathology cancer images by a deep-learning-derived \u201ccomputational stain\u201d developed by Saltz et al. They processed 5,202 digital images from 13 cancer types. Resulting TIL maps were correlated with TCGA molecular data, relating TIL content to survival, tumor subtypes, and immune profiles

Estimation of multiple illuminants from a single image of arbitrary known geometry

Abstract. We present a new method for the detection and estimation of multiple illuminants, using one image of any object withknown geometry and Lambertian reflectance. Our method obviates the need to modify the imaged scene by inserting calibration objects of any particular geometry, relying instead on partial knowledge of the geometry of the scene. Thus, the recovered multiple illuminants can be used both for image-based rendering and for shape reconstruction. We first develop our method for the case of a sphere with known size, illuminated by a set of directional light sources. In general, eachpoint of sucha sphere will be illuminated by a subset of these sources. We propose a novel, robust way to segment the surface into regions, witheachregion illuminated by a different set of sources. The regions are separated by boundaries consisting of critical points (points where one illuminant is perpendicular to the normal). Our region-based recursive least-squares method is impervious to noise and missing data and significantly outperforms a previous boundary-based method using spheres[21]. This robustness to missing data is crucial to extending the method to surfaces of arbitrary smooth geometry, other than spheres. We map the normals of the arbitrary shape onto a sphere, which we can then segment, even when only a subset of the normals is available on the scene. We demonstrate experimentally the accuracy of our method, both in detecting the number of light sources and in estimating their directions, by testing on images of a variety of synthetic and real objects. 1

Breathing under water: a transcontinental conversation about the "why" of co-facilitating transdiscilinary self-study learning communities

The preceding chapters in this book exemplify polyvocal professional learning through self-study research as phenomenon (what) and method (how). Overall, these transdisciplinary exemplars comprise a complex conversation about supporting and enacting professional learning, with self-study methodology at the centre.