LEARNING ENVIRONMENTS IN THE CONTEXT OF A MASTER DEGREE IN ANIMATION AND ILLUSTRATION

This paper aims to describe the processes of teaching illustration and animation, together, in the context of a masters degree program. In Portugal, until very recently, illustration and animation higher education courses, were very scarce and only provided by a few private universities, which offered separated programs - either illustration or animation. The MA in Illustration and Animation (MIA) based in the Instituto Politécnico do Cávado e Ave in Portugal, dared to join these two creative areas in a common learning model and is already starting it’s third edition with encouraging results and will be supported by the first international conference on illustration and animation (CONFIA). This masters program integrates several approaches and techniques (in illustration and animation) and integrates and encourages creative writing and critique writing. This paper describes the iterative process of construction, and implementation of the program as well as the results obtained on the initial years of existence in terms of pedagogic and learning conclusions. In summary, we aim to compare pedagogic models of animation or illustration teaching in higher education opposed to a more contemporary and multidisciplinary model approach that integrates the two - on an earlier stage - and allows them to be developed separately – on the second part of the program. This is based on the differences and specificities of animation (from classic techniques to 3D) and illustration (drawing the illustration) and the intersection area of these two subjects within the program structure focused on the students learning and competencies acquired to use in professional or authorial projects.

Automatic segmentation and 3D feature extraction of protein aggregates in Caenorhabditis Elegans

In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a process often used as disease progression readout and to develop therapeutic strategies. This work presents an image processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode Caenorhabditis Elegans. A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals of distinct genetic conditions. Because of the animals’ transparency, most of the slices pixels appeared dark, hampering their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm that uses the Tukey’s biweight as edge-stopping function. The image histogram median of this outcome was used to dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations were subsequently segmented based on an iso-value and blended with the resulting volume mesh. The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative diseases treatment planning and interventions prevention