A New Hybrid Method for Gland Segmentation in Histology Images

Gland segmentation has become an important task in biomedical image analysis. An accurate gland segmentation could be instrumental in designing of personalised treatments, potentially leading to improved patient survival rate. Different gland instance segmentation architectures have been tested in the work reported here. A hybrid method that combines two-level classification has been described. The proposed method achieved very good image-level classification results with 100% classification accuracy on the available test data. Therefore, the overall performance of the proposed hybrid method highly depends on the results of the pixel-level classification. Diverse image features reflecting various morphological gland structures visible in histology images have been tested in order to improve the performance of the gland instance segmentation. Based on the reported experimental results, the hybrid approach, which combines two-level classification, achieved overall the best results among the tested methods

Search for large extra dimensions in the production of jets and missing transverse energy in p(p)over-bar collisions at root s=1.96 TeV

We present the results of a search for new physics in the jets plus missing transverse energy data sample collected from 368 pb(-1) of p (p) over bar collisions at root s = 1.96 TeV recorded by the Collider Detector at Fermilab. We compare the number of events observed in the data with a data-based estimate of the standard model backgrounds contributing to this signature. We observe no significant excess of events, and we interpret this null result in terms of lower limits on the fundamental Planck scale for a large extra dimensions scenario

Physicality, modeling, and agency in a computational physics class

Computation is intertwined with essentially all aspects of physics research and is invaluable for physicists’ careers. Despite its disciplinary importance, integration of computation into physics education remains a challenge and, moreover, has tended to be constructed narrowly as a route to solving physics problems. Here, we broaden Physics Education Research’s conception of computation by constructing a metamodel—a model of modeling—incorporating insights on computational modeling from the philosophy of science and prior work. The metamodel is formulated in terms of practices, things physicists do, and how these inform one another. We operationalize this metamodel in an educational environment that incorporates making, the creation of shared physical and digital artifacts, intended to promote students’ agency, creativity, and self-expression alongside doing physics. We present a content analysis of student work from initial implementations of this approach to illustrate the very complex epistemic maneuvers students make as they engaged in computational modeling. We demonstrate how our metamodel can be used to understand student practices and conclude with implications of the metamodel for instruction and future research