Talking about Meaning and Loss with Relatives of Persons with Dementia: An Ethnographic Study in a Nursing Home.

Advance care planning (ACP) can help prepare for future losses and decisions to be taken. However, relatives of persons with dementia may wait for healthcare professionals to initiate ACP conversations which may not adequately address their individual information needs. To evaluate inducing and enhancing conversations about meaning and loss, we conducted an ethnographic study on nurse-led ACP conversations using a question prompt list (QPL) on six dementia wards of a nursing home in the Netherlands from January to September 2021.

Inspirational Adversarial Image Generation.

The task of image generation started receiving some attention from artists and designers, providing inspiration for new creations. However, exploiting the results of deep generative models such as Generative Adversarial Networks can be long and tedious given the lack of existing tools. In this work, we propose a simple strategy to inspire creators with new generations learned from a dataset of their choice, while providing some control over the output.

BRANE Clust: Cluster-Assisted Gene Regulatory Network Inference Refinement.

Discovering meaningful gene interactions is crucial for the identification of novel regulatory processes in cells. Building accurately the related graphs remains challenging due to the large number of possible solutions from available data. Nonetheless, enforcing a priori on the graph structure, such as modularity, may reduce network indeterminacy issues.

BARCHAN: Blob Alignment for Robust CHromatographic ANalysis.

Two-dimensional gas chromatography (GC×GC) plays a central role into the elucidation of complex samples. The automation of the identification of peak areas is of prime interest to obtain a fast and repeatable analysis of chromatograms. To determine the concentration of compounds or pseudo-compounds, templates of blobs are defined and superimposed on a reference chromatogram.

BRANE Cut: biologically-related a priori network enhancement with graph cuts for gene regulatory network inference.

Background: Inferring gene networks from high-throughput data constitutes an important step in the discovery of relevant regulatory relationships in organism cells. Despite the large number of available Gene Regulatory Network inference methods, the problem remains challenging: the underdetermination in the space of possible solutions requires additional constraints that incorporate a priori information on gene interactions.

Methods: Weighting all possible pairwise gene relationships by a probability of edge presence, we formulate the regulatory network inference as a discrete variational problem on graphs.