Pompeu Fabra University

About: Pompeu Fabra University is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Population & Gene. The organization has 8093 authors who have published 23570 publications receiving 858431 citations. The organization is also known as: Universitat Pompeu Fabra & UPF.

Ancient human genomes suggest three ancestral populations for present-day Europeans

Abstract: We sequenced the genomes of a ∼7,000-year-old farmer from Germany and eight ∼8,000-year-old hunter-gatherers from Luxembourg and Sweden. We analysed these and other ancient genomes with 2,345 contemporary humans to show that most present-day Europeans derive from at least three highly differentiated populations: west European hunter-gatherers, who contributed ancestry to all Europeans but not to Near Easterners; ancient north Eurasians related to Upper Palaeolithic Siberians, who contributed to both Europeans and Near Easterners; and early European farmers, who were mainly of Near Eastern origin but also harboured west European hunter-gatherer related ancestry. We model these populations' deep relationships and show that early European farmers had ∼44% ancestry from a 'basal Eurasian' population that split before the diversification of other non-African lineages.

Navier-stokes, fluid dynamics, and image and video inpainting

Abstract: Image inpainting involves filling in part of an image or video using information from the surrounding area. Applications include the restoration of damaged photographs and movies and the removal of selected objects. We introduce a class of automated methods for digital inpainting. The approach uses ideas from classical fluid dynamics to propagate isophote lines continuously from the exterior into the region to be inpainted. The main idea is to think of the image intensity as a 'stream function for a two-dimensional incompressible flow. The Laplacian of the image intensity plays the role of the vorticity of the fluid; it is transported into the region to be inpainted by a vector field defined by the stream function. The resulting algorithm is designed to continue isophotes while matching gradient vectors at the boundary of the inpainting region. The method is directly based on the Navier-Stokes equations for fluid dynamics, which has the immediate advantage of well-developed theoretical and numerical results. This is a new approach for introducing ideas from computational fluid dynamics into problems in computer vision and image analysis.

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Abstract: Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.

Body size in ecological networks

Abstract: Body size determines a host of species traits that can affect the structure and dynamics of food webs, and other ecological networks, across multiple scales of organization. Measuring body size provides a relatively simple means of encapsulating and condensing a large amount of the biological information embedded within an ecological network. Recently, important advances have been made by incorporating body size into theoretical models that explore food web stability, the patterning of energy fluxes, and responses to perturbations. Because metabolic constraints underpin body-size scaling relationships, metabolic theory offers a potentially useful new framework within which to develop novel models to describe the structure and functioning of ecological networks and to assess the probable consequences of biodiversity change.