A membrane parallel rapidly-exploring random tree algorithm for robotic motion planning

© 2020-IOS Press and the authors. All rights reserved. In recent years, incremental sampling-based motion planning algorithms have been widely used to solve robot motion planning problems in high-dimensional configuration spaces. In particular, the Rapidly-exploring Random Tree (RRT) algorithm and its asymptotically-optimal counterpart called RRT∗ are popular algorithms used in real-life applications due to its desirable properties. Such algorithms are inherently iterative, but certain modules such as the collision-checking procedure can be parallelized providing significant speedup with respect to sequential implementations. In this paper, the RRT and RRT∗ algorithms have been adapted to a bioinspired computational framework called Membrane Computing whose models of computation, a.k.a. P systems, run in a non-deterministic and massively parallel way. A large number of robotic applications are currently using a variant of P systems called Enzymatic Numerical P systems (ENPS) for reactive controlling, but there is a lack of solutions for motion planning in the framework. The novel models in this work have been designed using the ENPS framework. In order to test and validate the ENPS models for RRT and RRT*, we present two ad-hoc implementations able to emulate the computation of the models using OpenMP and CUDA. Finally, we show the speedup of our solutions with respect to sequential baseline implementations. The results show a speedup up to 6x using OpenMP with 8 cores against the sequential implementation and up to 24x using CUDA against the best multi-threading configuration

CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools

[EN] The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the four types of cancer that currently have the highest prevalence worldwide: lung, breast, prostate and colorectal. To allow this, clinical partners and external collaborators will populate the repository with multimodality (MR, CT, PET/CT) imaging and related clinical data. Subsequently, AI developers will enable a multimodal analytical data engine facilitating the interpretation, extraction and exploitation of the information stored at the repository. The development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and image harmonization. By the end of the project, the usability and performance of the repository as a tool fostering AI experimentation will be technically validated, including a validation subphase by world-class European AI developers, participating in Open Challenges to the AI Community. Upon successful validation of the repository, a set of selected AI tools will undergo early in-silico validation in observational clinical studies coordinated by leading experts in the partner hospitals. Tool performance will be assessed, including external independent validation on hallmark clinical decisions in response to some of the currently most important clinical end points in cancer. The project brings together a consortium of 18 European partners including hospitals, universities, R & D centers and private research companies, constituting an ecosystem of infrastructures, biobanks, AI/in-silico experimentation and cloud computing technologies in oncology.CHAIMELEON has been funded by as a Horizon 2020 project (RIA, topic DT-TDS-05-2020-AI for Health Imaging; call SC1-FA-DTS-2019-1, under Grant Agreement No. 952172)Martí Bonmatí, L.; Miguel, A.; Suárez, A.; Aznar, M.; Beregi, JP.; Fournier, L.; Neri, E.... (2022). CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools. Frontiers in Oncology. 12:1-11. https://doi.org/10.3389/fonc.2022.7427011111

Los desafíos de Educación Preescolar, Básica y Media en América Latina

En la mayoría de los países estudiados se registran altas tasa de cobertura en la enseñanza básica y media, con tasas netas por sobre el 90% en el nivel básico y en torno al 75-80% en el nivel medio. Todos los países experimentan mejoras sustanciales en las tasas de acceso al nivel medio y preescolar, mientras que las tasas de acceso en el nivel básico se mantienen tan altas o incluso aumentan con respecto a sus niveles en la década del ’90. Subsisten déficits importantes a nivel de retención, en particular en el tránsito entre el nivel básico y medio, lo que es evidente por las altas brechas en las tasas de acceso netas que se registran entre estos dos niveles. Uno de los países con mejor cobertura, Uruguay, reporta que entre los jóvenes de entre 15-17 años, un 26% está atrasado en el sistema escolar, mientras que otro 22,8% abandona sus estudios. En varios de los capítulos (ver Chile, Argentina y Uruguay, por ejemplo) se nota una preocupación fuerte por los logros en aprendizajes y las altas brechas que existen entre estudiantes de alto y bajo nivel socioeconómico. En la mayoría de los países se destaca como una debilidad la decadencia del rol del docente tanto en la escuela, como su estatus en la sociedad. En varios de los países (Argentina, Chile, México, Venezuela, por ejemplo) ha habido un aumento sistemático en los recursos asignados a educación. En todos los países se pone en evidencia problemas asociados a la calidad de los docentes. Estos problemas están asociados a la misma formación de los docentes (tanto en lo que se refiere a planes de formación y perfiles de egreso, como al tipo de estudiante que opta por carreras pedagógicas), como también a la manera de organizar los recursos docentes y directivos hacia adentro de los establecimientos escolares

Influence of Antisynthetase Antibodies Specificities on Antisynthetase Syndrome Clinical Spectrum TimeCourse

Introduction: Increased cardiovascular (CV) morbidity and mortality is observed in inflammatory joint diseases (IJDs) such as rheumatoid arthritis, ankylosing spondylitis, and psoriatic arthritis. However, the management of CV disease in these conditions is far from being well established.Areas covered: This review summarizes the main epidemiologic, pathophysiological, and clinical risk factors of CV disease associated with IJDs. Less common aspects on early diagnosis and risk stratification of the CV disease in these conditions are also discussed. In Europe, the most commonly used risk algorithm in patients with IJDs is the modified SCORE index based on the revised recommendations proposed by the EULAR task force in 2017.Expert opinion: Early identification of IJD patients at high risk of CV disease is essential. It should include the use of complementary noninvasive imaging techniques. A multidisciplinary approach aimed to improve heart-healthy habits, including strict control of classic CV risk factors is crucial. Adequate management of the underlying IJD is also of main importance since the reduction of disease activity decreases the risk of CV events. Non-steroidal anti-inflammatory drugs may have a lesser harmful effect in IJD than in the general population, due to their anti-inflammatory effects along with other potential beneficial effects.This research was partially funded by FOREUM—Foundation for Research in Rheumatolog

Management of intra-abdominal infections : recommendations by the WSES 2016 consensus conference

This paper reports on the consensus conference on the management of intra-abdominal infections (IAIs) which was held on July 23, 2016, in Dublin, Ireland, as a part of the annual World Society of Emergency Surgery (WSES) meeting. This document covers all aspects of the management of IAIs. The Grading of Recommendations Assessment, Development and Evaluation recommendation is used, and this document represents the executive summary of the consensus conference findings.Peer reviewe