Role of quorum sensing in bacterial infections

<p>Quorum sensing (QS) is cell communication that is widely used by bacterial pathogens to coordinate the expression of several collective traits, including the production of multiple virulence factors, biofilm formation, and swarming motility once a population threshold is reached. Several lines of evidence indicate that QS enhances virulence of bacterial pathogens in animal models as well as in human infections; however, its relative importance for bacterial pathogenesis is still incomplete. In this review, we discuss the present evidence from <i>in vitro</i> and <i>in vivo</i> experiments in animal models, as well as from clinical studies, that link QS systems with human infections. We focus on two major QS bacterial models, the opportunistic Gram negative bacteria <i>Pseudomonas aeruginosa</i> and the Gram positive <i>Staphylococcus aureus</i>, which are also two of the main agents responsible of nosocomial and wound infections. In addition, QS communication systems in other bacterial, eukaryotic pathogens, and even immune and cancer cells are also reviewed, and finally, the new approaches proposed to combat bacterial infections by the attenuation of their QS communication systems and virulence are also discussed.</p&gt

The complete mitochondrial and plastid genomes of Corallina chilensis (Corallinaceae, Rhodophyta) from Tomales Bay, California, USA

Genomic analysis of the marine alga Corallina chilensis from Tomales Bay, California, USA, resulted in the assembly of its complete mitogenome (GenBank accession number MK598844) and plastid genome (GenBank MK598845). The mitogenome is 25,895 bp in length and contains 50 genes. The plastid genome is 178,350 bp and contains 233 genes. The organellar genomes share a high-level of gene synteny to other Corallinales. Comparison of rbcL and cox1 gene sequences of C. chilensis from Tomales Bay reveals it is identical to three specimens from British Columbia, Canada and very similar to a specimen of C. chilensis from southern California. These genetic data confirm that C. chilensis is distributed in Pacific North America

La investigación universitaria y sus contribuciones en Mesoamérica

La Universidad Autónoma de Chiapas a través de su Proyecto Académico 2014-2018, reafirma su compromiso con el desarrollo de nuestra región, al establecer líneas de desarrollo de nuestra región, al establecer líneas de desarrollo institucional, donde la vinculación de la investigación ocupa un lugar preponderante; en este sentido, a partir de 2015, junto con la comunidad académica internacional, se unió a la Agenda 2030 para el Desarrollo sostenible de la ONU y priorizó los 17 Objetivos de Desarrollo Sostenible (ODS) y sus 169 metas, con la finalidad de dar soluciona los grandes desafíos sociales, económicos y medioambientales que enfrenta la sociedad. Este libro es la recopilación de trabajos realizados por académicos de diversas Instituciones de Educación Superior y Centros de Investigación, de manera multidisciplinaria, interinstitucional e internacional, los cuales han permitido compartir intereses en diversas líneas de generación y aplicación del conocimiento

La investigación universitaria y sus contribuciones en Mesoamérica

La Universidad Autónoma de Chiapas a través de su Proyecto Académico 2014-2018, reafirma su compromiso con el desarrollo de nuestra región, al establecer líneas de desarrollo de nuestra región, al establecer líneas de desarrollo institucional, donde la vinculación de la investigación ocupa un lugar preponderante; en este sentido, a partir de 2015, junto con la comunidad académica internacional, se unió a la Agenda 2030 para el Desarrollo sostenible de la ONU y priorizó los 17 Objetivos de Desarrollo Sostenible (ODS) y sus 169 metas, con la finalidad de dar soluciona los grandes desafíos sociales, económicos y medioambientales que enfrenta la sociedad. Este libro es la recopilación de trabajos realizados por académicos de diversas Instituciones de Educación Superior y Centros de Investigación, de manera multidisciplinaria, interinstitucional e internacional, los cuales han permitido compartir intereses en diversas líneas de generación y aplicación del conocimiento

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation