Secondary use of data extracted from a clinical information system to assess the adherence of tidal volume and its impact on outcomes.

Objectives To extract data from clinical information systems to automatically calculate high-resolution quality indicators to assess adherence to recommendations for low tidal volume. Design We devised two indicators: the percentage of time under mechanical ventilation with excessive tidal volume (>8 mL/kg predicted body weight) and the percentage of patients who received appropriate tidal volume (≤8 mL/kg PBW) at least 80% of the time under mechanical ventilation. We developed an algorithm to automatically calculate these indicators from clinical information system data and analyzed associations between them and patients’ characteristics and outcomes. Settings This study has been carried out in our 30-bed polyvalent intensive care unit between January 1, 2014 and November 30, 2019. Patients All patients admitted to intensive care unit ventilated >72 h were included. Intervention Use data collected automatically from the clinical information systems to assess adherence to tidal volume recommendations and its outcomes. Main variables of interest Mechanical ventilation days, ICU length of stay and mortality. Results Of all admitted patients, 340 met the inclusion criteria. Median percentage of time under mechanical ventilation with excessive tidal volume was 70% (23%–93%); only 22.3% of patients received appropriate tidal volume at least 80% of the time. Receiving appropriate tidal volume was associated with shorter duration of mechanical ventilation and intensive care unit stay. Patients receiving appropriate tidal volume were mostly male, younger, taller, and less severely ill. Adjusted intensive care unit mortality did not differ according to percentage of time with excessive tidal volume or to receiving appropriate tidal volume at least 80% of the time. Conclusions Automatic calculation of process-of-care indicators from clinical information systems high-resolution data can provide an accurate and continuous measure of adherence to recommendations. Adherence to tidal volume recommendations was associated with shorter duration of mechanical ventilation and intensive care unit stay.pre-print1126 K

Ensemble and Greedy Approach for the Reconstruction of Large Gene Co-Expression Networks

In the recent years, the vast amount of genetic information generated by high-throughput approaches, have led to the need of new methods for data handling. The integrative analysis of diverse-nature gene information could provide a much-sought overview to study complex biological systems and processes. In this sense, Co-expression Gene Networks (CGN) have become a powerful tool in the comprehensive analysis of gene expression. Such networks represent relationships between genes (or gene products) by means of a graph composed of nodes and edges, where nodes represent genes and edges the relationships among them. Amongst the main features of CGN, sparseness and scale-free topology may notably affect the latter network analysis. Within this framework, structure optimization techniques are also important in the reduction of the size of the networks, not only improving their topology but also keeping a positive prediction ratio. On the other hand, ensemble strategies have significantly improved the precision of results by combining different measures or methods. In this work, we present Ensemble and Greedy networks (EnGNet), a novel two-step method for CGN inference. First, EnGNet uses an ensemble strategy for co-expression networks generation. Final score is estimated by major voting among three different methdos, i.e. Spearman and Kendall coefficients and Normalized Mutual Information. Second, a greedy algorithm optimizes both the size and the topological features of the network. Not only do achieved results show that this method is able to obtain reliable networks, but also that it significantly improves the topology of the networks. Moreover, the usefulness of the method is proven by an application to a human dataset on post-traumatic stress disorder (PTSD), revealing an innate immunity-mediated response to this pathology in accordance with previous studies. These results are indicative of the potential of CGN, and EnGNet in particular, in the unveiling of the genetic causes for complex diseases. Finally, the implications of CGN in biomarkers discovery, could lead research towards earlier detection and effective treatment of these diseases

Automatización y computación distribuida para laboratorios de informática forense

El crecimiento del volumen digital en las pericias informáticas es uno de los factores críticos que lleva al colapso de los laboratorios de informática forense y al aumento de las listas de espera de casos en trámite. Urge entonces comenzar a transitar un nuevo camino en lo que refiere a aspectos metodológicos, junto con la aplicación de nuevas técnicas y herramientas de informática forense apoyadas en recursos tecnológicos que provean una razonable capacidad de cómputo y de almacenamiento masivo de información digital. Este trabajo presenta la implementación y experimentación sobre una infraestructura de cómputo distribuido llevada adelante en el Gabinete de Pericias Informáticas del Poder Judicial del Neuquén, integrando diversas aplicaciones de informática forense con el objeto de automatizar y agilizar aquellas actividades operativas que demandan tiempos elevados durante el proceso forense digital. A la luz de los cambios en los plazos procesales para la investigación penal que operan luego de la última reforma al Código Procesal Penal de la Provincia del Neuquén se procura maximizar la disponibilidad de recursos computacionales para el procesamiento de evidencia digital.Sociedad Argentina de Informática e Investigación Operativa (SADIO

The effects of dynamical substructure on Milky Way mass estimates from the high velocity tail of the local stellar halo

We investigate the impact of dynamical streams and substructure on estimates of the local escape speed and total mass of Milky Way-mass galaxies from modelling the high velocity tail of local halo stars. We use a suite of high-resolution, magneto-hydrodynamical cosmological zoom-in simulations, which resolve phase space substructure in local volumes around solar-like positions. We show that phase space structure varies significantly between positions in individual galaxies and across the suite. Substructure populates the high velocity tail unevenly and leads to discrepancies in the mass estimates. We show that a combination of streams, sample noise and truncation of the high velocity tail below the escape speed leads to a distribution of mass estimates with a median that falls below the true value by $\sim 20 \%$, and a spread of a factor of 2 across the suite. Correcting for these biases, we derive a revised value for the Milky Way mass presented in Deason et al. of $1.29 ^{+0.37}_{-0.47} \times 10^{12}$ $\rm M_{\odot}$.Comment: Re-submitted to MNRAS Letters after minor revisio

The effects of dynamical substructure on Milky Way mass estimates from the high-velocity tail of the local stellar halo

We investigate the impact of dynamical streams and substructure on estimates of the local escape speed and total mass of Milky-Way-mass galaxies from modelling the high-velocity tail of local halo stars. We use a suite of high-resolution magnetohydrodynamical cosmological zoom-in simulations that resolve phase space substructure in local volumes around solar-like positions. We show that phase space structure varies significantly between positions in individual galaxies and across the suite. Substructure populates the high-velocity tail unevenly and leads to discrepancies in the mass estimates. We show that a combination of streams, sample noise, and truncation of the high-velocity tail below the escape speed leads to a distribution of mass estimates with a median that falls below the true value by ∼20 per cent ∼20 per cent ⁠, and a spread of a factor of 2 across the suite. Correcting for these biases, we derive a revised value for the Milky Way mass presented in Deason et al. of 1.29 +0.37 −0.47 × 10 12 M ⊙ 1.29−0.47+0.37×1012M⊙ ⁠

Fluid structural analysis of urine flow in a stented ureter

Many urologists are currently studying new designs of ureteral stents to improve the quality of their operations and the subsequent recovery of the patient. In order to help during this design process, many computational models have been developed to simulate the behaviour of different biological tissues and provide a realistic computational environment to evaluate the stents. However, due to the high complexity of the involved tissues, they usually introduce simplifications to make these models less computationally demanding. In this study, the interaction between urine flow and a double-J stented ureter with a simplified geometry has been analysed.The Fluid-Structure Interaction (FSI) of urine and the ureteral wall was studied using three models for the solid domain: Mooney-Rivlin, Yeoh, and Ogden. The ureter was assumed to be quasi-incompressible and isotropic. Data obtained in previous studies fromex vivo and in vivo mechanical characterization of different ureters were used to fit thementioned models.The results show that the interaction between the stented ureter and urine is negligible. Therefore, we can conclude that this type of models does not need to include the FSI and could be solved quite accurately assuming that the ureter is a rigid body and, thus, using the more simple Computational Fluid Dynamics (CFD) approach

Machine Learning for Galactic Archaeology: A chemistry-based neural network method for identification of accreted disc stars

We develop a method ('Galactic Archaeology Neural Network', GANN) based on neural network models (NNMs) to identify accreted stars in galactic discs by only their chemical fingerprint and age, using a suite of simulated galaxies from the Auriga Project. We train the network on the target galaxy's own local environment defined by the stellar halo and the surviving satellites. We demonstrate that this approach allows the detection of accreted stars that are spatially mixed into the disc. Two performance measures are defined - recovery fraction of accreted stars, and the probability that a star with a positive (accreted) classification is a true-positive result, P(TP). As the NNM output is akin to an assigned probability, we are able to determine positivity based on flexible threshold values that can be adjusted easily to refine the selection of presumed-accreted stars. We find that GANN identifies accreted disc stars within simulated galaxies, with high recovery fraction and/or high P(TP). We also find that stars in Gaia-Enceladus-Sausage (GES) mass systems are over 50% recovered by our NNMs in the majority (18/24) of cases. Additionally, nearly every individual source of accreted stars is detected at 10% or more of its peak stellar mass in the disc. We also demonstrate that a conglomerated NNM, trained on the halo and satellite stars from all of the Auriga galaxies provides the most consistent results, and could prove to be an intriguing future approach as our observational capabilities expand.Comment: 19 pages, 12 figure