Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

Network intrusion detection based on a general regression neural network optimized by an improved artificial immune algorithm.

To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accuracy. In this paper, the mean square errors (MSEs) were considered the affinity function. The AIAE was used to optimize the smooth factors of the GRNN; then, the optimal smooth factor was solved and substituted into the trained GRNN. Thus, the intrusive data were classified. The paper selected a GRNN that was separately optimized using a genetic algorithm (GA), particle swarm optimization (PSO), and fuzzy C-mean clustering (FCM) to enable a comparison of these approaches. As shown in the results, the AIAE-GRNN achieves a higher classification accuracy than PSO-GRNN, but the running time of AIAE-GRNN is long, which was proved first. FCM and GA-GRNN were eliminated because of their deficiencies in terms of accuracy and convergence. To improve the running speed, the paper adopted principal component analysis (PCA) to reduce the dimensions of the intrusive data. With the reduction in dimensionality, the PCA-AIAE-GRNN decreases in accuracy less and has better convergence than the PCA-PSO-GRNN, and the running speed of the PCA-AIAE-GRNN was relatively improved. The experimental results show that the AIAE-GRNN has a higher robustness and accuracy than the other algorithms considered and can thus be used to classify the intrusive data

Efficient Processing of Multiple XML Twig Queries

Abstract. Finding all occurrences of a twig pattern in an XML document is a core operation for XML query processing. The emergence of XML as a common mark-up language for data interchange has spawned great interest in techniques for filtering and content-based routing of XML data. In this paper, we aim to use the state-of-art holistic twig join technique to address multiple twig queries in a large scale XML database. We propose a new twig query technique which is specially tailored to match documents with large numbers of twig pattern queries. We introduce the super-twig to represent multiple twig queries. Based on the super-twig, we design a holistic twig join algorithm, called MTwigStack, to find all matches for multiple twig queries by scanning an XML document only once.

DR & FPR of each intrusion category by the different algorithms.

<p>The result showed that the DR and FPR of PSO-GRNN and AIAE-GRNN were higher than GA-GRNN and FCM. And the DR and FPR of AIAE-GRNN were higher slightly than PSO-GRNN.</p><p>DR & FPR of each intrusion category by the different algorithms.</p

The evaluation indexes for PCA-PSO-GRNN and PCA-AIAE-GRNN.

<p>By reducing dimensions in PCA, compared with <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120976#pone.0120976.t002" target="_blank">Table 2</a>, the convergence and relative running time were improved. This result showed that the robustness of AIAE-GRNN was better than PSO-GRNN.</p><p>The evaluation indexes for PCA-PSO-GRNN and PCA-AIAE-GRNN.</p

The algorithm flow chart of AIAE-GRNN.

<p>The algorithm flow chart of AIAE-GRNN.</p

The output performances of GA-GRNN, PSO-GRNN and AIAE-GRNN.

<p>(A) The relationship between the optimized GRNN smooth factors and the iterations of GA-GRNN, PSO-GRNN and AIAE-GRNN. (B) The relationship between MSE and the iterations of GA-GRNN, PSO-GRNN and AIAE-GRNN.</p

The output performances of PCA-AIAE-GRNN and PCA-PSO-GRNN.

<p>(A) The relationship between the optimized GRNN smooth factors and the iterations of PCA-AIAE-GRNN and PCA-PSO-GRNN. (B) The relationship between MSE and the iterations of PCA-AIAE-GRNN and PCA-PSO-GRNN.</p

DR & FPR of each intrusion category by PCA-PSO-GRNN and PCA-AIAE-GRNN.

<p>By reducing dimensions in PCA, compared with <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0120976#pone.0120976.t001" target="_blank">Table 1</a>, the DR and FPR of PSO-GRNN and AIAE-GRNN declined to a certain extent, but the DR and FPR of AIAE-GRNN was still higher than PSO-GRNN.</p><p>DR & FPR of each intrusion category by PCA-PSO-GRNN and PCA-AIAE-GRNN.</p

The evaluation indexes for the different algorithms.

<p>The result showed that the GA-GRNN had the premature convergence problem. In contrast, the PSO-GRNN and AIAE-GRNN overcame this problem. The running time of PSO-GRNN was shorter than AIAE-GRNN.</p><p>The evaluation indexes for the different algorithms.</p