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

Height, selected genetic markers and prostate cancer risk: results from the PRACTICAL consortium.

This corrects the article DOI: 10.1038/bjc.2017.231

2.5D Simulated Keyframe Animation in Blender

3D animation requires specialized skills and tends to limit creative expression in favor of physical feasibility, while 2D animation does the opposite. Another duality exists between simulated and keyframe animation. While simulations provide physical believability, keyframes give animators fine timing control. This project seeks to bridge the gap between these approaches to animation: leveraging the expressiveness of 2D animation, the robustness of 3D environment and camera movement, the physical feasibility of simulation, and the control of keyframing. To this end, we present a 2.5D animation interface that takes 2D drawn keyframes and 3D context (object, environment and camera movement) to generate simulated animations that adhere to the user-drawn keyframes

Data1

Part 1 of Labelled EPG data from recordings of Asian Citrus Psyllid (Diaphorina citri) feeding on citrus cultivars

Data from: Machine learning for characterization of insect vector feeding

Insects that feed by ingesting plant and animal fluids cause devastating damage to humans, livestock, and agriculture worldwide, primarily by transmitting pathogens of plants and animals. The feeding processes required for successful pathogen transmission by sucking insects can be recorded by monitoring voltage changes across an insect-food source feeding circuit. The output from such monitoring has traditionally been examined manually, a slow and onerous process. We taught a computer program to automatically classify previously described insect feeding patterns involved in transmission of the pathogen causing citrus greening disease. We also show how such analysis contributes to discovery of previously unrecognized feeding states and can be used to characterize plant resistance mechanisms. This advance greatly reduces the time and effort required to analyze insect feeding, and should facilitate developing, screening, and testing of novel intervention strategies to disrupt pathogen transmission affecting agriculture, livestock and human health

Data2

Part 2 of Labelled EPG recordings of Asian Citrus Psyllid (Diaphorina citri) feeding on citrus cultivars

Prediction of insect feeding states from electrical penetration graph recordings.

<p>Insect feeding states (C, D, E1, E2, G, NP) as predicted by random forest models trained on five percent of human classified data. Feeding states were classified with 97.4 ± 0.1% (95% CI) out of sample accuracy. Black time series are voltages across an insect plant circuit for Asian citrus psyllid feeding on Carrizo citrange (a common citrus rootstock). Actual feeding states were determined and manually annotated through visual examination of frequencies on a second by second basis. Large depolarizations (feeding states E1 and E2), where the time series drops to approximately minus two volts are characteristic of phloem feeding when acquisition and inoculation of the greening pathogen are presumed to occur.</p

Resistance to pathogen transmission.

<p>Phloem feeding (feeding states E1 and E2) by Asian citrus psyllid on trifoliate and non-trifoliate citrus varieties. The vertical axis is the median percent time an insect spends on each bout of phloem feeding, where pathogen transmission and inoculation can occur. Trifoliate varieties are significantly more (<i>α</i> = 0.05) resistant to phloem feeding, an explanation for observed tolerance of trifoliate varieties to citrus greening disease.</p

Computers can recognize additional feeding states.

<p>Hidden Markov Models (HMMs) of insect feeding states. (A) Bayesian information criterion (BIC) for HMMs of different numbers of feeding states. BIC conservatively penalizes the likelihood function with increasing numbers of feeding states. Minimum BIC scores indicate a more appropriate number of feeding states; the decreasing BIC scores suggest that the model can resolve more feeding states than the six currently recognized. (B) Three and half hour sample of electrical penetration graph recordings from Asian citrus psyllid on Carrizo citrange citrus. (C) Human-annotated insect feeding states from visual inspection of (B) on a second by second basis. (D) Feeding states recovered from an eight state Hidden Markov Model. The model resolves phloem feeding states E1 and E2 in accordance with human annotation and recognizes more feeding states within the human annotated C feeding state (dashed box in (C) and (D).</p

Citrus Genotypes.

<p>Nine citrus genotypes and associated varieties used in this analysis. Trifoliates and trifoliate hybrids are being considered for their potential tolerance to citrus greening disease.</p