Probability tree algorithm for general diffusion processes

Motivated by path-integral numerical solutions of diffusion processes, PATHINT, we present a new tree algorithm, PATHTREE, which permits extremely fast accurate computation of probability distributions of a large class of general nonlinear diffusion processes

The diffusion-induced nova scenario. CK Vul and PB 8 as possible observational counterparts

We propose a scenario for the formation of DA white dwarfs with very thin helium buffers. For these stars we explore the possible occurrence of diffusion-induced CNO- flashes, during their early cooling stage. In order to obtain very thin helium buffers, we simulate the formation of low mass remnants through an AGB final/late thermal pulse (AFTP/LTP scenario). Then we calculate the consequent white dwarf cooling evolution by means of a consistent treatment of element diffusion and nuclear burning. Based on physically sounding white dwarf models, we find that the range of helium buffer masses for these diffusion-induced novas to occur is significantly smaller than that predicted by the only previous study of this scenario. As a matter of fact, we find that these flashes do occur only in some low-mass (M < 0.6M) and low metallicity (Z_ZAMS <0.001) remnants about 10^6 - 10^7 yr after departing from the AGB. For these objects, we expect the luminosity to increase by about 4 orders of magnitude in less than a decade. We also show that diffusion-induced novas should display a very typical eruption lightcurve, with an increase of about a few magnitudes per year before reaching a maximum of M_V ~ -5 to -6. Our simulations show that surface abundances after the outburst are characterized by logNH/NHe ~ -0.15...0.6 and N>C>O by mass fractions. Contrary to previous speculations we show that these events are not recurrent and do not change substantially the final H-content of the cool (DA) white dwarf. (Abridged)Comment: 16 pages, 8 figures, 3 tables. Replaced to match the final version published by MNRAS. The definitive version is available at http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291365-296

White dwarf-main sequence binaries from LAMOST: the DR5 catalogue

We present the data release (DR) 5 catalogue of white dwarf-main sequence (WDMS) binaries from the Large Area Multi-Object fiber Spectroscopic Telescope (LAMOST). The catalogue contains 876 WDMS binaries, of which 757 are additions to our previous LAMOST DR1 sample and 357 are systems that have not been published before. We also describe a LAMOST-dedicated survey that aims at obtaining spectra of photometrically-selected WDMS binaries from the Sloan Digital Sky Survey (SDSS) that are expected to contain cool white dwarfs and/or early type M dwarf companions. This is a population under-represented in previous SDSS WDMS binary catalogues. We determine the stellar parameters (white dwarf effective temperatures, surface gravities and masses, and M dwarf spectral types) of the LAMOST DR5 WDMS binaries and make use of the parameter distributions to analyse the properties of the sample. We find that, despite our efforts, systems containing cool white dwarfs remain under-represented. Moreover, we make use of LAMOST DR5 and SDSS DR14 (when available) spectra to measure the Na I λλ 8183.27, 8194.81 absorption doublet and/or Hα emission radial velocities of our systems. This allows identifying 128 binaries displaying significant radial velocity variations, 76 of which are new. Finally, we cross-match our catalogue with the Catalina Surveys and identify 57 systems displaying light curve variations. These include 16 eclipsing systems, two of which are new, and nine binaries that are new eclipsing candidates. We calculate periodograms from the photometric data and measure (estimate) the orbital periods of 30 (15) WDMS binaries

The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients

Background: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. Methods: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. Results: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). Conclusions: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort

Background Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.Conclusions During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis

1.1. Path Integral Solution of Diffusion Processes

Motivated by path-integral numerical solutions of diffusion processes, PATHINT, wepresent anew tree algorithm, PATHTREE, which permits extremely fast accurate computation of probability distributions of a large class of general nonlinear diffusion processes. Ke ywords: path integral; statistical mechanic