Autoantibodies against type I IFNs in patients with life-threatening COVID-19

Interindividual clinical variability in the course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is vast. We report that at least 101 of 987 patients with life-threatening coronavirus disease 2019 (COVID-19) pneumonia had neutralizing immunoglobulin G (IgG) autoantibodies (auto-Abs) against interferon-w (IFN-w) (13 patients), against the 13 types of IFN-a (36), or against both (52) at the onset of critical disease; a few also had auto-Abs against the other three type I IFNs. The auto-Abs neutralize the ability of the corresponding type I IFNs to block SARS-CoV-2 infection in vitro. These auto-Abs were not found in 663 individuals with asymptomatic or mild SARS-CoV-2 infection and were present in only 4 of 1227 healthy individuals. Patients with auto-Abs were aged 25 to 87 years and 95 of the 101 were men. A B cell autoimmune phenocopy of inborn errors of type I IFN immunity accounts for life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men

Modelling non-homogeneous dynamic Bayesian networks with piece-wise linear regression models

In statistical genomics and systems biology non-homogeneous dynamic Bayesian networks (NH-DBNs) have become an important tool for learning regulatory networks and signalling pathways from post-genomic data, such as gene expression time series. This chapter gives an overview of various state-of-the-art NH-DBN models with a variety of features. All NH-DBNs, presented here, have in common that they are Bayesian models that combine linear regression with multiple changepoint processes. The NH-DBN models can be used for learning the network structures of time-varying regulatory processes from data, where the regulatory interactions are subject to temporal change. We conclude this chapter with an illustration of the methodology on two applications, related to morphogenesis in Drosophila and synthetic biology in yeast

Effects of InAlN underlayer on deep traps detected in near-UV InGaN/GaN single quantum well light-emitting diodes

Two types of near-UV light-emitting diodes (LEDs) with an InGaN/GaN single quantum well (QW) differing only in the presence or absence of an underlayer (UL) consisting of an InAlN/GaN superlattice (SL) were examined. The InAlN-based ULs were previously shown to dramatically improve internal quantum efficiency of near-UV LEDs, via a decrease in the density of deep traps responsible for nonradiative recombination in the QW region. The main differences between samples with and without UL were (a) a higher compensation of Mg acceptors in the p-GaN:Mg contact layer of the sample without UL, which correlates with the presence of traps with an activation energy of 0.06\u2009eV in the QW region, (b) the presence of deep electron traps with levels 0.6\u2009eV below the conduction band edge (Ec) (ET1) and at Ec 0.77\u2009eV (ET2) in the n-GaN spacer underneath the QW, and the presence of hole traps (HT1) in the QW, 0.73\u2009eV above the valence band edge in the sample without UL (no traps could be detected in the sample with UL), and (c) a high density of deep traps with optical ionization energy close to 1.5\u2009eV for the LEDs without UL. Irradiation with 5\u2009MeV electrons led to a strong decrease in the electroluminescence (EL) intensity in the LEDs without UL, while for the samples with UL, such irradiation had little effect on the EL signal at high driving current, although the level of driving currents necessary to have a measurable EL signal increased by about an order of magnitude. This is despite the 5 times higher starting EL signal of the sample with UL. Irradiation also led to the appearance in the LEDs with UL of the ET1 and HT1 deep traps, but with concentration much lower than without the UL, and to a considerable increase in the Mg compensation rati