AN APPLICATION OF SCHUR’S ALGORITHM TO VARIABILITY REGIONS OF CERTAIN ANALYTIC FUNCTIONS II

We extend our study of variability regions, Ali et al. [‘An application of Schur algorithm to variability regions of certain analytic functions–I’, Comput. Methods Funct. Theory, to appear] from convex domains to starlike domains. Let $\mathcal {CV}(\Omega )$ be the class of analytic functions f in ${\mathbb D}$ with $f(0)=f'(0)-1=0$ satisfying $1+zf''(z)/f'(z) \in {\Omega }$ . As an application of the main result, we determine the variability region of $\log f'(z_0)$ when f ranges over $\mathcal {CV}(\Omega )$ . By choosing a particular $\Omega $ , we obtain the precise variability regions of $\log f'(z_0)$ for some well-known subclasses of analytic and univalent functions.

A Putative Role of de-Mono-ADP-Ribosylation of STAT1 by the SARS-CoV-2 Nsp3 Protein in the Cytokine Storm Syndrome of COVID-19

As more cases of COVID-19 are studied and treated worldwide, it had become apparent that the lethal and most severe cases of pneumonia are due to an out-of-control inflammatory response to the SARS-CoV-2 virus. I explored the putative causes of this specific feature through a detailed genomic comparison with the closest SARS-CoV-2 relatives isolated from bats, as well as previous coronavirus strains responsible for the previous epidemics (SARS-CoV and MERS-CoV). The high variability region of the nsp3 protein was confirmed to exhibit the most variations between closest strains. It was then studied in the context of physiological and molecular data available in the literature. A number of convergent findings suggest de-mono-ADP-ribosylation (de-MARylation) of STAT1 by the SARS-CoV-2 nsp3 as a putative cause of the cytokine storm observed in the most severe cases of COVID-19. This may suggest new therapeutic approaches and help in designing assays to predict the virulence of naturally circulating SARS-like animal coronaviruses.

The Likely Role of de-Mono-ADP-ribosylation of STAT1 by the SARS-CoV-2 nsp3 Protein in the Cytokine Storm Syndrome of COVID-19

As more cases of COVID-19 are studied and treated world-wide, it had become apparent that lethal and most severe cases of pneumonia are due to an out-of-control inflammatory response to the SARS-CoV2 virus. I explored the putative causes of this specific feature through a detailed genomic comparison with the closest SARS-CoV-2 relatives isolated from bats, as well as previous coronavirus strains responsible for the previous epidemics (SARS-CoV, and MERS-CoV). The high variability region of the nsp3 protein was confirmed to exhibit the most variations between closest strains. It was then studied in the context of physiological and molecular data available in the literature. A number of convergent findings point out de-mono-ADP-ribosylation (de-MARylation) of STAT1 by the SARS-CoV-2 nsp3 as a likely cause of the cytokine storm observed in the most severe cases of COVID-19. This may suggest new therapeutic approaches and an assay to predict the virulence of naturally circulating SARS-like animal coronaviruses.

The Emergent Behaviour of Thermal Networks and Its Impact on the Thermal Conductivity of Heterogeneous Materials and Systems

The properties of thermal networks are examined to understand the effective thermal conductivity of heterogeneous two-phase composite materials and systems. At conditions of high contrast in thermal conductivity of the individual phases (k1 and k2), where k1 << k2 or k1 >> k2, the effective thermal conductivity of individual networks of the same composition was seen to be highly sensitive to the distribution of the phases and the presence of percolation paths across the network. However, when the contrast in thermal conductivities of the two phases was modest (k1/k2 ~ 10−2 to 102), the thermal networks were observed to exhibit an emergent response with a low variability in the effective thermal conductivity of mixtures of the same composition. A logarithmic mixing rule is presented to predict the network response in the low variability region. Excellent agreement between the model, mixing rule and experimental data is observed for a range two-phase porous and granular media. The modelling approach provides new insights into the design of multi-phase composites for thermal management applications and the interpretation or prediction of their heat transfer properties.

An Ultraviolet Look at the Blue Edge of the ZZ Ceti Instability Strip

It has already been shown that most, and probably all, of the DA white dwarfs become variable in a narrow temperature range as they cool down (Fontaine et al. 1982). Optical photometry and spectrophotometry has led to several determinations of the boundaries of this instability strip. The strip has been found to cover the range 10300 - 13600 K (McGraw 1979), 10400 - 12100 K (Greenstein 1982), 10000 - 13000 K (Weidemann and Koester 1984) and 11000 - 13000 K (Fontaine et al. 1985). Theoretical calculations show that the location of the blue edge is very sensitive to the efficiency of convection used in the unpertubed models (Winget et al. 1982; Winget and Fontaine 1982; Fontaine, Tassoul, and Wesemael 1984). Also, the sharpness of this boundary depends on the range of stellar mass and thickness of the hydrogen envelope found in ZZ Ceti stars. Recently, Wesemael, Lamontagne, and Fontaine (1986) and Lamontagne, Wesemael, and Fontaine (1987) have obtained and compared ultraviolet observations of several DA white dwarfs, in or near the instability strip, with published model calculations from Nelan and Wegner (1985), hereafter NW, and Koester et al. (1985), hereafter KWZV. They determined the boundaries of the variability region at 11400 - 12500 K or 11700 - 13000 K depending on which grid was used. We present here a reanalysis of these IUE observations with an improved grid of model atmospheres in order to define more precisely the location of the blue edge.