Systemic cascades on inhomogeneous random financial networks.

This article presents a model of the financial system as an inhomogeneous random financial network (IRFN) with N nodes that represent different types of institutions such as banks or funds and directed weighted edges that signify counterparty relationships between nodes. The onset of a systemic crisis is triggered by a large exogenous shock to banks' balance sheets. Their behavioural response is modelled by a cascade mechanism that tracks the propagation of damaging shocks and possible amplification of the crisis, and leads the system to a cascade equilibrium. The mathematical properties of the stochastic framework are investigated for the first time in a generalization of the Eisenberg-Noe solvency cascade mechanism that accounts for fractional bankruptcy charges. New results include verification of a "tree independent cascade property" of the solvency cascade mechanism, and culminate in an explicit recursive stochastic solvency cascade mapping conjectured to hold in the limit as the number of banks N goes to infinity. It is shown how this cascade mapping can be computed numerically, leading to a rich picture of the systemic crisis as it evolves toward the cascade equilibrium.

Impact of asymptomatic COVID-19 carriers on pandemic policy outcomes.

This paper provides a mathematical model that makes it clearly visible why the underestimation of r, the fraction of asymptomatic COVID-19 carriers in the general population, may lead to a catastrophic reliance on the standard policy intervention that attempts to isolate all confirmed infectious cases. The SE(A+O)R model with infectives separated into asymptomatic and ordinary carriers, supplemented by a model of the data generation process, is calibrated to standard early pandemic datasets for two countries. It is shown that certain fundamental parameters, critically r, are unidentifiable with this data. A general analytical framework is presented that projects the impact of different types of policy intervention. It is found that the lack of parameter identifiability implies that some, but not all, potential policy interventions can be correctly predicted. In an example representing Italy in March 2020, a hypothetical optimal policy of isolating confirmed cases that aims to reduce the basic reproduction number R 0 of the outbreak from 4.4 to 0.8 assuming r = 0, only achieves 3.8 if it turns out that r = 40%.

COVID-19: Analytics of contagion on inhomogeneous random social networks.

Motivated by the need for robust models of the Covid-19 epidemic that adequately reflect the extreme heterogeneity of humans and society, this paper presents a novel framework that treats a population of N individuals as an inhomogeneous random social network (IRSN). The nodes of the network represent individuals of different types and the edges represent significant social relationships. An epidemic is pictured as a contagion process that develops day by day, triggered by a seed infection introduced into the population on day 0. Individuals' social behaviour and health status are assumed to vary randomly within each type, with probability distributions that vary with their type. A formulation and analysis is given for a SEIR (susceptible-exposed-infective-removed) network contagion model, considered as an agent based model, which focusses on the number of people of each type in each compartment each day. The main result is an analytical formula valid in the large N limit for the stochastic state of the system on day t in terms of the initial conditions. The formula involves only one-dimensional integration. The model can be implemented numerically for any number of types by a deterministic algorithm that efficiently incorporates the discrete Fourier transform. While the paper focusses on fundamental properties rather than far ranging applications, a concluding discussion addresses a number of domains, notably public awareness, infectious disease research and public health policy, where the IRSN framework may provide unique insights.

Disulphide formation on mitochondrial protein thiols.

A large number of proteins contain free thiols that can be modified by the formation of internal disulphides or by mixed disulphides with low-molecular-mass thiols. The majority of these latter modifications result from the interaction of protein thiols with the endogenous glutathione pool. Protein glutathionylation and disulphide formation are of significance both for defence against oxidative damage and in redox signalling. As mitochondria are central to both oxidative damage and redox signalling within the cell, these modifications of mitochondrial proteins are of particular importance. In the present study, we review the mechanisms and physiological significance of these processes.

Lipophilic triphenylphosphonium cations as tools in mitochondrial bioenergetics and free radical biology.

Lipophilic phosphonium cations were first used to investigate mitochondrial biology by Vladimir Skulachev and colleagues in the late 1960s. Since then, these molecules have become important tools for exploring mitochondrial bioenergetics and free radical biology. Here we review why these molecules are useful in mitochondrial research and outline some of the ways in which they are now being utilized.