Distant X-ray Galaxies: Insights from the Local Population

A full understanding of the origin of the hard X-ray background requires a complete and accurate census of the distant galaxies that produce it. Unfortunately, distant X-ray galaxies tend to be very faint at all wavelengths, which hinders efforts to perform this census. This chapter discusses the insights that can be obtained through comparison of the distant population to local X-ray galaxies, whose properties are well characterized. Such comparisons will ultimately aid investigations into the cosmic evolution of supermassive black holes and their environments.Comment: 19 pages, 10 figures, to appear as Chapter 7 in "Supermassive Black Holes in the Distant Universe" (2004), ed. A. J. Barger, Kluwer Academic Publishers, in pres

Dynamic causal modelling of immune heterogeneity

An interesting inference drawn by some COVID-19 epidemiological models is that there exists a proportion of the population who are not susceptible to infection-even at the start of the current pandemic. This paper introduces a model of the immune response to a virus. This is based upon the same sort of mean-field dynamics as used in epidemiology. However, in place of the location, clinical status, and other attributes of people in an epidemiological model, we consider the state of a virus, B and T-lymphocytes, and the antibodies they generate. Our aim is to formalise some key hypotheses as to the mechanism of resistance. We present a series of simple simulations illustrating changes to the dynamics of the immune response under these hypotheses. These include attenuated viral cell entry, pre-existing cross-reactive humoral (antibody-mediated) immunity, and enhanced T-cell dependent immunity. Finally, we illustrate the potential application of this sort of model by illustrating variational inversion (using simulated data) of this model to illustrate its use in testing hypotheses. In principle, this furnishes a fast and efficient immunological assay-based on sequential serology-that provides a (1) quantitative measure of latent immunological responses and (2) a Bayes optimal classification of the different kinds of immunological response (c.f., glucose tolerance tests used to test for insulin resistance). This may be especially useful in assessing SARS-CoV-2 vaccines

The benefits and limitations of animal models for translational research in cartilage repair.

Much research is currently ongoing into new therapies for cartilage defect repair with new biomaterials frequently appearing which purport to have significant regenerative capacity. These biomaterials may be classified as medical devices, and as such must undergo rigorous testing before they are implanted in humans. A large part of this testing involves in vitro trials and biomechanical testing. However, in order to bridge the gap between the lab and the clinic, in vivo preclinical trials are required, and usually demanded by regulatory approval bodies. This review examines the in vivo models in current use for cartilage defect repair testing and the relevance of each in the context of generated results and applicability to bringing the device to clinical practice. Some of the preclinical models currently used include murine, leporine, ovine, caprine, porcine, canine, and equine models. Each of these has advantages and disadvantages in terms of animal husbandry, cartilage thickness, joint biomechanics and ethical and licencing issues. This review will examine the strengths and weaknesses of the various animal models currently in use in preclinical studies of cartilage repair

Modelling the radio to X-ray SED of galaxies

We present our model to interpret the SED of galaxies. The model for the UV to sub-mm SED is already well established (Silva et al 1998). We remind here its main features and show some applications. Recently we have extended the model to the radio range (Bressan et al 2001), and we have started to include the X-ray emission from the stellar component.Comment: 4 pages, to be published in "The link between stars and cosmology", 26-30 March, 2001, Puerto Vallarta, Mexico, by Kluwer, eds. M. Chavez, A. Bressan, A. Buzzoni, and D. Mayy

Inorganic Nitrate Promotes Glucose Uptake and Oxidative Catabolism in White Adipose Tissue through the XOR Catalyzed Nitric Oxide Pathway

An ageing global population combined with sedentary lifestyles and unhealthy diets has contributed to an increasing incidence of obesity and type 2 diabetes. These metabolic disorders are associated with perturbations to nitric oxide (NO) signaling and impaired glucose metabolism. Dietary inorganic nitrate, found in high concentration in green leafy vegetables, can be converted to NO in vivo and demonstrates anti-diabetic and anti-obesity properties in rodents. Alongside tissues including skeletal muscle and liver, white adipose tissue is also an important physiological site of glucose disposal. However, the distinct molecular mechanisms governing the effect of nitrate on adipose tissue glucose metabolism, and the contribution of this tissue to the glucose tolerant phenotype, remain to be determined. Using a metabolomic and stable-isotope labeling approach, combined with transcriptional analysis, we found that nitrate increases glucose uptake and oxidative catabolism in primary adipocytes and white adipose tissue of nitrate-treated rats. Mechanistically, we determine that nitrate induces these phenotypic changes in primary adipocytes through the xanthine oxidoreductase catalysed reduction of nitrate to nitric oxide and independently of Peroxisome Proliferator-Activated Receptor α. The nitrate-mediated enhancement of glucose uptake and catabolism in white adipose tissue may be a key contributor to the anti-diabetic effects of this anion

The changing patterns of group politics in Britain

Two interpretations of ways in which group politics in Britain have presented challenges to democracy are reviewed: neo-corporatism or pluralistic stagnation and the rise of single issue interest groups. The disappearance of the first paradigm created a political space for the second to emerge. A three-phase model of group activity is developed: a phase centred around production interests, followed by the development of broadly based 'other regarding' groups, succeeded by fragmented, inner directed groups focusing on particular interests. Explanations of the decay of corporatism are reviewed. Single issue group activity has increased as party membership has declined and is facilitated by changes in traditional media and the development of the internet. Such groups can overload the policy-making process and frustrate depoliticisation. Debates about the constitution and governance have largely ignored these issues and there is need for a debate

Dynamic causal modelling of COVID-19 [version 1; peer review: 1 approved, 1 not approved]

This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process

Testing and tracking in the UK: A dynamic causal modelling study [version 1; peer review: 1 approved with reservations, 1 not approved]

By equipping a previously reported dynamic causal modelling of COVID-19 with an isolation state, we were able to model the effects of self-isolation consequent on testing and tracking. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic—and could only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For example, under plausible (graded) relaxations of social distancing, a rebound of infections is highly unlikely. The emergence of a second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A testing and tracking policy—implemented at the present time—will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with 50,000 tests per day). These conclusions are based upon a dynamic causal model for which we provide some construct and face validation—using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies

Second waves, social distancing, and the spread of COVID-19 across the USA [version 3; peer review: 1 approved, 1 approved with reservations]

We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several instantiations of this (epidemic) model to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity—and the exchange of people between regions—and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium

Second waves, social distancing, and the spread of COVID-19 across the USA [version 2; peer review: 2 approved with reservations]

We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several instantiations of this (epidemic) model to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity—and the exchange of people between regions—and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium