Fault-tolerant routing mechanism in 3D optical network-on-chip based on node reuse

The three-dimensional Network-on-Chips (3D NoCs) has become a mature multi-core interconnection architecture in recent years. However, the traditional electrical lines have very limited bandwidth and high energy consumption, making the photonic interconnection promising for future 3D Optical NoCs (ONoCs). Since existing solutions cannot well guarantee the fault-tolerant ability of 3D ONoCs, in this paper, we propose a reliable optical router (OR) structure which sacrifices less redundancy to obtain more restore paths. Moreover, by using our fault-tolerant routing algorithm, the restore path can be found inside the disabled OR under the deadlock-free condition, i.e., fault-node reuse. Experimental results show that the proposed approach outperforms the previous related works by maximum 81.1 percent and 33.0 percent on average for throughput performance under different synthetic and real traffic patterns. It can improve the system average optical signal to noise ratio (OSNR) performance by maximum 26.92 percent and 12.57 percent on average, and it can improve the average energy consumption performance by 0.3 percent to 15.2 percent under different topology types/sizes, failure rates, OR structures, and payload packet sizes

On the role of CD8+ T cells in determining recovery time from influenza virus infection

Myriad experiments have identified an important role for CD8+ T cell response mechanisms in determining recovery from influenza A virus infection. Animal models of influenza infection further implicate multiple elements of the immune response in defining the dynamical characteristics of viral infection. To date, influenza virus models, while capturing particular aspects of the natural infection history, have been unable to reproduce the full gamut of observed viral kinetic behaviour in a single coherent framework. Here, we introduce a mathematical model of influenza viral dynamics incorporating innate, humoral and cellular immune components and explore its properties with a particular emphasis on the role of cellular immunity. Calibrated against a range of murine data, our model is capable of recapitulating observed viral kinetics from a multitude of experiments. Importantly, the model predicts a robust exponential relationship between the level of effector CD8+ T cells and recovery time, whereby recovery time rapidly decreases to a fixed minimum recovery time with an increasing level of effector CD8+ T cells. We find support for this relationship in recent clinical data from influenza A(H7N9) hospitalized patients. The exponential relationship implies that people with a lower level of naive CD8+ T cells may receive significantly more benefit from induction of additional effector CD8+ T cells arising from immunological memory, itself established through either previous viral infection or T cell-based vaccines

Interval between infections and viral hierarchy are determinants of viral interference following influenza virus infection in a ferret model

Background.Epidemiological studies suggest that, following infection with influenza virus, there is a short period during which a host experiences a lower susceptibility to infection with other influenza viruses. This viral interference appears to be independent of any antigenic similarities between the viruses. We used the ferret model of human influenza to systematically investigate viral interference. Methods.Ferrets were first infected then challenged 1-14 days later with pairs of influenza A(H1N1)pdm09, influenza A(H3N2), and influenza B viruses circulating in 2009 and 2010. Results.Viral interference was observed when the interval between initiation of primary infection and subsequent challenge was <1 week. This effect was virus specific and occurred between antigenically related and unrelated viruses. Coinfections occurred when 1 or 3 days separated infections. Ongoing shedding from the primary virus infection was associated with viral interference after the secondary challenge. Conclusions.The interval between infections and the sequential combination of viruses were important determinants of viral interference. The influenza viruses in this study appear to have an ordered hierarchy according to their ability to block or delay infection, which may contribute to the dominance of different viruses often seen in an influenza season

Re-exposure behaviour of Model R1 for different IFN production rates.

<p>A smaller IFN production for the primary virus for Model R1 does not lead to any qualitative difference, in terms of the dependence of model behaviours for the challenge virus on the IEI, from the case of very large IFN production rate of the first virus. The pattern is also independent of the choice of <i>q</i>₂. The meaning of each colour is explained in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.g008" target="_blank">Fig 8</a>. They all exhibit four types of behaviours (seen vertically, separated by dashed lines) and within each type the phase decomposition and their order are preserved.</p

Innate Immunity and the Inter-exposure Interval Determine the Dynamics of Secondary Influenza Virus Infection and Explain Observed Viral Hierarchies

<div><p>Influenza is an infectious disease that primarily attacks the respiratory system. Innate immunity provides both a very early defense to influenza virus invasion and an effective control of viral growth. Previous modelling studies of virus–innate immune response interactions have focused on infection with a single virus and, while improving our understanding of viral and immune dynamics, have been unable to effectively evaluate the relative feasibility of different hypothesised mechanisms of antiviral immunity. In recent experiments, we have applied consecutive exposures to different virus strains in a ferret model, and demonstrated that viruses differed in their ability to induce a state of temporary immunity or viral interference capable of modifying the infection kinetics of the subsequent exposure. These results imply that virus-induced early immune responses may be responsible for the observed viral hierarchy. Here we introduce and analyse a family of within-host models of re-infection viral kinetics which allow for different viruses to stimulate the innate immune response to different degrees. The proposed models differ in their hypothesised mechanisms of action of the non-specific innate immune response. We compare these alternative models in terms of their abilities to reproduce the re-exposure data. Our results show that 1) a model with viral control mediated solely by a virus-resistant state, as commonly considered in the literature, is not able to reproduce the observed viral hierarchy; 2) the synchronised and desynchronised behaviour of consecutive virus infections is highly dependent upon the interval between primary virus and challenge virus exposures and is consistent with virus-dependent stimulation of the innate immune response. Our study provides the first mechanistic explanation for the recently observed influenza viral hierarchies and demonstrates the importance of understanding the host response to multi-strain viral infections. Re-exposure experiments provide a new paradigm in which to study the immune response to influenza and its role in viral control.</p></div

Re-exposure experimental data for primary exposure with influenza B virus followed by challenge with A(H1N1)pdm09.

<p>In contrast to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.g001" target="_blank">Fig 1</a>, changing the IEI does not block the infection of A(H1N1)pdm09 virus for any infection period. It does, however, result in a reduced growth rate and delayed time to peak virus titre for A(H1N1)pdm09 at short IEIs. Missing data points in the curves indicate undetectable levels of viral load and no sample was taken on the day of challenge. Each graph represents the data from a single ferret, so that two ferrets within each interval are shown here. All symbols are the same as those in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.g001" target="_blank">Fig 1</a>. Data used from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.ref010" target="_blank">10</a>].</p

Stage analysis of the three models with different antiviral mechanisms.

<p>For each model, we compute the solutions for two different IFN production rates, <i>q</i> = 10⁻⁷ and <i>q</i> = 5 × 10⁻⁶ (shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.s003" target="_blank">S1</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.s005" target="_blank">S3</a> Figs in the <i>Supporting Information</i>). Focusing on understanding the mechanisms of control of viral load, we present here the time series for the four terms on the right-hand side of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.e001" target="_blank">Eq 1</a>, <i>pI</i>/(1+<i>sF</i>) (viral growth), <i>cV</i> (viral natural decay), <i>μAV</i> (killed by antibody), and <i>βVT</i> (binding to target cells), which are calculated based on the model solutions and represent the contribution of each term to the change of viral load (<i>dV</i>/<i>dt</i>). Model 1: panels (A) and (B); Model 2: panels (C) and (D); Model 3: panels (E) and (F). For each of the cases shown, we use black trangles to roughly indicate three consecutive phases, which are characterised by the dominant factors involved in controlling the change of viral load.</p

Re-exposure experimental data showing the four dominant patterns observed in the viral kinetics for primary–challenge virus pairs.

<p>The data shown here are distinct from those shown in Figs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.g001" target="_blank">1</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.g002" target="_blank">2</a>, where the same phenomena may also be observed, and a subset of the full data presented in [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.ref010" target="_blank">10</a>]. Top panels show the case of co-infection, whereby both the primary (H1N1) and challenge (H3N2) viruses experience a synchronised increase in the very early stage of infection, followed by a synchronised decrease. Panels in the second row show examples of delayed infection, in which an initial synchronised decrease gives way to growth and successful infection with the challenge virus. The undetectable points between days 15 and 19 for the challenge virus in the right figure (second row) show a rapid decrease to undetectable viral level followed by a rapid upstroke back to a detectable level. Desynchronised viral kinetics in the early stage of infection are also observed for short IEIs, with examples shown in the third row of panels. The last well-observed pattern is that of a complete block, whereby the challenge virus is unable to replicate to a productive infection level (bottom panel). All symbols are the same as those in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.g001" target="_blank">Fig 1</a>. Data used from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.ref010" target="_blank">10</a>].</p

Parameter values for the three models.

<p>The units of <i>V</i>, <i>F</i> and <i>A</i> are denoted as <i>u</i>_<i>v</i>, <i>u</i>_<i>F</i> and <i>u</i>_<i>A</i> respectively. <i>T</i>, <i>I</i>, and <i>R</i> have the same unit of <i>u</i>_<i>T</i>; the number of cells. Time (<i>t</i>) has a unit of days (<i>d</i>). Some units are symbolised, as our study is highly qualitative and thus substantially independent of the choice of units. Such units would need to be transformed for different experimental protocols. “varied” indicates that the parameter was assigned different values for different simulations, with the value/s specified whenever necessary. Other parameters are taken or estimated from the literature (references are provided beside those parameter values), and others chosen such that 1) the viral load during infection experiences at least a three orders of magnitude increase and peaks at around the second day post infection [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.ref010" target="_blank">10</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.ref017" target="_blank">17</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.ref022" target="_blank">22</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.ref028" target="_blank">28</a>]; 2) IFN is maximally activated at around 2–4 days post infection [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.ref028" target="_blank">28</a>]; and 3) antibodies are observable (i.e. rise above a lower detection threshold limit) later than six days post infection [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.ref024" target="_blank">24</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004334#pcbi.1004334.ref035" target="_blank">35</a>].</p