Isolating and Reconstructing Key Components of North Atlantic Ocean Variability From a Sclerochronological Spatial Network

This is the final version. Available from AGU via the DOI in this record.Our understanding of North Atlantic Ocean variability within the coupled climate system is limited by the brevity of instrumental records and a deficiency of absolutely dated marine proxies. Here we demonstrate that a spatial network of marine stable oxygen isotope series derived from molluscan sclerochronologies (δ18Oshell) can provide skillful annually resolved reconstructions of key components of North Atlantic Ocean variability with absolute dating precision. Analyses of the common δ18Oshell variability, using principal component analysis, highlight strong connections with tropical North Atlantic and subpolar gyre (SPG) sea surface temperatures and sea surface salinity in the North Atlantic Current (NAC) region. These analyses suggest that low-frequency variability is dominated by the tropical Atlantic signal while decadal variability is dominated by variability in the SPG and salinity transport in the NAC. Split calibration and verification statistics indicate that the composite series produced using the principal component analysis can provide skillful quantitative reconstructions of tropical North Atlantic and SPG sea surface temperatures and NAC sea surface salinities over the industrial period (1864–2000). The application of these techniques with extended individual δ18Oshell series provides powerful baseline records of past North Atlantic variability into the unobserved preindustrial period. Such records are essential for developing our understanding of natural climate variability in the North Atlantic Ocean and the role it plays in the wider climate system, especially on multidecadal to centennial time scales, potentially enabling reduction of uncertainties in future climate predictions

Annually resolved North Atlantic marine climate over the last millennium

This is the final version of the article. Available from Nature Publishing Group via the DOI in this record.Owing to the lack of absolutely dated oceanographic information before the modern instrumental period, there is currently significant debate as to the role played by North Atlantic Ocean dynamics in previous climate transitions (for example, Medieval Climate Anomaly-Little Ice Age, MCA-LIA). Here we present analyses of a millennial-length, annually resolved and absolutely dated marine δ(18)O archive. We interpret our record of oxygen isotope ratios from the shells of the long-lived marine bivalve Arctica islandica (δ(18)O-shell), from the North Icelandic shelf, in relation to seawater density variability and demonstrate that solar and volcanic forcing coupled with ocean circulation dynamics are key drivers of climate variability over the last millennium. During the pre-industrial period (AD 1000-1800) variability in the sub-polar North Atlantic leads changes in Northern Hemisphere surface air temperatures at multi-decadal timescales, indicating that North Atlantic Ocean dynamics played an active role in modulating the response of the atmosphere to solar and volcanic forcing.We thank the members of the RV Bjarni Sæmundsson (Cruise No. B05-2006). This work was supported by the NERC-funded ULTRA project (Grant Number NE/H023356/1), NERC-funded CLAM project; (Project No. NE/N001176/1) and EU Millennium Project (Project number 017008). This study is a contribution to the Climate Change Consortium for Wales (C3W). We thank Brian Long (Bangor University) and Dr Julia Becker (Cardiff University) for their technical support, and Dr Manfred Mudelsee for his assistance with the trend analysis. We thank Dr Jessica Tierney and an anonymous reviewer for providing the constructive comments in the reviewing process

Modelers' Perception of Mathematical Modeling in Epidemiology: A Web-Based Survey

International audienceBackground: Mathematical modeling in epidemiology (MME) is being used increasingly. However, there are many uncertainties in terms of definitions, uses and quality features of MME. Methodology/Principal Findings: To delineate the current status of these models, a 10-item questionnaire on MME was devised. Proposed via an anonymous internet-based survey, the questionnaire was completed by 189 scientists who had published in the domain of MME. A small minority (18%) of respondents claimed to have in mind a concise definition of MME. Some techniques were identified by the researchers as characterizing MME (e.g. Markov models), while others–at the same level of sophistication in terms of mathematics–were not (e.g. Cox regression). The researchers' opinions were also contrasted about the potential applications of MME, perceived as higly relevant for providing insight into complex mechanisms and less relevant for identifying causal factors. The quality criteria were those of good science and were not related to the size and the nature of the public health problems addressed. Conclusions/Significance: This study shows that perceptions on the nature, uses and quality criteria of MME are contrasted, even among the very community of published authors in this domain. Nevertheless, MME is an emerging discipline in epidemiology and this study underlines that it is associated with specific areas of application and methods. The development of this discipline is likely to deserve a framework providing recommendations and guidance at various steps of the studies, from design to report

Genetic Predisposition of Donors Affects the Allograft Outcome in Kidney Transplantation; Polymorphisms of Stromal-Derived Factor-1 and CXC Receptor 4

Genetic interaction between donor and recipient may dictate the impending responses after transplantation. In this study, we evaluated the role of the genetic predispositions of stromal-derived factor-1 (SDF1) [rs1801157 (G>A)] and CXC receptor 4 (CXCR4) [rs2228014 (C>T)] on renal allograft outcomes. A total of 335 pairs of recipients and donors were enrolled. Biopsy-proven acute rejection (BPAR) and long-term graft survival were traced. Despite similar allele frequencies between donors and recipients, minor allele of SDF1 rs1801157 (GA+AA) from donor, not from recipients, has a protective effect on the development of BPAR compared to wild type donor (GG) (P = 0.005). Adjustment for multiple covariates did not affect this result (odds ratio 0.39, 95% C.I 0.20–0.76, P = 0.006). CXCR4 rs2228014 polymorphisms from donor or recipient did not affect the incidence of acute rejection. SDF1 was differentially expressed in renal tubular epithelium with acute rejection according to genetic variations of donor rs1801157 showing higher expressions in the grafts from GG donors. Contrary to the development of BPAR, the presence of minor allele rs1801157 A, especially homozygocity, predisposed poor graft survival (P = 0.001). This association was significant after adjusting for several risk factors (hazard ratio 3.01; 95% C.I = 1.19–7.60; P = 0.020). The allelic variation of recipients, however, was not associated with graft loss. A donor-derived genetic polymorphism of SDF1 has influenced the graft outcome. Thus, the genetic predisposition of donor should be carefully considered in transplantation

Factors associated with completion of bowel cancer screening and the potential effects of simplifying the screening test algorithm

BACKGROUND: The primary colorectal cancer screening test in England is a guaiac faecal occult blood test (gFOBt). The NHS Bowel Cancer Screening Programme (BCSP) interprets tests on six samples on up to three test kits to determine a definitive positive or negative result. However, the test algorithm fails to achieve a definitive result for a significant number of participants because they do not comply with the programme requirements. This study identifies factors associated with failed compliance and modifications to the screening algorithm that will improve the clinical effectiveness of the screening programme. METHODS: The BCSP Southern Hub data for screening episodes started in 2006–2012 were analysed for participants aged 60–69 years. The variables included age, sex, level of deprivation, gFOBt results and clinical outcome. RESULTS: The data set included 1 409 335 screening episodes; 95.08% of participants had a definitively normal result on kit 1 (no positive spots). Among participants asked to complete a second or third gFOBt, 5.10% and 4.65%, respectively, failed to return a valid kit. Among participants referred for follow up, 13.80% did not comply. Older age was associated with compliance at repeat testing, but non-compliance at follow up. Increasing levels of deprivation were associated with non-compliance at repeat testing and follow up. Modelling a reduction in the threshold for immediate referral led to a small increase in completion of the screening pathway. CONCLUSIONS: Reducing the number of positive spots required on the first gFOBt kit for referral for follow-up and targeted measures to improve compliance with follow-up may improve completion of the screening pathway

Impact of Emerging Antiviral Drug Resistance on Influenza Containment and Spread: Influence of Subclinical Infection and Strategic Use of a Stockpile Containing One or Two Drugs

BACKGROUND: Wide-scale use of antiviral agents in the event of an influenza pandemic is likely to promote the emergence of drug resistance, with potentially deleterious effects for outbreak control. We explored factors promoting resistance within a dynamic infection model, and considered ways in which one or two drugs might be distributed to delay the spread of resistant strains or mitigate their impact. METHODS AND FINDINGS: We have previously developed a novel deterministic model of influenza transmission that simulates treatment and targeted contact prophylaxis, using a limited stockpile of antiviral agents. This model was extended to incorporate subclinical infections, and the emergence of resistant virus strains under the selective pressure imposed by various uses of one or two antiviral agents. For a fixed clinical attack rate, R(0) rises with the proportion of subclinical infections thus reducing the number of infections amenable to treatment or prophylaxis. In consequence, outbreak control is more difficult, but emergence of drug resistance is relatively uncommon. Where an epidemic may be constrained by use of a single antiviral agent, strategies that combine treatment and prophylaxis are most effective at controlling transmission, at the cost of facilitating the spread of resistant viruses. If two drugs are available, using one drug for treatment and the other for prophylaxis is more effective at preventing propagation of mutant strains than either random allocation or drug cycling strategies. Our model is relatively straightforward, and of necessity makes a number of simplifying assumptions. Our results are, however, consistent with the wider body of work in this area and are able to place related research in context while extending the analysis of resistance emergence and optimal drug use within the constraints of a finite drug stockpile. CONCLUSIONS: Combined treatment and prophylaxis represents optimal use of antiviral agents to control transmission, at the cost of drug resistance. Where two drugs are available, allocating different drugs to cases and contacts is likely to be most effective at constraining resistance emergence in a pandemic scenario

How to Minimize the Attack Rate during Multiple Influenza Outbreaks in a Heterogeneous Population

<div><h3>Background</h3><p>If repeated interventions against multiple outbreaks are not feasible, there is an optimal level of control during the first outbreak. Any control measures above that optimal level will lead to an outcome that may be as sub-optimal as that achieved by an intervention that is too weak. We studied this scenario in more detail.</p> <h3>Method</h3><p>An age-stratified ordinary-differential-equation model was constructed to study infectious disease outbreaks and control in a population made up of two groups, adults and children. The model was parameterized using influenza as an example. This model was used to simulate two consecutive outbreaks of the same infectious disease, with an intervention applied only during the first outbreak, and to study how cumulative attack rates were influenced by population composition, strength of inter-group transmission, and different ways of triggering and implementing the interventions. We assumed that recovered individuals are fully immune and the intervention does not confer immunity.</p> <h3>Results/Conclusion</h3><p>The optimal intervention depended on coupling between the two population sub-groups, the length, strength and timing of the intervention, and the population composition. Population heterogeneity affected intervention strategies only for very low cross-transmission between groups. At more realistic values, coupling between the groups led to synchronization of outbreaks and therefore intervention strategies that were optimal in reducing the attack rates for each subgroup and the population overall coincided. For a sustained intervention of low efficacy, early intervention was found to be best, while at high efficacies, a delayed start was better. For short interventions, a delayed start was always advantageous, independent of the intervention efficacy. For most scenarios, starting the intervention after a certain cumulative proportion of children were infected seemed more robust in achieving close to optimal outcomes compared to a strategy that used a specified duration after an outbreak’s beginning as the trigger.</p> </div

A simulation analysis to characterize the dynamics of vaccinating behaviour on contact networks

<p>Abstract</p> <p>Background</p> <p>Human behavior influences infectious disease transmission, and numerous "prevalence-behavior" models have analyzed this interplay. These previous analyses assumed homogeneously mixing populations without spatial or social structure. However, spatial and social heterogeneity are known to significantly impact transmission dynamics and are particularly relevant for certain diseases. Previous work has demonstrated that social contact structure can change the individual incentive to vaccinate, thus enabling eradication of a disease under a voluntary vaccination policy when the corresponding homogeneous mixing model predicts that eradication is impossible due to free rider effects. Here, we extend this work and characterize the range of possible behavior-prevalence dynamics on a network.</p> <p>Methods</p> <p>We simulate transmission of a vaccine-prevetable infection through a random, static contact network. Individuals choose whether or not to vaccinate on any given day according to perceived risks of vaccination and infection.</p> <p>Results</p> <p>We find three possible outcomes for behavior-prevalence dynamics on this type of network: small final number vaccinated and final epidemic size (due to rapid control through voluntary ring vaccination); large final number vaccinated and significant final epidemic size (due to imperfect voluntary ring vaccination), and little or no vaccination and large final epidemic size (corresponding to little or no voluntary ring vaccination). We also show that the social contact structure enables eradication under a broad range of assumptions, except when vaccine risk is sufficiently high, the disease risk is sufficiently low, or individuals vaccinate too late for the vaccine to be effective.</p> <p>Conclusion</p> <p>For populations where infection can spread only through social contact network, relatively small differences in parameter values relating to perceived risk or vaccination behavior at the individual level can translate into large differences in population-level outcomes such as final size and final number vaccinated. The qualitative outcome of rational, self interested behaviour under a voluntary vaccination policy can vary substantially depending on interactions between social contact structure, perceived vaccine and disease risks, and the way that individual vaccination decision-making is modelled.</p

Transplantation of mesenchymal stem cells from young donors delays aging in mice

Increasing evidence suggests that the loss of functional stem cells may be important in the aging process. Our experiments were originally aimed at testing the idea that, in the specific case of age-related osteoporosis, declining function of osteogenic precursor cells might be at least partially responsible. To test this, aging female mice were transplanted with mesenchymal stem cells from aged or young male donors. We find that transplantation of young mesenchymal stem cells significantly slows the loss of bone density and, surprisingly, prolongs the life span of old mice. These observations lend further support to the idea that age-related diminution of stem cell number or function may play a critical role in age-related loss of bone density in aging animals and may be one determinant of overall longevity

Influenza Infectious Dose May Explain the High Mortality of the Second and Third Wave of 1918–1919 Influenza Pandemic

BACKGROUND: It is widely accepted that the shift in case-fatality rate between waves during the 1918 influenza pandemic was due to a genetic change in the virus. In animal models, the infectious dose of influenza A virus was associated to the severity of disease which lead us to propose a new hypothesis. We propose that the increase in the case-fatality rate can be explained by the dynamics of disease and by a dose-dependent response mediated by the number of simultaneous contacts a susceptible person has with infectious ones. METHODS: We used a compartment model with seasonality, waning of immunity and a Holling type II function, to model simultaneous contacts between a susceptible person and infectious ones. In the model, infected persons having mild or severe illness depend both on the proportion of infectious persons in the population and on the level of simultaneous contacts between a susceptible and infectious persons. We further allowed for a high or low rate of waning immunity and volunteer isolation at different times of the epidemic. RESULTS: In all scenarios, case-fatality rate was low during the first wave (Spring) due to a decrease in the effective reproduction number. The case-fatality rate in the second wave (Autumn) depended on the ratio between the number of severe cases to the number of mild cases since, for each 1000 mild infections only 4 deaths occurred whereas for 1000 severe infections there were 20 deaths. A third wave (late Winter) was dependent on the rate for waning immunity or on the introduction of new susceptible persons in the community. If a group of persons became voluntarily isolated and returned to the community some days latter, new waves occurred. For a fixed number of infected persons the overall case-fatality rate decreased as the number of waves increased. This is explained by the lower proportion of infectious individuals in each wave that prevented an increase in the number of severe infections and thus of the case-fatality rate. CONCLUSION: The increase on the proportion of infectious persons as a proxy for the increase of the infectious dose a susceptible person is exposed, as the epidemic develops, can explain the shift in case-fatality rate between waves during the 1918 influenza pandemic.TD acknowledges the support of the Faculdade de Ciencias e Tecnologia through grant PPCDT/AMB/55701/2004. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript