Characteristics of the dengue viruses used in this study.

a<p>genome equivalents/mL.</p>b<p>C, C6/36 mosquito cell line; M, mosquito; S, suckling mouse.</p

The influence of magnetic vortices motion on the inverse ac Josephson effect in asymmetric arrays

We report on the influence a preferential magnetic vortices motion has on the magnitude of the inverse ac Josephson effect (the appearance of dc current Shapiro steps) and the coherent operation of asymmetrical parallel arrays of YBa2Cu3O7−δ Josephson junctions (JJ) irradiated with microwave (MW) radiation in the presence of an applied magnetic field B. The preferential direction of motion of the Josephson vortices is due to the asymmetry-induced ratchet effect and has a dramatic impact: for a particular positive dc bias current I when the flux-flow is robust multiple pronounced Shapiro-steps are observed consistent with a coherent operation of the array. This suggests an efficient emission/detection of MW in related applications. In contrast, when we reverse the direction of I, the flux-flow is reduced and the Shapiro steps are strongly suppressed due to a highly incoherent operation that suggests an inefficient emission/detection of MW. Remarkably, by changing B slightly, the situation is reversed: Shapiro steps are now suppressed for a positive I while well pronounced for a reverse current − I. Our results suggest that a preferential vortex-flow has a very significant impact on the coherent MW operation of superconducting devices consisting of either multiple JJs or an asymmetrically biased single long JJ. This is particularly relevant in the case of flux-flow oscillators for sub-terahertz integrated-receivers, flux-driven Josephson (travelling-wave) parametric amplifiers, or on-chip superconducting MW generators, which usually operate at bias currents in the Shapiro step region.</p

Estimates of effect of significant domicile construction materials on <i>Triatoma dimidiata</i> intradomiciliary prevalence >5%.

<p>Univariate logistic regression models were developed to investigate the effect of each domicile construction material on <i>Triatoma dimidiata</i> intradomiciliary village prevalence >5% by survey and department. Odds ratios (OR) and 95% confidence intervals for significant risk factors are reported. Domicile construction risk factors represent the proportion of domiciles per village constructed with each material as determined by the 2002 national census of the Guatemalan National Institute of Statistics <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001035#pntd.0001035-INE1" target="_blank">[40]</a>.</p

Summary of environmental and socioeconomic databases used in analyses.

<p>Key to database abbreviations: LST, land surface temperature; MIR, middle infrared; NDVI, normalized difference vegetation index; RH, relative humidity; max, maximum average value; min, minimum average value. Key to database source abbreviations: CGIAR_CSI, Consultative Group for International Agriculture Research – Consortium for Spatial Information; MODIS, moderate resolution imaging spectroradiometer; AVHRR/TFA, advanced very high resolution radiometer transformed by temporal fourier analysis; CRU/UEA, Climate Research Unit,/University of East Anglia; INE, Instituto Nacional de Estadistica de Guatemala.</p

Diagnostic statistics for predictive models of <i>Triatoma dimidiata</i> intradomiciliary prevalence >5%.

<p>Key to department and study abbreviations: Dept, department; BV, Baja Verapaz; JU, Jutiapa; UVG, Universidad del Valle de Guatemala; GNMH; Guatemala National Ministry of Health. Key to model abbreviations: ENV, environmental model; DOM, domicile construction material model; ALL, combination of census and environmental models. Key to accuracy measure abbreviations: AUC, area under receiver-operator curve; Max κ, maximum kappa; PPV, positive predictive value; NPV, negative predictive value.</p><p>Multivariate logistic regression models were developed to estimate the predictive probability of <i>Triatoma dimidiata</i> intradomiciliary village prevalence >5%. For each department and study, predictive models of environmental and domicile construction risk factors were developed separately and together. Overall model accuracy was compared using the area under the receiver-operator curve (AUC). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated using the probability threshold with maximum value of kappa (κ).</p

Estimates of effect of significant environmental risk factors on <i>Triatoma dimidiata</i> intradomiciliary prevalence >5%.

<p>Key to risk factor abbreviations: LST, land surface temperature; MIR, middle infrared; NDVI, normalized difference vegetation index; RH, relative humidity.</p><p>Univariate logistic regression models were developed to investigate the effect of each environmental covariate on <i>Triatoma dimidiata</i> intradomiciliary village prevalence >5% by survey and department. Odds ratios (OR) and 95% confidence intervals for significant risk factors are reported. Land cover classes represent the proportion of each land cover type within a 2 km buffer of analyzed villages.</p

Significant grouped climate variables with highest Akaike weight (<i>w_i</i>).

<p>Key to covariate abbreviations: LST, land surface temperature; MIR, middle infrared; NDVI, normalized difference vegetation index; RH, relative humidity. Key to database statistical abbreviations: AICc: Akaike information criterion for small sample sizes; <i>w_i</i>, Akaike weight.</p><p>Univariate logistic regression models were fitted to each of the grouped climate variables to determine the covariates that best discriminated intradomiciliary village prevalence. Model performance was evaluated by the selecting the covariate with the highest Akaike weight (<i>w_i</i>).</p

MOESM1 of Use of different transmission metrics to describe malaria epidemiology in the highlands of western Kenya

Additional file 1. Seroconversion rates (SCR) and corresponding 95 % confidence interval (CI) stratified by elevation and mosquito control categories. The table shows the seroconversion rates by elevation and mosquito control category, demonstrating lower exposure to malaria at altitudes above 1530 m and in households with both IRS and ITNs in their households

Simulated effect of intervention combinations.

<p>Compared to a scenario with no interventions outside the existing case management system, the mean and inter-quartile range of the impact of different intervention combinations (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107700#pone-0107700-t001" target="_blank"><b>Table 1</b></a>) on epidemiological outcomes in a population of 100,000 individuals over a time period of five years*. <b>Bold</b> figures indicate mean results improved from the current strategy.</p><p><i>*Unless otherwise indicated.</i></p><p>Simulated effect of intervention combinations.</p

Sensitivity analysis.

<p>Tornado diagram of the change in the ACER of an intervention with 80% LLIN use, 90% IRS coverage, and 80% IST coverage twice per term in relation to variation in component costs.</p