Genotyping of Francisella tularensis strains by pulsed-field gel electrophoresis, amplified fragment length polymorphism fingerprinting, and 16S rRNA gene sequencing

We evaluated three molecular methods for identification of Francisella strains: pulsed-field gel electrophoresis (PFGE), amplified fragment length polymorphism (AFLP) analysis, and 16S rRNA gene sequencing. The analysis was performed with 54 Francisella tularensis subsp. holarctica, 5 F. tularensis subsp. tularensis, 2 F. tularensis subsp. novicida, and 1 F. philomiragia strains. On the basis of the combination of results obtained by PFGE with the restriction enzymes XhoI and BamHI, PFGE revealed seven pulsotypes, which allowed us to discriminate the strains to the subspecies level and which even allowed us to discriminate among some isolates of F. tularensis subsp. holarctica. The AFLP analysis technique produced some degree of discrimination among F. tularensis subsp. holarctica strains (one primary cluster with three major subclusters and minor variations within subclusters) when EcoRI-C and MseI-A, EcoRI-T and MseI-T, EcoRI-A and MseI-C, and EcoRI-0 and MseI-CA were used as primers. The degree of similarity among the strains was about 94%. The percent similarities of the AFLP profiles of this subspecies compared to those of F. tularensis subsp. tularensis, F. tularensis subsp. novicida, and F. philomiragia were less than 90%, about 72%, and less than 24%, respectively, thus permitting easy differentiation of this subspecies. 16S rRNA gene sequencing revealed 100% similarity for all F. tularensis subsp. holarctica isolates compared in this study. These results suggest that although limited genetic heterogeneity among F. tularensis subsp. holarctica isolates was observed, PFGE and AFLP analysis appear to be promising tools for the diagnosis of infections caused by different subspecies of F. tularensis and suitable techniques for the differentiation of individual strains.

Occurrence, quantification, and genotyping of Mycoplasma conjunctivae in wild Caprinae with and without infectious keratoconjunctivitis

Mycoplasma conjunctivae, the causative agent of infectious keratoconjunctivitis (IKC), was recently detected in asymptomatic Alpine ibex (Capra ibex ibex). This suggested that an external source of infection may not be required for an IKC outbreak in wildlife but might be initiated by healthy carriers, which contradicted previous serologic investigations in chamois. Our aims were to 1) assess the prevalence of M. conjunctivae among asymptomatic ibex and Alpine chamois (Rupicapra rupicapra rupicapra) and its frequency in IKC-affected animals, 2) determine mycoplasma loads in different disease stages, and 3) characterize the M. conjunctivae strains involved. Eye swabs from 654 asymptomatic and 204 symptomatic animals were collected in diverse Swiss regions between 2008 and 2010, and tested by TaqMan real-time PCR. Data analysis was performed considering various patterns of IKC occurrence in the respective sampling regions. Strains from 24 animals were compared by cluster analysis. Prevalence of M. conjunctivae was 5.6% (95% confidence interval [CI]: 3.7-8.1%) in asymptomatic ibex and 5.8% (CI: 3.0-9.9%) in asymptomatic chamois, with significant differences between years and regions in both species. Detection frequency in symptomatic animals was significantly higher during IKC outbreaks than in nonepidemic situations (i.e., regular but low incidence or sporadic occurrence). Mycoplasma load was significantly lower in eyes from healthy carriers and animals with mild signs than from animals with moderate and severe signs. Although some strains were found in both asymptomatic and diseased animals of the same species, others apparently differed in their pathogenic potential depending on the infected species. Overall, we found a widespread occurrence of M. conjunctivae in wild Caprinae with and without IKC signs. Our results confirm the central role of M. conjunctivae in outbreaks but suggest that other infectious agents may be involved in IKC cases in nonepidemic situations. Additionally, presence and severity of signs are related to the quantity of M. conjunctivae in the eyes rather than to the strain. We propose that individual or environmental factors influence the clinical expression of the disease and that persistence of M. conjunctivae in populations of wild Caprinae cannot be excluded.

A multiplex PCR for the simultaneous detection and genotyping of the Echinococcus granulosus complex.

Echinococcus granulosus is characterized by high intra-specific variability (genotypes G1-G10) and according to the new molecular phylogeny of the genus Echinococcus, the E. granulosus complex has been divided into E. granulosus sensu stricto (G1-G3), E. equinus (G4), E. ortleppi (G5), and E. canadensis (G6-G10). The molecular characterization of E. granulosus isolates is fundamental to understand the spatio-temporal epidemiology of this complex in many endemic areas with the simultaneous occurrence of different Echinococcus species and genotypes. To simplify the genotyping of the E. granulosus complex we developed a single-tube multiplex PCR (mPCR) allowing three levels of discrimination: (i) Echinococcus genus, (ii) E. granulosus complex in common, and (iii) the specific genotype within the E. granulosus complex. The methodology was established with known DNA samples of the different strains/genotypes, confirmed on 42 already genotyped samples (Spain: 22 and Bulgaria: 20) and then successfully applied on 153 unknown samples (Tunisia: 114, Algeria: 26 and Argentina: 13). The sensitivity threshold of the mPCR was found to be 5 ng Echinoccoccus DNA in a mixture of up to 1 µg of foreign DNA and the specificity was 100% when template DNA from closely related members of the genus Taenia was used. Additionally to DNA samples, the mPCR can be carried out directly on boiled hydatid fluid or on alkaline-lysed frozen or fixed protoscoleces, thus avoiding classical DNA extractions. However, when using Echinococcus eggs obtained from fecal samples of infected dogs, the sensitivity of the mPCR was low (<40%). Thus, except for copro analysis, the mPCR described here has a high potential for a worldwide application in large-scale molecular epidemiological studies on the Echinococcus genus.

Quantifying uncertainty in cost effectiveness analyses

This study compared four alternative approaches (Taylor, Fieller, percentile bootstrap, and bias-corrected bootstrap methods) to estimating confidence intervals (CIs) around cost-effectiveness (CE) ratio. The study consisted of two components: (1) Monte Carlo simulation was conducted to identify characteristics of hypothetical cost-effectiveness data sets which might lead one CI estimation technique to outperform another. These results were matched to the characteristics of an (2) extant data set derived from the National AIDS Demonstration Research (NADR) project. The methods were used to calculate (CIs) for data set. These results were then compared. The main performance criterion in the simulation study was the percentage of times the estimated (CIs) contained the “true” CE. A secondary criterion was the average width of the confidence intervals. For the bootstrap methods, bias was estimated. ^ Simulation results for Taylor and Fieller methods indicated that the CIs estimated using the Taylor series method contained the true CE more often than did those obtained using the Fieller method, but the opposite was true when the correlation was positive and the CV of effectiveness was high for each value of CV of costs. Similarly, the CIs obtained by applying the Taylor series method to the NADR data set were wider than those obtained using the Fieller method for positive correlation values and for values for which the CV of effectiveness were not equal to 30% for each value of the CV of costs. ^ The general trend for the bootstrap methods was that the percentage of times the true CE ratio was contained in CIs was higher for the percentile method for higher values of the CV of effectiveness, given the correlation between average costs and effects and the CV of effectiveness. The results for the data set indicated that the bias corrected CIs were wider than the percentile method CIs. This result was in accordance with the prediction derived from the simulation experiment. ^ Generally, the bootstrap methods are more favorable for parameter specifications investigated in this study. However, the Taylor method is preferred for low CV of effect, and the percentile method is more favorable for higher CV of effect. ^

Single hadron response measurement and calorimeter jet energy scale uncertainty with the ATLAS detector at the LHC

The uncertainty on the calorimeter energy response to jets of particles is derived for the ATLAS experiment at the Large Hadron Collider (LHC). First, the calorimeter response to single isolated charged hadrons is measured and compared to the Monte Carlo simulation using proton-proton collisions at centre-of-mass energies of root s = 900 GeV and 7 TeV collected during 2009 and 2010. Then, using the decay of K-s and Lambda particles, the calorimeter response to specific types of particles (positively and negatively charged pions, protons, and anti-protons) is measured and compared to the Monte Carlo predictions. Finally, the jet energy scale uncertainty is determined by propagating the response uncertainty for single charged and neutral particles to jets. The response uncertainty is 2-5 % for central isolated hadrons and 1-3 % for the final calorimeter jet energy scale.

Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set

Stepwise uncertainty reduction (SUR) strategies aim at constructing a sequence of points for evaluating a function  f in such a way that the residual uncertainty about a quantity of interest progressively decreases to zero. Using such strategies in the framework of Gaussian process modeling has been shown to be efficient for estimating the volume of excursion of f above a fixed threshold. However, SUR strategies remain cumbersome to use in practice because of their high computational complexity, and the fact that they deliver a single point at each iteration. In this article we introduce several multipoint sampling criteria, allowing the selection of batches of points at which f can be evaluated in parallel. Such criteria are of particular interest when f is costly to evaluate and several CPUs are simultaneously available. We also manage to drastically reduce the computational cost of these strategies through the use of closed form formulas. We illustrate their performances in various numerical experiments, including a nuclear safety test case. Basic notions about kriging, auxiliary problems, complexity calculations, R code, and data are available online as supplementary materials.

Clinical outcomes and molecular genotyping of Staphylococcus aureus isolated from milk samples of dairy primiparous Mediterranean buffaloes (Bubalus bubalis)

Staphylococcus aureus is one of the most important pathogens causing mastitis in dairy cows and in Mediterranean buffaloes. Genotype B (GTB) is contagious in dairy cows and may occur in up to 87% of cows of a dairy herd. It was the aim of this study to evaluate genotypes present, clinical outcomes, and prevalence of Staph. aureus in milk samples of primiparous Mediterranean dairy buffaloes. Two hundred composite milk samples originating from 40 primiparous buffaloes were collected from May to June 2012, at d 10, 30, 60, 90, and 150 d in milk (DIM) to perform somatic cell counts and bacteriological cultures. Daily milk yields were recorded. Before parturition until 40 to 50 DIM, all primiparous animals were housed separated from the pluriparous animals. Milking was performed in the same milking parlor, but the primiparous animals were milked first. After 50 DIM, the primiparous were mixed with the pluriparous animals, including the milking procedure. Individual quarter samples were collected from each animal, and aliquots of 1 mL were mixed and used for molecular identification and genotyping of Staph. aureus. The identification of Staph. aureus was performed verifying the presence of nuc gene by nuc gene PCR. All the nuc-positive isolates were subjected to genotype analysis by means of PCR amplification of the 16S-23S rRNA intergenic spacer region and analyzed by a miniaturized electrophoresis system. Of all 200 composite samples, 41 (20.5%) were positive for Staph. aureus, and no genotype other than GTB was identified. The prevalence of samples positive for Staph. aureus was 0% at 10 DIM and increased to a maximum of 22/40 (55%) at 90 DIM. During the period of interest, 14 buffaloes tested positive for Staph. aureus once, 6 were positive twice, and 5 were positive 3 times, whereas 15 animals were negative at every sampling. At 90 and 150 DIM, 7 (17.5%) and 3 buffaloes (7.5%), respectively, showed clinical mastitis (CM), and only 1 (2.5%) showed CM at both samplings. At 60, 90, and 150 DIM, 1 buffalo was found with subclinical mastitis at each sampling. At 30, 60, 90, and 150 DIM, 2.5 (1/40), 22.5 (9/40), 35 (14/40), and 10% (4/40) were considered affected by intramammary infection, respectively. Buffaloes with CM caused by Staph. aureus had statistically significantly higher mean somatic cell count values (6.06 ± 0.29, Log10 cells/mL ± standard deviation) and statistically significantly lower mean daily milk yields (7.15 ± 1.49, liters/animal per day) than healthy animals (4.69 ± 0.23 and 13.87 ± 2.64, respectively), buffaloes with IMI (4.82 ± 0.23 and 11.16 ± 1.80, respectively), or with subclinical mastitis (5.47 ± 0.10 and 10.33 ± 0.68, respectively). Based on our knowledge, this is the first time that Staph. aureus GTB has been identified in milk samples of dairy Mediterranean buffaloes.

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob’ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob’ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.

Networks of Information Exchange Under Uncertainty: The Case of Unconventional Gas Development in the United Kingdom

Modern policy-making is increasingly influenced by different types of uncertainty. Political actors are supposed to behave differently under the context of uncertainty then in “usual” decision-making processes. Actors exchange information in order to convince other actors and decision-makers, to coordinate their lobbying activities and form coalitions, and to get information and learn on the substantive issue. The literature suggests that preference similarity, social trust, perceived power and functional interdependence are particularly important drivers of information exchange. We assume that social trust as well as being connected to scientific actors is more important under uncertainty than in a setting with less uncertainty. To investigate information exchange under uncertainty analyze the case of unconventional shale gas development in the UK from 2008 till 2014. Our study will rely on statistical analyses of survey data on a diverse set of actors dealing with shale gas development and regulation in the UK.

Policy Instrument Selection under Uncertainty: The Case of Micropollution Regulation

The paper addresses the question of which factors drive the formation of policy preferences when there are remaining uncertainties about the causes and effects of the problem at stake. To answer this question we examine policy preferences reducing aquatic micropollutants, a specific case of water protection policy and different actor groups (e.g. state, science, target groups). Here, we contrast two types of policy preferences: a) preventive or source-directed policies, which mitigate pollution in order to avoid contact with water; and b) reactive or end-of-pipe policies, which filter water already contaminated by pollutants. In a second step, we analyze the drivers for actors’ policy preferences by focusing on three sets of explanations, i.e. participation, affectedness and international collaborations. The analysis of our survey data, qualitative interviews and regression analysis of the Swiss political elite show that participation in the policy-making process leads to knowledge exchange and reduces uncertainties about the policy problem, which promotes preferences for preventive policies. Likewise, actors who are affected by the consequences of micropollutants, such as consumer or environmental associations, opt for anticipatory policies. Interestingly, we find that uncertainties about the effectiveness of preventive policies can promote preferences for end-of-pipe policies. While preventive measures often rely on (uncertain) behavioral changes of target groups, reactive policies are more reliable when it comes to fulfilling defined policy goals. Finally, we find that in a transboundary water management context, actors with international collaborations prefer policies that produce immediate and reliable outcomes.