Intrusive versus domiciliated triatomines and the challenge of adapting vector control practices against Chagas disease

Chagas disease prevention remains mostly based on triatomine vector control to reduce or eliminate house infestation with these bugs. The level of adaptation of triatomines to human housing is a key part of vector competence and needs to be precisely evaluated to allow for the design of effective vector control strategies. In this review, we examine how the domiciliation/intrusion level of different triatomine species/populations has been defined and measured and discuss how these concepts may be improved for a better understanding of their ecology and evolution, as well as for the design of more effective control strategies against a large variety of triatomine species. We suggest that a major limitation of current criteria for classifying triatomines into sylvatic, intrusive, domiciliary and domestic species is that these are essentially qualitative and do not rely on quantitative variables measuring population sustainability and fitness in their different habitats. However, such assessments may be derived from further analysis and modelling of field data. Such approaches can shed new light on the domiciliation process of triatomines and may represent a key tool for decision-making and the design of vector control interventions.

Assessing the mitochondrial DNA diversity of the Chagas disease vector Triatoma sordida (Hemiptera: Reduviidae)

Triatoma sordida is a species that transmits Trypanosoma cruzi to humans. In Brazil, T. sordida currently deserves special attention because of its wide distribution, tendency to invade domestic environments and vectorial competence. For the planning and execution of control protocols to be effective against Triatominae, they must consider its population structure. In this context, this study aimed to characterise the genetic variability of T. sordida populations collected in areas with persistent infestations from Minas Gerais, Brazil. Levels of genetic variation and population structure were determined in peridomestic T. sordida by sequencing a polymorphic region of the mitochondrial cytochrome b gene. Low nucleotide and haplotype diversity were observed for all 14 sampled areas; π values ranged from 0.002-0.006. Most obtained haplotypes occurred at low frequencies, and some were exclusive to only one of the studied populations. Interpopulation genetic diversity analysis revealed strong genetic structuring. Furthermore, the genetic variability of Brazilian populations is small compared to that of Argentinean and Bolivian specimens. The possible factors related to the reduced genetic variability and strong genetic structuring obtained for studied populations are discussed in this paper.

Towards a system analysis of the early steps of infectious endocarditis pathogenesis

L'endocardite infectieuse (EI) est une maladie potentiellement mortelle qui doit être prévenue dans toute la mesure du possible. Au cours de ces dernières 50 années, les recommandations Américaines et Européennes pour la prophylaxie de PEI proposaient aux patients à risques de prendre un antibiotique, préventif avant de subir une intervention médico-chirurgicale susceptible d'induire une bactériémie transitoire. Cependant, des études épidémiologiques récentes ont montré que la plupart des EI survenaient en dehors de tous actes médico-chirurgicaux, et indépendamment de la prise ou non de prophylaxie antibiotique . L'EI pourrait donc survenir suite à la cumulation de bactériémies spontanées de faibles intensités, associées à des activités de la vie courante telle que le brossage dentaire pour le streptocoques, ou à partir de tissus colonisés ou de cathéters infectés pour les staphylocoques. En conséquence, les recommandations internationales pour la prophylaxie de PEI ont été revues et proposent une diminution drastique de l'utilisation d'antibiotiques. Cependant, le risque d'EI représenté par le cumul de bactériémies de faibles intensités n'a pas été démontré expérimentalement. Nous avons développé un nouveau modèle d'EI expérimentale induite par une inoculation en continu d'une faible quantité de bactéries, simulant le cumul de bactériémies de faibles intensités chez l'homme, et comparé l'infection de Streptococcus gordonii et de Staphylococcus aureus dans ce modèle avec celle du modèle d'IE induite par une bactériémie brève, mais de forte intensité. Nous avons démontré, après injection d'une quantité égale de bactéries, que le nombre de végétations infectées était similaire dans les deux types d'inoculations. Ces résultats expérimentaux ont confirmé l'hypothèse qu'une exposition cumulée à des bactériémies de faibles intensités, en dehors d'une procédure médico-chirurgicale, représentait un risque pour le développement d'une El, comme le suggéraient les études épidémiologiques. En plus, ces résultats ont validé les nouvelles recommandations pour la prophylaxie de l'El, limitant drastiquement l'utilisation d'antibiotiques. Cependant, ces nouvelles recommandations laissent une grande partie (> 90%) de cas potentiels d'EI sans alternatives de préventions, et des nouvelles stratégies prophylactiques doivent être investiguées. Le nouveau modèle d'EI expérimentale représente un modèle réaliste pour étudier des nouvelles mesures prophylactiques potentielles appliquées à des expositions cumulées de bactériémies de faible nombre. Dans un contexte de bactériémies spontanées répétitives, les antibiotiques ne peuvent pas résoudre le problème de la prévention de l'EI. Nous avons donc étudié la une alternative de prévention par l'utilisation d'agents antiplaquettaires. La logique derrière cette approche était basée sur le fait que les plaquettes sont des composants clés dans la formation des végétations cardiaques, et le fait que les bactéries capables d'interagir avec les plaquettes sont plus enclines à induire une El. Les agents antiplaquettaires utilisés ont été l'aspirine (inhibiteur du COX1), la ticlopidine (inhibiteur du P2Y12, le récepteur de l'ADP), et l'eptifibatide et Pabciximab, deux inhibiteurs du GPIIb/IIIa, le récepteur plaquettaire pour le fibrinogène. Les anticoagulants étaient le dabigatran etexilate, inhibant lathrombine et l'acenocumarol, un antagoniste de la vitamine K. L'aspirine, la ticlopidine ou l'eptifibatide seuls n'ont pas permis de prévenir l'infection valvulaire (> 75% animaux infectés). En revanche, la combinaison d'aspirine et de ticlopidine, aussi bien que l'abciximab, ont protégé 45% - 88% des animaux de l'EI par S. gordonii et par S. aureus. L'antithrombotique dabigatran etexilate à protégé 75% des rats contre l'EI par S. aureus, mais pas (< 30% de protection) par S. gordonii. L'acenocoumarol n'a pas eu d'effet sur aucun des deux organismes. En général, ces résultats suggèrent un possible rôle pour les antiplaquettaires et du dabigatran etexilate dans la prophylaxie de l'EI dans un contexte de bactériémies récurrentes de faibles intensités. Cependant, l'effet bénéfique des antiplaquettaires doit être soupesé avec le risque d'hémorragie inhérent à ces molécules, et le fait que les plaquettes jouent un important rôle dans les défenses de l'hôte contre les infections endovasculaires. En plus, le double effet bénéfique du dabigatran etexilate devrait être revu chez les patients porteurs de valves prothétiques, qui ont besoin d'une anticoagulation à vie, et chez lesquels l'EI à S. aureus est associée avec une mortalité de près de 50%. Comme l'approche avec des antiplaquettaires et des antithrombotiques pourrait avoir des limites, une autre stratégie prophylactique pourrait être la vaccination contre des adhésines de surfaces des pathogènes. Chez S. aureus, la protéine de liaison au fibrinogène, ou dumping factor A (ClfA), et la protéine de liaison à la fibronectine (FnbpA) sont des facteurs de virulence nécessaires à l'initiation et l'évolution de PEI. Elles représentent donc des cibles potentielles pour le développement de vaccins contre cette infection. Récemment, des nombreuses publications ont décrit que la bactérie Lactococcus lactis pouvait être utilisée comme vecteur pour la diffusion d'antigènes bactériens in vivo, et que cette approche pourrait être une stratégie de vaccination contre les infections bactériennes. Nous avons exploré l'effet de l'immunisation par des recombinant de L. lactis exprimant le ClfA, la FnbpA, ou le ClfA ensemble avec et une forme tronquée de la FnbpA (Fnbp, comprenant seulement le domaine de liaison à la fibronectine mais sans le domaine A de liaison au fibrinogène [L. lactis ClfA/Fnbp]), dans la prophylaxie de PIE expérimentale à S. aureus. L. lactis ClfA a été utilisés comme agent d'immunisation contre la souche S. aureus Newman (qui a particularité de n'exprimer que le ClfA, mais pas la FnbpA). L. lactis ClfA, L. lactis FnbpA, et L. lactis ClfA/Fnbp, ont été utilisé comme agents d'immunisation contre une souche isolée d'une IE, S. aureus P8 (exprimant ClfA et FnbpA). L'immunisation avec L. lactis ClfA a généré des anticorps anti-ClfA fonctionnels, capables de bloquer la liaison de S. aureus Newman au fibrinogène in vitro et protéger 13/19 (69%) animaux d'une El due à S. aureus Newman (P < 0.05 comparée aux contrôles). L'immunisation avec L. lactis ClfA, L. lactis FnbpA, ou L. lactis ClfA/Fnbp, a généré des anticorps contre chacun de ces antigènes. Cependant, ils n'ont pas permis de bloquer l'adhésion de S. aureus P8 au fibrinogène et à la fibronectine in vitro. De plus, l'immunisation avec L. lactis ClfA ou L. lactis FnbpA s'est avérée inefficace in vivo (< 10% d'animaux protégés d'une El) et l'immunisation avec L. lactis ClfA/Fnbp a fourni une protection limitée de l'EI (8/23 animaux protégés; P < 0.05 comparée aux contrôles) après inoculation avec S. aureus P8. Dans l'ensemble, ces résultats indiquent que L. lactis est un système efficace pour la présentation d'antigènes in vivo et potentiellement utile pour la prévention de PEI à S. aureus. Cependant, le répertoire de protéines de surface de S. aureus capable d'évoquer une panoplie d'anticorps efficace reste à déterminer.. En résumé, notre étude a démontré expérimentalement, pour la première fois, qu'une bactériémie répétée de faible intensité, simulant la bactériémie ayant lieu, par exemple, lors des activités de la vie quotidienne, est induire un taux d'EI expérimentale similaire à celle induite par une bactériémie de haute intensité suite à une intervention médicale. Dans ce contexte, où l'utilisation d'antibiotiques est pas raisonnable, nous avons aussi montré que d'autres mesures prophylactiques, comme l'utilisation d'agents antiplaquettaires ou antithrombotiques, ou la vaccination utilisant L. lactis comme vecteur d'antigènes bactériens, sont des alternatives prometteuses qui méritent d'être étudiées plus avant. Thesis Summary Infective endocarditis (IE) is a life-threatening disease that should be prevented whenever possible. Over the last 50 years, guidelines for IE prophylaxis proposed the use of antibiotics in patients undergoing dental or medico-surgical procedures that might induce high, but transient bacteremia. However, recent epidemiological studies indicate that IE occurs independently of medico-surgical procedures and the fact that patients had taken antibiotic prophylaxis or not, i.e., by cumulative exposure to random low-grade bacteremia, associated with daily activities (e.g. tooth brushing) in the case of oral streptococci, or with a colonized site or infected device in the case of staphylococci. Accordingly, the most recent American and European guidelines for IE prophylaxis were revisited and updated to drastically restrain antibiotic use. Nevertheless, the relative risk of IE represented by such cumulative low-grade bacteremia had never been demonstrated experimentally. We developed a new model of experimental IE due to continuous inoculation of low-grade bacteremia, mimicking repeated low-grade bacteremia in humans, and compared the infectivity of Streptococcus gordonii and Staphylococcus aureus in this model to that in the model producing brief, high-level bacteremia. We demonstrated that, after injection of identical bacterial numbers, the rate of infected vegetations was similar in both types of challenge. These experimental results support the hypothesis that cumulative exposure to low-grade bacteremia, outside the context of procedure-related bacteremia, represents a genuine risk of IE, as suggested by human epidemiological studies. In addition, they validate the newer guidelines for IE prophylaxis, which drastic limit the procedures in which antibiotic prophylaxis is indicated. Nevertheless, these refreshed guidelines leave the vast majority (> 90%) of potential IE cases without alternative propositions of prevention, and novel strategies must be considered to propose effective alternative and "global" measures to prevent IE initiation. The more realistic experimental model of IE induced by low-grade bacteremia provides an accurate experimental setting to study new preventive measures applying to cumulative exposure to low bacterial numbers. Since in a context of spontaneous low-grade bacteremia antibiotics are unlikely to solve the problem of IE prevention, we addressed the role of antiplatelet and anticoagulant agents for the prophylaxis of experimental IE induced by S. gordonii and S. aureus. The logic of this approach was based on the fact that platelets are key players in vegetation formation and vegetation enlargement, and on the fact that bacteria capable of interacting with platelets are more prone to induce IE. Antiplatelet agents included the COX1 inhibitor aspirin, the inhibitor of the ADP receptor P2Y12 ticlopidine, and two inhibitors of the platelet fibrinogen receptor GPIIb/IIIa, eptifibatide and abciximab. Anticoagulants included the thrombin inhibitor dabigatran etexilate and the vitamin K antagonist acenocoumarol. Aspirin, ticlopidine or eptifibatide alone failed to prevent aortic infection (> 75% infected animals). In contrast, the combination of aspirin with ticlopidine, as well as abciximab, protected 45% to 88% of animals against IE due to S. gordonii and S. aureus. The antithrombin dabigatran etexilate protected 75% of rats against IE due to S. aureus, but failed (< 30% protection) against S. gordonii. Acenocoumarol had no effect against any bacteria. Overall, these results suggest a possible role for antiplatelet agents and dabigatran etexilate in the prophylaxis of IE in humans in a context of recurrent low- grade bacteremia. However, the potential beneficial effect of antiplatelet agents should be balanced against the risk of bleeding and the fact that platelets play an important role in the host defenses against intravascular infections. In addition, the potential dual benefit of dabigatran etexilate might be revisited in patients with prosthetic valves, who require life-long anticoagulation and in whom S. aureus IE is associated with high mortality rate. Because the antiplatelet and anticoagulant approach might be limited in the context of S. aureus bacteremia, other prophylactic strategies for the prevention of S. aureus IE, like vaccination with anti-adhesion proteins was tested. The S. aureus surface proteins fibrinogen-binding protein clumping-factor A (ClfA) and the fibronectin-binding protein A (FnbpA) are critical virulence factors for the initiation and development of IE. Thus, they represent key targets for vaccine development against this disease. Recently, numerous reports have described that the harmless bacteria Lactococcus lactis can be used as a bacterial vector for the efficient delivery of antigens in vivo, and that this approach is a promising vaccination strategy against bacterial infections. We therefore explored the immunization capacity of non- living recombinant L. lactis ClfA, L. lactis FnbpA, or L. lactis expressing ClfA together with Fnbp (a truncated form of FnbpA with only the fibronectin-binding domain but lacking the fibrinogen-binding domain A [L. lactis ClfA/Fnbp]), to protect against S. aureus experimental IE. L. lactis ClfA was used as immunization agent against the laboratory strain S. aureus Newman (expressing ClfA, but lacking FnbpA). L. lactis ClfA, L. lactis FnbpA, as well as L. lactis ClfA/Fnbp, were used as immunization agents against the endocarditis isolate S. aureus P8 (expressing both ClfA and FnbpA). Immunization with L. lactis ClfA produced anti-ClfA functional antibodies, which were able to block the binding of S. aureus Newman to fibrinogen in vitro and protect 13/19 (69%) animals from IE due to S. aureus Newman (P < 0.05 compared to controls). Immunization with L. lactis ClfA, L. lactis FnbpA or L. lactis ClfA/Fnbp, produced antibodies against each antigen. However, they were not sufficient to block S. aureus P8 binding to fibrinogen and fibronectin in vitro. Moreover, immunization with L. lactis ClfA or L. lactis FnbpA was ineffective (< 10% protected animals) and immunization with L. lactis ClfA/Fnbp conferred limited protection from IE (8/23 protected animals; P < 0.05 compared to controls) after challenge with S. aureus P8. Together, these results indicate that L. lactis is an efficient delivering antigen system potentially useful for preventing S. aureus IE. They also demonstrate that expressing multiple antigens in L. lactis, yet to be elucidated, will be necessary to prevent IE due to clinical S. aureus strains fully equipped with virulence determinants. In summary, our study has demonstrated experimentally, for the first time, the hypothesis that low-grade bacteremia, mimicking bacteremia occurring outside of a clinical intervention, is equally prone to induce experimental IE as high-grade bacteremia following medico-surgical procedures. In this context, where the use of antibiotics for the prophylaxis of IE is limited, we showed that other prophylactic measures, like the use of antiplatelets, anticoagulants, or vaccination employing L. lactis as delivery vector of bacterial antigens, are reasonable alternatives that warrant to be further investigated.

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

Asymptotic robustness in multi-sample analysis of multivariate linear relations

Standard methods for the analysis of linear latent variable models oftenrely on the assumption that the vector of observed variables is normallydistributed. This normality assumption (NA) plays a crucial role inassessingoptimality of estimates, in computing standard errors, and in designinganasymptotic chi-square goodness-of-fit test. The asymptotic validity of NAinferences when the data deviates from normality has been calledasymptoticrobustness. In the present paper we extend previous work on asymptoticrobustnessto a general context of multi-sample analysis of linear latent variablemodels,with a latent component of the model allowed to be fixed across(hypothetical)sample replications, and with the asymptotic covariance matrix of thesamplemoments not necessarily finite. We will show that, under certainconditions,the matrix $\Gamma$ of asymptotic variances of the analyzed samplemomentscan be substituted by a matrix $\Omega$ that is a function only of thecross-product moments of the observed variables. The main advantage of thisis thatinferences based on $\Omega$ are readily available in standard softwareforcovariance structure analysis, and do not require to compute samplefourth-order moments. An illustration with simulated data in the context ofregressionwith errors in variables will be presented.

Asymptotic robust inferences in the analysis of mean and covariance structures

Structural equation models are widely used in economic, socialand behavioral studies to analyze linear interrelationships amongvariables, some of which may be unobservable or subject to measurementerror. Alternative estimation methods that exploit different distributionalassumptions are now available. The present paper deals with issues ofasymptotic statistical inferences, such as the evaluation of standarderrors of estimates and chi--square goodness--of--fit statistics,in the general context of mean and covariance structures. The emphasisis on drawing correct statistical inferences regardless of thedistribution of the data and the method of estimation employed. A(distribution--free) consistent estimate of $\Gamma$, the matrix ofasymptotic variances of the vector of sample second--order moments,will be used to compute robust standard errors and a robust chi--squaregoodness--of--fit squares. Simple modifications of the usual estimateof $\Gamma$ will also permit correct inferences in the case of multi--stage complex samples. We will also discuss the conditions under which,regardless of the distribution of the data, one can rely on the usual(non--robust) inferential statistics. Finally, a multivariate regressionmodel with errors--in--variables will be used to illustrate, by meansof simulated data, various theoretical aspects of the paper.

Spatial evaluation of larvae of Culicidae (Diptera) from different breeding sites: application of a geospatial method and implications for vector control

Spatial evaluation of Culicidae (Diptera) larvae from different breeding sites: application of a geospatial method and implications for vector control. This study investigates the spatial distribution of urban Culicidae and informs entomological monitoring of species that use artificial containers as larval habitats. Collections of mosquito larvae were conducted in the São Paulo State municipality of Santa Bárbara d' Oeste between 2004 and 2006 during house-to-house visits. A total of 1,891 samples and nine different species were sampled. Species distribution was assessed using the kriging statistical method by extrapolating municipal administrative divisions. The sampling method followed the norms of the municipal health services of the Ministry of Health and can thus be adopted by public health authorities in disease control and delimitation of risk areas. Moreover, this type of survey and analysis can be employed for entomological surveillance of urban vectors that use artificial containers as larval habitat.

Spatial analysis of corresponding fingerprint features from match and close non-match populations

The development of statistical models for forensic fingerprint identification purposes has been the subject of increasing research attention in recent years. This can be partly seen as a response to a number of commentators who claim that the scientific basis for fingerprint identification has not been adequately demonstrated. In addition, key forensic identification bodies such as ENFSI [1] and IAI [2] have recently endorsed and acknowledged the potential benefits of using statistical models as an important tool in support of the fingerprint identification process within the ACE-V framework. In this paper, we introduce a new Likelihood Ratio (LR) model based on Support Vector Machines (SVMs) trained with features discovered via morphometric and spatial analyses of corresponding minutiae configurations for both match and close non-match populations often found in AFIS candidate lists. Computed LR values are derived from a probabilistic framework based on SVMs that discover the intrinsic spatial differences of match and close non-match populations. Lastly, experimentation performed on a set of over 120,000 publicly available fingerprint images (mostly sourced from the National Institute of Standards and Technology (NIST) datasets) and a distortion set of approximately 40,000 images, is presented, illustrating that the proposed LR model is reliably guiding towards the right proposition in the identification assessment of match and close non-match populations. Results further indicate that the proposed model is a promising tool for fingerprint practitioners to use for analysing the spatial consistency of corresponding minutiae configurations.

An adenovirus-based fluorescent reporter vector to identify and isolate HIV-infected cells.

A procedure is described that allows the simple identification and sorting of live human cells that transcribe actively the HIV virus, based on the detection of GFP fluorescence in cells. Using adenoviral vectors for gene transfer, an expression cassette including the HIV-1 LTR driving the reporter gene GFP was introduced into cells that expressed stably either the Tat transcriptional activator, or an inactive mutant of Tat. Both northern and fluorescence-activated cell sorting (FACS) analysis indicate that cells containing the functional Tat protein presented levels of GFP mRNA and GFP fluorescence several orders of magnitude higher than control cells. Correspondingly, cells infected with HIV-1 showed similar enhanced reporter gene activation. HIV-1-infected cells of the lymphocytic line Jurkat were easily identified by fluorescence-activated cell sorting (FACS) as they displayed a much higher green fluorescence after transduction with the reporter adenoviral vector. This procedure could also be applied on primary human cells as blood monocyte-derived macrophages exposed to the adenoviral LTR-GFP reporter presented a much higher fluorescence when infected with HIV-1 compared with HIV-uninfected cells. The vector described has the advantages of labelling cells independently of their proliferation status and that analysis can be carried on intact cells which can be isolated subsequently by fluorescence-activated cell sorting (FACS) for further culture. This work suggests that adenoviral vectors carrying a virus-specific transcriptional control element controlling the expressions of a fluorescent protein will be useful in the identification and isolation of cells transcribing actively the viral template, and to be of use for drug screening and susceptibility assays.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Molecular analysis of the bacterial diversity in a specialized consortium for diesel oil degradation

Diesel oil is a compound derived from petroleum, consisting primarily of hydrocarbons. Poor conditions in transportation and storage of this product can contribute significantly to accidental spills causing serious ecological problems in soil and water and affecting the diversity of the microbial environment. The cloning and sequencing of the 16S rRNA gene is one of the molecular techniques that allows estimation and comparison of the microbial diversity in different environmental samples. The aim of this work was to estimate the diversity of microorganisms from the Bacteria domain in a consortium specialized in diesel oil degradation through partial sequencing of the 16S rRNA gene. After the extraction of DNA metagenomics, the material was amplified by PCR reaction using specific oligonucleotide primers for the 16S rRNA gene. The PCR products were cloned into a pGEM-T-Easy vector (Promega), and Escherichia coli was used as the host cell for recombinant DNAs. The partial clone sequencing was obtained using universal oligonucleotide primers from the vector. The genetic library obtained generated 431 clones. All the sequenced clones presented similarity to phylum Proteobacteria, with Gammaproteobacteria the most present group (49.8 % of the clones), followed by Alphaproteobacteira (44.8 %) and Betaproteobacteria (5.4 %). The Pseudomonas genus was the most abundant in the metagenomic library, followed by the Parvibaculum and the Sphingobium genus, respectively. After partial sequencing of the 16S rRNA, the diversity of the bacterial consortium was estimated using DOTUR software. When comparing these sequences to the database from the National Center for Biotechnology Information (NCBI), a strong correlation was found between the data generated by the software used and the data deposited in NCBI.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Optimal priming of poxvirus vector (NYVAC)-based HIV vaccine regimens requires 3 DNA injections. Results of the EV03/ANRS Vac20 Phase I/II Trial

Background: Two or three DNA primes have been used in previous smaller clinical trials, but the number required for optimal priming of viral vectors has never been assessed in adequately powered clinical trials. The EV03/ANRS Vac20 phase I/II trial investigated this issue using the DNA prime/poxvirus NYVAC boost combination, both expressing a common HIV-1 clade C immunogen consisting of Env and Gag-Pol-Nef polypeptide. Methods: 147 healthy volunteers were randomly allocated through 8 European centres to either 3xDNA plus 1xNYVAC (weeks 0, 4, 8 plus 24; n¼74) or to 2xDNA plus 2xNYVAC (weeks 0, 4 plus 20, 24; n¼73), stratified by geographical region and sex. T cell responses were quantified using the interferon g Elispot assay and 8 peptide pools; samples from weeks 0, 26 and 28 (time points for primary immunogenicity endpoint), 48 and 72 were considered for this analysis. Results: 140 of 147 participants were evaluable at weeks 26 and/ or 28. 64/70 (91%) in the 3xDNA arm compared to 56/70 (80%) in the 2xDNA arm developed a T cell response (P¼0.053). 26 (37%) participants of the 3xDNA arm developed a broader T cell response (Env plus at least to one of the Gag, Pol, Nef peptide pools) versus 15 (22%) in the 2xDNA arm (P¼0.047). At week 26, the overall magnitude of responses was also higher in the 3xDNA than in the 2xDNA arm (similar at week 28), with a median of 545 versus 328 SFUs/106 cells at week 26 (P<0.001). Preliminary overall evaluation showed that participants still developed T-cell response at weeks 48 (78%, n¼67) and 72 (70%, n¼66). Conclusion: This large clinical trial demonstrates that optimal priming of poxvirus-based vaccine regimens requires 3 DNA regimens and further confirms that the DNA/NYVAC prime boost vaccine combination is highly immunogenic and induced durable T-cell responses.