A recombinant hybrid protein as antigen for an anti-blood stage malaria vaccine: a study on the conservation of a protective component

Recently we have shown that two hybrid proteins expressed in Escherichia coli confer protective immunity to Aotus monkeys against an experimental Plasmodium falciparum infection (Knapp et al., 1992). Both hybrid proteins carry a sequence containing amino acids 631 to 764 of the serine stretch protein SERP (Knapp et al., 1989b). We have studied the diversity of this SERP region in field isolates of P. falciparum. Genomic DNA was extracted from the blood of six donors from different endemic areas of Brazil and West Africa. The SERP region encoding amino acids 630 to 781 was amplified by polymerase chain reaction (PCR) and sequenced. Only conserved amino acid substitutions in maximally two positions of the analyzed SERP fragment could be detected which supports the suitability of this SERP region as a component of anti-blood stage malaria vaccine.

Study of gene flow through a hybrid zone in the common shrew (Sorex araneus) using microsatellites

The common shrew (Sorer araneus) is subdivided into several chromosomal races. As hybrid zones between them have been characterized, this organism is of particular interest in studying the role of chromosomes in speciation. Six microsatellite loci were used to evaluate the level of gene how in the S. araneus hybrid zone between the Cordon and Valais races. Most of these loci were very polymorphic, the total number of alleles detected per locus ranging from 3 to 20. Using Mantel tests, we showed that the effect of rivers as barriers to gene flow is less important at this sampling scale. The effect of the chromosomal race is of particular importantance in diminishing gene flow.

Mechanisms of reproductive isolation between an ant species of hybrid origin and one of its parents.

The establishment of new species by hybridization is difficult because it requires the development of reproductive isolation (RI) in sympatry to escape the homogenizing effects of gene flow from the parental species. Here we investigated the role of two pre- and two postzygotic mechanisms of RI in a system comprising two interdependent Pogonomyrmex harvester ant lineages (the H1 and H2 lineages) of hybrid origin and one of their parental species (P. rugosus). Similar to most other ants, P. rugosus is characterized by an environmental system of caste determination with female brood developing either into queens or workers depending on nongenetic factors. By contrast, there is a strong genetic component to caste determination in the H1 and H2 lineages because the developmental fate of female brood depends on the genetic origin of the parents, with interlineage eggs developing into workers and intralineage eggs developing into queens. The study of a mixed mating aggregation revealed strong differences in mating flight timing between P. rugosus and the two lineages as a first mechanism of RI. A second important prezygotic mechanism was assortative mating. Laboratory experiments also provided support for one of the two investigated mechanisms of postzygotic isolation. The majority of offspring produced from the few matings between P. rugosus and the lineages aborted at the egg stage. This hybrid inviability was under maternal influence, with hybrids produced by P. rugosus queens being always inviable whereas a small proportion of H2 lineage queens produced large numbers of adult hybrid offspring. Finally, we found no evidence that genetic caste determination acted as a second postzygotic mechanism reducing gene flow between P. rugosus and the H lineages. The few viable P. rugosus-H hybrids were not preferentially shunted into functionally sterile workers but developed into both workers and queens. Overall, these results reveal that the nearly complete (99.5%) RI between P. rugosus and the two hybrid lineages stems from the combination of two typical prezygotic mechanisms (mating time divergence and assortative mating) and one postzygotic mechanism (hybrid inviability).

Textural classification of land cover using support vector machines: an empirical comparison with parametric, non parametric and hybrid classifiers in the Bolivian Amazon

Land cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims to establish an efficient classification approach to accurately map all broad land cover classes in a large, heterogeneous tropical area of Bolivia, as a basis for further studies (e.g., land cover-land use change). Specifically, we compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbour and four different support vector machines - SVM), and hybrid classifiers, using both hard and soft (fuzzy) accuracy assessments. In addition, we test whether the inclusion of a textural index (homogeneity) in the classifications improves their performance. We classified Landsat imagery for two dates corresponding to dry and wet seasons and found that non-parametric, and particularly SVM classifiers, outperformed both parametric and hybrid classifiers. We also found that the use of the homogeneity index along with reflectance bands significantly increased the overall accuracy of all the classifications, but particularly of SVM algorithms. We observed that improvements in producer’s and user’s accuracies through the inclusion of the homogeneity index were different depending on land cover classes. Earlygrowth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land cover classes were mapped with producer’s and user’s accuracies of around 90%. Our approach seems very well suited to accurately map land cover in tropical regions, thus having the potential to contribute to conservation initiatives, climate change mitigation schemes such as REDD+, and rural development policies.

Hybrid Multiscale Finite Volume method for two-phase flow in porous media

We present a novel hybrid (or multiphysics) algorithm, which couples pore-scale and Darcy descriptions of two-phase flow in porous media. The flow at the pore-scale is described by the Navier?Stokes equations, and the Volume of Fluid (VOF) method is used to model the evolution of the fluid?fluid interface. An extension of the Multiscale Finite Volume (MsFV) method is employed to construct the Darcy-scale problem. First, a set of local interpolators for pressure and velocity is constructed by solving the Navier?Stokes equations; then, a coarse mass-conservation problem is constructed by averaging the pore-scale velocity over the cells of a coarse grid, which act as control volumes; finally, a conservative pore-scale velocity field is reconstructed and used to advect the fluid?fluid interface. The method relies on the localization assumptions used to compute the interpolators (which are quite straightforward extensions of the standard MsFV) and on the postulate that the coarse-scale fluxes are proportional to the coarse-pressure differences. By numerical simulations of two-phase problems, we demonstrate that these assumptions provide hybrid solutions that are in good agreement with reference pore-scale solutions and are able to model the transition from stable to unstable flow regimes. Our hybrid method can naturally take advantage of several adaptive strategies and allows considering pore-scale fluxes only in some regions, while Darcy fluxes are used in the rest of the domain. Moreover, since the method relies on the assumption that the relationship between coarse-scale fluxes and pressure differences is local, it can be used as a numerical tool to investigate the limits of validity of Darcy's law and to understand the link between pore-scale quantities and their corresponding Darcy-scale variables.

Merging the hypothetical extraction method and the classical multiplier approach: a hybrid possibility for identifying key distributive sectors

The two main alternative methods used to identify key sectors within the input-output approach, the Classical Multiplier method (CMM) and the Hypothetical Extraction method (HEM), are formally and empirically compared in this paper. Our findings indicate that the main distinction between the two approaches stems from the role of the internal effects. These internal effects are quantified under the CMM while under the HEM only external impacts are considered. In our comparison, we find, however that CMM backward measures are more influenced by within-block effects than the proposed forward indices under this approach. The conclusions of this comparison allow us to develop a hybrid proposal that combines these two existing approaches. This hybrid model has the advantage of making it possible to distinguish and disaggregate external effects from those that a purely internal. This proposal has also an additional interest in terms of policy implications. Indeed, the hybrid approach may provide useful information for the design of ''second best'' stimulus policies that aim at a more balanced perspective between overall economy-wide impacts and their sectoral distribution.

Exogenous selection shapes germination behaviour and seedling traits of populations at different altitudes in a Senecio hybrid zone.

BACKGROUND AND AIMS: The Senecio hybrid zone on Mt Etna, Sicily, is characterized by steep altitudinal clines in quantitative traits and genetic variation. Such clines are thought to be maintained by a combination of 'endogenous' selection arising from genetic incompatibilities and environment-dependent 'exogenous' selection leading to local adaptation. Here, the hypothesis was tested that local adaptation to the altitudinal temperature gradient contributes to maintaining divergence between the parental species, S. chrysanthemifolius and S. aethnensis. METHODS: Intra- and inter-population crosses were performed between five populations from across the hybrid zone and the germination and early seedling growth of the progeny were assessed. KEY RESULTS: Seedlings from higher-altitude populations germinated better under low temperatures (9-13 °C) than those from lower altitude populations. Seedlings from higher-altitude populations had lower survival rates under warm conditions (25/15 °C) than those from lower altitude populations, but also attained greater biomass. There was no altitudinal variation in growth or survival under cold conditions (15/5 °C). Population-level plasticity increased with altitude. Germination, growth and survival of natural hybrids and experimentally generated F(1)s generally exceeded the worse-performing parent. CONCLUSIONS: Limited evidence was found for endogenous selection against hybrids but relatively clear evidence was found for divergence in seed and seedling traits, which is probably adaptive. The combination of low-temperature germination and faster growth in warm conditions might enable high-altitude S. aethnensis to maximize its growth during a shorter growing season, while the slower growth of S. chrysanthemifolius may be an adaptation to drought stress at low altitudes. This study indicates that temperature gradients are likely to be an important environmental factor generating and maintaining adaptive divergence across the Senecio hybrid zone on Mt Etna.

Evaluation of organ-specific peripheral doses after 2-dimensional, 3-dimensional and hybrid intensity modulated radiation therapy for breast cancer based on Monte Carlo and convolution/superposition algorithms: implications for secondary cancer risk assessment.

To make a comprehensive evaluation of organ-specific out-of-field doses using Monte Carlo (MC) simulations for different breast cancer irradiation techniques and to compare results with a commercial treatment planning system (TPS). Three breast radiotherapy techniques using 6MV tangential photon beams were compared: (a) 2DRT (open rectangular fields), (b) 3DCRT (conformal wedged fields), and (c) hybrid IMRT (open conformal+modulated fields). Over 35 organs were contoured in a whole-body CT scan and organ-specific dose distributions were determined with MC and the TPS. Large differences in out-of-field doses were observed between MC and TPS calculations, even for organs close to the target volume such as the heart, the lungs and the contralateral breast (up to 70% difference). MC simulations showed that a large fraction of the out-of-field dose comes from the out-of-field head scatter fluence (>40%) which is not adequately modeled by the TPS. Based on MC simulations, the 3DCRT technique using external wedges yielded significantly higher doses (up to a factor 4-5 in the pelvis) than the 2DRT and the hybrid IMRT techniques which yielded similar out-of-field doses. In sharp contrast to popular belief, the IMRT technique investigated here does not increase the out-of-field dose compared to conventional techniques and may offer the most optimal plan. The 3DCRT technique with external wedges yields the largest out-of-field doses. For accurate out-of-field dose assessment, a commercial TPS should not be used, even for organs near the target volume (contralateral breast, lungs, heart).

An hybrid methodology for RL-based behavior coordination in a target following mission with an AUV

Proposes a behavior-based scheme for high-level control of autonomous underwater vehicles (AUVs). Two main characteristics can be highlighted in the control scheme. Behavior coordination is done through a hybrid methodology, which takes in advantages of the robustness and modularity in competitive approaches, as well as optimized trajectories

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors