Geomodelling of a fluvial system with semi-supervised support vector regression

Fluvial deposits are a challenge for modelling flow in sub-surface reservoirs. Connectivity and continuity of permeable bodies have a major impact on fluid flow in porous media. Contemporary object-based and multipoint statistics methods face a problem of robust representation of connected structures. An alternative approach to model petrophysical properties is based on machine learning algorithm ? Support Vector Regression (SVR). Semi-supervised SVR is able to establish spatial connectivity taking into account the prior knowledge on natural similarities. SVR as a learning algorithm is robust to noise and captures dependencies from all available data. Semi-supervised SVR applied to a synthetic fluvial reservoir demonstrated robust results, which are well matched to the flow performance

Example-based support vector machine for drug concentration analysis

Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.

Wave-vector dependence of spin and density multipole excitations in quantum dots

We have employed time-dependent local-spin density-functional theory to analyze the multipole spin and charge density excitations in GaAs-AlxGa1-xAs quantum dots. The on-plane transferred momentum degree of freedom has been taken into account, and the wave-vector dependence of the excitations is discussed. In agreement with previous experiments, we have found that the energies of these modes do not depend on the transferred wave vector, although their intensities do. Comparison with a recent resonant Raman scattering experiment [C. Schüller et al., Phys. Rev. Lett. 80, 2673 (1998)] is made. This allows us to identify the angular momentum of several of the observed modes as well as to reproduce their energies

Forest insects and environmental variation in stand edges

Summary

Generation of a tightly regulated all-cis beta cell-specific tetracycline-inducible vector.

Ability to induce protein expression at will in a cell is a powerful strategy used by scientists to better understand the function of a protein of interest. Various inducible systems have been designed in eukaryotic cells to achieve this goal. Most of them rely on two distinct vectors, one encoding a protein that can regulate transcription by binding a compound X, and one hosting the cDNA encoding the protein of interest placed downstream of promoter sequences that can bind the protein regulated by compound X (e.g., tetracycline, ecdysone). The commercially available systems are not designed to allow cell- or tissue-specific regulated expression. Additionally, although these systems can be used to generate stable clones that can be induced to express a given protein, extensive screening is often required to eliminate the clones that display poor induction or high basal levels. In the present report, we aimed to design a pancreatic beta cell-specific tetracycline-inducible system. Since the classical two-vector based tetracycline-inducible system proved to be unsatisfactory in our hands, a single vector was eventually designed that allowed tight beta cell-specific tetracycline induction in unselected cell populations.

An evolutionary perspective on self-organized division of labor in social insects

Division of labor is a complex phenomenon observed throughout nature. Theoretical studies have focused either on its emergence through self-organization mechanisms or on its adaptive consequences. We suggest that the interaction of self-organization, which undoubtedly characterizes division of labor in social insects, and evolution should be further explored. We review the factors empirically shown to influence task choice. In light of these factors, we review the most important self-organization and evolutionary models for division of labor and outline their advantages and limitations. We describe ways to unify evolution and self-organization in the theoretical study of division of labor and recent results in this area. Finally, we discuss some benchmarks and primary challenges of this approach.

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.

Cutaneous leishmaniasis: progress towards a vaccine.

Leishmaniases are vector-borne diseases due to the protozoan parasite Leishmania . Since no prevention method is available and as current therapy is costly, often poorly tolerated and not always efficacious, the development of alternative therapies, including vaccines, constitutes the priority in the fight of Leishmania infection. This review focuses on recent advances in the development of vaccines against leishmaniasis, with emphasis on the cutaneous form. Indeed, the fact that recovery from leishmaniasis is associated with immunity against new infection provides a rational basis for the development of vaccination strategy against infection with Leishmania . Evidence from animal studies demonstrate that protection can be achieved following infection with live-attenuated Leishmania as well as through immunization with purified proteins or DNA vaccines. In addition, recent results have shown that immunization against the saliva of the insect vector could have synergistic effects with conventional vaccination. Finally, vaccination using dendritic cells was recently demonstrated as a possible tool for Leishmania vaccination.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Hydrocarbon divergence and reproductive isolation in Timema stick insects.

BACKGROUND: Individuals commonly prefer certain trait values over others when choosing their mates. If such preferences diverge between populations, they can generate behavioral reproductive isolation and thereby contribute to speciation. Reproductive isolation in insects often involves chemical communication, and cuticular hydrocarbons, in particular, serve as mate recognition signals in many species. We combined data on female cuticular hydrocarbons, interspecific mating propensity, and phylogenetics to evaluate the role of cuticular hydrocarbons in diversification of Timema walking-sticks. RESULTS: Hydrocarbon profiles differed substantially among the nine analyzed species, as well as between partially reproductively-isolated T. cristinae populations adapted to different host plants. In no-choice trials, mating was more likely between species with similar than divergent hydrocarbon profiles, even after correcting for genetic divergences. The macroevolution of hydrocarbon profiles, along a Timema species phylogeny, fits best with a punctuated model of phenotypic change concentrated around speciation events, consistent with change driven by selection during the evolution of reproductive isolation. CONCLUSION: Altogether, our data indicate that cuticular hydrocarbon profiles vary among Timema species and populations, and that most evolutionary change in hydrocarbon profiles occurs in association with speciation events. Similarities in hydrocarbon profiles between species are correlated with interspecific mating propensities, suggesting a role for cuticular hydrocarbon profiles in mate choice and speciation in the genus Timema.