Localization of HLA-A2.1-restricted T cell epitopes in the circumsporozoite protein of Plasmodium falciparum.

Localization of human MHC class I-restricted T cell epitopes in the circumsporozoite (CS) protein of the human parasite Plasmodium falciparum is an important objective in the development of antimalarial vaccines. To this purpose, we synthesized a series of overlapping synthetic 20-mer peptides, spanning the entire sequence of the 7G8 CS molecule except for the central repeat B cell domain. The P.f.CS peptides were first tested for their ability to bind to the human MHC class I HLA-A2.1 molecule on T2, a human cell line. Subsequently, the use of a series of shorter peptide analogues allowed us to determine the optimal A2.1 binding sequence present in several of the 20-mers. Binding P.f.CS peptides were further tested for their capacity to activate PBL from HLA-A2.1+ immune donors living in a malaria-endemic area. Specific IFN-gamma production was detected in the supernatant of cultures of PBL from exposed individuals. Cytotoxic T cell lines and clones were derived from the PBL of one responder, and their activity was shown to be HLA-A2.1-restricted and specific for the peptide 334-342 of the CS protein. In addition, double transgenic HLA-A2.1 x human beta 2-microglobulin mice were immunized with peptide 1-10 of the CS protein. T cells derived from immune lymph nodes displayed a peptide-specific HLA-A2.1-restricted cytolytic activity after one in vitro stimulation.

Expression hierarchy of T cell epitopes from melanoma differentiation antigens: unexpected high level presentation of tyrosinase-HLA-A2 Complexes revealed by peptide-specific, MHC-restricted, TCR-like antibodies.

Peptide Ags presented by class I MHC molecules on human melanomas and that are recognized by CD8(+) T cells are the subjects of many studies of antitumor immunity and represent attractive candidates for therapeutic approaches. However, no direct quantitative measurements exist to reveal their expression hierarchy on the cell surface. Using novel recombinant Abs which bind these Ags with a peptide-specific, MHC-restricted manner, we demonstrate a defined pattern of expression hierarchy of peptide-HLA-A2 complexes derived from three major differentiation Ags: gp100, Melan-A/Mart-1, and tyrosinase. Studying melanoma cell lines derived from multiple patients, we reveal a surprisingly high level of presentation of tyrosinase-derived complexes and moderate to very low expression of complexes derived from other Ags. No correlation between Ag presentation and mRNA expression was found; however, protein stability may play a major role. These results provide new insights into the characteristics of Ag presentation and are particularly important when such targets are being considered for immunotherapy. These results may shed new light on relationships between Ag presentation and immune response to cancer Ags.

Restricted transgene expression in the brain with cell-type specific neuronal promoters.

Tissue-targeted expression is of major interest for studying the contribution of cellular subpopulations to neurodegenerative diseases. However, in vivo methods to investigate this issue are limited. Here, we report an analysis of the cell specificity of expression of fluorescent reporter genes driven by six neuronal promoters, with the ubiquitous phosphoglycerate kinase 1 (PGK) promoter used as a reference. Quantitative analysis of AcGFPnuc expression in the striatum and hippocampus of rodents showed that all lentiviral vectors (LV) exhibited a neuronal tropism; however, there was substantial diversity of transcriptional activity and cell-type specificity of expression. The promoters with the highest activity were those of the 67 kDa glutamic acid decarboxylase (GAD67), homeobox Dlx5/6, glutamate receptor 1 (GluR1), and preprotachykinin 1 (Tac1) genes. Neuron-specific enolase (NSE) and dopaminergic receptor 1 (Drd1a) promoters showed weak activity, but the integration of an amplification system into the LV overcame this limitation. In the striatum, the expression profiles of Tac1 and Drd1a were not limited to the striatonigral pathway, whereas in the hippocampus, Drd1a and Dlx5/6 showed the expected restricted pattern of expression. Regulation of the Dlx5/6 promoter was observed in a disease condition, whereas Tac1 activity was unaffected. These vectors provide safe tools that are more selective than others available, for the administration of therapeutic molecules in the central nervous system (CNS). Nevertheless, additional characterization of regulatory elements in neuronal promoters is still required.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

Restricted gene flow at specific parts of the shrew genome in chromosomal hybrid zones.

The species and races of the shrews of the Sorex araneus group exhibit a broad range of chromosomal polymorphisms. European taxa of this group are parapatric and form contact or hybrid zones that span an extraordinary variety of situations, ranging from absolute genetic isolation to almost free gene flow. This variety seems to depend for a large part on the chromosome composition of populations, which are primarily differentiated by various Robertsonian fusions of a subset of acrocentric chromosomes. Previous studies suggested that chromosomal rearrangements play a causative role in the speciation process. In such models, gene flow should be more restricted for markers on chromosomes involved in rearrangements than on chromosomes common in both parent species. In the present study, we address the possibility of such differential gene flow in the context of two genetically very similar but karyotypically different hybrid zones between species of the S. araneus group using microsatellite loci mapped to the chromosome arm level. Interspecific genetic structure across rearranged chromosomes was in general larger than across common chromosomes. However, the difference between the two classes of chromosomes was only significant in the hybrid zone where the complexity of hybrids is expected to be larger. These differences did not distinguish populations within species. Therefore, the rearranged chromosomes appear to affect the reproductive barrier between karyotypic species, although the strength of this effect depends on the complexity of the hybrids produced.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

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.

Why microalgal biofuels won't save the internal combustion machine

Proponents of microalgae biofuel technologies often claim that the world demand of liquid fuels, about 5 trillion liters per year, could be supplied by microalgae cultivated on only a few tens of millions of hectares. This perspective reviews this subject and points out that such projections are greatly exaggerated, because (1) the pro- ductivities achieved in large-scale commercial microalgae production systems, operated year-round, do not surpass those of irrigated tropical crops; (2) cultivating, harvesting and processing microalgae solely for the production of biofuels is simply too expensive using current or prospective technology; and (3) currently available (limited) data suggest that the energy balance of algal biofuels is very poor. Thus, microalgal biofuels are no panacea for depleting oil or global warming, and are unlikely to save the internal combustion machine.