Serosurvey Study of Toscana Virus in Domestic Animals, Granada, Spain

Toscana virus (TOSV) is transmitted by infected sandflies. In Mediterranean countries, TOSV is one of the major viral pathogens involved in aseptic meningitis and meningoencephalitis in humans. It remains unclear if there are animal reservoirs able to maintain the virus through the cold months of the year, when the vector is not circulating. From May to October of 2006 and 2007, we conducted a serosurvey study on domestic animals from Granada province (southern Spain). TOSV was investigated in 1186 serum samples from horses, goats, pigs, cats, dogs, sheep, and cows by serology (indirect fluorescence assay), viral culture, and RT-polymerase chain reaction. Specific anti-TOSV antibodies were detected in 429 (36.2%) serum samples. The highest seropositivity rates were observed in cats (59.6%) and dogs (48.3%). These results suggest that an important percentage of the domestic animals have been infected by TOSV. Significantly different seroprevalence rates were detected in goats among distinct geographical areas. All viral cultures were negative. TOSV was detected by RT-polymerase chain reaction in only one serum sample from a goat. Thus, the studied animals do not seem to act as reservoirs for TOSV; otherwise, they could be amplifying hosts for the virus.

Genetic diversity of Toscana virus

Distribution of Toscana virus (TOSV) is evolving with climate change, and pathogenicity may be higher in nonexposed populations outside areas of current prevalence (Mediterranean Basin). To characterize genetic diversity of TOSV, we determined the coding sequences of isolates from Spain and France. TOSV is more diverse than other well-studied phleboviruses (e.g.,Rift Valley fever virus).

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).