Protection of Spanish Ibex (Capra pyrenaica) against Bluetongue Virus Serotypes 1 and 8 in a Subclinical Experimental Infection

Many wild ruminants such as Spanish ibex (Capra pyrenaica) are susceptible to Bluetongue virus (BTV) infection, which causes disease mainly in domestic sheep and cattle. Outbreaks involving either BTV serotypes 1 (BTV-1) and 8 (BTV-8) are currently challenging Europe. Inclusion of wildlife vaccination among BTV control measures should be considered in certain species. In the present study, four out of fifteen seronegative Spanish ibexes were immunized with a single dose of inactivated vaccine against BTV-1, four against BTV-8 and seven ibexes were non vaccinated controls. Seven ibexes (four vaccinated and three controls) were inoculated with each BTV serotype. Antibody and IFN-gamma responses were evaluated until 28 days after inoculation (dpi). The vaccinated ibexes showed significant (P<0.05) neutralizing antibody levels after vaccination compared to non vaccinated ibexes. The non vaccinated ibexes remained seronegative until challenge and showed neutralizing antibodies from 7 dpi. BTV RNA was detected in the blood of non vaccinated ibexes from 2 to the end of the study (28 dpi) and in target tissue samples obtained at necropsy (8 and 28 dpi). BTV-1 was successfully isolated on cell culture from blood and target tissues of non vaccinated ibexes. Clinical signs were unapparent and no gross lesions were found at necropsy. Our results show for the first time that Spanish ibex is susceptible and asymptomatic to BTV infection and also that a single dose of vaccine prevents viraemia against BTV-1 and BTV-8 replication

PreCLAS: An evolutionary tool for unsupervised feature selection

Several research areas are being faced with data matrices that are not suitable to be managed with traditional clustering, regression, or classification strategies. For example, biological so-called omic problems present models with thousands or millions of rows and less than a hundred columns. This matrix structure hinders the successful progress of traditional data analysis methods and thus needs some means for reducing the number of rows. This article presents an unsupervised approach called PreCLAS for preprocessing matrices with dimension problems to obtain data that are apt for clustering and classification strategies. The PreCLAS was implemented as an unsupervised strategy that aims at finding a submatrix with a drastically reduced number of rows, preferring those rows that together present some group structure. Experimentation was carried out in two stages. First, to assess its functionality, a benchmark dataset was studied in a clustering context. Then, a microarray dataset with genomic information was analyzed, and the PreCLAS was used to select informative genes in the context of classification strategies. Experimentation showed that the new method performs successfully at drastically reducing the number of rows of a matrix, smartly performing unsupervised feature selection for both classification and clustering problems.Fil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Cecchini, Rocío Luján. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina15th International Conference on Hybrid Artificial Intelligence SystemsGuijónEspañaUniversidad de Ovied

From supervised instance and feature selection algorithms to dual selection: a review

International audienceThis chapter reviews the data reduction problem for instance and feature selection methods in the context of supervised classification. In the first part, instance and feature selections are studied separatively. As instance and feature selection are not independent, algorithms dealing with simultaneous selection are then presented. To provide a comprehensive and tractable view of this field, the strategy was to start from the fundamental and original contributions go towards state of the art algorithms, paying particular attention to large scale selections. Detailed pseudo codes of representative algorithms are given to consolidate the whole

Rare earth elements (REE) in biology and medicine

AbstractThis survey reports on topics that were presented at the workshop on "Challenges with Rare Earth Elements. The Periodic Table at work for new Science & Technology" hold at the Academia dei Lincei in November 2019. The herein reported materials refer to presentations dealing with studies and applications of rare earth elements (REE) in several areas of Biology and Medicine. All together they show the tremendous impact REE have in relevant fields of living systems and highlight, on one hand, the still existing knowledge gap for an in-depth understanding of their function in natural systems as well as the very important role they already have in providing innovative scientific and technological solutions in a number of bio-medical areas and in fields related to the assessment of the origin of food and on their manufacturing processes. On the basis of the to-date achievements one expects that new initiatives will bring, in a not too far future, to a dramatic increase of our understanding of the REE involvement in living organisms as well as a ramp-up in the exploitation of the peculiar properties of REE for the design of novel applications in diagnostic procedures and in the set-up of powerful medical devices. This scenario calls the governmental authorities for new responsibilities to guarantee a continuous availability of REE to industry and research labs together with providing support to activities devoted to their recovery/recycling