Multivariate Statistical Analyses Demonstrate Unique Host Immune Responses to Single and Dual Lentiviral Infection

Feline immunodeficiency virus (FIV) and human immunodeficiency virus (HIV) are recently identified lentiviruses that cause progressive immune decline and ultimately death in infected cats and humans. It is of great interest to understand how to prevent immune system collapse caused by these lentiviruses. We recently described that disease caused by a virulent FIV strain in cats can be attenuated if animals are first infected with a feline immunodeficiency virus derived from a wild cougar. The detailed temporal tracking of cat immunological parameters in response to two viral infections resulted in high-dimensional datasets containing variables that exhibit strong co-variation. Initial analyses of these complex data using univariate statistical techniques did not account for interactions among immunological response variables and therefore potentially obscured significant effects between infection state and immunological parameters.Here, we apply a suite of multivariate statistical tools, including Principal Component Analysis, MANOVA and Linear Discriminant Analysis, to temporal immunological data resulting from FIV superinfection in domestic cats. We investigated the co-variation among immunological responses, the differences in immune parameters among four groups of five cats each (uninfected, single and dual infected animals), and the "immune profiles" that discriminate among them over the first four weeks following superinfection. Dual infected cats mount an immune response by 24 days post superinfection that is characterized by elevated levels of CD8 and CD25 cells and increased expression of IL4 and IFNgamma, and FAS. This profile discriminates dual infected cats from cats infected with FIV alone, which show high IL-10 and lower numbers of CD8 and CD25 cells.Multivariate statistical analyses demonstrate both the dynamic nature of the immune response to FIV single and dual infection and the development of a unique immunological profile in dual infected cats, which are protected from immune decline

Spatial functionnal analysis application on fisheries acoustics data coupled with fine scale environmental data [résumé de poster]

ICES. Working Group of Fisheries Acoustics, Science and Technology (WGFAST), Somone, SEN, 25-/04/2022 - 28/04/2022In this work, we were interested in the application of functional, spatial data analysis (FSDA) on coupling acoustic (Sv) and environmental (water temperature, fluorescence, salinity and turbid-ity) data. To do this we use data from an acoustics fisheries surveys (R/V Thalassa, Ifremer, AWA campaign) carry out in West African waters using multifrequency echosounder (18, 38, 70, 120, 333 kHz) and a scanfish (high performance towed undulator). FSDA were compared to classical statistical methods namely multivariate functional principal component analysis, classical prin-cipal component analysis, classification on principal component scores, classical additive model, spatial functional additive model. The interest to improve such statistical analysis is applied here to the study the effect at fine scale of environmental parameters on the distribution of coastal sound scattered layers. We first considered an aggregated analysis of the environmental data then we considered a more complete analysis of the data via their functional characters

Nonparametric prediction for spatial dependent functional data : application to demersal coastal fish off Senegal

Fisheries research shows, in an ecosystem approach, how the environment or ecological parameters affect the variability of density or biomass of one species or a group of species of wildlife by studying data at different capture locations over a long period of time. Classical multivariate statistical techniques such as multivariate spatial parametric prediction (Kriging) models are commonly applied to evaluate and predict fish abundance. This chapter discusses ways to model and predict high-dimensional oceanological data by functional data analysis for a better management of fishery resources. It commences with an introduction of the regression model, which allows to define the predictor. The chapter proposes a non-parametric spatial predictor of the catch per unit of effort of Senegalese coastal demersal fish species. Finally, it gives the application of demersal coastal fish off Senegal to spatial prediction

Asymptotic properties of nonparametric quantile estimation with spatial dependency

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