Silencing T-bet Defines a Critical Role in the Differentiation of Autoreactive T Lymphocytes

AbstractAs a means of developing therapies that target the pathogenic T cells in multiple sclerosis (MS) without compromising the immune system or eliciting systemic side effects, we investigated the use of T-bet-specific antisense oligonucleotides and small interfering RNAs (siRNA) to silence T-bet expression in autoreactive encephalitogenic T cells and evaluated the biological consequences of this suppression in experimental autoimmune encephalomyelitis, a model for MS. The T-bet-specific AS oligonucleotide and siRNA suppressed T-bet expression, IFNγ production, and STAT1 levels during antigen-specific T cell differentiation. In vitro suppression of T-bet during differentiation of myelin-specific T cells and in vivo administration of a T-bet-specific antisense oligonucleotide or siRNA inhibited disease. T-bet was shown to bind the IFNγ and STAT1 promoters, but did not regulate the IL-12/STAT4 pathway. Since T-bet regulates IFNγ production in CD4+ T cells, but to a lesser extent in most other IFNγ-producing cells, T-bet may be a target for therapeutics for Th1-mediated diseases

Uncertainty in ecosystem mapping by remote sensing

142013 ROC 1reservedInternationalThe classification of remotely sensed images such as aerial photographs or satellite sensor images for deriving ecosystem-related maps (e.g. land cover, land use, vegetation, soil) is generally based on clustering of spatial entities within a spectral space. In most cases, Boolean logic is applied in order to map landscape patterns. One major concern is that this implies an ability to divide the gradual variability of the Earth’s surface into a finite number of discrete non-overlapping classes, which are considered to be exhaustively defined and mutually exclusive. This type of approach is often inappropriate given the continuous nature of many ecosystem properties. Moreover, the standard data processing and image classification methods used will involve the loss of information as the continuous quantitative spectral information is degraded into a set of discrete classes. This leads to uncertainty in the products resulting from the use of remote sensing tools. It follows that any estimated ecosystem property has an associated error and/or uncertainty of unknown magnitude, and that the statistical quantification of uncertainty should be a core part of scientific research using remote sensing. In this paper we will review recent attempts to take explicitly into account uncertainty when mapping ecosystems.restrictedRocchini, D.; Foody, G.M.; Nagendra, H.; Ricotta, C.; Anand, M.; He, K.S.; Amici, V.; Kleinschmit, B.; Förster, M.; Schmidtlein, S.; Feilhauer, H.; Ghisla, A.; Metz, M.; Neteler, M.Rocchini, D.; Foody, G.M.; Nagendra, H.; Ricotta, C.; Anand, M.; He, K.S.; Amici, V.; Kleinschmit, B.; Förster, M.; Schmidtlein, S.; Feilhauer, H.; Ghisla, A.; Metz, M.; Neteler, M.G

Spatial Algorithms Applied to Landscape Diversity Estimate from Remote Sensing Data

The causal relationship between species diversity and environmental (landscape) heterogeneity has been a long-lasting interest among ecologists. In fact, environmental heterogeneity is considered to be one of the main factors associated with biodiversity given that areas with higher environmental heterogeneity can host more species due to their higher number of available niches. In particular, entropy (also referred to as “heterogeneity”) measured by the spatial variation of remotely sensed spectral signals has been proposed as a proxy for species diversity. The aim of this book chapter is to review the main spatial algorithms for measuring landscape heterogeneity based on remote sensing