H-2D ligand expression by Ly49A+ natural killer (NK) cells precludes ligand uptake from environmental cells: implications for NK cell function.

To study the adaptation of natural killer (NK) cells to their major histocompatibility complex (MHC) class I environment we have established a novel mouse model with mosaic expression of H-2D(d) using a Cre/loxP system. In these mice, we noticed that NK cells expressing the inhibitory receptor for D(d), Ly49A, were specifically underrepresented among cells with low D(d) levels. That was due to the acquisition of D(d) molecules by the Ly49A+ NK cells that have lost their D(d) transgene. The uptake of H-2D molecules via the Ly49A receptor was restricted to strong ligands of Ly49A. Surprisingly, when Ly49A+ NK cells were D(d+), uptake of the alternative ligand D(k) was not detectable. Similarly, one anti-Ly49A mAb (A1) bound inefficiently when Ly49A was expressed on D(d+) NK cells. Concomitantly, functional assays demonstrated a reduced capacity of Ly49A to inhibit H-2(b)D(d) as compared with H-2(b) NK cells, rendering Ly49A+ NK cells in D(d+) mice particularly reactive. Minor reductions of D(d) levels and/or increases of activating ligands on environmental cells may thus suffice to abrogate Ly49A-mediated NK cell inhibition. The mechanistic explanation for all these phenomena is likely the partial masking of Ly49A by D(d) on the same cell via a lateral binding site in the H-2D(d) molecule.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

The category 'natural' in the discourse of couples who used medically assisted procreation

As a constantly evolving set of complex biotechnologies, medically assisted procreation (MAP) jeopardises a category that seems to be taken for granted: that of 'natural'. What is 'natural' or not when MAP is used to procreate? What are the boundaries between a 'natural' and a 'non-natural' fertilisation? Drawing upon a dialogical approach to language and cognition, our study examined the semantic field of the category 'natural' as expressed in interviews between a psychiatrist and seven couples who resorted to MAP and had to decide whether to keep their frozen pre-embryonic cells (zygotes) for further procreation or to allow them be destroyed. We examined how these couples evoked the category 'natural' and showed that in their argumentation, the category 'natural' encompassed a wide variety of phenomena, which shifted the boundaries between the 'natural' and 'non-natural'. In so doing, the couples 'renaturalised' MAP, normalized it, moved the boundaries between what is legitimate or not, and showed their accountability. Hence, reference to the category 'natural' seemed to act both as an argumentative and a psychological resource in the elaboration of the person's experience in resorting to MAP.