A gravitationally lensed quasar with quadruple images separated by 14.62 arcseconds.

Gravitational lensing is a powerful tool for the study of the distribution of dark matter in the Universe. The cold-dark-matter model of the formation of large-scale structures (that is, clusters of galaxies and even larger assemblies) predicts the existence of quasars gravitationally lensed by concentrations of dark matter so massive that the quasar images would be split by over 7 arcsec. Numerous searches for large-separation lensed quasars have, however, been unsuccessful. All of the roughly 70 lensed quasars known, including the first lensed quasar discovered, have smaller separations that can be explained in terms of galaxy-scale concentrations of baryonic matter. Although gravitationally lensed galaxies with large separations are known, quasars are more useful cosmological probes because of the simplicity of the resulting lens systems. Here we report the discovery of a lensed quasar, SDSS J1004 + 4112, which has a maximum separation between the components of 14.62 arcsec. Such a large separation means that the lensing object must be dominated by dark matter. Our results are fully consistent with theoretical expectations based on the cold-dark-matter model.

Identification and quantification of disease-related gene clusters.

MOTIVATION: DNA microarray technology and the completion of human and mouse genome sequencing programs are now offering new avenues for the investigation of complex genetic diseases. In particular, this makes possible the study of the spatial distribution of disease-related genes within the genome. We report on the first systematic search for clustering of genes associated with a polygenic autoimmune disease. RESULTS: Using a set of cDNA microarray chip experiments in two mouse models of rheumatoid arthritis, we have identified approximately 200 genes based on their expression in inflamed joints and mapped them into the genome. We compute the spatial autocorrelation function of the selected genes and find that they tend to cluster over scales of a few megabase pairs. We then identify significant gene clusters using a friends-of-friends algorithm. This approach should aid in discovering functionally related gene clusters in the mammalian genome.