Intelligent ranking for photo galleries using sharing intent

Users often share digital photographs over communication links, e.g., via SMS, chat, URLs, etc. This disclosure describes techniques to determine and present ranked candidate images for sharing to other users and user devices. The ranking is based on a variety of characteristics of the images, including time of capture of the images, as well as other user-permitted factors that can indicate user intent, such as history of previous sharing of images by the user, the current or recent device context of the user (chat or conversation, application being used, data on the user’s screen, and so on), etc. By intelligently ranking images in sharing galleries, the user\u27s time spent searching for and sharing images can be reduced, which can improve user satisfaction and sharing frequency

Impacts, Tillites, and the Breakup of Gondwanaland

Mathematical analysis demonstrates that substantial impact crater deposits should have been produced during the last 2 Gy of Earth\u27s history. Textures of impact deposits are shown to resemble textures of tillites and diamictites of Precambrian and younger ages. The calculated thickness distribution for impact crater deposits produced during 2 Gy is similar to that of tillites and diamictites ≤2 Ga. We suggest, therefore, that some tillites/diamictites could be of impact origin. Extensive tillite/diamictite deposits predated continental flood basalts on the interior of Gondwa- naland. Significantly, other investigators have already associated impact cratering with flood basalt volcanism and continental rifting. Thus, it is proposed that the breakup of Gondwanaland could have been initiated by crustal fracturing from impacts

Genetic diversity and striatal gene networks: focus on the heterogeneous stock-collaborative cross (HS-CC) mouse

Abstract Background The current study focused on the extent genetic diversity within a species (Mus musculus) affects gene co-expression network structure. To examine this issue, we have created a new mouse resource, a heterogeneous stock (HS) formed from the same eight inbred strains that have been used to create the collaborative cross (CC). The eight inbred strains capture > 90% of the genetic diversity available within the species. For contrast with the HS-CC, a C57BL/6J (B6) × DBA/2J (D2) F2 intercross and the HS4, derived from crossing the B6, D2, BALB/cJ and LP/J strains, were used. Brain (striatum) gene expression data were obtained using the Illumina Mouse WG 6.1 array, and the data sets were interrogated using a weighted gene co-expression network analysis (WGCNA). Results Genes reliably detected as expressed were similar in all three data sets as was the variability of expression. As measured by the WGCNA, the modular structure of the transcriptome networks was also preserved both on the basis of module assignment and from the perspective of the topological overlap maps. Details of the HS-CC gene modules are provided; essentially identical results were obtained for the HS4 and F2 modules. Gene ontology annotation of the modules revealed a significant overrepresentation in some modules for neuronal processes, e.g., central nervous system development. Integration with known protein-protein interactions data indicated significant enrichment among co-expressed genes. We also noted significant overlap with markers of central nervous system cell types (neurons, oligodendrocytes and astrocytes). Using the Allen Brain Atlas, we found evidence of spatial co-localization within the striatum for several modules. Finally, for some modules it was possible to detect an enrichment of transcription binding sites. The binding site for Wt1, which is associated with neurodegeneration, was the most significantly overrepresented. Conclusions Despite the marked differences in genetic diversity, the transcriptome structure was remarkably similar for the F2, HS4 and HS-CC. These data suggest that it should be possible to integrate network data from simple and complex crosses. A careful examination of the HS-CC transcriptome revealed the expected structure for striatal gene expression. Importantly, we demonstrate the integration of anatomical and network expression data.</p