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We present a general encoding scheme for the efficient management of spatial RDF data. The scheme approximates the geometries of the RDF entities inside their (integer) IDs and can be used, along with several operators and optimizations we introduce, to accelerate queries with spatial predicates and to re-encode entities dynamically in case of updates. We implement our ideas in SRX, a system built on top of the popular RDF-3X system. SRX extends RDF-3X with support for three types of spatial queries: range selections (e.g., find entities within a given polygon), spatial joins (e.g., find pairs of entities whose locations are close to each other), and spatial k-nearest neighbors (e.g., find the three closest entities from a given location). We evaluate SRX on spatial queries and updates with real RDF data, and we also compare its performance with the latest versions of three popular RDF stores. The results show SRX ’s superior performance over the competitors; compared to RDF-3X, SRX improves its performance for queries with spatial predicates while incurring little overhead during updates.

BACKGROUND Hirschsprung's disease-associated enterocolitis (HE) is a life-threatening septic complication of Hirschsprung's disease (HD), leading to bacterial translocation (BT) and sepsis. Many factors, such as intestinal stasis, HD-related inherited immune disorders and abnormal mucosal secretion have been implicated in its pathogenesis. OBJECTIVES To investigate the effect of intestinal stasis as an independent factor in the pathogenesis of HE intestinal lesions and its systematic effects. MATERIAL AND METHODS The rectal ganglion cells of 46 Wistar rats were chemically ablated through local benzalkonium chloride (BAC) injection, in order to create a HD model (megacolon rats) that does not carry the possible genetic burden of HD. The animals were sacrificed either on the 20th or 25th day after ablation and were examined for histopathological changes on the wall of the small intestine, presence of bacterial translocation in body organs, body biometrics, and white blood cell count (WBC) and hemoglobin concentration. The results were compared to control animals. RESULTS In the megacolon rats, severe damage on the small intestine as well as BT proportional to the extent of the intestinal damage and to the time elapsed after ablation was observed. Significant effects on the WBCs, hemoglobin concentration and biometric parameters were also observed. CONCLUSIONS In megacolon rats, intestinal stasis can lead by itself to a full-blown HE. The HE lesions that promote BT are present even in regions distant from the aganglionic bowel and are proportional to the time elapsed under the influence of intestinal stasis. Systematic effects such as growth retardation are also produced.

How do we create content that will become viral in a whole network after we share it with friends or followers? Significant research activity has been dedicated to the problem of strategically selecting a seed set of initial adopters so as to maximize a meme’s spread in a network. This line of work assumes that the success of such a campaign depends solely on the choice of a tunable seed set of adopters, while the way users perceive the propagated meme is fixed. Yet, in many real-world settings, the opposite holds: a meme’s propagation depends on users’ perceptions of its tunable characteristics, while the set of initiators is fixed.In this paper, we address the natural problem that arises in such circumstances: Suggest content, expressed as a limited set of attributes, for a creative promotion campaign that starts out from a given seed set of initiators, so as to maximize its expected spread over a social network. To our knowledge, no previous work ad-dresses this problem. We find that the problem is NP-hard and inapproximable. As a tight approximation guarantee is not admissible, we design an efficient heuristic, Explore-Update, as well as a conventional Greedy solution. Our experimental evaluation demonstrates that Explore-Update selects near-optimal attribute sets with real data, achieves 30% higher spread than baselines, and runs an order of magnitude faster than the Greedy solution.