The dramatic growth in the number of application domains that naturally generate probabilistic, uncertain data has resulted in a need for efficiently supporting complex querying and decision-making over such data. In this paper, we present a unified approach to ranking and top-k query processing in probabilistic databases by viewing it as a multi-criteria optimization problem, and by deriving a set of features that capture the key properties of a probabilistic dataset that dictate the ranked result. We contend that a single, specific ranking function may not suffice for probabilistic databases, and we instead propose two parameterized ranking functions, called P RF ω and P RF e , that generalize or can approximate many of the previously proposed ranking functions. We present novel generating functions-based algorithms for efficiently ranking large datasets according to these ranking functions, even if the datasets exhibit complex correlations modeled using probabilistic and/xor trees or Markov networks. We further propose that the parameters of the ranking function be learned from user preferences, and we develop an approach to learn those parameters. Finally, we present a comprehensive experimental study that illustrates the effectiveness of our parameterized ranking functions, especially P RF e , at approximating other ranking functions and the scalability of our proposed algorithms for exact or approximate ranking.

Hu, X. F., Guo, Y. M., Li, J. H., Yan, G. L., Bun, S. and Huang, B. Y. 2011. Effects of an early lipopolysaccharide challenge on growth and small intestinal structure and function of broiler chickens. Can. J. Anim. Sci. 91: 379–384. Two experiments were conducted to determine the effect of early exposure to lipopolysaccharide (LPS) on small intestinal structure and function of broiler chickens. Seven-day-old birds were randomly allotted to two equal treatments: an LPS-injected treatment in which the birds were injected intraperitoneally with LPS 500 µg kg−1 body weight (dissolved in 1 mL saline) on 8, 10, 12, 15, 17, and 19 d of age, i.e., on days 1, 3, and 5 d for 2 continuous weeks, and a control treatment (CTRL) in which the birds were similarly injected with 1 mL saline as a placebo. In exp. 1, food intake and weight gain were monitored over the 2 wk, the weight of the small bowel was determined at 14 and 21 d of age and duodenal and jejunal villus height and crypt depth, D-xylose uptake were also measured at 21 d. In exp. 2, additional measurements of the intestinal peristalsis ratio and the BrdU-labeling index and duodenal sodium-glucose co-transporter-1 (SGLT1) mRNA level were made at 21 d of age. The results showed that LPS challenge decreased feed intake, daily gain, duodenal and jejunal villus height and crypt depth, plasma D-xylose concentration and intestinal BrdUrd-labeling index, respectively (P<0.05) as well as small bowel weight at 14 and 21 d of age (P<0.05). Conversely, LPS injection increased SGLT1 mRNA level in the small intestine (P<0.05) and the small intestinal relative weight at 14 (P<0.05) and 21 d of age (P=0.063). Following LPS injection there were non-significant changes in feed conversion ratio and intestinal peristalsis ratio (P>0.05). In conclusion, early LPS challenge delayed the growth of intestine and impaired small intestinal structure and absorptive function.

Streaming analytics require real-time aggregation and processing of geographically distributed data streams continuously over time. The typical analytics infrastructure for processing such streams follow a hub-andspoke model, comprising multiple edges connected to a center by a wide-area network (WAN). The aggregation of such streams often require that the results be available at the center within a certain acceptable delay bound. Further, the WAN bandwidth available between the edges and the center is often scarce or expensive, requiring that the traffic between the edges and the center be minimized. We propose a novel Time-to-Live (TTL-)based mechanism for real-time aggregation that provably optimizes both delay and traffic, providing a theoretical basis for understanding the delay-traffic tradeoff that is fundamental to streaming analytics. Our TTL-based optimization model provides analytical answers to how much aggregation should be performed at the edge versus the center, how much delay can be incurred at the edges, and how the edge-to-center bandwidth must be apportioned across applications with different delay requirements. To evaluate our approach, we implement our TTL-based aggregation mechanism in Apache Flink, a popular stream analytics framework. We deploy our Flink implementation in a hub-and-spoke architecture on geodistributed Amazon EC2 data centers and a WAN-emulated local testbed, and run aggregation tasks for realistic workloads derived from extensive Akamai and Twitter traces. The delay-traffic tradeoff achieved by our Flink implementation agrees closely with theoretical predictions of our model. We show that by deriving the optimal TTLs using our model, our system can achieve a "sweet spot" where both delay and traffic are minimized, in comparison to traditional aggregation schemes such as batching and streaming.

The highly pathogenic avian influenza A virus poses a global threat to human health. Avian influenza RNA polymerase protein PAC was used in the screening of two herbal plant extracts for anti-influenza agents. As a result, chlorogenic acid was identified to be PAC ligand and was discovered to inhibit polymerase activity. Hence, this work revealed a potential anti-influenza lead compound and provided an important step in the discovery of new anti-influenza drugs.

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