We have characterized the 5' region of the rice actin 1 gene (Act1) and show that it is an efficient promoter for regulating the constitutive expression of a foreign gene in transgenic rice. By constructing plasmids with 5' regions from the rice Act1 gene fused to the coding sequence of a gene encoding bacterial beta-glucuronidase, we demonstrate that a region 1.3 kilobases upstream of the Act1 translation initiation codon contains all of the 5'-regulatory elements necessary for high-level beta-glucuronidase (GUS) expression in transient assays of transformed rice protoplasts. The rice Act1 primary transcript has a noncoding exon separated by a 5' intron from the first coding exon. Fusions that lack this Act1 intron showed no detectable GUS activity in transient assays of transformed rice protoplasts. Deletion analysis of the Act1 5' intron suggests that the intron-mediated stimulation of GUS expression is associated, in part, with an in vivo requirement for efficient intron splicing.

No abstract

This study used data from 1994 to 1999 from one large county in Pennsylvania to estimate the Medicaid expenditures of children diagnosed with autism spectrum disorders (ASD) and to compare these expenditures with those of other Medicaid-eligible children. On average, children diagnosed with ASD had expenditures 10 times those of other children. Differences in expenditures were driven in large part by inpatient psychiatric care. Further research is required to determine whether hospitalized children could be served in less restrictive and less expensive settings. Lack of differences in ambulatory care expenditures suggests that children with ASD are not receiving additional primary care services that would be indicative of appropriately coordinated services as suggested by the medical home model.

Selective and fast: A flavylium derivative‐based ratiometric fluorescent probe (1) for H2S is reported. The reaction mechanism (see scheme) is based on the nucleophilic addition of H2S towards the electrically positive benzopyrylium moiety of 1, which can efficiently differentiate H2S from other competitive species. The probe exhibits a fast response toward H2S, within 10 s, which is superior to most of the reported H2S probes.

No abstract

Event matching is the process of checking high volumes of events against large numbers of subscriptions and is a fundamental issue for the overall performance of a largescale distributed publish/subscribe system. Most existing algorithms are based on counting satisfied component constraints in each subscription. As the scale of a system grows, these algorithms inevitably suffer from performance degradation. We present REIN (REctangle INtersection), a fast event matching approach for large-scale content-based publish/subscribe systems. The idea behind REIN is to quickly filter out unlikely matched subscriptions. In REIN, the event matching problem is first transformed into the rectangle intersection problem. Then, an efficient index structure is designed to address the problem by using bit operations. Experimental results show that REIN has a better matching performance than its counterparts. In particular, the event matching speed is faster by an order of magnitude when the selectivity of subscriptions is high and the number of subscriptions is large.

No abstract

Recommender systems (RS) have become an integral part of our daily life. However, most current RS often repeatedly recommend items to users with similar profiles. We argue that recommendation should be diversified by leveraging session contexts with personalized user profiles. For this, current session-based RS (SBRS) often assume a rigidly ordered sequence over data which does not fit in many real-world cases. Moreover, personalization is often omitted in current SBRS. Accordingly, a personalized SBRS over relaxedly ordered user-session contexts is more pragmatic. In doing so, deep-structured models tend to be too complex to serve for online SBRS owing to the large number of users and items. Therefore, we design an efficient SBRS with shallow wide-in-wide-out networks, inspired by the successful experience in modern language modelings. The experiments on a real-world e-commerce dataset show the superiority of our model over the state-of-the-art methods.