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Despite the great agricultural and ecological importance of efficient use of urea-containing nitrogen fertilizers by crops, molecular and physiological identities of urea transport in higher plants have been investigated only in Arabidopsis. We performed short-time urea-influx assays which have identified a low-affinity and high-affinity (Km of 7.55 mu M) transport system for urea-uptake by rice roots (Oryza sativa). A high-affinity urea transporter OsDUR3 from rice was functionally characterized here for the first time among crops. OsDUR3 encodes an integral membrane-protein with 721 amino acid residues and 15 predicted transmembrane domains. Heterologous expression demonstrated that OsDUR3 restored yeast dur3-mutant growth on urea and facilitated urea import with a Km of c. 10 mu M in Xenopus oocytes. Quantitative reverse-transcription polymerase chain reaction (qPCR) analysis revealed upregulation of OsDUR3 in rice roots under nitrogen-deficiency and urea-resupply after nitrogen-starvation. Importantly, overexpression of OsDUR3 complemented the Arabidopsis atdur3-1 mutant, improving growth on low urea and increasing root urea-uptake markedly. Together with its plasma membrane localization detected by green fluorescent protein (GFP)-tagging and with findings that disruption of OsDUR3 by T-DNA reduces rice growth on urea and urea uptake, we suggest that OsDUR3 is an active urea transporter that plays a significant role in effective urea acquisition and utilisation in rice.
Intrusion Detection Systems (IDS) have been widely deployed in practice for detecting malicious behavior on network communication and hosts. False-positive alerts are a popular problem for most IDS approaches. The solution to address this problem is to enhance the detection process by correlation and clustering of alerts. To meet the practical requirements, this process needs to be finished fast, which is a challenging task as the amount of alerts in large-scale IDS deployments is significantly high. We identifytextitdata storage and processing algorithms to be the most important factors influencing the performance of clustering and correlation. We propose and implement a highly efficient alert correlation platform. For storage, a column-based database, an In-Memory alert storage, and memory-based index tables lead to significant improvements of the performance. For processing, algorithms are designed and implemented which are optimized for In-Memory databases, e.g. an attack graph-based correlation algorithm. The platform can be distributed over multiple processing units to share memory and processing power. A standardized interface is designed to provide a unified view of result reports for end users. The efficiency of the platform is tested by practical experiments with several alert storage approaches, multiple algorithms, as well as a local and a distributed deployment.