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The present study was to determine whether clonidine could induce calcitonin gene-related peptide (CGRP) production and the underlying mechanisms. Human umbilical vein endothelial cells were treated with clonidine and the dose-effect or time-effect relationship of clonidine on CGRP production was examined. Yohimbine (a alpha(2)-adrenoceptor blocker) and L-NAME (an antagonist of nitric oxide synthase, NOS) were chosen to explore the role of alpha(2)-adrenoceptor and nitric oxide pathway in the effect of clonidine on endothelial cell-derived CGRP production. The level of CGRP mRNA or protein was detected by Real Time-PCR or radioimmunoassay. Nitric oxide content was measured by nitroreduction assay. The study showed that clonidine was able to induce CGRP mRNA (alpha- and beta-isoforms) expression in a dose-dependent manner in endothelial cells. The effect of clonidine on endothelial cell-derived CGRP synthesis and secretion was attenuated in the presence of yohimbine. L-NAME treatment could also inhibit clonidine-induced CGRP synthesis and secretion concomitantly with the decreased NO content in culture medium. These results suggest that clonidine could stimulate CGRP synthesis and secretion in endothelial cells through the activation of alpha(2)-adrenoceptor, which is related to the NO pathway.

A frequent item of a data stream is a data point whose occurrence frequency is above a given threshold. Finding frequent item of data stream has wide applications in various fields, such as network traffic monitor, data stream OLAP and data stream mining, etc. In data stream model, the algorithm can only scan the data in one pass and the available memory space is very limited relative to the volume of a data stream, therefore it is usually unable to find all the accurate frequent items of a data stream. This paper proposes a novel algorithm to find e-approximate frequent items of a data stream, its space complexity is O(e −1 ) and the processing time for each item is O(1) in average. Moreover, the frequency error bound of the results returned by the proposed algorithm is e(1−s+e)N.

In wireless sensor networks, observers are interested in spatio-temporal information monitored by sensors. Observers are not interested in sensor itself or massive irrelevant readings from sensors. They often issue spatio-temporal queries such as "Which events did happen in region R from 10:00 to 12:00?". Since battery supply of sensors is limited, energy-efficient spatio-temporal query processing in sensor networks has become an important research problem. This paper presents a spatio-temporal query processing algorithm based on data-centric storage. The energy consumption of sensors in three storage strategies, namely external storage, local storage and data-centric storage, is analyzed and compared in this paper. The paper also studies the influence of the probability of an event occurring, node density, number of event types, number of queries, temporal window size and spatial area size in spatial-temporal query on energy consumption. Analytical and experimental results show that in most cases the spatio-temporal query processing algorithm proposed in this paper can save more energy than those algorithms based on the external storage and local storage strategies.

It is very important in a lot of applications to forecast future trend of data streams. For example, using predictive queries to a sensor network for monitoring environment, observers can forecast future average temperature and humidity in the area covered by the network to determine abnormal events. Recent works on query processing over data streams mainly focused on approximate queries over newly arriving data. To the best of the knowledge, there is nothing to date in the literature on predictive query processing over data streams. Adopting multivariable linear regression, a predictive mathematical model for forecasting the aggregate value over data streams is first proposed. Then, based on the model, a predictive aggregate query processing method over data streams is proposed in the paper. When the frequency of forecast failing is greater than a predefined threshold, an adaptive strategy for the predictive mathematical model is proposed. A mathematical model that characterizes the affects of the updating cycle of sliding window and data stream rate on predictive accuracy is also presented. Analytical and experimental results show that the proposed method is very effective, and the proposed algorithms have higher performance and provide better prediction of aggregate values over data streams to users. In experiments the TPC-H data and ocean air temperature data measured by TAO (tropical atmosphere ocean) are used to construct data streams.