LORRAINE B. WARE,1 XIOHUI FANG,2 YIBING WANG,2 TSUTOMU SAKUMA,3 TIMOTHY S. HALL,4 AND MICHAEL A. MATTHAY2,5 1Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee 37232-2650; 2Cardiovascular Research Institute, University of California, San Francisco, California 94143-0130; 3Thoracic Surgery, Kanazawa Medical University, Kanaza...

survivorship of the immature stage of anopheles culicifacies in damar district were studied through sampling larval and pupal stages from stream bed pools and rice fields during may and august 1993. the duration of each larval instar and the pupal stage was also determined in the laboratory for preparing the survivorship curves and the construction of the life tables. mortality from egg to adul...

Stream network density exerts a strong influence on ecohydrologic processes in watersheds, yet existing stream maps fail to capture most headwater streams and therefore underestimate stream density. Furthermore, discrepancies between mapped and actual stream length vary between watersheds, confounding efforts to understand the impacts of land use on stream ecosystems. Here we report on research...

* Salk Institute for Biological Studies, La Jolla, CA 92037-1099, USA § Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093-0130, USA ‡ Department of Molecular and Human Genetics, Baylor College of Medicine, Houston TX 77030, USA 1 current address: Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA Genetics: Published Articles Ahead of Print,...

A streaming data clustering algorithm is presented building upon the density-based self-organizing stream clustering algorithm SOSTREAM. Many density-based clustering algorithms are limited by their inability to identify clusters with heterogeneous density. SOSTREAM addresses this limitation through the use of local (nearest neighbor-based) density determinations. Additionally, many stream clus...

Many real-world applications share the property that the data they process arrives in streams. The transient and volatile nature of these streams renders the application of common processing and analysis techniques difficult. In particular, the mining of streams has proved to be a difficult task due to the rigid processing requirements that must be met within the data stream scenario. We propos...

To solve the problem of distributed data streams clustering, the algorithm DB-DDSC (Density-Based Distribute Data Stream Clustering) was proposed. The algorithm consisted of two stages. First presented the concept of circular-point based on the representative points and designed the iterative algorithm to find the densityconnected circular-points, then generated the local model at the remote si...

In many business applications, large data workloads such as sales figures or process performance measures need to be monitored in real-time. The data analysts want to catch problems in flight to reveal the root cause of anomalies. Immediate actions need to be taken before the problems become too expensive or consume too many resources. In the meantime, analysts need to have the “big picture” of...

Most density based stream clustering algorithms separate the clustering process into an online and offline component. Exact summarized statistics are being employed for defining micro-clusters or grid cells during the online stage followed by macro-clustering during the offline stage. This paper proposes a novel alternative to the traditional two phase stream clustering scheme, introducing sket...