Abstract. As part of the GBC (Global Bioanalysis Consortium), the L3 assay format team has focused on reviewing common platforms used to support ligand binding assays in the detection of biotherapeutics.The following review is an overview of discussions and presentations from around the globe with a group of experts from different companies to allow an international harmonization of common practices and suggestions for different platforms. Some of the major platforms include Gyrolab, Erenna, RIA, AlphaLISA, Delfia, Immuno-PCR, Luminex, BIAcore, and ELISAs. The review is meant to support bioanalysts in taking decisions between different platforms depending on the needs of the analyte with a number of recommendations to help integration of platforms into a GLP environment.
As the uneven distribution of contrast agents and the perspective projection principle of X-ray, the vasculatures in angiographic image are with low contrast and are generally superposed with other organic tissues; therefore, it is very difficult to identify the vasculature and quantitatively estimate the blood flow directly from angiographic images. In this paper, we propose a fully automatic algorithm named adaptive geometrical vessel tracking (AGVT) for coronary artery identification in X-ray angiograms. Initially, the ridge enhancement (RE) image is obtained utilizing multiscale Hessian information. Then, automatic initialization procedures including seed points detection, and initial directions determination are performed on the RE image. The extracted ridge points can be adjusted to the geometrical centerline points adaptively through diameter estimation. Bifurcations are identified by discriminating connecting relationship of the tracked ridge points. Finally, all the tracked centerlines are merged and smoothed by classifying the connecting components on the vascular structures. Synthetic angiographic images and clinical angiograms are used to evaluate the performance of the proposed algorithm. The proposed algorithm is compared with other two vascular tracking techniques in terms of the efficiency and accuracy, which demonstrate successful applications of the proposed segmentation and extraction scheme in vasculature identification.
With the growth of e-commerce web sites, the demand of writing reviews on these portals have gained huge popularity. This huge data must be mined to analyze the opinion and for making better decisions in different domains. In this paper, we have proposed an aspect based opinion mining algorithm for the tourism domain. It first determines the aspects, and then extracts the opinion words related to the aspects. The opinion words are provided a score based on the Senti-Wordnet and the final score of each aspect is calculated by the summation of the scores of the opinions. The final score is visualized depicting ranking of scores of different aspects for different hotels.
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