Spinorphin is an endogenous heptapeptide (leucylvalylvalyltyrosylprolyltryptophylthreonine), first isolated from bovine spinal cord, whose sequence matches a conserved region of β-hemoglobin. Also referred to as LVV-hemorphin-4 and a member of the nonclassical opioid hemorphin family, spinorphin inhibits enkephalin-degrading enzymes and is analgesic. Recently, spinorphin was reported to block neutrophil activation induced by the chemotactic N-formylpeptide N-formylmethionylleucylphenylalanine (fMLF), suggesting a potential role as an endogenous negative regulator of inflammation. Here we use both gain- and loss-of-function genetic tests to identify the specific mechanism of spinorphin action on neutrophils. Spinorphin induced calcium flux in normal mouse neutrophils, but was inactive in neutrophils from mice genetically deficient in the fMLF receptor subtype FPR (N-formylpeptide receptor). Consistent with this, spinorphin induced calcium flux in human embryonic kidney 293 cells transfected with mouse FPR, but had no effect on cells expressing the closely related fMLF receptor subtype FPR2. Despite acting as a calcium-mobilizing agonist at FPR, spinorphin was a weak chemotactic agonist and effectively blocked neutrophil chemotaxis induced by fMLF at concentrations selective for FPR. Spinorphin did not affect mouse neutrophil chemotaxis induced by concentrations of fMLF that selectively activate FPR2. Thus, spinorphin blocks fMLF-induced neutrophil chemotaxis by acting as a specific antagonist at the fMLF receptor subtype FPR.
-Naïve-Bayes and k-NN classifiers are two machine learning approaches for text classification. Rocchio is the classic method for text classification in information retrival. Based on these three approaches and using classifier fusion methods, we propose a novel approach in text classification. Our approach is a supervised method, meaning that the list of categories should be defined and a set of training data should be provided for training the system. In this approach, documents are represented as vectors where each component is associated with a particular word. We propose voting methods and OWA operator and Decision Template method for combining classifiers. Experimental results show that these methods decrese the classification error 15 percent as measured on 2000 training data from 20 newsgroups dataset.
Today, advanced multimedia services such as video conferencing and video streaming are widely used. In these applications, coded video signals are transmitted over error prone channels. Lossy source coding together with channel errors cause more degradation in the quality of received video. Hence, many error control techniques have been developed in the last ten years. Unequal error protection (UEP) is one of the promising techniques to address this issue. The efficiency of UEP increases by carefully considering the error sensitivity of each protected part. In addition, the video layers in a scalable video coding (SVC) stream have different importance. Consequently, applying UEP on scalable video signal improves the efficiency and reliability of a video transmission system. In this paper, we propose a protection method for enhancing the quality of scalable video over error prone networks for a wide range of error rates. The proposed method makes use of Reed Solomon codes for unequal error protection. The scalable extension of H.264/AVC is chosen as the encoder module. Experimental results show a significant improvement of 1.27dB in average, when compared with conventional methods. In addition comparing the results with Equal Error Protection shows an improvement of 4.13dB in high packet loss rates. It should be noted that the proposed method is far less complex compared to other existing methods like genetic algorithm (GA) and hillclimbing which use a UEP approach .1. Index Terms -Unequal error protection (UEP), scalable video coding (SVC), H.264/AVC, Reed Solomon coding.
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