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Activation of tetrodotoxin-resistant sodium channels contributes to action potential electrogenesis in neurons. Antisense oligonucleotide studies directed against Na(v)1.8 have shown that this channel contributes to experimental inflammatory and neuropathic pain. We report here the discovery of A-803467, a sodium channel blocker that potently blocks(More)
Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Most previous studies have focused on designing special algorithms to effectively exploit the unlabeled data in conjunction with labeled data. Our goal is to improve the classification accuracy of any given supervised learning algorithm by(More)
The Hippo (Hpo) kinase cascade restricts tissue growth by inactivating the transcriptional coactivator Yorkie (Yki), which regulates the expression of target genes such as the cell death inhibitor diap1 by unknown mechanisms. Here we identify the TEAD/TEF family protein Scalloped (Sd) as a DNA-binding transcription factor that partners with Yki to mediate(More)
A novel search-based approach to software quality modeling with multiple software project repositories is presented. Training a software quality model with only one software measurement and defect data set may not effectively encapsulate quality trends of the development organization. The inclusion of additional software projects during the training process(More)
BACKGROUND On March 30, a novel influenza A subtype H7N9 virus (A/H7N9) was detected in patients with severe respiratory disease in eastern China. Virological factors associated with a poor clinical outcome for this virus remain unclear. We quantified the viral load and analysed antiviral resistance mutations in specimens from patients with A/H7N9. (More)
In this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics: (1) diversity -- which indicates the variance of topics in a group of documents; (2) information richness -- which measures the coverage of a single document to its topic. Both of the two metrics are calculated from a directed(More)
In recent years, local pattern based object detection and recognition have attracted increasing interest in computer vision research community. However, to our best knowledge no previous work has focused on utilizing local patterns for the task of human detection. In this paper we develop a novel human detection system in personal albums based on LBP (local(More)
We present an approach to query expansion in answer retrieval that uses Statistical Machine Translation (SMT) techniques to bridge the lexical gap between questions and answers. SMT-based query expansion is done by i) using a full-sentence paraphraser to introduce synonyms in context of the entire query, and ii) by translating query terms into answer terms(More)
We present a novel framework for multi-label learning that explicitly addresses the challenge arising from the large number of classes and a small size of training data. The key assumption behind this work is that two examples tend to have large overlap in their assigned class memberships if they share high similarity in their input patterns. We capitalize(More)
Learning application-specific distance metrics from labeled data is critical for both statistical classification and information retrieval. Most of the earlier work in this area has focused on finding metrics that simultaneously optimize compactness and separability in a global sense. Specifically, such distance metrics attempt to keep all of the data(More)