45 research outputs found

    Oncogenic Transformation by Inhibitor-Sensitive and -Resistant EGFR Mutants

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    BACKGROUND: Somatic mutations in the kinase domain of the epidermal growth factor receptor tyrosine kinase gene EGFR are common in lung adenocarcinoma. The presence of mutations correlates with tumor sensitivity to the EGFR inhibitors erlotinib and gefitinib, but the transforming potential of specific mutations and their relationship to drug sensitivity have not been described. METHODS AND FINDINGS: Here, we demonstrate that EGFR active site mutants are oncogenic. Mutant EGFR can transform both fibroblasts and lung epithelial cells in the absence of exogenous epidermal growth factor, as evidenced by anchorage-independent growth, focus formation, and tumor formation in immunocompromised mice. Transformation is associated with constitutive autophosphorylation of EGFR, Shc phosphorylation, and STAT pathway activation. Whereas transformation by most EGFR mutants confers on cells sensitivity to erlotinib and gefitinib, transformation by an exon 20 insertion makes cells resistant to these inhibitors but more sensitive to the irreversible inhibitor CL-387,785. CONCLUSION: Oncogenic transformation of cells by different EGFR mutants causes differential sensitivity to gefitinib and erlotinib. Treatment of lung cancers harboring EGFR exon 20 insertions may therefore require the development of alternative kinase inhibition strategies

    Epidermal Growth Factor Receptor Activation in Glioblastoma through Novel Missense Mutations in the Extracellular Domain

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    BACKGROUND: Protein tyrosine kinases are important regulators of cellular homeostasis with tightly controlled catalytic activity. Mutations in kinase-encoding genes can relieve the autoinhibitory constraints on kinase activity, can promote malignant transformation, and appear to be a major determinant of response to kinase inhibitor therapy. Missense mutations in the EGFR kinase domain, for example, have recently been identified in patients who showed clinical responses to EGFR kinase inhibitor therapy. METHODS AND FINDINGS: Encouraged by the promising clinical activity of epidermal growth factor receptor (EGFR) kinase inhibitors in treating glioblastoma in humans, we have sequenced the complete EGFR coding sequence in glioma tumor samples and cell lines. We identified novel missense mutations in the extracellular domain of EGFR in 13.6% (18/132) of glioblastomas and 12.5% (1/8) of glioblastoma cell lines. These EGFR mutations were associated with increased EGFR gene dosage and conferred anchorage-independent growth and tumorigenicity to NIH-3T3 cells. Cells transformed by expression of these EGFR mutants were sensitive to small-molecule EGFR kinase inhibitors. CONCLUSIONS: Our results suggest extracellular missense mutations as a novel mechanism for oncogenic EGFR activation and may help identify patients who can benefit from EGFR kinase inhibitors for treatment of glioblastoma

    The application of information and communication technologies on digital learning to reduce the digital divide

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    [[abstract]]Digital learning is gaining a lot of attention in these few years. However, digital learning is still not yet widely adapted. One of the main reasons is due to the digital divide. Digital learning requires the learners to possess certain equipments and skills to participate. Due to the education level, income, generation, behavior, geographic location, etc., learners may not have the required equipments or skills or both to join digital learning. Moreover, insufficient professionalism and the lack of content also hinder the promotion of digital learning. Many nations have national policies to reduce the digital divide, mainly in establishing the necessary infrastructure such as network and computer to broaden the accessibility of digital technology, and deploying online government services to encourage the usage of the digital technology for public. One the other hand, the quality and quantity of the digital content for digital learning is still insufficient. The lack of an open platform, authoring tool chain, convenient digital right management mechanism, system supporting mobile behavior and customization, etc., are some sources of the problem. We will describe several ongoing projects that are aimed to solve or reduce these issues. Combined with the efforts from the industries, such as system integrator, chip manufacturer, and mobile communication vendor, these projects will help encouraging the industries to invest in the creation of digital content

    Joint playout and FEC control for multi-stream voice over IP networks

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    [[abstract]]Packet loss and delay are the major network impairments for transporting real-time voice over internet protocol (IP) networks. In the proposed system, multiple descriptions of the speech are used to take advantage of packet path diversity. A new objective method is presented for predicting the perceived quality of multi-stream voice transmission. Also proposed is a joint playout buffer and forward error control (FEC) adjustment scheme that maximizes the perceived speech quality via delay-loss trading. Experimental results showed that the proposed multi-stream voice transmission scheme achieves significant reductions in delay- and packet-loss rates as well as improved speech quality

    Perceptual-based playout mechanisms for multi-stream voice over IP networks

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    [[abstract]]Packet loss and delay are two essential problems to real-time voice transmission over IP networks. In the proposed system, multiple descriptions of the speech are transmitted to take advantage of largely uncorrelated delay and loss characteristics on different network paths. Adaptive playout scheduling of multiple voice streams is formulated as an optimization problem leading to a better delay-loss tradeoff. Also proposed is a perceptually motivated optimization criterion based on a simplified version of the ITU-T E-model. Experimental results show that the proposed multi-stream playout algorithm improves the delay-loss tradeoff as well as speech reconstruction quality

    Design for failure: intelligent systems learning from their mistakes

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    Smart environments aim to make the life of their inhabitants more comfortable by having context-aware systems continuously work together to assist people with their daily tasks. However, all too often these assistive technologies are naively or optimistically developed assuming that systems can always anticipate what users want. Furthermore, the more these smart systems grow in complexity, the more prone to failure they become. The overall goal of this paper is to define new concepts and methodologies for the development of more reliable smart applications, and propose middleware support to analyze failures in context-aware behavior, culminating in a software-based safeguard that improves robustness against unforeseen human interventions, exceptional circumstances and unexpected events.status: publishe

    Context-aware computing using a shared contextual information service

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    International audienceThe Aura ubiquitous computing project is investigating how we can reduce user distractions by having applications automatically adapt to the user's context. Context-aware applications rely on a shared service, the Contextual Information Service, to obtain context information. In this paper we describe our experience in implementing four very different applications using the CIS and in porting the applications to a different environment. One of the services also integrates technologies developed by a sister project that focuses on using the Semantic Web to support context awareness and privacy

    The BBN BYBLOS continuous speech recognition system

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    In this paper we describe the algorithms used in the BBN BYBLOS Continuous Speech Recognition system. The BYBLOS system uses context-dependent hidden Markov models of phonemes to provide a robust model of phonetic coarticulation. We provide an update of the ongoing research aimed at improving the recognition accuracy. In the first experiment we confirm the large improvement in accuracy that can be derived by using spectral derivative parameters in the recognition. In particular, the word error rate is reduced by a factor of two. Currently the system achieves a word error rate of 2.9% when tested on the speaker-dependent part of the standard 1000-Word DARPA Resource Management Database using the Word-Pair grammar supplied with the database. When no grammar was used, the error rate is 15.3%. Finally, we present a method for smoothing the discrete densities on the states of the HMM, which is intended to alleviate the problem of insufficient training for detailed phonetic models. At BBN we have been involved in the development o
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