Assessment of ABT-263 activity across a cancer cell line collection leads to a potent combination therapy for small-cell lung cancer

BH3 mimetics such as ABT-263 induce apoptosis in a subset of cancer models. However, these drugs have shown limited clinical efficacy as single agents in small-cell lung cancer (SCLC) and other solid tumor malignancies, and rational combination strategies remain underexplored. To develop a novel therapeutic approach, we examined the efficacy of ABT-263 across >500 cancer cell lines, including 311 for which we had matched expression data for select genes. We found that high expression of the proapoptotic gene Bcl2-interacting mediator of cell death (BIM) predicts sensitivity to ABT-263. In particular, SCLC cell lines possessed greater BIM transcript levels than most other solid tumors and are among the most sensitive to ABT-263. However, a subset of relatively resistant SCLC cell lines has concomitant high expression of the antiapoptotic myeloid cell leukemia 1 (MCL-1). Whereas ABT-263 released BIM from complexes with BCL-2 and BCL-XL, high expression of MCL-1 sequestered BIM released from BCL-2 and BCL-XL, thereby abrogating apoptosis. We found that SCLCs were sensitized to ABT-263 via TORC1/2 inhibition, which led to reduced MCL-1 protein levels, thereby facilitating BIM-mediated apoptosis. AZD8055 and ABT-263 together induced marked apoptosis in vitro, as well as tumor regressions in multiple SCLC xenograft models. In a Tp53; Rb1 deletion genetically engineered mouse model of SCLC, the combination of ABT-263 and AZD8055 significantly repressed tumor growth and induced tumor regressions compared with either drug alone. Furthermore, in a SCLC patient-derived xenograft model that was resistant to ABT-263 alone, the addition of AZD8055 induced potent tumor regression. Therefore, addition of a TORC1/2 inhibitor offers a therapeutic strategy to markedly improve ABT-263 activity in SCLC.United States. Dept. of Defense (Grant W81-XWH-13-1-0323)National Cancer Institute (U.S.) (Cancer Center Support Grant P30-CA14051

Active surveillance of patients who have sentinel node positive melanoma:An international, multi-institution evaluation of adoption and early outcomes after the Multicenter Selective Lymphadenectomy trial II (MSLT-2)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/168248/1/cncr33483.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/168248/2/cncr33483_am.pd

스페클 노이즈에 강인한 CNN에 기반한 SAR 자동 표적 식별 알고리즘

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A CFD analysis of the dynamics of a direct-operated safety relief valve mounted on a pressure vessel

In this study, a numerical model is developed to investigate the fluid and dynamic characteristics of a direct-operated safety relieve valve (SRV). The CFX code has been used to employ advanced computational fluid dynamics (CFD) techniques including moving mesh capabilities, multiple domains and valve piston motion using the CFX Expression Language (CEL). With a geometrically accurate CFD model of the SRV and the vessel, the complete transient process of the system from valve opening to valve closure is simulated. A detailed picture of the compressible fluid flowing through the SRV is obtained, including small-scale flow features in the seat regions. In addition, the flow forces on the disc and the lift are monitored and analyzed and the comparison of the effects of design parameters, are examined; including the adjusting ring position, vessel volume and spring stiffness. Results from the model allow the fluid and dynamic characteristics of the SRV to be investigated and shows that the model has great potential of assisting engineers in the preliminary design of SRVs, operating under actual conditions which are often found to be difficult to interpret in practice

Classification of Working Memory Performance from EEG with Deep Artificial Neural Networks

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Speckle-Noise-Invariant Convolutional Neural Network for SAR Target Recognition

Speckle noise is inherent to synthetic aperture radar (SAR) images and degrades the target recognition performance. Deep learning based on convolutional neural networks (CNNs) has been widely applied for SAR target recognition, but the extracted features are still sensitive to speckle noise. In addition, speckle noise has been seldom considered in such CNN-based approaches. In this letter, we propose a speckle-noise-invariant CNN that employs regularization for minimizing feature variations caused by this noise. Before CNN training, we performed SAR image despeckling using the improved Lee sigma filter for feature extraction. Then, we generated SAR images for CNN training by adding speckle noise to the despeckled images. The proposed regularization improves both the feature robustness to speckle noise and SAR target recognition. Experiments on the moving and stationary target acquisition and recognition database demonstrate that the proposed CNN notably improves the classification accuracy compared with the conventional methods.11Nsciescopu

FGANet: fNIRS-guided Attention Network for Hybrid EEG-fNIRS Brain-Computer Interfaces

AuthorNon-invasive brain-computer interfaces (BCIs) have been widely used for neural decoding, linking neural signals to control devices. Hybrid BCI systems using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention for overcoming the limitations of EEG- and fNIRS-standalone BCI systems. However, most hybrid EEG-fNIRS BCI studies have focused on late fusion because of discrepancies in their temporal resolutions and recording locations. Despite the enhanced performance of hybrid BCIs, late fusion methods have difficulty in extracting correlated features in both EEG and fNIRS signals. Therefore, in this study, we proposed a deep learning-based early fusion structure, which combines two signals before the fully-connected layer, called the fNIRS-guided attention network (FGANet). First, 1D EEG and fNIRS signals were converted into 3D EEG and fNIRS tensors to spatially align EEG and fNIRS signals at the same time point. The proposed fNIRS-guided attention layer extracted a joint representation of EEG and fNIRS tensors based on neurovascular coupling, in which the spatially important regions were identified from fNIRS signals, and detailed neural patterns were extracted from EEG signals. Finally, the final prediction was obtained by weighting the sum of the prediction scores of the EEG and fNIRS-guided attention features to alleviate performance degradation owing to delayed fNIRS response. In the experimental results, the FGANet significantly outperformed the EEG-standalone network. Furthermore, the FGANet has 4.0% and 2.7% higher accuracy than the state-of-the-art algorithms in mental arithmetic and motor imagery tasks, respectively.11Nsciescopu

Multilevel Feature Fusion with 3D Convolutional Neural Network for EEG-Based Workload Estimation

Mental workload is defined as the proportion of the information processing capability used to perform a task. High cognitive load requires additional resources to process information; this demand for additional resources may reduce the processing efficiency and performance. Therefore, the technique of workload estimation can ensure a proper working environment to promote the working efficiency of each person. In this paper, we propose a three-dimensional convolutional neural network (3D CNN) employing a multilevel feature fusion algorithm for mental workload estimation using electroencephalogram (EEG) signals. The 1D EEG signals are converted to 3D EEG images to enable the 3D CNN to learn the spectral and spatial information over the scalp. The multilevel feature fusion framework integrates local and global neuronal activities by workload tasks in the 3D CNN algorithm. Multilevel features are extracted in each layer of the 3D convolution operation and each multilevel feature is multiplied by a weighting factor, which determines the importance of the feature. The weighting factor is adaptively estimated for each EEG image by a backpropagation process. Furthermore, we generate subframes from each EEG image and propose a temporal attention technique based on the long short-term memory model (LSTM) to extract a significant subframe at each multilevel feature that is strongly correlated with task difficulty. To verify the performance of our network, we performed the Sternberg task to measure the mental workload of the participant, which was classified according to its difficulty as low or high workload condition. We showed that the difficulty of the workload was well designed, which was reflected in the behavior of the participant. Our network is trained on this dataset and the accuracy of our network is 90.8 %, which is better than that of conventional algorithms. We also evaluated our method using the public EEG dataset and achieved 93.9 % accuracy. © 2013 IEEE.11Ysciescopu