20 research outputs found
Tight bound on coherent-state-based entanglement generation over lossy channels
The first stage of the hybrid quantum repeaters is entanglement generation
based on transmission of pulses in coherent states over a lossy channel.
Protocols to make entanglement with only one type of error are favorable for
rendering subsequent entanglement distillation efficient. Here we provide the
tight upper bound on performances of these protocols that is determined only by
the channel loss. In addition, we show that this bound is achievable by
utilizing a proposed protocol [arXiv:0811.3100] composed of a simple
combination of linear optical elements and photon-number-resolving detectors.Comment: 12 pages, 3 figure
In-situ biogas upgrading with H-2 addition in an anaerobic membrane bioreactor (AnMBR) digesting waste activated sludge
Biological in-situ biogas upgrading is a promising approach for sustainable energy-powered technologies. This method increases the CH4 content in biogas via hydrogenotrophic methanogenesis with an external H-2 supply. In this study, an anaerobic membrane bioreactor (AnMBR) was employed for in-situ biogas upgrading. The AnMBR was operated in semi-batch mode using waste activated sludge as the substrate. Pulsed H-2 addition into the reactor and biogas recirculation effectively increased the CH4 content in the biogas. The addition of 4 equivalents of H-2 relative to CO2 did not lead to appreciable biogas upgrading, although the acetate concentration increased significantly. When 11 equivalents of H-2 were introduced, the biogas was successfully upgraded, and the CH4 content increased to 92%. The CH4 yield and CH4 production rate were 0.31 L/g-VSinput and 0.086 L/L/d, respectively. In this phase of the process, H-2 addition increased the acetate concentration and the pH because of CO2 depletion. Compared with a continuously-stirred tank reactor, the AnMBR system attained higher CH4 content, even without the addition of H-2. The longer solid retention time (100 d) in the AnMBR led to greater degradation of volatile solids. Severe membrane fouling was not observed, and the transmembrane pressure remained stable under 10 kPa for 117 d of continuous filtration without cleaning of the membrane. The AnMBR could be a promising reactor configuration to achieve in-situ biogas upgrading during sludge digestion
Collinear injection-seeded terahertz parametric generator
In a conventional injection-seeded terahertz (THz) parametric generator (is-TPG), a complicated achromatic optical system controlling the angle of incidence of the seed beam is used to ensure tunability because both the pump and seed beams must satisfy non-collinear phase-matching conditions. In this study, we found that a THz output and tunability similar to those characteristics of a conventional is-TPG can be obtained even when the pump and seed beams are coaxially injected into the LiNbO3 crystal. In this new generation mechanism, a weak THz-wave is generated by the difference frequency of optical mixing between the pump and seed beams at the Cherenkov phase-matching angle and then strongly parametrically amplified. Thus, we describe our device as a collinear is-TPG. Simplicity is improved by eliminating any need for achromatic optics
In-situ biogas upgrading with H-2 addition in an anaerobic membrane bioreactor (AnMBR) digesting waste activated sludge
Biological in-situ biogas upgrading is a promising approach for sustainable energy-powered technologies. This method increases the CH4 content in biogas via hydrogenotrophic methanogenesis with an external H-2 supply. In this study, an anaerobic membrane bioreactor (AnMBR) was employed for in-situ biogas upgrading. The AnMBR was operated in semi-batch mode using waste activated sludge as the substrate. Pulsed H-2 addition into the reactor and biogas recirculation effectively increased the CH4 content in the biogas. The addition of 4 equivalents of H-2 relative to CO2 did not lead to appreciable biogas upgrading, although the acetate concentration increased significantly. When 11 equivalents of H-2 were introduced, the biogas was successfully upgraded, and the CH4 content increased to 92%. The CH4 yield and CH4 production rate were 0.31 L/g-VSinput and 0.086 L/L/d, respectively. In this phase of the process, H-2 addition increased the acetate concentration and the pH because of CO2 depletion. Compared with a continuously-stirred tank reactor, the AnMBR system attained higher CH4 content, even without the addition of H-2. The longer solid retention time (100 d) in the AnMBR led to greater degradation of volatile solids. Severe membrane fouling was not observed, and the transmembrane pressure remained stable under 10 kPa for 117 d of continuous filtration without cleaning of the membrane. The AnMBR could be a promising reactor configuration to achieve in-situ biogas upgrading during sludge digestion
Knockout of all ErbB-family genes delineates their roles in proliferation, survival, and migration
The ErbB-family receptors play pivotal roles in the proliferation, migration, and survival of epithelial cells. Because our knowledge on the ErbB-family receptors was obtained largely by the exogenous application of their ligands, it remains unknown to which extent each of the ErbB contributes to these outputs. We here knocked out each ErbB gene, various combinations of ErbB genes, or all in Madin-Darby canine kidney cells to delineate the contribution of each gene. ERK activation waves during collective cell migration were mediated primarily by ErbB1 and secondarily by the ErbB2/ErbB3 heterodimer. Either ErbB1 or the ErbB2/ErbB3 complex was sufficient for the G1/S progression. The saturation cell density was markedly reduced in cells deficient in all ErbB-proteins, but not cells retaining only ErbB2, which cannot bind to ligands. Thus, the ligand-independent ErbB2 activity is sufficient for preventing apoptosis at high cell density. In short, systematic knockout of ErbB-family genes delineated the roles of each ErbB receptor
Performance improvement of automated melanoma diagnosis system by data augmentation
Color information is an important tool for diagnosing melanoma. In this study, we used a hyper-spectral imager (HSI), which can measure color information in detail, to develop an automated melanoma diagnosis system. In recent years, the effectiveness of deep learning has become more widely accepted in the field of image recognition. We therefore integrated the deep convolutional neural network with transfer learning into our system. We tried data augmentation to demonstrate how our system improves diagnostic performance. 283 melanoma lesions and 336 non-melanoma lesions were used for the analysis. The data measured by HSI, called the hyperspectral data (HSD), were converted to a single-wavelength image averaged over plus or minus 3 nm. We used GoogLeNet which was pre-trained by ImageNet and then was transferred to analyze the HSD. In the transfer learning, we used not only the original HSD but also artificial augmentation dataset to improve the melanoma classification performance of GoogLeNet. Since GoogLeNet requires three-channel images as input, three wavelengths were selected from those single-wavelength images and assigned to three channels in wavelength order from short to long. The sensitivity and specificity of our system were estimated by 5-fold cross-val-idation. The results of a combination of 530, 560, and 590 nm (combination A) and 500, 620, and 740 nm (com-bination B) were compared. We also compared the diagnostic performance with and without the data augmentation. All images were augmented by inverting the image vertically and/or horizontally. Without data augmentation, the respective sensitivity and specificity of our system were 77.4% and 75.6% for combination A and 73.1% and 80.6% for combination B. With data augmentation, these numbers improved to 79.9% and 82.4% for combination A and 76.7% and 82.2% for combination B. From these results, we conclude that the diagnostic performance of our system has been improved by data augmentation. Furthermore, our system suc-ceeds to differentiate melanoma with a sensitivity of almost 80%
Automatic diagnosis of melanoma using hyperspectral data and GoogLeNet
Background: Melanoma is a type of superficial tumor. As advanced melanoma has a poor prognosis, early detection and therapy are essential to reduce melanoma-related deaths. To that end, there is a need to develop a quantitative method for diagnosing melanoma. This paper reports the development of such a diagnostic system using hyperspectral data (HSD) and a convolutional neural network, which is a type of machine learning. Materials and Methods: HSD were acquired using a hyperspectral imager, which is a type of spectrometer that can simultaneously capture information about wavelength and position. GoogLeNet pre-trained with Imagenet was used to model the convolutional neural network. As many CNNs (including GoogLeNet) have three input channels, the HSD (involving 84 channels) could not be input directly. For that reason, a “Mini Network” layer was added to reduce the number of channels from 84 to 3 just before the GoogLeNet input layer. In total, 619 lesions (including 278 melanoma lesions and 341 non-melanoma lesions) were used for training and evaluation of the network. Results and Conclusion: The system was evaluated by 5-fold cross-validation, and the results indicate sensitivity, specificity, and accuracy of 69.1%, 75.7%, and 72.7% without data augmentation, 72.3%, 81.2%, and 77.2% with data augmentation, respectively. In future work, it is intended to improve the Mini Network and to increase the number of lesions