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HsfAld, a Protein Identified via FOX Hunting Using Thellungiella salsuginea cDNAs Improves Heat Tolerance by Regulating Heat-Stress-Responsive Gene Expression

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Molecular Plant 2013年第2期6卷 411-422页

作者： Yukari Higashi Naohiko Ohama Tomoko Ishikawa Taku Katori Ayaka Shimura Kazuya Kusakabe Kazuko Yamaguchi-Shinozaki junko ishida Maho Tanaka Motoaki Seki Kazuo Shinozaki Yoichi Sakata Takahisa Hayashi Teruaki Taji Department of Bio-Science Tokyo University of Agriculture 1-1-1 Sakuragaoka Setagaya Tokyo 156-8502 Japan Graduate School of Agriculture and Life Sciences University of Tokyo Tokyo 113-8657 Japan RIKEN Yokohama Institute 1-7-22 Suehiro-cho Tsururni-ku Yokohama 230-0045 Japan

Thellungiella salsuginea （formerly T. halophila）, a species closely related to Arabidopsis （Arabidopsis thali-ana）, is tolerant not only to high salt levels, but also to chilling, freezing, and ozone. Here, we report that T. salsuginea also shows greater heat tolerance than Arabidopsis. We identified T. salsuginea HsfAld （TsHsfAld） as a gene that can confer marked heat tolerance on Arabidopsis. TsHsfAld was identified via Full-length cDNA Over-eXpressing gene （FOX） hunt-ing from among a collection of heat-stress-related T. salsuginea cDNAs. Transgenic Arabidopsis overexpressing TsHsfAld showed constitutive up-regulation of many genes in the Arabidopsis AtHsfA1 regulon under normal growth tempera-ture. in Arabidopsis mesophyll protoplasts, TsHsfAld was localized in both the nucleus and the cytoplasm. TsHsfAld also interacted with AtHSP90, which negatively regulates AtHsfAls by forming HsfA1-HSP90 complexes in the cytoplasm. It is likely that the partial nuclear localization of TsHsfAld induced the expression of the AtHsfAld regulon in the transgenic plants at normal temperature. We also discovered that transgenic Arabidopsis plants overexpressing AtHsfAldwere more heat-tolerant than wild-type plants and up-regulated the expression of the HsfAld regulon, as was observed in TsHsfAld-overexpressing plants. We propose that the products of both TsHsfAld and AtHsfAld function as positive regulators of Arabidopsis heat-stress response and would be useful for the improvement of heat-stress tolerance in other plants.

关键词： Thellungiella salsuginea heat-stress tolerance HsfA1 FOX hunting.

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Super-Resolution Imaging of Mammograms Based on the Super-Resolution Convolutional Neural Network

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Open Journal of Medical Imaging 2017年第4期7卷 180-195页

作者： Kensuke Umehara junko Ota Takayuki ishida Department of Medical Physics and Engineering Graduate School of Medicine Osaka University Suita Japan

Purpose: To apply and evaluate a super-resolution scheme based on the super-resolution convolutional neural network (SRCNN) for enhancing image resolution in digital mammograms. Materials and Methods: A total of 711 mediolateral oblique (MLO) images including breast lesions were sampled from the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM). We first trained the super-resolution convolutional neural network (SRCNN), which is a deep-learning based super-resolution method. Using this trained SRCNN, high-resolution images were reconstructed from low-resolution images. We compared the image quality of the super-resolution method and that obtained using the linear interpolation methods (nearest neighbor and bilinear interpolations). To investigate the relationship between the image quality of the SRCNN-processed images and the clinical features of the mammographic lesions, we compared the image quality yielded by implementing the SRCNN, in terms of the breast density, the Breast Imaging-Reporting and Data System (BI-RADS) assessment, and the verified pathology information. For quantitative evaluation, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were measured to assess the image restoration quality and the perceived image quality. Results: The super-resolution image quality yielded by the SRCNN was significantly higher than that obtained using linear interpolation methods (p p Conclusion: SRCNN can significantly outperform conventional interpolation methods for enhancing image resolution in digital mammography. SRCNN can significantly improve the image quality of magnified mammograms, especially in dense breasts, high-risk BI-RADS assessment groups, and pathology-verified malignant cases.

关键词： Super-Resolution Deep-Learning Artificial Intelligence Breast Imaging Reporting and Data System (BI-RADS) Mammography

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Application of Sparse-Coding Super-Resolution to 16-Bit DICOM Images for Improving the Image Resolution in MRI

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Open Journal of Medical Imaging 2017年第4期7卷 144-155页

作者： junko Ota Kensuke Umehara Naoki Ishimaru Takayuki ishida Department of Medical Physics and Engineering Graduate School of Medicine Osaka University Suita Japan

Purpose: To improve the image resolution of magnetic resonance imaging (MRI), conventional interpolation methods are commonly used to magnify images via various image processing approaches;however, these methods tend to produce artifacts. While super-resolution (SR) schemes have been introduced as an alternative approach to apply medical imaging, previous studies applied SR only to medical images in 8-bit image format. This study aimed to evaluate the effectiveness of sparse-coding super-resolution (ScSR) for improving the image quality of reconstructed high-resolution MR images in 16-bit digital imaging and communications in medicine (DICOM) image format. Materials and Methods: Fifty-nine T1-weighted images (T1), 84 T2-weighted images (T2), 85 fluid attenuated inversion recovery (FLAIR) images, and 30 diffusion-weighted images (DWI) were sampled from The Repository of Molecular Brain Neoplasia Data as testing datasets, and 1307 non-medical images were sampled from the McGill Calibrated Color Image Database as a training dataset. We first trained the ScSR to prepare dictionaries, in which the relationship between low- and high-resolution images was learned. Using these dictionaries, a high-resolution image was reconstructed from a 16-bit DICOM low-resolution image downscaled from the original test image. We compared the image quality of ScSR and 4 interpolation methods (nearest neighbor, bilinear, bicubic, and Lanczos interpolations). For quantitative evaluation, we measured the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Results: The PSNRs and SSIMs for the ScSR were significantly higher than those of the interpolation methods for all 4 MRI sequences (PSNR: p p Conclusion: ScSR provides significantly higher image quality in terms of enhancing the resolution of MR images (T1, T2, FLAIR, and DWI) in 16-bit DICOM format compared to the interpolation methods.

关键词： Super-Resolution Sparse-coding Super-Resolution (ScSR) MRI DICOM 16-Bit

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Performance Evaluation of Super-Resolution Methods Using Deep-Learning and Sparse-Coding for Improving the Image Quality of Magnified Images in Chest Radiographs

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Open Journal of Medical Imaging 2017年第3期7卷 100-111页

作者： Kensuke Umehara junko Ota Naoki Ishimaru Shunsuke Ohno Kentaro Okamoto Takanori Suzuki Takayuki ishida Department of Medical Physics and Engineering Graduate School of Medicine Osaka University Suita Japan

Purpose: To detect small diagnostic signals such as lung nodules in chest radiographs, radiologists magnify a region-of-interest using linear interpolation methods. However, such methods tend to generate over-smoothed images with artifacts that can make interpretation difficult. The purpose of this study was to investigate the effectiveness of super-resolution methods for improving the image quality of magnified chest radiographs. Materials and Methods: A total of 247 chest X-rays were sampled from the JSRT database, then divided into 93 training cases with non-nodules and 154 test cases with lung nodules. We first trained two types of super-resolution methods, sparse-coding super-resolution (ScSR) and super-resolution convolutional neural network (SRCNN). With the trained super-resolution methods, the high-resolution image was then reconstructed using the super-resolution methods from a low-resolution image that was down-sampled from the original test image. We compared the image quality of the super-resolution methods and the linear interpolations (nearest neighbor and bilinear interpolations). For quantitative evaluation, we measured two image quality metrics: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). For comparative evaluation of the super-resolution methods, we measured the computation time per image. Results: The PSNRs and SSIMs for the ScSR and the SRCNN schemes were significantly higher than those of the linear interpolation methods (p p p Conclusion: Super-resolution methods provide significantly better image quality than linear interpolation methods for magnified chest radiograph images. Of the two tested schemes, the SRCNN scheme processed the images fastest;thus, SRCNN could be clinically superior for processing radiographs in terms of both image quality and processing speed.

关键词： Deep Learning Super-Resolution Super-Resolution Convolutional Neural Network (SRCNN) Sparse-Coding Super-Resolution (ScSR) Chest X-Ray

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