現生・太古代熱水系におけるマンガン・リンの地球化学的研究：原始的な微生物活動の重要性について

要約のみTohoku University掛川武課

現生・太古代熱水系におけるマンガン・リンの地球化学的研究：原始的な微生物活動の重要性について

要約のみTohoku University掛川武課

A Raman Lidar with a Deep Ultraviolet Laser for Continuous Water Vapor Profiling in the Atmospheric Boundary Layer

A Raman lidar with a deep ultraviolet laser was constructed to continuously monitor water vapor distributions in the atmospheric boundary layer for twenty-four hours. We employ a laser at a wavelength of 266 nm and detects the light separated into an elastic backscatter signal and vibrational Raman signals of oxygen, nitrogen, and water vapor. The lidar was encased in a temperature-controlled and vibration-isolated compact container, resistant to a variety of environmental conditions. Water vapor profile observations were made for twelve months from November 24, 2017, to November 29, 2018. These observations were compared with collocated radiosonde measurements for daytime and nighttime conditions

Effect of diamagnetic contribution of water on harmonics distribution in a dilute solution of iron oxide nanoparticles measured using high-Tc SQUID magnetometer

The magnetization curve of iron oxide nanoparticles in low-concentration solutions was investigated by a highly sensitive high-Tc superconducting quantum interference device (SQUID) magnetometer. The diamagnetic contribution of water that was used as the carrier liquid was observed in the measured magnetization curves in the high magnetic field region over 100 mT. The effect of the diamagnetic contribution of water on the generation of harmonics during the application of AC and DC magnetic fields was simulated on the basis of measured magnetization curves. Although the diamagnetic effect depends on concentration, a linear relation was observed between the detected harmonics and concentration in the simulated and measured results. The simulation results suggested that improvement could be expected in harmonics generation because of the diamagnetic effect when the iron concentration was lower than 72 μg/ml. The use of second harmonics with an appropriate bias of the DC magnetic field could be utilized for realization of a fast and highly sensitive detection of magnetic nanoparticles in a low-concentration solution

A Raman Lidar with a Deep Ultraviolet Laser for Continuous Water Vapor Profiling in the Atmospheric Boundary Layer

A Raman lidar with a deep ultraviolet laser was constructed to continuously monitor water vapor distributions in the atmospheric boundary layer for twenty-four hours. We employ a laser at a wavelength of 266 nm and detects the light separated into an elastic backscatter signal and vibrational Raman signals of oxygen, nitrogen, and water vapor. The lidar was encased in a temperature-controlled and vibration-isolated compact container, resistant to a variety of environmental conditions. Water vapor profile observations were made for twelve months from November 24, 2017, to November 29, 2018. These observations were compared with collocated radiosonde measurements for daytime and nighttime conditions

Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks

Lung cancer is a leading cause of death worldwide. Although computed tomography (CT) examinations are frequently used for lung cancer diagnosis, it can be difficult to distinguish between benign and malignant pulmonary nodules on the basis of CT images alone. Therefore, a bronchoscopic biopsy may be conducted if malignancy is suspected following CT examinations. However, biopsies are highly invasive, and patients with benign nodules may undergo many unnecessary biopsies. To prevent this, an imaging diagnosis with high classification accuracy is essential. In this study, we investigate the automated classification of pulmonary nodules in CT images using a deep convolutional neural network (DCNN). We use generative adversarial networks (GANs) to generate additional images when only small amounts of data are available, which is a common problem in medical research, and evaluate whether the classification accuracy is improved by generating a large amount of new pulmonary nodule images using the GAN. Using the proposed method, CT images of 60 cases with confirmed pathological diagnosis by biopsy are analyzed. The benign nodules assessed in this study are difficult for radiologists to differentiate because they cannot be rejected as being malignant. A volume of interest centered on the pulmonary nodule is extracted from the CT images, and further images are created using axial sections and augmented data. The DCNN is trained using nodule images generated by the GAN and then fine-tuned using the actual nodule images to allow the DCNN to distinguish between benign and malignant nodules. This pretraining and fine-tuning process makes it possible to distinguish 66.7% of benign nodules and 93.9% of malignant nodules. These results indicate that the proposed method improves the classification accuracy by approximately 20% in comparison with training using only the original images