Hideo Yokota

Abstract The Hyper Suprime-Cam (HSC) is an 870 megapixel prime focus optical imaging camera for the 8.2 m Subaru telescope. The wide-field corrector delivers sharp images of 0${^{\prime\prime}_{.}}$2 (FWHM) in the HSC-i band over the entire 1${^{\circ}_{.}}$5 diameter field of view. The collimation of the camera with respect to the optical axis of the primary mirror is done with hexapod actuators, the mechanical accuracy of which is a few microns. Analysis of the remaining wavefront error in off-focus stellar images reveals that the collimation of the optical components meets design specifications. While there is a flexure of mechanical components, it also is within the design specification. As a result, the camera achieves its seeing-limited imaging on Maunakea during most of the time; the median seeing over several years of observing is 0${^{\prime\prime}_{.}}$67 (FWHM) in the i band. The sensors use p-channel, fully depleted CCDs of 200 μm thickness (2048 × 4176 15 μm square pixels) and we employ 116 of them to pave the 50 cm diameter focal plane. The minimum interval between exposures is 34 s, including the time to read out arrays, to transfer data to the control computer, and to save them to the hard drive. HSC on Subaru uniquely features a combination of a large aperture, a wide field of view, sharp images and a high sensitivity especially at longer wavelengths, which makes the HSC one of the most powerful observing facilities in the world.

Hyper Suprime-Cam (HSC) is an 870 Mega pixel prime focus camera for the 8.2 m Subaru telescope. The wide field corrector delivers sharp image of 0.25 arc-sec FWHM in r-band over the entire 1.5 degree (in diameter) field of view. The collimation of the camera with respect to the optical axis of the primary mirror is realized by hexapod actuators whose mechanical accuracy is few microns. As a result, we expect to have seeing limited image most of the time. Expected median seeing is 0.67 arc-sec FWHM in i-band. The sensor is a p-ch fully depleted CCD of 200 micron thickness (2048 x 4096 15 μm square pixel) and we employ 116 of them to pave the 50 cm focal plane. Minimum interval between exposures is roughly 30 seconds including reading out arrays, transferring data to the control computer and saving them to the hard drive. HSC uniquely features the combination of large primary mirror, wide field of view, sharp image and high sensitivity especially in red. This enables accurate shape measurement of faint galaxies which is critical for planned weak lensing survey to probe the nature of dark energy. The system is being assembled now and will see the first light in August 2012.

Genome information, which is three-dimensionally organized within cells as chromatin, is searched and read by various proteins for diverse cell functions. Although how the protein factors find their targets remains unclear, the dynamic and flexible nature of chromatin is likely crucial. Using a combined approach of fluorescence correlation spectroscopy, single-nucleosome imaging, and Monte Carlo computer simulations, we demonstrate local chromatin dynamics in living mammalian cells. We show that similar to interphase chromatin, dense mitotic chromosomes also have considerable chromatin accessibility. For both interphase and mitotic chromatin, we observed local fluctuation of individual nucleosomes (~50 nm movement/30 ms), which is caused by confined Brownian motion. Inhibition of these local dynamics by crosslinking impaired accessibility in the dense chromatin regions. Our findings show that local nucleosome dynamics drive chromatin accessibility. We propose that this local nucleosome fluctuation is the basis for scanning genome information.

This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional optical coherence tomography (OCT) data and color fundus images. In this study, 208 glaucomatous and 149 healthy eyes were enrolled, and color fundus images and volumetric OCT data from the optic disc and macular area of these eyes were captured with a spectral-domain OCT (3D OCT-2000, Topcon). Thickness and deviation maps were created with a segmentation algorithm. Transfer learning of convolutional neural network (CNN) was used with the following types of input images: (1) fundus image of optic disc in grayscale format, (2) disc retinal nerve fiber layer (RNFL) thickness map, (3) macular ganglion cell complex (GCC) thickness map, (4) disc RNFL deviation map, and (5) macular GCC deviation map. Data augmentation and dropout were performed to train the CNN. For combining the results from each CNN model, a random forest (RF) was trained to classify the disc fundus images of healthy and glaucomatous eyes using feature vector representation of each input image, removing the second fully connected layer. The area under receiver operating characteristic curve (AUC) of a 10-fold cross validation (CV) was used to evaluate the models. The 10-fold CV AUCs of the CNNs were 0.940 for color fundus images, 0.942 for RNFL thickness maps, 0.944 for macular GCC thickness maps, 0.949 for disc RNFL deviation maps, and 0.952 for macular GCC deviation maps. The RF combining the five separate CNN models improved the 10-fold CV AUC to 0.963. Therefore, the machine learning system described here can accurately differentiate between healthy and glaucomatous subjects based on their extracted images from OCT data and color fundus images. This system should help to improve the diagnostic accuracy in glaucoma.