Defective TiO(2-x) was synthesized via a facile anodization technique. Electron paramagnetic resonance spectra confirmed the presence of oxygen vacancy, which extended the photon-absorbance deeply into the visible-light region. By stripping off the nanotubes on top, a hexagonally dimpled layer of black TiO(2-x) was exposed and exhibited remarkable photocatalytic activity.

Deci-hertz Interferometer Gravitational Wave Observatory (DECIGO) is the future Japanese space mission with a frequency band of 0.1 Hz to 10 Hz. DECIGO aims at the detection of primordial gravitational waves, which could be produced during the inflationary period right after the birth of the universe. There are many other scientific objectives of DECIGO, including the direct measurement of the acceleration of the expansion of the universe, and reliable and accurate predictions of the timing and locations of neutron star/black hole binary coalescences. DECIGO consists of four clusters of observatories placed in the heliocentric orbit. Each cluster consists of three spacecraft, which form three Fabry-Perot Michelson interferometers with an arm length of 1,000 km. Three clusters of DECIGO will be placed far from each other, and the fourth cluster will be placed in the same position as one of the three clusters to obtain the correlation signals for the detection of the primordial gravitational waves. We plan to launch B-DECIGO, which is a scientific pathfinder of DECIGO, before DECIGO in the 2030s to demonstrate the technologies required for DECIGO, as well as to obtain fruitful scientific results to further expand the multi-messenger astronomy.

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Mesoporous photocatalytic titanium dioxide films with periodic structures were prepared by molding from two-dimensionally ordered arrays of monodisperse SiO2 particles. The morphology of the porous structures in these films was dependent upon the annealing temperature. The photocatalytic activity of the films was confirmed, as evidenced by the photodeposition of silver on the textured film surfaces. Attachment of the free-standing films to rigid supports allowed us to locate specific microscopic areas at will and to monitor the progress of the silver photodeposition in these areas using optical microscopy and scanning electron microscopy, with both secondary electron and backscattered electron detection. However, the repeated electron beam exposure in these selected areas was found to adversely affect the photoreducibility. Although the detailed film morphology did not affect the macroscopic photocatalytic activity, we found subtle differences in the silver nucleation process which depended upon the pore wall thickness.

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Four patterned surfaces with hydrophilic areas of different sizes were prepared using photolithography with a smooth octadecyltrimethoxysilane (ODS) hydrophobic coating. The hydrophilic area in the surfaces was aligned hexagonally with a constant area fraction. The sliding angle and contact angle hysteresis of the water droplets increased concomitantly with increasing pattern size. The increase of the contact line distortion between defects at the receding side plays an important role in this trend. The droplet sliding velocity also increased concomitantly with increasing pattern size. This trend was simulated by a simple flow model. The contribution of the interface between the ODS region and the hydrophilic area was deduced from this trend. This study demonstrated the different size dependency of the chemical surface defects for sliding behavior between the critical moment at which a droplet slides down and the period when a droplet is sliding.

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Scratching is one of the most important behaviours in experimental animals because it can reflect itching and/or psychological stress. Here, we aimed to establish a novel method to detect scratching using deep neural network. Scratching was elicited by injecting a chemical pruritogen lysophosphatidic acid to the back of a mouse, and behaviour was recorded using a standard handy camera. Images showing differences between two consecutive frames in each video were generated, and each frame was manually labelled as showing scratching behaviour or not. Next, a convolutional recurrent neural network (CRNN), composed of sequential convolution, recurrent, and fully connected blocks, was constructed. The CRNN was trained using the manually labelled images and then evaluated for accuracy using a first-look dataset. Sensitivity and positive predictive rates reached 81.6% and 87.9%, respectively. The predicted number and durations of scratching events correlated with those of the human observation. The trained CRNN could also successfully detect scratching in the hapten-induced atopic dermatitis mouse model (sensitivity, 94.8%; positive predictive rate, 82.1%). In conclusion, we established a novel scratching detection method using CRNN and showed that it can be used to study disease models.