Wide expression of ZEB1 in sarcomatous component of spindle cell carcinoma of the esophagus

学位記番号：医博甲1561

Spectral evolution of GRB 060904A observed with Swift and Suzaku -- Possibility of Inefficient Electron Acceleration

We observed an X-ray afterglow of GRB 060904A with the Swift and Suzaku satellites. We found rapid spectral softening during both the prompt tail phase and the decline phase of an X-ray flare in the BAT and XRT data. The observed spectra were fit by power-law photon indices which rapidly changed from $\Gamma = 1.51^{+0.04}_{-0.03}$ to $\Gamma = 5.30^{+0.69}_{-0.59}$ within a few hundred seconds in the prompt tail. This is one of the steepest X-ray spectra ever observed, making it quite difficult to explain by simple electron acceleration and synchrotron radiation. Then, we applied an alternative spectral fitting using a broken power-law with exponential cutoff (BPEC) model. It is valid to consider the situation that the cutoff energy is equivalent to the synchrotron frequency of the maximum energy electrons in their energy distribution. Since the spectral cutoff appears in the soft X-ray band, we conclude the electron acceleration has been inefficient in the internal shocks of GRB 060904A. These cutoff spectra suddenly disappeared at the transition time from the prompt tail phase to the shallow decay one. After that, typical afterglow spectra with the photon indices of 2.0 are continuously and preciously monitored by both XRT and Suzaku/XIS up to 1 day since the burst trigger time. We could successfully trace the temporal history of two characteristic break energies (peak energy and cutoff energy) and they show the time dependence of $\propto t^{-3} \sim t^{-4}$ while the following afterglow spectra are quite stable. This fact indicates that the emitting material of prompt tail is due to completely different dynamics from the shallow decay component. Therefore we conclude the emission sites of two distinct phenomena obviously differ from each other.Comment: 19 pages, 9 figures, accepted for publication in PASJ (Suzaku 2nd Special Issue

Japanese glaciological activities at NEEM, Greenland

第2回極域科学シンポジウム　氷床コアセッション 11月16日（水） 国立極地研究所 2階大会議室前フロ

Digital flexion contracture caused by tophaceous gout in flexor tendon

Gouty tophus is an unusual cause of digital flexion contracture. Awareness of this pathophysiology will lead to more confidence in proper treatment and surgical management of this rare condition. This report describes a case of digital flexion contracture by tophaceous gout distinguished between trigger finger and locking of the metacarpophalangeal joint. We found the flexor tendon with a deposited chalky white substance suggestive of gouty tophus intraoperatively. We performed tenosynovectomy and removed the chalky white substance to the greatest degree possible. Histological findings confirmed the diagnosis of gout. Postoperatively, the patient recovered nearly to a full range of motion of the affected digits. When meeting with the patient who has had hyperuricemia and who is unable to extend the affected digits suddenly, one must keep in mind digital flexion contracture caused by tophaceous gout

A Method for Detecting Damage of Traffic Marks by Half Celestial Camera Attached to Cars

Roads are becoming deterioration in everywhere. In some places, traffic marks painted on roads are damaged thus needed to be updated. Municipalities must manage road condition and traffic marks (road painting). It is the municipalities task to manage those roads using, for example, special inspection cars and human eyes. However, the management cost is high if a city contains many roads. This paper proposes a mechanism that automates this management. Our idea is to leverage cameras attached to garbage trucks, which run through the entire city almost everyday. The mechanism collects road images and detects damaged traffic marks using an image recognition algorithm. This paper shows the algorithm and reports the benchmark results. The benchmark showed that the mechanism can detect the damaged traffic marks with 76.6% precision

C's: Sensing the Quality of Traffic Markings Using Camera-Attached Cars

Road maintenance requires local city governments to dedicate a substantial amount of funds in finding and repairing damaged traffic marks and pavements. In developed cities, the total road length is so large that the cost becomes unreasonably high. In this paper, we propose a method of sensing damaged traffic marks from images captured by a camera mounted to a car, for the purpose of reducing road maintenance cost. In particular, we utilized convolutional neural networks (CNN), as well as linear support vector machines (SVM) and Random Forest, in developing a system of damage detection. The experiments used thousands of images captured in the wild and showed that the method can detect damages using CNN with 93% accuracy, at maximum, and at reasonable speed (55 images per second)