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A pulsed laser cause vaporization of tissue by plasma if a laser can provide high-density energy within a very short pulse duration. Such phenomena are called laser-induced plasma ablation. The influence of the laser-induced plasma ablation for tissue is unclear because the ablation mechanism is differing regardless of two lasers provide almost the same power density. The two kinds of lasers' vaporization mechanism (Nanosecond laser output could cause an optical breakdown in the air depending on power density and pulse duration of the laser and Femtosecond laser output could cause a breakdown only on solids surface since pulse irradiation time is shorter than energy transfer time) are evaluated by using thermal damage and destruction of tissue. The experimental results show that nanosecond laser caused vaporization without thermal damage and destruction at the tissue approximant 300 µm away from the ablation area. The pulsed laser which has high power density and longer pulse duration than energy transfer time is suitable for plasma ablation not depending on thermal process.

A mouse’s scratching behavior is one of the reaction effected by a new drug, which is rapid movement of its hind leg around its head. Currently, the number of mouse scratching behavior is counted by manual human’s decision, and conventional automatic detections used a high-speed camera. Improving the accuracy of the detection, the automation for counting the scratching behavior is necessary, and the evaluation must be quantitative. We propose the detection of the mouse's scratching behavior based on the shape and the motion feature using a wide-used camera. We apply Improved Dense Trajectories to the classifier to identify the scratching behavior. The classifier could detect the scratching around 90 % accuracy. Based on the length of scratching, we could decrease the ratio of the detection failure to half using a median processing.

Background: Diabetic peripheral neuropathy (DPN) is one of the major risks to diabetic foot diseases (DFD). Currently, there are few quantitative methods to assess progression of DFD if no obvious ulcerations, microtraumas, and other skin lesions are presented. We investigated the differences of sole images (SI) on hard and soft floors in patients with or without diabetes and evaluated the availability to predict DPN and severity of DFD. Material and Methods: We prospectively recruited 104 patients from December 2017 to December 2018. Both SI on hard and soft floors were taken by the specific device with a camera underneath and were analyzed according to total area, shape, distal and proximal variations and bilateral differences. Severity of DFD was defined and calculated according to classical neurological and hemodynamic assessments. Results: SI variations were significantly increased in diabetic patients with DPN compared with those without DPN (p Conclusion: Variations of SI on hard and soft floors increase along with diabetes duration and have significantly positive correlation with severity of DFD. Because of good sensitivity and specificity for screening of DPN, this approach may be effective and useful in daily practice to diabetic patients. Disclosure S. Chou: None. M. Haraguchi: None. H. Komori: None. K. Watanabe: None. S. Tsuichihara: None. S. Nakamura: None. H. Takemura: None.

Purpose: A remedy for ankle osteoarthritis is artificial joint replacement surgery. By estimating the relative positional difference (alignment) between the bone before and after deformation, precise artificial joint replacement surgery can be performed. By using the estimated alignment, an artificial ankle joint can ensure high satisfaction with less pain and can perform necessary functions. Although bone alignment is currently estimated from X-rays and computed tomography (CT) images, it is difficult to measure three-dimensional (3D) data from two-dimensional (2D) images. Although 3D data can be estimated using a 2D–3D registration method, it requires capturing the bone images multiple times and imposing considerable burden on the patient. In this paper, we propose a method to estimate the 3D bone alignment based on the size of the bone using principal component analysis (PCA), which requires scanning only one data. Method: In the proposed method, PCA is used to create a 3D bone model based on the bone thickness, and the bone alignment is estimated by the Go-ICP algorithm. In this study, the images of the foot were captured using a CT device, and the 3D bone model was created by stacking CT images. For improving the accuracy of the alignment, a reference model based on the bone thickness was created using PCA. Subsequently, the bone was overlapped and the alignment was estimated. Result: For the evaluation of the accuracy of alignment in the proposed method, three types of methods were used to create a bone model. The proposed method was found to be the most accurate with respect to the true value in five of the six evaluation criteria. In addition, the alignment of the tibiofibular joint was evaluated using the proposed method. Regarding the displacement in the [Formula: see text]-axis direction, there were significant differences in both Stage 3B and Stage 4 compared with the healthy subjects. In addition, the magnitude of rotation in the [Formula: see text]-axis direction showed a significant difference in stage 3B as compared with a healthy subject. Conclusion: Using the proposed method, we have shown that bone alignment can be estimated three-dimensionally by scanning the bone data once only. In addition, by comparing with the reference model using PCA based on the thickness of the bone, the accuracy of alignment is improved as compared with the reference model, which is not based on the bone thickness.