A novel scanning Thermal Microscopy System

MEMS 2007, Kobe, Japan, 21-25 January 200

マサバはイワシノコバンの新宿主

大分県佐賀関沖の豊予海峡で漁獲されたマサバの尾柄部に等脚類ウオノエ科のイワシノコバンNerocila phaiopleura Bleeker, 1857の寄生を認めた。マサバはイワシノコバンの新宿主である。本報告は，瀬戸内海と周辺水域からのイワシノコバンの第4記録となる。An ovigerous female of Nerocila phaiopleura Bleeker, 1857 was collected from the caudal peduncle of a chub mackerel, Scomber japonicus Houttuyn, 1782 (Perciformes: Scombridae), at the Hōyo Strait located between the western Seto Inland Sea and the Bungo Channell in western Japan. This represents a new host record for N. phaioplueura and its fourth record from the Seto Inland Sea and adjacent region

Accuracy of deep learning, a machine learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes

We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-field fundus images (Optos) with deep learning, which is a machine learning technology. The study included 910 Optos color images (715 normal images, 195 MH images). Of these 910 images, 637 were learning images (501 normal images, 136 MH images) and 273 were test images (214 normal images and 59 MH images). We conducted training with a deep convolutional neural network (CNN) using the images and constructed a deep-learning model. The CNN exhibited high sensitivity of 100% (95% confidence interval CI [93.5–100%]) and high specificity of 99.5% (95% CI [97.1–99.9%]). The area under the curve was 0.9993 (95% CI [0.9993–0.9994]). Our findings suggest that MHs could be diagnosed using an approach involving wide angle camera images and deep learning

Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naïve proliferative diabetic retinopathy

Purpose We investigated using ultrawide-field fundus images with a deep convolutional neural network (DCNN), which is a machine learning technology, to detect treatment-naïve proliferative diabetic retinopathy (PDR). Methods We conducted training with the DCNN using 378 photographic images (132 PDR and 246 non-PDR) and constructed a deep learning model. The area under the curve (AUC), sensitivity, and specificity were examined. Result The constructed deep learning model demonstrated a high sensitivity of 94.7% and a high specificity of 97.2%, with an AUC of 0.969. Conclusion Our findings suggested that PDR could be diagnosed using wide-angle camera images and deep learning

A Highly Sensitive Strain Sensor Using Surface Acoustic Wave and Its Evaluation for Wireless Battery-less Sensor Network

Proceedings of The 6th IEEE Conference on Sensors (SENSORS2007

A Double Planetary System around the Evolved Intermediate-Mass Star HD 4732

We report the detection of a double planetary system orbiting around the evolved intermediate-mass star HD 4732 from precise Doppler measurements at Okayama Astrophysical Observatory (OAO) and Anglo-Australian Observatory (AAO). The star is a K0 subgiant with a mass of 1.7 M_sun and solar metallicity. The planetary system is composed of two giant planets with minimum mass of msini=2.4 M_J, orbital period of 360.2 d and 2732 d, and eccentricity of 0.13 and 0.23, respectively. Based on dynamical stability analysis for the system, we set the upper limit on the mass of the planets to be about 28 M_J (i>5 deg) in the case of coplanar prograde configuration.Comment: 12 pages, 7 figures, accepted for publication in Ap

Accuracy of deep learning, a machine learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes

We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-field fundus images (Optos) with deep learning, which is a machine learning technology. The study included 910 Optos color images (715 normal images, 195 MH images). Of these 910 images, 637 were learning images (501 normal images, 136 MH images) and 273 were test images (214 normal images and 59 MH images). We conducted training with a deep convolutional neural network (CNN) using the images and constructed a deep-learning model. The CNN exhibited high sensitivity of 100% (95% confidence interval CI [93.5–100%]) and high specificity of 99.5% (95% CI [97.1–99.9%]). The area under the curve was 0.9993 (95% CI [0.9993–0.9994]). Our findings suggest that MHs could be diagnosed using an approach involving wide angle camera images and deep learning

Preliminary techno-economic analysis of non-commercial ceramic and organosilica membranes for hydrogen peroxide ultrapurification

Polymeric membrane cascades have demonstrated their technical and economic viability for hydrogen peroxide ultrapurification. Nevertheless, these membranes suffer from fast degradation under such oxidative conditions. Alternative membranes with higher chemical resistance could improve the ultrapurification process. Therefore, this work presents the preliminary techno-economic analysis of two non-commercial membranes (a ceramic one and a hybrid organosilica one). This analysis is complemented with further research regarding the competitiveness of these alternative membranes compared to polymeric ones. The results confirm the technical viability for both membranes, but the ceramic membrane is not appropriate when Na is considered as the limiting impurity (because it has too low rejection coefficient). The economic viability of the proposed ultrapurification processes is also probed, but not under competitive conditions, as the polyamide membrane appears to be the optimal choice. Nonetheless, improvements in the permeability of the hybrid membrane (an increase in the membrane permeability by a factor of 10) or the rejection performance of the ceramic membrane (an increase in the reflection coefficient above 0.85) could transform these non-commercial membranes into the most profitable alternative.This research has been financially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) through CTQ2014-56820-JIN Project, co-financed by FEDER funds. R. Abejón acknowledges the assistance of the Japan Society for Promotion of Science (JSPS) for the award of a Post-Doctoral Fellowship (Short-Term) for North American and European Researchers (PE14057)

Effect of maximum grip strength on controlled force exertion measured by a computer-generated sinusoidal waveform in young adult males

Context: It is important to develop a method to accurately measure controlled force exertion (CFE). Objectives: This study aimed to examine the effect of maximum grip strength on CFE measured by a sinusoidal waveform in 81 right-handed young males aged 15-29 years. Methods: On the basis of grip strength measurements, participants were divided into the following three groups: low (males 20; mean age 19.5 years; standard deviation (SD) = 5.0 years), medium (males 41; mean age 22.8 years; SD = 4.2 years), and high (males 20; mean age 23.7 years; SD = 3.4 years). Participants adjusted the submaximal grip strength of the dominant hand with changes in the demand values that were displayed as a sinusoidal waveform with a frequency of 0.1 Hz on a computer screen. The abovementioned test was performed three times with a 1-min interval after one practice trial. Each trial lasted 40 s. The sum of the differences between the demand value and grip exertion strength value for 25 s was considered as the evaluation parameter. Results: Controlled force exertion values demonstrated insignificant correlations with age and maximum grip strength in all groups (r = 0.07; r = -0.12; p > 0.05). No significant differences were found between CFE mean scores that was adjusted for age and varying maximum grip strength in the three groups (F = 1.95; p > 0.05). Conclusions: Based on the sinusoidal waveform display, we inferred that maximum grip strength has little effect on CFE evaluation in young males. © 2014 Springer-Verlag Italia