Development of Superior Heat Resistant Cu-Si Alloys Dispersed with Fine Mo5Si3 Particles

Proceedings of 17th International Federation for Heat Treatment and Surface Engineering Congress (IFHTSE 2008), 27-30 October 2008, Kobe, Japa

Predicting reliable H2_2 column density maps from molecular line data using machine learning

The total mass estimate of molecular clouds suffers from the uncertainty in the H$_2$-CO conversion factor, the so-called $X_{\rm CO}$ factor, which is used to convert the $^{12}$CO (1--0) integrated intensity to the H$_2$ column density. We demonstrate the machine learning's ability to predict the H$_2$ column density from the $^{12}$CO, $^{13}$CO, and C$^{18}$O (1--0) data set of four star-forming molecular clouds; Orion A, Orion B, Aquila, and M17. When the training is performed on a subset of each cloud, the overall distribution of the predicted column density is consistent with that of the Herschel column density. The total column density predicted and observed is consistent within 10\%, suggesting that the machine learning prediction provides a reasonable total mass estimate of each cloud. However, the distribution of the column density for values $> \sim 2 \times 10^{22}$ cm$^{-2}$, which corresponds to the dense gas, could not be predicted well. This indicates that molecular line observations tracing the dense gas are required for the training. We also found a significant difference between the predicted and observed column density when we created the model after training the data on different clouds. This highlights the presence of different $X_{\rm CO}$ factors between the clouds, and further training in various clouds is required to correct for these variations. We also demonstrated that this method could predict the column density toward the area not observed by Herschel if the molecular line and column density maps are available for the small portion, and the molecular line data are available for the larger areas.Comment: Accepted for publication in MNRA

Distance determination of molecular clouds in the 1st quadrant of the Galactic plane using deep learning : I. Method and Results

Machine learning has been successfully applied in varied field but whether it is a viable tool for determining the distance to molecular clouds in the Galaxy is an open question. In the Galaxy, the kinematic distance is commonly employed as the distance to a molecular cloud. However, there is a problem in that for the inner Galaxy, two different solutions, the ``Near'' solution, and the ``Far'' solution, can be derived simultaneously. We attempted to construct a two-class (``Near'' or ``Far'') inference model using a Convolutional Neural Network (CNN), a form of deep learning that can capture spatial features generally. In this study, we used the CO dataset toward the 1st quadrant of the Galactic plane obtained with the Nobeyama 45-m radio telescope (l = 62-10 degree, |b| < 1 degree). In the model, we applied the three-dimensional distribution (position-position-velocity) of the 12CO (J=1-0) emissions as the main input. The dataset with ``Near'' or ``Far'' annotation was made from the HII region catalog of the infrared astronomy satellite WISE to train the model. As a result, we could construct a CNN model with a 76% accuracy rate on the training dataset. By using the model, we determined the distance to molecular clouds identified by the CLUMPFIND algorithm. We found that the mass of the molecular clouds with a distance of < 8.15 kpc identified in the 12CO data follows a power-law distribution with an index of about -2.3 in the mass range of M >10^3 Msun. Also, the detailed molecular gas distribution of the Galaxy as seen from the Galactic North pole was determined.Comment: 29 pages, 12 figure

Effects of a non-cyclodextrin cyclic carbohydrate on mouse melanoma cells: Characterization of a new type of hypopigmenting sugar.

Cyclic nigerosyl nigerose (CNN) is a cyclic tetrasaccharide that exhibits properties distinct from other conventional cyclodextrins. Herein, we demonstrate that treatment of B16 melanoma with CNN results in a dose-dependent decrease in melanin synthesis, even under conditions that stimulate melanin synthesis, without significant cytotoxity. The effects of CNN were prolonged for more than 27 days, and were gradually reversed following removal of CNN. Undigested CNN was found to accumulate within B16 cells at relatively high levels. Further, CNN showed a weak but significant direct inhibitory effect on the enzymatic activity of tyrosinase, suggesting one possible mechanism of hypopigmentation. While a slight reduction in tyrosinase expression was observed, tyrosinase expression was maintained at significant levels, processed into a mature form, and transported to late-stage melanosomes. Immunocytochemical analysis demonstrated that CNN treatment induced drastic morphological changes of Pmel17-positive and LAMP-1-positive organelles within B16 cells, suggesting that CNN is a potent organelle modulator. Colocalization of both tyrosinase-positive and LAMP-1-positive regions in CNN-treated cells indicated possible degradation of tyrosinase in LAMP-1-positive organelles; however, that possibility was ruled out by subsequent inhibition experiments. Taken together, this study opens a new paradigm of functional oligosaccharides, and offers CNN as a novel hypopigmenting molecule and organelle modulator

Association between the Perception of Behavior Change and Habitual Exercise during COVID-19: A Cross-Sectional Online Survey in Japan

In general, the perception of behavior change may be associated with habitual exercise. However, this association might not be well-understood due to the state of emergency of the COVID-19 pandemic. This study collected data from 1499 internet users aged 20&ndash;86 years living in Japan who participated in the online survey from 26 to 27 February 2021. Having a perception of behavior change was defined as preparation, action, and maintenance of the transtheoretical model. The habitual exercise was defined as 600 metabolic equivalent min/week or more based on the International Physical Activity Questionnaire. Multivariate logistic regression analysis was used to calculate the odds ratio of habitual exercise and a 95% confidence interval was estimated after adjusting for related factors. We found that perception of behavior change was positively associated with habitual exercise (adjusted odds ratio = 2.41, 95%CI = 1.89&ndash;3.08), and similar associations were found in states of emergency (2.69, 1.97&ndash;3.69) and non-emergency (2.01, 1.34&ndash;3.01). Moreover, women were negatively associated in all analyses with habitual exercise compared to men (0.63, 0.49&ndash;0.80; 0.65, 0.44&ndash;0.96; 0.62, and 0.45&ndash;0.84, respectively). Thus, the perception of behavior change may be involved in the implementation of habitual exercise, suggesting that women face difficulties in engaging in habitual exercise

CNN exhibits partial inhibitory effects on tyrosinase activity.

<p>CNN exhibits partial inhibitory effects on tyrosinase activity.</p

CNN reduced melanin synthesis in B16 melanoma in a dose-dependent manner.

<p>B16 cells were treated with CNN (2 mM, 10 mM, and 50 mM) for 4 days, and the levels of melanin and total protein in the treated cells were determined after preparing cell extracts with 3 M NaOH. Kojic acid and APO were used as positive controls of hypopigmenting agents. Levels of melanin were normalized to the total protein concentration. Graphs are shown as percentage of the untreated control. **, <i>p</i><0.01; *, <i>p</i><0.05; statistically significant differences were analyzed by one-way ANOVA and Dunnett’s post hoc testing. Data are representative results of at least three independent experiments.</p