The only official method that can detect the skin sensitizing potential of chemicals, including the elicitation response, is the OECD test guideline (TG) 406. However, this guideline uses guinea pigs, which requires complex procedures. Since a simple and complete test method for evaluating skin sensitization is needed, especially for mechanistic studies of skin sensitization, this study confirmed the reactivity of mice to skin sensitizing substances. We set up a protocol involving one induction exposure of the test substance to the back skin, followed by three challenge exposures to the auricle (Protocol 2), and compared their skin sensitization responses with the results of two exposures to the auricle and back skin every 2 weeks (Protocol 1) and a local lymph node assay (TG442B). A hapten 2,4-dinitrofluorobenzene caused significant auricular thickening, skin inflammation, and enlarged auricular lymph nodes in Protocols 1 and 2. These changes were more pronounced in Protocol 2. Plasma IgE and IgG1 and gene expression of IL4, IFNγ, and perforin were significantly increased in Protocol 2. Cell proliferation in the auricular lymph nodes was observed in both protocols as in TG442B. These results indicate that Protocol 2 can be a good candidate for a relatively simple skin sensitization test.
In the our previous study, we proposed the visualization system of three-dimensional sound intensity measured by handy 4-ch microphones using Mixed Reality. In the previous system, since the sound intensity was estimated by the cross-spectrum method. the error at the higher frequency
than the spatial Nyquist frequency becomes larger due to the microphone intervals. In addition, visibility of sound intensity map was degraded because the microphone array was moved by hands and the intervals of measurement points for sound intensity map are irregular. In this study, we improve
the previous mixed reality system for sound intensity by applying the PAGE method (Phase and Amplitude Gradient Estimation), which can accurately estimate the sound intensity up to higher frequencies. Furthermore, by using spatial interpolation of an irregularly-sampled intensity map, we improve
visibility of sound intensity map with mixed reality. The proposed system interpolates sound intensities at equal intervals from the sound intensities measured at irregular intervals. For evaluation, we conducted visualization experiments of sound intensities surrounding single loudspeaker.
In the field of room acoustics, it is important to find the absorption coefficients of the wall surface, which is a boundary condition for modeling the room acoustic field. However, it is not easy to measure the acoustic impedances of the entire room because it requires many measurement
points near the wall surface. Recently, a method to estimate the acoustic impedance and absorption coefficients by using both measurement and simulation methods has been proposed. However, a large number of measurement points are required to obtain sufficient estimation accuracy. In this study,
we proposed estimation method of the sound absorption coefficients using machine learning with virtually increasing the number of microphones. First, the transfer functions at the virtual microphones are obtained from small number of transfer functions based on the sound field modeling by
sparse equivalent sources. Then, the both transfer functions at the virtual and real microphones are used as the training data for machine learning. To evaluate estimation accuracy of the proposed method, we conducted the two-dimensional simulation experiments based on the boundary element
method.
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