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Summary The reconstruction of lost neural circuits by cell replacement is a possible treatment for neurological deficits after cerebral cortex injury. Cerebral organoids can be a novel source for cell transplantation, but because the cellular composition of the organoids changes along the time course of the development, it remains unclear which developmental stage of the organoids is most suitable for reconstructing the corticospinal tract. Here, we transplanted human embryonic stem cell-derived cerebral organoids at 6 or 10 weeks after differentiation (6w- or 10w-organoids) into mouse cerebral cortices. 6w-organoids extended more axons along the corticospinal tract but caused graft overgrowth with a higher percentage of proliferative cells. Axonal extensions from 10w-organoids were smaller in number but were enhanced when the organoids were grafted 1 week after brain injury. Finally, 10w-organoids extended axons in cynomolgus monkey brains. These results contribute to the development of a cell-replacement therapy for brain injury and stroke.

This paper describes a method of modeling the characteristics of a singing voice from polyphonic musical audio signals including sounds of various musical instruments. Because singing voices play an important role in musical pieces with vocals, such representation is useful for music information retrieval systems. The main problem in modeling the characteristics of a singing voice is the negative influences caused by accompaniment sounds. To solve this problem, we developed two methods, accompaniment sound reduction and reliable frame selection. The former makes it possible to calculate feature vectors that represent a spectral envelope of a singing voice after reducing accompaniment sounds. It first extracts the harmonic components of the predominant melody from sound mixtures and then resynthesizes the melody by using a sinusoidal model driven by these components. The latter method then estimates the reliability of frame of the obtained melody (i.e., the influence of accompaniment sound) by using two Gaussian mixture models (GMMs) for vocal and nonvocal frames to select the reliable vocal portions of musical pieces. Finally, each song is represented by its GMM consisting of the reliable frames. This new representation of the singing voice is demonstrated to improve the performance of an automatic singer identification system and to achieve an MIR system based on vocal timbre similarity. Index Terms-Music information retrieval (MIR), singer identification, singing voice, vocal, vocal timbre similarity.Hiromasa Fujihara received the B.S. and M.S. degrees from Kyoto University, Kyoto, Japan, in 2005 and 2007, respectively. He is currently pursuing the Ph.D. degree in the Japan. He was a Visiting Scholar at Stanford University, Stanford, CA, from 1986 to 1988. He has done research in programming languages, parallel processing, and reasoning mechanism in AI. He is currently engaged in computational auditory scene analysis, music scene analysis, and robot audition.

We provide a new solution to the problem of feature variations caused by the overlapping of sounds in instrument identification in polyphonic music. When multiple instruments simultaneously play, partials (harmonic components) of their sounds overlap and interfere, which makes the acoustic features different from those of monophonic sounds. To cope with this, we weight features based on how much they are affected by overlapping. First, we quantitatively evaluate the influence of overlapping on each feature as the ratio of the within-class variance to the between-class variance in the distribution of training data obtained from polyphonic sounds. Then, we generate feature axes using a weighted mixture that minimizes the influence via linear discriminant analysis. In addition, we improve instrument identification using musical context. Experimental results showed that the recognition rates using both feature weighting and musical context were 84.1% for duo, 77.6% for trio, and 72.3% for quartet; those without using either were 53.4, 49.6, and 46.5%, respectively.

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A great He two-ribbon flare of 12 October, 1981 was observed with the Domeless Solar Telescope at the Hida Observatory and its detailed photometry was made with a two dimensional microdensitometer. The principal results are as follows: (1)The impulsive phase of the flare started with the progressive brightenings oftlare points forming the front lines of the He two ribbons at both sides of the magnetic neutral line. These are followed by the explosive expansion of Hc~ two ribbons at the main impulsive phase. (2) Three typical shapes of Hc~light curves were found. The type 1 light curve is characterized by the primary impulsive rise and rapid fall of intensity. The light curve of type 3 has no impulsive component but has a very gradual maximum. The type 2 profile attains the main gradual maximum with a few small impulsive peaks. These different types of light curves are made by different heating mechanisms, those are electron precipitation, heat conduction and soft X-ray radiation respectively. (3) The light curve of total intensity, which was made by integrating He -1.0 A intensities of the whole main H~ flare region, shows a primary impulsive peak and a later gradual maximum. The former peak coincides in time with that of the hard X-ray emission. The latter maximum is well correlated with the soft X-ray maximum. (4) The brightest flare points with time profiles of type 1 are closely related to the impulsive hard X-ray emissions of highest energy.