Auditory noise improves balance control by cross-modal stochastic resonance.

It is known that enhanced somatosensory function leads to improved balance, and somatosensory function can be enhanced by the appropriate level of mechanical, visual, or auditory noise. In this study, we tested the potential benefit of an auditory noise on balance control. We first assessed static balance by measuring 10 times the duration of standing on the toes of one leg with closed eyes. For the 18 healthy adult participants, the median standing times ranged from 2.1 to 45.6 s, and the median of the distribution was 9.9 s. From the above, the participants were divided into two groups: lower (below 10 s, n = 9) and higher (above 10 s, n = 9) balance groups. We then investigated the effect on balance control of an auditory white noise emitted at the detection threshold. Each individual performed 20 trials. The auditory noise was applied in half the trials, while the remaining trials were conducted without noise. The order of the noise and no-noise trials was quasi-random. In the lower-balance group, the median standing time significantly increased during the noise trials (10.3 s) compared with the time in the no-noise controls (5.2 s). On the other hand, noise had no significant effect in the higher-balance group, presumably because of a ceiling effect. These findings suggest that static balance in the lower-balance participants can be improved by applying a weak noise through cross-modal stochastic resonance.

Application of Deep Learning in the Identification of Cerebral Hemodynamics Data Obtained from Functional Near-Infrared Spectroscopy: A Preliminary Study of Pre- and Post-Tooth Clenching Assessment.

In fields using functional near-infrared spectroscopy (fNIRS), there is a need for an easy-to-understand method that allows visual presentation and rapid analysis of data and test results. This preliminary study examined whether deep learning (DL) could be applied to the analysis of fNIRS-derived brain activity data. To create a visual presentation of the data, an imaging program was developed for the analysis of hemoglobin (Hb) data from the prefrontal cortex in healthy volunteers, obtained by fNIRS before and after tooth clenching. Three types of imaging data were prepared: oxygenated hemoglobin (oxy-Hb) data, deoxygenated hemoglobin (deoxy-Hb) data, and mixed data (using both oxy-Hb and deoxy-Hb data). To differentiate between rest and tooth clenching, a cross-validation test using the image data for DL and a convolutional neural network was performed. The network identification rate using Hb imaging data was relatively high (80‒90%). These results demonstrated that a method using DL for the assessment of fNIRS imaging data may provide a useful analysis system.

A mechanistic investigation of anti-elimination in (Z)-1,2-bis(arylseleno)-1-alkenes and their sulfur analogs.

The oxidation of (Z)-1,2-bis(arylseleno)-1-alkenes is known to afford alkynyl selenoxides via a unique selenoxide anti-elimination mechanism; however, to date, there have been no mechanistic studies of this reaction. During our studies of this transformation, monoselenoxides 6 and 7 were unexpectedly isolated as stable reaction intermediates. In addition, (77)Se NMR studies of the reaction mixture revealed the presence of an intramolecular Se···O interaction and the formation of alkynyl selenoxides. Meanwhile, even at higher temperatures, the reaction of a (Z)-1,2-bis(arylsulfinyl)-1-alkene, the sulfur analog of (Z)-1,2-bis(arylseleninyl)-1-alkenes, did not proceed via sulfoxide elimination but proceeded via isomerization and disproportionation. Therefore, the intramolecular Se···O interaction can be concluded to play a pivotal role in the anti-elimination reaction.