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Abstract Background Many people use the snooze function of digital alarm clocks for morning awakening, but the effects of a snooze alarm on waking are unclear. We examined the effects of a snooze alarm on sleep inertia, which is a transitional state characterized by reduced arousal and impaired cognitive and behavioral performance immediately upon awakening. Methods In study 1, healthy Japanese university students responded to a sleep survey during a psychology class (study 1), and we collected 293 valid responses. In study 2, we compared a separate sample of university students (n = 10) for the effects of using or not using a snooze alarm on sleep inertia immediately after awakening from normal nocturnal sleep in a sleep laboratory. Results Of 293 valid respondents in study 1, 251 often used a tool to wake up in the morning (85.7%). Moreover, 70.5% reported often using the snooze function of their mobile phones, mainly to reduce anxiety about oversleeping. Study 2 indicated no differences in the sleep quality or quantity before awakening with or without the snooze alarm, except in the last 20 min. However, during the last 20 min of sleep with snooze alarm, the snooze alarm prolonged waking and stage N1 sleep. Stage N1 sleep is non-rapid eye movement sleep that is primarily defined as a drowsy state. Furthermore, Global Vigor values were enhanced after awakening compared to pre-sleep in the no-snooze condition. Conclusions Using a snooze alarm prolongs sleep inertia compared to a single alarm, possibly because snooze alarms induce repeated forced awakenings.

PurposeOptic nerve head (ONH) blood flow decrease without changes in intraocular pressure in a possible rat model of retinal ganglion cell loss by systemic administration of aldosterone. To compare the blood flow in the ONH, using laser speckle flowgraphy (LSFG), in healthy eyes and in eyes with primary aldosteronism (PA).MethodsThe ONH tissue area mean blur rate (MT) was evaluated in this single center, retrospective, cross-sectional study using LSFG. In order to compare the MT between PA patients and normal subjects, mixed-effects models were used, with adjustments made for the mean arterial pressure, disc area, and β-peripapillary atrophy (β-PPA) area. Mixed-effects models were also used to analyze the risk factors affecting the MT.ResultsThis study evaluated a total of 29 eyes of 17 PA patients and 61 eyes of 61 normal subjects. There was a significantly lower MT in PA patients (10.8 ± 0.4) as compared to the normal subjects (12.3 ± 0.3) (P = 0.004). The MT was significantly lower in PA patients (10.8 ± 0.6) even after adjusting for the potential confounding factors when compared to normal subjects (12.3 ± 0.3) (P = 0.046). Multivariate mixed-effects model analysis demonstrated that the MT was significantly associated with the PA and β-PPA.ConclusionsThere was a significantly lower ONH blood flow in PA patients as compared to normal subjects.

In this paper, we propose a noise-robust pulse wave estimation method from near-infrared face video images. Pulse wave estimation in a near-infrared environment is expected to be applied to non-contact monitoring in dark areas. The conventional method cannot consider noise when performing estimation. As a result, the accuracy of pulse wave estimation in noisy environments is not very high. This may adversely affect the accuracy of heart rate data and other data obtained from pulse wave signals. Therefore, the objective of this study is to perform pulse wave estimation robust to noise. The Wiener estimation method, which is a simple linear computation that can consider noise, was used in this study. Experimental results showed that the combination of the proposed method and signal processing (detrending and bandpass filtering) increased the SNR (signal to noise ratio) by more than 2.5 dB compared to the conventional method and signal processing. The correlation coefficient between the pulse wave signal measured using a pulse wave meter and the estimated pulse wave signal was 0.30 larger on average for the proposed method. Furthermore, the AER (absolute error rate) between the heart rate measured with the pulse wave meter was 0.82% on average for the proposed method, which was lower than the value of the conventional method (12.53% on average). These results show that the proposed method is more robust to noise than the conventional method for pulse wave estimation.

Several traditional Japanese Kampo formulas are known to have inhibitory effects on infections with viruses that cause respiratory symptoms. Although some herbs and their components have been reported to suppress SARS-CoV-2 replication in vitro, it is difficult to compare effective Kampo formulas because of the different methods used in studies. Thus, we carried out in vitro experiments on the suppression of SARS-CoV-2 infection by Kampo formulas and crude drugs used for the common cold to compare their suppressive effects on virus infection. After infecting VeroE6/TMPRSS2 cells with SARS-CoV-2, lysates of the Kampo formulas and crude drugs were added, and after 24 h, the infectious titer in the medium was measured by the TCID50 method. Maoto was the most effective among the Kampo formulas, and Ephedrae herba was the most effective among the constituent crude drugs. However, a comparison of the suppressive effects of Ephedrae herba and Kampo formulas containing Ephedrae herba showed that the suppressive effect on virus infection did not depend on the content of Ephedrae herba. Based on the results, we believe that the use of Maoto among Kampo formulas is suitable as a countermeasure against COVID-19.

Colored product textures correspond to particle size distributions. The microscopic images of colorants must be divided into regions to determine the particle size distribution. The conventional method used for this process involves manually dividing images into areas, which may be inefficient. In this paper, we have overcome this issue by developing two different modified architectures of U-Net convolution neural networks to automatically determine the particle sizes. To develop these modified architectures, a significant amount of ground truth data must be prepared to train the U-Net, which is difficult for big data as the labeling is performed manually. Therefore, we also aim to reduce this process by using incomplete labeling data. The first objective of this study is to determine the accuracy of our modified U-Net architectures for this type of image. The second objective is to reduce the difficulty of preparing the ground truth data by testing the accuracy of training on incomplete labeling data. The results indicate that efficient segmentation can be realized using our modified U-Net architectures, and the generation of ground truth data can be simplified. This paper presents a preliminary study to improve the efficiency of determining particle size distributions with incomplete labeling data.

Photoplethysmographic (PPG) pulses contain information about cardiovascular parameters. In particular, blood pressure can be estimated using PPG pulse decomposition analysis, which assumes that a PPG pulse is composed of the original heart ejection blood wave and its reflections in arterial branchings. Among pulse decomposition wave functions that have been studied in the literature, Gaussian waves are the most successful ones. However, a more adequate pulse decomposition function could be found to improve blood pressure estimates. In this paper, we propose pulse decomposition analysis using hyperbolic secant (sech) waves and compare results with corresponding Gaussian wave decomposition. We analyze how the parameters of each of the two types of decomposition waves correlate with blood pressure. For this analysis, continuous blood pressure data and PPG data were acquired from ten healthy volunteers. The blood pressure of volunteers was varied by asking them to hold their breath for up to 60 s. The results suggested sech wave decomposition had higher accuracy in estimating blood pressure than the Gaussian function. Thus, sech wave decomposition should be considered as a more robust alternative to Gaussian wave pulse decomposition for blood pressure estimation models.