THERMAL HYDRAULIC ISSUES OF CONTAINMENT FILTERED VENTING SYSTEM FOR A LONG OPERATING TIME

This study investigated the thermal hydraulic issues in the Containment Filtered Venting System (CFVS) for a long operating time using the MELCOR computer code. The modeling of the CFVS, including the models for pool scrubbing and the filter, was added to the input file for the OPR-1000, and a Station Blackout (SBO) was chosen as an accident scenario. Although depressurization in the containment building as a primary objective of the CFVS was successful, the decontamination feature by scrubbing and filtering in the CFVS for a long operating time could fail by the continuous evaporation of the scrubbing solution. After the operation of the CFVS, the atmosphere temperature in the CFVS became slightly above the water saturation temperature owing to the release of an amount of steam with high temperature from the containment building to the scrubbing solution. Reduced pipe diameters at the inlet and outlet of the CFVS vessel mitigated the evaporation of scrubbing water by controlling the amount of high-temperature steam and the water saturation temperature

Multi-Signal Reconstruction Using Masked Autoencoder From EEG During Polysomnography

Polysomnography (PSG) is an indispensable diagnostic tool in sleep medicine, essential for identifying various sleep disorders. By capturing physiological signals, including EEG, EOG, EMG, and cardiorespiratory metrics, PSG presents a patient's sleep architecture. However, its dependency on complex equipment and expertise confines its use to specialized clinical settings. Addressing these limitations, our study aims to perform PSG by developing a system that requires only a single EEG measurement. We propose a novel system capable of reconstructing multi-signal PSG from a single-channel EEG based on a masked autoencoder. The masked autoencoder was trained and evaluated using the Sleep-EDF-20 dataset, with mean squared error as the metric for assessing the similarity between original and reconstructed signals. The model demonstrated proficiency in reconstructing multi-signal data. Our results present promise for the development of more accessible and long-term sleep monitoring systems. This suggests the expansion of PSG's applicability, enabling its use beyond the confines of clinics.Comment: Proc. 12th IEEE International Winter Conference on Brain-Computer Interfac

Relationship Between Mood, Sleepiness, and EEG Functional Connectivity by 40 Hz Monaural Beats

The monaural beat is known that it can modulate brain and personal states. However, which changes in brain waves are related to changes in state is still unclear. Therefore, we aimed to investigate the effects of monaural beats and find the relationship between them. Ten participants took part in five separate random sessions, which included a baseline session and four sessions with monaural beats stimulation: one audible session and three inaudible sessions. Electroencephalogram (EEG) were recorded and participants completed pre- and post-stimulation questionnaires assessing mood and sleepiness. As a result, audible session led to increased arousal and positive mood compared to other conditions. From the neurophysiological analysis, statistical differences in frontal-central, central-central, and central-parietal connectivity were observed only in the audible session. Furthermore, a significant correlation was identified between sleepiness and EEG power in the temporal and occipital regions. These results suggested a more detailed correlation for stimulation to change its personal state. These findings have implications for applications in areas such as cognitive enhancement, mood regulation, and sleep management

Impact of Nap on Performance in Different Working Memory Tasks Using EEG

Electroencephalography (EEG) has been widely used to study the relationship between naps and working memory, yet the effects of naps on distinct working memory tasks remain unclear. Here, participants performed word-pair and visuospatial working memory tasks pre- and post-nap sessions. We found marked differences in accuracy and reaction time between tasks performed pre- and post-nap. In order to identify the impact of naps on performance in each working memory task, we employed clustering to classify participants as high- or low-performers. Analysis of sleep architecture revealed significant variations in sleep onset latency and rapid eye movement (REM) proportion. In addition, the two groups exhibited prominent differences, especially in the delta power of the Non-REM 3 stage linked to memory. Our results emphasize the interplay between nap-related neural activity and working memory, underlining specific EEG markers associated with cognitive performance.Comment: Submitted to 2024 12th IEEE International Winter Conference on Brain-Computer Interfac

Neurophysiological Response Based on Auditory Sense for Brain Modulation Using Monaural Beat

Brain modulation is a modification process of brain activity through external stimulations. However, which condition can induce the activation is still unclear. Therefore, we aimed to identify brain activation conditions using 40 Hz monaural beat (MB). Under this stimulation, auditory sense status which is determined by frequency and power range is the condition to consider. Hence, we designed five sessions to compare; no stimulation, audible (AB), inaudible in frequency, inaudible in power, and inaudible in frequency and power. Ten healthy participants underwent each stimulation session for ten minutes with electroencephalogram (EEG) recording. For analysis, we calculated the power spectral density (PSD) of EEG for each session and compared them in frequency, time, and five brain regions. As a result, we observed the prominent power peak at 40 Hz in only AB. The induced EEG amplitude increase started at one minute and increased until the end of the session. These results of AB had significant differences in frontal, central, temporal, parietal, and occipital regions compared to other stimulations. From the statistical analysis, the PSD of the right temporal region was significantly higher than the left. We figure out the role that the auditory sense is important to lead brain activation. These findings help to understand the neurophysiological principle and effects of auditory stimulation.Comment: Accepted to EMBC 202

17β-estradiol reduces inflammation and modulates antioxidant enzymes in colonic epithelial cells

Background/Aims: Estrogen is known to have protective effect in colorectal cancer development. The aims of this study are to investigate whether estradiol treatment reduces inflammation in CCD841CoN, a female human colonic epithelial cell line and to uncover underlying mechanisms of estradiol effects. Methods: 17 beta-Estradiol (E2) effect was measured by Western blot after inducing inflammation of CCD841CoN by tumor necrosis factor alpha (TNF-alpha). Expression levels of estrogen receptor alpha (ER alpha) and beta (ER beta), cyclooxygenase-2 (COX-2), nuclear factor-kappa B (NF-kappa B), heme oxygenase-1 (HO-1), and NAD(P)H-quinone oxidoreductase-1 (NQO-1) were also evaluated. Results: E2 treatment induced expression of ERO but did not increase that of ER alpha. E2 treatment for 48 hours significantly elevated the expression of anti-oxidant enzymes, HO-1 and NQO-1. TNF-alpha treatment significantly increased the level of activated NF-kappa B (p < 0.05), and this increase was significantly suppressed by treatment of to nM of E2 (p < 0.05). E2 treatment ameliorated TNF-alpha-induced COX-2 expression and decrease of HO-1 expression. 4-(2-phenyl-5,7-bis(trifluoromethyl) pyrazolo(1,5-a)pyrimidin-3-yl)phenol (PHTPP), antagonist of ER beta, removed the inhibitory effect of E2 in the TNF-alpha-induced COX-2 expression (p = 0.05). Conclusions: Estrogen seems to inhibit inflammation in female human colonic epithelial cell lines, through down-regulation of NF-kappa B and COX-2 expression and induction of anti-oxidant enzymes such as HO-1 and NQO-1.

17β-Estradiol supplementation changes gut microbiota diversity in intact and colorectal cancer-induced ICR male mice

The composition of the gut microbiota is influenced by sex hormones and colorectal cancer (CRC). Previously, we reported that 17 beta -estradiol (E2) inhibits azoxymethane/dextran sulfate sodium (AOM/DSS)-induced tumorigenesis in male mice. Here, we investigated whether the composition of the gut microbiota is different between male and female, and is regulated by estrogen as a secondary outcome of previous studies. We established four groups of mice based on the sex and estrogen status [ovariectomized (OVX) female and E2-treated male]. Additionally, three groups of males were established by treating them with AOM/DSS, and E2, after subjecting them to AOM/DSS treatment. The mice were sacrificed at 21 weeks old. The composition of the gut microbiota was analyzed using 16S rRNA metagenomics sequencing. We observed a significant increase in the microbial diversity (Chao1 index) in females, males supplemented with E2, and males treated with AOM/DSS/E2 compared with normal males. In normal physiological condition, sex difference and E2 treatment did not affect the ratio of Firmicutes/Bacteroidetes (F/B). However, in AOM/DSS-treated male mice, E2 supplementation showed significantly lower level of the F/B ratio. The ratio of commensal bacteria to opportunistic pathogens was higher in females and E2-treated males compared to normal males and females subjected to OVX. Unexpectedly, this ratio was higher in the AOM/DSS group than that determined in other males and the AOM/DSS/E2 group. Our findings suggest that estrogen alters the gut microbiota in ICR (CrljOri:CD1) mice, particularly AOM/DSS-treated males, by decreasing the F/B ratio and changing Shannon and Simpson index by supply of estrogen. This highlights another possibility that estrogen could cause changes in the gut microbiota, thereby reducing the risk of developing CRC.

Dysfunction in Configural Face Processing in Patients With Schizophrenia

Background: Face recognition has important implications for patients with schizophrenia, who exhibit poor interpersonal and social skills. Previous reports have suggested that patients with schizophrenia have deficits in their ability to recognize faces, and because face recognition relies heavily on information about the configuration of faces, we hypothesized that patients with schizophrenia would have specific problems in processing configural information. Methods: We measured the performance of 20 patients with schizophrenia and 20 normal subjects in a face-discrimination task, using upright and inverted pairs of face photographs that differed in featural or configural information. Results: The patients with schizophrenia showed disproportionately poorer performance in discriminating configural compared with featural face sets. Conclusion: The result suggests that the face-recognition deficit in schizophrenic patients is due to specific impairments in configural processing of faces

Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning

A correction method using machine learning aims to improve the conventional linear regression (LR) based method for correction of atmospheric pressure data obtained by smartphones. The method proposed in this study conducts clustering and regression analysis with time domain classification. Data obtained in Gyeonggi-do, one of the most populous provinces in South Korea surrounding Seoul with the size of 10,000 km2, from July 2014 through December 2014, using smartphones were classified with respect to time of day (daytime or nighttime) as well as day of the week (weekday or weekend) and the user’s mobility, prior to the expectation-maximization (EM) clustering. Subsequently, the results were analyzed for comparison by applying machine learning methods such as multilayer perceptron (MLP) and support vector regression (SVR). The results showed a mean absolute error (MAE) 26% lower on average when regression analysis was performed through EM clustering compared to that obtained without EM clustering. For machine learning methods, the MAE for SVR was around 31% lower for LR and about 19% lower for MLP. It is concluded that pressure data from smartphones are as good as the ones from national automatic weather station (AWS) network