Concert recording 2019-03-29

[Track 1]. Ionhontsiá:te (Mother earth). Grandmother moon for soprano and cello / Dawn Ierihò:kwats Avery -- [Track 2]. Lieux ecartez, paisible solitude from Céphale et Procris / Élisabeth Jacquet de La Guerre -- [Track 3]. Four Dickinson songs / Lori Laitman Emily Dickinson -- [Track 4]. Guendae Issume (Because you are here) / Sunae Kim -- [Track 5]. Lee Eung and Arirang (Arirang) / YoungRan Park -- [Track 6]. She tell her love [Track 7]. A boy and a girl / Stephen Caldwell -- [Track 8]. Morgen, op. 27, no. 4 [Track 9]. Heimliche Aufforderung, op. 27, no. 3 / Richard Strauss -- [Track 10]. Suleika, op. 14, no. 1 / Franz Schubert -- [Track 11]. Philine: Singet nicht in Trauertönen, op. 98a, no. 7 / Robert Schumann -- [Track 12]. Mignon: Kennst du das Land / Hugo Wolf -- [Track 13]. From Song of Almah. III. Cedar of Lebanon / Andrew Beall

Towards Neural Decoding of Imagined Speech based on Spoken Speech

Decoding imagined speech from human brain signals is a challenging and important issue that may enable human communication via brain signals. While imagined speech can be the paradigm for silent communication via brain signals, it is always hard to collect enough stable data to train the decoding model. Meanwhile, spoken speech data is relatively easy and to obtain, implying the significance of utilizing spoken speech brain signals to decode imagined speech. In this paper, we performed a preliminary analysis to find out whether if it would be possible to utilize spoken speech electroencephalography data to decode imagined speech, by simply applying the pre-trained model trained with spoken speech brain signals to decode imagined speech. While the classification performance of imagined speech data solely used to train and validation was 30.5 %, the transferred performance of spoken speech based classifier to imagined speech data displayed average accuracy of 26.8 % which did not have statistically significant difference compared to the imagined speech based classifier (p = 0.0983, chi-square = 4.64). For more comprehensive analysis, we compared the result with the visual imagery dataset, which would naturally be less related to spoken speech compared to the imagined speech. As a result, visual imagery have shown solely trained performance of 31.8 % and transferred performance of 26.3 % which had shown statistically significant difference between each other (p = 0.022, chi-square = 7.64). Our results imply the potential of applying spoken speech to decode imagined speech, as well as their underlying common features.Comment: 4 pages, 2 figure

Fully immersive virtual reality exergames with dual-task components for patients with Parkinsons disease: a feasibility study

Abstract Background Dual-task training in Parkinsons disease (PD) improves spatiotemporal gait parameters, cognition, and quality of life. Virtual reality (VR) has been used as a therapeutic tool for patients to participate in activities in a safe environment, engage in multisensory experiences, and improve motivation and interest in rehabilitation. This study aimed to investigate the feasibility of fully immersive VR exergames with dual-task components in patients with PD. Methods We developed VR exergames (go/no-go punch game, go/no-go stepping game, and number punch game) to improve habitual behavior control using motor–cognitive dual-task performance in patients with PD. The participants underwent 10 sessions 2–3 times a week, consisting of 30min per session. The Unified Parkinsons Disease Rating Scale, Timed Up and Go test (TUG) under single- and dual-task (cognitive and physical) conditions, Berg balance scale (BBS), Stroop test, trail-making test, and digit span were evaluated before and after intervention. The Simulator Sickness Questionnaire (SSQ) was used to assess VR cybersickness. Usability was assessed using a self-reported questionnaire. Results Twelve patients were enrolled and completed the entire training session. The mean age of participants was 73.83 ± 6.09years; mean disease duration was 128.83 ± 76.96months. The Hoehn and Yahr stages were 2.5 in seven patients and 3 in five patients. A significant improvement was observed in BBS and Stroop color–word test (p = 0.047 and p = 0.003, respectively). TUG time and dual-task interferences showed positive changes, but these changes were not statistically significant. The median SSQ total score was 28.05 (IQR: 29.92), 13.09 (IQR: 11.22), and 35.53 (IQR: 52.36) before, after the first session, and after the final session, respectively; the differences were not significant. Overall satisfaction with the intervention was 6.0 (IQR: 1.25) on a 7-point Likert-type scale. Conclusions Fully immersive VR exergames combined with physical and cognitive tasks may be used for rehabilitation of patients with PD without causing serious adverse effects. Furthermore, the exergames using dual-task components improved executive function and balance. Further development of VR training content may be needed to improve motor and dual-task performances. Trial registration NCT04787549 (https://clinicaltrials.gov/ct2/show/NCT04787549)This study was supported by Grant no. 03-2020-2020 from the Seoul National University Hospital Research Fund

Phytohormone abscisic acid control RNA-dependent RNA polymerase 6 gene expression and post-transcriptional gene silencing in rice cells

RNA-dependent RNA polymerase 6 (RDR6) catalyses dsRNA synthesis for post-transcriptional gene silencing (PTGS)-associated amplification and the generation of endogeneous siRNAs involved in developmental determinations or stress responses. The functional importance of RDR6 in PTGS led us to examine its connection to the cellular regulatory network by analyzing the hormonal responses of RDR6 gene expression in a cultured cell system. Delivery of dsRNA, prepared in vitro, into cultured rice (Oryza sativa cv. Japonica Dongjin) cells successfully silenced the target isocitrate lyase (ICL) transcripts. Silencing was transient in the absence of abscisic acid (ABA), while it became persistent in the presence of ABA in growth medium. A transcription assay of the OsRDR6 promoter showed that it was positively regulated by ABA. OsRDR6-dependent siRNA(ICL) generation was also significantly up-regulated by ABA. The results showed that, among the five rice OsRDR isogenes, only OsRDR6 was responsible for the observed ABA-mediated amplification and silencing of ICL transcripts. We propose that ABA modulates PTGS through the transcriptional control of the OsRDR6 gene

Anti-malarial activity of 6-(8'Z-pentadecenyl)-salicylic acid from Viola websteri in mice

<p>Abstract</p> <p>Background</p> <p>Petroleum ether extracts of <it>Viola websteri </it>Hemsl (Violaceae) were reported to have anti-plasmodial activity against <it>Plasmodium falciparum in vitro</it>, with this activity being largely attributable to 6-(8'Z-pentadecenyl)-salicylic acid (6-SA).</p> <p>Methods</p> <p>The schizontocidal activity of 6-SA on early <it>Plasmodium berghei </it>infections was evaluated in a four-day test. The possible 'repository' activity of 6-SA was assessed using the method described by Peters. The median lethal dose (LD₅₀) of 6-SA, when given intraperitoneally, was also determined using uninfected ICR mice and the method of Lorke.</p> <p>Results</p> <p>In the present study, 6-SA was found to have anti-malarial activity <it>in vivo</it>, when tested against <it>P. berghei </it>in mice. 6-SA at 5, 10 and 25 mg/kg·day exhibited a significant blood schizontocidal activity in four-day early infections, repository evaluations and established infections with a significant mean survival time comparable to that of the standard drug, chloroquine (5 mg/kg·day).</p> <p>Conclusion</p> <p>6-SA possesses a moderate anti-malarial activity that could be exploited for malaria therapy.</p

Brain-Driven Representation Learning Based on Diffusion Model

Interpreting EEG signals linked to spoken language presents a complex challenge, given the data's intricate temporal and spatial attributes, as well as the various noise factors. Denoising diffusion probabilistic models (DDPMs), which have recently gained prominence in diverse areas for their capabilities in representation learning, are explored in our research as a means to address this issue. Using DDPMs in conjunction with a conditional autoencoder, our new approach considerably outperforms traditional machine learning algorithms and established baseline models in accuracy. Our results highlight the potential of DDPMs as a sophisticated computational method for the analysis of speech-related EEG signals. This could lead to significant advances in brain-computer interfaces tailored for spoken communication

Experimental approach to evaluate software reliability in hardware-software integrated environment

Reliability in safety-critical systems and equipment is of vital importance, so the probabilistic safety assessment (PSA) has been widely used for many years in the nuclear industry to address reliability in a quantitative manner. As many nuclear power plants (NPPs) become digitalized, evaluating the reliability of safety-critical software has become an emerging issue. Due to a lack of available methods, in many conventional PSA models only hardware reliability is addressed with the assumption that software reliability is perfect or very high compared to hardware reliability. This study focused on developing a new method of safety-critical software reliability quantification, derived from hardware-software integrated environment testing. Since the complexity of hardware and software interaction makes the possible number of test cases for exhaustive testing well beyond a practically achievable range, an importance-oriented testing method that assures the most efficient test coverage was developed. Application to the test of an actual NPP reactor protection system demonstrated the applicability of the developed method and provided insight into complex software-based system reliability. (C) 2020 Korean Nuclear Society, Published by Elsevier Korea LLC