Published papers

CAlcium fluoride for the study of Neutrinos and Dark matters by Low-energy Spectrometer (CANDLES) searches for neutrino-less double beta decay of $^{48}$Ca using a CaF$_2$ scintillator array. A high Q-value of $^{48}$Ca at 4,272 keV enabled us to achieve very low background condition, however, at the same it causes difficulties in calibrating the detector's Q-value region because of the absence of a standard high-energy $\gamma$-ray source. Therefore, we have developed a novel calibration system based on $\gamma$-ray emission by neutron capture on $^{28}$Si, $^{56}$Fe and $^{58}$Ni nuclei. In the paper, we report the development of the new calibration system as well as the results of energy calibration in CANDLES up to 9 MeV.

© 2020 The Author(s). Background: The novel coronavirus pandemic calls for a rapid adaptation of conventional medical practices to meet the evolving needs of such vulnerable patients. People with coronavirus disease (COVID-19) may frequently require treatment with psychotropic medications, but are at the same time at higher risk for safety issues because of the complex underlying medical condition and the potential interaction with medical treatments. Methods: In order to produce evidence-based practical recommendations on the optimal management of psychotropic medications in people with COVID-19, an international, multi-disciplinary working group was established. The methodology of the WHO Rapid Advice Guidelines in the context of a public health emergency and the principles of the AGREE statement were followed. Available evidence informing on the risk of respiratory, cardiovascular, infective, hemostatic, and consciousness alterations related to the use of psychotropic medications, and drug-drug interactions between psychotropic and medical treatments used in people with COVID-19, was reviewed and discussed by the working group. Results: All classes of psychotropic medications showed potentially relevant safety risks for people with COVID-19. A set of practical recommendations was drawn in order to inform frontline clinicians on the assessment of the anticipated risk of psychotropic-related unfavorable events, and the possible actions to take in order to effectively manage this risk, such as when it is appropriate to avoid, withdraw, switch, or adjust the dose of the medication. Conclusions: The present evidence-based recommendations will improve the quality of psychiatric care in people with COVID-19, allowing an appropriate management of the medical condition without worsening the psychiatric condition and vice versa.

It is widely acknowledged that over 90% of those who committed suicide had a psychiatric diagnosis at the time of death. Hence, suicide prevention is one of the top priorities for psychiatrists to address. However, the field of neuropsychiatry lacks biomarkers that could serve as objective indicators for diagnosis and severity assessment. Suicide is a complicated result caused by multiple factors and can be difficult to accurately predict. In recent years, research based on data analysis using artificial intelligence (AI) supported by machine learning has attracted attention in the medical field. In suicide prevention, an unprecedented amount of research using machine learning have been reported. In this paper, we will introduce new information about suicide prevention using machine learning as well as comment on the ethical, legal, and social implications (ELSI) that should be considered for implementation into society. There are no potential conflicts of interest to disclose.

PURPOSE: The assessment of anterior eye diseases and the understanding of psychological functions of blinking can benefit greatly from a validated blinking detection technology. In this work, we proposed an algorithm based on facial recognition built on current video processing technologies to automatically filter and analyze blinking movements. We compared electrooculography (EOG), the gold standard of blinking measurement, with manual video tape recording counting (mVTRc) and our proposed automated video tape recording analysis (aVTRa) in both static and dynamic conditions to validate our aVTRa method. METHODS: We measured blinking in both static condition, where the subject was sitting still with chin fixed on the table, and dynamic condition, where the subject's face was not fixed and natural communication was taking place between the subject and interviewer. We defined concordance of blinks between measurement methods as having less than 50 ms difference between eyes opening and closing. RESULTS: The subjects consisted of seven healthy Japanese volunteers (3 male, four female) without significant eye disease with average age of 31.4±7.2. The concordance of EOG vs. aVTRa, EOG vs. mVTRc, and aVTRa vs. mVTRc (average±SD) were found to be 92.2±10.8%, 85.0±16.5%, and 99.6±1.0% in static conditions and 32.6±31.0%, 28.0±24.2%, and 98.5±2.7% in dynamic conditions, respectively. CONCLUSIONS: In static conditions, we have found a high blink concordance rate between the proposed aVTRa versus EOG, and confirmed the validity of aVTRa in both static and dynamic conditions.

© Springer Nature Switzerland AG 2020. Diagnoses of autism spectrum disorder (ASD) are difficult due to difference of interviewers and environments, etc. We show relations between utterance features and ASD severity scores, which were manually given by clinical psychologists. These scores are based on the Autism Diagnostic Observation Schedule (ADOS), which is the standard metrics for symptom evaluation for subjects who are suspected as ASD. We built our original corpus where we transcribed voice records of our ADOS evaluation experiment movies. Our corpus is the world largest as speech/dialog of ASD subjects, and there has been no such ADOS corpus available in Japanese language as far as we know. We investigated relationships between ADOS scores (severity) and our utterance features, automatically estimated their scores using support vector regression (SVR). Our average estimation errors were around error rates that human ADOS experts are required not to exceed. Because our detailed analysis for each part of the ADOS test (“puzzle toy assembly + story telling” part and the “depiction of a picture” part) shows different error rates, effectiveness of our features would depend on the contents of the records. Our entire results suggest a new automatic way to assist humans’ diagnosis, which could help supporting language rehabilitation for individuals with ASD in future.