睡眠覚醒恒常性におけるカルシウム依存的過分極経路の役割の理解

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 松崎 政紀, 東京大学教授 狩野 方伸, 東京大学教授 岡部 繁男, 東京大学教授 吉川 雅英, 東京大学教授 饗場 篤University of Tokyo(東京大学

Impact of domestic travel restrictions on transmission of COVID-19 infection using public transportation network approach

The international spread of COVID-19 infection has attracted global attention, but the impact of local or domestic travel restriction on public transportation network remains unclear. Passenger volume data for the domestic public transportation network in Japan and the time at which the first confirmed COVID-19 case was observed in each prefecture were extracted from public data sources. A survival approach in which a hazard was modeled as a function of the closeness centrality on the network was utilized to estimate the risk of importation of COVID-19 in each prefecture. A total of 46 prefectures with imported cases were identified. Hypothetical scenario analyses indicated that both strategies of locking down the metropolitan areas and restricting domestic airline travel would be equally effective in reducing the risk of importation of COVID-19. While caution is necessary that the data were limited to June 2020 when the pandemic was in its initial stage and that no other virus spreading routes have been considered, domestic travel restrictions were effective to prevent the spread of COVID-19 on public transportation network in Japan. Instead of lockdown that might seriously damage the economy, milder travel restrictions could have the similar impact on controlling the domestic transmission of COVID-19. © 2021, The Author(s)

Early SNS-based monitoring system for the COVID-19 outbreak in Japan: a population-level observational study

Background: The World Health Organization declared the novel coronavirus outbreak (COVID-19) to be a pandemic on March 11, 2020. Large-scale monitoring for capturing the current epidemiological situation of COVID-19 in Japan would improve preparation for and prevention of a massive outbreak. Methods: A chatbot-based healthcare system named COOPERA (COvid-19: Operation for Personalized Empowerment to Render smart prevention And care seeking) was developed using the LINE app to evaluate the current Japanese epidemiological situation. LINE users could participate in the system either though a QR code page in the prefecture’s website, or a banner at the top of the LINE app screen. COOPERA asked participants questions regarding personal information, preventive actions, and non-specific symptoms related to COVID-19 and their duration. We calculated daily cross correlation functions between the reported number of infected cases confirmed by PCR and the symptom-positive group captured by COOPERA. Results: We analyzed 206,218 participants from three prefectures reported between March 5 and 30, 2020. The mean (standard deviation) age of participants was 44.2 (13.2). No symptoms were reported by 96.93% of participants, but there was a significantly positive correlation between the reported number of COVID-19 cases and self-reported fevers, suggesting that massive monitoring of fever might help to estimate the scale of the COVID-19 epidemic in real time. Conclusions: COOPERA is the first real-time system being used to monitor trends in COVID-19 in Japan, and provides useful insights to assist political decisions to tackle the epidemic

Mutation Accumulation in a Selfing Population: Consequences of Different Mutation Rates between Selfers and Outcrossers

Currently existing theories predict that because deleterious mutations accumulate at a higher rate, selfing populations suffer from more intense genetic degradation relative to outcrossing populations. This prediction may not always be true when we consider a potential difference in deleterious mutation rate between selfers and outcrossers. By analyzing the evolutionary stability of selfing and outcrossing in an infinite population, we found that the genome-wide deleterious mutation rate would be lower in selfing than in outcrossing organisms. When this difference in mutation rate was included in simulations, we found that in a small population, mutations accumulated more slowly under selfing rather than outcrossing. This result suggests that under frequent and intense bottlenecks, a selfing population may have a lower risk of genetic extinction than an outcrossing population

Reduced mortality during the COVID-19 outbreak in Japan, 2020: a two-stage interrupted time-series design.

BACKGROUND: Coronavirus disease 2019 (COVID-19) continues to be a major global health burden. This study aims to estimate the all-cause excess mortality occurring in the COVID-19 outbreak in Japan, 2020, by sex and age group. METHODS: Daily time series of mortality for the period January 2015-December 2020 in all 47 prefectures of Japan were obtained from the Ministry of Health, Labour and Welfare, Japan. A two-stage interrupted time-series design was used to calculate excess mortality. In the first stage, we estimated excess mortality by prefecture using quasi-Poisson regression models in combination with distributed lag non-linear models, adjusting for seasonal and long-term variations, weather conditions and influenza activity. In the second stage, we used a random-effects multivariate meta-analysis to synthesize prefecture-specific estimates at the nationwide level. RESULTS: In 2020, we estimated an all-cause excess mortality of -20 982 deaths [95% empirical confidence intervals (eCI): -38 367 to -5472] in Japan, which corresponded to a percentage excess of -1.7% (95% eCI: -3.1 to -0.5) relative to the expected value. Reduced deaths were observed for both sexes and in all age groups except those aged <60 and 70-79 years. CONCLUSIONS: All-cause mortality during the COVID-19 outbreak in Japan in 2020 was decreased compared with a historical baseline. Further evaluation of cause-specific excess mortality is warranted

Estimating Infection-Related Human Mobility Networks Based on Time Series Data of COVID-19 Infection in Japan

Background: Comprehensive and evidence-based countermeasures against emerging infectious diseases have become increasingly important in recent years. COVID-19 and many other infectious diseases are spread by human movement and contact, but complex transportation networks in the 21st century make it difficult to predict disease spread in rapidly changing situations. It is especially challenging to estimate the network of infection transmission in countries where traffic and human movement data infrastructure is not yet developed. Methods: In this study, we devised a method utilizing an ordinary and partial differential equations-based mathematical model and a modified mathematical optimization method to estimate the network of transmission of COVID-19 from the time series data of its infection and applied it to determine its spread across areas in Japan. Furthermore, utilizing the estimated human mobility network, we predicted the spread of infection using the Tokyo Olympics as a model. Findings: We incorporated the effects of soft lockdowns, such as the declaration of a state of emergency, and changes in the infection network due to government-sponsored travel promotion, and revealed that the estimated effective distance captured human mobility changing dynamically in the different stages of the pandemic. The model predicted that the Tokyo Olympic and Paralympic Games would increase the number of infected cases in the host prefectures by up to 80%. Interpretation: The models used in this study are available online, and our data-driven infection network models are scalable, whether it be at the level of a city, town, country, or continent, and applicable anywhere in the world, as long as the time-series data of infections per region is available. These estimations of effective distance and the depiction of infectious disease networks based on actual infection data are expected to be useful in devising data-driven countermeasures against emerging infectious diseases worldwide

The extent of inbreeding depression as a function of deleterious mutation rate.

<p>Mean inbreeding depression under complete outcrossing (<i>S</i> = 0; solid line) and complete selfing (<i>S</i> = 1; broken line), were obtained analytically for an infinite population (equations 1a, b). The dashed-dotted line shows the threshold level of inbreeding depression ( = 0.5), below which selfing is evolutionarily stable. Selection parameters are set to be <i>h</i> = 0.2 and <i>s</i> = 0.1, based on available data for <i>Drosophila melanogaster </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033541#pone.0033541-Mukai1" target="_blank">[19]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033541#pone.0033541-Crow1" target="_blank">[20]</a>.</p

Mammalian reverse genetics without crossing reveals Nr3a as a short-sleeper gene

The identification of molecular networks at the system level in mammals is accelerated by next-generation mammalian genetics without crossing, which requires both the efficient production of whole-body biallelic knockout (KO) mice in a single generation and high-performance phenotype analyses. Here, we show that the triple targeting of a single gene using the CRISPR/Cas9 system achieves almost perfect KO efficiency (96%–100%). In addition, we developed a respiration-based fully automated noninvasive sleep phenotyping system, the Snappy Sleep Stager (SSS), for high-performance (95.3% accuracy) sleep/wake staging. Using the triple-target CRISPR and SSS in tandem, we reliably obtained sleep/wake phenotypes, even in double-KO mice. By using this system to comprehensively analyze all of the N-methyl-D-aspartate (NMDA) receptor family members, we found Nr3a as a short-sleeper gene, which is verified by an independent set of triple-target CRISPR. These results demonstrate the application of mammalian reverse genetics without crossing to organism-level systems biology in sleep research