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Article ; Online: A design principle of spindle oscillations in mammalian sleep

Tetsuya Yamada / Shoi Shi / Hiroki R. Ueda

iScience, Vol 25, Iss 3, Pp 103873- (2022)

2022

Abstract: Summary: Neural oscillations are mainly regulated by molecular mechanisms and network connectivity of neurons. Large-scale simulations of neuronal networks have driven the population-level understanding of neural oscillations. However, cell-intrinsic ... ...

Abstract	Summary: Neural oscillations are mainly regulated by molecular mechanisms and network connectivity of neurons. Large-scale simulations of neuronal networks have driven the population-level understanding of neural oscillations. However, cell-intrinsic mechanisms, especially a design principle, of neural oscillations remain largely elusive. Herein, we developed a minimal, Hodgkin-Huxley-type model of groups of neurons to investigate molecular mechanisms underlying spindle oscillation, which is synchronized oscillatory activity predominantly observed during mammalian sleep. We discovered that slowly inactivating potassium channels played an essential role in characterizing the firing pattern. The detailed analysis of the minimal model revealed that leak sodium and potassium channels, which controlled passive properties of the fast variable (i.e., membrane potential), competitively regulated the base value and time constant of the slow variable (i.e., cytosolic calcium concentration). Consequently, we propose a theoretical design principle of spindle oscillations that may explain intracellular mechanisms behind the flexible control over oscillation density and calcium setpoint.
Keywords	Neuroscience ; Complex system biology ; Computer modeling ; Science ; Q
Subject code	612
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A design principle of spindle oscillations in mammalian sleep.

Yamada, Tetsuya / Shi, Shoi / Ueda, Hiroki R

iScience

2022 Volume 25, Issue 3, Page(s) 103873

Abstract: Neural oscillations are mainly regulated by molecular mechanisms and network connectivity of neurons. Large-scale simulations of neuronal networks have driven the population-level understanding of neural oscillations. However, cell-intrinsic mechanisms, ... ...

Abstract	Neural oscillations are mainly regulated by molecular mechanisms and network connectivity of neurons. Large-scale simulations of neuronal networks have driven the population-level understanding of neural oscillations. However, cell-intrinsic mechanisms, especially a design principle, of neural oscillations remain largely elusive. Herein, we developed a minimal, Hodgkin-Huxley-type model of groups of neurons to investigate molecular mechanisms underlying spindle oscillation, which is synchronized oscillatory activity predominantly observed during mammalian sleep. We discovered that slowly inactivating potassium channels played an essential role in characterizing the firing pattern. The detailed analysis of the minimal model revealed that leak sodium and potassium channels, which controlled passive properties of the fast variable (i.e., membrane potential), competitively regulated the base value and time constant of the slow variable (i.e., cytosolic calcium concentration). Consequently, we propose a theoretical design principle of spindle oscillations that may explain intracellular mechanisms behind the flexible control over oscillation density and calcium setpoint.
Language	English
Publishing date	2022-02-05
Publishing country	United States
Document type	Journal Article
ISSN	2589-0042
ISSN (online)	2589-0042
DOI	10.1016/j.isci.2022.103873
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Article ; Online: Estimating infection-related human mobility networks based on time series data of COVID-19 infection in Japan

Yamada, Tetsuya / Shi, Shoi

medRxiv

Abstract: 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 ... ...

Abstract	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 21 century make it difficult to predict disease spread in rapidly changing situations. It is especially challenging to estimate the network of infection transmission in the countries that the traffic and human movement data infrastructure is not yet developed. In this study, we devised a 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. 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 predicted the spread of infection using the Tokyo Olympics as a model. 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.
Keywords	covid19
Language	English
Publishing date	2021-08-04
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2021.08.02.21261486
Database	COVID19

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Article ; Online: The 103,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes.

Katori, Machiko / Shi, Shoi / Ode, Koji L / Tomita, Yasuhiro / Ueda, Hiroki R

Proceedings of the National Academy of Sciences of the United States of America

2022 Volume 119, Issue 12, Page(s) e2116729119

Abstract: SignificanceHuman sleep phenotypes are diversified by genetic and environmental factors, and a quantitative classification of sleep phenotypes would lead to the advancement of biomedical mechanisms underlying human sleep diversity. To achieve that, a ... ...

Abstract	SignificanceHuman sleep phenotypes are diversified by genetic and environmental factors, and a quantitative classification of sleep phenotypes would lead to the advancement of biomedical mechanisms underlying human sleep diversity. To achieve that, a pipeline of data analysis, including a state-of-the-art sleep/wake classification algorithm, the uniform manifold approximation and projection (UMAP) dimension reduction method, and the density-based spatial clustering of applications with noise (DBSCAN) clustering method, was applied to the 100,000-arm acceleration dataset. This revealed 16 clusters, including seven different insomnia-like phenotypes. This kind of quantitative pipeline of sleep analysis is expected to promote data-based diagnosis of sleep disorders and psychiatric disorders that tend to be complicated by sleep disorders.
MeSH term(s)	Acceleration ; Biological Specimen Banks ; Humans ; Phenotype ; Sleep ; Sleep Wake Disorders ; United Kingdom
Language	English
Publishing date	2022-03-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.2116729119
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Article ; Online: Impact of airline network on the global importation risk of mpox, 2022.

Kinoshita, Ryo / Sassa, Miho / Otake, Shogo / Yoshimatsu, Fumi / Shi, Shoi / Ueno, Ryo / Suzuki, Motoi / Yoneoka, Daisuke

Epidemiology and infection

2023 Volume 151, Page(s) e60

Abstract: From 1 January 2022 to 4 September 2022, a total of 53 996 mpox cases were confirmed globally. Cases are predominantly concentrated in Europe and the Americas, while other regions are also continuously observing imported cases. This study aimed to ... ...

Abstract	From 1 January 2022 to 4 September 2022, a total of 53 996 mpox cases were confirmed globally. Cases are predominantly concentrated in Europe and the Americas, while other regions are also continuously observing imported cases. This study aimed to estimate the potential global risk of mpox importation and consider hypothetical scenarios of travel restrictions by varying passenger volumes (PVs) via airline travel network. PV data for the airline network, and the time of first confirmed mpox case for a total of 1680 airports in 176 countries (and territories) were extracted from publicly available data sources. A survival analysis technique in which the hazard function was a function of effective distance was utilised to estimate the importation risk. The arrival time ranged from 9 to 48 days since the first case was identified in the UK on 6 May 2022. The estimated risk of importation showed that regardless of the geographic region, most locations will have an intensified importation risk by 31 December 2022. Travel restrictions scenarios had a minor impact on the global airline importation risk against mpox, highlighting the importance to enhance local capacities for the identification of mpox and to be prepared to carry out contact tracing and isolation.
MeSH term(s)	Humans ; Mpox (monkeypox) ; Travel ; Airports ; Contact Tracing ; Europe/epidemiology
Language	English
Publishing date	2023-03-21
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	632982-2
ISSN	1469-4409 ; 0950-2688
ISSN (online)	1469-4409
ISSN	0950-2688
DOI	10.1017/S0950268823000456
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Article ; Online: Time to Reconsider Diverse Ways of Working in Japan to Promote Social Distancing Measures against the COVID-19.

Nomura, Shuhei / Yoneoka, Daisuke / Tanoue, Yuta / Kawashima, Takayuki / Shi, Shoi / Eguchi, Akifumi / Miyata, Hiroaki

Journal of urban health : bulletin of the New York Academy of Medicine

2020 Volume 97, Issue 4, Page(s) 457–460

Keywords	covid19
Language	English
Publishing date	2020-06-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	1435288-6
ISSN	1468-2869 ; 1099-3460
ISSN (online)	1468-2869
ISSN	1099-3460
DOI	10.1007/s11524-020-00464-4
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Article ; Online: Ca

Shi, Shoi / Ueda, Hiroki R

BioEssays : news and reviews in molecular, cellular and developmental biology

2017 Volume 40, Issue 1

Abstract: Although we are beginning to understand the neuronal and biochemical nature of sleep regulation, questions remain about how sleep is homeostatically regulated. Beyond its importance in basic physiology, understanding sleep may also shed light on ... ...

Abstract	Although we are beginning to understand the neuronal and biochemical nature of sleep regulation, questions remain about how sleep is homeostatically regulated. Beyond its importance in basic physiology, understanding sleep may also shed light on psychiatric and neurodevelopmental disorders. Recent genetic studies in mammals revealed several non-secretory proteins that determine sleep duration. Interestingly, genes identified in these studies are closely related to psychiatric and neurodevelopmental disorders, suggesting that the sleep-wake cycle shares some common mechanisms with these disorders. Here we review recent sleep studies, including reverse and forward genetic studies, from the perspectives of sleep duration and homeostasis. We then introduce a recent hypothesis for mammalian sleep in which the fast and slow Ca
MeSH term(s)	Animals ; Calcium/physiology ; Calcium Channels/physiology ; Circadian Rhythm/genetics ; Circadian Rhythm/physiology ; Computational Biology ; Electrophysiological Phenomena ; Homeostasis/physiology ; Humans ; Models, Biological ; Neurodevelopmental Disorders/diagnosis ; Neurodevelopmental Disorders/genetics ; Neurodevelopmental Disorders/physiopathology ; Neurons/physiology ; Sleep/genetics ; Sleep/physiology
Chemical Substances	Calcium Channels ; Calcium (SY7Q814VUP)
Language	English
Publishing date	2017-12-04
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	50140-2
ISSN	1521-1878 ; 0265-9247
ISSN (online)	1521-1878
ISSN	0265-9247
DOI	10.1002/bies.201700105
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Zs.A 2024: Show issues

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Article ; Online: A jerk-based algorithm ACCEL for the accurate classification of sleep–wake states from arm acceleration

Koji L. Ode / Shoi Shi / Machiko Katori / Kentaro Mitsui / Shin Takanashi / Ryo Oguchi / Daisuke Aoki / Hiroki R. Ueda

iScience, Vol 25, Iss 2, Pp 103727- (2022)

2022

Abstract: Summary: Arm acceleration data have been used to measure sleep–wake rhythmicity. Although several methods have been developed for the accurate classification of sleep–wake episodes, a method with both high sensitivity and specificity has not been fully ... ...

Abstract	Summary: Arm acceleration data have been used to measure sleep–wake rhythmicity. Although several methods have been developed for the accurate classification of sleep–wake episodes, a method with both high sensitivity and specificity has not been fully established. In this study, we developed an algorithm, named ACceleration-based Classification and Estimation of Long-term sleep–wake cycles (ACCEL) that classifies sleep and wake episodes using only raw accelerometer data, without relying on device-specific functions. The algorithm uses a derivative of triaxial acceleration (jerk), which can reduce individual differences in the variability of acceleration data. Applying a machine learning algorithm to the jerk data achieved sleep–wake classification with a high sensitivity (>90%) and specificity (>80%). A jerk-based analysis also succeeded in recording periodic activities consistent with pulse waves. Therefore, the ACCEL algorithm will be a useful method for large-scale sleep measurement using simple accelerometers in real-world settings.
Keywords	Chronobiology ; Diagnostic technique in health technology ; Health technology ; Science ; Q
Subject code	006
Language	English
Publishing date	2022-02-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A jerk-based algorithm ACCEL for the accurate classification of sleep-wake states from arm acceleration.

Ode, Koji L / Shi, Shoi / Katori, Machiko / Mitsui, Kentaro / Takanashi, Shin / Oguchi, Ryo / Aoki, Daisuke / Ueda, Hiroki R

iScience

2022 Volume 25, Issue 2, Page(s) 103727

Abstract: Arm acceleration data have been used to measure sleep-wake rhythmicity. Although several methods have been developed for the accurate classification of sleep-wake episodes, a method with both high sensitivity and specificity has not been fully ... ...

Abstract	Arm acceleration data have been used to measure sleep-wake rhythmicity. Although several methods have been developed for the accurate classification of sleep-wake episodes, a method with both high sensitivity and specificity has not been fully established. In this study, we developed an algorithm, named ACceleration-based Classification and Estimation of Long-term sleep-wake cycles (ACCEL) that classifies sleep and wake episodes using only raw accelerometer data, without relying on device-specific functions. The algorithm uses a derivative of triaxial acceleration (jerk), which can reduce individual differences in the variability of acceleration data. Applying a machine learning algorithm to the jerk data achieved sleep-wake classification with a high sensitivity (>90%) and specificity (>80%). A jerk-based analysis also succeeded in recording periodic activities consistent with pulse waves. Therefore, the ACCEL algorithm will be a useful method for large-scale sleep measurement using simple accelerometers in real-world settings.
Language	English
Publishing date	2022-01-01
Publishing country	United States
Document type	Journal Article
ISSN	2589-0042
ISSN (online)	2589-0042
DOI	10.1016/j.isci.2021.103727
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: "KAIZEN" method realizing implementation of deep-learning models for COVID-19 CT diagnosis in real world hospitals.

Okada, Naoki / Umemura, Yutaka / Shi, Shoi / Inoue, Shusuke / Honda, Shun / Matsuzawa, Yohsuke / Hirano, Yuichiro / Kikuyama, Ayano / Yamakawa, Miho / Gyobu, Tomoko / Hosomi, Naohiro / Minami, Kensuke / Morita, Natsushiro / Watanabe, Atsushi / Yamasaki, Hiroyuki / Fukaguchi, Kiyomitsu / Maeyama, Hiroki / Ito, Kaori / Okamoto, Ken /

Harano, Kouhei / Meguro, Naohito / Unita, Ryo / Koshiba, Shinichi / Endo, Takuro / Yamamoto, Tomonori / Yamashita, Tomoya / Shinba, Toshikazu / Fujimi, Satoshi

Scientific reports

2024 Volume 14, Issue 1, Page(s) 1672

Abstract: Numerous COVID-19 diagnostic imaging Artificial Intelligence (AI) studies exist. However, none of their models were of potential clinical use, primarily owing to methodological defects and the lack of implementation considerations for inference. In this ... ...

Abstract	Numerous COVID-19 diagnostic imaging Artificial Intelligence (AI) studies exist. However, none of their models were of potential clinical use, primarily owing to methodological defects and the lack of implementation considerations for inference. In this study, all development processes of the deep-learning models are performed based on strict criteria of the "KAIZEN checklist", which is proposed based on previous AI development guidelines to overcome the deficiencies mentioned above. We develop and evaluate two binary-classification deep-learning models to triage COVID-19: a slice model examining a Computed Tomography (CT) slice to find COVID-19 lesions; a series model examining a series of CT images to find an infected patient. We collected 2,400,200 CT slices from twelve emergency centers in Japan. Area Under Curve (AUC) and accuracy were calculated for classification performance. The inference time of the system that includes these two models were measured. For validation data, the slice and series models recognized COVID-19 with AUCs and accuracies of 0.989 and 0.982, 95.9% and 93.0% respectively. For test data, the models' AUCs and accuracies were 0.958 and 0.953, 90.0% and 91.4% respectively. The average inference time per case was 2.83 s. Our deep-learning system realizes accuracy and inference speed high enough for practical use. The systems have already been implemented in four hospitals and eight are under progression. We released an application software and implementation code for free in a highly usable state to allow its use in Japan and globally.
MeSH term(s)	Humans ; COVID-19/diagnostic imaging ; Artificial Intelligence ; Deep Learning ; Tomography, X-Ray Computed/methods ; Software ; COVID-19 Testing
Language	English
Publishing date	2024-01-19
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-52135-y
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