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X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260508T100000
DTEND;TZID=Asia/Seoul:20260508T120000
DTSTAMP:20260610T090536
CREATED:20260406T041825Z
LAST-MODIFIED:20260506T033947Z
UID:12360-1778234400-1778241600@www.ibs.re.kr
SUMMARY:Impact of daylight saving time on physical activity patterns - Myna Lim
DESCRIPTION:In this talk\, we discuss the paper “Impact of daylight saving time on physical activity patterns” by Hayoung Jeong et al.\, Nature Health\, 2026. \nAbstract\nDaylight saving time (DST) remains contentious: some policymakers highlight behavioural benefits\, while others emphasize health risks. Here we estimated the behavioural and physiological impacts of DST using longitudinal Fitbit measures from the National Institutes of Health All of Us Research Program. Avoiding strict modelling assumptions\, we used a natural difference-in-differences design with Arizona (no DST) as a control against neighbouring Mountain Time states (observing DST). Contrary to common belief\, DST transitions produced no net change in total daily steps. Instead\, activity was reallocated to other times of day: fall transitions increased morning steps by 202 (confidence interval = [78\, 326]\, P = 0.001) while reducing evening steps by 180 (confidence interval = [−263\, −97]\, P < 0.001); spring transitions showed the opposite. Importantly\, these treatment effects varied by demographics and across data-driven activity phenotypes (‘morning walker’\, ‘neutral walker’ and ‘evening walker’). These disparities suggest that structural factors (for example\, rigid work schedules\, perceived safety) may constrain the capacity to flexibly adapt to time shifts for some populations. Physiologically\, resting heart rate showed subtle intraday shifts mirroring behavioural changes\, although differences were clinically insignificant. Our study provides a large-scale causal analysis of DST’s influence using continuous wearables data\, illustrating how observational data can generate real-world evidence to inform health-relevant policies.
URL:https://www.ibs.re.kr/bimag/event/digital-biomarkers-for-brain-health-passive-and-continuous-assessment-from-wearable-sensors-myna-lim/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260515T100000
DTEND;TZID=Asia/Seoul:20260515T120000
DTSTAMP:20260610T090536
CREATED:20260403T080250Z
LAST-MODIFIED:20260429T070938Z
UID:12338-1778839200-1778846400@www.ibs.re.kr
SUMMARY:High-order Michaelis-Menten equations allow inference of hidden kinetic parameters in enzyme catalysis - Hyeong Jun Jang
DESCRIPTION:In this talk\, we discuss the paper “High-order Michaelis-Menten equations allow inference of hidden kinetic parameters in enzyme catalysis” by Divya Singh et al.\, Nat. Comm.\, 2025. \nAbstract \nSingle-molecule measurements provide a platform for investigating the dynamical properties of enzymatic reactions. To this end\, the single-molecule Michaelis-Menten equation was instrumental as it asserts that the first moment of the enzymatic turnover time depends linearly on the reciprocal of the substrate concentration. This\, in turn\, provides robust and convenient means to determine the maximal turnover rate and the Michaelis-Menten constant. Yet\, the information provided by these parameters is incomplete and does not allow access to key observables such as the lifetime of the enzyme-substrate complex\, the rate of substrate-enzyme binding\, and the probability of successful product formation. Here we show that these quantities and others can be inferred via a set of high-order Michaelis-Menten equations that we derive. These equations capture universal linear relations between the reciprocal of the substrate concentration and distinguished combinations of turnover time moments\, essentially generalizing the Michaelis-Menten equation to moments of any order. We demonstrate how key observables such as the lifetime of the enzyme-substrate complex\, the rate of substrate-enzyme binding\, and the probability of successful product formation\, can all be inferred using these high-order Michaelis-Menten equations. We test our inference procedure to show that it is robust\, producing accurate results with only several thousand turnover events per substrate concentration.
URL:https://www.ibs.re.kr/bimag/event/high-order-michaelis-menten-equations-allow-inference-of-hidden-kinetic-parameters-in-enzyme-catalysis-hyeong-jun-jang/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260529T100000
DTEND;TZID=Asia/Seoul:20260529T120000
DTSTAMP:20260610T090536
CREATED:20260429T070610Z
LAST-MODIFIED:20260518T051101Z
UID:12398-1780048800-1780056000@www.ibs.re.kr
SUMMARY:Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies - Chitaranjan Mahapatra
DESCRIPTION:In this talk\, we discuss the paper “Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies” by Sam vidil et al.\, Nature Communications\, 2025. \nAbstract: \nAccelerometers allow objective measures of dimensions (rest-activity rhythm (RAR)\, daytime activity\, sleep\, and chronotype) of the bio-behavioural manifestation of circadian rhythm (CR) using multiple metrics in large-scale studies. These dimensions are rarely examined together due to methodological challenges of using correlated data. To address this challenge\, we propose a two-step approach consisting of data reduction of CR metrics using principal component analyses\, followed by k-means clustering to identify groups of individuals with a similar profile using data from the Whitehall II (N = 3\,991\, mean age=69.4years) and UK Biobank (N = 54\,995\, mean age=67.5years) cohort studies. Our analyses identified nine CR clusters: two presented extreme (most robust/poorest) RAR and (highest/lowest) daytime activity\, two robust RAR with opposite sleep profiles (longer and efficient/shorter and fragmented)\, one high-intensity physical activity\, and four poor RAR (one characterised by late chronotype\, two by low activity but opposite sleep profiles\, and one by restless (agitated) sleep). The participants in these nine clusters differed on sociodemographic\, behavioural and health-related factors. Findings were similar in these two independent cohort studies\, highlighting the validity of our approach. Most previous studies have used only the RAR dimension of circadian rhythm\, and here we show that this might be an oversimplification as demonstrated by nine clusters characterised by combinations of RAR\, daytime activity\, sleep\, and chronotype. Our innovative approach demonstrates feasibility of using all dimensions to study the impact of circadian rhythm dysregulation on health.
URL:https://www.ibs.re.kr/bimag/event/circadian-rhythm-profiles-derived-from-accelerometer-measures-of-the-sleep-wake-cycle-in-two-cohort-studies-chitaranjan-mahapatra/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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