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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
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260508T100000
DTEND;TZID=Asia/Seoul:20260508T120000
DTSTAMP:20260422T104830
CREATED:20260406T041825Z
LAST-MODIFIED:20260406T060631Z
UID:12360-1778234400-1778241600@www.ibs.re.kr
SUMMARY:Digital biomarkers for brain health: passive and continuous assessment from wearable sensors - Myna Lim
DESCRIPTION:In this tallk\, we discuss the paper “Digital biomarkers for brain health: passive and continuous assessment from wearable sensors” by Igor Matias et al.\, npj digital medicine\, 2026. \nAbstract\nContinuous and scalable monitoring of cognition and affective states is critical for the early detection of brain health\, which is currently limited by the burden of active assessments. This study investigated the potential of consumer-grade wearable and mobile technologies to passively predict 21 cognitive and mental health outcomes in real-world conditions. We collected data from 82 cognitively healthy adults\, including passively measured behaviour\, physiology\, and environmental exposures longitudinally\, for 10 months. Active data were gathered in four waves using validated patient- and performance-reported outcomes. Data quality assurance involved a data filtering resulting in average wearable data coverage of 96% per day. Artificial Intelligence-powered prediction was applied\, and performance was assessed using subject- and wave-dependent cross-validation. Cognitive and affective outcomes were predicted with low scaled errors. Patient-reported outcomes were more predictable than performance-based ones. Environmental and physiological metrics emerged as the most informative predictors. Passive multimodal data captured meaningful variability in cognition and affect\, demonstrating the feasibility of low-burden\, scalable approaches to continuous brain-health monitoring. Feature-importance analyses suggested that environmental exposures better explained inter-individual differences\, whereas physiological and behavioural rhythms captured within-person changes. These findings highlight the potential of everyday technologies for population-level tracking of brain-health and deviations from expected trajectories.
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:20260422T104830
CREATED:20260403T080250Z
LAST-MODIFIED:20260406T060706Z
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 tallk\, 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
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