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METHOD:PUBLISH
X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20240101T000000
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END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250103T140000
DTEND;TZID=Asia/Seoul:20250103T160000
DTSTAMP:20260423T133233
CREATED:20250101T061847Z
LAST-MODIFIED:20250101T061847Z
UID:10503-1735912800-1735920000@www.ibs.re.kr
SUMMARY:Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model - Seokhwan Moon
DESCRIPTION:In this talk\, we discuss the paper “Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model” by F. W. Townes et.al.\, Genome Biology\, 2019. \nAbstract  \nSingle-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls\, we show UMI counts follow multinomial sampling with no zero inflation. Current normalization procedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We propose simple multinomial methods\, including generalized principal component analysis (GLM-PCA) for non-normal distributions\, and feature selection using deviance. These methods outperform the current practice in a downstream clustering assessment using ground truth datasets.
URL:https://www.ibs.re.kr/bimag/event/feature-selection-and-dimension-reduction-for-single-cell-rna-seq-based-on-a-multinomial-model-seokhwan-moon/
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:20250110T140000
DTEND;TZID=Asia/Seoul:20250110T160000
DTSTAMP:20260423T133233
CREATED:20250104T003730Z
LAST-MODIFIED:20250107T122054Z
UID:10529-1736517600-1736524800@www.ibs.re.kr
SUMMARY:CARE as a wearable derived feature linking circadian amplitude to human cognitive functions - Dongju Lim
DESCRIPTION:In this talk\, we discuss the paper “CARE as a wearable derived feature linking circadian amplitude to human cognitive functions” by Shuya Cui et.al.\, npj Digital Medicine\, 2023. \nAbstract \nCircadian rhythms are crucial for regulating physiological and behavioral processes. Pineal hormone melatonin is often used to measure circadian amplitude but its collection is costly and time-consuming. Wearable activity data are promising alternative\, but the most commonly used measure\, relative amplitude\, is subject to behavioral masking. In this study\, we firstly derive a feature named circadian activity rhythm energy (CARE) to better characterize circadian amplitude and validate CARE by correlating it with melatonin amplitude (Pearson’s r = 0.46\, P = 0.007) among 33 healthy participants. Then we investigate its association with cognitive functions in an adolescent dataset (Chinese SCHEDULE-A\, n = 1703) and an adult dataset (UK Biobank\, n = 92\,202)\, and find that CARE is significantly associated with Global Executive Composite (β = 30.86\, P = 0.016) in adolescents\, and reasoning ability\, short-term memory\, and prospective memory (OR = 0.01\, 3.42\, and 11.47 respectively\, all P < 0.001) in adults. Finally\, we identify one genetic locus with 126 CARE-associated SNPs using the genome-wide association study\, of which 109 variants are used as instrumental variables in the Mendelian Randomization analysis\, and the results show a significant causal effect of CARE on reasoning ability\, short-term memory\, and prospective memory (β = -59.91\, 7.94\, and 16.85 respectively\, all P < 0.0001). The present study suggests that CARE is an effective wearable-based metric of circadian amplitude with a strong genetic basis and clinical significance\, and its adoption can facilitate future circadian studies and potential intervention strategies to improve circadian rhythms and cognitive functions.
URL:https://www.ibs.re.kr/bimag/event/mapping-the-physiological-changes-in-sleep-regulation-across-infancy-and-young-childhood-dongju-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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250124T140000
DTEND;TZID=Asia/Seoul:20250124T160000
DTSTAMP:20260423T133233
CREATED:20250104T005711Z
LAST-MODIFIED:20250104T005711Z
UID:10531-1737727200-1737734400@www.ibs.re.kr
SUMMARY:Plausible\, robust biological oscillations through allelic buffering - Eui Min Jeong
DESCRIPTION:In this talk\, we discuss the paper “Plausible\, robust biological oscillations through allelic buffering” by F-S. Hsieh et.al\, Cell Systems\, 2024. at the Journal Club.  \nAbstract \nBiological oscillators can specify time- and dose-dependent functions via dedicated control of their oscillatory dynamics. However\, how biological oscillators\, which recurrently activate noisy biochemical processes\, achieve robust oscillations remains unclear. Here\, we characterize the long-term oscillations of p53 and its negative feedback regulator Mdm2 in single cells after DNA damage. Whereas p53 oscillates regularly\, Mdm2 from a single MDM2 allele exhibits random unresponsiveness to ∼9% of p53 pulses. Using allelic-specific imaging of MDM2 activity\, we show that MDM2 alleles buffer each other to maintain p53 pulse amplitude. Removal of MDM2 allelic buffering cripples the robustness of p53 amplitude\, thereby elevating p21 levels and cell-cycle arrest. In silico simulations support that allelic buffering enhances the robustness of biological oscillators and broadens their plausible biochemical space. Our findings show how allelic buffering ensures robust p53 oscillations\, highlighting the potential importance of allelic buffering for the emergence of robust biological oscillators during evolution. A record of this paper’s transparent peer review process is included in the supplemental information. 
URL:https://www.ibs.re.kr/bimag/event/plausible-robust-biological-oscillations-through-allelic-buffering-eui-min-jeong/
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:20250131T140000
DTEND;TZID=Asia/Seoul:20250131T160000
DTSTAMP:20260423T133233
CREATED:20250126T021153Z
LAST-MODIFIED:20250203T004702Z
UID:10696-1738332000-1738339200@www.ibs.re.kr
SUMMARY:Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality - Yun Min Song
DESCRIPTION:In this talk\, we discuss the paper “Self-supervised learning of accelerometer data provides new insights for sleep and\nits association with mortality” by H. Yuan et.al\, npj digital medicine\, 2024\, at the Journal Club. \nAbstract  \nSleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus\, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion\, 1113 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 48.2 min (95% limits of agreement (LoA): −50.3 to 146.8 min) for total sleep duration\, −17.1 min for REM duration (95% LoA: −56.7 to 91.0 min) and 31.1 min (95% LoA: −67.3 to 129.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with ~100\,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66\,262 UK Biobank participants\, 1644 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration 6–7.9 h\, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.36; 95% confidence intervals (CIs): 1.18 to 1.58) or high sleep efficiency (HRs: 1.29; 95% CIs: 1.04–1.61). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.
URL:https://www.ibs.re.kr/bimag/event/self-supervised-learning-of-accelerometer-data-provides-new-insights-for-sleep-and-its-association-with-mortality-yun-min-song/
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
END:VCALENDAR