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1. Hyperphosphorylation-Induced Phase Transition in Vesicle Delivery Dynamics of Motor Proteins in Neuronal Cells

Author

Lee, Eunsang, Kim, Donghee, Song, Yo Han, Shin, Kyujin, Song, Sanggeun, Lee, Minho, Goh, Yeongchang, Lim, Mi Hee, Kim, Ji-Hyun, Sung, Jaeyoung, and Lee, Kang Taek

Subjects
Physics - Biological Physics
Abstract

Synaptic vesicle transport by motor proteins along microtubules is a crucial active process underlying neuronal communication. It is known that microtubules are destabilized by tau-hyperphosphorylation, which causes tau proteins to detach from microtubules and form neurofibril tangles. However, how tau-phosphorylation affects transport dynamics of motor proteins on the microtubule remains unknown. Here, we discover that long-distance unidirectional motion of vesicle-motor protein multiplexes (VMPMs) in living cells is suppressed under tau-hyperphosphorylation, with the consequent loss of fast vesicle-transport along the microtubule. The VMPMs in hyperphosphorylated cells exhibit seemingly bidirectional random motion, with dynamic properties far different from VMPM motion in normal cells. We establish a parsimonious physicochemical model of VMPM's active motion that provides a unified, quantitative explanation and predictions for our experimental results. Our analysis reveals that, under hyperphosphorylation conditions, motor-protein-multiplexes have both static and dynamic motility fluctuations. The loss of the fast vesicle-transport along the microtubule can be a mechanism of neurodegenerative disorders associated with tau-hyperphosphorylation.

Published
2024
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2. CNG-SFDA: Clean-and-Noisy Region Guided Online-Offline Source-Free Domain Adaptation

Author

Cho, Hyeonwoo, Park, Chanmin, Kim, Donghee, Kim, Jinyoung, and Kim, Won Hwa

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Domain shift occurs when training (source) and test (target) data diverge in their distribution. Source-Free Domain Adaptation (SFDA) addresses this domain shift problem, aiming to adopt a trained model on the source domain to the target domain in a scenario where only a well-trained source model and unlabeled target data are available. In this scenario, handling false labels in the target domain is crucial because they negatively impact the model performance. To deal with this problem, we propose to update cluster prototypes (i.e., centroid of each sample cluster) and their structure in the target domain formulated by the source model in online manners. In the feature space, samples in different regions have different pseudo-label distribution characteristics affected by the cluster prototypes, and we adopt distinct training strategies for these samples by defining clean and noisy regions: we selectively train the target with clean pseudo-labels in the clean region, whereas we introduce mix-up inputs representing intermediate features between clean and noisy regions to increase the compactness of the cluster. We conducted extensive experiments on multiple datasets in online/offline SFDA settings, whose results demonstrate that our method, CNG-SFDA, achieves state-of-the-art for most cases., Comment: 15 pages, 5 figures

Published
2024

4. Real-Time Tracking of Vesicles in Living Cells Reveals That Tau-Hyperphosphorylation Suppresses Unidirectional Transport by Motor Proteins

Author

Lee, Eunsang, Kim, Donghee, Song, Yo Han, Shin, Kyujin, Song, Sanggeun, Lee, Minho, Goh, Yeongchang, Lim, Mi Hee, Kim, Ji-Hyun, Sung, Jaeyoung, and Lee, Kang Taek

Subjects
Biochemistry and Cell Biology, Biological Sciences, Neurodegenerative, Dementia, Neurosciences, Aging, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Brain Disorders, Acquired Cognitive Impairment, Underpinning research, 1.1 Normal biological development and functioning, Neurological
Abstract

Synaptic vesicle transport by motor proteins along microtubules is a crucially active process underlying neuronal communication. It is known that microtubules are destabilized by tau-hyperphosphorylation, which causes tau proteins to detach from microtubules and form neurofibril tangles. However, how tau-phosphorylation affects the transport dynamics of motor proteins on the microtubule remains unknown. Here, we discover that the long-distance unidirectional motion of vesicle-motor protein multiplexes (VMPMs) in living cells is suppressed under tau-hyperphosphorylation, with the consequent loss of fast vesicle-transport along the microtubule. The VMPMs in hyperphosphorylated cells exhibit seemingly bidirectional random motion, with dynamic properties far different from those of VMPM motion in normal cells. We establish a parsimonious physicochemical model of VMPM’s active motion that provides a unified, quantitative explanation and predictions for our experimental results. Our analysis reveals that, under hyperphosphorylation conditions, motor protein multiplexes have both static and dynamic motility fluctuations. The loss of fast vesicle-transport along the microtubule can be a mechanism of neurodegenerative disorders associated with tau-hyperphosphorylation.

Published
2024

8. Disparities in metabolic dysfunction-associated steatotic liver disease and cardiometabolic conditions in low and lower middle-income countries: a systematic analysis from the global burden of disease study 2019

Author

Danpanichkul, Pojsakorn, Suparan, Kanokphong, Dutta, Priyata, Kaeosri, Chuthathip, Sukphutanan, Banthoon, Pang, Yanfang, Kulthamrongsri, Narathorn, Jaisa-aad, Methasit, Ng, Cheng Han, Teng, Margaret, Nakano, Masahito, Morishita, Asahiro, Alkhouri, Naim, Yang, Ju Dong, Chen, Vincent L., Kim, Donghee, Fallon, Michael B., Diaz, Luis Antonio, Arab, Juan Pablo, Mantzoros, Christos S., Noureddin, Mazen, Lazarus, Jeffrey V., and Wijarnpreecha, Karn

Published
2024
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10. A Daily Tourism Demand Prediction Framework Based on Multi-head Attention CNN: The Case of The Foreign Entrant in South Korea

Author

Kim, Dong-Keon, Shyn, Sung Kuk, Kim, Donghee, Jang, Seungwoo, and Kim, Kwangsu

Subjects
Computer Science - Machine Learning
Abstract

Developing an accurate tourism forecasting model is essential for making desirable policy decisions for tourism management. Early studies on tourism management focus on discovering external factors related to tourism demand. Recent studies utilize deep learning in demand forecasting along with these external factors. They mainly use recursive neural network models such as LSTM and RNN for their frameworks. However, these models are not suitable for use in forecasting tourism demand. This is because tourism demand is strongly affected by changes in various external factors, and recursive neural network models have limitations in handling these multivariate inputs. We propose a multi-head attention CNN model (MHAC) for addressing these limitations. The MHAC uses 1D-convolutional neural network to analyze temporal patterns and the attention mechanism to reflect correlations between input variables. This model makes it possible to extract spatiotemporal characteristics from time-series data of various variables. We apply our forecasting framework to predict inbound tourist changes in South Korea by considering external factors such as politics, disease, season, and attraction of Korean culture. The performance results of extensive experiments show that our method outperforms other deep-learning-based prediction frameworks in South Korea tourism forecasting., Comment: Accepted to IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021)

Published
2021

11. Metabolic Syndrome and Metabolic Dysfunction-Associated Steatotic Liver Disease in Premenopausal Women: Global Trends and Projections to 2040

Author

Danpanichkul, Pojsakorn, Ng, Cheng Han, Muthiah, Mark D., Duangsonk, Kwanjit, Kongarin, Siwanart, Srisurapanont, Karan, Pingwang, Phakkamon, Songmueang, Nonlawan, Nonthasoot, Chalida, Manosroi, Worapaka, Nathisuwan, Surakit, Li, Fang, Yang, Ju Dong, Chen, Vincent L., Kim, Donghee, Noureddin, Mazen, Huang, Daniel Q., and Wijarnpreecha, Karn

Published
2024
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16. FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning

Author

Shyn, Sung Kuk, Kim, Donghee, and Kim, Kwangsu

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Computer Science and Game Theory
Abstract

Client contribution evaluation, also known as data valuation, is a crucial approach in federated learning(FL) for client selection and incentive allocation. However, due to restrictions of accessibility of raw data, only limited information such as local weights and local data size of each client is open for quantifying the client contribution. Using data size from available information, we introduce an empirical evaluation method called Federated Client Contribution Evaluation through Accuracy Approximation(FedCCEA). This method builds the Accuracy Approximation Model(AAM), which estimates a simulated test accuracy using inputs of sampled data size and extracts the clients' data quality and data size to measure client contribution. FedCCEA strengthens some advantages: (1) enablement of data size selection to the clients, (2) feasible evaluation time regardless of the number of clients, and (3) precise estimation in non-IID settings. We demonstrate the superiority of FedCCEA compared to previous methods through several experiments: client contribution distribution, client removal, and robustness test to partial participation.

Published
2021

17. Predictors of Outcomes of COVID-19 in Patients With Chronic Liver Disease: US Multi-center Study

Author

Kim, Donghee, Adeniji, Nia, Latt, Nyann, Kumar, Sonal, Bloom, Patricia P, Aby, Elizabeth S, Perumalswami, Ponni, Roytman, Marina, Li, Michael, Vogel, Alexander S, Catana, Andreea M, Wegermann, Kara, Carr, Rotonya M, Aloman, Costica, Chen, Vincent L, Rabiee, Atoosa, Sadowski, Brett, Nguyen, Veronica, Dunn, Winston, Chavin, Kenneth D, Zhou, Kali, Lizaola-Mayo, Blanca, Moghe, Akshata, Debes, José, Lee, Tzu-Hao, Branch, Andrea D, Viveiros, Kathleen, Chan, Walter, Chascsa, David M, Kwo, Paul, and Dhanasekaran, Renumathy

Subjects
Clinical Research, Liver Disease, Liver Cancer, Digestive Diseases, Chronic Liver Disease and Cirrhosis, Cancer, Prevention, Substance Misuse, Alcoholism, Alcohol Use and Health, Rare Diseases, Good Health and Well Being, Adult, Aged, COVID-19, COVID-19 Testing, Carcinoma, Hepatocellular, Female, Humans, Liver Cirrhosis, Liver Neoplasms, Male, Middle Aged, Risk Factors, United States, COVID-19 Drug Treatment, Cirrhosis, Alcohol, Mortality, Clinical Sciences, Gastroenterology & Hepatology
Abstract

Background & aimsChronic liver disease (CLD) represents a major global health burden. We undertook this study to identify the factors associated with adverse outcomes in patients with CLD who acquire the novel coronavirus-2019 (COVID-19).MethodsWe conducted a multi-center, observational cohort study across 21 institutions in the United States (US) of adult patients with CLD and laboratory-confirmed diagnosis of COVID-19 between March 1, 2020 and May 30, 2020. We performed survival analysis to identify independent predictors of all-cause mortality and COVID-19 related mortality, and multivariate logistic regression to determine the risk of severe COVID-19 in patients with CLD.ResultsOf the 978 patients in our cohort, 867 patients (mean age 56.9 ± 14.5 years, 55% male) met inclusion criteria. The overall all-cause mortality was 14.0% (n = 121), and 61.7% (n = 535) had severe COVID-19. Patients presenting with diarrhea or nausea/vomiting were more likely to have severe COVID-19. The liver-specific factors associated with independent risk of higher overall mortality were alcohol-related liver disease (ALD) (hazard ratio [HR] 2.42, 95% confidence interval [CI] 1.29-4.55), decompensated cirrhosis (HR 2.91 [1.70-5.00]) and hepatocellular carcinoma (HCC) (HR 3.31 [1.53-7.16]). Other factors were increasing age, diabetes, hypertension, chronic obstructive pulmonary disease and current smoker. Hispanic ethnicity (odds ratio [OR] 2.33 [1.47-3.70]) and decompensated cirrhosis (OR 2.50 [1.20-5.21]) were independently associated with risk for severe COVID-19.ConclusionsThe risk factors which predict higher overall mortality among patients with CLD and COVID-19 are ALD, decompensated cirrhosis and HCC. Hispanic ethnicity and decompensated cirrhosis are associated with severe COVID-19. Our results will enable risk stratification and personalization of the management of patients with CLD and COVID-19. Clinicaltrials.gov number NCT04439084.

Published
2021

21. Liver Injury in Liver Transplant Recipients With Coronavirus Disease 2019 (COVID‐19): U.S. Multicenter Experience

Author

Rabiee, Atoosa, Sadowski, Brett, Adeniji, Nia, Perumalswami, Ponni V, Nguyen, Veronica, Moghe, Akshata, Latt, Nyan L, Kumar, Sonal, Aloman, Costica, Catana, Andreea M, Bloom, Patricia P, Chavin, Kenneth D, Carr, Rotonya M, Dunn, Winston, Chen, Vincent L, Aby, Elizabeth S, Debes, Jose D, Dhanasekaran, Renumathy, Kim, Donghee, Roytman, Marina, Viveiros, Kathleen, Chan, Walter, Li, Michael, Vogel, Alexander, Wegerman, Kara, Lee, Tzu‐Hao, and Zhou, Kali

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Genetics, Transplantation, Clinical Research, Organ Transplantation, Clinical Trials and Supportive Activities, Prevention, Liver Disease, Digestive Diseases, Oral and gastrointestinal, Good Health and Well Being, Acute Lung Injury, Adult, Aged, Aged, 80 and over, Alanine Transaminase, COVID-19, Cohort Studies, Female, Humans, Immunosuppression Therapy, Liver Transplantation, Logistic Models, Male, Middle Aged, SARS-CoV-2, COLD Consortium, Medical Biochemistry and Metabolomics, Immunology, Gastroenterology & Hepatology, Clinical sciences
Abstract

Background and aimsCoronavirus disease 2019 (COVID-19) is associated with liver injury, but the prevalence and patterns of liver injury in liver transplantation (LT) recipients with COVID-19 are open for study.Approach and resultsWe conducted a multicenter study in the United States of 112 adult LT recipients with COVID-19. Median age was 61 years (interquartile range, 20), 54.5% (n = 61) were male, and 39.3% (n = 44) Hispanic. Mortality rate was 22.3% (n = 25); 72.3% (n = 81) were hospitalized and 26.8% (n = 30) admitted to the intensive care unit (ICU). Analysis of peak values of alanine aminotransferase (ALT) during COVID-19 showed moderate liver injury (ALT 2-5× upper limit of normal [ULN]) in 22.2% (n = 18) and severe liver injury (ALT > 5× ULN) in 12.3% (n = 10). Compared to age- and sex-matched nontransplant patients with chronic liver disease and COVID-19 (n = 375), incidence of acute liver injury was lower in LT recipients (47.5% vs. 34.6%; P = 0.037). Variables associated with liver injury in LT recipients were younger age (P = 0.009; odds ratio [OR], 2.06; 95% confidence interval [CI], 1.20-3.54), Hispanic ethnicity (P = 0.011; OR, 6.01; 95% CI, 1.51-23.9), metabolic syndrome (P = 0.016; OR, 5.87; 95% CI, 1.38-24.99), vasopressor use (P = 0.018; OR, 7.34; 95% CI, 1.39-38.52), and antibiotic use (P = 0.046; OR, 6.93; 95% CI, 1.04-46.26). Reduction in immunosuppression (49.4%) was not associated with liver injury (P = 0.156) or mortality (P = 0.084). Liver injury during COVID-19 was significantly associated with mortality (P = 0.007; OR, 6.91; 95% CI, 1.68-28.48) and ICU admission (P = 0.007; OR, 7.93; 95% CI, 1.75-35.69) in LT recipients.ConclusionsLiver injury is associated with higher mortality and ICU admission in LT recipients with COVID-19. Hence, monitoring liver enzymes closely can help in early identification of patients at risk for adverse outcomes. Reduction of immunosuppression during COVID-19 did not increase risk for mortality or graft failure.

Published
2020

31. SAT-278 FIB-4 score as a predictor of mortality outcomes in individuals with metabolic dysfunction-associated steatotic liver disease with moderate alcohol consumption: a nationwide cohort study

Author

Aboona, Majd, primary, Danpanichkul, Pojsakorn, additional, Chen, Vincent, additional, Rangan, Pooja, additional, Kim, Donghee, additional, Alkhouri, Naim, additional, Noureddin, Mazen, additional, Arab, Juan Pablo, additional, and Wijarnpreecha, Karn, additional

Published
2024
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32. FRI-236-YI Racial disparities in incidence of cirrhosis and extrahepatic manifestations in metabolic dysfunction-associated steatotic liver disease

Author

Aboona, Majd, primary, Ghani, Leith, additional, Rangan, Pooja, additional, Chen, Vincent, additional, Ng, Cheng Han, additional, Huang, Daniel, additional, Muthiah, Mark, additional, Kim, Donghee, additional, Rubin, Moises Nevah, additional, Han, Ma Ai Thanda, additional, Fallon, Michael, additional, and Wijarnpreecha, Karn, additional

Published
2024
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39. Incidence of liver cancer in young adults according to the global burden of disease database 2019

Author

Danpanichkul, Pojsakorn, primary, Aboona, Majd B., additional, Sukphutanan, Banthoon, additional, Kongarin, Siwanart, additional, Duangsonk, Kwanjit, additional, Ng, Cheng Han, additional, Muthiah, Mark D., additional, Huang, Daniel Q., additional, Seko, Yuya, additional, Díaz, Luis Antonio, additional, Arab, Juan Pablo, additional, Yang, Ju Dong, additional, Chen, Vincent L., additional, Kim, Donghee, additional, Noureddin, Mazen, additional, Liangpunsakul, Suthat, additional, and Wijarnpreecha, Karn, additional

Published
2024
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49. Canine Behavior Interpretation Framework Using Deep Graph Model

Author

Lim, Jongmin, Kim, Donghee, Kim, Kwangsu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rutkowski, Leszek, editor, Scherer, Rafał, editor, Korytkowski, Marcin, editor, Pedrycz, Witold, editor, Tadeusiewicz, Ryszard, editor, and Zurada, Jacek M., editor

Published
2021
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