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Article ; Online: Effects of sleep quality on diurnal variation of brain volume in older adults: A retrospective cross-sectional study.

Kim, Jun Sung / Han, Ji Won / Oh, Dae Jong / Suh, Seung Wan / Kwon, Min Jeong / Park, Jieun / Jo, Sungman / Kim, Jae Hyoung / Kim, Ki Woong

NeuroImage

2024 Volume 288, Page(s) 120533

Abstract: Aim: Brain volume is influenced by several factors that can change throughout the day. In addition, most of these factors are influenced by sleep quality. This study investigated diurnal variation in brain volume and its relation to overnight sleep ... ...

Abstract	Aim: Brain volume is influenced by several factors that can change throughout the day. In addition, most of these factors are influenced by sleep quality. This study investigated diurnal variation in brain volume and its relation to overnight sleep quality. Methods: We enrolled 1,003 healthy Koreans without any psychiatric disorders aged 60 years or older. We assessed sleep quality and average wake time using the Pittsburgh Sleep Quality Index, and divided sleep quality into good, moderate, and poor groups. We estimated the whole and regional brain volumes from three-dimensional T1-weighted brain MRI scans. We divided the interval between average wake-up time and MRI acquisition time (INT) into tertile groups: short (INT Results: Whole and regional brain volumes showed no significance with respect to INT. However, the `interaction between INT and sleep quality showed significance for whole brain, cerebral gray matter, and cerebrospinal fluid volumes (p < .05). The INT Conclusion: Human brain volume changes significantly within a day associated with overnight sleep in the individuals with good sleep quality.
MeSH term(s)	Humans ; Aged ; Cross-Sectional Studies ; Sleep Quality ; Retrospective Studies ; Brain/diagnostic imaging ; Gray Matter/diagnostic imaging ; Magnetic Resonance Imaging/methods
Language	English
Publishing date	2024-02-08
Publishing country	United States
Document type	Journal Article
ZDB-ID	1147767-2
ISSN	1095-9572 ; 1053-8119
ISSN (online)	1095-9572
ISSN	1053-8119
DOI	10.1016/j.neuroimage.2024.120533
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Article: Effect of type of coagulants on removal efficiency and removal mechanisms of antibiotic resistance genes in anaerobic digestion of primary sludge produced via a chemically enhanced primary treatment process

Mezmir Damtie, Mekdimu / Shin, Jingyeong / Lee, Sungman / Min Park, Chang / Wang, Jinhua / Mo Kim, Young

Bioresource technology. 2022 Feb., v. 346

2022

Abstract: The potential impact of the trivalent coagulant cations on the removal mechanisms, removal efficiencies and removal patterns of antibiotic resistance genes (ARGs) during anaerobic digestion (AD) of chemically enhanced primary treatment sludge (CEPTS) was ...

Abstract	The potential impact of the trivalent coagulant cations on the removal mechanisms, removal efficiencies and removal patterns of antibiotic resistance genes (ARGs) during anaerobic digestion (AD) of chemically enhanced primary treatment sludge (CEPTS) was investigated using polyaluminium chloride (PACl), ferric chloride (FeCl₃) and mixed FeCl₃-PACl. The removal efficiency of 23 ARGs and intI1 improved to 72.1% in AD of primary sludge with 100 mg/L FeCl₃ and was lowest (only 54.4 %) in AD of primary sludge with 25 mg/L PACl. The removal of ARGs in AD of CEPTS with addition of single or mixed types of Al-based coagulant began to increase rapidly at the onset of batch operation. On the other hand, both the rapid increase in the removal efficiency of ARGs in AD with FeCl₃ and the maximum removal efficiency were attained later than in the other ADs.
Keywords	anaerobic digestion ; antibiotic resistance ; coagulants ; ferric chloride ; sludge
Language	English
Dates of publication	2022-02
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	1065195-0
ISSN	1873-2976 ; 0960-8524
ISSN (online)	1873-2976
ISSN	0960-8524
DOI	10.1016/j.biortech.2021.126599
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Article ; Online: Deep reinforcement learning in an ultrafiltration system: Optimizing operating pressure and chemical cleaning conditions.

Park, Sanghun / Shim, Jaegyu / Yoon, Nakyung / Lee, Sungman / Kwak, Donggeun / Lee, Seungyong / Kim, Young Mo / Son, Moon / Cho, Kyung Hwa

Chemosphere

2022 Volume 308, Issue Pt 2, Page(s) 136364

Abstract: Enhancing engineering efficiency and reducing operating costs are permanent subjects that face all engineers over the world. To effectively improve the performance of filtration systems, it is necessary to determine an optimal operating condition beyond ... ...

Abstract	Enhancing engineering efficiency and reducing operating costs are permanent subjects that face all engineers over the world. To effectively improve the performance of filtration systems, it is necessary to determine an optimal operating condition beyond conventional methods of periodic and empirical operation. Herein, this paper proposes an effective approach to finding an optimal operating strategy using deep reinforcement learning (DRL), particularly for an ultrafiltration (UF) system. Deep learning was developed to represent the UF system utilizing a long-short term memory and provided an environment for DRL. DRL was designed to control three actions; operating pressure, cleaning time, and cleaning concentration. Ultimately, DRL proposed the UF system to actively change the operating pressure and cleaning conditions over time toward better water productivity and operating efficiency. DRL denoted ∼20.9% of specific energy consumption can be reduced by increasing average water flux (39.5-43.7 L m
MeSH term(s)	Filtration ; Humans ; Membranes, Artificial ; Ultrafiltration/methods ; Water ; Water Purification/methods
Chemical Substances	Membranes, Artificial ; Water (059QF0KO0R)
Language	English
Publishing date	2022-09-07
Publishing country	England
Document type	Journal Article
ZDB-ID	120089-6
ISSN	1879-1298 ; 0045-6535 ; 0366-7111
ISSN (online)	1879-1298
ISSN	0045-6535 ; 0366-7111
DOI	10.1016/j.chemosphere.2022.136364
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Article ; Online: Textural and Volumetric Changes of the Temporal Lobes in Semantic Variant Primary Progressive Aphasia and Alzheimer's Disease.

Kwon, Min Jeong / Lee, Subin / Park, Jieun / Jo, Sungman / Han, Ji Won / Oh, Dae Jong / Lee, Jun-Young / Park, Joon Hyuk / Kim, Jae Hyoung / Kim, Ki Woong

Journal of Korean medical science

2023 Volume 38, Issue 41, Page(s) e316

Abstract: Background: Texture analysis may capture subtle changes in the gray matter more sensitively than volumetric analysis. We aimed to investigate the patterns of neurodegeneration in semantic variant primary progressive aphasia (svPPA) and Alzheimer's ... ...

Abstract	Background: Texture analysis may capture subtle changes in the gray matter more sensitively than volumetric analysis. We aimed to investigate the patterns of neurodegeneration in semantic variant primary progressive aphasia (svPPA) and Alzheimer's disease (AD) by comparing the temporal gray matter texture and volume between cognitively normal controls and older adults with svPPA and AD. Methods: We enrolled all participants from three university hospitals in Korea. We obtained T1-weighted magnetic resonance images and compared the gray matter texture and volume of regions of interest (ROIs) between the groups using analysis of variance with Bonferroni posthoc comparisons. We also developed models for classifying svPPA, AD and control groups using logistic regression analyses, and validated the models using receiver operator characteristics analysis. Results: Compared to the AD group, the svPPA group showed lower volumes in five ROIs (bilateral temporal poles, and the left inferior, middle, and superior temporal cortices) and higher texture in these five ROIs and two additional ROIs (right inferior temporal and left entorhinal cortices). The performances of both texture- and volume-based models were good and comparable in classifying svPPA from normal cognition (mean area under the curve [AUC] = 0.914 for texture; mean AUC = 0.894 for volume). However, only the texture-based model achieved a good level of performance in classifying svPPA and AD (mean AUC = 0.775 for texture; mean AUC = 0.658 for volume). Conclusion: Texture may be a useful neuroimaging marker for early detection of svPPA in older adults and its differentiation from AD.
MeSH term(s)	Humans ; Aged ; Alzheimer Disease/diagnosis ; Semantics ; Aphasia, Primary Progressive/diagnostic imaging ; Brain/diagnostic imaging ; Temporal Lobe/diagnostic imaging ; Magnetic Resonance Imaging
Language	English
Publishing date	2023-10-23
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	639262-3
ISSN	1598-6357 ; 1011-8934
ISSN (online)	1598-6357
ISSN	1011-8934
DOI	10.3346/jkms.2023.38.e316
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Article ; Online: Effect of type of coagulants on removal efficiency and removal mechanisms of antibiotic resistance genes in anaerobic digestion of primary sludge produced via a chemically enhanced primary treatment process.

Mezmir Damtie, Mekdimu / Shin, Jingyeong / Lee, Sungman / Min Park, Chang / Wang, Jinhua / Mo Kim, Young

Bioresource technology

2021 Volume 346, Page(s) 126599

Abstract	The potential impact of the trivalent coagulant cations on the removal mechanisms, removal efficiencies and removal patterns of antibiotic resistance genes (ARGs) during anaerobic digestion (AD) of chemically enhanced primary treatment sludge (CEPTS) was investigated using polyaluminium chloride (PACl), ferric chloride (FeCl
MeSH term(s)	Anaerobiosis ; Anti-Bacterial Agents/pharmacology ; Drug Resistance, Microbial ; Genes, Bacterial ; Sewage ; Waste Water
Chemical Substances	Anti-Bacterial Agents ; Sewage ; Waste Water
Language	English
Publishing date	2021-12-23
Publishing country	England
Document type	Journal Article
ZDB-ID	1065195-0
ISSN	1873-2976 ; 0960-8524
ISSN (online)	1873-2976
ISSN	0960-8524
DOI	10.1016/j.biortech.2021.126599
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Article: Deep reinforcement learning in an ultrafiltration system: Optimizing operating pressure and chemical cleaning conditions

Park, Sanghun / Shim, Jaegyu / Yoon, Nakyung / Lee, Sungman / Kwak, Donggeun / Lee, Seungyong / Kim, Young Mo / Son, Moon / Cho, Kyung Hwa

Chemosphere. 2022 Dec., v. 308

2022

Abstract	Enhancing engineering efficiency and reducing operating costs are permanent subjects that face all engineers over the world. To effectively improve the performance of filtration systems, it is necessary to determine an optimal operating condition beyond conventional methods of periodic and empirical operation. Herein, this paper proposes an effective approach to finding an optimal operating strategy using deep reinforcement learning (DRL), particularly for an ultrafiltration (UF) system. Deep learning was developed to represent the UF system utilizing a long-short term memory and provided an environment for DRL. DRL was designed to control three actions; operating pressure, cleaning time, and cleaning concentration. Ultimately, DRL proposed the UF system to actively change the operating pressure and cleaning conditions over time toward better water productivity and operating efficiency. DRL denoted ∼20.9% of specific energy consumption can be reduced by increasing average water flux (39.5–43.7 L m⁻² h⁻¹) and reducing operating pressure (0.617–0.540 bar). Moreover, the optimal action of DRL was reasonable to achieve better performance beyond the conventional operation. Crucially, this study demonstrated that due to the nature of DRL, the approach is tractable for engineering systems that have structurally complex relationships among operating conditions and resultants.
Keywords	neural networks ; specific energy ; ultrafiltration
Language	English
Dates of publication	2022-12
Publishing place	Elsevier Ltd
Document type	Article
ZDB-ID	120089-6
ISSN	1879-1298 ; 0045-6535 ; 0366-7111
ISSN (online)	1879-1298
ISSN	0045-6535 ; 0366-7111
DOI	10.1016/j.chemosphere.2022.136364
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Identification of Akt interaction protein PHF20/TZP that transcriptionally regulates p53.

Park, Sungman / Kim, Donghwa / Dan, Han C / Chen, Huihua / Testa, Joseph R / Cheng, Jin Q

The Journal of biological chemistry

2016 Volume 291, Issue 43, Page(s) 22852

Language	English
Publishing date	2016-11-07
Publishing country	United States
Document type	Journal Article ; Retraction of Publication
ZDB-ID	2997-x
ISSN	1083-351X ; 0021-9258
ISSN (online)	1083-351X
ISSN	0021-9258
DOI	10.1074/jbc.A111.333922
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Article ; Online: Compassionate use of hzVSF-v13 in two patients with severe COVID-19.

Kang, Chang Kyung / Choe, Pyoeng Gyun / Park, Sungman / Kim, Taek Soo / Seong, Moon-Woo / Kim, Nam-Joong / Oh, Myoung-Don / Park, Wan Beom / Kim, Yoon-Won

Journal of medical virology

2020 Volume 92, Issue 11, Page(s) 2371–2373

MeSH term(s)	Adult ; Aged, 80 and over ; Antibodies, Monoclonal/therapeutic use ; Antiviral Agents/therapeutic use ; COVID-19/immunology ; COVID-19/therapy ; Compassionate Use Trials ; Humans ; Immunization, Passive ; Immunoglobulin G/therapeutic use ; Male ; Republic of Korea ; Treatment Outcome
Chemical Substances	Antibodies, Monoclonal ; Antiviral Agents ; Immunoglobulin G
Keywords	covid19
Language	English
Publishing date	2020-06-03
Publishing country	United States
Document type	Case Reports ; Letter
ZDB-ID	752392-0
ISSN	1096-9071 ; 0146-6615
ISSN (online)	1096-9071
ISSN	0146-6615
DOI	10.1002/jmv.26063
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Article: Compassionate use of hzVSF-v13 in two patients with severe COVID-19

Kang, Chang Kyung / Choe, Pyoeng Gyun / Park, Sungman / Kim, Taek Soo / Seong, Moon-Woo / Kim, Nam-Joong / Oh, Myoung-Don / Park, Wan Beom / Kim, Yoon-Won

J. med. virol

Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #378302
Database	COVID19

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Article ; Online: An Open Medical Platform to Share Source Code and Various Pre-Trained Weights for Models to Use in Deep Learning Research.

Kim, Sungchul / Cho, Sungman / Cho, Kyungjin / Seo, Jiyeon / Nam, Yujin / Park, Jooyoung / Kim, Kyuri / Kim, Daeun / Hwang, Jeongeun / Yun, Jihye / Jang, Miso / Lee, Hyunna / Kim, Namkug

Korean journal of radiology

2021 Volume 22, Issue 12, Page(s) 2073–2081

Abstract: Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by ... ...

Abstract	Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by simultaneously playing pioneering and gatekeeping roles. The application of deep learning technology in medicine is sometimes restricted by ethical or legal issues, including patient privacy and confidentiality, data ownership, and limitations in patient agreement. In this paper, we present an open platform, MI2RLNet, for sharing source code and various pre-trained weights for models to use in downstream tasks, including education, application, and transfer learning, to encourage deep learning research in radiology. In addition, we describe how to use this open platform in the GitHub environment. Our source code and models may contribute to further deep learning research in radiology, which may facilitate applications in medicine and healthcare, especially in medical imaging, in the near future. All code is available at https://github.com/mi2rl/MI2RLNet.
MeSH term(s)	Databases, Factual ; Deep Learning ; Humans ; Radiologists ; Radiology ; Software
Language	English
Publishing date	2021-10-26
Publishing country	Korea (South)
Document type	Journal Article ; Review
ZDB-ID	2046981-0
ISSN	2005-8330 ; 1229-6929
ISSN (online)	2005-8330
ISSN	1229-6929
DOI	10.3348/kjr.2021.0170
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