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Article ; Online: Role of the proline-rich disordered domain of DROSHA in intronic microRNA processing.

Son, Soomin / Kim, Baekgyu / Yang, Jihye / Kim, V Narry

2023 Volume 37, Issue 9-10, Page(s) 383–397

Abstract: DROSHA serves as a gatekeeper of the microRNA (miRNA) pathway by processing primary transcripts (pri-miRNAs). While the functions of structured domains of DROSHA have been well documented, the contribution of N-terminal proline-rich disordered domain ( ... ...

Abstract	DROSHA serves as a gatekeeper of the microRNA (miRNA) pathway by processing primary transcripts (pri-miRNAs). While the functions of structured domains of DROSHA have been well documented, the contribution of N-terminal proline-rich disordered domain (PRD) remains elusive. Here we show that the PRD promotes the processing of miRNA hairpins located within introns. We identified a DROSHA isoform (p140) lacking the PRD, which is produced by proteolytic cleavage. Small RNA sequencing revealed that p140 is significantly impaired in the maturation of intronic miRNAs. Consistently, our minigene constructs demonstrated that PRD enhances the processing of intronic hairpins, but not those in exons. Splice site mutations did not affect the PRD's enhancing effect on intronic constructs, suggesting that the PRD acts independently of splicing reaction by interacting with sequences residing within introns. The N-terminal regions from zebrafish and
MeSH term(s)	Animals ; Humans ; Introns/genetics ; MicroRNAs/genetics ; MicroRNAs/metabolism ; Proline/genetics ; Proline/metabolism ; Ribonuclease III/genetics ; Ribonuclease III/metabolism ; RNA Processing, Post-Transcriptional ; Zebrafish
Chemical Substances	DROSHA protein, human (EC 3.1.26.3) ; MicroRNAs ; Proline (9DLQ4CIU6V) ; Ribonuclease III (EC 3.1.26.3)
Language	English
Publishing date	2023-05-26
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	806684-x
ISSN	1549-5477 ; 0890-9369
ISSN (online)	1549-5477
ISSN	0890-9369
DOI	10.1101/gad.350275.122
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Zs.A 2292: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MG 54: Show issues

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Article ; Online: Environmentally Robust Triboelectric Tire Monitoring System for Self-Powered Driving Information Recognition via Hybrid Deep Learning in Time-Frequency Representation.

Kim, BaekGyu / Song, Jin Yeong / Kim, Do Young / Cho, Min Woo / Park, Ji Gyo / Choi, Dongwhi / Lee, Chengkuo / Park, Sang Min

Small (Weinheim an der Bergstrasse, Germany)

2024 , Page(s) e2400484

Abstract: Developing a robust artificial intelligence of things (AIoT) system with a self-powered triboelectric sensor for harsh environment is challenging because environmental fluctuations are reflected in triboelectric signals. This study presents an ... ...

Abstract	Developing a robust artificial intelligence of things (AIoT) system with a self-powered triboelectric sensor for harsh environment is challenging because environmental fluctuations are reflected in triboelectric signals. This study presents an environmentally robust triboelectric tire monitoring system with deep learning to capture driving information in the triboelectric signals generated from tire-road friction. The optimization of the process and structure of a laser-induced graphene (LIG) electrode layer in the triboelectric tire is conducted, enabling the tire to detect universal driving information for vehicles/robotic mobility, including rotation speeds of 200-2000 rpm and contact fractions of line. Employing a hybrid model combining short-term Fourier transform with a convolution neural network-long short-term memory, the LIG-based triboelectric tire monitoring (LTTM) system decouples the driving information, such as traffic lines and road states, from varied environmental conditions of humidity (10%-90%) and temperatures (50-70 °C). The real-time line and road state recognition of the LTTM system is confirmed on a mobile platform across diverse environmental conditions, including fog, dampness, intense sunlight, and heat shimmer. This work provides an environmentally robust monitoring AIoT system by introducing a self-powered triboelectric sensor and hybrid deep learning for smart mobility.
Language	English
Publishing date	2024-04-02
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2168935-0
ISSN	1613-6829 ; 1613-6810
ISSN (online)	1613-6829
ISSN	1613-6810
DOI	10.1002/smll.202400484
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Article ; Online: fCLIP-seq for transcriptomic footprinting of dsRNA-binding proteins: Lessons from DROSHA.

Kim, Baekgyu / Kim, V Narry

Methods (San Diego, Calif.)

2018 Volume 152, Page(s) 3–11

Abstract: CLIP-seq (crosslinking immunoprecipitation and sequencing) is widely used to map the binding sites of a protein of interest on the transcriptome, and generally employs UV to induce the covalent bonds between protein and RNA, which allows stringent ... ...

Abstract	CLIP-seq (crosslinking immunoprecipitation and sequencing) is widely used to map the binding sites of a protein of interest on the transcriptome, and generally employs UV to induce the covalent bonds between protein and RNA, which allows stringent washing. However, dsRNA is inefficiently crosslinked by UV, making it difficult to study the interactions between dsRNA binding proteins and their substrates. A dsRNA endoribonuclease DROSHA initiates the maturation of microRNA (miRNA) by cleaving primary miRNA (pri-miRNA). Despite the importance of DROSHA in miRNA maturation and sequence determination, accurate mapping of DROSHA cleavage sites has not been feasible due to rapid processing, modification, and degradation of the cleaved products in cells. Here, we present a high-throughput sequencing method that allows the mapping of in vivo DROSHA cleavage sites at single nucleotide resolution, termed formaldehyde crosslinking, immunoprecipitation, and sequencing (fCLIP-seq). The fCLIP-seq protocol has been improved significantly over the standard CLIP-seq methods by (1) using formaldehyde for efficient and reversible crosslinking, (2) employing polyethylene glycol and adaptors with randomized sequences to enhance ligation efficiency and minimize bias, and (3) performing ligation after elution, which exposes the RNA termini for efficient ligation. fCLIP-seq successfully captures the nascent products of DROSHA, which allows precise mapping of the DROSHA processing sites. Moreover, from the analysis of the distinctive cleavage pattern, we discover previously unknown substrates of DROSHA. fCLIP-seq is a useful tool to obtain transcriptome-wide information on DROSHA activity and can be applied further to investigate other dsRNA-protein interactions.
MeSH term(s)	HEK293 Cells ; HeLa Cells ; Humans ; RNA-Binding Proteins/chemistry ; Ribonuclease III/chemistry ; Ribonuclease III/physiology ; Sequence Analysis, RNA/methods
Chemical Substances	RNA-Binding Proteins ; DROSHA protein, human (EC 3.1.26.3) ; Ribonuclease III (EC 3.1.26.3)
Language	English
Publishing date	2018-06-12
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1066584-5
ISSN	1095-9130 ; 1046-2023
ISSN (online)	1095-9130
ISSN	1046-2023
DOI	10.1016/j.ymeth.2018.06.004
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Zs.A 3239: Show issues

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Jg. 1995 - 2021: Lesesall (2.OG)
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Book ; Online: LEAF + AIO

Wang, Haoxin / Kim, BaekGyu / Xie, Jiang / Han, Zhu

Edge-Assisted Energy-Aware Object Detection for Mobile Augmented Reality

2022

Abstract: Today very few deep learning-based mobile augmented reality (MAR) applications are applied in mobile devices because they are significantly energy-guzzling. In this paper, we design an edge-based energy-aware MAR system that enables MAR devices to ... ...

Abstract	Today very few deep learning-based mobile augmented reality (MAR) applications are applied in mobile devices because they are significantly energy-guzzling. In this paper, we design an edge-based energy-aware MAR system that enables MAR devices to dynamically change their configurations, such as CPU frequency, computation model size, and image offloading frequency based on user preferences, camera sampling rates, and available radio resources. Our proposed dynamic MAR configuration adaptations can minimize the per frame energy consumption of multiple MAR clients without degrading their preferred MAR performance metrics, such as latency and detection accuracy. To thoroughly analyze the interactions among MAR configurations, user preferences, camera sampling rate, and energy consumption, we propose, to the best of our knowledge, the first comprehensive analytical energy model for MAR devices. Based on the proposed analytical model, we design a LEAF optimization algorithm to guide the MAR configuration adaptation and server radio resource allocation. An image offloading frequency orchestrator, coordinating with the LEAF, is developed to adaptively regulate the edge-based object detection invocations and to further improve the energy efficiency of MAR devices. Extensive evaluations are conducted to validate the performance of the proposed analytical model and algorithms. Comment: This is a personal copy of the authors. Not for redistribution. The final version of this paper was accepted by IEEE Transactions on Mobile Computing
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Multimedia ; Computer Science - Networking and Internet Architecture
Subject code	600
Publishing date	2022-05-27
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Genome-wide Mapping of DROSHA Cleavage Sites on Primary MicroRNAs and Noncanonical Substrates.

Kim, Baekgyu / Jeong, Kyowon / Kim, V Narry

Molecular cell

2017 Volume 66, Issue 2, Page(s) 258–269.e5

Abstract: MicroRNA (miRNA) maturation is initiated by DROSHA, a double-stranded RNA (dsRNA)-specific RNase III enzyme. By cleaving primary miRNAs (pri-miRNAs) at specific positions, DROSHA serves as a main determinant of miRNA sequences and a highly selective ... ...

Abstract	MicroRNA (miRNA) maturation is initiated by DROSHA, a double-stranded RNA (dsRNA)-specific RNase III enzyme. By cleaving primary miRNAs (pri-miRNAs) at specific positions, DROSHA serves as a main determinant of miRNA sequences and a highly selective gatekeeper for the canonical miRNA pathway. However, the sites of DROSHA-mediated processing have not been annotated, and it remains unclear to what extent DROSHA functions outside the miRNA pathway. Here, we establish a protocol termed "formaldehyde crosslinking, immunoprecipitation, and sequencing (fCLIP-seq)," which allows identification of DROSHA cleavage sites at single-nucleotide resolution. fCLIP identifies numerous processing sites, suggesting widespread end modifications during miRNA maturation. fCLIP also finds many pri-miRNAs that undergo alternative processing, yielding multiple miRNA isoforms. Moreover, we discovered dozens of DROSHA substrates on non-miRNA loci, which may serve as cis-elements for DROSHA-mediated gene regulation. We anticipate that fCLIP-seq could be a general tool for investigating interactions between dsRNA-binding proteins and structured RNAs.
MeSH term(s)	Base Sequence ; Binding Sites ; Cross-Linking Reagents/chemistry ; Formaldehyde/chemistry ; HEK293 Cells ; HeLa Cells ; High-Throughput Nucleotide Sequencing ; Humans ; Immunoprecipitation ; MicroRNAs/chemistry ; MicroRNAs/genetics ; MicroRNAs/metabolism ; Nucleic Acid Conformation ; Protein Binding ; RNA Interference ; RNA Processing, Post-Transcriptional ; Ribonuclease III/chemistry ; Ribonuclease III/genetics ; Ribonuclease III/metabolism ; Sequence Analysis, RNA/methods ; Structure-Activity Relationship ; Substrate Specificity ; Transfection
Chemical Substances	Cross-Linking Reagents ; MicroRNAs ; Formaldehyde (1HG84L3525) ; DROSHA protein, human (EC 3.1.26.3) ; Ribonuclease III (EC 3.1.26.3)
Language	English
Publishing date	2017-04-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	1415236-8
ISSN	1097-4164 ; 1097-2765
ISSN (online)	1097-4164
ISSN	1097-2765
DOI	10.1016/j.molcel.2017.03.013
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Zs.A 5055: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article: Genome-wide Mapping of DROSHA Cleavage Sites on Primary MicroRNAs and Noncanonical Substrates

Kim, Baekgyu / Kyowon Jeong / V. Narry Kim

Molecular cell. 2017 Apr. 20, v. 66, no. 2

2017

Abstract	MicroRNA (miRNA) maturation is initiated by DROSHA, a double-stranded RNA (dsRNA)-specific RNase III enzyme. By cleaving primary miRNAs (pri-miRNAs) at specific positions, DROSHA serves as a main determinant of miRNA sequences and a highly selective gatekeeper for the canonical miRNA pathway. However, the sites of DROSHA-mediated processing have not been annotated, and it remains unclear to what extent DROSHA functions outside the miRNA pathway. Here, we establish a protocol termed “formaldehyde crosslinking, immunoprecipitation, and sequencing (fCLIP-seq),” which allows identification of DROSHA cleavage sites at single-nucleotide resolution. fCLIP identifies numerous processing sites, suggesting widespread end modifications during miRNA maturation. fCLIP also finds many pri-miRNAs that undergo alternative processing, yielding multiple miRNA isoforms. Moreover, we discovered dozens of DROSHA substrates on non-miRNA loci, which may serve as cis-elements for DROSHA-mediated gene regulation. We anticipate that fCLIP-seq could be a general tool for investigating interactions between dsRNA-binding proteins and structured RNAs.
Keywords	crosslinking ; double-stranded RNA ; formaldehyde ; genes ; loci ; microRNA ; precipitin tests ; proteins ; ribonucleases
Language	English
Dates of publication	2017-0420
Size	p. 258-269.e5.
Publishing place	Elsevier Inc.
Document type	Article
ZDB-ID	1415236-8
ISSN	1097-4164 ; 1097-2765
ISSN (online)	1097-4164
ISSN	1097-2765
DOI	10.1016/j.molcel.2017.03.013
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Energy Drain of the Object Detection Processing Pipeline for Mobile Devices

Wang, Haoxin / Kim, BaekGyu / Xie, Jiang / Han, Zhu

Analysis and Implications

2020

Abstract: Applying deep learning to object detection provides the capability to accurately detect and classify complex objects in the real world. However, currently, few mobile applications use deep learning because such technology is computation-intensive and ... ...

Abstract	Applying deep learning to object detection provides the capability to accurately detect and classify complex objects in the real world. However, currently, few mobile applications use deep learning because such technology is computation-intensive and energy-consuming. This paper, to the best of our knowledge, presents the first detailed experimental study of a mobile augmented reality (AR) client's energy consumption and the detection latency of executing Convolutional Neural Networks (CNN) based object detection, either locally on the smartphone or remotely on an edge server. In order to accurately measure the energy consumption on the smartphone and obtain the breakdown of energy consumed by each phase of the object detection processing pipeline, we propose a new measurement strategy. Our detailed measurements refine the energy analysis of mobile AR clients and reveal several interesting perspectives regarding the energy consumption of executing CNN-based object detection. Furthermore, several insights and research opportunities are proposed based on our experimental results. These findings from our experimental study will guide the design of energy-efficient processing pipeline of CNN-based object detection. Comment: This is a personal copy of the authors. Not for redistribution. The final version of this paper was accepted by IEEE Transactions on Green Communications and Networking
Keywords	Computer Science - Performance ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Multimedia ; Computer Science - Networking and Internet Architecture
Subject code	690
Publishing date	2020-11-25
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Impact of Sleep Disorder as a Risk Factor for Dementia in Men and Women.

Jee, Hye Jin / Shin, Wonseok / Jung, Ho Joong / Kim, Baekgyu / Lee, Bo Kyung / Jung, Yi-Sook

Biomolecules & therapeutics

2019 Volume 28, Issue 1, Page(s) 58–73

Abstract: Sleep is an essential physiological process, especially for proper brain function through the formation of new pathways and processing information and cognition. Therefore, when sleep is insufficient, this can result in pathophysiologic conditions. Sleep ...

Abstract	Sleep is an essential physiological process, especially for proper brain function through the formation of new pathways and processing information and cognition. Therefore, when sleep is insufficient, this can result in pathophysiologic conditions. Sleep deficiency is a risk factor for various conditions, including dementia, diabetes, and obesity. Recent studies have shown that there are differences in the prevalence of sleep disorders between genders. Insomnia, the most common type of sleep disorder, has been reported to have a higher incidence in females than in males. However, sex/gender differences in other sleep disorder subtypes are not thoroughly understood. Currently, increasing evidence suggests that gender issues should be considered important when prescribing medicine. Therefore, an investigation of the gender-dependent differences in sleep disorders is required. In this review, we first describe sex/gender differences not only in the prevalence of sleep disorders by category but in the efficacy of sleep medications. In addition, we summarize sex/gender differences in the impact of sleep disorders on incident dementia. This may help understand gender-dependent pathogenesis of sleep disorders and develop therapeutic strategies in men and women.
Language	English
Publishing date	2019-12-18
Publishing country	Korea (South)
Document type	Journal Article ; Review
ZDB-ID	2734146-X
ISSN	2005-4483 ; 1976-9148
ISSN (online)	2005-4483
ISSN	1976-9148
DOI	10.4062/biomolther.2019.192
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Are Self-Driving Cars Secure? Evasion Attacks against Deep Neural Networks for Steering Angle Prediction

Chernikova, Alesia / Oprea, Alina / Nita-Rotaru, Cristina / Kim, BaekGyu

2019

Abstract: Deep Neural Networks (DNNs) have tremendous potential in advancing the vision for self-driving cars. However, the security of DNN models in this context leads to major safety implications and needs to be better understood. We consider the case study of ... ...

Abstract	Deep Neural Networks (DNNs) have tremendous potential in advancing the vision for self-driving cars. However, the security of DNN models in this context leads to major safety implications and needs to be better understood. We consider the case study of steering angle prediction from camera images, using the dataset from the 2014 Udacity challenge. We demonstrate for the first time adversarial testing-time attacks for this application for both classification and regression settings. We show that minor modifications to the camera image (an L2 distance of 0.82 for one of the considered models) result in mis-classification of an image to any class of attacker's choice. Furthermore, our regression attack results in a significant increase in Mean Square Error (MSE) by a factor of 69 in the worst case. Comment: Preprint of the work accepted for publication at the IEEE Workshop on the Internet of Safe Things, San Francisco, CA, USA, May 23, 2019
Keywords	Computer Science - Machine Learning ; Computer Science - Cryptography and Security ; Statistics - Machine Learning
Subject code	006 ; 380
Publishing date	2019-04-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Runtime-Safety-Guided Policy Repair

Zhou, Weichao / Gao, Ruihan / Kim, BaekGyu / Kang, Eunsuk / Li, Wenchao

2020

Abstract: We study the problem of policy repair for learning-based control policies in safety-critical settings. We consider an architecture where a high-performance learning-based control policy (e.g. one trained as a neural network) is paired with a model-based ... ...

Abstract	We study the problem of policy repair for learning-based control policies in safety-critical settings. We consider an architecture where a high-performance learning-based control policy (e.g. one trained as a neural network) is paired with a model-based safety controller. The safety controller is endowed with the abilities to predict whether the trained policy will lead the system to an unsafe state, and take over control when necessary. While this architecture can provide added safety assurances, intermittent and frequent switching between the trained policy and the safety controller can result in undesirable behaviors and reduced performance. We propose to reduce or even eliminate control switching by `repairing' the trained policy based on runtime data produced by the safety controller in a way that deviates minimally from the original policy. The key idea behind our approach is the formulation of a trajectory optimization problem that allows the joint reasoning of policy update and safety constraints. Experimental results demonstrate that our approach is effective even when the system model in the safety controller is unknown and only approximated.
Keywords	Computer Science - Artificial Intelligence
Subject code	629
Publishing date	2020-08-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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