期刊文献+
共找到5篇文章
< 1 >
每页显示 20 50 100
The LBD11-ROS feedback regulatory loop modulates vascular cambium proliferation and secondary growth in Arabidopsis 被引量:2
1
作者 Tuong Vi T.Dang seungchul lee +2 位作者 Hyunwoo Cho Kyuha Choi IIdoo Hwang 《Molecular Plant》 SCIE CSCD 2023年第7期1131-1145,共15页
Vascular cambium produces the phloem and xylem,vascular tissues that transport resources and provide mechanical support,making it an ideal target for crop improvement.However,much remains unknown about how vascular ca... Vascular cambium produces the phloem and xylem,vascular tissues that transport resources and provide mechanical support,making it an ideal target for crop improvement.However,much remains unknown about how vascular cambium proliferates.In this study,through pharmaceutical and genetic manipulation of reactive oxygen species(ROS)maxima,we demonstrate a direct link between levels of ROS and activity of LATERAL ORGAN BOUNDARIES DOMAIN 11(LBD11)in maintaining vascular cambium activity.LBD11 activates the transcriptionof several keyROS metabolic genes,including PEROXIDASE71and RESPIRATORY BURST OXIDASE HOMOLOGS D and F,to generate local ROS maxima in cambium,which in turn enhance the proliferation of cambial cells.In a negative feedback mechanism,higher Ros levels then repress LBD11 expression and maintain the balance of cambial cell proliferation.Our findings thus reveal the role of a novel LBD11/ROS-dependent feedback regulatory system in maintaining vascular cambiumspecific redox homeostasis and radial growth inplants. 展开更多
关键词 vascular cambium radial growth reactive oxygen species
原文传递
Spider-inspired tunable mechanosensor for biomedical applications
2
作者 Taewi Kim Insic Hong +21 位作者 Yeonwook Roh Dongjin Kim Sungwook Kim Sunghoon Im Changhwan Kim Kiwon Jang Seongyeon Kim Minho Kim Jieun Park Dohyeon Gong Kihyeon Ahn Jingoo lee Gunhee lee Hak-Seung lee Jeehoon Kang Ji Man Hong seungchul lee Sungchul Seo Bon-Kwon Koo Je-sung Koh Seungyong Han Daeshik Kang 《npj Flexible Electronics》 SCIE 2023年第1期444-452,共9页
The recent advances of wearable sensors are remarkable but there are still limitations that they need to be refabricated to tune the sensor for target signal.However,biological sensory systems have the inherent potent... The recent advances of wearable sensors are remarkable but there are still limitations that they need to be refabricated to tune the sensor for target signal.However,biological sensory systems have the inherent potential to adjust their sensitivity according to the external environment,allowing for a broad and enhanced detection.Here,we developed a Tunable,Ultrasensitive,Nature-inspired,Epidermal Sensor(TUNES)that the strain sensitivity was dramatically increased(GF~30k)and the pressure sensitivity could be tuned(10–254 kPa^(−1))by preset membrane tension.The sensor adjusts the sensitivity to the pressure regime by preset tension,so it can measure a wide range(0.05 Pa–25 kPa)with the best performance:from very small signals such as minute pulse to relatively large signals such as muscle contraction and respiration.We verified its capabilities as a wearable health monitoring system by clinical trial comparing with pressure wire which is considered the current gold standard of blood pressure(r=0.96)and home health care system by binary classification of Old’s/Young’s pulse waves via machine learning(accuracy 95%). 展开更多
关键词 SENSOR ADJUST SPIDER
原文传递
Deep learning acceleration of multiscale superresolution localization photoacoustic imaging 被引量:3
3
作者 Jongbeom Kim Gyuwon Kim +7 位作者 Lei Li Pengfei Zhang Jin Young Kim Yeonggeun Kim Hyung Ham Kkim Lihong V.Wang seungchul lee Chulhong Kim 《Light(Science & Applications)》 SCIE EI CAS CSCD 2022年第6期1166-1177,共12页
A superresolution imaging approach that localizes very small targets,such as red blood cells or droplets of injected photoacoustic dye,has significantly improved spatial resolution in various biological and medical im... A superresolution imaging approach that localizes very small targets,such as red blood cells or droplets of injected photoacoustic dye,has significantly improved spatial resolution in various biological and medical imaging modalities.However,this superior spatial resolution is achieved by sacrificing temporal resolution because many raw image frames,each containing the localization target,must be superimposed to form a sufficiently sampled high-density superresolution image.Here,we demonstrate a computational strategy based on deep neural networks(DNNs)to reconstruct high-density superresolution images from far fewer raw image frames.The localization strategy can be applied for both 3D label-free localization optical-resolution photoacoustic microscopy(OR-PAM)and 2D labeled localization photoacoustic computed tomography(PACT).For the former,the required number of raw volumetric frames is reduced from tens to fewer than ten.For the latter,the required number of raw 2D frames is reduced by 12 fold.Therefore,our proposed method has simultaneously improved temporal(via the DNN)and spatial(via the localization method)resolutions in both label-free microscopy and labeled tomography.Deep-learning powered localization PA imaging can potentially provide a practical tool in preclinical and clinical studies requiring fast temporal and fine spatial resolutions. 展开更多
关键词 DEEP FRAMES NETWORKS
原文传递
Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis 被引量:1
4
作者 Jaimyun Jung Juwon Na +4 位作者 Hyung Keun Park Jeong Min Park Gyuwon Kim seungchul lee Hyoung Seop Kim 《npj Computational Materials》 SCIE EI CSCD 2021年第1期867-877,共11页
The digitized format of microstructures,or digital microstructures,plays a crucial role in modern-day materials research.Unfortunately,the acquisition of digital microstructures through experimental means can be unsuc... The digitized format of microstructures,or digital microstructures,plays a crucial role in modern-day materials research.Unfortunately,the acquisition of digital microstructures through experimental means can be unsuccessful in delivering sufficient resolution that is necessary to capture all relevant geometric features of the microstructures.The resolution-sensitive microstructural features overlooked due to insufficient resolution may limit one’s ability to conduct a thorough microstructure characterization and material behavior analysis such as mechanical analysis based on numerical modeling.Here,a highly efficient super-resolution imaging based on deep learning is developed using a deep super-resolution residual network to super-resolved low-resolution(LR)microstructure data for microstructure characterization and finite element(FE)mechanical analysis.Microstructure characterization and FE model based mechanical analysis using the super-resolved microstructure data not only proved to be as accurate as those based on high-resolution(HR)data but also provided insights on local microstructural features such as grain boundary normal and local stress distribution,which can be only partially considered or entirely disregarded in LR data-based analysis. 展开更多
关键词 MICROSTRUCTURE MICROSTRUCTURE ANALYSIS
原文传递
iNID: An Analytical Framework for Identifying Network Models for Interplays among Developmental Signaling in Arabidopsis
5
作者 Daeseok Choi Jaemyung Choi +4 位作者 Byeongsoo Kang seungchul lee Young-hyun Cho Ildoo Hwang Daehee Hwang 《Molecular Plant》 SCIE CAS CSCD 2014年第5期792-813,共22页
Integration of internal and external cues into developmental programs is indispensable for growth and development of plants, which involve complex interplays among signaling pathways activated by the internal and exte... Integration of internal and external cues into developmental programs is indispensable for growth and development of plants, which involve complex interplays among signaling pathways activated by the internal and external factors (IEFs). However, decoding these complex interplays is still challenging. Here, we present a web-based platform that identifies key regulators and Network models delineating Interplays among Developmental signaling (iNID) in Arabidopsis. iNID provides a comprehensive resource of (1) transcriptomes previously collected under the conditions treated with a broad spectrum of IEFs and (2) protein and genetic interactome data in Arabidopsis. In addition, iNID provides an array of tools for identifying key regulators and network models related to interplays among IEFs using transcriptome and interactome data. To demonstrate the utility of iNID, we investigated the interplays of (1) phytohormones and light and (2) phytohormones and biotic stresses. The results revealed 34 potential regulators of the interplays, some of which have not been reported in association with the interplays, and also network models that delineate the involvement of the 34 regulators in the interplays, providing novel insights into the interplays collectively defined by phytohormones, light, and biotic stresses. We then experimentally verified that BME3 and TEM1, among the selected regulators, are involved in the auxin-brassinosteroid (BR)-blue light interplay. Therefore, iNID serves as a useful tool to provide a basis for understanding interplays among IEFs. 展开更多
关键词 transcriptome analysis network analysis signal interplays development Arabidopsis.
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部