Damping implementation issues for in‐structure response estimation of seismically isolated nuclear structures

Author(s):  
Satyam Kumar ◽  
Manish Kumar
2021 ◽  
Vol 230 ◽  
pp. 111705
Author(s):  
Yuxing Yang ◽  
Amit H. Varma ◽  
Michael E. Kreger ◽  
Ying Wang ◽  
Kai Zhang

2006 ◽  
Vol 17 (3) ◽  
pp. 1126-1140 ◽  
Author(s):  
Lei Li ◽  
Ken Roy ◽  
Sachin Katyal ◽  
Xuejun Sun ◽  
Stacey Bléoo ◽  
...  

DDX1 bodies, cleavage bodies, Cajal bodies (CBs), and gems are nuclear suborganelles that contain factors involved in RNA transcription and/or processing. Although all four nuclear bodies can exist as distinct entities, they often colocalize or overlap with each other. To better understand the relationship between these four nuclear bodies, we examined their spatial distribution as a function of the cell cycle. Here, we report that whereas DDX1 bodies, CBs and gems are present throughout interphase, CPSF-100-containing cleavage bodies are predominantly found during S and G2 phases, whereas CstF-64-containing cleavage bodies are primarily observed during S phase. All four nuclear bodies associate with each other during S phase, with cleavage bodies colocalizing with DDX1 bodies, and cleavage bodies/DDX1 bodies residing adjacent to gems and CBs. Although inhibitors of RNA transcription had no effect on DDX1 bodies or cleavage bodies, inhibitors of DNA replication resulted in loss of CstF-64-containing cleavage bodies. A striking effect on nuclear structures was observed with latrunculin B, an inhibitor of actin polymerization, resulting in the formation of needlelike nuclear spicules made up of CstF-64, CPSF-100, RNA, and RNA polymerase II. Our results suggest that cleavage body components are highly dynamic in nature.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Grzegorz Bokota ◽  
Jacek Sroka ◽  
Subhadip Basu ◽  
Nirmal Das ◽  
Pawel Trzaskoma ◽  
...  

Abstract Background Bioimaging techniques offer a robust tool for studying molecular pathways and morphological phenotypes of cell populations subjected to various conditions. As modern high-resolution 3D microscopy provides access to an ever-increasing amount of high-quality images, there arises a need for their analysis in an automated, unbiased, and simple way. Segmentation of structures within the cell nucleus, which is the focus of this paper, presents a new layer of complexity in the form of dense packing and significant signal overlap. At the same time, the available segmentation tools provide a steep learning curve for new users with a limited technical background. This is especially apparent in the bulk processing of image sets, which requires the use of some form of programming notation. Results In this paper, we present PartSeg, a tool for segmentation and reconstruction of 3D microscopy images, optimised for the study of the cell nucleus. PartSeg integrates refined versions of several state-of-the-art algorithms, including a new multi-scale approach for segmentation and quantitative analysis of 3D microscopy images. The features and user-friendly interface of PartSeg were carefully planned with biologists in mind, based on analysis of multiple use cases and difficulties encountered with other tools, to offer an ergonomic interface with a minimal entry barrier. Bulk processing in an ad-hoc manner is possible without the need for programmer support. As the size of datasets of interest grows, such bulk processing solutions become essential for proper statistical analysis of results. Advanced users can use PartSeg components as a library within Python data processing and visualisation pipelines, for example within Jupyter notebooks. The tool is extensible so that new functionality and algorithms can be added by the use of plugins. For biologists, the utility of PartSeg is presented in several scenarios, showing the quantitative analysis of nuclear structures. Conclusions In this paper, we have presented PartSeg which is a tool for precise and verifiable segmentation and reconstruction of 3D microscopy images. PartSeg is optimised for cell nucleus analysis and offers multi-scale segmentation algorithms best-suited for this task. PartSeg can also be used for the bulk processing of multiple images and its components can be reused in other systems or computational experiments.


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