Interference microscopy volume illustration for biomedical data

2012 ◽  
Author(s):  
Hanqi Guo ◽  
Xiaoru Yuan ◽  
Jie Liu ◽  
Guihua Shan ◽  
Xuebin Chi ◽  
...  
Keyword(s):  
Interference Microscopy ◽  
Biomedical Data ◽  
Volume Illustration

Scanning Electron Microscopy and Electron Microprobe Analysis of Malignant Cell Cultures

1968 ◽  
Vol 26 ◽  
pp. 164-165
Author(s):  
R. I. Johnsson-Hegyeli ◽  
A. F. Hegyeli ◽  
D. K. Landstrom ◽  
W. C. Lane
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Cell Cultures ◽  
Electron Microprobe ◽  
Microprobe Analysis ◽  
Electron Microprobe Analysis ◽  
Three Dimensional ◽  
Malignant Cell ◽  
Interference Microscopy ◽  
Scanning Electron

Last year we reported on the use of reflected light interference microscopy (RLIM) for the direct color photography of the surfaces of living normal and malignant cell cultures without the use of replicas, fixatives, or stains. The surface topography of living cells was found to follow underlying cellular structures such as nuceloli, nuclear membranes, and cytoplasmic organelles, making possible the study of their three-dimensional relationships in time. The technique makes possible the direct examination of cells grown on opaque as well as transparent surfaces. The successful in situ electron microprobe analysis of the elemental composition and distribution within single tissue culture cells was also reported.This paper deals with the parallel and combined use of scanning electron microscopy (SEM) and the two previous techniques in a study of living and fixed cancer cells. All three studies can be carried out consecutively on the same experimental specimens without disturbing the cells or their structural relationships to each other and the surface on which they are grown. KB carcinoma cells were grown on glass coverslips in closed Leighto tubes as previously described. The cultures were photographed alive by means of RLIM, then fixed with a fixative modified from Sabatini, et al (1963).

Feature Selection Algorithm for High-dimensional Biomedical Data Using Information Gain and Improved Chemical Reaction Optimization

2021 ◽  
Vol 15 (8) ◽  
pp. 912-926
Author(s):  
Ge Zhang ◽  
Pan Yu ◽  
Jianlin Wang ◽  
Chaokun Yan
Keyword(s):  
Feature Selection ◽  
Chemical Reaction ◽  
Information Gain ◽  
Feature Selection Method ◽  
Search Space ◽  
Neighborhood Search ◽  
Biomedical Data ◽  
Chemical Reaction Optimization ◽  
Search Mechanism ◽  
Reaction Optimization

Background: There have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. However, these datasets usually involve thousands of features and include much irrelevant or redundant information, which leads to confusion during diagnosis. Feature selection is a solution that consists of finding the optimal subset, which is known to be an NP problem because of the large search space. Objective: For the issue, this paper proposes a hybrid feature selection method based on an improved chemical reaction optimization algorithm (ICRO) and an information gain (IG) approach, which called IGICRO. Methods: IG is adopted to obtain some important features. The neighborhood search mechanism is combined with ICRO to increase the diversity of the population and improve the capacity of local search. Results: Experimental results of eight public available data sets demonstrate that our proposed approach outperforms original CRO and other state-of-the-art approaches.

scholarly journals Author Correction: Deep transfer learning for reducing health care disparities arising from biomedical data inequality

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Yan Gao ◽  
Yan Cui
Keyword(s):  
Health Care ◽  
Transfer Learning ◽  
Health Care Disparities ◽  
Biomedical Data

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20480-x

scholarly journals Commonalities across computational workflows for uncovering explanatory variants in undiagnosed cases

2021 ◽  
Author(s):  
Shilpa Nadimpalli Kobren ◽  
◽  
Dustin Baldridge ◽  
Matt Velinder ◽  
Joel B. Krier ◽  
...  
Keyword(s):  
Online Survey ◽  
Variant Calling ◽  
Theoretical Method ◽  
The United States ◽  
Genomic Sequencing ◽  
Biomedical Data ◽  
Sequencing Data ◽  
Multimodal Data ◽  
Undiagnosed Diseases ◽  
Undiagnosed Diseases Network

Abstract Purpose Genomic sequencing has become an increasingly powerful and relevant tool to be leveraged for the discovery of genetic aberrations underlying rare, Mendelian conditions. Although the computational tools incorporated into diagnostic workflows for this task are continually evolving and improving, we nevertheless sought to investigate commonalities across sequencing processing workflows to reveal consensus and standard practice tools and highlight exploratory analyses where technical and theoretical method improvements would be most impactful. Methods We collected details regarding the computational approaches used by a genetic testing laboratory and 11 clinical research sites in the United States participating in the Undiagnosed Diseases Network via meetings with bioinformaticians, online survey forms, and analyses of internal protocols. Results We found that tools for processing genomic sequencing data can be grouped into four distinct categories. Whereas well-established practices exist for initial variant calling and quality control steps, there is substantial divergence across sites in later stages for variant prioritization and multimodal data integration, demonstrating a diversity of approaches for solving the most mysterious undiagnosed cases. Conclusion The largest differences across diagnostic workflows suggest that advances in structural variant detection, noncoding variant interpretation, and integration of additional biomedical data may be especially promising for solving chronically undiagnosed cases.

scholarly journals Monitoring Reactivation of Latent HIV by Label-Free Gradient Light Interference Microscopy

iScience ◽  
2021 ◽  
pp. 102940
Author(s):  
Neha Goswami ◽  
Yiyang Lu ◽  
Mikhail E. Kandel ◽  
Michael J. Fanous ◽  
Kathrin Bohn-Wippert ◽  
...  
Keyword(s):  
Interference Microscopy ◽  
Label Free ◽  
Light Interference ◽  
Latent Hiv
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