scholarly journals Spatial heterogeneity of the cytosol revealed by machine learning-based 3D particle tracking

2020 ◽  
Vol 31 (14) ◽  
pp. 1498-1511 ◽  
Author(s):  
Grace A. McLaughlin ◽  
Erin M. Langdon ◽  
John M. Crutchley ◽  
Liam J. Holt ◽  
M. Gregory Forest ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spatial Heterogeneity ◽  
Particle Tracking ◽  
Cell Biology ◽  
Physical State ◽  
Biological Systems ◽  
Physical Structure ◽  
Length Scales ◽  
3D Motion ◽  
In Cells

The structure of the cytosol across different length scales is a debated topic in cell biology. Here we present tools to measure the physical state of the cytosol by analyzing the 3D motion of nanoparticles expressed in cells. We find evidence that the physical structure of the cytosol is a fundamental source of variability in biological systems.

scholarly journals 3P189 Movement of nucleus in cells cultured on micro-patterned substrates(Cell biology,Poster,The 52th Annual Meeting of the Biophysical Society of Japan(BSJ2014))

2014 ◽  
Vol 54 (supplement1-2) ◽  
pp. S280
Author(s):  
Zhu Xinfeng ◽  
Kuribayashi-Shigetomi Kaori ◽  
Cai Pinggen ◽  
Subagyo Agus ◽  
Sueoka Kazuhisa ◽  
...  
Keyword(s):  
Annual Meeting ◽  
Cell Biology ◽  
Patterned Substrates ◽  
Biophysical Society ◽  
Cells Cultured ◽  
In Cells

scholarly journals A MACHINE LEARNING DATASET FOR LARGE-SCOPE HIGH RESOLUTION REMOTE SENSING IMAGE INTERPRETATION CONSIDERING LANDSCAPE SPATIAL HETEROGENEITY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 731-736
Author(s):  
Y. Xu ◽  
X. Hu ◽  
Y. Wei ◽  
Y. Yang ◽  
D. Wang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Cover ◽  
Spatial Heterogeneity ◽  
Satellite Remote Sensing ◽  
Sampling Method ◽  
National Level ◽  
Remote Sensing Image ◽  
Training Sample ◽  
Large Scope

<p><strong>Abstract.</strong> The demand for timely information about earth’s surface such as land cover and land use (LC/LU), is consistently increasing. Machine learning method shows its advantage on collecting such information from remotely sensed images while requiring sufficient training sample. For satellite remote sensing image, however, sample datasets covering large scope are still limited. Most existing sample datasets for satellite remote sensing image built based on a few frames of image located on a local area. For large scope (national level) view, choosing a sufficient unbiased sampling method is crucial for constructing balanced training sample dataset. Dependable spatial sample locations considering spatial heterogeneity of land cover are needed for choosing sample images. This paper introduces an ongoing work on establishing a national scope sample dataset for high spatial-resolution satellite remote sensing image processing. Sample sites been chosen sufficiently using spatial sampling method, and divided sample patches been grouped using clustering method for further uses. The neural network model for road detection trained our dataset subset shows an increased performance on both completeness and accuracy, comparing to two widely used public dataset.</p>

scholarly journals Norbornene Probes for the Detection of Cysteine Sulfenic Acid in Cells

2018 ◽  
Author(s):  
Lisa Alcock ◽  
Bruno Oliveira ◽  
Michael Deery ◽  
Tara Pukala ◽  
Michael Perkins ◽  
...  
Keyword(s):  
Biological Systems ◽  
Live Cells ◽  
Cysteine Oxidation ◽  
Sulfenic Acid ◽  
Sulfenic Acids ◽  
Norbornene Derivatives ◽  
In Cells

Norbornene derivatives were validated as probes for cysteine sulfenic acid on proteins and in live cells. Trapping sulfenic acids with norbornene probes is highly selective and revealed a different reactivity profile than the traditional dimedone reagent. The norbornene probe also revealed a superior chemoselectivity when compared to a commonly used dimedone probe. Together, these results advance the study of cysteine oxidation in biological systems.

Forming Canonical 8D Data Clouds to Explore the Transient Dynamics of Physical Stiffened Plates: A Machine Learning Approach for Complex Mechanical Structures

2020 ◽  
Author(s):  
Ioannis T. Georgiou
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Complex Structure ◽  
Spatial Dimension ◽  
Physical Structure ◽  
Transient Dynamics ◽  
Alloy Plate ◽  
Considerable Uncertainty ◽  
Acceleration Signal ◽  
The Impact

Abstract This work presents a data-driven explorative study of the physics of the dynamics of a physical structure of complicated geometry. The geometric complexity of the physical system renders the typical single sensor acceleration signal quite complicated for a physics interpretation. We need the spatial dimension to resolve the single sensory signal over its entire time horizon. Thus we are introducing the spatial dimension by the canonical eight-dimensional data cloud (Canonical 8D-Data Cloud) concept to build methods to explore the impact-induced free dynamics of physical complex mechanical structures. The complex structure in this study is a large scale aluminum alloy plate stiffened by a frame made of T-section beams. The Canonical 8D-Data Cloud is identified with the simultaneous acceleration measurements by eight piezoelectric sensors equally spaced and attached on the periphery of a circular material curve drawn on the uniform surface of the stiffened plate. The Data Cloud approach leads to a systematic exploration-discovery-quantification of uncertainty in this physical complex structure. It is found that considerable uncertainty is stemming from the sensitivity of transient dynamics on the parameters of space-time localized force pulses, the latter being used as a means to diagnose the presence of structural anomalies. The Data Cloud approach leads to aspects of machine learning such as reduced dynamics analytics of big sensory data by means of heavenly machine-assisted computations to carry out the unparalleled data reduction analysis enabled by the Advanced Proper Orthogonal Decomposition Transform. Emphasized is the connection between the characteristic geometric features of high-dimensional datasets as a whole, the Data Cloud, and the modal physics of the dynamics.

scholarly journals Machine learning in cell biology – teaching computers to recognize phenotypes

2013 ◽  
Vol 126 (24) ◽  
pp. 5529-5539 ◽  
Author(s):  
Christoph Sommer ◽  
Daniel W. Gerlich
Keyword(s):  
Machine Learning ◽  
Cell Biology ◽  
Biology Teaching

Machine learning based position‐rendering algorithms for radioactive particle tracking experimentation

AIChE Journal ◽  
2020 ◽  
Vol 66 (6) ◽  
Author(s):  
Ashutosh Yadav ◽  
Tuntun Kumar Gaurav ◽  
Harish J. Pant ◽  
Shantanu Roy
Keyword(s):  
Machine Learning ◽  
Particle Tracking ◽  
Radioactive Particle Tracking ◽  
Radioactive Particle
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