3-D Color Video Microscopy of Intact Plants

2000 ◽  
pp. 273-290
Keyword(s):  
Video Microscopy ◽  
Intact Plants ◽  
Color Video

scholarly journals Axonal Membrane Proteins Are Transported in Distinct Carriers: A Two-Color Video Microscopy Study in Cultured Hippocampal Neurons

2000 ◽  
Vol 11 (4) ◽  
pp. 1213-1224 ◽  
Author(s):  
Christoph Kaether ◽  
Paul Skehel ◽  
Carlos G. Dotti
Keyword(s):  
Membrane Proteins ◽  
Hippocampal Neurons ◽  
Fluorescent Protein ◽  
Yellow Fluorescent Protein ◽  
Microscopy Study ◽  
Video Microscopy ◽  
Axonal Membrane ◽  
Color Video ◽  
Green Fluorescent ◽  
Living Neurons

Neurons transport newly synthesized membrane proteins along axons by microtubule-mediated fast axonal transport. Membrane proteins destined for different axonal subdomains are thought to be transported in different transport carriers. To analyze this differential transport in living neurons, we tagged the amyloid precursor protein (APP) and synaptophysin (p38) with green fluorescent protein (GFP) variants. The resulting fusion proteins, APP-yellow fluorescent protein (YFP), p38-enhanced GFP, and p38-enhanced cyan fluorescent protein, were expressed in hippocampal neurons, and the cells were imaged by video microscopy. APP-YFP was transported in elongated tubules that moved extremely fast (on average 4.5 μm/s) and over long distances. In contrast, p38-enhanced GFP-transporting structures were more vesicular and moved four times slower (0.9 μm/s) and over shorter distances only. Two-color video microscopy showed that the two proteins were sorted to different carriers that moved with different characteristics along axons of doubly transfected neurons. Antisense treatment using oligonucleotides against the kinesin heavy chain slowed down the long, continuous movement of APP-YFP tubules and increased frequency of directional changes. These results demonstrate for the first time directly the sorting and transport of two axonal membrane proteins into different carriers. Moreover, the extremely fast-moving tubules represent a previously unidentified type of axonal carrier.

Freeze-Fracture Studies of Membranes in Developing Sieve Elements in a Plant Tissue Culture

1980 ◽  
Vol 38 ◽  
pp. 636-637
Author(s):  
R. D. Sjolund ◽  
C. Y. Shih
Keyword(s):  
Plant Tissue ◽  
Cultured Cells ◽  
Freeze Fracture ◽  
Tissue Cultures ◽  
Sieve Elements ◽  
Intact Plants ◽  
Plant Tissue Cultures ◽  
Whole Plants ◽  
Energy Dependent ◽  
Similar Elements

The differentiation of phloem in plant tissue cultures offers a unique opportunity to study the development and structure of sieve elements in a manner that avoids the injury responses associated with the processing of similar elements in intact plants. Short segments of sieve elements formed in tissue cultures can be fixed intact while the longer strands occuring in whole plants must be cut into shorter lengths before processing. While iyuch controversy surrounds the question of phloem function in tissue cultures , sieve elements formed in these cultured cells are structurally similar to those of Intact plants. We are particullarly Interested In the structure of the plasma membrane and the peripheral ER in these cells because of their possible role in the energy-dependent active transport of sucrose into the sieve elements.

How nanoscale surface steps promote ice growth on feldspar: microscopy observation of morphology-enhanced condensation and freezing

Nanoscale ◽  
2019 ◽  
Vol 11 (44) ◽  
pp. 21147-21154 ◽  
Author(s):  
Raymond W. Friddle ◽  
Konrad Thürmer
Keyword(s):  
Surface Topography ◽  
Video Microscopy ◽  
Microscopy Observation ◽  
Atmospheric Dust ◽  
Ice Growth ◽  
Surface Steps

Video microscopy and AFM are used to relate surface topography to a mineral's ability to promote ice growth. On feldspar, abundant as atmospheric dust, basic surface steps can facilitate condensation and freezing when air becomes saturated.

The effect of 8 days of microgravity on regeneration of intact plants from protoplasts

1994 ◽  
Vol 92 (3) ◽  
pp. 404-411 ◽  
Author(s):  
Ole Rasmussen ◽  
Carolyn A. Baggerud ◽  
Hilde C. Larssen ◽  
Kjell Evjen ◽  
Tor-Henning Iversen
Keyword(s):  
Intact Plants

Video-microscopy of freezing in aqueous solutions and blood cell suspensions

Cryobiology ◽  
1987 ◽  
Vol 24 (6) ◽  
pp. 551-552
Author(s):  
Ch. Körber ◽  
S. Englich ◽  
G. Lipp
Keyword(s):  
Blood Cell ◽  
Aqueous Solutions ◽  
Video Microscopy ◽  
Cell Suspensions

scholarly journals Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ossama Mahmoud ◽  
Mahmoud El-Sakka ◽  
Barry G. H. Janssen
Keyword(s):  
Machine Learning ◽  
Blood Flow ◽  
Human Error ◽  
Image Data ◽  
Video Microscopy ◽  
Microvascular Blood Flow ◽  
Image Processing Algorithm ◽  
Step Algorithm ◽  
3D Cnn

AbstractMicrovascular blood flow is crucial for tissue and organ function and is often severely affected by diseases. Therefore, investigating the microvasculature under different pathological circumstances is essential to understand the role of the microcirculation in health and sickness. Microvascular blood flow is generally investigated with Intravital Video Microscopy (IVM), and the captured images are stored on a computer for later off-line analysis. The analysis of these images is a manual and challenging process, evaluating experiments very time consuming and susceptible to human error. Since more advanced digital cameras are used in IVM, the experimental data volume will also increase significantly. This study presents a new two-step image processing algorithm that uses a trained Convolutional Neural Network (CNN) to functionally analyze IVM microscopic images without the need for manual analysis. While the first step uses a modified vessel segmentation algorithm to extract the location of vessel-like structures, the second step uses a 3D-CNN to assess whether the vessel-like structures have blood flowing in it or not. We demonstrate that our two-step algorithm can efficiently analyze IVM image data with high accuracy (83%). To our knowledge, this is the first application of machine learning for the functional analysis of microvascular blood flow in vivo.

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