scholarly journals SWATH-MS Quantitative Analysis of Proteins in the Rice Inferior and Superior Spikelets during Grain Filling

2016 ◽  
Vol 7 ◽  
Author(s):  
Fu-Yuan Zhu ◽  
Mo-Xian Chen ◽  
Yu-Wen Su ◽  
Xuezhong Xu ◽  
Neng-Hui Ye ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Grain Filling ◽  
Superior Spikelets

scholarly journals GC-MS-Based Metabolite Profiling of Key Differential Metabolites Between Superior and Inferior Spikelets of Rice During the Grain Filling Stage

2021 ◽  
Author(s):  
Xiumei Min ◽  
Hailong Xu ◽  
Fenglian Huang ◽  
Yidong Wei ◽  
Wenxiong Lin ◽  
...  
Keyword(s):  
Soluble Sugars ◽  
Starch Synthesis ◽  
Grain Filling ◽  
Absolute Quantification ◽  
Metabolite Level ◽  
Inferior Spikelets ◽  
Expression Of Genes ◽  
Proteome Level ◽  
Superior Spikelets ◽  
The Difference

Abstract Background:The asynchronous filling between superior spikelets (SS) and inferior spikelets (IS) in rice has become a research hotspot. The stagnant development and poor grain filling of IS limit yields and the formation of good quality rice. A large number of studies on this phenomenon have been carried out from the genome, transcriptome and proteome level, indicating that asynchronous filling of SS and IS filling is a complex, but orderly physiological and biochemical process involving changes of a large number of genes, protein expression and modification. However, the analysis of metabolomics differences between SS and IS is rarely reported currently.Results:This study utilized untargeted metabolomics and identified 162 metabolites in rice spikelets. Among them, 17 differential metabolites associated with unsynchronized grain filling between superior spikelets (SS) and IS, 27 metabolites were related to the stagnant development of IS and 35 metabolites related to the lower maximum grain-filling rate of IS compared with the SS. We found that soluble sugars were an important metabolite during grain filling for SS and IS. Absolute quantification was used to further analyze the dynamic changes of 4 types of soluble sugars (sucrose, fructose, glucose, and trehalose) between SS and IS. The results showed that sucrose and trehalose were closely associated with the dynamic characteristics of grain filling between SS and IS. The application of exogenous sugar showed that trehalose functioned as a key sugar signal during grain filling. Trehalose regulated the expression of genes related to sucrose conversion and starch synthesis, thereby promoting the conversion of sucrose to starch. The difference in the spatiotemporal expression of TPS-2 and TPP-1 between SS and IS was an important reason that led to the difference in trehalose contents between SS and IS.Conclusions:The results from this study are helpful for understanding the difference in grain filling between SS and IS at the metabolite level. In addition, the present results can also provide a theoretical basis for the next step of using metabolites to regulate the filling of IS.

scholarly journals Effect of Removing Superior Spikelets on Grain Filling of Inferior Spikelets in Rice

2016 ◽  
Vol 7 ◽  
Author(s):  
Cuicui You ◽  
Honglei Zhu ◽  
Beibei Xu ◽  
Wenxiao Huang ◽  
Shaohua Wang ◽  
...  
Keyword(s):  
Grain Filling ◽  
Inferior Spikelets ◽  
Superior Spikelets

An Improved Quantitative Image Analyser

1977 ◽  
Vol 35 ◽  
pp. 226-227
Author(s):  
J.P. Fallon ◽  
P.J. Gregory ◽  
C.J. Taylor
Keyword(s):  
Feature Extraction ◽  
Quantitative Analysis ◽  
Frequency Content ◽  
General Sense ◽  
Physical Measurements ◽  
Quantitative Image ◽  
Image Analyser ◽  
Automatic Methods ◽  
Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

Quantification of Silica Uptake by Alveolar Macrophages - An Emperical Scanning Electron Microprobe Method

1982 ◽  
Vol 40 ◽  
pp. 332-333
Author(s):  
V. V. Damiano ◽  
R. P. Daniele ◽  
H. T. Tucker ◽  
J. H. Dauber
Keyword(s):  
Quantitative Analysis ◽  
Alveolar Macrophages ◽  
Bulk Sample ◽  
Silica Particles ◽  
Empirical Method ◽  
Phagocytic Cells ◽  
Calibration Standards ◽  
X Ray ◽  
Accurate Quantitative Analysis ◽  
Scanning Electron

An important example of intracellular particles is encountered in silicosis where alveolar macrophages ingest inspired silica particles. The quantitation of the silica uptake by these cells may be a potentially useful method for monitoring silica exposure. Accurate quantitative analysis of ingested silica by phagocytic cells is difficult because the particles are frequently small, irregularly shaped and cannot be visualized within the cells. Semiquantitative methods which make use of particles of known size, shape and composition as calibration standards may be the most direct and simplest approach to undertake. The present paper describes an empirical method in which glass microspheres were used as a model to show how the ratio of the silicon Kα peak X-ray intensity from the microspheres to that of a bulk sample of the same composition correlated to the mass of the microsphere contained within the cell. Irregular shaped silica particles were also analyzed and a calibration curve was generated from these data.

Lateral resolution of the electron microbeam analysis

1978 ◽  
Vol 36 (1) ◽  
pp. 542-543
Author(s):  
H.J. Dudek
Keyword(s):  
Quantitative Analysis ◽  
Depth Resolution ◽  
Lateral Resolution ◽  
Cylindrical Particle ◽  
Correction Procedure ◽  
X Ray ◽  
Element Concentrations ◽  
Microbeam Analysis ◽  
The Matrix

The chemical inhomogenities in modern materials such as fibers, phases and inclusions, often have diameters in the region of one micrometer. Using electron microbeam analysis for the determination of the element concentrations one has to know the smallest possible diameter of such regions for a given accuracy of the quantitative analysis.In th is paper the correction procedure for the quantitative electron microbeam analysis is extended to a spacial problem to determine the smallest possible measurements of a cylindrical particle P of high D (depth resolution) and diameter L (lateral resolution) embeded in a matrix M and which has to be analysed quantitative with the accuracy q. The mathematical accounts lead to the following form of the characteristic x-ray intens ity of the element i of a particle P embeded in the matrix M in relation to the intensity of a standard S

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

The use of a Bent Grid to Improve the take-Off Angle for Electron Probe Analysis

1976 ◽  
Vol 34 ◽  
pp. 360-361
Author(s):  
Delbert E. Philpott ◽  
David Leaffer
Keyword(s):  
Quantitative Analysis ◽  
Tilt Angle ◽  
Count Rate ◽  
Electron Probe ◽  
Open Area ◽  
Electron Probe Analysis ◽  
Background Ratio ◽  
Probe Analysis

There are certain advantages for electron probe analysis if the sample can be tilted directly towards the detector. The count rate is higher, it optimizes the geometry since only one angle need be taken into account for quantitative analysis and the signal to background ratio is improved. The need for less tilt angle may be an advantage because the grid bars are not moved quite as close to each other, leaving a little more open area for observation. Our present detector (EDAX) and microscope (Philips 300) combination precludes moving the detector behind the microscope where it would point directly at the grid. Therefore, the angle of the specimen was changed in order to optimize the geometry between the specimen and the detector.

Solving the mechanisms responsible for organelle behavior in vivo by same-cell correlative light and electron microscopy

1992 ◽  
Vol 50 (1) ◽  
pp. 14-15
Author(s):  
Conly L. Rieder
Keyword(s):  
Electron Microscopy ◽  
Quantitative Analysis ◽  
Light Microscopy ◽  
Living Cell ◽  
Light And Electron Microscopy ◽  
Time Behavior ◽  
Dynamic Interactions ◽  
Positive Identification ◽  
Cellular Components

The behavior of many cellular components, and their dynamic interactions, can be characterized in the living cell with considerable spatial and temporal resolution by video-enhanced light microscopy (video-LM). Indeed, under the appropriate conditions video-LM can be used to determine the real-time behavior of organelles ≤ 25-nm in diameter (e.g., individual microtubules—see). However, when pushed to its limit the structures and components observed within the cell by video-LM cannot be resolved nor necessarily even identified, only detected. Positive identification and a quantitative analysis often requires the corresponding electron microcopy (EM).

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