scholarly journals Architecture and function analysis of integrated energy service stations considering cyber‐physical integration

2021 ◽  
Author(s):  
Haoyu Liu ◽  
Qi Wang ◽  
Yi Tang
Keyword(s):  
Function Analysis ◽  
Service Stations ◽  
Energy Service ◽  
And Function

Structure and function analysis of a CC-NBS-LRR protein AT1G12290

2021 ◽  
Vol 534 ◽  
pp. 206-211
Author(s):  
Jianzhong Huang ◽  
Xiaoqiu Wu ◽  
Kaiting Sun ◽  
Zhiyong Gao
Keyword(s):  
Function Analysis ◽  
Structure And Function ◽  
And Function ◽  
Lrr Protein

Characterization of diacylglycerol acyltransferase 2 from Idesia polycarpa and function analysis

2021 ◽  
Vol 234 ◽  
pp. 105023
Author(s):  
Ruishen Fan ◽  
Gui Cai ◽  
Xuanyuan Zhou ◽  
Yuxin Qiao ◽  
Jiabao Wang ◽  
...  
Keyword(s):  
Function Analysis ◽  
Diacylglycerol Acyltransferase ◽  
And Function

scholarly journals Molecular characterization and function analysis of the rice OsDUF617 family

2021 ◽  
Vol 35 (1) ◽  
pp. 862-872
Author(s):  
Chunliu Li ◽  
Dejia Hou ◽  
Lin Zhang ◽  
Xiaohong Li ◽  
Jiangbo Fan ◽  
...  
Keyword(s):  
Molecular Characterization ◽  
Function Analysis ◽  
And Function

scholarly journals Platelet Phenotyping and Function Testing in Thrombocytopenia

2021 ◽  
Vol 10 (5) ◽  
pp. 1114
Author(s):  
Kerstin Jurk ◽  
Yavar Shiravand
Keyword(s):  
Function Analysis ◽  
Point Of Care ◽  
Bleeding Complications ◽  
Diagnostic Significance ◽  
Blood Samples ◽  
Skilled Personnel ◽  
Life Threatening ◽  
And Function ◽  
Function Testing

Patients who suffer from inherited or acquired thrombocytopenia can be also affected by platelet function defects, which potentially increase the risk of severe and life-threatening bleeding complications. A plethora of tests and assays for platelet phenotyping and function analysis are available, which are, in part, feasible in clinical practice due to adequate point-of-care qualities. However, most of them are time-consuming, require experienced and skilled personnel for platelet handling and processing, and are therefore well-established only in specialized laboratories. This review summarizes major indications, methods/assays for platelet phenotyping, and in vitro function testing in blood samples with reduced platelet count in relation to their clinical practicability. In addition, the diagnostic significance, difficulties, and challenges of selected tests to evaluate the hemostatic capacity and specific defects of platelets with reduced number are addressed.

Predicting Chromosome Flexibility from the Genomic Sequence Based on Deep Learning Neural Networks

2021 ◽  
Vol 16 ◽  
Author(s):  
Jinghao Peng ◽  
Jiajie Peng ◽  
Haiyin Piao ◽  
Zhang Luo ◽  
Kelin Xia ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Function Analysis ◽  
Double Helix ◽  
Gm12878 Cell ◽  
Genomic Sequence Analysis ◽  
And Function ◽  
Nuclear Processes

Background: The open and accessible regions of the chromosome are more likely to be bound by transcription factors which are important for nuclear processes and biological functions. Studying the change of chromosome flexibility can help to discover and analyze disease markers and improve the efficiency of clinical diagnosis. Current methods for predicting chromosome flexibility based on Hi-C data include the flexibility-rigidity index (FRI) and the Gaussian network model (GNM), which have been proposed to characterize chromosome flexibility. However, these methods require the chromosome structure data based on 3D biological experiments, which is time-consuming and expensive. Objective: Generally, the folding and curling of the double helix sequence of DNA have a great impact on chromosome flexibility and function. Motivated by the success of genomic sequence analysis in biomolecular function analysis, we hope to propose a method to predict chromosome flexibility only based on genomic sequence data. Method: We propose a new method (named "DeepCFP") using deep learning models to predict chromosome flexibility based on only genomic sequence features. The model has been tested in the GM12878 cell line. Results: The maximum accuracy of our model has reached 91%. The performance of DeepCFP is close to FRI and GNM. Conclusion: The DeepCFP can achieve high performance only based on genomic sequence.

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