scholarly journals A Primal Analysis System of Brain Neurons Data

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Dong-Mei Pu ◽  
Da-Qi Gao ◽  
Yu-Bo Yuan
Keyword(s):  
Human Brain ◽  
Original Data ◽  
Type Number ◽  
Brain Neurons ◽  
Basic Procedure ◽  
Analysis System ◽  
Neuron Growth ◽  
Feature Database ◽  
Primal System

It is a very challenging work to classify the 86 billions of neurons in the human brain. The most important step is to get the features of these neurons. In this paper, we present a primal system to analyze and extract features from brain neurons. First, we make analysis on the original data of neurons in which one neuron contains six parameters: room type,X,Y,Zcoordinate range, total number of leaf nodes, and fuzzy volume of neurons. Then, we extract three important geometry features including rooms type, number of leaf nodes, and fuzzy volume. As application, we employ the feature database to fit the basic procedure of neuron growth. The result shows that the proposed system is effective.

scholarly journals ZNF804A Protein Is Widely Expressed in Human Brain Neurons: Possible Implications on Normal Brain Structure and Pathomorphologic Changes in Schizophrenia

2014 ◽  
Vol 40 (3) ◽  
pp. 499-500 ◽  
Author(s):  
Hans-Gert Bernstein ◽  
Johann Steiner ◽  
Henrik Dobrowolny ◽  
Bernhard Bogerts
Keyword(s):  
Human Brain ◽  
Brain Structure ◽  
Normal Brain ◽  
Brain Neurons

Visualisation of bradykinin B2 receptors on human brain neurons

1996 ◽  
Vol 33 (1-3) ◽  
pp. 104-107 ◽  
Author(s):  
Deshandra M. Raidoo ◽  
Nirasha Ramchurren ◽  
Yugen Naidoo ◽  
Strini Naidoo ◽  
Werner Müller-Esterl ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Neurons ◽  
B2 Receptors

Long non-coding pseudogene transcripts compete with mRNAs that share microRNA recognition elements with them in human brain neurons

2016 ◽  
Vol 26 ◽  
pp. S139
Author(s):  
H. Soreq ◽  
S. Barbash ◽  
A. Simchovitz ◽  
A.S. Buchman ◽  
D.A. Bennett ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Neurons ◽  
Recognition Elements

scholarly journals Specific Regional Transcription of Apolipoprotein E in Human Brain Neurons

1999 ◽  
Vol 154 (2) ◽  
pp. 601-611 ◽  
Author(s):  
Pu-Ting Xu ◽  
John R. Gilbert ◽  
Hui-Ling Qiu ◽  
John Ervin ◽  
Tracie R. Rothrock-Christian ◽  
...  
Keyword(s):  
Human Brain ◽  
Apolipoprotein E ◽  
Brain Neurons

Human Brain Neurons Grown in Culture

Science News ◽  
1990 ◽  
Vol 137 (18) ◽  
pp. 276
Author(s):  
R. Weiss
Keyword(s):  
Human Brain ◽  
Brain Neurons

Development of human brain neurons

Science ◽  
2018 ◽  
Vol 362 (6416) ◽  
pp. 788.12-790
Author(s):  
Pamela J. Hines
Keyword(s):  
Human Brain ◽  
Brain Neurons

scholarly journals Acid-Sensing Ion Channels in Acidosis-Induced Injury of Human Brain Neurons

2010 ◽  
Vol 30 (6) ◽  
pp. 1247-1260 ◽  
Author(s):  
Minghua Li ◽  
Koichi Inoue ◽  
Deborah Branigan ◽  
Eric Kratzer ◽  
Jillian C Hansen ◽  
...  
Keyword(s):  
Ion Channels ◽  
Human Brain ◽  
Cortical Neurons ◽  
Cell Injury ◽  
Acidic Solution ◽  
Brain Neurons ◽  
Acid Sensing Ion Channels ◽  
Intracellular Ca ◽  
Inward Currents ◽  
High Level

Acidosis is a common feature of the human brain during ischemic stroke and is known to cause neuronal injury. However, the mechanism underlying acidosis-mediated injury of the human brain remains elusive. We show that a decrease in the extracellular pH evoked inward currents characteristic of acid-sensing ion channels (ASICs) and increased intracellular Ca2+ in cultured human cortical neurons. Acid-sensing ion channels in human cortical neurons show electrophysiological and pharmacological properties distinct from those in neurons of the rodent brain. Reverse transcriptase-PCR and western blot detected a high level of the ASIC1a subunit with little or no expression of other ASIC subunits. Treatment of human cortical neurons with acidic solution induced substantial cell injury, which was attenuated by the ASIC1a blockade. Thus, functional homomeric ASIC1a channels are predominantly expressed in neurons from the human brain. Activation of these channels has an important role in acidosis-mediated injury of human brain neurons.

scholarly journals Noninvasive Load Identification Method Based on Feature Similarity

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hongyan Li ◽  
Xianfeng Ding ◽  
Dan Qu ◽  
Jiang Lin
Keyword(s):  
Reactive Power ◽  
Electrical Equipment ◽  
Original Data ◽  
Load Identification ◽  
Device Identification ◽  
Power Load ◽  
Similarity Model ◽  
Feature Similarity ◽  
Low Efficiency ◽  
Feature Database

The traditional power load identification is greatly restricted in application because of its high cost and low efficiency. In this paper, the similarity model is established to realize the noninvasive load identification of power by determining the feature database for the equipment. Firstly, the wavelet decomposition method and the wavelet threshold processing method are used to remove abnormal points and reduce noise of the original data, respectively. Secondly, the transient and steady-state characteristics of electrical equipment (active power and reactive power, harmonic current, and voltage-current trajectory) are extracted, and the feature database for the equipment is established. Thirdly, the feature similarity is defined to describe the similarity degree of any two devices under a certain feature, and the similarity model of automatic recognition of a single device is established. Finally, the device identification and calculation of power consumption are carried out for the part of data in annex 2 of question A in the 6th “teddy cup” data mining challenge competition.

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