東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Statistical physics approaches to Al...

Peng, Shouyong.

Linked to FindBook

Google Book

Amazon

博客來

Statistical physics approaches to Alzheimer's disease.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical physics approaches to Alzheimer's disease./
Author:	Peng, Shouyong.
Description:	101 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4268.
Contained By:	Dissertation Abstracts International66-08B.
Subject:	Physics, General. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3186526
ISBN:	9780542284915

Statistical physics approaches to Alzheimer's disease.
Peng, Shouyong.

Statistical physics approaches to Alzheimer's disease. - 101 p.

Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4268.

Thesis (Ph.D.)--Boston University, 2006.

Alzheimer's disease (AD) is the most common cause of late life dementia. In the brain of an AD patient, neurons are lost and spatial neuronal organizations (microcolumns) are disrupted. An adequate quantitative analysis of microcolumns requires that we automate the neuron recognition stage in the analysis of microscopic images of human brain tissue. We propose a recognition method based on statistical physics. Specifically, Monte Carlo simulations of an inhomogeneous Potts model are applied for image segmentation. Unlike most traditional methods, this method improves the recognition of overlapped neurons, and thus improves the overall recognition percentage.

ISBN: 9780542284915Subjects--Topical Terms:

1018488
Physics, General.

Statistical physics approaches to Alzheimer's disease.
LDR:03561nmm 2200301 4500 001 1824211
005 20061128083234.5
008 130610s2006 eng d
020 $a 9780542284915
035 $a (UnM)AAI3186526
035 $a AAI3186526
040 $a UnM $c UnM
100 1 $a Peng, Shouyong. $3 1913298
245 1 0 $a Statistical physics approaches to Alzheimer's disease.
300 $a 101 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4268.
500 $a Major Professor: H. Eugene Stanley.
502 $a Thesis (Ph.D.)--Boston University, 2006.
520 $a Alzheimer's disease (AD) is the most common cause of late life dementia. In the brain of an AD patient, neurons are lost and spatial neuronal organizations (microcolumns) are disrupted. An adequate quantitative analysis of microcolumns requires that we automate the neuron recognition stage in the analysis of microscopic images of human brain tissue. We propose a recognition method based on statistical physics. Specifically, Monte Carlo simulations of an inhomogeneous Potts model are applied for image segmentation. Unlike most traditional methods, this method improves the recognition of overlapped neurons, and thus improves the overall recognition percentage.
520 $a Although the exact causes of AD are unknown, as experimental advances have revealed the molecular origin of AD, they have continued to support the amyloid cascade hypothesis, which states that early stages of aggregation of amyloid beta (Abeta) peptides lead to neurodegeneration and death. X-ray diffraction studies reveal the common cross-beta structural features of the final stable aggregates-amyloid fibrils. Solid-state NMR studies also reveal structural features for some well-ordered fibrils. But currently there is no feasible experimental technique that can reveal the exact structure or the precise dynamics of assembly and thus help us understand the aggregation mechanism.
520 $a Computer simulation offers a way to understand the aggregation mechanism on the molecular level. Because traditional all-atom continuous molecular dynamics simulations are not fast enough to investigate the whole aggregation process, we apply coarse-grained models and discrete molecular dynamics methods to increase the simulation speed. First we use a coarse-grained two-bead (two beads per amino acid) model. Simulations show that peptides can aggregate into multilayer beta-sheet structures, which agree with X-ray diffraction experiments. To better represent the secondary structure transition happening during aggregation, we refine the model to four beads per amino acid. Typical essential interactions, such as backbone hydrogen bond, hydrophobic and electrostatic interactions, are incorporated into our model. We study the aggregation of Abeta16-22, a peptide that can aggregate into a well-ordered fibrillar structure in experiments. Our results show that randomly-oriented monomers can aggregate into fibrillar subunits, which agree not only with X-ray diffraction experiments but also with solid-state NMR studies. Our findings demonstrate that coarse-grained models and discrete molecular dynamics simulations can help researchers understand the aggregation mechanism of amyloid peptides.
590 $a School code: 0017.
650 4 $a Physics, General. $3 1018488
650 4 $a Statistics. $3 517247
690 $a 0605
690 $a 0463
710 2 0 $a Boston University. $3 1017454
773 0 $t Dissertation Abstracts International $g 66-08B.
790 1 0 $a Stanley, H. Eugene, $e advisor
790 $a 0017
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3186526