Sökresultat

Flat-histogram techniques provide a powerful approach to the simulation of first-order-like phase transitions and are potentially very useful for protein studies. Here, we test this approach by implicit solvent all-atom Monte Carlo (MC) simulations of peptide aggregation, for a 7-residue fragment (GIIFNEQ) of the Cu/Zn superoxide dismutase 1 protein (SOD1). In simulations with 8 chains, we observe

Single-molecule pulling experiments on unstructured proteins linked to neurodegenerative diseases have measured rupture forces comparable to those for stable folded proteins. To investigate the structural mechanisms of this unexpected force resistance, we perform pulling simulations of the amyloid β-peptide (Aβ) and α-synuclein (αS), starting from simulated conformational ensembles for the free mo

Immunohistochemical expression of CD40 is seen in 60-70% of diffuse large B-cell lymphoma (DLBCL) and is associated with a superior prognosis. By using gene expression profiling we aimed to further explore the underlying mechanisms for this effect. Ninety-eight immunohistochemically defined CD40 positive or negative DLBCL tumors, 63 and 35 respectively, were examined using spotted 55K oligonucleot

Small soluble oligomers, as well as dimers in particular, of the amyloid β-peptide (Aβ) are believed to play an important pathological role in Alzheimer's disease. Here, we investigate the spontaneous dimerization of Aβ42, with 42 residues, by implicit solvent all-atom Monte Carlo simulations, for the wild-type peptide and the mutants F20E, E22G and E22G/I31E. The observed dimers of these variants

The purpose of this study was to develop a computer-assisted diagnosis (CAD) system based on image-processing techniques and artificial neural networks for the interpretation of bone scans performed to determine the presence or absence of metastases. METHODS: A training group of 810 consecutive patients who had undergone bone scintigraphy due to suspected metastatic disease were included in the st

We present and study a minimal structure-based model for the self-assembly of peptides into ordered β-sheet-rich fibrils. The peptides are represented by unit-length sticks on a cubic lattice and interact by hydrogen bonding and hydrophobicity forces. Using Monte Carlo simulations with >10^{5} peptides, we show that fibril formation occurs with sigmoidal kinetics in the model. To determine the mec

Background Soft tissue sarcoma (STS) diagnosis is challenging because of a multitude of histopathological subtypes, different genetic characteristics, and frequent intratumoral pleomorphism. One-third of STS metastasize and current risk-stratification is suboptimal, therefore, novel diagnostic and prognostic markers would be clinically valuable. We assessed the diagnostic and prognostic value of

Purpose: To develop a completely automated method based on image processing techniques and artificial neural networks for the interpretation of combined [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) images for the diagnosis and staging of lung cancer. Methods: A total of 87 patients who underwent PET/CT examinations due to suspected lung cancer comp

The properties of the amyloid-beta peptide that lead to aggregation associated with Alzheimer's disease are not fully understood. This study aims at identifying conformational differences among four variants of full-length Abeta42 that are known to display very different aggregation properties. By extensive all-atom Monte Carlo simulations, we find that a variety of beta-sheet structures with dist

Cities today consume over 80% of the world’s energy and are responsible for 75% of the total GHG emissions. Over 80% of the population in Europe live in Urban areas. The mobility system, being at the heart of urban activities is responsible for the movement of people, goods and services and is responsible for attracting investments into cities. Playing such a key role in urban development, the sec

Th e performance of a mean field th eory (MFT) neu ralnetwork technique for finding approximate solutions to optimi zationproblems is invest igat ed for the case of th e minimum cut graph bisection problem, which is NP- complete. We address the issues of solut ionquality, programming complexity, convergence tim es and scala bility.Both standard random gr aphs an d mor e st ruct ured geomet ric gra

Based on t he Boltzmann Machine concept, we derive alear ning algorithm in which time-consuming stochastic measurementsof correlations a re replaced by solutions to dete rminist ic mean fieldtheory equ ations. T he method is applied to t he XOR (exclusive-or ),encoder, and line sym metry problems with substantial success. Weobserve speedup facto rs ranging from 10 to 30 for these ap plicat ionsand

We propose a simple architecture for implementing supervised neural network models optically with photorefractive technology. The architecture is very versatile: a wide range of supervised learning algorithms can be implemented including mean-field-theory, backpropagation, and Kanerva-style networks. Our architecture is based on a single crystal with spatial multiplexing rather than the more commo

Artificial neural networks is one of the most commonly used machine learning algorithms in medical applications. However, they are still not used in practice in the clinics partly due to their lack of explanatory capacity. We compare two case-based explanation methods to two trained physicians on analysis of electrocardiogram (ECG) data from patients with a suspected acute coronary syndrome (ACS).