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Non-averaged single-molecule tertiary structures reveal RNA self-folding through individual-particle cryo-electron tomography.
Nat Commun
October 2024
The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
Large-scale and continuous conformational changes in the RNA self-folding process present significant challenges for structural studies, often requiring trade-offs between resolution and observational scope. Here, we utilize individual-particle cryo-electron tomography (IPET) to examine the post-transcriptional self-folding process of designed RNA origami 6-helix bundle with a clasp helix (6HBC). By avoiding selection, classification, averaging, or chemical fixation and optimizing cryo-ET data acquisition parameters, we reconstruct 120 three-dimensional (3D) density maps from 120 individual particles at an electron dose of no more than 168 eÅ, achieving averaged resolutions ranging from 23 to 35 Å, as estimated by Fourier shell correlation (FSC) at 0.
View Article and Find Full Text PDFMassively parallel classification of single-trial EEG signals using a min-max modular neural network.
IEEE Trans Biomed Eng
March 2004
Department of Computer Science and Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Rd., Shanghai 200030, PR China.
This paper presents a method for classifying single-trial electroencephalogram (EEG) signals using min-max modular neural networks implemented in a massively parallel way. The method has three main steps. First, a large-scale, complex EEG classification problem is simply divided into a reasonable number of two-class subproblems, as small as needed.
View Article and Find Full Text PDFClassification of non-averaged EEG data by learning vector quantisation and the influence of signal preprocessing.
Med Biol Eng Comput
September 1994
Department of Medical Informatics, University of Technology, Graz, Austria.
Neural network based classification of non-averaged event-related EEG responses.
Med Biol Eng Comput
March 1994
Department of Medical Informatics, University of Technology, Graz, Austria.
Classification of non-averaged task-related EEG responses with different types of classifier, including self-organising feature map and learning vector quantiser, K-mean, back-propagation and a combination of the last two, is reported. EEG data are collected from approximately one second periods prior to movement of the right or left index finger. A cue stimulus indicating which hand to use is employed.
View Article and Find Full Text PDFNeural network based classification of single-trial EEG data.
Artif Intell Med
December 1993
Department of Medical Informatics, Graz University of Technology, Austria.
Standard Back Propagation (BP), Partially Recurrent (PR) and Cascade-Correlation (CC) neural networks were used to predict the side of finger movement on the basis of non-averaged single trial multi-channel EEG data recorded prior to movement. From these EEG data, power values were calculated and used as parameters for classification. The results obtained on three subjects show that the Cascade-Correlation neural network is an appropriate choice for neural network based classification of spatio-temporal single-trial EEG patterns.
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