AI Article Synopsis
- Singular value decomposition (SVD) is effective for filtering noise and reducing data complexity in spectroscopic analysis, specifically in crystallography.
- SVD was applied to simulated difference Fourier maps related to time-resolved studies of a photoactive yellow protein, using various realistic experimental conditions to assess its effectiveness.
- The technique successfully differentiates between signal and noise, allowing for the recovery of phase information and time-independent structures, showcasing its potential in analyzing time-resolved crystallographic data.
Article Abstract
Singular value decomposition (SVD) is a technique commonly used in the analysis of spectroscopic data that both acts as a noise filter and reduces the dimensionality of subsequent least-squares fits. To establish the applicability of SVD to crystallographic data, we applied SVD to calculated difference Fourier maps simulating those to be obtained in a time-resolved crystallographic study of photoactive yellow protein. The atomic structures of one dark state and three intermediates were used in qualitatively different kinetic mechanisms to generate time-dependent difference maps at specific time points. Random noise of varying levels in the difference structure factor amplitudes, different extents of reaction initiation, and different numbers of time points were all employed to simulate a range of realistic experimental conditions. Our results show that SVD allows for an unbiased differentiation between signal and noise; a small subset of singular values and vectors represents the signal well, reducing the random noise in the data. Due to this, phase information of the difference structure factors can be obtained. After identifying and fitting a kinetic mechanism, the time-independent structures of the intermediates could be recovered. This demonstrates that SVD will be a powerful tool in the analysis of experimental time-resolved crystallographic data.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1302779 | PMC |
| http://dx.doi.org/10.1016/S0006-3495(03)75018-8 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model.
Sci Rep
January 2025
School of Civil Engineering, Qingdao University of Technology, Qingdao, 266525, China.
In the field of Structural Health Monitoring (SHM), complete datasets are fundamental for modal identification analysis and risk prediction. However, data loss due to sensor failures, transmission interruptions, or hardware issues is a common problem. To address this challenge, this study develops a method combining Variational Mode Decomposition (VMD) and Sparrow Search Algorithm (SSA)-optimized Gate Recurrent Unit (GRU) for recovering structural response data.
View Article and Find Full Text PDFA hybrid model for carbon price forecasting based on SSA-NSTransformer: Considering the role of multi-stage carbon reduction targets.
J Environ Manage
January 2025
School of Economics and Management, North China Electric Power University, Beijing, China. Electronic address:
In order to reduce the unpredictability of carbon prices caused by their increasingly prominent environmental and market attributes, and to minimize their negative impact on carbon trading, further research on forecasting models for carbon price is urgently needed. To improve the accuracy of prediction, this paper proposes a carbon price forecasting method based on SSA-NSTransformer. The method includes four main steps: Firstly, decomposition of carbon price signals, using Singular Spectrum Analysis to remove noise signals; Secondly, analysis of influencing factors, using Random Forest to identify and select key influencing factors of carbon price signal components from energy price, financial market, socio-economic, and environmental aspects; Furthermore, influencing factors prediction, considering the impact of different carbon reduction targets and predicting future trends of influencing factors; And finally, carbon price prediction, considering the impact of factors based on multi-stage carbon reduction targets, using Non-stationary Transformer to predict the signal components of carbon prices, reconstructing the carbon price time series, and testing the model accuracy.
View Article and Find Full Text PDFModel-based perfusion reconstruction with time separation technique in cone-beam CT dynamic liver perfusion imaging.
Med Phys
January 2025
Institute for Medical Engineering, Otto von Guericke University Magdeburg, Magdeburg, Germany.
Background: The success of embolization, a minimally invasive treatment of liver cancer, could be evaluated in the operational room with cone-beam CT by acquiring a dynamic perfusion scan to inspect the contrast agent flow.
Purpose: The reconstruction algorithm must address the issues of low temporal sampling and higher noise levels inherent in cone-beam CT systems, compared to conventional CT.
Methods: Therefore, a model-based perfusion reconstruction based on the time separation technique (TST) was applied.
Precision of in vivo pressure gradient estimations using synthetic aperture ultrasound.
Ultrasonics
January 2025
The Center for Fast Ultrasound Imaging, Department of Health Technology. Technical University of Denmark, Ørsteds Plads Building 349, Lyngby, DK-2800, Denmark.
Non-invasive estimation of pressure differences using 2D synthetic aperture ultrasound imaging offers a precise, low-cost, and risk-free diagnostic tool. Unlike invasive techniques, this preserves natural blood flow and avoids the limitations of devices that occupy lumen space. This paper evaluates a previously published estimator, modified to incorporate Singular Value Decomposition (SVD) echo-cancellation, using data from ten healthy volunteers and one patient.
View Article and Find Full Text PDFEvaluating Neural Network Performance in Predicting Disease Status and Tissue Source of JC Polyomavirus from Patient Isolates Based on the Hypervariable Region of the Viral Genome.
Viruses
December 2024
Life Sciences, Health, and Engineering Department, The Roux Institute, Northeastern University, Portland, ME 04101, USA.
JC polyomavirus (JCPyV) establishes a persistent, asymptomatic kidney infection in most of the population. However, JCPyV can reactivate in immunocompromised individuals and cause progressive multifocal leukoencephalopathy (PML), a fatal demyelinating disease with no approved treatment. Mutations in the hypervariable non-coding control region (NCCR) of the JCPyV genome have been linked to disease outcomes and neuropathogenesis, yet few metanalyses document these associations.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!