This work proposed a novel approach based on principal component analyses (PCAs) to monitor the very early-age hydration of self-compacting concrete (SCC) with varying replacement ratios of fly ash (FA) to cement at 0%, 15%, 30%, 45%, and 60%, respectively. Based on the conductance signatures obtained from electromechanical impedance (EMI) tests, the effect of the FA content on the very early-age hydration of SCCs was indicated by the predominant resonance shifts, the statistical metrics, and the contribution ratios of principal components, quantitatively. Among the three, the PCA-based approach not only provided robust indices to predict the setting times with physical implications but also captured the liquid-solid transition elongation (1.5 h) during the hydration of SCC specimens with increasing FA replacement ratios from 0% to 45%. The results demonstrated that the PCA-based approach was more accurate and robust for quantitative hydration monitoring than the conventional penetration resistance test and the other two counterpart indices based on EMI tests.
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http://dx.doi.org/10.3390/s23073627 | DOI Listing |
Bioinformatics
January 2025
Institute of Medical Systems Biology, Center for Biomedical AI (bAIome), Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany.
Motivation: The identification of biologically meaningful domains is a central step in the analysis of spatial transcriptomic data.
Results: Following Occam's razor, we show that a simple PCA-based algorithm for unsupervised spatial domain identification rivals the performance of ten competing state-of-the-art methods across six single-cell spatial transcriptomic datasets. Our reductionist approach, NichePCA, provides researchers with intuitive domain interpretation and excels in execution speed, robustness, and scalability.
Anal Chem
January 2025
Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Belfast BT9 5DL, Northern Ireland.
Maximizing the extraction of true, high-quality, nonredundant features from biofluids analyzed via LC-MS systems is challenging. Here, the R packages IPO and AutoTuner were used to optimize XCMS parameter settings for the retrieval of metabolite or lipid features in both ionization modes from either faecal or urine samples from two cohorts ( = 621). The feature lists obtained were compared with those where the parameter values were selected manually.
View Article and Find Full Text PDFJ Magn Reson
December 2024
Department of Low-Temperature Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 747/2, 180 00 Prague 8, Czech Republic.
PCA-based denoising usually implies either discarding a number of high-index principal components (PCs) of a data matrix or their attenuation according to a regularization model. This work introduces an alternative, model-free, approach to high-index PC attenuation that seeks to average values of PC vectors as if they were expected from noise perturbation of data. According to the perturbation theory, the average PCs are attenuated versions of the clean PCs of noiseless data - the higher the noise-related content in a PC vector, the lower is its average's norm.
View Article and Find Full Text PDFBMC Public Health
December 2024
Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, Goyang-si, Gyeonggi-do, Korea.
Background: Given the rapid increase in the prevalence of prostate cancer (PCa), identifying its risk factors and developing suitable risk prediction models has important implications for public health. We used machine learning (ML) approach to screen participants with high risk of PCa and, specifically, investigated whether participants with metabolic syndrome (MetS) exhibited an elevated PCa risk.
Methods: A prospective cohort study was performed with 41,837 participants in South Korea.
bioRxiv
October 2024
Center for Magnetic Resonance Research, Radiology, Medical School, University of Minnesota, Minneapolis, Minnesota.
Purpose: to propose a two-step non-local principal component analysis (PCA) method and demonstrate its utility for denoising diffusion tensor MRI (DTI) with a few diffusion directions.
Methods: A two-step denoising pipeline was implemented to ensure accurate patch selection even with high noise levels and was coupled with data preprocessing for g-factor normalization and phase stabilization before data denoising with a non-local PCA algorithm. At the heart of our proposed pipeline was the use of a data-driven optimal shrinkage algorithm to manipulate the singular values in a way that would optimally estimate the noise-free signal.
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