Quantitative Accuracy and Precision in Multiplexed Single-Cell Proteomics
Sign Up to like & getrecommendations! Published in 2021 at "Analytical Chemistry"
DOI: 10.1021/acs.analchem.1c04174
Abstract: Single-cell proteomics workflows have considerably improved in sensitivity and reproducibility to characterize as-yet unknown biological phenomena. With the emergence of multiplexed single-cell proteomics, studies increasingly present single-cell measurements in conjunction with an abundant congruent carrier… read more here.
Keywords: multiplexed single; single cell; cell; cell proteomics ... See more keywords
Assessment of the quantitative accuracy of Rietveld/XRD analysis of crystalline and amorphous phases in fly ash
Sign Up to like & getrecommendations! Published in 2017 at "Analytical Methods"
DOI: 10.1039/c7ay00337d
Abstract: An internal standard method based on Rietveld/XRD whole-pattern fitting analysis of fly ash is used to assess the quantitative accuracy to determine its crystalline and amorphous phases under various conditions such as internal standards (types,… read more here.
Keywords: amorphous phases; rietveld xrd; internal standard; crystalline amorphous ... See more keywords
An investigation of quantitative accuracy for deep learning based denoising in oncological PET.
Sign Up to like & getrecommendations! Published in 2019 at "Physics in medicine and biology"
DOI: 10.1088/1361-6560/ab3242
Abstract: Reducing radiation dose is important for PET imaging. However, reducing injection doses causes increased image noise and low signal-to-noise ratio (SNR), subsequently affecting diagnostic and quantitative accuracies. Deep learning methods have shown a great potential… read more here.
Keywords: image; deep learning; sub; learning based ... See more keywords
Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy.
Sign Up to like & getrecommendations! Published in 2020 at "Physical review letters"
DOI: 10.1103/physrevlett.124.156401
Abstract: Simulations of excited state properties, such as spectral functions, are often computationally expensive and therefore not suitable for high-throughput modeling. As a proof of principle, we demonstrate that graph-based neural networks can be used to… read more here.
Keywords: machine learning; ray absorption; quantitative accuracy; learning ray ... See more keywords