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  1. Rapid progress in machine learning offers new opportunities for the automated analysis of multidimensional NMR spectra ranging from protein NMR to metabolomics applications. Most recently, it has been demonstrated how deep neural networks (DNN) designed for spectral peak picking are capable of deconvoluting highly crowded NMR spectra rivaling the facilities of human experts. Superior DNN-based peak picking is one of a series of critical steps during NMR spectral processing, analysis, and interpretation where machine learning is expected to have a major impact. In this perspective, we lay out some of the unique strengths as well as challenges of machine learningmore »approaches in this new era of automated NMR spectral analysis. Such a discussion seems timely and should help define common goals for the NMR community, the sharing of software tools and standardization of protocols, and calibrate expectations. It will also help prepare for an NMR future where machine learning and artificial intelligence tools will be common place.« less
    Free, publicly-accessible full text available April 1, 2023
  2. Sissa (Ed.)
    We discuss recent CTEQ-TEA group activities after the publication of the CT18 global analysis of parton distribution functions (PDFs) in the proton. In particular, we discuss a new calculation for the photon content in the proton, termed as CT18lux and CT18qed PDFs, and the impact of novel charm- and bottom-quark production cross section measurements at HERA on the CT18 global analysis.
    Free, publicly-accessible full text available March 8, 2023
  3. Matsuno, F. ; Azuma, Si. ; Yamamoto, M. (Ed.)
    Free, publicly-accessible full text available January 3, 2023
  4. Free, publicly-accessible full text available December 1, 2022
  5. The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems. Here, we introduce DEEP Picker, a deep neural network (DNN)-based approach for peak picking and spectral deconvolution which semi-automates the analysis of two-dimensional NMR spectra. DEEP Picker includes 8 hidden convolutional layers and was trained on a large number of synthetic spectra of known composition with variable degrees of crowdedness. We show that our method is able to correctly identify overlapping peaks, including ones that aremore »challenging for expert spectroscopists and existing computational methods alike. We demonstrate the utility of DEEP Picker on NMR spectra of folded and intrinsically disordered proteins as well as a complex metabolomics mixture, and show how it provides access to valuable NMR information. DEEP Picker should facilitate the semi-automation and standardization of protocols for better consistency and sharing of results within the scientific community.« less
    Free, publicly-accessible full text available October 1, 2022
  6. Free, publicly-accessible full text available October 1, 2022
  7. Free, publicly-accessible full text available July 1, 2022