Session ThOC. There are 5 abstracts in this session.

Session: SPEED (BEYOND FOURIER) AND SENSITIVITY, time: 08:30 - 8:45 am

Using Deep Neural Networks to Reconstruct and Analyse Non-Uniformly Sampled NMR Spectra

D. Flemming Hansen
ISMB, Univ. College London, London, United Kingdom

Non-uniform and sparse sampling of multi-dimensional NMR spectra has over the last decade become an important tool to allow for fast acquisition of multi-dimensional NMR spectra with high resolution. Also over the last decade, deep neural networks and artificial intelligence have seen new applications in an enormous range of sciences, including analysis of MRI spectra. A proof-of-principle and deep neural networks trained to reconstruct sparsely sampled 2D and 3D NMR spectra will be presented. For reconstruction of two-dimensional NMR spectra, deep neural network performs as well, if not better than, the currently used techniques. It is anticipated that deep learning provides a very valuable tool for the analysis of sparsely sampled NMR spectra in the near future to come.

Session: SPEED (BEYOND FOURIER) AND SENSITIVITY, time: 08:45-9:10 am

Acceleration of in vivo magnetic resonance spectroscopic imaging using non-conventional approaches

Anke Henning
UT Southwestern Medical Center, Dallas, TX

Over more than 30 years in vivo magnetic resonance spectroscopic imaging (MRSI) has undergone an enormous evolution. The development of both fast and efficient sampling and reconstruction techniques has played a fundamental role in this process. State-of-the-art MRSI has grown from a slow purely cartesian phase-encoded acquisition technique to a method that combines the benefits of different acceleration techniques. These include shortening of repetition times, spatial-spectral encoding, uniform and non-uniform undersampling of k-space and time domain, non-cartesian encoding and use of spatial/spectral prior knowledge in the reconstruction. This presentation highlights recent developments with respect to MRSI acceleration that exploit novel trends in image reconstruction such as the use of machine learning and low rank approximation and much improved computation power.

Session: SPEED (BEYOND FOURIER) AND SENSITIVITY, time: 09:20 -9:35 am

Evaluation of NUS 13C-1H HSQC NMR Spectroscopy for Semi-Quantitative Metabolomics

Bo Zhang1; Robert Powers2; Elizabeth ODay1
1Olaris, Waltham, Massachusetts; 2University of Nebraska-Lincoln, Lincoln, NE

Metabolomics is the study of metabolism, the biochemical processes that allows organisms to grow, reproduce, maintain their structures and respond to genetic and environmental factors. By comparing metabolites between healthy and disease states new insights can be uncovered. Non-uniform sampling (NUS) is a well-accepted mode of acquiring multi-dimensional NMR data enabling either reduced acquisition times or increased sensitivity in equivalent time.  Despite these advantages the technique has not been widely applied to metabolomics. Here we demonstrate using model mixtures and human plasma that NUS based HSQC NMR spectroscopy provides a sensitive, quantitative and highly reproducible platform for metabolite profiling. 

Session: SPEED (BEYOND FOURIER) AND SENSITIVITY, time: 09:35 - 9:50 am

NMRFx:  New Advances in an Integrated Cross-Platform NMR Software Application

Martha Beckwith; Teddy Cohen; Ellen Koag; Audrigue Jean-Louis; Bruce Johnson
CUNY Advanced Science Research Ctr., New York, NY

NMRFx is an integrated suite of three cross-platform programs for the analysis of NMR data.  NMRFx Processor is a processing program for full signal processing of 1D and multi-dimensional NMR data.  NMRFx Structure is a molecular structure program that can generate three-dimensional structures based on input constraints and can predict NMR chemical shifts.  NMRFx Analyst is a new NMR visualization and analysis program and has access to both the processing and structure analysis features of the other two components.  In this presentation we describe recent developments in NMRFx that include new processing tools for NUS datasets, improved chemical shift prediction tools, and new tools for following titrations of ligands and pressure induced changes.

Session: SPEED (BEYOND FOURIER) AND SENSITIVITY, time: 09:50 - 10:05 am

SCRUFI: A Matlab toolbox for the analysis of INADEQUATE spectra

David Bennett; Iain Day
University of Sussex, Brighton, United Kingdom

Determining the heavy atom connectivity is the one of the key steps for structure determination. In principle, despite its inherently poor sensitivity, the INADEQUATE experiment gives these connectivities directly. 

The automation of spectral analysis allows correlations to be identified. Parameterisation of the peak shape allows an automated search over all possible expected correlations. Correlations can then be classified by comparison with the  spectral noise floor. Ultimately a connectivity diagram can be produced directly from the spectrum.

Here we present SCRUFI (Skeletal Connectivity Revealed Using Fitted Inadequate), a Matlab toolbox and utility designed to automate the analysis of INADEQUATE spectra. We achieve this by a modern implementation of the approach of Dunkel.