Analysis Of Nuclear Magnetic Resonance Data Research Articles

Determination of the blending parameters of ternary blends containing a terpolymer as a component copolymer by multivariate analysis of NMR data

Heat-resistant ABS resin, an industrial polymer material, is generally prepared by mixing AN/MS bipolymer and/or AN/ST/MS terpolymer with the matrix of ABS resin, where AN, ST and MS denote acrylonitrile, styrene and α-methylstyrene, respectively. In order to predict the thermal properties of ABS resins, it is important to know which copolymers are blended and in what ratio. In this study, statistical structural analysis was performed on polymer blends consisting of AN/ST bipolymer, AN/MS bipolymer and AN/ST/MS terpolymer to predict blending parameters for heat-resistant ABS resin. Nuclear magnetic resonance (NMR) spectral data of the polymer blends were used as explanatory variables to determine the chemical composition and mole fraction of the component copolymers. Partial least-squares (PLS) regression and least absolute shrinkage and selection operator (LASSO) regression were used as the exploratory techniques. The use of 1H NMR spectral data showed poor prediction, probably because of low resolution due to the narrow spectral width. Accordingly, 13C NMR spectra, which have wider spectral widths, were used, resulting in the successful prediction of the blending parameters of the ternary blends containing a terpolymer as a component. It should be noted that it was not necessary to obtain the NMR spectra of all the polymer blends as the explanatory variables, i.e., linear combinations of the NMR spectra of the component copolymers were sufficient.

Supporting the assignment of NMR spectra with variable-temperature experiments.

Nuclear magnetic resonance (NMR) spectroscopy is one of the most powerful tools in analytical chemistry. An important step in the analysis of NMR data is the assignment of resonance frequencies to the corresponding atoms in the molecule being investigated. The traditional approach considers the spectrum's characteristic parameters: chemical shift values, internuclear couplings, and peak intensities. In this paper, we show how to support the process of assigning a series of spectra of similar organic compounds by using temperature coefficients, that is, the rates of change in chemical shift values associated with given changes in temperature.

Efficient NMR measurement and data analysis supported by the Bayesian inference: The case of the heavy fermion compound YbCo[formula omitted]Zn20

We propose a data-driven technique to infer microscopic physical quantities from nuclear magnetic resonance (NMR) spectra, in which the data size and quality required for the Bayesian inference are investigated. The 59Co-NMR measurement of YbCo2Zn20 single crystal generates complex spectra with 28 peaks. By exploiting the site symmetry in the crystal structure, the isotropic Knight shift Kiso and nuclear quadrupole resonance (NQR) frequency νQ were respectively estimated to be Kiso=0.7822±0.0090% and νQ=2.008±0.016 MHz (T=20 K and H≃10.2 T) by analyzing only 30 data points from one spectrum. The estimated νQ is consistent with the precise value obtained in the NQR experiment. Our method can significantly reduce the measurement time and the computational cost of data analysis in NMR experiments.

Determination of nuclear magnetic resonance T2 cutoff in remoulded loess by the freezing point

AbstractProton nuclear magnetic resonance (NMR) is widely applied to characterize the microscopic properties of hydrogen‐containing porous media. The transverse relaxation time cutoff (T2c) value is the crucial parameter for the quantitative analysis of NMR data. Currently, there is no universal method for the determination of the T2c in clayey soils. This study aimed to develop a laboratory method for determining the T2c of remoulded loess by the freezing point of loosely bound water. Malan loess, a kind of typical clayey silt, was used as test material. Based on the soil freezing characteristic, NMR measurements were performed on remoulded loess with different macro‐parameter controls during the cooling process to obtain the T2 spectrum at each target temperature. By analysing the variation of unfrozen water content with temperature reduction, the freezing point of loosely bound water and the T2c value within the freezing‐point range was determined. The freezing point of loosely bound water in remoulded loess is about −3 to −5°C and that of firmly bound water is less than −5°C. Accordingly, the T2c value of remoulded loess is determined to be 1.5–1.8 ms. The assessment of heating and cooling process and different methods for determining the T2c shows that the laboratory method by the freezing point is effective and reliable, and the T2c determined by statistical methods is worthy of further study and improvement. The saturated permeability of remoulded loess is evaluated according to the determined T2c, and two NMR‐based permeability equations can well reflect pore water distribution in remoulded loess, but to a certain extent, both equations ignore soil microstructure, pore connectivity and chemical effects of pore solution. The laboratory method by the freezing point and the determined T2c value of remoulded loess fill the gap of NMR measurement in loess analysis and are of great significance for low‐plastic clays and clay types.

NMR Analysis of the Interactions and Conformational Plasticity of Dynein Intermediate Chain.

Cytoplasmic dynein complexes play crucial roles in intracellular transport of cellular organelles. While the motor domain of dynein is well characterized by techniques such as X-ray crystallography and cryo-electron microscopy (Cryo-EM), structural representations of dynein usually include only the more packed and easily resolved regions and omit the long flexible and poorly structured regions. One such flexible region is the N-terminal half of the intermediate chain (IC), which contains almost 300 amino acids that are predicted to be disordered. This level of disorder makes IC impossible to study by X-ray crystallography and Cryo-EM, but amenable to study by solution nuclear magnetic resonance (NMR), a powerful technique that can elucidate residue-specific information in adynamic ensemble of structures, and transient binding interactions of associated proteins. Here, we describe the methods we use to characterize flexible and disordered proteins including protein expression, purification, sample preparation, and NMR data acquisition and analysis.

Rapid protein assignments and structures from raw NMR spectra with the deep learning technique ARTINA

Nuclear Magnetic Resonance (NMR) spectroscopy is a major technique in structural biology with over 11,800 protein structures deposited in the Protein Data Bank. NMR can elucidate structures and dynamics of small and medium size proteins in solution, living cells, and solids, but has been limited by the tedious data analysis process. It typically requires weeks or months of manual work of a trained expert to turn NMR measurements into a protein structure. Automation of this process is an open problem, formulated in the field over 30 years ago. We present a solution to this challenge that enables the completely automated analysis of protein NMR data within hours after completing the measurements. Using only NMR spectra and the protein sequence as input, our machine learning-based method, ARTINA, delivers signal positions, resonance assignments, and structures strictly without human intervention. Tested on a 100-protein benchmark comprising 1329 multidimensional NMR spectra, ARTINA demonstrated its ability to solve structures with 1.44 Å median RMSD to the PDB reference and to identify 91.36% correct NMR resonance assignments. ARTINA can be used by non-experts, reducing the effort for a protein assignment or structure determination by NMR essentially to the preparation of the sample and the spectra measurements.

Combining NMR and MS to Describe Pyrrole-2-Carbaldehydes in Wheat Bran of Radiation.

Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are indispensable analytical tools to provide chemical fingerprints in metabolomics studies. The present study evaluated radiation breeding wheat lines for chemical changes by non-targeted NMR-based metabolomics analysis of bran extracts. Multivariate analysis following spectral binning suggested pyrrole-2-carbaldehydes as chemical markers of four mutant lines with distinct NMR fingerprints in a δH range of 9.28-9.40 ppm. Further NMR and MS data analysis, along with chromatographic fractionation and synthetic preparation, aimed at structure identification of marker metabolites and identified five pyrrole-2-carbaldehydes. Quantum-mechanical driven 1H iterative full spin analysis (QM-HiFSA) on synthetic pyrrole-2-carbaldehydes provided a precise description of complex peak patterns. Biological evaluation of pyrrole-2-carbaldehydes was performed with nine synthetic products, and six compounds showed hepatoprotective effects via modulation of reactive oxygen species production. Given that three out of five identified in wheat bran of radiation were described for hepatoprotective activity, the value of radiation mutation to greatly enhance pyrrole-2-carbaldehyde production was supported.

Occurrence characteristics of the movable fluid in heterogeneous sandstone reservoir based on fractal analysis of NMR data: A case study of the Chang 7 Member of Ansai Block, Ordos Basin, China

In the Ordos Basin, Chang 7 sandstone is considered an essential tight sandstone reservoir. In this research, the occurrence characteristics of the movable fluid, fractal characteristics, and their relationship in Chang 7 reservoir of the Ansai Block, Ordos Basin, have been quantitatively evaluated through nuclear magnetic resonance (NMR) and fractal theory. Moreover, the factors influencing the fractal dimensions and movable fluid distribution have been determined through a series of experiments, i.e., conventional physical property, cast thin section, scanning electron microscope (SEM), high-pressure mercury intrusion (HPMI), and X-ray diffraction technique (XRD). It has been noticed the movable fluid distribution in the tight sandstone reservoirs is greatly affected by the pore structure heterogeneity. Results show the main pore types in Chang 7 are the intergranular dissolution pores, intragranular dissolution pores, residual intergranular pores, and inter-crystalline pores. The pore size chiefly ranges from 0.1 nm–5 μm, while the main fluids storage spaces are nanopores and submicron pores. The reservoir has been divided into three types (type Ⅰ, Ⅱ, and Ⅲ) based on the distribution of the T2 spectrum and petrophysical parameters. Moreover, these reservoirs have various movable fluid distributions and fractal structures. Multiple factors such as pore structure, pore size distribution, and mineral compositions are discussed, which influence the fractal dimension and movable fluid saturation. The reservoirs having more nanopores are often associated with strong heterogeneity and low movable fluid content. The fractal dimension is weakly affected by pore structure parameters and mineral compositions. The movable fluid saturation is significantly affected by pore structure while weakly affected by mineral compositions. The relationship between heterogeneity and movable fluid distribution varies with reservoir types. The inhibition of heterogeneity on the movable fluid of different reservoirs is mainly reflected in various pores. Movable fluids controlled by throats are primarily distributed in different pores, resulting in diverse effects of heterogeneity on controlling the effect of different throats in various reservoirs. The findings of this research can help better understand the occurrence characteristics of the movable fluid in tight sandstone reservoirs with different fractal features.

Chemometric strategy for aligning chemical shifts in 1H NMR to improve geographical origin discrimination: A case study for Chinese Goji honey

1H nuclear magnetic resonance (NMR)-based untargeted metabolomics has been extensively used for the geographical discrimination of food samples. In analyzing complex samples, this technique faces the challenges of baseline drift and NMR peak position shifts, resulting in inaccurate geographical discrimination by current chemometric models, like partial least squares. To address this problem, we provide a novel automatic untargeted chemometrics strategy named AntDAS-NMR in this work. AntDAS-NMR utilizes the originally acquired NMR spectra as inputs to automatically perform baseline drift correction, NMR peak detection, NMR peak-based spectrum alignment, and geographical discrimination analysis. The developed strategy is used for the geographical discrimination of Goji honey samples from the northwest zones of China. Results indicate that with the aid of AntDAS-NMR, the artifacts of baseline drifts and NMR peak shift that influence the geographical discrimination model can be reasonably resolved and that Goji honey samples from various provinces of China can be satisfactorily classified. Additionally, the performance of AntDAS-NMR is comparable with classic NMR data analysis tools, like COW, icoshift, PAFFT, and RAFFT. In conclusion, the AntDAS-NMR may be used as a candidate for 1H NMR-based untargeted metabolomics.

5f -electron magnetism in single crystal UN probed by N14 NMR

Spin susceptibility and low-frequency dynamics of uranium $5f$ electrons have been investigated by nuclear magnetic resonance (NMR) on the $^{14}\mathrm{N}$ nuclei in paramagnetic and magnetically ordered phases for single crystalline and polycrystalline samples of uranium mononitride (UN). NMR spectra, shifts of the $^{14}\mathrm{N}$ NMR lines, and the spin-lattice relaxation times ${T}_{1}$ have been obtained in the temperature range $T=10--760\phantom{\rule{0.16em}{0ex}}\mathrm{K}$ in magnetic field $B=92.8\phantom{\rule{0.16em}{0ex}}\mathrm{kOe}$. It is shown that in the UN paramagnetic phase, temperature dependence of the $^{14}\mathrm{N}$ NMR line shift is proportional to the spin susceptibility of the uranium $5f$ electrons. Joint analysis of NMR and magnetic susceptibility data allows us to determine temperature dependence of spin fluctuation energy ${\mathrm{\ensuremath{\Gamma}}}_{\mathrm{nmr}}(T)$ of the uranium $5f$ electrons and to demonstrate that its temperature variation is close to $\mathrm{\ensuremath{\Gamma}}(T)\ensuremath{\propto}{T}^{0.5}$ dependence which is characteristic of the concentrated Kondo systems above the coherent state formation temperature. In the magnetically ordered UN phase the $^{14}\mathrm{N}$ NMR spectra consist of several lines that can be explained in terms of the model of type $I$ antiferromagnetic order corresponding to $1k$ structure in the presence of magnetic domains.

Applying multimodal data fusion based on manifold learning with nuclear magnetic resonance (NMR) and near infrared spectroscopy (NIRS) to maize haploid identification

The fusion of multi-source data obtained by multimodal sensors has recently attracted attention. The manifold learning method has been applied to data fusion problems because of its ability to extract the underlying structure of data, and a new data fusion method has been developed called alternating diffusion maps. In practical application, some shortcomings of this method were discovered. Firstly, it is based on diffusion process, which is more suitable for clustering tasks than classification tasks because of its clustering characteristics; secondly, in possible data overlapping areas the diffusion process between different classes of samples makes the performance of the algorithm decline rapidly; finally, the lack of explicit mapping makes it difficult to extend to new samples. In response to these problems, this paper proposes a new kernel width selection method with discrimination effect, which can be applied to classification tasks and possible overlapping area. In addition, Nystrom method is extended to solve out-of-sample problem. The improved framework to the identification of maize haploids and proposed for the first time to carry out fusion analysis on the data obtained by NMR and NIRS measurement equipment. The experimental results show that our method has a significant improvement in the classification task of unclear class boundaries of maize kernel, up to about 9%, which confirmed the effectiveness of the fusion of NMR and NIRS data for classification and the superiority of our proposed framework. • Fusion analysis of NMR and NIR data was proposed. • Data fusion method based on manifold learning applied to haploid identification. • Fusion of data improved classification accuracy of haploid maize kernels.

Magnetic properties of the itinerant ferromagnet LaCrGe3 under pressure studied by La139 NMR

$^{139}$La nuclear magnetic resonance (NMR) measurements under pressure ($p = 0-2.64$ GPa) have been carried out to investigate the static and dynamic magnetic properties of the itinerant ferromagnet LaCrGe$_3$. $^{139}$La-NMR spectra for all measured pressures in the ferromagnetically ordered state show a large shift due to the internal field induction $|$$B_{\rm int}$$|$ $\sim$ 4 T at the La site produced by Cr ordered moments. The change in $B_{\rm int}$ by less than 5\% with $p$ up to 2.64~GPa indicates that the Cr 3$d$ moments are robust under pressure. The temperature dependence of NMR shift and $B_{\rm int}$ suggest that the ferromagnetic order develops below $\sim$ 50~K under higher pressures in a magnetic field of $\sim$ 7.2 T. Based on the analysis of NMR data using the self-consistent-renormalization (SCR) theory, the spin fluctuations in the paramagnetic state well above $T_{\rm C}$ are revealed to be three dimensional ferromagnetic throughout the measured $p$ region.

Solid-state NMR spectroscopy.

Solid-state nuclear magnetic resonance (NMR) spectroscopy is an atomic-level method used to determine the chemical structure, three-dimensional structure, and dynamics of solids and semi-solids. This Primer summarizes the basic principles of NMR as applied to the wide range of solid systems. The fundamental nuclear spin interactions and the effects of magnetic fields and radiofrequency pulses on nuclear spins are the same as in liquid-state NMR. However, because of the anisotropy of the interactions in the solid state, the majority of high-resolution solid-state NMR spectra is measured under magic-angle spinning (MAS), which has profound effects on the types of radiofrequency pulse sequences required to extract structural and dynamical information. We describe the most common MAS NMR experiments and data analysis approaches for investigating biological macromolecules, organic materials, and inorganic solids. Continuing development of sensitivity-enhancement approaches, including 1H-detected fast MAS experiments, dynamic nuclear polarization, and experiments tailored to ultrahigh magnetic fields, is described. We highlight recent applications of solid-state NMR to biological and materials chemistry. The Primer ends with a discussion of current limitations of NMR to study solids, and points to future avenues of development to further enhance the capabilities of this sophisticated spectroscopy for new applications.

NMR-Based Metabolomics.

Nuclear magnetic resonance (NMR) spectroscopy is a major analytical method used in the growing field of metabolomics. Although NMR is relatively less sensitive than mass spectrometry, this analytical platform has numerous characteristics including its high reproducibility and quantitative abilities, its nonselective and noninvasive nature, and the ability to identify unknown metabolites in complex mixtures and trace the downstream products of isotope labeled substrates ex vivo, in vivo, or in vitro. Metabolomic analysis of highly complex biological mixtures has benefitted from the advances in both NMR data acquisition and analysis methods. Although metabolomics applications span a wide range of disciplines, a majority has focused on understanding, preventing, diagnosing, and managing human diseases. This chapter describes NMR-based methods relevant to the rapidly expanding metabolomics field.

CHESPA/CHESCA-SPARKY: automated NMR data analysis plugins for SPARKY to map protein allostery.

Correlated Nuclear Magnetic Resonance (NMR) chemical shift changes identified through the CHEmical Shift Projection Analysis (CHESPA) and CHEmical Shift Covariance Analysis (CHESCA) reveal pathways of allosteric transitions in biological macromolecules. To address the need for an automated platform that implements CHESPA and CHESCA and integrates them with other NMR analysis software packages, we introduce here integrated plugins for NMRFAM-SPARKY that implement the seamless detection and visualization of allosteric networks. CHESCA-SPARKY and CHESPA-SPARKY are available in the latest version of NMRFAM-SPARKY from the National Magnetic Resonance Facility at Madison (http://pine.nmrfam.wisc.edu/download_packages.html), the NMRbox Project (https://nmrbox.org) and to subscribers to the SBGrid (https://sbgrid.org). The assigned spectra involved in this study and tutorial videos using this dataset are available at https://sites.google.com/view/chescachespa-sparky. Supplementary data are available at Bioinformatics Online.

Screening for Early Gastric Cancer Using a Noninvasive Urine Metabolomics Approach.

Simple SummaryThere are currently no effective specific biomarkers for the screening of early gastric cancer. Recently, metabolomics has been used to profile small endogenous metabolites, demonstrating significant potential in the diagnosis/screening of cancer, owing to its ability to conduct a noninvasive sample analysis. Here, we performed a urine metabolomics analysis in the context of an early diagnosis of gastric cancer. This approach showed very high diagnostic sensitivity and specificity and performed significantly better than the analysis of serum tumor markers modalities. An additional genomic data analysis revealed the up-regulation of several genes in gastric cancer. This metabolomics-based early diagnosis approach may have the potential for mass screening an average-risk population and may facilitate endoscopic examination through risk stratification.The early detection of gastric cancer (GC) could decrease its incidence and mortality. However, there are currently no accurate noninvasive markers for GC screening. Therefore, we developed a noninvasive diagnostic approach, employing urine nuclear magnetic resonance (NMR) metabolomics, to discover putative metabolic markers associated with GC. Changes in urine metabolite levels during oncogenesis were evaluated using samples from 103 patients with GC and 100 age- and sex-matched healthy controls. Approximately 70% of the patients with GC (n = 69) had stage I GC, with the majority (n = 56) having intramucosal cancer. A multivariate statistical analysis of the urine NMR data well discriminated between the patient and control groups and revealed nine metabolites, including alanine, citrate, creatine, creatinine, glycerol, hippurate, phenylalanine, taurine, and 3-hydroxybutyrate, that contributed to the difference. A diagnostic performance test with a separate validation set exhibited a sensitivity and specificity of more than 90%, even with the intramucosal cancer samples only. In conclusion, the NMR-based urine metabolomics approach may have potential as a convenient screening method for the early detection of GC and may facilitate consequent endoscopic examination through risk stratification.

CRAFT for NMR lipidomics: Targeting lipid metabolism in leucine-supplemented tumor-bearing mice.

Lipid profiling by 1 H-NMR has gained increasing utility in many fields because of its intrinsically quantitative, nondestructive nature and the ability to differentiate small molecules based on their spectral location. Most nuclear magnetic resonance (NMR) techniques for metabolite quantification use frequency domain analysis that involves many user-dependent steps such as phase and baseline correction and quantification by either manual integration or peak fitting. Recently, Bayesian analysis of time-domain NMR data has been shown to reduce operator bias and increase automation in NMR spectroscopy. In this study, we demonstrate the use of CRAFT (complete reduction to amplitude-frequency table), a Bayesian-based approach to automate processing in NMR-based lipidomics using lipid standards and tissue samples of healthy and tumor-bearing mice supplemented with leucine. Complex mixtures of lipid standards were prepared and examined using CRAFT to validate it against conventional Fourier transform (FT)-NMR and derive a fingerprint to be used for analyzing lipid profiles of serum and liver samples. CRAFT and FT-NMR were comparable in accuracy, with CRAFT achieving higher correlation in quantifying several lipid species. Analysis of the serum lipidome of tumor-bearing mice revealed hyperlipidemia and no signs of hepatic triglyceride accumulation compared with that of the healthy group demonstrating that the tumor-bearing mice were in a state of precachexia. Leucine-supplementation was associated with minimal changes in the lipid profile in both tissues. In conclusion, our study demonstrates that the CRAFT method can accurately identify and quantify lipids in complex lipid mixtures and murine tissue samples and, hence, will increase automation and reproducibility in NMR-based lipidomics.

13C NMR Dataset Qualitative Analysis of Grecian Wines

The development of analytical techniques for characterizing food samples, especially for the wine industry, is a main topic of research. Regarding the classification of wines based on their geographical origin, nuclear magnetic resonance (NMR) spectroscopy represents a fast and effective tool for determining chemical fingerprints. Herein, a 13C NMR dataset, which was acquired for classification of Grecian wines through multivariate statistics, is reported and described. Thus, the main qualitative differences between grapes of the same geographical origin, observable by the visual analysis of the 13C NMR data, are discussed.

Discovery of Three 22-Membered Macrolides by Deciphering the Streamlined Genome of Mangrove-Derived Streptomyces sp. HM190.

Strain HM190, a moderate halophile, was isolated from rhizosphere soil of the mangrove Kandelia obovata in Fugong village, China. The 16S ribosomal RNA (rRNA) gene sequence and the results of phylogenetic analysis revealed that strain HM190 belonged to the genus Streptomyces and had the highest sequence similarity of 99.79% to Streptomyces heilongjiangensis NEAU-W2T. The complete genome of strain HM190 comprised 7,762,826 bp in a linear chromosome with 71.97% G + C content. According to antiSMASH analysis, a total of 30 biosynthetic gene clusters (BGCs) were predicted to be involved in secondary metabolism, 12 of which were responsible for the production of polyketide- and non-ribosomal peptide-derived secondary metabolites. Gene cluster 5 was responsible for macrolide biosynthesis in a strain-specific 126,331-bp genomic island belonging to the left-arm region. Combined genomics–metabolomics analysis led to the discovery of three 22-membered macrolides (compounds 1–3). Their structures were elucidated by using spectroscopic techniques including high-resolution electrospray ionization mass spectroscopy (HRESIMS) and nuclear magnetic resonance (NMR). The absolute configurations of compounds 1–3 were determined by the X-ray single crystal diffraction and NMR data analysis. All three compounds displayed moderate cytotoxic activities toward tumor cell lines HepG2, A549, and HCT116.

Extraction of High-Purity Lignins via Catalyst-free Organosolv Pulping from Low-Input Crops.

A catalyst-free organosolv pulping process was applied to cup plant (Silphium perfoliatum, S), Miscanthus grass (Miscanthus x giganteus, M), and the Paulownia tree (Paulownia tomentosa, P), resulting in high-purity lignins with no signals for cellulose, hemicellulose, or other impurities in two-dimensional heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra. Different biomass particle sizes used for the organosolv pulping (1.6-2.0 mm (1); 0.5-1.0 mm (2); <0.25 mm (3)) influenced the molecular weight and chemical structure of the isolated lignins. Principal component analysis (PCA) of 1H NMR data revealed a high intergroup variance of Miscanthus and Paulownia lignins, separating the small particle fraction from the larger ones. Furthermore, monolignol ratios identified via HSQC NMR differ significantly: Miscanthus lignins were composed of all three monolignols (guaiacyl (G), p-hydroxyphenyl (H), syringyl (S)), while for Paulownia and Silphium lignins only G and S units were observed (except for P3).