Spin Liquid State in an Organic Mott Insulator with Triangular Lattice

$^{1}$H NMR and static susceptibility measurements have been performed in an organic Mott insulator with nearly isotropic triangular lattice, $\kappa$-(BEDT-TTF)$_{2}$Cu$_{2}$(CN)$_{3}$, which is a model system of frustrated quantum spins. The static susceptibility is described by the spin $S$ = 1/2 antiferromagnetic triangular-lattice Heisenberg model with the exchange constant $J$ $\sim$ 250 K. Regardless of the large magnetic interactions, the $^{1}$H NMR spectra show no indication of long-range magnetic ordering down to 32 mK, which is four-orders of magnitude smaller than $J$. These results suggest that a quantum spin liquid state is realized in the close proximity of the superconducting state appearing under pressure.Comment: 4 pages, 4 figure

Metabolomic profiling predicts outcome of rituximab therapy in rheumatoid arthritis.

ObjectiveTo determine whether characterisation of patients' metabolic profiles, utilising nuclear magnetic resonance (NMR) and mass spectrometry (MS), could predict response to rituximab therapy. 23 patients with active, seropositive rheumatoid arthritis (RA) on concomitant methotrexate were treated with rituximab. Patients were grouped into responders and non-responders according to the American College of Rheumatology improvement criteria, at a 20% level at 6 months. A Bruker Avance 700 MHz spectrometer and a Thermo Scientific Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometer were used to acquire (1)H-NMR and ultra high pressure liquid chromatography (UPLC)-MS/MS spectra, respectively, of serum samples before and after rituximab therapy. Data processing and statistical analysis were performed in MATLAB. 14 patients were characterised as responders, and 9 patients were considered non-responders. 7 polar metabolites (phenylalanine, 2-hydroxyvalerate, succinate, choline, glycine, acetoacetate and tyrosine) and 15 lipid species were different between responders and non-responders at baseline. Phosphatidylethanolamines, phosphatidyserines and phosphatidylglycerols were downregulated in responders. An opposite trend was observed in phosphatidylinositols. At 6 months, 5 polar metabolites (succinate, taurine, lactate, pyruvate and aspartate) and 37 lipids were different between groups. The relationship between serum metabolic profiles and clinical response to rituximab suggests that (1)H-NMR and UPLC-MS/MS may be promising tools for predicting response to rituximab

CHARACTERIZATION OF EGCG COMPOUND USE H NMR SPECTRUM ON CAMELLIA SINENSIS (L.) CALLUS

Epigallocatechin gallate (EGCG) are secondary metabolite on Camellia sinensis L as obesity preventing agent. The characterisation of this plant use 1 H NMR spectroscopy often have been done, however characterisation on callus both drying with open air and without drying is rare. The purpose of this research is characterize EGCG of tea callus via process both drying with open air and vacuum. Tis method use 1 H NMR spectroscopy. The result show that EGCG character of tea callus via process both drying with open air and vacuum are significantly differen

Anomalous Larmour Frequency Dependence of Proton Spin-Lattice Relaxation Time (T1_1) in the Ferroelectric Glycine Phosphite

We report here the results of $^1$H NMR spin-lattice relaxation time (T$_1$) studies in Glycine phosphite which is a ferroelectric below 224 K. The experiments have been carried out in the temperature range from 200 K to 419 K and at two Larmour frequencies of 11.40MHz and 23.56 MHz. We have noticed a Larmour frequency dependence on the high temperature side of the T$_1$ minimum >. A model is proposed based on the BPP theory to explain the observation.Comment: 4 pages, 2 ps figs included, revtex forma

Marked increase in PROP taste responsiveness following oral supplementation with selected salivary proteins or their related free amino acids

The genetic predisposition to taste 6-n-propylthiouracil (PROP) varies among individuals and is associated with salivary levels of Ps-1 and II-2 peptides, belonging to the basic proline-rich protein family (bPRP). We evaluated the role of these proteins and free amino acids that selectively interact with the PROP molecule, in modulating bitter taste responsiveness. Subjects were classified by their PROP taster status based on ratings of perceived taste intensity for PROP and NaCl solutions. Quantitative and qualitative determinations of Ps-1 and II-2 proteins in unstimulated saliva were performed by HPLC-ESI-MS analysis. Subjects rated PROP bitterness after supplementation with Ps-1 and II-2, and two amino acids (L-Arg and L-Lys) whose interaction with PROP was demonstrated by (1)H-NMR spectroscopy. ANOVA showed that salivary levels of II-2 and Ps-1 proteins were higher in unstimulated saliva of PROP super-tasters and medium tasters than in non-tasters. Supplementation of Ps-1 protein in individuals lacking it in saliva enhanced their PROP bitter taste responsiveness, and this effect was specific to the non-taster group.(1)H-NMR results showed that the interaction between PROP and L-Arg is stronger than that involving L-Lys, and taste experiments confirmed that oral supplementation with these two amino acids increased PROP bitterness intensity, more for L-Arg than for L-Lys. These data suggest that Ps-1 protein facilitates PROP bitter taste perception and identifies a role for free L-Arg and L-Lys in PROP tasting

Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection.

We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data

NMR Evidence for Antiferromagnetic Transition in the Single-Component Molecular Conductor, [Au(tmdt)_{2}] at 110 K

We present the results of a ^{1}H NMR study of the single-component molecular conductor, [Au(tmdt)_{2}]. A steep increase in the NMR line width and a peak formation of the nuclear spin-lattice relaxation rate, 1/T_{1}, were observed at around 110 K. This behavior provides clear and microscopic evidences for a magnetic phase transition at considerably high temperature among organic conductors. The observed variation in 1/T_{1} with respect to temperature indicates the highly correlated nature of the metallic phase.Comment: 5pages, 6figures to be published in J. Phys. Soc. Jp

Differentiation of meat species of raw and processed meat based on polar metabolites using 1H NMR spectroscopy combined with multivariate data analysis

Meat species of raw meat and processed meat products were investigated by $^1$H NMR spectroscopy with subsequent multivariate data analysis. Sample preparation was based on aqueous extraction combined with ultrafiltration in order to reduce macromolecular components in the extracts. $^1$H NMR data was analyzed by using a non—targeted approach followed by principal component analysis (PCA), linear discrimination analysis (LDA), and cross-validation (CV) embedded in a Monte Carlo (MC) resampling approach. A total of 379 raw meat samples (pork, beef, poultry, and lamb) and 81 processed meat samples (pork, beef, poultry) were collected between the years 2018 and 2021. A 99% correct prediction rate was achieved if the raw meat samples were classified according to meat species. Predicting processed meat products was slightly less successful (93 %) with this approach. Furthermore, identification of spectral regions that are relevant for the classification via polar chemical markers was performed. Finally, data on polar metabolites were fused with previously published $^1$H NMR data on non-polar metabolites in order to build a broader classification model and to improve prediction accuracy