Deep learning can accelerate and quantify simulated localized correlated spectroscopy

Nuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic structures and concentrations of different chemicals in a biochemical sample of interest. MRS is used in vivo clinically to aid in the diagnosis of several pathologies that affect metabolic pathways in the body. Typically, this experiment produces a one dimensional (1D) 1H spectrum containing several peaks that are well associated with biochemicals, or metabolites. However, since many of these peaks overlap, distinguishing chemicals with similar atomic structures becomes much more challenging. One technique capable of overcoming this issue is the localized correlated spectroscopy (L-COSY) experiment, which acquires a second spectral dimension and spreads overlapping signal across this second dimension. Unfortunately, the acquisition of a two dimensional (2D) spectroscopy experiment is extremely time consuming. Furthermore, quantitation of a 2D spectrum is more complex. Recently, artificial intelligence has emerged in the field of medicine as a powerful force capable of diagnosing disease, aiding in treatment, and even predicting treatment outcome. In this study, we utilize deep learning to: (1) accelerate the L-COSY experiment and (2) quantify L-COSY spectra. All training and testing samples were produced using simulated metabolite spectra for chemicals found in the human body. We demonstrate that our deep learning model greatly outperforms compressed sensing based reconstruction of L-COSY spectra at higher acceleration factors. Specifically, at four-fold acceleration, our method has less than 5% normalized mean squared error, whereas compressed sensing yields 20% normalized mean squared error. We also show that at low SNR (25% noise compared to maximum signal), our deep learning model has less than 8% normalized mean squared error for quantitation of L-COSY spectra. These pilot simulation results appear promising and may help improve the efficiency and accuracy of L-COSY experiments in the future.

Conformational Assignments of Thujopsene and Related Compounds

Conformational analysis suggests that thujopsene (1) can exist as 2 possible conformations, steroidal and nonsteroidal. The conformation of 1 has been investigated using a NMR spectroscopic method. Analyses of the 1H NMR, ROESY, and long-range 1H-1H COSY spectra indicate that 1 exists in a steroidal conformation. Although the conformations of related compounds 2 and 3 were originally assigned as nonsteroidal, careful reexamination of the published NMR data indicates that both compounds exist in a steroidal conformation.

2D 7Li Ultrafast CT-COSY: a new tool for the rapid measurement of tiny homonuclear lithium scalar couplings

We report on the first examples of 7Li Ultrafast (UF) CT-COSY spectra from lithiated samples and on the usefulness of these experiments for the quantitative estimation of the 2J7Li–7Li in a much shorter time.

ISOLASI DAN ELUSIDASI STRUKTUR TRITERPENOID KULIT BATANG SURIAN Toona sinensis DAN UJI TERHADAP HAMA Crosidolomia pavonana

ABSTRACT Tryterpenoid compound (3-hydroxyeupha-7-ene) were isolated from hexane fraction the steam bark of Toona sinensis (Meliaceae). The structures of these compounds were established base on spectroscopic evidence, including UV, IR, 3C-NMR, 1H-NMR, HMBC, HMQC and COSY spectra and literature survey this is new compound in family Meliaceae. Insect activity of the hexane fraction (1.0%) and against Crosidolomia pavonana were tested. The activity of hexane were mortality (37.5%), antifeeding (75.5%). The activity of the 3-hydroxyeupha-7-ene showed mortality (52.0%), Lc50; 0.39847 and Lt50; 5.53931. Keywords: Toona sinensis (Meliaceae), 3-hydroxyeupha-7-ene, Crosidolomia pavonana, Antifeeding, Mortality Lc50 and Lt50