DESPERATE: A Python library for processing and denoising NMR spectra

Integrated LSPR Biosensing Signal Processing Strategy and Visualization Implementation

The LSPR biosensor chip is a groundbreaking tool popular in laboratory settings for identifying disease markers. However, its use in clinical environments is not as widespread. One notable gap is the lack of a universal signal processing tool for LSPR biosensing. To escalate its precision, there is an emerging need for software that not only optimizes signal processing but also incorporates self-verification functionalities within LSPR biochemical sensors. Enter the visual LSPR sensor software-an innovative platform that processes real-time transmission or reflection spectra. This advanced software adeptly captures the nuanced structural changes at the nanostructure interface prompted by environmental fluctuations. It diligently records and computes a suite of parameters, including the resonance wavelength shift, full width at half maximum, sensitivity, and quality factor. These features empower users to tailor processing algorithms for each data capture session. Transcending traditional instruments, this method accommodates a multitude of parameters and ensures robust result validation while tactfully navigating nanostructure morphology complexities. Forsaking third-party tool dependencies, the software tackles challenges of precision and cost-effectiveness head-on, heralding a significant leap forward in nanophotonics, especially for high-throughput LSPR biosensing applications. This user-centric innovation marks substantial progress in biochemical detection. It is designed to serve both researchers and practitioners in the field of nanophotonic sensing technology, simplifying complexity while enhancing reliability and efficiency.

Pure-Shift-Based Proton Magnetic Resonance Spectroscopy for High-Resolution Studies of Biological Samples

Proton magnetic resonance spectroscopy (1H MRS) presents a powerful tool for revealing molecular-level metabolite information, complementary to the anatomical insight delivered by magnetic resonance imaging (MRI), thus playing a significant role in in vivo/in vitro biological studies. However, its further applications are generally confined by spectral congestion caused by numerous biological metabolites contained within the limited proton frequency range. Herein, we propose a pure-shift-based 1H localized MRS method as a proof of concept for high-resolution studies of biological samples. Benefitting from the spectral simplification from multiplets to singlet peaks, this method addresses the challenge of spectral congestion encountered in conventional MRS experiments and facilitates metabolite analysis from crowded NMR resonances. The performance of the proposed pure-shift 1H MRS method is demonstrated on different kinds of samples, including brain metabolite phantom and in vitro biological samples of intact pig brain tissue and grape tissue, using a 7.0 T animal MRI scanner. This proposed MRS method is readily implemented in common commercial NMR/MRI instruments because of its generally adopted pulse-sequence modules. Therefore, this study takes a meaningful step for MRS studies toward potential applications in metabolite analysis and disease diagnosis.

Stationary wavelet denoising of solid-state NMR spectra using multiple similar measurements

Accumulating several scans of free induction decays is always needed to improve the signal-to-noise ratio of NMR spectra, especially for the low gyromagnetic ratio solid-state NMR. In this study, we present a new denoising approach based on the correlations between multiple similar NMR spectra. Contrary to the simple averaging of multiple scans or denoising the final averaged spectrum, we propose a Wavelet-based Denoising technique for Multiple Similar scans(WDMS). Firstly, the stationary wavelet transform is applied to decompose every spectrum into approximation coefficients and detail coefficients. Then, the detail coefficients are multiplied by weights calculated based on Pearson's correlation coefficient and structural similarity index between approximation coefficients of different spectra. Finally, the average of these detailed components is used to denoise the spectra. The proposed method is carried on the assumption that noise between multiple spectra is uncorrelated while peak signal information is similar between different spectra, thus preserving the possibility of applying further processing to the data. As a demonstration, the standard wavelet denoise is applied to the WDMS-processed spectra, achieving a further increase in the S/N ratio. We confirm the reliability of the denoising approach based on multiple scans on 1D/2D solid-state MAS/static NMR spectra. In addition, we also show that this method can be used to deal with a single Car-Purcell-Meiboom-Gill (CPMG) echo train.

On the effects of quadrupolar relaxation in Earth's field NMR spectra

There is growing interest in using low-field magnetic resonance experiments for routine chemical characterization. Earth's field NMR is one such technique that can garner structural information and enable sample differentiation with low cost and highly portable designs. The resulting NMR spectra are primarily influenced by J-couplings, resulting in so-called J-coupled spectra (JCS). Many small molecules include atoms with NMR-active nuclei that are quadrupolar either at natural abundance or are often isotopically enriched (e.g.,2H, 6Li, 11B, 14N, 17O, etc.) where the effects of quadrupolar J-couplings and relaxation on JCS of strongly- and weakly-coupled spin systems have not been explored to date. Herein, using a set of seven fluoropyridine samples with unique substitution and J-couplings, we demonstrate that the 14N relaxation rates can induce drastic line-broadening in the JCS. This includes a previously unexplored unique line broadening mechanism enabled by strongly coupled spins at low-field. Numerical simulations are used to model and refine the magnitudes and signs of J-couplings, as well as indirectly determine the 14N relaxation rates in a single 1D experiment that has a higher fidelity than observed in high-field NMR experiments.