Acquisition Of Spectroscopic Data Research Articles

NMR-Based Analysis of Cellular Central Energy Metabolism and Exchange Rates.

Cell cultures are widely used in studies to gain mechanistic insights of metabolic processes. The foundation of these studies lies on the quantification of intracellular and extracellular metabolites, and nuclear magnetic resonance (NMR) is one of the key analytical platforms used to this aim. Among the factors influencing the quality of the produced data are the sampling procedures as well as the acquisition and processing of spectroscopic data. Here we provide our workflow for obtaining quantitative metabolic data from adherent mammalian cells using NMR spectroscopy. The described protocol is compatible with other analytical methods like LC- or GC-MS-based lipidomics and untargeted metabolomics from the same sample. We also show how the collected extracellular data can be used to extract exchange flux rates, particularly useful for flux analysis studies and metabolic engineering of human-induced pluripotent stem cells.

Synchronization processes in fNIRS visibility networks

We employ Kuramoto model to assess the presence of synchronization in individuals who fulfill a cooperation task. Our input data is a couple of signals obtained from functional Near-Infrared Spectroscopy Data Acquisition and Pre-processing technology that is used to capture the brain activity of an individual by measuring the oxyhemoglobin (HbO) level. We consider 1 min signal for individuals in three distinct states: (i) rest; (ii) before a disturb happens; (iii) after the disturbance. We estimate global and local order parameters synchronization with the purpose to compare the conditions of reaching a synchronous state in the networks corresponding to different states for distinct individuals and hemispheres of the prefrontal cortices of same individual. Experimental results confirmed once more that coherent state is reached not for same conditions in both individuals and hemispheres of the prefrontal cortices. Furthermore, condition changes even for different events. The computation of the effective frequencies for each degree class indicates clearly the network difference in rest, before and after disturb. Finally, we investigate the dynamic connectivity matrix and consider the similarity between distinct prefrontal cortices over time.

Neuroprotective Effects of Chemical Constituents of Leaves of Euonymus hamiltonianus Wall.

Euonymus hamiltonianus Wall. is considered a medicinal plant and is used to treat pain, cough, dysuria, and cancer, but a clear phytochemical investigation of its biological activities has yet to be performed. Investigation of chemical constituents of the leaves of Euonymus hamiltonianus Wall. led to the isolation of three new compounds by chromatography techniques, euonymusins A-C (1, 10, and 11), and the acquisition of new spectroscopic data for euonymusin D (2), along with the identification of ten known compounds. The chemical structures of the compounds were established using extensive spectroscopic techniques, including NMR, MS, and hydrolysis, and compared with the published data. These compounds were tested in vitro for their inhibitory effects on beta amyloid production (Aβ42). Compounds 13 and 14 displayed weak inhibition, with IC50 values ranging from 53.15 to 65.43 µM. Moreover, these compounds were also assessed for their inhibitory effects on nitric oxide production. Of these compounds, 3, 4, and 14 displayed inhibitory effects on NO production, with IC50 values ranging from 14.38 to 17.44 µM. Compounds 3, 4, and 14 also suppressed LPS-induced expression of nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) protein.

New, fast, and precise method of COVID-19 detection in nasopharyngeal and tracheal aspirate samples combining optical spectroscopy and machine learning.

Fast, precise, and low-cost diagnostic testing to identify persons infected with SARS-CoV-2 virus is pivotal to control the global pandemic of COVID-19 that began in late 2019. The gold standard method of diagnostic recommended is the RT-qPCR test. However, this method is not universally available, and is time-consuming and requires specialized personnel, as well as sophisticated laboratories. Currently, machine learning is a useful predictive tool for biomedical applications, being able to classify data from diverse nature. Relying on the artificial intelligence learning process, spectroscopic data from nasopharyngeal swab and tracheal aspirate samples can be used to leverage characteristic patterns and nuances in healthy and infected body fluids, which allows to identify infection regardless of symptoms or any other clinical or laboratorial tests. Hence, when new measurements are performed on samples of unknown status and the corresponding data is submitted to such an algorithm, it will be possible to predict whether the source individual is infected or not. This work presents a new methodology for rapid and precise label-free diagnosing of SARS-CoV-2 infection in clinical samples, which combines spectroscopic data acquisition and analysis via artificial intelligence algorithms. Our results show an accuracy of 85% for detection of SARS-CoV-2 in nasopharyngeal swab samples collected from asymptomatic patients or with mild symptoms, as well as an accuracy of 97% in tracheal aspirate samples collected from critically ill COVID-19 patients under mechanical ventilation. Moreover, the acquisition and processing of the information is fast, simple, and cheaper than traditional approaches, suggesting this methodology as a promising tool for biomedical diagnosis vis-à-vis the emerging and re-emerging viral SARS-CoV-2 variant threats in the future.

Digital restoration of colour cinematic films using imaging spectroscopy and machine learning

Digital restoration is a rapidly growing methodology within the field of heritage conservation, especially for early cinematic films which have intrinsically unstable dye colourants that suffer from irreversible colour fading. Although numerous techniques to restore film digitally have emerged recently, complex degradation remains a challenging problem. This paper proposes a novel vector quantization (VQ) algorithm for restoring movie frames based on the acquisition of spectroscopic data with a custom-made push-broom VNIR hyperspectral camera (380–780 nm). The VQ algorithm utilizes what we call a multi-codebook that correlates degraded areas with corresponding non-degraded ones selected from reference frames. The spectral-codebook was compared with a professional commercially available film restoration software (DaVinci Resolve 17) tested both on RGB and on hyperspectral providing better results in terms of colour reconstruction.

Photothermal Gas Detection Using a Mode-Locked Laser Signal Readout

In this work, a novel configuration of a photothermal gas sensor is demonstrated. The measured gas sample is delivered to a gas cell placed inside the linear cavity of a 1.55 μm mode-locked fiber laser. The gas refractive index modulation resulting from the excitation by an auxiliary continuous wave laser is efficiently probed by analyzing the phase shift of the self-heterodyne beatnote signal of the mode-locked laser. IQ demodulation combined with Wavelength Modulation Spectroscopy-based signal analysis was employed to optimize and simplify the spectroscopic data acquisition and analysis. A proof-of-concept experiment with detection of carbon dioxide at 2 μm wavelength yielded a noise equivalent absorption of 2.25·10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−7</sup> for 1000 s integration time. The proposed sensor configuration is versatile and can be used to probe any gas molecule, provided an appropriate excitation source is used to induce the photothermal effect.

Machine learning approach to optimization of parameters for impedance measurements of Quartz Crystal Microbalance to improve limit of detection

Impedance spectroscopy allows real time monitoring of fundamental resonance frequency and harmonics of AT cut Quartz Crystal Microbalance (QCM) biosensors. Acquisition of accurate and high resolution spectroscopic data requires optimization of parameters, which is rarely performed as it requires an excessive number of experiments. In this work, machine learning clustering and classification algorithms are applied in an attempt to minimize the number of experiments by designating a small representative subset of chosen values of parameters, while preserving the variation of those values. Our results indicated it is possible to reduce the number of experiments required by more than 10 fold. The constructed model has both an unsupervised clustering part (k-means algorithm) and a supervised classification part (Support Vector Machine algorithm), working independently. From those two different approaches 83% compatibility is obtained. Such optimization can improve limit of detection (LOD) of glycerol concentrations by a factor of 12 using a QCM with a fundamental resonance frequency of 10 MHz. This approach can be applied not only to biosensors but also to chemical sensors involving resonance frequency monitoring by means of impedance spectroscopy, and has the potential to be expanded to systems with different transducer types, monitoring resonance by other means.

Plug-and-play advanced magnetic resonance spectroscopy.

Advanced MRS protocols improve data quality and reproducibility relative to vendor-provided protocols; however, they are challenging to incorporate into the clinical workflow and require local MRS expertise for successful implementation. Here, we developed an automated advanced MRS acquisition protocol at 3T to facilitate acquisition of high-quality spectroscopic data without local MRS expertise. First, a B0 shimming protocol was selected for automation by comparing 3 widely used B0 algorithms (2 vendor protocols and FAST(EST)MAP). Next, voxel-based B0 and B1 calibrations were incorporated into the consensus-recommended semi-LASER sequence and combined with an automated VOI prescription tool, a recently developed method for automated voxel prescription. The efficiency of collecting single-voxel data from a clinical cohort (N = 40) with the automated protocol (calibration time and fraction of usable datasets) was compared with the nonautomated semi-LASER protocol (N = 35) whereby all prescan calibrations were executed manually in the academic hospital setting with rotating MR technologists in the neuroradiology unit. A multi-iteration FAST(EST)MAP protocol resulted in narrower water linewidths than vendor's B0 shim protocols for data acquired from 6 brain locations (p < 1e-5) and was selected for automation. The automated B0 and B1 calibrations resulted in a time saving of ~4.5 minutes per voxel relative to the same advanced protocol executed manually. All spectra acquired with the automated protocol were usable, whereas only 86% of those collected with the manual protocol were usable and spectral quality was more variable. The plug-and-play advanced MRS protocol allows automated acquisition of high-quality MRS data with high success rate and consistency on a clinical 3T platform.

New Algorithm by Maximizing Mutual Information for Correction of Frequency Drifts Arising from One-Dimensional NMR Spectroscopic Data Acquisition.

Benchtop nuclear magnetic resonance (NMR) instruments are getting popular these days. However, the obtained spectra sometimes suffer from significant frequency drifts, which cause difficulty in accumulating the raw data. In this paper, a new algorithm for correction of frequency drifts is proposed, which operates by maximizing mutual information between the obtained spectroscopic data. The algorithm worked well for both 1H and 19F NMR spectroscopic data, even in the case of very noisy ones. In comparison with the previously reported algorithms, the present algorithm has an advantage that NMR spectra complicated by signal overlapping and spin coupling can be handled without difficulty. This makes the present algorithm particularly advantageous for application of benchtop NMR spectrometers in organic chemistry.

A LabVIEW based virtual instrument system for mid infrared laser spectroscopy measurements

The development of a virtual instrument (VI) interface to acquire spectroscopic measurements using a quantum cascade laser-based on LabVIEWTM is presented. The interface can enable mid-infrared laser spectroscopic data acquisition using a fast response mercury-cadmium-telluride (MCT) detector. The MCT is connected to an analog/digital converter card, and the data is displayed in a virtual high-speed oscilloscope. The VI processes the acquired data, displays the information, and saves it to a file through several operations using a “state machine” architecture. Transmission measurements of a polystyrene film were used to calibrate the system. They were compared to the National Institute of Standards and Technology-based FT-IR reference spectrum, showing excellent agreement. The system was tested with four different optical setups for IR Transmission, Diffuse Reflectance Infrared Spectroscopy, Reflection Absorption Infrared Spectroscopy, and Attenuated Total Reflection. The VI can be easily customized, allowing users to adapt it according to their specific laboratory needs.

Synchronizing gas injections and time-resolved data acquisition for perturbation-enhanced APXPS experiments.

An experimental approach is described in which well-defined perturbations of the gas feed into an Ambient Pressure X-ray Photoelectron Spectroscopy (APXPS) cell are fully synchronized with the time-resolved x-ray photoelectron spectroscopy data acquisition. These experiments unlock new possibilities for investigating the properties of materials and chemical reactions mediated by their surfaces, such as those in heterogeneous catalysis, surface science, and coating/deposition applications. Implementation of this approach, which is termed perturbation-enhanced APXPS, at the SPECIES beamline of MAX IV Laboratory is discussed along with several experimental examples including individual pulses of N2 gas over a Au foil, a multi-pulse titration of oxygen vacancies in a pre-reduced TiO2 single crystal with O2 gas, and a sequence of alternating precursor pulses for atomic layer deposition of TiO2 on a silicon wafer substrate.

Cycle-StarNet: Bridging the Gap between Theory and Data by Leveraging Large Data Sets

Abstract Advancements in stellar spectroscopy data acquisition have made it necessary to accomplish similar improvements in efficient data analysis techniques. Current automated methods for analyzing spectra are either (a) data driven, which requires prior knowledge of stellar parameters and elemental abundances, or (b) based on theoretical synthetic models that are susceptible to the gap between theory and practice. In this study, we present a hybrid generative domain-adaptation method that turns simulated stellar spectra into realistic spectra by applying unsupervised learning to large spectroscopic surveys. We apply our technique to the APOGEE H-band spectra at R = 22,500 and the Kurucz synthetic models. As a proof of concept, two case studies are presented. The first is the calibration of synthetic data to become consistent with observations. To accomplish this, synthetic models are morphed into spectra that resemble observations, thereby reducing the gap between theory and observations. Fitting the observed spectra shows an improved average reduced from 1.97 to 1.22, along with a mean residual reduced from 0.16 to −0.01 in normalized flux. The second case study is the identification of the elemental source of missing spectral lines in the synthetic modeling. A mock data set is used to show that absorption lines can be recovered when they are absent in one of the domains. This method can be applied to other fields that use large data sets and are currently limited by modeling accuracy. The code used in this study is made publicly available on GitHub (https://github.com/teaghan/Cycle_SN).

The clinical utility of proton magnetic resonance spectroscopy in traumatic brain injury: recommendations from the ENIGMA MRS working group.

Proton (1H) magnetic resonance spectroscopy provides a non-invasive and quantitative measure of brain metabolites. Traumatic brain injury impacts cerebral metabolism and a number of research groups have successfully used this technique as a biomarker of injury and/or outcome in both pediatric and adult TBI populations. However, this technique is underutilized, with studies being performed primarily at centers with access to MR research support. In this paper we present a technical introduction to the acquisition and analysis of in vivo 1H magnetic resonance spectroscopy and review 1H magnetic resonance spectroscopy findings in different injury populations. In addition, we propose a basic 1H magnetic resonance spectroscopy data acquisition scheme (Supplemental Information) that can be added to any imaging protocol, regardless of clinical magnetic resonance platform. We outline a number of considerations for study design as a way of encouraging the use of 1H magnetic resonance spectroscopy in the study of traumatic brain injury, as well as recommendations to improve data harmonization across groups already using this technique.

Reduction of 4‐Nitrothiophenol on Ag/Au Bimetallic Alloy Surfaces Studied Using Bipolar Raman Spectroelectrochemistry

AbstractHerein, we describe an experimental approach for adapting the principles of Raman spectroelectrochemistry to electrodes controlled using a bipolar circuit. This method allows the simultaneous acquisition of spectroscopic data as a function of both the electrode potential and the chemical composition of a bimetallic alloy and can be generalized to other system variables. The electrochemical reduction of 4‐nitrothiophenol (4‐NTP) was carried out on bimetallic Ag/Au alloy gradients and monitored in situ using a confocal Raman microscope with 785 nm excitation. Continuous Ag/Au alloy gradients, in which the alloy composition varied from approximately 0.5 to 1.0 mole fraction Ag, were prepared by using bipolar electrodeposition and then modified with a monolayer of 4‐NTP using self‐assembly. 4‐NTP monolayers on Au/Ag alloys were placed in a bipolar electrochemical cell and characterized as a function of applied potential and chemical composition by using surface‐enhanced Raman scattering. The E1/2 for NTP reduction was observed to be a strong function of the alloy composition, increasing by over 100 mV as the mole fraction of Ag varied from 0.5 to 1.0. In addition, spectroscopic evidence for the formation of the partially reduced intermediate, 4,4’‐dimercaptoazobenzene (DMAB) at intermediate applied potentials, was also found. Bipolar Raman spectroelectrochemistry (BRSE) is a powerful tool for acquiring in situ multidimensional Raman spectroelectrochemical data.

Self-Gated Respiratory Motion Rejection for Optoacoustic Tomography

Respiratory motion in living organisms is known to result in image blurring and loss of resolution, chiefly due to the lengthy acquisition times of the corresponding image acquisition methods. Optoacoustic tomography can effectively eliminate in vivo motion artifacts due to its inherent capacity for collecting image data from the entire imaged region following a single nanoseconds-duration laser pulse. However, multi-frame image analysis is often essential in applications relying on spectroscopic data acquisition or for scanning-based systems. Thereby, efficient methods to correct for image distortions due to motion are imperative. Herein, we demonstrate that efficient motion rejection in optoacoustic tomography can readily be accomplished by frame clustering during image acquisition, thus averting excessive data acquisition and post-processing. The algorithm’s efficiency for two- and three-dimensional imaging was validated with experimental whole-body mouse data acquired by spiral volumetric optoacoustic tomography (SVOT) and full-ring cross-sectional imaging scanners.

Inside Cover: Raman ChemLighter: Fiber optic Raman probe imaging in combination with augmented chemical reality (J. Biophotonics 7/2019)

The newly developed Raman ChemLighter allows the real‐time acquisition of spectroscopic data using a handheld probe. By intelligently combining the fiber‐based imaging approach with computational modeling, we can directly extract molecular information of a sample provide augmented chemical reality to visualize chemistry.Further details can be found in the article by Wei Yang, Abdullah S. Mondol, Clara Stiebing, Laura Marcu, Jürgen Popp, Iwan W. Schie (e201800447). image

Diagonal slice four-wave mixing: natural separation of coherent broadening mechanisms.

We present an ultrafast coherent spectroscopy data acquisition scheme that samples slices of the time domain used in multidimensional coherent spectroscopy to achieve faster data collection than full spectra. We derive analytical expressions for resonance lineshapes using this technique that completely separate homogeneous and inhomogeneous broadening contributions into separate projected lineshapes for arbitrary inhomogeneous broadening. These lineshape expressions are also valid for slices taken from full multidimensional spectra and allow direct measurement of the parameters contributing to the lineshapes in those spectra as well as our own.

In situ characterization of nanoscale contaminations adsorbed in air using atomic force microscopy.

Under ambient conditions, surfaces are rapidly modified and contaminated by absorbance of molecules and a variety of nanoparticles that drastically change their chemical and physical properties. The atomic force microscope tip–sample system can be considered a model system for investigating a variety of nanoscale phenomena. In the present work we use atomic force microscopy to directly image nanoscale contamination on surfaces, and to characterize this contamination by using multidimensional spectroscopy techniques. By acquisition of spectroscopy data as a function of tip–sample voltage and tip–sample distance, we are able to determine the contact potential, the Hamaker constant and the effective thickness of the dielectric layer within the tip–sample system. All these properties depend strongly on the contamination within the tip–sample system. We propose to access the state of contamination of real surfaces under ambient conditions using advanced atomic force microscopy techniques.

Rapid detection of cAMP content in red jujube using near-infrared spectroscopy

In this paper, a new method for the rapid, economical and convenient detection of cyclic adenosine monophosphate (cAMP) in jujube is proposed and verified. Based on near-infrared (NIR) fiber spectroscopy combined with stoichiometric analysis, the cAMP content in red jujube can be quickly detected. 68 red jujube samples were used for the NIR spectroscopy data acquisition and the corresponding chemical values were determined. The sample set was adjusted based on the joint XY distance (SPXY) to select the correction sample set. After different preprocessing on the spectra, the partial least squares (PLS) method was used to establish the model, and the smoothed and normalized PLS model result was obtained better. The model’s correction correlation coefficient (Rc), correction set mean square error (RMSEC), prediction correlation coefficient (Rp), and prediction and mean square error (RMSEP) are 0.951 5, 25.793 7, 0.910 8 and 28.228 0, respectively. The results show that NIR combined with specific chemometric methods can achieve rapid detection of cAMP in red jujube.

Advantages of Direct Detection and Electron Counting for High-Energy Resolution and Monochromated Electron Energy Loss Spectroscopy Data Acquisition

Advantages of Direct Detection and Electron Counting for High-Energy Resolution and Monochromated Electron Energy Loss Spectroscopy Data Acquisition