Signal Overlap Research Articles

Application of probing signals with zero autocorrelation zone for suppression of recurrent range interference in synthetic aperture radars

The problem of overlap of echo signals of adjacent probing cycles in synthetic aperture radars (SARs), called range ambiguity interference, or recurrent interference, is considered, which is usually solved by using orthogonal linear frequency modulated (chirp) and phase-code shift keyed (PCSK) in adjacent SAR probing cycles, which allows you to successfully suppress false signals from bright point targets from adjacent repetition periods. For SAR, various types of probing PCSK signals with a zero autocorrelation zone are considered, which make it possible to successfully solve the problem of suppressing recurrent interference from both point targets and extended objects. A comparative analysis of the correlation characteristics of orthogonal chirp and PCSK signals with various types of PCSK signals with a zero autocorrelation zone has been carried out.

Leveraging Ion-Ion and Ion-Photon Activation to Improve the Sequencing of Proteins Carrying Multiple Disulfide Bonds: The Human Serum Albumin Case Study.

Gas-phase sequencing of large intact proteins (>30 kDa) via tandem mass spectrometry is an inherently challenging process that is further complicated by the extensive overlap of multiply charged product ion peaks, often characterized by a low signal-to-noise ratio. Disulfide bonds exacerbate this issue because of the need to cleave both the S-S and backbone bonds to liberate sequence informative fragments. Although electron-based ion activation techniques such as electron transfer dissociation (ETD) have been proven to rupture disulfide bonds in whole protein ions, they still struggle to produce extensive sequencing when multiple, concatenated S-S bonds are present on the same large polypeptide chain. Here, we evaluate the increase in sequence coverage obtained by combining activated-ion ETD (AI-ETD) and proton transfer charge reduction (PTCR) in the analysis of 66 kDa human serum albumin, which holds 17 disulfide bridges. We also describe the combination of AI-ETD with supplemental postactivation of the ETD reaction products via higher-energy collisional dissociation─a hybrid fragmentation method termed AI-EThcD. AI-EThcD leads to a further improvement compared to AI-ETD in both the global number of cleaved backbone bonds and the number of ruptured backbone bonds from disulfide-protected regions. Our results also demonstrate that the full potential of AI-ETD and AI-EThcD is unveiled only when combined with PTCR: reduction in overlap of ion signals leads to a sequence coverage as high as 39% in a single experiment, highlighting the relevance of spectral simplification in top-down mass spectrometry of large proteins.

Speedy Component Resolution Using Spatially Encoded Diffusion NMR Data.

Diffusion-ordered NMR spectroscopy (DOSY) is a powerful tool to analyse mixtures. Spatially encoded (SPEN) DOSY enables recording a full DOSY dataset in just one scan by performing spatial parallelisation of the gradient dimension. The simplest and most widely used approach to processing DOSY data is to fit each peak in the spectrum with a single or multiple exponential decay. However, when there is peak overlap, and/or when the diffusion decays of the contributing components are too similar, this method has limitations. Multivariate analysis of DOSY data, which is an attractive alternative, consists of decomposing the experimental data, into compound-specific diffusion decays and 1D NMR spectra. Multivariate analysis has been very successfully used for conventional DOSY data, but its use for SPEN DOSY data has only recently been reported. Here, we present a comparison, for SPEN DOSY data, of two widely used algorithms, SCORE and OUTSCORE, that aim at unmixing the spectra of overlapped species through a least square fit or a cross-talk minimisation, respectively. Data processing was performed with the General NMR Analysis Toolbox (GNAT), with custom-written code elements that now expands the capabilities, and makes it possible to import and process SPEN DOSY data. This comparison is demonstrated on three different two-component mixtures, each with different characteristics in terms of signal overlap, diffusion coefficient similarity, and component concentration.

Homonuclear Super-Resolution NMR Spectroscopy.

Homonuclear 1H NMR (nuclear magnetic resonance) spectra such as the [1H,1H]-NOESY (Nuclear Overhauser Enhancement spectroscopy) are essential for biomolecular structural biology. However, the spectrum contains hundreds to thousands of cross peaks meaning that within approximately 100 ppm2 there is significant signal overlap. Spectral resolution is thus a limiting factor for data interpretation for dynamics and structure elucidation. Acquiring the spectra at higher magnetic fields such as at a 1.2 GHz 1H frequency helps to reduce spectral crowding, since resolution scales proportionally to the magnetic field strength. Here, we show that the linewidths' of cross peaks in [1H,1H]-NOESY and [1H,1H]-TOCSY spectra can be further reduced by a factor of 2-3 in each dimension by super-resolution spectroscopy. In the indirect dimension a composite exponential-cosine weighted number of scans along the time increments are recorded and digitally smoothened by a window function. Furthermore, measurement time saving by reduced-acquisition super-resolution (RASR) is introduced. Application to the 20 kDa protein KRAS shows that highly resolved NMR spectra suitable for automated analysis can be acquired within less than 3 hours. The method opens an avenue towards automated chemical shift assignment, dynamics and structure determination of unlabeled small and medium size proteins within 24 hours.

Joint HIV and hepatitis C virus phylogenetic analyses signal network overlap among women engaged in sex work and men who purchase sex.

Transmission of HIV and hepatitis C virus (HCV) are heavily influenced by complex interactions within sexual or injecting networks where risk behaviors occur. In Ukraine, women engaged in sex work (WSW) and men who purchase sex (MWPS) are disproportionately affected by both viruses. The aim of our study was to the investigate the influence of underlying networks on transmission of HIV and HCV. A cross-sectional integrated bio-behavioural survey was implemented among 560 WSW and 370 MWPS representative of sex work hotspots in Dnipro, Ukraine (December 2017 to March 2018). A portion of the HIV reverse transcriptase gene (n = 13; 62% WSW, 38% MWPS) and HCV NS5B gene (n = 46; 70% WSW, 30% MWPS) were sequenced from dried blood spot specimens. Tip-to-tip distances on phylogenetic trees were used to infer phylogenetic clusters for identifying potential transmission clusters. Phylogenetic analyses identified two HIV clusters containing four sequences (50% WSW; 50% MWPS) and 11 HCV clusters containing 31 sequences - the majority comprising infections in WSW (83.9%). Nearly half (45.4%) of HCV clusters contained at least one WSW with a history of injecting drugs. Joint analyses of HIV and HCV signal overlap in sex work and injecting networks in Ukraine, suggesting implications for the comprehensive coverage of prevention programs for WSW including harm reduction services. Conducting phylogenetic analyses with HCV may provide a more complete appraisal of underlying transmission networks than HIV alone, particularly in the context of high HIV treatment coverage yielding viral suppression.

Hydrogen isotopic ratio by residual gas analysis during the JET DT campaigns

Abstract The analysis of hydrogen isotope content is crucial for understanding the operation of fusion devices. Hydrogen isotopic analysis in the core and plasma edge is conducted through neutron and spectroscopic diagnostics. In the case of exhaust gas, mass spectrometry is employed using residual gas analyzers (RGAs), along with optical gas analysis (OGA) utilizing an optical Penning gauge. The use of traditional quadrupole mass spectrometers for RGA encounters challenges during discharges with tritium gas due to signal overlap of different hydrogen molecules at the same mass number. This paper introduces a novel technique to address this issue through a cross-related analysis of RGA data with OGA results. Another consideration in mass spectrometry analysis is the instrument’s varying sensitivity concerning the gas mass number. The paper includes gas calibration data results for all the quadrupoles used in the JET gas analysis. Hydrogen isotopic ratios are calculated from RGA detected currents using simple formulas. The results of this procedure are presented for selected DT JET discharges in relation to the JET pulse time, and they are compared with corresponding optical data. Time-averaged hydrogen isotopic ratio values are computed for numerous discharges during the DT and T campaigns.

Towards a universal method for middle-down analysis of antibodies via proton transfer charge reduction-Orbitrap mass spectrometry.

Modern mass spectrometry technology allows for extensive sequencing of the ~ 25kDa subunits of monoclonal antibodies (mAbs) produced by IdeS proteolysis followed by disulfide bond reduction, an approach known as middle-down mass spectrometry (MD MS). However, the spectral congestion of tandem mass spectra of large polypeptides dramatically complicates fragment ion assignment. Here, we report the development and benchmark of an MD MS strategy based on the combination of different ion fragmentation techniques with proton transfer charge reduction (PTCR) to simplify the gas-phase sequencing of mAb subunits. Applied on the liquid chromatography time scale using an Orbitrap Tribrid mass spectrometer, PTCR produces easy-to-interpret mass spectra with limited ion signal overlap. We demonstrate that the accurate estimation of the number of charges submitted to the Orbitrap mass analyzer after PTCR allows for the detection of charge-reduced product ions over a wide mass-over-charge (m/z) window with low parts per million m/z accuracy. Therefore, PTCR-based MD MS analysis increases not only sequence coverage, number of uniquely identified fragments, and number of assigned complementary ion pairs, but also the general confidence in the assignment of subunit fragments. This data acquisition method can be readily applied to any class of mAbs without an apparent need for optimization, and benefits from the high resolving power of the Orbitrap mass analyzer to return sequence coverage of individual subunits exceeding 80% in a single run, and > 90% when just two experiments are combined.

Improving standardization and accuracy of in vivo omega plot exchange parameter determination using rotating-frame model-based fitting of quasi-steady-state Z-spectra.

Although Ω-plot-driven quantification of in vivo amide exchange properties has been demonstrated, differences in scan parameters may complicate the fidelity of determination. This work systematically evaluated the use of quasi-steady-state (QUASS) Z-spectra reconstruction to standardize in vivo amide exchange quantification across acquisition conditions and further determined it in vivo. Simulation and in vivo rodent brain chemical exchange saturation transfer (CEST)data at 4.7 T were fit with and without QUASS reconstruction using both multi-Lorentzian and model-based fitting approaches. pH modulation was accomplished both in simulation and in vivo by inducing global ischemia via cardiac arrest. Amide parameters were determined via Ω-plots and compared across methods. Simulation showed that Ω-plots using multi-Lorentzian fitting could underestimate the exchange rate, with error increasing as conditions diverged from the steady state. In comparison, model-based fitting using QUASS estimated the same exchange rate within 2%. These results aligned with in vivo findings where multi-Lorentzian fitting of native Z-spectra resulted in an exchange rate of 64 ± 13 s-1 (38 ± 16 s-1 after cardiac arrest), whereas model-based fitting of QUASS Z-spectra yielded an exchange rate of 126 ± 25 s-1 (49 ± 13 s-1). The model-based fitting of QUASS CEST Z-spectra enables consistent and accurate quantification of exchange parameters through Ω-plot construction by reducing error due to signal overlap and nonequilibrium CEST effects.

Multi-targets detection via combination of multi-stimulus-response engineered bacteria and hydrogel-based separation platform

Establishing a method similar to ICP-MS that can quantitatively analyze multiple heavy metals simultaneously, conveniently, and in situ is highly anticipated. In this study, we integrated the sensing elements of multiple targets and different fluorescence reporting elements to construct an engineered Escherichia coli. When these targets are present, the engineered bacteria can emit a fluorescent signal at the corresponding wavelength. To avoid the inability to accurately distinguish and quantify the content of each target due to the overlap of fluorescence signals when multiple targets coexist, a hydrogel-based separation platform similar to a separation column was constructed. The hydrogel platform can change the detection limit (LOD) and sensitivity by adjusting the adsorption strength towards different targets, so as to realize the differentiation and recognition of their respective detection signals. The LODs of this new detection method for Cd(II), Hg(II), As(III), and Pb(II) are 1.249, 0.380, 3.917, and 0.755 μg/L, respectively. In addition, this biosensor system was applied to detect coexisting Cd(II), Hg(II), As(III), and Pb(II) in actual samples with a recovery rate of 85.61–110.30 %, which is consistent with the classical ICP-MS detection results, confirming the accuracy and reliability of the method for detecting multiple heavy metal coexisting samples.

ATP, the 31P Spectral Modulus, and Metabolism.

Adenosine triphosphate (ATP) has a high intracellular millimolar concentration (ca. 2.4 mM) throughout the phylogenetic spectrum of eukaryotes, archaea, and prokaryotes. In addition, the function of ATP as a hydrotrope in the prevention of protein aggregation and maintenance of protein solubilization is essential to cellular, tissue, and organ homeostasis. The 31P spectral modulus (PSM) is a measure of the health status of cell, tissue, and organ systems, as well as of ATP, and it is based on in vivo 31P nuclear magnetic resonance (31P NMR) spectra. The PSM is calculated by dividing the area of the 31P NMR integral curve representing the high-energy phosphates by that of the low-energy phosphates. Unlike the difficulties encountered in measuring organophosphates such as ATP or any other phosphorylated metabolites in a conventional 31P NMR spectrum or in processed tissue samples, in vivo PSM measurements are possible with NMR surface-coil technology. The PSM does not rely on the resolution of individual metabolite signals but uses the total area derived from each of the NMR integral curves of the above-described spectral regions. Calculation is based on a simple ratio of the high- and low-energy phosphate bands, which are conveniently arranged in the high- and low-field portions of the 31P NMR spectrum. In practice, there is essentially no signal overlap between these two regions, with the dividing point being ca. -3 δ. ATP is the principal contributor to the maintenance of an elevated PSM that is typically observed in healthy systems. The purpose of this study is to demonstrate that (1) in general, the higher the metabolic activity, the higher the 31P spectral modulus, and (2) the modulus calculation does not require highly resolved 31P spectral signals and thus can even be used with reduced signal-to-noise spectra such as those detected as a result of in vivo analyses or those that may be obtained during a clinical MRI examination. With increasing metabolic stress or maturation of metabolic disease in cells, tissues, or organ systems, the PSM index declines; alternatively, with decreasing stress or resolution of disease states, the PSM increases. The PSM can serve to monitor normal homeostasis as a diagnostic tool and may be used to monitor disease processes with and without interventional treatment.

Temperature Estimation of Lithium-Ion Batteries Using Electrochemical Techniques

With their high energy and high power density, lithium-ion batteries (LIBs) are widely used as power sources for electric vehicles and energy storage systems, and their demand continues to grow. However, the increasing energy and power density of LIBs leads to significant heat generation issues, and reflecting these safety concerns there have been a number of recent fires of high-performance batteries. Under these circumstances, it is increasingly important to have a reliable assessment of battery safety through precise diagnostics of battery state of health (SoH). As a matter of fact, the battery management system diagnoses and evaluates battery SoH by analyzing various parameters such as voltage, current, and surface temperature of battery, and among these there is no doubt that battery temperature is a critical safety factor. As LIBs become more high-performance and large-scale, the temperature distribution inside the battery becomes more uneven, which possibly degrades performance and eventually leads to failure, so it is very important to secure the uniformity of the battery temperature and at the same time to analyze the temperature inhomogeneity and sudden temperature changes inside the battery.A typical way to estimate battery temperature is to insert a temperature sensor into the battery or attach it to the battery surface. While these methods enable direct temperature measurement, inserting a sensor can mechanically affect the internal structure of the battery, and attaching a sensor to a surface can result in delayed detection of internal cell temperature changes and may not fully reflect the battery temperature especially if there is a large temperature distribution. As an alternative, temperature estimation using electrochemical impedance spectroscopy (EIS) has received significant attention. Although EIS analysis is an indirect method of obtaining internal cell information through the electrochemical signal, it has the great advantage that it is a non-destructive method that does not affect the cell structure, and the internal cell situation is fully reflected in the electrochemical signal. Nevertheless, typical temperature estimation methods using EIS are time-consuming because they require deriving temperature-related impedance components from impedance data measured over a wide frequency range (kHz to Hz). To solve this problem, Srinivasan pioneered a method that utilizes impedance values at a single frequency for temperature estimation, which uses a specific response signal that has a clear temperature dependence but is largely unaffected by other factors (such as state of charge (SoC)). While this method can effectively reduce the cost and time spent on temperature estimation using EIS and is one of the most promising methods of estimating cell internal temperature known to date, it has been reported that the accuracy of temperature estimation is low at high temperatures for reasons that are not yet fully understood.In this study, we diagnose the problems with the existing method of estimating the internal temperature of batteries through EIS and propose a way to improve the accuracy of temperature estimation at high temperatures. For this purpose, first, we measured the impedance at various SoCs and derived the characteristic frequency representing the reaction at the solid electrolyte interphase (SEI), which is the main reaction used for temperature estimation in the previous work. In addition, we thoroughly analyzed the impedance change with temperature to more clearly understand the resistance components contributing to the impedance at the characteristic frequency. The analysis showed that impedance value at characteristic frequency, which was thought to represent only one reaction at the SEI, contained signals from other reactions (e.g. interfacial charge transfer) at higher temperatures. This situation becomes more severe as the temperature increases, and is believed to be the reason why traditional impedance-based temperature estimation methods have limited accuracy at high temperatures. We also observed that increasing the characteristic frequency to avoid this signal overlap prevented signal disturbance due to charge transfer reactions, but instead introduced another signal disturbance due to stray inductance. Based on these analyses, this study proposes a new frequency value to be used for battery temperature estimation through EIS, and at the same time presents an impedance correction method that can reliably take only the impedance signal of a specific reaction with temperature dependence without interference from other reaction signals at that frequency. It is confirmed that the temperature estimation accuracy is greatly improved when using the method presented in this study compared to the existing temperature estimation method. In this presentation, we will critically discuss why existing methods have poor temperature estimation accuracy at high temperatures, and propose an impedance-based temperature estimation method that can achieve high accuracy over a wide operating temperature range.

Ultraselective, Ultrahigh Resolution 1D TOCSY

AbstractSolution state 1H NMR spectroscopy provides valuable insights into molecular structure and conformation. However, when the spectrum exhibits severe signal overlap, it hampers the extraction of key structural information. Here, an ultraselective, ultrahigh resolution TOCSY method is introduced that greatly reduces spectral complexity, allowing the extraction of previously inaccessible spectral information. It combines the recently developed GEMSTONE excitation with homonuclear decoupling to provide highly simplified through‐bond correlation 1D 1H NMR spectra, showing all signals within the selected spin system as singlets. The new method can greatly facilitate the analysis of mixtures, as shown here for a mixture of Cinchona alkaloids (popular catalysts in asymmetric synthesis) and a mixture of glucocorticoids (used for treating conditions such as asthma).

Signatures of selection in Angus and Hanwoo beef cattle using imputed whole genome sequence data.

In this study, we detected signatures of selection in Hanwoo and Angus beef cattle using allele frequency and haplotype-based methods based on imputed whole genome sequence variants. Our dataset included 13,202 Angus animals with 10,057,633 imputed SNPs and 10,437 Hanwoo animals with 13,241,550 imputed SNPs. The dataset was subset down to 6,873,624 SNPs in common between the two populations to identify within population (runs of homozygosity, extended haplotype homozygosity) and between population signals of selection (allele fixation index, extended haplotype homozygosity). Assuming these selection signals were complementary to each other, they were combined into a decorrelated composite of multiple signals to identify regions under selection for each of the breeds. 27 genomic regions spanning 25.15Mb and harboring 360 genes were identified in Angus on chromosomes 1,3, 4, 5, 6, 7, 8, 12, 13, 14, 16, 20, 21 and 28. Similarly, in Hanwoo, 59 genes and 17 genomic regions spanning 5.21Mb on chromosomes 2, 4, 5, 6, 7, 8, 9, 10, 13, 17, 20 and 24 were identified. Apart from a small region on chromosome 13, there was no major overlap of selection signals between the two breeds reflecting their largely different selection histories, environmental challenges, breeding objectives and breed characteristics. Positional candidate genes identified in selected genomic regions in Angus have been previously associated with growth, immunity, reproductive development, feed efficiency and adaptation to environment while the candidate genes identified in Hanwoo included important genes regulating meat quality, fat deposition, cholesterol metabolism, lipid synthesis, neuronal development, and olfactory reception.

Super-resolution NMR spectroscopy

Spectral resolution is one of the limiting factors in nuclear magnetic resonance (NMR) spectroscopy of biological systems where signal overlap often interferes with chemical shift assignment as well as dynamics and structure analysis. This problem can be addressed in part by using higher magnetic field NMR spectrometers operating at up to 1.2 GHz 1H frequency to enhance the resolution proportionally with the field strength, and by deuteration in combination with transverse relaxation-optimized spectroscopy that reduces the transverse relaxation rate and proportionally the resonance linewidth of the peaks yielding higher spectral resolution. As a complement or alternative to these expensive and often insufficient approaches, we present here a generally applicable method to reduce the linewidth of peaks in indirect dimensions of multi-dimensional NMR spectra by increasing the number of scans per time increment exponentially as a function of time in order to compensate, in part, the decay of the signal caused by transverse relaxation. This enables to achieve a user-defined linewidth of the peaks without undue increase of the noise. Optimization by including in the number of scans also a cosine apodization function as well as processing spectra with an exponential-cosine window function in the direct dimension results typically in a resolution enhancement (linewidth reduction) by a factor of 1.5–2 in comparison to a standard measurement with a constant number of scans per time increment. This is comparable to the 2-fold resolution enhancement that can be obtained by going from a 600 MHz 1H frequency NMR spectrometer to a 1.2 GHz instrument, or from 1.2 GHz to a spectrum measured hypothetically at 2.4 GHz 1H frequency. A factor of two resolution enhancement causes thereby a signal to noise loss of a factor of three. The sensitivity gain by dynamic number of scan sampling is thereby ∼20 % over the use of a digital apodization function.

Characterization of shale oil and water micro-occurrence based on a novel method for fluid identification by NMR T2 spectrum

Nuclear magnetic resonance (NMR) has become a crucial technique for determining complex fluid occurrence characteristics. However, the overlap of fluid signals limits the application of the one-dimensional (1D) T2 (transverse relaxation time). Conventional methods are unable to accurately detect fluid signals that overlap entirely and cannot directly determine the petrophysical significance of each component spectrum. Leveraging the additional T1 (longitudinal relaxation time) information of two-dimensional (2D) NMR T1-T2, this study introduced a novel T2 spectra fluid identification method using the mixed Gaussian function. Six states of T2 and T1-T2 experiments were conducted on shale samples, including as-received (AR), water restoration of AR (WR-AR), water–oil restoration of AR (WOR-AR), oil-washed and dry (dry), oil-saturated (SO), and water-saturated (SW). By introducing the T2 projection spectrum, linear relationships between the 1D and 2D NMR signal amplitudes of various fluids were established to help separate the overlapping signals in the T2 spectrum. The results have demonstrated an inconsistent linear correlation between the 1D and 2D NMR signal amplitudes of various fluids, influenced by fluid composition and occurrence state. The conversion coefficient of adsorbed oil is 1.745 times higher than that of bound oil in the SO state. The adsorbed-to-bound ratio conversion coefficient was proposed as an additional input in the mixed Gaussian function to identify overlapping fluids in the oil–water coexistence state. The conversion coefficient of bound water determined by peak area analysis of 1D and 2D NMR before and after water restoration validated the identification results. The pore water altered the occurrence characteristics of oil, reducing the conversion coefficient. Furthermore, the pore-size characteristics of fluid occurrence were described. Compared with the conventional method, the proposed method bridges the gap between 1D and 2D NMR, significantly enhancing the accuracy and reliability of fluid identification by T2 spectra and providing new insights for NMR fluid evaluation in shale reservoirs.

One-to-Nine Single Spectroscopic Intelligent Probe for Risk Assessment of Multiple Metals in Drinking Water.

26% of the world's population lacks access to clean drinking water; clean water and sanitation are major global challenges highlighted by the UN Sustainable Development Goals, indicating water security in public water systems is at stake today. Water monitoring using precise instruments by skilled operators is one of the most promising solutions. Despite decades of research, the professionalism-convenience trade-off when monitoring ubiquitous metal ions remains the major challenge for public water safety. Thus, to overcome these disadvantages, an easy-to-use and highly sensitive visual method is desirable. Herein, an innovative strategy for one-to-nine metal detection is proposed, in which a novel thiourea spectroscopic probe with high 9-metal affinity is synthesized, acting as "one", and is detected based on the 9 metal-thiourea complexes within portable spectrometers in the public water field; this is accomplished by nonspecialized personnel as is also required. During the processing of multimetal analysis, issues arise due to signal overlap and reproducibility problems, leading to constrained sensitivity. In this innovative endeavor, machine learning (ML) algorithms were employed to extract key features from the composite spectral signature, addressing multipeak overlap, and completing the detection within 30-300 s, thus achieving a detection limit of 0.01 mg/L and meeting established conventional water quality standards. This method provides a convenient approach for public drinking water safety testing.

Universally Quantitative Band-Selective Pure Shift NMR Spectroscopy.

NMR spectroscopy is often described as a quantitative analytical technique. Strictly, only the simple pulse-acquire experiment is universally quantitative, but the poor signal resolution of the 1H NMR pulse-acquie experiment frequently complicates quantitative analysis. Pure shift NMR techniques provide higher resolution, by reducing signal overlap, but they are susceptible to a variety of sources of site-dependent signal loss. Here, we introduce a new method that corrects for signal loss from such sources in band-selective pure shift NMR experiments, by performing different numbers of iterations of the same pulse sequence elements before acquisition to allow extrapolation back to the loss-free signal. We apply this method to both interferogram and semi-realtime acquisition modes, obtaining integrals within 1% of those acquired from a pulse-acquire experiment for a three-component mixture.

Efficient Segmental Isotope Labeling of Integral Membrane Proteins for High-Resolution NMR Studies.

High-resolution structural NMR analyses of membrane proteins are challenging due to their large size, resulting in broad resonances and strong signal overlap. Among the isotope labeling methods that can remedy this situation, segmental isotope labeling is a suitable strategy to simplify NMR spectra and retain high-resolution structural information. However, protein ligation within integral membrane proteins is complicated since the hydrophobic protein fragments are insoluble, and the removal of ligation side-products is elaborate. Here, we show that a stabilized split-intein system can be used for rapid and high-yield protein trans-splicing of integral membrane proteins under denaturing conditions. This setup enables segmental isotope labeling experiments within folded protein domains for NMR studies. We show that high-quality NMR spectra of markedly reduced complexity can be obtained in detergent micelles and lipid nanodiscs. Of note, the nanodisc insertion step specifically selects for the ligated and correctly folded membrane protein and simultaneously removes ligation byproducts. Using this tailored workflow, we show that high-resolution NMR structure determination is strongly facilitated with just two segmentally isotope-labeled membrane protein samples. The presented method will be broadly applicable to structural and dynamical investigations of (membrane-) proteins and their complexes by solution and solid-state NMR but also other structural methods where segmental labeling is beneficial.

Noncontact visualization of multiscale defects in CFRP composites using eddy current testing with T-R probe

Carbon fiber reinforced polymer (CFRP) is widely used in modern industries, and damages may occur from manufacturing to utilization, highlighting the importance of CFRP detection. As being a multi-phase structural material, the CFRP's mesostructural features contained in eddy current signals can obscure macroscopic defect characteristics. Phase rotation is a common method for eddy current signal processing, but the specific criteria for evaluation of processing effect are lacking. This paper proposes three reliable criteria for assessment of the phase rotation processing effects of Transmitter-Receiver (T-R) probe signals, namely: signal overlap ratio (SOR), signal-to-noise ratio (SNR), and image average gradient (IAG). To begin, the SOR serves as a crucial indicator for assessing signal similarity and the degree of overlap, with a low SOR indicating the method's effectiveness in distinguishing defect characteristics. Subsequently, by comparing the SNRs of images, the clarity of defects under different rotation angles can be effectively quantified. Finally, a novel criterion, i.e. the IAG, is introduced. What distinguishes this criterion is its higher degree of automation, facilitating a comprehensive evaluation of processing effects without excessive subjective intervention. After analyzing these criteria, it is clear that the IAG criterion excels in automation and objectivity for evaluating eddy current signal processing effects.

Detection of EWSR1 gene rearrangement by fluorescence in situ hybridization in bone and soft tissue tumors: clinical application evaluation and atypical signal analysis

Objective: To investigate the clinical application of EWSR1 gene rearrangement by fluorescence in situ hybridization (FISH) in bone and soft tissue tumors and to analyze the cases with atypical signal pattern. Methods: The cases detected for EWSR1 gene rearrangement by FISH in Beijing Jishuitan Hospital, Capital Medical University from 2014 to 2021 were collected, and the value of detecting EWSR1 gene rearrangement for diagnosing bone and soft tissue tumors was analyzed. The cases with atypical positive signals were further analyzed by next generation sequencing (NGS). Results: FISH using EWSR1 break-apart probe kit was successfully performed in 97% (205/211) of cases, 6 cases failed. Four of the 6 failures were due to improper decalcification, 1 case due to signal overlap caused by thick slices, and 1 case due to signal amplification and disorder. EWSR1 gene rearrangements were positive in 122 cases (122/205, 59%), atypical positive signal in 8 cases (8/205, 4%), and negative in 75 cases (75/205, 37%). In cases testing positive, the percentage of positive cells ranged from 34% to 98%, with 120 cases (120/122, 98%) showing a positive cell percentage greater than 50%. Among the 205 successfully tested cases, 156 cases were histologically diagnosed as Ewing's sarcoma, of which 110 were positive (110/156, 71%), 7 were atypical positive (7/156, 4%), and 39 were negative (39/156, 25%). Nine cases were histologically diagnosed as clear cell sarcoma of soft tissue, of which 6 were positive (6/9), 1 was atypical positive (1/9), and 2 were negative (2/9). Five cases were histologically diagnosed as extraskeletal myxoid chondrosarcoma, of which 2 were positive (2/5) and 3 were negative (3/5). Three cases were histologically diagnosed as angiomatoid fibrous histiocytoma, of which 2 were positive (2/3) and 1 was negative (1/3). Two cases were histologically diagnosed as myoepithelioma of soft tissue, of which 1 was positive (1/2) and 1 was negative (1/2). One case was histologically diagnosed as olfactory neuroblastoma with a positive result. The 29 other tumor cases including osteosarcoma, synovial sarcoma, and malignant melanoma and others were all negative. Basing on histology as the standard for diagnosis and considering atypical positive cases as negative, comparing with the 29 cases of other tumors as control group, the sensitivity for diagnosing Ewing's sarcoma through the detection of EWSR1 gene rearrangement was 71%, and the specificity was 100%; the sensitivity for diagnosing clear cell sarcoma of soft tissue was 67%, and the specificity was 100%; the sensitivity for diagnosing extraskeletal myxoid chondrosarcoma was 40%, and the specificity was 100%; the sensitivity for diagnosing angiomatoid fibrous histiocytoma was 67%, and the specificity was 100%; the sensitivity for diagnosing myoepithelioma of soft tissue was 50%, and the specificity was 100%; the sensitivity for diagnosing olfactory neuroblastoma was 100%, and the specificity was 100%. Four of 8 cases with atypical positive signals analyzed by NGS showed EWSR1 rearrangement, including EWSR1::FLI1 in one case of Ewing sarcoma, EWSR1::NFATC2 in one case of EWSR1::NFATC2-rearranged sarcoma, EWSR1::ATF1 in one case of clear cell sarcoma of soft tissue and EWSR1::NR4A3 in one case of extraskeletal myxoid chondrosarcoma. Conclusions: Detection of EWSR1 rearrangement by FISH is of utmost significance in the diagnosis of bone and soft tissue tumors. Cases with atypical positive signals should be further scrutinized, correlating with their histomorphology and verifying by NGS if necessary.