NMR Spectra Denoising with Vandermonde Constraints

Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering. However, NMR signals are easily contaminated by noise during the data acquisition, which can affect subsequent quantitative analysis. Therefore, denoising NMR signals has been a long-time concern. In this work, we propose an optimization model-based iterative denoising method, CHORD-V, by treating the time-domain NMR signal as damped exponentials and maintaining the exponential signal form with a Vandermonde factorization. Results on both synthetic and realistic NMR data show that CHORD-V has a superior denoising performance over typical Cadzow and rQRd methods, and the state-of-the-art CHORD method. CHORD-V restores low-intensity spectral peaks more accurately, especially when the noise is relatively high.Comment: 10 pages, 9 figure

The Existence of Spanning Ended System on Claw-Free Graphs

We prove that every connected claw-free graph G contains a spanning k-ended system if and only if cl(G) contains a spanning k-ended system, where cl(G) denotes Ryjáček closure of G

Clinical outcomes of active specific immunotherapy in advanced colorectal cancer and suspected minimal residual colorectal cancer: a meta-analysis and system review

<p>Abstract</p> <p>Background</p> <p>To evaluate the objective clinical outcomes of active specific immunotherapy (ASI) in advanced colorectal cancer (advanced CRC) and suspected minimal residual colorectal cancer (suspected minimal residual CRC).</p> <p>Methods</p> <p>A search was conducted on Medline and Pub Med from January 1998 to January 2010 for original studies on ASI in colorectal cancer (CRC). All articles included in this study were assessed with the application of predetermined selection criteria and were divided into two groups: ASI in advanced CRC and ASI in suspected minimal residual CRC. For ASI in suspected minimal residual CRC, a meta-analysis was executed with results regarding the overall survival (OS) and disease-free survival (DFS). Regarding ASI in advanced colorectal cancer, a system review was performed with clinical outcomes.</p> <p>Results</p> <p>1375 colorectal carcinoma patients with minimal residual disease have been enrolled in Meta-analysis. A significantly improved OS and DFS was noted for suspected minimal residual CRC patients utilizing ASI (For OS: HR = 0.76, P = 0.007; For DFS: HR = 0.76, P = 0.03). For ASI in stage II suspected minimal residual CRC, OS approached significance when compared with control (HR = 0.71, P = 0.09); however, the difference in DFS of ASI for the stage II suspected minimal residual CRC reached statistical significance (HR = 0.66, P = 0.02). For ASI in stage III suspected minimal residual CRC compared with control, The difference in both OS and DFS achieved statistical significance (For OS: HR = 0.76, P = 0.02; For DFS: HR = 0.81, P = 0.03). 656 advanced colorectal patients have been evaluated on ASI in advanced CRC. Eleven for CRs and PRs was reported, corresponding to an overall response rate of 1.68%. No serious adverse events have been observed in 2031 patients.</p> <p>Conclusions</p> <p>It is unlikely that ASI will provide a standard complementary therapeutic approach for advanced CRC in the near future. However, the clinical responses to ASI in patients with suspected minimal residual CRC have been encouraging, and it has become clear that immunotherapy works best in situations of patients with suspected minimal residual CRC.</p

High-resolution intermolecular zero-quantum coherence spectroscopy under inhomogeneous fields with effective solvent suppression

Intermolecular zero-quantum coherences (iZQCs) are not susceptible to magnetic field inhomogeneities significantly larger than the dipolar correlation distance and can be used to obtain 1D high-resolution spectra in an inhomogeneous field. However, with the iZQC methods proposed previously, residual conventional single-quantum coherences (SQCs) originating mainly from solvent resonance result in strong t(1) ridge noises. A modified HOMOGENIZED with an intermolecular double-quantum. filter (iDQF), named iDQF-HOMOGENIZED, is presented in this work to suppress the residual conventional SQC signals as well as solvent iZQC signals. The solvent-suppression efficiency of the iDQF-HOMOGENIZED is analyzed and a thorough comparison of the new sequence with several relevant pulse sequences is made. Dramatic resolution enhancement and solvent suppression in the measurements of a piece of grape sarcocarp suggest potential applications of the method in in vivo spectroscopy

CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis

Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful analytical tool for studying molecular structure and dynamics in chemistry and biology. However, the processing of raw data acquired from NMR spectrometers and subsequent quantitative analysis involves various specialized tools, which necessitates comprehensive knowledge in programming and NMR. Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation. Thus, NMR processing is not an easy task for chemist and biologists. In this work, we present CloudBrain-NMR, an intelligent online cloud computing platform designed for NMR data reading, processing, reconstruction, and quantitative analysis. The platform is conveniently accessed through a web browser, eliminating the need for any program installation on the user side. CloudBrain-NMR uses parallel computing with graphics processing units and central processing units, resulting in significantly shortened computation time. Furthermore, it incorporates state-of-the-art deep learning-based algorithms offering comprehensive functionalities that allow users to complete the entire processing procedure without relying on additional software. This platform has empowered NMR applications with advanced artificial intelligence processing. CloudBrain-NMR is openly accessible for free usage at https://csrc.xmu.edu.cn/CloudBrain.htmlComment: 11 pages, 13 figure

High-resolution magnetic resonance spectroscopy in unstable fields via intermolecular zero-quantum coherences

Intermolecular zero-quantum coherences (iZQCs) have been utilized to achieve high-resolution nuclear magnetic resonance (NMR) proton spectra under inhomogeneous and/or unstable fields. In this paper, we demonstrated that despite the insensitivity of iZQCs to B-0 variations, the influence of unstable fields on the observable single-quantum coherence signals causes strong t(1) noises in the high-resolution iZQC spectra. Short-time acquisition (STA) and phase spectrum schemes were proposed for noise suppression in in vivo iZQC magnetic resonance spectroscopy (MRS) under temporal B-0 variations. The feasibility of these schemes were verified by localized spectroscopic studies under B-0 variations generated by the Z(0) coil current oscillations and by voxel position variations in the presence of field gradients, which simulate the field conditions of MRS in the presence of physiological motions. The phase scheme not only improves the signal-to-noise ratio but also further reduces the linewidth by half.NNSF of China [10774125, 10875101, 10974164]; Research Fund for the Doctoral Program of Higher Education of China [200803840019

Ankylosing spondylitis and glaucoma in European population: A Mendelian randomization study

BackgroundThe relationship between ankylosing spondylitis (AS) and glaucoma in the European population remains unclear. In the present study, we applied a two-sample Mendelian randomization (MR) method to investigate their causal relationship.MethodsMR analysis was conducted to validate the causal associations between AS with glaucoma using summary statistics from the genome-wide association studies of AS (9,069 cases and 13,578 control subjects) and glaucoma (8,591 cases and 210,201 control subjects). The inverse variance weighting method was performed to evaluate the causal relationship. The MR–Egger regression approach was applied to assess pleiotropy, while Cochran’s Q test was used to analyze heterogeneity. Subgroup analysis was performed according to primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG).ResultsThe results of the MR study reveal a risk-increasing causal relationship between AS and glaucoma among European populations (OR = 1.35, 95%CI = 1.16–1.57, P = 8.81 × 10-5). Pleiotropy and heterogeneity were not found in our study. In the subgroup analysis, AS was also causal with POAG (OR = 1.48, 95%CI = 1.17–1.86, P = 8.80 × 10-4) and PACG (OR = 1.91, 95%CI = 1.03–3.51, P = 3.88 × 10-2).ConclusionThe results of the MR analysis suggested a causal relationship between AS and glaucoma in the European population. Further studies are needed to identify the specific mechanism between these two diseases

Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal

Objective: Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of non-ideal acquisition conditions, and interference with strong background signals mainly from macromolecules. The most popular method, LCModel, adopts complicated non-linear least square to quantify metabolites and addresses these problems by designing empirical priors such as basis-sets, imperfection factors. However, when the signal-to-noise ratio of MRS signal is low, the solution may have large deviation. Methods: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification. First, a neural network is designed to explicitly predict the imperfection factors and the overall signal from macromolecules. Then, metabolite quantification is solved analytically with the introduced LLS. In our Quantification Network (QNet), LLS takes part in the backpropagation of network training, which allows the feedback of the quantification error into metabolite spectrum estimation. This scheme greatly improves the generalization to metabolite concentrations unseen for training compared to the end-to-end deep learning method. Results: Experiments show that compared with LCModel, the proposed QNet, has smaller quantification errors for simulated data, and presents more stable quantification for 20 healthy in vivo data at a wide range of signal-to-noise ratio. QNet also outperforms other end-to-end deep learning methods. Conclusion: This study provides an intelligent, reliable and robust MRS quantification. Significance: QNet is the first LLS quantification aided by deep learning