Design and Modeling for 2D Plate Type MR Damper

A two-dimensional magnetorheological damper is developed for the engineering two-dimensional damping need. The velocity and pressure distribution model of the two-dimensional plate-type damper, and the damping force calculation model are established based on the Navier-Stokes equation. Several structural and physical parameters, including the working gap δ, the length a, and the width a of the middle slide plate, are analyzed theoretically. The damping performance of the two-dimensional plate-type magnetorheological damper was evaluated using a two-dimensional vibration test-bed, with the effect of the excitation current analyzed. The experimental results suggest a significant influence of Coulomb damping force on the damping force of magnetorheological damper when using appropriate magnetorheological fluid. As the excitation current increases, the damping force of magnetorheological damper becomes larger while the system amplitude decreases gradually in both directions, a maximum reduction of 2.5956 times. It's confirmed that the design of the two-dimensional plate-type magnetorheological damper is reasonable

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

CloudBrain-MRS: An Intelligent Cloud Computing Platform for in vivo Magnetic Resonance Spectroscopy Preprocessing, Quantification, and Analysis

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectra plots and metabolite quantification, the widespread clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: 1) Automatically statistical analysis to find biomarkers for diseases; 2) Consistency verification between the classic and artificial intelligence quantification algorithms; 3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, both healthy and mild cognitive impairment patient data are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing free access and service for two years. Please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.Comment: 11 pages, 12 figure

Denoising Single Voxel Magnetic Resonance Spectroscopy with Deep Learning on Repeatedly Sampled In Vivo Data

Objective: Magnetic Resonance Spectroscopy (MRS) is a noninvasive tool to reveal metabolic information. One challenge of MRS is the relatively low Signal-Noise Ratio (SNR) due to low concentrations of metabolites. To improve the SNR, the most common approach is to average signals that are acquired in multiple times. The data acquisition time, however, is increased by multiple times accordingly, resulting in the scanned objects uncomfortable or even unbearable. Methods: By exploring the multiple sampled data, a deep learning denoising approach is proposed to learn a mapping from the low SNR signal to the high SNR one. Results: Results on simulated and in vivo data show that the proposed method significantly reduces the data acquisition time with slightly compromised metabolic accuracy. Conclusion: A deep learning denoising method was proposed to significantly shorten the time of data acquisition, while maintaining signal accuracy and reliability. Significance: Provide a solution of the fundamental low SNR problem in MRS with artificial intelligence

Joint application of plant immunity-inducing elicitors and fungicides to control Phytophthora diseases

Abstract Phytophthora are destructive plant pathogens that pose a serious threat to crop production. Traditional control methods rely heavily on chemical fungicides, which are harmful to the environment and human health. Currently, effective green prevention and control methods for Phytophthora pathogens are lacking. Plants rely primarily on their innate immune system to resist pathogens. Plant cells perceive pathogen invasion and activate immune responses by recognizing specific pathogen-derived molecules, called elicitors, which mainly include pathogen-associated molecular patterns (PAMPs) and microbial effector proteins. PAMPs, which are conserved molecular features of microbes and recognized by plant cell surface-localized pattern-recognition receptors (PRRs), activate mild and broad-spectrum disease resistance. However, there are few reports on elicitor proteins that induce broad resistance against Phytophthora pathogens. In this study, we identified BcIEB1, a fungal-derived PAMP, which activated plant immune responses in a BAK1- and SOBIR1-dependent manner. BcIEB1 could induce plant resistance to various Phytophthora pathogens, including P. capsici, P. infestans, and P. parasitica. We further found that the combination of lower concentrations of BcIEB1 with fungicides, such as pyraclostrobin, azoxystrobin, and metalaxyl-M, could enhance the effect on Phytophthora disease control while reducing the dependence on fungicides, thereby reducing environmental pollution. This study identified a novel, less toxic strategy for controlling Phytophthora diseases

Preparation of Liposomes Coated Superparamagnetic Iron Oxide Nanoparticles for Targeting and Imaging Brain Glioma

Effective and specific diagnostic imaging of brain glioma remains a challenge. Magnetic nanoparticles are actively being developed as contrast agents for diagnosis of tumor. In this work, we developed the targeted magnetic nanoparticles as T1-positive magnetic resonance imaging (MRI) contrast agents. Zn-doped Fe3O4 NPs were synthesized by solvothermal method, coated with liposome and conjugated to a tumor-penetrating peptide (RGERPPR). The effect of zinc doping on the magnetic properties of Fe3O4 nanoparticles was studied. Zn0.4Fe2.6O4-PEG nanoparticles exhibited T1 MR contrast enhancement. Cytotoxicity assay indicated that nanoparticles have good biocompatibility and low toxicity. And the in vitro cellular uptake assays on U87 cells confirmed that the conjugation of RGERPPR increased the uptake of the Zn0.4Fe2.6O4 NPs. In vivo MR imaging showed the contrast enhance of U87 brain glioma in rat model after injection

A Rapid and High-Sensitive Real-Time Reverse Transcription-Polymerase Chain Reaction Assay Used for the Detection of Severe Acute Respiratory Syndrome Coronavirus 2

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a public health emergency of international concern. Real-time reverse transcription-polymerase chain reaction (RT-PCR) is widely used as the gold standard method for the diagnosis of SARS-CoV-2 infection. However, the reliability of current real-time RT-PCR assays is questioned due to some false-negative reports. In this study, we improved the real-time RT-PCR method based on three target regions (ORF1ab, E, and N) of SARS-CoV-2. Results showed that real-time RT-PCR assays herein could complete detection within one hour after viral RNA preparation and had high sensitivity down to 5 copies of viral RNA. In addition, six clinical specimens were detected to evaluate the availability of this method. Among them, four samples were 3-plex SARS-CoV-2 positive and two were negative by real-time RT-PCR. The sensitivity was 100% (4/4), and specificity was 100% (2/2). These results demonstrate that we develop a rapid and high-sensitive real-time RT-PCR method for SARS-CoV-2 detection, which will be a powerful tool for COVID-19 identification and for monitoring suspected patients