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

A new anisotropic continuum traffic flow model with anticipation driving behavior

Based on the anticipation driving car-following model, a new macro traffic flow model is established in this paper by considering the relationship between micro and macro variables. Therefore, the evolution law of traffic flow with anticipation driving effect can be studied from macroscopic level. By using approaches of linear stability analysis, the linear stability discriminant condition of the new macro model to keep the traffic flow stable against small disturbance is obtained. Numerical experiments verify that the model can not only simulate the unique shock wave, rarefaction wave mutation and the dynamic propagation process of small disturbance, but also improve the stability of traffic flow by introducing the information of anticipation driving behavior

DEPRESSION, SUICIDAL IDEATION, AND KNOWLEDGE OF SUICIDAL BEHAVIOR AMONG CHINESE UNIVERSITY FRESHMEN

We explored the relationship between depression and suicidal ideation among Chinese university freshmen, and also the moderating role of knowledge of suicidal behavior in this relationship. A sample of 1,150 Chinese university freshmen completed the Symptom Checklist-90-Revised to evaluate their depression symptoms, the Suicide Attitude Inventory to evaluate their knowledge of suicidal behavior, and the Youth Self-Report Form to evaluate suicidal ideation. Results showed that knowledge of suicidal behavior had a negative relationship with both depression and suicidal ideation, between which there was a significant positive relationship. This relationship was moderated by knowledge of suicidal behavior. The enhancement of freshmen&#39;s knowledge of suicidal behavior may help prevent suicidal ideation among the students, and buffer the effect of depression on suicidal ideation.</p

Study on Extreme Precipitation Trends in Northeast China Based on Non-Stationary Generalized Extreme Value Distribution

Northeast China is the learding food productive base of China. The extreme precipitation (EP) event seriously impacts agricultural production and social life. Given the limited understanding of the EP in Northeast China, we investigate the trend and potential risk of the EP in Northeast China(107 stations) during 1959-2017, especially in early and later summer. For the first time, the non-stationary generalized extreme value (GEV) model is used to analyze the trend and potential risk of EP in Northeast China. Moreover, the mechanisms of EP trends over Northeast China in early and later summer were studied separately. Negative trends dominate EP in early summer but positive trends prevail in last summer. It is reasonable to discuss separately in the two periods. Meanwhile, all return levels are shown to increase trends in EP in early summer, corresponding to more frequent EP events. Nevertheless, in later summer, the 2-year return level decreases in location parameter diminish slightly, and the rare EP (20, 50, and 100-year return levels) slightly increase with scale parameter. Also, our results show that normal EP frequently occurs in Liaoning Province, and extreme EP is more likely to occur in Jilin Province and Heilongjiang Province. The increase of EP in early summer is mainly influenced by the northeast cold vortex. However, in later summer, the effect of cold air on EP is stronger in Northeast China, which gives a clear explanation that the EP does not increase. This study analyzed the trends and mechanism of return level and EP, which is beneficial for the development of policy strategies.Comment: 19page,10 figur

CVGG-Net: Ship Recognition for SAR Images Based on Complex-Valued Convolutional Neural Network

Ship target recognition is a vital task in synthetic aperture radar (SAR) imaging applications. Although convolutional neural networks have been successfully employed for SAR image target recognition, surpassing traditional algorithms, most existing research concentrates on the amplitude domain and neglects the essential phase information. Furthermore, several complex-valued neural networks utilize average pooling to achieve full complex values, resulting in suboptimal performance. To address these concerns, this paper introduces a Complex-valued Convolutional Neural Network (CVGG-Net) specifically designed for SAR image ship recognition. CVGG-Net effectively leverages both the amplitude and phase information in complex-valued SAR data. Additionally, this study examines the impact of various widely-used complex activation functions on network performance and presents a novel complex max-pooling method, called Complex Area Max-Pooling. Experimental results from two measured SAR datasets demonstrate that the proposed algorithm outperforms conventional real-valued convolutional neural networks. The proposed framework is validated on several SAR datasets

Traditional and emerging organophosphate esters (OPEs) in indoor dust of Nanjing, eastern China: Occurrence, human exposure, and risk assessment

Here, fifteen OPEs were investigated in n = 50 floor dust samples collected from six types of indoor spaces in Nanjing, eastern China, in the year 2018. Ten OPEs, including tris(2-chloroethyl) phosphate (TCEP), tris(2-chloroisopropyl) phosphate (TCIPP), tris(1,3-dichloro-isopropyl) phosphate (TDCIPP), tris(2-ethylhexyl) phosphate (TEHP), tris(2-butoxyethyl) phosphate (TBOEP), 2-ethylhexyl-diphenyl phosphate (EHDPP), triphenyl phosphate (TPHP), tris(methyl-phenyl) phosphate (TMPP), 4-biphenylyl diphenyl phosphate (4-BPDP) and tris(2-biphenylyl) phosphate (TBPP), were detected in at least one of the analyzed samples (>method limits of quantification). Regardless of indoor spaces, EHDPP (34% of Sigma 8OPEs, mean: 1.43 mu g/g) and TDCIPP (19%, 0.81 mu g/g) were the ascendant OPEs in indoor floor dust. 4-BPDP and TBPP were detectable in indoor floor dust samples, but at relatively low detection frequencies with 2% and 10%, respectively. Various indoor microenvironments exhibited different pollution characteristics of OPEs. Floor dust collected from electronic product maintenance centers contained the richest OPE contaminants with highest mean Sigma 8OPEs concentration of 7.92 mu g/g. On the basis of measured Sigma 10OPEs concentrations in dust sample, we estimated daily intake via floor dust ingestion to be 1.37, 0.75 and 1.24 ng/kg BW/day for electronic engineers, undergraduates, and graduate students under mean-exposure scenario, respectively. Overall, our study reported the occurrence of 4-BPDP and TBPP in environmental samples for the first time, and demonstrated that indoor floor dust ingestion exposure does values were far less than reference dosage values of oral toxicity proposed by United States Environmental Protection Agency (USEPA) Integrated Risk Information System. (C) 2020 Elsevier B.V. All rights reserved

Improved dual‐mode compressive tracking integrating balanced colour and texture features

Discriminative tracking methods can achieve state‐of‐the‐art performance by considering tracking as a classification problem tackled with both object and background information. As a high efficient discriminative tracker, compressive tracking (CT) has attracted much attention recently. However, it may easily fail when the object suffers from long‐term occlusions, and severe appearance and illumination changes. To address these issues, the authors develop a robust tracking framework based on CT by considering balanced feature representation as well as dual‐mode classifier construction. First, the original measurement matrix of CT works as a dominated texture feature extractor. To obtain a balanced feature representation, they propose to induce a complementary measurement matrix by considering both texture and colour features. Then, they develop two classifiers (dual mode) by using previous and current sample sets, respectively, and subsequently combine them into one ensemble classifier to track the target, which can help to avoid tracking failure suffering from severe appearance changes and long term occlusion. Moreover, they propose a classifier updating schema to prevent the inclusion of unsatisfied positive samples by predicting the occlusions with their ensemble classifier. The extensive experiments demonstrate the superior performance of their tracking framework under various situations

Paper device combining CRISPR/Cas12a and reverse-transcription loop-mediated isothermal amplification for SARS-CoV-2 detection in wastewater

Wastewater-based surveillance of the COVID-19 pandemic holds great promise; however, a point-of-use detection method for SARS-CoV-2 in wastewater is lacking. Here, a portable paper device based on CRISPR/Cas12a and reverse-transcription loop-mediated isothermal amplification (RT-LAMP) with excellent sensitivity and specificity was developed for SARS-CoV-2 detection in wastewater. Three primer sets of RT-LAMP and guide RNAs (gRNAs) that could lead Cas12a to recognize target genes via base pairing were used to perform the high-fidelity RT-LAMP to detect the N, E, and S genes of SARS-CoV-2. Due to the trans-cleavage activity of CRISPR/Cas12a after high-fidelity amplicon recognition, carboxyfluorescein-ssDNA-Black Hole Quencher-1 and carboxyfluorescein-ssDNA-biotin probes were adopted to realize different visualization pathways via a fluorescence or lateral flow analysis, respectively. The reactions were integrated into a paper device for simultaneously detecting the N, E, and S genes with limits of detection (LODs) of 25, 310, and 10 copies/mL, respectively. The device achieved a semiquantitative analysis from 0 to 310 copies/mL due to the different LODs of the three genes. Blind experiments demonstrated that the device was suitable for wastewater analysis with 97.7% sensitivity and 82% semiquantitative accuracy. This is the first semiquantitative endpoint detection of SARS-CoV-2 in wastewater via different LODs, demonstrating a promising point-of-use method for wastewater-based surveillance

Supporting Information from Synthesis and photocatalytic activity of mesoporous g-C₃N₄/MoS₂ hybrid catalysts

The key to solving environmental and energy issues through photocatalytic technology requires highly efficient, stable and eco-friendly photocatalysts. Graphitic carbon nitride (g-C₃N₄) is one of the most promising candidates except for its limited photoactivity. In this work, a facile and scalable one-step method is developed to fabricate an efficient heterostructural g-C₃N₄ photocatalyst <i>in situ</i> coupled with MoS₂. The strong coupling effect between the MoS₂ nanosheets and g-C₃N₄ scaffold, numerous mesopores and enlarged specific surface area helped form an effective heterojunction. As such, the photocatalytic activity of the g-C₃N₄/MoS₂ is more than three times higher than that of the pure g-C₃N₄ in the degradation of RhB under visible light irradiation. Improvement of g-C₃N₄/MoS₂ photocatalytic performance is mainly ascribed to the effective suppression of the recombination of charge carriers