Application of Time-Fractional Order Bloch Equation in Magnetic Resonance Fingerprinting

Magnetic resonance fingerprinting (MRF) is one novel fast quantitative imaging framework for simultaneous quantification of multiple parameters with pseudo-randomized acquisition patterns. The accuracy of the resulting multi-parameters is very important for clinical applications. In this paper, we derived signal evolutions from the anomalous relaxation using a fractional calculus. More specifically, we utilized time-fractional order extension of the Bloch equations to generate dictionary to provide more complex system descriptions for MRF applications. The representative results of phantom experiments demonstrated the good accuracy performance when applying the time-fractional order Bloch equations to generate dictionary entries in the MRF framework. The utility of the proposed method is also validated by in-vivo study.Comment: Accepted at 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019

A survey of speech enhancement algorithms

speech is easy to be interfered by the external environment in real applications, resulting in the reduction of speech intelligibility and signal-to-noise ratio. In the past few decades, due to the wide application of speech based solutions in practical applications, speech enhancement of noisy speech signals has aroused considerable research interest. This paper classifi es and introduces several main speech enhancement methods, summarizes the advantages and disadvantages of several main methods, and fi nally puts forward the next research direction of speech enhancement methods

Study on Qi Deficiency Syndrome Identification Modes of Coronary Heart Disease Based on Metabolomic Biomarkers

Coronary heart disease (CHD) is one of the most important types of heart disease because of its high incidence and mortality. With the era of systems biology bursting into reality, the analysis of the whole biological systems whether they are cells, tissues, organs, or the whole organisms has now become the norm of biological researches. Metabolomics is the branch of science concerned with the quantitative understandings of the metabolite complement of integrated living systems and their dynamic responses to the changes of both endogenous and exogenous factors. The aim of this study is to discuss the characteristics of plasma metabolites in CHD patients and CHD Qi deficiency syndrome patients and explore the composition and concentration changes of the plasma metabolomic biomarkers. The results show that 25 characteristic metabolites related to the CHD patients comparing with the healthy people, and 4 identifiable variables had significant differences between Qi deficiency and non-Qi deficiency patients. On the basis of identifying the different plasma endogenous metabolites between CHD patients and healthy people, we further prompted the metabolic rules, pathogenesis, and biological essence in Qi deficiency syndrome patients

Study on TCM Syndrome Identification Modes of Coronary Heart Disease Based on Data Mining

Coronary heart disease (CHD) is one of the most important types of heart disease because of its high incidence and high mortality. TCM has played an important role in the treatment of CHD. Syndrome differentiation based on information from traditional four diagnostic methods has met challenges and questions with the rapid development and wide application of system biology. In this paper, methods of complex network and CHAID decision tree were applied to identify the TCM core syndromes of patients with CHD, and to establish TCM syndrome identification modes of CHD based on biological parameters. At the same time, external validation modes were also constructed to confirm the identification modes

Metabolomics-Based Study of Clinical and Animal Plasma Samples in Coronary Heart Disease with Blood Stasis Syndrome

The aim of this study is to explore a bridge connecting the mechanism basis and macro syndromes of coronary heart disease with experimental animal models. GC-MS technique was used to detect the metabolites of plasma samples in mini swine models with myocardial infarction (MI) and patients with unstable angina (UA). 30 metabolites were detected in the plasma samples of more than 50 percent of model group and control group in swine, while 37 metabolites were found in the plasma samples of UA patients and healthy control group. 21 metabolites in the plasma samples of swine model and 20 metabolites in patients with UA were found of significant value. Among which, 8 shared metabolites were found of low level expression in both swine model and UA patients. Independent Student's t-test, principal component analysis (PCA), and hierarchicalcluster analysis (HCA) were orderly applied to comprehend inner rules of variables in the data. The 8 shared metabolites could take place of the 21 or 20 metabolites in classification of swine model with MI and UA patients, which could be considered as a bridge connecting the mechanism basis and macrosyndromes of swine model with MI and UA patients

In Silico Syndrome Prediction for Coronary Artery Disease in Traditional Chinese Medicine

Coronary artery disease (CAD) is the leading causes of deaths in the world. The differentiation of syndrome (ZHENG) is the criterion of diagnosis and therapeutic in TCM. Therefore, syndrome prediction in silico can be improving the performance of treatment. In this paper, we present a Bayesian network framework to construct a high-confidence syndrome predictor based on the optimum subset, that is, collected by Support Vector Machine (SVM) feature selection. Syndrome of CAD can be divided into asthenia and sthenia syndromes. According to the hierarchical characteristics of syndrome, we firstly label every case three types of syndrome (asthenia, sthenia, or both) to solve several syndromes with some patients. On basis of the three syndromes' classes, we design SVM feature selection to achieve the optimum symptom subset and compare this subset with Markov blanket feature select using ROC. Using this subset, the six predictors of CAD's syndrome are constructed by the Bayesian network technique. We also design Naïve Bayes, C4.5 Logistic, Radial basis function (RBF) network compared with Bayesian network. In a conclusion, the Bayesian network method based on the optimum symptoms shows a practical method to predict six syndromes of CAD in TCM

Dual functions of the ZmCCT-associated quantitative trait locus in flowering and stress responses under long-day conditions

Gene ontology enrichment of differentially expressed genes in HZ4 and HZ4-NIL in three development stages. (XLS 21 kb

Transcriptomics yields valuable information regarding the response mechanisms of Chinese Min pigs infected with PEDV

Porcine epidemic diarrhea virus (PEDV) causes porcine epidemic diarrhea (PED), a highly infectious disease, which has resulted in huge economic losses for the pig industry. To date, the pathogenic and immune response mechanism was not particularly clear. The purpose of this study was to investigate the pathogenic and immune responses of pigs infected with PEDV.In this study, 12 Min pigs were randomly selected without taking colostrum. At 3 days old, eight piglets were infected with 1 mL of PEDV solution (10 TCID50/ml), and the remaining four piglets were handled by 1 mL of 0.9% normal saline. Within the age of 7 days old, four piglets died and were considered as the death group. Correspondingly, four alive individuals were classified into the resistance group. Tissues of the duodenum, jejunum, ileum, colon, cecum, and rectum of piglets in the three groups were collected to measure the PEDV content. Additionally, the jejunum was used for the measurements and analyses of Hematoxylin-eosinstaining (HE), immunohistochemical sections, and transcriptomics. The phenotypes of Min piglets infected with PEDV showed that the viral copy numbers and jejunal damage had significant differences between the death and resistance groups. We also observed the transcriptome of the jejunum, and the differentially expressed (DE) analysis observed 6,585 DE protein-coding genes (PCGs), 3,188 DE long non-coding RNAs (lncRNAs), and 350 DE microRNAs (miRNAs), which were mainly involved in immune response and metabolic pathways. Furthermore, the specific expressed molecules for each group were identified, and 97 PCGs,108 lncRNAs, and 51 miRNAs were included in the ceRNA-regulated networks. By weighted gene co-expression network analysis (WGCNA) and transcription factor (TF) prediction, 27 significant modules and 32 significant motifs (E-value < 0.05) annotated with 519 TFs were detected. Of these TFs, 53 were DE PCGs. In summary, the promising key PCGs, lncRNAs, and miRNAs related to the pathogenic and immunological response of pigs infected with PEDV were detected and provided new insights into the pathogenesis of PEDV

Monitoring Prevalence and Persistence of Environmental Contamination by SARS-CoV-2 RNA in a Makeshift Hospital for Asymptomatic and Very Mild COVID-19 Patients

Objective: To investigate the details of environmental contamination status by SARS-CoV-2 in a makeshift COVID-19 hospital.Methods: Environmental samples were collected from a makeshift hospital. The extent of contamination was assessed by quantitative reverse transcription polymerase chain reaction (RT-qPCR) for SARS-CoV-2 RNA from various samples.Results: There was a wide range of total collected samples contaminated with SARS-CoV-2 RNA, ranging from 8.47% to 100%. Results revealed that 70.00% of sewage from the bathroom and 48.19% of air samples were positive. The highest rate of contamination was found from the no-touch surfaces (73.07%) and the lowest from frequently touched surfaces (33.40%). The most contaminated objects were the top surfaces of patient cubic partitions (100%). The median Ct values among strongly positive samples were 33.38 (IQR, 31.69–35.07) and 33.24 (IQR, 31.33–34.34) for ORF1ab and N genes, respectively. SARS-CoV-2 relic RNA can be detected on indoor surfaces for up to 20 days.Conclusion: The findings show a higher prevalence and persistence in detecting the presence of SARS-CoV-2 in the makeshift COVID-19 hospital setting. The contamination mode of droplet deposition may be more common than contaminated touches