Dynamic Regularized Adaptive Cluster Optimization (DRACO) for Quantitative Cardiac Cine MRI in Complex Arrhythmias

BACKGROUND

Irregular cardiac motion can render conventional segmented cine MRI nondiagnostic. Clustering has been proposed for cardiac motion binning and may be optimized for complex arrhythmias.

PURPOSE

To develop an adaptive cluster optimization method for irregular cardiac motion, and to generate the corresponding time-resolved cine images.

STUDY TYPE

Prospective.

SUBJECTS

Thirteen with atrial fibrillation, four with premature ventricular contractions, and one patient in sinus rhythm.

FIELD STRENGTH/SEQUENCE

Free-running balanced steady state free precession (bSSFP) with sorted golden-step, reference real-time sequence.

ASSESSMENT

Each subject underwent both the sorted golden-step bSSFP and the reference Cartesian real-time imaging. Golden-step bSSFP images were reconstructed using the dynamic regularized adaptive cluster optimization (DRACO) method and k-means clustering. Image quality (4-point Likert scale), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge sharpness, and ventricular function were assessed.

STATISTICAL TESTS

Paired t-tests, Friedman test, regression analysis, Fleiss' Kappa, Bland-Altman analysis. Significance level P < 0.05.

RESULTS

The DRACO method had the highest percent of images with scores ≥3 (96% for diastolic frame, 93% for systolic frame, and 93% for multiphase cine) and the percentages were significantly higher compared with both the k-means and real-time methods. Image quality scores, SNR, and CNR were significantly different between DRACO vs. k-means and between DRACO vs. real-time. Cardiac function analysis showed no significant differences between DRACO vs. the reference real-time.

CONCLUSION

DRACO with time-resolved reconstruction generated high quality images and has early promise for quantitative cine cardiac MRI in patients with complex arrhythmias including atrial fibrillation.

LEVEL OF EVIDENCE: 1

TECHNICAL EFFICACY

Stage 2.

A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN

Positioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both static and dynamic conditions. Since there is no ground truth for the pseudorange biases due to the multipath/NLoS scenarios, the proposed method is validated based on the positioning performance. Positioning solutions are computed by subtracting the estimated biases from the raw pseudoranges and comparing them to the ground truth.

Nanomechanical-based classification of prostate tumor using atomic force microscopy

BACKGROUND

The loss of mechanical homeostasis between tumor cells and microenvironment is an important factor in tumor metastasis. In the process, mechanical forces affect cell proliferation, differentiation, migration and tissue development.

AIMS

Using high spatial resolution of Atomic force microscopy (AFM) technology, our study provides the direct measurement of the nanomechanical properties of prostate cancer clinical tissue specimens.

MATERIALS AND METHODS

AFM was used to determine the biomechanical properties of prostate tissue with different grade scores. K-means clustering method and fuzzy C-means were used to distinguish the cellular component in prostate tissue from non-cellular component based on their viscoelasticity. Futhermore, AFM measurements in vitro cells, including metastatic prostate cells (PC-3) and normal human prostate cells (PZ-HPV-7) were carried out.

RESULTS

The Young's modulus was decreased in prostate cancer progression, and the elasticity of cellular component in prostate cancer tissue was smaller than that of normal prostate tissue. PC-3 cells were softer than PZ-HPV-7 cells. Further mechanism investigation showed that the difference in modulus between cancerous and normal prostate tissue may be associated with a greater actin cytoskeleton distribution inside the cancer cells.

CONCLUSION

The results suggests that the nanomechanical properties can classify the prostate tumor, which could be used as an index for the identification and classification of cancer at cellular level.

Data-driven crash prediction by injury severity using a recurrent neural network model based on Keras framework

With the development of big data technology and the improvement of deep learning technology, data-driven and machine learning application have been widely employed. By adopting the data-driven machine learning method, with the help of clustering processing of data sets, a recurrent neural network (RNN) model based on Keras framework is proposed to predict the injury severity in urban areas. First, with crash data from 2014 to 2017 in Nevada, OPTICS clustering algorithm is employed to extract the crash injury in Las Vegas. Next, by virtue of Keras' high efficiency and strong scalability, the parameters of loss function, activation function and optimizer of the deep learning model are determined to realize the training of the model and the visualization of the training results, and the RNN model is constructed. Finally, on the basis of training and testing data, the model can predict the injury severity with high accuracy and high training speed. The results provide an alternative and some potential insights on the injury severity prediction.

A Novel Four-Step Algorithm for Detecting a Single Circle in Complex Images

Single-circle detection is vital in industrial automation, intelligent navigation, and structural health monitoring. In these fields, the circle is usually present in images with complex textures, multiple contours, and mass noise. However, commonly used circle-detection methods, including random sample consensus, random Hough transform, and the least squares method, lead to low detection accuracy, low efficiency, and poor stability in circle detection. To improve the accuracy, efficiency, and stability of circle detection, this paper proposes a single-circle detection algorithm by combining Canny edge detection, a clustering algorithm, and the improved least squares method. To verify the superiority of the algorithm, the performance of the algorithm is compared using the self-captured image samples and the GH dataset. The proposed algorithm detects the circle with an average error of two pixels and has a higher detection accuracy, efficiency, and stability than random sample consensus and random Hough transform.

Experimental study and clustering of operating staff of search systems in the sense of stress resistance

Introduction

The main goal of this study is to develop a methodology for the organization of experimental selection of operator personnel based on the analysis of their behavior under the influence of micro-stresses.

Methods

A human-machine interface model has been developed, which considers the change in the functional state of the human operator. The presented concept of the difficulty of detecting the object of attention contributed to developing a particular sequence of ordinary test images with stressor images included in it and presented models of the flow of presenting test images to the recipient.

Results

With the help of descriptive statistics, the parameters of individual box-plot diagrams were determined, and the recipient group was clustered.

Discussion

Overall, the proposed approach based on the example of the conducted grouping makes it possible to ensure the objectivity and efficiency of the professional selection of applicants for operator specialties.

TransFNN: A Novel Overtemperature Prediction Method for HVDC Converter Valves Based on an Improved Transformer and the F-NN Algorithm

Appropriate cooling of the converter valve in a high-voltage direct current (HVDC) transmission system is highly significant for the safety, stability, and economical operation of a power grid. The proper adjustment of cooling measures is based on the accurate perception of the valve's future overtemperature state, which is characterized by the valve's cooling water temperature. However, very few previous studies have focused on this need, and the existing Transformer model, which excels in time-series predictions, cannot be directly applied to forecast the valve overtemperature state. In this study, we modified the Transformer and present a hybrid Transformer-FCM-NN (TransFNN) model to predict the future overtemperature state of the converter valve. The TransFNN model decouples the forecast process into two stages: (i) The modified Transformer is used to obtain the future values of the independent parameters; (ii) the relation between the valve cooling water temperature and the six independent operating parameters is fit, and the output of the Transformer is used to calculate the future values of the cooling water temperature. The results of the quantitative experiments showed that the proposed TransFNN model outperformed other models with which it was compared; with TransFNN being applied to predict the overtemperature state of the converter valves, the forecast accuracy was 91.81%, which was improved by 6.85% compared with that of the original Transformer model. Our work provides a novel approach to predicting the valve overtemperature state and acts as a data-driven tool for operation and maintenance personnel to use to adjust valve cooling measures punctually, effectively, and economically.

Gate-Level Circuit Partitioning Algorithm Based on Clustering and an Improved Genetic Algorithm

Gate-level circuit partitioning is an important development trend for improving the efficiency of simulation in EDA software. In this paper, a gate-level circuit partitioning algorithm, based on clustering and an improved genetic algorithm, is proposed for the gate-level simulation task. First, a clustering algorithm based on betweenness centrality is proposed to quickly identify clusters in the original circuit and achieve the circuit coarse. Next, a constraint-based genetic algorithm is proposed which provides absolute and probabilistic genetic strategies for clustered circuits and other circuits, respectively. This new genetic strategy guarantees the integrity of clusters and is effective for realizing the fine partitioning of gate-level circuits. The experimental results using 12 ISCAS '89 and ISCAS '85 benchmark circuits show that the proposed algorithm is 5% better than Metis, 80% better than KL, and 61% better than traditional genetic algorithms for finding the minimum number of connections between subsets.

Free-Form Surface Partitioning and Simulation Verification Based on Surface Curvature

To address the problem of low overall machining efficiency of free-form surfaces and difficulty in ensuring machining quality, this paper proposes a MATLAB-based free-form surface division method. The surface division is divided into two stages: Partition area identification and area boundary determination. In the first stage, the free-form surface is roughly divided into convex, concave, and saddle regions according to the curvature of the surface, and then the regions are subdivided based on the fuzzy c-means clustering algorithm. In the second stage, according to the clustering results, the Voronoi diagram algorithm is used to finally determine the boundary of the surface patch. We used NURBS to describe free-form surfaces and edit a set of MATLAB programs to realize the division of surfaces. The proposed method can easily and quickly divide the surface area, and the simulation results show that the proposed method can shorten machining time by 36% compared with the traditional machining method. It is proved that the method is practical and can effectively improve the machining efficiency and quality of complex surfaces.

Research on autonomous route generation method based on AIS ship trajectory big data and improved LSTM algorithm

The autonomous generation of routes is an important part of ship intelligence and it can be realized by deep learning of the big data of automatic identification system (AIS) ship trajectories. In this study, to make the routes generated by long short-term memory (LSTM) artificial neural network more accurate and efficient, a ship route autonomous generation scheme is proposed based on AIS ship trajectory big data and improved multi-task LSTM artificial neural network. By introducing an unsupervised trajectory separation mechanism into LSTM, a fast and accurate separation of trajectories with similar paths is realized. In the process of route generation, first of all, a clustering algorithm is used to cluster the trajectories in massive AIS data according to the density of trajectory points, so as to eliminate the trajectories in the routes that do not belong to the target area. Furthermore, the routes are classified according to the type of ships, and then the classified trajectories are processed and used as datasets. Based on these datasets, an improved LSTM algorithm is used to generate ship routes autonomously. The results show the improved LSTM works better than LSTM when the generated route trajectories are short.

A Single-Image Noise Estimation Algorithm Based on Pixel-Level Low-Rank Low-Texture Patch and Principal Component Analysis

Noise level is an important parameter for image denoising in many image-processing applications. We propose a noise estimation algorithm based on pixel-level low-rank, low-texture subblocks and principal component analysis for white Gaussian noise. First, an adaptive clustering algorithm, based on a dichotomy merge, adaptive pixel-level low-rank matrix construction method and a gradient covariance low-texture subblock selection method, is proposed to construct a pixel-level low-rank, low-texture subblock matrix. The adaptive clustering algorithm can improve the low-rank property of the constructed matrix and reduce the content of the image information in the eigenvalues of the matrix. Then, an eigenvalue selection method is proposed to eliminate matrix eigenvalues representing the image to avoid an inaccurate estimation of the noise level caused by using the minimum eigenvalue. The experimental results show that, compared with existing state-of-the-art methods, our proposed algorithm has, in most cases, the highest accuracy and robustness of noise level estimation for various scenarios with different noise levels, especially when the noise is high.

Emotional analysis of evaluation discourse in business English translation based on language big data mining of public health environment

Purpose

This paper conducts sentiment analysis on the evaluation discourse of business English translation based on language big data mining of public health environment, and aims to find a reasonable algorithm to conduct detailed research on all aspects of sentiment analysis.

Methodology

This paper focuses on three areas of sentiment information, extraction, sentiment information retrieval, and sentiment information submission, using scale analysis and feedback analysis, combined with related algorithms of big data mining technology, such as decision trees and clustering algorithms, through the level of emotional words appearing in the corpus, phrase-level, text-level, etc., and combine the text model with the combined reliability to output the evaluation object and evaluation feature separately, and propose an evaluation method to calculate the sensitivity of the evaluation feature, so as to accurately improve the sensitivity of the evaluation feature. It is mainly divided into two categories for data analysis. One is to focus on the public health environment of the characteristics of business English translation itself, and the other is to conduct research on the application of big data mining in the evaluation of translation discourse.

Research findings

The research data show that the smallest gap between the sentiment orientation of the discourse evaluation perspective is the output of the language discourse, and the smallest gap in the attributes of the evaluation object is at the phrase level, and the gap value is 3.5; for the evaluation object, the maximum difference is 3.4.

Research implications

With the development of science and technology and the economy, the public health environment has become more and more complex, and business English translation has received more and more attention. The sentiment analysis of evaluation discourse in this field is a means of expressing language characteristics. In order to enrich research in this field, the study of this article is necessary.

Practical implications

This study has a deeper understanding of the affective analysis of evaluation discourse in public health environment business English translation. The clustering algorithm of big data mining technology applied can provide an important guarantee for the actual conclusion of this research and quantitative analysis of the positive evaluation and criticism of evaluation. To solve the various problems encountered in translation, so as to improve the translator's own translation level, and promote the research of translation methods in Chinese translation.

Precision DNA Mixture Interpretation with Single-Cell Profiling

Wet-lab based studies have exploited emerging single-cell technologies to address the challenges of interpreting forensic mixture evidence. However, little effort has been dedicated to developing a systematic approach to interpreting the single-cell profiles derived from the mixtures. This study is the first attempt to develop a comprehensive interpretation workflow in which single-cell profiles from mixtures are interpreted individually and holistically. In this approach, the genotypes from each cell are assessed, the number of contributors (NOC) of the single-cell profiles is estimated, followed by developing a consensus profile of each contributor, and finally the consensus profile(s) can be used for a DNA database search or comparing with known profiles to determine their potential sources. The potential of this single-cell interpretation workflow was assessed by simulation with various mixture scenarios and empirical allele drop-out and drop-in rates, the accuracies of estimating the NOC, the accuracies of recovering the true alleles by consensus, and the capabilities of deconvolving mixtures with related contributors. The results support that the single-cell based mixture interpretation can provide a precision that cannot beachieved with current standard CE-STR analyses. A new paradigm for mixture interpretation is available to enhance the interpretation of forensic genetic casework.

A Cluster-Based Approach for the Discovery of Copy Number Variations From Next-Generation Sequencing Data

The next-generation sequencing technology offers a wealth of data resources for the detection of copy number variations (CNVs) at a high resolution. However, it is still challenging to correctly detect CNVs of different lengths. It is necessary to develop new CNV detection tools to meet this demand. In this work, we propose a new CNV detection method, called CBCNV, for the detection of CNVs of different lengths from whole genome sequencing data. CBCNV uses a clustering algorithm to divide the read depth segment profile, and assigns an abnormal score to each read depth segment. Based on the abnormal score profile, Tukey's fences method is adopted in CBCNV to forecast CNVs. The performance of the proposed method is evaluated on simulated data sets, and is compared with those of several existing methods. The experimental results prove that the performance of CBCNV is better than those of several existing methods. The proposed method is further tested and verified on real data sets, and the experimental results are found to be consistent with the simulation results. Therefore, the proposed method can be expected to become a routine tool in the analysis of CNVs from tumor-normal matched samples.

Consensus clustering of single-cell RNA-seq data by enhancing network affinity

Elucidation of cell subpopulations at high resolution is a key and challenging goal of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data analysis. Although unsupervised clustering methods have been proposed for de novo identification of cell populations, their performance and robustness suffer from the high variability, low capture efficiency and high dropout rates which are characteristic of scRNA-seq experiments. Here, we present a novel unsupervised method for Single-cell Clustering by Enhancing Network Affinity (SCENA), which mainly employed three strategies: selecting multiple gene sets, enhancing local affinity among cells and clustering of consensus matrices. Large-scale validations on 13 real scRNA-seq datasets show that SCENA has high accuracy in detecting cell populations and is robust against dropout noise. When we applied SCENA to large-scale scRNA-seq data of mouse brain cells, known cell types were successfully detected, and novel cell types of interneurons were identified with differential expression of gamma-aminobutyric acid receptor subunits and transporters. SCENA is equipped with CPU + GPU (Central Processing Units + Graphics Processing Units) heterogeneous parallel computing to achieve high running speed. The high performance and running speed of SCENA combine into a new and efficient platform for biological discoveries in clustering analysis of large and diverse scRNA-seq datasets.

Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree

The diagnosis of the degree of differentiation of tumor cells can help physicians to make timely detection and take appropriate treatment for the patient's condition. In this study, the original dataset is clustered into two independent types by the Kohonen clustering algorithm. One type is used as the development sets to find correlation indicators and establish predictive models of differentiation, while the other type is used as the validation sets to test the correlation indicators and models. In the development sets, thirteen indicators significantly associated with the degree of differentiation of esophageal squamous cell carcinoma are found by the Kohonen clustering algorithm. Thirteen relevant indicators are used as input features and the degree of tumor differentiations is used as output. Ten classification algorithms are used to predict the differentiation of esophageal squamous cell carcinoma. Artificial bee colony-support vector machine (ABC-SVM) predicts better than the other nine algorithms, with an average accuracy of 81.5% for the 10-fold cross-validation. Based on logistic regression and ReliefF algorithm, five models with the greater merit for the degree of differentiation are found in the development sets. The AUC values of the five models are 0.672, 0.628, 0.630, 0.628, and 0.608 (P < 0.05). The AUC values of the five models in the validation sets are 0.753, 0.728, 0.744, 0.776, and 0.868 (P < 0.0001). The predicted values of the five models are constructed as the input features of ABC-SVM. The accuracy of the 10-fold cross-validation reached 82.0 and 86.5% in the development sets and the validation sets, respectively.

Genetic-Based Hypertension Subtype Identification Using Informative SNPs

In this work, we proposed a process to select informative genetic variants for identifying clinically meaningful subtypes of hypertensive patients. We studied 575 African American (AA) and 612 Caucasian hypertensive participants enrolled in the Hypertension Genetic Epidemiology Network (HyperGEN) study and analyzed each race-based group separately. All study participants underwent GWAS (Genome-Wide Association Studies) and echocardiography. We applied a variety of statistical methods and filtering criteria, including generalized linear models, F statistics, burden tests, deleterious variant filtering, and others to select the most informative hypertension-related genetic variants. We performed an unsupervised learning algorithm non-negative matrix factorization (NMF) to identify hypertension subtypes with similar genetic characteristics. Kruskal–Wallis tests were used to demonstrate the clinical meaningfulness of genetic-based hypertension subtypes. Two subgroups were identified for both African American and Caucasian HyperGEN participants. In both AAs and Caucasians, indices of cardiac mechanics differed significantly by hypertension subtypes. African Americans tend to have more genetic variants compared to Caucasians; therefore, using genetic information to distinguish the disease subtypes for this group of people is relatively challenging, but we were able to identify two subtypes whose cardiac mechanics have statistically different distributions using the proposed process. The research gives a promising direction in using statistical methods to select genetic information and identify subgroups of diseases, which may inform the development and trial of novel targeted therapies.

A Novel Weighted Clustering Algorithm Supported by a Distributed Architecture for D2D Enabled Content-Centric Networks

Next generation cellular systems need efficient content-distribution schemes. Content-sharing via Device-to-Device (D2D) clustered networks has emerged as a popular approach for alleviating the burden on the cellular network. In this article, we utilize Content-Centric Networking and Network Virtualization to propose a distributed architecture, that supports efficient content delivery. We propose to use clustering at the user level for content-distribution. A weighted multifactor clustering algorithm is proposed for grouping the D2D User Equipment (DUEs) sharing a common interest. The proposed algorithm is evaluated in terms of energy efficiency, area spectral efficiency, and throughput. The effect of the number of clusters on these performance parameters is also discussed. The proposed algorithm has been further modified to allow for a tradeoff between fairness and other performance parameters. A comprehensive simulation study demonstrates that the proposed clustering algorithm is more flexible and outperforms several classical and state-of-the-art algorithms.

BHCMDA: A New Biased Heat Conduction Based Method for Potential MiRNA-Disease Association Prediction

Recent studies have indicated that microRNAs (miRNAs) are closely related to sundry human sophisticated diseases. According to the surmise that functionally similar miRNAs are more likely associated with phenotypically similar diseases, researchers have proposed a variety of valid computational models through integrating known miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity to discover the potential miRNA-disease relationships in biomedical researches. Taking account of the limitations of previous computational models, a new computational model based on biased heat conduction for MiRNA-Disease Association prediction (BHCMDA) was proposed in this paper, which can achieve the AUC of 0.8890 in LOOCV (Leave-One-Out Cross Validation) and the mean AUC of 0.9060, 0.8931 under the framework of twofold cross validation, fivefold cross validation, respectively. In addition, BHCMDA was further implemented to the case studies of three vital human cancers, and simulation results illustrated that there were 88% (Esophageal Neoplasms), 92% (Colonic Neoplasms) and 92% (Lymphoma) out of top 50 predicted miRNAs having been confirmed by experimental literatures, separately, which demonstrated the good performance of BHCMDA as well. Thence, BHCMDA would be a useful calculative resource for potential miRNA-disease association prediction.

Innovating Computational Biology and Intelligent Medicine: ICIBM 2019 Special Issue

The International Association for Intelligent Biology and Medicine (IAIBM) is a nonprofit organization that promotes intelligent biology and medical science. It hosts an annual International Conference on Intelligent Biology and Medicine (ICIBM), which was established in 2012. The ICIBM 2019 was held from 9 to 11 June 2019 in Columbus, Ohio, USA. Out of the 105 original research manuscripts submitted to the conference, 18 were selected for publication in a Special Issue in Genes. The topics of the selected manuscripts cover a wide range of current topics in biomedical research including cancer informatics, transcriptomic, computational algorithms, visualization and tools, deep learning, and microbiome research. In this editorial, we briefly introduce each of the manuscripts and discuss their contribution to the advance of science and technology.

[Study on medication regularity of traditional Chinese medicine in treatment of COVID-19 based on data mining]

The coronavirus disease 2019(COVID-19) is developing rapidly and posing great threat to public health. There is no specific medicine available for treating the disease. Luckily, traditional Chinese medicine has played a positive role in the fighting against COVID-19. In this paper, We collected and sorted the prescriptions of modern Chinese medicine for COVID-19 released by national government, different provinces, autonomous regions and municipalities, as well as online databases, such as CNKI, WanFang medical network, and VIP database. These prescriptions were combined with the inheritance of traditional Chinese medicine auxiliary V2.5, and the complex system entropy clustering method was used to determine the association rules and frequency of single drug and drug combination in the prescription. In the end, 96 effective prescriptions were included. Among them, the four properties were mainly concentrated in temperature, cold and level, the five tastes were mainly concentrated in bitter, hot and sweet, and the meridians were mainly concentrated in lung, stomach and spleen. The high-frequency drugs were Glycyrrhizae Radix et Rhizoma, Armeniacae Semen Amarum, Gypsum Fibrosum, etc., and the high-frequency combinations are Gypsum Fibrosum-Armeniacae Semen Amarum, Gypsum Fibrosum-Glycyrrhizae Radix et Rhizoma, Armeniacae Semen Amarum-Glycyrrhizae Radix et Rhizoma, the core combinations are Lepidii Semen-Armeniacae Semen Amarum-Gypsum Fibrosum, Pogostemonis Herba-Zingiberis Rhizoma Recens-Magnoliae Officinalis Cortex, Ophiopogonis Radix-Armeniacae Semen Amarum-Scutellariae Radix and so on. Form new prescriptions Lepidii Semen, Armeniacae Semen Amarum, Gypsum Fibrosum, Pogostemonis Herba, Zingiberis Rhizoma Recens, Magnoliae Officinalis Cortex. Ophiopogonis Radix, Armeniacae Semen Amarum, Scutellariae Radix, Schisandrae Sphenantherae Fructus, Panacis Quinquefolii Radix. From the medicinal properties to high-frequency drugs and new prescriptions, it could be seen that the overall treatment of COVID-19 by traditional Chinese medicine was to strengthen body resistance, eliminate pathogenic factors, and give attention to Qi and Yin.

Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study

With recent advances in single-cell RNA sequencing, enormous transcriptome datasets have been generated. These datasets have furthered our understanding of cellular heterogeneity and its underlying mechanisms in homogeneous populations. Single-cell RNA sequencing (scRNA-seq) data clustering can group cells belonging to the same cell type based on patterns embedded in gene expression. However, scRNA-seq data are high-dimensional, noisy, and sparse, owing to the limitation of existing scRNA-seq technologies. Traditional clustering methods are not effective and efficient for high-dimensional and sparse matrix computations. Therefore, several dimension reduction methods have been introduced. To validate a reliable and standard research routine, we conducted a comprehensive review and evaluation of four classical dimension reduction methods and five clustering models. Four experiments were progressively performed on two large scRNA-seq datasets using 20 models. Results showed that the feature selection method contributed positively to high-dimensional and sparse scRNA-seq data. Moreover, feature-extraction methods were able to promote clustering performance, although this was not eternally immutable. Independent component analysis (ICA) performed well in those small compressed feature spaces, whereas principal component analysis was steadier than all the other feature-extraction methods. In addition, ICA was not ideal for fuzzy C-means clustering in scRNA-seq data analysis. K-means clustering was combined with feature-extraction methods to achieve good results.

Evaluation of the Physical Properties of Bedding Materials for Dairy Cattle Using Fuzzy Clustering Analysis

The bedding materials used in dairy cow housing systems are extremely important for animal welfare and performance. A wide range of materials can be used as bedding for dairy cattle, but their physical properties must be analysed to evaluate their potential. In the present study, the physical properties of various bedding materials for dairy cattle were investigated, and different fuzzy clustering algorithms were employed to cluster these materials based on their physical properties. A total of 51 different bedding materials from various places in Europe were collected and tested. Physical analyses were carried out for the following parameters: bulk density (BD), water holding capacity (WHC), air-filled porosity (AFP), global density (GD), container capacity (CC), total effective porosity (TEP), saturated humidity (SH), humidity (H), and average particle size (APS). These data were analysed by principal components analysis (PCA) to reduce the amount of data and, subsequently, by fuzzy clustering analysis. Three clustering algorithms were tested: k-means (KM), fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. Furthermore, different numbers of clusters (2-8) were evaluated and subsequently compared using five validation indexes. The GK clustering algorithm with eight clusters fit better regarding the division of materials according to their properties. From this clustering analysis, it was possible to understand how the physical properties of the bedding materials may influence their behaviour. Among the materials that fit better as bedding materials for dairy cows, Posidonia oceanica (Cluster 6) can be considered an alternative material.

Identifying Interaction Clusters for MiRNA and MRNA Pairs in TCGA Network

Existing methods often fail to recognize the conversions for the biological roles of the pairs of genes and microRNAs (miRNAs) between the tumor and normal samples. We have developed a novel cluster scoring method to identify messenger RNA (mRNA) and miRNA interaction pairs and clusters while considering tumor and normal samples jointly. Our method has identified 54 significant clusters for 15 cancer types selected from The Cancer Genome Atlas project. We also determined the shared clusters across tumor types and/or subtypes. In addition, we compared gene and miRNA overlap between lists identified in our liver hepatocellular carcinoma (LIHC) study and regulatory relationships reported from human and rat nonalcoholic fatty liver disease studies (NAFLD). Finally, we analyzed biological functions for the single significant cluster in LIHC and uncovered a significantly enriched pathway (phospholipase D signaling pathway) with six genes represented in the cluster, symbols: DGKQ, LPAR2, PDGFRB, PIK3R3, PTGFR and RAPGEF3.

Design, Optimization and Improvement of FBG Flexible Sensor for Slope Displacement Profiles Measurement

The accurate measurement of slope displacement profiles using a fiber Bragg grating flexible sensor is limited due to the influence of accumulative measurement errors. The measurement errors vary with the deformation forms of the sensor, which dramatically affects the measurement accuracy of the slope displacement profiles. To tackle the limitations and improve the measurement precision of displacement profiles, a segmental correction method based on strain increments clustering was proposed. A K-means clustering algorithm was used to automatically identify the deformation segments of a flexible sensor with different bending shapes. Then, the particle swarm optimization method was adopted to determine the correction coefficients corresponding to different deformation segments. Both finite element simulations and experiments were performed to validate the superiority of the proposed method. The experimental results indicated that the mean absolute errors (MAEs) percentages of the reconstructed displacements using the proposed method for six different bending shapes were 1.87%, 5.28%, 6.98%, 7.62%, 4.16% and 8.31%, respectively, which had improved the accuracy by 26.83%, 18.94%, 29.49%, 26.35%, 7.39%, and 19.65%, respectively. Therefore, it was confirmed that the proposed correction method was competent for effectively mitigating the measurement errors and improving the measurement accuracy of slope displacement profiles, and it presented a vital significance and application promotion value.

[Medication rules of Professor Zhang Bing in treatment of skin itching based on data mining]

Skin itching is a subjective sensation that causes the desire to scratch. It is one of the most common clinical symptoms at department of dermatology, even the only complaint of dermatological patients, which seriously affects the quality life of patients. Therefore, based on the software of traditional Chinese medicine inheritance auxiliary platform, association rules and complex system entropy clustering were adopted to collect and analyze Zhang Bing's prescriptions for skin itching, and get the drug use frequency and the relationship between drugs. Based on that, we could conclude the experience for skin itching. A total of 147 prescriptions were collected, 20 drugs with a frequency of 34 or more and 20 high-frequency drug combinations were analyzed, and 14 core combinations and 7 new prescriptions were excavated. The high-frequency drugs included Kochiae Fructus, Dictamni Cortex, Mori Cortex. The high-frequency drug combinations included "Kochiae Fructus-Dictamni Cortex" "Angelicae Dahuricae Radix-Chuanxiong Rhizoma" "Paeoniae Radix Rubra-Paeoniae Radix Alba", and the core combinations included "Schizonepetae Herba-Saposhnikoviae Radix-Cinnamomi Ramulus" "Arctii Fructus-Cicadae Periostracum-Houttuyniae Herba" "Ghrysanthemi Indici Flos-Kochiae Fructus-Dictamni Cortex", and new formulations include "Schizonepetae Herba, Saposhnikoviae Radix, Cinnamomi Ramulus, Clematidis Radix et Rhizoma, Tribuli Fructus, Dictamni Cortex", "Phellodendri Chinensis Coritex, Lonicerae Japonicae Flos, Atractylodis Rhizoma, Ghrysanthemi Indici Flos, Kochiae Fructus, Dictamni Cortex" "Arctii Fructus, Cicadae Periostracum, Houttuyniae Herba, Trichosanthis Fructus". The result of this research shows that Professor Zhang Bing's experience in the treatment of skin itching is mainly to dispelling wind and arresting itching, clearing heat and drying dampness.

Is More Always Better?: Discovering Incentivized mHealth Intervention Engagement Related to Health Behavior Trends

Behavioral medicine is devoting increasing attention to the topic of participant engagement and its role in effective mobile health (mHealth) behavioral interventions. Several definitions of the term "engagement" have been proposed and discussed, especially in the context of digital health behavioral interventions. We consider that engagement refers to specific interaction and use patterns with the mHealth tools such as smartphone applications for intervention, whereas adherence refers to compliance with the directives of the health intervention, independent of the mHealth tools. Through our analysis of participant interaction and self-reported behavioral data in a college student health study with incentives, we demonstrate an example of measuring "effective engagement" as engagement behaviors that can be linked to the goals of the desired intervention. We demonstrate how clustering of one year of weekly health behavior self-reports generate four interpretable clusters related to participants' adherence to the desired health behaviors: healthy and steady, unhealthy and steady, decliners, and improvers. Based on the intervention goals of this study (health promotion and behavioral change), we show that not all app usage metrics are indicative of the desired outcomes that create effective engagement. As such, mHealth intervention design might consider eliciting not just more engagement or use overall, but rather, effective engagement defined by use patterns related to the desired behavioral outcome.

Corrigendum: Comparative Studies of Gene Expression Kinetics: Methodologies and Insights on Development and Evolution

[This corrects the article DOI: 10.3389/fgene.2018.00339.].

Comparative Studies of Gene Expression Kinetics: Methodologies and Insights on Development and Evolution

Across the animal kingdom, embryos of closely related species show high morphological similarity despite genetic and environmental distances. Deciphering the molecular mechanisms that underlie morphological conservation and those that support embryonic adaptation are keys to understand developmental robustness and evolution. Comparative studies of developmental gene regulatory networks can track the genetic changes that lead to evolutionary novelties. However, these studies are limited to a relatively small set of genes and demand extensive experimental efforts. An alternative approach enabled by next-generation sequencing, is to compare the expression kinetic of large sets of genes between different species. The advantages of these comparisons are that they can be done relatively easily, for any species and they provide information of all expressed genes. The challenge in these experiments is to compare the kinetic profiles of thousands of genes between species that develop in different rates. Here we review recent comparative studies that tackled the challenges of accurate staging and large-scale analyses using different computational approaches. These studies reveal how correct temporal scaling exposes the striking conservation of developmental gene expression between morphologically similar species. Different clustering approaches are used to address various comparative questions and identify the conservation and divergence of large gene sets. We discuss the unexpected contribution of housekeeping genes to the interspecies correlations and how this contribution distorts the hourglass pattern generated by developmental genes. Overall, we demonstrate how comparative studies of gene expression kinetics can provide novel insights into the developmental constraints and plasticity that shape animal body plans.

Application of PZT Technology and Clustering Algorithm for Debonding Detection of Steel-UHPC Composite Slabs

A lightweight composite bridge deck system composed of steel orthotropic deck stiffened with thin Ultra-High Performance Concrete (UHPC) layer has been proposed to eliminate fatigue cracks in orthotropic steel decks. The debonding between steel deck and UHPC layer may be introduced during construction and operation phases, which could cause adverse consequences, such as crack-induced water invasion and distinct reduction of the shear resistance. The piezoelectric lead zirconate titanate (PZT)-based technologies are used to detect interfacial debonding defects between the steel deck and the UHPC layer. Both impedance analysis and wave propagation method are employed to extract debonding features of the steel-UHPC composite slab with debonding defect in different sizes and thicknesses. Experimental tests are performed on two steel-UHPC composite slabs and a conventional steel-concrete composite deck. Additionally, an improved Particle Swarm Optimization (PSO)-k-means clustering algorithm is adopted to obtain debonding patterns based on the feature data set. The laboratory tests demonstrate that the proposed approach provides an effective way to detect interfacial debonding of steel-UHPC composite deck.

Clustering-Constrained ICA for Ballistocardiogram Artifacts Removal in Simultaneous EEG-fMRI

Combination of electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) plays a potential role in neuroimaging due to its high spatial and temporal resolution. However, EEG is easily influenced by ballistocardiogram (BCG) artifacts and may cause false identification of the related EEG features, such as epileptic spikes. There are many related methods to remove them, however, they do not consider the time-varying features of BCG artifacts. In this paper, a novel method using clustering algorithm to catch the BCG artifacts' features and together with the constrained ICA (ccICA) is proposed to remove the BCG artifacts. We first applied this method to the simulated data, which was constructed by adding the BCG artifacts to the EEG signal obtained from the conventional environment. Then, our method was tested to demonstrate the effectiveness during EEG and fMRI experiments on 10 healthy subjects. In simulated data analysis, the value of error in signal amplitude (Er) computed by ccICA method was lower than those from other methods including AAS, OBS, and cICA (p < 0.005). In vivo data analysis, the Improvement of Normalized Power Spectrum (INPS) calculated by ccICA method in all electrodes was much higher than AAS, OBS, and cICA methods (p < 0.005). We also used other evaluation index (e.g., power analysis) to compare our method with other traditional methods. In conclusion, our novel method successfully and effectively removed BCG artifacts in both simulated and vivo EEG data tests, showing the potentials of removing artifacts in EEG-fMRI applications.

Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments

Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS). Due to the absence of satellite signal in Global Navigation Satellite System (GNSS), various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP), which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC), is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1) and the XiDan Joy City (Floors 1 and 2, as Test-bed 2), and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means.

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.

Adaptive partitioning by local density-peaks: An efficient density-based clustering algorithm for analyzing molecular dynamics trajectories

We present an efficient density-based adaptive-resolution clustering method APLoD for analyzing large-scale molecular dynamics (MD) trajectories. APLoD performs the k-nearest-neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high-density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2-3 orders of magnitude for systems ranging from alanine dipeptide to a 370-residue Maltose-binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low-density regions, while smaller clusters at high-density regions), which is a clear advantage over other popular clustering algorithms including k-centers and k-medoids. We anticipate that APLoD can be widely applied to split ultra-large MD datasets containing millions of conformations for subsequent construction of Markov State Models. © 2016 Wiley Periodicals, Inc.

High-dimensional cluster analysis with the masked EM algorithm

Cluster analysis faces two problems in high dimensions: the "curse of dimensionality" that can lead to overfitting and poor generalization performance and the sheer time taken for conventional algorithms to process large amounts of high-dimensional data. We describe a solution to these problems, designed for the application of spike sorting for next-generation, high-channel-count neural probes. In this problem, only a small subset of features provides information about the cluster membership of any one data vector, but this informative feature subset is not the same for all data points, rendering classical feature selection ineffective. We introduce a "masked EM" algorithm that allows accurate and time-efficient clustering of up to millions of points in thousands of dimensions. We demonstrate its applicability to synthetic data and to real-world high-channel-count spike sorting data.

Clustering the lexicon in the brain: a meta-analysis of the neurofunctional evidence on noun and verb processing

Although it is widely accepted that nouns and verbs are functionally independent linguistic entities, it is less clear whether their processing recruits different brain areas. This issue is particularly relevant for those theories of lexical semantics (and, more in general, of cognition) that suggest the embodiment of abstract concepts, i.e., based strongly on perceptual and motoric representations. This paper presents a formal meta-analysis of the neuroimaging evidence on noun and verb processing in order to address this dichotomy more effectively at the anatomical level. We used a hierarchical clustering algorithm that grouped fMRI/PET activation peaks solely on the basis of spatial proximity. Cluster specificity for grammatical class was then tested on the basis of the noun-verb distribution of the activation peaks included in each cluster. Thirty-two clusters were identified: three were associated with nouns across different tasks (in the right inferior temporal gyrus, the left angular gyrus, and the left inferior parietal gyrus); one with verbs across different tasks (in the posterior part of the right middle temporal gyrus); and three showed verb specificity in some tasks and noun specificity in others (in the left and right inferior frontal gyrus and the left insula). These results do not support the popular tenets that verb processing is predominantly based in the left frontal cortex and noun processing relies specifically on temporal regions; nor do they support the idea that verb lexical-semantic representations are heavily based on embodied motoric information. Our findings suggest instead that the cerebral circuits deputed to noun and verb processing lie in close spatial proximity in a wide network including frontal, parietal, and temporal regions. The data also indicate a predominant-but not exclusive-left lateralization of the network.

CpGIF: an algorithm for the identification of CpG islands

CpG islands (CGIs) play a fundamental role in genome analysis and annotation, and contribute to improving the accuracy of promoter prediction. Besides, CGIs in promoter regions are abnormally methylated in cancer cells and thus can be used as tumor markers. However, current methods for identifying CGIs suffer from various drawbacks. We present a new algorithm for detecting CGIs, called CpG Island Finder (CpGIF), which combines the best features in the most commonly used algorithms and avoids their disadvantages as much as possible. Five public tools for CpG island searching are used to compare with CpGIF for the assessment of accuracy and computational efficiency. The results reveal that CpGIF has higher performance coefficient and correlation coefficient than these previous methods, which indicates that CpGIF is able to provide high sensitivity and specificity at the same time. CpGIF is also faster than those methods with comparable prediction accuracy.