Search

Human lying is influenced by cognitive neural mechanisms in the brain, and conducting research on lie detection in speech can help to reveal the cognitive mechanisms of the human brain. Inappropriate deception detection features can easily lead to dimension disaster and make the generalization ability of the widely used semi-supervised speech deception detection model worse. Because of this, this paper proposes a semi-supervised speech deception detection algorithm combining acoustic statistical features and time-frequency two-dimensional features. Firstly, a hybrid semi-supervised neural network based on a semi-supervised autoencoder network (AE) and a mean-teacher network is established. Secondly, the static artificial statistical features are input into the semi-supervised AE to extract more robust advanced features, and the three-dimensional (3D) mel-spectrum features are input into the mean-teacher network to obtain features rich in time-frequency two-dimensional information. Finally, a consistency regularization method is introduced after feature fusion, effectively reducing the occurrence of over-fitting and improving the generalization ability of the model. This paper carries out experiments on the self-built corpus for deception detection. The experimental results show that the highest recognition accuracy of the algorithm proposed in this paper is 68.62% which is 1.2% higher than the baseline system and effectively improves the detection accuracy. [ABSTRACT FROM AUTHOR]

Subcellular localization attempts to assign proteins to one of the cell compartments that performs specific biological functions. Finding the link between proteins, biological functions, and subcellular localization is an effective way to investigate the general organization of living cells in a systematic manner. However, determining the subcellular localization of proteins by traditional experimental approaches is difficult. Here, protein–protein interaction networks, functional enrichment on gene ontology and pathway, and a set of proteins having confirmed subcellular localization were applied to build prediction models for human protein subcellular localizations. To build an effective predictive model, we employed a variety of robust machine learning algorithms, including Boruta feature selection, minimum redundancy maximum relevance, Monte Carlo feature selection, and LightGBM. Then, the incremental feature selection method with random forest and support vector machine was used to discover the essential features. Furthermore, 38 key features were determined by integrating results of different feature selection methods, which may provide critical insights into the subcellular location of proteins. Their biological functions of subcellular localizations were discussed according to recent publications. In summary, our computational framework can help advance the understanding of subcellular localization prediction techniques and provide a new perspective to investigate the patterns of protein subcellular localization and their biological importance. [ABSTRACT FROM AUTHOR]

The immune system is a complicated defensive system that comprises multiple functional cells and molecules acting against endogenous and exogenous pathogenic factors. Identifying immune cell subtypes and recognizing their unique immunological functions are difficult because of the complicated cellular components and immunological functions of the immune system. With the development of transcriptomics and high-throughput sequencing, the gene expression profiling of immune cells can provide a new strategy to explore the immune cell subtyping. On the basis of the new profiling data of mouse immune cell gene expression from the Immunological Genome Project (ImmGen), a novel computational pipeline was applied to identify different immune cell subtypes, including αβ T cells, B cells, γδ T cells, and innate lymphocytes. First, the profiling data was analyzed by a powerful feature selection method, Monte-Carlo Feature Selection, resulting in a feature list and some informative features. For the list, the two-stage incremental feature selection method, incorporating random forest as the classification algorithm, was applied to extract essential gene signatures and build an efficient classifier. On the other hand, a rule learning scheme was applied on the informative features to construct quantitative expression rules. A group of gene signatures was found as qualitatively related to the biological processes of four immune cell subtypes. The quantitative expression rules can efficiently cluster immune cells. This work provides a novel computational tool for immune cell quantitative subtyping and biomarker recognition. [ABSTRACT FROM AUTHOR]

Based on improved back propagation (BP) neural network, information fusion state estimation problem for multi-sensor system is considered. Firstly, particle swarm optimization, search dynamic learning rate and additional momentum method are introduced to train the initial weights and thresholds of BP neural network. Then, the improved neural network is used to optimize the estimated value of Kalman filter. Finally, the sate estimators are fused by weighting matrices. A simulation example verifies the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

It is well known that the unscented Kalman filter (UKF) has been successfully implemented for the integration of inertial and global positioning system (GPS) measurements. This paper proposes combining an aggregate constraint method with the UKF to solve the GPS/inertial navigation system (INS) integration with equality and inequality constraints. The proposed algorithm comprehensively combines the characteristics of the aggregate constraint method and the UKF, and achieves the results in a computationally efficient way. A numerical example shows the proposed algorithm avoids large computational expense and can achieve the same level of good applicability compared with the existing constrained Kalman filter. [ABSTRACT FROM AUTHOR]

As non-coding RNAs, circular RNAs (cirRNAs) and long non-coding RNAs (lncRNAs) have attracted an increasing amount of attention. They have been confirmed to participate in many biological processes, including playing roles in transcriptional regulation, regulating protein-coding genes, and binding to RNA-associated proteins. Until now, the differences between these two types of non-coding RNAs have not been fully uncovered. It is still quite difficult to detect cirRNAs from other lncRNAs using simple techniques. In this study, we investigated these two types of non-coding RNAs using several computational methods. The purpose was to extract important factors that could distinguish cirRNAs from other lncRNAs and build an effective classification model to distinguish them. First, we collected cirRNAs, lncRNAs and their representations from a previous study, in which each cirRNA or lncRNA was represented by 188 features derived from its graph representation, sequence and conservation properties. Second, these features were analyzed by the minimum redundancy maximum relevance (mRMR) method. The obtained mRMR feature list, incremental feature selection method and hierarchical extreme learning machine algorithm were employed to build an optimal classification model with sensitivity of 0.703, specificity of 0.850, accuracy of 0.789 and a Matthews correlation coefficient of 0.561. Finally, we analyzed the 16 most important features. Of them, the sequences and structures of the RNA molecule were top ranking, implying they can be potential indicators of differences between cirRNAs and other lncRNAs. Meanwhile, other features of evolutionary conversation, sequence consecution were also important. [ABSTRACT FROM AUTHOR]

Identifying essential genes in a given organism is important for research on their fundamental roles in organism survival. Furthermore, if possible, uncovering the links between core functions or pathways with these essential genes will further help us obtain deep insight into the key roles of these genes. In this study, we investigated the essential and non-essential genes reported in a previous study and extracted gene ontology (GO) terms and biological pathways that are important for the determination of essential genes. Through the enrichment theory of GO and KEGG pathways, we encoded each essential/non-essential gene into a vector in which each component represented the relationship between the gene and one GO term or KEGG pathway. To analyze these relationships, the maximum relevance minimum redundancy (mRMR) was adopted. Then, the incremental feature selection (IFS) and support vector machine (SVM) were employed to extract important GO terms and KEGG pathways. A prediction model was built simultaneously using the extracted GO terms and KEGG pathways, which yielded nearly perfect performance, with a Matthews correlation coefficient of 0.951, for distinguishing essential and non-essential genes. To fully investigate the key factors influencing the fundamental roles of essential genes, the 21 most important GO terms and three KEGG pathways were analyzed in detail. In addition, several genes was provided in this study, which were predicted to be essential genes by our prediction model. We suggest that this study provides more functional and pathway information on the essential genes and provides a new way to investigate related problems. [ABSTRACT FROM AUTHOR]

A robust object tracking system that is invariant to object appearance variations and background clutter is proposed. Multiple instance learning with a boosting algorithm is applied to select discriminant texture information between the object and background data. Additionally, depth information, which is important to distinguish the object from a complicated background, is integrated. We propose two depth-based models that can compensate texture information to cope with both appearance variants and background clutter. Moreover, in order to reduce the risk of drifting problem increased for the textureless depth templates, an update mechanism is proposed to select more precise tracking results to avoid incorrect model updates. In the experiments, the robustness of the proposed system is evaluated and quantitative results are provided for performance analysis. Experimental results show that the proposed system can provide the best success rate and has more accurate tracking results than other well-known algorithms. [ABSTRACT FROM AUTHOR]

The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived from the aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request. [ABSTRACT FROM AUTHOR]

The time delay of the pulsar integrated pulse profile relative to standard pulse profile is one of the important observations in X-ray pulsar-based navigation system, which accuracy directly affects the accuracy of the pulsar-based navigation system. In order to improve the measuring accuracy of the time delay of the pulsar integrated pulse profile and reduce computation complexity, a novel optimization algorithm based on sparse representation is proposed in this paper. This method firstly constructs a waveform-matched dictionary in accordance with standard pulse profile. On the basis of the dictionary, first order sparse coefficient vector that contains the phase information of time delay is used to represent measurement pulse profile. Then, the time delay for integrated pulse profile is calculated with the sparse coefficient by the matching pursuit algorithm. The proposed algorithm is validated with the real data observed by Rossi X-ray Timing Explorer. Comparing with the state-of-the-art fast Fourier transform algorithm, experiments show that the proposed algorithm can significantly reduce the impact of signal-to-noise ratio of the observed pulse profile on the measuring accuracy of the time delay. Even in a short observation time, the proposed algorithm can still maintain the high measuring accuracy. In addition, the measuring accuracy of the proposed algorithm can be further improved when the redundant dictionary is constructed with more sampling phase bins of the standard pulse profile. [ABSTRACT FROM AUTHOR]

In this paper a Bayesian method is proposed to estimate dynamic origin–destination (O–D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O–D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O–D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O–D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O–D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen–Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O–D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O–D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and "true" O–D demands is relatively small, and the O–D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O–D demands with fine accuracy. [ABSTRACT FROM AUTHOR]

Platinum-cobalt bimetallic clusters (N=11–15) were modeled by a many-body Sutton-Chen (SC) potential. The basin-hopping algorithm and Monte Carlo (MC) energy minimization were used to determine the global minima of bimetallic clusters. The structural changes with cluster size were observed. Most of the structures had built on icosahedral packing. Second energy difference analyzes were performed to investigate the relative stability of a cluster with respect to its size and composition. The dependence of energetics and structures on size and composition is discussed. [ABSTRACT FROM AUTHOR]

Objectives. We aimed to establish an effective machine learning (ML) model for predicting the risk of distant metastasis (DM) in medullary thyroid carcinoma (MTC). Methods. Demographic data of MTC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database of the National Institutes of Health between 2004 and 2015 to develop six ML algorithm models. Models were evaluated based on accuracy, precision, recall rate, F1-score, and area under the receiver operating characteristic curve (AUC). The association between clinicopathological characteristics and target variables was interpreted. Analyses were performed using traditional logistic regression (LR). Results. In total, 2049 patients were included and 138 developed DM. Multivariable LR showed that age, sex, tumor size, extrathyroidal extension, and lymph node metastasis were predictive features for DM in MTC. Among the six ML models, the random forest (RF) had the best predictability in assessing the risk of DM in MTC, with an accuracy, precision, recall rate, F1-score, and AUC higher than those of the traditional binary LR model. Conclusion. RF was superior to traditional LR in predicting the risk of DM in MTC and can provide a valuable reference for clinicians in decision-making. [ABSTRACT FROM AUTHOR]

Methylation is one of the most common and considerable modifications in biological systems mediated by multiple enzymes. Recent studies have shown that methylation has been widely identified in different RNA molecules. RNA methylation modifications have various kinds, such as 5-methylcytosine (m5C). However, for individual methylation sites, their functions still remain to be elucidated. Testing of all methylation sites relies heavily on high-throughput sequencing technology, which is expensive and labor consuming. Thus, computational prediction approaches could serve as a substitute. In this study, multiple machine learning models were used to predict possible RNA m5C sites on the basis of mRNA sequences in human and mouse. Each site was represented by several features derived from k -mers of an RNA subsequence containing such site as center. The powerful max-relevance and min-redundancy (mRMR) feature selection method was employed to analyse these features. The outcome feature list was fed into incremental feature selection method, incorporating four classification algorithms, to build efficient models. Furthermore, the sites related to features used in the models were also investigated. [ABSTRACT FROM AUTHOR]

In order to obtain the shockwave load simplified algorithm model for the semiarmored projectile internal explosion in the cabin, this research made use of AUTODYN to provide a numerical modeling method for explosion in the cabin and verified the accuracy of the method via the experiment. Internal explosion simulation calculation was conducted on the operating condition numerical model with different cabin structural dimensions and different explosive loads. The cabin internal explosion space was divided into the noncorner central area, near-wall area, two-sided corner area, and three-sided corner area. Through regression of the abovementioned calculation results, an engineering model to calculate the shockwave load was obtained. It is hoped that the model can offer some references to the antiexplosion design for the ship cabin and for damage assessment of the internal explosion. [ABSTRACT FROM AUTHOR]

COVID-19, a severe respiratory disease caused by a new type of coronavirus SARS-CoV-2, has been spreading all over the world. Patients infected with SARS-CoV-2 may have no pathogenic symptoms, i.e., presymptomatic patients and asymptomatic patients. Both patients could further spread the virus to other susceptible people, thereby making the control of COVID-19 difficult. The two major challenges for COVID-19 diagnosis at present are as follows: (1) patients could share similar symptoms with other respiratory infections, and (2) patients may not have any symptoms but could still spread the virus. Therefore, new biomarkers at different omics levels are required for the large-scale screening and diagnosis of COVID-19. Although some initial analyses could identify a group of candidate gene biomarkers for COVID-19, the previous work still could not identify biomarkers capable for clinical use in COVID-19, which requires disease-specific diagnosis compared with other multiple infectious diseases. As an extension of the previous study, optimized machine learning models were applied in the present study to identify some specific qualitative host biomarkers associated with COVID-19 infection on the basis of a publicly released transcriptomic dataset, which included healthy controls and patients with bacterial infection, influenza, COVID-19, and other kinds of coronavirus. This dataset was first analysed by Boruta, Max-Relevance and Min-Redundancy feature selection methods one by one, resulting in a feature list. This list was fed into the incremental feature selection method, incorporating one of the classification algorithms to extract essential biomarkers and build efficient classifiers and classification rules. The capacity of these findings to distinguish COVID-19 with other similar respiratory infectious diseases at the transcriptomic level was also validated, which may improve the efficacy and accuracy of COVID-19 diagnosis. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) detection is an effective way to find defects and diagnose the insulation condition of power equipment. During manufacturing and operating, there may exist multi-source PD signals in the equipment, which could seriously affect the accuracy of subsequent defect analyses. In this paper, a universal separation methodology using linear prediction analysis (LPA) and the isolation forest algorithm (IFA) is introduced. By approximating the present waveform point by a linear combination of several points in the past, a 12-D linear prediction cepstrum coefficient (LPCC) feature space can be established which can accurately characterize PD waveforms. In this paper, we applied principal component analysis (PCA) to reduce the feature space to 2-D space. The IFA was adopted to separate multi-source PD signals which quantified the degree of clustering and added one more parameter based on the original features. Thus, the proposed method can separate multiple PD sources effectively even if the difference in the features of different types of PDs is small. In addition, the proposed algorithm easily removed the noise points during the separation process. The algorithm was applied to different types of PD signals on 35-kV transformers and a three-source PD data set on a 252-kV gas-insulated switchgear (GIS) platform. The separation results confirm that the proposed algorithm can effectively separate and distinguish various PD signals. [ABSTRACT FROM AUTHOR]

Background and objectives Automatic delineation of the myocardium in echocardiography can assist radiologists to diagnosis heart problems. However, it is still challenging to distinguish myocardium from other tissue due to a low signal-to-noise ratio, low contrast, vague boundary, and speckle noise. The purpose of this study is to automatically detect myocardium region in left ventricle myocardial contrast echocardiography (LVMCE) images to help radiologists’ diagnosis and further measurement on infarction size. Methods The LVMCE image is firstly mapped into neutrosophic similarity (NS) domain using the intensity and homogeneity features. Then, a neutrosophic active contour model (NACM) is proposed and the energy function is defined by the NS values. Finally, the ventricle is detected using the curve evolving results. The ventricle's boundary is identified as the endocardium. To speed up the evolution procedure and increase the detection accuracy, a clustering algorithm is employed to obtain the initial ventricle region. The curve evolution procedure in NACM is utilized again to obtain the epicardium, where the initial contour uses the detected endocardium and the anatomy knowledge on the thickness of the myocardium. Results Echocardiographic studies are performed on 10 male Sprague-Dawley rats using a Vivid 7 system including 5 normal cases and 5 rats with myocardial infarction. The myocardium boundaries manually outlined by an experienced radiologist are used as the reference standard for the performance evaluation. Two metrics, Hdist and AvgDist, are employed to evaluate the detection results. The NACM method was compared with those from the eliminated particle swarm optimization (EPSO) and active contour model without edges (ACMWE) methods. The mean and standard deviation of the Hdist and AvgDist on endocardium are 6.83 ± 1.12 mm and 0.79 ± 0.28 mm using EPSO method, 7.12 ± 0.98 mm and 0.82 ± 0.32 mm using ACMWE method, and 4.55 ± 0.9 mm and 0.58 ± 0.18 mm using NACM method, respectively. The improvement on epicardium is much more significant, and two metrics are decreased from 7.45 ± 1.24 mm, and 1.47 ± 0.34 mm using EPSO method, and 8.21±0.43 mm, and 1.73±0.47 mm using ACMWE method, to 4.94 ± 0.82 mm, and 0.84 ± 0.22 mm using NACM method, respectively. Conclusions The proposed method can automatically detect myocardium accurately, and is helpful for clinical therapeutics to measure myocardial perfusion and infarct size. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a novel method is proposed from the perspective of restructuring the gratings to be modulated to restrain the spectrum overlap in composite structure light 3D measurement. Previously, kinds of methods are presented to expand the distance between the adjacent carrier channels, but it still cannot eliminate the spectrum overlap in the orthogonal direction. By the new method, three sinusoidal gratings with equal phase shifting in traditional Phase Measuring Profilometry (PMP) are replaced by two sinusoidal gratings with π/2 phase shifting and a uniform flat image. Moreover, set the carrier frequency modulated by the flat image between the frequencies modulated by the phase-shifting sinusoidal gratings. For the flat image, carrier is only modulated by the “DC” component. Therefore the spectrum overlap between adjacent carrier channels is smaller than the traditional composite pattern (CP) projection. Computer simulation and experiment results prove the new method can effectively restrain the effect of spectrum overlap, and improve the measurement accuracy about 1.74 times. [Copyright &y& Elsevier]