Lane detection plays an important role in improving autopilot’s safety. In this paper, a novel lane-division-lines detection method is proposed, which exhibits good performances in abnormal illumination and lane occlusion. It includes three major components: First, the captured image is converted to aerial view to make full use of parallel lanes’ characteristics. Second, a ridge detector is proposed to extract each lane’s feature points and remove noise points with an adaptable neural network (ANN). Last, the lane-division-lines are accurately fitted by an improved random sample consensus (RANSAC), termed the (regional) gaussian distribution random sample consensus (G-RANSAC). To test the performances of this novel lane detection method, we proposed a new index named the lane departure index (LDI) describing the departure degree between true lane and predicted lane. Experimental results verified the superior performances of the proposed method over others in different testing scenarios, respectively achieving 99.02%, 96.92%, 96.65% and 91.61% true-positive rates (TPR); and 66.16, 54.85, 55.98 and 52.61 LDIs in four different types of testing scenarios.
Background Hypoxia-induced pulmonary hypertension (HPH) is a lethal cardiovascular disease with the characteristic of severe remodeling of pulmonary vascular. Although a large number of dysregulated mRNAs, lncRNAs, circRNAs, and miRNAs related to HPH have been identified from extensive studies, the competitive endogenous RNA (ceRNA) regulatory network in the pulmonary artery that responds to hypoxia remains largely unknown. Results Transcriptomic profiles in the pulmonary arteries of HPH rats were characterized through high-throughput RNA sequencing in this study. Through relatively strict screening, a set of differentially expressed RNAs (DERNAs) including 19 DEmRNAs, 8 DElncRNAs, 19 DEcircRNAs, and 23 DEmiRNAs were identified between HPH and normal rats. The DEmRNAs were further found to be involved in cell adhesion, axon guidance, PPAR signaling pathway, and calcium signaling pathway, suggesting their crucial role in HPH. Moreover, a hypoxia-induced ceRNA regulatory network in the pulmonary arteries of HPH rats was constructed according to the ceRNA hypothesis. More specifically, the ceRNA network was composed of 10 miRNAs as hub nodes, which might be sponged by 6 circRNAs and 7 lncRNAs, and directed the expression of 18 downstream target genes that might play important role in the progression of HPH. The expression patterns of selected DERNAs in the ceRNA network were then validated to be consistent with sequencing results in another three independent batches of HPH and normal control rats. The diagnostic effectiveness of several hub mRNAs in ceRNA network was further evaluated through investigating their expression profiles in patients with pulmonary artery hypertension (PAH) recorded in the Gene Expression Omnibus (GEO) dataset GSE117261. Dysregulated POSTN, LTBP2, SPP1, and LSAMP were observed in both the pulmonary arteries of HPH rats and lung tissues of PAH patients. Conclusions A ceRNA regulatory network in the pulmonary arteries of HPH rats was constructed, 10 hub miRNAs and their corresponding interacting lncRNAs, circRNAs, and mRNAs were identified. The expression patterns of selected DERNAs were further validated to be consistent with the sequencing result. POSTN, LTBP2, SPP1, and LSAMP were suggested to be potential diagnostic biomarkers and therapeutic targets for PAH.
Background Hypoxia-induced pulmonary hypertension (HPH) is a lethal cardiovascular disease with the characteristic of severe remodelling of pulmonary vascular. Although large number of dysregulated mRNAs, lncRNAs, circRNAs and miRNAs related to HPH have been identified in extensive studies, the RNA regulatory network in pulmonary artery that respond to hypoxia remains poorly understood. Results Transcriptomic profiles in pulmonary arteries of HPH rats were interrogated through high-throughput RNA sequencing in this study. The differentially expressed RNAs (DERNAs) including DEmRNAs, DElncRNAs, DEcircRNAs and DEmiRNAs between HPH and normal rats were investigated. A set of 19 DEmRNAs, 8 DElncRNAs, 19 DEcircRNAs and 23 DEmiRNAs were identified through a relatively strict screening. The DEmRNAs were further found to be involved in cell adhesion, axon guidance, PPAR signalling pathway and calcium signalling pathway, suggesting their crucial role in HPH. Furthermore, according to the competitive endogenous RNA (ceRNA) hypothesis, a hypoxia induced ceRNA regulatory network in pulmonary arteries of HPH rats was constructed. More specifically, the ceRNA network was composed of 10 miRNAs as hub nodes, which might be sponged by 6 circRNAs and 7 lncRNAs, and directed the expression of 18 downstream target genes that might play important role in the progression of HPH. Expression pattern of selected DERNAs in the ceRNA network were validated to be in consistent with sequencing results. Diagnostic effectiveness of several hub mRNAs were further evaluated through investigating their expression profiles in patients with pulmonary artery hypertension (PAH) recorded in the Gene Expression Omnibus (GEO) dataset GSE117261. Dysregulated POSTN, LTBP2, SPP1 and LSAMP were observed in both the pulmonary arteries of HPH rats and lung tissues of PAH patients. Conclusions A ceRNA regulatory network in pulmonary arteries of HPH rats was constructed, 10 hub miRNAs and their corresponding interacting lncRNAs, circRNAs and mRNAs were identified. The expression pattern of selected DERNAs were further validated to be in consistent with sequencing result. POSTN, LTBP2, SPP1 and LSAMP were suggested to be potential diagnostic biomarkers and therapeutic targets for PAH.
In this study, a classification algorithm based on complex number feature is proposed. Specifically, the SVM framework is reformulated, so each example would be classified in the unitary space. The cost function is redefined by considering the maximum margin of real and imaginary units of the complex number feature at the same time. The cost function is based on the expectation of the hinge loss, and its derivatives can be calculated in closed forms. Using a stochastic gradient descent (SGD) algorithm, this method allows for efficient implementation. For complex number feature, the example uncertainty is modeled by a sample preprocessing method based on within-class Euclidean distance Gaussian distribution sample (DGS). In addition, a complex number feature selection method based on improved hybrid discrimination analysis (HDA) is proposed by considering the correlation between real and imaginary units of complex number feature. The proposed classification algorithm is tested on synthetic data and three publicly available and popular datasets, namely, MNIST, WDBC, and Voc2012. Experimental results verify the effectiveness of the proposed method.
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