Long non-coding RNAs (lncRNAs) have recently been shown as novel promising diagnostic or prognostic biomarkers for various cancers. However, lncRNA expression patterns and their predictive value in early diagnosis of myocardial infarction (MI) have not been systematically investigated. In our study, we performed a comprehensive analysis of lncRNA expression profiles in MI and found altered lncRNA expression pattern in MI compared to healthy samples. We then constructed a lncRNA-mRNA dysregulation network (DLMCEN) by integrating aberrant lncRNAs, mRNAs and their co-dysregulation relationships, and found that some of mRNAs were previously reported to be involved in cardiovascular disease, suggesting the functional roles of dysregulated lncRNAs in the pathogenesis of MI. Therefore, using support vector machine (SVM) and leave one out cross-validation (LOOCV), we developed a 9-lncRNA signature (termed 9LncSigAMI) from the discovery cohort which could distinguish MI patients from healthy samples with accuracy of 95.96%, sensitivity of 93.88% and specificity of 98%, and validated its predictive power in early diagnosis of MI in another completely independent cohort. Functional analysis demonstrated that these nine lncRNA biomarkers in the 9LncSigAMI may be involved in myocardial innate immune and inflammatory response, and their deregulation may lead to the dysfunction of the inflammatory and immune system contributing to MI recurrence. With prospective validation, the 9LncSigAMI identified by our work will provide additional diagnostic information beyond other known clinical parameters, and increase the understanding of the molecular mechanism underlying the pathogenesis of MI.

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In order to mimic the natural appearance, motion, and perception of the human hand, a biomechatronic approach to design an anthropomorphic prosthetic hand À À À HIT/DLR Prosthetic Hand has been presented. It reproduces human hand in its fundamental structure such as appearance, weight, and dimensions. Its thumb can move along a cone surface in 3D space. Similar with that of human's, it combines with abduction and adduction from palmar position to lateral position. Actuated by only one motor, the middle¯nger, ring¯nger, and little¯nger can envelop complex-shaped objects. In addition, the bio-mechatronic integration and cosmetic designation make it much more like a genuine human hand.HIT/DLR Prosthetic Hand can be controlled through voice control strategy, Electromyography (EMG) control strategy, EMG, and electrocutaneous sensory feedback (ESF) close loop control strategy. In EMG control system, 10 types of hand posture are recognized through six electrodes on the basis of support vector machine (SVM). The last control strategy can help an amputee recover the grasp perception, further improve the e±ciency of EMG control, and reduce the hand mis-manipulation and force delivery mistakes.

SUMMARY Visual tracking is an essential building block for target tracking and capture of the underwater vehicles. On the basis of remotely autonomous control architecture, this paper has proposed an improved kernelized correlation filter (KCF) tracker and a novel fuzzy controller. The model is trained to learn an online correlation filter from a plenty of positive and negative training samples. In order to overcome the influence from occlusion, the improved KCF tracker has been designed with an added self-discrimination mechanism based on system confidence uncertainty. The novel fuzzy logic tracking controller can automatically generate and optimize fuzzy rules. Through Q-learning algorithm, the fuzzy rules are acquired through the estimating value of each state action pairs. An S surface based fitness function has been designed for the improvement of learning based particle swarm optimization. Tank and channel experiments have been carried out to verify the proposed tracker and controller through pipe tracking and target grasp on the basis of designed open frame underwater vehicle.

Coxsackievirus B3 (CVB3) is a common causative agent in the development of inflammatory cardiomyopathy. However, whether the expression of peripheral blood microRNAs (miRNAs) is altered in this process is unknown. The present study investigated changes to miRNA expression in the peripheral blood of CVB3-infected mice. Utilizing miRNA microarray technology, differential miRNA expression was examined between normal and CVB3-infected mice. The present results suggest that specific miRNAs were differentially expressed in the peripheral blood of mice infected with CVB3, varying with infection duration. Using miRNA microarray analysis, a total of 96 and 89 differentially expressed miRNAs were identified in the peripheral blood of mice infected with CVB3 for 3 and 6 days, respectively. Quantitative polymerase chain reaction was used to validate differentially expressed miRNAs, revealing a consistency of these results with the miRNA microarray analysis results. The biological functions of the differentially expressed miRNAs were then predicted by bioinformatics analysis. The potential biological roles of differentially expressed miRNAs included hypertrophic cardiomyopathy, dilated cardiomyopathy and arrhythmogenic right ventricular cardiomyopathy. These results may provide important insights into the mechanisms responsible for the progression of CVB3 infection.

Congenital heart diseases (CHD) are a large group of prevalent and complex anatomic malformations of the heart, with the genetic basis remaining largely unknown. Since genes or factors associated with the differentiation of human embryonic stem (HES) cells would affect the development of all embryonic tissues, including cardiac progenitor cells, we postulated their potential roles in CHD. In this study, we focused on ZW10, a kinetochore protein involved in the process of proper chromosome segregation, and conducted comparative studies between CHD patients and normal controls matched in gender and age in Chinese Han populations. We identified three variations in the ZW10 gene, including rs2885987, rs2271261 and rs2459976, which all had high genetic heterozygosity. Association analysis of these genetic variations with CHD showed correlation between rs2459976 and the risk of CHD. We conclude that rs2459976 in the ZW10 gene is associated with CHD in Chinese Han populations.

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BackgroundU‐Net and its variations have achieved remarkable performances in medical image segmentation. However, they have two limitations. First, the shallow layer feature of the encoder always contains background noise. Second, semantic gaps exist between the features of the encoder and the decoder. Skip‐connections directly connect the encoder to the decoder, which will lead to the fusion of semantically dissimilar feature maps.PurposeTo overcome these two limitations, this paper proposes a novel medical image segmentation algorithm, called feature‐guided attention network, which consists of U‐Net, the cross‐level attention filtering module (CAFM), and the attention‐guided upsampling module (AUM).MethodsIn the proposed method, the AUM and the CAFM were introduced into the U‐Net, where the AUM learns to filter the background noise in the low‐level feature map of the encoder and the CAFM tries to eliminate the semantic gap between the encoder and the decoder. Specifically, the AUM adopts a top‐down pathway to use the high‐level feature map so as to filter the background noise in the low‐level feature map of the encoder. The AUM uses the encoder features to guide the upsampling of the corresponding decoder features, thus eliminating the semantic gap between them. Four medical image segmentation tasks, including coronary atherosclerotic plaque segmentation (Dataset A), retinal vessel segmentation (Dataset B), skin lesion segmentation (Dataset C), and multiclass retinal edema lesions segmentation (Dataset D), were used to validate the proposed method.ResultsFor Dataset A, the proposed method achieved higher Intersection over Union (IoU) (), dice (), accuracy (), and sensitivity () than the previous best method: CA‐Net. For Dataset B, the proposed method achieved higher sensitivity (83.50%) and accuracy (97.55%) than the previous best method: SCS‐Net. For Dataset C, the proposed method had highest IoU () and dice () than those of all compared previous methods. For Dataset D, the proposed method had highest dice (average: 81.53%; retina edema area [REA]: 83.78%; pigment epithelial detachment [PED] 77.13%), sensitivity (REA: 89.01%; SRF: 85.50%), specificity (REA: 99.35%; PED: 100.00), and accuracy (98.73%) among all compared previous networks. In addition, the number of parameters of the proposed method was 2.43 M, which is less than CA‐Net (3.21 M) and CPF‐Net (3.07 M).ConclusionsThe proposed method demonstrated state‐of‐the‐art performance, outperforming other top‐notch medical image segmentation algorithms. The CAFM filtered the background noise in the low‐level feature map of the encoder, while the AUM eliminated the semantic gap between the encoder and the decoder. Furthermore, the proposed method was of high computational efficiency.