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Structural health monitoring (SHM) system provides valuable information for the fatigue assessment of existing rail-cum-road bridges. This paper aims to develop an effective fatigue assessment approach for the rail-cum-road bridge, Jiujiang Yangtze River Bridge, by utilising the SHM data, focusing on dynamic modal analysis, finite element model updating and fatigue assessment. First, the traffic load spectrum and modal characteristics of the bridge are investigated from the SHM data. A three-dimensional finite element model is constructed and then updated by using the measured modal data through the proposed regularised model updating method. Then, the updated numerical model is verified with the measured dynamic response data, which can be utilised for calculating stress response at critical structural components of the rail-cum-road bridge. Finally, an improved Corten-Dolan’s model is proposed to analyse the fatigue damage and structural reliability of the critical structural components of the bridge, taking into account the combined effects of train and highway vehicle loads. The results demonstrate that the proposed fatigue assessment method provides more reliable results for the rail-cum-road bridge by considering the combined effect of multi-level traffic loads and the non-linear fatigue damage accumulation. It is concluded that the short H-shaped suspender is identified as the most vulnerable structural member of the rail-cum-road bridge, and the remaining fatigue service life of the typical components of the bridge should meet the design requirement.

Abstract Background Multiple carpometacarpal fractures and dislocations are rare. This case report describes a novel multiple carpometacarpal injury, namely, ‘diagonal’ carpometacarpal joint fracture and dislocation. Case presentation A 39-year-old male general worker sustained a compression injury to his right hand in the dorsiflexion position. Radiography indicated a Bennett fracture, hamate fracture, and fracture at the base of the second metacarpal. Subsequent computed tomography and intraoperative examination confirmed an injury to the first to fourth carpometacarpal joint along a diagonal line. The normal anatomy of the patient’s hand was successfully restored via open reduction combined with Kirschner wire and steel plate fixation. Conclusion Our findings highlight the importance of taking the injury mechanism into account to avoid a missed diagnosis and to choose the best treatment approach. This is the first case of ‘diagonal’ carpometacarpal joint fracture and dislocation to be reported in the literature.

Background/Aims: ALT1 is a novel long non-coding RNA derived from the alternatively spliced transcript of the deleted in lymphocytic leukemia 2 (DLEU2). To date, ALT1 biological roles in human vascular endothelial cells have not been reported. Methods: ALT1 was knocked down by siRNAs. Cell proliferation was analyzed by cck-8. The existence and sequence of human ALT1 were identified by 3’ rapid amplification of cDNA ends. The interaction between lncRNA and proteins was analyzed by RNA-Protein pull down assay, RNA immunoprecipitation, and mass spectrometry analysis. Results: ALT1 was expressed in human umbilical vein endothelial cells (HUVECs). The expression of ALT1 was significantly downregulated in contact-inhibited HUVECs and in hypoxia-induced, growth-arrested HUVECs. Knocking down of ALT1 inhibited the proliferation of HUVECs by G0/G1 cell cycle arrest. We observed that angiotensin converting enzyme Ⅱ(ACE2) was a direct target gene of ALT1. Knocking-down of ALT1 or its target gene ACE2 could efficiently decrease the expression of cyclin D1 via the enhanced ubiquitination and degradation, in which HIF-1α and protein von Hippel-Lindau (pVHL) might be involved. Conclusion: The results suggested the human long non-coding RNA ALT1 is a novel regulator for cell cycle of HUVECs via ACE2 and cyclin D1 pathway.

Background: Aberrant vascular smooth muscle cell (VSMC) proliferation and migration contribute to the development of vascular pathologies, such as atherosclerosis and post-angioplasty restenosis. The aim of this study was to determine whether miR-22-3p plays a role in regulating human artery vascular smooth muscle cell (HASMC) function and neointima formation. Methods: Quantitative real-time PCR (qRT-PCR) and fluorescence in situ hybridization (FISH) were used to detect miR-22-3p expression in human arteries. Cell Counting Kit-8 (CCK-8) and EdU assays were performed to assess cell proliferation, and transwell and wound closure assays were performed to assess cell migration. Moreover, luciferase reporter assays were performed to identify the target genes of miR-22-3p. Finally, a rat carotid artery balloon-injury model was used to determine the role of miR-22-3p in neointima formation. Results: MiR-22-3p expression was downregulated in arteriosclerosis obliterans (ASO) arteries compared with normal arteries, as well as in platelet-derived growth factor-BB (PDGF-BB)-stimulated HASMCs compared with control cells. MiR-22-3p overexpression had anti-proliferative and anti-migratory effects and dual-luciferase assay showed that high mobility group box-1 (HMGB1) is a direct target of miR-22-3p in HASMCs. Furthermore, miR-22-3p expression was negatively correlated with HMGB1 expression in ASO tissue specimens. Finally, LV-miR-22-3p-mediated miR-22-3p upregulation significantly suppressed neointimal hyperplasia specifically by reducing HMGB1 expression in vivo. Conclusions: Our results indicate that miR-22-3p is a key molecule in regulating HASMC proliferation and migration by targeting HMGB1 and that miR-22-3p and HMGB1 may be therapeutic targets in the treatment of human ASO.

Aims: To explore the expression of miR-24-3p in human arteries with arteriosclerosis obliterans (ASO) as well as the role of miR-24-3p in the pathogenesis of ASO. Methods: We used quantitative real-time PCR (qRT-PCR) and in situ hybridization to monitor miR-24-3p expression in human arteries. To investigate the effect of miR-24-3p on human arterial smooth muscle cells (HASMCs), we applied cell counting and EdU assays to monitor proliferation and transwell and wound healing assays to investigate migration and flow cytometry to investigate apoptosis. Furthermore, we applied 3'-untranslated region (3'-UTR) luciferase assays to investigate the role of miR-24-3p in targeting platelet-derived growth factor receptor B (PDGFRB) and c-Myc. Results: MiR-24-3p was mainly located in the media of arteries and was downregulated in ASO arteries compared with normal arteries. Platelet-derived growth factor BB (PDGF-BB) treatment reduced the expression of miR-24-3p in primary cultured HASMCs. MiR-24-3p mimic oligos inhibited the proliferation and migration, and promotes apoptosis of HASMCs. Our 3'-UTR luciferase assays confirmed that PDGFRB and c-Myc were targets of miR-24-3p. Conclusion: The results suggest that miR-24-3p regulates the proliferation and migration of HASMCs by targeting PDGFRB and c-Myc. The PDGF/miR-24-3p/PDGFRB and PDGF/miR-24-3p/c-Myc pathways may play critical roles in the pathogenesis of ASO. These findings highlight the potential for new therapeutic targets for ASO.

Detecting the faults related to the operating condition of induction motors is a very important task for avoiding system failure. In this paper, a novel methodology is demonstrated to detect the working condition of a three-phase induction motor and classify it as a faulty or healthy motor. The electrical current signal data is collected for five different types of fault and one normal operating condition of the induction motors. The first part of the methodology illustrates a pattern recognition technique based on the empirical wavelet transform, to transform the raw current signal into two dimensional (2-D) grayscale images comprising the information related to the faults. Second, a deep CNN (Convolutional Neural Network) model is proposed to automatically extract robust features from the grayscale images to diagnose the faults in the induction motors. The experimental results show that the proposed methodology achieves a competitive accuracy in the fault diagnosis of the induction motors and that it outperformed the traditional statistical and other deep learning methods.

Induction motors face various stresses under operating conditions leading to some failure modes. Hence, health monitoring for motors becomes essential. In this paper, we introduce an effective framework for fault diagnosis of 3-phase induction motors. The proposed framework mainly consists of two parts. The first part explains the preprocessing method, in which the time-series data signals are converted into two-dimensional (2D) images. The preprocessing method generates recurrence plots (RP), which represent the transformation of time-series data such as 3-phase current signals into 2D texture images. The second part of the paper explains how the proposed convolutional neural network (CNN) extracts the robust features to diagnose the induction motor’s fault conditions by classifying the images. The generated RP images are considered as input for the proposed CNN in the texture image recognition task. The proposed framework is tested on the dataset collected from different 3-phase induction motors working with different failure modes. The experimental results of the proposed framework show its competitive performance over traditional methodologies and other machine learning methods.

Objective To explore the potential biomarkers for the diagnosis of primary brain stem injury （PBSI） by using metabonomics method to observe the changes of metabolites in rats with PBSI caused death. Methods PBSI, non-brain stem brain injury and decapitation rat models were established, and metabolic maps of brain stem were obtained by LC-MS metabonomics method and annotated to the HMDB database. Partial least square-discriminant analysis （PLS-DA） and random forest methods were used to screen potential biomarkers associated with PBSI diagnosis. Results Eighty-six potential metabolic markers associated with PBSI were screened by PLS-DA. They were modeled and predicted by random forest algorithm with an accuracy rate of 83.3%. The 818 metabolic markers annotated to HMDB database were used for random forest modeling and prediction, and the accuracy rate was 88.9%. According to the importance in the identification of cause of death, the most important metabolic markers that were significantly up-regulated in PBSI group were HMDB0038126 （genipinic acid, GA）, HMDB0013272 （N-lauroylglycine）, HMDB0005199 [（R）-salsolinol] and HMDB0013645 （N,N-dimethylsphingosine）. Conclusion GA, N-lauroylglycine, （R）-salsolinol and N,N-dimethylsphingosine are expected to be important metabolite indicators in the diagnosis of PBSI caused death, thus providing clues for forensic medicine practice.