Isosorbide mononitrate inhibits myocardial fibrosis in diabetic rats by up-regulating exosomal MiR-378

Purpose: To investigate the effect of isosorbide mononitrate on diabetic cardiomyopathy (DCM), and the potential mechanism of action.Methods: The effects of isosorbide mononitrate and isosorbide mononitrate + GW4869 on cardiac function and myocardial fibrosis in DCM rats were determined via hemodynamics, hematoxylin-eosin (H&E) staining and Masson staining. Exosomes were extracted from the serum, and the differential expressions of microribonucleic acids (miRNAs) related to myocardial fibrosis were determined by reverse transcription-polymerase chain reaction (qRT-PCR). Western blotting was performed to determine the effects of isosorbide mononitrate and isosorbide mononitrate + GW4869 on IGF1R/STAT3 signaling pathway.Results: Isosorbide mononitrate exerted a protective effect against DCM--induced cardiac dysfunction and myocardial fibrosis, while such a protective effect was suppressed by the exosome inhibitor GW4869 (p < 0.05). The expression of miR-378 in exosomes significantly rose in isosorbide mononitrate group. The increased expression of miR-378 in vitro inhibited the proliferation of primary myocardial fibroblasts, and reduced the expression of myocardial fibrosis markers (p < 0.05). Luciferase reporter assay data showed that miR-378 negatively regulated the expression of IGF1R by direct binding to IGF1R mRNA 3'-untranslated region (3'UTR). In primary myocardial fibroblasts, miR-378 mimic significantly reduced the protein expressions of IGF1R, p-STAT3/STAT3 and c-Myc (p < 0.05). Isosorbide mononitrate lowered the protein expressions of IGF1R, p-STAT3/STAT3 and c-Myc, but the inhibitory effect was weakened by the exosome inhibitor, GW4869 (p < 0.05).Conclusion: Isosorbide mononitrate inhibits myocardial fibrosis in diabetic rats by up-regulating exosomal miR-378, and targeting the axis of STAT3/IGF1R. The results of this study may provide a new insight into the treatment of DCM

KOH-mediated transition metal-free synthesis of imines from alcohols and amines

National Basic Research Program of China [2012CB821600]; Chinese National Natural Science Foundation [21173178, 20972130]; NFFTBS [J1030415, TJAB-2009-023]The various imines were prepared from alcohols and amines in moderate to good yields under an air atmosphere promoted by KOH, eliminating the need for toxic transition metal catalysts. Due to its simplicity, this protocol will have wide application in synthesis

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

An Adaptive Fusion Convolutional Denoising Network and Its Application to the Fault Diagnosis of Shore Bridge Lift Gearbox

Traditional fault diagnosis methods are limited in the condition detection of shore bridge lifting gearboxes due to their limited ability to extract signal features and their sensitivity to noise. In order to solve this problem, an adaptive fusion convolutional denoising network (AF-CDN) was proposed in this paper. First, a novel 1D and 2D adaptive fused convolutional neural network structure is built. The fusion of both 1D and 2D convolutional models can effectively improve the feature extraction capability of the network. Then, a gradient updating method based on the Kalman filter mechanism is designed. The effectiveness of the developed method is evaluated by using the benchmark datasets and the actual data collected for the shore bridge lift gearbox. Finally, the effectiveness of the proposed algorithm is proved through the experimental validation in the paper. The main contributions of this paper are described as follows: the proposed AF-CDN can improve the diagnosis accuracy by 1.5–9.1% when compared with the normal CNN methods. The robustness of the diagnostic network can be significantly improved

A Correction Method for the Motion Measurement of the Ship-Borne Mechanical Platform Based on Multi-Sensor Fusion

In order to perform multi-degree-of-freedom motion measurements of marine machinery, such as ship-borne mechanical platforms, in an absolute environment without a reference, absolute measurement methods using acceleration sensors and tilt gyroscopes are typically employed. However, the influence of wave forces on ship-borne mechanical platforms can cause coupling between different degrees of freedom, resulting in significant measurement disturbances that make efficient computation and real-time analysis challenging. To address these challenges, a correction method for the motion measurement of the ship-borne mechanical platform based on multi-sensor fusion is proposed by analyzing the influence of the inclination angle of the ship-borne mechanical platform on the sensor measurement based on the working principles of the acceleration sensor and angle sensor. In this article, we first analyzed the influence of the inclination angle on the integral effect in the heave direction. Then, we proposed a configuration using four groups of acceleration sensors to correct the integral effect. Finally, the optimal inclination angle is determined through Kalman filtering based on the measured values of the angle sensors and estimated values from the acceleration sensor sets. Experiments have proved that the average error of the corrected heave displacement signal is 25.34 mm, which is better than the integral displacement signal of a single acceleration sensor. At the same time, we use the acceleration sensor to calculate the roll angle and pitch angle of the ship-borne mechanical platform and combine it with the angle sensor signal to perform Kalman filtering. This filters out the errors caused by the shaking and instability of the mechanical platform and can more accurately estimate the true inclination of the platform. Therefore, this method can enhance the precision and accuracy of ship-borne mechanical platform motion signal acquisition, providing more valuable experimental data for research in marine engineering and related fields

Tensor Dictionary Self-Taught Learning Classification Method for Hyperspectral Image

Precise object classification based on Hyperspectral imagery with limited training data presents a challenging task. We propose a tensor-based dictionary self-taught learning (TDSL) classification method to provide some insight into these challenges. The idea of TDSL is to utilize a small amount of unlabeled data to improve the supervised classification. The TDSL trains tensor feature extractors from unlabeled data, extracts joint spectral-spatial tensor features and performs classification on the labeled data set. These two data sets can be gathered over different scenes even by different sensors. Therefore, TDSL can complete cross-scene and cross-sensor classification tasks. For training tensor feature extractors on unlabeled data, we propose a sparse tensor-based dictionary learning algorithm for three-dimensional samples. In the algorithm, we initialize dictionaries using Tucker decomposition and update these dictionaries based on the K higher-order singular value decomposition. These dictionaries are feature extractors, which are used to extract sparse joint spectral-spatial tensor features on the labeled data set. To provide classification results, the support vector machine as the classifier is applied to the tensor features. The TDSL with the majority vote (TDSLMV) can reduce the misclassified pixels in homogenous regions and at the edges of different homogenous regions, which further refines the classification. The proposed methods are evaluated on Indian Pines, Pavia University, and Houston2013 datasets. The classification results show that TDSLMV achieves as high as 99.13%, 99.28%, and 99.76% accuracies, respectively. Compared with several state-of-the-art methods, the classification accuracies of the proposed methods are improved by at least 2.5%

Accurate Variable Control System for Boom Sprayer Based on Auxiliary Antidrift System

Control accuracy significantly affects the performances of boom sprayer. In this study, we develop a precise autocontrol technology based on the vehicle speed feedback. We utilize the auxiliary antidrift system of wind-curtain type air flow and the variable spraying control system for adaptive fertilizing and online measuring of working conditions. Experimental results demonstrate that the variable spraying control system could keep the speed error less than 3%. The air flow significantly improves the penetration of spraying, decreases the fog drip, and increases the pesticide utility. Benefitting from the auxiliary air flow, the average utility of pesticide is improved from 26.76% to 37.98%. Additionally, the speed feedback control reduces the consumption of pesticide by more than 12%

Frame vibration states identification for corn harvester based on joint improved empirical mode decomposition - Support vector machine method

The frame of corn harvester is prone to vibration bending and torsional deformation due to the vibration caused by field road bumps and fluctuations. It poses a serious challenge to the reliability of machinery. Therefore it is critical to explore the vibration mechanism, and to identify the vibration states under different working conditions. To address the above problem, a vibration state identification method is proposed in this paper. An improved empirical mode decomposition (EMD) algorithm was used to decrease noise for signals of high noise and non-stationary vibration in the field. The support vector machine (SVM) model was used for identification of frame vibration states under different working conditions. The results showed that: (1) an improved EMD algorithm could effectively reduce noise interference and restore the effective information of the original signal. (2) based on improved EMD – SVM method identify the vibration states of the frame with the accuracy of 99.21%. (3) The corn ears in grain tank were not sensitive to low order vibration, but had an absorption effect on high order vibration. The proposed method has the potential to be applied for accurately identifying vibration state and improving frame safety