Search

The extinguishing and re-burning of the closed fire area in an underground coal mine were investigated by laboratory-scale physical simulation. Temperatures in the center of the fire source were recorded, and the typical cooling process was observed to include the rapid cooling stage (900-400 °C) and dilatory cooling stage (400-100 °C). With the increase of coal mass from 20 to 80 kg, the rate of cooling decreases and the time required for fire extinguishing increases by 69.5%-193.2%. At temperatures ranging between 500 and 100 °C, yields of CO and H

Background Increasing evidence has indicated that circular RNAs (circRNAs) play a role in various diseases. However, the influence of circRNAs in nephritis remains unknown. Methods Microarray analysis and RT-qPCR were used to detect the expression of circRNA. Type I IFN were administrated to RMC and HEK293 cells to establish a nephritis cell model. CCK-8, MTT assay, and flow cytometry were used to assess cell proliferation, viability, and apoptosis of cells. Bioinformatics analysis and dual luciferase reporter assay detect the interaction of circ_0007059, miRNA-1278, and SHP-1. Glomerulonephritis was performed in a mouse model by administration of IFNα-expressing adenovirus. IHC staining showed the pathogenic changes. Results In the present study, the expression of circ_0007059 in type I interferon (IFN)-treated renal mesangial cells (RMCs), lupus nephritis (LN) specimens, and HEK293 cells was downregulated compared with that in normal healthy samples and untreated cells. Circ_0007059 overexpression resulted in increased cell proliferation, cell viability, apoptosis, and inflammation-associated factors (CXCL10, IFIT1, ISG15, and MX1) in RMCs and HEK293 cells. In addition, circ_0007059 overexpression significantly restored cell proliferation and viability and inhibited IFN-induced apoptosis. Further, the increased expression resulted in reduced inflammation and the downregulation of CXCL10, IFIT1, ISG15, and MX1 in RMCs and HEK293 cells. Circ_0007059 serves as a sponge for miR-1278 so that the latter can target the 3′-untranslated region of SHP-1. Overexpressed circ_0007059 inhibited miR-1278 expression and elevated SHP-1 expression, subsequently reducing STAT3 phosphorylation. Meanwhile, miR-1278 was upregulated and SHP-1 was downregulated in LN samples and IFN-treated cells. The restoration of miR-1278 counteracted the effect of circ_0007059 on viability, apoptosis, and inflammation as well as on SHP-1/STAT3 signaling in RMCs and HEK293 cells. We also investigated the role of SHP-1 overexpression in IFN-treated RMCs and HEK293 cells; SHP-1 overexpression resulted in a similar phenotype as that observed with circ_0007059 expression. Conclusions The study indicates that circ_0007059 protects RMCs against apoptosis and inflammation during nephritis by attenuating miR-1278/SHP-1/STAT3 signaling.

Background Renal tubular epithelial cells play an important role in renal function and are a major site of injury from inflammation. Emerging evidence suggests that CYR61 is involved in the regulation of autophagy. However, there are few studies on CYR61 in nephropathy and associated inflammation. This study aimed to clarify how CYR61 regulates autophagy in human renal epithelial cells while in an inflammatory state and regulates the upstream pathway of CYR61 levels. Methods The human renal tubular epithelial cells (HK-2) cell line treated by lipopolysaccharide (LPS) was used as an inflammatory model of human epithelial cells. Short hairpin RNA (shRNA) was used to down-regulate CYR61, and the changes in the transcription and expression levels of related molecules, as well as the morphological changes of HK-2 cells, were detected by quantitative real time-PCR (qRT-PCR), western blot (WB), and transmission electron microscopy. Either CYR61 or MALAT1 were up-regulated by overexpression vectors, or MALAT1 was down-regulated by miR-22-3p mimics. Subsequently, the levels of CYR61, MALAT1, related inflammatory factors, and autophagy factors were measured by qPCR, WB, and enzyme-linked immunosorbent assay (ELISA). Cell apoptosis was detected by flow cytometry and acridine-orange assay. Results We observed that down-regulation of CYR61 could down-regulate 1B-light chain 3 (LC3) level and inhibit autophagy in the LPS-induced inflammation model of HK-2 cells. The expression levels of CYR61, Beclin1, Atg5, LC3, interleukin 6 (IL-6), and tumor necrosis factor-α (TNF-α) were significantly increased by upregulating CYR61 or MALAT1 by overexpression vector, while the expression level of p62 was significantly decreased, intracellular reactive oxygen species (ROS) content was increased, and the proportion of autophagy and apoptosis was increased. The use of miR-22-3p mimics significantly reversed the changes induced by up-regulation of CYR61 or MALAT1 at the molecular and cellular levels. Conclusions Our data indicated that CYR61 positively regulates autophagy of HK-2 cells under an inflammatory state, and was negatively regulated by miR-22-3p, while miR-22-3p and MALAT1 were negatively regulated by each other.

Organic elastomers such as polyurethane are widely used as a filling material in underground coal mines. However, the risk of fire hazard in coal seams is increased due to the flammability of polyurethane. Laboratory tests illustrate that in a thermostatic reactor with initial temperature of 25 °C, the temperature during the production of polyurethane increases up to > 140 °C. Adiabatic oxidation experiments and TG–DTG analysis showed that the heating rate of the mixed samples of polyurethane and coal was higher than that of any single component in the initial oxidation stage. From the initial temperature to 100 °C, increasing the proportion of polyurethane was conducive to the acceleration of temperature rise. From 0.180 to 0.850 mm, the self-heating rate was inversely proportional to the particle size. At the initial stage, the low ventilation rate promoted the spontaneous combustion of mixed samples, but when the temperature is > 70 °C, an opposite trend was seen. Kinetic parameters suggested that the activation energy decreases with the introduction of polyurethane. According to the mathematical model, the variation of the spontaneous combustion period of coal and polyurethane was predicted.

High dielectric loss is one of the current obstacles to the application of dielectric materials. In this paper, we synthesized a new dielectric material, i.e., (Yb + Ta)-co-doped TiO2 dielectric material. The (Yb0.5Ta0.5)xTi1−xO2 ceramics were synthesized by the solid-state reaction method. It has been found that (Yb0.5Ta0.5)xTi1−xO2 ceramics had a dense ceramic microstructure, and (Yb0.5Ta0.5)xTi1−xO2 ceramics exhibited the rutile TiO2 structure. All (Yb0.5Ta0.5)xTi1−xO2 ceramics exhibited low dielectric loss ( 105). The optimal dielectric properties are obtained at a doping level of x = 0.05 with dielectric constant of 5.1 × 105 and dielectric loss of 0.037. Further study of the thermal stability of the dielectric properties was performed in the temperature range from − 50 to 250 ℃, which indicates that the ceramic sample with x = 0.05 co-dopant concentration maintains good dielectric properties in the temperature range from − 50 to 100 ℃. XPS shows that high dielectric properties can be explained by the electron-pinned defect-dipole mechanism.

Few cases of uraemic tumoral calcinosis (UTC) have been reported. This study aimed to investigate the clinical efficacy of parathyroidectomy for UTC. Historical clinical data of patients with end-stage renal disease and UTC who underwent parathyroidectomy were analysed. Absorption of metastatic calcification was compared before and after operation. Changes in intact parathyroid hormone, serum calcium, phosphorus, and alkaline phosphatase levels were analysed before parathyroidectomy and at 1 week and 3, 6, and 12 months after parathyroidectomy. Eight patients met the enrolment criteria (men, 6; mean age, 38.6 SD 10.9 years). Uraemic tumoral calcinosis, which developed 2–8 years after dialysis began, was caused by secondary hyperparathyroidism. Massive calcium deposition was found in the shoulder (n = 6), hip (n = 3), and elbow (n = 2). Four patients had > 2 joints affected, and a single joint was involved for four patients. Seven patients had rapid remission (p p > 0.05). Parathyroidectomy has promising efficacy for UTC treatment and regulation of serum intact parathyroid hormone and phosphorus. Hypocalcaemia is a common complication after parathyroidectomy. Current Controlled Trials ChiCTR2000041311, date of registration: Dec. 23, 2020.

Current development of high-performance fiber-reinforced cementitious composites (HPFRCC) mainly relies on intensive experiments. The main purpose of this study is to develop a machine learning method for effective and efficient discovery and development of HPFRCC. Specifically, this research develops machine learning models to predict the mechanical properties of HPFRCC through innovative incorporation of micromechanics, aiming to increase the prediction accuracy and generalization performance by enriching and improving the datasets through data cleaning, principal component analysis (PCA), and K-fold cross-validation. This study considers a total of 14 different mix design variables and predicts the ductility of HPFRCC for the first time, in addition to the compressive and tensile strengths. Different types of machine learning methods are investigated and compared, including artificial neural network (ANN), support vector regression (SVR), classification and regression tree (CART), and extreme gradient boosting tree (XGBoost). The results show that the developed machine learning models can reasonably predict the concerned mechanical properties and can be applied to perform parametric studies for the effects of different mix design variables on the mechanical properties. This study is expected to greatly promote efficient discovery and development of HPFRCC.

The acoustic signal generated by mechanical motion contains the information of its motion state, but when the signal-to-noise ratio (SNR) is low, the accuracy of real-time monitoring mechanical motion state by the acoustic signal is low. This study proposes an adaptive noise reduction method based on the dislocation superposition method (DSM), which can realize the adaptive noise reduction and the extraction of fault a component from the automobile engine abnormal noise signal of low SNR. Firstly, the wavelet coefficients of engine abnormal noise signal are obtained by continuous wavelet transform (CWT), and the fault feature points of the abnormal noise signal in each period are extracted by setting hard threshold function, window function, and feature points extraction algorithm. Then, the signal segments containing fault components are obtained by using the position of feature points to extend the length of the fault component forward and backward, respectively, and Pearson’s correlation is calculated by traversal to determine the starting superposition point of each signal segment containing fault components. Finally, the signal segments of the odd group and even group are selected for superposition calculation. When the superposition stop condition is not satisfied, the number of superpositions increased until the stop condition is satisfied, and the superposition signal can be used as a fault component. The experimental results show that, compared with the improved DSM, this method has a good effect on the noise reduction and extraction of fault components of automobile engine cylinder knocking fault, and the effectiveness of this method is verified. This method is used to reduce the noise and extract the fault components of automobile engine cylinder missing fault and knock fault, and good results are obtained.

Beneficial from fusion of various external sensors such as LiDAR and camera, intelligent vehicles are able to be informed of their surroundings with details in real time. The characteristics of each type of sensors determine its specificity in raw data, leading to inconformity of content, precision, range and timing, which eventually increases the complexity of environment descriptions and effective information extraction for further applications. In order to efficiently describe the environment, as well as remain the diversity of necessary information, we propose a unified description model independent of specific sensors for intelligent vehicle environment perception, containing 3D positions, semantics and time. The spatio-temporal relationship between different types of collected data are established to express all the elements in the same system. As a potential implementation scheme, we take advantages of LiDAR point cloud and color images to acquire the positions and semantics of the environment components. Semantic segmentation based on convolutional neural networks (CNN) provides rich semantics for the model, which could also be enhanced with the fusion of LiDAR point cloud. We validate the effectiveness of our proposed model on the KITTI dataset and our own collected data and exhibit qualitative results.

Ultra-high-performance concrete (UHPC) is a type of cement-based composite for new construction and/or restoration of existing structures to extend service life. UHPC features superior workability, mechanical properties, and durability compared with conventional concrete. However, some challenges limit the wider application of UHPC, such as low workability for large-volume production, high autogenous shrinkage, insufficient flexural/tensile properties, and unpredictable durability after concrete cracking. Therefore, this paper reviews the state-of-the-art technologies for developing UHPC mixtures with improved properties. This review covers the following aspects: (1) the existing design methodologies; (2) the typical ingredients (e.g., binders, aggregates, chemical admixtures, and fibers) for preparation of UHPC and the underlying working principals; (3) the technologies for improving and controlling key properties (e.g., workability, autogenous shrinkage, compressive performance, tensile/flexural properties, and durability); and (4) the representative successful applications. This review is expected to advance the fundamental knowledge of UHPC and promote further research and applications of UHPC.

High-performance fiber-reinforced cementitious composites (HPFRCCs) feature high mechanical strengths, crack resistance, and durability. Under excessive loading, HPFRCCs demonstrate dense microcracks that are difficult to identify using existing methods. This study presents a computer vision method for identification, quantification, and visualization of microcracks in HPFRCCs based on deep learning. The presented method integrates multiple deep learning models and computer vision techniques in a hierarchical architecture. The crack pattern (e.g., number, width, and spacing of cracks) are automatically determined from pictures without human intervention. This study shows that the presented method achieves an accuracy of 0.992 for crack detection and an accuracy finer than 50 μm (R2 > 0.984) for quantification of crack width when deep learning models are trained using only 200 pictures of HPFRCCs and 200 pictures of conventional concrete with incorporation of data augmentation. The presented method is expected to be also applicable to other materials featuring complex cracks.

Background: Fractalkine is a chemokine with several roles, including chemotaxis, adhesion, and immune damage. It participates in cell inflammation and apoptosis and responds to the pathogenesis of autoimmune diseases. On the other hand, Treg cells are involved in autoimmune diseases. This study aimed to explore the regulatory mechanism of Fractalkine in renal injury and Treg apoptosis via the p38MAPK signalling pathway in lupus-prone mice. Methods: Lupus was induced in BALB/c female mice by injection of pristane. Then, CD4+ CD5+ Treg cells were isolated from the spleen of lupus mice. To deplete Fractalkine, first, mice received an injection of anti-Fractalkine antibody. Later, the transfection of Treg cells with Fractalkine siRNA was conducted. Lupus mice and Treg cells were treated with the p38MAPK inhibitor SB203580 and/or activator U-46619, respectively. The levels of urine protein, serum urea nitrogen, creatinine, and autoantibodies were measured from mouse samples, and renal histopathological features were analysed. The expression of Fractalkine, p-p38, Foxp3, IL-6, and IL-17 in the mouse kidney and Treg cells was detected. The levels of apoptosis and key apoptotic factors: Bax, Bcl-2, and Cyt-c in Treg cells were analysed. Results: The expression of Fractalkine, p-p38, IL-6, and IL-17 increased; meanwhile, that of Foxp3 decreased in the kidneys of mice with lupus. The depletion of Fractalkine and p38MAPK levels ameliorated proteinuria and renal functions and significantly reduced serum autoantibodies, renal Fractalkine, p-p38, IL-6, and IL-17 levels, while increasing renal Foxp3 concentration in lupus mice. The effects of Fractalkine knockdown (KD) and p38MAPK inhibitor on Treg cells, derived from lupus mice, were consistent with those observed in the kidneys. In addition, Fractalkine KD reduced cell apoptosis and suppressed the activation of p38MAPK signalling in Treg cells, derived from lupus mice. Meanwhile, the p38MAPK activator U-46619 had the opposite effect. Conclusion: Together, our data indicated that Fractalkine promoted the nephritis progression in lupus mice, most likely through the regulation of Treg cell apoptosis and activation of the p38MAPK signalling pathway. It suggested that targeting Fractalkine is a potential therapeutic strategy for treating LN.

As an increasing amounts of waste glass are produced annually, recycling glass in manufacturing concrete shows economic and environmental benefits. Recently, glass has been used as one of the ingredients to prepare high-performance fiber-reinforced cementitious composites (HPFRCCs), like strain-hardening cementitious composite and ultra-high-performance concrete, featuring high mechanical properties and long-term durability. While the application of glass particles may reduce the material cost, however, fundamental knowledge of the effects of using glass to replace concrete components on the key properties (e.g., mechanical properties, fresh properties, durability) and the underlying mechanisms is still lacking. This study aims to reveal the roles of waste glass particles on the key properties, clarify the fundamental mechanisms, and point out viable strategies to enhance the key properties of HPFRCCs incorporating glass. To this end, the paper reviews existing studies on using glass in preparing HPFRCCs and discusses possible methods to develop HPFRCCs with glass particles. While there are different types of glass, this review focuses on soda-lime glass, which is representative among all types of glass. At last, life cycle analysis is conducted to assess the effect of using glass particles on reducing the cost, CO2 emission, and energy consumption. This review aims to enhance the fundamental knowledge of HPFRCC and propose further research of HPFRCCs incorporating glass.

Background/Aims: Transforming growth factor beta 1 (TGF-β1) plays a critical role in the pathogenesis of glomerulosclerosis. The purpose of this study was to examine the effects of inhibition of miR-155 on podocyte injury induced by TGF-β1 and to determine further molecular mediators involved in the effects of miR-155. Methods: Conditionally immortalized podocytes were cultured in vitro and they were divided into four groups: control; TGF-β1 treatment; TGF-β1 with miR-155 knockdown [using antisense oligonucleotides against miR-155 (ASO-miR-155)] and TGF-β1 with negative control antisense oligonucleotides (ASO-NC). Real time RT-PCR and Western blot analysis were employed to determine the mRNA and protein expression of nephrin, desmin and caspase-9, respectively. Flow cytometry was used to examine the apoptotic rate of podocytes and DAPI fluorescent staining was used to determine apoptotic morphology. In addition, we examined the levels of miR-155, TGF-β1, nephrin, desmin and caspase-9 in glomerular tissues of nephropathy induced by intravenous injections of adriamycin in rats. Results: mRNA and protein expression of desmin and caspase-9 was increased in cultured TGF-β1-treated podocytes, whereas nephrin was decreased as compared with the control group. Importantly, miR-155 knockdown significantly attenuated upregulation of desmin and caspase-9, and alleviated impairment of nephrin induced by TGF-β1. Moreover, the number of apoptotic podocytes was increased after exposure to TGF-β1 and this was alleviated after miR-155 knockdown. Knocking down miR-155 also decreased an apoptosis rate of TGF-β1-treated podocytes. Note that negative control antisense oligonucleotides failed to alter an increase of the apoptosis rate in TGF-β1-treated podocytes. Consistent with in vitro results, expression of miR-155, TGF-β1, desmin and caspase-9 was increased and nephrin was decreased in glomerular tissues with nephropathy in vivo experiments. Conclusions: TGF-β1 impairs the protein expression of nephrin and amplifies the protein expression of desmin and caspase -9 via miR-155 signal pathway. Inhibition of miR-155 alleviates these changes in podocytes-treated with TGF-β1 and attenuated apoptosis of podocytes. Our data suggest that miR-155 plays a role in mediating TGF-β1-induced podocyte injury via nephrin, desmin and caspase-9. Results of the current study also indicate that blocking miR-155 signal has a protective effect on podocyte injury. Targeting one or more of these signaling molecules may present new opportunities for treatment and management of podocyte injury observed in glomerulosclerosis.

OBJECTIVES: This study aims to investigate the positive detection rate and predictive value of autoantibodies, including anti-double stranded deoxyribonucleic acid (anti-dsDNA) antibodies, anti-histone antibodies (AHAs), anti-ribosomal (anti-Rib) P antibodies, anti-Smith (anti-Sm) antibodies, anti-U1 ribonucleoprotein (anti-U1RNP) antibodies, anti-Sjögren's syndrome type A antibodies and anti-Sjögren's syndrome type B antibodies, on organ damage in patients with systemic lupus erythematous (SLE). PATIENTS AND METHODS: A total of 225 patients with SLE (37 males, 188 females; mean age 37.4±15.9 years; range, 7 to 80 years) were evaluated retrospectively. Statistical analysis was performed to obtain the positive detection rate of autoantibodies and to investigate the predictive value. RESULTS: There were statistically significant differences of positive anti-dsDNA antibodies in renal damage, photosensitization, hematological abnormalities and serositis (p0.05). CONCLUSION: Autoantibody spectrum is an important serological basis for SLE diagnosis. There are differences in the autoantibodies distribution of SLE patients with different organ damage, suggesting a certain clinical value for prediction of organ damage in SLE.

Search engines usually return relevant sorted results based on the keywords. Because of the lack of considering the user's current search interest and intention, this kind of strategy may not meet users' personalized search requirements. In order to retrieve results associated with the user's current search interest, existing researches integrate the user's interest information into the search process and ranking, so as to make the search results fit to the user's current search target more better. Different from the unified model of interest which most current related researches used, we propose a model of combing user's interests for the purpose of ranking in the light of different query types. This paper first classified queries into several types based on the current keywords and the sources of user's interests, such as short-term interest in user clicks, and long-term interest in user's search history, search logs of the search system etc. Then based on the sources of interests, we established a model of combination of interests which based on the artificial neural network and was trained by related approaches and data sets. Depending on different query types, we combined these sources of user's interests that the most best reflecting the user's current search intention to enhance the user's search experience. Comparing with the existing model of single interest, the results of the experiments show that our model can obtain better ranking on the results.

Social tagging is one of the most important ways to organize and index online resources. Recommendation in social tagging systems, e.g. tag recommendation, item recommendation and user recommendation, is used to improve the quality of tags and to ease the tagging or searching process. Existing works usually provide recommendations by analyzing relation information in social tagging systems, suffering a lot from the over sparse problem. These approaches ignore information contained in the content of resources, which we believe should be considered to improve recommendation quality and to deal with the over sparse problem. In this paper we propose a recommendation approach for social tagging systems that combines content and relation analysis in a single model. By modeling the generating process of social tagging systems in a latent Dirichlet allocation approach, we build a fully generative model for social tagging, leverage it to estimate the relation between users, tags and resources and achieve tag, item and user recommendation tasks. The model is evaluated using a CiteULike data snapshot, and results show improvements in metrics for various recommendation tasks.

In order to make search results better fit users’ current search interest, this paper proposes an algorithm of repeated results re-ranking using a model of polysemy. The algorithm considers the characteristics of the keywords to improve the rank of repeated results. Based on the analysis of polysemy of the keywords, we propose a polysemous model of concept lattice, then we combine with the user interest model to change the rank of repeated results in a search session. The method of this paper considers the impact of polysemy of keywords which may improve the ranking. The experimental results show that the process based on the polysemy of the keyword can reduce the length of the search session, especially when the keywords have multiple meanings.

Social Tagging is a free but also uncontrolled way to index and organize Web 2.0 resources. Many works have been proposed to leverage such tagging information. However, although previous studies have shown that users could reach consensus on which tags should be attached to a resource, the study about the consensus showing how to use a tag is still lack. This paper proposes a text chance discovery and subjective Bayes based approach to model and detect consensus of tags. In the proposed approach, tag consensus is modeled using characteristics of resources annotated by the tag. Experiment results show that the proposed method could more properly capture to what extent the users have reached consensus about the tag in advance of usage frequency.

Blog provides a simple way for people to share personal experiences and ideas, and has already become an important tool for people to communicate with each other. This fact turns blog into one of the most important information providers of the World Wide Web, but also makes how to find useful blogs for people to read an urgent problem. In this paper, we propose a blog clustering algorithm BCBSM (Blog Clustering based on Blog Set similarity and Mergence) aims at providing a general purpose blog friend recommendation system with high efficiency and effectiveness. By applying BCBSM, blogs are clustered into blog sets and to help improving the effectiveness and efficiency of friend recommendation. We evaluate our method by compare it with traditional blog similarity based recommendation method and measure the result with an automatic measurement. Result shows that our method could help provide better and more reasonable results.

According to the characteristic of auto-body inner packaging, the object-oriented case representation models of auto-body inner packaging were presented by employing the extended object model, in which the index and solution objects were appended. Knowledge of auto-body inner packaging was represented as rule and constraints, and their definitions were described with object models. The nearest neighborhood method was adopted for case retrieval. The strategies for case adaptation, integrating rule-based reasoning and constrain satisfaction, were presented to modify case solution to fit the current problem definition.

Clustered Web pages, such as blog posts, could be used to improve Web search. In this paper, we propose a folksonomy extending framework using relations in the Blogosphere and demonstrate how the framework could be used to help clustering blog posts. We evaluate the framework by comparing it with content-based folksonomy extending approach. Experiment results show that the framework does help the clustering process.