Aberrant allele frequencies of the SNPs located in microRNA target sites are potentially associated with human cancers

MicroRNAs (miRNAs) are a class of noncoding small RNAs that regulate gene expression by base pairing with target mRNAs at the 3′-terminal untranslated regions (3′-UTRs), leading to mRNA cleavage or translational repression. Single-nucleotide polymorphisms (SNPs) located at miRNA-binding sites (miRNA-binding SNPs) are likely to affect the expression of the miRNA target and may contribute to the susceptibility of humans to common diseases. We herein performed a genome-wide analysis of SNPs located in the miRNA-binding sites of the 3′-UTR of various human genes. We found that miRNA-binding SNPs are negatively selected in respect to SNP distribution between the miRNA-binding ‘seed’ sequence and the entire 3′-UTR sequence. Furthermore, we comprehensively defined the expression of each miRNA-binding SNP in cancers versus normal tissues through mining EST databases. Interestingly, we found that some miRNA-binding SNPs exhibit significant different allele frequencies between the human cancer EST libraries and the dbSNP database. More importantly, using human cancer specimens against the dbSNP database for case-control association studies, we found that twelve miRNA-binding SNPs indeed display an aberrant allele frequency in human cancers. Hence, SNPs located in miRNA-binding sites affect miRNA target expression and function, and are potentially associated with cancers

MicroRNAs preferentially target the genes with high transcriptional regulation complexity

Over the past few years, microRNAs (miRNAs) have emerged as a new prominent class of gene regulatory factors that negatively regulate expression of approximately one-third of the genes in animal genomes at post-transcriptional level. However, it is still unclear why some genes are regulated by miRNAs but others are not, i.e. what principles govern miRNA regulation in animal genomes. In this study, we systematically analyzed the relationship between transcription factors (TFs) and miRNAs in gene regulation. We found that the genes with more TF-binding sites have a higher probability of being targeted by miRNAs and have more miRNA-binding sites on average. This observation reveals that the genes with higher cis-regulation complexity are more coordinately regulated by TFs at the transcriptional level and by miRNAs at the post-transcriptional level. This is a potentially novel discovery of mechanism for coordinated regulation of gene expression. Gene ontology analysis further demonstrated that such coordinated regulation is more popular in the developmental genes.Comment: supplementary data available at http://www.bri.nrc.ca/wan

Global analysis of microRNA target gene expression reveals that miRNA targets are lower expressed in mature mouse and Drosophila tissues than in the embryos

MicroRNAs (miRNAs) are non-coding small RNAs of ∼22 nt that regulate the gene expression by base pairing with target mRNAs, leading to mRNA cleavage or translational repression. It is currently estimated that miRNAs account for ∼1% of predicted genes in higher eukaryotic genomes and that up to 30% of genes might be regulated by miRNAs. However, only very few miRNAs have been functionally characterized and the general functions of miRNAs are not globally studied. In this study, we systematically analyzed the expression patterns of miRNA targets using several public microarray profiles. We found that the expression levels of miRNA targets are lower in all mouse and Drosophila tissues than in the embryos. We also found miRNAs more preferentially target ubiquitously expressed genes than tissue-specifically expressed genes. These results support the current suggestion that miRNAs are likely to be largely involved in embryo development and maintaining of tissue identity

Recombinant Human Endostatin Endostar Inhibits Tumor Growth and Metastasis in a Mouse Xenograft Model of Colon Cancer

To investigate the effects of recombinant human endostatin Endostar on metastasis and angiogenesis and lymphangiogenesis of colorectal cancer cells in a mouse xenograft model. Colon cancer cells SW620 were injected subcutaneously into the left hind flank of nude mice to establish mouse xenograft models. The mice were treated with normal saline or Endostar subcutaneously every other day. The growth and lymph node metastasis of tumor cells, angiogenesis and lymphangiogenesis in tumor tissue were detected. Apoptosis and cell cycle distribution were studied by flow cytometry. The expression of VEGF-A, -C, or -D in SW620 cells was determined by immunoblotting assays. Endostar inhibited tumor growth and the rate of lymph node metastasis (P < 0.01). The density of blood vessels in or around the tumor area was 12.27 ± 1.21 and 22.25 ± 2.69 per field in Endostar-treated mice and controls (P < 0.05), respectively. Endostar also decreased the density of lymphatic vessels in tumor tissues (7.84 ± 0.81 vs. 13.83 ± 1.08, P < 0.05). Endostar suppresses angiogenesis and lymphangiogenesis in the lymph nodes with metastases, simultaneously. The expression of VEGF-A, -C and -D in SW620 cells treated with Endostar was substantially lower than that of controls. Endostar inhibited growth and lymph node metastasis of colon cancer cells by inhibiting angiogenesis and lymphangiogenesis in a mouse xenograft model of colon cancer

Research and Application of Construction of Operation Integration for Smart Power Distribution and Consumption Based on “Integration of Marketing with Distribution”

The “information integrated platform of marketing and distribution integration system” researched and developed by this article is an advanced application platform to concurrently design and develop the automation of marketing and power distribution through integration and analysis of existing data based on the data platform of Jiaozuo Power Supply Corporation. It uses data mining and data bus technology, uniform analysis of comprehensive marketing and distribution data. And it conducts a real time monitoring on power utilization information for marketing and early warning maintenance business of power distribution according to electric business model, which realizes an integration of marketing and distribution business, achieves the target of integrated operation of marketing and distribution, improves the operation level of business, reduces maintenance costs of distribution grid, increases electricity sales of distribution grid and provide reliable practical basis for operation and maintenance of Jiaozuo power marketing and distribution

Hydraulic Pump Fault Diagnosis Method Based on EWT Decomposition Denoising and Deep Learning on Cloud Platform

An axial piston pump fault diagnosis algorithm based on empirical wavelet transform (EWT) and one-dimensional convolutional neural network (1D-CNN) is presented. The fault vibration signals and pressure signals of axial piston pump are taken as the analysis objects. Firstly, the original signals are decomposed by EWT, and each signal component is screened and reconstructed according to the energy characteristics. Then, the time-domain features and the frequency-domain features of the denoised signal are extracted, and features of time domain and frequency domain are fused. Finally, the 1D-CNN model was deployed to the WISE-Platform as a Service (WISE-PaaS) cloud platform to realize the real-time fault diagnosis of axial piston pump based on the cloud platform. Compared with ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD), the results show that the axial piston pump fault diagnosis algorithm based on EWT and 1D-CNN has higher fault identification accuracy

A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives

A Method for Internal Curing Water Calculation of Concrete with Super Absorbent Polymer

The internal curing method is effective in reducing the self-desiccation of concrete, and the amount of internal curing water (IC water) is greatly important to the shrinkage and strength of concrete. A method for calculating IC water of concrete with and without mineral admixture has been developed. The method is derived from Powers’ model for the phase distribution of a hydrating cement paste. To verify the method, a series of autogenous shrinkage and compressive strength of concrete with and without super absorbent polymer (SAP) were evaluated compared with the method proposed previously. To explain the macro performance of hardened concrete, the nonevaporable water content and calcium hydroxide content measurement were utilized to evaluate the degree of hydration of cement pastes. And, mercury intrusion method and image analysis method were used to explore the pore structure in hardened cement pastes and air void characteristics in hardened concrete, respectively. Furthermore, the evolution process was also studied for the relative humidity inside the concrete

Promoting hydrogen-evolution activity and stability of perovskite oxides via effectively lattice doping of molybdenum

Electrocatalysts are the most compelling objectives in realizing highly efficient renewable energy conversion and storage applications. Rational doping is an effective strategy for the development of cost-effective perovskite oxides with high electrochemical performance. In this study, we report facilely prepared molybdenum (Mo)-doped SrCo0.70Fe0.30O3-δ perovskites such as SrCo0.7Fe0.25Mo0.05O3-δ (SCFM0.05) and SrCo0.7Fe0.20Mo0.10O3-δ (SCFM0.10) for boosting the hydrogen evolution reaction (HER) activity and stability. Among them, SCFM0.05 delivers a promising overpotential of ∼323 mV at the current density of 10 mA cmdisk^-2 and keeps almost stable for 5 h and after accelerated 1000 cycles. The promoted HER activity of SCFM0.05 regarding the decreased overpotential, increased catalytic current density, and improved charge transfer kinetics, might originate from the combined effects of distortion of octahedral coordination, low oxygen vacancy/high oxidation state of Co, abundant lattice oxygen and highly oxidative oxygen species, long B–O length, and strong OH− adsorption compared to the un-doped counterpart. We ascribe the enhanced operational stability to the formation of a low concentration of oxygen vacancy that stabilizes the crystal structure of Mo-doped SrCo0.7Fe0.3O3-δ and prevents the surface from Sr leaching/surface amorphization. These findings suggest that tuning perovskite oxide using a redox-inactive dopant featured with high valence state may provide further avenues to HER optimization.This research is supported by the National Natural Science Foundation of China (No. 51702125 & No. 21808080), Pearl River S&T Nova Program of Guangzhou (No. 201806010054), Fundamental Research Funds for the Central Universities (No. 21616301), and the China Postdoctoral Science Foundation (No. 2017M620401)

Research on the Vertical Vibration Characteristics of Hydraulic Screw Down System of Rolling Mill under Nonlinear Friction

The rolling mill with hydraulic system is widely used in the production of strip steel. For the problem of vertical vibration of the rolling mill, the effects of different equivalent damping coefficient, leakage coefficient, and proportional coefficient of the controller on the hydraulic screw down system of the rolling mill are studied, respectively. First, a vertical vibration model of a hydraulic screw down system was established, considering the nonlinear friction and parameter uncertainty of the press cylinder. Second, the correlation between different equivalent damping coefficient, internal leakage coefficient, proportional coefficient, vertical vibration was analyzed. The simulation results show that, in the closed-loop state, when Proportional-Integral-Derivative (PID) controller parameters are fixed, due to the change of the equivalent damping coefficient and internal leakage coefficient, the system will have parameter uncertainty, which may lead to the failure of the PID controller and the vertical vibration of the system. This study has theoretical and practical significance for analyzing the mechanism of vertical vibration of the rolling mill