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Background: Long noncoding RNA secretory carrier membrane protein 1 (SCAMP1) has been recently reported to be an oncogene in several cancers, including glioma and ovarian cancer. However, its clinical significance and functional role in hepatocellular carcinoma (HCC) remain unknown.Methods: The expression of SCAMP1 was determined in tissue samples and cell lines using quantitative reverse transcription PCR. The clinical significance of SCAMP1 in HCC was evaluated using chi-squared test, Kaplan-Meier survival, as well as univariate and multivariate analysis. Cell proliferation, cell cycle distribution and apoptosis were evaluated using CCK-8 assay, colony formation assay, Flow cytometry analysis. Related protein expression levels were measured by western blot analysis.Results: We found SCAMP1 expression levels were remarkably up-regulated in HCC tissues compared with that in matched adjacent tissues. Increased SCAMP1 expression was significantly correlated with large tumor size, advanced TNM stage and poor survival prognosis in HCC. Knockdown of SCAMP1 significantly inhibited cell proliferation, induced G0/G1 phase arrest and apoptosis in HepG2 and SNU-182 cells. More importantly, knockdown of SCAMP1 downregulated the expression levels of CDK4, Cyclin D1 and Bcl-2, while upregulated the expression levels of p21, p53 and Bax.Conclusions: In summary, our study clarified the oncogenic role of SCAMP1 in HCC, and provided a potential therapeutic target for HCC treatment.

A design of measuring system with zero temperature drift is proposed for the tri-component array induction tool. The designed down-hole system is able to survive high temperature up to $175 ^{\circ}\mathrm{C}$. It deploys multiplexers before pre-amplifiers to switch 20 signals, including zero signals, calibration signals, and coil signals. A DSP chip and a FPGA chip are integrated for digital signal processing. An algorithm of digital phase sensitive detection is employed for calculating the amplitudes and phases of these three kinds of signals. In order to perform zero-temperature-drift measurements, real time calibrations are applied to the four analog conditioning channels, and the common-mode interference signals of the receiving coils are cancelled during the processing. Experimental data illustrate that, this high temperature system survives in the process of heating without obvious temperature drift.

To realize the online multi-parameter measurement of the complex part with high precision, the method for measuring part shape based on integration technology of projection optics and image processing is proposed. Key techniques of this method including the projection optics system, the image feature extraction and the image feature matching are deeply studied. After a detailed analysis of the technical characteristics of object telecentric optical path adopted in the part shape measurement, an optical system for measuring instrument is designed. Through the in-depth study of algorithm in image feature extraction and matching, and after the actual test of shaft part, the feasibility of these researched techniques and the accuracy of the developed instrument are verified. The test results also show that this measurement technique of part shape has the characteristics of measurement speed ≤3 s, measurement accuracy ≤±2 μm, real-time 15 parameters measurement in a single. That fully meets the requirements of automated testing.

Identifying protein–protein interactions (PPIs) is crucial to comprehend various biological processes in cells. Although high-throughput techniques generate many PPI data for various species, they are only a petty minority of the entire PPI network. Furthermore, these approaches are costly and time-consuming and have a high error rate. Therefore, it is necessary to design computational methods for efficiently detecting PPIs. In this study, a random projection ensemble classifier (RPEC) was explored to identify novel PPIs using evolutionary information contained in protein amino acid sequences. The evolutionary information was obtained from a position-specific scoring matrix (PSSM) generated from PSI-BLAST. A novel feature fusion scheme was then developed by combining discrete cosine transform (DCT), fast Fourier transform (FFT), and singular value decomposition (SVD). Finally, via the random projection ensemble classifier, the performance of the presented approach was evaluated on Yeast, Human, and H. pylori PPI datasets using 5-fold cross-validation. Our approach achieved high prediction accuracies of 95.64%, 96.59%, and 87.62%, respectively, effectively outperforming other existing methods. Generally speaking, our approach is quite promising and supplies a practical and effective method for predicting novel PPIs.

In the previous study of user identification, most of the researchers improved the recognition algorithm. In this paper, we use large data technology to extract electricity feature from different angles and study the impact of different features on recognition. Firstly, the raw data was cleaned. In order to obtain the key information of power theft user identification, the features of the data set are extracted from three aspects: basic attribute feature, statistical feature under different time scale and similarity feature under different time scale. Then we use feature sets of different combinations to carry out experiments under the KNN model, the random forest (RF) model and the XGBoost model. The experimental results show that the experimental results of the BF+SF+PF feature set in the three classifiers are obviously better than the other two feature sets. Therefore, it is concluded that different features have obvious effects on the recognition results.

In order to realize the current requirements of video image data memory card such as low power consumption, high storage density, high reading/writing speed, and enhancing the storage medium compatibility with improving the storage capacity and stability, we present a design of Video image data memory card using ATA interface hard-disk, and use modularized design in this program, consists four parts: the image data buffer module, ATA logic module, ATA interface module, host communication module. Wherein the image data buffer module selects the CPLD plus external FIFO to complete makes the system only need to modify the CPLD program while working together with different external devices, which can improve the reliability and maintainability of the memory card that we designed. The functions of ATA logic module and ATA interface module are realized based on FPGA. The host communication module is completed by the AVR chips with TUSB6250 chip, ensuring the memory card has the advantages of low power consumption, internal short circuit protection and so on. In this paper, the proposed memory card scheme has the superiority such as lower cost, better compatibility, easy to expand, especially suitable for huge amounts of data in video store.

This paper presents the numerical approach based on FEM with the hierarchical vector basis functions has been developed to simulate the response of multi-component induction tool in anisotropic formations. To improve computational efficiency, the mixed order of hierarchical vector bases are applied to FEM, which means different order of the bases are used for different elements in accordance with the variation speed of the field. The mixed order scheme reduces the number of unknowns greatly, and attains much higher computational efficiency. In addition, the numerical results have been given to demonstrate the capabilities and the advantages of algorithm in simulating the response of multi-component induction tool in arbitrary 3-D anisotropic formations.