Inhibition of PC cell-derived growth factor (PCDGF)/granulin-epithelin precursor (GEP) decreased cell proliferation and invasion through downregulation of cyclin D and CDK 4 and inactivation of MMP-2

BACKGROUND: PC cell-derived growth factor (PCDGF), also called epithelin/granulin precursor (GEP), is an 88-kDa secreted glycoprotein with the ability to stimulate cell proliferation in an autocrine fashion. In addition, some studies indicated that PCDGF participated in invasion, metastasis and survival of cancer cells by regulating cell migration, adhesion and proliferation. Yet the effects of PCDGF on proliferation and invasion of ovarian cancer cells in vitro and the mechanisms by which PCDGF mediates biological behaviors of ovarian cancer have rarely been reported. In the present study we investigated whether and how PCDGF/GEP mediated cell proliferation and invasion in ovarian cancer. METHODS: PCDGF/GEP expression level in three human ovarian cancer cell lines of different invasion potential were detected by RT-PCR and western blot. Effects of inhibition of PCDGF expression on cell proliferation and invasion capability were determined by MTT assay and Boyden chamber assay. Expression levels of cyclin D1 and CDK4 and MMP-2 activity were evaluated in a pilot study. RESULTS: PCDGF mRNA and protein were expressed at a high level in SW626 and A2780 and at a low level in SKOV3. PCDGF expression level correlated well with malignant phenotype including proliferation and invasion in ovarian cancer cell lines. In addition, the proliferation rate and invasion index decreased after inhibition of PCDGF expression by antisense PCDGF cDNA transfection in SW626 and A2780. Furthermore expression of CyclinD1 and CDK4 were downregulated and MMP-2 was inactivated after PCDGF inhibition in the pilot study. CONCLUSION: PCDGF played an important role in stimulating proliferation and promoting invasion in ovarian cancer. Inhibition of PCDGF decreased proliferation and invasion capability through downregulation of cyclin D1 and CDK4 and inactivation of MMP-2. PCDGF could serve as a potential therapeutic target in ovarian cancer

A Systems Biology-Based Classifier for Hepatocellular Carcinoma Diagnosis

AIM: The diagnosis of hepatocellular carcinoma (HCC) in the early stage is crucial to the application of curative treatments which are the only hope for increasing the life expectancy of patients. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with HCC progression. However, those marker sets shared few genes in common and were poorly validated using independent data. Therefore, we developed a systems biology based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC diagnosis. METHODS AND RESULTS: In the Oncomine platform, genes differentially expressed in HCC tissues relative to their corresponding normal tissues were filtered by a corrected Q value cut-off and Concept filters. The identified genes that are common to different microarray datasets were chosen as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and the hub genes were chosen. After that, an HCC diagnostic classifier was constructed by Partial Least Squares modeling based on the microarray gene expression data of the hub genes. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.88∼92.71%) and area under ROC curve (approximating 1.0), and that the network topological features integrated into this classifier contribute greatly to improving the predictive performance. Furthermore, it has been demonstrated that this modeling strategy is not only applicable to HCC, but also to other cancers. CONCLUSION: Our analysis suggests that the systems biology-based classifier that combines the differential gene expression and topological features of human protein interaction network may enhance the diagnostic performance of HCC classifier

A Marine Object Detection Algorithm Based on SSD and Feature Enhancement

Autonomous detection and fishing by underwater robots will be the main way to obtain aquatic products in the future; sea urchins are the main research object of aquatic product detection. When the classical Single-Shot MultiBox Detector (SSD) algorithm is applied to the detection of sea urchins, it also has disadvantages of being inaccurate to small targets and insensitive to the direction of the sea urchin. Based on the classic SSD algorithm, this paper proposes a feature-enhanced sea urchin detection algorithm. Firstly, according to the spiny-edge characteristics of a sea urchin, a multidirectional edge detection algorithm is proposed to enhance the feature, which is taken as the 4th channel of image and the original 3 channels of underwater image together as the input for the further deep learning. Then, in order to improve the shortcomings of SSD algorithm’s poor ability to detect small targets, resnet 50 is used as the basic framework of the network, and the idea of feature cross-level fusion is adopted to improve the feature expression ability and strengthen semantic information. The open data set provided by the National Natural Science Foundation of China underwater Robot Competition will be used as the test set and training set. Under the same training and test conditions, the AP value of the algorithm in this paper reaches 81.0%, 7.6% higher than the classic SSD algorithm, and the confidence of small target analysis is also improved. Experimental results show that the algorithm in this paper can effectively improve the accuracy of sea urchin detection

An Underwater Image Enhancement Algorithm Based on MSR Parameter Optimization

The quality of underwater images is often affected by the absorption of light and the scattering and diffusion of floating objects. Therefore, underwater image enhancement algorithms have been widely studied. In this area, algorithms based on Multi-Scale Retinex (MSR) represent an important research direction. Although the visual quality of underwater images can be improved to some extent, the enhancement effect is not good due to the fact that the parameters of these algorithms cannot adapt to different underwater environments. To solve this problem, based on classical MSR, we propose an underwater image enhancement optimization (MSR-PO) algorithm which uses the non-reference image quality assessment (NR-IQA) index as the optimization index. First of all, in a large number of experiments, we choose the Natural Image Quality Evaluator (NIQE) as the NR-IQA index and determine the appropriate parameters in MSR as the optimization object. Then, we use the Gravitational Search Algorithm (GSA) to optimize the underwater image enhancement algorithm based on MSR and the NIQE index. The experimental results show that this algorithm has an excellent adaptive ability to environmental changes

Construction of Fe₃O₄@Fe₂P Heterostructures as Electrode Materials for Supercapacitors

Considering their high abundance in the earth, iron-based materials have occasionally been regarded as promising electrode materials for supercapacitors. However, monometallic iron-based electrodes still demonstrate an insufficient specific capacitance value in comparison to monometallic Mn-, Ni-, and Co-based compounds and their combined materials. Herein, an enhanced iron-based heterostructure of Fe3O4@Fe2P was prepared via the in situ phosphorization of Fe3O4. Compared to pristine Fe3O4, the Fe3O4@Fe2P heterostructure showed a capacity enhancement in KOH aqueous solution. The improved electrochemical performance can be attributed to both the core shell structure, which favors buffering the collapse of the electrode, and the synergistic effect between the two iron compounds, which may provide abundant interfaces and additional electrochemically active sites. Moreover, the assembled asymmetric supercapacitor device using the Fe3O4@Fe2P heterostructure as the positive electrode and activated carbon as the negative electrode delivers a high energy density of 13.47 Wh kg−1, a high power density of 424.98 W kg−1, and an acceptable capacitance retention of 78.5% after 5000 cycles. These results clarify that monometallic Fe based materials can deliver a potential practical application. In addition, the construction method for the heterostructure developed here, in which different anion species are combined, may represent a promising strategy for designing high-performance electrodes

TB-NUCA: A Temperature-Balanced 3D NUCA Based on Bayesian Optimization

Three-dimensional network-on-chip (NoC) is the primary interconnection method for 3D-stacked multicore processors due to their excellent scalability and interconnect flexibility. With the support of 3D NoC, 3D non-uniform cache architecture (NUCA) is commonly used to organize the last-level cache (LLC) due to its high capacity and fast access latency. However, owing to the layered structure that leads to longer heat dissipation paths and variable inter-layer cooling efficiency, 3D NoC experiences a severe thermal problem that has a big impact on the reliability and performance of the chip. In traditional memory-to-LLC mapping in 3D NUCA, the traffic load in each node is inconsistent with its heat dissipation capability, causing thermal hotspots. To solve the above problem, we propose a temperature-balanced NUCA mapping mechanism named TB-NUCA. First, the Bayesian optimization algorithm is used to calculate the probability distribution of cache blocks in each node in order to equalize the node temperature. Secondly, the structure of TB-NUCA is designed. Finally, comparative experiments were conducted under random, transpose-2, and shuffle traffic patterns. The experimental results reveal that, compared with the classical NUCA mapping mechanism (S-NUCA), TB-NUCA can increase the mean-time-to-failure (MTTF) of routers by up to 28.13% while reducing the maximum temperature, average temperature, and standard deviation of temperature by a maximum of 4.92%, 4.48%, and 20.46%, respectively

Towards Convolutional Neural Network Acceleration and Compression Based on Simonk-Means

Convolutional Neural Networks (CNNs) are popular models that are widely used in image classification, target recognition, and other fields. Model compression is a common step in transplanting neural networks into embedded devices, and it is often used in the retraining stage. However, it requires a high expenditure of time by retraining weight data to atone for the loss of precision. Unlike in prior designs, we propose a novel model compression approach based on Simonk-means, which is specifically designed to support a hardware acceleration scheme. First, we propose an extension algorithm named Simonk-means based on simple k-means. We use Simonk-means to cluster trained weights in convolutional layers and fully connected layers. Second, we reduce the consumption of hardware resources in data movement and storage by using a data storage and index approach. Finally, we provide the hardware implementation of the compressed CNN accelerator. Our evaluations on several classifications show that our design can achieve 5.27× compression and reduce 74.3% of the multiply–accumulate (MAC) operations in AlexNet on the FASHION-MNIST dataset

TECED: A Two-Dimensional Error-Correction Codes Based Energy-Efficiency SRAM Design

The reliability of memory is an important issue. The rapid development of transistor technology makes the memory more prone to soft errors. Several recent efforts have proposed various designs to avoid the corruption of stored data by using Error Correction Codes (ECC). However, these designs tend to focus on one indicator, which means they cannot balance the electrical timing, area and power consumption constraints with the increasing of the chip-scale and the operating frequency. In this paper, we propose a design named TECED: A Two-Dimensional Error-Correction Codes Based Energy-Efficiency SRAM Design. We achieve higher energy-efficiency and lower hardware cost by using a two-dimensional error correction codes, and evaluate the design by considering the overall system performance. Comparing with the traditional Hamming code, the evaluation shows that the TECED reduces most of fifty percent of the area overhead and twenty-eight point five percent power consumption of the memory at a specific storage capacity

TECED: A Two-Dimensional Error-Correction Codes Based Energy-Efficiency SRAM Design

The reliability of memory is an important issue. The rapid development of transistor technology makes the memory more prone to soft errors. Several recent efforts have proposed various designs to avoid the corruption of stored data by using Error Correction Codes (ECC). However, these designs tend to focus on one indicator, which means they cannot balance the electrical timing, area and power consumption constraints with the increasing of the chip-scale and the operating frequency. In this paper, we propose a design named TECED: A Two-Dimensional Error-Correction Codes Based Energy-Efficiency SRAM Design. We achieve higher energy-efficiency and lower hardware cost by using a two-dimensional error correction codes, and evaluate the design by considering the overall system performance. Comparing with the traditional Hamming code, the evaluation shows that the TECED reduces most of fifty percent of the area overhead and twenty-eight point five percent power consumption of the memory at a specific storage capacity