Integrated analysis of the expression profiles of the lncRNA-miRNA-mRNA ceRNA network in granulosa and cumulus cells from yak ovaries.

BACKGROUND: Growing oocytes acquire the ability to mature through two-way communication between gametes and surrounding somatic cumulus cells (CCs). Granulosa cells (GCs) support oocyte growth, regulate meiosis progression, and modulate global oocyte transcription activity. However, the proliferation and differentiation of the yak ovary in GCs and CCs remain unclear. To characterize the important roles of long non-coding RNA, (lncRNA), microRNA (miRNA), and messenger RNA (mRNA), whole-transcriptome analysis was performed. Real-time quantitative fluorescence PCR was performed to verify the selected RNA sequences. RESULTS: Important gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways related to differentiation and oocyte development were identified for the target genes of differentially expressed lncRNAs, miRNAs, and mRNAs. In total,6223 mRNAs (2197 upregulated, 4026 downregulated), 643 lncRNAs (204 upregulated, 479 downregulated), and 559 miRNAs (311 upregulated, 248 downregulated) were significantly altered between the two groups. Target genes involved in cell adhesion, cell differentiation, regulation of developmental processes, cell proliferation, embryo development, signal transduction, apoptosis, and aromatic compound biosynthetic processes were significantly enriched. These RNAs were involved in ECM-receptor interaction, MAPK signaling, Hippo signaling, PI3K-Akt signaling, cell cycle, cell adhesion, leukocyte trans-endothelial migration, and actin cytoskeleton regulation. CONCLUSIONS: A comprehensive analysis of the co-expression network of competing endogenous RNAs (ceRNAs) will facilitate the understanding of the process of granulosa cell proliferation and differentiation and offer a theoretical basis for the development of oocytes.

Breast Mass Detection in Mammography Based on Image Template Matching and CNN.

In recent years, computer vision technology has been widely used in the field of medical image processing. However, there is still a big gap between the existing breast mass detection methods and the real-world application due to the limited detection accuracy. It is known that humans locate the regions of interest quickly and further identify whether these regions are the targets we found. In breast cancer diagnosis, we locate all the potential regions of breast mass by glancing at the mammographic image from top to bottom and from left to right, then further identify whether these regions are a breast mass. Inspired by the process of human detection of breast mass, we proposed a novel breast mass detection method to detect breast mass on a mammographic image by stimulating the process of human detection. The proposed method preprocesses the mammographic image via the mathematical morphology method and locates the suspected regions of breast mass by the image template matching method. Then, it obtains the regions of breast mass by classifying these suspected regions into breast mass and background categories using a convolutional neural network (CNN). The bounding box of breast mass obtained by the mathematical morphology method and image template matching method are roughly due to the mathematical morphology method, which transforms all of the brighter regions into approximate circular areas. For regression of a breast mass bounding box, the optimal solution should be searched in the feasible region and the Particle Swarm Optimization (PSO) is suitable for solving the problem of searching the optimal solution within a certain range. Therefore, we refine the bounding box of breast mass by the PSO algorithm. The proposed breast mass detection method and the compared detection methods were evaluated on the open database Digital Database for Screening Mammography (DDSM). The experimental results demonstrate that the proposed method is superior to all of the compared detection methods in detection performance.

An Efficient Seam Elimination Method for UAV Images Based on Wallis Dodging and Gaussian Distance Weight Enhancement.

The rapid development of Unmanned Aerial Vehicle (UAV) remote sensing conforms to the increasing demand for the low-altitude very high resolution (VHR) image data. However, high processing speed of massive UAV data has become an indispensable prerequisite for its applications in various industry sectors. In this paper, we developed an effective and efficient seam elimination approach for UAV images based on Wallis dodging and Gaussian distance weight enhancement (WD-GDWE). The method encompasses two major steps: first, Wallis dodging was introduced to adjust the difference of brightness between the two matched images, and the parameters in the algorithm were derived in this study. Second, a Gaussian distance weight distribution method was proposed to fuse the two matched images in the overlap region based on the theory of the First Law of Geography, which can share the partial dislocation in the seam to the whole overlap region with an effect of smooth transition. This method was validated at a study site located in Hanwang (Sichuan, China) which was a seriously damaged area in the 12 May 2008 enchuan Earthquake. Then, a performance comparison between WD-GDWE and the other five classical seam elimination algorithms in the aspect of efficiency and effectiveness was conducted. Results showed that WD-GDWE is not only efficient, but also has a satisfactory effectiveness. This method is promising in advancing the applications in UAV industry especially in emergency situations.

Exploration of the Tolerance Ability of a Cell-Free Biosynthesis System to Toxic Substances.

Cell-free synthetic biology has become a robust technology platform for synthesizing proteins and chemicals in recent years. Cell-free synthetic biology system activates biological machinery without the use of living cells, which opens new opportunities for the addition or synthesis of toxic substances at high concentrations in biological systems. Although it is generally accepted that an in vitro cell-free synthesis system has a high tolerance ability to toxic substances, there is a lack of relevant data to support this view. To explore particular tolerance ability, a range of different surfactants, lipids, materials, biofuels, and chemical drugs were selected for testing their effects in Escherichia coli-based cell-free protein synthesis system. The results showed the limit concentrations of different toxic substances. Moreover, the results demonstrated that the tolerance ability of a cell-free system is much higher than that of a cell system. This study further provides a rationale for the synthesis of toxic biopharmaceuticals, biochemicals, and biofuels by using cell-free systems.

MicroRNA-16 suppresses the activation of inflammatory macrophages in atherosclerosis by targeting PDCD4.

Programmed cell death 4 (PDCD4) is involved in a number of bioprocesses, such as apoptosis and inflammation. However, its regulatory mechanisms in atherosclerosis remain unclear. In this study, we investigated the role and mechanisms of action of PDCD4 in high-fat diet-induced atherosclerosis in mice and in foam cells (characteristic pathological cells in atherosclerotic lesions) derived from ox-LDL-stimulated macrophages. MicroRNA (miR)-16 was predicted to bind PDCD4 by bioinformatics analysis. In the mice with atherosclerosis and in the foam cells, PDCD4 protein expression (but not the mRNA expression) was enhanced, while that of miR­16 was reduced. Transfection with miR­16 mimic decreased the activity of a luciferase reporter containing the 3' untranslated region (3'UTR) of PDCD4 in the macrophage-derived foam cells. Conversely, treatment with miR­16 inhibitor enhanced the luciferase activity. However, by introducing mutations in the predicted binding site located in the 3'UTR of PDCD4, the miR­16 mimic and inhibitor were unable to alter the level of PDCD4, suggesting that miR­16 is a direct negative regulator of PDCD4 in atherosclerosis. Furthermore, transfection wtih miR­16 mimic and siRNA targeting PDCD4 suppressed the secretion and mRNA expression of pro-inflammatory factors, such as interleukin (IL)-6 and tumor necrosis factor-α (TNF­α), whereas it enhanced the secretion and mRNA expression of the anti-inflammatory factor, IL-10. Treatment with miR­16 inhibitor exerted the opposite effects. In addition, the phosphorylation of p38 and extracellular signal-regulated kinase (ERK), and nuclear factor-κB (NF-κB) expression were altered by miR­16. In conclusion, our data demonstrate that the targeting of PDCD4 by miR­16 may suppress the activation of inflammatory macrophages though mitogen-activated protein kinase (MAPK) and NF-κB signaling in atherosclerosis; thus, PDCD4 may prove to be a potential therapeutic target in the treatment of atherosclerosis.