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The blue egg is both of biological interest and economic importance for consumers, egg retailers, and scientists. To date, the genetic mechanisms underlying pigment have mainly focused on protein-coding genes. However, the underpinning mechanism of non-coding RNAs on the pigment deposition among different eggshell colors remains unknown. In this study, RNA sequencing was employed to profile the uterine gland transcriptome (lncRNA and mRNA) of 15 Changshun blue eggshell layers, to better understand the genetic mechanisms of deposition of blue eggshell color. Results showed that differentially expressed mRNAs, GO terms, and KEGG pathways among pink-eggshell and blue-eggshell chickens were mainly targeting immune- and transporter-related terms with the SLC family, IgJ, CD family, and MTMR genes. Furthermore, the progesterone-mediated oocyte maturation and cortisol synthesis and secretion pathway with targeted gene PGR and Pbx1 were significantly enriched between blue- and pink-eggshell chickens. Integrating analysis of lncRNA and mRNA profiles predicted 4 and 25 lncRNA–gene pairs by antisense and cis analysis. They were relative to immune, nerve, and lipids and amino acid metabolisms, porphyrin, and chlorophyll metabolism with targeted gene FECH and oxidative phosphorylation and cardiac muscle contraction pathways with targeted gene COX6A1. Within blue-eggshell chickens, the GO terms hindbrain tangential cell migration and phosphatidylinositol monophosphate phosphatase activity with targeted gene Plxna2 and MTRM1 were identified. Integrating analysis of lncRNA and mRNA profiles predicted 8 and 22 lncRNA–gene pairs. Most pathways were mainly enriched on lipid-related metabolisms as found in mRNA sequencing. The lncRNAs did exert similar functions in color formation by modulating pigment disposition and immune- and lipid-related metabolisms. Our results provide a catalog of chicken uterine lncRNAs and genes worthy of further studies to understand their roles in the selection for blue eggshell color layers.

The transient electromagnetic method (TEM) is extremely important in geophysics. However, the secondary field signal (SFS) in the TEM received by coil is easily disturbed by random noise, sensor noise and man-made noise, which results in the difficulty in detecting deep geological information. To reduce the noise interference and detect deep geological information, we apply autoencoders, which make up an unsupervised learning model in deep learning, on the basis of the analysis of the characteristics of the SFS to denoise the SFS. We introduce the SFSDSA (secondary field signal denoising stacked autoencoders) model based on deep neural networks of feature extraction and denoising. SFS-DSA maps the signal points of the noise interference to the high-probability points with a clean signal as reference according to the deep characteristics of the signal, so as to realize the signal denoising and reduce noise interference. The method is validated by the measured data comparison, and the comparison results show that the noise reduction method can (i) effectively reduce the noise of the SFS in contrast with the Kalman, principal component analysis (PCA) and wavelet transform methods and (ii) strongly support the speculation of deeper underground features.