Integrated Analysis Reveals a lncRNA–miRNA–mRNA Network Associated with Pigeon Skeletal Muscle Development

Growing evidence has demonstrated the emerging role of long non-coding RNA as competitive endogenous RNA (ceRNA) in regulating skeletal muscle development. However, the mechanism of ceRNA regulated by lncRNA in pigeon skeletal muscle development remains unclear. To reveal the function and regulatory mechanisms of lncRNA, we first analyzed the expression profiles of lncRNA, microRNA (miRNA), and mRNA during the development of pigeon skeletal muscle using high-throughput sequencing. We then constructed a lncRNA–miRNA–mRNA ceRNA network based on differentially expressed (DE) lncRNAs, miRNAs, and mRNAs according to the ceRNA hypothesis. Functional enrichment and short time-series expression miner (STEM) analysis were performed to explore the function of the ceRNA network. Hub lncRNA–miRNA–mRNA interactions were identified by connectivity degree and validated using dual-luciferase activity assay. The results showed that a total of 1625 DE lncRNAs, 11,311 DE mRNAs, and 573 DE miRNAs were identified. A ceRNA network containing 9120 lncRNA–miRNA–mRNA interactions was constructed. STEM analysis indicated that the function of the lncRNA-associated ceRNA network might be developmental specific. Functional enrichment analysis identified potential pathways regulating pigeon skeletal muscle development, such as cell cycle and MAPK signaling. Based on the connectivity degree, lncRNAs TCONS_00066712, TCONS_00026594, TCONS_00001557, TCONS_00001553, and TCONS_00003307 were identified as hub genes in the ceRNA network. lncRNA TCONS_00026594 might regulate the FSHD region gene 1 (FRG1)/ SRC proto-oncogene, non-receptor tyrosine kinase (SRC) by sponge adsorption of cli-miR-1a-3p to affect the development of pigeon skeletal muscle. Our findings provide a data basis for in-depth elucidation of the lncRNA-associated ceRNA mechanism underlying pigeon skeletal muscle development.

Development of Intellectual Web System for Morph Analyzing of Uzbek Words

Currently, there is an active development of the Uzbek sector of the Internet. In it, as in other national sectors, the most common form of presentation of textual information is semi-structured documents, work that presupposes the availability of reliable algorithms for text analysis, including its lexical characteristics. The article offers an intelligent web application developed for morphological analysis of words in the Uzbek language. The web application is based on the concept of generation and stem analysis of the Uzbek language word forms. A well-known Porter algorithm was chosen as the basis for stemming. The morphoanalyzer generates word forms of the Uzbek language based on the division of words into certain classes, taking into account the specifics and structure of this language. For example, nouns can be classified by meaning (related, nominal), by quantity (singular and plural), by case, and also, by the endings of belonging (possessive).

A MicroRNA-Based Network Provides Potential Predictive Signatures and Reveals the Crucial Role of PI3K/AKT Signaling for Hepatic Lineage Maturation

BackgroundFunctions of miRNAs involved in tumorigenesis are well reported, yet, their roles in normal cell lineage commitment remain ambiguous. Here, we investigated a specific “transcription factor (TF)-miRNA-Target” regulatory network during the lineage maturation of biliary tree stem cells (BTSCs) into adult hepatocytes (hAHeps).MethodBioinformatic analysis was conducted based on our RNA-seq and microRNA-seq datasets with four human hepatic-lineage cell lines, including hBTSCs, hepatic stem cells (hHpSCs), hepatoblasts (hHBs), and hAHeps. Short time-series expression miner (STEM) analysis was performed to reveal the time-dependent dynamically changed miRNAs and mRNAs. GO and KEGG analyses were applied to reveal the potential function of key miRNAs and mRNAs. Then, the miRDB, miRTarBase, TargetScan, miRWalk, and DIANA-microT-CDS databases were adopted to predict the potential targets of miRNAs while the TransmiR v2.0 database was used to obtain the experimentally supported TFs that regulate miRNAs. The TCGA, Kaplan–Meier Plotter, and human protein atlas (HPA) databases and more than 10 sequencing data, including bulk RNA-seq, microRNA-seq, and scRNA-seq data related to hepatic development or lineage reprogramming, were obtained from both our or other published studies for validation.ResultsSTEM analysis showed that during the maturation from hBTSCs to hAHeps, 52 miRNAs were downwardly expressed and 928 mRNA were upwardly expressed. Enrichment analyses revealed that those 52 miRNAs acted as pluripotency regulators for stem cells and participated in various novel signaling pathways, including PI3K/AKT, MAPK, and etc., while 928 mRNAs played important roles in liver-functional metabolism. With an extensive sorting of those key miRNAs and mRNAs based on the target prediction results, 23 genes were obtained which not only functioned as the targets of 17 miRNAs but were considered critical for the hepatic lineage commitment. A “TF-miRNA-Target” regulatory network for hepatic lineage commitment was therefore established and had been well validated by various datasets. The network revealed that the PI3K/AKT pathway was gradually suppressed during the hepatic commitment.ConclusionA total of 17 miRNAs act as suppressors during hepatic maturation mainly by regulating 23 targets and modulating the PI3K/AKT signaling pathway. The regulatory network uncovers possible signatures and guidelines enabling us to identify or obtain the functional hepatocytes for future study.

A Tree Ring Measurement Method Based on Error Correction in Digital Image of Stem Analysis Disk

Stem analysis is an essential aspect in forestry investigation and forest management, as it is a primary method to study the growth law of trees. Stem analysis requires measuring the width and number of tree rings to ensure the accurate measurement, expand applicable tree species, and reduce operation cost. This study explores the use of Open Source Computer Vision Library (Open CV) to measure the ring radius of analytic wood disk digital images, and establish a regression equation of ring radius based on image geometric distortion correction. Here, a digital camera was used to photograph the stem disks’ tree rings to obtain digital images. The images were preprocessed with Open CV to measure the disk’s annual ring radius. The error correction model based on the least-square polynomial fitting method was established for digital image geometric distortion correction. Finally, a regression equation for tree ring radius based on the error correction model was established. Through the above steps, click the intersection point between the radius line and each ring to get the pixel distance from the ring to the pith, then the size of ring radius can be calculated by the regression equation of ring radius. The study’s method was used to measure the digital image of the Chinese fir stem disk and compare it with the actual value. The results showed that the maximum error of this method was 0.15 cm, the average error was 0.04 cm, and the average detection accuracy reached 99.34%, which met the requirements for measuring the tree ring radius by stem disk analysis. This method is simple, accurate, and suitable for coniferous and broad-leaved species, which allows researchers to analyze tree ring radius measurement, and is of great significance for analyzing the tree growth process.