Background
During intrauterine development, the formation and function of synaptic vesicles (SVs) are thought to be fundamental conditions essential for normal development of the brain. Lacking advanced technology during the intrauterine period, such as longitudinal real-time monitoring of the SV-associated transcripts (SVATs), which include six pairs of lncRNA-mRNA, has limited acquisition of the dynamic gene expression profile (GEP) of SVATs. We previously reported the differential expression of SVATs in the peripheral blood of autistic children. The current study was designed to determine the dynamic profiles of differentially-expressed SVATs in circulating exosomes (EXs) derived from autistic children and pregnant women at different gestational ages.
Methods
Blood samples were collected from autistic children and women with variant physiopathologic pregnancies. EXs were isolated with an ExoQuick Exosome Precipitation Kit and characterized by transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and Western blotting. The expression of lncRNAs and lncRNA-targeted mRNAs were quantified using real-time PCR.
Results
SVAT-associated lncRNAs-mRNAs were detected in autistic children and differentially expressed from the first trimester of pregnancy to the term of delivery. Pathologic pregnancies, including spontaneous preterm birth (sPTB), preeclampsia (PE), and gestational diabetes mellitus (GDM), were compared to normal physiologic pregnancies, and shown to exhibit specific correlations between SVAT-lncRNA and SVAT-mRNA of STX8, SLC18A2, and SYP with sPTB; SVAT-lncRNA and SVAT-mRNA of STX8 with PE; and SVAT-lncRNA and SVAT-mRNA of SV2C as well as SVAT-mRNA of SYP with GDM.
Conclusion
Variant complications in pathologic pregnancies may alter the GEP of SVATs, which is likely to affect the intrauterine development of neural circuits and consequently influence fetal brain development.
The most prominent area where the potential of neural networks under study is that of engineering applications, such as image and speech recognition. In this paper, the design of neural networksfor sequential domains is another interesting direction. This paper describes a 3-D motion detection system. This system consists of three stages; the Rough Motion Detection stage, the Moving Object Eztraction stage, and the Object Identification and the 9-D Motion Detection stage. Five neural networksthe Correlation Network, the Rough Motion Detection network, the Edge Enhancement Network, the Background Remover, and the Normalization Network, are designed for the implementation of these three stages.
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