The solitary LTRs of ERV-9 human endogenous retrovirus are middle repetitive DNAs associated with 3,000 -4,000 human gene loci including the ␤-globin gene locus where the ERV-9 LTR is juxtaposed to the locus control region (␤-LCR) far upstream of the globin genes. The ERV-9 LTRs are conserved during primate evolution, but their function in the primate genomes is unknown. Here, we show that in transgenic zebrafish harboring the ␤-globin ERV-9 LTR coupled to the GFP gene, the LTR enhancer was active and initiated synthesis of GFP mRNA in oocytes but not in spermatozoa, and GFP expression in the embryos was maternally inherited. The LTR enhancer was active also in stem͞ progenitor cell regions of adult tissues of transgenic zebrafish. In human tissues, ERV-9 LTR enhancer was active also in oocytes and stem͞progenitor cells but not in spermatozoa and a number of differentiated, adult somatic cells. Transcriptional analyses of the human ␤-globin gene locus showed that the ␤-globin ERV-9 LTR enhancer initiated RNA synthesis from the LTR in the direction of the downstream ␤ locus control region and globin genes in ovary and erythroid progenitor cells. The findings suggest that, during oogenesis, ERV-9 LTR enhancers in the human genome could activate the cis-linked gene loci to synthesize maternal mRNAs required for early embryogenesis. Alternatively, the ERV-9 LTR enhancers, in initiating RNA syntheses into the downstream genomic DNAs, could transcriptionally potentiate and preset chromatin structure of the cis-linked gene loci in oocytes and adult stem͞progenitor cells.

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Converged access networks consolidating 5G and beyond and fixed optical fiber access are expected to support future latency-sensitive human-to-machine applications over the Tactile Internet. Making intelligent bandwidth allocation decisions among end users/machines/robots of the converged network is thus crucial to meeting stringent latency requirements. The recent renewed interest in machine learning (ML) has contributed towards a plethora of undeniable performance improvements in communication networks. Current insights into how ML can be exploited to provide intelligent bandwidth allocation decisions to enhance latency performance, along with guidance on the most suitable ML technique in that regard, remain elusive. This paper provides the first insights, to the best of our knowledge, into the suitability of commonly adopted ML techniques for this purpose by first presenting an in-depth survey focusing on the technical details of these techniques and how each technique is used in existing studies. The benefits, drawbacks, resultant time and space complexity incurred, and prediction accuracy are then evaluated for each ML technique reviewed. Next, a comprehensive comparative study of the ML techniques is presented for the first time, to our best knowledge, to provide guidance on the selection of ML technique that provides intelligent bandwidth allocation decisions towards supporting emerging latency-sensitive applications. The uplink latency performance of a converged network adopting an artificial neural network (ANN) supervised bandwidth allocation scheme is then compared with those arising from using existing bandwidth allocation schemes. Results highlight the ability of the ANN to learn the association among bandwidth demand, network parameters, and the resulting uplink latency such that, in operation, the allocated bandwidth will always be optimized to enhance latency performance.

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