Ageing populations pose one of the main public health crises of our time. Reprogramming gene expression by altering the activities of sequence-specific transcription factors (TFs) can ameliorate deleterious effects of age. Here we explore how a circuit of TFs coordinates pro-longevity transcriptional outcomes, which reveals a multi-tissue and multi-species role for an entire protein family: the E-twenty-six (ETS) TFs. In Drosophila , reduced insulin/IGF signalling (IIS) extends lifespan by coordinating activation of Aop , an ETS transcriptional repressor, and Foxo , a Forkhead transcriptional activator. Aop and Foxo bind the same genomic loci, and we show that, individually, they effect similar transcriptional programmes in vivo . In combination, Aop can both moderate or synergise with Foxo , dependent on promoter context. Moreover, Foxo and Aop oppose the gene-regulatory activity of Pnt , an ETS transcriptional activator. Directly knocking down Pnt recapitulates aspects of the Aop / Foxo transcriptional programme and is sufficient to extend lifespan. The lifespan-limiting role of Pnt appears to be balanced by a requirement for metabolic regulation in young flies, in which the Aop-Pnt-Foxo circuit determines expression of metabolic genes, and Pnt regulates lipolysis and responses to nutrient stress. Molecular functions are often conserved amongst ETS TFs, prompting us to examine whether other Drosophila ETS-coding genes may also affect ageing. We show that five out of eight Drosophila ETS TFs play a role in fly ageing, acting from a range of organs and cells including the intestine, adipose and neurons. We expand the repertoire of lifespan-limiting ETS TFs in C . elegans , confirming their conserved function in ageing and revealing that the roles of ETS TFs in physiology and lifespan are conserved throughout the family, both within and between species.

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Employing channel adaptive resource allocation can yield to a large enhancement in almost any performance metric of Device-to-Device (D2D) communications. We observe that D2D users are able to estimate their local Channel State Information (CSI), however the base station needs some signaling exchange to acquire this information. Based on the D2D users' knowledge of their local CSI, we provide a scheduling framework that shows how distributed approach outperforms centralized one. We start by proposing a centralized scheduling that requires the knowledge of D2D links' CSI at the base station level. This CSI reporting suffers from the limited number of resources available for feedback transmission. Therefore, we benefit from the users' knowledge of their local CSI to develop a distributed algorithm for D2D resource allocation. In distributed approach, collisions may occur between the different CSI reporting; thus a collision reduction algorithm is proposed. We give a description on how both centralized and distributed algorithms can be implemented in practice. Furthermore, numerical results are presented to corroborate our claims and demonstrate the gain that the proposed scheduling algorithms bring to cellular networks.

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Reconfigurable Intelligent Surface (RIS) is one of the key technologies for the upcoming 6th Generation (6G) communications, which can improve the signal strength at the receivers by adding artificial propagation paths. In the context of Downlink (DL) Multi-User Multiple-Input Multiple-Output (MU-MIMO) communications, designing an appropriate Beamforming (BF) scheme to take full advantage of this reconfigured propagation environment and improve the network capacity is a major challenge. Due to the spatial dimension provided by MIMO systems, independent data streams can be transmitted to multiple users simultaneously on the same radio resources. It is important to note that serving the same subset of users over a period of time may lead to undesired areas where the average Electromagnetic Field Exposure (EMFE) exceeds regulatory limits. To address this challenge, in this paper, we propose a Dual Gradient Descent (Dual-GD)-based Electromagnetic Field (EMF)-aware MU-MIMO BF scheme that aims to optimize the overall capacity under EMFE constraints in RIS-aided 6G cellular networks.

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