http://bura.brunel.ac.uk/handle/2438/9134 Sun, 05 Apr 2026 21:37:47 GMT 2026-04-05T21:37:47Z http://bura.brunel.ac.uk/handle/2438/28791 Title: Reduced chromosome aberration complexity in normal human bronchial epithelial cells exposed to low-LET γ-rays and high-LET α-particles Authors: Themis, M; Garimberti, E; Hill, MA; Anderson, RM Abstract: Purpose Cells of the lung are at risk from exposure to low and moderate doses of ionising radiation from a range of environmental and medical sources. To help assess human health risks from such exposures, a better understanding of the frequency and types of chromosome aberration initially-induced in human lung cell types is required to link initial DNA damage and rearrangements with transmission potential and, to assess how this varies with radiation quality. Materials and Methods We exposed normal human bronchial lung epithelial (NHBE) cells in vitro to 0.5 and 1 Gy low-linear energy transfer (LET) γ-rays and a low fluence of high-LET α-particles and assayed for chromosome aberrations in premature chromosome condensation (PCC) spreads by 24-colour multiplex-fluorescence in situ hybridisation (M-FISH). Results Both simple and complex aberrations were induced in a LET and dose dependent manner however, the frequency and complexity observed were reduced in comparison to that previously reported in spherical cell types after exposure to comparable doses or fluence of radiation. Approximately 1-2% of all exposed cells were categorised as being capable of transmitting radiation-induced chromosomal damage to future NHBE cell generations, irrespective of dose. Conclusion One possible mechanistic explanation for this reduced complexity is the differing geometric organisation of chromosome territories within ellipsoid nuclei compared to spherical nuclei. This study highlights the need to better understand the role of nuclear organisation in the formation of exchange aberrations and, the influence three-dimensional (3D) tissue architecture may have on this in vivo. Description: Supplemental material is available online at: https://www.tandfonline.com/doi/full/10.3109/09553002.2013.805889#supplemental-material-section . Thu, 13 Jun 2013 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/28791 2013-06-13T00:00:00Z http://bura.brunel.ac.uk/handle/2438/26744 Title: No substantial changes in estrogen receptor and estrogen-related receptor orthologue gene transcription in Marisa cornuarietis exposed to estrogenic chemicals Authors: Bannister, R; Beresford, N; Granger, DW; Pounds, NA; Rand-Weaver, M; White, R; Jobling, S; Routledge, EJ Abstract: Copyright © 2013 The Authors. Estrogen receptor orthologues in molluscs may be targets for endocrine disruptors, although mechanistic evidence is lacking. Molluscs are reported to be highly susceptible to effects caused by very low concentrations of environmental estrogens which, if substantiated, would have a major impact on the risk assessment of many chemicals. The present paper describes the most thorough evaluation to-date of the susceptibility of Marisa cornuarietis ER and ERR gene transcription to modulation by vertebrate estrogens in vivo and in vitro. We investigated the effects of estradiol-17β and 4-tert-Octylphenol exposure on in vivo estrogen receptor (ER) and estrogen-related receptor (ERR) gene transcription in the reproductive and neural tissues of the gastropod snail M. cornuarietis over a 12-week period. There was no significant effect (p > 0.05) of treatment on gene transcription levels between exposed and non-exposed snails. Absence of a direct interaction of estradiol-17β and 4-tert-Octylphenol with mollusc ER and ERR protein was also supported by in vitro studies in transfected HEK-293 cells. Additional in vitro studies with a selection of other potential ligands (including methyl-testosterone, 17α-ethinylestradiol, 4-hydroxytamoxifen, diethylstilbestrol, cyproterone acetate and ICI182780) showed no interaction when tested using this assay. In repeated in vitro tests, however, genistein (with mcER-like) and bisphenol-A (with mcERR) increased reporter gene expression at high concentrations only (>10−6 M for Gen and >10−5 M for BPA, respectively). Like vertebrate estrogen receptors, the mollusc ER protein bound to the consensus vertebrate estrogen-response element (ERE). Together, these data provide no substantial evidence that mcER-like and mcERR activation and transcript levels in tissues are modulated by the vertebrate estrogen estradiol-17β or 4-tert-Octylphenol in vivo, or that other ligands of vertebrate ERs and ERRs (with the possible exception of genistein and bisphenol A, respectively) would do otherwise. Description: Supplementary data are available online at https://www.sciencedirect.com/science/article/pii/S0166445X1300115X?via%3Dihub#sec0105 . Fri, 17 May 2013 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/26744 2013-05-17T00:00:00Z http://bura.brunel.ac.uk/handle/2438/25794 Title: Accelerating UN Sustainable Development Goals with AI-Driven Technologies: A Systematic Literature Review of Women’s Healthcare Authors: Lau, PL; Nandy, M; Chakraborty, S Abstract: Copyright © 2023 by the authors. n this paper, we critically examine if the contributions of artificial intelligence (AI) in healthcare adequately represent the realm of women’s healthcare. This would be relevant for achieving and accelerating the gender equality and health sustainability goals (SDGs) defined by the United Nations. Following a systematic literature review (SLR), we examine if AI applications in health and biomedicine adequately represent women’s health in the larger scheme of healthcare provision. Our findings are divided into clusters based on thematic markers for women’s health that are commensurate with the hypotheses that AI-driven technologies in women’s health still remain underrepresented, but that emphasis on its future deployment can increase efficiency in informed health choices and be particularly accessible to women in small or underrepresented communities. Contemporaneously, these findings can assist and influence the shape of governmental policies, accessibility, and the regulatory environment in achieving the SDGs. On a larger scale, in the near future, we will extend the extant literature on applications of AI-driven technologies in health SDGs and set the agenda for future research. Description: Data Availability Statement This study is primarily a reanalysis of existing publicly available data as cited in the “References” section. Notwithstanding, in some sections of this publication, the data underpinning parts thereof can be accessed from Brunel University London’s data repository, Brunelfigshare here under a CCBY license: https://brunel.figshare.com/ publication (accessed on 21 July 2022), where it is supported by multiple datasets cited in the “References” section of this paper. Tue, 31 Jan 2023 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/25794 2023-01-31T00:00:00Z http://bura.brunel.ac.uk/handle/2438/24944 Title: Profit maximization for large-scale energy storage systems to enable fast EV charging infrastructure in distribution networks Authors: Lai, CS; Chen, D; Zhang, J; Zhang, X; Xu, X; Taylor, G; Lai, LL Abstract: Coppyright © 2022 The Author(s). Large-scale integration of battery energy storage systems (BESS) in distribution networks has the potential to enhance the utilization of photovoltaic (PV) power generation and mitigate the negative effects caused by electric vehicles (EV) fast charging behavior. This paper presents a novel deep reinforcement learning-based power scheduling strategy for BESS which is installed in an active distribution network. The network includes fast EV charging demand, PV power generation, and electricity arbitrage from main grid. The aim is to maximize the profit of BESS operator whilst maintaining voltage limits. The novel strategy adopts a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and requires forecasted PV power generation and EV smart charging demand. The proposed strategy is compared with Deep Deterministic Policy Gradient (DDPG), Particle Swarm Optimization and Simulated Annealing algorithms to verify its effectiveness. Case studies are conducted with smart EV charging dataset from Project Shift (UK Power Networks Innovation) and the UK photovoltaic dataset. The Internal Rate of Return results with TD3 and DDPG algorithms are 9.46% and 8.69%, respectively, which show that the proposed strategy can enhance power scheduling and outperforms the mainstream methods in terms of reduced levelized cost of storage and increased net present value. Fri, 05 Aug 2022 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/24944 2022-08-05T00:00:00Z