Department of CS newsletter

For 2019-20 the University’s Open Access award from UKRI to support the Research Councils’ policy is £1.2m. The new grant year begins on 1st April and we propose continuing the first come, first served allocation model that we reverted to in December. We will continue to assess requests against funder criteria and, for fully Open Access journals, membership of the Director of Open Access Journals or the Open Access Scholarly Publishers Association. 

The new Open Access grant year for research papers funded by one of the UK Research Councils begins on 1st April 2019. The grant will continue to be allocated on a first come, first served basis and we anticipate that it will be possible to maintain this approach for the full grant year. Further information on Open Access funding at the University is available on the Library website.

Get easy access to Library e-resources directly from your browser! Library Access is a browser extension (available for Google Chrome, Mozilla Firefox, Safari, Opera and Microsoft Edge) that makes it easier to access electronic resource content courtesy of The University of Manchester Library. When you are off-campus, you can use the extension to read electronic journal articles, e-books, reviews and other online material provided by the Library without having to visit the Library website first.  To find out more about Library Access, including how to install it, please see here.

Mercury Talk on Wednesday 9th October 2019 in Kilburn Mercury room at 2pm including refreshments

Speaker: Dario Panada

Host: Xiao-Jun Zeng

Title: Modelling Expression Rates of Hypoxia-inducible Factors in Growing Tumours: Investigating Potentials for Non-Invasive Cancer Therapies

Abstract:

In-silico modelling has the potential to complement laboratory studies by accurately simulating biological processes, offering insights into tumour dynamics, significantly reducing experimental costs, and improving the quality of measurements. Furthermore, in-silico studies allow to explore counterfactual situations and test multiple hypotheses in a quick and efficient manner. We have developed a comprehensive artificial model (informed by the medical literature) to investigate the potential for a novel non-invasive therapy aimed at containing cancer growth in tumour cells. Hypoxia-Inducible Factors have been previously identified as a target for non-invasive cancer therapy, where the requirement being that their expression could be artificially enhanced. While anti-angiogenic therapy has been suggested as potential vehicle to achieve this, the extent to which it is possible remains unclear, due to the challenges of making the necessary in-vivo measurements. We executed in-silico experiments to explore the effects of anti-angiogenic therapy. The results suggest that it can successfully act as a necessary precursor predisposing the tumour mass to non-invasive anti-cancer therapies. In addition to the medical value of the results, the results also highlight the importance of in-silico approaches as a complement to laboratory studies. The present study paves the way for future in-silico and laboratory experiments aiming at devising non-invasive therapies based on the joint action of anti-angiogenic and hypoxia-inducible-factor targeting.

Outline Proposals deadline: 7 Nov 2019
Full Proposals deadline: 28 Feb 2020

https://nerc.ukri.org/innovation/activities/environmentaldata/digitalsolutions/news/ao-digital-solutions/

The Natural Environment Research Council (NERC) invites applications to lead and deliver a £5M, 5-year programme that will exploit environmental and other data to create innovative digital services that deliver economic, societal and environmental benefits. There’s NERC capital investment of up to £3.0m over 5 years; NERC Environmental Data Service (EDS) costs to support this programme: up to £0.4m pa; plus any further direct or in-kind investment made or leveraged by the host institution. 
NERC expects at least a third of the programme budget to be sub-contracted to other partners for collaborative activities.

Deadline: 12 Nov 2019
https://www.ukri.org/funding/funding-opportunities/citizen-science-exploration/ 

The a call is designed to encourage researchers and innovators to experiment with citizen science, whether through collecting data, analysing data, or helping researchers and innovators to develop better questions. 
 
Up to 20 successful applicants will be awarded up to £20k each to conduct explorative work that will help groups to develop opportunities for building citizen science methodologies into their research. These projects must be conducted between 16 Dec 2019 and 30 April 2020. This could form the basis of future projects which may be supported through other funding sources, including through a proposed follow-up phase of UKRI funding from 2020-2023. Applicants should be undertaking research that is related to one of UKRI’s key strategic areas of research and innovation.

https://nerc.ukri.org/latest/events/list/pre-announcement-spf-food-systems/

UK Research and Innovation (UKRI) will shortly announce a call for research to fundamentally transform the UK food system, by placing healthy people and a healthy natural environment at its centre. Proposals will be invited for interdisciplinary consortia to take a food systems approach, linking healthy and accessible diets with sustainable food production and supply to help drive food system transformation

ARM, in collaboration with Manchester, are developing workflows to enable bespoke Machine Learning predictors in ultra-low-power plastic substrates, otherwise known as flexible electronics. The work featured in IEEs Spectrum publication and in The Economist. 

Prof Gavin Brown is leading the Machine Learning aspects of the project, advising ARM on how to use/modify ML technology to push the boundaries. In terms of energy consumption, this is the opposite end of the spectrum to where big industry (Google, Facebook, etc) is sitting - using large data-centres for ML compute.  Here, all compute is on an ultra-low budget, using just a few thousand logic gates, instead of many billions in off-the-shelf chip technology.   The ML algorithms must be customised and modified for these challenging scenarios, hence the close collaboration.