NIHRDataInsights/HRCSData huggingface dataset:
Title: Enabling the big data revolution through skills training
Funder: Medical Research Council

Abstract: Biomedicine research is at the crossroads of a transformative change if an effective use of all the data gathered in the field can be effectively rationalised and integrated. At present, data are stored in separate silos that are specific to a research field and its culture. It is becoming apparent that cross-disciplinary training encompassing these cultures is the best way forward to connect researchers and to integrate data from those silos. Non-standardised data formats and lack of generic data pipelines are hampering effective integration and translation across disciplines and therefore limiting scientific cross-communication. We present a training programme with the ambitious task of bridging the gap between real-world clinical care data, genetics, molecular and imaging data with computational statistical modelling and Artificial Intelligence. We believe we have excellent research portfolio in these areas and proven success in a number of active teaching activities, but we see the opportunity of integrating these in a truly interdisciplinary programme that is flexible and modular and allows for the design of personalised training pathways that can be initiated at any time. The programme will have a circular structure with no specific entry point and with the possibility of pick and mix courses from the three areas: Health Data Science exploring electronic data records (WS1); 'Omics harnessing genetics and molecular data collected in online databases (WS2); Artificial Intelligence focusing data image analysis and understanding AI through practical applications (WS3). The courses will cover the understanding of online 'omics data resources, harnessing the heterogeneity of health informatics and imaging records by interacting with the data at different levels: From the implementation of simple pipelines to analyse data, to the mastering of statistical analyses, to clinical prediction modelling, machine learning and artificial intelligence data analyses.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Train the Trainer in Data Science for Pathogen Genomics and Surveillance
Funder: Medical Research Council

Abstract: There is a growing need for strengthening training capacity in data science, tailored specifically for pathogen genomics and surveillance, for professionals who incorporate genomics and sequencing in microbiology and clinical diagnostics, perform bioinformatics analyses and interpret data from genomic analysis, routinely conduct pathogen surveillance and work on large-scale national projects the to track samples, link metadata, analyse and interpret sequencing data during disease outbreaks and pandemics. The increased demand for these skills requires building capacity by training educators and professionals with relevant skills to provide specific training in Data Science for Pathogen Genomics and Surveillance. We plan to achieve this in three steps. First, we will assess the current training needs for data science and pathogen genomics among healthcare professionals working in the field of clinical microbiology. With guidance from our scientific and clinical advisory boards, we will design and deliver 3 Train-the-trainer courses that integrate pedagogical and scientific concepts, which will enable participants to transfer to others knowledge and skills in data collection, linkage, analysis and interpretation for pathogen genomics and surveillance. The first modules of the course will run virtually and will expand participants' pedagogy and scientific knowledge. Then, a project-based learning approach, where participants will be mentored to build their own training materials will be applied. This will be followed by in-person modules, where they will demonstrate delivering training to others and will receive feedback on their methods. Furthermore, participants will be supported to deliver training in their work environments, through mentorship and networking. An evaluation framework will be designed to follow-up trainees and determine impact on professional development, careers and genomic data science skills among professionals in clinical microbiology.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Cloud-SPAN: Specialised analyses for environmental 'omics with Cloud-based High Performance Computing
Funder: Medical Research Council

Abstract: Environmental Biotechnology (EB) is an interdisciplinary field involving advanced molecular and applied microbiologists, environmental chemists and engineers that addresses global challenges using engineered microbial systems. This proposal aims to trainer these researchers to generate, analyse and mine big data relating to EB microbiomes which are larger than those found in the human gut and require different approaches to both measurement and analysis in order to manage reagent costs and effectively leverage available HPC resources. Easily accessible and scalable HPC-based training is required to provide researchers with the skills and self-confidence to manipulate and analyse big data generated from 'omics technologies and generate biological insights from these highly interconnected systems. This area of bioinformatics involves a steep learning curve which can be confounded by the need to install packages with multiple dependencies onto different HPC architectures based on what is available at a researcher's home institution, even before the user can engage with writing scripts to manage workflows, manipulate or visualise data, or manage job schedulers. We will deploy Cloud-based containerised instances which are (1) accessible to anyone anywhere as long as they have an adequate internet connection, (2) have a very low hardware entry requirement and (3) allow for easily scalable and replicable installations of software that will not become deprecated as quickly as might occur on a local server. The cloud providers we are working with run grant schemes that provide significant resources to researchers that will support deployment of production instances of the images we will generate. We expect that our resources will be easily deployed and used by groups who do not necessarily have devoted bioinformaticians or expertise in HPC, providing a cost-effective route to useful analyses for researchers in a strategically key area of expansion in the biosciences.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: Bayesian causal learning: A novel framework for drug-target discovery using Mendelian randomization on single-cell transcriptomics
Funder: Medical Research Council

Abstract: Our proposal details the development of a novel computational toolkit, named single-cell Mendelian randomization (scMR), for causal inference of single-cell RNA sequencing (scRNA-seq) data combined with genotype information. We plan to develop novel statistical models with the aims to detect causal transcripts for diseases at single cell-type resolution, to understand how transcripts interact with each other within and across different cell-types and finally, to identify shared molecular mechanism affecting more than one disease. Specifically, scMR combines two causal inference concepts to derive causal conclusions from genetic associations. First, scRNA-seq expression quantitative trait loci (eQTL) are used as genetic anchors or, technically speaking, instrumental variables to define causal regulators of disease independent of confounders or technical artifacts such as batch effects. Second, we will build on recent advantages in Bayesian causal structural learning to infer directed acyclic graphs from genetic data to learn how genes interact with each other and across different cell-types. ScMR takes as input genomic regions of interest and is formulated as a multivariable multi-response MR model that accounts for measured pleiotropy by including the expression of any transcript in any cell-type in the region of interest into the model and estimates their shared or distinct effect on more than one outcome. Defined on genetic associations on the summary-level, scMR allows the integration of scRNA-seq eQTL with publicly available genome-wide association studies. As an application example, we leverage one of the first scRNA-seq eQTL from healthy brain tissue and integrate it with a wide range of more than twenty neurological phenotypes and brain diseases. Our translational aims are to identify which genes need to be altered to treat disease, but also which cells need to be targeted and how molecular processes propagate through different molecular layers.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);5.1 PHARMACEUTICALS
Health categories: GENERIC HEALTH RELEVANCE
Title: COLLABORATIVE AND DIGITAL ANALYSIS OF BIG QUAL DATA IN TIME SENSITIVE CONTEXTS - LISTEN
Funder: Medical Research Council

Abstract: Evidence-based emergency response requires the rapid collection, synthesis and analysis of information to inform response efforts, but qualitative data providing insight into the behaviours, beliefs, and contextual factors that shape complex and, often, unprecedented situations are seldom integrated in emergency response research plans. The aim of this project is to refine, consolidate and disseminate the first method for the rapid analysis of large qualitative datasets, so big qualitative data can be routinely used to inform response efforts in the context of emergencies. Our multidisciplinary team based at University College London and Oxford University is strategically well placed to carry out this work due to our state of the art knowledge of research methods, our large international network of researchers, and popular training courses in English and Spanish. We will convene a Consultation Group with a range of stakeholders to advise across all stages of the project. We will review the evidence on existing methods for the rapid analysis of qualitative data to inform the development of LISTEN, a method that will combine the use of digital analysis software and collaborative rapid qualitative methods to make sense of large volumes of qualitative data in short timeframes. We will evaluate the method's quality and implementation requirements on three existing datasets: 1) a large interview study capturing the experiences healthcare workers during the COVID-19 pandemic in the UK, 2) social media data on Long Covid, and 3) open-ended survey responses on the impact of environmental disasters on mental health in Peru. We will present LISTEN to a network of researchers in an international symposium offered in person and streamed online. The primary outputs of our study will include a dynamic website and a project page on the Open Science Framework (OSF) to share openly available and accessible study results.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE
Title: Data driven life science skills development - equipping society for the future
Funder: Medical Research Council

Abstract: Our aim is to enable researchers at all career stages to harness the power of data-driven research and innovation for health and bioscience, by providing training in the management and analysis of biological data. We will address this by scaling our successful training programs, adding new curricula, and training new instructors. We will use open-source platforms developed by The Carpentries, a community-based project that is a global leader in teaching data and coding skills. The Carpentries paradigm of open access training leads participants on a skills development path, first as a learner, then helping an experienced instructor, finally leading instruction themselves. Open platforms such as The Carpentries add value by enabling open peer review, re-use, and long-term archiving. Our objectives are to: 1. Develop peer-reviewed training modules for biological data science that address identified skill gaps in health and bioscience researchers (Statistics; Open science, FAIR principles & data management; Data science computing with workflows) using The Carpentries platform. 2. Deliver 98 days of remote training of our new workshops, plus established introductory material, to a diverse community of researchers in academia and industry across all career stages. 3. Offer a clinic after every workshop for learners to ask advice regarding their own projects. 4. Train 30 new instructors to deliver these workshops, building a scalable training community. Our remotely delivered training programme will be piloted locally and made available nationally, publicized via our strong links to organizations involved in data science UK-wide. Our team will bring together subject-matter expertise in 'omics, statistics, and computation, with strengths in research data management and the UK's largest Carpentries chapter, to build an extensive cohort of confident practitioners and a scalable and sustainable network of health and bioscience data science training for the UK.


Research activity codes: 1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING);2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY)
Health categories: GENERIC HEALTH RELEVANCE
Title: Digital design and fabrication of advanced biopurification materials
Funder: Medical Research Council

Abstract: 3D imaging, analysis and flow simulations have been pioneered at UCL for successfully improving designs and operability of commercially relevant technologies, for example batteries and fuel cells. This research vision proposes the development of a digital design cycle in order to produce novel biomanufacturing devices to be specifically tailored for valuable biologic products of interest through case studies, in particular vaccines (including adenovirus and mRNA based products) and antibody biotherapeutics. The proposed approach uses high resolution imaging, analysis and simulation to understand the impact of structure on the separation material's performance. This knowledge will enable the design of new materials that will be precisely fabricated using 3D printing. This advance is possible due to recent improvements in the resolution achievable in 3D imaging and printing which now match the microscale pores fundamental to the behaviour of the separation media, making this research timely. The approach addresses limitations in existing biopseparation technology, that of structural heterogeneity and use of undefined components, to enable more efficient, cost-effective medicines manufacturing by enhancing purification performance. Pall Biotech are a world leading international bioprocess vendor and applications expert, providing a great opportunity on secondment to receive training from Pall experts on device development. Additionally, using Pall bioprocess materials will result in 3D printed prototypes being directly optimised on industrially applicable products, interfacing academic research with industrial product translation. Pall will significantly benefit in hosting this secondment through involvement in an innovative approach for designing advanced materials that will augment their product development portfolio. This project delivers industrial innovation by: combing multiple disciplines, enabling technology transfer and demonstrating case study relevance.


Research activity codes: 5.9 RESOURCES AND INFRASTRUCTURE (TREATMENT DEVELOPMENT)
Health categories: GENERIC HEALTH RELEVANCE
Title: Accelerating medicine development timelines through new approaches in knowledge extraction from diverse biological data sets
Funder: Medical Research Council

Abstract: Early stage drug discovery involves conducting panels of well-validated assays to generate datasets on collections of candidate chemical, biological or genetic entities. Such datasets include metabolomic, proteomic, transcriptomic and image-based cellular profiles and provide decision-making data on the suitability of a candidate to be moved to clinical phases of development. Historically, these types of datasets have been used in isolation but growing evidence shows that by considering the same datasets in relation to one another through improved machine learning strategies, rates of attrition in drug discovery can be reduced; an effect mediated through identification of disease-associated phenotypes and mechanisms in early screening and greater fidelity in early prediction of efficacy and toxicity. To this aim the secondee will utilise immunological datasets derived from primary T cell and iPSC-derived macrophage assays to develop ontological frameworks within the GSK R&D information platform in order to integrate imaging and omics datasets and simultaneously provide a structure suitable for future reuse. The framework will provide inter-relation of previously disparate databases enabling the secondee to deploy deep learning and machine learning tools to map responses to fingerprints from compounds or disease profiles annotated by in vivo or clinical data. Critically, as the effect of drug candidates are highly dose-dependent, the analytics developed will trace dose across multidimensional space to inform upon pathway preferentiality, off-target effects, and action of multiple targets in each therapeutic mechanism. This will provide new ranking metrics for medicinal chemists to optimise a drug series and inform on opportunities for combination screening. The work will provide the secondee with valuable networking opportunities and insights across the pharmaceutical industry through collaboration with units including AI/machine learning and medicinal chemistry.


Research activity codes: 2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);5.1 PHARMACEUTICALS
Health categories: GENERIC HEALTH RELEVANCE
Title: X-ray-excited nanoparticle imaging - a new hybrid modality for deep, in-vivo imaging in preclinical research
Funder: Medical Research Council

Abstract: Medical research relies on imaging to study the ways in which tissue responds to pathogens and other agents that cause damage, and the effectiveness of potential treatments in live, whole-body environments. The near-infrared (NIR) transmission window from 700-900 nm allows NIR light to escape from mammalian tissue with relatively low absorption, albeit highly scattered. X-rays, on the other hand, experience much less absorption or scattering. We are developing a hybrid imaging modality for preclinical research that exploits the ability of X-rays to traverse mammalian tissue relatively undeviated, and therein to excite NIR emission that can be detected outside the body. This enables us to trace the distribution of NIR emitters in deeper-lying organs and other physiological structures, and to probe physiological function through the sequestering of the NIR emitters in localised regions of the body. To date we have demonstrated this capability using NIR emitting quantum dots, nanometre-sized particles in aqueous suspension, which we have excited at depth in whole mouse using clinical X-ray sources. Our aim is to develop this hybrid imaging technique further using Xstrahl's Small Animal Radiation Research Platform - SARRP - which is in operation at more than 700 treatment and research facilities worldwide, and delivers targeted radiation in pre-clinical studies of animals bearing human disease models, such as tumour-bearing mice. The SARRP is ideally suited to this hybrid imaging modality because it already provides complete support for X-ray planning, delivery and monitoring, with integrated CT and bioluminescent imaging capabilities, alongside animal welfare infrastructure. It also provides the flexibility needed by the developmental nature of this translational research. Our ultimate goal is to elevate this technique from being a bespoke, emerging technology, to one that is readily available to thousands of biomedical researchers through the Xstrahl SARRP.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: GENERIC HEALTH RELEVANCE
Title: Risk Elimination on Walks to School (REmWAlkS)
Funder: Medical Research Council

Abstract: More than 350,000 children around their world lose their lives to road traffic crashes and traffic-related air pollutants every year, with 93% of these children living in low- or middle-income countries. Children walking to school are at particular risk as they often walk along congested, unsafe roads with vehicles travelling at high speeds and emitting high levels of pollutants. School are also frequently located along busy highways. While some work is being conducted in African countries around making the journey to school safer for school children, none, to our knowledge are addressing the issue of both road safety and air pollution despite many overlapping interventions. This proposal therefore aims to develop a set of interventions that will reduce both the risk of injury and exposure to poor air quality for children aged 12-16 years who walk to school in Blantyre, Malawi. This will be achieved through the follow specific objectives: 1. To describe how children, aged 12-16 years, get to school in Blantyre, Malawi. 2. To understand the risks that children encounter, both in terms of road safety and air quality, on their journeys to school. 3. To develop, through a participatory approach with children and their caregivers as well as local/international experts, a set of interventions that will reduce exposure to both road traffic collisions and poor air quality. 4. To assess the acceptability of the proposed set of interventions among a group of stakeholders. 5. To understand the pathways to policy and practice change in Blantyre, Malawi. A novel, participatory, mixed methods approach, guided by the 6SQuID framework and the Safe Systems Approach, will be used to understand risks & develop a set of interventions. PhotoVoice, a Delphi Technique and Focus Group Discussions will be employed to develop, prioritize and test the set of interventions in anticipation of conducting an appropriate intervention study in the next phase.


Research activity codes: 3.2 INTERVENTIONS TO ALTER PHYSICAL AND BIOLOGICAL ENVIRONMENTAL RISKS
Health categories: GENERIC HEALTH RELEVANCE