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Amazon Fund
UK - Department for Energy Security and Net Zero
The Amazon Fund is a REDD+ mechanism created to raise donations for non-reimbursable investments in efforts to prevent, monitor and combat deforestation, as well as to promote the preservation and sustainable use in the Brazilian Amazon. The UK committed to funding £115 million total for results-based finance at $5 per tonne and £3.5 million for technical assistance, of which £2 million will be destined for GIZ Action for Forests programme. £1.5 million is for MEL.
Darwin Initiative
Department for Environment, Food, and Rural Affairs
The Darwin Initiative is the UK’s flagship international challenge fund for biodiversity conversation and poverty reduction, established at the Rio Earth Summit in 1992. The Darwin Initiative is a grant scheme working on projects that aim to slow, halt, or reverse the rates of biodiversity loss and degradation, with associated reductions in multidimensional poverty. To date, the Darwin Initiative has awarded more than £195m to over 1,280 projects in 159 countries to enhance the capability and capacity of national and local stakeholders to deliver biodiversity conservation and multidimensional poverty reduction outcomes in low and middle-income countries. More information at https://www.gov.uk/government/groups/the-darwin-initiative. This page contains information about Rounds 27 onwards. For information about Rounds 1 to 26, please see the Darwin Initiative website -https://www.darwininitiative.org.uk/
Illegal Wildlife Trade Challenge Fund
Department for Environment, Food, and Rural Affairs
Illegal wildlife trade (IWT) is a widespread and lucrative criminal activity causing major global environmental and social harm. The IWT has been estimated to be worth up to £17 billion a year. Nearly 6,000 different species of fauna and flora are impacted, with almost every country in the world playing a role in the illicit trade. The UK government is committed to tackling illegal trade of wildlife products and is a long-standing leader in efforts to eradicate the IWT. Defra manages the Illegal Wildlife Trade Challenge Fund, which is a competitive grants scheme with the objective of tackling IWT and, in doing so, contributing to sustainable development in developing countries. Projects funded under the Illegal Wildlife Trade Challenge Fund address one, or more, of the following themes: • Developing sustainable livelihoods to benefit people directly affected by IWT, • Strengthening law enforcement, • Ensuring effective legal frameworks, • Reducing demand for IWT products. By 2023 over £51 million has been committed to 157 projects since the Illegal Wildlife Trade Challenge Fund was established in 2013. This page contains information about Rounds 7 onwards. For information about Rounds 1 to 6, please see the IWTCF website -https://iwt.challengefund.org.uk/
Legacy Landscapes Fund
Department for Environment, Food, and Rural Affairs
Legacy Landscapes Fund aims to guarantee long-term conservation funding to protect biodiversity, promote climate resilience, and foster equitable development in some of the world’s most outstanding landscapes. The UK will work together with LLF and its partners to help narrow the biodiversity finance gap and deliver the global 30by30 target on land by sourcing significant and sustained funding for protected areas with high biodiversity and critical ecosystems. LLF are a multi-donor conservation trust fund established in 2020 that deliver long-term support to vital protected areas and their buffer zones in the global south. Their ambition is to fund 30 landscapes by 2030, and they benefit from partnerships with a range of public and private donors and NGOs who provide strategic support and effective, inclusive implementation. Central to LLF's approach is an understanding that long term and predictable funding helps them to deliver better outcomes and builds capacity more effectively. LLF, it's partners and Defra are committed to the equitable delivery of 30by30, and this funding will focus on maximising benefits for Indigenous peoples and local communities and promoting gender equity.
Low-carbon Agriculture for avoided deforestation and poverty reduction Phase II - Rural Sustentável
Department for Environment, Food, and Rural Affairs
As a follow-up phase to a similar ICF intervention in Brazil, Rural Sustentável aims to promote low-carbon agriculture (LCA) on small and medium-scale farms to reduce greenhouse gas (GHG) emissions through avoided deforestation, enhance producers’ income and quality of life, increase the adoption of sustainable practices, and foster policy replications in Brazil and abroad. The programme operates through three distinct projects in separate Brazilian biomes: PRS Amazon, PRS Cerrado, and PRS Caatinga. Each project has its own budget, implementing agency, timelines, and activities but despite their differences, all three projects share a common theory of change: by providing small- and medium-scale farmers and landowners with alternative methods of production and income generation, the rate of deforestation can be significantly reduced.
Land Degradation Neutrality Fund
Department for Environment, Food, and Rural Affairs
The LDN Fund invests in projects which reduce or reverse land degradation and thereby contribute to ‘Land Degradation Neutrality’. The LDN Fund is co-promoted by the Global Mechanism of the United Nations Convention to Combat Desertification (UNCCD) and Mirova. It is a public-private partnership using public money to increase private sector investment in sustainable development. The fund invests in sustainable agriculture, forestry and other land uses globally. The Fund was launched at the UNCCD’s COP 13 in China in 2017.
Tsiino Hiiwiida: Unveiling multiple dimensions of plant and fungal biodiversity of the Upper Rio Negro
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
The project “Tsiino Hiiwiida: Unveiling multiple dimensions of the plant and fungal biodiversity in the Upper Rio Negro” addresses a critical gap in knowledge of the plant and fungal diversity in one of the least explored regions of the Amazon Basin, the Cabeça de Cachorro (or Tsiino Hiiwiida in the indigenous language of the Baniwa people) of Brazil. In the face of increasing anthropogenic change in the area due to mining and deforestation, conservation efforts are impeded by lack of knowledge of key components that maintain ecosystem integrity. In a region that has been significantly less explored than the rest of Brazil, Cabeça de Cachorro is a critical gap for effective conservation and sustainable development. Among the outcomes of the project that will directly benefit Brazil are 1) creation of a network of scientists, students, parataxonomists and indigenous people with common purpose to understand and document diversity, 2) discovery and description of hitherto undocumented plant and fungal diversity in a global hotspot, 3) new insights into the evolution of Amazonian biodiversity that will directly aid conservation, 4) locally relevant tools for future monitoring of local diversity by local people and 5) improvement of higher level and academic training for people based in the Amazonian region. The project Tsiino Hiiwiida will specifically address the following UN Sustainable Development Goals (SDGs): 4 (quality education), 10 (reduce inequalities), 13 (climate action), and 15 (life on land). Involvement of local communities in both the research and the production of research products will engender lifelong learning and contribute to the levelling up of the Amazon within Brazilian society (4, 10). Building better knowledge of plant and fungal diversity contributes directly to Goals 13 and 15. The complete taxonomically verified catalogue of plant and fungal diversity of the focal area, coupled with capacity building and co-designed tools for further documentation of plant and fungal diversity will empower Brazilian scientists and local peoples. Novel methods for exploration, monitoring and describing the diversity of this rich area will create a collaborative traditional and western scientific knowledge system to truly understand and protect the biodiversity of this culturally rich region of the Brazilian Amazon.
(UKRI-Brazil) Participatory monitoring of traditional territories: digital platform for co-production of data on sociobiodiversity in Amazonian areas
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
This proposal seeks to develop a mobile, digital platform that records and catalogs socio-biodiversity through the co-creation of local, traditional and indigenous knowledge(s). Carried out in 9 communities within 3 states in the Legal Amazon: Pará, Amazonas and Maranhão, researchers will cooperate with traditional Amazonian communities with aim of developing an Artificial Intelligence (AI) system to develop an inventory of traditional knoweldges with the biodiversity of traditional territories. The co-creation strategy associated with the digital platform will enable these traditional knowledges associated with biodiversity to be better integrated with more normative Scientific ecological (i.e. socio-biodiversity) data. The main objective of the proposed project is for this digital tool to record and scientifically validate traditional practices and knowledge of biodiversity and relate them to globally available scientific databases, whilst enabling communities to maintain epistemic control over their knowledges and consequently territories. The records made by traditional peoples and communities will be collated with information from the collections of the Brazilian Biodiversity Information System (SiBBR) – an online platform that integrates data and information about biodiversity and ecosystems from different sources, making them accessible for different uses (SIBBR, 2024). The co-creation strategy will also allow the platform to be regularly updated by traditional communities, and thus to become a tool for monitoring biodiversity in their territories. The platform will also consist of a tool-kit that can be used resolve conflicts between these communities (and similarly positioned social groups) and market-based actors that enter traditional territories to extract, profit and otherwise exploit from their rich biodiversity. The recognition and validation of such traditional knowledge associated with biodiversity in these Amazonian territories is crucial for the development of institutional strategies that enable the continuity of conservation practices of traditional peoples and communities, thus ensuring compliance with the provisions of Article 8 of the Biodiversity Convention – specifically that pertaining to legal disputes between market-agents and traditional Amazonian peoples and communities. KEY WORDS Amazônia; traditional populations; traditional knowledge; biodiversity; monitoring platforms
Amazonian BioTechQuilombo - Amazonian Biodiversity, Technology Assessment and Knowledge Exchange with Quilombos
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
Our research project stands at the forefront of integrating traditional Quilombola knowledge with cutting-edge scientific methodologies to address critical biodiversity challenges in Brazil's Amazon region. This collaborative effort aims to not only meet but exceed the Official Development Assistance (ODA) requirements of the funding opportunity, embodying a holistic approach that recognizes and values the diverse ways of knowing. Our aim is to diagnose and analyse biodiversity data gaps by integrating traditional Quilombola community knowledge and technologies in various conservation areas of the Amazon. These communities, rooted in their specific relationships to land, territory, ancestry, traditions, and cultural practices, provide invaluable insights into the preservation of natural ecosystems and their resilience to environmental challenges such as deforestation, land use expansion, and climate change. Brazil is the primary beneficiary of our research activities, given the critical importance of the Amazon region in global biodiversity and environmental sustainability. The encroachment of deforestation into various Quilombolas territories serves as compelling evidence of the urgent need to integrate their traditional knowledge with state-of-the-art technologies to address biodiversity loss and promote sustainable practices. Our project combines traditional Quilombola knowledge with advanced technologies such as environmental DNA (eDNA), remote sensing, and artificial intelligence (AI) to comprehensively record biota and characterise landscapes. By engaging Quilombola communities as active partners in the research process, we ensure the effectiveness and cultural relevance of our conservation efforts. Our methodology leverages the convergence of these advanced technologies to map and understand biodiversity across numerous taxa, including mammals, aquatic fauna, birds, and trees. This integration of diverse methodologies not only ensures an internationally excellent standard of research but also fosters collaborations and knowledge exchange among diverse communities. We have identified clear pathways to impact that prioritise community participatory-based biodiversity assessment within Quilombola territories and adjacent areas. By co-developing and validating automated frameworks for biodiversity assessment and monitoring with Quilombola communities, we empower them to actively participate in research and conservation efforts, thereby promoting a participatory and inclusive approach to sustainable development. The expected impact of this biodiversity monitoring framework will be to inform conservation policies and sustainable management. In summary, our project embodies a transformative vision that celebrates the convergence of different epistemologies, leading to new insights and solutions to the environmental challenges facing Brazil and the global community. Through collaborative partnerships and innovative methodologies, we aim to combine scientific methods with traditional knowledge to strengthen the role of traditional Quilombola communities in biodiversity conservation and make an important contribution to the preservation of Brazil's invaluable natural heritage.
Voices of Indigenous Amazonia: historical processes of sociobiodiversity in the face of the challenges of the Anthropocene
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
The Voices of Indigenous Amazonia project proposes to study Amazonian biodiversity and its long-term interactions with Indigenous peoples in three regions characterized by complex sociocultural systems: the Upper Negro Indigenous Territory (Amazonas state); the Xingu Indigenous Territory (Matto Grosso state); and the Kayapó Indigenous Territory (Pará state). These territories stand out for their varied and complex ethnic, historical, and socio-environmental configurations, which include ethnobiological knowledge that is specific to each region. In this project we propose to combine human and biological sciences with Indigenous knowledge to increase our efficiency in producing knowledge about Amazonia. We propose to document biodiversity and its relationship with knowledge and sociocultural practices of present and past Indigenous peoples through: 1) biological inventories of species little known to Western science; 2) characterizing Indigenous landscapes through participatory mapping and remote sensing; 3) fostering exchanges of biodiversity-related knowledge between scientific and Indigenous knowledge; 4) recording long-term anthropogenic changes in vegetation, fauna, and soils ; and 5) collaboratively producing relevant ethnographic, linguistic, and sociocultural documentation. Supported by multifaceted biological studies (descriptions of new species, taxonomic revisions, morphological and molecular phylogenetic analyses, distribution modelling and species richness) integrated with studies of traditional Indigenous knowledge, including its role in the domestication of plants and landscapes, as well as studies of millennia-old environmental management technologies within different Indigenous territories, the project will enable large-scale analyses of biological and sociocultural diversity while mitigating existing taxonomic gaps in poorly sampled yet well-preserved regions of Brazilian Legal Amazonia. At a broader level, the project will produce relevant contributions to tackle the current climate emergency and socio-environmental challenges of the Anthropocene, which compromises forests, resources, and the continuity of the lifeways of our partners, Indigenous peoples of Amazonia.
Brazil-UKRI: The recovery of the adaptive capacity of Pre-Columbian tree crops to environmental changes
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
Multiple large-scale forest restoration strategies are emerging globally to counteract ecosystems degradation and biodiversity loss. However, these strategies often remain insufficient to offset the loss caused by anthropogenic development. At least two reasons could explain this incomplete performance: i) we ignore how human disturbance affects species genetic variability and their potential to evolve and adapt to the ongoing global changes; ii) there is a major gap in the knowledge about long-term (>100 years) ecosystem dynamics after human disturbance ends. In this project, we propose to investigate the adaptative potential of the Brazil nut and other Amazonian tree crops associated with Brazil nut areas, after anthropic disturbance cessation. We will sample plant leaf and cambium tissue and roots on Pre-Columbian archaeological sites, today known as Terras Pretas Amazônicas (TPA), where the descendants of ancient Brazilian nut trees still grow today. With selected TPA sites sequentially abandoned that have never been reoccupied, we will build a 2,000-year chronosequence. This chronosequence will allow us understand how the Brazilian nut trees and associated Amazonian tree crops recover their adaptive potential after they are released from domestication after Pre-Columbian peoples sequentially abandoned their lands to finally collapse around the XV century with the Spanish invasion. Our team that includes experts in forest restoration, domestication, and genomics will explore changes in the whole genome of the Brazilian nut tree and associated tree crops, as well as its associated soil microbiome, along the chronosequence. The results will help find genomes with increased genetic variability and thus adaptive potential, by identifying specific functions related to an enhanced adaptive potential. Propagules from individuals with these functions can then be used in tropical forest restoration, and agriculture, increasing the resilience and resistance of forests to ongoing global changes.
Climate Science for Service Partnership (CSSP) Brazil - Calls- tender-UNIVERSITY OF LEEDS
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
Collaborative climate science research programme between Brazilian and UK to improve understanding of recent climate changes and Brazil’s role in mitigation activities to inform international negotiations; to enhance projections of future weather and climate extremes and impacts to inform decision making and contribute to disaster risk reduction in Brazil. Research on Moisture Transport and Deforestation.
Climate Science for Service Partnership (CSSP) Brazil - Calls- tender-UNIVERSITY OF LEEDS
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
Collaborative climate science research programme between Brazil and UK to improve understanding of recent climate changes and Brazil’s role in mitigation activities to inform international negotiations; to enhance projections of future weather and climate extremes and impacts to inform decision making and contribute to disaster risk reduction in Brazil. Research hydrological cycle responses to land-use change and climate change over Brazil
Climate Science for Service Partnership (CSSP) Brazil - Calls- tender-UNIVERSITY OF READING
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
Collaborative climate science research programme between Brazil and UK to improve understanding of recent climate changes and Brazil’s role in mitigation activities to inform international negotiations; to enhance projections of future weather and climate extremes and impacts to inform decision making and contribute to disaster risk reduction in Brazil. Research into Sub-seasonal and seasonal predictions for advancing climate services in Brazil. Specifically this grant will support the development of communication materials to support partner uptake of programme outputs.
Climate Science for Service Partnership (CSSP) Brazil - Calls- tender-UNIVERSITY OF LEEDS WP3
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
Constraining future projections of wildfire and air quality in Brazil This project will bring together and analyse data on fire, climate, air pollution and human health to improve our understanding of the climate and human drivers of wildfire and poor air quality across Brazil. We will use new understanding gained from analysis of historical fires to help constrain future model projections of wildfire and air quality in Brazil. We will provide new evidence of how fire and land management alongside other mitigations could reduce exposure to poor air quality. We will work to develop UK-Brazil collaborations on wildfire and air quality and ensure outputs from the research inform policy and decision making in Brazil.
Innovative AI-Empowered Organoid Platform for Illuminating Early Neural Tube Development and Related Neural Tube Defects
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
The central nervous system (CNS) plays a crucial role in regulating essential functions and behaviors, making it a key area of medical research. The CNS begins developing with the formation of the neural tube during early embryogenesis. Neural tube defects (NTDs), originating at this stage, result in severe CNS birth defects like spina bifida and anencephaly. In Brazil, NTDs are a significant public health issue, with an estimated prevalence of 0.29 per 1,000 live births. This underscores the necessity of understanding neural tube development to enhance prevention and treatment strategies. Recent advancements in the field have yielded insights into neural stem cell behavior and adult brain neurogenesis, suggesting novel approaches for CNS repair and neurodegenerative disease treatment. However, research is hindered by the inaccessibility of human tissue and ethical considerations, leaving gaps in knowledge about the molecular mechanisms of neural tube formation. Traditional research models, such as cell lines and animal studies, often fail to replicate the complex 3D architecture and specific development processes of the human CNS, impeding the study of NTDs and related diseases. Human organoids have transformed CNS research by accurately modeling human-specific conditions and the 3D structure of the CNS. Early neural tube organoid models, derived from human induced pluripotent stem cells (iPSCs), mimic the initial stages of neural tube formation. These organoids offer valuable insights into neural differentiation and the etiology of NTDs, enabling researchers to study neural progenitor behavior and the cellular environment during critical developmental stages. Patient-specific iPSC-derived organoids help uncover the molecular bases of NTDs, overcoming the limitations of traditional models and highlighting potential therapeutic targets. Cell image assays using fluorescence microscopy are essential for studying cellular responses in CNS-related organoid models. These assays allow for the identification of specific cellular components, analysis of molecular interactions, and detection of early disease markers. Advanced microscopy techniques like STORM and STED offer nanoscale resolution, enabling detailed visualization of subcellular structures and providing unprecedented insights into cellular dynamics within CNS organoid models. Despite their advantages, these assays are often labor-intensive, time-consuming, and limited by the need for specific markers. The integration of artificial intelligence (AI) into biomedical research has revolutionized image analysis. Techniques like convolutional neural networks (CNNs) and deep learning significantly enhance the accuracy and interpretation of microscopy data. Generative AI models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), advance microscopy-based imaging analysis in organoid research. GANs improve the visualization of synapses, aiding in the differentiation between healthy and diseased structures. VAEs generate high-resolution images that capture detailed neuronal morphology, enabling more accurate mapping of neuronal circuits and connectivity. AI technologies thus enhance the potential of microscopy-based imaging, offering a comprehensive understanding of CNS intricacies and disease mechanisms. The project specifically targets addressing NTDs in countries on the OECD DAC list, with a notable focus on Brazil, which grapple with both a high prevalence of NTDs and considerable economic and healthcare burdens.
ISPF-265 Brazil Global talent workshop (pilot)
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
A workshop to bring together awardees and the funders to share learning, receive advice and support for potential next steps, analyse impact activities connected with exchanges, and explore opportunities for future collaborations. Researchers (ECRs) in the UK and Brazil with the potential to create new knowledge, understanding, technologies, products and services to enrich and improve lives in the UK, Brazil and around the world. The opportunity will support researchers based in the UK and Brazil to establish and develop collaborative partnerships around a specific, jointly defined, research topic that could serve as the backbone for high quality applications for future funding.This programme is being developed in partnership with Science and Innovation Network (SIN) Brazil and Brazilian National Council for the State Funding Agencies (CONFAP).
Minimising inequalities in care access and quality for patients with UTI in Brazil: application of intelligent data linkage and machine learning
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
Brazil reported the highest UTI incidence and UTI-associated mortality and morbidity in the world. Treating UTI has become increasingly difficult to due to antimicrobial resistance (AMR) in the causative pathogens, predominantly Gram-negative bacteria. Inappropriately treated UTI leads to recurrent cases and bacterial invasion to other body parts and drives AMR emergence and spread. Diagnosing, prescribing, and follow-up of UTI remain sub-optimal, with half of the UTI antibiotic prescriptions in primary care being inappropriate. Limited data is available to evaluate of UTI management as it requires tracking patients along care pathway to identify re-prescribing, (re)admissions to primary care and hospitals, and deaths and disabilities due to UTI complications and AMR. Artificial intelligent (AI)/machine-learning (ML) supported by data integration can improve care for UTI by identifying cases, detecting AMR, and guiding patient stratification and antibiotic prescribing. In this proposal, University of São Paulo (USP) and Imperial College London (ICL) team will co-develop data linkage and case identification algorithms to better monitor UTI across primary and secondary care in Brazil. The linked data enables piloting and validation of three ML-based algorithms to perform risk stratification, guide antibiotic prescribing, and predict adverse events in hospitals. Routine electronic health records (EHR) and laboratory data from primary care units and hospitals in São Caetano do Sul, covering a population of 165,655 residents, will be deterministically linked. Using the linked data, we will develop tiered-case identification algorithms to identify cases and risk factors of community-acquired UTI, assess antibiotic prescribing appropriateness, and evaluate patient outcomes including urine-sourced BSI and other UTI complications. Three ML-based tools will be piloted, including a support Vector Machine (SVM) classifier to estimate the likelihood of UTI and BSI using routine biomarkers, a case-based reasoning (CDR) decision support to guide antibiotic empiric prescribing and review, and a random forest model to predict patient's risk of experiencing acute kidney injury and other adverse events subsequent to antibiotic treatment. This proposal aims to minimise the inequalities in access and quality of care in different socioeconomic groups. The case identification algorithms with probable and definite ontological concepts mapping and automated natural language processing (NLP) will monitor patients who are particularly vulnerable, including those who are socially deprived, with low health or technology literacy, living in care homes, or with multiple long-term conditions. Led by Dr Silvia Figueiredo Costa (USP) and Prof Alison Holmes (ICL), this multidisciplinary team has strong expertise in infectious disease epidemiology, data analytics, clinical microbiology, health economics, and health management, with a track record of ethical research and implementation of AI to address social determinants of health. São Caetano do Sul is one of the few cities in Brazil with fully implemented and routinised EHR. USP's established connection with local care providers and public health authorities will facilitate secure and timely access to data, and support validation and dissemination of the findings. This proposal is expected to generate direct benefit to patients in Brazil by enhancing surveillance and providing evidence to guide stewardship, infection prevention, and health service delivery. The co-developed, externally validated ML-based tools can be adopted/adapted for management of other infectious diseases and wider health systems strengthening. The USP-ICL partnership directly responds to the UK National Action Plan (NAP) by fostering a sustainable channel for knowledge exchange and innovation co-development, and engaging workforce and society within pluralistic health systems.
Speaking up for COPD through Artificial Intelligence in Brazil
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
CONTEXT: The burden and disability associated with chronic respiratory diseases (CRDs) is considerable, often falling on the most vulnerable in societies including those living in low- and middle-income countries such as Brazil. Specifically, the prevalence of chronic obstructive pulmonary disease (COPD) is increasing, and COPD presents a particular health challenge in Brazil where the prevalence in adults exceeds 17%. Most people with COPD in Brazil remain undiagnosed, and therefore untreated, because of limited access to the current diagnostic test called 'spirometry'. Spirometry is not widely, or equitably available in many primary healthcare settings in Brazil - including in Sao Paulo state. Innovative approaches to the diagnosis and management of COPD are therefore urgently required. THE CHALLENGE WE ADDRESS: We seek to transform the diagnosis of CRDs in primary care in Sao Paulo state, Brazil. We will do this through the use of vocal biomarkers derived by artificial-intelligence analysis of speech patterns. This technique has shown promise in English and Dutch languages, as a diagnostic and prognostic marker in CRDs, but has not been applied in (Brazilian) Portuguese, nor been deployed in real-life primary care settings where the need for easier tools to diagnose CRDs is greatest. AIMS and OBJECTIVES: Our over-arching aim is to develop and test AI-derived vocal biomarkers to support better diagnosis and management of CRDs in Brazil. To do this, we will work as an equitable partnership between the Federal University of Sao Carlos (Brazil) and University College London (UCL), with voice analysis experts at the University of Maastrict (Netherlands). We will: AIM 1: establish a dataset of voices from individuals with and without CRDs in Brazil. AIM 2: test the discriminative accuracy of AI-derived vocal biomarkers to distinguish those with CRDs from those with normal lung function. AIM 3: evaluate the utility of vocal biomarkers in COPD to detect the development of exacerbations of disease which are the major cause of ill-health and lost productivity in COPD. AIM 4: evaluate the utility of vocal biomarkers in COPD to provide objective evidence of benefit from pulmonary rehabilitation (PR) programmes, reflecting improvements in breathlessness, health status, and exercise capacity. POTENTIAL APPLICATIONS and BENEFIT: Transforming diagnosis and management of CRDs in Brazil would have wide health, social and economic benefits and provide an exemplar AI-health solution in an area of considerable unmet need
I-GAME: Integrated Genomics and AI as a tool for Malaria Elimination in Brazil
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
Malaria, caused by Plasmodium parasites, continues to be a major global health concern, with millions of cases and hundreds of thousands of deaths annually. In 2023, Brazil reported over 140,000 cases, marking it as the country with the highest malaria burden in South America. Notably, there are significant data gaps, particularly in high-risk regions such as indigenous communities and gold mining areas around the Amazon. Efforts to control malaria worldwide are further complicated by the emergence of Plasmodium drug resistance (DR), especially against artemisinin-based treatments. While resistance to artemisinin has primarily been observed in Southeast Asia, there is concern that similar issues may arise in other regions with comparable transmission dynamics, including parts of South America like the Brazilian Amazon. The generation and analysis of Plasmodium genomic data are critical for identifying DR mutations and understanding transmission patterns, including the cross-border movement of strains. Advanced genomic techniques, such as whole-genome sequencing (WGS) and targeted gene amplicon sequencing (AMP-SEQ), are used to identify species, DR mutations, and genetic diversity. Platforms like Oxford Nanopore and Illumina provide detailed clinical and epidemiological insights, thereby enhancing surveillance strategies. However, the effective utilisation of extensive genomic datasets is often hampered by a shortage of bioinformatics expertise and advanced informatics tools. Developing AI-driven informatics tools, such as the Malaria-Profiler software, is crucial for the rapid analysis and interpretation of WGS data. These tools can provide actionable insights into species identification, DR profiles, and geographic origins, which are essential for guiding clinical management, surveillance efforts, and public health interventions, particularly in data-limited regions like Brazil. Leveraging a well-established collaboration in malaria epidemiology, with extensive field site access and expertise in genomics and AI methodologies, the London School of Hygiene & Tropical Medicine (LSHTM) and the Institute of Biomedical Sciences at the University of São Paulo (ICB-USP) aim to further enhance these informatics tools. The project seeks to integrate AI models to continuously update mutation libraries and improve the predictive accuracy for species identification, DR profiling, and geographic profiling, alongside other genomic information that could support the National Malaria Control Programme (NMCP). This initiative includes conducting WGS/AMP-SEQ in Brazilian malaria hotspots to better understand genetic diversity and inform strategies for disease control and elimination in the country. The integration of AI methods with genomic data for parasite profiling has the potential to revolutionise malaria control. It enables proactive surveillance, personalised treatment strategies, and rapid responses to emerging threats, such as DR, including the identification of critical emerging Plasmodium mutations. This approach not only improves clinical care but also strengthens public health systems by facilitating informed decision-making and promoting collaborative data sharing among researchers and healthcare providers globally. The project will also involve key stakeholders, including Brazil's NMCP, to enhance capacity in AI and genomics through workshops and the development of dashboards and end-user reports. These resources will aid in implementing and validating the informatics platform, incorporating AI functionalities such as spatial analysis for public health applications. These efforts aim to ensure the tools' readiness for clinical and surveillance purposes, thereby contributing to reducing malaria and other infectious diseases in Brazil and aligning with the World Health Organization's regional elimination goals and global health objectives.