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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.
Low-carbon Agriculture for avoided deforestation and poverty reduction Phase II (Rural Sustentavel)
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.
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/
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.
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.
Climate Science for Service Partnership (CSSP) Brazil - Calls- tender-UNIVERSITY OF OXFORD
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.
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 OXFORD
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 in understanding and attributing weather and climate events, and their socio-economic impacts on key food, water and health sectors in Brazil.
Climate Science for Service Partnership (CSSP) Brazil - Calls- tender-UNIVERSITY OF EXETER
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 ecosystems responses to extremes.
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 (WCSSP) Brazil - Met Office
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
Climate Science for Service Partnership (WCSSP) Brazil - Calls - tender
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
Developing innovative arts-based approaches to prevent gender-based violence through feminist activism among youth in the favelas of Brazil
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
"This project develops an innovative translational arts-based approach to prevent GBV in Brazil and creates engagement and policy pathways that can be scaled-up transnationally. Drawing on established successful international collaborations, it will be conducted in the favelas of Maré in Rio de Janeiro with Redes da Maré (Redes) (a community-based human rights NGO), together with People’s Palace Projects (an arts centre using creativity for transformation - PPP) and Women of the World Foundation (a global movement creating a gender equal world – WOW). Through exploring ways to address and prevent GBV in Brazil, one of the main factors undermining sustainable development is addressed illustrating that this project is directly and primarily relevant to the addressing the Sustainable Development Goals (SDGs) in Brazil. More specifically, SDG 5 on achieving gender equality and empowering all women and girls is at the core of this project. It directly addresses target 5.1 ‘End all forms of discrimination against all women and girls everywhere’ and Target 5.2 ‘Eliminate all forms of violence against all women and girls in the public and private spheres’. The project also addresses SDG 11 on making cities inclusive, safe, resilient and sustainable, especially in relation to the importance of making public spaces safe for everyone (Indicator 11.7.2)
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.
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.
Breast Cancer Diagnosis and Risk Prediction in Multi-Incidence Mammograms: Leveraging UK and Brazilian Data and Expertise
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
This collaborative project aims to develop novel AI (Artificial Intelligence) methods for breast cancer diagnosis and risk prediction using mammograms, by leveraging the combined expertise and diverse mammogram datasets from the UK and Brazil. Breast cancer remains a significant global health burden, with an estimated 2.3 million new cases and 685,000 deaths in 2020 alone. Early detection is crucial for improving patient outcomes. However, AI-based cancer detection and risk prediction models can be biased if the training sample is not representative of the entire population. The UK and Brazil have distinct demographic characteristics. By leveraging mammogram data from both countries, this project aims to reduce bias and improve the generalizability of the diagnosis and risk prediction models, contributing to more equitable and effective breast cancer screening worldwide. The UK-based team has developed a deep learning model called BREST (Breast Risk Evaluation from Screening Test) for three-year risk assessment using mammograms. The Brazilian team has proposed a breast cancer diagnosing algorithm called "Patch to Multi-View" (P2MV) that simultaneously uses the two standard views of the breast to significantly increase the accuracy, compared to other strategies that also use the two views. We will test whether the breast cancer risk prediction provided by BREST can be improved using multiple mammographic views via the P2MV algorithm. When a radiologist finds a suspicious lesion, he/she may request complementary mammogram views, such as cone view, cleavage view, compression view, etc., to better evaluate the detected abnormality. We propose to investigate whether using these complementary views can help to improve breast cancer detection and risk prediction. P2MV algorithm is well-suited for this task, as it can extract information from multiple views. A recent study analyzed 134,870 breast cancer deaths in Brazil in women aged 20 to 69, from 1996 to 2013. Unfortunately, there was a temporal trend of increased breast cancer mortality in young women aged 20 to 49. Therefore, early diagnosis of cancer in young women becomes increasingly important. However, young women have dense breasts, making it difficult to diagnose cancer using X-rays. We want to determine how well AI models perform in detecting and predicting breast cancer risk in young women. This would allow us to propose the best strategies for early cancer diagnosis for this age group.
AI-Based Support for Mental Health Communication (AIM-Health)
DEPARTMENT FOR SCIENCE, INNOVATION AND TECHNOLOGY
Depression is a mental health disorder that affects a large portion of the global population, being the second largest contributor to a decrease in healthy life expectancy. Depression is characterised by a clinically significant form of psychological suffering that leads to significant impairment in someone's functionality, reduced quality of life and, in severe cases, can lead to death due to the risk of suicide. However, according to the World Health Organization, only a quarter of individuals suffering from mental health disorders receive proper care. Advances in Artificial Intelligence (AI) and Natural Language Processing (NLP) research have been developed to a level that can be used for proposing computational solutions that assist in the detection and intervention in mental health conditions. AI and NLP based solutions that aid in the identification of signs of depression can be useful both in individual treatment and in making public policy decisions. Similarly, solutions that offer autonomous, ethical, reliable, controlled, and engaging intervention, in real time, can help mitigate the damage caused by depression. This project works on proposing and developing AI and NLP based solutions for the detection and intervention of mental health conditions that can have a broader reach and allow mental health support to individuals and populations that would not otherwise have access to it. Furthermore, as social determinants are frequently mentioned as risk factors for mental health conditions, this project also aims at furthering the understanding about them in two contexts (Brazil and the United Kingdom). This project aims to address scientific challenges that are still present and very relevant in this context: (i) dealing with more abstract language (such as figurative language) commonly used in mental health self-narratives, and (ii) outputting personalised interventions suitable for an individual's context.
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.