The Bill & Melinda Gates Foundation (BMGF), a merging of the William H. Gates Foundation and the Gates Learning Foundation, is an American private foundation founded by Bill Gates and Melinda French Gates. Based in Seattle, Washington, it was launched in 2000 and is reported as of 2020 to be the second largest charitable foundation in the world, holding $49.8 billion in assets. On his 43rd birthday, Bill Gates gave the foundation $1 billion. The primary stated goals of the foundation are to enhance healthcare and reduce extreme poverty across the world, and to expand educational opportunities and access to information technology in the U.S. Key individuals of the foundation include Bill Gates, Melinda French Gates, Warren Buffett, chief executive officer Mark Suzman, and Michael Larson.The BMGF had an endowment of approximately $50 billion as of December 31, 2020[update]. The scale of the foundation and the way it seeks to apply business techniques to giving makes it one of the leaders in venture philanthropy, though the foundation itself notes that the philanthropic role has limitations. In 2007, its founders were ranked as the second most generous philanthropists in the U.S., behind Warren Buffett. As of 2018, Bill Gates and Melinda French Gates had donated around $36 billion to the foundation. Since its founding, the foundation has endowed and supported a broad range of social, health, and education developments, including the establishment of the Gates Cambridge Scholarships at Cambridge University.....S.S.S.S.S.
The advancement of speech technology has predominantly favored high-resource languages, creating a significant digital divide for speakers of most Sub-Saharan African languages. To address this gap, we introduce WAXAL, a large-scale, openly accessible speech dataset for 21 languages representing over 100 million speakers. The collection consists of two main components: an Automated Speech Recognition (ASR) dataset containing approximately 1,250 hours of transcribed, natural speech from a diverse range of speakers, and a Text-to-Speech (TTS) dataset with over 180 hours of high-quality, single-speaker recordings reading phonetically balanced scripts. This paper details our methodology for data collection, annotation, and quality control, which involved partnerships with four African academic and community organizations. We provide a detailed statistical overview of the dataset and discuss its potential limitations and ethical considerations. The WAXAL datasets are released at https://huggingface.co/datasets/google/WaxalNLP under the permissive CC-BY-4.0 license to catalyze research, enable the development of inclusive technologies, and serve as a vital resource for the digital preservation of these languages.
The implications of climate change for malaria eradication this century remain poorly resolved1,2. Many studies focus on parasite and vector ecology in isolation, neglecting the interactions between climate, malaria control and the socioeconomic environment, including disruption from extreme weather3,4. Here we integrate 25 years of African data on climate, malaria burden and control, socioeconomic factors, and extreme weather. Using a geotemporal model linked to an ensemble of climate projections under the Shared Socioeconomic Pathway 2-4.5 (SSP 2-4.5) scenario5, we estimate the future impact of climate change on malaria burden in Africa, including both ecological and disruptive effects. Our findings indicate that climate change could lead to 123 million (projection range 49.5 million to 203 million) additional malaria cases and 532,000 (195,000-912,000) additional deaths in Africa between 2024 and 2050 under current control levels. Contrary to the prevailing focus on ecological mechanisms, extreme weather events emerge as the primary driver of increased risk, accounting for 79% (50-94%) of additional cases and 93% (70-100%) of additional deaths. Most increases stem from intensification in existing endemic areas rather than range expansion, with significant regional variation in impact. These results highlight the urgent need for climate-resilient malaria control strategies and robust emergency response systems to safeguard progress towards malaria eradication.
Hepatitis E virus (HEV) remains a leading cause of acute viral hepatitis globally, particularly in South Asia and Africa. However, epidemiological prioritization is hampered by fragmented data and discordant disease burden estimates. Following JBI and PRISMA-Sc guidelines, we conducted a scoping review of global HEV evidence. We used the PCC framework: (P) general and high-risk populations (pregnant women, immunocompromised, and displaced groups); (C) quantitative estimates of burden, risk factors, or virological gaps; and (C) global evidence across all WHO regions to include studies. We searched PubMed, Scopus, and Web of Science, supplemented by country-specific searches in Google Scholar and IHME. From 11,583 citations, 395 articles met the inclusion criteria. The temporal distribution shows a marked increase in research volume, with 65.3% of studies published after 2010; however, 54.9% relied on observational descriptive designs while experimental investigations remained infrequent (4.3%). We identified three estimates of the global burden of HEV: the IHME Global Burden of Disease (GBD) published in 2021 (19.4 million cases) and two widely cited systematic reviews published in 2012 (20.1 million infections) and 2020 (939 million infections). A significant virological "blind spot" was observed, as 47.8% of studies did not report genotype information, though Genotype 3 (21.8%) was the most frequently identified among specified reports. Key risk domains identified were environmental (sanitation/water contamination) and cultural/occupational practices. Pregnant women, immunocompromised patients, and patients with pre-existing liver conditions were high at-risk populations. Key knowledge gaps identified were limited confidence in burden of disease estimates: severe molecular blind spots and evidence deserts, limited public health resources for surveillance, diagnostics, and reporting of cases and deaths in highest risk settings; exclusion of outbreaks from estimates of the burden of disease and unreliable convenience sample derived estimates. Hepatitis E virus is often neglected by international communities, global actors and national governments. However, it is difficult for stakeholders to prioritize a pathogen with highly variable and unreliable global burden of disease estimates. Comprehensive country level data based on more access to routine testing could facilitate global initiatives to devise strategies for equitable vaccination and mitigate the morbidity and mortality associated with this vaccine-preventable disease.
Ultra-low-field (ULF) MRI is emerging as an alternative modality to high-field (HF) MRI due to its lower cost, minimal siting requirements, portability, and enhanced accessibility factors that enable large-scale deployment. Although ULF-MRI exhibits lower signal-to-noise ratio (SNR), advanced imaging and data-driven denoising methods enabled by high-performance computing have made contrasts like diffusion-weighted imaging (DWI) feasible at ULF. This study investigates the potential and limitations of ULF tractography, using data acquired on a 0.064 T commercially available mobile point-of-care MRI scanner. The results demonstrate that most major white matter bundles can be successfully retrieved in healthy adult brains within clinically tolerable scan times. This study also examines the recovery of diffusion tensor imaging (DTI)-derived scalar maps, including fractional anisotropy and mean diffusivity. Strong correspondence is observed between scalar maps obtained with ULF-MRI and those acquired at high field strengths. Furthermore, fibre orientation distribution functions reconstructed from ULF data show good agreement with high-field references, supporting the feasibility of using ULF-MRI for reliable tractography. These findings open new opportunities to use ULF-MRI in studies of brain health, development, and disease progression particularly in populations traditionally underserved due to geographic or economic constraints. The results show that robust assessments of white matter microstructure can be achieved with ULF-MRI, effectively democratising microstructural MRI and extending advanced imaging capabilities to a broader range of research and clinical settings where resources are typically limited.
Generative AI emulators are increasingly used in scientific domains where we already have strong theory, benchmarks, and physical intuition. This raises a central evaluation and interpretability question: when a foundation-style model can reproduce known continuum dynamics, what internal mechanism supports that behavior, is the internal behaviour consistent with known physics, and how does it relate to where the emulator succeeds or fails? We investigate a cross-domain foundation model for continuum dynamics, Walrus by Polymathic, using mechanistic interpretability guided by physical principles. We apply a sparse autoencoder (SAE) to probe a selected layer, and address the practical challenge of triaging a large feature set (over 20,000) using enstrophy as a physically grounded metric. As a deliberately simple testbed, we focus on shear flow and compare feature recruitment across multiple shear-flow setups, i.e. parameter values in the numerical simulation. Across setups we find evidence of piecewise consistency, with subsets of features recurring in similar roles, but this structure is intermittent and does not map cleanly onto standard physical decompositions. In parallel, direct comparisons between numerical simulation and the emulator reveal systematic output-level discrepancies, including regimes where energy/structures become too diffuse or too localized. We connect parts of these discrepancies to changes in specific SAE feature usage. Our work highlights open questions for scientific foundation models: how to robustly prioritize mechanistically meaningful features, how to separate stable structure from analysis artifacts (including single-layer and SAE limitations), and how to use established benchmarks to decide when "different" internal representations are genuinely informative rather than merely effective.
