Microplastics (<5 mm) pollution is a growing problem affecting coastal communities, marine ecosystems, aquatic life, and human health. The widespread occurrence of marine microplastics, and the need to curb its threats, require expansive, and continuous monitoring. While microplastic research has increased in recent years and generated significant volumes of data, there is a lack of a robust, open access, and long-term aggregation of this data. The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) now provides a global open access to marine microplastics data on an easily discoverable and accessible GIS web map and data portal (https://www.ncei.noaa.gov/products/microplastics). The objective of this data portal is to develop a repository where microplastics data are aggregated, archived, and served in a user friendly, consistent, and reliable manner. This work contributes to NCEI’s efforts towards data standardization, integration, harmonization, and interoperability among national and international collaborators for monitoring global marine microplastics. This paper describes the NOAA NCEI global marine microplastics database, its creation, quality control procedures, and future directions.

The global proliferation of plastics and their degradation into microplastics (<5 mm) have created a pervasive environmental crisis with severe ecological and human health consequences. Despite the exponential growth in microplastic research over the past decade, standardized protocols are still lacking. The absence of consistent sampling, analysis, and reporting methods limits data comparability, interoperability, and harmonization across studies. This study conducted a systematic bibliographic review of 355 peer-reviewed articles published between 2010 and 2022 that investigated microplastics in freshwater as well as marine water and sediment environments. The goal was to evaluate methodological consistency, sampling instruments, measurement units, reported characteristics, and data-sharing practices to identify pathways toward harmonized and FAIR (Findable, Accessible, Interoperable, and Reusable) microplastic data. Results show that 80.6% of studies focused on marine environments, 18% on freshwater, and 1.4% on both. This highlights persistent data gaps in freshwater systems, which function as key transport pathways for plastics to the ocean. Most studies targeted water (59%) rather than sediment (41%) and were mostly based on single-time sampling, limiting long-term analyses. Surface layers (<1 m) were predominantly sampled, while deeper layers remain understudied. Nets, particularly Manta, neuston, and plankton nets were the dominant tools for water sampling, whereas grabs, corers, and metallic receptacles were used for sediments. However, variations in mesh size and sampling depth introduce substantial biases in particle size recovery and reduce comparability across studies. The most common units were counts/volume for water and counts/g dry weight for sediments, but more than ten unit expressions were identified, complicating conversions. Only 35% of studies reported all four key microplastic characteristics (color, polymer type, shape, and size), and less than 20% made datasets publicly available. To advance harmonization, we recommend the adoption of consistent measurement units, mandatory reporting of key metadata, and wider implementation of open data practices aligned with the FAIR principles. These insights provide a foundation for developing robust monitoring strategies and evidence-based management frameworks. This is especially important for freshwater systems, where data remain scarce, and policy intervention is urgently needed.

2512 Background: Precision medicine has been studied in patients (pts) with advanced, heavily-treated cancers by administering molecularly matched monotherapies. With increasing availability of large gene assays and cognate agents, we hypothesized that offering customized combination therapies to treatment-naïve tumors would be feasible and improve response rates. Methods: Investigation of Profile-Related Evidence Determining Individualized Cancer Therapy (I-PREDICT, NCT02534675) targeted metastatic and/or unresectable, untreated lethal cancers ( > 50% 2-yr mortality). Comprehensive genomic profiling (CGP, Foundation Medicine; 315 genes), and, if possible, PD-(L)1 IHC, tumor mutational burden (TMB) and circulating tumor DNA were performed. A molecular tumor board discussed results immediately upon receipt, and emphasized customized combinations. Final decisions were the treating physician’s choice. Results: CGP was evaluable in 40/47 treatment-naïve pts (85.1%); 22 (46.8%) were treated [17 matched (36.2%); 5 unmatched (10.6%); 11 different diagnoses). The other 25 pts (53.2%) are awaiting therapy (8, 17%) or could not be treated (17, 36.2%), mainly due to patient deterioration or payor limitations. Each tumor had a unique genomic portfolio. The median (range) of genomic alterations/patient was 5 (1-12). TMB was available in 17 pts (12 low; 4 intermediate; 1 high). The median (range) Matching Score [(matches (#)/characterized genomic alterations (#)] was 33% (14-100%; 100% designated immunotherapy match or all alterations matched to targeted agents) [Reference PMID 2719717]. Nine/17 matched pts (53%) achieved SD > 6 months (N = 2) or CR (1)/PR (6). The median progression-free survival (PFS) for matched vs. unmatched pts was 4.7 vs. 1.0 months ( P= 0.0019). There were no drug-related deaths. Conclusions: With the use of broad-based DNA sequencing assays, inclusion of pts earlier in their disease course, timely mandated molecular tumor board discussions, and increasing availability of cognate drugs for customized combinations, we report: 1) high molecular matching rates (~36%); 2) high rates of SD > 6 months/CR/PR (~53%); and 3) improved PFS. Study expansion is ongoing. Clinical trial information: NCT0253467.

Climate change is increasing the number, severity, and frequency of extreme weather events, such as heatwaves, which kill thousands of people worldwide and strain healthcare systems, particularly in cities. Those responsible for managing the societal consequences of these events require easy and uniform access to the best data, models, and decision support tools in order to mitigate impacts on affected populations and enable appropriate responses. To achieve this, spatially and temporally accurate information on the occurrence, location, and duration of heatwaves is required.A digital replica of the Earth system, a foundational component to a digital twin (DT), can provide such information by producing bespoke cutting-edge numerical simulations of upcoming heatwaves and combining several components of the value chain – from observations to modelling, to answering strategic and socioeconomic questions – in a single workflow. To better understand how heatwaves may affect a city across different neighbourhoods, however, km-resolution DT forecasts must be zoomed in at the metre scale as events unfold. This necessitates taking into account urban complexity, which can be achieved by combining operational weather forecasts with quality-controlled station observations and land-use data. By using such an approach, machine learning (ML) has shown to enhance forecasts in urban areas to much higher resolutions than standard operational forecasts. Yet, the use of ML to forecast downscaling has so far involved offline, city-specific applications with limited scalability and testing in real-world settings.The US-EU AI for the Public Good partnership seeks to address these limitations. As part of the partnership, ML solutions are being implemented to deliver hyper-local data from the European Commission's Destination Earth (DestinE) initiative and operationally generate forecast maps of heat in terms of thermophysiological stress. This presentation will provide an overview, plans, and current status of the partnership.