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Small water bodies (i.e., ponds; <0.01 km2) play an important role in Earth System processes, including carbon cycling and emissions of methane. Detection and monitoring of ponds using satellite imagery has been extremely difficult and many water maps are biased toward lakes (>0.01 km2). We leverage high‐resolution (3 m) optical satellite imagery from Planet Labs and deep learning methods to map seasonal changes in pond and lake areal extent across four regions in Alaska. Our water maps indicate that changes in open water extent over the snow‐free season are especially pronounced in ponds. To investigate potential impacts of seasonal changes in pond area on carbon emissions, we provide a case study of open water methane emission budgets using the new water maps. Our approach has widespread applications for water resources, habitat and land cover change assessments, wildlife management, risk assessments, and other biogeochemical modeling efforts. Plain Language Summary Small water bodies (<0.01 km2) are an important driver of many Earth system processes. Despite their importance, many existing water mapping products have difficulty detecting these small water features and their seasonal changes in surface area. We used deep learning and high‐resolution (3 m) satellite imagery to map and monitor seasonal changes in the areal extent of lakes and small ponds across four regions in Alaska. The resulting water maps accounted for considerably more water coverage than existing products. The maps also effectively tracked widespread seasonal changes in pond and lake area that were not previously identified. This demonstrates the importance of monitoring surface water at high spatial resolutions and across seasons. Key Points Deep learning and 3 m resolution satellite imagery from Planet Labs can detect and track ponds and lakes >0.0001 km2 Total surface area for ponds (<0.01 km2) in boreal forest and tundra environments can vary by 20%–40% throughout an individual season Ponds can contribute to a broad range (8%–37%) of total methane emissions from lakes and ponds in northern boreal forest and tundra

The permafrost region contains a significant portion of the world's soil organic carbon, and its thawing, driven by accelerated Arctic warming, could lead to substantial release of greenhouse gases, potentially disrupting the global climate system. Accurate predictions of carbon cycling in permafrost ecosystems hinge on the robust calibration of model parameters. However, manually calibrating numerous parameters in complex process-based models is labor-intensive and is complicated further by equifinality – the presence of multiple parameter sets that can equally fit the observed data. Incorrect calibration can lead to unrealistic ecological predictions. In this study, we employed the Model Analysis and Decision Support (MADS) software package to automate and enhance the accuracy of parameter calibration for carbon dynamics within the coupled Dynamic Vegetation Model, Dynamic Organic Soil Model, and Terrestrial Ecosystem Model (DVM-DOS-TEM), a process-based ecosystem model designed for high-latitude regions. The calibration process involved adjusting rate-limiting parameters to accurately replicate observed carbon and nitrogen fluxes and stocks in both soil and vegetation. Gross primary productivity, net primary productivity, vegetation carbon, vegetation nitrogen, and soil carbon and nitrogen pools served as synthetic observations for a black spruce boreal forest ecosystem. To validate the efficiency of this new calibration method, we utilized model-generated synthetic and actual observations. When matching model outputs to observed data, we encountered difficulties in maintaining mineral soil carbon stocks. Additionally, due to strong interdependencies between parameters and target values, the model consistently overestimated carbon and nitrogen allocation to the stems of evergreen trees. This study demonstrates the calibration workflow, offers an in-depth analysis of the relationships between parameters and observations (synthetic and actual), and evaluates the accuracy of the calibrated parameter values.

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long‐term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, and grass‐tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE <390 g C m−2) relative to net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE <180 g C m−2). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long‐term network‐based monitoring of vegetation biomass, C fluxes, and SOC stocks. Plain Language Summary Rangelands play a crucial role in providing various ecosystem services, including potential climate change mitigation through increased soil organic carbon (SOC) storage. Accurate estimates of changes in carbon (C) storage are challenging due to the heterogeneous nature of rangelands and the limited availability of field observations. In this work, we leveraged remote sensing observations, tower‐based C flux measurements from over 60 rangeland sites in the Western and Midwestern US, and other environmental data sets to build the process‐based Rangeland Carbon Tracking and Management (RCTM) modeling system. The RCTM system is designed to simulate the past 20 years of rangeland C dynamics and is regionally calibrated. The RCTM system performs well in estimating spatial and temporal rangeland C fluxes as well as spatial SOC storage. Model simulation results revealed increased SOC storage and rangeland productivity driven by annual precipitation patterns. The RCTM system developed by this work can be used to generate accurate spatial and temporal estimates of SOC storage and C fluxes at fine spatial (30 m) and temporal (every 5 days) resolutions, and is well‐suited for informing rangeland C management strategies and improving broad‐scale policy making. Key Points The Rangeland Carbon Tracking and Monitoring System was calibrated to simulate vegetation type‐specific rangeland C dynamics Regional variability in carbon fluxes and soil organic carbon is well represented by a remote sensing‐driven process modeling approach Soil organic carbon stocks in Western and Midwestern US rangelands increased over the past 20 years due to increased precipitation

In response to the emergence of SARS-CoV-2 variants of concern, the global scientific community, through unprecedented effort, has sequenced and shared over 11 million genomes through GISAID, as of May 2022. This extraordinarily high sampling rate provides a unique opportunity to track the evolution of the virus in near real-time. Here, we present outbreak.info , a platform that currently tracks over 40 million combinations of Pango lineages and individual mutations, across over 7,000 locations, to provide insights for researchers, public health officials and the general public. We describe the interpretable visualizations available in our web application, the pipelines that enable the scalable ingestion of heterogeneous sources of SARS-CoV-2 variant data and the server infrastructure that enables widespread data dissemination via a high-performance API that can be accessed using an R package. We show how outbreak.info can be used for genomic surveillance and as a hypothesis-generation tool to understand the ongoing pandemic at varying geographic and temporal scales. The outbreak.info genomic reports provides comprehensive and detailed information about SARS-CoV-2 lineages and mutations worldwide, which facilitates near real-time genomic surveillance.

Outbreak.info Research Library is a standardized, searchable interface of coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) publications, clinical trials, datasets, protocols and other resources, built with a reusable framework. We developed a rigorous schema to enforce consistency across different sources and resource types and linked related resources. Researchers can quickly search the latest research across data repositories, regardless of resource type or repository location, via a search interface, public application programming interface (API) and R package. Outbreak.info Research Library collects and integrates COVID-19 information from various repositories, enabling one-stop search for publications, clinical trials, datasets and other resources.

Background Biomedical researchers are strongly encouraged to make their research outputs more Findable, Accessible, Interoperable, and Reusable (FAIR). While many biomedical research outputs are more readily accessible through open data efforts, finding relevant outputs remains a significant challenge. Schema.org is a metadata vocabulary standardization project that enables web content creators to make their content more FAIR. Leveraging Schema.org could benefit biomedical research resource providers, but it can be challenging to apply Schema.org standards to biomedical research outputs. We created an online browser-based tool that empowers researchers and repository developers to utilize Schema.org or other biomedical schema projects. Results Our browser-based tool includes features which can help address many of the barriers towards Schema.org-compliance such as: The ability to easily browse for relevant Schema.org classes, the ability to extend and customize a class to be more suitable for biomedical research outputs, the ability to create data validation to ensure adherence of a research output to a customized class, and the ability to register a custom class to our schema registry enabling others to search and re-use it. We demonstrate the use of our tool with the creation of the Outbreak.info schema—a large multi-class schema for harmonizing various COVID-19 related resources. Conclusions We have created a browser-based tool to empower biomedical research resource providers to leverage Schema.org classes to make their research outputs more FAIR.

Thwaites Glacier is one of the fastest-changing ice–ocean systems in Antarctica 1 – 3 . Much of the ice sheet within the catchment of Thwaites Glacier is grounded below sea level on bedrock that deepens inland 4 , making it susceptible to rapid and irreversible ice loss that could raise the global sea level by more than half a metre 2 , 3 , 5 . The rate and extent of ice loss, and whether it proceeds irreversibly, are set by the ocean conditions and basal melting within the grounding-zone region where Thwaites Glacier first goes afloat 3 , 6 , both of which are largely unknown. Here we show—using observations from a hot-water-drilled access hole—that the grounding zone of Thwaites Eastern Ice Shelf (TEIS) is characterized by a warm and highly stable water column with temperatures substantially higher than the in situ freezing point. Despite these warm conditions, low current speeds and strong density stratification in the ice–ocean boundary layer actively restrict the vertical mixing of heat towards the ice base 7 , 8 , resulting in strongly suppressed basal melting. Our results demonstrate that the canonical model of ice-shelf basal melting used to generate sea-level projections cannot reproduce observed melt rates beneath this critically important glacier, and that rapid and possibly unstable grounding-line retreat may be associated with relatively modest basal melt rates. Despite observations from a hot-water-drilled access hole showing warm ocean waters beneath Thwaites Glacier Eastern Ice Shelf, the basal melt rate is strongly suppressed due to the low current speeds and strong density stratification.

Developing synchronous bilateral Wilms tumor suggests an underlying (epi)genetic predisposition. Here, we evaluate this predisposition in 68 patients using whole exome or genome sequencing ( n  = 85 tumors from 61 patients with matched germline blood DNA), RNA-seq ( n  = 99 tumors), and DNA methylation analysis ( n  = 61 peripheral blood, n  = 29 non-diseased kidney, n  = 99 tumors). We determine the predominant events for bilateral Wilms tumor predisposition: 1)pre-zygotic germline genetic variants readily detectable in blood DNA [ WT1 (14.8%), NYNRIN (6.6%) , TRIM28 (5%) , and BRCA-related genes (5%)] or 2)post-zygotic epigenetic hypermethylation at 11p15.5 H19/ICR1 that may require analysis of multiple tissue types for diagnosis. Of 99 total tumor specimens, 16 (16.1%) have 11p15.5 normal retention of imprinting, 25 (25.2%) have 11p15.5 copy neutral loss of heterozygosity, and 58 (58.6%) have 11p15.5 H19/ICR1 epigenetic hypermethylation (loss of imprinting). Here, we ascertain the epigenetic and genetic modes of bilateral Wilms tumor predisposition. The genetic and epigenetic predisposition of bilateral Wilms tumour remains to be investigated. Here, the authors perform multiomics analysis and identify the predominant genetic and epigenetic events associated with bilateral Wilms tumour predisposition.

Microscopic-scale processes significantly influence benthic marine ecosystems such as coral reefs and kelp forests. Due to the ocean’s complex and dynamic nature, it is most informative to study these processes in the natural environment yet it is inherently difficult. Here we present a system capable of non-invasively imaging seafloor environments and organisms in situ at nearly micrometre resolution. We overcome the challenges of underwater microscopy through the use of a long working distance microscopic objective, an electrically tunable lens and focused reflectance illumination. The diver-deployed instrument permits studies of both spatial and temporal processes such as the algal colonization and overgrowth of bleaching corals, as well as coral polyp behaviour and interspecific competition. By enabling in situ observations at previously unattainable scales, this instrument can provide important new insights into micro-scale processes in benthic ecosystems that shape observed patterns at much larger scales. Underwater microscopes have limited spatial and temporal resolutions. Here, Mullen et al . present a small non-invasive underwater microscope for both direct and fluorescence microscopy. They image coral bleaching and interspecific competition with resolutions approaching a micron and hundreds of milliseconds.

Germinal centres are the engines of antibody evolution. Here, using human immunodeficiency virus (HIV) Env protein immunogen priming in rhesus monkeys followed by a long period without further immunization, we demonstrate germinal centre B (B GC ) cells that last for at least 6 months. A 186-fold increase in B GC cells was present by week 10 compared with conventional immunization. Single-cell transcriptional profiling showed that both light- and dark-zone germinal centre states were sustained. Antibody somatic hypermutation of B GC cells continued to accumulate throughout the 29-week priming period, with evidence of selective pressure. Env-binding B GC cells were still 49-fold above baseline at 29 weeks, which suggests that they could remain active for even longer periods of time. High titres of HIV-neutralizing antibodies were generated after a single booster immunization. Fully glycosylated HIV trimer protein is a complex antigen, posing considerable immunodominance challenges for B cells 1 , 2 . Memory B cells generated under these long priming conditions had higher levels of antibody somatic hypermutation, and both memory B cells and antibodies were more likely to recognize non-immunodominant epitopes. Numerous B GC cell lineage phylogenies spanning more than the 6-month germinal centre period were identified, demonstrating continuous germinal centre activity and selection for at least 191 days with no further antigen exposure. A long-prime, slow-delivery (12 days) immunization approach holds promise for difficult vaccine targets and suggests that patience can have great value for tuning of germinal centres to maximize antibody responses. Using HIV Env protein immunogen priming in rhesus monkeys followed by a long period without further immunization, we demonstrate germinal centre B cells lasting at least 6 months, showing promise in regard to difficult vaccine targets.