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Terrestrial vegetation and soils hold three times more carbon than the atmosphere. Much debate concerns how anthropogenic activity will perturb these surface reservoirs, potentially exacerbating ongoing changes to the climate system. Uncertainties specifically persist in extrapolating point-source observations to ecosystemscale budgets and fluxes, which require consideration of vertical and lateral processes on multiple temporal and spatial scales. To explore controls on organic carbon (OC) turnover at the river basin scale, we present radiocarbon (14C) ages on two groups of molecular tracers of plant-derived carbon—leaf-wax lipids and lignin phenols—from a globally distributed suite of rivers. We find significant negative relationships between the 14C age of these biomarkers and mean annual temperature and precipitation. Moreover, riverine biospheric-carbon ages scale proportionally with basinwide soil carbon turnover times and soil 14C ages, implicating OC cycling within soils as a primary control on exported biomarker ages and revealing a broad distribution of soil OC reactivities. The ubiquitous occurrence of a long-lived soil OC pool suggests soil OC is globally vulnerable to perturbations by future temperature and precipitation increase. Scaling of riverine biosphericcarbon ages with soil OC turnover shows the former can constrain the sensitivity of carbon dynamics to environmental controls on broad spatial scales. Extracting this information from fluvially dominated sedimentary sequences may inform past variations in soil OC turnover in response to anthropogenic and/or climate perturbations. In turn, monitoring riverine OC composition may help detect future climate-change–induced perturbations of soil OC turnover and stocks.

Sources of dissolved and particulate carbon to the Fraser River system vary significantly in space and time. Tributaries in the northern interior of the basin consistently deliver higher concentrations of dissolved organic carbon (DOC) to the main stem than other tributaries. Based on samples collected near the Fraser River mouth throughout 2013, the radiocarbon age of DOC exported from the Fraser River does not change significantly across seasons despite a spike in DOC concentration during the freshet, suggesting modulation of heterogeneous upstream chemical and isotopic signals during transit through the river basin. Dissolved inorganic carbon (DIC) concentrations are highest in the Rocky Mountain headwater region where carbonate weathering is evident, but also in tributaries with high DOC concentrations, suggesting that DOC respiration may be responsible for a significant portion of DIC in this basin. Using an isotope and major ion mass balance approach to constrain the contributions of carbonate and silicate weathering and DOC respiration, we estimate that up to 33 ± 11% of DIC is derived from DOC respiration in some parts of the Fraser River basin. Overall, these results indicate close coupling between the cycling of DOC and DIC, and that carbon is actively processed and transformed during transport through the river network.

The radiocarbon content of dissolved organic carbon (DOC) in rivers, lakes, and other non-saline waters can provide valuable information on carbon cycling dynamics in the environment. DOC is typically prepared for 14C analysis by accelerator mass spectrometry (AMS) either by ultraviolet (UV) oxidation or by freeze-drying and sealed tube combustion. We present here a new method for the rapid analysis of 14C of DOC using wet chemical oxidation (WCO) and automated headspace sampling of CO2. The approach is an adaption of recently developed methods using aqueous persulfate oxidant to determine the δ13C of DOC in non-saline water samples and the 14C content of volatile organic acids. One advantage of the current method over UV oxidation is higher throughput: 22 samples and 10 processing standards can be prepared in one day and analyzed in a second day, allowing a full suite of 14C processing standards and blanks to be run in conjunction with samples. A second advantage is that there is less potential for cross-contamination between samples.

The radiocarbon content of dissolved organic carbon (DOC) in rivers, lakes, and other non-saline waters can provide valuable information on carbon cycling dynamics in the environment. DOC is typically prepared for 14 C analysis by accelerator mass spectrometry (AMS) either by ultraviolet (UV) oxidation or by freeze-drying and sealed tube combustion. We present here a new method for the rapid analysis of 14 C of DOC using wet chemical oxidation (WCO) and automated headspace sampling of CO 2 . The approach is an adaption of recently developed methods using aqueous persulfate oxidant to determine the δ 13 C of DOC in non-saline water samples and the 14 C content of volatile organic acids. One advantage of the current method over UV oxidation is higher throughput: 22 samples and 10 processing standards can be prepared in one day and analyzed in a second day, allowing a full suite of 14 C processing standards and blanks to be run in conjunction with samples. A second advantage is that there is less potential for cross-contamination between samples.

Wildfires and incomplete combustion of fossil fuel produce large amounts of black carbon. Black carbon production and transport are essential components of the carbon cycle. Constraining estimates of black carbon exported from land to ocean is critical, given ongoing changes in land use and climate, which affect fire occurrence and black carbon dynamics. Here, we present an inventory of the concentration and radiocarbon content (∆14C) of particulate black carbon for 18 rivers around the globe. We find that particulate black carbon accounts for about 15.8 ± 0.9% of river particulate organic carbon, and that fluxes of particulate black carbon co-vary with river-suspended sediment, indicating that particulate black carbon export is primarily controlled by erosion. River particulate black carbon is not exclusively from modern sources but is also aged in intermediate terrestrial carbon pools in several high-latitude rivers, with ages of up to 17,000 14C years. The flux-weighted 14C average age of particulate black carbon exported to oceans is 3,700 ± 400 14C years. We estimate that the annual global flux of particulate black carbon to the ocean is 0.017 to 0.037 Pg, accounting for 4 to 32% of the annually produced black carbon. When buried in marine sediments, particulate black carbon is sequestered to form a long-term sink for CO2.

In the version of this Article originally published, the units of the x and y axes in Fig. 3a were incorrectly given as ‘mg km–2 yr–1’; the correct units are ‘Mg km–2 yr–1’. These errors have now been corrected in the online versions.

Accurate cancer subtyping with accompanying molecular characterization is critical for precision oncology. While machine learning approaches have been applied to both digital pathology and cancer genomics, previous work has been limited in sample size and has typically aggregated granular cancer subtypes into coarse groupings , likely obfuscating informative molecular and prognostic associations and phenotypic variation of more detailed tumor subtypes. Accordingly, we collated 378,123 hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) with matched targeted DNA clinical sequencing results and OncoTree detailed cancer subtypes from a real-world cohort of 71,142 patients. Using this scaled, granular dataset and a cancer subtype knowledge graph, we developed Mosaic: a family of calibrated machine learning models using H&E WSI embeddings to classify tumors and identify molecular phenotypes across 163 detailed subtypes. The cancer subtyping module (Aeon) achieved an area under the receiver operating characteristic curve (AUROC) of 0.992 overall, with 161/163 subtypes reaching an AUROC ≥ 0.90 and improved performance over a state-of-the-art genomics-based classifier. The genomic inference module (Paladin) achieved an AUROC ≥ 0.80 for 167 pairs of detailed subtypes and genomic targets. We further used the learned histopathologic representations to i) identify key associations of the histopathologic embeddings with clinical biomarkers; ii) identify unsupervised sub-clusters of tumors with genomic determinants of tumor phenotype; iii) specify granular diagnoses for cancers of unknown primary, evaluated by genomic associations and expected clinical outcome distributions; iv) annotate functional significance for variants of uncertain significance (VUS); and v) identify cases that mimic the phenotypic effect of known DNA variants on H&E in the absence of detectable DNA alterations. Taken together, this work advances our understanding of phenotypic variation of granular tumor subtypes, their relevance to enhanced diagnostics, and their potential utility in risk stratification with multimodal machine learning in cancer.