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Representativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major phases: Phase I—Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Phase II—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Phase III—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.

The U.S. Department of Energy's (DOE) Colorado East River Community Observatory (ER) in the Upper Colorado River Basin was established in 2015 as a representative mountainous, snow-dominated watershed to study hydrobiogeochemical responses to hydrological perturbations in headwater systems. The ER is characterized by steep elevation, geologic, hydrologic and vegetation gradients along floodplain, montane, subalpine, and alpine life zones, which makes it an ideal location for researchers to understand how different mountain subsystems contribute to overall watershed behaviour. The ER has both long-term and spatially-extensive observations and experimental campaigns carried out by the Watershed Function Scientific Focus Area (SFA), led by Lawrence Berkeley National Laboratory, and researchers from over 30 organizations who conduct cross-disciplinary process-based investigations and modelling of watershed behaviour. The heterogeneous data generated at the ER include hydrological, genomic, biogeochemical, climate, vegetation, geological, and remote sensing data, which combined with model inputs and outputs comprise a collection of datasets and value-added products within a mountainous watershed that span multiple spatiotemporal scales, compartments, and life zones. Within 5 years of collection, these datasets have revealed insights into numerous aspects of watershed function such as factors influencing snow accumulation and melt timing, water balance partitioning, and impacts of floodplain biogeochemistry and hillslope ecohydrology on riverine geochemical exports. Data generated by the SFA are managed and curated through its Data Management Framework. The SFA has an open data policy, and over 70 ER datasets are publicly available through relevant data repositories. A public interactive map of data collection sites run by the SFA is available to inform the broader community about SFA field activities. Here, we describe the ER and the SFA measurement network, present the public data collection generated by the SFA and partner institutions, and highlight the value of collecting multidisciplinary multiscale measurements in representative catchment observatories.

Major components of hydrologic and elemental cycles reside underground, where their complex dynamics and linkages to surface waters are obscure. We delineated seasonal subsurface flow and transport dynamics along a hillslope in the Rocky Mountains (USA), where precipitation occurs primarily as winter snow and drainage discharges into the East River, a tributary of the Gunnison River. Hydraulic and geochemical measurements down to 10 m below ground surface supported application of transmissivity feedback of snowmelt to describe subsurface flow and transport through three zones: soil, weathering shale, and saturated fractured shale. Groundwater flow is predicted to depths of at least 176 m, although a shallower limit exists if hillslope-scale hydraulic conductivities are higher than our local measurements. Snowmelt during the high snowpack water year 2017 sustained flow along the weathering zone and downslope within the soil, while negligible downslope flow occurred along the soil during the low snowpack water year 2018. We introduce subsurface concentration-discharge (C-Q) relations for explaining hillslope contributions to C-Q observed in rivers and demonstrate their calculations based on transmissivity fluxes and measured pore water specific conductance and dissolved organic carbon. The specific conductance data show that major ions in the hillslope pore waters, primarily from the weathering and fractured shale, are about six times more concentrated than in the river, indicating hillslope solute loads are disproportionately high, while flow from this site and similar regions are relatively smaller. This methodology is applicable in different representative environments within snow-dominated watersheds for linking their subsurface exports to surface waters.

The U.S. Department of Energy's Watershed Function Scientific Focus Area (SFA), centered in the East River, Colorado, generates diverse datasets including hydrological, geological, geochemical, geophysical, ecological, microbiological and remote sensing data. The project has deployed extensive field infrastructure involving hundreds of sensors that measure highly diverse phenomena (e.g. stream and groundwater hydrology, water quality, soil moisture, weather) across the watershed. Data from the sensor network are telemetered and automatically ingested into a queryable database. The data are subsequently quality checked, integrated with the United States Geological Survey's stream monitoring network using a custom data integration broker, and published to a portal with interactive visualizations. The resulting data products are used in a variety of scientific modeling and analytical efforts. This paper describes the SFA's end-to-end infrastructure and services that support the generation of integrated datasets from a watershed sensor network. The development and maintenance of this infrastructure, presents a suite of challenges from practical field logistics to complex data processing, which are addressed through various solutions. In particular, the SFA adopts a holistic view for data collection, assessment and integration, which dramatically improves the products generated, and enables a co-design approach wherein data collection is informed by model results and vice-versa.

We use 3-D high-resolution reactive transport modeling to investigate whether the spatial distribution of organic-carbon-rich and chemically reduced sediments located in the riparian zone and temporal variability in groundwater flow direction impact the formation and distribution of nitrogen hot spots (regions that exhibit higher reaction rates when compared to other locations nearby) and hot moments (times that exhibit high reaction rates as compared to longer intervening time periods) within the Rifle floodplain in Colorado. Groundwater flows primarily toward the Colorado River from the floodplain but changes direction at times of high river stage. The result is that oxic river water infiltrates the Rifle floodplain during these relatively short-term events. Simulation results indicate that episodic rainfall in the summer season leads to the formation of nitrogen hot moments associated with Colorado River rise and resulting river infiltration into the floodplain. The results further demonstrate that the naturally reduced zones (NRZs) present in sediments of the Rifle floodplain have a higher potential for nitrate removal, approximately 70% greater than non-NRZs for typical hydrological conditions. During river water infiltration, nitrate reduction capacity remains the same within the NRZs, however, these conditions impact non-NRZs to a greater extent (approximately 95% less nitrate removal). Model simulations indicate chemolithoautotrophs are primarily responsible for the removal of nitrate in the Rifle floodplain. These nitrogen hot spots and hot moments are sustained by microbial respiration and the chemolithoautotrophic oxidation of reduced minerals in the riparian zone.

Embryonal tumor with multilayered rosettes (ETMR, previously known as ETANTR) is a highly aggressive embryonal CNS tumor, which almost exclusively affects infants and is associated with a dismal prognosis. Accurate diagnosis is of critical clinical importa

Introduction: Cribriform growth pattern (CP) in prostate cancer (PCa) has been associated with different unfavourable oncological outcomes. This study addresses if CP in prostate biopsies is an independent risk factor for metastatic disease on PSMA PET/CT. Methods: Treatment-naive patients with ISUP GG ≥ 2 staged with 68Ga-PSMA-11 PET/CT diagnosed from 2020 to 2021 were retrospectively enrolled. To test if CP in biopsies was an independent risk factor for metastatic disease on 68Ga-PSMA PET/CT, regression analyses were performed. Secondary analyses were performed in different subgroups. Results: A total of 401 patients were included. CP was reported in 252 (63%) patients. CP in biopsies was not an independent risk factor for metastatic disease on the 68Ga-PSMA PET/CT (p = 0.14). ISUP grade group (GG) 4 (p = 0.006), GG 5 (p = 0.003), higher PSA level groups per 10 ng/ml until > 50 (p-value between 0.02 and > 0.001) and clinical EPE (p > 0.001) were all independent risk factors. In the subgroups with GG 2 (n = 99), GG 3 (n = 110), intermediate-risk group (n = 129) or the high-risk group (n = 272), CP in biopsies was also not an independent risk factor for metastatic disease on 68Ga-PSMA PET/CT. If the EAU guideline recommendation for performing metastatic screening was applied as threshold for PSMA PET/CT imaging, in 9(2%) patients, metastatic disease was missed, and 18% fewer PSMA PET/CT would have been performed. Conclusion: This retrospective study found that CP in biopsies was not an independent risk factor for metastatic disease on 68Ga-PSMA PET/CT. [ABSTRACT FROM AUTHOR]