Hyporheic exchange has been hypothesized to have basin‐scale consequences; however, predictions throughout river networks are limited by available geomorphic and hydrogeologic data and by models that ...can analyze and aggregate hyporheic exchange flows across large spatial scales. We developed a parsimonious but physically based model of hyporheic flow for application in large river basins: Networks with EXchange and Subsurface Storage (NEXSS). We applied NEXSS across a broad range of geomorphic diversity in river reaches and synthetic river networks. NEXSS demonstrates that vertical exchange beneath submerged bed forms rather than lateral exchange through meanders dominates hyporheic fluxes and turnover rates along river corridors. Per kilometer, low‐order streams have a biogeochemical potential at least 2 orders of magnitude larger than higher‐order streams. However, when biogeochemical potential is examined per average length of each stream order, low‐ and high‐order streams were often found to be comparable. As a result, the hyporheic zone's intrinsic potential for biogeochemical transformations is comparable across different stream orders, but the greater river miles and larger total streambed area of lower order streams result in the highest cumulative impact from low‐order streams. Lateral exchange through meander banks may be important in some cases but generally only in large rivers.
Key Points
Physically based model predicts hyporheic exchange in any lowland riverVertical exchange rather than lateral exchange dominates hyporheic flow in river networksReaction potential is comparable for small and large streams
Abstract
MoonProt 3.0 (http://moonlightingproteins.org) is an updated open-access database storing expert-curated annotations for moonlighting proteins. Moonlighting proteins have two or more ...physiologically relevant distinct biochemical or biophysical functions performed by a single polypeptide chain. Here, we describe an expansion in the database since our previous report in the Database Issue of Nucleic Acids Research in 2018. For this release, the number of proteins annotated has been expanded to over 500 proteins and dozens of protein annotations have been updated with additional information, including more structures in the Protein Data Bank, compared with version 2.0. The new entries include more examples from humans, plants and archaea, more proteins involved in disease and proteins with different combinations of functions. More kinds of information about the proteins and the species in which they have multiple functions has been added, including CATH and SCOP classification of structure, known and predicted disorder, predicted transmembrane helices, type of organism, relationship of the protein to disease, and relationship of organism to cause of disease.
Many biological and man-made networked systems are characterized by the simultaneous presence of different sub-networks organized in separate layers, with links and nodes of qualitatively different ...types. While during the past few years theoretical studies have examined a variety of structural features of complex networks, the outstanding question is whether such features are characterizing all single layers, or rather emerge as a result of coarse-graining, i.e. when going from the multilayered to the aggregate network representation. Here we address this issue with the help of real data. We analyze the structural properties of an intrinsically multilayered real network, the European Air Transportation Multiplex Network in which each commercial airline defines a network layer. We examine how several structural measures evolve as layers are progressively merged together. In particular, we discuss how the topology of each layer affects the emergence of structural properties in the aggregate network.
New experimental techniques are allowing, for the first time, direct visualization of mass and momentum transport across the sediment‐water interface in streams. These experimental insights are ...catalyzing a renaissance in our understanding of the role stream turbulence plays in a host of critical ecosystem services, including nutrient cycling. In this commentary, we briefly review the nature of stream turbulence and its role in hyporheic exchange and nutrient cycling in streams. A simple process‐based model, borrowed from biochemical engineering, provides the link between empirical relationships for grain‐scale turbulent mixing and nutrient processing at reach, catchment, continental, and global scales.
Plain Language Summary
Streams transport excess nitrogen and phosphorus from point and nonpoint sources in a watershed to downstream receiving waters. But streams are not pipes. Microorganisms living in streambed sediments catalyze a broad range of redox reactions that reduce the impacts of nutrient pollution or in some cases exacerbate it. In this commentary we discuss recent advances in our understanding of how turbulence influences the transport and biogeochemical processing of nutrients in streambed sediments and explore how these concepts might be incorporated into stream network models of nutrient fate and transport at local‐to‐global scales.
Key Points
Nutrient processing in streambed sediments is facilitated by hyporheic exchange across submerged bedforms
New evidence suggests that stream turbulence plays an outsized but underappreciated role in this process
Incorporating stream turbulence into stream network models should greatly improve local‐to‐global assessments of nutrient pollution
Discharge varies in space and time, driving hyporheic exchange processes in river corridors that affect biogeochemical cycling and ultimately control the dynamics of biogeochemical hot spots and hot ...moments. Herein, we use a reduced‐order model to conduct the systematic analysis of the interplay between discharge variability (peak flow intensities, duration, and skewness) and streambed topography (bedform aspect ratios and channel slopes) and their role in the flow and transport characteristics of hyporheic zones (HZs). We use a simple and robust conceptualization of single peak flow events for a series of periodic sinusoidal bedforms. Using the model, we estimate the spatial extent of the HZ, the total amount of exchange, and the residence time of water and solutes within the reactive environment and its duration relative to typical time scales for oxygen consumption (i.e., a measure of the denitrification potential). Our results demonstrate that HZ expansion and contraction is controlled by events yet modulated by ambient groundwater flow. Even though the change in hyporheic exchange flux (%) relative to baseflow conditions is invariant for different values of channel slopes, absolute magnitudes varied substantially. Primarily, peak flow events cause more discharge of older water for the higher aspect ratios (i.e., for dunes and ripples) and lower channel slopes. Variations in residence times during peak flow events lead to the development of larger areas of potential nitrification and denitrification in the HZ for longer durations. These findings have potential implications for river management and restoration, particularly the need for (re)consideration of the importance of hyporheic exchange under dynamic flow conditions.
Key Points
A reduced‐order model is used to systematically explore the dynamics of hyporheic zones during single peak flow events
Exchange fluxes and residence times varied substantially with various combinations of peak flow characteristics, channel gradient, and streambed topography
Even though the potential denitrification efficiency increased with high intensity and duration of the peak flow event, the extent of increase was determined by the interplay between geomorphological, biological, and hydrological controls
Downstream flow in rivers is repeatedly delayed by hydrologic exchange with off‐channel storage zones where biogeochemical processing occurs. We present a dimensionless metric that quantifies river ...connectivity as the balance between downstream flow and the exchange of water with the bed, banks, and floodplains. The degree of connectivity directly influences downstream water quality — too little connectivity limits the amount of river water exchanged and leads to biogeochemically inactive water storage, while too much connectivity limits the contact time with sediments for reactions to proceed. Using a metric of reaction significance based on river connectivity, we provide evidence that intermediate levels of connectivity, rather than the highest or lowest levels, are the most efficient in removing nitrogen from Northeastern United States’ rivers. Intermediate connectivity balances the frequency, residence time, and contact volume with reactive sediments, which can maximize the reactive processing of dissolved contaminants and the protection of downstream water quality. Our simulations suggest denitrification dominantly occurs in riverbed hyporheic zones of streams and small rivers, whereas vertical turbulent mixing in contact with sediments dominates in mid‐size to large rivers. The metrics of connectivity and reaction significance presented here can facilitate scientifically based prioritizations of river management strategies to protect the values and functions of river corridors.
Research Impact Statement: We quantify river connectivity as the balance between downstream flow and the exchange of water with the bed, banks, and floodplains of rivers, and demonstrate the impact on downstream water quality.
On 31 December, 2019, an outbreak of a novel coronavirus, SARS-CoV-2, that causes the COVID-19 disease, was first reported in Hubei, mainland China. This epidemics’ health threat is probably one of ...the biggest challenges faced by our interconnected modern societies. According to the epidemiological reports, the large basic reproduction numberR0∼3.0, together with a huge fraction of asymptomatic infections, paved the way for a major crisis of the national health capacity systems. Here, we develop an age-stratified mobility-based metapopulation model that encapsulates the main particularities of the spreading of COVID-19 regarding (i) its transmission among individuals, (ii) the specificities of certain demographic groups with respect to the impact of COVID-19, and (iii) the human mobility patterns inside and among regions. The full dynamics of the epidemic is formalized in terms of a microscopic Markov chain approach that incorporates the former elements and the possibility of implementing containment measures based on social distancing and confinement. With this model, we study the evolution of the effective reproduction numberR(t), the key epidemiological parameter to track the evolution of the transmissibility and the effects of containment measures, as it quantifies the number of secondary infections generated by an infected individual. The suppression of the epidemic is directly related to this value and is attained whenR<1. We find an analytical expression connectingRwith nonpharmacological interventions, and its phase diagram is presented. We apply this model at the municipality level in Spain, successfully forecasting the observed incidence and the number of fatalities in the country at each of its regions. The expression forRshould assist policymakers to evaluate the epidemics’ response to actions, such as enforcing or relaxing confinement and social distancing.
Denitrification in the hyporheic zone (HZ) of river corridors is crucial to removing excess nitrogen in rivers from anthropogenic activities. However, previous modeling studies of the effectiveness ...of river corridors in removing excess nitrogen via denitrification were often limited to the reach‐scale and low‐order stream watersheds. We developed a basin‐scale river corridor model for the Columbia River Basin with random forest models to identify the dominant factors associated with the spatial variation of HZ denitrification. Our modeling results suggest that the combined effects of hydrologic variability in reaches and substrate availability influenced by land use are associated with the spatial variability of modeled HZ denitrification at the basin scale. Hyporheic exchange flux can explain most of spatial variation of denitrification amounts in reaches of different sizes, while among the reaches affected by different land uses, the combination of hyporheic exchange flux and stream dissolved organic carbon (DOC) concentration can explain the denitrification differences. Also, we can generalize that the most influential watershed and channel variables controlling denitrification variation are channel morphology parameters (median grain size (D50), stream slope), climate (annual precipitation and evapotranspiration), and stream DOC‐related parameters (percent of shrub area). The modeling framework in our study can serve as a valuable tool to identify the limiting factors in removing excess nitrogen pollution in large river basins where direct measurement is often infeasible.
Key Points
Hyporheic exchange flux controls the spatial variation of denitrification across reaches with different sizes and land uses
The combination of hyporheic exchange flux and stream dissolved organic carbon (DOC) explains the differences in denitrification for different land use streams
D50, stream slope, precipitation, evapotranspiration, and shrub area can explain most of the spatial variability in denitrification
Bed form‐induced hyporheic interactions are characterized by a nested system of flow paths that continuously exchange water, solutes, momentum, and energy. At the local scale, sediment heterogeneity ...plays a key role in the hydrodynamics and potential for biogeochemical transformations within the hyporheic zone. This manuscript explores the role of low‐permeability sedimentary layers on the interplay between bed form‐induced hyporheic exchange and groundwater upwelling. A hydrodynamic conceptualization that sequentially couples fully‐turbulent flow in the water column and Darcian flow in the sediment is used. Low‐permeability layers are characterized by long residence times and solute accumulation. Furthermore, these layers induce hydrodynamic sequestration due to the relocation and, in some cases, emergence of new stagnation zones. Spatial patterns of residence time distributions and flushing intensities indicate that the interface of the low‐permeability layers has the potential to be a hot spot for biogeochemical transformations and flow acceleration near such interface can increase the mobilization capacity for the products of redox chemical and microbial processes. A discussion about the possible implications that hydrodynamic changes have on the biogeochemistry of hyporheic zones is presented; however, further biogeochemical experimentation and modeling are needed to validate these arguments.
Key Points
Low‐permeability layers show long residence times and solute accumulation
Low‐permeability layers induce hydrodynamic sequestration
Interface of low‐permeability layer has the potential to be a biogeochemical hot spot
To assess the cost-effectiveness of artificial intelligence (AI) for supporting clinicians in detecting and grading diseases in dermatology, dentistry, and ophthalmology.
AI has been referred to as a ...facilitator for more precise, personalized, and safer health care, and AI algorithms have been reported to have diagnostic accuracies at or above the average physician in dermatology, dentistry, and ophthalmology.
This economic evaluation analyzed data from 3 Markov models used in previous cost-effectiveness studies that were adapted to compare AI vs standard of care to detect melanoma on skin photographs, dental caries on radiographs, and diabetic retinopathy on retina fundus imaging. The general US and German population aged 50 and 12 years, respectively, as well as individuals with diabetes in Brazil aged 40 years were modeled over their lifetime. Monte Carlo microsimulations and sensitivity analyses were used to capture lifetime efficacy and costs. An annual cycle length was chosen. Data were analyzed between February 2021 and August 2021.
AI vs standard of care.
Association of AI with tooth retention-years for dentistry and quality-adjusted life-years (QALYs) for individuals in dermatology and ophthalmology; diagnostic costs.
In 1000 microsimulations with 1000 random samples, AI as a diagnostic-support system showed limited cost-savings and gains in tooth retention-years and QALYs. In dermatology, AI showed mean costs of $750 (95% CI, $608-$970) and was associated with 86.5 QALYs (95% CI, 84.9-87.9 QALYs), while the control showed higher costs $759 (95% CI, $618-$970) with similar QALY outcome. In dentistry, AI accumulated costs of €320 (95% CI, €299-€341) (purchasing power parity PPP conversion, $429 95% CI, $400-$458) with 62.4 years per tooth retention (95% CI, 60.7-65.1 years). The control was associated with higher cost, €342 (95% CI, €318-€368) (PPP, $458; 95% CI, $426-$493) and fewer tooth retention-years (60.9 years; 95% CI, 60.5-63.1 years). In ophthalmology, AI accrued costs of R $1321 (95% CI, R $1283-R $1364) (PPP, $559; 95% CI, $543-$577) at 8.4 QALYs (95% CI, 8.0-8.7 QALYs), while the control was less expensive (R $1260; 95% CI, R $1222-R $1303) (PPP, $533; 95% CI, $517-$551) and associated with similar QALYs. Dominance in favor of AI was dependent on small differences in the fee paid for the service and the treatment assumed after diagnosis. The fee paid for AI was a factor in patient preferences in cost-effectiveness between strategies.
The findings of this study suggest that marginal improvements in diagnostic accuracy when using AI may translate into a marginal improvement in outcomes. The current evidence supporting AI as decision support from a cost-effectiveness perspective is limited; AI should be evaluated on a case-specific basis to capture not only differences in costs and payment mechanisms but also treatment after diagnosis.
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