Systems Biology Graphical Notation: Process Diagram Level 1

Standard graphical representations have played a crucial role in science and engineering throughout the last century. Without electrical symbolism, it is very likely that our industrial society would not have evolved at the same pace. Similarly, specialised notations such as the Feynmann notation or the process flow diagrams did a lot for the adoption of concepts in their own fields. With the advent of Systems Biology, and more recently of Synthetic Biology, the need for precise and unambiguous descriptions of biochemical interactions has become more pressing. While some ideas have been advanced over the last decade, with a few detailed proposals, no actual community standard has emerged. The Systems Biology Graphical Notation (SBGN) is a graphical representation crafted over several years by a community of biochemists, modellers and computer scientists. Three orthogonal and complementary languages have been created, the Process Diagrams, the Entity Relationship Diagrams and the Activity Flow Diagrams. Using these three idioms a scientist can represent any network of biochemical interactions, which can then be interpreted in an unambiguous way. The set of symbols used is limited, and the grammar quite simple, to allow its usage in textbooks and its teaching directly in high schools. The first level of the SBGN Process Diagram has been publicly released. Software support for SBGN Process Diagram was developed concurrently with its specification in order to speed-up public adoption. Shared by the communities of biochemists, genomicians, theoreticians and computational biologists, SBGN languages will foster efficient storage, exchange and reuse of information on signalling pathways, metabolic networks and gene regulatory maps

Suspended-sediment induced stratification inferred from concentration and velocity profile measurements in the lower Yellow River, China

Despite a multitude of models predicting sediment transport dynamics in open‐channel flow, self‐organized vertical density stratification that dampens flow turbulence due to the interaction between fluid and sediment, has not been robustly validated with field observations from natural rivers. Turbulence‐suppressing density stratification can develop in channels with low channel‐bed slope and high sediment concentration. As the Yellow River, China, maintains one of the highest sediment loads in the world for a low sloping system, this location is ideal for documenting particle and fluid interactions that give rise to density stratification. Herein, we present analyses from a study conducted over a range of discharge conditions (e.g., low flow, rising limb, and flood peak) from a lower reach of the Yellow River, whereby water samples were collected at targeted depths to measure sediment concentration and, simultaneously, velocity measurements were collected throughout the flow depth. Importantly, sediment concentration varied by an order of magnitude between base and flood flows. By comparing measured concentration and velocity profiles to predictive models, we show that the magnitude of density stratification increases with sediment concentration. Furthermore, a steady‐state calculation of sediment suspension is used to determine that sediment diffusivity increases with grain size. Finally, we calculate concentration and velocity profiles, showing that steady‐state sediment suspensions are reliably predicted over a range of stratification conditions larger than had been previously documented in natural river flows. We determine that the magnitude of density stratification can be predicted by a function considering an entrainment parameter, sediment concentration, and bed slope

Suspended-sediment induced stratification inferred from concentration and velocity profile measurements in the lower Yellow River, China

Despite a multitude of models predicting sediment transport dynamics in open‐channel flow, self‐organized vertical density stratification that dampens flow turbulence due to the interaction between fluid and sediment, has not been robustly validated with field observations from natural rivers. Turbulence‐suppressing density stratification can develop in channels with low channel‐bed slope and high sediment concentration. As the Yellow River, China, maintains one of the highest sediment loads in the world for a low sloping system, this location is ideal for documenting particle and fluid interactions that give rise to density stratification. Herein, we present analyses from a study conducted over a range of discharge conditions (e.g., low flow, rising limb, and flood peak) from a lower reach of the Yellow River, whereby water samples were collected at targeted depths to measure sediment concentration and, simultaneously, velocity measurements were collected throughout the flow depth. Importantly, sediment concentration varied by an order of magnitude between base and flood flows. By comparing measured concentration and velocity profiles to predictive models, we show that the magnitude of density stratification increases with sediment concentration. Furthermore, a steady‐state calculation of sediment suspension is used to determine that sediment diffusivity increases with grain size. Finally, we calculate concentration and velocity profiles, showing that steady‐state sediment suspensions are reliably predicted over a range of stratification conditions larger than had been previously documented in natural river flows. We determine that the magnitude of density stratification can be predicted by a function considering an entrainment parameter, sediment concentration, and bed slope

Modeling deltaic lobe‐building cycles and channel avulsions for the Yellow River delta, China

River deltas grow by repeating cycles of lobe development punctuated by channel avulsions, so that over time, lobes amalgamate to produce a composite landform. Existing models have shown that backwater hydrodynamics are important in avulsion dynamics, but the effect of lobe progradation on avulsion frequency and location has yet to be explored. Herein, a quasi‐2‐D numerical model incorporating channel avulsion and lobe development cycles is developed. The model is validated by the well‐constrained case of a prograding lobe on the Yellow River delta, China. It is determined that with lobe progradation, avulsion frequency decreases, and avulsion length increases, relative to conditions where a delta lobe does not prograde. Lobe progradation lowers the channel bed gradient, which results in channel aggradation over the delta topset that is focused farther upstream, shifting the avulsion location upstream. Furthermore, the frequency and location of channel avulsions are sensitive to the threshold in channel bed superelevation that triggers an avulsion. For example, avulsions occur less frequently with a larger superelevation threshold, resulting in greater lobe progradation and avulsions that occur farther upstream. When the delta lobe length prior to avulsion is a moderate fraction of the backwater length (0.3–0.5L_b), the interplay between variable water discharge and lobe progradation together set the avulsion location, and a model capturing both processes is necessary to predict avulsion timing and location. While this study is validated by data from the Yellow River delta, the numerical framework is rooted in physical relationships and can therefore be extended to other deltaic systems

Entrainment and suspension of sand and gravel

The entrainment and suspension of sand and gravel are important for the evolution of rivers, deltas, coastal areas, and submarine fans. The prediction of a vertical profile of suspended sediment concentration typically consists of assessing (1) the concentration near the bed using an entrainment relation and (2) the upward vertical distribution of sediment in the water column. Considerable uncertainty exists in regard to both of these steps, especially the near-bed concentration. Most entrainment relations have been tested against limited grain-size-specific data, and no relations have been evaluated for gravel suspension, which can be important in bedrock and mountain rivers. To address these issues, we compiled a database with suspended sediment data from natural rivers and flume experiments, taking advantage of the increasing availability of high-resolution grain size measurements. We evaluated 12 dimensionless parameters that may determine entrainment and suspension relations and applied multivariate regression analysis. A best-fit two-parameter equation (r² = 0.79) shows that near-bed entrainment, evaluated at 10 % of the flow depth, decreases with the ratio of settling velocity to skin-friction shear velocity (w_(si)/u_(∗ skin)), as in previous relations, and increases with Froude number (Fr), possibly due to its role in determining bedload-layer concentrations. We used the Rouse equation to predict concentration upward from the reference level and evaluated the coefficient β_i, which accounts for differences in the turbulent diffusivity of sediment from the parabolic eddy viscosity model used in the Rouse derivation. The best-fit relation for β_i (r² = 0.40) indicates greater relative sediment diffusivities for rivers with greater flow resistance, possibly due to bedform-induced turbulence, and larger w_(si)/u_(∗ skin); the latter dependence is nonlinear and therefore different from standard Rouse theory. In addition, we used empirical relations for gravel saltation to show that our relation for near-bed concentration also provides good predictions for coarse-grained sediment. The new relations extend the calibrated parameter space over a wider range in sediment sizes and flow conditions compared to previous work and result in 95 % of concentration data throughout the water column predicted within a factor of 9

Entrainment and suspension of sand and gravel

The entrainment and suspension of sand and gravel are important for the evolution of rivers, deltas, coastal areas, and submarine fans. The prediction of a vertical profile of suspended sediment concentration typically consists of assessing (1) the concentration near the bed using an entrainment relation and (2) the upward vertical distribution of sediment in the water column. Considerable uncertainty exists in regard to both of these steps, especially the near-bed concentration. Most entrainment relations have been tested against limited grain-size-specific data, and no relations have been evaluated for gravel suspension, which can be important in bedrock and mountain rivers. To address these issues, we compiled a database with suspended sediment data from natural rivers and flume experiments, taking advantage of the increasing availability of high-resolution grain size measurements. We evaluated 12 dimensionless parameters that may determine entrainment and suspension relations and applied multivariate regression analysis. A best-fit two-parameter equation (r² = 0.79) shows that near-bed entrainment, evaluated at 10 % of the flow depth, decreases with the ratio of settling velocity to skin-friction shear velocity (w_(si)/u_(∗ skin)), as in previous relations, and increases with Froude number (Fr), possibly due to its role in determining bedload-layer concentrations. We used the Rouse equation to predict concentration upward from the reference level and evaluated the coefficient β_i, which accounts for differences in the turbulent diffusivity of sediment from the parabolic eddy viscosity model used in the Rouse derivation. The best-fit relation for β_i (r² = 0.40) indicates greater relative sediment diffusivities for rivers with greater flow resistance, possibly due to bedform-induced turbulence, and larger w_(si)/u_(∗ skin); the latter dependence is nonlinear and therefore different from standard Rouse theory. In addition, we used empirical relations for gravel saltation to show that our relation for near-bed concentration also provides good predictions for coarse-grained sediment. The new relations extend the calibrated parameter space over a wider range in sediment sizes and flow conditions compared to previous work and result in 95 % of concentration data throughout the water column predicted within a factor of 9

Dynamic Treatment Regimen Estimation via Regression-Based Techniques: Introducing R Package DTRreg

Personalized medicine, whereby treatments are tailored to a specific patient rather than a general disease or condition, is an area of growing interest in the fields of biostatistics, epidemiology, and beyond. Dynamic treatment regimens (DTRs) are an integral part of this framework, allowing for personalized treatment of patients with long-term conditions while accounting for both their present circumstances and medical history. The identification of the optimal DTR in any given context, however, is a non-trivial problem, and so specialized methodologies have been developed for that purpose. Here we introduce the R package DTRreg which implements two regression-based approaches: G-estimation and dynamic weighted ordinary least squares regression. We outline the theory underlying these methods, discuss the implementation of DTRreg and demonstrate its use with hypothetical and real-world inspired simulated datasets

Challenges and Enablers to Integrate Land-Sea-Interactions in Cross-Border Marine and Coastal Planning:Experiences from the Pan Baltic Scope Collaboration

202

Systems Biology Markup Language (SBML) Level 3 Package: Distributions, Version 1, Release 1

Biological models often contain elements that have inexact numerical values, since they are based on values that are stochastic in nature or data that contains uncertainty. The Systems Biology Markup Language (SBML) Level 3 Core specification does not include an explicit mechanism to include inexact or stochastic values in a model, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactic constructs. The SBML Distributions package for SBML Level 3 adds the necessary features to allow models to encode information about the distribution and uncertainty of values underlying a quantity

Universal relation with regime transition for sediment transport in fine-grained rivers

Fine-grained sediment (grain size under 2,000 μm) builds floodplains and deltas, and shapes the coastlines where much of humanity lives. However, a universal, physically based predictor of sediment flux for fine-grained rivers remains to be developed. Herein, a comprehensive sediment load database for fine-grained channels, ranging from small experimental flumes to megarivers, is used to find a predictive algorithm. Two distinct transport regimes emerge, separated by a discontinuous transition for median bed grain size within the very fine sand range (81 to 154 μm), whereby sediment flux decreases by up to 100-fold for coarser sand-bedded rivers compared to river with silt and very fine sand beds. Evidence suggests that the discontinuous change in sediment load originates from a transition of transport mode between mixed suspended bed load transport and suspension-dominated transport. Events that alter bed sediment size near the transition may significantly affect fluviocoastal morphology by drastically changing sediment flux, as shown by data from the Yellow River, China, which, over time, transitioned back and forth 3 times between states of high and low transport efficiency in response to anthropic activities