Modeling of nitrogen in river water using a detailed and a simplified model

To model catchment surface water quantity and quality, different model types are available. They vary from detailed physically based models to simplified conceptual and empirical models. The most appropriate model type for a certain application depends on the project objectives and the data availability. The detailed models are very useful for short-term simulations of representative events. They cannot be used for long-term statistical information or as a management tool. For those purposes, more simplified (conceptual or meta-) models must be used. In this study, nitrogen dynamics are modeled in a river in Flanders. Nitrogen sources from agricultural leaching and domestic point sources are considered. Based on this input, concentrations of ammonium (NH4-N) and nitrate (NO3-N) in the river water are modeled in MIKE 11 by taking into consideration advection and dispersion and the most important biological and chemical processes. Model calibration was done on the basis of available measured water quality data. To this detailed model, a more simplified model was calibrated with the objective to more easily yield long-term simulation results that can be used in a statistical analysis. The results show that the conceptual simplified model is 1800 times faster than the MIKE 11 model. Moreover the two models have almost the same accuracy. The detailed models are recommended for short-term simulations unless there are enough data for model input and model parameters. The conceptual simplified model is recommended for long-term simulations.     

VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data

This work is in line with an on-going effort tending toward a computational (quantitative and refutable) understanding of human neuro-cognitive processes. Many sophisticated models for behavioural and neurobiological data have flourished during the past decade. Most of these models are partly unspecified (i.e. they have unknown parameters) and nonlinear. This makes them difficult to peer with a formal statistical data analysis framework. In turn, this compromises the reproducibility of model-based empirical studies. This work exposes a software toolbox that provides generic, efficient and robust probabilistic solutions to the three problems of model-based analysis of empirical data: (i) data simulation, (ii) parameter estimation/model selection, and (iii) experimental design optimization.

This is a PLOS Computational Biology Software Article

     

A Class of Pairwise Models for Epidemic Dynamics on Weighted Networks

In this paper, we study the SIS (susceptible–infected–susceptible) and SIR (susceptible–infected–removed) epidemic models on undirected, weighted networks by deriving pairwise-type approximate models coupled with individual-based network simulation. Two different types of theoretical/synthetic weighted network models are considered. Both start from non-weighted networks with fixed topology followed by the allocation of link weights in either (i) random or (ii) fixed/deterministic way. The pairwise models are formulated for a general discrete distribution of weights, and these models are then used in conjunction with stochastic network simulations to evaluate the impact of different weight distributions on epidemic thresholds and dynamics in general. For the SIR model, the basic reproductive ratio R ₀ is computed, and we show that (i) for both network models R ₀ is maximised if all weights are equal, and (ii) when the two models are ‘equally-matched’, the networks with a random weight distribution give rise to a higher R ₀ value. The models with different weight distributions are also used to explore the agreement between the pairwise and simulation models for different parameter combinations.     

Exact Solutions of Linear Reaction-Diffusion Processes on a Uniformly Growing Domain: Criteria for Successful Colonization

Many processes during embryonic development involve transport and reaction of molecules, or transport and proliferation of cells, within growing tissues. Mathematical models of such processes usually take the form of a reaction-diffusion partial differential equation (PDE) on a growing domain. Previous analyses of such models have mainly involved solving the PDEs numerically. Here, we present a framework for calculating the exact solution of a linear reaction-diffusion PDE on a growing domain. We derive an exact solution for a general class of one-dimensional linear reaction—diffusion process on 0<x<L(t), where L(t) is the length of the growing domain. Comparing our exact solutions with numerical approximations confirms the veracity of the method. Furthermore, our examples illustrate a delicate interplay between: (i) the rate at which the domain elongates, (ii) the diffusivity associated with the spreading density profile, (iii) the reaction rate, and (iv) the initial condition. Altering the balance between these four features leads to different outcomes in terms of whether an initial profile, located near x = 0, eventually overcomes the domain growth and colonizes the entire length of the domain by reaching the boundary where x = L(t).     

Functional arginine-containing amino acid sequences in peptides and proteins

L-Arginine is a source of nitrogen oxide and plays a great role in a number of other biochemical processes. Functions and prospects for practical application of five groups of arginine-containing amino acid sequences and synthetic polyarginine sequences are considered. The physiological characteristics of well-known arginine-containing peptides, such as RGD containing, kyotorphin, and tuftsin, are described in detail.     

Statistical modeling of nitrogen-dependent modulation of root system architecture in Arabidopsis thaliana

Plant root development is strongly affected by nutrient availability. Despite the importance of structure and function of roots in nutrient acquisition,statistical modeling approaches to evaluate dynamic and temporal modulations of root system architecture in response to nutrient availability have remained as widely open and exploratory areas in root biology. In this study,we developed a statistical modeling approach to investigate modulations of root system architecture in response to nitrogen availability. Mathematical models were designed for quantitative assessment of root growth and root branching phenotypes and their dynamic relationships based on hierarchical con figuration of primary and lateral roots formulating the fishbone-shaped root system architecture in Arabidopsis thaliana. Time-series datasets reporting dynamic changes in root developmental traits on different nitrate or ammonium concentrations were generated for statistical analyses. Regression analyses unraveled key parameters associated with:(i) inhibition of primary root growth under nitrogen limitation or on ammonium;(ii) rapid progression of lateral root emergence in response to ammonium; and(iii) inhibition of lateral root elongation in the presence of excess nitrate or ammonium. This study provides a statistical framework for interpreting dynamic modulation of root system architecture,supported by metaanalysis of datasets displaying morphological responses of roots to diverse nitrogen supplies.     

Crop response to magnesium fertilization as affected by nitrogen supply

Background

Crop yield depends in large part on the availability and accessibility of nitrogen in the soil. For optimal yield, the soil nitrogen must be available at critical periods of crop development, and in a form that is accessible for plant uptake and use. Ancillary crop nutrients can alter the plant’s ability to access and utilize nitrogen. Therefore, crop fertilization with magnesium should focus on its effect on nitrogen management. This conceptual review aims to assess the present state of knowledge regarding the importance of magnesium in fulfilling both objectives.

Scope

The response to fertilizer magnesium of high-yielding wheat, maize, sugar beet and potato crops was evaluated using published and unpublished data on yield, yield components and nitrogen uptake. A simple, stepwise regression and path analysis was applied to explain the effect of fertilizer magnesium on yield and yield components.

Conclusions

The effect of soil or foliar applied magnesium on yield of crops was inconsistent due to (i) weather experienced during the growth season, (ii) rates of applied fertilizer nitrogen, and (iii) the (natural background levels of?) magnesium available in the soil. The yield increase due to magnesium application was related to the extra supply of nitrogen. In cereals, magnesium application resulted in a higher number of ears and/or thousand grain weight (TGW), stressing the magnesium-sensitive stages of yield formation. The increase of sugar beet yield was most pronounced in dry years. The main conclusion gleaned from the review underlines a positive effect of magnesium on nitrogen uptake efficiency. The optimal yield forming effect of fertilizer magnesium can generally occur under conditions of relatively low nitrogen supply (soil + fertilizer nitrogen), but high supply of magnesium. This phenomenon can best be described as “magnesium-induced nitrogen uptake”.     

Reaction between nitric oxide,glutathione, and oxygen in the presence and absence of protein: How are S-nitrosothiols formed?

The reaction between NO, thiols, and oxygen has been studied in some detail in vitro due to its perceived importance in the mechanism of NO-dependent signal transduction. The formation of S-nitrosothiols and thiol disulfides from this chemistry has been suggested to be an important component of the biological chemistry of NO, and such subsequent thiol modifications may result in changes in cellular function and phenotype. In this study we have reinvestigated this reaction using both experiment and simulation and conclude that: (i) S-nitrosation through radical and nonradical pathways is occurring simultaneously, (ii) S-nitrosation through direct addition of NO to thiol does not occur to any meaningful extent, and (iii) protein hydrophobic environments do not catalyze or enhance S-nitrosation of either themselves or of glutathione. We conclude that S-nitrosation and disulfide formation in this system occur only after the initial reaction between NO and oxygen to form nitrogen dioxide, and that hydrophobic protein environments are unlikely to play any role in enhancing and targeting S-nitrosothiol formation.     

Soil Nitrogen Availability and Plant Genotype Modify the Nutrition Strategies of M. truncatula and the Associated Rhizosphere Microbial Communities

Plant and soil types are usually considered as the two main drivers of the rhizosphere microbial communities. The aim of this work was to study the effect of both N availability and plant genotype on the plant associated rhizosphere microbial communities, in relation to the nutritional strategies of the plant-microbe interactions, for six contrasted Medicago truncatula genotypes. The plants were provided with two different nutrient solutions varying in their nitrate concentrations (0 mM and 10 mM). First, the influence of both nitrogen availability and Medicago truncatula genotype on the genetic structure of the soil bacterial and fungal communities was determined by DNA fingerprint using Automated Ribosomal Intergenic Spacer Analysis (ARISA). Secondly, the different nutritional strategies of the plant-microbe interactions were evaluated using an ecophysiological framework. We observed that nitrogen availability affected rhizosphere bacterial communities only in presence of the plant. Furthermore, we showed that the influence of nitrogen availability on rhizosphere bacterial communities was dependent on the different genotypes of Medicago truncatula. Finally, the nutritional strategies of the plant varied greatly in response to a modification of nitrogen availability. A new conceptual framework was thus developed to study plant-microbe interactions. This framework led to the identification of three contrasted structural and functional adaptive responses of plant-microbe interactions to nitrogen availability.     

Biofuel production improvement with genome-scale models: The role of cell composition

Genome-scale models have developed into a vital tool for rational metabolic engineering. These models balance cofactors and energetic requirements and determine biosynthetic precursor availability in response to environmental and genetic perturbations. In particular, allocation of additional reducing power is an important strategy for engineering potential biofuel production from microbes. Many potential biofuel solvents induce biomolecular changes on the host organism that are not yet captured by genome-scale models. Here, methods of construction for several biomass constituting equations are reviewed along with potential changes to cellular composition with potential biofuels exposure. The biomass constituting equations of potential host organisms with existing genome-scale models are compared side-by-side to explore their evolution over the years and to explore differences that arise when these equations are compiled by different research groups. Genome-scale model simulation results attempt to address and provide guidance for further research into: (i) whether inconsistencies in the biomass constituting equations are relevant to predictions of solvent production, (ii) what level of detail is necessary to accurately describe cellular composition, and (iii) future developments that may enable more accurate characterizations of biomolecular composition.     

Multifractal analysis of K+ channel activity

The Multifractal version of the Detrended Fluctuation Analysis was used for the study of non-stationary dwell time series of Ca²⁺-activated K⁺ channels (K_Ca channels) in cultured kidney Vero cells and of voltage-dependent K⁺ channels (K_v channels) in mollusc (Lymnaea stagnalis) neurons. The data obtained can briefly be summarized as follows: (i) The generalized fluctuation function F _q (l) strongly depends on the index (order) q; for monofractal time series, such dependence is nonexistent; (ii) The relationship between the scaling exponent τ commonly employed in standard multifractal analysis and q is characterized by two slopes and a transitory region, whereas monofractal processes are characterized by the linear dependence; (iii) The relationship between the singularity spectrum f(h) and the Hurst exponent h is bell-shaped, while in the case of monofractal processes it is represented by a single point f(h) = 1. Random mixing of the time series resulted in the narrowing of the spectrum f(h) and a shift of f(h) towards the value more characteristic of stochastic (monofractal) processes (h ～ 0.5). It is concluded that the activities of both K_Ca channels in kidney Vero cells and of K_V channels in mollusc (Lymnaea stagnalis) neurons can be characterized as multifractal processes.     

Adaptive feedback control models of the vestibulocerebellum and spinocerebellum

We extend the cerebellar learning model proposed by Kawato and Gomi (1992) to the case where a specific region of the cerebellum executes adaptive feed-back control as well as feedforward control. The model is still based on the feedback-error-learning scheme. The proposed adaptive feedback control model is developed in detail as a specific neural circuit model for three different regions of the cerebellum and the learning of the corresponding representative movements: (i) the flocculus and adaptive modification of the vestibulo-ocular reflex and optokinetic eye-movement responses, (ii) the vermis and adaptive posture control, and (iii) the intermediate zones of the hemisphere and adaptive control of locomotion. As a representative example, simultaneous adaptation of the vestibulo-ocular reflex and the optokinetic eye-movement response was successfully simulated while the Purkinje cells receive copies of motor commands through recurrent neural connections as well as vestibular and retinal-slip parallel-fiber inputs.     

The simulation of soil organic matter and nitrogen accumulation in Scots pine plantations on bare parent material using the combined forest model EFIMOD

The individual-based combined forest model EFIMOD including the soil-sub model SOMM has been used for the simulation of Scots pine stand growth and soil organic matter (SOM) accumulation on a humus-free bare mineral surface. The growth of Scots pine plantation, with an initial density of 10 000 trees ha⁻¹ and average tree biomass of 0.01 kg was simulated for 50 yr under Central European climatic conditions (i) with varying atmospheric nitrogen inputs and (ii) different rates of initial application of raw undecomposed organic material or compost, on humus-free parent material. The accumulation of typical raw humus was simulated in all cases. The accumulation was most intensive in the simulation of high atmospheric nitrogen input. The humus pool in the mineral topsoil was small but achieved its maximum value with compost application. SOM nitrogen accumulation was scant in all cases, except the compost applications with low atmospheric nitrogen input. No statistically significant differences of SOM and stand parameters were found between variants without organic matter and those with low input of organic manure. However, the maximum relative rate of SOM and nitrogen accumulation was found in the scenario without organic manure, under slowly growing unstable Scots pine plantation. This revised version was published online in June 2006 with corrections to the Cover Date. This revised version was published online in June 2006 with corrections to the Cover Date. This revised version was published online in June 2006 with corrections to the Cover Date.     

A peptide containing residues 26-44 of tau protein impairs mitochondrial oxidative phosphorylation acting at the level of the adenine nucleotide translocator

Having confirmed that adenovirus-mediated overexpression of NH₂-tau fragment lacking the first 25 aminoacids evokes a potent neurotoxic effect, sustained by protracted stimulation of NMDA receptors, in primary neuronal cultures we investigated whether and how chemically synthesized NH₂-derived tau peptides, i.e. NH₂-26-44 and NH₂-1-25 fragments, affect mitochondrial function. We tested both fragments on each step of the processes leading to ATP synthesis via oxidative phosphorylation: i) electron flow via the respiratory chain from physiological substrates to oxygen with the activity of each individual complex of the respiratory chain investigated in some detail, ii) membrane potential generation arising from externally added succinate and iii) the activity of both the adenine nucleotide translocator and iv) ATP synthase. Oxidative phosphorylation is not affected by NH₂-1-25 tau fragment, but dramatically impaired by NH₂-26-44 tau fragment. Both cytochrome c oxidase and the adenine nucleotide translocator are targets of NH₂-26-44 tau fragment, but adenine nucleotide translocator is the unique mitochondrial target responsible for impairment of oxidative phosphorylation by the NH₂-26-44 tau fragment, which then exerts deleterious effects on cellular availability of ATP synthesized into mitochondria.     

Implementation of the perfect plasticity approximation with biogeochemical compartments in R

Modeling forest ecosystems is a landmark challenge in science, due to the complexity of the processes involved and their importance in predicting future planetary conditions. While there are a number of open-source forest biogeochemistry models, few papers exist detailing the software development approach used to develop these models. This has left many forest biogeochemistry models large, opaque and/or difficult to use, typically implemented in compiled languages for speed. Here, we present a forest biogeochemistry model from the SORTIE-PPA class of models, PPA-SiBGC. Our model is based on the perfect plasticity approximation with simple biogeochemistry compartments and uses empirical vegetation dynamics rather than detailed prognostic processes to drive the estimation of carbon and nitrogen fluxes. This allows our model to be used with traditional forest inventory data, making it widely applicable and simple to parameterize. We detail the conceptual design of the model as well as the software implementation in the R language for statistical computing. Our aim is to provide a useful tool for the biogeochemistry modeling community that demonstrates the importance of vegetation dynamics in biogeochemical models.     

Linkage of MIKE SHE to Wetland-DNDC for carbon budgeting and anaerobic biogeochemistry simulation

This study reports the linkage between MIKE SHE and Wetland-DNDC for carbon dynamics and greenhouse gases (GHGs) emissions simulation in forested wetland.Wetland-DNDC was modified by parameterizing management measures, refining anaerobic biogeochemical processes, and was linked to the hydrological model – MIKE SHE. As a preliminary application, we simulated the effect of water table position and forest management practices on GHGs emissions and carbon dynamics to test the capabilities of the models for simulating seasonal and long-term carbon budget. Simulation results show that water table changes had a remarkable effect on GHGs fluxes. Anaerobic conditions in forested wetland soils reduce organic matter decomposition and stimulate CH₄ production. Decrease in the water table from the wetland surface decreases methane flux, while CO₂ emission was lower with a rise in the water table. When there is a drop in water availability, wetlands can become a net source of atmospheric CO₂ as photosynthesis is decreased and respiration loss enhanced. Forest management activities i.e. harvest, fertilization and reforestation practices were parameterized in the model. We predicted carbon fluxes and stores on a pine forest under different forest management scenarios during 160 years. Results show that average long-term carbon storage in ecosystem pools increased with increasing rotation length; Soil carbon showed only minor, long-term responses to harvesting events. In contrast, carbon sequestered in tree biomass and litter fluctuated widely, in concert with the harvest cycle. Application of nitrogen fertilizer increased average carbon storage in all ecosystem pools and wood products. We presented the linkage of MIKE SHE and Wetland-DNDC as a way to use of simulation modeling tools for assessing GHGs mitigation strategies, carbon budgeting and forest management.     

Systematic construction of a conceptual minimal model of plasma cholesterol levels based on knockout mouse phenotypes

Elevated plasma cholesterol, a well-known risk factor for cardiovascular diseases, is the result of the activity of many genes and their encoded proteins in a complex physiological network. We aim to develop a minimal kinetic computational model for predicting plasma cholesterol levels. To define the scope of this model, it is essential to discriminate between important and less important processes influencing plasma cholesterol levels. To this end, we performed a systematic review of mouse knockout strains and used the resulting dataset, named KOMDIP, for the identification of key genes that determine plasma cholesterol levels. Based on the described phenotype of mouse knockout models, 36 of the 120 evaluated genes were marked as key genes that have a pronounced effect on the plasma cholesterol concentration. The key genes include well-known genes, e.g., Apoe and Ldlr, as well as genes hardly linked to cholesterol metabolism so far, e.g., Plagl2 and Slc37a4. Based on the catalytic function of the genes, a minimal conceptual model was defined. A comparison with nine conceptual models from literature revealed that each of the individual published models is less complete than our model. Concluding, we have developed a conceptual model that can be used to develop a physiologically based kinetic model to quantitatively predict plasma cholesterol levels.     

The role of asparagine-linked glycosylation site on the catalytic domain of matriptase in its zymogen activation

Matriptase is a type II transmembrane serine protease containing one potential site for asparagine-linked glycosylation (N-glycosylation) on the catalytic domain (Asn772). It has been found that the activation of matriptase zymogen occurs via a mechanism requiring its own activity and that the N-glycosylation site is critical for the activation. The present study aimed to determine the underlying reasons for the site requirement using Madin–Darby canine kidney cells stably expressing recombinant variants of rat matriptase. A full-length variant with glutamine substitution at Asn772 appeared to be unable to undergo activation because of its catalytic incompetence (i.e., decreased availability of the soluble catalytic domain and/or of the correctly folded domain). This was evidenced by the observations that (i) a recombinant catalytic domain of matriptase with glutamine substitution at the site corresponding to matriptase Asn772 [N772Q-CD-Myc(His)₆] was not detected in the medium conditioned by transfected cells but was on the cell surface and (ii) purified N772Q-CD-Myc(His)₆ exhibited markedly reduced activity toward a peptide substrate. It is concluded that N-glycosylation site at Asn772 of matriptase is required for the zymogen activation because it plays an important role in rendering this protease catalytically competent in the cellular environment.     

Stochastic determinants of assemblage patterns in coral reef fishes: a quantification by means of two models

Synopsis One perspective emphasizing the importance of stochastic processes in determining coral reef fish assemblages implies that there is little organization in species richness, abundance structure, and spatial distribution. We examine the degree to which this perspective is correct by analyzing distribution of fishes on a collection of patch reefs (Discovery Bay, Jamaica). We ask the question whether these patches accumulate species and individuals in a manner consistent with stochastic expectations. To address this question we use two conceptual models, each permitting a different insight. One assumes that fish are distributed stochastically on patches while the other assumes presence of restrictions on fish distribution due to habitat structure. For each conceptual model we use two types of benchmark: we compare observed patterns to those predicted by theoretical models, and we also compare observed patterns to those obtained from a random reallocation of fish individuals to patches. We found that the conceptual model assuming stochastic processes appeared to provide weaker explanation of patterns than the conceptual model that includes restrictions due to habitat structure. Further, and more importantly, we found that (i) the community is shaped by a mixture of stochastic and non-stochastic mechanisms, and (ii) the stochastic assembly processes decrease in importance for species restricted to fewer microhabitat types and sites. Our study therefore indicates that patches do accumulate individuals and species in a manner consistent with stochastic expectations, however, this applies primarily to the habitat generalists (unrestricted species). By the same token, increased habitat specialization by some species imposes constraints on the stochastic model such that it eventually fails.