Personalised follow-up and management schema for patients with screen-detected pulmonary nodules: A dynamic modelling study

BACKGROUND

Selecting the time target for follow-up testing in lung cancer screening is challenging. We aim to devise dynamic, personalized lung cancer screening schema for patients with pulmonary nodules detected through low-dose computed tomography.

METHODS

We developed and validated dynamic models using data of pulmonary nodule patients (aged 55-74 years) from the National Lung Screening Trial. We predicted patient-specific risk profiles at baseline (R0) and updated the risk evaluation results in repeated screening rounds (R1 and R2). We used risk cutoffs to optimize time-dependent sensitivity at an early decision point (3 months) and time-dependent specificity at a late decision point (1 year).

RESULTS

In validation, area under receiver operating characteristic curve for predicting 12-month lung cancer onset was 0.867 (95 % confidence interval: 0.827-0.894) and 0.807 (0.765-0.948) at R0 and R1-R2, respectively. The personalized schema, compared with National Comprehensive Cancer Network (NCCN) guideline and Lung-RADS, yielded lower rates of delayed diagnosis (1.7% vs. 1.7% vs. 6.9 %) and over-testing (4.9% vs. 5.6% vs. 5.6 %) at R0, and lower rates of delayed diagnosis (0.0% vs. 18.2% vs. 18.2 %) and over-testing (2.6% vs. 8.3% vs. 7.3 %) at R2. Earlier test recommendation among cancer patients was more frequent using the personalized schema (vs. NCCN: 29.8% vs. 20.9 %, p = 0.0065; vs. Lung-RADS: 33.2% vs. 22.8 %, p = 0.0025), especially for women, patients aged ≥65 years, and part-solid or non-solid nodules.

CONCLUSIONS

The personalized schema is easy-to-implement and more accurate compared with rule-based protocols. The results highlight value of personalized approaches in realizing efficient nodule management.

Modelling of high-rate activated sludge process: Assessment of model parameters by sensitivity and uncertainty analyses

The objective of this study is to develop a mechanistic model to predict the long-term dynamic performance of High-Rate Activated Sludge (HRAS) process, including the removal of carbon (COD), nitrogen (N), and phosphorus (P). The model was formulated with inspiration from Activated Sludge Models No. 1 and 3 (ASM1 and ASM3) to incorporate essential mechanisms, such as adsorption and storage substrate, specific to HRAS systems. A stepwise protocol was followed for calibration with dynamic data from a pilot-scale HRAS plant. Sensitivity analysis identified influential model parameters, including maximum specific growth rate (μ), growth yield (YH), storage yield (YSTO), storage rate (kSTO), decay rate (b), and half saturation of the readily biodegradable substrate for growth (KS1). The calibrated model achieved prediction efficiencies above the normalized Mean Absolute Error (MAE) of 70 % for mixed liquor suspended solids (MLSS), total chemical oxygen demand (TCOD), soluble COD (SCOD), particulate COD (XCOD), total nitrogen (TN), ammonia nitrogen (SNH), total phosphorus (TP), soluble TP (STP), and particulate TP (XTP). Uncertainty analysis revealed that SCOD was underestimated. Based on the dynamic profiles of uncertainty bands and observed data, there is potential for improving the estimation of dynamic behavior in STP. The observed discrepancies may be attributed to variations in wastewater characteristics during the monitoring period, particularly concerning the phosphorus (P) fractions of the readily biodegradable substrate (SS) and soluble inerts (SI), which were not considered as dynamically changing parameters in the model.

Thermal inactivation kinetics of Listeria monocytogenes in milk under isothermal and dynamic conditions

Thermal processing is a widely used method to ensure the microbiological safety of milk. Predictive microbiology plays a crucial role in quantifying microbial growth and decline, providing valuable guidance on the design and optimization of food processing operations. This study aimed to investigate the thermal inactivation kinetics of Listeria monocytogenes in milk under both isothermal and dynamic conditions. The thermal inactivation of L. monocytogenes was conducted under isothermal and non-isothermal conditions in sterilized and pasteurized milk, with and without background microbiota, respectively. Furthermore, a secondary model was developed between the shoulder effect and temperature, which was then integrated into the dynamic model. The results showed that L. monocytogenes grown in Tryptic Soy Yeast Extract Broth (TSBYE) prior to thermal inactivation exhibited higher heat resistance compared to cells grown in sterilized milk at isothermal temperatures of 60.0, 62.5, and 65℃. Moreover, the presence of background microbiota in milk significantly enhanced the heat resistance of L. monocytogenes, as evidenced by the increased D-values from 1.13 min to 2.34 min, from 0.46 min to 0.53 min, and from 0.25 min to 0.34 min at 60.0, 62.5, and 65 °C, respectively, regardless of whether the background microbiota was inactivated after co-growth or co-inactivated with L. monocytogenes. For non-isothermal inactivation, the one-step dynamic model based on the log-linear with shoulder model effectively described the microbial inactivation curve and exhibited satisfactory model performance. The model developed contributes to improved risk assessment, enabling dairy processors to optimize thermal treatment and ensure microbiological safety.

A mechanistic mathematical model for the treatment of synthetic oil-field wastewater (produced water) by electrocoagulation process using aluminium electrodes

Produced water (PW) is the largest by-product that comes out of the oil wells during oil and gas (O&G) field exploration. PW contains high-salt concentration along with other organic and inorganic components; therefore, PW must be treated before disposal. Electrocoagulation (EC) is an effective treatment method to remove pollutants from PW which has been the focus of many experimental studies; however, a mathematical model specifically for PW treatment by EC has not been developed yet. In this work, a comprehensive mathematical model has been developed to elucidate the role of EC operating parameters on the PW treatment performance and determine the mechanism for COD (Chemical Oxygen Demand) removal. The present model considers and identifies the dominant Al-hydroxy complex species and their contribution to the COD removal from synthetic PW samples by estimating their rate constants and comparing their magnitudes and investigates multi-scale modelling of the EC reactor. The influence of working parameters such as current density, initial pH, interelectrode distance, mixing speed and solution volume of PW on Al coagulant production and COD removal was investigated and modelled. The study estimates the rate constants of the reactions taking place for COD removal by EC process and by comparing their magnitudes identifies the dominant reactions and coagulant species involved in the process. The mathematical model prediction of COD removal fits well with the experimental data at 10 mA cm-2, 15 mA cm-2 and 20 mA cm-2 current density with R2 value of 0.96, 0.97 and 0.92, respectively and for dissolved Al concentration R2 value of 0.96, 0.99, and 0.97, respectively. The simulated results reproduced a good fit at initial pH of 6.1, 7.3 and 8.6 with R2 value of 0.92, 0.96 and 0.98, respectively for COD removal. The mathematical model and the experimental results showed the role of dominant Al-hydroxy complex species such asAl OH 2 + ,Al OH 2 + , Al OH 3 ,Al 2 OH 2 + 4 andAl OH 4 - in controlling the COD removal process. Under different operating conditions considered in the study, the model also predicted the COD removal performance of the EC reactors at different reactor volumes with R2 value of 0.96 for higher solution volume and larger reactor. The model presented and rate constants determined in the study will provide a theoretical basis for designing, scaling up and operating the EC reactor for oil-field PW treatment.

Comparison of two 2-D numerical models for snow avalanche simulation

Snow avalanches are gravitational processes characterised by the rapid movement of a snow mass, threatening inhabitants and damaging infrastructure in mountain areas. Such phenomena are complex events, and for this reason, different numerical models have been developed to reproduce their dynamics over a given topography. In this study, we focus on the two-dimensional numerical simulation tools RAMMS::AVALANCHE and FLO-2D, aiming to compare their performance in predicting the deposition area of snow avalanches. We also aim to assess the employment of the FLO-2D simulation model, normally used in water flood or mud/debris flow simulations, in predicting the motion of snow avalanches. For this purpose, two well-documented avalanche events that occurred in the Province of Bolzano (IT) were analyzed (Knollgraben, Pichler Erschbaum avalanches). The deposition area of each case study was simulated with both models through back-analysis processes. The simulation results were evaluated primarily by comparing the simulated deposition area with the observed one through statistical indices. Subsequently, the maximum flow depth, velocity and deposition depth were also compared between the simulation results. The results showed that RAMMS::AVALANCHE generally reproduced the observed deposits better compared to FLO-2D simulation. FLO-2D provided suitable results for wet and dry snow avalanches after a meticulous calibration of the rheological parameters, since they are not those typically considered in avalanche rheology studies. The results showed that FLO-2D can be used to study the propagation of snow avalanches and could also be adopted by practitioners to define hazard areas, expanding its field of application.

Dynamic finite element analysis of hip replacement edge loading: Balancing precision and run time in a challenging model

An important aspect in evaluating the resilience of hip replacement designs is testing their performance under adverse conditions that cause edge loading of the acetabular liner. The representation of edge loading conditions in finite element models is computationally challenging due to the changing contact locations, need for fine meshes, and dynamic nature of the system. In this study, a combined mesh and mass-scaling sensitivity study was performed to identify an appropriate compromise between convergence and solution time of explicit finite element analysis in investigating edge loading in hip replacement devices. The optimised model was then used to conduct a sensitivity test investigating the effect of different hip simulator features (the mass of the translating fixture and mediolateral spring damping) on the plastic strain in the acetabular liner. Finally, the effect of multiple loading cycles on the progressive accumulation of plastic strain was then also examined using the optimised model. A modelling approach was developed which provides an effective compromise between mass-scaling effects and mesh refinement for a solution time per cycle of less than 1 h. This 'Recommended Mesh' model underestimated the plastic strains by less than 10%, compared to a 'Best Estimate' model with a run time of ∼190 h. Starting with this model setup would therefore significantly reduce any new model development time while also allowing the flexibility to incorporate additional complexities as required. The polyethylene liner plastic strain was found to be sensitive to the simulator mass and damping (doubling the mass or damping had a similar magnitude effect to doubling the swing phase load) and these should ideally be described in future experimental studies. The majority of the plastic strain (99%) accumulated within the first three load cycles.

Modelling Subarctic watershed dissolved organic carbon response to hydroclimatic regime

Shifts in hydroclimatic regimes associated with global climate change may impact freshwater availability and quality. In high latitudes of the northern hemisphere, where vast quantities of carbon are stored terrestrially, explaining landscape-scale carbon (C) budgets and associated pollutant transfer is necessary for understanding the impact of changing hydroclimatic regimes. We used a dynamic modelling approach to simulate streamflow, DOC concentration, and DOC export in a northern Canadian catchment that has undergone notable climate warming, and will continue to for the remainder of this century. The Integrated Catchment model for Carbon (INCA-C) was successfully calibrated to a multi-year period (2012-2016) that represents a range in hydrologic conditions. The model was subsequently run over 30-year periods representing baseline and two future climate scenarios. Average discharge is predicted to decrease under an elevated temperature scenario (22-27 % of baseline) but increase (116-175 % of baseline) under an elevated temperature and precipitation scenario. In the latter scenario the nival hydroclimatic regime is expected to shift to a combined nival and pluvial regime. Average DOC flux over 30 years is predicted to decrease (24-27 % of baseline) under the elevated temperature scenario, as higher DOC concentrations are offset by lower runoff. Under the elevated temperature and precipitation scenario, results suggest an increase in carbon export of 64-81 % above baseline. These increases are attributed to greater connectivity of the catchment. The largest increase in DOC export is expected to occur in early winter. These predicted changes in DOC export, particularly under a climate that is warmer and wetter could be part of larger ecosystem change and warrant additional monitoring efforts in the region.

Dynamic alpha factors: Prediction in time and evolution along reactors

The performance of aeration - one of the most costly processes at water resource recovery facilities - is heavily impacted by actual wastewater characteristics which are commonly taken into account using the alpha factor (α). This factor varies depending on hydraulic and organic loading; such variance includes both time and spatial fluctuations. In standard design practice, it is often considered as a fixed number, or at best, a predefined time series. The objective of this paper is to propose a new method of predicting plantwide trends in the α factor through the use of process modelling which can accommodate diurnal and seasonal variations. The authors' concept takes into account the dependence of α on sludge retention time in the form of degradation kinetics, the effects of organic loading (influent filtered COD), the presence or absence of anoxic zones, diffuser depth, and the impact of high MLSS found in certain, e.g., MBR, technologies. The developed model was calibrated using data from numerous facilities, relying on off-gas measurements and tests in clean and process water. Model validation was carried out against averaged α factor gradient data from one plant, and against diurnal air flow measurements from another. The Benchmark Simulation Model 1 configuration was used to demonstrate the applicability of the proposed model - in estimation of blower energy consumption and peak air flow requirements - comparing it with constant and scheduled α factor-based approaches.

Quantifying the embodied carbon saving potential of recycling construction and demolition waste in the Greater Bay Area, China: Status quo and future scenarios

Comparing with the enduring efforts to reduce carbon emissions in design, construction, and operation stages of a construction project, less attention has been paid to emission abatement potential in the end-of-life stage, particularly by recycling waste generated by construction and demolition (C&D) activities. This research aims to cover this knowledge void by quantifying the embodied carbon saving potential of recycling C&D waste. It does so by adopting a Life Cycle Assessment (LCA) and choosing the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in South China for a case study. The carbon emission is treated as embodied in construction materials, by recycling which the equivalent amount of carbon generated from the virgin materials can be saved. It is estimated that the GBA produced 128.49 Mt. of C&D waste in 2018, which implies an embodied carbon saving potential of 92.26 Mt. carbon emissions. The research goes further to understand the future C&D waste generation and their corresponding embodied carbon saving potential. A first-of-its-kind dynamic approach is developed to simulate the future 42-year saving potential under four construction development scenarios. Depending on different construction growth rates, the embodied carbon saving potential in 2060 can be up to 894.80 Mt. and down to 166.34 Mt. This research can help achieve China's 2060 carbon neutral goal by focusing on a non-negligible sector in an economically important region. Methods proposed in this paper are also applicable to other regions worldwide, especially where C&D waste data is insufficient.

Adsorption of pharmaceuticals in a fixed-bed column using tyre-based activated carbon: Experimental investigations and numerical modelling

Magnetic tyre char (MTC), activated tyre char (ATC) and commercial activated carbon (CAC) were used as packing materials in lab-scale column study for the adsorption of three pharmaceuticals: propranolol (PRO), ciprofloxacin (CIP) and clomipramine (CLO), from aqueous solution. The obtained breakthrough curves (BTCs) suggest that, lower flow rate, greater bed height, higher pH and nano particle size led to increased adsorption of PRO. The lowest adsorption capacity was observed for CIP either from single or ternary solution while it was significantly higher for CLO. Surface area of ATC increased nearly twelve-fold (38.17 to 453.81 m²/g), after thermal and chemical activation and adsorption capacity was comparable to commercial activated carbon. The suitability of Hydrus-1D model incorporating chemical non-equilibrium process to simulate the pharmaceutical transport and fit experimental BTCs was demonstrated (97.29 <R² <99.22) in comparison to other common models (Adams-Bohart, Thomas and Yoon-Nelson). The modelling suggests the existence of non-equilibrium conditions and rate-limited sorption sites and the effect of dispersion and mass transfer mechanisms in the solute transport under dynamic conditions. The cost analysis showed that unit cost for treatment of wastewater using fixed-bed columns of tyre char was calculated to be 1.57 US$/m³ which can be deemed as commercially feasible.

Circular economy for phosphorus supply chain and its impact on social sustainable development goals

To be able to grow crops, we have interfered with Earth's reserves of one of top three essential elements, phosphorus (P), as to which we face a problem related to its high consumption compared to available resources. This forces us to follow the alternative of closing the phosphorus loop from a circular economy perspective. However, there is a lack of research on regional and global social sustainability in this area, as emphasized in the United Nations' Agenda 2030 goals for sustainable development. In this paper, we address social challenges involved in global phosphorus supply chain, such as eradicating poverty, child labor and malnutrition; promoting gender equality; providing decent work and economic growth; maintaining sustainable water use; and achieving food security. Our research is driven by the question of whether the circular economy aims to direct phosphorus management towards tackling social issues associated with its supply chain. We use system dynamics modelling by combining the concept of material flow analysis and social life cycle assessment. Detailed analysis at regional and global levels indicates a paradoxical social impact of phosphorus circular model. This reflects the multiple stakeholders involved, and the regional interactions with phosphorus circular economy transitions. Improvements can be demonstrated in reducing poverty and providing safer work environment in many regions, e.g., Western Asia (93%), New Zealand, Central Asia, and Europe (44-61%), while achieving employment targets is limited in Northern and Eastern Europe. Circular model fails to promote gender equality, it also exacerbates exploitative child work problem for the Caribbean and most Africa. It provides sufficient nutrition to North America, Australia/New Zealand, and Northern Europe. It achieves water use targets in several regions with 53% savings worldwide. Finally, circular model contributes to P efficiency (average balance of 1.21 kgP/ha) and strengthens P security within most regions with an average of 64%.

Structural identifiability and observability of compartmental models of the COVID-19 pandemic

The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of contradictory model predictions has highlighted their limitations. If some parameters and/or state variables of a model cannot be determined from output measurements, its ability to yield correct insights - as well as the possibility of controlling the system - may be compromised. Epidemic dynamics are commonly analysed using compartmental models, and many variations of such models have been used for analysing and predicting the evolution of the COVID-19 pandemic. In this paper we survey the different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information. We address the problem using the control theoretic concepts of structural identifiability and observability. Since some parameters can vary during the course of an epidemic, we consider both the constant and time-varying parameter assumptions. We analyse the structural identifiability and observability of all of the models, considering all plausible choices of outputs and time-varying parameters, which leads us to analyse 255 different model versions. We classify the models according to their structural identifiability and observability under the different assumptions and discuss the implications of the results. We also illustrate with an example several alternative ways of remedying the lack of observability of a model. Our analyses provide guidelines for choosing the most informative model for each purpose, taking into account the available knowledge and measurements.

Dynamic modelling of growth and flavonoid production from Ocimum tenuiflorum suspension culture

A structured-segregated dynamic model for biomass growth, sucrose utilization and flavonoid production in Ocimum tenuiflorum suspension culture is proposed, considering a dynamic heterogeneous population of viable active, viable nonactive and dead cell. The sucrose hydrolysis (into glucose and fructose), substrate uptake by biomass and intracellular flavonoid production are modelled using Contois kinetics, a competitive double-substrate Monod, and Luedeking-Piret model, respectively. The conversion of active to viable-nonactive biomass has been formulated as a function of the total substrate and biomass concentrations. Parameters for the dynamic model are evaluated while minimizing the sum of square errors between modelled and measured biomass, cell viability, glucose, fructose and intracellular flavonoid contents. Bootstrap confidence intervals and dynamic relative sensitivity analysis of these model parameters are presented. The knowledge gained from the population-based model in plant suspension culture can provide the basic framework for prediction and optimization of the bioprocess system for phytochemical production.

A dynamic model-based framework to test the effectiveness of biocontrol targeting a new plant invader- the case of Alternanthera philoxeroides in the Iberian Peninsula

Biological invasions are one of the major threats to biodiversity at the global scale, causing numerous environmental impacts and having high direct and indirect costs associated with their management, control and eradication. In this work, we present a system-dynamic modelling approach for the biocontrol of the invasive plant species Alternanthera philoxeroides using its natural predator, Agasicles hygrophila, as a biocontrol agent. We have simulated different scenarios in the Finisterre region (Spain), where a single population of the invasive plant has been recently described. To assess the effectiveness of A. hygrophila as a biocontrol agent in the region, a population dynamic model was developed in order to include the life-cycle of both species, as well as the interaction among them. The results of the simulations indicate that the control of this new invasive plant is possible, as long as several releases of the biocontrol agent are made over time. The proposed model can support the control or even the eradication of the population of A. philoxeroides with a minimal impact on the environment. Additionally, the proposed framework also represents a versatile dynamic tool, adjustable to different local management specificities (objectives and parameters) and capable of responding under different contexts. Hence, this approach can be used to guide eradication efforts of new invasive species, to improve the applicability of early management measures as biocontrol, and to support decision-making by testing several alternative management scenarios.

Modelling assisted phytoremediation of soils contaminated with heavy metals - Main opportunities, limitations, decision making and future prospects

The heavy metals (HMs) soils contamination is a growing concern since HMs are not biodegradable and can accumulate in all living organisms causing a threat to plants and animals, including humans. Phytoremediation is a cost-efficient technology that uses plants to remove, transform or detoxify contaminants. In recent years, phytoremediation is entering the stage of large-scale modelling via various mathematical models. Such models can be useful tools to further our understanding and predicting of the processes that influence the efficiency of phytoremediation and to precisely plan such actions on a large-scale. When dealing with extremely complicated and challenging variables like the interactions between the climate, soil and plants, modelling before starting an operation can significantly reduce the time and cost of such process by granting us an accurate prediction of possible outcomes. Research on the applicability of different modelling approaches is ongoing and presented work compares and discusses available models in order to point out their specific strengths and weaknesses in given scenarios. The main aim of this paper is to critically evaluate the main advantages and limitations of available models for large-scale phytoremediation including, among others, the Decision Support System (DSS), Response Surface Methodology (RSM), BALANS, PLANTIX and various regression models. Study compares their applicability and highlight existing gaps in current knowledge with a special reference to improving the efficiency of large-scale phytoremediation of sites contaminated with heavy-metals. The presented work can serve as a useful tool when choosing the most suitable model for the phytoremediation of contaminated sites.

On the dynamics of a curved microtubule-associated proteins by considering viscoelastic properties of the living biological cells

Over the last few years, some novel researches in the field of medical science made a tendency to have therapy without any complications or side-effects of the disease with the aid of prognosis about the behaviors of the microtubules. Regarding this issue, the stability/instability analysis of curved microtubule-associated protein in axons with attention to different size effect parameters based on an exact continuum method is presented. The real property of the living biological cells is presented as the Kelvin-Voight viscoelastic properties. Considering length scale parameter (l/R = 0.2) in modified couple stress theory (MCST) leads to a better agreement with experimental results in comparison by other theories that in the results section is presented, in detail. Based on presented exact results, the effect of R1/R parameter on the relative frequency changes of the microtubules is hardly depended to the value of the external forced load that should be attention to this value. Another important consequence is that the influence of the microtubule curvature parameter on the relative frequency changes of the living substructure is hardly depended on the value of the time-dependent viscoelastic property, that researchers in the analysis of the microtubule should be attention to this important issue.Communicated by Ramaswamy H. Sarma.

Mathematical modelling for antibiotic resistance control policy: do we know enough?

BACKGROUND

Antibiotics remain the cornerstone of modern medicine. Yet there exists an inherent dilemma in their use: we are able to prevent harm by administering antibiotic treatment as necessary to both humans and animals, but we must be mindful of limiting the spread of resistance and safeguarding the efficacy of antibiotics for current and future generations. Policies that strike the right balance must be informed by a transparent rationale that relies on a robust evidence base.

MAIN TEXT

One way to generate the evidence base needed to inform policies for managing antibiotic resistance is by using mathematical models. These models can distil the key drivers of the dynamics of resistance transmission from complex infection and evolutionary processes, as well as predict likely responses to policy change in silico. Here, we ask whether we know enough about antibiotic resistance for mathematical modelling to robustly and effectively inform policy. We consider in turn the challenges associated with capturing antibiotic resistance evolution using mathematical models, and with translating mathematical modelling evidence into policy.

CONCLUSIONS

We suggest that in spite of promising advances, we lack a complete understanding of key principles. From this we advocate for priority areas of future empirical and theoretical research.

Calibration, Selection and Identifiability Analysis of a Mathematical Model of the in vitro Erythropoiesis in Normal and Perturbed Contexts

The in vivo erythropoiesis, which is the generation of mature red blood cells in the bone marrow of whole organisms, has been described by a variety of mathematical models in the past decades. However, the in vitro erythropoiesis, which produces red blood cells in cultures, has received much less attention from the modelling community. In this paper, we propose the first mathematical model of in vitro erythropoiesis. We start by formulating different models and select the best one at fitting experimental data of in vitro erythropoietic differentiation obtained from chicken erythroid progenitor cells. It is based on a set of linear ODE, describing 3 hypothetical populations of cells at different stages of differentiation. We then compute confidence intervals for all of its parameters estimates, and conclude that our model is fully identifiable. Finally, we use this model to compute the effect of a chemical drug called Rapamycin, which affects all states of differentiation in the culture, and relate these effects to specific parameter variations. We provide the first model for the kinetics of in vitro cellular differentiation which is proven to be identifiable. It will serve as a basis for a model which will better account for the variability which is inherent to the experimental protocol used for the model calibration.

Personal assessment of the external exposome during pregnancy and childhood in Europe

The human exposome affects child development and health later in life, but its personal external levels, variability, and correlations are largely unknown. We characterized the personal external exposome of pregnant women and children in eight European cities. Panel studies included 167 pregnant women and 183 children (aged 6-11 years). A personal exposure monitoring kit composed of smartphone, accelerometer, ultraviolet (UV) dosimeter, and two air pollution monitors were used to monitor physical activity (PA), fine particulate matter (PM_2.5), black carbon, traffic-related noise, UV-B radiation, and natural outdoor environments (NOE). 77% of women performed the adult recommendation of ≥150 min/week of moderate to vigorous PA (MVPA), while only 3% of children achieved the childhood recommendation of ≥60 min/day MVPA. 11% of women and 17% of children were exposed to daily PM_2.5 levels higher than recommended (≥25μg/m³). Mean exposure to noise ranged from Lden 51.1 dB in Kaunas to Lden 65.2 dB in Barcelona. 4% of women and 23% of children exceeded the recommended maximum of 2 Standard-Erythemal-Dose of UV-B at least once a week. 33% of women and 43% of children never reached the minimum NOE contact recommendation of ≥30 min/week. The variations in air and noise pollution exposure were dominated by between-city variability, while most of the variation observed for NOE contact and PA was between-participants. The correlations between all personal exposures ranged from very low to low (Rho < 0.30). The levels of personal external exposures in both pregnant women and children are above the health recommendations, and there is little correlation between the different exposures. The assessment of the personal external exposome is feasible but sampling requires from one day to more than one year depending on exposure due to high variability between and within cities and participants.

Environmental sustainability assessment using dynamic Autoregressive-Distributed Lag simulations-Nexus between greenhouse gas emissions, biomass energy, food and economic growth

Increasing population demand has triggered the enhancement of food production, energy consumption and economic development, however, its impact on climate change has become a global concern. This study applied a novel environmental sustainability assessment tool using dynamic Autoregressive-Distributed Lag (ARDL) simulations for model estimation of the relationships between greenhouse gas (GHG) emissions, energy, biomass, food and economic growth for Australia using data spanning from 1970 to 2017. The study found an inversed-U shaped relationship between energy consumption and income level, showing a decarbonized and services economy, hence, improved energy efficiency. While energy consumption increases emissions by 0.4 to 2.8%, biomass consumption supports Australia's transition to a decarbonized economy by reducing GHG emissions by 0.13% and shifts the demand for fossil fuel. Food and energy consumption underpin socio-economic development and vice versa. However, food waste from production and consumption increases ecological footprint, implying a lost opportunity to improve food security and reduce environmental pressure from agricultural production. There is no single path to achieving environmental sustainability, nonetheless, the integrated approach applied in this study reveals conceptual tools which are applicable for decision making.

Parameter estimation in models of biological oscillators: an automated regularised estimation approach

BACKGROUND

Dynamic modelling is a core element in the systems biology approach to understanding complex biosystems. Here, we consider the problem of parameter estimation in models of biological oscillators described by deterministic nonlinear differential equations. These problems can be extremely challenging due to several common pitfalls: (i) a lack of prior knowledge about parameters (i.e. massive search spaces), (ii) convergence to local optima (due to multimodality of the cost function), (iii) overfitting (fitting the noise instead of the signal) and (iv) a lack of identifiability. As a consequence, the use of standard estimation methods (such as gradient-based local ones) will often result in wrong solutions. Overfitting can be particularly problematic, since it produces very good calibrations, giving the impression of an excellent result. However, overfitted models exhibit poor predictive power. Here, we present a novel automated approach to overcome these pitfalls. Its workflow makes use of two sequential optimisation steps incorporating three key algorithms: (1) sampling strategies to systematically tighten the parameter bounds reducing the search space, (2) efficient global optimisation to avoid convergence to local solutions, (3) an advanced regularisation technique to fight overfitting. In addition, this workflow incorporates tests for structural and practical identifiability.

RESULTS

We successfully evaluate this novel approach considering four difficult case studies regarding the calibration of well-known biological oscillators (Goodwin, FitzHugh-Nagumo, Repressilator and a metabolic oscillator). In contrast, we show how local gradient-based approaches, even if used in multi-start fashion, are unable to avoid the above-mentioned pitfalls.

CONCLUSIONS

Our approach results in more efficient estimations (thanks to the bounding strategy) which are able to escape convergence to local optima (thanks to the global optimisation approach). Further, the use of regularisation allows us to avoid overfitting, resulting in more generalisable calibrated models (i.e. models with greater predictive power).

Temporalis, a generic method and tool for dynamic Life Cycle Assessment

The limitations of the static nature of Life Cycle Assessment (LCA) are well known. To overcome the loss of temporal information due to the aggregation of flows in the Life Cycle Inventory (LCI), several dynamic LCA methodologies have been proposed. In this paper we present a new generic and operational methodology for dynamic LCA that allows for the introduction of temporal information in both in the inventory and the Life Cycle Impact Assessment (LCIA) phases. The method makes use of graph traversal and convolution to calculate the temporally differentiated inventory, and makes it possible to use several types of dynamic impact assessment. We describe our method and apply it to a cradle-to-grave dynamic LCA of a glued laminated timber (glulam) product. We also test the sensitivity of the global warming results to temporal explicit LCI data. There is a considerable difference in outcome between the static and dynamic approaches. We have implemented our framework in the free and open source software Temporalis that is fully operational and can be used with existing LCA databases.

Initial state perturbations as a validation method for data-driven fuzzy models of cellular networks

Background

Data-driven methods that automatically learn relations between attributes from given data are a popular tool for building mathematical models in computational biology. Since measurements are prone to errors, approaches dealing with uncertain data are especially suitable for this task. Fuzzy models are one such approach, but they contain a large amount of parameters and are thus susceptible to over-fitting. Validation methods that help detect over-fitting are therefore needed to eliminate inaccurate models.

Results

We propose a method to enlarge the validation datasets on which a fuzzy dynamic model of a cellular network can be tested. We apply our method to two data-driven dynamic models of the MAPK signalling pathway and two models of the mammalian circadian clock. We show that random initial state perturbations can drastically increase the mean error of predictions of an inaccurate computational model, while keeping errors of predictions of accurate models small.

Conclusions

With the improvement of validation methods, fuzzy models are becoming more accurate and are thus likely to gain new applications. This field of research is promising not only because fuzzy models can cope with uncertainty, but also because their run time is short compared to conventional modelling methods that are nowadays used in systems biology.

Cost-effectiveness of sex-neutral HPV-vaccination in Sweden, accounting for herd-immunity and sexual behaviour

INTRODUCTION

The aim was to assess cost-effectiveness of expanding the Swedish HPV-vaccination program to include preadolescent boys, by comparing health-effects and costs of HPV-related disease, with a sex-neutral vaccination program versus only vaccinating girls.

METHODS

We used a dynamic compartmental model to simulate the burden of HPV16/18-related disease in Sweden, accounting for indirect effects of vaccination through herd-immunity. The model accounted for sexual behaviour, such as age preferences and men who have sex with men. The main outcome was number of individuals with HPV-related cancers (cervical, genital, anal and oropharyngeal cancer) and cervical intraepithelial neoplasia (CIN). Costs included in the analysis were those incurred when treating HPV-related cancer and CIN, production losses during sick-leave, and acquisition and administration of vaccine. Health effects were measured as quality-adjusted life years (QALY). The time horizon was set to 100 years, and both effects and costs were discounted by 3% annually. Health effects and costs were accumulated over the time horizon and used to create an incremental cost-effectiveness ratio.

RESULTS

A sex-neutral vaccination program would reduce HPV-related cancer and CIN, both due to direct effects among vaccinated as well as through herd-immunity, further decreasing HPV-related cancer burden annually by around 60 cases among men and women respectively in steady-state. The cost per gained QALY was estimated to 40,000 euro. Applying the procurement price of 2017, sex-neutral vaccination was dominant.

CONCLUSION

Introducing a sex-neutral HPV-vaccination program would be good value for money also in Sweden where there this 80% coverage in the current HPV-vaccination program for preadolescent girls. The cost-effectiveness of a sex-neutral program is highly dependent on the price of the vaccine, the lower the price the more favourable it is to also vaccinate boys.

Modelling studies for photocatalytic degradation of organic dyes using TiO2 nanofibers

In this work, modelling of the photocatalytic degradation of para-nitrophenol (PNP) using synthesized electrospun TiO₂ nanofibers under UV light illumination is reported. A dynamic model was developed in order to understand the behaviour of operating parameters, i.e. light intensity and catalyst loading on the photocatalytic activity. This model was simulated and analysed for both TiO₂ solid nanofibers and TiO₂ hollow nanofibers, applied as photocatalysts in the Langmuir-Hinshelwood kinetic framework. The entire photocatalytic degradation rate follows pseudo-first-order kinetics. The simulated results obtained from the developed model are in good agreement with the experimental results. At a catalyst loading of 1.0 mg mL^-1, better respective degradation rates were achieved at UV light irradiance of 4 mW cm^-2, for both the TiO₂ solid and hollow nanofibers. However, it was also observed that TiO₂ hollow nanofibers have a higher adsorption rate than that of TiO₂ solid nanofibers resulting in a higher photocatalytic degradation rate of PNP.

Dynamic elementary mode modelling of non-steady state flux data

BACKGROUND

A novel framework is proposed to analyse metabolic fluxes in non-steady state conditions, based on the new concept of dynamic elementary mode (dynEM): an elementary mode activated partially depending on the time point of the experiment.

RESULTS

Two methods are introduced here: dynamic elementary mode analysis (dynEMA) and dynamic elementary mode regression discriminant analysis (dynEMR-DA). The former is an extension of the recently proposed principal elementary mode analysis (PEMA) method from steady state to non-steady state scenarios. The latter is a discriminant model that permits to identify which dynEMs behave strongly different depending on the experimental conditions. Two case studies of Saccharomyces cerevisiae, with fluxes derived from simulated and real concentration data sets, are presented to highlight the benefits of this dynamic modelling.

CONCLUSIONS

This methodology permits to analyse metabolic fluxes at early stages with the aim of i) creating reduced dynamic models of flux data, ii) combining many experiments in a single biologically meaningful model, and iii) identifying the metabolic pathways that drive the organism from one state to another when changing the environmental conditions.

A design metric for safety assessment of industrial robot design suitable for power- and force-limited collaborative operation

This research presents a novel design metric based on maximum power flux density for the assessment of the severity of a transient physical contact between a robot manipulator and a human body region. Such incidental transient contact can occur in the course of a collaborative application of the power- and force-limiting type. The proposed metric is intended for the design and development of the robot manipulator as well as for the design of manufacturing applications. Such safety metric can also aid in controlling the robot’s speeds during manufacturing operations by carrying out rapid risk assessments of impending collisions that could arise due to the proximity to the human co-worker. Furthermore, this study contributes by expressing the physical impact between the robot and the human body region as a linear spring-damper model. The influence of the restitution coefficient and the elasticity of the human tissues on the contact duration and contact area during the collision is analysed. With the demonstrated analysis model, the dependence of the power flux density with respect to the robot’s effective mass, speed, and geometrical and damping coefficients during the human-industrial robot manipulator collision process is investigated.

Simultaneous use of caustic and oxygen for efficient sulfide control in sewers

Periodic caustic shock-loading is a commonly used method for sulfide control in sewers. Caustic shock-loading relies on the elevation of the sewage pH to ≥10.5 for several hours, thereby removing sewer pipe biofilms as well as deactivating SRB activity in the remaining biofilm. Although a widely used method, SRB activity is often not completely inhibited, and as such sulfide is still being generated. Here, we propose and experimentally demonstrate an innovative approach which combines caustic with oxygen, another commonly used method, as a dosing strategy for overcoming the drawbacks of caustic shock-loading. Six laboratory-scale rising main reactors were subjected to three dosing schemes over a period of three months, namely (i) simultaneous caustic and oxygen addition, (ii) caustic addition and (iii) no chemical addition. Our results showed that the combination of caustic and oxygen achieved efficient sulfide control, leading to a prolonged biofilm recovery period in between caustic shocks. In addition, methane emissions were reduced to a negligible level compared to caustic treatment only. To translate the findings to real-life application, the key parameters obtained during the long-term lab-scale experiments were subjected to extensive simulation studies using the SeweX model under a wide range of conditions commonly found in sewers. Overall, this study highlights the potential of periodic shock-loading and intermittent oxygen injection as combined dosing strategy for efficient sulfide control in sewers.

Experimental and modelling of Arthrospira platensis cultivation in open raceway ponds

In this study, the growth of Arthrospira platensis was studied in an open raceway pond. Furthermore, dynamic model for algae growth and CFD modelling of hydrodynamics in open raceway pond were developed. The dynamic behaviour of the algal system was developed by solving mass balance equations of various components, considering light intensity and gas-liquid mass transfer. A CFD modelling of the hydrodynamics of open raceway pond was developed by solving mass and momentum balance equations of the liquid medium. The prediction of algae concentration from the dynamic model was compared with the experimental data. The hydrodynamic behaviour of the open raceway pond was compared with the literature data for model validation. The model predictions match the experimental findings. Furthermore, the hydrodynamic behaviour and residence time distribution in our small raceway pond were predicted. These models can serve as a tool to assess the pond performance criteria.

The public health value of vaccines beyond efficacy: methods, measures and outcomes

BACKGROUND

Assessments of vaccine efficacy and safety capture only the minimum information needed for regulatory approval, rather than the full public health value of vaccines. Vaccine efficacy provides a measure of proportionate disease reduction, is usually limited to etiologically confirmed disease, and focuses on the direct protection of the vaccinated individual. Herein, we propose a broader scope of methods, measures and outcomes to evaluate the effectiveness and public health impact to be considered for evidence-informed policymaking in both pre- and post-licensure stages.

DISCUSSION

Pre-licensure: Regulatory concerns dictate an individually randomised clinical trial. However, some circumstances (such as the West African Ebola epidemic) may require novel designs that could be considered valid for licensure by regulatory agencies. In addition, protocol-defined analytic plans for these studies should include clinical as well as etiologically confirmed endpoints (e.g. all cause hospitalisations, pneumonias, acute gastroenteritis and others as appropriate to the vaccine target), and should include vaccine-preventable disease incidence and 'number needed to vaccinate' as outcomes. Post-licensure: There is a central role for phase IV cluster randomised clinical trials that allows for estimation of population-level vaccine impact, including indirect, total and overall effects. Dynamic models should be prioritised over static models as the constant force of infection assumed in static models will usually underestimate the effectiveness and cost-effectiveness of the immunisation programme by underestimating indirect effects. The economic impact of vaccinations should incorporate health and non-health benefits of vaccination in both the vaccinated and unvaccinated populations, thus allowing for estimation of the net social value of vaccination.

CONCLUSIONS

The full benefits of vaccination reach beyond direct prevention of etiologically confirmed disease and often extend across the life course of a vaccinated person, prevent outcomes in the wider community, stabilise health systems, promote health equity, and benefit local and national economies. The degree to which vaccinations provide broad public health benefits is stronger than for other preventive and curative interventions.

Modelling impacts of atmospheric deposition and temperature on long-term DOC trends

It is increasingly recognised that widespread and substantial increases in Dissolved organic carbon (DOC) concentrations in remote surface, and soil, waters in recent decades are linked to declining acid deposition. Effects of rising pH and declining ionic strength on DOC solubility have been proposed as potential dominant mechanisms. However, since DOC in these systems is derived mainly from recently-fixed carbon, and since organic matter decomposition rates are considered sensitive to temperature, uncertainty persists over the extent to which other drivers that could influence DOC production. Such potential drivers include fertilisation by nitrogen (N) and global warming. We therefore ran the dynamic soil chemistry model MADOC for a range of UK soils, for which time series data are available, to consider the likely relative importance of decreased deposition of sulphate and chloride, accumulation of reactive N, and higher temperatures, on soil DOC production in different soils. Modelled patterns of DOC change generally agreed favourably with measurements collated over 10-20years, but differed markedly between sites. While the acidifying effect of sulphur deposition appeared to be the predominant control on the observed soil water DOC trends in all the soils considered other than a blanket peat, the model suggested that over the long term, the effects of nitrogen deposition on N-limited soils may have been sufficient to raise the "acid recovery DOC baseline" significantly. In contrast, reductions in non-marine chloride deposition and effects of long term warming appeared to have been relatively unimportant. The suggestion that future DOC concentrations might exceed preindustrial levels as a consequence of nitrogen pollution has important implications for drinking water catchment management and the setting and pursuit of appropriate restoration targets, but findings still require validation from reliable centennial-scale proxy records, such as those being developed using palaeolimnological techniques.

Optimization of municipal solid waste transportation by integrating GIS analysis, equation-based, and agent-based model

The amount of municipal solid waste (MSW) has been increasing steadily over the last decade by reason of population rising and waste generation rate. In most of the urban areas, disposal sites are usually located outside of the urban areas due to the scarcity of land. There is no fixed route map for transportation. The current waste collection and transportation are already overloaded arising from the lack of facilities and insufficient resources. In this paper, a model for optimizing municipal solid waste collection will be proposed. Firstly, the optimized plan is developed in a static context, and then it is integrated into a dynamic context using multi-agent based modelling and simulation. A case study related to Hagiang City, Vietnam, is presented to show the efficiency of the proposed model. From the optimized results, it has been found that the cost of the MSW collection is reduced by 11.3%.

Weather dependent dynamics of the herbicides florasulam, carfentrazone-ethyl, fluroxypyr-meptyl and fluroxypyr in wheat fields through field studies and computational simulation

A dynamic model of dynamiCROP was applied to study environmental fate and behavior of four herbicides in wheat including florasulam, carfentrazone-ethyl, fluroxypyr-meptyl, and fluroxypyr. Meantime, their residue in wheat and dissipation half-lives in plant determined by field trials using QuEChERS liquid chromatography tandem mass spectrometry were used to verify modelling results. The combination of experimental verification and modelling prediction deciphered the fate of four pesticides in wheat field ecosystem. Besides, temperature difference of 3 °C only resulted in lower than 15% half-life difference. By quantifying the contribution of temperature, the predominant role of rain on pesticide dissipation was highlighted for the first time, namely higher precipitation leaded to faster degradation and vice versa.

Dynamic modelling of a forward osmosis-nanofiltration integrated process for treating hazardous wastewater

Dynamic modelling and simulation of a nanofiltration-forward osmosis integrated complete system was done along with economic evaluation to pave the way for scale up of such a system for treating hazardous pharmaceutical wastes. The system operated in a closed loop not only protects surface water from the onslaught of hazardous industrial wastewater but also saves on cost of fresh water by turning wastewater recyclable at affordable price. The success of dynamic modelling in capturing the relevant transport phenomena is well reflected in high overall correlation coefficient value (R ² > 0.98), low relative error (<0.1) and Willmott d-index (<0.95). The system could remove more than 97.5 % chemical oxygen demand (COD) from real pharmaceutical wastewater having initial COD value as high as 3500 mg/L while ensuring operation of the forward osmosis loop at a reasonably high flux of 56-58 l per square meter per hour.

Data for the qualitative modeling of the osmotic stress response to NaCl in Escherichia coli

Qualitative modeling approaches allow to provide a coarse-grained description of the functioning of cellular networks when experimental data are scarce and heterogeneous. We translate the primary literature data on the response of Escherichia coli to hyperosmotic stress caused by NaCl addition into a piecewise linear (PL) model. We provide a data file of the qualitative model, which can be used for simulation of changes of protein concentrations and of DNA coiling during the physiological response of the bacterium to the stress. The qualitative model predictions are directly comparable to the available experimental data. This data is related to the research article entitled “Piecewise linear approximations to model the dynamics of adaptation to osmotic stress by food-borne pathogens” (Metris et al., 2016) [1].

The complexities and versatility of the RAS-to-ERK signalling system in normal and cancer cells

The intricate dynamic control and plasticity of RAS to ERK mitogenic, survival and apoptotic signalling has mystified researches for more than 30 years. Therapeutics targeting the oncogenic aberrations within this pathway often yield unsatisfactory, even undesired results, as in the case of paradoxical ERK activation in response to RAF inhibition. A direct approach of inhibiting single oncogenic proteins misses the dynamic network context governing the network signal processing. In this review, we discuss the signalling behaviour of RAS and RAF proteins in normal and in cancer cells, and the emerging systems-level properties of the RAS-to-ERK signalling network. We argue that to understand the dynamic complexities of this control system, mathematical models including mechanistic detail are required. Looking into the future, these dynamic models will build the foundation upon which more effective, rational approaches to cancer therapy will be developed.

Piecewise linear approximations to model the dynamics of adaptation to osmotic stress by food-borne pathogens

Addition of salt to food is one of the most ancient and most common methods of food preservation. However, little is known of how bacterial cells adapt to such conditions. We propose to use piecewise linear approximations to model the regulatory adaptation of Escherichiacoli to osmotic stress. We apply the method to eight selected genes representing the functions known to be at play during osmotic adaptation. The network is centred on the general stress response factor, sigma S, and also includes a module representing the catabolic repressor CRP-cAMP. Glutamate, potassium and supercoiling are combined to represent the intracellular regulatory signal during osmotic stress induced by salt. The output is a module where growth is represented by the concentration of stable RNAs and the transcription of the osmotic gene osmY. The time course of gene expression of transport of osmoprotectant represented by the symporter proP and of the osmY is successfully reproduced by the network. The behaviour of the rpoS mutant predicted by the model is in agreement with experimental data. We discuss the application of the model to food-borne pathogens such as Salmonella; although the genes considered have orthologs, it seems that supercoiling is not regulated in the same way. The model is limited to a few selected genes, but the regulatory interactions are numerous and span different time scales. In addition, they seem to be condition specific: the links that are important during the transition from exponential to stationary phase are not all needed during osmotic stress. This model is one of the first steps towards modelling adaptation to stress in food safety and has scope to be extended to other genes and pathways, other stresses relevant to the food industry, and food-borne pathogens. The method offers a good compromise between systems of ordinary differential equations, which would be unmanageable because of the size of the system and for which insufficient data are available, and the more abstract Boolean methods.

Applying a system dynamics modelling approach to explore policy options for improving neonatal health in Uganda

BACKGROUND

The most recent reports on global trends in neonatal mortality continue to show alarmingly slow progress on improvements in neonatal mortality rates, with sub-Saharan Africa still lagging behind. This emphasised the urgent need to innovatively employ alternative solutions that take into account the intricate complexities of neonatal health and the health systems in which the various strategies operate.

METHODS

In our first paper, we empirically explored the causes of the stagnating neonatal mortality in Uganda using a dynamic synthesis methodology (DSM) approach. In this paper, we completed the last three stages of DSM, which involved the development of a quantitative (simulation) model, using STELLA modelling software. We used statistical data to populate the model. Through brainstorming sessions with stakeholders, iterations to test and validate the model were undertaken. The different strategies and policy interventions that could possibly lower neonatal mortality rates were tested using what-if analysis. Sensitivity analysis was used to determine the strategies that could have a great impact on neonatal mortality.

RESULTS

We developed a neonatal health simulation model (NEOSIM) to explore potential interventions that could possibly improve neonatal health within a health system context. The model has four sectors, namely population, demand for services, health of the mothers and choices of clinical care. It tests the effects of various interventions validated by a number of Ugandan health practitioners, including health education campaigns, free delivery kits, motorcycle coupons, kangaroo mother care, improving neonatal resuscitation and labour management skills, and interventions to improve the mothers health, i.e. targeting malaria, anaemia and tetanus. Among the tested interventions, the package with the highest impact on reducing neonatal mortality rates was a combination of the free delivery kits in a setting where delivery services were free and motorcycle coupons to take women to hospital during emergencies.

CONCLUSIONS

This study presents a System Dynamics model with a broad and integrated view of the neonatal health system facilitating a deeper understanding of its current state and constraints and how these can be mitigated. A tool with a user friendly interface presents the dynamic nature of the model using 'what-if' scenarios, thus enabling health practitioners to discuss the consequences or effects of various decisions. Key findings of the research show that proposed interventions and their impact can be tested through simulation experiments thereby generating policies and interventions with the highest impact for improved healthcare service delivery.

Modelling the nitrogen loadings from large yellow croaker (Larimichthys crocea) cage aquaculture

Large yellow croaker (LYC) cage farming is a rapidly developing industry in the coastal areas of the East China Sea. However, little is known about the environmental nutrient loadings resulting from the current aquaculture practices for this species. In this study, a nitrogenous waste model was developed for LYC based on thermal growth and bioenergetic theories. The growth model produced a good fit with the measured data of the growth trajectory of the fish. The total, dissolved and particulate nitrogen outputs were estimated to be 133, 51 and 82 kg N tonne(-1) of fish production, respectively, with daily dissolved and particulate nitrogen outputs varying from 69 to 104 and 106 to 181 mg N fish(-1), respectively, during the 2012 operational cycle. Greater than 80 % of the nitrogen input from feed was predicted to be lost to the environment, resulting in low nitrogen retention (<20 %) in the fish tissues. Ammonia contributed the greatest proportion (>85 %) of the dissolved nitrogen generated from cage farming. This nitrogen loading assessment model is the first to address nitrogenous output from LYC farming and could be a valuable tool to examine the effects of management and feeding practices on waste from cage farming. The application of this model could help improve the scientific understanding of offshore fish farming systems. Furthermore, the model predicts that a 63 % reduction in nitrogenous waste production could be achieved by switching from the use of trash fish for feed to the use of pelleted feed.

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Acid deposition in the Athabasca Oil Sands Region: a policy perspective

Industrial emissions of sulphur (S) and nitrogen (N) to the atmosphere associated with the oil sands industry in north-eastern Alberta are of interest as they represent the largest localized source in Canada (with potential for future growth) and the region features acid-sensitive upland terrain. Existing emission management policy for the Regional Municipality of Wood Buffalo, where the industry is located, is based on a time-to-effect approach that relies on dynamic model simulations of temporal changes in chemistry and features highly protective chemical criteria. In practice, the policy is difficult to implement and it is unlikely that a scientifically defensible estimate of acidification risk can be put forward due to the limitations primarily associated with issues of scale, chemical endpoint designation (selection of chemical limit for ecosystem protection from acidification) and data availability. A more implementable approach would use a steady-state critical load (CL) assessment approach to identify at-risk areas. The CL assessment would consider areas of elevated acid deposition associated with oil sands emissions rather than targeted political jurisdictions. Dynamic models should only be (strategically) used where acidification risk is identified via CL analysis, in order to characterize the potential for acidification-induced changes that can be detrimental to sensitive biota within the lifespan of the industry.

Evaluation of new alternatives in wastewater treatment plants based on dynamic modelling and life cycle assessment (DM-LCA)

With a view to quantifying the energy and environmental advantages of Urine Source-Separation (USS) combined with different treatment processes, five wastewater treatment plant (WWTP) scenarios were compared to a reference scenario using Dynamic Modelling (DM) and Life Cycle Assessment (LCA), and an integrated DM-LCA framework was thus developed. Dynamic simulations were carried out in BioWin(®) in order to obtain a realistic evaluation of the dynamic behaviour and performance of plants under perturbation. LCA calculations were performed within Umberto(®) using the Ecoinvent database. A Python™ interface was used to integrate and convert simulation data and to introduce them into Umberto(®) to achieve a complete LCA evaluation comprising foreground and background processes. Comparisons between steady-state and dynamic simulations revealed the importance of considering dynamic aspects such as nutrient and flow peaks. The results of the evaluation highlighted the potential of the USS scenario for nutrient recovery whereas the Enhanced Primary Clarification (EPC) scenario gave increased biogas production and also notably decreased aeration consumption, leading to a positive energy balance. Both USS and EPC scenarios also showed increased stability of plant operation, with smaller daily averages of total nitrogen and phosphorus. In this context, USS and EPC results demonstrated that the coupled USS + EPC scenario and its combinations with agricultural spreading of N-rich effluent and nitritation/anaerobic deammonification could present an energy-positive balance with respectively 27% and 33% lower energy requirements and an increase in biogas production of 23%, compared to the reference scenario. The coupled scenarios also presented lesser environmental impacts (reduction of 31% and 39% in total endpoint impacts) along with effluent quality well within the specified limits. The marked environmental performance (reduction of global warming) when nitrogen is used in agriculture shows the importance of future research on sustainable solutions for nitrogen recovery. The contribution analysis of midpoint impacts also showed hotspots that it will be important to optimize further, such as plant infrastructure and direct N2O emissions.

A consensus approach for estimating the predictive accuracy of dynamic models in biology

Mathematical models that predict the complex dynamic behaviour of cellular networks are fundamental in systems biology, and provide an important basis for biomedical and biotechnological applications. However, obtaining reliable predictions from large-scale dynamic models is commonly a challenging task due to lack of identifiability. The present work addresses this challenge by presenting a methodology for obtaining high-confidence predictions from dynamic models using time-series data. First, to preserve the complex behaviour of the network while reducing the number of estimated parameters, model parameters are combined in sets of meta-parameters, which are obtained from correlations between biochemical reaction rates and between concentrations of the chemical species. Next, an ensemble of models with different parameterizations is constructed and calibrated. Finally, the ensemble is used for assessing the reliability of model predictions by defining a measure of convergence of model outputs (consensus) that is used as an indicator of confidence. We report results of computational tests carried out on a metabolic model of Chinese Hamster Ovary (CHO) cells, which are used for recombinant protein production. Using noisy simulated data, we find that the aggregated ensemble predictions are on average more accurate than the predictions of individual ensemble models. Furthermore, ensemble predictions with high consensus are statistically more accurate than ensemble predictions with large variance. The procedure provides quantitative estimates of the confidence in model predictions and enables the analysis of sufficiently complex networks as required for practical applications.

The silicon trypanosome: a test case of iterative model extension in systems biology

The African trypanosome, Trypanosoma brucei, is a unicellular parasite causing African Trypanosomiasis (sleeping sickness in humans and nagana in animals). Due to some of its unique properties, it has emerged as a popular model organism in systems biology. A predictive quantitative model of glycolysis in the bloodstream form of the parasite has been constructed and updated several times. The Silicon Trypanosome is a project that brings together modellers and experimentalists to improve and extend this core model with new pathways and additional levels of regulation. These new extensions and analyses use computational methods that explicitly take different levels of uncertainty into account. During this project, numerous tools and techniques have been developed for this purpose, which can now be used for a wide range of different studies in systems biology.

Dynamic metabolism modelling of urban water services--demonstrating effectiveness as a decision-support tool for Oslo, Norway

Urban water services are challenged from many perspectives and different stakeholders demand performance improvements along economic, social and environmental dimensions of sustainability. In response, urban water utilities systematically give more attention to criteria such as water safety, climate change adaptation and mitigation, environmental life cycle assessment (LCA), total cost efficiency, and on how to improve their operations within the water-energy-carbon nexus. The authors of this paper collaborated in the development of a 'Dynamic Metabolism Model' (DMM). The model is developed for generic use in the sustainability assessment of urban water services, and it has been initially tested for the city of Oslo, Norway. The purpose has been to adopt a holistic systemic perspective to the analysis of metabolism and environmental impacts of resource flows in urban water and wastewater systems, in order to offer a tool for the examination of future strategies and intervention options in such systems. This paper describes the model and its application to the city of Oslo for the analysis time period 2013-2040. The external factors impacting decision-making and interventions are introduced along with realistic scenarios developed for the testing, after consultation with officials at the Oslo Water and Wastewater Works (Norway). Possible interventions that the utility intends to set in motion are defined and numerically interpreted for incorporation into the model, and changes in the indicator values over the time period are determined. This paper aims to demonstrate the effectiveness and usefulness of the DMM, as a decision-support tool for water-wastewater utilities. The scenarios considered and interventions identified do not include all possible scenarios and interventions that can be relevant for water-wastewater utilities.

An intermediate complexity dynamic model for predicting accumulation of atmospherically-deposited metals (Ni, Cu, Zn, Cd, Pb) in catchment soils: 1400 to present

The Intermediate Dynamic Model for Metals (IDMM) is a model for prediction of the pools of metals (Ni, Cu, Zn, Cd, Pb) in topsoils of catchments resulting from deposition of metals from the atmosphere. We used the model to simulate soil metal pools from 1400 onwards in ten UK catchments comprising semi-natural habitats, and compared the results with present day observations of soil metal pools. Generally the model performed well in simulating present day pools, and further improvements were made to simulations of Ni, Cu, Zn and Cd by adjusting the strength of metal adsorption to the soils. Some discrepancies between observation and prediction for Pb appeared to be due either to underestimation of cumulative deposition, or to overestimation of the metal pool under 'pristine', pre-industrial conditions. The IDMM provides a potential basis for large scale assessment of metal dynamics in topsoils.

A complete dynamic model of primary sedimentation

A dynamic mathematical model for the primary clarifier of a wastewater treatment plant is described, which is represented by a general tanks-in-series model, to simulate insufficient mixing. The model quantifies successfully the diurnal response of both the suspended and dissolved species. It is general enough, so that the values of the parameters can be replaced with those applicable to a specific case. The model was verified through data from the Biological Centre of Metamorfosi, in Athens, Greece, and can be used to assist in the design of new plants or in the analysis and output predictions of existing ones.