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Neuropathic pain is caused by nerve injury and involves brain areas such as the central nucleus of the amygdala (CeA). We developed the first 3-D agent-based model (ABM) of neuropathic pain-related neurons in the CeA using NetLogo3D. The execution time of a single ABM simulation using realistic parameters (e.g., 13,000 neurons and 22,000+ neural connections) is an important factor in the model's usability. In this paper, we describe our efforts to improve the computational efficiency of our 3-D ABM, which resulted in a 28% reduction in execution time on average for a typical simulation. With this upgraded model, we performed one- and two-parameter sensitivity analyses to study the sensitivity of model output to variability in several key parameters along the anterior to posterior axis of the CeA. These results highlight the importance of computational modeling in exploring spatial and cell-type specific properties of brain regions to inform future wet lab experiments.

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Textile and medical effluents causing bioaccumulation and biomagnification have been successfully biodegraded by fungal laccases. Here, a decision-making tool was developed and applied to evaluate 45 different laccase production strategies which determined the best potential source from a techno-economical perspective. Laccase production cost was calculated with a fixed output of 109 enzymatic units per batch (USD$per109U) and a sensitivity analysis was performed. Results indicate that optimization of enzymatic kinetics for each organism is essential to avoid exceeding the fermentation time point at which production titer reaches its peak and, therefore, higher production costs. Overall, the most cost-effective laccase-producing strategy was obtained when using Pseudolagarobasidium acaciicola with base production cost of USD $42.46 per 109 U. This works serves as platform for decision-making to find the optimal laccase production strategy based on techno-economic parameters.

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BACKGROUND: Irresponsible dog ownership in urban areas is a public health concern with significant implications for human, animal, and environmental welfare. Factors such as abandonment, variations in adoption, insufficient supervision, emerging identification initiatives, and collective feeding impact the growth of stray dog populations and the transmission of diseases. Developing a modeling tool to understand the dynamics of canine population growth and the effect of human behavior on this phenomenon is essential. METHODS: An ordinary differential equation model was developed to depict the growth dynamics and movements of urban dog populations, distinguishing between those with owners (restricted and semi-restricted) and those without (stray and community dogs). Two equilibrium states of the system were analyzed: with and without the presence of individually owned dogs. An increase rate for the population of individually owned dogs was calculated, and a local sensitivity analysis was conducted to assess the impact of parameters on the reduction of this population. Additionally, two global sensitivity analysis methods were used to evaluate the simultaneous influence of the parameters. RESULTS: Findings indicate that system equilibrium depends on various dog categories. Although total eradication of stray and community dogs is unlikely, equilibrium levels are directly related to subpopulation growth rates, responsible ownership practices, and adoption and abandonment rates. The growth rates of the population of dogs without individual owners have a direct and proportional influence on their regulation, while adoption rates have an inverse and proportional effect. The study, through global sensitivity analysis, identifies key parameters for each dog subpopulation. For restricted dogs, environmental carrying capacity is the most variable factor; for semi-restricted dogs, awareness of responsible ownership is crucial. The abandonment of restricted dogs significantly impacts stray dog dynamics, while the transition from stray to community status is an important variable factor for community dogs. CONCLUSION: Addressing the situation of unowned dogs requires a collective effort to reduce risks associated with the spread of zoonotic diseases, environmental pollution, and biodiversity loss, thus contributing to public health and environmental conservation.

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Phalaris brachystachys (short-spiked canary grass) is considered to be among the most troublesome cereal weeds in Mediterranean areas. A bioeconomic model, based on population dynamics, competition and economic sub-models, was developed to simulate the long-term economic consequence of using herbicide-based strategies: no herbicide application, full herbicide dose (standard rate) and two reduced dose rates (75 and 50% of the standard rate) to control P. brachystachys in a biennial wheat-sunflower rotation. Simulation results indicated that only herbicide application at a full dose (90% control) and 3/4 dose (80% control) produced positive economic results, with the full dose being the best strategy (EUR 98.65 ha-1 year-1). A sensitivity analysis showed that the economic outcome, in terms of annualized net return, was strongly influenced by changes in yield, price, and fixed costs. In addition, the annualized net return was more sensitive to parameter changes at reduced herbicide doses than at full rate. In the wheat-sunflower rotation system, the application of the full dose of herbicide was the most economical and stable strategy in the long-term. Reduced doses are not a recommended option from an economic point of view. Bioeconomic models provide practical insight into different management approaches for effective weed control.

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Chikungunya is a vector-borne viral disease transmitted by Aedes aegypti and Aedes albopictus mosquitoes. It does not have any specific treatment, and there is no vaccine. Recent epidemiological data have indicated that a relapse of the infection can occur within three months of the initial infection; however, until now, mathematical models for the spread of the disease have not considered this factor. We propose a mathematical model for the transmission of the Chikungunya virus that considers relapse. We calculated the basic reproductive number $ (R_0) $ of the disease by using the next-generation operator method. We proved the existence of a forward bifurcation. We determined the existence and the global stability of the equilibrium points by using the Lyapunov function method. We fitted the model to data from an outbreak in 2015 in Acapulco, Mexico to estimate the model parameters and $ R_0 $ with the Bayesian approach via a Hamiltonian Monte Carlo method. In the local sensitivity analysis, we found that the fraction of infected individuals who become asymptomatic has a strong impact on the basic reproductive number and makes some control measures insufficient. The impact of the fraction of infected individuals who become asymptomatic should be considered in Chikungunya control strategies.

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Environmental vulnerability is an important tool to understand the natural and anthropogenic impacts associated with the susceptibility to environmental damage. This study aims to assess the environmental vulnerability of the Doce River basin in Brazil through Multicriteria Decision Analysis based on Geographic Information Systems (GIS-MCDA). Natural factors (slope, elevation, relief dissection, rainfall, pedology, and geology) and anthropogenic factors (distance from urban centers, roads, mining dams, and land use) were used to determine the environmental vulnerability index (EVI). The EVI was classified into five classes, identifying associated land uses. Vulnerability was verified in water resource management units (UGRHs) and municipalities using hot spot analysis. The study employed the water quality index (WQI) to assess the EVI and global sensitivity analysis (GSA) to evaluate the model input parameters that most influence the basin's environmental vulnerability. The results showed that the regions near the middle Doce River were considered environmentally more vulnerable, especially the UGRHs Guandu, Manhuaçu, and Caratinga; and 35.9% of the basin has high and very high vulnerabilities. Hot spot analysis identified regions with low EVI values (cold spot) in the north and northwest, while areas with high values (hot spot) were concentrated mainly in the middle Doce region. Water monitoring stations with the worst WQI values were found in the most environmentally vulnerable areas. The GSA determined that land use and slope were the primary factors influencing the model's response. The results of this study provide valuable information for supporting environmental planning in the Doce River basin.

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The aim of this study was to analyze the financial and economic risks of tilapia cage culture across different production water volumes (m³). The production water volumes evaluated were 10 to 50 thousand m³ (Small Volume, SV), 51 to 150 thousand m³ (Medium Volume, MV), 151 to 300 thousand m³ (Large Volume, LV), and >301 thousand m³ (Extra-Large Volume, ELV). Productivity and economic data were obtained from a commercial Nile tilapia cage farm with 232 net cages installed in a neotropical reservoir, in Brazil, from 2017 to 2019. Cost and profitability analyses, economic feasibility, and risk and sensitivity analyses were performed using a Monte Carlo simulation. The implementation of commercial tilapia cage farming relies mainly on feed prices. The initial investment demand is proportional to the size of the farms. On the other hand, MV, LV, and ELV tilapia farms showed the lowest financial risks despite the higher investments. These farms presented a medium-low risk at ≈39% probability, whereas the SV farm presented a medium to medium-high risk at 51.17% probability. Thus, fish farms with a production volume above 51 thousand m³ tend to be more profitable and have a ≈36% probability of low financial and economic risk with a Payback period of fewer than 10 years, mainly due to the lower feed costs per mass of fish produced. This study assists investors in choosing a better path toward a more viable and profitable activity.

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Applying complex mathematical models of physiological systems is challenging due to the large number of parameters. Identifying these parameters through experimentation is difficult, and although procedures for fitting and validating models are reported, no integrated strategy exists. Additionally, the complexity of optimization is generally neglected when the number of experimental observations is restricted, obtaining multiple solutions or results without physiological justification. This work proposes a fitting and validation strategy for physiological models with many parameters under various populations, stimuli, and experimental conditions. A cardiorespiratory system model is used as a case study, and the strategy, model, computational implementation, and data analysis are described. Using optimized parameter values, model simulations are compared to those obtained using nominal values, with experimental data as a reference. Overall, a reduction in prediction error is achieved compared to that reported for model building. Furthermore, the behavior and accuracy of all the predictions in the steady state were improved. The results validate the fitted model and provide evidence of the proposed strategy's usefulness.

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The work proposes a new family of survival models called the Odd log-logistic generalized Neyman type A long-term. We consider different activation schemes in which the number of factors M has the Neyman type A distribution and the time of occurrence of an event follows the odd log-logistic generalized family. The parameters are estimated by the classical and Bayesian methods. We investigate the mean estimates, biases, and root mean square errors in different activation schemes using Monte Carlo simulations. The residual analysis via the frequentist approach is used to verify the model assumptions. We illustrate the applicability of the proposed model for patients with gastric adenocarcinoma. The choice of the adenocarcinoma data is because the disease is responsible for most cases of stomach tumors. The estimated cured proportion of patients under chemoradiotherapy is higher compared to patients undergoing only surgery. The estimated hazard function for the chemoradiotherapy level tends to decrease when the time increases. More information about the data is addressed in the application section.

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The integration between physiologically-based pharmacokinetics (PBPK) models and pharmacodynamics (PD) models makes it possible to describe the absorption, distribution, metabolism and excretion processes of drugs, together with the concentration-response relationship, being a fundamental framework with wide applications in pharmacology. Nevertheless, the enormous complexity of PBPK models and the large number of parameters that define them leads to the need to study and understand how the uncertainty of the parameters affects the variability of the models output. To study this issue, this paper proposes a global sensitivity analysis (GSA) to identify the parameters that have the greatest influence on the response of the model. It has been selected as study cases the PBPK models of an inhaled anesthetic and an analgesic, along with two PD interaction models that describe two relevant clinical effects, hypnosis and analgesia during general anesthesia. The subset of the most relevant parameters found adequately with the GSA method has been optimized for the generation of a virtual population that represents the theoretical output variability of various model responses. The generated virtual population has the potential to be used for the design, development and evaluation of physiological closed-loop control systems.

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We propose a mathematical model to study the antibody-dependent enhancement (ADE) phenomenon. Here, we explore the interaction between macrophages, dengue virus and plasma cells, especially the effect of a limitation on plasma cell proliferation, which occurs due to immunological memory. The model has up to three equilibrium points: one virus-free equilibrium and two virus-presence equilibrium, depending on the value of two thresholds. We determine the existence regions for the model equilibrium points and their stability, a sensitivity analysis was performed in the model thresholds. Numerical simulations illustrate that ADE can occur even when the basic reproduction number is less than one.

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In response to the ongoing pandemic of COVID-19, several companies across the world have proposed a wide variety of vaccines of different mechanisms of action. As a consequence, a new scenario of multiple imperfect vaccines against the SARS-CoV-2 arose. Mathematical modeling needs to consider this complex situation with different vaccines, some of them with two required doses. Using compartmental models we can simplify, simulate and most importantly, answer questions related to the development of the outbreak and the vaccination campaign. We present a model that addresses the current situation of COVID-19 and vaccination. Two important questions were considered in this paper: are more vaccines useful to reduce the spread of the coronavirus? How can we know if the vaccination campaign is sufficient? Two sensitivity criteria are helpful to answer these questions. The first criterion is the Multiple Vaccination Theorem, which indicates whether a vaccine is giving a positive or negative impact on the reproduction number. The second result (Insufficiency Theorem) provides a condition to answer the second question. Finally, we fitted the parameters with data and discussed the empirical results of six countries: Israel, Germany, the Czech Republic, Portugal, Italy, and Lithuania.

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The cabbage looper, Trichoplusia ni Hübner (Lep.: Noctuidae), is a destructive pest of Brassica crops. Their larvae defoliate plants, leading to reduced crop yield. Understanding and modeling pest seasonal dynamics is central to management programs because it allows one to set up sampling and control efforts. This study aimed to train, with field-collected data, artificial neural networks (ANN) for T. ni forecasting on Brassica crops. ANNs were used due to their suitability to fit complex models with multiple predictors. Three weather variables (air temperature, rainfall, and relative humidity lagged at different intervals from the day of pest assessment) and three host plants (broccoli, cabbage, and cauliflower) along with another plant-related variable (days after transplanting) were used as input variables to build ANNs with different topologies. Two outputs (T. ni eggs or larvae) were tested to verify which one would yield more precise models. ANNs forecasting T. ni eggs performed better, based on Pearson's correlation (rv) of observed with fitted values. The winning ANN (rv = 0.706) had weather data lagged by 15 days, 2 neurons in the hidden layer, hyperbolic tangent as the activation function, and resilient propagation as the learning algorithm. Broccoli and cauliflower were the hosts with major contributions for T. ni occurrence. Rainfall was the primary environmental predictor and affected T. ni negatively. Therefore, the winning ANN may be used to forecast T. ni egg densities 15 days in advance, allowing for timely management of this pest.

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In a population with ongoing vaccination, the trajectory of a pandemic is determined by how the virus spreads in unvaccinated and vaccinated individuals that exhibit distinct transmission dynamics based on different levels of natural and vaccine-induced immunity. We developed a mathematical model that considers both subpopulations and immunity parameters, including vaccination rates, vaccine effectiveness, and a gradual loss of protection. The model forecasted the spread of the SARS-CoV-2 delta variant in the US under varied transmission and vaccination rates. We further obtained the control reproduction number and conducted sensitivity analyses to determine how each parameter may affect virus transmission. Although our model has several limitations, the number of infected individuals was shown to be a magnitude greater (~10×) in the unvaccinated subpopulation compared to the vaccinated subpopulation. Our results show that a combination of strengthening vaccine-induced immunity and preventative behavioral measures like face mask-wearing and contact tracing will likely be required to deaccelerate the spread of infectious SARS-CoV-2 variants.

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Some deterministic models deal with environmental conditions and use parameter estimations to obtain experimental parameters, but they do not consider anthropogenic or environmental disturbances, e.g., chemical control or climatic conditions. Even more, they usually use theoretical or measured in-lab parameters without worrying about uncertainties in initial conditions, parameters, or changes in control inputs. Thus, in this study, we estimate parameters (including chemical control parameters) and confidence contours under uncertainty conditions using data from the municipality of Bello (Colombia) during 2010-2014, which includes two epidemic outbreaks. Our study shows that introducing non-periodic pulse inputs into the mathematical model allows us to: (i) perform parameter estimation by fitting real data of consecutive dengue outbreaks, (ii) highlight the importance of chemical control as a method of vector control, and (iii) reproduce the endemic behavior of dengue. We described a methodology for parameter and sub-contour box estimation under uncertainties and performed reliable simulations showing the behavior of dengue spread in different scenarios.

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An experiment was conducted to evaluate the effect of the intake of a mixture of fish and sacha inchi oils (iOM), organic selenium (iSe), and organic chromium (iCr) on egg production (EP) and feed conversion ratio (FCR) of Isa Brown second-cycle laying hens (SCLH) for 16 weeks (91-106 weeks old). Egg production and FCR were evaluated using multivariate models that included conventional equations and artificial neural networks (ANN) to study multiple nutritional interactions as alternatives to univariate dose-response models. Based on the best models, iOM, iSe, and iCr levels were optimized, and a global sensitivity analysis was implemented to quantify their influence on EP and FCR. The modified logistic model was selected as the best strategy to represent EP. In the case of FCR, an ANN model with a feed-forward architecture and softmax transfer function was selected as the best alternative. One of the scenarios to simultaneously optimize EP (89.1%) and FCR (1.94 kg feed/kg egg) at 16 weeks of production was established with 3.3 g/hen·day of iOM, 0.132 mg/ hen·day of iSe, and 0.176 mg/hen·day of iCr. However, optimization considering only FCR results in much lower optimal iCr levels (between 0.083 and 0.105 mg/hen·day) with a slight decrease in EP (87.9%). The global sensitivity analysis showed that iSe is an essential factor associated with the increase in EP, and iCr is the most influential factor for the decrease in FCR. When both criteria were taken into account simultaneously from a desirability function, iSe was the most critical factor.(AU)

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In recent decades, new aquaculture technologies have been developed and improved, such as the Biofloc Technology system, which is considered an alternative to the conventional aquaculture model. This study compared the bioeconomic viability of intensive production in nurseries and super-intensive production of shrimp Litopenaeus vannamei bioflocs greenhouses. The investment for implementing the project was US$ 767,190.18 for intensive production and US$ 807,669.16 for super-intensive production. The analyses showed Net Present Value of US$ 363,718.21 and US$ 385,477.42, Equivalent annual value of US$ 59,830.66 and US$ 63,410.00, Net future value of US$ 965,052.69 and US$ 1,022,786.35, Payback Period 4.12 and 4.11, Discounted payback period 5.64 and 5.63, Profitability Index 1.47 and 1.48, Internal Rate of Return 20.49 and 20.55%, and Modified Internal Rate of Return 14.61 and 14.64%. The investment analysis used in this study showed that super-intensive production in a greenhouse is the best investment option. The development of a new scenario simulating the super-intensive production of shrimp in a Biofloc Technology system, considering land use as a premise, made it possible to observe the possibility of obtaining financial gains in scale, both in the reduction of production costs and in the economic performance of the enterprise. However, the financial contribution for the implementation and operation of the project increased substantially.

Nas últimas décadas, novas tecnologias aquícolas têm sido desenvolvidas e aprimoradas, como o sistema de bioflocos, considerado uma alternativa ao modelo convencional aquícola. O presente estudo compara a viabilidade bioeconômica da produção intensiva em viveiros com a da produção superintensiva em estufas do camarão Litopenaeus vannamei em bioflocos. O investimento para implantação do projeto foi de US$ 767.190,18 para produção intensiva e US$ 807.669,16 para superintensiva. As análises apresentaram valor presente líquido de US$ 363.718,21 e US$ 385.477,42, valor anual equivalente de US$ 59.830,66 e US$ 63.410,00, valor futuro líquido de US$ 965.052,69 e US$ 1.022.786,35, período de payback 4,12 e 4,11, payback descontado 5,64 e 5,63, índice de lucratividade 1,47 e 1,48, taxa interna de retorno 20,49 e 20,55% e taxa interna de retorno modificada 14,61 e 14,64%. As análises de investimentos neste estudo mostraram que a produção superintensiva em estufas é a melhor opção. O desenvolvimento de um novo cenário simulando a produção superintensiva de camarões em sistema de bioflocos, considerando o uso da terra como premissa, permitiu observar a possibilidade de ganhos financeiros em escala tanto na redução dos custos de produção quanto no desempenho econômico do empreendimento. No entanto, a contribuição financeira para implantar e operar o projeto aumentou substancialmente.

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The power prior is a popular tool for constructing informative prior distributions based on historical data. The method consists of raising the likelihood to a discounting factor in order to control the amount of information borrowed from the historical data. However, one often wishes to assign this discounting factor a prior distribution and estimate it jointly with the parameters, which in turn necessitates the computation of a normalizing constant. In this article, we are concerned with how to approximately sample from joint posterior of the parameters and the discounting factor. We first show a few important properties of the normalizing constant and then use these results to motivate a bisection-type algorithm for computing it on a fixed budget of evaluations. We give a large array of illustrations and discuss cases where the normalizing constant is known in closed-form and where it is not. We show that the proposed method produces approximate posteriors that are very close to the exact distributions and also produces posteriors that cover the data-generating parameters with higher probability in the intractable case. Our results suggest that the proposed method is an accurate and easy to implement technique to include this normalization, being applicable to a large class of models. They also reinforce the notion that proper inclusion of the normalizing constant is crucial to the drawing of correct inferences and appropriate quantification of uncertainty.

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BACKGROUND: The global diet quality score (GDQS) is a simple, standardized metric appropriate for population-based measurement of diet quality globally. OBJECTIVES: We aimed to operationalize data collection by modifying the quantity of consumption cutoffs originally developed for the GDQS food groups and to statistically evaluate the performance of the operationalized GDQS relative to the original GDQS against nutrient adequacy and noncommunicable disease (NCD)-related outcomes. METHODS: The GDQS application uses a 24-h open-recall to collect a full list of all foods consumed during the previous day or night, and automatically classifies them into corresponding GDQS food group. Respondents use a set of 10 cubes in a range of predetermined sizes to determine if the quantity consumed per GDQS food group was below, or equal to or above food group-specific cutoffs established in grams. Because there is only a total of 10 cubes but as many as 54 cutoffs for the GDQS food groups, the operationalized cutoffs differ slightly from the original GDQS cutoffs. RESULTS: A secondary analysis using 5 cross-sectional datasets comparing the GDQS with the original and operationalized cutoffs showed that the operationalized GDQS remained strongly correlated with nutrient adequacy and was equally sensitive to anthropometric and other clinical measures of NCD risk. In a secondary analysis of a longitudinal cohort study of Mexican teachers, there were no differences between the 2 modalities with the beta coefficients per 1 SD change in the original and operationalized GDQS scores being nearly identical for weight gain (-0.37 and -0.36, respectively, P < 0.001 for linear trend for both models) and of the same clinical order of magnitude for waist circumference (-0.52 and -0.44, respectively, P < 0.001 for linear trend for both models). CONCLUSION: The operationalized GDQS cutoffs did not change the performance of the GDQS and therefore are recommended for use to collect GDQS data in the future.

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