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Ecosystem services in bolstering human well-being and steering environmental management garnered increasing recognition. In this realm, the Soil and Water Assessment Tool (SWAT) rose as an instrumental tool in ecosystem services. The heterogeneous applications of SWAT across diverse studies underscore an imperative for bibliometric analysis to decipher these evolving trends. This study endeavors to execute a comprehensive analysis of SWAT's application for ecosystem services, delineating key thematic development and exploring its utilization in ecosystem services. We conducted a comprehensive literature review by searching the Web of Science database, retrieving a total of 534 articles. The CiteSpace facilitated our co-citation analysis, enabling the identification of seminal publications and burgeoning themes within SWAT. Our analysis delineated thematic development in SWAT pertaining to ecosystem services. Initially concentrated on hydrological processes, the focus progressively broadened to encompass comprehensive ecosystem services evaluations. We examined 81 peer-reviewed publications directly related to ecosystem services, and most of them addressed certain ecosystem services, such as water yield, soil retention, regulation of water flow, food, and carbon storage. SWAT holds a unique advantage in quantifying water-related processes. Future studies should focus more on ecosystem service flows based on SWAT, which contributes to elucidating the relationship between nature and humans, facilitating comprehensive ecosystem management.

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Climate change could lead to high economic burden for individuals (i.e. low income and high prices). While economic conditions are important determinants of climate change vulnerability, environmental epidemiological studies focus primarily on the direct impact of temperature on morbidity and mortality without accounting for climate-induced impacts on the economy. More integrated approaches are needed to provide comprehensive assessments of climate-induced direct and indirect impacts on health. This paper provides some perspectives on how epidemiological and economic impact assessments could be better integrated. We argue that accounting for the economic repercussions of climate change on people's health and, vice versa, the consequences of health effects on the economy could provide more realistic scenario projections and could be more useful for adaptation policy.

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The health and welfare of livestock are significant for ensuring the sustainability and profitability of the agricultural industry. Addressing efficient ways to monitor and report the health status of individual cows is critical to prevent outbreaks and maintain herd productivity. The purpose of the study is to develop a machine learning (ML) model to classify the health status of milk cows into three categories. In this research, data are collected from existing non-invasive IoT devices and tools in a dairy farm, monitoring the micro- and macroenvironment of the cow in combination with particular information on age, days in milk, lactation, and more. A workflow of various data-processing methods is systematized and presented to create a complete, efficient, and reusable roadmap for data processing, modeling, and real-world integration. Following the proposed workflow, the data were treated, and five different ML algorithms were trained and tested to select the most descriptive one to monitor the health status of individual cows. The highest result for health status assessment is obtained by random forest classifier (RFC) with an accuracy of 0.959, recall of 0.954, and precision of 0.97. To increase the security, speed, and reliability of the work process, a cloud architecture of services is presented to integrate the trained model as an additional functionality in the Amazon Web Services (AWS) environment. The classification results of the ML model are visualized in a newly created interface in the client application.

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Genotype-to-phenotype (G2P) prediction has become a mainstream paradigm to facilitate genomic selection (GS)-assisted breeding in the seed industry. Many methods have been introduced for building GS models, but their prediction precision may vary depending on species and specific traits. Therefore, evaluation of multiple models and selection of the appropriate one is crucial to effective GS analysis. Here, we present the G2P container developed for the Singularity platform, which not only contains a library of 16 state-of-the-art GS models and 13 evaluation metrics. G2P works as an integrative environment offering comprehensive, unbiased evaluation analyses of the 16 GS models, which may be run in parallel on high-performance computing clusters. Based on the evaluation outcome, G2P performs auto-ensemble algorithms that not only can automatically select the most precise models but also can integrate prediction results from multiple models. This functionality should further improve the precision of G2P prediction. Another noteworthy function is the refinement design of the training set, in which G2P optimizes the training set based on the genetic diversity analysis of a studied population. Although the training samples in the optimized set are fewer than in the original set, the prediction precision is almost equivalent to that obtained when using the whole set. This functionality is quite useful in practice, as it reduces the cost of phenotyping when constructing training population. The G2P container and source codes are freely accessible at https://g2p-env.github.io/.

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Understanding factors influencing microbial interactions, and designing methods to identify key taxa that are candidates for synthetic communities, or SynComs, are complex challenges for achieving microbiome-based agriculture. Here, we study how grafting and the choice of rootstock influences root-associated fungal communities in a grafted tomato system. We studied three tomato rootstocks (BHN589, RST-04-106, and Maxifort) grafted to a BHN589 scion and profiled the fungal communities in the endosphere and rhizosphere by sequencing the internal transcribed spacer (ITS2). The data provided evidence for a rootstock effect (explaining ~2% of the total captured variation, P < 0.01) on the fungal community. Moreover, the most productive rootstock, Maxifort, supported greater fungal species richness than the other rootstocks or controls. We then constructed a phenotype-operational taxonomic unit (OTU) network analysis (PhONA) using an integrated machine learning and network analysis approach based on fungal OTUs and associated tomato yield as the phenotype. PhONA provides a graphical framework to select a testable and manageable number of OTUs to support microbiome-enhanced agriculture. We identified differentially abundant OTUs specific to each rootstock in both endosphere and rhizosphere compartments. Subsequent analyses using PhONA identified OTUs that were directly associated with tomato fruit yield and others that were indirectly linked to yield through their links to these OTUs. Fungal OTUs that are directly or indirectly linked with tomato yield may represent candidates for synthetic communities to be explored in agricultural systems. IMPORTANCE The realized benefits of microbiome analyses for plant health and disease management are often limited by the lack of methods to select manageable and testable synthetic microbiomes. We evaluated the composition and diversity of root-associated fungal communities from grafted tomatoes. We then constructed a phenotype-OTU network analysis (PhONA) using these linear and network models. By incorporating yield data in the network, PhONA identified OTUs that were directly predictive of tomato yield and others that were indirectly linked to yield through their links to these OTUs. Follow-up functional studies of taxa associated with effective rootstocks, identified using approaches such as PhONA, could support the design of synthetic fungal communities for microbiome-based crop production and disease management. The PhONA framework is flexible for incorporation of other phenotypic data, and the underlying models can readily be generalized to accommodate other microbiome or 'omics data.

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Aboveground transmission oil pipelines can cross debris flow-prone areas. Currently, there are no available methodologies to assess pipeline failure status with the different pipeline arrangements (location, direction, and segment lengths) and different operating conditions. For solving the research gap, this study proposes a novel methodology to simulate the cascade processes of debris flow propagation, the impact of debris flow on pipelines, and pipeline failure. With consideration of different pipeline arrangement and operating conditions. We introduce the polar coordinate system to set up locations and directions scenarios for the first time. Also, we use the 3-D debris flow simulation model (DebrisInterMixing solver in OpenFOAM) coupled with a modified pipeline mechanical model considering operating conditions for the first time. The proposed methodology shows the different trends of pipeline failure probability with the increase of pipeline segment length for the different pipeline locations and directions. The result shows, for the pipelines of 30° the tensile stress has a more moderate increase rate with the increase of pipeline segment length, and the pipeline failure probability keeps 0 at the 5-m location. At 5 m and 15 m locations, the failure probabilities of the pipelines of 60° and 90° start to increase when the segment length is 13-14 m, while for other pipelines the segment length is 17-19 m. The findings of this study can support the decisions of government authorities, stakeholders, and operators for risk assessment, prioritization of hazard mitigation measures and emergency planning, or concerning decisions regarding pipeline siting during the design, routing, construction, operation, and maintenance stage.

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Root anatomy is an important determinant of root metabolic costs, soil exploration, and soil resource capture. Root anatomy varies substantially within and among plant species. RootSlice is a multicellular functional-structural model of root anatomy developed to facilitate the analysis and understanding of root anatomical phenotypes. RootSlice can capture phenotypically accurate root anatomy in three dimensions of different root classes and developmental zones, of both monocotyledonous and dicotyledonous species. Several case studies are presented illustrating the capabilities of the model. For maize nodal roots, the model illustrated the role of vacuole expansion in cell elongation; and confirmed the individual and synergistic role of increasing root cortical aerenchyma and reducing the number of cortical cell files in reducing root metabolic costs. Integration of RootSlice for different root zones as the temporal properties of the nodal roots in the whole-plant and soil model OpenSimRoot/maize enabled the multiscale evaluation of root anatomical phenotypes, highlighting the role of aerenchyma formation in enhancing the utility of cortical cell files for improving plant performance over varying soil nitrogen supply. Such integrative in silico approaches present avenues for exploring the fitness landscape of root anatomical phenotypes.

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Cardiotocography (CTG) recorded fetal heart rate and its temporal relationship with uterine contractions. CTG intelligent classification plays an important role in evaluating fetal health and protecting fetal normal growth and development throughout pregnancy. At the feature selection level, this study uses the Apriori algorithm to search frequent item sets for feature extraction. At the level of the classification model, the combination model of AdaBoost and random forest with the highest classification accuracy is finally selected by comparing various models. The suspicious class data in the CTG data set affect the overall classification accuracy. The number of suspicious class data is predicted by the multi-model ensemble method. Finally, the data set is fused from three classifications to two classifications. The classification accuracy is 0.976, and the AUC is 0.98, which significantly improves the classification effect. In conclusion, the method used in this study has high accuracy in model classification, which is helpful to improve the accuracy of fetal abnormality detection.

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The current production of a number of commodity chemicals relies on the exploitation of fossil fuels and hence has an irreversible impact on the environment. Biotechnological processes offer an attractive alternative by enabling the manufacturing of chemicals by genetically modified microorganisms. However, this alternative approach poses some important technical challenges that must be tackled to make it competitive. On the one hand, the design of biotechnological processes is based on trial-and-error approaches, which are not only costly in terms of time and money, but also result in suboptimal designs. On the other hand, the manufacturing of chemicals by biological processes is almost exclusively carried out by batch or fed-batch cultures. Given that batch cultures are expensive and not easy to scale, technical means must be developed to make continuous cultures feasible and efficient. In order to address these challenges, we have developed a mathematical model able to integrate in a single model both the genome-scale metabolic model for the organism synthesizing the chemical of interest and the dynamics of the bioreactor in which the organism is cultured. Such a model is based on the use of Flexible Nets, a modeling formalism for dynamical systems. The integration of a microscopic (organism) and a macroscopic (bioreactor) model in a single net provides an overall view of the whole system and opens the door to global optimizations. As a case study, the production of citramalate with respect to the substrate consumed by E. coli is modeled, simulated and optimized in order to find the maximum productivity in a steady-state continuous culture. The predicted computational results were consistent with the wet lab experiments.

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Estimating exposure in receiving waterbodies is a key step in the regulatory process to evaluate potential ecological risks posed by the use of agricultural pesticides. The United States Environmental Protection Agency (USEPA) currently uses the Variable Volume Water Model (VVWM) to predict environmental concentrations of pesticides in static waterbodies (ponds) that receive edge-of-field runoff inputs from the Pesticide Root Zone Model (PRZM). This regulatory model, however, does not adequately characterize potential pesticide concentrations in flowing water systems (streams and rivers) drained from watershed areas. This study aims at addressing this gap by coupling the regulatory PRZM model with a watershed-level hydrological model, the Soil and Water Assessment Tool (SWAT), to predict pesticide concentrations in flowing water habitats for aquatic organisms. This coupled PRZM-SWAT model was applied in a test watershed (~HUC12), a headwater watershed of Goodwater Creek in Missouri, and simulation results at the outlet of this watershed were compared to daily and near-daily measured streamflow and atrazine concentration data from a decade-long sampling campaign. Overall, the PRZM-SWAT model captured (1) the general magnitude and temporal trend of daily atrazine concentrations, (2) the observed high-end of exposure levels (>3 ppb) of atrazine concentrations, and (3) the 90th centile annual maximum for various exposure durations (1-, 4-, 7-, 21-, and 60-day rolling average), which are important exposure metrics used in assessing the potential ecological risks posed by the application of pesticides. The PRZM-SWAT model is expected to expand the utility of the field-scale regulatory model to include pesticide exposure prediction capability in flowing waterbodies from agricultural watersheds. Integr Environ Assess Manag 2022;18:1678-1693. © 2022 SETAC.

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Terrestrial ecosystems regulate Earth's climate through water, energy, and biogeochemical transformations. Despite a key role in regulating the Earth system, terrestrial ecology has historically been underrepresented in the Earth system models (ESMs) that are used to understand and project global environmental change. Ecology and Earth system modeling must be integrated for scientists to fully comprehend the role of ecological systems in driving and responding to global change. Ecological insights can improve ESM realism and reduce process uncertainty, while ESMs offer ecologists an opportunity to broadly test ecological theory and increase the impact of their work by scaling concepts through time and space. Despite this mutualism, meaningfully integrating the two remains a persistent challenge, in part because of logistical obstacles in translating processes into mathematical formulas and identifying ways to integrate new theories and code into large, complex model structures. To help overcome this interdisciplinary challenge, we present a framework consisting of a series of interconnected stages for integrating a new ecological process or insight into an ESM. First, we highlight the multiple ways that ecological observations and modeling iteratively strengthen one another, dispelling the illusion that the ecologist's role ends with initial provision of data. Second, we show that many valuable insights, products, and theoretical developments are produced through sustained interdisciplinary collaborations between empiricists and modelers, regardless of eventual inclusion of a process in an ESM. Finally, we provide concrete actions and resources to facilitate learning and collaboration at every stage of data-model integration. This framework will create synergies that will transform our understanding of ecology within the Earth system, ultimately improving our understanding of global environmental change, and broadening the impact of ecological research.

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Most previous studies have indicated inconsistent relationships between rice cadmium (Cd) and the soil properties of paddy fields at a regional scale under the adverse effects of confounding factors and spatial heterogeneity. In order to reduce these effects, this study integrates Geodetector, a stepwise regression model, and a hierarchical Bayesian method (collectively called GDSH). The GDSH framework is validated in a large typical rice production area in southeastern China. According to the results, significant stratified heterogeneity of the bioaccumulation factor is observed among different subregions and pH strata (q = 0.23, p < 0.01). Additionally, the soil-rice relationships and dominant factors vary by the subregions, and the available soil Cd and pH are found to be the dominant factors in 64% and 50% of subregions, respectively. In the entire region, when the pH < 6, the dominant factors are organic matter and available Cd, and when pH ≥ 6 they are organic matter, pH, and available Cd. Furthermore, these factors presented different sensitivity to the spatial heterogeneity. The results indicate that, at the subregional level, the GDSH framework can reduce the confounding effects and accurately identify the dominant factors of rice Cd. At the regional level, this model can evaluate the sensitivity of the dominant factors to spatial heterogeneity in a large area. This study provides a new scheme for the complete utilization of regional field survey data, which is conducive to formulating precise pollution control strategies.

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This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application with a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures since the software supports structural normalization, molecular descriptor calculation, and machine learning model generation using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code, adding unlimited model customization. Models generated with Flame can be easily exported, facilitating collaborative model development. All models are stored in a model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly 3 years of development in collaboration with users from the pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high-quality predictive models based on shared legacy data for assessing the safety of drug candidates.

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The development of modeling technology to adequately simulate water and pesticide movement within the rice paddy environment faces several challenges. These include: (1) adequately representing ponded conditions; (2) the collection/implementation of temporal/spatial pesticide application data at field scales; (3) the integration of various mixed-landuses simulation schemes. Currently available models do not fully consider these challenges and results may not be sufficiently accurate to represent fate and transport of rice pesticides at watershed scales. Therefore, in this study, an integrated simulation system, "RiceWQ-AnnAGNPS", was developed to fully address these challenges and is illustrated in a California watershed with rice farming practices. The integrated system successfully extends field level simulations to watershed scales while considering the impact of mixed landuses on downstream loadings. Moreover, the system maintains the application information at fine spatial scales and handles varying treated paddy areas via the "split and adjust" approach. The new system was evaluated by investigating the fate and transport of thiobencarb residues in the Colusa Basin, California as a case study. Thiobencarb concentrations in both water and sediment phases were accurately captured by the calibrated RiceWQ model at the edge of field. After spatial upscaling, the integrated system successfully reflected both the seasonal pattern of surface runoff and the timing of monthly thiobencarb loadings. Incorporating future enhancements can further improve model performance by including more detailed water drainage schedules and management practices, improving the accuracy of summer runoff estimations, and incorporating a more sophisticated in-stream process module. This integrated system provides a framework for evaluating rice pesticide impacts as part of a basin level management approach to improve water quality, which can be extended to other rice agrochemicals, or other areas with fine-scale spatial information of pesticide applications.

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The trade-off between yield and quality, a major problem for the production of fleshy fruits, involves fruit expansive growth and sugar metabolism. Here we developed an integrative model by coupling a biophysical model of fleshy fruit growth processes, including water and carbon fluxes and organ expansion, with an enzyme-based kinetic model of sugar metabolism to better understand the interactions between these two processes. The integrative model was initially tested on tomato fruit, a model system for fleshy fruit. The integrative model closely simulated the biomass and major carbon metabolites of tomato fruit developing under optimal or stress conditions. The model also performed robustly when simulating the fruit size and sugar concentrations of different tomato genotypes including wild species. The validated model was used to explore ways of uncoupling the size-sweetness trade-off in fruit. Model-based virtual experiments suggested that larger sweeter tomatoes could be obtained by simultaneously manipulating certain biophysical factors and transmembrane transports. The integrative fleshy fruit model provides a promising tool to facilitate the targeted bioengineering and breeding of tomatoes and other fruits.

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The degree to which we can understand the multi-scale organization of cellular life is tied to how well our models can represent this organization and the processes that drive its evolution. This paper uses Vivarium-an engine for composing heterogeneous computational biology models into integrated, multi-scale simulations. Vivarium's approach is demonstrated by combining several sub-models of biophysical processes into a model of chemotactic E. coli that exchange molecules with their environment, express the genes required for chemotaxis, swim, grow, and divide. This model is developed incrementally, highlighting cross-compartment mechanisms that link E. coli to its environment, with models for: (1) metabolism and transport, with transport moving nutrients across the membrane boundary and metabolism converting them to useful metabolites, (2) transcription, translation, complexation, and degradation, with stochastic mechanisms that read real gene sequence data and consume base pairs and ATP to make proteins and complexes, and (3) the activity of flagella and chemoreceptors, which together support navigation in the environment.

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Knowledge management is one of the key priorities of many organizations. They face different challenges in the implementation of knowledge management processes, including the transformation of tacit knowledge-experience, skills, insights, intuition, judgment and know-how-into explicit knowledge. Furthermore, the increasing number of information sources and services in some domains, such as healthcare, increase the amount of information available. Therefore, there is a need to transform that information in knowledge. In this context, learning ecosystems emerge as solutions to support knowledge management in a different context. On the other hand, the dashboards enable the generation of knowledge through the exploitation of the data provided from different sources. The model-driven development of these solutions is possible through two meta-models developed in previous works. Even though those meta-models solve several problems, the learning ecosystem meta-model has a lack of decision-making support. In this context, this work provides two main contributions to face this issue. First, the definition of a holistic meta-model to support decision-making processes in ecosystems focused on knowledge management, also called learning ecosystems. The second contribution of this work is an instantiation of the presented holistic meta-model in the healthcare domain.

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Gene regulatory network Inference with high accuracy based on gene expression data sets is one of the most challenging problems in computational biology. To improve the accuracy of gene regulatory network inference and find hub genes, we proposed a novel model integration network inference method with clustering and hub genes finding called MINICHG. The method is divided into three main steps: (1) using single models inference results based on three machine learning algorithms to construct feature matrix; (2) using k-means to cluster gene pairs according to feature matrix; (3) hub genes finding. MINICHG integrates RF(Random Forest), GBDT (Gradient Boosting Decision Tree) and Pearson Correlation results with a novel weighted strategy in a semi-unsupervised way. The designed optimization scheme in MINICHG considering sparse gold standard data characteristics is suitable for most gene regulatory network reconstruction. We evaluated the proposed method on simulated data sets from five Dream4 multifactorial data sets and Dream5 in silico data set and real data set from E.coli. The performance was better than other network inference methods with high accuracy and robustness.

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Genome-scale metabolic models have been successfully applied to study the metabolism of multiple plant species in the past decade. While most existing genome-scale modelling studies have focussed on studying the metabolic behaviour of individual plant metabolic systems, there is an increasing focus on combining models of multiple tissues or organs to produce multi-tissue models that allow the investigation of metabolic interactions between tissues and organs. Multi-tissue metabolic models were constructed for multiple plants including Arabidopsis, barley, soybean and Setaria. These models were applied to study various aspects of plant physiology including the division of labour between organs, source and sink tissue relationship, growth of different tissues and organs and charge and proton balancing. In this review, we outline the process of constructing multi-tissue genome-scale metabolic models, discuss the strengths and challenges in using multi-tissue models, review the current status of plant multi-tissue and whole plant metabolic models and explore the approaches for integrating genome-scale metabolic models into multi-scale plant models.

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