Understanding the strength of trade-off and synergistic relationships among ecosystem services (ESs) is crucial for ecological management and restoration in the Fenhe River Basin. However, there is still a lack of sufficient research on the driving variables and spatial pattern optimization of the strength of ESs relationships in this area. Based on the quantitative assessment of six ESs in the Fenhe River Basin in 2000 and 2020, the ecosystem services trade-off synergy index (TSI) was introduced to quantitatively measure the strength of trade-off and synergistic relationships between each pair of ESs. A Bayesian network was constructed to identify the driving variables of trade-off and synergistic relationships, and sensitivity analysis was conducted to determine the degree of influence of key variables on the strength of these relationships. The optimization area of the strength of ESs trade-off and synergistic relationships was characterized in spatial patterns. The results showed that:① There were significant spatiotemporal differences in the six ESs in the Fenhe River Basin in 2000 and 2020. In terms of time scale, water yield, net primary productivity, crop productivity, soil conservation, and carbon storage all showed a trend of fluctuating increase. In terms of spatial scale, the spatial distribution changes in the six ESs were relatively small over the 20 years. ② The TSI of carbon storage was high in the surrounding area and low in the middle, showing a four-high and four-low pattern. The areas with the highest TSI between grain supply and other services were distributed from north to south. ③ Sensitivity analysis found that the strength of water yield, soil conservation, and habitat quality were significantly affected by precipitation, plant root depth restriction, and rainfall erosion. According to the conditional probability of different states of key variables, Wenshui County, Qingxu County, and Qi County in the central part of the Fenhe River Basin were identified as high-value areas for trade-off and synergistic relationships, which could be used as key areas for ecological restoration. These findings have important theoretical and practical significance for understanding the complex relationship between multiple ESs trade-off and synergistic relationships and their driving variables and for proposing sustainable ecological environment management policies.

An understanding of the combined effects of climate change (CC) and other anthropogenic stressors, such as chemical exposures, is essential for improving ecological risk assessments of vulnerable ecosystems. In the Great Barrier Reef, coral reefs are under increasingly severe duress from increasing ocean temperatures, acidification, and cyclone intensities associated with CC. In addition to these stressors, inshore reef systems, such as the Mackay-Whitsunday coastal zone, are being impacted by other anthropogenic stressors, including chemical, nutrient, and sediment exposures related to more intense rainfall events that increase the catchment runoff of contaminated waters. To illustrate an approach for incorporating CC into ecological risk assessment frameworks, we developed an adverse outcome pathway network to conceptually delineate the effects of climate variables and photosystem II herbicide (diuron) exposures on scleractinian corals. This informed the development of a Bayesian network (BN) to quantitatively compare the effects of historical (1975-2005) and future projected climate on inshore hard coral bleaching, mortality, and cover. This BN demonstrated how risk may be predicted for multiple physical and biological stressors, including temperature, ocean acidification, cyclones, sediments, macroalgae competition, and crown of thorns starfish predation, as well as chemical stressors such as nitrogen and herbicides. Climate scenarios included an ensemble of 16 downscaled models encompassing current and future conditions based on multiple emission scenarios for two 30-year periods. It was found that both climate-related and catchment-related stressors pose a risk to these inshore reef systems, with projected increases in coral bleaching and coral mortality under all future climate scenarios. This modeling exercise can support the identification of risk drivers for the prioritization of management interventions to build future resilient reefs. Integr Environ Assess Manag 2024;20:401-418. © 2023 Norwegian Institute for Water Research and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Bayesian network (BN) models are increasingly used as tools to support probabilistic environmental risk assessments (ERAs), because they can better account for uncertainty compared with the simpler approaches commonly used in traditional ERA. We used BNs as metamodels to link various sources of information in a probabilistic framework, to predict the risk of pesticides to aquatic communities under given scenarios. The research focused on rice fields surrounding the Albufera Natural Park (Valencia, Spain), and considered three selected pesticides: acetamiprid (an insecticide), 2-methyl-4-chlorophenoxyacetic acid (MCPA; a herbicide), and azoxystrobin (a fungicide). The developed BN linked the inputs and outputs of two pesticide models: a process-based exposure model (Rice Water Quality [RICEWQ]), and a probabilistic effects model (Predicts the Ecological Risk of Pesticides [PERPEST]) using case-based reasoning with data from microcosm and mesocosm experiments. The model characterized risk at three levels in a hierarchy: biological endpoints (e.g., molluscs, zooplankton, insects, etc.), endpoint groups (plants, invertebrates, vertebrates, and community processes), and community. The pesticide risk to a biological endpoint was characterized as the probability of an effect for a given pesticide concentration interval. The risk to an endpoint group was calculated as the joint probability of effect on any of the endpoints in the group. Likewise, community-level risk was calculated as the joint probability of any of the endpoint groups being affected. This approach enabled comparison of risk to endpoint groups across different pesticide types. For example, in a scenario for the year 2050, the predicted risk of the insecticide to the community (40% probability of effect) was dominated by the risk to invertebrates (36% risk). In contrast, herbicide-related risk to the community (63%) resulted from risk to both plants (35%) and invertebrates (38%); the latter might represent (in the present study) indirect effects of toxicity through the food chain. This novel approach combines the quantification of spatial variability of exposure with probabilistic risk prediction for different components of aquatic ecosystems. Environ Toxicol Chem 2024;43:182-196. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

In the digital transformation of Chinese pharmaceutical industry, how to efficiently govern and analyze industrial data and excavate the valuable information contained therein to guide the production of drug products has always been a research hotspot and application difficulty. Generally, the Chinese pharmaceutical technique is relatively extensive, and the consistency of drug quality needs to be improved. To address this problem, we proposed an optimization method combining advanced calculation tools(e.g., Bayesian network, convolutional neural network, and Pareto multi-objective optimization algorithm) with lean six sigma tools(e.g., Shewhart control chart and process performance index) to dig deeply into historical industrial data and guide the continuous improvement of pharmaceutical processes. Further, we employed this strategy to optimize the manufacturing process of sporoderm-removal Ganoderma lucidum spore powder. After optimization, we preliminarily obtained the possible interval combination of critical parameters to ensure the P_(pk) values of the critical quality properties including moisture, fineness, crude polysaccharide, and total triterpenes of the sporoderm-removal G. lucidum spore powder to be no less than 1.33. The results indicate that the proposed strategy has an industrial application value.

Due to the influence of complex regional climate, water quality perturbation factors of lakes in cold regions are complicated, and the uncertainty of each factor needs further study. This study coupled two algorithms (clustering and EM) to establish a water quality uncertainty model of Chagan Lake, a typical cold region lake in China. A BN model containing nine influencing factors (including water temperature (WT), total phosphorus (TP), total nitrogen (TN), etc.) was established and optimized, and sensitivity analysis was also performed. The results indicate that the water quality status of the lake is class III and 27.47% risk of exceeding the standard. The water quality of the lake is more susceptible to disturbance during the freezing period (WT < 1 °C). TP is the most sensitive factor for water quality disturbance in the lake followed by chemical oxygen demand (COD), TN, and fluoride (F). Parameter control result displays, and the multifactor synergistic control scheme could reduce the water quality risk of the lake by 36.47%. This study demonstrates that our proposed method can be used to predict both sudden water quality events and the overall trend of water quality fluctuation, which is important for rapid water quality evaluation and management decisions.

Mycotoxin contamination of corn results in significant agroeconomic losses and poses serious health issues worldwide. This paper presents the first report utilizing machine learning and historical aflatoxin and fumonisin contamination levels in-order-to develop models that can confidently predict mycotoxin contamination of corn in Illinois, a major corn producing state in the USA. Historical monthly meteorological data from a 14-year period combined with corresponding aflatoxin and fumonisin contamination data from the State of Illinois were used to engineer input features that link weather, fungal growth, and aflatoxin production in combination with gradient boosting (GBM) and bayesian network (BN) modeling. The GBM and BN models developed can predict mycotoxin contamination with overall 94% accuracy. Analyses for aflatoxin and fumonisin with GBM showed that meteorological and satellite-acquired vegetative index data during March significantly influenced grain contamination at the end of the corn growing season. Prediction of high aflatoxin contamination levels was linked to high aflatoxin risk index in March/June/July, high vegetative index in March and low vegetative index in July. Correspondingly, high levels of fumonisin contamination were linked to high precipitation levels in February/March/September and high vegetative index in March. During corn flowering time in June, higher temperatures range increased prediction of high levels of fumonisin contamination, while high aflatoxin contamination levels were linked to high aflatoxin risk index. Meteorological events prior to corn planting in the field have high influence on predicting aflatoxin and fumonisin contamination levels at the end of the year. These early-year events detected by the models can directly assist farmers and stakeholders to make informed decisions to prevent mycotoxin contamination of Illinois grown corn.

Efficient food safety monitoring should achieve optimal resource allocation. In this article, a methodology is presented to optimize the use of resources for food safety monitoring aimed at identifying noncompliant samples and estimating background level of hazards in food products. A Bayesian network (BN) model and an optimization model were combined in a single framework. The framework was applied to monitoring dioxins and dioxin-like polychlorinated biphenyls (DL-PCBs) in primary animal-derived food products in the Netherlands. The BN model was built using a national dataset with monitoring results of dioxins and DL-PCBs in animal-derived food products over a 10-year period (2008-2017). These data were used to estimate the probability of detecting suspect samples with dioxins and DL-PCBs levels above preset thresholds, given certain sample conditions. The results of the BN model were then inserted into the optimization model to compute an optimal monitoring scheme. Model estimates showed that the probability of dioxins and DL-PCBs exceeding threshold limits was higher in laying hen eggs and sheep meat than in other animal-derived food (except deer meat). Compared with the monitoring scheme used in the Netherlands in 2018, the optimal monitoring scheme would save around 10,000 EUR per year. This could be obtained by reallocating monitoring resources from products with lower probability of dioxin and DL-PCBs exceeding threshold limits (e.g., pig meat) to products with higher probability (e.g., bovine animal meat), and by shifting sample collection from the last quarter of the year toward the first three quarters of the year.

OBJECTIVE: Preoperative risk stratification of newly diagnosed endometrial carcinoma (EC) patients has been hindered by only moderate prediction performance for many years. Recently ENDORISK, a Bayesian network model, showed high predictive performance. It was the aim of this study to validate ENDORISK by applying the model to a population-based case series of EC patients.
METHODS: ENDORISK was applied to a retrospective cohort of women surgically treated for EC from 2003 to 2013. Prediction accuracy for LNM as well as 5-year DSS was investigated. The model\'s overall performance was quantified by the Brier score, discriminative performance by area under the curve (AUC).
RESULTS: A complete dataset was evaluable from 247 patients. 78.1% cases were endometrioid histotype. The majority of patients (n = 156;63.2%) had stage IA disease. Overall, positive lymph nodes were found in 20 (8.1%) patients. Using ENDORISK predicted probabilities, most (n = 156;63.2%) patients have been assigned to low or very low risk group with a false-negative rate of 0.6%. AUC for LNM prediction was 0.851 [95% confidence interval (CI) 0.761-0.941] with a Brier score of 0.06. For 5-year DSS the AUC was 0.698 (95% CI 0.595-0.800) as Brier score has been calculated 0.09.
CONCLUSIONS: We were able to successfully validate ENDORISK for prediction of LNM and 5-year DSS. Next steps will now have to focus on ENDORISK performance in daily clinical practice. In addition, incorporating TCGA-derived molecular subtypes will be of key importance for future extended use. This study may support further promoting of data-based decision-making tools for personalized treatment of EC.

Brucellosis is a common zoonotic infection of humans from livestock. This bacterial infection is acquired from infected animals and their products. The pathogen of this disease is a genus of bacilli called Brucella, and no effective vaccine has been discovered yet for the prevention of human brucellosis.
The present study is mainly conducted to diagnose brucellosis accurately and timely, using Data Mining techniques. Based on the knowledge discovered with Data Mining and opinions of specialist physicians, this study aims to propose instructions for diagnosing brucellosis.
The dataset used in this study contains 340 samples and is extracted from the files of patients at Tehran Imam Khomeini Hospital from the years 2010-2020. Attributes of this dataset have been determined based on domain expert opinions, namely specialist physicians. After initial analysis and data pre-processing, various Data Mining techniques have been employed to diagnose brucellosis, including neural networks, Bayesian networks, and decision trees.
According to the recorded data, 270 people (approximately 79% of samples) had brucellosis. Some clinical symptoms were more prominent among infected patients, including fever, arthritis, tremor, decreased appetite, and nightly perspiration. Among all employed Data Mining techniques in this study, the decision tree with C5.0 pruning algorithm possessed the highest accuracy in diagnosing patients with brucellosis (approximately 99% accuracy). Based on the obtained final model, the most important factors for diagnosing brucellosis are the Wright test, Coombs Wright test, blood culture test, and living place.
According to the results of this study, brucellosis can be diagnosed with a high accuracy using Data Mining techniques. Furthermore, the most significant factors for diagnosing brucellosis disease can be identified by Data Mining. Among all investigated techniques in this study, the decision tree with C5.0 pruning algorithm has the most accuracy in diagnosing brucellosis. Given the decision tree created by the C5.0 algorithm and the opinions of specialist physicians, some instructions are proposed based on a decision-making framework to classify referents into patient and non-patient groups. These instructions can accelerate the diagnosis, reduce therapeutic costs, and decrease treatment period.

To analyze the risk of gas overrun in coal mines and improve the risk analysis, a novel risk analysis method was proposed based on FAHP and Bayesian network. The risk analysis framework consisted of causal reasoning, logical reasoning, and sensitivity analysis. The gas overrun risk analysis was conducted by taking the Laohutai Coal Mine in China as the research object. Specifically, based on prior knowledge and sample data, the probability of the gas overrun was 3.2%, belonging to a small probability event. However, the probability of gas concentration exceeding 1% was 12%, and there was still potential danger. Logical reasoning diagnosed and identified that wind speed and air leakage were the direct causes of gas overrun. Sensitivity analysis indicated that wind speed, human error, and ground stress were key factors of the gas overrun. The case study showed this fuzzy analytic hierarchy process (FAHP)-Bayesian network (BN)-based risk analysis method can provide real-time and dynamic decision support for gas overrun control and treatment in coal mines to ensure the safe and efficient mining.