Evaluation of best management practices for mitigating harmful algal blooms risk in an agricultural lake basin using a watershed model integrated with Bayesian Network approach.

Lake eutrophication caused by nitrogen and phosphorus has led to frequent harmful algal blooms (HABs), especially under the unknown challenges of climate change, which have seriously damaged human life and property. In this study, a coupled SWAT-Bayesian Network (SWAT-BN) model framework was constructed to elucidate the mechanisms between non-point source nitrogen pollution in agricultural lake watersheds and algal activities. A typical agricultural shallow lake basin, the Taihu Basin (TB), China, was chosen in this study, aiming to investigate the effectiveness of best management practices (BMPs) in controlling HABs risks in TB. By modeling total nitrogen concentration of Taihu Lake from 2007 to 2022 with four BMPs (filter strips, grassed waterway, fertilizer application reduction and no-till agriculture), the results indicated that fertilizer application reduction proved to be the most effective BMP with 0.130 of Harmful Algal Blooms Probability Reduction (HABs-PR) when reducing 40% of fertilizer, followed by filter strips with 0.01 of HABs-PR when 4815ha of filter strips were conducted, while grassed waterway and no-till agriculture showed no significant effect on preventing HABs. Furthermore, the combined practice between 40% fertilizer application reduction and 4815ha filter strips construction showed synergistic effects with HABs-PR increasing to 0.171. Precipitation and temperature data were distorted to model scenarios of extreme events. As a result, the combined approach outperformed any single BMP in terms of robustness under extreme climates. This research provides a watershed-level perspective on HABs risks mitigation and highlights the strategies to address HABs under the influence of climate change.

A novel approach to assessing natural resource injury with Bayesian networks.

Quantifying the effects of environmental stressors on natural resources is problematic because of complex interactions among environmental factors that influence endpoints of interest. This complexity, coupled with data limitations, propagates uncertainty that can make it difficult to causally associate specific environmental stressors with injury endpoints. The Natural Resource Damage Assessment and Restoration (NRDAR) regulations under the Comprehensive Environmental Response, Compensation, and Liability Act and Oil Pollution Act aim to restore natural resources injured by oil spills and hazardous substances released into the environment; exploration of alternative statistical methods to evaluate effects could help address NRDAR legal claims. Bayesian networks (BNs) are statistical tools that can be used to estimate the influence and interrelatedness of abiotic and biotic environmental variables on environmental endpoints of interest. We investigated the application of a BN for injury assessment using a hypothetical case study by simulating data of acid mine drainage (AMD) affecting a fictional stream-dwelling bird species. We compared the BN-generated probability estimates for injury with a more traditional approach using toxicity thresholds for water and sediment chemistry. Bayesian networks offered several distinct advantages over traditional approaches, including formalizing the use of expert knowledge, probabilistic estimates of injury using intermediate direct and indirect effects, and the incorporation of a more nuanced and ecologically relevant representation of effects. Given the potential that BNs have for natural resource injury assessment, more research and field-based application are needed to determine their efficacy in NRDAR. We expect the resulting methods will be of interest to many US federal, state, and tribal programs devoted to the evaluation, mitigation, remediation, and/or restoration of natural resources injured by releases or spills of contaminants. Integr Environ Assess Manag 2024;20:562-573. Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

How can multiscenario flow paths of water supply services be simulated? A supply-flow-demand model of ecosystem services across a typical basin in China.

Ecosystems provide many benefits to humans, and among them, water supply is crucial for human survival and development. This research focused on the Yangtze River Basin as the research area, quantitatively evaluated the temporal-spatial dynamic changes in the supply and demand of water supply services and determined the spatial relationship between the supply and demand regions of water supply services. We constructed the supply-flow-demand model of water supply service to quantify its flow. In our research, the Bayesian model was used to establish a multiscenario model of the water supply service flow path to simulate it and clarify its spatial flow path, flow direction and flow magnitude from the supply region to the demand region and determine its changing characteristics and driving factors in the basin. The results show that (1) In 2010, 2015 and 2020, the amount of water supply services showed a decreasing trend and was approximately 133.57 × 1012 m3, 129.97 × 1012 m3 and 120.82 × 1012 m3, respectively. (2) From 2010 to 2020, the trend of the cumulative flow of water supply service flow decreased each year and was 59.814 × 1012 m3, 56.930 × 1012 m3, 56.325 × 1012 m3 respectively. (3) Under the multiscenario simulation, the flow path of the water supply service was generally the same. The proportion of the water supply region was the highest under the green environmental protection scenario, at 73.8 %, and the proportion of the water demand region was the highest under the economic development and social progress scenario, at 27.3 %. (4) The provinces and municipalities in the basin were divided into three types of regions according to the matching relationship between supply and demand: catchment region, flow pass-through region and outflow region. The number of outflow regions was lowest, accounting for 23.53 %% of the regions, while the number of flow pass-through regions was the highest, accounting for 52.94 %.

Establishment of a Bayesian network model to predict the survival of malignant peritoneal mesothelioma patients after cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy.

OBJECTIVES: To establish a Bayesian network (BN) model to predict the survival of patients with malignant peritoneal mesothelioma (MPM) treated with cytoreductive surgery (CRS) plus hyperthermic intraperitoneal chemotherapy (HIPEC). METHODS: The clinicopathological data of 154 MPM patients treated with CRS + HIPEC at our hospital from April 2015 to November 2022 were retrospectively analyzed. They were randomly divided into two groups in a 7:3 ratio. Survival analysis was conducted on the training set and a BN model was established. The accuracy of the model was validated using a confusion matrix of the testing set. The receiver operating characteristic (ROC) curve and area under the curve were used to evaluate the overall performance of the BN model. RESULTS: Survival analysis of 107 patients (69.5%) in the training set found ten factors affecting patient prognosis: age, Karnofsky performance score, surgical history, ascites volume, peritoneal cancer index, organ resections, red blood cell transfusion, pathological types, lymphatic metastasis, and Ki-67 index (all p < 0.05). The BN model was successfully established after the above factors were included, and the BN model structure was adjusted according to previous research and clinical experience. The results of confusion matrix obtained by internal validation of 47 cases in the testing set showed that the accuracy of BN model was 72.7%, and the area under ROC was 0.74. CONCLUSIONS: The BN model was established successfully with good overall performance and can be used as a clinical decision reference.

Weighted Bayesian network for the classification of unbalanced food safety data: Case study of risk-based monitoring of heavy metals.

Historical data on food safety monitoring often serve as an information source in designing monitoring plans. However, such data are often unbalanced: a small fraction of the dataset refers to food safety hazards that are present in high concentrations (representing commodity batches with a high risk of being contaminated, the positives) and a high fraction of the dataset refers to food safety hazards that are present in low concentrations (representing commodity batches with a low risk of being contaminated, the negatives). Such unbalanced datasets complicate modeling to predict the probability of contamination of commodity batches. This study proposes a weighted Bayesian network (WBN) classifier to improve the model prediction accuracy for the presence of food and feed safety hazards using unbalanced monitoring data, specifically for the presence of heavy metals in feed. Applying different weight values resulted in different classification accuracies for each involved class; the optimal weight value was defined as the value that yielded the most effective monitoring plan, that is, identifying the highest percentage of contaminated feed batches. Results showed that the Bayesian network classifier resulted in a large difference between the classification accuracy of positive samples (20%) and negative samples (99%). With the WBN approach, the classification accuracy of positive samples and negative samples were both around 80%, and the monitoring effectiveness increased from 31% to 80% for pre-set sample size of 3000. Results of this study can be used to improve the effectiveness of monitoring various food safety hazards in food and feed.

Single-cell multiomics revealed the dynamics of antigen presentation, immune response and T cell activation in the COVID-19 positive and recovered individuals.

Introduction: Despite numerous efforts to describe COVID-19's immunological landscape, there is still a gap in our understanding of the virus's infections after-effects, especially in the recovered patients. This would be important to understand as we now have huge number of global populations infected by the SARS-CoV-2 as well as variables inclusive of VOCs, reinfections, and vaccination breakthroughs. Furthermore, single-cell transcriptome alone is often insufficient to understand the complex human host immune landscape underlying differential disease severity and clinical outcome. Methods: By combining single-cell multi-omics (Whole Transcriptome Analysis plus Antibody-seq) and machine learning-based analysis, we aim to better understand the functional aspects of cellular and immunological heterogeneity in the COVID-19 positive, recovered and the healthy individuals. Results: Based on single-cell transcriptome and surface marker study of 163,197 cells (124,726 cells after data QC) from the 33 individuals (healthy=4, COVID-19 positive=16, and COVID-19 recovered=13), we observed a reduced MHC Class-I-mediated antigen presentation and dysregulated MHC Class-II-mediated antigen presentation in the COVID-19 patients, with restoration of the process in the recovered individuals. B-cell maturation process was also impaired in the positive and the recovered individuals. Importantly, we discovered that a subset of the naive T-cells from the healthy individuals were absent from the recovered individuals, suggesting a post-infection inflammatory stage. Both COVID-19 positive patients and the recovered individuals exhibited a CD40-CD40LG-mediated inflammatory response in the monocytes and T-cell subsets. T-cells, NK-cells, and monocyte-mediated elevation of immunological, stress and antiviral responses were also seen in the COVID-19 positive and the recovered individuals, along with an abnormal T-cell activation, inflammatory response, and faster cellular transition of T cell subtypes in the COVID-19 patients. Importantly, above immune findings were used for a Bayesian network model, which significantly revealed FOS, CXCL8, IL1ß, CST3, PSAP, CD45 and CD74 as COVID-19 severity predictors. Discussion: In conclusion, COVID-19 recovered individuals exhibited a hyper-activated inflammatory response with the loss of B cell maturation, suggesting an impeded post-infection stage, necessitating further research to delineate the dynamic immune response associated with the COVID-19. To our knowledge this is first multi-omic study trying to understand the differential and dynamic immune response underlying the sample subtypes.

Differences in Lipid Profiles and Atherogenic Indices Between Hypertensive and Normotensive Populations: A Cross-Sectional Study of 11 Chinese Cities.

Background: Several previous studies have reported that dyslipidemia is associated with the risk of hypertension, but these studies are mainly conducted in European and US populations, with a very few studies in the Asian population. Moreover, the effects of atherosclerotic indices, including atherogenic coefficient (AC) and atherogenic risk of plasma (AIP), on hypertension in Asians have not been well described so far. Methods: From 2010 to 2016, altogether 211,833 Chinese adults were ultimately recruited at the health centers in 11 Chinese cities (including Shanghai, Beijing, Nanjing, Suzhou, Shenzhen, Changzhou, Chengdu, Guangzhou, Hefei, Wuhan, and Nantong). Differences in continuous variables between the two groups were analyzed by the Mann-Whitney test, while those in categorical variables were examined by the Chi-squared test. Logistic regression was applied to evaluate the association between lipid profiles and the risk of hypertension. The predictive values of AC and AIP for the incidence of hypertension were analyzed using the area under the receiver operating characteristic (ROC) curve. Meanwhile, Bayesian network (BN) models were performed to further analyze the associations between the different covariates and the incidence of hypertension. Results: A total of 117,056 participants were included in the final analysis. There were significant differences in baseline characteristics between normotension and hypertension groups (p < 0.001). In multivariate logistic regression, the risk of hypertension increased by 0.2% (1.002 [1.001-1.003]), 0.2% (1.002 [1.001-1.003]), and 0.2% (1.002 [1.001-1.003]) per 1 mg/dl increase in total cholesterol (TC), low-density lipoprotein (LDL), and non-high-density lipoprotein cholesterol (non-HDL-c), respectively. However, after adjusting for body mass index (BMI), an increase in HDL level was associated with a higher risk of hypertension (p for a trend < 0.001), and the risk of hypertension increased by 0.6% per 1 mg/dl increase in HDL-c (1.006 [1.003-1.008]). In women, AC had the highest predictive value for the incidence of hypertension with an area under the curve (AUC) of 0.667 [95% confidence interval (CI): 0.659-0.674]. BN models suggested that TC and LDL were more closely related to the incidence of hypertension. Conclusions: Overall, lipid profiles were significantly abnormal in the hypertensive population than in the normotensive population. TC and LDL were strongly associated with the incidence of hypertension. TC, LDL, and non-HDL-c levels show a positive association, HDL-c shows a negative association, while TG is not significantly associated with the risk of hypertension. After adjusting for BMI, HDL-c turns out to be positively associated with the risk of hypertension. In addition, AC has a good predictive value for the incidence of hypertension in women.

On-farm evaluation of a predictive model for Australian beef and sheep producers' vulnerability to an outbreak of foot and mouth disease.

To explore Australian sheep and beef producer vulnerability to an emergency animal disease outbreak, Bayesian Network models have been developed, with the ultimate goal of creating risk management tool for outbreak preparedness. These models were developed using multiple stakeholder elicitation including modelling experts, epidemiologists and on-farm stakeholders, including on-farm/survey data. An evaluation of the model's predictive capacity was conducted, using independent, blinded on-farm vulnerability assessments. Nine properties were visited, four each with sheep and beef enterprises, and one mixed enterprise. There were some discrepancies between the model predictions and on-farm assessment in the beef enterprises, with greater disparity with the sheep properties. Discrepancies between the model predictions and on-farm assessments have created opportunities for examination of the data collection process for the model development, the model itself and the on-farm assessment process. Bayesian Network approaches that allow for the inclusion of both continuous and discrete variables may improve the usefulness of these models, avoiding the loss of nuanced data by the need for discretisation of continuous variables, as will the inclusion of input from on-farm stakeholders in model development. Future work includes more data collection to improve the sensitivity of the model predictions, and a deeper, systemic exploration of the factors that may impact Australian producers' vulnerability to an emergency animal disease outbreak.

Probabilistic elderly person's mood analysis based on its activities of daily living using smart facilities.

The world's population is aging, and eldercare services that use smart facilities such as smart homes are widely common in societies now. With the aid of smart facilities, the present study aimed at understanding an elder's moods based on the person's activities of daily living (ADLs). With this end in view, an explainable probabilistic graphical modeling approach, applying the Bayesian network (BN), was proposed. The proposed BN-based model was capable of defining the relationship between the elder's ADLs and moods in three different levels: Activity-based Feature (AbF), Category of Activity (CoA), and the mood state. The model also allowed us to explain the transformations among the different levels/nodes on the defined BNs. A framework featured with smart facilities, including a smart home, a smartphone, and a wristband, was utilized to assess the model. The smart home was an elderly woman's house, equipped with a set of binary-based sensors. For about five months, the ADLs' data have been recorded through daily behavioral-based information, registered by experts using a defined questionnaire. The obtained results proved that the proposed BN-based model of the current study could promisingly estimate the elder's moods and CoA states. Moreover, in contrast to the machine learning techniques that behave like a black box, the effect of each feature from the lower levels to the higher levels of information of the BNs can be traced. Implications of the findings for future diagnosis and treatment of the elderly are considered.

Using a Bayesian Network Predictive Model to Understand Vulnerability of Australian Sheep Producers to a Foot and Mouth Disease Outbreak.

To maintain and strengthen Australia's competitive international advantage in sheep meat and wool markets, the biosecurity systems that support these industries need to be robust and effective. These systems, strengthened by jurisdictional and livestock industry investments, can also be enhanced by a deeper understanding of individual producer risk of exposure to animal diseases and capacity to respond to these risks. This observational study developed a Vulnerability framework, built from current data from Australian sheep producers around behaviors and beliefs that may impact on their likelihood of Exposure and Response Capacity (willingness and ability to respond) to an emergency animal disease (EAD). Using foot and mouth disease (FMD) as a model, a cross-sectional survey gathered information on sheep producers' demographics, and their practices and beliefs around animal health management and biosecurity. Using the Vulnerability framework, a Bayesian Network (BN) model was developed as a first attempt to develop a decision making tool to inform risk based surveillance resource allocation. Populated by the data from 448 completed questionnaires, the BN model was analyzed to investigate relationships between variables and develop producer Vulnerability profiles. Respondents reported high levels of implementation of biosecurity practices that impact the likelihood of exposure to an EAD, such as the use of appropriate animal movement documentation (75.4%) and isolation of incoming stock (64.9%). However, adoption of other practices relating to feral animal control and biosecurity protocols for visitors were limited. Respondents reported a high uptake of Response Capacity practices, including identifying themselves as responsible for observing (94.6%), reporting unusual signs of disease in their animals (91.0%) and daily/weekly inspection of animals (90.0%). The BN analysis identified six Vulnerability typologies, with three levels of Exposure (high, moderate, low) and two levels of Response Capacity (high, low), as described by producer demographics and practices. The most influential Exposure variables on producer Vulnerability included adoption levels of visitor biosecurity and visitor access protocols. Findings from this study can guide decisions around resource allocation to improve Australia's readiness for EAD incursion and strengthen the country's biosecurity system.

Bayesian network models with decision tree analysis for management of childhood malaria in Malawi.

BACKGROUND: Malaria is a major cause of death in children under five years old in low- and middle-income countries such as Malawi. Accurate diagnosis and management of malaria can help reduce the global burden of childhood morbidity and mortality. Trained healthcare workers in rural health centers manage malaria with limited supplies of malarial diagnostic tests and drugs for treatment. A clinical decision support system that integrates predictive models to provide an accurate prediction of malaria based on clinical features could aid healthcare workers in the judicious use of testing and treatment. We developed Bayesian network (BN) models to predict the probability of malaria from clinical features and an illustrative decision tree to model the decision to use or not use a malaria rapid diagnostic test (mRDT). METHODS: We developed two BN models to predict malaria from a dataset of outpatient encounters of children in Malawi. The first BN model was created manually with expert knowledge, and the second model was derived using an automated method. The performance of the BN models was compared to other statistical models on a range of performance metrics at multiple thresholds. We developed a decision tree that integrates predictions with the costs of mRDT and a course of recommended treatment. RESULTS: The manually created BN model achieved an area under the ROC curve (AUC) equal to 0.60 which was statistically significantly higher than the other models. At the optimal threshold for classification, the manual BN model had sensitivity and specificity of 0.74 and 0.42 respectively, and the automated BN model had sensitivity and specificity of 0.45 and 0.68 respectively. The balanced accuracy values were similar across all the models. Sensitivity analysis of the decision tree showed that for values of probability of malaria below 0.04 and above 0.40, the preferred decision that minimizes expected costs is not to perform mRDT. CONCLUSION: In resource-constrained settings, judicious use of mRDT is important. Predictive models in combination with decision analysis can provide personalized guidance on when to use mRDT in the management of childhood malaria. BN models can be efficiently derived from data to support clinical decision making.

Effect of femoral posterior condyle offset on knee joint function after total knee replacement: a network meta-analysis and a sequential retrospective cohort study.

BACKGROUND: This study was conducted with the aim to compare the effect of posterior condyle offset (PCO) changes on knee joint function of patients following total knee replacement (TKR). METHODS: Electronic and manual searches were performed in the PubMed, Embase, and Cochrane Library databases from inception to September 2019. Network meta-analysis combined direct and indirect evidence to assess the weighted mean difference (WMD) and surface under the cumulative ranking curves (SUCRA) of different PCO changes (PCO ≤ - 2 mm, - 2 mm < PCO < 0 mm, 0 mm ≤ PCO < 2 mm and PCO ≥ 2 mm) on knee joint function after TKR. Then 103 OA patients undergoing unilateral TKR were included and the effect of PCO on the postoperative knee function was examined. RESULTS: Totally, 5 cohort studies meeting the inclusion criteria were enrolled in this analysis. The results of meta-analysis showed that patients with 0 mm ≤ PCO < 2 mm after TKR had a better recovery of joint function (flexion contracture: 28.67%; KS functional score: 78.67%; KS knee score: 75.00%) than the remaining three groups. However, the knee flexion (77.00%) of patients with PCO ≤ - 2 mm after TKR was superior to the other three groups. Retrospective study also revealed a significant correlation between PCO changes and the flexion contracture, further flexion and KS functional score of patients after TKR, in which each functional knee score of patients with 0 mm ≤ PCO < 2 mm was better than the others. CONCLUSION: These findings suggest a close correlation between PCO magnitude and knee joint function after TKR and that 0 mm ≤ PCO < 2 mm is superior to other changes for joint function after TKR.

A network meta-analysis for efficacies and toxicities of different therapeutic regimens in the treatment of advanced nasopharyngeal carcinoma.

PURPOSE: The current study set out to compare the efficacies and toxicities (grad 3 and 4) between concurrent chemoradiotherapy (CCRT), induction chemotherapy plus radiotherapy (IC + RT), IC + CCRT, RT and CCRT + adjuvant chemotherapy (CCRT + AC) in regard to advanced nasopharyngeal carcinoma (NPC) treatment using a network meta-analysis. METHODS: Literature retrieval was conducted using PubMed, Cochrane Library and other English databases. Eligible randomized controlled trails (RCTs) of 5 different regimens were included. The network meta-analysis combined direct and indirect comparisons to measure pooled odd ratios (OR) and the surface under the cumulative ranking curves (SUCRA). RESULTS: A total of eight eligible RCTs were enrolled into this network meta-analysis after initial exclusion. With respect to hematologic toxicity, CCRT + AC exhibited higher toxicity in patients with advanced NPC in terms of anemia and leukopenia/neutropenia compared to RT. As for anemia, the toxicity of IC + CCRT was higher than those with advanced NPC. In addition, CCRT exhibited higher toxicity than RT in relation to leukopenia/neutropenia. Non-hematologic toxicity in regard to nausea/vomiting suggested that CCRT, IC + CCRT and CCRT + AC presented with higher levels of toxicity in patients with advanced NPC, in contrast to RT. Lastly, RT was found to be less toxic but with higher five-year overall survival (OS) rate in patients with advanced NPC, while CCRT, IC + CCRT and CCRT + AC were more toxic in patients with advanced NPC. CONCLUSION: Among the five therapeutic regimens, the survival rate of IC + RT was similar to that of CCRT, and the toxicity SUCRA value of IC + RT was lower than that of CCRT. Together, our findings indicate that IC + RT may be a potentially acceptable treatment alternative to CCRT for advanced NPC, and is worthy of further investigation.

The goat industry in Australia: Using Bayesian network analysis to understand vulnerability to a foot and mouth disease outbreak.

Australia's goat industry is one of the largest goat product exporters in the world, managing both farmed and wild caught animals. To protect and maintain the competitive advantage afforded to the Australian goat industry by the absence of many diseases endemic elsewhere, it is important to identify the vulnerability of producers to livestock disease incursions. This study developed a framework of producer vulnerability built from the beliefs and practices of producers that may impact on their likelihood of exposure and response capacity to an emergency animal disease (EAD), using foot and mouth disease as a model. A cross-sectional questionnaire gathered information on producer/enterprise demographics, animal health management and biosecurity practices, with 107 participating in the study. The biosecurity measures that were most commonly implemented by producers were always using animal movement documentation for purchased stock (74.7 %) and isolating new stock (73.1 %). However, moderate to low uptake of biosecurity protocols related to visitors to the property were reported. Response capacity variables such as checking animals daily (72.0 %) and record keeping (91.7 %) were reported by the majority of respondents, with 40.7 % reporting yearly veterinary inspection of their animals. Using the vulnerability framework, a Bayesian Network model was developed and populated by the survey data, and the relationships between variables were investigated. Six vulnerability profiles were developed, with three levels of exposure (high, moderate, low) and two levels of response capacity (high, low), as described by producer demographics and practices. The most sensitive exposure variables on producer vulnerability included implementation of visitor biosecurity and control of feral animals. Results from this study can inform risk based perspectives and decisions around biosecurity and surveillance resource allocation within the goat industry. The results also highlight opportunities for improving Australia's preparedness for a future EAD incursion by considering producer behaviour and beliefs by applying a vulnerability framework.

[Characteristics of methadone maintenance treatment clinic patients and influencing factors for HBsAg positivity based on Bayesian network model].

Objective: To understand the characteristics and explore the influencing factors of HBsAg positivity in methadone maintenance treatment (MMT) clinic patients. Methods: A face to face interview and medical record review were conducted in 1 040 patients at three MMT clinics in Guangxi from September to November in 2014. The questionnaire information included general demographic characteristics, drug use history, MMT status, sexual behaviors, and health status, etc. Blood samples were collected from the patients at the same time for the detections of the level of HBsAg, anti-HBs and anti-HCV. By using χ(2) test, unconditional logistic regression model and Bayesian network model the influencing factors for HBsAg positivity in MMT clinic patients and the complex network relationship among these factors were explored. Results: A total of 1 031 MMT clinic patients were surveyed, the HBsAg positive rate was 11.35% (117/1 031). The anti-HCV positive rate was 71.77% (740/1 031), among the anti-HCV positive patients, the HBsAg positive rate was 10.27% (76/740). After adjusting for the confounding factors, anti-HBs positive persons might not be HBsAg positive (OR=0.05, 95%CI: 0.03-0.09), and anti-HCV positive persons might not be HBsAg positive too (OR=0.30, 95%CI: 0.17-0.52) compared with anti-HBs negative and anti-HCV negative persons, respectively. The persons with family history of hepatitis B virus infection were more likely to be HBsAg positive compared those with no such family history (OR=5.30, 95%CI: 2.68-10.52). Bayesian network model analysis results showed that family history of hepatitis B virus infection and anti-HBs were directly related with HBsAg positivity. Anti-HCV, intravenous drug use in the past three months and other drug using during treatment were indirectly related with HBsAg positivity. Conclusions: Anti-HBs, family history of hepatitis B virus infection, anti-HCV, intravenous drug use in past three months and other drug use during treatment were related with the HBsAg positivity in MMT clinic patients. So, it is necessary to enhance health education, improve health awareness and decrease high risk behaviors to reduce the rate of HBV infection.

Designing flows to enhance ecosystem functioning in heavily altered rivers.

More than a century of dam construction and water development in the western United States has led to extensive ecological alteration of rivers. Growing interest in improving river function is compelling practitioners to consider ecological restoration when managing dams and water extraction. We developed an Ecological Response Model (ERM) for the Cache la Poudre River, northern Colorado, USA, to illuminate effects of current and possible future water management and climate change. We used empirical data and modeled interactions among multiple ecosystem components to capture system-wide insights not possible with the unintegrated models commonly used in environmental assessments. The ERM results showed additional flow regime modification would further alter the structure and function of Poudre River aquatic and riparian ecosystems due to multiple and interacting stressors. Model predictions illustrated that specific peak flow magnitudes in spring and early summer are critical for substrate mobilization, dynamic channel morphology, and overbank flows, with strong subsequent effects on instream and riparian biota that varied seasonally and spatially, allowing exploration of nuanced management scenarios. Instream biological indicators benefitted from higher and more stable base flows and high peak flows, but stable base flows with low peak flows were only half as effective to increase indicators. Improving base flows while reducing peak flows, as currently proposed for the Cache la Poudre River, would further reduce ecosystem function. Modeling showed that even presently depleted annual flow volumes can achieve substantially different ecological outcomes in designed flow scenarios, while still supporting social demands. Model predictions demonstrated that implementing designed flows in a natural pattern, with attention to base and peak flows, may be needed to preserve or improve ecosystem function of the Poudre River. Improved regulatory policies would include preservation of ecosystem-level, flow-related processes and adaptive management when water development projects are considered.

Risk Assessment of An Earthquake-Collapse-Landslide Disaster Chain by Bayesian Network and Newmark Models.

Severe natural disasters and related secondary disasters are a huge menace to society. Currently, it is difficult to identify risk formation mechanisms and quantitatively evaluate the risks associated with disaster chains; thus, there is a need to further develop relevant risk assessment methods. In this research, we propose an earthquake disaster chain risk evaluation method that couples Bayesian network and Newmark models that are based on natural hazard risk formation theory with the aim of identifying the influence of earthquake disaster chains. This new method effectively considers two risk elements: hazard and vulnerability, and hazard analysis, which includes chain probability analysis and hazard intensity analysis. The chain probability of adjacent disasters was obtained from the Bayesian network model, and the permanent displacement that was applied to represent the potential hazard intensity was calculated by the Newmark model. To validate the method, the Changbai Mountain volcano earthquake-collapse-landslide disaster chain was selected as a case study. The risk assessment results showed that the high-and medium-risk zones were predominantly located within a 10 km radius of Tianchi, and that other regions within the study area were mainly associated with very low-to low-risk values. The verified results of the reported method showed that the area of the receiver operating characteristic (ROC) curve was 0.817, which indicates that the method is very effective for earthquake disaster chain risk recognition and assessment.

A comprehensive framework for student stress monitoring in fog-cloud IoT environment: m-health perspective.

Over the last few years, Internet of Things (IoT) has opened the doors to innovations that facilitate interactions among things and humans. Focusing on healthcare domain, IoT devices such as medical sensors, visual sensors, cameras, and wireless sensor network are leading this evolutionary trend. In this direction, the paper proposes a novel, IoT-aware student-centric stress monitoring framework to predict student stress index at a particular context. Bayesian Belief Network (BBN) is used to classify the stress event as normal or abnormal using physiological readings collected from medical sensors at fog layer. Abnormal temporal structural data which is time-enriched dataset sequence is analyzed for various stress-related parameters at cloud layer. To compute the student stress index, a two-stage Temporal Dynamic Bayesian Network (TDBN) model is formed. This model computes stress based on four parameters, namely, leaf node evidences, workload, context, and student health trait. After computing the stress index of the student, decisions are taken in the form of alert generation mechanism with the deliverance of time-sensitive information to caretaker or responder. Experiments are conducted both at fog and cloud layer which hold evidence for the utility and accuracy of the BBN classifier and TDBN predictive model in our proposed system. Graphical Abstract Student stress monitoring in IoT-Fog Environment.

Efficacy and adverse events of five targeted agents in the treatment of advanced or metastatic non-small-cell lung cancer: A network meta-analysis of nine eligible randomized controlled trials involving 5,059 patients.

Recently, targeted agents were reported to improve overall survival, progression-free survival (PFS), response rate, and quality of life compared with cytotoxic chemotherapies, which provides hope for the treatment of non-small-cell lung cancer (NSCLC). The network meta-analysis is applied to compare the efficacies and adverse events of five targeted agents (erlotinib, gefitinib, vandetanib, dacomitinib, and icotinib) for advanced or metastatic NSCLC. Nine eligible randomized controlled trials from PubMed and Cochrane Library database were included. Weighted mean difference, odds ratio, and surface under the cumulative ranking curve (SUCRA) values were evaluated for the efficacy and adverse events of the five targeted agents in the treatment of NSCLC. With regard to efficacy, the overall response rate (ORR) of advanced or metastatic NSCLC patients treated with gefitinib was relatively higher than those treated with placebo. Compared with patients treated with placebo, the disease control rate (DCR) of patients treated with erlotinib and with gefitinib was relatively higher. Furthermore, in terms of PFS and DCR, the SUCRA value of icotinib was the highest among the five targeted drugs. With regard to ORR, the SUCRA value of gefitinib was the highest among the five targeted drugs. In terms of fatigue, rash, and cough, erlotinib had the lowest SUCRA value, whereas vandetanib exhibited the lowest SUCRA value in terms of diarrhea. Our study suggests that the efficacies of gefitinib and icotinib for advanced or metastatic NSCLC were comparatively better, whereas the toxicities of erlotinib and vandetanib were relatively greater.

Quantitative risk assessment of Listeria monocytogenes in bulk cooked meat from production to consumption in China: a Bayesian approach.

BACKGROUND: To estimate the public health risk related to cooked meat in bulk products contaminated with Listeria monocytogenes, a generic Bayesian network (BN) risk-assessment model was developed to simulate influencing factors and processes of products from the industry level to the consumer level. To quantify the model, parameter values of prior distributions were acquired from the literature, websites, and expert opinions. Using the Markov chain Monte Carlo (MCMC) simulation approach, posterior probability distributions were calculated according to the incorporated evidence, which allowed us to predict various risks affected by processing variability from production to consumption. RESULTS: The average risks of listeriosis from consuming cooked meat in bulk products are 8.40 × 10-7 , 2.58 × 10-8 , 8.24 × 10-7 , and 1.05 × 10-6 per meal for children, young people, elderly people, and pregnant women, respectively. The estimated mean number of listeriosis cases is 5 per 100 000 people per year in China. CONCLUSION: Although only a conceptual BN model is given, it manifests the principles and characteristics of mathematical methods. The BN model can also provide significant benefits for quantitative risk assessment by incorporating all available data and by updating beliefs. © 2018 Society of Chemical Industry.