High-Frequency Notable HBV Mutations Identified in Blood Donors With Occult Hepatitis B Infection From Heyuan City of Southern China.

Background: All Chinese blood centers have implemented mini pool (MP) HBV nucleic acid testing (NAT) together with HBsAg ELISA in routine donor screening since 2015. The prevalence of occult hepatitis B virus infection (OBI) in donors from different regions varies, and the molecular characterization of the HBV DNA and clinical outcomes of these OBIs remain largely unexplored. Methods: Blood donations from Heyuan city in Southern China were screened by HBsAg ELISA and HBV MP8 NAT. Donations with HBsAg-/HBV DNA+ were collected for this study. Molecular characterizations of HBV DNAs were further analyzed by various DNA amplification assays including quantitative PCR (qPCR) and nested PCR, amplifying the basic core and pre-core promoter regions (BCP/PC). The HBsAg (S) region from HBV DNA was isolated by high-volume nucleic acid extraction. Notable mutations were identified by comparison to the HBV reference sequences. The clinical outcomes of the donors with OBIs were further followed for nearly 3 years. Results: Seventy OBIs from 44,592 donations (0.15%) that we identified and reported previously were enrolled for this current study. HBV sequences were obtained from 44/70 OBIs, and genotyping analysis showed that 42/44 (95.2%) OBIs were genotype B, and 2/44 (4.8%) were genotype C. Interestingly, mutation analysis revealed that various mutations including M133L/T, F134L, P142L, V168A, R169H, S174N, L175S, and V177A of HBV DNA affecting HBsAg detection were observed in genotype B OBIs. Two notable mutations, T47K and L53S, were identified in genotype C OBIs. Follow-up studies showed that 3/31 (9.7%) OBIs converted to HBsAg+ as chronic infections while 1/31 (3.2%) HBV DNA was undetectable (classified as recovery) and 27/31 (87.1%) remained as OBIs. Conclusion: Various notable mutations affecting HBsAg detection were observed in blood donors with OBIs in Heyuan city of Southern China. Follow-up studies showed that most OBIs remained as OBIs with fluctuating or low viral loads. Higher sensitive HBV ID NAT is recommended for donor screening to further reduce the transmission risk of OBIs.

An integrated multi-level watershed-reservoir modeling system for examining hydrological and biogeochemical processes in small prairie watersheds.

Eutrophication of small prairie reservoirs presents a major challenge in water quality management and has led to a need for predictive water quality modeling. Studies are lacking in effectively integrating watershed models and reservoir models to explore nutrient dynamics and eutrophication pattern. A water quality model specific to small prairie water bodies is also desired in order to highlight key biogeochemical processes with an acceptable degree of parameterization. This study presents a Multi-level Watershed-Reservoir Modeling System (MWRMS) to simulate hydrological and biogeochemical processes in small prairie watersheds. It integrated a watershed model, a hydrodynamic model and an eutrophication model into a flexible modeling framework. It can comprehensively describe hydrological and biogeochemical processes across different spatial scales and effectively deal with the special drainage structure of small prairie watersheds. As a key component of MWRMS, a three-dimensional Willows Reservoir Eutrophication Model (WREM) is developed to addresses essential biogeochemical processes in prairie reservoirs and to generate 3D distributions of various water quality constituents; with a modest degree of parameterization, WREM is able to meet the limit of data availability that often confronts the modeling practices in small watersheds. MWRMS was applied to the Assiniboia Watershed in southern Saskatchewan, Canada. Extensive efforts of field work and lab analysis were undertaken to support model calibration and validation. MWRMS demonstrated its ability to reproduce the observed watershed water yield, reservoir water levels and temperatures, and concentrations of several water constituents. Results showed that the aquatic systems in the Assiniboia Watershed were nitrogen-limited and sediment flux played a crucial role in reservoir nutrient budget and dynamics. MWRMS can provide a broad context of decision support for water resources management and water quality protection in the prairie region.

Planning of municipal solid waste management under dual uncertainties.

Municipal solid waste management is a complex and multidisciplinary problem, involving a number of impact factors associated with various uncertainties. In this study, a hybrid interval-parameter possibilistic programming (IPP) approach was developed and applied for planning municipal solid waste management under dual uncertainties. The IPP improves upon the existing management approaches by allowing possibility distributions of the lower and upper bounds of some interval parameters in the objective function and interval information in the modelling coefficients to be effectively incorporated within its optimization. By introducing the concept of possibilistic interval numbers, the dual uncertainties can be communicated into the optimization process and the resulting solutions, such that the generated decision schemes can effectively reflect the highly complex system features under uncertainty. The results of the case study indicate that useful information can be obtained for providing feasible decision schemes for waste flow allocation. Different decision schemes can be generated by adjusting waste flow allocation patterns within the solution intervals. Lower decision variable values should be used to obtain lower system cost of waste treatment and disposal under advantageous conditions, and higher decision variable values should be used under demanding conditions (worst case conditions). A strong desire to acquire the lower system cost will lead to the decreased probability of meeting the treatment and disposal requirements (i.e. the increased risk of unforeseen conditions); willingness to accept the upper limit of the system cost will guarantee that waste treatment and disposal requirements are met.

Capacity planning for waste management systems: an interval fuzzy robust dynamic programming approach.

This study integrates the concepts of interval numbers and fuzzy sets into optimization analysis by dynamic programming as a means of accounting for system uncertainty. The developed interval fuzzy robust dynamic programming (IFRDP) model improves upon previous interval dynamic programming methods. It allows highly uncertain information to be effectively communicated into the optimization process through introducing the concept of fuzzy boundary interval and providing an interval-parameter fuzzy robust programming method for an embedded linear programming problem. Consequently, robustness of the optimization process and solution can be enhanced. The modeling approach is applied to a hypothetical problem for the planning of waste-flow allocation and treatment/disposal facility expansion within a municipal solid waste (MSW) management system. Interval solutions for capacity expansion of waste management facilities and relevant waste-flow allocation are generated and interpreted to provide useful decision alternatives. The results indicate that robust and useful solutions can be obtained, and the proposed IFRDP approach is applicable to practical problems that are associated with highly complex and uncertain information.

Robust stochastic fuzzy possibilistic programming for environmental decision making under uncertainty.

Nonpoint source (NPS) water pollution is one of serious environmental issues, especially within an agricultural system. This study aims to propose a robust chance-constrained fuzzy possibilistic programming (RCFPP) model for water quality management within an agricultural system, where solutions for farming area, manure/fertilizer application amount, and livestock husbandry size under different scenarios are obtained and interpreted. Through improving upon the existing fuzzy possibilistic programming, fuzzy robust programming and chance-constrained programming approaches, the RCFPP can effectively reflect the complex system features under uncertainty, where implications of water quality/quantity restrictions for achieving regional economic development objectives are studied. By delimiting the uncertain decision space through dimensional enlargement of the original fuzzy constraints, the RCFPP enhances the robustness of the optimization processes and resulting solutions. The results of the case study indicate that useful information can be obtained through the proposed RCFPP model for providing feasible decision schemes for different agricultural activities under different scenarios (combinations of different p-necessity and p(i) levels). A p-necessity level represents the certainty or necessity degree of the imprecise objective function, while a p(i) level means the probabilities at which the constraints will be violated. A desire to acquire high agricultural income would decrease the certainty degree of the event that maximization of the objective be satisfied, and potentially violate water management standards; willingness to accept low agricultural income will run into the risk of potential system failure. The decision variables under combined p-necessity and p(i) levels were useful for the decision makers to justify and/or adjust the decision schemes for the agricultural activities through incorporation of their implicit knowledge. The results also suggest that this developed approach is applicable to many practical problems where fuzzy and probabilistic distribution information simultaneously exist.

Two-stage fuzzy-stochastic robust programming: a hybrid model for regional air quality management.

In this study, a hybrid two-stage fuzzy-stochastic robust programming (TFSRP) model is developed and applied to the planning of an air-quality management system. As an extension of existing fuzzy-robust programming and two-stage stochastic programming methods, the TFSRP can explicitly address complexities and uncertainties of the study system without unrealistic simplifications. Uncertain parameters can be expressed as probability density and/or fuzzy membership functions, such that robustness of the optimization efforts can be enhanced. Moreover, economic penalties as corrective measures against any infeasibilities arising from the uncertainties are taken into account. This method can, thus, provide a linkage to predefined policies determined by authorities that have to be respected when a modeling effort is undertaken. In its solution algorithm, the fuzzy decision space can be delimited through specification of the uncertainties using dimensional enlargement of the original fuzzy constraints. The developed model is applied to a case study of regional air quality management. The results indicate that reasonable solutions have been obtained. The solutions can be used for further generating pollution-mitigation alternatives with minimized system costs and for providing a more solid support for sound environmental decisions.