A tick saliva serpin, IxsS17 inhibits host innate immune system proteases and enhances host colonization by Lyme disease agent.

Lyme disease (LD) caused by Borrelia burgdorferi is among the most important human vector borne diseases for which there is no effective prevention method. Identification of tick saliva transmission factors of the LD agent is needed before the highly advocated tick antigen-based vaccine could be developed. We previously reported the highly conserved Ixodes scapularis (Ixs) tick saliva serpin (S) 17 (IxsS17) was highly secreted by B. burgdorferi infected nymphs. Here, we show that IxsS17 promote tick feeding and enhances B. burgdorferi colonization of the host. We show that IxsS17 is not part of a redundant system, and its functional domain reactive center loop (RCL) is 100% conserved in all tick species. Yeast expressed recombinant (r) IxsS17 inhibits effector proteases of inflammation, blood clotting, and complement innate immune systems. Interestingly, differential precipitation analysis revealed novel functional insights that IxsS17 interacts with both effector proteases and regulatory protease inhibitors. For instance, rIxsS17 interacted with blood clotting proteases, fXII, fX, fXII, plasmin, and plasma kallikrein alongside blood clotting regulatory serpins (antithrombin III and heparin cofactor II). Similarly, rIxsS17 interacted with both complement system serine proteases, C1s, C2, and factor I and the regulatory serpin, plasma protease C1 inhibitor. Consistently, we validated that rIxsS17 dose dependently blocked deposition of the complement membrane attack complex via the lectin complement pathway and protected complement sensitive B. burgdorferi from complement-mediated killing. Likewise, co-inoculating C3H/HeN mice with rIxsS17 and B. burgdorferi significantly enhanced colonization of mouse heart and skin organs in a reverse dose dependent manner. Taken together, our data suggests an important role for IxsS17 in tick feeding and B. burgdorferi colonization of the host.

Comparison between U-shaped structural deep learning models to detect landslide traces.

Landslides endanger lives and public infrastructure in mountainous areas. Monitoring landslide traces in real-time is difficult for scientists, sometimes costly and risky because of the harsh terrain and instability. Nowadays, modern technology may be able to identify landslide-prone locations and inform locals for hours or days when the weather worsens. This study aims to propose indicators to detect landslide traces on the fields and remote sensing images; build deep learning (DL) models to identify landslides from Sentinel-2 images automatically; and apply DL-trained models to detect this natural hazard in some particular areas of Vietnam. Nine DL models were trained based on three U-shaped architectures, including U-Net, U2-Net, and U-Net3+, and three options of input sizes. The multi-temporal Sentinel-2 images were chosen as input data for training all models. As a result, the U-Net, using an input image size of 32 × 32 and a performance of 97 % with a loss function of 0.01, can detect typical landslide traces in Vietnam. Meanwhile, the U-Net (64 × 64) can detect more considerable landslide traces. Based on multi-temporal remote sensing data, a different case study in Vietnam was chosen to see landslide traces over time based on the trained U-Net (32 × 32) model. The trained model allows mountain managers to track landslide occurrences during wet seasons. Thus, landslide incidents distant from residential areas may be discovered early to warn of flash floods.

Tick extracellular vesicles impair epidermal homeostasis through immune-epithelial networks during hematophagy.

Hematophagous ectoparasites, such as ticks, rely on impaired wound healing for skin attachment and blood feeding. Wound healing has been extensively studied through the lens of inflammatory disorders and cancer, but limited attention has been given to arthropod-borne diseases. Here, we used orthogonal approaches combining single-cell RNA sequencing (scRNAseq), flow cytometry, murine genetics, and intravital microscopy to demonstrate how tick extracellular vesicles (EVs) disrupt networks involved in tissue repair. Impairment of EVs through silencing of the SNARE protein vamp33 negatively impacted ectoparasite feeding and survival in three medically relevant tick species, including Ixodes scapularis. Furthermore, I. scapularis EVs affected epidermal γδ T cell frequencies and co-receptor expression, which are essential for keratinocyte function. ScRNAseq analysis of the skin epidermis in wildtype animals exposed to vamp33-deficient ticks revealed a unique cluster of keratinocytes with an overrepresentation of pathways connected to wound healing. This biological circuit was further implicated in arthropod fitness when tick EVs inhibited epithelial proliferation through the disruption of phosphoinositide 3-kinase activity and keratinocyte growth factor levels. Collectively, we uncovered a tick-targeted impairment of tissue repair via the resident γδ T cell-keratinocyte axis, which contributes to ectoparasite feeding.

Ixodes scapularis nymph saliva protein blocks host inflammation and complement-mediated killing of Lyme disease agent, Borrelia burgdorferi.

Tick serine protease inhibitors (serpins) play crucial roles in tick feeding and pathogen transmission. We demonstrate that Ixodes scapularis (Ixs) nymph tick saliva serpin (S) 41 (IxsS41), secreted by Borrelia burgdorferi (Bb)-infected ticks at high abundance, is involved in regulating tick evasion of host innate immunity and promoting host colonization by Bb. Recombinant (r) proteins were expressed in Pichia pastoris, and substrate hydrolysis assays were used to determine. Ex vivo (complement and hemostasis function related) and in vivo (paw edema and effect on Bb colonization of C3H/HeN mice organs) assays were conducted to validate function. We demonstrate that rIxsS41 inhibits chymase and cathepsin G, pro-inflammatory proteases that are released by mast cells and neutrophils, the first immune cells at the tick feeding site. Importantly, stoichiometry of inhibition analysis revealed that 2.2 and 2.8 molecules of rIxsS41 are needed to 100% inhibit 1 molecule of chymase and cathepsin G, respectively, suggesting that findings here are likely events at the tick feeding site. Furthermore, chymase-mediated paw edema, induced by the mast cell degranulator, compound 48/80 (C48/80), was blocked by rIxsS41. Likewise, rIxsS41 reduced membrane attack complex (MAC) deposition via the alternative and lectin complement activation pathways and dose-dependently protected Bb from complement killing. Additionally, co-inoculating C3H/HeN mice with Bb together with rIxsS41 or with a mixture (rIxsS41 and C48/80). Findings in this study suggest that IxsS41 markedly contributes to tick feeding and host colonization by Bb. Therefore, we conclude that IxsS41 is a potential candidate for an anti-tick vaccine to prevent transmission of the Lyme disease agent.

A new species of Sterculia (Malvaceae) from Vietnam.

A new species of Sterculia from Vietnam - S.konchurangensis - is described, illustrated, and compared with the similar S.lanceolata. S.konchurangensis differs from S.lanceolata by the length of the petiole (7.0-9.5 vs. 25-35 mm), shape of the leaf blade (obovate or elliptic vs. elliptic, lanceolate or elliptic-lanceolate), length of the leaf blade (6-8 vs. 9-20 cm), and length of the calyx lobe (11-12.5 vs. 4-6 mm). A diagnostic key of the 22 Sterculia species occurring in Vietnam is also provided.

Monitoring the effects of urbanization and flood hazards on sandy ecosystem services.

Urbanization, storms, and floods have compromised the benefits derived from various types of sand dune landscapes, particularly in developing countries located in humid monsoon tropical regions. One pertinent question is which driving forces have had a dominant impact on the contributions of sand dune ecosystems to human well-being. Has the decline in sand dune ecosystem services (ES) been primarily due to urbanization or flooding hazards? This study aims to address these issues by developing a Bayesian Belief Network (BBN) to analyze six different sand dune landscapes worldwide. The study uses various data types, including multi-temporal and -sensor remote sensing (SAR and optical data), expert knowledge, statistics, and GIS to analyze the trends in sand dune ecosystems. A support tool based on probabilistic approaches was developed to assess changes in ES over time due to the effects of urbanization and flooding. The developed BBN has the potential to assess the ES values of sand dunes during both rainy and dry seasons. The study calculated and tested the ES values in detail over six years (from 2016 to 2021) in Quang Nam province, Vietnam. The results showed that urbanization has led to an increase in the total ES values since 2016, while floods only had a minimal impact on dune ES values during the rainy season. The fluctuations of ES values were found to be more significant due to urbanization than floods. The study's approach can be useful in future research on coastal ecosystems.

Spherical Body Protein 4 from Babesia bigemina: A Novel Gene That Contains Conserved B-Cell Epitopes and Induces Cross-Reactive Neutralizing Antibodies in Babesia ovata.

Bovine babesiosis is a tick-transmitted disease caused by intraerythrocytic protozoan parasites of the genus Babesia. Its main causative agents in the Americas are Babesia bigemina and Babesia bovis, while Babesia ovata affects cattle in Asia. All Babesia species secrete proteins stored in organelles of the apical complex, which are involved in all steps of the invasion process of vertebrate host cells. Unlike other apicomplexans, which have dense granules, babesia parasites instead have large, round intracellular organelles called spherical bodies. Evidence suggests that proteins from these organelles are released during the process of invading red blood cells, where spherical body proteins (SBPs) play an important role in cytoskeleton reorganization. In this study, we characterized the gene that encodes SBP4 in B. bigemina. This gene is transcribed and expressed in the erythrocytic stages of B. bigemina. The sbp4 gene consists of 834 nucleotides without introns that encode a protein of 277 amino acids. In silico analysis predicted a signal peptide that is cleaved at residue 20, producing a 28.88-kDa protein. The presence of a signal peptide and the absence of transmembrane domains suggest that this protein is secreted. Importantly, when cattle were immunized with recombinant B. bigemina SBP4, antibodies identified B. bigemina and B. ovata merozoites according to confocal microscopy observations and were able to neutralize parasite multiplication in vitro for both species. Four peptides with predicted B-cell epitopes were identified to be conserved in 17 different isolates from six countries. Compared with the pre-immunization sera, antibodies against these conserved peptides reduced parasite invasion in vitro by 57%, 44%, 42%, and 38% for peptides 1, 2, 3, and 4, respectively (p < 0.05). Moreover, sera from cattle infected with B. bigemina cattle contained antibodies that recognized the individual peptides. All these results support the concept of spb4 as a new gene in B. bigemina that should be considered a candidate for a vaccine to control bovine babesiosis.

Chemical Fractionations of Lead and Zinc in the Contaminated Soil Amended with the Blended Biochar/Apatite.

Heavy metal contamination in agricultural land is an alarming issue in Vietnam. It is necessary to develop suitable remediation methods for environmental and farming purposes. The present study investigated the effectiveness of using peanut shell-derived biochar to remediate the two heavy metals Zn and Pb in laboratory soil assays following Tessier's sequential extraction procedure. The concentration of heavy metals was analyzed using Inductively coupled plasma mass spectrometry (ICP-MS). This study also compared the effectiveness of the blend of biochar and apatite applied and the mere biochar amendment on the chemical fractions of Pb and Zn in the contaminated agricultural soil. Results have shown that the investigated soil was extremely polluted by Pb (3047.8 mg kg−1) and Zn (2034.3 mg kg−1). In addition, the pH, organic carbon, and electrical conductivity values of amended soil samples increased with the increase in the amendment's ratios. The distribution of heavy metals in soil samples was in the descending order of carbonate fraction (F2) > residue fraction (F5) > exchangeable fraction (F1) > Fe/Mn oxide fraction (F3) > organic fraction (F4) for Pb and F5 ≈ F2 > F1 > F3 > F4 for Zn. The peanut shell-derived biochar produced at 400 °C and 600 °C amended at a 10% ratio (PB4:10 and PB6:10) could significantly reduce the exchangeable fraction Zn from 424.82 mg kg−1 to 277.69 mg kg−1 and 302.89 mg kg−1, respectively, and Pb from 495.77 mg kg−1 to 234.55 mg kg−1 and 275.15 mg kg−1, respectively, and immobilize them in soil. Amending the biochar and apatite combination increased the soil pH, then produced a highly negative charge on the soil surface and facilitated Pb and Zn adsorption. This study shows that the amendment of biochar and biochar blended with apatite could stabilize Pb and Zn fractions, indicating the potential of these amendments to remediate Pb and Zn in contaminated soil.

Dependence between Chinese stock market and Vietnamese stock market during the Covid-19 pandemic.

The study aims to investigate the tail dependence between Chinese stock market and Vietnamese stock market in the context of the Covid-19 pandemic. Using the data on the Ho Chi Minh City Stock Exchange (VNI) and the Shanghai Stock Exchange (SSEC) representing for Vietnam and China stock markets, the study reveals the tail dependence across three periods including: pre-pandemic, during the chaos of the pandemic, and the period of adaptation to the pandemic. Using the copula method including Normal, Clayton, Plackett, Frank, Student, Symmetrised Joe-Clayton copulas, the research results confirm that there is no dependent relationship between the stock market between the two countries in the pre-pandemic. During the pandemic, the Vietnamese stock market is heavily dependent on Chinese stock market, especially the upper tail dependence. During the period of adaptation to the pandemic, this dependence relationship still exists but less than that in the pandemic.

A new deep learning approach based on bilateral semantic segmentation models for sustainable estuarine wetland ecosystem management.

Nowadays, estuarial areas have been strongly affected by the construction of electrical power dams from upstream, downstream urbanization and many types of hazards along the coastal regions. It has resulted in significant changes in estuarine wetland ecosystems between rainy and dry seasons. To avoid estuary vulnerability, monitoring and evaluation of the estuarine ecosystems are very critical tasks. The main goal of this research is to propose and implement a novel deep learning method in monitoring various ecosystems in estuarine regions. The processing speed and accuracy of common neural networks is improved more than ten times through spatial and context paths integrated into a novel Bilateral Segmentation Network (BiSeNet). The multi-sensor and multi-temporal satellite images (including Sentinel-2, ALOS-DEM, and NOAA-DEM images) served as input data. As a result, four BiSeNet models out of 20 trained models achieved a greater than 90% accuracy, especially for interpreting estuarine waters, intertidal forested wetlands, and aquacultural lands in subtidal regions. These models outperformed Random Forest and Support Vector Machine approaches. The best one was used to map estuarine ecosystems from 12 satellite images over a five-year period in the largest estuary in northern Vietnam. The ecosystem changes between dry and rainy seasons were analyzed in detail to assess the ecological succession in estuaries. Furthermore, this model can potentially update new estuarine ecosystem types in other estuarine areas across the world, making possible real-time monitoring and assessing estuarine ecological conditions for sustainable management of wetland ecosystem.

Combination of in vivo phage therapy data with in silico model highlights key parameters for pneumonia treatment efficacy.

The clinical (re)development of bacteriophage (phage) therapy to treat antibiotic-resistant infections faces the challenge of understanding the dynamics of phage-bacteria interactions in the in vivo context. Here, we develop a general strategy coupling in vitro and in vivo experiments with a mathematical model to characterize the interplay between phage and bacteria during pneumonia induced by a pathogenic strain of Escherichia coli. The model allows the estimation of several key parameters for phage therapeutic efficacy. In particular, it quantifies the impact of dose and route of phage administration as well as the synergism of phage and the innate immune response on bacterial clearance. Simulations predict a limited impact of the intrinsic phage characteristics in agreement with the current semi-empirical choices of phages for compassionate treatments. Model-based approaches will foster the deployment of future phage-therapy clinical trials.

A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm.

A high-resolution soil moisture prediction method has recently gained its importance in various fields such as forestry, agricultural and land management. However, accurate, robust and non- cost prohibitive spatially monitoring of soil moisture is challenging. In this research, a new approach involving the use of advance machine learning (ML) models, and multi-sensor data fusion including Sentinel-1(S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and ALOS Global Digital Surface Model (ALOS DSM) to predict precisely soil moisture at 10 m spatial resolution across research areas in Australia. The total of 52 predictor variables generated from S1, S2 and ALOS DSM data fusion, including vegetation indices, soil indices, water index, SAR transformation indices, ALOS DSM derived indices like digital model elevation (DEM), slope, and topographic wetness index (TWI). The field soil data from Western Australia was employed. The performance capability of extreme gradient boosting regression (XGBR) together with the genetic algorithm (GA) optimizer for features selection and optimization for soil moisture prediction in bare lands was examined and compared with various scenarios and ML models. The proposed model (the XGBR-GA model) with 21 optimal features obtained from GA was yielded the highest performance (R2 = 0. 891; RMSE = 0.875%) compared to random forest regression (RFR), support vector machine (SVM), and CatBoost gradient boosting regression (CBR). Conclusively, the new approach using the XGBR-GA with features from combination of reliable free-of-charge remotely sensed data from Sentinel and ALOS imagery can effectively estimate the spatial variability of soil moisture. The described framework can further support precision agriculture and drought resilience programs via water use efficiency and smart irrigation management for crop production.

BRCA1/2 Mutations in Vietnamese Patients with Hereditary Breast and Ovarian Cancer Syndrome.

(1) Background: Individuals with BRCA1/2 gene mutations are at increased risk of breast and ovarian cancer. The prevalence of BRCA1/2 mutations varies by race and ethnicity, and the prevalence and the risks associated with most BRCA1/2 mutations has not been unknown in the Vietnamese population. We herein screen the entire BRCA1 and BRCA2 genes for breast and ovarian cancer patients with a family history of breast cancer and ovarian cancer, thereby, suggesting a risk score associated with carrier status and history for aiding personalized treatment; (2) Methods: Between December 2017 and December 2019, Vietnamese patients who had a pathological diagnosis of breast and epithelial ovarian cancer were followed up, prospectively, after treatment from two large institutions in Vietnam. Blood samples from 33 Vietnamese patients with hereditary breast and ovarian cancers (HBOC) syndrome were collected and analyzed using Next Generation Sequencing; (3) Results: Eleven types of mutations in both BRCA1 (in nine patients) and BRCA2 (in three patients) were detected, two of which (BRCA1:p.Tyr1666Ter and BRCA2:p.Ser1341Ter) have not been previously documented in the literature. Seven out of 19 patient's relatives had BRCA1/2 gene mutations. All selected patients were counselled about the likelihood of cancer rising and prophylactic screening and procedures. The study established a risk score associated with the cohorts based on carrier status and family history; (4) Conclusions: Our findings suggested the implications for the planning of a screening programme for BRCA1 and BRCA2 genes testing in breast and ovarian cancer patients and genetic screening in their relatives. BRCA1/2 mutation carriers without cancer should have early and regular cancer screening, and prophylactic measures. This study could be beneficial for a diverse group in a large population-specific cohort, related to HBOC Syndrome.

Efficient model-based bioequivalence testing.

The classical approach to analyze pharmacokinetic (PK) data in bioequivalence studies aiming to compare two different formulations is to perform noncompartmental analysis (NCA) followed by two one-sided tests (TOST). In this regard, the PK parameters area under the curve (AUC) and $C_{\max}$ are obtained for both treatment groups and their geometric mean ratios are considered. According to current guidelines by the U.S. Food and Drug Administration and the European Medicines Agency, the formulations are declared to be sufficiently similar if the $90\%$ confidence interval for these ratios falls between $0.8$ and $1.25 $. As NCA is not a reliable approach in case of sparse designs, a model-based alternative has already been proposed for the estimation of $\rm AUC$ and $C_{\max}$ using nonlinear mixed effects models. Here we propose another, more powerful test than the TOST and demonstrate its superiority through a simulation study both for NCA and model-based approaches. For products with high variability on PK parameters, this method appears to have closer type I errors to the conventionally accepted significance level of $0.05$, suggesting its potential use in situations where conventional bioequivalence analysis is not applicable.

A novel intelligence approach based active and ensemble learning for agricultural soil organic carbon prediction using multispectral and SAR data fusion.

Monitoring agricultural soil organic carbon (SOC) has played an essential role in sustainable agricultural management. Precise and robust prediction of SOC greatly contributes to carbon neutrality in the agricultural industry. To create more knowledge regarding the ability of remote sensing to monitor carbon soil, this research devises a state-of-the-art low cost machine learning model for quantifying agricultural soil carbon using active and ensemble-based decision tree learning combined with multi-sensor data fusion at a national and world scale. This work explores the use of Sentinel-1 (S1) C-band dual polarimetric synthetic aperture radar (SAR), Sentinel-2 (S2) multispectral data, and an innovative machine learning (ML) approach using an integration of active learning for land-use mapping and advanced Extreme Gradient Boosting (XGBoost) for robustness of the SOC estimates. The collected soil samples from a field survey in Western Australia were used for the model validation. The indicators including the coefficient of determination (R2) and root - mean - square - error (RMSE) were applied to evaluate the model's performance. A numerous features computed from optical and SAR data fusion were employed to build and test the proposed model performance. The effectiveness of the proposed machine learning model was assessed by comparing with the two well-known algorithms such as Random Forests (RF) and Support Vector Machine (SVM) to predict agricultural SOC. Results suggest that a combination of S1 and S2 sensors could effectively estimate SOC in farming areas by using ML techniques. Satisfactory accuracy of the proposed XGBoost with optimal features was achieved the highest performance (R2 = 0.870; RMSE = 1.818 tonC/ha) which outperformed RF and SVM. Thus, multi-sensor data fusion combined with the XGBoost lead to the best prediction results for agricultural SOC at 10 m spatial resolution. In short, this new approach could significantly contribute to various agricultural SOC retrieval studies globally.

Essential roles of buried phenylalanine in the structural stability of thioredoxin from a psychrophilic Arctic bacterium Sphingomonas sp.

Thioredoxin (Trx), a small redox protein, exhibits thermal stability at high temperatures regardless of its origin, including psychrophiles. Trxs have a common structure consisting of the central ß-sheet flanked by an aliphatic cluster on one side and an aromatic cluster on the other side. Although the roles of aromatic amino acids in the folding and stability of proteins have been studied extensively, the contributions of aromatic residues to the stability and function of Trx, particularly Trxs from cold-adapted organisms, have not been fully elucidated. This study examined the roles of aromatic amino acids in the aromatic cluster of a Trx from the psychrophilic Arctic bacterium Sphingomonas sp. PAMC 26621 (SpTrx). The aromatic cluster of SpTrx was comprised of W11, F26, F69, and F80, in which F26 at the ß2 terminus was buried inside. The substitution of tyrosine for F26 changed the SpTrx conformation substantially compared to that of F69 and F80. Further biochemical and spectroscopic investigations on F26 showed that the F26Y, F26W, and F26A mutants resulted in structural instability of SpTrx in both urea- and temperature-induced unfolding and lower insulin reduction activities. The Trx reductase (SpTR) showed lower catalytic efficiencies against F26 mutants compared to the wild-type SpTrx. These results suggest that buried F26 is essential for maintaining the active-site conformation of SpTrx as an oxidoreductase and its structural stability for interactions with SpTR at colder temperatures.

An Assessment of the Economic Impacts of the 2019 African Swine Fever Outbreaks in Vietnam.

The 2019 African swine fever (ASF) outbreaks in Vietnam imposed considerable impacts on the pig sector in Vietnam, resulting in the death or culling of nearly six million pigs, or more than 20% of the country's pig population. In order to assess the magnitude of the outbreak at sector level (both on farm and at value chain level), on livelihoods, and on the broader national economy, a comprehensive impact assessment was conducted using a mixed methods approach that integrated a value chain assessment with the use of quantitative modeling tools at sector and national levels. The results showed that the outbreak caused severe direct and indirect economic losses among farmers, particularly medium- and large-farmers whose livelihoods are largely derived from pig production. The outbreaks also affected other value chain actors due to a halving in the volume of pigs traded. At sector level, the outbreaks posed adverse impacts on the domestic supply and demand for pork, especially in the traditional sector. Meanwhile, the modern sector with higher levels of biosecurity and high technology growth was less likely to be affected and even benefited from the outbreak, which was evidenced by increased supply and income throughout the simulation period in this sector. At national level, different model simulation scenarios showed a sharp reduction in total gross domestic product (GDP) and a substantial loss of jobs. Improvements in the system of ASF compensation scheme are needed, both in terms of its administration, but also in its targeting, with greater emphasis needed on developing improved risk-sharing and funding mechanisms across national and local levels.

Finding optimal design in nonlinear mixed effect models using multiplicative algorithms.

BACKGROUND AND OBJECTIVES: To optimize designs for longitudinal studies analyzed by nonlinear mixed effect models (NLMEMs), the Fisher information matrix (FIM) can be used. In this work, we focused on the multiplicative algorithms, previously applied in standard individual regression, to find optimal designs for NLMEMs. METHODS: We extended multiplicative algorithms to mixed models and implemented the algorithm both in R and in C. Then, we applied the algorithm to find D-optimal designs in two longitudinal data examples, one with continuous and one with binary outcome. RESULTS: For these examples, we quantified the improved speed when C is used instead of R. Design optimization using the multiplicative algorithm led to designs with D-efficiency gains between 13% and 25% compared to non-optimized designs. CONCLUSION: We found that the multiplicative algorithm can be used efficiently to design longitudinal studies.

Integrated methods and scenarios for assessment of sand dunes ecosystem services.

Anthropogenic and natural ecosystems in coastal dunes provide considerable benefits to human well-being. However, to date, we still lack a good understanding of how ecosystem services (ES) supply varies from young dunes (e.g., embryo and fore dunes) to mature dunes (e.g., brown and red dunes). This study proposed a novel modelling methodology by integrating an expert-based matrix, a Bayesian Belief Network (BBN), a structural equation model, and a scenario development method. It aims at evaluating dune ecosystem services for the sustainable development of coastal areas. The model was tested using data collected from dunes in Vietnam. An expert-based matrix to assess the supply capacity of 18 ES in different types of dunes was generated with the participation of 21 interdisciplinary scientists. It was found that red dune ecosystems could supply the most regulation and cultural ecosystem services, while gray dunes provided the least amount. Results from a scenario analysis recommended that decision-making is able to optimize multiple ES by: (i) keeping embryo/fore dunes in their natural state instead of using them for mineral mining and urbanization; (ii) enlarging certified and protected forests areas in gray and yellow dunes; and (iii) optimizing cultural ES supply in red dunes.