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Subsea pipelines are the backbone of the modern oil and gas industry, transporting a total of 28% of global oil production. Due to several factors, such as corrosion or deformations, the pipelines might degrade over time, which might lead to serious economic and environmental damages if not addressed promptly. Therefore, it is crucial to detect any serious damage to subsea pipelines before they cause dangerous catastrophes. Inspections of subsea pipelines are usually made using a Remote Operating Vehicle and the inspection data is usually processed manually, which is subject to human errors, and requires experienced Remote Operating Vehicle operators. It is thus necessary to automate the inspection process to enable more efficiency as well as reduce costs. Besides, it is recognised that specific challenges of noisy and low-quality inspection data arising from the underwater environment prevent the industry from taking full advantage of the recent development in the Artificial Intelligence field to the problem of subsea pipeline inspection. In this paper, we developed an ensemble of deep learning classifiers to further improve the performance of single deep learning models in classifying anomalous events on the subsea pipeline inspection data. The output of the proposed ensemble was combined based on a weighted combining method. The weights of base classifiers were found by minimising the difference between the weighted combining result and the given associated ground truth annotation information. Three inspection datasets, gathered from different oil and gas companies in the United Kingdom, were analysed. These datasets were recorded under varying conditions and include a range of anomalies. The results showed that the proposed ensemble achieves around 78% accuracy on two datasets and more than 99% accuracy on one dataset, which is better compared to base classifiers and two popular ensembles. [ABSTRACT FROM AUTHOR]

Subsea pipelines are the backbone of the modern oil and gas industry, transporting a total of 28% of global oil production. Due to several factors, such as corrosion or deformations, the pipelines might degrade over time, which might lead to serious economic and environmental damages if not addressed promptly. Therefore, it is crucial to detect any serious damage to subsea pipelines before they cause dangerous catastrophes. Inspections of subsea pipelines are usually made using a Remote Operating Vehicle and the inspection data is usually processed manually, which is subject to human errors, and requires experienced Remote Operating Vehicle operators. It is thus necessary to automate the inspection process to enable more efficiency as well as reduce costs. Besides, it is recognised that specific challenges of noisy and low-quality inspection data arising from the underwater environment prevent the industry from taking full advantage of the recent development in the Artificial Intelligence field to the problem of subsea pipeline inspection. In this paper, we developed an ensemble of deep learning classifiers to further improve the performance of single deep learning models in classifying anomalous events on the subsea pipeline inspection data. The output of the proposed ensemble was combined based on a weighted combining method. The weights of base classifiers were found by minimising the difference between the weighted combining result and the given associated ground truth annotation information. Three inspection datasets, gathered from different oil and gas companies in the United Kingdom, were analysed. These datasets were recorded under varying conditions and include a range of anomalies. The results showed that the proposed ensemble achieves around 78% accuracy on two datasets and more than 99% accuracy on one dataset, which is better compared to base classifiers and two popular ensembles. [ABSTRACT FROM AUTHOR]

One of the most important areas in medical image analysis is segmentation, in which raw image data is partitioned into structured and meaningful regions to gain further insights. By using Deep Neural Networks (DNN), AI-based automated segmentation algorithms can potentially assist physicians with more effective imaging-based diagnoses. However, since it is difficult to acquire high-quality ground truths for medical images and DNN hyperparameters require significant manual tuning, the results by DNN-based medical models might be limited. A potential solution is to combine multiple DNN models using ensemble learning. We propose a two-layer ensemble of deep learning models in which the prediction of each training image pixel made by each model in the first layer is used as the augmented data of the training image for the second layer of the ensemble. The prediction of the second layer is then combined by using a weight-based scheme which is found by solving linear regression problems. To the best of our knowledge, our paper is the first work which proposes a two-layer ensemble of deep learning models with an augmented data technique in medical image segmentation. Experiments conducted on five different medical image datasets for diverse segmentation tasks show that proposed method achieves better results in terms of several performance metrics compared to some well-known benchmark algorithms. Our proposed two-layer ensemble of deep learning models for segmentation of medical images shows effectiveness compared to several benchmark algorithms. The research can be expanded in several directions like image classification. [ABSTRACT FROM AUTHOR]

Groundwater exploitation, soft ground, and urban development are the main causes of land subsidence in Vietnam. This study focuses on identifying, zoning, monitoring, and evaluating land subsidence using PSI technique with IBI method applied to Ho Chi Minh City. The results show diverse land subsidence trends from 2014 to 2021 with different levels in varying locations 98,278 km2 had subsided by over 10 cm, 214,593 km2 had subsided by 5 to 10 cm, and 1.377,897 km2 had subsided by less than 5 cm. Notably, certain areas have higher subsidence funnel centres. To ensure accuracy when using PSI techniques, both natural and human-induced factors must be considered during data collection, which influences the actual subsidence rate. Though difficult, this technique provides reliable insights into this complex phenomenon, e.g. in the geodetic precise levelling method to detect millimetre-level deformations in urban areas. The average PS density in urban areas is between 0,5% and 2,5% of the original number of pixels. The positioning accuracy of a PS is within 1 m in all three directions if a large number of SAR scenes are used. Therefore, the results have been documented with 1 mm precision. [ABSTRACT FROM AUTHOR]

The study aimed to evaluate the influence of various parameters of the blanching and drying condition of small shrimp (Acetes) on some product quality parameters. Some criteria were assessed, such as salt content, astaxanthin content (ATC), and color of small shrimp. The results showed that the blanching time significantly affected the salt content. When increasing the blanching time, the salt content was found to increase from 17.35 ± 2.48 mg/g DW to 39.51 ± 0.45 mg/g DW. The astaxanthin content achieved the highest value (0.026 ± 0.001 mg/g DW) in a 2% salt solution. The study showed that the blanching and drying processes significantly affected the salt content, astaxanthin, and color of the small shrimp, and the optimized temperatures for blanching and drying were 70°C and 60°C, respectively. [ABSTRACT FROM AUTHOR]

Honey is a natural product made by honeybees, its composition depends on factors such as climate, soil and plant source. In this study, the nutritional parameters, phenolic composition, antioxidant activity and antibacterial ability of 30 different types of honey of different botanical and geographical origins in Vietnam were investigated. The study focused on the characterization and evaluation of the influence of plant origin and geographical location on physical–chemical properties and biological activities (antioxidant and antibacterial). The obtained results show that all honey samples meet quality standards according to international standards and Vietnamese standards, except for some exceptions recorded in moisture, 5-hydroxymethylfurfural (HMF) value and ash. These samples were explored for the detection of 13 polyphenols by using high-performance liquid chromatography (HPLC). The classification of honey samples collected from different regions and botanical sources was performed by principal component analysis (PCA), and it was observed that certain phenolic compounds contributed to the identification of honey samples. In addition, the correlation between physicochemical properties, chemical composition and biological activity of most honeys was also first clarified in this study. Overall, our data provide an overview data set and essential results in creating a database on the world honey trait map. [ABSTRACT FROM AUTHOR]

Background: Bone metastatic prostate cancer does not completely respond to androgen-targeted therapy and generally evolves into lethal castration resistant prostate cancer (CRPC). Expression of AR-V7- a constitutively active, ligand independent splice variant of AR is one of the critical resistant mechanisms regulating metastatic CRPC. TNC is an extracellular matrix glycoprotein, crucial for prostate cancer progression, and associated with prostate cancer bone metastases. In this study, we investigated the mechanisms that regulate AR-V7 expression in prostate cancer cells interacting with osteogenic microenvironment including TNC. Methods: Prostate cancer/preosteoblast heterotypical organoids were evaluated via immunofluorescence imaging and gene expression analysis using RT-qPCR to assess cellular compartmentalization, TNC localization, and to investigate regulation of AR-V7 in prostate cancer cells by preosteoblasts and hormone or antiandrogen action. Prostate cancer cells cultured on TNC were assessed using RT-qPCR, Western blotting, cycloheximide chase assay, and immunofluorescence imaging to evaluate (1) regulation of AR-V7, and (2) signaling pathways activated by TNC. Identified signaling pathway induced by TNC was targeted using siRNA and a small molecular inhibitor to investigate the role of TNC-induced signaling activation in regulation of AR-V7. Both AR-V7- and TNC-induced signaling effectors were targeted using siRNA, and TNC expression assessed to evaluate potential feedback regulation. Results: Utilizing heterotypical organoids, we show that TNC is an integral component of prostate cancer interaction with preosteoblasts. Interaction with preosteoblasts upregulated both TNC and AR-V7 expression in prostate cancer cells which was suppressed by testosterone but elevated by antiandrogen enzalutamide. Interestingly, the results demonstrate that TNC-induced Src activation regulated AR-V7 expression, post-translational stability, and nuclear localization in prostate cancer cells. Treatment with TNC neutralizing antibody, Src knockdown, and inhibition of Src kinase activity repressed AR-V7 transcript and protein. Reciprocally, both activated Src and AR-V7 were observed to upregulate autocrine TNC gene expression in prostate cancer cells. Conclusion: Overall, the findings reveal that prostate cancer cell interactions with the cellular and ECM components in the osteogenic microenvironment plays critical role in regulating AR-V7 associated with metastatic CRPC. 9BxMEZaJbqgytBg6QvviEa Video Abstract [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) is the second most diagnosed cancer in the United States and is associated with metabolic reprogramming and significant disparities in clinical outcomes among African American (AA) men. While the cause is likely multi-factorial, the precise reasons for this are unknown. Here, we identified a higher expression of the metabolic enzyme UGT2B28 in localized PCa and metastatic disease compared to benign adjacent tissue, in AA PCa compared to benign adjacent tissue, and in AA PCa compared to European American (EA) PCa. UGT2B28 was found to be regulated by both full-length androgen receptor (AR) and its splice variant, AR-v7. Genetic knockdown of UGT2B28 across multiple PCa cell lines (LNCaP, LAPC-4, and VCaP), both in androgen-replete and androgen-depleted states resulted in impaired 3D organoid formation and a significant delay in tumor take and growth rate of xenograft tumors, all of which were rescued by re-expression of UGT2B28. Taken together, our findings demonstrate a key role for the UGT2B28 gene in promoting prostate tumor growth. [ABSTRACT FROM AUTHOR]

Altered glycosylation plays an important role during development and is also a hallmark of increased tumorigenicity and metastatic potentials of several cancers. We report here that Tankyrase-1 (TNKS1) controls protein glycosylation by Poly-ADP-ribosylation (PARylation) of a Golgi structural protein, Golgin45, at the Golgi. TNKS1 is a Golgi-localized peripheral membrane protein that plays various roles throughout the cell, ranging from telomere maintenance to Glut4 trafficking. Our study indicates that TNKS1 localization to the Golgi apparatus is mediated by Golgin45. TNKS1-dependent control of Golgin45 protein stability influences protein glycosylation, as shown by Glycomic analysis. Further, FRAP experiments indicated that Golgin45 protein level modulates Golgi glycosyltransferease trafficking in Rab2-GTP-dependent manner. Taken together, these results suggest that TNKS1-dependent regulation of Golgin45 may provide a molecular underpinning for altered glycosylation at the Golgi during development or oncogenic transformation. Xihua Yue et al. report that Tankyrase-1 controls poly-ADP-ribosylation of Golgin45, a Golgi structural protein, which has a profound influence on glycosyltransferase trafficking and protein glycosylation at the Golgi. This may shed new light onto mechanisms involved in development and oncogenic transformation. [ABSTRACT FROM AUTHOR]

The work was proceeded to define the suitable period for planting cassava crops in Han Thuan Bac district of Binh Thuan province, Vietnam to reduce the negative impacts of weather factors. The work was deployed applying the FAO-Aqua Crop model to calculate the tuber yield of cassava plants under the cassava farming practices (CFP) to seek the suitable planting period (SPP). The applied model was appraised through the calibration and validation procedures with the index of agreement (IA), correlation coefficient (CC) and the Root Mean Square Error (RMSE) varying from 0.82 to 0.88, 0.81 to 0.89 and 0.21 to 0.29, respectively. Based on the calibrated and validated procedures it can state that the proposed model is suitable for simulating the tuber yield of cassava across the study area. The simulated results indicated that the application of the CFP on Julian days from 110 to 150 for Vu Xuan crop and from 100 to 140 for Vu He crop the tuber yield of cassava can improve up to 8.9 per cent and 6.0 per cent, respectively compared to the current farming practices. [ABSTRACT FROM AUTHOR]

Stromal cells in the tumor microenvironment regulate the immune landscape and tumor progression. Yet, the ontogeny and heterogeneity of reactive stromal cells within tumors is not well understood. Carcinoma‐associated fibroblasts exhibiting an inflammatory phenotype (iCAFs) have been identified within multiple cancers; however, mechanisms that lead to their recruitment and differentiation also remain undefined. Targeting these mechanisms therapeutically may be important in managing cancer progression. Here, we identify the ELF3 transcription factor as the canonical mediator of IL‐1α‐induced differentiation of prostate mesenchymal stem cells to an iCAF phenotype, typical of the tumor microenvironment. Furthermore, IL‐1α‐induced iCAFs were subsequently refractive to TGF‐β1 induced trans‐differentiation to a myofibroblast phenotype (myCAF), another key carcinoma‐associated fibroblast subtype typical of reactive stroma in cancer. Restricted trans‐differentiation was associated with phosphorylation of the YAP protein, indicating that interplay between ELF3 action and activation of the Hippo pathway are critical for restricting trans‐differentiation of iCAFs. Together, these data show that the IL‐1α/ELF3/YAP pathways are coordinate for regulating inflammatory carcinoma‐associated fibroblast differentiation. [ABSTRACT FROM AUTHOR]

Background: The medicinal plants consider as a rich resource of ingredients which can be used in drug development and synthesis. Blumea lacera (Burm. f.) DC. is generally used in traditional medicine for the treatment of cough, bronchitis, dysentery, wound healing. The aim of this study is to isolate and identify the compounds from the aerial parts of Blumea lacera. Methods: The aerial parts of B. lacera were dried, powdered and extracted using EtOH, and the concentrated extract was partitioned in succession with n-hexane, CH2 Cl2, and EtOAc. From the EtOAc fraction, the compounds were isolated through column chromatography and their chemical structures were elucidated by NMR spectroscopy and confirmed by comparison of their NMR data with literature data. Results: Repeated column chromatography of the EtOAc-soluble fraction from the aerial parts of B. lacera resulted in the isolation of β-sitosterol (1), campesterol (2), artemetin (3) and acid paracatechuic (4). [ABSTRACT FROM AUTHOR]

Background: Conformational transitions are implicated in the biological function of many proteins. Structural changes in proteins can be described approximately as the relative movement of rigid domains against each other. Despite previous efforts, there is a need to develop new domain segmentation algorithms that are capable of analysing the entire structure database efficiently and do not require the choice of protein-dependent tuning parameters such as the number of rigid domains. Results: We develop a graph-based method for detecting rigid domains in proteins. Structural information from multiple conformational states is represented by a graph whose nodes correspond to amino acids. Graph clustering algorithms allow us to reduce the graph and run the Viterbi algorithm on the associated line graph to obtain a segmentation of the input structures into rigid domains. In contrast to many alternative methods, our approach does not require knowledge about the number of rigid domains. Moreover, we identified default values for the algorithmic parameters that are suitable for a large number of conformational ensembles. We test our algorithm on examples from the DynDom database and illustrate our method on various challenging systems whose structural transitions have been studied extensively. Conclusions: The results strongly suggest that our graph-based algorithm forms a novel framework to characterize structural transitions in proteins via detecting their rigid domains. The web server is available at http://azifi.tz.agrar.uni-goettingen.de/webservice/. [ABSTRACT FROM AUTHOR]

3,24-acetonideclethric acid (1), a new ursane-type triterpene, and four known compounds including ursolic acid (2), randiasaponin IV (3), ilekudinoside W (4) and (25S)-1β,3β,24β- trihydroxyspirost-5-en 1-O-α-L-rhamnopyranosyl-(1→2)- α-L-arabinopyranoside (5), and were isolated from the fermented shallot Allium ascalonicum. Their structures were determined by analysis of HR-ESI-MS, NMR spectral data, as well as comparison with those reported in the literature. All of the saponins (3-5) exhibited antimicrobial activity against three strains Staphylococcus aureus, Escherichia coli, and Candida albicans with IC50 values in the range from 89.49 ± 2.24 to 95.71 ± 3.86 µM. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to evaluate the rainfall intensities and their limits for durations from 0.25 to 8 h with return periods from 2 to 100 years for Ca Mau City in Vietnam. Design/methodology/approach: First, the quality of the historical rainfall data series in 44 years (1975–2018) at Ca Mau station was assessed using the standard normal homogeneity test and the Pettitt test. Second, the appraised rainfall data series are used to establish the rainfall intensity-duration-frequency curve for the study area. Findings: Based on the findings, a two-year return period, the extreme rainfall intensities (ERIs) ranged from 9.1 mm/h for 8 h rainstorms to 91.2 mm/h for 0.25 h. At a 100-year return period, the ERIs ranged from 18.4 mm/h for 8 h rainstorms to 185.8 mm/h for 0.25 h. The results also show that the narrowest uncertainty level between the lower and upper limits recorded 1.6 mm at 8 h for the two-year return period while the widest range is at 42.5 mm at 0.25 h for the 100-year return period. In general, the possibility of high-intensity rainfall values compared to the extreme rainfall intensities is approximately 2.0% at the 100-year return period. Originality/value: The results of the rainfall IDF curves can provide useful information for policymakers to make the right decisions in controlling and minimizing flooding in the study area. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate aspects related to the changing trends of the rainfall extremes in the entire Mekong Delta in the period of 32 years (1984-2015) applying rainfall extreme indices. First, the homogeneity tests were applied to assess the quality of observed rainfall data series. The authors, then, investigated three rainfall indices including the number of very heavy rainfall days 20 mm (R20), number of days above 50 mm (R50) and number of days above 100 mm (R100) applying the Mann-Kendall test and Sen's slope estimate. Design/methodology/approach: First, the homogeneity tests were applied to assess the quality of observed rainfall data series. The authors, then, investigated three rainfall indices including the number of very heavy rainfall days 20 mm (R20), number of days above 50 mm (R50) and number of days above 100 mm (R100) applying the Mann-Kendall test and Sen's slope estimate. Findings: The results of R20 pointed out that an insignificant upward tendency was found in the coastal provinces, whereas an insignificant downward tendency was also recorded in the inland provinces. Regarding the number of R50, a similar trend to R20 was recorded with five stations slightly increased and five stations slightly decreased. For the number of R100, the results recorded an absence of significant trends over the entire study area. Approximately 58.5% of stations show a slightly decreasing trend, while 41.5% of the remaining stations recorded a slightly increasing trend. Originality/value: For the number of R100, the results recorded an absence of the significant trends over the entire study area. Approximately 58.5% of stations show a slightly decreasing trend, while 41.5% of the remaining stations recorded a slightly increasing trend. Of note is the fact that the number of R100 occurred more frequently in the northern provinces, which means the northern region is facing a high risk of flooding. [ABSTRACT FROM AUTHOR]

This study was conducted to investigate the developmental processes of tomato plants in greenhouses covered with polyethene agricultural film that applied a smart irrigation system. The study tested applying different irrigation water levels (IWLs) of 60, 80, 100, and 120% of crop evapotranspiration (ETc). Based on the findings for the tomato rows sampled to collect information about leaf area, biomass, and yield, maximal growth of tomatoes varied from 80 to 90 days after transplanting (DAT) for all IWLs. Among the applied IWLs, approximately 80% of ETc was recorded as the most appropriate for the crop growth rate (CGR), leaf area index (LAI), and yield, while the IWLs with 60%, 100%, and 120% of ETc also gave an equivalent output. The results of this work can provide useful information for farmers to make the right decisions about the amount of irrigation water to use. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) numerical models are useful tools for studying river morphology. Frequently, 2D numerical models are often applied to predict the impacts of the artificial changes to rivers and estuaries. These changes may be caused by altered watershed hydrology, variations in the sediment supply and the construction of artificial works such as dams, embankments and tidal gutters. The aim of this study was to apply the CCHE2D model in simulating riverbed morphological variation in the Co Chien Estuary of Vietnam with complex morphology under the combined impacts of hydrodynamic processes such as waves, flow field, tidal currents and sediment transport. First, the proposed model was calibrated using water surface level and current speed data during dry and flood seasons in 2010. Calibrated results showed satisfactory coefficients (root mean square error smaller than 0.10 and Brier skill score (BSS) criteria varying between 0.63 and 0.94). Second, the proposed model is applied to simulate riverbed level variation for Co Chien Estuary after the 6-year flood (2010–2015). The results were evaluated comparing deviations between simulated and measured elevations at multiple monitoring cross sections and longitudinal bed profiles after the 6-year flood. Compared results confirmed that the proposed model is suitable for simulating hydrodynamic processes and riverbed morphological changes in the study area with BSS criteria greater than 0.68. The proposed model is a useful tool to help efficiently manage resources and minimizing the unwanted influences of wave, current, tide and sediment transport process. [ABSTRACT FROM AUTHOR]

This work evaluates the impacts of climate change on water use-demand of three rice planting crops including winter–spring (WS), summer–autumn (SA) and autumn–winter crops for Long Xuyen Quadrangle Delta of Vietnam. Climatic variables scenarios were obtained from the updated report on emission scenarios which are issued by Vietnam's Ministry of Natural Resources and Environment for three future timescales (2016–2035, 2046–2065 and 2080–2099) of representative concentration pathways (RCP) 4.5 and RCP8.5 scenarios. Cropwat model was selected to evaluate the irrigation water demand of three rice planting crops based on simulating actual evapotranspiration (ETc) and effective rainfall for the study area. The results showed that the WS and SA planting crops need more irrigation water demand in the growing and developmental stage for timescales of RCP4.5 and RCP8.5 scenarios. Results also pointed out that compared with current climate condition the tendency to decrease in irrigation water demand in the initial and developmental stages of autumn–winter planting crop with arranging from 2.9–12.9 to 10.0–18.2%, respectively, corresponding to timescales of RCP4.5 and RCP8.5 scenarios is found and a significant downward trend in the late stage approximately 5.8–20.0% and 13.6–20.7%, respectively, for RCP4.5 and RCP8.5 scenarios also recorded. [ABSTRACT FROM AUTHOR]

In recent years, Mekong Delta of Vietnam is severely affected by salinity intrusion and water scarcity due to climate variability. In this study, a comprehensive analysis of meteorology drought was conducted to detect drought events using the Standardized Precipitation Index at 3-, 6-, 9- and 12-month time scale based on monthly precipitation data from 46 precipitation gauge stations for a period of 1984-2015. The aim of the study is to assess the degree of meteorology drought from 1984 to 2015 in potential crop growing areas to provide early warnings and monitor drought events to minimize their negative effects. The results indicated that meteorological drought occurred at the central provinces of the study area in the period 1985-1994, the northeastern and northwestern provinces in the period 1995-2004 and 10 recent years (2005-2014) meteorological drought shifted toward southern coastal provinces. The analyzed results also showed a tendency to decrease in frequency of drought is recorded while a tendency to increase in the spatial distribution of drought with moderate and severe droughts is recorded. Among the major droughts, 1990-1992 was evaluated the most extreme drought with 85% of the study area covered by the extreme drought with peak value of − 2.63 recorded and lasting for 29 months. [ABSTRACT FROM AUTHOR]

The spatio-temporal variability of meteorological droughts in the Mekong delta area of Vietnam were analysed to detect drought events using the standardized precipitation index (SPI) at different time scales based on 32-year (1984-2015) monthly rainfall data. The degree and intensity of drought in the years of El Nino phenomenon during the study period were also analysed. The results confirmed that the frequency of drought scales decreased while their spatial distribution tends to increase with main scales including moderate and severe droughts. During the period of study, 1990-1992 drought stage was defined the most extreme drought with 11 out of 13 provinces of the study area experienced the extreme drought as the peak value of SPI12 was -2.63 and the duration of the drought lasted for 29 months. [ABSTRACT FROM AUTHOR]

Long Xuyen Quadrangle is one of the important agricultural areas of the Mekong Delta of Vietnam accounting for 25% of rice production. In recent years, the area faces drought and salinization problems, as part of climate change impact and sea level rise. These are the main causes that led to the crop water deficits for agricultural production. Therefore, this work was conducted to predict crop water requirement (CWR) based on consideration of other related meteorological factors and further redefine the crop planting calendar (CPC) for three main cropping seasons including winter-spring (WS), summer-autumn (SA) and autumn-spring (AS) using the Cropwat crop model based on the current climate conditions and future climate change scenarios. Meteorological data for the baseline period (2006-2016) and future corresponding to timescales 2020s, 2055s and 2090s of Representative Concentration Pathways (RCP)4.5 and RCP8.5 scenarios are used to predict CWR and CPC for the study area. The results showed that WS and SA crops needed more irrigation water than AS crop and the highest irrigation water requirement of the WS and SA crops occurred on developmental stage, while the AW crop appeared on growth, developmental and late stage for the baseline and timescales of RCP4.5 and RCP8.5 scenarios. Calculation results of the shift of CPC indicated that the CWR of the AW crop decreased lowest approximately 6.6-20.6% for timescales of RCP4.5 scenario and 20.6-25.5% for RCP8.5 scenario compared with the baseline. [ABSTRACT FROM AUTHOR]

This study presented simulation results of runoff discharge combining the Mike NAM and Mike SHE models for lower Sai Gon-Dong Nai River basin (SG-DNRB). Sai Gon-Dong Nai River basin is Vietnam’s largest population center and main industrial area in Ho Chi Minh City. In recent years, the area faces flooding problems because of heavy rainfalls and high tides, as part of the impacts of climate change and sea level rise. The lower of SG-DNRB was selected as a case study to highlight the necessity to investigate combination of the rainfall-runoff model and the hydrodynamic model for long-term strategies to resource water in the future. The models were calibrated using water level and runoff discharge data during low and flood seasons in 2014. The calibrated results showed satisfactory coefficients (NASH index, R2 up to 0.70 and RMSE is smaller than 0.20). The results confirmed that the combination of Mike NAM and Mike SHE models is well suitable to simulate runoff discharge in the lower SG-DNRB. Combination of the models can be considered a useful tool to help professional agency operator water resources management projects in other areas in the context of climate change and sea level rise. [ABSTRACT FROM AUTHOR]

Many Vietnamese citizens have been and continue to be inadvertently exposed to dioxins and dioxin-like compounds deposited in the country during the Vietnam War. Dioxins may be involved in the pathogenesis of inflammatory diseases in part via by affecting expression of aryl hydrocarbon receptor (Ahr) and inflammatory cytokines in animal models. As the role of the Ahr in dioxin-exposed people is not well defined, a study was conducted to examine gene expression levels ofAhr, inflammatory cytokines, and the incidence of diseases in dioxin-exposed citizens who had/still resided near a heavily dioxin-contaminated area in Vietnam. Whole blood from citizens at/around Da Nang airbase and control individuals living in unsprayed areas was collected. Serum levels of dioxins were analyzed by using a dioxins-responsive chemical-activated luciferase gene expression bioassay. Gene expression ofAhr,interleukin (IL)-1β,TNFα, IL-6, and IL-22in whole blood was examined by quantitative real-time PCR. The results showed levels of dioxins and expression ofAhr,IL-1β,TNFα,andIL-6were up-regulated whileIL-22expression was down-regulated in dioxin-exposed people. Various disease incidences in the study subjects was also examined. Interestingly, the incidence of rheumatoid arthritis (RA) in these individuals was increased compared to the estimated prevalence of this disease in the general Vietnamese population. Analyses also showed that expression levels ofAhrcorrelated to those ofIL-6andIL-22in the dioxin-exposed people. Taken together, dioxins might be involved in an up-regulated expression ofAhrthat might possibly relate to changes in level of inflammatory cytokines and, ultimately, in the incidence of select diseases in residents of Vietnam who had/continue to live near a dioxins-contaminated site. [ABSTRACT FROM PUBLISHER]

Numerical models are useful tools that play an important role in many research projects. Mike21C is one of the most well-established models for simulating a variety of processes, including bank erosion, bed level variations, aggradation, and degradation. Such processes are caused by a variety of activities, such as construction and dredging, as well as seasonal flow fluctuations. Mike21C based on an orthogonal curvilinear grid, which enables a computational speed that is faster than that of other grids. The hydrodynamic part of the model is based on solving the Saint-Venant equations. In this research, the Mike21C model was applied to simulate water depth, flow discharge distribution, and suspended transport rate along reaches of the Bassac River the on Vietnam. The data files of the curvilinear grid and bathymetry were used to generate the model. A time series for the discharge and water level of hydrological stations was established. The model was calibrated using the water level and suspended load data collected during high and low flow discharges. The simulation results show that the model can be applied well to other areas. [ABSTRACT FROM AUTHOR]

Studies of sediment transport problems in mountainous rivers with steep slopes are difficult due to rapid variations in flow regimes, abrupt changes in topography, etc. Sediment transport in mountainous rivers with steep slopes is a complicated subject because bed materials in mountainous rivers are often heterogeneous and contain a wide range of bed material sizes, such as gravel, cobbles, boulders, etc. This paper presents a numerical model that was developed to simulate the river morphology in mountainous rivers where the maximum bed material size is in the range of cobbles. The governing equations were discretized using a finite difference method. In addition, an empirical bed load formula was established to calculate the bed load transport rate. The flow and sediment transport modules were constructed in a decoupled manner. The developed model was tested to simulate the river morphology in an artificial channel and in the Asungjun River section of the mountainous Yangyang Namdae River (South Korea). The simulation results exhibited good agreement with field data. [ABSTRACT FROM AUTHOR]

Studies of sediment transport problems in mountainous rivers with steep slopes are difficult due to rapid variations in flow regimes, abrupt changes in topography, etc. Sediment transport in mountainous rivers with steep slopes is a complicated subject because bed materials in mountainous rivers are often heterogeneous and contain a wide range of bed material sizes, such as gravel, cobbles, boulders, etc. This paper presents a numerical model that was developed to simulate the river morphology in mountainous rivers where the maximum bed material size is in the range of cobbles. The governing equations were discretized using a finite difference method. In addition, an empirical bed load formula was established to calculate the bed load transport rate. The flow and sediment transport modules were constructed in a decoupled manner. The developed model was tested to simulate the river morphology in an artificial channel and in the Asungjun River section of the mountainous Yangyang Namdae River (South Korea). The simulation results exhibited good agreement with field data. [ABSTRACT FROM AUTHOR]

Within human blood there are two subsets of monocytes that can be identified by differential expression of CD16. Although numerous phenotypic and functional differences between the subsets have been described, little is known of the mechanisms underlying the distinctive properties of the two subsets. Micro RNAs (mi RNAs) are small non-coding RNAs that can regulate gene expression through promoting mRNA degradation or repressing translation, leading to alterations in cellular processes. Their potential influence on the functions of monocyte subsets has not been investigated. In this study, we employed microarray analysis to define the mi RNA expression profile of human monocyte subsets. We identified 66 mi RNAs that were differentially expressed ( DE) between CD16+ and CD16− monocytes. Gene ontology analysis revealed that the predicted targets of the DE mi RNAs were predominantly associated with cell death and cellular movement. We validated the functional impacts of selected DE mi RNAs in CD16− monocytes, over-expression of miR-432 significantly increases apoptosis, and inhibiting miR-19a significantly reduces cell motility. Furthermore, we found that miR-345, another DE mi RNA directly targets the transcription factor RelA in monocytes, which resulted in the differential expression of RelA in monocyte subsets. This implicates miR-345 indirect regulation of many genes downstream of RelA, including important inflammatory mediators. Together, our data show that DE mi RNAs could contribute substantially to regulating the functions of human blood monocytes. [ABSTRACT FROM AUTHOR]

Human blood monocytes are heterogeneous and conventionally subdivided into two subsets based on CD16 expression. Recently, the official nomenclature subdivides monocytes into three subsets, the additional subset arising from the segregation of the CD16+ monocytes into two based on relative expression of CD14. Recent whole genome analysis reveal that specialized functions and phenotypes can be attributed to these newly defined monocyte subsets. In this review, we discuss these recent results, and also the description and utility of this new segregation in several disease conditions. We also discuss alternative markers for segregating the monocyte subsets, for example using Tie-2 and slan, which do not necessarily follow the official method of segregating monocyte subsets based on relative CD14 and CD16 expressions. [ABSTRACT FROM AUTHOR]

TGF-β1 is overexpressed in wound repair and in most proliferative disorders including benign prostatic hyperplasia and prostate cancer. The stromal microenvironment at these sites is reactive and typified by altered phenotype, matrix deposition, inflammatory responses, and alterations in nerve density and biology. TGF-β1 is known to modulate several stromal responses; however there are few transgenic models to study its integrated biology. To address the actions of TGF-β1 in prostate disorders, we targeted expression of an epitope tagged and constitutively active TGF-β1 via the enhanced probasin promoter to the murine prostate gland epithelium. Transgenic mice developed age-dependent lesions leading to severe, yet focal attenuation of epithelium, and a discontinuous basal lamina. These changes were associated with elevated fibroplasia and frequency of collagenous micronodules in collapsed acini, along with an induced inflammation in nerve ganglia and small vessels. Elevated recruitment of CD115+ myeloid cells but not mature macrophages was observed in nerve ganglia, also in an age-dependent manner. Similar phenotypic changes were observed using a human prostate epithelium tissue recombination xenograft model, where epithelial cells engineered to overexpress TGF-β1 induced fibrosis and altered matrix deposition concurrent with inflammation in the stromal compartment. Together, these data suggest that elevated TGF-β1 expression induces a fibroplasia stromal response associated with breach of epithelial wall structure and inflammatory involvement of nerve ganglia and vessels. The novel findings of ganglia and vessel inflammation associated with formation of collagenous micronodules in collapsed acini is important as each of these are observed in human prostate carcinoma and may play a role in disease progression. [ABSTRACT FROM AUTHOR]

Chitin is the second most abundant polysaccharide in nature and linked to fungal infection and asthma. However, bona fide immune receptors directly binding chitin and signaling immune activation and inflammation have not been clearly identified because polymeric crude chitin with unknown purity and molecular composition has been used. By using defined chitin (N‐acetyl‐glucosamine) oligomers, we here identify six‐subunit‐long chitin chains as the smallest immunologically active motif and the innate immune receptor Toll‐like receptor (TLR2) as a primary fungal chitin sensor on human and murine immune cells. Chitin oligomers directly bind TLR2 with nanomolar affinity, and this fungal TLR2 ligand shows overlapping and distinct signaling outcomes compared to known mycobacterial TLR2 ligands. Unexpectedly, chitin oligomers composed of five or less subunits are inactive, hinting to a size‐dependent system of immuno‐modulation that appears conserved in plants and humans. Since blocking of the chitin‐TLR2 interaction effectively prevents chitin‐mediated inflammation in vitro and in vivo, our study highlights the chitin‐TLR2 interaction as a potential target for developing novel therapies in chitin‐related pathologies and fungal disease. Synopsis: Chitin, a polysaccharide linked to fungal infection and allergic asthma, directly binds to the innate immune receptor TLR2 and triggers inflammation dependent on oligomer size. Blocking the chitin‐TLR2 interaction effectively prevents chitin‐mediated inflammation. Oligomeric chains of fungal chitin directly bind to TLR2 and trigger inflammation.Blocking of the chitin‐TLR2 interaction prevents chitin‐mediated inflammation in vitro and in vivo.Size‐dependent chitin recognition based on oligomers is found in both plants and humans. Chitin, a polysaccharide linked to fungal infection and allergic asthma, directly binds to the innate immune receptor TLR2 and triggers inflammation dependent on oligomer size. Blocking the chitin‐TLR2 interaction effectively prevents chitin‐mediated inflammation. [ABSTRACT FROM AUTHOR]

A correction to the article "Experimental analysis and numerical simulation of bed elevation change in mountain rivers," by Troung An Dang and Sang Deog Park that was published in the 2016 issue is presented.