Multi-Decadal Changes in Mangrove Extent, Age and Species in the Red River Estuaries of Viet Nam

This research investigated the performance of four different machine learning supervised image classifiers: artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machine (SVM) using SPOT-7 and Sentinel-1 images to classify mangrove age and species in 2019 in a Red River estuary, typical of others found in northern Viet Nam. The four classifiers were chosen because they are considered to have high accuracy, however, their use in mangrove age and species classifications has thus far been limited. A time-series of Landsat images from 1975 to 2019 was used to map mangrove extent changes using the unsupervised classification method of iterative self-organizing data analysis technique (ISODATA) and a comparison with accuracy of K-means classification, which found that mangrove extent has increased, despite a fall in the 1980s, indicating the success of mangrove plantation and forest protection efforts by local people in the study area. To evaluate the supervised image classifiers, 183 in situ training plots were assessed, 70% of them were used to train the supervised algorithms, with 30% of them employed to validate the results. In order to improve mangrove species separations, Gram–Schmidt and principal component analysis image fusion techniques were applied to generate better quality images. All supervised and unsupervised (2019) results of mangrove age, species, and extent were mapped and accuracy was evaluated. Confusion matrices were calculated showing that the classified layers agreed with the ground-truth data where most producer and user accuracies were greater than 80%. The overall accuracy and Kappa coefficients (around 0.9) indicated that the image classifications were very good. The test showed that SVM was the most accurate, followed by DT, ANN, and RF in this case study. The changes in mangrove extent identified in this study and the methods tested for using remotely sensed data will be valuable to monitoring and evaluation assessments of mangrove plantation projects

A Clinical and Epidemiological Investigation of the First Reported Human Infection With the Zoonotic Parasite Trypanosoma evansi in Southeast Asia

Background. Trypanosoma is a genus of unicellular parasitic flagellate protozoa. Trypanosoma brucei species and Trypanosoma cruzi are the major agents of human trypanosomiasis; other Trypanosoma species can cause human disease, but are rare. In March 2015, a 38-year-old woman presented to a healthcare facility in southern Vietnam with fever, headache, and arthralgia. Microscopic examination of blood revealed infection with Trypanosoma. Methods. Microscopic observation, polymerase chain reaction (PCR) amplification of blood samples, and serological testing were performed to identify the infecting species. The patient's blood was screened for the trypanocidal protein apolipoprotein L1 (APOL1), and a field investigation was performed to identify the zoonotic source. Results. PCR amplification and serological testing identified the infecting species as Trypanosoma evansi. Despite relapsing 6 weeks after completing amphotericin B therapy, the patient made a complete recovery after 5 weeks of suramin. The patient was found to have 2 wild-type APOL1 alleles and a normal serum APOL1 concentration. After responsive animal sampling in the presumed location of exposure, cattle and/or buffalo were determined to be the most likely source of the infection, with 14 of 30 (47%) animal blood samples testing PCR positive for T. evansi. Conclusions. We report the first laboratory-confirmed case of T. evansi in a previously healthy individual without APOL1 deficiency, potentially contracted via a wound while butchering raw beef, and successfully treated with suramin. A linked epidemiological investigation revealed widespread and previously unidentified burden of T. evansi in local cattle, highlighting the need for surveillance of this infection in animals and the possibility of further human cases

Evaluation of an algorithm for integrated management of childhood illness in an area of Vietnam with dengue transmission

OBJECTIVES: To determine whether nurses, using the WHO/UNICEF algorithm for integrated management of childhood illness (IMCI), modified to include dengue infection, satisfactorily classified children in an area endemic for dengue haemorrhagic fever (DHF). METHODS: Nurses assessed and classified, using the modified IMCI algorithm, a systematic sample of 1250 children aged 2 months to 10 years (n = 1250) presenting to a paediatric hospital in Dong Nai Province, Vietnam. Their classification was compared with that of a paediatrician, blind to the result of the nurses' assessment, which could be modified in the light of simple investigations, e.g. dengue serology. RESULTS: In children aged 2-59 months (n = 859), the nurses were able to classify, using the modified chart, the presenting illness in >99% of children and found more than one classification in 70%. For the children with pneumonia, diarrhoea, dengue shock syndrome, severe DHF and severe disease requiring urgent admission, the nurse's classification was >60% sensitive and >85% specific compared with that of the paediatrician. For the nurse's classification of DHF the specificity was 50-55% for the children <5 years and in children with definitive dengue serology. Alterations in the DHF algorithm improved specificity at the expense of sensitivity. CONCLUSION: Using the IMCI chart, nurses classified appropriately many of the major clinical problems in sick children <5 years in southern Vietnam. However, further modifications will be required in the fever section, particularly for dengue. The impact of using the IMCI chart in peripheral health stations remains to be evaluated