Political transition and emergent forest-conservation issues in Myanmar.

Political and economic transitions have had substantial impacts on forest conservation. Where transitions are underway or anticipated, historical precedent and methods for systematically assessing future trends should be used to anticipate likely threats to forest conservation and design appropriate and prescient policy measures to counteract them. Myanmar is transitioning from an authoritarian, centralized state with a highly regulated economy to a more decentralized and economically liberal democracy and is working to end a long-running civil war. With these transitions in mind, we used a horizon-scanning approach to assess the 40 emerging issues most affecting Myanmar's forests, including internal conflict, land-tenure insecurity, large-scale agricultural development, demise of state timber enterprises, shortfalls in government revenue and capacity, and opening of new deforestation frontiers with new roads, mines, and hydroelectric dams. Averting these threats will require, for example, overhauling governance models, building capacity, improving infrastructure- and energy-project planning, and reforming land-tenure and environmental-protection laws. Although challenges to conservation in Myanmar are daunting, the political transition offers an opportunity for conservationists and researchers to help shape a future that enhances Myanmar's social, economic, and environmental potential while learning and applying lessons from other countries. Our approach and results are relevant to other countries undergoing similar transitions

Political transition and emergent forest-conservation issues in Myanmar.

Political and economic transitions have had substantial impacts on forest conservation. Where transitions are underway or anticipated, historical precedent and methods for systematically assessing future trends should be used to anticipate likely threats to forest conservation and design appropriate and prescient policy measures to counteract them. Myanmar is transitioning from an authoritarian, centralized state with a highly regulated economy to a more decentralized and economically liberal democracy and is working to end a long-running civil war. With these transitions in mind, we used a horizon-scanning approach to assess the 40 emerging issues most affecting Myanmar's forests, including internal conflict, land-tenure insecurity, large-scale agricultural development, demise of state timber enterprises, shortfalls in government revenue and capacity, and opening of new deforestation frontiers with new roads, mines, and hydroelectric dams. Averting these threats will require, for example, overhauling governance models, building capacity, improving infrastructure- and energy-project planning, and reforming land-tenure and environmental-protection laws. Although challenges to conservation in Myanmar are daunting, the political transition offers an opportunity for conservationists and researchers to help shape a future that enhances Myanmar's social, economic, and environmental potential while learning and applying lessons from other countries. Our approach and results are relevant to other countries undergoing similar transitions

Detection of East/Central/South African Genotype of Chikungunya Virus in Myanmar, 2010

In 2010, chikungunya virus of the East Central South African genotype was isolated from 4 children in Myanmyar who had dengue-like symptoms. Phylogenetic analysis of the E1 gene revealed that the isolates were closely related to isolates from China, Thailand, and Malaysia that harbor the A226V mutation in this gene

Prevalence and seroprevalence of Plasmodium infection in Myanmar reveals highly heterogeneous transmission and a large hidden reservoir of infection.

Malaria incidence in Myanmar has significantly reduced over recent years, however, completeness and timeliness of incidence data remain a challenge. The first ever nationwide malaria infection and seroprevalence survey was conducted in Myanmar in 2015 to better understand malaria epidemiology and highlight gaps in Annual Parasite Index (API) data. The survey was a cross-sectional two-stage stratified cluster-randomised household survey conducted from July-October 2015. Blood samples were collected from household members for ultra-sensitive PCR and serology testing for P. falciparum and P. vivax. Data was gathered on demography and a priori risk factors of participants. Data was analysed nationally and within each of four domains defined by API data. Prevalence and seroprevalence of malaria were 0.74% and 16.01% nationwide, respectively. Prevalent infection was primarily asymptomatic P. vivax, while P. falciparum was predominant in serology. There was large heterogeneity between villages and by domain. At the township level, API showed moderate correlation with P. falciparum seroprevalence. Risk factors for infection included socioeconomic status, domain, and household ownership of nets. Three K13 P. falciparum mutants were found in highly prevalent villages. There results highlight high heterogeneity of both P. falciparum and P. vivax transmission between villages, accentuated by a large hidden reservoir of asymptomatic P. vivax infection not captured by incidence data, and representing challenges for malaria elimination. Village-level surveillance and stratification to guide interventions to suit local context and targeting of transmission foci with evidence of drug resistance would aid elimination efforts

Comparison of Performance of Machine Learning Algorithms for Wine Type Classification

Supervised Machine Learning is the search for algorithms that reason from externally supplied instances to produce general hypothesis, which then make predictions about future instances. This paper describes various Supervised Machine Learning classification techniques, compares several machine learning algorithms for classifying wine types. Wine dataset is taken from UCI datasets. Six different machine learning algorithms that involve Logistic Regression(LR), Linear Discriminant Analysis(LDA), k-Nearest Neighbors(KNN), Classification and Regression Trees(CART), Gaussian Naïve Bayes (NB) and support vector machine(SVM) are proposed and assessed for this classification. The result shows that LDA was found to be the algorithm with most precision and accuracy, Gaussian Naïve Bayes and LR algorithms are found to be the next accurate after LDA accordingly. Machine Learning algorithms requires precision, accuracy and minimum error to have supervised predictive machine learning

Sale Forecasting for Hot-Drink Productivity Using Naïve Bayesian Classification

Classification is one of the most popular data mining tasks with a wide ranges of application and lots of algorithms have been proposed to build scalable classifiers. Several data mining techniques and classification methods have been widely applied to extract knowledge from databases. Naïve Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surprising, because the conditional independence assumption on which it the conditional independence assumption on which it is based, rarely true in real-world applications. This system will present sale forecasting productivity using Naïve Bayesian Classification

Problem of Golden Apple Snail Pomacea canaliculata (Lamarck) (Gastropoda: Ampullariidae) in Selected Rice Growing Areas of Myanmar

A survey of 142 farmers was conducted in the seven townships of major rice growing areas of Myanmar to observe the pest status and damages, integrated management practices and farmers’ perception of GASs. The farm-ers were stratified by the level of GAS damage i.e. low, medium and high damage. The majority of interviewed farmers grow the rice by transplanting method (78% of respondents), followed by direct seeding method (14% of respondents) and only 8% of farmers used both methods. The farmers identified that GAS was the key pest on their rice (77%) followed by rice yellow stem borer (3%), rodent (1.7%), BPH (1.7%) and caseworm (0.8%). GASs were firstly aware in Shan State since early 1990s and were introduced as a food item, biological control agent for aquatic weeds. To control the GASs, most of the farmers (89%) used hand picking of snails and egg masses, molluscicides application (39%) followed by the biological (duck herding 32%) and cultural (replanting 32%) measures. Some farmers (17% of respondents) managed the irrigation water into their fields and some farmers (6%) are reluctant to grow rice in their fields as they are afraid of GAS infestation. GAS is clearly the most important problem of rice farmers in the surveyed areas of Myanmar. Farmers from Ayeyarwaddy Delta might suffer more seriously the GASs damages than those of Kayin and Mon States because the farmers from Ayeyarwaddy grow the paddy year round and so many streams and waterways make easy the spread of GASs rapidly and create the breeding grounds for GASs

Preliminary Investigation of Water Level Fluctuation in Yemyet In

Wetlands are among the world's most productive environments. Meanwhile, they are also the most threatened ecosystems in the world. Yemyet In is located in Sagaing Township, also an important wetland and natural resource for its surrounding area in the Dry Zone area of Myanmar. There has an area of 28.5 square miles (18240 acres) in the period of flood and 23.15 square miles (14813 acres) in that of normal. Small changes in precipitation and stream inflows strongly affect the extent of the lake surface area. For times when there are no satellite images, it is difficult to determine the extent of the lake from observations. Water balance computations were performed to create a water-level series for Yemyet In extending back in time. The water-balance computations confirm the crude Local people knowledge about historical lake status. It is found that if the average monthly precipitation is less than 2.45 feet during the wet season around Yemyet In, there is a risk that this shallow lake dries out in the dry season. This paper also conducts the fluctuation patterns of Water level and processes of fluctuation from the perspective of physical condition and human activitie

Triterpenoids and Their Glycosides from Glinus Oppositifolius with Antifungal Activities against Microsporum Gypseum and Trichophyton Rubrum

Four new triterpenoids, 3β,12β,16β,21β,22-pentahydroxyhopane (1), 12β,16β,21β,22-tetrahydroxyhopan-3-one (2), 3-oxo-olean-12-ene-28,30-dioic acid (3), and 3β-hydroxyoleana-11,13(18)-diene-28,30-dioic acid 30-methyl ester (4); 21 new triterpenoid saponins, glinusopposides A–U (5–25); and 12 known compounds (26–37) were isolated from the whole plants of Glinus oppositifolius. The structures of the new compounds were elucidated based on the analysis of one-dimensional (1D) and two-dimensional (2D) nuclear magnetic resonance (NMR) and mass spectrometry (MS) data. All compounds from the plants were measured for antifungal activities against Microsporum gypseum and Trichophyton rubrum. Glinusopposide B (6), glinusopposide Q (21), glinusopposide T (24), and glinusopposide U (25) showed strong inhibitory activities against M. gypseum (MIC50 7.1, 6.7, 6.8, and 11.1 μM, respectively) and T. rubrum (MIC50 14.3, 13.4, 11.9, and 13.0 μM, respectively). For those active compounds with an oleanane skeleton, glycosylation (21–26) or oxidation (3) of 3-OH was helpful in increasing the activity; replacement of the 30-methyl group (29) by a carboxymethyl group (26) enhanced the activity; the presence of 11,13(18) double bonds (20) decreased the activity