Carbon assessment for Robusta coffee production systems in Vietnam: a case study in Dak Lak

Carbon assessments have proliferated to identify climate friendly practices in Arabica producing systems, though little attention is given to Robusta. In this study, we evaluated the climate impact of Robusta production via quantification of carbon stock and greenhouse gas (GHG) emissions in the intensive shaded and unshaded coffee farms of the world’s largest Robusta producing region, Vietnam’s Central Highlands. We find due to the linear relationship between fertilizer use and yields, emissions from input use on a per unit product basis are not significantly different between the intensive and less intensive systems. However, when accounting for carbon sequestered in shade and coffee trees, the less intensive systems are carbon positive (sequestering more than they emit) per unit of green coffee bean produced

A Case-Control Study of Risk Factors for Type 2 Diabetes Mellitus in Vietnam

A case-control study including 599 type 2 diabetes (T2D) cases and 599 controls was conducted in Northern Vietnam between 2013 and 2015 to investigate the associations between dietary, lifestyle factors and T2D risk. High consumption of green tea, coffee, and moderate intensity of activity were inversely associated with reduced odds of T2D. The present study found that there are inverse associations between habitual tea, coffee consumption, physical activity and risk of T2D among Vietnamese adults

Research on the stability of the 3D frame on coral foundation subjected to impact load

This article presents an application of the finite element method (FEM) for the stability analysis of 3D frame (space bar system) on the coral foundation impacted by collision impulse. One-way joints between the rod and the coral foundation are described by the contact element. Numerical analysis shows the effect of some factors on the stability of the bar system on coral foundation. The results of this study can be used for stability analysis of the bar system on coral foundation subjected to sea wave load

Inferring pleiotropy by network analysis: linked diseases in the human PPI network

<p>Abstract</p> <p>Background</p> <p>Earlier, we identified proteins connecting different disease proteins in the human protein-protein interaction network and quantified their mediator role. An analysis of the networks of these mediators shows that proteins connecting heart disease and diabetes largely overlap with the ones connecting heart disease and obesity.</p> <p>Results</p> <p>We quantified their overlap, and based on the identified topological patterns, we inferred the structural disease-relatedness of several proteins. Literature data provide a functional look of them, well supporting our findings. For example, the inferred structurally important role of the PDZ domain-containing protein GIPC1 in diabetes is supported despite the lack of this information in the Online Mendelian Inheritance in Man database. Several key mediator proteins identified here clearly has pleiotropic effects, supported by ample evidence for their general but always of only secondary importance.</p> <p>Conclusions</p> <p>We suggest that studying central nodes in mediator networks may contribute to better understanding and quantifying pleiotropy. Network analysis provides potentially useful tools here, as well as helps in improving databases.</p

A lossless online Bayesian classifier.

We are living in a world progressively driven by data. Besides the issue that big data cannot be entirely stored in the main memory as required by traditional offline learning methods, the problem of learning data that can only be collected over time is also very prevalent. Consequently, there is a need of online methods which can handle sequentially arriving data and offer the same accuracy as offline methods. In this paper, we introduce a new lossless online Bayesian-based classifier which uses the arriving data in a 1-by-1 manner and discards each data right after use. The lossless property of our proposed method guarantees that it can reach the same prediction performance as its offline counterpart regardless of the incremental training order. Experimental results demonstrate its superior performance over many well-known state-of-the-art online learning methods in the literature

Facile preparation of water soluble curcuminoids extracted from turmeric (Curcuma longa L.) powder by using steviol glucosides

AbstractCurcuminoids from rhizomes of Curcuma longa possess various biological activities. However, low aqueous solubility and consequent poor bioavailability of curcuminoids are major limitations to their use. In this study, curcuminoids extracted from turmeric powder using stevioside (Ste), rebaudioside A (RebA), or steviol glucosides (SG) were solubilized in water. The optimum extraction condition by Ste, RebA, or SG resulted in 11.3, 9.7, or 6.7mg/ml water soluble curcuminoids. Curcuminoids solubilized in water showed 80% stability at pH from 6.0 to 10.0 after 1week of storage at 25°C. The particle sizes of curcuminoids prepared with Ste, RebA, and SG were 110.8, 95.7, and 32.7nm, respectively. The water soluble turmeric extracts prepared with Ste, RebA, and SG showed the 2,2-diphenyl-1-picrylhydrazyl radical scavenging (SC50) activities of 127.6, 105.4, and 109.8μg/ml, and the inhibition activities (IC50) against NS2B-NS3pro from dengue virus type IV of 14.1, 24.0 and 15.3μg/ml, respectively