State of research on carbon sequestration in Bangladesh: a comprehensive review

<p>A deep interest is evident in carbon sequestration modeling in Bangladesh from the development of several allometric equations to estimate carbon sequestration by plants. It is linked to the evolving carbon offsetting approaches, for example, Clean Development Mechanism (CDM) and the REDD+, which require certifiable estimate of carbon captured by trees and forests. This review compiled a snapshot of state of the art in carbon modeling in Bangladesh. More than half of the published research focused on the development of allometric equations and forest carbon estimation. The comparison among available studies was challenging due to the use of different terminologies and assumptions and arbitrary combinations of parameters including age, topography, season, slope, crown diameter, etc. The spatial distribution of reports indicated narrow geographical focus outside forests in Chittagong and Sundarbans. Surprisingly, no attempts were evident to explore carbon stocks at the Chittagong Hill Tracts (CHTs) where majority of pristine forest areas of the country occurs. Bangladesh is likely to reforest the vast deforested areas in CHTs under CDM and REDD+ projects which requires extensive carbon modeling. Majority of the reports used conversion factor to calculate soil carbon instead of analytical estimation which might cause inaccurate estimation of soil carbon. Blue carbon assessment and policy implication of carbon studies are two areas where insufficient attention is evident. Bangladesh apparently needs to conduct wide-scale carbon modeling through the integration of GIS, remote sensing, etc to increase precision and accuracy of carbon stock assessments. .</p

Regional Variation in the Prevalence of <i>E. coli</i> O157 in Cattle: A Meta-Analysis and Meta-Regression

<div><p>Background</p><p><i>Escherichia coli</i> O157 (EcO157) infection has been recognized as an important global public health concern. But information on the prevalence of EcO157 in cattle at the global and at the wider geographical levels is limited, if not absent. This is the first meta-analysis to investigate the point prevalence of EcO157 in cattle at the global level and to explore the factors contributing to variation in prevalence estimates.</p><p>Methods</p><p>Seven electronic databases- CAB Abstracts, PubMed, Biosis Citation Index, Medline, Web of Knowledge, Scirus and Scopus were searched for relevant publications from 1980 to 2012. A random effect meta-analysis model was used to produce the pooled estimates. The potential sources of between study heterogeneity were identified using meta-regression.</p><p>Principal findings</p><p>A total of 140 studies consisting 220,427 cattle were included in the meta-analysis. The prevalence estimate of EcO157 in cattle at the global level was 5.68% (95% CI, 5.16–6.20). The random effects pooled prevalence estimates in Africa, Northern America, Oceania, Europe, Asia and Latin America-Caribbean were 31.20% (95% CI, 12.35–50.04), 7.35% (95% CI, 6.44–8.26), 6.85% (95% CI, 2.41–11.29), 5.15% (95% CI, 4.21–6.09), 4.69% (95% CI, 3.05–6.33) and 1.65% (95% CI, 0.77–2.53), respectively. Between studies heterogeneity was evidenced in most regions. World region (p<0.001), type of cattle (p<0.001) and to some extent, specimens (p = 0.074) as well as method of pre-enrichment (p = 0.110), were identified as factors for variation in the prevalence estimates of EcO157 in cattle.</p><p>Conclusion</p><p>The prevalence of the organism seems to be higher in the African and Northern American regions. The important factors that might have influence in the estimates of EcO157 are type of cattle and kind of screening specimen. Their roles need to be determined and they should be properly handled in any survey to estimate the true prevalence of EcO157.</p></div

Meta-regression for prevalence of <i>E. coli</i> O157 in cattle.

<p>*Estimated prevalence was calculated separately, Coef.  =  Regression coefficient, Ref.  =  Reference category, IMS = Immunomagnetic separation.</p

Forest plot of prevalence of <i>E. coli</i> O157 in cattle amongst studies conducted in North America.

<p>Forest plot of prevalence of <i>E. coli</i> O157 in cattle amongst studies conducted in North America.</p

Forest plot of prevalence of <i>E. coli</i> O157 in cattle amongst studies conducted in Europe.

<p>Forest plot of prevalence of <i>E. coli</i> O157 in cattle amongst studies conducted in Europe.</p

Estimated pooled prevalence of <i>E. coli</i> O157 in cattle by world region.

<p>Estimated pooled prevalence of <i>E. coli</i> O157 in cattle by world region.</p

Funnel plot for examination of publication bias.

<p>(ppercent, prevalence percent; se, standard error).</p

Forest plot of prevalence of <i>E. coli</i> O157 in cattle amongst studies conducted in Latin America and Caribbean.

<p>Forest plot of prevalence of <i>E. coli</i> O157 in cattle amongst studies conducted in Latin America and Caribbean.</p

Estimated prevalence of <i>E. coli</i> O157 in cattle in different countries.

<p>The prevalence is based on a meta-analysis of 140 studies comprising 220,427 cattle from different production system. Regional adjusted prevalence is denoted by different colors and it represents the quartile distribution of prevalence by country.</p

Comparison of the Performance of the TPTest, Tubex, Typhidot and Widal Immunodiagnostic Assays and Blood Cultures in Detecting Patients with Typhoid Fever in Bangladesh, Including Using a Bayesian Latent Class Modeling Approach - Fig 1

<p>Venn diagram indicating (A) the patients in the six groups, (B) the healthy controls and (C) the patients with other febrile diseases are positive with each of the three tests (TPTest, Tubex and Typhidot). </p