Correlation between caffeine contents of green coffee beans and altitudes of the coffee plants grown in southwest Ethiopia

Caffeine contents of 45 green coffee bean samples collected from coffee plants grown at different altitudes in Southwest Ethiopia was determined by UV-Vis spectrophotometry. The caffeine contents were found in the range of 0.62 - 1.2% (w/w). A moderate negative correlation (R = 0.5463) was found between the caffeine contents of green coffee beans and the altitudes at which the coffee plants were grown. The caffeine contents of 9 of the green coffee bean samples analyzed by high performance liquid chromatography (HPLC) provided comparable results in the range of 0.60−1.1% (w/w). Statistical analysis of data (t-test) indicated absence of significant differences between the caffeine contents obtained by the two methods. Nonetheless, HPLC method is precise, accurate and reliable in determining caffeine content in green coffee bean samples while the UV-Vis spectrophotometry is simple, rapid, precise and more economical. KEY WORDS: Green coffee beans, Caffeine, Correlation between caffeine content and altitude of coffee plant, UV-Vis spectrophotometry, High performance liquid chromatography, Ethiopia Bull. Chem. Soc. Ethiop. 2018, 32(1), 13-25DOI: https://dx.doi.org/10.4314/bcse.v32i1.

The multispecific thyroid hormone transporter OATP1C1 mediates cell-specific sulforhodamine 101-labeling of hippocampal astrocytes

Sulforhodamine 101 (SR101) is widely used for astrocyte identification, though the labeling mechanism remains unknown and the efficacy of labeling in different brain regions is heterogeneous. By combining region-specific isolation of astrocytes followed by transcriptome analysis, two-photon excitation microscopy, and mouse genetics, we identified the thyroid hormone transporter OATP1C1 as the SR101-uptake transporter in hippocampus and cortex. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00429-013-0645-0) contains supplementary material, which is available to authorized users

Waste-waste treatment technology and environmental management using sawdust bio-mixture

AbstractThe industrial wastewater (WW) of potato-chips factory is characterized by its high biological oxygen demand (BOD) and chemical oxygen demand (COD), in addition to a medium content of oil & grease (O&G), total dissolved slats (TDS) and total suspended solids (TSS). A new technique for wastewater treatment has been applied using bio-mixture of selected strains of Aspergillus terreus or Rhizopus sexualis in addition to the natural flora of sawdust (SD-BIOMIX) in the form of mobile micro-carrier in activated sludge system. Different kinds of composted sawdust were used as a microbial carrier, support and source of nutrients and enzymes to enhance the wastewater treatment process; in order to improve the quality of treated wastewater and resulting sludge. The parameters of treated wastewater in terms of BOD, COD, O&G, TDS and TSS were greatly improved by 85.0, 79.0, 82.7, 74.6 and 87.7% respectively, in relation to the retention time and kind of tested materials. The 14 days microbial–treated (composted) sawdust by A. terreus, or R. sexualis as (SD-BIOMIX) exhibited the highest enzymes contents and was the most efficient materials for the wastewater treatment process in comparison with commercial biomixture products e.g. C157 and EM solution. Furthermore, the retention time of the treatment process could be reduced to 4 hr only. Finally, the resulting sludge(s) of (SD-BIOMIX) was easy to separate (in 5–10 min.) from wastewater. The sludge, according the chemical analysis, can be safely used in agriculture as an organic fertilizer and soil conditioner. In addition, different kinds of resulting sludge have been tested as biosorbents and exhibited high ability to remove chromium (89.1 – 99.3%), nickel (84.3 – 98.0%) and zinc (85.6 – 97.7%) from the heavy industrial wastewater. Data indicated the possibility of magnifying the introduced (SD-BIOMIX) as a new technique for the treatment of wastewater and as new trend for wastes management and pollution prevention and could be applied in Kingdom of Saudi Arabia as one of advanced biotechnology to solve many of environmental problems in KSA

Agricultural technology adoption, seed access constraints and commercialization in Ethiopia

This article examines the driving forces behind farmers’ decisions to adopt agricultural technologies and the causal impact of adoption on farmers’ integration into output market using data obtained from a random cross-section sample of 700 farmers in Ethiopia. We estimate a Double-Hurdle model to analyze the determinants of the intensity of technology adoption conditional on overcoming seed access constraints. We estimate the impact of technology adoption on farmers’ integration into output market by utilizing treatment effect model, regression based on propensity score as well as matching techniques to account for heterogeneity in the adoption decision, and for unobservable characteristics of farmers and their farm. Results show that knowledge of existing varieties, perception about the attributes of improved varieties, household wealth (livestock and land) and availability of active labor force are major determinants for adoption of improved technologies. Our results suggest that the adoption of improved agricultural technologies has a significant positive impact on farmers’ integration into output market and the findings are consistent across the three models suggesting the robustness of the results. This confirms the potential direct role of technology adoption on market participation among rural households, as higher productivity from improved technology translates into higher output market integration

The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2

A new weakly coupled land data assimilation (WCLDA) system based on the four-dimensional ensemble variational (4DEnVar) method is developed and applied to the fully coupled Energy Exascale Earth System Model version 2 (E3SMv2). The dimension-reduced projection four-dimensional variational (DRP-4DVar) method is employed to implement 4DVar using the ensemble technique instead of the adjoint technique. With an interest in providing initial conditions for decadal climate predictions, monthly mean anomalies of soil moisture and temperature from the Global Land Data Assimilation System (GLDAS) reanalysis from 1980 to 2016 are assimilated into the land component of E3SMv2 within the coupled modeling framework with a 1-month assimilation window. The coupled assimilation experiment is evaluated using multiple metrics, including the cost function, assimilation efficiency index, correlation, root-mean-square error (RMSE), and bias, and compared with a control simulation without land data assimilation. The WCLDA system yields improved simulation of soil moisture and temperature compared with the control simulation, with improvements found throughout the soil layers and in many regions of the global land. In terms of both soil moisture and temperature, the assimilation experiment outperforms the control simulation with reduced RMSE and higher temporal correlation in many regions, especially in South America, central Africa, Australia, and large parts of Eurasia. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought, highlighting the crucial role of land surface in drought lifecycle. The WCLDA system is intended to be a foundational resource for research to investigate land-derived climate predictability.</p