Designing Strategies to Support a Transformation of Agriculture in Ethiopia

The paper consists of three parts. The first part of the paper is a review of agricultural performance in Ethiopia over the past forty years. The second part diagnoses agricultural system performance and food security problems in Ethiopia and discusses some tentative practical strategies for promoting an agricultural transformation, and with it, increased productivity, income growth, and food security over the long run. The third part describes the general approach to promoting an agricultural transformation and food security for Ethiopia. It is conceptual and procedural. It draws from the lessons of economic history and theory applied to the current situation in Ethiopia.food security, food policy, Ethiopia, Farm Management, Food Security and Poverty, Q18,

Households Willingness to Pay for Improved Urban Solid Waste Management: The Case of Mekelle City, Ethiopia

Cities in developing countries experiencing rapid urbanization and population growth too often lack the financial resources and institutional capacity to provide needed municipal infrastructure for adequate solid waste management, despite citizens’ demand for it. This paper uses a cross-sectional survey of 226 randomly selected households in Mekelle city, Ethiopia, to assess the current municipal sanitary fees and the willingness to pay (WTP) of residents for improved urban waste management, and suggest mechanisms for cost recovery. We used Tobit and probit models in the empirical analysis to determine the factors that influence households’ WTP for improved solid waste management. Results reveal that residents’ WTP for improved solid waste management is significantly related to income and awareness of environmental quality, among other factors. The results suggest that the current city fee for sanitation is far below the WTP of the residents. The mean WTP we found can be a guide for municipal officials in setting a more appropriate fee that can finance improvements in city solid waste management, where all households receive collection services, waste is disposed of properly, and recycling features are added.Keywords: Urban waste management, willingness to pay, cost recovery, Ethiopia, citiesJEL Classification: D13, Q51, Q5

MESUAFERRIN A-BIOACTIVE FLAVONOID ISOLATED FROM THE BARK OF MESUA FERREA L. AGAINST PHOSPHOLIPASE A2, CYCLOOXYGENASE AND LIPOXYGENASE: AN IN VITRO, IN VIVO AND IN SILICO APPROACH

Objective: The main objective of the present study was to evaluate the anti-inflammatory activity of isolated bioactive flavonoid Mesuaferrin-A from the bark of Mesuaferrea L. by in vitro, in vivo and in silico approach.Methods: To evaluate the effect of isolated bioactive flavonoid Mesuaferrin-A on arachidonic acid metabolizing enzymes (PLA2, COX-2 and 5-LOX) using in vitro methods, followed by carrageenan-induced paw edema model by in vivo and to determine the binding orientation and interactions of Mesuaferrin-A onarachidonic acid metabolizing enzymes (PLA2, COX-2 and 5-LOX) crystal proteins using molecular docking (in silico) studies.Results: Mesuaferrin-A exhibited a dose-dependent significant 5-LOX inhibitory and considerable COX-2 inhibitory activity by in vitro, The inhibitory activities of 5-LOX and COX-2 at 100µg/ml were found to be 78.67%, 81.03% with IC50 values of 45.22µg/ml and 35.74µg/ml respectively. Whereas Mesuaferrin-A showed less PLA2 inhibitory activity. Mesuaferrin-A showed 68.34% inhibitory activity at 400 mg/kg body weight at the late phase of carrageenan-induced paw edema, and In silico studies demonstrated that Mesuaferrin-A strongly binds with 5-LOX and COX-2, these strong binding affinity of Mesuaferrin-A on active site amino acids of 5-LOX and COX-2 may be responsible for inhibition of enzyme activity. Mesuaferrin-A showeda comparable 5-LOX and COX-2 inhibition activity with (positive control).Conclusion: It was concluded that Mesuaferrin-A act as 5-LOX and COX dual inhibitor, from the results it was suggests that Mesuaferrin-A, may be an effective preventive and therapeutic approach for patients with inflammatory-related diseases

1,3-Bis(2,6-diisopropyl­phen­yl)imidazolidinium tetra­phenyl­borate dichloro­methane disolvate

The title compound, C27H39N2 +·C24H20B−·2CH2Cl2, is the first reported imidazolidinium cation with the sterically demanding 2,6-diisopropyl­phenyl groups in the 1,3-positions. The crystal structure is stabilized by weak inter­molecular C—H⋯π(arene) inter­actions. Due to the bulky nature of both the flanking 2,6-diisopropyl­phenyl substituents and the tetra­phenyl­borate counter-ion, anion inter­actions with the imidazolidinium H atom in the 2-position are not observed, also a first for this class of ortho-alkyl-substituted Arduengo-type carbene precursors

1,1,2,2-Tetra­kis(1,3-benzoxazol-2-yl)ethene

The title compound, C30H16N4O4, reveals crystallographic and mol­ecular symmetry and accordingly the asymmetric unit comprises one half-mol­ecule. The dihedral angle between the planes of the two geminal benzoxazole rings is 74.39 (5)°. The packing features weak C—H⋯N and π–π inter­actions [centroid–centroid distance = 3.652 (1) Å]

Cell abundance aware deep learning for cell detection on highly imbalanced pathological data

Automated analysis of tissue sections allows a better understanding of disease biology, and may reveal biomarkers that could guide prognosis or treatment selection. In digital pathology, less abundant cell types can be of biological significance, but their scarcity can result in biased and sub-optimal cell detection model. To minimize the effect of cell imbalance on cell detection, we proposed a deep learning pipeline that considers the abundance of cell types during model training. Cell weight images were generated, which assign larger weights to less abundant cells and used the weights to regularize Dice overlap loss function. The model was trained and evaluated on myeloma bone marrow trephine samples. Our model obtained cell detection F1-score of 0.78, a 2% increase compared to baseline models, and it outperformed baseline models at detecting rare cell types. We found that scaling deep learning loss function by the abundance of cells improves cell detection performance. Our results demonstrate the importance of incorporating domain knowledge on deep learning methods for pathological data with class imbalance

1,3-Dimesitylimidazolidinium tetra­chloridogold(III) dichloro­methane solvate

The title ionic compound, (C21H27N2)[AuCl4]·CH2Cl2, was obtained from the reaction of 1,3-dimesitylimidazolidinium chloride with t-BuOK and a solution of AuCl3 in tetra­hydro­furan. In the crystal structure, numerous weak C—H⋯Cl hydrogen bonds form double layers parallel to (100), which are further stabilized by π–π inter­actions between mesitylene rings [centroid–centroid distance = 4.308 (4) Å], resulting in the formation of a three-dimensional supra­molecular assembly