Household food security status and its associated factors among pensioners in Arba Minch town, South Ethiopia

Predicting the binding mode of flexible polypeptides to proteins is an important task that falls outside the domain of applicability of most small molecule and protein-protein docking tools. Here, we test the small molecule flexible ligand docking program Glide on a set of 19 non-α-helical peptides and systematically improve pose prediction accuracy by enhancing Glide sampling for flexible polypeptides. In addition, scoring of the poses was improved by post-processing with physics-based implicit solvent MM-GBSA calculations. Using the best RMSD among the top 10 scoring poses as a metric, the success rate (RMSD ≤ 2.0 Å for the interface backbone atoms) increased from 21% with default Glide SP settings to 58% with the enhanced peptide sampling and scoring protocol in the case of redocking to the native protein structure. This approaches the accuracy of the recently developed Rosetta FlexPepDock method (63% success for these 19 peptides) while being over 100 times faster. Cross-docking was performed for a subset of cases where an unbound receptor structure was available, and in that case, 40% of peptides were docked successfully. We analyze the results and find that the optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach.

Background Access to sufficient food is essential for household welfare as well as for accomplishing other development activities. Households with insufficient access to food often face other challenges related to food insecurity including poor health and a decline in productivity. These challenges can often create a vicious circle whereby households are unable to produce enough food even during a good crop season. Thus, this study aimed to determine the magnitude of food insecurity and its determinants in rural households of Farta District, Northwest Ethiopia. Methods A community based cross-sectional study was conducted from September to October 2012. Household heads were recruited using a multistage random sampling technique. Data were collected by face-to-face interviews using the Household Food Insecurity Access Scale (HFIAS) tool after verbal informed consent. Data were entered to Epi info 2002 and exported to SPSS version 16 for analysis. Multiple logistic regressions were fitted and odds ratios with 95% confidence intervals were calculated to identify associated factors and control confounding effect. Results A total of 836 households were included in this study. Nearly three quarters of the households (70.7%) had food insecurity. Households headed by females (AOR = 3.18, 95% CI:1.08, 15.21), lack of education (AOR = 2.59, 95% CI: 1.46, 4.60), family size of 4-7 (AOR = 2.39, 95% CI: 1.21,4.70), family size of >7 (AOR = 13.23,95% CI:6.18, 28.32), few or absence of livestock (AOR = 5.60, 95% CI:1.28, 24.43), absence of income from off-farm activities (AOR = 3.12, 95% CI:1.53, 6.36), lack of irrigation (AOR = 3.54, 95% CI:2.14, 5.18) and lack of perennial income (AOR = 3.15, 95% CI:1.88, 5.27) were factors associated with food insecurity. Conclusions This study revealed that most households of the district were food insecure. Hence, the promotion of contraceptive use, off-farm employment activities and the development of small scale irrigation are important recommendations to reduce food insecurity.