Promoting nutrition sensitive and climate smart agriculture through increased use of traditional underutilised species in the Pacific

Poster presented at Tropentag 2014. International Conference on Research on Food Security, Natural Resource Management and Rural Development. "Bridging the Gap between Increasing Knowledge and Decreasing Resources" Prague (Czech Republic) Sep 17-19 2014

QUASI-EXPERIMENTAL DESIGNS FOR MEASURING IMPACTS OF DEVELOPMENTAL HIGHWAYS IN RURAL AREAS

Quasi-experimental techniques were developed to provide decision-making tools for documenting the impacts of developmental highways in rural areas. Regression discontinuity analysis (RDA) with limited observations was used to compare economic changes in highway counties to those in adjacent and non-adjacent control counties. The RDA models found statistically significant changes in population, per capita income, and taxable sales related to highway development. The study found that some counties benefited from developmental highways, some were unchanged, while some experienced economic decline. RDA models with adjacent controls had better explanatory powers while those with non-adjacent controls were more sensitive to highway-related changes in economic activity. When significant non-highway activities were present, adjacent control models may have understated highway-related impacts, while non-adjacent control models may have overstated these impacts. Arguments for using adjacent and non-adjacent experimental designs are discussed.Community/Rural/Urban Development,

Combining algorithms to predict bacterial protein sub-cellular location: Parallel versus concurrent implementations

We describe a novel and potentially important tool for candidate subunit vaccine selection through in silico reverse-vaccinology. A set of Bayesian networks able to make individual predictions for specific subcellular locations is implemented in three pipelines with different architectures: a parallel implementation with a confidence level-based decision engine and two serial implementations with a hierarchical decision structure, one initially rooted by prediction between membrane types and another rooted by soluble versus membrane prediction. The parallel pipeline outperformed the serial pipeline, but took twice as long to execute. The soluble-rooted serial pipeline outperformed the membrane-rooted predictor. Assessment using genomic test sets was more equivocal, as many more predictions are made by the parallel pipeline, yet the serial pipeline identifies 22 more of the 74 proteins of known location

Toward bacterial protein sub-cellular location prediction: single-class discrimminant models for all gram- and gram+ compartments

Based on Bayesian Networks, methods were created that address protein sequence-based bacterial subcellular location prediction. Distinct predictive algorithms for the eight bacterial subcellular locations were created. Several variant methods were explored. These variations included differences in the number of residues considered within the query sequence - which ranged from the N-terminal 10 residues to the whole sequence - and residue representation - which took the form of amino acid composition, percentage amino acid composition, or normalised amino acid composition. The accuracies of the best performing networks were then compared to PSORTB. All individual location methods outperform PSORTB except for the Gram+ cytoplasmic protein predictor, for which accuracies were essentially equal, and for outer membrane protein prediction, where PSORTB outperforms the binary predictor. The method described here is an important new approach to method development for subcellular location prediction. It is also a new, potentially valuable tool for candidate subunit vaccine selection

Multi-class subcellular location prediction for bacterial proteins

Two algorithms, based on Bayesian Networks (BNs), for bacterial subcellular location prediction, are explored in this paper: one predicts all locations for Gram+ bacteria and the other all locations for Gram- bacteria. Methods were evaluated using different numbers of residues (from the N-terminal 10 residues to the whole sequence) and residue representation (amino acid-composition, percentage amino acid-composition or normalised amino acid-composition). The accuracy of the best resulting BN was compared to PSORTB. The accuracy of this multi-location BN was roughly comparable to PSORTB; the difference in predictions is low, often less than 2%. The BN method thus represents both an important new avenue of methodological development for subcellular location prediction and a potentially value new tool of true utilitarian value for candidate subunit vaccine selection

A predictor of membrane class: Discriminating α-helical and β-barrel membrane proteins from non-membranous proteins

Accurate protein structure prediction remains an active objective of research in bioinformatics. Membrane proteins comprise approximately 20% of most genomes. They are, however, poorly tractable targets of experimental structure determination. Their analysis using bioinformatics thus makes an important contribution to their on-going study. Using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we have addressed the alignment-free discrimination of membrane from non-membrane proteins. The method successfully identifies prokaryotic and eukaryotic α-helical membrane proteins at 94.4% accuracy, β-barrel proteins at 72.4% accuracy, and distinguishes assorted non-membranous proteins with 85.9% accuracy. The method here is an important potential advance in the computational analysis of membrane protein structure. It represents a useful tool for the characterisation of membrane proteins with a wide variety of potential applications

Alpha helical trans-membrane proteins: Enhanced prediction using a Bayesian approach

Membrane proteins, which constitute approximately 20% of most genomes, are poorly tractable targets for experimental structure determination, thus analysis by prediction and modelling makes an important contribution to their on-going study. Membrane proteins form two main classes: alpha helical and beta barrel trans-membrane proteins. By using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we addressed α-helical topology prediction. This method has accuracies of 77.4% for prokaryotic proteins and 61.4% for eukaryotic proteins. The method described here represents an important advance in the computational determination of membrane protein topology and offers a useful, and complementary, tool for the analysis of membrane proteins for a range of applications

LIPPRED: A web server for accurate prediction of lipoprotein signal sequences and cleavage sites

Bacterial lipoproteins have many important functions and represent a class of possible vaccine candidates. The prediction of lipoproteins from sequence is thus an important task for computational vaccinology. Naïve-Bayesian networks were trained to identify SpaseII cleavage sites and their preceding signal sequences using a set of 199 distinct lipoprotein sequences. A comprehensive range of sequence models was used to identify the best model for lipoprotein signal sequences. The best performing sequence model was found to be 10-residues in length, including the conserved cysteine lipid attachment site and the nine residues prior to it. The sensitivity of prediction for LipPred was 0.979, while the specificity was 0.742. Here, we describe LipPred, a web server for lipoprotein prediction; available at the URL: http://www.jenner.ac.uk/LipPred/. LipPred is the most accurate method available for the detection of SpaseIIcleaved lipoprotein signal sequences and the prediction of their cleavage sites

TATPred:a Bayesian method for the identification of twin arginine translocation pathway signal sequences

The twin arginine translocation (TAT) system ferries folded proteins across the bacterial membrane. Proteins are directed into this system by the TAT signal peptide present at the amino terminus of the precursor protein, which contains the twin arginine residues that give the system its name. There are currently only two computational methods for the prediction of TAT translocated proteins from sequence. Both methods have limitations that make the creation of a new algorithm for TAT-translocated protein prediction desirable. We have developed TATPred, a new sequence-model method, based on a Nave-Bayesian network, for the prediction of TAT signal peptides. In this approach, a comprehensive range of models was tested to identify the most reliable and robust predictor. The best model comprised 12 residues: three residues prior to the twin arginines and the seven residues that follow them. We found a prediction sensitivity of 0.979 and a specificity of 0.942

Educational outcomes of Helping Babies Breathe training at a community hospital in Honduras

Objectives: Helping Babies Breathe is an evidence-based curriculum designed to teach basic neonatal resuscitation in low-resource countries. The purpose of this study was to evaluate the acquisition of knowledge and skills following this training and correlation of learner characteristics to performance in a Spanish-speaking setting. Methods: Thirty-one physicians and 39 nurses completed Helping Babies Breathe training at a Honduran community hospital. Trainee knowledge and skills were evaluated before and after the training using a multiple-choice questionnaire, bag-mask ventilation skills test, and two objective structured clinical exams (OSCEs). Linear mixed-effects models were used to analyze assessment scores pre- and post-training by profession (physician or nurse) while controlling for covariates. Results: Helping Babies Breathe training resulted in significant increases in mean scores for the multiple-choice question test, bag-mask ventilation skills test, and OSCE B. Time to initiation of effective bag-mask ventilation decreased from a mean of 74.8 to 68.4 s. Despite this improvement in bag-mask ventilation, only 42 % of participants were able to initiate effective bag-mask ventilation within the Golden Minute. Although physicians scored higher on the pre-test multiple-choice questions and bag-mask ventilation, nurses demonstrated a greater mean difference in scores after training. OSCE B scores pre- and post-training increased similarly between professions. Nurses’ and physicians’ performance in simulation was not significantly different after the training. Assessment scores and course feedback indicated a need for more skills practice, particularly with bag-mask ventilation. Conclusions: When evaluated immediately after an initial workshop, Helping Babies Breathe training resulted in significant gains in neonatal resuscitation knowledge and skills. Following training, nurses, who commonly do not perform these skills in real-life situations, were able to perform at a similar level to physicians. Further studies are necessary to determine how to sustain this knowledge and skills over time, tailor the course to learner characteristics, and whether this training translates into improvements in clinical practice