Toronto Addis Ababa Academic Collaboration: A Relational, Partnership Model for Building Educational Capacity Between a High- and Low-Income University.

Educational partnerships between academic health sciences centers in high- and low-resource settings are often formed as attempts to address health care disparities. In this Perspective, the authors describe the Toronto Addis Ababa Academic Collaboration (TAAAC), an educational partnership between the University of Toronto and Addis Ababa University. The TAAAC model was designed to help address an urgent need for increased university faculty to teach in the massive expansion of universities in Ethiopia. As TAAAC has developed and expanded, faculty at both institutions have recognized that the need to understand contextual factors and to have clarity about funding, ownership, expertise, and control are essential elements of these types of collaborative initiatives. In describing the TAAAC model, the authors aim to contribute to wider conversations and deeper theoretical understandings about these issues.

The importance of accounting for larval detectability in mosquito habitat-association studies.

BACKGROUND: Mosquito habitat-association studies are an important basis for disease control programmes and/or vector distribution models. However, studies do not explicitly account for incomplete detection during larval presence and abundance surveys, with potential for significant biases because of environmental influences on larval behaviour and sampling efficiency. METHODS: Data were used from a dip-sampling study for Anopheles larvae in Ethiopia to evaluate the effect of six factors previously associated with larval sampling (riparian vegetation, direct sunshine, algae, water depth, pH and temperature) on larval presence and detectability. Comparisons were made between: (i) a presence-absence logistic regression where samples were pooled at the site level and detectability ignored, (ii) a success versus trials binomial model, and (iii) a presence-detection mixture model that separately estimated presence and detection, and fitted different explanatory variables to these estimations. RESULTS: Riparian vegetation was consistently highlighted as important, strongly suggesting it explains larval presence (-). However, depending on how larval detectability was estimated, the other factors showed large variations in their statistical importance. The presence-detection mixture model provided strong evidence that larval detectability was influenced by sunshine and water temperature (+), with weaker evidence for algae (+) and water depth (-). For larval presence, there was also some evidence that water depth (-) and pH (+) influenced site occupation. The number of dip-samples needed to determine if larvae were likely present at a site was condition dependent: with sunshine and warm water requiring only two dips, while cooler water and cloud cover required 11. CONCLUSIONS: Environmental factors influence true larval presence and larval detectability differentially when sampling in field conditions. Researchers need to be more aware of the limitations and possible biases in different analytical approaches used to associate larval presence or abundance with local environmental conditions. These effects can be disentangled using data that are routinely collected (i.e., multiple dip samples at each site) by employing a modelling approach that separates presence from detectability.

Genetic diversity and differentiation of cultivated amochi (Arisaema schimperianum Schott) in Ethiopia as revealed by AFLP markers.

Amochi (Arisaema schimperianum Schott) is an off-season crop plant in southern Ethiopia, grown during the dry season on residual moisture, for its edible tubers. It has gained importance as a "security crop" especially during the years of moisture stress and food shortage. Amochi is irritating in contact to the skin. Removal of this effect is an important question for breeding. As the first step, however we attempt to establish base line information of its breeding system and genetic variability using AFLPs. The extent of genetic differentiation among 11 populations (96 individuals) of amochi sampled along altitudinal gradients that varied from 1700 to 3200 m a.s.l. was investigated. The populations were classified in to three altitudinal groups: lowland (1700 to 2200 m a.s.l.), central-highland (2201 to 2600 m a.s.l.) and highland (2601 to 3200 m a.s.l.). Polymorphic loci (167) scored from four primer pair combinations, were used for principal component analysis (PCA), and analysis of molecular variance (AMOVA). Both PCA and unweighed pair group with arithmetic mean (UPGMA) clearly differentiated populations into their respective altitude groups, with large genetic distances. AMOVA analysis revealed 70.5%, 16.7% and 12.8% variability between altitude groups, between populations and within populations respectively. Average diversity indices within populations were also low. Since the largest proportion of variation is located between altitude groups, rather than within populations, we suggest future studies on the chemical composition, low irritation, and other desirable traits should consider populations from different altitude ranges.