Contribution mapping: a method for mapping the contribution of research to enhance its impact.

Background: At a time of growing emphasis on both the use of research and accountability, it is important for research funders, researchers and other stakeholders to monitor and evaluate the extent to which research contributes to better action for health, and find ways to enhance the likelihood that beneficial contributions are realized. Past attempts to assess research 'impact' struggle with operationalizing 'impact', identifying the users of research and attributing impact to research projects as source. In this article we describe Contribution Mapping, a novel approach to research monitoring and evaluation that aims to assess contributions instead of impacts. The approach focuses on processes and actors and systematically assesses anticipatory efforts that aim to enhance contributions, so-called alignment efforts. The approach is designed to be useful for both accountability purposes and for assisting in better employing research to contribute to better action for health.Methods: Contribution Mapping is inspired by a perspective from social studies of science on how research and knowledge utilization processes evolve. For each research project that is assessed, a three-phase process map is developed that includes the main actors, activities and alignment efforts during research formulation, production and knowledge extension (e.g. dissemination and utilization). The approach focuses on the actors involved in, or interacting with, a research project (the linked actors) and the most likely influential users, who are referred to as potential key users. In the first stage, the investigators of the assessed project are interviewed to develop a preliminary version of the process map and first estimation of research-related contributions. In the second stage, potential key-users and other informants are interviewed to trace, explore and triangulate possible contributions. In the third stage, the presence and role of alignment efforts is analyzed and the preliminary results are shared with relevant stakeholders for feedback and validation. After inconsistencies are clarified or described, the results are shared with stakeholders for learning, improvement and accountability purposes.Conclusion: Contribution Mapping provides an interesting alternative to existing methods that aim to assess research impact. The method is expected to be useful for research monitoring, single case studies, comparing multiple cases and indicating how research can better be employed to contribute to better action for health. © 2012 Kok and Schuit; licensee BioMed Central Ltd

PANGEA: pipeline for analysis of next generation amplicons

High-throughput DNA sequencing can identify organisms and describe population structures in many environmental and clinical samples. Current technologies generate millions of reads in a single run, requiring extensive computational strategies to organize, analyze and interpret those sequences. A series of bioinformatics tools for high-throughput sequencing analysis, including preprocessing, clustering, database matching and classification, have been compiled into a pipeline called PANGEA. The PANGEA pipeline was written in Perl and can be run on Mac OSX, Windows or Linux. With PANGEA, sequences obtained directly from the sequencer can be processed quickly to provide the files needed for sequence identification by BLAST and for comparison of microbial communities. Two different sets of bacterial 16S rRNA sequences were used to show the efficiency of this workflow. The first set of 16S rRNA sequences is derived from various soils from Hawaii Volcanoes National Park. The second set is derived from stool samples collected from diabetes-resistant and diabetes-prone rats. The workflow described here allows the investigator to quickly assess libraries of sequences on personal computers with customized databases. PANGEA is provided for users as individual scripts for each step in the process or as a single script where all processes, except the χ(2) step, are joined into one program called the ‘backbone’

Ketogenic diet improves behaviors in a maternal immune activation model of autism spectrum disorder

Prenatal factors influence autism spectrum disorder (ASD) incidence in children and can increase ASD symptoms in offspring of animal models. These may include maternal immune activation (MIA) due to viral or bacterial infection during the first trimesters. Unfortunately, regardless of ASD etiology, existing drugs are poorly effective against core symptoms. For nearly a century a ketogenic diet (KD) has been used to treat seizures, and recent insights into mechanisms of ASD and a growing recognition that immune/inflammatory conditions exacerbate ASD risk has increased interest in KD as a treatment for ASD. Here we studied the effects of KD on core ASD symptoms in offspring exposed to MIA. To produce MIA, pregnant C57Bl/6 mice were injected with the viral mimic polyinosinic-polycytidylic acid; after weaning offspring were fed KD or control diet for three weeks. Consistent with an ASD phenotype of a higher incidence in males, control diet-fed MIA male offspring were not social and exhibited high levels of repetitive self-directed behaviors; female offspring were unaffected. However, KD feeding partially or completely reversed all MIA-induced behavioral abnormalities in males; it had no effect on behavior in females. KD-induced metabolic changes of reduced blood glucose and elevated blood ketones were quantified in offspring of both sexes. Prior work from our laboratory and others demonstrate KDs improve relevant behaviors in several ASD models, and here we demonstrate clear benefits of KD in the MIA model of ASD. Together these studies suggest a broad utility for metabolic therapy in improving core ASD symptoms, and support further research to develop and apply ketogenic and/or metabolic strategies in patients with ASD