Bringing results to fruition through publication: An analysis of a peer-reviewed, open access and context-focused journal's editorial practice

When it comes to helping to shape sustainable development, research is most useful when it bridges the science–implementation/management gap and when it brings development specialists and researchers into a dialogue (Hurni et al. 2004); can a peer-reviewed journal contribute to this aim? In the classical system for validation and dissemination of scientific knowledge, journals focus on knowledge exchange within the academic community and do not specifically address a ‘life-world audience’. Within a North-South context, another knowledge divide is added: the peer review process excludes a large proportion of scientists from the South from participating in the production of scientific knowledge (Karlsson et al. 2007). Mountain Research and Development (MRD) is a journal whose mission is based on an editorial strategy to build the bridge between research and development and ensure that authors from the global South have access to knowledge production, ultimately with a view to supporting sustainable development in mountains. In doing so, MRD faces a number of challenges that we would like to discuss with the td-net community, after having presented our experience and strategy as editors of this journal. MRD was launched in 1981 by mountain researchers who wanted mountains to be included in the 1992 Rio process. In the late 1990s, MRD realized that the journal needed to go beyond addressing only the scientific community. It therefore launched a new section addressing a broader audience in 2000, with the aim of disseminating insights into, and recommendations for, the implementation of sustainable development in mountains. In 2006, we conducted a survey among MRD’s authors, reviewers, and readers (Wymann et al. 2007): respondents confirmed that MRD had succeeded in bridging the gap between research and development. But we realized that MRD could become an even more efficient tool for sustainability if development knowledge were validated: in 2009, we began submitting ‘development’ papers (‘transformation knowledge’) to external peer review of a kind different from the scientific-only peer review (for ‘systems knowledge’). At the same time, the journal became open access in order to increase the permeability between science and society, and ensure greater access for readers and authors in the South. We are currently rethinking our review process for development papers, with a view to creating more space for communication between science and society, and enhancing the co-production of knowledge (Roux 2008). Hopefully, these efforts will also contribute to the urgent debate on the ‘publication culture’ needed in transdisciplinary research (Kueffer et al. 2007).

Interactions between dorsolateral and ventromedial prefrontal cortex underlie context-dependent stimulus valuation in goal-directed choice.

External circumstances and internal bodily states often change and require organisms to flexibly adapt valuation processes to select the optimal action in a given context. Here, we investigate the neurobiology of context-dependent valuation in 22 human subjects using functional magnetic resonance imaging. Subjects made binary choices between visual stimuli with three attributes (shape, color, and pattern) that were associated with monetary values. Context changes required subjects to deviate from the default shape valuation and to integrate a second attribute in order to comply with the goal to maximize rewards. Critically, this binary choice task did not involve any conflict between opposing monetary, temporal, or social preferences. We tested the hypothesis that interactions between regions of dorsolateral and ventromedial prefrontal cortex (dlPFC; vmPFC) implicated in self-control choices would also underlie the more general function of context-dependent valuation. Consistent with this idea, we found that the degree to which stimulus attributes were reflected in vmPFC activity varied as a function of context. In addition, activity in dlPFC increased when context changes required a reweighting of stimulus attribute values. Moreover, the strength of the functional connectivity between dlPFC and vmPFC was associated with the degree of context-specific attribute valuation in vmPFC at the time of choice. Our findings suggest that functional interactions between dlPFC and vmPFC are a key aspect of context-dependent valuation and that the role of this network during choices that require self-control to adjudicate between competing outcome preferences is a specific application of this more general neural mechanism.

Automatic multi-resolution shape modeling of multi-organ structures.

Point Distribution Models (PDM) are among the most popular shape description techniques and their usefulness has been demonstrated in a wide variety of medical imaging applications. However, to adequately characterize the underlying modeled population it is essential to have a representative number of training samples, which is not always possible. This problem is especially relevant as the complexity of the modeled structure increases, being the modeling of ensembles of multiple 3D organs one of the most challenging cases. In this paper, we introduce a new GEneralized Multi-resolution PDM (GEM-PDM) in the context of multi-organ analysis able to efficiently characterize the different inter-object relations, as well as the particular locality of each object separately. Importantly, unlike previous approaches, the configuration of the algorithm is automated thanks to a new agglomerative landmark clustering method proposed here, which equally allows us to identify smaller anatomically significant regions within organs. The significant advantage of the GEM-PDM method over two previous approaches (PDM and hierarchical PDM) in terms of shape modeling accuracy and robustness to noise, has been successfully verified for two different databases of sets of multiple organs: six subcortical brain structures, and seven abdominal organs. Finally, we propose the integration of the new shape modeling framework into an active shape-model-based segmentation algorithm. The resulting algorithm, named GEMA, provides a better overall performance than the two classical approaches tested, ASM, and hierarchical ASM, when applied to the segmentation of 3D brain MRI.