Metagenomics, Metadata, Metaanalysis, Models and Metainfrastructure (M5) Roundtable at the 13th International Society for Microbial Ecology Meeting in Seattle, WA, USA August 22-27, 2010. The Genomic Standards Consortium (GSC) hosted this meeting as a community engagement exercise to describe the GSC to the microbial ecology community during this important international meeting. The roundtable included five talks given by members of the GSC, and was followed by audience participation in the form of a roundtable discussion. This report summarizes this event. Further information on the GSC and its range of activities can be found at http://www.gensc.org.This report summarizes the proceedings of the  Bacteria, and for a much smaller number of the Archaea. Yet, by isolating microbial DNA from different ecosystems, we have demonstrated that these cultured representatives barely describe 30% of the phylum-level phylogenetic diversity that exists. Indeed, cultured isolates may represent less than 5% of the total microbial diversity in any given environment [Research in Microbial Ecology has entered a brave new world. For a few hundred years we have adequately understood the concept of microbial life; however, we have lacked the tools to explore that life in detail. Using microscopes and culturing techniques, we have managed to describe approximately 9-10 thousand species of the 30), with around 100 trillion existing on or in every human being. With this vast array of life comes potentially indescribable diversity. In part, this diversity is driven by the length of time  these organisms have had to evolve. However, the rate at which they evolve must also play a significant role. The species concept so important for our understanding of eukaryotic systems takes a lesser role and may be less well defined in Bacteria and Archaea. The so-called pan-genome concept [In terms of investigating the microbiome of a sample,  it is the numbers of bacteria involved that are truly staggering. It is estimated that there are more than 1 nonillion microbial cells on planet earth  is just learning how to explain functional and phylogenetic diversity appropriately. Strikingly, the more we uncover the more we have to adjust our metrics to cope. Whereas the phenotypic characteristics of eukaryotic macrofauna and macroflora enable relatively simple comparative analysis to be made among multiple projects, the same cannot be said for microbial ecology. The lack of understanding in how we delineate species or functions has led to a considerable gap in our ability for comparison across microbial projects.   It is therefore essential that we work as a community to explore ways of rectifying this.There are currently some 8,000 metagenomic projects available. However, as these have been run on different sequencing platforms, with different amplification strategies, different PCR primers, and different sampling regimens it is virtually impossible to make any comparisons between them.   In addition, very few projects are submitted to public repositories with any physical and chemical parameter data, which could help to describe the environment from which the samples were isolated.  Comprehensive metadata, which could be used to establish which projects can be more usefully compared, is generally not available; this lack of contextual information significantly compounds the difficulties.  Specifically, the metadata needs to include at least the minimum information that would enable us to explore ecological metrics among samples.  This is essential if we are to start exploring the factors that shape microbial ecology, which directly or indirectly will therefore influence all biochemical and climatic events on the planet. Doing so would allow us to turn the data from metagenomics research into ecological knowledge.There are currently three big challenges to overcome for microbial ecologists. Firstly, we must understand how to explore systems biology, community interactions and modeling, i.e. we need to know what parameters are required. Secondly, we must explore how to use these metrics and models to explore predictive biology, in much the same way as we physical scientists make predictions and extend the understanding of  physical world.    Third, only through deep exploration of natural microbial communities will we discover the "Dark Matter" of the microbial world and achieve the full potential for societal applications of novel microbial biology and biochemistry.th Meeting and promote increased future engagement. The Genomic Standards Consortium exists to improve the utility of public collections of completed genomes and metagenomes, through the development and dissemination of standards and data integration technologies. The GSC is an open-member international community consisting of biologists, bioinformaticians and computer scientists that includes representatives from EMBL, EMBL-EBI, DDBJ, NCBI, and major sequencing centers including JCVI, JGI, and the Wellcome Trust Sanger Institute (WTSI). Core work of the GSC includes the development and implementation of minimal information checklists. The first major accomplishment of the GSC was the publication of the ‘Minimum Information about a (Meta) Genome Sequence’ (MIGS/MIMS) specification [The M5 roundtable aimed to introduce the Genomic Standards Consortium to the microbial ecology community at the International Symposium on Microbial Ecology, 13fication , MG-RASTfication ), and isfication  ), the Gfication ; ) and Hfication  ). AlongThe topics presented and discussed at this roundtable were broad and focused on the value of richer stores of metadata associated with public data and ongoing GSC projects to improve metadata capture and exchange. The ultimate goal of the roundtable was to attract experimentalists in microbial ecology to appreciate and review  the recommendations of the GSC,  understand the rationale behind the development of new standards, and ultimately, participate in their usage  We, therefore, divided the roundtable into two parts, beginning with a set of five talks and following with an open forum in which the community could ask questions regarding GSC activities.Rob Knight (University of Colorado) provided numerous examples of the power of having metadata associated with environmental and host-associated 16S rRNA amplicon datasets. This umbrella talk helped to explore the ramifications of data standardization and potential routes for implementation. Importantly, this talk was from an experimentalist’s perspective and helped to drive forward the concepts from that standpoint. Dawn Field (Centre for Ecology and Hydrology), the president of the GSC, described the history of the organization and gave an overview of all of the current organizational efforts. Jack Gilbert  provided an overview of a new mega sequencing initiative, the Earth Microbiome Project [Bacterial and Archaeal type strains from culture collections around the world. Both of these projects highlighted the vast information flux, which is become real for microbial ecology. With vast data come great responsibility; it is essential that these projects are made available for the whole community, and as such, it is vital that appropriate reporting standards are implanted. Project , which a Project , which aFolker Meyer  explored the computational requirements for coping with the data wave. The fast growth of data will require the various analysis providers to not only collaborate in order to avoid duplication of effort but also improve the computational cost of their various analysis steps dramatically. The M5 group  is working together to develop the standards required for sharing data and computational results in a democratized sequencing world, standards that will add transparency to and enable sharing among the providers of analysis like IMG/M [Pelin Yilmaz (MPI-Bremen), explained the  newest GSC minimum information checklist and how it should be used. The objective of the “Minimum Information about an Environmental Sequence” (MIENS) checklist is to aid in the description of phylogenetic and functional marker genes from all three domains of life, from surveys to cultured organisms and independent of the sequencing platform used. The talk highlighted the pressing need for better contextual metadata standards for marker genes in the age of mass-sequencing in microbial ecology, and briefly showed the GSC roadmap to tackle with this issue with the outcome as MIENS. The core standard was presented, with a focus on examples of compliant datasets and submission of compliant data to INSDC databases. The audience was encouraged to give feedback and participate in the further development and applications of MIENS.ke IMG/M , MG-RASTke IMG/M . StandarA wide disciplinary range of microbial ecologists participated in the roundtable, which was well attended (approximately 60-70 attendees), especially considering the many roundtables running in parallel.  A lively discussion ensued and a sense that the work of the GSC had a growing place in the world of microbial ecology was established. There was discussion about the practicalities of complying with the GSC family of standards and an emphasis on the fact that it was the responsibility of the GSC to make compliance as easy as possible for experimentalists. This would need to be achieved through documentation, development of tools and re-engineering of the core databases in this field. It was agreed that the vision of a future store of richly annotated genomes, metagenomes and marker sequences was worth working for, but would need both community buy-in and engagement as well as dedicated funding.Being chosen for an opportunity for the GSC to host a roundtable at ISME13, especially given such fierce competition for these fora, demonstrates that the microbial ecology community understands the importance of data standards in recording and interpreting biological information. Importantly, the community further understands that when working towards descriptive and predictive models that will require data generation from multiple sources, this endeavor is imperative.  The GSC looks forward to future opportunities in engaging the experimental community and advancing the potential of microbial ecology.


BMC Medicine, Roberts and colleagues have shown that breastfeeding interventions can have a significant role in reducing inequity in child health. With the proper attention paid to overcoming the barriers to scaling up breastfeeding interventions, deployment of effective interventions in health facilities and the community, and improvements in support for breastfeeding interventions across society, many countries that are struggling to meet their millennium development goals could make significant gains in child survival and inequity.Although the number of child deaths has declined globally over the past 20 years, many countries still lag behind their millennium development goal targets, and inequity in child health remains a pernicious problem both between and within countries. Breastfeeding is a key intervention to reduce child mortality, and in an article published in http://www.biomedcentral.com/1741-7015/11/254/abstract.Please see related research:  Significant progress has been made towards achieving Millennium Development Goal 4 (MDG 4), which pertains to reducing child mortality, but much more is still to be done . Since tet al. show the potential for breastfeeding interventions to reduce inequity in child mortality [It is in this context that Roberts ortality . Robertsortality . Therefoortality . Breastfortality .et al. present a mixed picture of success in this regard. Some countries have made remarkable progress in scaling up breastfeeding as a child health intervention since 1990: in Malawi, for example, rates of exclusive breastfeeding in the first 5 months of life have increased from 5.0% in 1990 to 49.7% in 2010, and over this time period the rates of predominant breastfeeding in some countries have doubled from a low base. However, many countries, often those with the highest rates of child mortality, have regressed during this time. In some countries, concerns about mother-to-child transmission of HIV are likely to contribute to low rates of breastfeeding [For the benefits of breastfeeding to be realized, however, rates of breastfeeding need to be high in all countries, and Roberts tfeeding . In thestfeeding . UnderstWhat is to be done to encourage breastfeeding in these countries? What interventions are complementary to and support breastfeeding, and what intersectoral gains need to be made to support breastfeeding interventions in those countries that are furthest from achieving MDG 4?The efficacy of basic interventions to promote breastfeeding is well established , and theet al. rightly indicate the role of legislative changes and the media in encouraging and supporting breastfeeding, and this shows the important role of intersectoral collaboration in building an environment supportive of the full benefits of breastfeeding. Legislative support for public breastfeeding, family friendly workplace policies, strict standards on the content and advertising of baby foods for complementary feeding should become commonplace and acceptable community-level interventions in low-income nations.Breastfeeding is also a nutrition intervention with significant developmental and welfare benefits. The success of breastfeeding programs is also tied to the quality of maternal nutrition, and to the level of social support for breastfeeding. Roberts Because breastfeeding does not rely on technology investment or extensive health infrastructure, it is also amenable to community based and grassroots initiatives to improve uptake , and theet al. have identified the powerful equity benefits of scaling up breastfeeding, and quantified its significant contribution to preventing illness and mortality in low-income and middle-income countries. With this knowledge, we can better prioritize funding and system organization both for improving child health and for reducing inequity in illness and mortality in some of the poorest countries in the world. Nonetheless, challenges to scaling up breastfeeding remain. Only through careful attention to what is known to be effective and cost-effective, coupled with strategic use of multisectoral agents and cooperation across society, can we realize the large benefits of breastfeeding’s unique contribution to reducing the burden of disease and inequity in disease distribution in low-income and middle-income countries. With the deadline for the MDGs approaching and many countries still lagging on the key indicators of child health, now is the time to redouble efforts to scale up this cheap, reliable and equitable intervention, and to achieve the promise of better and more equitable health made in 2000.Roberts The authors declare that they have no competing interests.Both authors contributed to conception of the article. SG drafted the article. Both authors were involved in editing and revision of the manuscript and both agreed to its publication.


Increasing numbers of homes are being destroyed by wildfire in the wildland-urban interface. With projections of climate change and housing growth potentially exacerbating the threat of wildfire to homes and property, effective fire-risk reduction alternatives are needed as part of a comprehensive fire management plan. Land use planning represents a shift in traditional thinking from trying to eliminate wildfires, or even increasing resilience to them, toward avoiding exposure to them through the informed placement of new residential structures. For land use planning to be effective, it needs to be based on solid understanding of where and how to locate and arrange new homes. We simulated three scenarios of future residential development and projected landscape-level wildfire risk to residential structures in a rapidly urbanizing, fire-prone region in southern California. We based all future development on an econometric subdivision model, but we varied the emphasis of subdivision decision-making based on three broad and common growth types: infill, expansion, and leapfrog. Simulation results showed that decision-making based on these growth types, when applied locally for subdivision of individual parcels, produced substantial landscape-level differences in pattern, location, and extent of development. These differences in development, in turn, affected the area and proportion of structures at risk from burning in wildfires. Scenarios with lower housing density and larger numbers of small, isolated clusters of development, i.e., resulting from leapfrog development, were generally predicted to have the highest predicted fire risk to the largest proportion of structures in the study area, and infill development was predicted to have the lowest risk. These results suggest that land use planning should be considered an important component to fire risk management and that consistently applied policies based on residential pattern may provide substantial benefits for future risk reduction. The recognition that homes are vulnerable to wildfire in the wildland-urban interface (WUI) has been established for decades ; but with a recent surge in structures burning, this issue is now receiving widespread attention in policy, the media, and the scientific literature. Single fire events, like those in Greece, Australia, southern California, and Colorado have resulted in scores of lost lives, thousands of structures burned, and billions of dollars in expenditures Traditional fire-risk reduction focuses heavily on fire suppression and manipulation of wildland vegetation to reduce hazardous fuels One alternative to traditional fire management that is receiving widespread attention is to prepare communities through the use of fire-safe building materials or creating defensible space around structures Land use planning is an alternative that represents a further shift in thinking, beyond the preparation of communities to withstand an inevitable fire, to preventing new residential structures from being exposed to fire in the first place. The reason homes are vulnerable to fires at the wildland-urban interface is a function of its very definition: “where homes meet or intermingle with wildland vegetation” Despite recent attention in the literature, land use planning for wildfire has yet to gain traction in practice, particularly in the United States. However, fire history has been used to help define land zoning for fire planning in Italy Research on how planning decisions contributed to structures burning in the past provides some guidance about what actions may work in the future. Analysis of hundreds of homes that burned in southern California the last decade showed that housing arrangement and location strongly influence fire risk, particularly through housing density and spacing, location along the perimeter of development, slope, and fire history These results suggest, for example, that placing new residential development within the boundaries of existing high-density developments or in areas of low relief may reduce fire risk. However, it is difficult to know whether broad-scale planning policies would actually result in the intended housing arrangement and pattern at the landscape scale, and whether those patterns would result in lower fire risk. Our objective here was to simulate three scenarios of future residential development, and to project wildfire risk, in a rapidly urbanizing and fire-prone region where we have studied past structure loss Although cities vary in extent, fragmentation, and residential density Do residential development policies reflecting broad growth types affect the resulting pattern and footprint of development across the landscape?Do differences in extent, location, and pattern of residential development translate into differences in wildfire risk, based on the current configuration of structures?Which development process, infill, expansion, or leapfrog, results in the lowest projected fire risk across the landscape?2. The region is topographically diverse with high levels of biodiversity, and urban development has been the primary cause of natural habitat loss and species extinction The study area included all land within the South Coast Ecoregion of San Diego County, California, US, encompassing an area of 8312 kmA host of alternative modeling approaches exist to simulate future land use scenarios Although we based the three development scenarios on generalized planning policies, we also wanted to ensure that the residential projections were realistic and adhered to current planning regulations. The objective of the econometric modeling was to estimate the likelihood that residential parcels will subdivide in the future. Therefore, we used a probit model to estimate the transition probability of each parcel based on a range of potential explanatory variables typically associated with parcel subdivision and housing development To develop the model of subdivision probability, we acquired GIS data of the county’s parcel boundaries in years 2005 and 2009 from the San Diego Association of Governments (SANDAG). The dependent variable was equal to 1 if a parcel subdivided between 2005 and 2009, and zero otherwise. Using these data layers we first determined which parcels were legally able to subdivide given current land use regulations. Minimum lot size restrictions are typically considered the most import restriction for determining future land use. We deemed a parcel eligible for subdivision if the current lot size was greater than twice the minimum legal size given the land class. To determine which parcels subdivided between 2005 and 2009, we queried parcel IDs where the total area was reduced by at least the minimum lot size between the two time periods. Finally, we were able to generate a suite of variables that determine the likelihood of a parcel developing in the future .http://www.sandag.org/index.asp?subclassid=100&fuseaction=home.subclasshome). Our explanatory variables included: parcel size, parcel size squared, six dummy variables which capture non-linear effects of parcel size, distance to the coast, distance to the coast squared; distance to city center and its square, current zoning, slope, land use, roads, if the parcel is in a protected area, if the parcel is in a development area, if the parcel is in the redevelopment area , expansion (development on the fringe of developed areas), and leapfrog (development in open areas). The model runs in four 5-year time steps from 2010 to 2030, and generates the spatial locations of new housing units in the county.Although development decisions could feasibly depend on fire risk, we did not model that here. There is no evidence that fire has influenced past regional planning decisions, so it was not used as an explanatory variable in the econometric model. Although we could have evaluated the potential for future development decisions to be based in part on fire risk, this would have required simulation of feedbacks between fires and probability of development. Because our objective in this study was to isolate the effects of the three distinct growth types, we modeled fire risk only as a function of development pattern and not vice versa.We constructed a complete spatial database of existing residential structures in the study area For future development scenarios, we wanted to allocate an equal number of new structures to the landscape. This was to ensure that any predicted difference in fire risk was a function of the arrangement and location of structures, not the total number of structures. Nevertheless, differences in the total number of structures were simulated with each of the 5-year time steps. We determined the number of housing units to add during the simulations based on projections made by San Diego County One output of the econometric model was the prediction of the maximum number of new dwelling units that could be added to each parcel. However, dwelling units may consist of apartments as well as single family homes. The mix of single and multifamily units in the region has remained relatively constant over time, and the overall trend has been a mix of roughly 1/3 multifamily and 2/3 single family units. Because the fire risk model is based on points representing structure locations across the landscape, regardless of the number of dwelling units per structure, we needed to generate a conversion factor from dwelling units to structures. We therefore defined a minimum lot size of 0.25 acre on which no more than a single structure could be built, regardless of the number of dwelling units in it . Then, once a parcel was selected for development by the model (see details below), we divided its total area by the maximum number of dwelling units to be added, according to the econometric model. If the result was larger than 0.25, we subdivided parcels according to the result. If not, we quantified how many 0.25 acre parcels fit into the original parcel, and generated the new parcel boundaries accordingly.Using the initial map of parcels (year 2010), we classified each parcel that was defined as eligible for development (in the previous stage) as suitable for one of the three planning scenarios described above, according to the number of developed parcels in its immediate neighborhood . We defined ‘developed parcels’ as ones that had more than one house per 20 acres (8.09 ha). Therefore, according to these density thresholds, we allowed some parcels with nonzero housing density to be considered as ‘undeveloped’ because these large, rural parcels might contain a single or a handful of houses but they exist within a large open area. In other words, the overall land cover of these parcels was effectively undeveloped, and we therefore assumed that development in adjacent parcels would be akin to development in open areas.We defined infill parcels as those that were completely surrounded by developed parcels. Expansion parcels had at least one neighboring parcel that was undeveloped; and leapfrog parcels were those with no developed parcels in their immediate surroundings. We reclassified the type of each available parcel in the same manner after each time step, to account for changing dynamics in the development map of the county.We conducted three simulations, one for each development scenario . In each simulation, all parcels were eligible to subdivide, regardless of their class. Therefore, to build a simulation for a specific scenario, we increased the development probability of parcels of the selected scenario by 20%, to favor their development compared to the other types of parcels, without prohibiting development in the other parcel types. This approach was necessary because the projected number of dwelling units was much larger than it would be possible to fit in infill and leapfrog class parcels solely. For example, as the spatial coverage of developed parcel expands, there is less contiguous area that is undevelopable and suitable for leapfrog development. Therefore, the scenarios are not exclusive, but rather a mixture of the three development types. Yet, in each scenario, there is one main type of development, and smaller amounts of development events of the other two types.Due to the immense computational demand of the simulations, we adopted a deterministic, rather than a stochastic approach to decide on which parcels were subdivided. After enhancing the transition probability according to the corresponding scenario, we ranked and then sorted all parcels according to their probability of subdivision. We then sequentially selected parcels, while simultaneously tallying the number of dwelling units in them, until the development target in that time step  was reached. Once the development target was reached, we moved to the next time step. After each time step, the remaining parcels that were still eligible for development were re-classified to development types according to the new spatial configuration of the landscape.Once a parcel was selected for subdivision, and the number of new parcels to develop in it was calculated (as detailed above), an equal-area spatial splitting model was employed to split the parent parcel to the predefined number of equal-area child parcels. We developed a simple splitting model which is based on iterative splitting of larger parcels into two smaller parcels using a straight line splitting boundary. Once the parcel was fully split into the needed number of sub-parcels, we allocated a new structure inside each new parcel by generating a point at its centroid (center of gravity). The point datasets of all structure locations per time step per scenario were passed over to the fire risk model, which is described below.To project the distribution of fire risk under alternative scenarios, we used MaxEnt The MaxEnt software uses a machine-learning algorithm that iteratively evaluates contrasts among values of predictor values at locations where structures burned versus values distributed across the entire study area. The model assumes that the best approximation of an unknown distribution  is the one with maximum entropy. The output is an exponential function that assigns a probability to every cell of a map. Thus, the resulting continuous maps of fire risk represented the probability of a structure being destroyed by fire. In these output maps, areas of predicted high fire risk that did not have structures on them represented environmental conditions similar to those in which structures have actually burned.We based the explanatory variables on those that were significantly related to burned structures in Syphard et al. Because burned structures were significantly related to small housing clusters Because the MaxEnt model randomly selects background samples in the map to compare with locations of destroyed structures, we used a mask to restrict sampling to the developed environment within cluster boundaries; the distance to the edge of the cluster would represent a different relationship inside a cluster boundary versus outside in the wildland. We also modified the grids to ensure that any random sample located within the 100m buffer zone would receive a value of 100m; thus, all points within the buffer were considered “the edge of the development”.After creating the grids representing housing pattern and arrangement of the current configuration of structures, we applied the same algorithms to the maps of simulated future structure locations. We thus generated grids representing future housing pattern and arrangement under alternative development scenarios. The other explanatory variables, including fire history, slope, fuel type, southwest aspect, and distance to coast We conditioned the MaxEnt model on present distributions of housing using ten thousand random background points and destroyed structures located no closer than 500-m to minimize any effect of spatial autocorrelation. We used 80% (260 records) of these data for model training, and 20% [66 records) for testing. We repeated the process using cross-validation with five replicates and used the average of these five models for analyses. For smoother functions of the explanatory variables, we used hinge features, linear, and quadratic with an increase in regularization of beta set at 2.5, based on Elith et al. We first developed the model using mapped explanatory variables derived from the current configuration of structures. To project fire risk under the different time steps of the alternative development scenarios, projected the model conditioned upon current conditions onto maps representing future conditions by substituting the grids representing future housing pattern and arrangement. This is similar to how potential future distributions of species are projected under climate change scenarios To quantify differences among current and future alternative scenarios, we calculated metrics representing housing density, pattern, and footprint to determine the extent to which the planning policies produced differences in housing pattern and location. We compared the modeled structure fire risk of the scenarios by overlaying all maps of structure locations with their respective mapped output grids from the MaxEnt models and calculating probability of burning for every structure point. We also calculated total area of risk by selecting three threshold criteria The probit econometric model, run on 113 001 observations, showed that larger parcels were most likely to subdivide, although the relationship between parcel size and subdivision probability was non-linear . ParcelsThe land use simulation model, based on a combination of the econometric subdivision model and three different growth policies, resulted in substantial differences in the extent and pattern of housing of the three scenarios. The total area of housing development, or the housing footprint, was largest for simulations where leapfrog growth dominated, followed by expansion-type development, and then infill . The difIn the first two time steps of the model (2015 and 2020), the simulated development pattern closely followed the desired pattern in the scenario, although some of the growth in the infill scenario ended up becoming expansion or leapfrog . In the The area under the curve (AUC) of receiver operating characteristic (ROC) plots, indicating the ability of the MaxEnt model to discriminate between burned and unburned structures, averaged across five cross-validated replicate runs was 0.91. The AUC represents the probability that, for a randomly selected set of observations, the model prediction was higher for a burned structure than for an unburned structure Maps showing the probability of a structure being destroyed in a wildfire, displayed as a gradient from low to high risk, show broad agreement relative to the general areas of the landscape that are riskiest, with correlation coefficients ranging from 0.85–0.91 . NeverthDifferences among current housing and the three development scenarios are clearly illustrated through the mean landscape risk, or total probability of all structures burning . All thrThe ranking of scenarios varied according to the proportion of structures located within different levels of risk defined through binary thresholding . When thOur simulations of residential development showed that planning policies based ondifferent growth types, applied locally for subdivision of individual parcels, willlikely produce substantial and cumulative landscape-level differences in pattern,location, and extent of development. These differences in development pattern, inturn, will likely affect the area and proportion of structures at risk from burningin wildfires. In particular, the scenarios with lower housing density and largernumbers of small, isolated clusters of development, i.e., leapfrog followed byexpansion and infill, were generally predicted to have the highest predicted firerisk to the largest proportion of structures in the study area. Nevertheless,rankings of scenarios were affected by the definition of risk.Theoretically, it makes sense that leapfrog development produced fragmenteddevelopment with larger numbers of small patches, lower housing density, and alarger housing footprint; and that infill resulted in the opposite, with expansionin the middle. By definition, leapfrog development requires open space around allsides of the newly developed parcel, whereas infill requires development on allsides, and expansion requires development on one side and open space on another.Implementing these planning policies on real landscapes, however, can be complex ifthere are more houses to build than there are parcels that meet the definitions ofthe three planning rules, and thus not all development conforms strictly to thepolicy The objective of the econometric model was to provide a baseline probability topredict which parcels were most likely to subdivide; thus, the econometric modelitself provides no explanation of how a given policy affects likelihood ofsubdivision, although it does indicate the correlation between the policy and theoutcome. In our setting, which areas are protected, marked for redevelopment, ormarked for development may be endogenous to the land owner decision to subdivide. Inthe case of these variables especially, our results should not be interpreted ascausal predictors. Likewise, we use data only from 2005–2009 to predictchanges to 2030. If major changes in the land market take place over this timehorizon our model will not be able to take this into account.Although some differences in predicted fire risk among the three scenarios likelystemmed from location of new structures relative to variables such as distance tocoast, fuel type, or slope, the most important variables in the fire risk model werehousing density and cluster size, with most structure loss historically occurring inareas with low housing density and in small, isolated housing clusters. Thus,leapfrog development was generally the riskiest scenario and infill the least risky.The most surprising result was the variation in predicted risk for the expansionscenario over time and at different thresholds. While leapfrog and infill showedsimilar trajectories across thresholds, expansion went from being the highest-riskscenario at the low threshold to being the lowest-risk scenario at the highestthreshold. Because the threshold is merely a way to group structures into a binaryclassification, this means that, while the average risk calculated across all homesshows expansion to rank in the middle of infill and leapfrog throughout thesimulation , the othAlthough leapfrog development clearly ranked highest in terms of fire risk, theinterpretation of which planning policy is best may depend on fire managementobjectives and resources, as well as other considerations such as biodiversity orecological impacts. The spatial pattern of development affects multiple ecologicalfunctions and services Different perceptions of the fire risk results could also potentially translate intodifferent planning priorities for management. For example, if the priority is toplan for the lowest overall risk to structures, then the mean landscape risk clearlydelineates the rankings of options, with infill being the winner. However, if theobjective is to reduce the number of structures at the highest risk threshold, i.e.,> = 0.5, then expansion is the best option, at least by2030. An important consideration for fire management is the total area that needs tobe protected, as well as the length of wildland-urban interface Although we did not create separate scenarios for high or low growth, the results atdifferent time steps can be substituted to envision the potential outcome ofdeveloping more or fewer houses. In the short term, the total fire risk is projectedto increase proportionately as more land is developed. However, given the inverserelationship between housing density and fire risk, it is possible that this trendcould reverse if housing growth eventually resulted in expansive high-densitydevelopment.Land use planning is one of a range of options available for reducing fire risk, andthe best outcome will likely be achieved through a combination of strategies thatinclude homeowner actions, improvements in fire-safe building codes, and advancedfire suppression tactics. Although we isolated the effect of land use planningpolicy in the three development scenarios, the fire risk model nevertheless showedthat the pattern and location of structures in this study area were the mostimportant out of a suite of factors influencing structure loss. We used acorrelative approach that did not incorporate mechanisms or feedbacks, but ourmodels clearly illustrated differences in the cumulative effects of individualplanning decisions. The relationship between spatial pattern of development and firerisk is likely related to the intermixing of development and wildland vegetationWith projections of substantial global change in climate and human development, werecommend that land use planning should be considered as an important component tofire risk management, potentially to become as successful as the prevention ofbuilding on flood plains Table S1Definitions and summary statistics for variables used in the probit model.(DOCX)Click here for additional data file.


The students’ motivation to learn basic sciences in health science curricula is poorly understood. The purpose of this study was to investigate the influence of different components of motivation  on learning human histology in health science curricula and their relationship with the final performance of the students in histology.Glynn Science Motivation Questionnaire II was used to compare students’ motivation components to learn histology in 367 first-year male and female undergraduate students enrolled in medical, dentistry and pharmacy degree programs.For intrinsic motivation, career motivation and self-efficacy, the highest values corresponded to medical students, whereas dentistry students showed the highest values for self-determination and grade motivation. Genders differences were found for career motivation in medicine, self-efficacy in dentistry, and intrinsic motivation, self-determination and grade motivation in pharmacy. Career motivation and self-efficacy components correlated with final performance in histology of the students corresponding to the three curricula.Our results show that the overall motivational profile for learning histology differs among medical, dentistry and pharmacy students. This finding is potentially useful to foster their learning process, because if they are metacognitively aware of their motivation they will be better equipped to self-regulate their science-learning behavior in histology. This information could be useful for instructors and education policy makers to enhance curricula not only on the cognitive component of learning but also to integrate students’ levels and types of motivation into the processes of planning, delivery and evaluation of medical education. The affective or motivational dimension of the educational process is important because of its influence on students’ learning behavior. Motivation affects the not only the decision to begin, persevere in or end a specific learning behavior, but also the choice of a specific behavior . AccordiSeveral authors ,26-28 haMotivation is recognized as an important factor in health science education because it helps students achieve good academic performance, well-being and satisfaction, and also helps them to become good professionals. However, research that centers on motivation in the health science disciplines is scarce ,17,29. THuman histology is a branch of Biology and Health Sciences dealing with the study of levels of organization that are intercalated between the atomic-molecular level (Biochemistry) and the gross morphological level (Anatomy) in the human body. Medical, Dentistry and Pharmacy programs include histology as part of their basic or preclinical curricula -35. AlthResearch into the motivations to learn science has thus far paid little attention to basic sciences in the health science curricula . MotivatAs noted by Brian and cols. , the difThis study was carried out to evaluate the motivational component profiles in university students learning histology using a validated questionnaire described by Glynn and cols . To compThe study was done at the School of Medicine, School of Dentistry and School of Pharmacy at the University of Granada in Granada, Spain. The sample consisted of 367 first-year undergraduate students (average age 18 years) enrolled in the histology course that formed part of their medical, dentistry or pharmacy curriculum in accordance with European Union regulations. In all these degrees, Histology is a basic science subject included in the core curriculum. Teaching is carried out by using lectures, practical sessions with the use of microscopes, and sessions with histology problem-solving strategies. There were 132 participants  at the medical school, 125  at the dentistry school and 110  at the pharmacy school. All participants agreed to participate in the study. The students’ participation was voluntary and consistent with the procedures of the university research review boards. The students were given no extra credit or compensation for participating. They were informed that their participation would help improve histology instruction. The final performance of the students in the histology subject was recorded at the end of the course to be used in the study.http://www.coe.uga.edu/smq/.To evaluate the different components of the motivation to learn histology in students at the medical, dentistry and pharmacy schools, all participants responded to the Science Motivation Questionnaire II (SMQII) developed by Glynnn and cols. , which uAverage values and standard deviations were calculated for each item, for each group of students and for each gender. Mean values were also calculated for each component of motivation by determining the average value of the 5 items included in each component. To compare the results between males and female students or between two different curricula  we used the Mann-Whitney test, since the variables did not fit the normal distribution as demonstrated by the Kolmogorov-Smirnov test. Finally, to determine if the final performance of the students in each curriculum was correlated with the components of motivation, we used the Kendall tau correlation test. To determine the reliability of the questionnaire, we determined the alpha coefficient of Cronbach.All statistical analyses were two-tailed and values of p less than 0.05 were considered statistically significant.In our results, the reliability of the scale was 0.8596 as determined by the Cronbach’s alpha coefficient. The average scores obtained for each component and for each item in each group of students  are shown in Table Statistical analysis showed that there were significant differences (p < 0.05) for the three comparisons between groups of students in the overall mean scores for components 2 and 5 . In addition, we found significant differences in component 1 scores between dentistry and pharmacy students, and between dentistry and medical students. Significant differences (p < 0.05) were also found in component 3 scores between medical and pharmacy students, and between dentistry and pharmacy students  between genders were found in medical students in overall mean scores for component 2, in dentistry students for component 4 and in pharmacy students for components 1, 3 and 5  for the components 2 and 4. Specifically, all 5 items in component 2 were positively correlated (p < 0.05 and r = 0.6666) with performance, whereas items 2, 4 and 5 were correlated with performance in component 4.Evaluating the main components of motivation for learning histology in medical, dentistry and pharmacy students is important because it can help instructors to help their students, aid in monitoring their motivation to learn science, and support efforts to better organize collaborative learning activities based on an appropriate selection of highly motivated students . In thisOne of the most innovative aspects of the SMQII is the transformation of the classical scale termed extrinsic motivation (or “learning science as a means to a tangible end”) into two scales, i.e. grade motivation and career motivation, which more clearly target the objectives that students perceive to be important in this stage of their education. This is especially important in health science education because the motivation to learn health science disciplines is influenced differently by short-term goals such as obtaining a high course grade and long-term goals such as success in their professional career practice, which in health science professions is explicitly regulated by professional and government bodies.The results of the present study show that in relation specifically to learning histology, the profiles for components of motivation defined as career motivation and self-efficacy were similar in all three degree programs. The scores for both components were highest in the group of medical students, followed in decreasing order by dentistry and pharmacy. In the career motivation component there were significant differences when the overall mean scores for the three groups were compared  whereas differences were not significant in self-efficacy. Interestingly, both profiles were positively correlated with the students’ final performance in histology, pointing out that the extent of career motivation and self-efficacy are clearly influencing the final outcome of the students in histology. Our study confirms some previous reports using different methods suggesting that self-efficacy is associated with effort, persistence, and performance ,50. The In the health professions the long-term goal of professional career practice is an important component of motivation, which is related more closely with future competencies to be used in regulated professional practice than with knowledge and skills to be acquired in the learning process during university study. In this regard our results are consistent with several studies which found that compared to dentistry students, medical students had a more professional attitude, whereas dentistry students showed a greater commitment to personal and financial gain ,55,56. RIn the present study the self-determination and grade motivation components yielded a similar motivational profile in all three groups of students. Mean scores for these components were highest in dentistry, followed in decreasing order by medicine and pharmacy. The differences between degree programs were statistically significant for the grade motivation, and between pharmacy and medicine students and between pharmacy and dentistry students for self-determination. The similarities in the response profiles for these components among the groups shows that a relationship exists between self-determination, and hence the control that students believe they have over their learning process, and the grade motivation, that is, the short-term primary goals of students enrolled in different degree programs. It is therefore unsurprising that dentistry students scored highest in both of these components of motivation, especially in grade motivation. Our results suggest, as Boiche and cols.  showed fThe use of this questionnaire allowed us to analyze independently the two major components of the extrinsic motivation -career motivation and grade motivation-. Our data illustrates clear differences in the results obtained for these components among groups of students, and suggests that it may be possible to identify relationships between components that might otherwise be overlooked. In addition, our results show that both components have different influence on motivation for learning histology in health sciences curricula. Whereas the career motivation, which showed the same profile that self-efficacy, was correlated with the final outcome of the students, grade motivation, whose profile was similar to that of self-determination, was not correlated with the final performance.Finally, intrinsic motivation showed the highest mean scores in medicine, followed in decreasing order by pharmacy and dentistry. The differences between medical and pharmacy students vs. dentistry students were statistically significant. The lower mean scores for this component in dentistry students vs. medicine and pharmacy students is likely related with the general perception, supported by several studies, that there is no statistical correlation between the amount of undergraduate basic science education and performance in dentistry school or on board exams, although dentistry graduates and practitioners do perceive the importance of these science a posteriori, including histology ,58. No cRegarding the differences between genders, our results suggest that only one of the components of motivation was different in the case of medicine and dentistry students, although three components were statistically different in pharmacy students. Although Hulsman and cols.  reportedOur findings with regard to the relative influence of different components of motivation in different groups of health science undergraduates are potentially useful in future efforts to answer some interesting questions. For example, are there gender differences in how students self-regulate in order to control their motivation and behavior? What role do the different components of motivation play in the overall motivation of males and females to perform well in these degree programs?The motivational process is a substantially undervalued factor in curriculum development . BecauseLimitations of the present study are related to the lack of knowledge on the previous motivation of the students before being incorporated to the study, on their previous knowledge on histology and other related sciences that could influence their perceptions. In addition, we do not know to what extent we can generalize our findings to other students and other disciplines.A strength of our study is that the histology course of interest was taught with the same methods by instructors from the same department to all participants, who were enrolled in different undergraduate programs at the same university. We feel our results contribute to our knowledge about the close relationships among different components of motivation in specific disciplines within different health sciences curricula. Ultimately, these findings can support improved efforts to develop more effective medical education curricula.Our results show that our histology students enrolled in each of three undergraduate health science programs differed in their personal motivational profiles. This finding is potentially useful to foster their learning process, because if they are metacognitively aware of their motivation they will be better equipped to self-regulate their science-learning behavior in histology. But our results also show that the overall motivational profile for learning histology differs among medical, dentistry and pharmacy students, and that instructors as well as education policy makers can use their awareness of these differences to enhance curricula so that they focus not only on fostering the cognitive component of learning but also integrate students’ levels and types of motivation into the processes of planning, delivery and evaluation of medical education. Although the differences between genders were not significant for many of the components of motivation, certain differences stand out. These differences suggest a need to increase our knowledge of possible variations and take advantage of them in developing both individually-targeted and curriculum-based teaching processes.The authors declare that they have no competing interests.ACS participated in the design of the work, analysis of the results, discussion and writing the manuscript. JALN contributed to the study design, selection of literature and critical review of the manuscript. VC took part in the analysis and interpretation of the data, and literature review. MAMP participated in data collection and management of the results. TS contributed to writing the manuscript and critical review of the literature. MA contributed to design the work and interpret the data, revising the article and critically appraising the content. All authors have approved the final version of the article submitted.ACS is a Master Degree and PhD student at the University of Granada. He is involved in education research with a focus on self-learning methodologies.JALN is a professor in the Department of Didactics and School Organization at the Faculty of Education Sciences, University of Granada.VD is a Master Degree and PhD in Tissue Engineering and a research fellow in the Department of Histology at the University of Granada, Pharmacy School.MAMP is a Master Degree and PhD student at the University of Granada. He is involved in education research in Dentistry Schools.TS is a full professor in the Department of Didactics and School Organization at the Faculty of Education Sciences, University of Granada.MA is a full professor in the Department of Histology at the Medical School, University of Granada.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6920/14/46/prepub


Diarrhea is one of the major causes of death in children under five years of age, disproportionately affecting children in low- and middle-income countries. Treatment of diarrhea with oral rehydration solution addresses dehydration and reduces diarrhea related deaths. The World Health Organization Programme for the Control of Diarrhoeal Disease began in 1978 and while global ORS access rates have improved substantially over the past forty years, rates of ORS use have stagnated. Investigation is required to understand which interventions are effective in promoting the use of ORS, and where there are gaps in the literature.We conducted a systematic search of peer-reviewed and grey literature and included interventions to promote the use of ORS for the treatment of acute diarrhea in children under 6 years. We used a standardized grading format based on the Child Health Epidemiology Research Group guidelines and performed meta-analysis for all categories with more than one data point.We identified 19 studies for abstraction. For co-promotion of zinc and ORS, mothers in the intervention group were 1.82  times more likely to use ORS to treat their child’s diarrhea episode than mothers in the comparison group. Meta-analysis of ORS social marketing and mass media strategies indicates that mothers exposed to messages were 2.05  times more likely to use ORS to treat their child’s diarrhea episode than unexposed mothers. However, this is not statistically significant. Both meta-analysis had significant heterogeneity and were graded as moderate/low and low quality, respectively.We found few studies of interventions to promote the use of ORS; many categories of interventions had only one study. While there are some promising results, this analysis reinforces the need for further investigation into approaches to increasing ORS use. Diarrhea is one of the major causes of death in children under five years of age, leading to an estimated 1.071 million annual deaths and disproportionately affecting children in low- and middle-income countries . This caThe WHO Programme for the Control of Diarrhoeal Disease began in 1978. Activities in diarrheal disease control programs vary widely from country to country and have included social marketing and mass media campaigns, the involvement of political figures and religious leaders, educational campaigns in schools, training of partly skilled health care workers, changes to medical school curricula, distribution schemes, as well as the establishment of outpatient oral rehydration centers .A 2010 systematic review by Munos et al. indicated that universal coverage with ORS would reduce diarrhea related deaths by 93% . While OIn a recent paper, Boschi-Pinto et al. found that more than half of the countries included in their analysis had no significant improvement, or had a reduction in the coverage of oral rehydration therapy for diarrhea (17/29 countries) , where cThis review aims to evaluate the effectiveness of strategies to promote and scale-up ORS for the treatment of acute childhood diarrhea. Collating the available evidence will also shed light on areas where more research is needed. Based on the various strategies identified through the literature search, a conceptual framework has been developed to help elucidate the processes by which ORS interventions influence caregivers’ knowledge and behaviours, within a particular environmental context, ultimately impacting use rates of ORS and reducing rates of diarrhea-related mortality . We did not include studies looking at the use of home-prepared sugar-salt solutions or home-available fluids. The outcome of interest was ORS use: whether the care provider had used ORS to treat their child’s current or most recent episode of diarrhea.Studies were screened using a two-stage process: one investigator screened titles and abstracts in order to select studies published in English that potentially met the inclusion criteria. Subsequently, two investigators reviewed the shortlist of studies to assess whether studies met the inclusion criteria and contained the relevant outcome indicator. Disagreements between investigators were resolved through discussion. Studies were abstracted separately by two reviewers into a standardized excel spreadsheet.The Child Health Epidemiology Research Group (CHERG) adaptation of the GRADE criteria  was applQuality assessment was also performed at the outcome level, considering the overall quality of the body of evidence along the dimensions of: volume and consistency of results across studies, the size of the pooled effect estimate, and the strength of evidence for the effect estimate as indicated by the p-value. The quality of the body of evidence was graded as high, moderate, low or very low.Studies graded as very low quality were excluded from the meta-analysis, as they were not deemed to be sufficiently reliable. For each category of intervention with more than one study, a meta-analysis was conducted using Review Manager version 5.1®. Due to the range in quality of evidence found through this review, we performed a separate meta-analysis on randomized-controlled trials and quasi-experimental, as well as a meta-analysis on observational studies. We applied the generic inverse variance method to all meta-analyses and report the random effects pooled relative risk (DerSimonian Laird method) and 95% confidence interval. We made an a priori decision to apply a random effects model to all meta-analyses, as we were not expecting the effects being estimated in each study to be identical.Electronic and hand searches returned 1187 studies. After removing duplicates and excluding studies based on reviewing titles and abstracts for relevant inclusion criteria, 100 full text articles were reviewed figure . From thMeta-analysis for other intervention categories was not possible. After excluding very low quality studies and after separating the observational studies from the quasi-experimental and randomized controlled trials, we were left with only one data point in each category. Additionally, there are two low quality studies, a community education intervention  and a coThe results of the meta-analyses are presented below. Characteristics and outcome data for the individual studies included in the meta-analysis, as well as the studies meeting the inclusion criteria which were not included in any meta-analysis, can be found in additional file We included four randomized controlled trials evaluating the effectiveness of promoting zinc therapy along with ORS for the treatment of diarrhea in a community setting. Three are published studies -18 and oMeta-analysis of the studies investigating the co-promotion of zinc and ORS indicate that mothers in the intervention arm were 1.82  times more likely to treat their child’s diarrhea episode than mothers in the comparison group figure . The quaWe found three observational studies looking at the impact of social marketing and mass media strategies on mother’s use of ORS to treat their child’s diarrhea episode -21. The Overall we found very few trials or high quality observational studies investigating the effectiveness of interventions to promote the use of ORS. The paucity of high quality research on interventions to improve ORS use limits the insight that can be gleaned from this analysis, nevertheless the results of this review highlight promising areas as well as gaps that need to be addressed through further research.The strongest evidence found through this systematic review relates to the co-promotion of zinc and ORS for the treatment of childhood diarrhea, largely because this evidence is derived from four randomized controlled trials. However, the generalizability of this analysis is limited by the fact that there was a geographical bias towards South Asia. Additionally, the effect appears to be driven by one of the four studies. There is also evidence suggesting beneficial effects of social marketing and mass media for promoting the use of ORS to treat diarrhea, yet the meta-analysis results are not statistically significant. Challenges with measuring exposure to mass media may have influenced the results.The remaining studies included in this review indicate beneficial effects to varying degrees of interventions such as community education ,22, distThere are several limitations in this review. One issue with this analysis, inherent in the design of the individual studies, is the inability to separate out the effects of multiple interventions occurring simultaneously. In some cases the intervention is designed around a multi-pronged approach, for example, the combined delivery of zinc and ORS. Furthermore, other health interventions occurring in the community could impact on the mother’s use of ORS to treat their child’s diarrhea episode. This is particularly problematic if researchers are not aware of, or are not measuring these other interventions. The majority of studies in this review did not provide a robust description of services offered at baseline in the intervention or comparison group, and did not explore this potential confounding variable in their analyses.An additional issue relates to the approach to the meta-analysis. Studies included in this review were categorized under a primary intervention, yet most included additional intervention activities. As much as possible, we tried to group studies according to similar profiles of interventions; however, there is likely to be some heterogeneity in terms of the types of intervention. The impact of the intervention may be due not just the main intervention, but to other activities as well. Full details of the intervention activities are available in additional file There is an expanse of descriptive data available on the topic of patterns ORS use; yet very few rigorous investigations of interventions to scale up the use of ORS. We only found randomized controlled trials on zinc interventions. The remaining body of literature is quasi-experimental or observational, and the study designs were largely before/after study designs or program/non-program comparison designs. A major limitation of before/after studies is that without a control group, it is not possible to determine whether the observed change in the outcome is due to the program, or to other factors . StudiesWhile observational studies can be rigorously executed, not all of the studies meeting the inclusion criteria for this review applied techniques that would increase the quality, for example adjusting for potential confounding factors. However, in the meta-analysis of social marketing and mass media campaigns, two of the three studies adjusted for major sociodemographic characteristics ,20; the As this review relied on published and grey literature, it may not accurately reflect the landscape of country-level experiences with ORS promotion. The review collated data only from studies measuring the effect of a specific intervention on the use of ORS, meaning that where studies of this nature do not exist, a country will not be represented. However, there is an upcoming country-level case study analysis of the promotion of ORS use  that will be able to fill in some of the gaps not be covered by this review.ORS is clearly efficacious in preventing diarrhea-related mortality, yet there are barriers towards promoting its use, which have led to stagnated global rates of ORS use for the treatment of childhood diarrhea. High quality research is needed to understand how best to promote the uptake of ORS for the treatment of acute childhood diarrhea, not only at the household consumption level, but from multiple vantage points within the system. Moreover, in the studies included in this review the baseline population level coverage of ORS was not systematically reported. This is a key piece of information to report, as different interventions may be more effective at raising use from low to moderate levels, while others may be more successful at raising use from moderate to high levels. Finally, more research is needed in regions other than South Asia, such as Sub-Saharan Africa, given that the effect of behaviour change interventions will likely differ across settings.Beyond researching the promotion of ORS use at the household level, research is needed to explore other aspects of the equation necessary for scaling up ORS use, including strategies to educate health care providers about ORS treatment, methods to ensure a reliable supply of ORS at no cost or low-cost through the public sector, or methods to make the sale of ORS profitable through financing mechanisms that are attractive to private sector investment .Most child deaths occur due to a small number of conditions that are preventable, even in the poorest settings, through interventions that are well known, affordable and deliverable via simple technologies . Oral reThe systematic review returned studies looking at a variety of interventions to increase the use of ORS to treat diarrhea in children. Strategies included zinc supplementation for the treatment of diarrhea, social marketing and mass media, community education, microcredit interventions, distribution programs and multi-pronged nationwide strategies. A multi-pronged approach, including elements of mass media, health force training and novel products such as zinc may have the potential to increase the use of ORS to treat diarrhea episodes in children. While the interventions in this review show promise, firm conclusions cannot be drawn due to issues with the small volume of the evidence and high levels of heterogeneity within the meta-analyses. Research is needed specifically investigating strategies to scale-up the use of ORS, looking at the system from multiple vantage points, in a range of settings where ORS use has been historically low.LML was involved in the literature search, data abstraction, data analysis and writing the manuscript. JKD was involved in the review conceptualization, data abstraction, data analysis and writing the manuscript. ZAB made significant contributions to the conceptualization of this review as well as manuscript revisions.The authors declare they have no conflict of interest.Medline Search StrategyClick here for fileData abstraction table and list of excluded studiesClick here for fileTable of characteristics of the studies included in the reviewClick here for file