Land-use intensification differentially affects bacterial, fungal and protist communities and decreases microbiome network complexity

Background: Soil microbial communities are major drivers of cycling of soil nutrients that sustain plant growth and productivity. Yet, a holistic understanding of the impact of land-use intensification on the soil microbiome is still poorly understood. Here, we used a field experiment to investigate the long-term consequences of changes in land-use intensity based on cropping frequency (continuous cropping, alternating cropping with a temporary grassland, perennial grassland) on bacterial, protist and fungal communities as well as on their co-occurrence networks. Results: We showed that land use has a major impact on the structure and composition of bacterial, protist and fungal communities. Grassland and arable cropping differed markedly with many taxa differentiating between both land use types. The smallest differences in the microbiome were observed between temporary grassland and continuous cropping, which suggests lasting effects of the cropping system preceding the temporary grasslands. Land-use intensity also affected the bacterial co-occurrence networks with increased complexity in the perennial grassland comparing to the other land-use systems. Similarly, co-occurrence networks within microbial groups showed a higher connectivity in the perennial grasslands. Protists, particularly Rhizaria, dominated in soil microbial associations, as they showed a higher number of connections than bacteria and fungi in all land uses. Conclusions: Our findings provide evidence of legacy effects of prior land use on the composition of the soil microbiome. Whatever the land use, network analyses highlighted the importance of protists as a key element of the soil microbiome that should be considered in future work. Altogether, this work provides a holistic perspective of the differential responses of various microbial groups and of their associations to agricultural intensification

The collective impact of rare diseases in Western Australia: an estimate using a population-based cohort.

PURPOSE: It has been argued that rare diseases should be recognized as a public health priority. However, there is a shortage of epidemiological data describing the true burden of rare diseases. This study investigated hospital service use to provide a better understanding of the collective health and economic impacts of rare diseases. METHODS: Novel methodology was developed using a carefully constructed set of diagnostic codes, a selection of rare disease cohorts from hospital administrative data, and advanced data-linkage technologies. Outcomes included health-service use and hospital admission costs. RESULTS: In 2010, cohort members who were alive represented approximately 2.0% of the Western Australian population. The cohort accounted for 4.6% of people discharged from hospital and 9.9% of hospital discharges, and it had a greater average length of stay than the general population. The total cost of hospital discharges for the cohort represented 10.5% of 2010 state inpatient hospital costs. CONCLUSIONS: This population-based cohort study provides strong new evidence of a marked disparity between the proportion of the population with rare diseases and their combined health-system costs. The methodology will inform future rare-disease studies, and the evidence will guide government strategies for managing the service needs of people living with rare diseases.Genet Med advance online publication 22 September 2016Genetics in Medicine (2016); doi:10.1038/gim.2016.143

EURO-WABB: an EU rare diseases registry for Wolfram syndrome, Alström syndrome and Bardet-Biedl syndrome

Background: Wolfram, Alström and Bardet-Biedl (WABB) syndromes are rare diseases with overlapping features of multiple sensory and metabolic impairments, including diabetes mellitus, which have caused diagnostic confusion. There are as yet no specific treatments available, little or no access to well characterized cohorts of patients, and limited information on the natural history of the diseases. We aim to establish a Europe-wide registry for these diseases to inform patient care and research. Methods: EURO-WABB is an international multicenter large-scale observational study capturing longitudinal clinical and outcome data for patients with WABB diagnoses. Three hundred participants will be recruited over 3 years from different sites throughout Europe. Comprehensive clinical, genetic and patient experience data will be collated into an anonymized disease registry. Data collection will be web-based, and forms part of the project's Virtual Research and Information Environment (VRIE). Participants who haven't undergone genetic diagnostic testing for their condition will be able to do so via the project. Conclusions: the registry data will be used to increase the understanding of the natural history of WABB diseases, to serve as an evidence base for clinical management, and to aid the identification of opportunities for intervention to stop or delay the progress of the disease. The detailed clinical characterisation will allow inclusion of patients into studies of novel treatment interventions, including targeted interventions in small scale open label studies; and enrolment into multi-national clinical trials. The registry will also support wider access to genetic testing, and encourage international collaborations for patient benefit

Evolving cohesion metrics of a research network on rare diseases: a longitudinal study over 14 years

[EN] Research collaboration is necessary, rewarding, and beneficial. Cohesion between team members is related to their collective efficiency. To assess collaboration processes and their eventual outcomes, agencies need innovative methods-and social network approaches are emerging as a useful analytical tool. We identified the research output and citation data of a network of 61 research groups formally engaged in publishing rare disease research between 2000 and 2013. We drew the collaboration networks for each year and computed the global and local measures throughout the period. Although global network measures remained steady over the whole period, the local and subgroup metrics revealed a growing cohesion between the teams. Transitivity and density showed little or no variation throughout the period. In contrast the following points indicated an evolution towards greater network cohesion: the emergence of a giant component (which grew from just 30 % to reach 85 % of groups); the decreasing number of communities (following a tripling in the average number of members); the growing number of fully connected subgroups; and increasing average strength. Moreover, assortativity measures reveal that, after an initial period where subject affinity and a common geographical location played some role in favouring the connection between groups, the collaboration was driven in the final stages by other factors and complementarities. The Spanish research network on rare diseases has evolved towards a growing cohesion-as revealed by local and subgroup metrics following social network analysis.The Spanish Ministry of Economics and Competitiveness partially supported this research (Grant Number ECO2014-59381-R).Benito Amat, C.; Perruchas, F. (2016). Evolving cohesion metrics of a research network on rare diseases: a longitudinal study over 14 years. Scientometrics. 108(1):41-56. https://doi.org/10.1007/s11192-016-1952-zS41561081Aymé, S., & Schmidtke, J. (2007). Networking for rare diseases: A necessity for Europe. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz, 50(12), 1477–1483. doi: 10.1007/s00103-007-0381-9 .Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3–4), 590–614. doi: 10.1016/S0378-4371(02)00736-7 .Bettencourt, L. M. A., Kaiser, D. I., & Kaur, J. (2009). Scientific discovery and topological transitions in collaboration networks. Journal of Informetrics, 3(3), 210–221. doi: 10.1016/j.joi.2009.03.001 .Bian, J., Xie, M., Topaloglu, U., Hudson, T., Eswaran, H., & Hogan, W. (2014). Social network analysis of biomedical research collaboration networks in a CTSA institution. Journal of Biomedical Informatics, 52, 130–140. doi: 10.1016/j.jbi.2014.01.015 .Bordons, M., Aparicio, J., González-Albo, B., & Díaz-Faes, A. A. (2015). The relationship between the research performance of scientists and their position in co-authorship networks in three fields. Journal of Informetrics, 9(1), 135–144. doi: 10.1016/j.joi.2014.12.001 .Börner, K., Dall’Asta, L., Ke, W., & Vespignani, A. (2005). Studying the emerging global brain: Analyzing and visualizing the impact of co-authorship teams. Complexity, 10(4), 57–67. doi: 10.1002/cplx.20078 .Casey-Campbell, M., & Martens, M. L. (2009). Sticking it all together: A critical assessment of the group cohesion–performance literature. International Journal of Management Reviews, 11(2), 223–246. doi: 10.1111/j.1468-2370.2008.00239.x .Chiocchio, F., & Essiembre, H. (2009). Cohesion and performance: A meta-analytic review of disparities between project teams, Production teams, and service teams. Small group research, 40(4), 382–420. doi: 10.1177/1046496409335103 .Cho, A. (2011). Particle physicists’ new extreme teams. Science, 333(6049), 1564–1567. doi: 10.1126/science.333.6049.1564 .Cooke, N. J., & Hilton, M. L. (2015). Enhancing the effectiveness of team science. Washington, D.C.: National Academies Press. Recuperado a partir de http://www.nap.edu/catalog/19007/enhancing-the-effectiveness-of-team-science .Cugmas, M., Ferligoj, A., & Kronegger, L. (2015). The stability of co-authorship structures. Scientometrics, 106(1), 163–186. doi: 10.1007/s11192-015-1790-4 .Estrada, E. (2011). The structure of complex networks: Theory and applications. Oxford: University Press.Gallivan, M., & Ahuja, M. (2015). Co-authorship, homophily, and scholarly influence in information systems research. Journal of the Association for Information Systems, 16(12), 980.Ghosh, J., Kshitij, A., & Kadyan, S. (2014). Functional information characteristics of large-scale research collaboration: Network measures and implications. Scientometrics, 102(2), 1207–1239. doi: 10.1007/s11192-014-1475-4 .Heymann, S. (2014). Gephi. In R. Alhajj & J. Rokne (Eds.), Encyclopedia of social network analysis and mining (pp. 612–625). New York: Springer.Himmelstein, D. S., & Powell, K. (2016). Analysis for “the history of publishing delays” blog post v1.0. Zenodo,. doi: 10.5281/zenodo.45516 .Hunt, J. D., Whipple, E. C., & McGowan, J. J. (2012). Use of social network analysis tools to validate a resources infrastructure for interinstitutional translational research: A case study. Journal of the Medical Library Association, 100(1), 48–54. doi: 10.3163/1536-5050.100.1.009 .Kolaczyk, E. D., & Csardi, G. (2014). Statistical analysis of network data with R (Vol. 65). New York: Springer.Kumar, S. (2015). Efficacy of a giant component in co-authorship networks: Evidence from a Southeast Asian dataset in economics. Aslib Journal of Information Management, 68(1), 19–32. doi: 10.1108/AJIM-12-2014-0172 .Larivière, V., Gingras, Y., Sugimoto, C. R., & Tsou, A. (2015). Team size matters: Collaboration and scientific impact since 1900. Journal of the Association for Information Science and Technology, 66(7), 1323–1332. doi: 10.1002/asi.23266 .Laudel, G. (2002). What do we measure by co-authorships? Research Evaluation, 11(1), 3–15. doi: 10.3152/147154402781776961 .Liu, X., Bollen, J., Nelson, M. L., & Van de Sompel, H. (2005). Co-authorship networks in the digital library research community. Information Processing and Management, 41(6), 1462–1480. doi: 10.1016/j.ipm.2005.03.012 .Liu, P., & Xia, H. (2015). Structure and evolution of co-authorship network in an interdisciplinary research field. Scientometrics, 103(1), 101–134. doi: 10.1007/s11192-014-1525-y .Ministerio de Sanidad y Consumo. Resolución de 30 de marzo de. (2006) del Instituto de Salud Carlos III, por la que se convocan ayudas destinadas a financiar estructuras estables de investigación cooperativa, en el área de biomedicina y ciencias de la salud, en el marco de la iniciativa Ingenio 2010, programa Consolider, acciones CIBER, 83 Boletín Oficial del Estado (pp. 13770–13777).Newman, M. E. J. (2001a). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E, 64(1), 016132. doi: 10.1103/PhysRevE.64.016132 .Newman, M. E. J. (2001b). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64(1), 016131. doi: 10.1103/PhysRevE.64.016131 .Newman, M. E. J. (2003a). Mixing patterns in networks. Physical Review E, 67(2), 026126. doi: 10.1103/PhysRevE.67.026126 .Newman, M. E. J. (2003b). The structure and function of complex networks. SIAM Review, 45, 167–256.OECD. (2010). Measuring innovation: A new perspective. Paris: OCDE Publishing.Ramasco, J., & Morris, S. (2006). Social inertia in collaboration networks. Physical Review E, 73(1), 016122. doi: 10.1103/PhysRevE.73.016122 .Sonnenwald, D. H. (2007). Scientific collaboration. Annual Review of Information Science and Technology, 41(1), 643–681. doi: 10.1002/aris.2007.1440410121 .Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039. doi: 10.1126/science.1136099

Patient/family views on data sharing in rare diseases: study in the European LeukoTreat project.: Survey assessing data sharing in leukodystrophies

International audienceThe purpose of this study was to explore patient and family views on the sharing of their medical data in the context of compiling a European leukodystrophies database. A survey questionnaire was delivered with help from referral centers and the European Leukodystrophies Association, and the questionnaires returned were both quantitatively and qualitatively analyzed. This study found that patients/families were strongly in favor of participating. Patients/families hold great hope and trust in the development of this type of research. They have a strong need for information and transparency on database governance, the conditions framing access to data, all research conducted, partnerships with the pharmaceutical industry, and they also need access to results. Our findings bring ethics-driven arguments for a process combining initial broad consent with ongoing information. On both, we propose key item-deliverables to database participants

Lab to Field Assessment of the Ecotoxicological Impact of Chlorpyrifos, Isoproturon, or Tebuconazole on the Diversity and Composition of the Soil Bacterial Community

Pesticides are intentionally applied to agricultural fields for crop protection. They can harm non-target organisms such as soil microorganisms involved in important ecosystem functions with impacts at the global scale. Within the frame of the pesticide registration process, the ecotoxicological impact of pesticides on soil microorganisms is still based on carbon and nitrogen mineralization tests, despite the availability of more extensive approaches analyzing the abundance, activity or diversity of soil microorganisms. In this study, we used a high-density DNA microarray (PhyloChip) and 16S rDNA amplicon next-generation sequencing (NGS) to analyze the impact of the organophosphate insecticide chlorpyrifos (CHL), the phenyl-urea herbicide isoproturon (IPU), or the triazole fungicide tebuconazole (TCZ) on the diversity and composition of the soil bacterial community. To our knowledge, it is the first time that the combination of these approaches are applied to assess the impact of these three pesticides in a lab-to-field experimental design. The PhyloChip analysis revealed that although no significant changes in the composition of the bacterial community were observed in soil microcosms exposed to the pesticides, significant differences in detected operational taxonomic units (OTUs) were observed in the field experiment between pesticide treatments and control for all three tested pesticides after 70 days of exposure. NGS revealed that the bacterial diversity and composition varied over time. This trend was more marked in the microcosm than in the field study. Only slight but significant transient effects of CHL or TCZ were observed in the microcosm and the field study, respectively. IPU was not found to significantly modify the soil bacterial diversity or composition. Our results are in accordance with conclusions of the Environmental Food Safety Authority (EFSA), which concluded that these three pesticides may have a low risk toward soil microorganisms

Child and family experiences with inborn errors of metabolism: a qualitative interview study with representatives of patient groups

© 2015, The Author(s). Background: Patient-centered health care for children with inborn errors of metabolism (IEM) and their families is important and requires an understanding of patient experiences, needs, and priorities. IEM-specific patient groups have emerged as important voices within these rare disease communities and are uniquely positioned to contribute to this understanding. We conducted qualitative interviews with IEM patient group representatives to increase understanding of patient and family experiences, needs, and priorities and inform patient-centered research and care. Methods: We developed a sampling frame of patient groups representing IEM disease communities from Canada, the United States, and United Kingdom. With consent, we interviewed participants to explore their views on experiences, needs, and outcomes that are most important to children with IEM and their families. We analyzed the data using a qualitative descriptive approach to identify key themes and sub-themes. Results: We interviewed 18 organizational representatives between February 28 and September 17, 2014, representing 16 IEMs and/or disease categories. Twelve participants voluntarily self-identified as parents and/or were themselves patients. Three key themes emerged from the coded data: managing the uncertainty associated with raising and caring for a child with a rare disease; challenges associated with the affected child’s life transitions, and; the collective struggle for improved outcomes and interventions that rare disease communities navigate. Conclusion: Health care providers can support children with IEM and their families by acknowledging and reducing uncertainty, supporting families through children’s life transitions, and contributing to rare disease communities’ progress toward improved interventions, experiences, and outcomes.The study was partially funded by the Rare Disease Foundation (RDF). In-kind support was provided by the Canadian Inherited Metabolic Diseases Research Network (CIMDRN) which is funded by the Canadian Institutes of Health Research (CIHR, grant TR3-119197) and administered by the University of Ottawa