Phase separation and rotor self-assembly in active particle suspensions

Adding a non-adsorbing polymer to passive colloids induces an attraction between the particles via the `depletion' mechanism. High enough polymer concentrations lead to phase separation. We combine experiments, theory and simulations to demonstrate that using active colloids (such as motile bacteria) dramatically changes the physics of such mixtures. First, significantly stronger inter-particle attraction is needed to cause phase separation. Secondly, the finite size aggregates formed at lower inter-particle attraction show unidirectional rotation. These micro-rotors demonstrate the self assembly of functional structures using active particles. The angular speed of the rotating clusters scales approximately as the inverse of their size, which may be understood theoretically by assuming that the torques exerted by the outermost bacteria in a cluster add up randomly. Our simulations suggest that both the suppression of phase separation and the self assembly of rotors are generic features of aggregating swimmers, and should therefore occur in a variety of biological and synthetic active particle systems.Comment: Main text: 6 pages, 5 figures. Supplementary information: 5 pages, 4 figures. Supplementary movies available from httP://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1116334109/-/DCSupplementa

Life below excellence: exploring the links between top-ranked universities and regional competitiveness

[EN] This paper examines interactions between the presence of top-ranked universities and other conditions that encourage regional competitiveness. Fuzzy-set qualitative comparative analysis (fsQCA) was conducted to assess the combined effect of the conditions. The analysis yields several noteworthy conclusions. First, no single condition is necessary for a region to be competitive. Second, R&D expenditure is important for regional competitiveness. Third, different configurations of conditions are sufficient for high competitiveness in different regional clusters. Furthermore, some of these configurations do not include the presence of top-ranked universities. A 'magic recipe' consists of the combination of a private research system, an inter-firm collaboration network and high levels of human capital. The analysis shows that university excellence is valuable. However, in terms of its contribution to regional development, it is not crucial and must always be contextualised. This conclusion is important for smart strategic planning of local knowledge systems.Jose-Maria Garcia-Alvarez-Coque and Francisco Mas-Verdu wish to thank Project RTI2018-093791-B-C22, funded by the Ministry of Science, Innovation and Universities (Spain), for supporting this research.García Alvarez-Coque, JM.; Mas Verdú, F.; Roig Tierno, H. (2021). Life below excellence: exploring the links between top-ranked universities and regional competitiveness. Studies in Higher Education. 46(2):369-384. https://doi.org/10.1080/03075079.2019.1637843S369384462Alamá-Sabater, L., Budí, V., García-Álvarez-Coque, J. M., & Roig-Tierno, N. (2019). Using mixed research approaches to understand rural depopulation. Economía Agraria y Recursos Naturales, 19(1), 99. doi:10.7201/earn.2019.01.06Baldacci, E., Clements, B., Gupta, S., & Cui, Q. (2008). Social Spending, Human Capital, and Growth in Developing Countries. World Development, 36(8), 1317-1341. doi:10.1016/j.worlddev.2007.08.003Bathelt, H., Malmberg, A., & Maskell, P. (2004). Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation. Progress in Human Geography, 28(1), 31-56. doi:10.1191/0309132504ph469oaBaumann, C., & Winzar, H. (2014). The role of secondary education in explaining competitiveness. Asia Pacific Journal of Education, 36(1), 13-30. doi:10.1080/02188791.2014.924387Berger, E. S. C. (2016). Is Qualitative Comparative Analysis an Emerging Method?—Structured Literature Review and Bibliometric Analysis of QCA Applications in Business and Management Research. FGF Studies in Small Business and Entrepreneurship, 287-308. doi:10.1007/978-3-319-27108-8_14Bjerke, L., & Johansson, S. (2015). Patterns of innovation and collaboration in small and large firms. The Annals of Regional Science, 55(1), 221-247. doi:10.1007/s00168-015-0712-yBoucher, G., Conway, C., & Van Der Meer, E. (2003). Tiers of Engagement by Universities in their Region’s Development. Regional Studies, 37(9), 887-897. doi:10.1080/0034340032000143896Bramwell, A., & Wolfe, D. A. (2008). Universities and regional economic development: The entrepreneurial University of Waterloo. Research Policy, 37(8), 1175-1187. doi:10.1016/j.respol.2008.04.016Breschi, S., & Lissoni, F. (2009). Mobility of skilled workers and co-invention networks: an anatomy of localized knowledge flows. Journal of Economic Geography, 9(4), 439-468. doi:10.1093/jeg/lbp008Camagni, R. (2017). Regional Competitiveness: Towards a Concept of Territorial Capital. Seminal Studies in Regional and Urban Economics, 115-131. doi:10.1007/978-3-319-57807-1_6Camagni, R., & Capello, R. (2013). Regional Competitiveness and Territorial Capital: A Conceptual Approach and Empirical Evidence from the European Union. Regional Studies, 47(9), 1383-1402. doi:10.1080/00343404.2012.681640Choi, J., & Lee, J. (2017). Repairing the R&D market failure: Public R&D subsidy and the composition of private R&D. Research Policy, 46(8), 1465-1478. doi:10.1016/j.respol.2017.06.009Cowan, R. (2000). The explicit economics of knowledge codification and tacitness. Industrial and Corporate Change, 9(2), 211-253. doi:10.1093/icc/9.2.211Cowan, R., & Zinovyeva, N. (2013). University effects on regional innovation. Research Policy, 42(3), 788-800. doi:10.1016/j.respol.2012.10.001Crilly, D. (2010). Predicting stakeholder orientation in the multinational enterprise: A mid-range theory. Journal of International Business Studies, 42(5), 694-717. doi:10.1057/jibs.2010.57Domenech, J., Escamilla, R., & Roig-Tierno, N. (2016). Explaining knowledge-intensive activities from a regional perspective. Journal of Business Research, 69(4), 1301-1306. doi:10.1016/j.jbusres.2015.10.096Duşa, A. (2008). A mathematical approach to the boolean minimization problem. Quality & Quantity, 44(1), 99-113. doi:10.1007/s11135-008-9183-xDuvivier, C., Polèse, M., & Apparicio, P. (2017). The location of information technology-led new economy jobs in cities: office parks or cool neighbourhoods? Regional Studies, 52(6), 756-767. doi:10.1080/00343404.2017.1322686European Commission. 2016a. European Regional Competitiveness Index. Brussels: Directorate-General for Regional and Urban Policy. Accessed June 2018. https://ec.europa.eu/regional_policy/en/information/maps/regional_competitiveness/.European Commission. 2016b. Regional Innovation Scoreboard (RIS). Brussels: Publications Office of the EU. Accessed June 2018. https://publications.europa.eu/en/publication-detail/-/publication/693eaaba-de16-11e6-ad7c-01aa75ed71a1/language-en/format-PDF/source-31233711.Fan, D., Li, Y., & Chen, L. (2017). Configuring innovative societies: The crossvergent role of cultural and institutional varieties. Technovation, 66-67, 43-56. doi:10.1016/j.technovation.2017.05.003Fiss, P. C. (2011). Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research. Academy of Management Journal, 54(2), 393-420. doi:10.5465/amj.2011.60263120Garcia-Alvarez-Coque, J.-M., Mas-Verdu, F., & Sanchez García, M. (2014). Determinants of Agri-food Firms’ Participation in Public Funded Research and Development. Agribusiness, 31(3), 314-329. doi:10.1002/agr.21407García Álvarez-Coque, J. M., Mas-Verdú, F., & Roig-Tierno, N. (2016). Technological innovation versus non-technological innovation: different conditions in different regional contexts? Quality & Quantity, 51(5), 1955-1967. doi:10.1007/s11135-016-0394-2Grossman, J. H., Reid, P. P., & Morgan, R. P. (2001). The Journal of Technology Transfer, 26(1/2), 143-152. doi:10.1023/a:1007848631448Harrison, R. T., & Leitch, C. M. (2005). Entrepreneurial Learning: Researching the Interface between Learning and the Entrepreneurial Context. Entrepreneurship Theory and Practice, 29(4), 351-371. doi:10.1111/j.1540-6520.2005.00089.xHarrison, J., & Turok, I. (2017). Universities, knowledge and regional development. Regional Studies, 51(7), 977-981. doi:10.1080/00343404.2017.1328189Hazelkorn, E. (2008). Learning to Live with League Tables and Ranking: The Experience of Institutional Leaders. Higher Education Policy, 21(2), 193-215. doi:10.1057/hep.2008.1Hewitt-Dundas, N., & Roper, S. (2011). Creating advantage in peripheral regions: The role of publicly funded R&D centres. Research Policy, 40(6), 832-841. doi:10.1016/j.respol.2011.03.005Hicks, D., Wouters, P., Waltman, L., de Rijcke, S., & Rafols, I. (2015). Bibliometrics: The Leiden Manifesto for research metrics. Nature, 520(7548), 429-431. doi:10.1038/520429aJaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations. The Quarterly Journal of Economics, 108(3), 577-598. doi:10.2307/2118401Jaumotte, F., and N. Pain. 2005. “From Ideas to Development: The Determinants of R&D and Patenting.” OECD Economics Department Working Papers, No. 457, OECD Publishing (NJ1).Kitson, M., Martin, R., & Tyler, P. (2004). Regional Competitiveness: An Elusive yet Key Concept? Regional Studies, 38(9), 991-999. doi:10.1080/0034340042000320816Lasagni, A. (2012). How Can External Relationships Enhance Innovation in SMEs? New Evidence for Europe*. Journal of Small Business Management, 50(2), 310-339. doi:10.1111/j.1540-627x.2012.00355.xLee, S., Park, G., Yoon, B., & Park, J. (2010). Open innovation in SMEs—An intermediated network model. Research Policy, 39(2), 290-300. doi:10.1016/j.respol.2009.12.009Lilles, A., & Rõigas, K. (2015). How higher education institutions contribute to the growth in regions of Europe? Studies in Higher Education, 42(1), 65-78. doi:10.1080/03075079.2015.1034264Lim, M. A. (2017). The building of weak expertise: the work of global university rankers. Higher Education, 75(3), 415-430. doi:10.1007/s10734-017-0147-8Mairesse, J., & Mohnen, P. (2010). Using Innovation Surveys for Econometric Analysis. Handbook of the Economics of Innovation, 1129-1155. doi:10.1016/s0169-7218(10)02010-1Isabel Maria, B. F., Rossi, F., & Geuna, A. (2013). Collaboration objectives and the location of the university partner: Evidence from the Piedmont region in Italy. Papers in Regional Science, 93, S203-S226. doi:10.1111/pirs.12054Marino, M., Lhuillery, S., Parrotta, P., & Sala, D. (2016). Additionality or crowding-out? An overall evaluation of public R&D subsidy on private R&D expenditure. Research Policy, 45(9), 1715-1730. doi:10.1016/j.respol.2016.04.009Medzihorsky, J., I. Oana, M. Quaranta, and C. Schneider. 2016. “SetMethods: Functions for Set-theoretic Multi-method Research and Advanced QCA.” R package version 2.1.Miozzo, M., Desyllas, P., Lee, H., & Miles, I. (2016). Innovation collaboration and appropriability by knowledge-intensive business services firms. Research Policy, 45(7), 1337-1351. doi:10.1016/j.respol.2016.03.018Olcay, G. A., & Bulu, M. (2017). Is measuring the knowledge creation of universities possible?: A review of university rankings. Technological Forecasting and Social Change, 123, 153-160. doi:10.1016/j.techfore.2016.03.029Paruolo, P., Saisana, M., & Saltelli, A. (2012). Ratings and rankings: voodoo or science? Journal of the Royal Statistical Society: Series A (Statistics in Society), 176(3), 609-634. doi:10.1111/j.1467-985x.2012.01059.xPinch, S., Henry, N., Jenkins, M., & Tallman, S. (2003). From «industrial districts» to «knowledge clusters»: a model of knowledge dissemination and competitive advantage in industrial agglomerations. Journal of Economic Geography, 3(4), 373-388. doi:10.1093/jeg/lbg019Pinheiro, R., Benneworth, P., & Jones, G. A. (Eds.). (2012). Universities and Regional Development. doi:10.4324/9780203112298Ragin, C. C. (2008). Redesigning Social Inquiry. doi:10.7208/chicago/9780226702797.001.0001Ragin, C. C. (2014). The Comparative Method. doi:10.1525/9780520957350Roig-Tierno, N., Gonzalez-Cruz, T. F., & Llopis-Martinez, J. (2017). An overview of qualitative comparative analysis: A bibliometric analysis. Journal of Innovation & Knowledge, 2(1), 15-23. doi:10.1016/j.jik.2016.12.002Schneider, M. R., Schulze-Bentrop, C., & Paunescu, M. (2009). Mapping the institutional capital of high-tech firms: A fuzzy-set analysis of capitalist variety and export performance. Journal of International Business Studies, 41(2), 246-266. doi:10.1057/jibs.2009.36Schneider, C. Q., & Wagemann, C. (2012). Set-Theoretic Methods for the Social Sciences. doi:10.1017/cbo9781139004244SCImago. 2016. Scimago Institutions Ranking. Accessed June 2018. https://www.scimagoir.com.Shapira, P., & Youtie, J. (2008). Learning to Innovate: Building Regional Technology Development Learning Networks in Midsized Cities. European Planning Studies, 16(9), 1207-1228. doi:10.1080/09654310802401631Soete, L., B. Verspagen, and T. Ziesemer. 2017. “The Productivity Effect of Public R&D in the Netherlands (No. 021).” United Nations University-Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).Suri, T., Boozer, M. A., Ranis, G., & Stewart, F. (2011). Paths to Success: The Relationship Between Human Development and Economic Growth. World Development, 39(4), 506-522. doi:10.1016/j.worlddev.2010.08.020Thiem, A. (2014). Analyzing multilevel data with QCA: yet another straightforward procedure. Quality & Quantity, 50(1), 121-128. doi:10.1007/s11135-014-0140-6Tobiassen, A. E., & Pettersen, I. B. (2017). Exploring open innovation collaboration between SMEs and larger customers. Baltic Journal of Management, 13(1), 65-83. doi:10.1108/bjm-01-2017-0018Van Raan, A. F. J. (2005). Fatal attraction: Conceptual and methodological problems in the ranking of universities by bibliometric methods. Scientometrics, 62(1), 133-143. doi:10.1007/s11192-005-0008-6Vis, B. (2012). The Comparative Advantages of fsQCA and Regression Analysis for Moderately Large-N Analyses. Sociological Methods & Research, 41(1), 168-198. doi:10.1177/0049124112442142Wolfe, D. A. (2005). 10. The Role of Universities in Regional Development and Cluster Formation. Creating Knowledge, Strengthening Nations, 167-194. doi:10.3138/9781442673564-013World Bank. (2017). GDP Per Capita in US Dollars. World Bank National Accounts Data, and OECD National Accounts Data Files. https://data.worldbank.org/indicator/ny.gdp.pcap.cd.Youtie, J., & Shapira, P. (2008). Building an innovation hub: A case study of the transformation of university roles in regional technological and economic development. Research Policy, 37(8), 1188-1204. doi:10.1016/j.respol.2008.04.01

The benefits of organic farming for biodiversity

Previous studies suggest widespread positive responses of biodiversity to organic farming. Many of these studies, however, have been small-scale. This project tested the generality of habitat and biodiversity differences between matched pairs of organic and non-organic farms containing cereal crops in lowland England on a large-scale across a range of taxa including plants, insects, birds and bats. The extent of both cropped and un-cropped habitats together with their composition and management on a range of scales were also compared. Organic farms was likely to favour higher levels of biodiversity and indeed organic farms tended to support higher numbers of species and overall abundance across most taxa. However, the magnitude of the response differed strikingly; plants showed stronger and more consistent responses than other taxa. Some, but not all, differences in biodiversity between systems appear to be a consequence of differences in habitat quantity

Dynamics of the spontaneous breakdown of superhydrophobicity

Drops deposited on rough and hydrophobic surfaces can stay suspended with gas pockets underneath the liquid, then showing very low hydrodynamic resistance. When this superhydrophobic state breaks down, the subsequent wetting process can show different dynamical properties. A suitable choice of the geometry can make the wetting front propagate in a stepwise manner leading to {\it square-shaped} wetted area: the front propagation is slow and the patterned surface fills by rows through a {\it zipping} mechanism. The multiple time scale scenario of this wetting process is experimentally characterized and compared to numerical simulations.Comment: 7 pages, 5 figure

Imaging the cool gas, dust, star formation, and AGN in the first galaxies

When, and how, did the first galaxies and supermassive black holes (SMBH) form, and how did they reionization the Universe? First galaxy formation and cosmic reionization are among the last frontiers in studies of cosmic structure formation. We delineate the detailed astrophysical probes of early galaxy and SMBH formation afforded by observations at centimeter through submillimeter wavelengths. These observations include studies of the molecular gas (= the fuel for star formation in galaxies), atomic fine structure lines (= the dominant ISM gas coolant), thermal dust continuum emission (= an ideal star formation rate estimator), and radio continuum emission from star formation and relativistic jets. High resolution spectroscopic imaging can be used to study galaxy dynamics and star formation on sub-kpc scales. These cm and mm observations are the necessary compliment to near-IR observations, which probe the stars and ionized gas, and X-ray observations, which reveal the AGN. Together, a suite of revolutionary observatories planned for the next decade from centimeter to X-ray wavelengths will provide the requisite panchromatic view of the complex processes involved in the formation of the first generation of galaxies and SMBHs, and cosmic reionization.Comment: 8 pages total. White paper submitted to the Astro 2010 Decadal Surve

Ebola Virus Disease: Implications for Solid Organ Transplantation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109839/1/ajt13093.pd

SYSTEMATIC REVIEW Involving older people in health research

Abstract Background: it is a UK policy requirement to involve patients and the public in health research as active partners. Objective: we reviewed published reports of studies which involved older people in commissioning, prioritising, designing, conducting or disseminating research. Search strategy and selection criteria: systematic searches of databases (PubMed, SCI-EXPANDED, SSCI, A&HCI, ASSIA, Embase, CINAHL and Medline) for English language studies published between 1995 and 2005 which had involved older people as partners in the research process as opposed to research subjects. Articles were reviewed by two authors using a standardised matrix for data extraction. Results: thirty studies were included and classified according to the stage in the research process in which older people were involved. Barriers to involving older people were: cultural divisions, language barriers, research skills capacity, ill health, time and resources. Four of the studies had been formally evaluated to identify the impact of involvement. Evaluation focussed on the impact on participants rather than on impact on research processes and outcomes. Benefits to participants included: increased knowledge, awareness and confidence, meeting others in similar situations, empowering older people to become active in their community regarding decisions/policies which affect them. Conclusions: factors hindering the involvement of older people in research were the same as reported factors hindering involvement of younger people, suggesting that age, per se, is not a barrier. To demonstrate the impact of user involvement on research quality, the definition of user involvement requires clarification, and systematic evaluation of research involving older people needs to be developed