97 research outputs found

    Artificial evolution with Binary Decision Diagrams: a study in evolvability in neutral spaces

    Get PDF
    This thesis develops a new approach to evolving Binary Decision Diagrams, and uses it to study evolvability issues. For reasons that are not yet fully understood, current approaches to artificial evolution fail to exhibit the evolvability so readily exhibited in nature. To be able to apply evolvability to artificial evolution the field must first understand and characterise it; this will then lead to systems which are much more capable than they are currently. An experimental approach is taken. Carefully crafted, controlled experiments elucidate the mechanisms and properties that facilitate evolvability, focusing on the roles and interplay between neutrality, modularity, gradualism, robustness and diversity. Evolvability is found to emerge under gradual evolution as a biased distribution of functionality within the genotype-phenotype map, which serves to direct phenotypic variation. Neutrality facilitates fitness-conserving exploration, completely alleviating local optima. Population diversity, in conjunction with neutrality, is shown to facilitate the evolution of evolvability. The search is robust, scalable, and insensitive to the absence of initial diversity. The thesis concludes that gradual evolution in a search space that is free of local optima by way of neutrality can be a viable alternative to problematic evolution on multi-modal landscapes

    Chronic type B dissecting aortoiliac aneurysm repair complicated by congenital pelvic kidney

    Get PDF
    Although the association between abdominal aortic aneurysm and pelvic kidney is rare, previous reports have described various methods of repair with successful preservation of pelvic kidney function. We describe a unique case complicated by aortic dissection. Successful intra-operative perfusion of the kidney was maintained via a temporary axillorenal shunt

    How can clinical research improve European health outcomes in cancer?

    Get PDF
    We review the mechanisms by which clinical cancer research can improve health outcomes and argue that this should be central to the development of policy. Recent series of major international studies have analysed large, often nationwide, datasets for cancer patient outcomes and participation in clinical research. They have evaluated and quantified the impact of new evidence generated by randomised controlled trials on cancer survival. They show a strong and probably causal relationship between the participation in clinical research in hospitals and the outcomes for patients with the disease under study in those hospitals. Also, institutions that are active in clinical trials appear to take up well evidenced innovations more rapidly than those which are not so engaged. Further work is necessary to confirm and examine the generalisability of these findings but we argue that all of these mechanisms are likely to lead to improved outcomes for patients as a consequence of the conduct of clinical research. The size of the benefit appears to be substantial and an active programme to promote clinical research across cancer care systems should be a part of National Cancer Plans and Cancer Control Strategies

    Different paths to the modern state in Europe: the interaction between domestic political economy and interstate competition

    Get PDF
    Theoretical work on state formation and capacity has focused mostly on early modern Europe and on the experience of western European states during this period. While a number of European states monopolized domestic tax collection and achieved gains in state capacity during the early modern era, for others revenues stagnated or even declined, and these variations motivated alternative hypotheses for determinants of fiscal and state capacity. In this study we test the basic hypotheses in the existing literature making use of the large date set we have compiled for all of the leading states across the continent. We find strong empirical support for two prevailing threads in the literature, arguing respectively that interstate wars and changes in economic structure towards an urbanized economy had positive fiscal impact. Regarding the main point of contention in the theoretical literature, whether it was representative or authoritarian political regimes that facilitated the gains in fiscal capacity, we do not find conclusive evidence that one performed better than the other. Instead, the empirical evidence we have gathered lends supports to the hypothesis that when under pressure of war, the fiscal performance of representative regimes was better in the more urbanized-commercial economies and the fiscal performance of authoritarian regimes was better in rural-agrarian economie

    Implementation of child-centred outcome measures in routine paediatric healthcare practice: a systematic review

    Get PDF
    Background: Person-centred outcome measures (PCOMs) are commonly used in routine adult healthcare to measure and improve outcomes, but less attention has been paid to PCOMs in children’s services. The aim of this systematic review is to identify and synthesise existing evidence of the determinants, strategies, and mechanisms that influence the implementation of PCOMs into paediatric healthcare practice. Methods: The review was conducted and reported in accordance with PRISMA guidelines. Databased searched included CINAHL, Embase, Medline, and PsycInfo. Google scholar was also searched for grey literature on 25th March 2022. Studies were included if the setting was a children’s healthcare service, investigating the implementation or use of an outcome measure or screening tool in healthcare practice, and reported outcomes relating to use of a measure. Data were tabulated and thematically analysed through deductive coding to the constructs of the adapted-Consolidated Framework for Implementation Research (CFIR). Results were presented as a narrative synthesis, and a logic model developed. Results: We retained 69 studies, conducted across primary (n = 14), secondary (n = 13), tertiary (n = 37), and community (n = 8) healthcare settings, including both child self-report (n = 46) and parent-proxy (n = 47) measures. The most frequently reported barriers to measure implementation included staff lack of knowledge about how the measure may improve care and outcomes; the complexity of using and implementing the measure; and a lack of resources to support implementation and its continued use including funding and staff. The most frequently reported facilitators of implementation and continued use include educating and training staff and families on: how to implement and use the measure; the advantages of using PCOMs over current practice; and the benefit their use has on patient care and outcomes. The resulting logic model presents the mechanisms through which strategies can reduce the barriers to implementation and support the use of PCOMs in practice. Conclusions: These findings can be used to support the development of context-specific implementation plans through a combination of existing strategies. This will enable the implementation of PCOMs into routine paediatric healthcare practice to empower settings to better identify and improve child-centred outcomes. Trial registration: Prospero CRD 42022330013

    Different Paths to the Modern State in Europe: The Interaction between Domestic Political Economy and Interstate Competition

    Full text link

    What Stimulates Researchers to Make Their Research Usable? Towards an Openness Approach

    Get PDF
    Ambiguity surrounding the effect of external engagement on academic research has raised questions about what motivates researchers to collaborate with third parties. We argue that what matters for society is research that can be absorbed by users. We define openness as a willingness by researchers to make research more usable by external partners by responding to external influences in their own research practices. We ask what kinds of characteristics define those researchers who are more open to creating usable knowledge. Our empirical study analyses a sample of 1583 researchers working at the Spanish Council for Scientific Research (CSIC). Results demonstrate that it is personal factors (academic identity and past experience) that determine which researchers have open behaviours. The paper concludes that policies to encourage external engagement should focus on experiences which legitimate and validate knowledge produced through user encounters, both at the academic formation career stage as well as through providing ongoing opportunities to engage with third parties.The data used for this study comes from the IMPACTO project funded by the Spanish Council for Scientific Research - CSIC (Ref. 200410E639). The work also benefited from a mobility grant awarded by Eu-Spri Forum to Julia Olmos Penuela & Paul Benneworth for her visiting research to the Center of Higher Education Policy Studies. Finally, Julia Olmos Penuela also benefited from a post-doctoral grant funded by the Generalitat Valenciana (APOSTD-2014-A-006).Olmos-Peñuela, J.; Benneworth, P.; Castro-MartĂ­nez, E. (2015). What Stimulates Researchers to Make Their Research Usable? Towards an Openness Approach. Minerva. 53(4):381-410. https://doi.org/10.1007/s11024-015-9283-4S381410534Abreu, Maria, Vadim Grinevich, Alan Hughes, and Michael Kitson. 2009. Knowledge exchange between academics and the business, public and third sectors. Cambridge: Centre for Business Research and UK-IRC.Aghion, Philippe, Mathias Dewatripont, and Jeremy C. Stein. 2008. Academic freedom, private-sector focus, and the process of innovation. RAND Journal of Economics 39: 617–635.Ajzen, Icek. 2001. Nature and operation of attitudes. Annual Review of Psychology 52(1): 27–58.AlrĂže, Hugo Fjelsted, and Erik Steen Kristensen. 2002. Towards a systemic research methodology in agriculture: Rethinking the role of values in science. Agriculture and Human Values 19(1): 3–23.Audretsch, David B., Werner Bönte, and Stefan Krabel. 2010. Why do scientists in public research institutions cooperate with private firms. In DRUID Working Paper, 10–27.Baldini, Nicola, Rosa Grimaldi, and Maurizio Sobrero. 2007. To patent or not to patent? A survey of Italian inventors on motivations, incentives, and obstacles to university patenting. Scientometrics 70(2): 333–354.Bandura, Albert. 1977. Social learning theory. Englewood Cliffs, NJ: Prentice-Hall.Barnett, R. 2009. Knowing and becoming in the higher education curriculum. Studies in Higher Education 34(4): 429–440.Becher, Tony. 1994. The significance of disciplinary differences. Studies in Higher Education 19(2): 151–161.Becher, Tony, and Paul Trowler. 2001. Academic tribes and territories: Intellectual enquiry and the culture of disciplines. McGraw-Hill International.Bekkers, Rudi, and Isabel Maria Bodas Freitas. 2008. Analysing knowledge transfer channels between universities and industry: To what degree do sectors also matter? Research Policy 37(10): 1837–1853.Belderbos, RenĂ©, Martin Carree, Bert Diederen, Boris Lokshin, and Reinhilde Veugelers. 2004. Heterogeneity in R&D cooperation strategies. International Journal of Industrial Organization 22(8): 1237–1263.Benner, Mats, and Ulf Sandström. 2000. Institutionalizing the triple helix: Research funding and norms in the academic system. Research Policy 29(2): 291–301.Bercovitz, Janet, and Maryann Feldman. 2008. Academic entrepreneurs: Organizational change at the individual level. Organization Science 19(1): 69–89.Berman, Elizabeth Popp. 2011. Creating the market university: How academic science became an economic engine. Princeton University Press.Bleiklie, Ivar, and Roar HĂžstaker. 2004. Modernizing research training-education and science policy between profession, discipline and academic institution. Higher Education Policy 17(2): 221–236.Bozeman, Barry, Daniel Fay, and Catherine P. Slade. 2013. Research collaboration in universities and academic entrepreneurship: The-state-of-the-art. The Journal of Technology Transfer 38(1): 1–67.Collini, Stefan. 2009. Impact on humanities: Researchers must take a stand now or be judged and rewarded as salesmen. The Times Literary Supplement 5563: 18–19.D’Este, Pablo, and Markus Perkmann. 2011. Why do academics engage with industry? The entrepreneurial university and individual motivations. The Journal of Technology Transfer 36(3): 316–339.D’Este, Pablo, Oscar Llopis, and Alfredo Yegros. 2013. Conducting pro-social research: Cognitive diversity, research excellence and awareness about the social impact of research: INGENIO (CSIC-UPV) Working Paper Series.Deem, Rosemary, and Lisa Lucas. 2007. Research and teaching cultures in two contrasting UK policy contexts: Academic life in education departments in five English and Scottish universities. Higher Education 54(1): 115–133.DiMaggio, Paul J., and Walter W. Powell. 1983. The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review 48(2): 147–160.Downing, David B. 2005. The knowledge contract: Politics and paradigms in the academic workplace. Lincoln: Nebraska University of Nebraska Press.Donovan, Claire. 2007. The qualitative future of research evaluation. Science and Public Policy 34(8): 585–597.Durning, Bridget. 2004. Planning academics and planning practitioners: Two tribes or a community of practice? Planning Practice and Research 19(4): 435–446.Edquist, Charles. 1997. System of innovation approaches: Their emergence and characteristics. In Systems of innovation: Technologies, institutions and organizations, ed. C. Edquist, 1–35. London: Pinter.Etzkowitz, Henry, and Loet Leydesdorff. 2000. The dynamics of innovation: from National Systems and “Mode 2” to a Triple Helix of university–industry–government relations. Research Policy 29(2): 109–123.Fromhold-Eisebith, Martina, Claudia Werker, and Marcel Vojnic. 2014. Tracing the social dimension in innovation networks. In The social dynamics of innovation networks, eds. Roel Rutten, Paul Benneworth, Frans Boekema, and Dessy Irawati. London: Routledge (in press).Geuna, Aldo, and Alessandro Muscio. 2009. The governance of university knowledge transfer: A critical review of the literature. Minerva 47(1): 93–114.Gibbons, Michael, Camille Limoges, Helga Nowotny, Simon Schwartzman, Peter Scott, and Martin Trow. 1994. The new production of knowledge: The dynamics of science and research in contemporary societies. London: Sage.GlĂ€ser, Jochen. 2012. How does Governance change research content? On the possibility of a sociological middle-range theory linking science policy studies to the sociology of scientific knowledge. Technical University Berlin. Technology Studies Working Papers. http://www.ts.tu-berlin.de/fileadmin/fg226/TUTS/TUTS-WP-1-2012.pdf . Accessed 16 Feb 2015.Goethner, Maximilian, Martin Obschonka, Rainer K. Silbereisen, and Uwe Cantner. 2012. Scientists’ transition to academic entrepreneurship: Economic and psychological determinants. Journal of Economic Psychology 33(3): 628–641.Gulbrandsen, Magnus, and Jens-Christian Smeby. 2005. Industry funding and university professors’ research performance. Research Policy 34(6): 932–950.Haeussler, Carolin, and Jeannette Colyvas. 2011. Breaking the ivory tower: Academic entrepreneurship in the life sciences in UK and Germany. Research Policy 40(1): 41–54.Hessels, Laurens K., Harro van Lente, John Grin, and Ruud E.H.M. Smits. 2011. Changing struggles for relevance in eight fields of natural science. Industry and Higher Education 25(5): 347–357.Hessels, Laurens K., and Harro Van Lente. 2008. Re-thinking new knowledge production: A literature review and a research agenda. Research Policy 37(4): 740–760.Hoye, Kate, and Fred Pries. 2009. ‘Repeat commercializers’, the ‘habitual entrepreneurs’ of university–industry technology transfer. Technovation 29(10): 682–689.Jacobson, Nora, Dale Butterill, and Paula Goering. 2004. Organizational factors that influence university-based researchers’ engagement in knowledge transfer activities. Science Communication 25(3): 246–259.Jain, Sanjay, Gerard George, and Mark Maltarich. 2009. Academics or entrepreneurs? Investigating role identity modification of university scientists involved in commercialization activity. Research Policy 38(6): 922–935.Jasanoff, Sheila, and Sang-Hyun Kim. 2013. Sociotechnical imaginaries and national energy policies. Science as Culture 22(2): 189–196.Jensen, Pablo. 2011. A statistical picture of popularization activities and their evolutions in France. Public Understanding of Science 20(1): 26–36.Kitcher, Philip. 2001. Science, truth, and democracy. Oxford: Oxford University Press.Knorr-Cetina, Karin. 1981. The manufacture of knowledge: An essay on the constructivist and contextual nature of science. Oxford: Pergamon Press.Kronenberg, Kristin, and Marjolein CaniĂ«ls. 2014. Professional proximity in research collaborations. In The social dynamics of innovation networks, eds. Roel Rutten, Paul Benneworth, Frans Boekema, and Dessy Irawati. London: Routledge (in press).Krueger, Rob, and David Gibbs. 2010. Competitive global city regions and sustainable development’: An interpretive institutionalist account in the South East of England. Environment and planning A 42: 821–837.Lam, Alice. 2011. What motivates academic scientists to engage in research commercialization: ‘Gold’, ‘ribbon’ or ‘puzzle’? Research Policy 40(10): 1354–1368.Landry, RĂ©jean, Malek SaĂŻhi, Nabil Amara, and Mathieu Ouimet. 2010. Evidence on how academics manage their portfolio of knowledge transfer activities. Research Policy 39(10): 1387–1403.Lee, Alison, and David Boud. 2003. Writing groups, change and academic identity: Research development as local practice. Studies in Higher Education 28(2): 187–200.Lee, Yong S. 1996. ‘Technology transfer’ and the research university: A search for the boundaries of university–industry collaboration. Research Policy 25(6): 843–863.Lee, Yong S. 2000. The sustainability of university–industry research collaboration: An empirical assessment. The Journal of Technology Transfer 25(2): 111–133.Leisyte, Liudvika, JĂŒrgen Enders, and Harry De Boer. 2008. The freedom to set research agendas—illusion and reality of the research units in the Dutch Universities. Higher Education Policy 21(3): 377–391.Louis, Karen Seashore, David Blumenthal, Michael E. Gluck, and Michael A. Stoto. 1989. Entrepreneurs in academe: An exploration of behaviors among life scientists. Administrative Science Quarterly 34(1): 110–131.Lowe, Philip, Jeremy Phillipson, and Katy Wilkinson. 2013. Why social scientists should engage with natural scientists. Contemporary Social Science 8(3): 207–222.MartĂ­n-Sempere, MarĂ­a JosĂ©, BelĂ©n GarzĂłn-GarcĂ­a, and JesĂșs Rey-Rocha. 2008. Scientists’ motivation to communicate science and technology to the public: Surveying participants at the Madrid Science Fair. Public Understanding of Science 17(3): 349–367.Martin, Ben. 2003. The changing social contract for science and the evolution of the university. In Science and innovation: Rethinking the rationales for funding and governance, eds. A. Geuna, A.J. Salter, and W.E. Steinmueller, 7–29. Cheltenhan: Edward Elgar.Merton, Robert K. 1973. The sociology of science: Theoretical and empirical investigations. Chicago: University of Chicago Press.Miller, Thaddeus R., and Mark W. Neff. 2013. De-facto science policy in the making: how scientists shape science policy and why it matters (or, why STS and STP scholars should socialize). Minerva 51(3): 295–315.MuthĂ©n, Bengt O. 1998–2004. Mplus Technical Appendices. MuthĂ©n & MuthĂ©n. Los Angeles, CA.: MuthĂ©n & MuthĂ©n.Nedeva, Maria. 2013. Between the global and the national: Organising European science. Research Policy 42(1): 220–230.Neff, Mark William. 2014. Research prioritization and the potential pitfall of path dependencies in coral reef science. Minerva 52(2): 213–235.Nelson, Richard R. 2001. Observations on the post-Bayh-Dole rise of patenting at American universities. The Journal of Technology Transfer 26(1): 13–19.Nowotny, Helga, Peter Scott, and Michael Gibbons. 2001. Re-thinking science: Knowledge and the public in an age of uncertainty. Cambridge: Polity Press.Olmos-Peñuela, Julia, Paul Benneworth, and Elena Castro-MartĂ­nez. 2014a. Are ‘STEM from Mars and SSH from Venus’? Challenging disciplinary stereotypes of research’s social value. Science and Public Policy 41: 384–400.Olmos-Peñuela, Julia, Elena Castro-MartĂ­nez, and Manuel FernĂĄndez-Esquinas. 2014b. Diferencias entre ĂĄreas cientĂ­ficas en las prĂĄcticas de divulgaciĂłn de la investigaciĂłn: un estudio empĂ­rico en el CSIC. Revista Española de DocumentaciĂłn CientĂ­fica. doi: 10.3989/redc.2014.2.1096 .Ouimet, Mathieu, Nabil Amara, RĂ©jean Landry, and John Lavis. 2007. Direct interactions medical school faculty members have with professionals and managers working in public and private sector organizations: A cross-sectional study. Scientometrics 72(2): 307–323.Perkmann, Markus, Valentina Tartari, Maureen McKelvey, Erkko Autio, Anders Brostrom, Pablo D’Este, Riccardo Fini, et al. 2013. Academic engagement and commercialisation: A review of the literature on university-industry relations. Research Policy 42(2): 423–442.Philpott, Kevin, Lawrence Dooley, Caroline O’Reilly, and Gary Lupton. 2011. The entrepreneurial university: Examining the underlying academic tensions. Technovation 31(4): 161–170.Rutten, Roel, and Frans Boekema. 2012. From learning region to learning in a socio-spatial context. Regional Studies 46(8): 981–992.Sarewitz, Daniel, and Roger A. Pielke. 2007. The neglected heart of science policy: reconciling supply of and demand for science. Environmental Science & Policy 10(1): 5–16.Sauermann, Henry, and Paula Stephan. 2013. Conflicting logics? A multidimensional view of industrial and academic science. Organization Science 24(3): 889–909.Schein, Edgar H. 1985. Organizational culture and leadership: A dynamic view. San Francisco, CA: Jossey-Bass.Shane, Scott. 2000. Prior knowledge and the discovery of entrepreneurial opportunities. Organization Science 11(4): 448–469.Spaapen, Jack, and Leonie van Drooge. 2011. Introducing ‘productive interactions’ in social impact assessment. Research Evaluation 20(3): 211–218.Stokes, Donald E. 1997. Pasteur’s quadrant: Basic science and technological innovation. Washington, DC: Brookings Institution Press.Tartari, Valentina, and Stefano Breschi. 2012. Set them free: scientists’ evaluations of the benefits and costs of university–industry research collaboration. Industrial and Corporate Change 21(5): 1117–1147.Tinker, Tony, and Rob Gray. 2003. Beyond a critique of pure reason: From policy to politics to praxis in environmental and social research. Accounting, Auditing & Accountability Journal 16(5): 727–761.van Rijnsoever, Frank J., Laurens K. Hessels, and Rens L.J. Vandeberg. 2008. A resource-based view on the interactions of university researchers. Research Policy 37(8): 1255–1266.Venkataraman, Sankaran. 1997. The distinctive domain of entrepreneurship research: An editor’s perspective. Advances in Entrepreneurship, Firm Emergence, and Growth 3: 119–138.Verspagen, Bart. 2006. University research, intellectual property rights and European innovation systems. Journal of Economic Surveys 20(4): 607–632.Villanueva-Felez, Africa, Jordi Molas-Gallart, and Alejandro EscribĂĄ-Esteve. 2013. Measuring personal networks and their relationship with scientific production. Minerva 51(4): 465–483.Watermeyer, Richard. 2015. Lost in the ‘third space’: the impact of public engagement in higher education on academic identity, research practice and career progression. European Journal of Higher Education (online first, doi: 10.1080/21568235.2015.1044546 ).Weingart, Peter. 2009. Editorial for Issue 47/3. Minerva 47(3): 237–239.Ziman, John. 1996. ‘Postacademic science’: Constructing knowledge with networks and norms. Science Studies 1: 67–80.Zomer, Arend H., Ben W.A. Jongbloed, and JĂŒrgen Enders. 2010. Do spin-offs make the academics’ heads spin? The impacts of spin-off companies on their parent research organisation. Minerva 48(3): 331–353

    Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity.

    Get PDF
    Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant
    • 

    corecore