1,082 research outputs found

    Complex Polysaccharide-Based Nanocomposites for Oral Insulin Delivery

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    Polyelectrolyte nanocomposites rarely reach a stable state and aggregation often occurs. Here, we report the synthesis of nanocomposites for the oral delivery of insulin composed of alginate, dextran sulfate, poly-(ethylene glycol) 4000, poloxamer 188, chitosan, and bovine serum albumin. The nanocomposites were obtained by Ca2+-induced gelation of alginate followed by an electrostatic-interaction process among the polyelectrolytes. Chitosan seemed to be essential for the final size of the nanocomposites and there was an optimal content that led to the synthesis of nanocomposites of 400–600 nm hydrodynamic size. The enhanced stability of the synthesized nanocomposites was assessed with LUMiSizer after synthesis. Nanocomposite stability over time and under variations of ionic strength and pH were assessed with dynamic light scattering. The rounded shapes of nanocomposites were confirmed by scanning electron microscopy. After loading with insulin, analysis by HPLC revealed complete drug release under physiologically simulated conditions

    Sheet metal plate design: a structured approach to product optimization in the presence of technological constraints

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    Geometrical optimization of structural components is a topic of high interest for engineers involved with design activities mainly related to mass reduction. The study described in these pages focuses on the optimization of plates subjected to bending for which stiffness is obtained by a pattern of ribs. Although stiffening by means of ribs is a well-known and old technique, the design of ribs for maximum stiffness is often based on practice and experience. Classical optimization methods such as topological, topographical and parametric optimization fail to give an efficient design with a reasonable programming effort, especially when dealing with many and complex constraints. These constraints are both technical and technological. A most promising technique to obtain optimal rib patterns was to define a set of feasible rib trajectories and then to select the subset with the most efficient combinations. The result is not unique and a method to select the optimal patterns is required. In fact, the stiffening effect increases with increasing rib length, but at a greater cost. A trade-off must be found between structural performance and cost: The tools to guide this selection process is the main objective of the paper, with particular attention in evaluating the stiffening due to the presence of beads on the plate with a close link with the production system and possible technological constraints which can occur during manufacturing processes, such as minimum rib distance or the presence of discontinuities or the presence of holes or other elements on the plate. A special tool with enforced rib cross section is considered, and optimal rib deployment has to be found. Numerical examples attached show the methodology and obtainable results. \ua9 2011 Springer-Verlag London Limited

    Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study

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    The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation, and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.Comment: Submitted to 'Systems Medicine' as a book chapte

    Do ResearchGate Scores create ghost academic reputations?

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    [EN] The academic social network site ResearchGate (RG) has its own indicator, RG Score, for its members. The high profile nature of the site means that the RG Score may be used for recruitment, promotion and other tasks for which researchers are evaluated. In response, this study investigates whether it is reasonable to employ the RG Score as evidence of scholarly reputation. For this, three different author samples were investigated. An outlier sample includes 104 authors with high values. A Nobel sample comprises 73 Nobel winners from Medicine and Physiology, Chemistry, Physics and Economics (from 1975 to 2015). A longitudinal sample includes weekly data on 4 authors with different RG Scores. The results suggest that high RG Scores are built primarily from activity related to asking and answering questions in the site. In particular, it seems impossible to get a high RG Score solely through publications. Within RG it is possible to distinguish between (passive) academics that interact little in the site and active platform users, who can get high RG Scores through engaging with others inside the site (questions, answers, social networks with influential researchers). Thus, RG Scores should not be mistaken for academic reputation indicators.Alberto Martin-Martin enjoys a four-year doctoral fellowship (FPU2013/05863) granted by the Ministerio de Educacion, Cultura, y Deporte (Spain). Enrique Orduna-Malea holds a postdoctoral fellowship (PAID-10-14), from the Polytechnic University of Valencia (Spain).Orduña Malea, E.; Martín-Martín, A.; Thelwall, M.; Delgado-López-Cózar, E. (2017). Do ResearchGate Scores create ghost academic reputations?. Scientometrics. 112(1):443-460. https://doi.org/10.1007/s11192-017-2396-9S4434601121Bosman, J. & Kramer, B. (2016). Innovations in scholarly communication—data of the global 2015–2016 survey. Available at: http://zenodo.org/record/49583 #. Accessed December 11, 2016.González-Díaz, C., Iglesias-García, M., & Codina, L. (2015). Presencia de las universidades españolas en las redes sociales digitales científicas: Caso de los estudios de comunicación. El profesional de la información, 24(5), 1699–2407.Goodwin, S., Jeng, W., & He, D. (2014). Changing communication on ResearchGate through interface updates. Proceedings of the American Society for Information Science and Technology, 51(1), 1–4.Hicks, D., Wouters, P., Waltman, L., de Rijcke, S., & Rafols, I. (2015). The Leiden Manifesto for research metrics. Nature, 520(7548), 429–431.Hoffmann, C. P., Lutz, C., & Meckel, M. (2015). A relational altmetric? Network centrality on ResearchGate as an indicator of scientific impact. Journal of the Association for Information Science and Technology, 67(4), 765–775.Jiménez-Contreras, E., de Moya Anegón, F., & Delgado López-Cózar, E. (2003). The evolution of research activity in Spain: The impact of the National Commission for the Evaluation of Research Activity (CNEAI). Research Policy, 32(1), 123–142.Jordan, K. (2014a). Academics’ awareness, perceptions and uses of social networking sites: Analysis of a social networking sites survey dataset (December 3, 2014). Available at: http://dx.doi.org/10.2139/ssrn.2507318 . Accessed December 11, 2016.Jordan, K. (2014b). Academics and their online networks: Exploring the role of academic social networking sites. First Monday, 19(11). Available at: http://dx.doi.org/10.5210/fm.v19i11.4937 . Accessed December 11, 2016.Jordan, K. (2015). Exploring the ResearchGate score as an academic metric: reflections and implications for practice. Quantifying and Analysing Scholarly Communication on the Web (ASCW’15), 30 June 2015, Oxford. Available at: http://ascw.know-center.tugraz.at/wp-content/uploads/2015/06/ASCW15_jordan_response_kraker-lex.pdf . Accessed December 11, 2016.Kadriu, A. (2013). Discovering value in academic social networks: A case study in ResearchGate. Proceedings of the ITI 2013—35th Int. Conf. on Information Technology Interfaces Information Technology Interfaces, pp. 57–62.Kraker, P. & Lex, E. (2015). A critical look at the ResearchGate score as a measure of scientific reputation. Proceedings of the Quantifying and Analysing Scholarly Communication on the Web workshop (ASCW’15), Web Science conference 2015. Available at: http://ascw.know-center.tugraz.at/wp-content/uploads/2016/02/ASCW15_kraker-lex-a-critical-look-at-the-researchgate-score_v1-1.pdf . Accessed December 11, 2016.Li, L., He, D., Jeng, W., Goodwin, S. & Zhang, C. (2015). Answer quality characteristics and prediction on an academic Q&A Site: A case study on ResearchGate. Proceedings of the 24th International Conference on World Wide Web Companion, pp. 1453–1458.Martín-Martín, A., Orduna-Malea, E., Ayllón, J. M. & Delgado López-Cózar, E. (2016). The counting house: measuring those who count. Presence of Bibliometrics, Scientometrics, Informetrics, Webometrics and Altmetrics in the Google Scholar Citations, ResearcherID, ResearchGate, Mendeley & Twitter. Available at: https://arxiv.org/abs/1602.02412 . Accessed December 11, 2016.Martín-Martín, A., Orduna-Malea, E. & Delgado López-Cózar, E. (2016). The role of ego in academic profile services: Comparing Google Scholar, ResearchGate, Mendeley, and ResearcherID. Researchgate, Mendeley, and Researcherid. The LSE Impact of Social Sciences blog. Available at: http://blogs.lse.ac.uk/impactofsocialsciences/2016/03/04/academic-profile-services-many-mirrors-and-faces-for-a-single-ego . Accessed December 11, 2016.Matthews, D. (2016). Do academic social networks share academics’ interests?. Times Higher Education. Available at: https://www.timeshighereducation.com/features/do-academic-social-networks-share-academics-interests . Accessed December 11, 2016.Memon, A. R. (2016). ResearchGate is no longer reliable: leniency towards ghost journals may decrease its impact on the scientific community. Journal of the Pakistan Medical Association, 66(12), 1643–1647.Mikki, S., Zygmuntowska, M., Gjesdal, Ø. L. & Al Ruwehy, H. A. (2015). Digital presence of norwegian scholars on academic network sites-where and who are they?. Plos One 10(11). Available at: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0142709 . Accessed December 11, 2016.Nicholas, D., Clark, D., & Herman, E. (2016). ResearchGate: Reputation uncovered. Learned Publishing, 29(3), 173–182.Orduna-Malea, E., Martín-Martín, A., & Delgado López-Cózar, E. (2016). The next bibliometrics: ALMetrics (Author Level Metrics) and the multiple faces of author impact. El profesional de la información, 25(3), 485–496.Ortega, Jose L. (2015). Relationship between altmetric and bibliometric indicators across academic social sites: The case of CSIC’s members. Journal of informetrics, 9(1), 39–49.Ortega, Jose L. (2016). Social network sites for scientists. Cambridge: Chandos.Ovadia, S. (2014). ResearchGate and Academia. edu: Academic social networks. Behavioral & Social Sciences Librarian, 33(3), 165–169.Thelwall, M., & Kousha, K. (2015). ResearchGate: Disseminating, communicating, and measuring Scholarship? Journal of the Association for Information Science and Technology, 66(5), 876–889.Thelwall, M. & Kousha, K. (2017). ResearchGate articles: Age, discipline, audience size and impact. Journal of the Association for Information Science and Technology, 68(2), 468–479.Van Noorden, R. (2014). Online collaboration: Scientists and the social network. Nature, 512(7513), 126–129.Wilsdon, J., Allen, L., Belfiore, E., Campbell, P., Curry, S., Hill, S. et al. (2015). The Metric Tide: Independent Review of the Role of Metrics in Research Assessment and Management. HEFCE. Available at: http://doi.org/10.13140/RG.2.1.4929.1363 . Accessed December 11, 2016
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