271 research outputs found
Analysis of factors influencing the ultrasonic fetal weight estimation
Objective: The aim of our study was the evaluation of sonographic fetal weight estimation taking into consideration 9 of the most important factors of influence on the precision of the estimation. Methods: We analyzed 820 singleton pregnancies from 22 to 42 weeks of gestational age. We evaluated 9 different factors that potentially influence the precision of sonographic weight estimation ( time interval between estimation and delivery, experts vs. less experienced investigator, fetal gender, gestational age, fetal weight, maternal BMI, amniotic fluid index, presentation of the fetus, location of the placenta). Finally, we compared the results of the fetal weight estimation of the fetuses with poor scanning conditions to those presenting good scanning conditions. Results: Of the 9 evaluated factors that may influence accuracy of fetal weight estimation, only a short interval between sonographic weight estimation and delivery (0-7 vs. 8-14 days) had a statistically significant impact. Conclusion: Of all known factors of influence, only a time interval of more than 7 days between estimation and delivery had a negative impact on the estimation
Predicting cell types and genetic variations contributing to disease by combining GWAS and epigenetic data
Genome-wide association studies (GWASs) identify single nucleotide polymorphisms (SNPs) that are enriched in individuals suffering from a given disease. Most disease-associated SNPs fall into non-coding regions, so that it is not straightforward to infer phenotype or function; moreover, many SNPs are in tight genetic linkage, so that a SNP identified as associated with a particular disease may not itself be causal, but rather signify the presence of a linked SNP that is functionally relevant to disease pathogenesis. Here, we present an analysis method that takes advantage of the recent rapid accumulation of epigenomics data to address these problems for some SNPs. Using asthma as a prototypic example; we show that non-coding disease-associated SNPs are enriched in genomic regions that function as regulators of transcription, such as enhancers and promoters. Identifying enhancers based on the presence of the histone modification marks such as H3K4me1 in different cell types, we show that the location of enhancers is highly cell-type specific. We use these findings to predict which SNPs are likely to be directly contributing to disease based on their presence in regulatory regions, and in which cell types their effect is expected to be detectable. Moreover, we can also predict which cell types contribute to a disease based on overlap of the disease-associated SNPs with the locations of enhancers present in a given cell type. Finally, we suggest that it will be possible to re-analyze GWAS studies with much higher power by limiting the SNPs considered to those in coding or regulatory regions of cell types relevant to a given disease
Neurobehavioral consequences of chronic intrauterine opioid exposure in infants and preschool children: a systematic review and meta-analysis
<b>Background</b><p></p>
It is assumed within the accumulated literature that children born of pregnant opioid dependent mothers have impaired neurobehavioral function as a consequence of chronic intrauterine opioid use.<p></p>
<b>Methods</b><p></p>
Quantitative and systematic review of the literature on the consequences of chronic maternal opioid use during pregnancy on neurobehavioral function of children was conducted using the Meta-analysis of Observational Studies in Epidemiology (MOOSE) and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We searched Cinahl, EMBASE, PsychINFO and MEDLINE between the periods of January 1995 to January 2012.<p></p>
<b>Results</b><p></p>
There were only 5 studies out of the 200 identified that quantitatively reported on neurobehavioral function of children after maternal opioid use during pregnancy. All 5 were case control studies with the number of exposed subjects within the studies ranging from 33–143 and 45–85 for the controls. This meta-analysis showed no significant impairments, at a non-conservative significance level of p < 0.05, for cognitive, psychomotor or observed behavioural outcomes for chronic intra-uterine exposed infants and pre-school children compared to non-exposed infants and children. However, all domains suggested a trend to poor outcomes in infants/children of opioid using mothers. The magnitude of all possible effects was small according to Cohen’s benchmark criteria.<p></p>
<b>Conclusions</b><p></p>
Chronic intra-uterine opioid exposed infants and pre-school children experienced no significant impairment in neurobehavioral outcomes when compared to non-exposed peers, although in all domains there was a trend to poorer outcomes. The findings of this review are limited by the small number of studies analysed, the heterogenous populations and small numbers within the individual studies. Longitudinal studies are needed to determine if any neuropsychological impairments appear after the age of 5 years and to help investigate further the role of environmental risk factors on the effect of ‘core’ phenotypes
Spatio-temporal Models of Lymphangiogenesis in Wound Healing
Several studies suggest that one possible cause of impaired wound healing is
failed or insufficient lymphangiogenesis, that is the formation of new
lymphatic capillaries. Although many mathematical models have been developed to
describe the formation of blood capillaries (angiogenesis), very few have been
proposed for the regeneration of the lymphatic network. Lymphangiogenesis is a
markedly different process from angiogenesis, occurring at different times and
in response to different chemical stimuli. Two main hypotheses have been
proposed: 1) lymphatic capillaries sprout from existing interrupted ones at the
edge of the wound in analogy to the blood angiogenesis case; 2) lymphatic
endothelial cells first pool in the wound region following the lymph flow and
then, once sufficiently populated, start to form a network. Here we present two
PDE models describing lymphangiogenesis according to these two different
hypotheses. Further, we include the effect of advection due to interstitial
flow and lymph flow coming from open capillaries. The variables represent
different cell densities and growth factor concentrations, and where possible
the parameters are estimated from biological data. The models are then solved
numerically and the results are compared with the available biological
literature.Comment: 29 pages, 9 Figures, 6 Tables (39 figure files in total
A cross-sectional study examining predictors of visit frequency to local green space and the impact this has on physical activity levels
Background Lack of physical activity (PA) is a growing public health concern. There is a growing body of literature that suggests a positive relationship may exist between the amount of local green space near one?s home and PA levels. For instance, park proximity has been shown to predict PA levels amongst certain populations. However, there is little evidence for the role of relatedness towards nature and perceptions of local green space on this relationship. The aim of this study was to examine, in a National UK sample, whether subjective indices associated with local green space were better predictors of visit frequency to local green space and PA levels compared to objectively measured quantity of local green space. Methods A cross-sectional survey was designed. From a random sample, 2079 working age adults responded to an online survey in September 2011. Demographics, self-reported PA, objective measures of the local environment (including local green space, road coverage, and environmental deprivation), were assessed in conjunction with perceptions of local green space and nature relatedness. Quantity of local green space was assessed by cross-referencing respondents? home postcodes with general land use databases. Regression models were conducted to assess which of our independent variables best predicted visit frequency to local green space and/or meeting PA guidelines. In addition, an ordinal regression was run to examine the relationship between visit frequency to local green space and the likelihood of meeting national PA guidelines. Results Nature relatedness was the strongest predictor for both visit frequency to local green space and meeting PA guidelines. Results show that perceived quality is a better predictor of visit frequency to local green space than objective quantity of local green space. The odds of achieving the recommended amount of PA was over four times greater for people who visited local green space once per week compared to never going (OR 4.151; 95 % CI, 2.40 to 7.17). Conclusions These results suggest that perceptions of local green space and nature relatedness play an important role in the relationship between local green space and PA. Considering the known health benefits of PA, our results are potentially important for public health interventions, policy making and environmental planning
Who leads research productivity growth? Guidelines for R&D policy-makers
[EN] This paper evaluates to what extent policy-makers have been able to promote the creation and consolidation of comprehensive research groups that contribute to the implementation of a successful innovation system. Malmquist productivity indices are applied in the case of the Spanish Food Technology Program, finding that a large size and a comprehensive multi-dimensional research output are the key features of the leading groups exhibiting high efficiency and productivity levels. While identifying these groups as benchmarks, we conclude that the financial grants allocated by the program, typically aimed at small-sized and partially oriented research groups, have not succeeded in reorienting them in time so as to overcome their limitations. We suggest that this methodology offers relevant conclusions to policy evaluation methods, helping policy-makers to readapt and reorient policies and their associated means, most notably resource allocation (financial schemes), to better respond to the actual needs of research groups in their search for excellence (micro-level perspective), and to adapt future policy design to the achievement of medium-long term policy objectives (meso and macro-level).Jiménez Saez, F.; Zabala Iturriagagoitia, JM.; Zofio, JL. (2013). Who leads research productivity growth? Guidelines for R&D policy-makers. Scientometrics. 94(1):273-303. doi:10.1007/s11192-012-0763-0S273303941Abbring, J. H., & Heckman, J. J. (2008). Dynamic policy analysis. In L. Mátyás & P. Sevestre (Eds.), The econometrics of panel data (3rd ed., pp. 795–863). Heidelberg: Springer.Acosta Ballesteros, J., & Modrego Rico, A. (2001). Public financing of cooperative R&D projects in Spain: the concerted projects under the national R&D plan. Research Policy, 30, 625–641.Arbel, A. (1981). Policy evaluation in the dynamic input–output model. International Journal of Systems Science, 12, 255–260.Arnold, E. (2004). Evaluation research and innovation policy: A systems world needs systems evaluations. Research Evaluation, 13, 3–17.Arrow, J. K. (1962). Economic welfare and the allocation of resources for inventions. In R. Nelson (Ed.), The rate and direction of inventive activity: Economic and social factor (pp. 609–625). Princeton: Princeton University Press and NBER.Autio, E. (1997). New, technology-based firms in innovation networks symplectic and generative impacts. Research Policy, 26, 263–281.Balk, B. (2001). Scale efficiency and productivity change. Journal of Productivity Analysis, 15, 153–183.Balzat, M., & Hanusch, H. (2004). Recent trends in the research on national innovation systems. Journal of Evolutionary Economics, 14, 197–210.Berg, S. A., Førsund, F. R., & Jansen, E. S. (1992). Malmquist indices of productivity growth during the deregulation of Norwegian banking. Scandinavian Journal of Economics, 94, S211–S228.Bergek, A., Carlsson, B., Lindmark, S., Rickne, A., & Jacobsson, S. (2008). Analyzing the functional dynamics of technological innovation systems: A scheme of analysis. Research Policy, 37, 407–429.Bonaccorsi, A., & Daraio, C. (2005). Exploring size and agglomeration effects on public research productivity. Scientometrics, 63(1), 87–120.Buisseret, T. J., Cameron, H., & Georghiou, L. (1995). What difference does it make? Additionality in the public support of R&D in large firms. International Journal of Technology Management, 10, 587–600.Bustelo, M. (2006). The potential role of standards and guidelines in the development of an evaluation culture in Spain. Evaluation, 12, 437–453.Chavas, J. P., & Cox, T. M. (1999). A generalized distance function and the analysis of production efficiency. Southern Economic Journal, 66, 295–318.CICYT. (1987). Programa Nacional de Tecnología de los Alimentos. Madrid: Ministerio de Educación y Ciencia.CICYT (1988). Plan Nacional de Investigación Científica y Desarrollo Tecnológico 1988–1991. Ministerio de Educación y Ciencia, Secretaría de Estado de Universidades e Investigación, Madrid.Cooper, W. W., Seiford, L. M., & Tone, K. (2000). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-software. Boston: Kluwer Academic Publishers.David, P., Mowery, D., & Steinmueller, W. E. (1994). Analyzing the economic payoffs from basic research. In D. Mowery (Ed.), Science and technology policy in interdependent economies (pp. 57–78). Boston: Kluwer Academic Publishers.Dopfer, K., Foster, J., & Potts, J. (2004). Micro-meso-macro. Journal of Evolutionary Economics, 14, 263–279.Edquist, C., & Hommen, L. (2008). Comparing national systems of innovation in Asia and Europe: Theory and comparative framework. In C. Edquist & L. Hommen (Eds.), Small country innovation systems: Globalisation, change and policy in Asia and Europe (pp. 1–28). Cheltenham: Edward Elgar.Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84, 66–83.Farrell, M. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A, General, 120(3), 253–281.Førsund, F. R. (1993). Productivity growth in Norwegian ferries. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency: Techniques and applications (pp. 352–373). New York: Oxford University Press.Førsund, F. R. (1997). The Malmquist productivity index, TFP and scale. University of Oslo, Oslo: Working Paper, Department of Economics and Business Administration.Freeman, C. (1987). Technology policy and economic performance: Lessons from Japan. London: Printer Publishers.García-Martínez, M., & Briz, J. (2000). Innovation in the Spanish food & drink industry. International Food and Agribusiness Management Review, 3, 155–176.Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: The dynamics of science and research in contemporary societies. London: Sage Publications.Grammatikopoulos, V., Kousteiios, A., Tsigilis, N., & Theodorakis, Y. (2004). Applying dynamic evaluation approach in education. Studies in Educational Evaluation, 30, 255–263.Grifell-Tatjé, E., & Lovell, C. A. K. (1999). A generalized Malmquist productivity index. Top, 7(1), 81–101.Grimpe, C., & Sofka, W. (2007). Search patterns and absorptive capacity: A comparison of low- and high-technology firms from thirteen European countries. Discussion paper no. 07-062. Centre for European Economic Research (ZEW), Mannheim, Germany.Guan, J., & Wang, J. (2004). Evaluation and interpretation of knowledge production efficiency. Scientometrics, 59(1), 131–155.Hekkert, M. P., Suurs, R. A. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. H. M. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change, 74, 413–432.Jiménez-Sáez, F. (2005). Una Evaluación del Programa Nacional de Tecnología de Alimentos: análisis de la articulación fomentada sobre el Sistema Alimentario de Innovación en España. PhD dissertation, Servicio de Publicaciones de la Universidad Politécnica de Valencia, Valencia.Jiménez-Sáez, F., Zabala-Iturriagagoitia, J. M., Zofío, J. L., & Castro-Martínez, E. (2011). Evaluating research efficiency within National R&D Programmes. Research Policy, 40, 230–241.Kao, C. (2008). Efficiency analysis of university departments: An empirical study. OMEGA, 36, 653–664.Kuhlmann, S. (2003). Evaluation of research and innovation policies: A discussion of trends with examples from Germany. International Journal of Technology Management, 26, 131–149.Laitinen, E. K. (2002). A dynamic performance measurement system: Evidence from small Finnish technology companies. Scandinavian Journal of Management, 18, 65–99.Laranja, M., Uyarra, E., & Flanagan, K. (2008). Policies for science, technology and innovation: Translating rationales into regional policies in a multi-level setting. Research Policy, 37(5), 823–835.Lee, T.-L., & von Tunzelman, N. (2005). A dynamic analytic approach to national innovation systems: The IC industry in Taiwan. Research Policy, 34, 425–440.Lipsey, R., & Carlaw, K. (1998). A structuralist assessment of technology policies: Taking Schumpeter seriously on policy. Ottawa: Industry Canada Research Publications Program.Lipsey, R., Carlaw, K., & Bekar, C. (2005). Economic transformations: General purpose technologies and long term economic growth. Oxford: Oxford University Press.Lundvall, B. Å. (1992). National systems of innovation: Toward a theory of innovation and interactive learning. London: Printer Publishers.Lundvall, B. Å., Johnson, B., Andersen, E. S., & Dalum, B. (2002). National systems of production, innovation and competence building. Research Policy, 31, 213–231.Markard, J., & Truffer, B. (2008). Actor-oriented analysis of innovation systems: Exploring micro-meso level linkages in the case of stationary fuel cells. Technology Analysis & Strategic Management, 20, 443–464.Metcalfe, J. S. (2002). Equilibrium and evolutionary foundations of competition and technology policy: New perspectives on the division of labour and the innovation process. CRIC Working Papers series, University of Manchester.Miettinen, R. (1999). The riddle of things. Activity theory and actor network theory as approaches of studying innovations. Mind, Culture and Activity, 6, 170–195.Molas-Gallart, J., & Davies, A. (2006). Toward theory-led evaluation: The experience of European science, technology, and innovation policies. American Journal of Evaluation, 27, 64–82.Mytelka, L. K., & Smith, K. (2002). Policy learning and innovation theory: An interactive and co-evolving process. Research Policy, 31, 1467–1479.Olazarán, M., Lavía, C., & Otero, B. (2004). ¿Hacia una segunda transición en la ciencia? Política científica y grupos de investigación. Revista Española de Sociología, 4, 143–172.Potts, J. (2007). The innovation system & economic evolution. Productivity commission submission, public support for science & innovation, productivity commission, Camberra.Ray, S., & Desli, E. (1997). Productivity growth, technical progress, and efficiency change in industrialized countries: Comment. American Economic Review, 87(5), 1033–1039.Rip, A., & Nederhof, A. J. (1986). Between dirigism and laissez-faire: Effects of implementing the science policy priority for biotechnology in the Netherlands. Research Policy, 15, 253–268.Schmidt, E. K., Graversen, E. K., & Langberg, K. (2003). Innovation and dynamics in public research environments in Denmark: A research-policy perspective. Science and Public Policy, 30, 107–116.Schmoch, U., & Schubert, T. (2009). Sustainability of incentives for excellent research—The German case. Scientometrics, 81(1), 195–218.Shephard, R. (1970). Theory of cost and production functions. New Jersey: Princeton University Press.Simar, L., & Wilson, P. W. (1998). Productivity growth in industrialized countries. Discussion paper 9810, Universite Catholique de Louvain, Belgium.Van Raan, A. F. J. (2000). R&D evaluation at the beginning of the new century. Research Evaluation, 8, 81–86.Zofio, J. L. (2007). Malmquist productivity index decompositions: A unifying framework. Applied Economics, 39, 2371–2387.Zofio, J. L., & Lovell, C. A. K. (1998). Yet another Malmquist productivity index decomposition. Working paper, Department of Economics, University of Georgia, Athens, GA 30602, USA.Zofio, J. L., & Lovell, C. A. K. (2001). Graph efficiency and productivity measures: An application to US agriculture. Applied Economics, 33(10), 1433–1442.Zofio, J. L., & Prieto, A. M. (2006). Return to dollar, generalized distance function and the Fisher productivity index. Spanish Economic Review, 8, 113–138
Control of Jupiter's radio emission and aurorae by the solar wind
Radio emissions from Jupiter provided the first evidence that this giant planet has a strong magnetic field(1,2) and a large magnetosphere(3). Jupiter also has polar aurorae(4), which are similar in many respects to Earth's aurorae(5). The radio emissions are believed to be generated along the high-latitude magnetic field lines by the same electrons that produce the aurorae, and both the radio emission in the hectometric frequency range and the aurorae vary considerably(6,7). The origin of the variability, however, has been poorly understood. Here we report simultaneous observations using the Cassini and Galileo spacecraft of hectometric radio emissions and extreme ultraviolet auroral emissions from Jupiter. Our results show that both of these emissions are triggered by interplanetary shocks propagating outward from the Sun. When such a shock arrives at Jupiter, it seems to cause a major compression and reconfiguration of the magnetosphere, which produces strong electric fields and therefore electron acceleration along the auroral field lines, similar to the processes that occur during geomagnetic storms at the Earth.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62740/1/415985a.pd
9-Genes Reinforce the Phylogeny of Holometabola and Yield Alternate Views on the Phylogenetic Placement of Strepsiptera
Background: The extraordinary morphology, reproductive and developmental biology, and behavioral ecology of twisted wing parasites (order Strepsiptera) have puzzled biologists for centuries. Even today, the phylogenetic position of these enigmatic “insects from outer space” [1] remains uncertain and contentious. Recent authors have argued for the placement of Strepsiptera within or as a close relative of beetles (order Coleoptera), as sister group of flies (order Diptera), or even outside of Holometabola.Methodology/Principal Findings Here, we combine data from several recent studies with new data (for a total of 9 nuclear genes and ∼13 kb of aligned data for 34 taxa), to help clarify the phylogenetic placement of Strepsiptera. Our results unequivocally support the monophyly of Neuropteroidea ( = Neuropterida + Coleoptera) + Strepsiptera, but recover Strepsiptera either derived from within polyphagan beetles (order Coleoptera), or in a position sister to Neuropterida. All other supra-ordinal- and ordinal-level relationships recovered with strong nodal support were consistent with most other recent studies. Conclusions/Significance: These results, coupled with the recent proposed placement of Strepsiptera sister to Coleoptera, suggest that while the phylogenetic neighborhood of Strepsiptera has been identified, unequivocal placement to a specific branch within Neuropteroidea will require additional study.Organismic and Evolutionary Biolog
Association of the rs738409 polymorphism in PNPLA3 with liver damage and the development of nonalcoholic fatty liver disease
<p>Abstract</p> <p>Background</p> <p>In a genome-wide association scan, the single-nucleotide polymorphism (SNP) rs738409 in the patatin-like phospholipase 3 gene (<it>PNPLA3</it>) was strongly associated with increased liver fat content. We investigated whether this SNP is associated with the occurrence and progression of nonalcoholic fatty liver disease (NAFLD) in the Japanese population.</p> <p>Methods</p> <p>SNP rs738409 was genotyped by the Taqman assay in 253 patients with NAFLD (189 with nonalcoholic steatohepatitis [NASH] and 64 with simple steatosis) and 578 control subjects. All patients with NAFLD underwent liver biopsy. Control subjects had no metabolic disorders. For a case-control study, the <it>χ</it><sup>2</sup>-test (additive model) was performed. Odds ratios (ORs) were adjusted for age, gender, and body mass index (BMI) by using multiple logistic regression analysis with genotypes (additive model), age, gender, and BMI as the independent variables. Multiple linear regression analysis was performed to test the independent effect of risk allele on clinical parameters while considering the effects of other variables (age, gender, and BMI), which were assumed to be independent of the effect of the SNP.</p> <p>Results</p> <p>The risk allele (G-allele) frequency of rs738409 was 0.44 in the control subjects and 0.60 in patients with NAFLD; this shows a strong association with NAFLD (additive model, <it>P </it>= 9.4 × 10<sup>-10</sup>). The OR (95% confidence interval) adjusted for age, gender, and BMI was 1.73 (1.25-2.38). Multiple linear regression analysis indicated that the G-allele of rs738409 was significantly associated with increases in aspartate transaminase (AST) (<it>P </it>= 0.00013), alanine transaminase (ALT) (<it>P </it>= 9.1 × 10<sup>-6</sup>), and ferritin levels (<it>P </it>= 0.014), and the fibrosis stage (<it>P </it>= 0.011) in the patients with NAFLD, even after adjustment for age, gender, and BMI. The steatosis grade was not associated with rs738409.</p> <p>Conclusions</p> <p>We found that in the Japanese population, individuals harboring the G-allele of rs738409 were susceptible to NAFLD, and that rs738409 was associated with plasma levels of ALT, AST, and ferritin, and the histological fibrosis stage. Our study suggests that <it>PNPLA3 </it>may be involved in the progression of fibrosis in NAFLD.</p
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