76 research outputs found

    Mathematical properties of weighted impact factors based on measures of prestige of the citing journals

    Full text link
    The final publication is available at Springer via http://dx.doi.org/10.1007/s11192-015-1741-0An abstract construction for general weighted impact factors is introduced. We show that the classical weighted impact factors are particular cases of our model, but it can also be used for defining new impact measuring tools for other sources of information as repositories of datasets providing the mathematical support for a new family of altmet- rics. Our aim is to show the main mathematical properties of this class of impact measuring tools, that hold as consequences of their mathematical structure and does not depend on the definition of any given index nowadays in use. In order to show the power of our approach in a well-known setting, we apply our construction to analyze the stability of the ordering induced in a list of journals by the 2-year impact factor (IF2). We study the change of this ordering when the criterium to define it is given by the numerical value of a new weighted impact factor, in which IF2 is used for defining the weights. We prove that, if we assume that the weight associated to a citing journal increases with its IF2, then the ordering given in the list by the new weighted impact factor coincides with the order defined by the IF2. We give a quantitative bound for the errors committed. We also show two examples of weighted impact factors defined by weights associated to the prestige of the citing journal for the fields of MATHEMATICS and MEDICINE, GENERAL AND INTERNAL, checking if they satisfy the increasing behavior mentioned above.Ferrer Sapena, A.; Sánchez Pérez, EA.; González, LM.; Peset Mancebo, MF.; Aleixandre Benavent, R. (2015). Mathematical properties of weighted impact factors based on measures of prestige of the citing journals. Scientometrics. 105(3):2089-2108. https://doi.org/10.1007/s11192-015-1741-0S208921081053Ahlgren, P., & Waltman, L. (2014). The correlation between citation-based and expert-based assessments of publication channels: SNIP and SJR vs. Norwegian quality assessments. Journal of Informetrics, 8, 985–996.Aleixandre Benavent, R., Valderrama Zurián, J. C., & González Alcaide, G. (2007). Scientific journals impact factor: Limitations and alternative indicators. El Profesional de la Información, 16(1), 4–11.Altmann, K. G., & Gorman, G. E. (1998). The usefulness of impact factor in serial selection: A rank and mean analysis using ecology journals. Library Acquisitions-Practise and Theory, 22, 147–159.Arnold, D. N., & Fowler, K. K. (2011). Nefarious numbers. Notices of the American Mathematical Society, 58(3), 434–437.Beliakov, G., & James, S. (2012). Using linear programming for weights identification of generalized bonferroni means in R. In: Proceedings of MDAI 2012 modeling decisions for artificial intelligence. Lecture Notes in Computer Science, Vol. 7647, pp. 35–44.Beliakov, G., & James, S. (2011). Citation-based journal ranks: The use of fuzzy measures. Fuzzy Sets and Systems, 167, 101–119.Buela-Casal, G. (2003). Evaluating quality of articles and scientific journals. Proposal of weighted impact factor and a quality index. Psicothema, 15(1), 23–25.Dorta-Gonzalez, P., & Dorta-Gonzalez, M. I. (2013). Comparing journals from different fields of science and social science through a JCR subject categories normalized impact factor. Scientometrics, 95(2), 645–672.Dorta-Gonzalez, P., Dorta-Gonzalez, M. I., Santos-Penate, D. R., & Suarez-Vega, R. (2014). Journal topic citation potential and between-field comparisons: The topic normalized impact factor. Journal of Informetrics, 8(2), 406–418.Egghe, L., & Rousseau, R. (2002). A general frame-work for relative impact indicators. Canadian Journal of Information and Library Science, 27(1), 29–48.Gagolewski, M., & Mesiar, R. (2014). Monotone measures and universal integrals in a uniform framework for the scientific impact assessment problem. Information Sciences, 263, 166–174.Garfield, E. (2006). The history and meaning of the journal impact factor. JAMA, 295(1), 90–93.Habibzadeh, F., & Yadollahie, M. (2008). Journal weighted impact factor: A proposal. Journal of Informetrics, 2(2), 164–172.Klement, E., Mesiar, R., & Pap, E. (2010). A universal integral as common frame for Choquet and Sugeno integral. IEEE Transaction on Fuzzy System, 18, 178–187.Leydesdorff, L., & Opthof, T. (2010). Scopus’s source normalized impact per paper (SNIP) versus a journal impact factor based on fractional counting of citations. Journal of the American Society for Information Science and Technology, 61, 2365–2369.Li, Y. R., Radicchi, F., Castellano, C., & Ruiz-Castillo, J. (2013). Quantitative evaluation of alternative field normalization procedures. Journal of Informetrics, 7(3), 746–755.Moed, H. F. (2010). Measuring contextual citation impact of scientific journals. Journal of Informetrics, 4, 265–277.NISO. (2014). Alternative metrics initiative phase 1. White paper. http://www.niso.org/apps/group-public/download.php/13809/Altmetrics-project-phase1-white-paperOwlia, P., Vasei, M., Goliaei, B., & Nassiri, I. (2011). Normalized impact factor (NIF): An adjusted method for calculating the citation rate of biomedical journals. Journal of Biomedical Informatics, 44(2), 216–220.Pinski, G., & Narin, F. (1976). Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics. Information Processing and Management, 12, 297–312.Pinto, A. C., & Andrade, J. B. (1999). Impact factor of scientific journals: What is the meaning of this parameter? Quimica Nova, 22, 448–453.Raghunathan, M. S., & Srinivas, V. (2001). Significance of impact factor with regard to mathematics journals. Current Science, 80(5), 605.Ruiz Castillo, J., & Waltman, L. (2015). Field-normalized citation impact indicators using algorithmically constructed classification systems of science. Journal of Informetrics, 9, 102–117.Saha, S., Saint, S., & Christakis, D. A. (2003). Impact factor: A valid measure of journal quality? Journal of the Medical Library Association, 91, 42–46.Torra, V., & Narukawa, Y. (2008). The h-index and the number of citations: Two fuzzy integrals. IEEE Transaction on Fuzzy System, 16, 795–797.Torres-Salinas, D., & Jimenez-Contreras, E. (2010). Introduction and comparative study of the new scientific journals citation indicators in journal citation reports and scopus. El Profesional de la Información, 19, 201–207.Waltman, L., & van Eck, N. J. (2008). Some comments on the journal weighted impact factor proposed by Habibzadeh and Yadollahie. Journal of Informetrics, 2(4), 369–372.Waltman, L., van Eck, N. J., van Leeuwen, T. N., & Visser, M. S. (2013). Some modifications to the SNIP journal impact indicator. Journal of Informetrics, 7, 272–285.Zitt, M. (2011). Behind citing-side normalization of citations: some properties of the journal impact factor. Scientometrics, 89, 329–344.Zitt, M., & Small, H. (2008). Modifying the journal impact factor by fractional citation weighting: The audience factor. Journal of the American Society for Information Science and Technology, 59, 1856–1860.Zyczkowski, K. (2010). Citation graph, weighted impact factors and performance indices. Scientometrics, 85(1), 301–315

    Impact Factor: outdated artefact or stepping-stone to journal certification?

    Full text link
    A review of Garfield's journal impact factor and its specific implementation as the Thomson Reuters Impact Factor reveals several weaknesses in this commonly-used indicator of journal standing. Key limitations include the mismatch between citing and cited documents, the deceptive display of three decimals that belies the real precision, and the absence of confidence intervals. These are minor issues that are easily amended and should be corrected, but more substantive improvements are needed. There are indications that the scientific community seeks and needs better certification of journal procedures to improve the quality of published science. Comprehensive certification of editorial and review procedures could help ensure adequate procedures to detect duplicate and fraudulent submissions.Comment: 25 pages, 12 figures, 6 table

    Dietary supplementation with inulin-propionate ester or inulin improves insulin sensitivity in adults with overweight and obesity with distinct effects on the gut microbiota, plasma metabolome and systemic inflammatory responses: A randomised cross-over trial

    Get PDF
    Objective: To investigate the underlying mechanisms behind changes in glucose homeostasis with delivery of propionate to the human colon by comprehensive and coordinated analysis of gut bacterial composition, plasma metabolome and immune responses. Design: Twelve non-diabetic adults with overweight and obesity received 20 g/day of inulin-propionate ester (IPE), designed to selectively deliver propionate to the colon, a high-fermentable fibre control (inulin) and a low-fermentable fibre control (cellulose) in a randomised, double-blind, placebo-controlled, cross-over design. Outcome measurements of metabolic responses, inflammatory markers and gut bacterial composition were analysed at the end of each 42-day supplementation period. Results: Both IPE and inulin supplementation improved insulin resistance compared with cellulose supplementation, measured by homeostatic model assessment 2 (mean±SEM 1.23±0.17 IPE vs 1.59±0.17 cellulose, p=0.001; 1.17±0.15 inulin vs 1.59±0.17 cellulose, p=0.009), with no differences between IPE and inulin (p=0.272). Fasting insulin was only associated positively with plasma tyrosine and negatively with plasma glycine following inulin supplementation. IPE supplementation decreased proinflammatory interleukin-8 levels compared with cellulose, while inulin had no impact on the systemic inflammatory markers studied. Inulin promoted changes in gut bacterial populations at the class level (increased Actinobacteria and decreased Clostridia) and order level (decreased Clostridiales) compared with cellulose, with small differences at the species level observed between IPE and cellulose. Conclusion: These data demonstrate a distinctive physiological impact of raising colonic propionate delivery in humans, as improvements in insulin sensitivity promoted by IPE and inulin were accompanied with different effects on the plasma metabolome, gut bacterial populations and markers of systemic inflammation

    Super-Aggregations of Krill and Humpback Whales in Wilhelmina Bay, Antarctic Peninsula

    Get PDF
    Ecological relationships of krill and whales have not been explored in the Western Antarctic Peninsula (WAP), and have only rarely been studied elsewhere in the Southern Ocean. In the austral autumn we observed an extremely high density (5.1 whales per km2) of humpback whales (Megaptera novaeangliae) feeding on a super-aggregation of Antarctic krill (Euphausia superba) in Wilhelmina Bay. The krill biomass was approximately 2 million tons, distributed over an area of 100 km2 at densities of up to 2000 individuals m−3; reports of such ‘super-aggregations’ of krill have been absent in the scientific literature for >20 years. Retentive circulation patterns in the Bay entrained phytoplankton and meso-zooplankton that were grazed by the krill. Tagged whales rested during daylight hours and fed intensively throughout the night as krill migrated toward the surface. We infer that the previously unstudied WAP embayments are important foraging areas for whales during autumn and, furthermore, that meso-scale variation in the distribution of whales and their prey are important features of this system. Recent decreases in the abundance of Antarctic krill around the WAP have been linked to reductions in sea ice, mediated by rapid climate change in this area. At the same time, baleen whale populations in the Southern Ocean, which feed primarily on krill, are recovering from past exploitation. Consideration of these features and the effects of climate change on krill dynamics are critical to managing both krill harvests and the recovery of baleen whales in the Southern Ocean

    Intravenous and intramyocardial injection of apoptotic white blood cell suspensions prevents ventricular remodelling by increasing elastin expression in cardiac scar tissue after myocardial infarction

    Get PDF
    Congestive heart failure developing after acute myocardial infarction (AMI) is a major cause of morbidity and mortality. Clinical trials of cell-based therapy after AMI evidenced only a moderate benefit. We could show previously that suspensions of apoptotic peripheral blood mononuclear cells (PBMC) are able to reduce myocardial damage in a rat model of AMI. Here we experimentally examined the biochemical mechanisms involved in preventing ventricular remodelling and preserving cardiac function after AMI. Cell suspensions of apoptotic cells were injected intravenously or intramyocardially after experimental AMI induced by coronary artery ligation in rats. Administration of cell culture medium or viable PBMC served as controls. Immunohistological analysis was performed to analyse the cellular infiltrate in the ischaemic myocardium. Cardiac function was quantified by echocardiography. Planimetry of the infarcted hearts showed a significant reduction of infarction size and an improvement of post AMI remodelling in rats treated with suspensions of apoptotic PBMC (injected either intravenously or intramoycardially). Moreover, these hearts evidenced enhanced homing of macrophages and cells staining positive for c-kit, FLK-1, IGF-I and FGF-2 as compared to controls. A major finding in this study further was that the ratio of elastic and collagenous fibres within the scar tissue was altered in a favourable fashion in rats injected with apoptotic cells. Intravenous or intramyocardial injection of apoptotic cell suspensions results in attenuation of myocardial remodelling after experimental AMI, preserves left ventricular function, increases homing of regenerative cells and alters the composition of cardiac scar tissue. The higher expression of elastic fibres provides passive energy to the cardiac scar tissue and results in prevention of ventricular remodelling

    Genetic Interactions between Chromosomes 11 and 18 Contribute to Airway Hyperresponsiveness in Mice

    Get PDF
    We used two-dimensional quantitative trait locus analysis to identify interacting genetic loci that contribute to the native airway constrictor hyperresponsiveness to methacholine that characterizes A/J mice, relative to C57BL/6J mice. We quantified airway responsiveness to intravenous methacholine boluses in eighty-eight (C57BL/6J X A/J) F2 and twenty-seven (A/J X C57BL/6J) F2 mice as well as ten A/J mice and six C57BL/6J mice; all studies were performed in male mice. Mice were genotyped at 384 SNP markers, and from these data two-QTL analyses disclosed one pair of interacting loci on chromosomes 11 and 18; the homozygous A/J genotype at each locus constituted the genetic interaction linked to the hyperresponsive A/J phenotype. Bioinformatic network analysis of potential interactions among proteins encoded by genes in the linked regions disclosed two high priority subnetworks - Myl7, Rock1, Limk2; and Npc1, Npc1l1. Evidence in the literature supports the possibility that either or both networks could contribute to the regulation of airway constrictor responsiveness. Together, these results should stimulate evaluation of the genetic contribution of these networks in the regulation of airway responsiveness in humans

    Do Dispersing Monkeys Follow Kin? Evidence from Gray-cheeked Mangabeys (Lophocebus albigena)

    Get PDF
    Among social vertebrates, immigrants may incur a substantial fitness cost when they attempt to join a new group. Dispersers could reduce that cost, or increase their probability of mating via coalition formation, by immigrating into groups containing first- or second-degree relatives. We here examine whether dispersing males tend to move into groups containing fathers or brothers in gray-cheeked mangabeys (Lophocebus albigena) in Kibale National Park, Uganda. We sampled blood from 21 subadult and adult male mangabeys in 7 social groups and genotyped them at 17 microsatellite loci. Twelve genotyped males dispersed to groups containing other genotyped adult males during the study; in only 1 case did the group contain a probable male relative. Contrary to the prediction that dispersing males would follow kin, relatively few adult male dyads were likely first- or second-degree relatives; opportunities for kin-biased dispersal by mangabeys appear to be rare. During 4 yr of observation, adult brothers shared a group only once, and for only 6 wk. Mean relatedness among adult males sharing a group was lower than that among males in different groups. Randomization tests indicate that closely related males share groups no more often than expected by chance, although these tests had limited power. We suggest that the demographic conditions that allow kin-biased dispersal to evolve do not occur in mangabeys, may be unusual among primates, and are worth further attention

    Multi-messenger observations of a binary neutron star merger

    Get PDF
    On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta
    corecore