130,751 research outputs found
MeSH term explosion and author rank improve expert recommendations
Get PDFInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
Get PDFBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
Liver transplantation and recurrence of Hepatitis C
No full textIl capitolo tratta delle indicazioni al trapianto di fegato per epatite C e della recidiva della patologia dopo il trapianto stesso
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Scholarly Communication and Publishing Lunch and Learn Talk #11: The ULS Open Access Author Fee Fund
No full textAt the May 2014 talk, you will learn about the ULS Open Access Author Fee Fund--what it is, why we do it, how it works, and how the program is going so far
The R&D Tax Incentives
No full textThis article sets out some background information and reflections of the author on the R&D tax incentive schemes included in the Common Corporate Tax Base (CCTB) Proposal. In particular the author analyzes the stimulus to private R&D through ad hoc tax incentives included in the CCTB Proposal and dives into the actual provisions included in the Proposal highlighting the most relevant issues connected with their design and interpretation. Moreover, the author explores the interaction between the CCTB Proposal and the granting by Member States of domestic R&D tax incentives
Supplemental Material for Sunitha et al., 2019
No full textSupplementary Figure S1. Size distributions of adapter-trimmed and pre-filtered reads in the 28 UV experimental sRNA libraries.Supplementary Figure S2. Functional validation of differentially expressed miRNA/tasiRNA activities under high-fluence solar UV-B in the field by qRT-PCR of target genes. A) Schematic alignment of vvi-TAS4a, -TAS4b and -TAS4c primary transcript sequences flanking the miR828 binding and TAS4 3’ D4(-) phasi-RNA positions, and location of locus-specific primers used for qRT-PCR. B) Expression profile of miR828* (left panel; only sRNA reads mapping to vvi-MIR828) and pre-MIR828 transcript abundance. Panels C-E: Expression profiles of TAS4a, TAS4b and TAS4c siRNAs (left panels) and their cognate primary undiced transcript abundances. C) TAS4a 3’ D1(+) trans acting siRNA expression profile and the expression profile of TAS4a primary transcript. D) TAS4b 3’ D4(-) trans acting siRNA expression profile and the expression profile of TAS4b primary transcript. E) TAS4c 3’ D4(-) trans acting siRNA expression profile and the expression profile of TAS4c primary transcript. F) TAS4-3'D4(-) target MYBA6 phasi-RNA-3'D6(+) expression profile and the expression profile of MYBA6 mRNA. G) TAS4-3'D4(-) target MYBA7 phasi-RNA-3'D6(+) expression profile and the expression profile of MYBA7 mRNA. Error bars are s.d., except panel B: s.e.m. Asterisks (*) denote significant differences based on analysis of variance (n=4 biological replicates) and comparisons using the Tukey-Kramer honestly significance test (HSD; p B-G: Target:miRNA/tasi-RNA binding positions (mRNA nucleotide coordinates) and primer binding sites (arrows) are depicted to scale. Target:miRNA binding depicted in Red; Target:tasiRNA binding depicted in Green; Target:phasiRNA binding depicted in Pink; miR828* species is depicted in Peach.Supplementary Figure S3. miR403f mature expression profile and the expression profile of MIR403f primary transcript, showing concordant inductions during berry development. Error bars are s.d. Asterisks (*) denote significant differences based on analysis of variance (n=4 biological replicates) and comparisons using the Tukey-Kramer honestly significance test (HSD; p Supplementary File S4. CleaveLand-validated miRNA target T plots.Supplementary File S5. PhaseTank Align and Cascades Output, UV-regulated PHASI loci.Supplementary File S6. Novel MIRNA loci characterized de novo by ShortStack, drawn from reads of libraries constructed from UV-B treated samples.Supplementary File S7. List of primers and parameters used in this study.Supplementary Table 1a and 1b. small RNA library (a) and degradome (b) quality control parameters.Supplementary Table 2a and 2b. a) List of validated miRNA targets by CleaveLand4.4 analysis of Pantaleo et al. (2010) degradome dataset. b) Meta-analysis of validated miRNA targets for three published datasets for transcriptome changes of berry skins in response to: UV-C (Suzuki et al., 2015), UV-B (Carbonell-Bejerano et al., 2017) and development (Massonnet et al., 2017).Supplementary Table 3a-d. DESeq2 output and associated ShortStack raw Counts dataset parameters for differentially expressed miRNA comparisons outlined in Table 1 summarized for target functions, and normalized reads per 20M for visualization of compared effects.Supplementary Table 4. PhaseTank output of degradome TAS loci targets and miRNA/phasi-RNA trigger predictions.</div
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