131,299 research outputs found
Voi teitä pappa (2/4 d)
No full textLaulun sanat: Voi teitä, pappa, ja voi teitä, mamma, kun talonne hävititte, nuorella iällä maailman kirjoin nimeni levititte
Valutazione rapida della qualità della pappa reale mediante spettroscopia NIR
No full textSviluppo di una metodica analitica ai fini di valutare nuovi marcatori molecolari quali indice di freschezza del prodotto e loro evoluzione nel tempo a diverse temperature di stoccaggi
Removal of <i>Mcs5c</i> TCE copies results in decreased <i>Pappa</i> expression <i>in vitro</i>.
No full text(A) CRISPR guides targeting the TCE were transfected into LA7 cells, and clones were screened for removal of the target region. Nine positive clones were assessed for remaining TCE copy number via qPCR, standardized to a non-targeted region within the Pappa gene. Copy number in wild-type LA7 cells (n = 4 independent cultures) was also assessed, and results were normalized to diploid MECs. (B) Interaction frequency (IF, y-axis) was calculated in select positive clones (n = 3) and WT LA7 cells between the TCE and Pappa bait regions P4-1 and P3-3. The IF for a positive control region, two nearby BglII fragments, is shown for reference. (C) Pappa expression in positive clones and WT LA7 cells (n = 6) was analyzed via qPCR and standardized to Tbp expression. (D) A scatterplot of Pappa expression and Mcs5c TCE copy number demonstrate a statistically significant positive correlation between the two (Pearson correlation coefficient, R, = 0.6245, n = 13, p-value = 0.0225). A linear trend line is shown (slope = 5.327). (E) WT LA7 cells were treated with 0μM (n = 4) or 1μM (n = 4) 5-aza-dC for 48hrs. Pappa expression was analyzed via qPCR and standardized to Tbp. (F) Methylation levels at Pappa CGI shore site 12 are shown for WT LA7 cells (n = 8) and CRISPR clones (n = 9). The p-value reflects Bonferroni correction. Scatterplots demonstrating negative correlations for shore site 12 methylation levels and Mcs5c TCE copy number (G) and Pappa expression (H) are shown (R = -0.8034/-0.6022, n = 13/17, p-value = 0.0009/0.011, respectively) along with linear tread lines (slope = -0.046/-0.004, respectively). For all bar graphs, p-values were obtained using the non-parametric Mann-Whitney U test, and standard error bars are shown (*, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001).</p
Role of PAPPA on growth of lung cancer cells <i>in vitro</i>.
No full text<p>(A) Protein levels of PAPPA in H1299 cells over-expressing PAPPA. PAPPA levels in protein extracts from H1299 cell lines stably transfected with PAPPA expression vector (H1299/PAPPAov) and empty control vector (H1299/pCR3.1) were determined by ELISA kit as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048799#s2" target="_blank">Materials and Methods</a>. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048799#s3" target="_blank">Results</a> shown are Mean ± SE of triplicate determinations. (B) IGF dependent protease activity in conditioned medium from H1299/pCR3.1 and H1299/PAPPAov. (C) Growth curve of H1299/pCR3.1 and H1299/PAPPAov cell lines. Viable cells at different time points were measured by CellTiter-Blue as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048799#s2" target="_blank">Materials and Methods</a>. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048799#s3" target="_blank">Results</a> were expressed as Mean ± SE of triplicate determinations of Relative Fluorescence Unit (RFU) of three independent experiments. (D) Protein levels of PAPPA in H1792 cells over-expressing PAPPA. PAPPA levels in protein extracts from H1792 cell lines stably transfected with PAPPA expression vector (H1792/PAPPAov) and empty control vector (H1792/pCR3.1) were determined by ELISA kit as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048799#s2" target="_blank">Materials and Methods</a>. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048799#s3" target="_blank">Results</a> shown are Mean ± SE of triplicate determinations. (E) IGF dependent protease activity in conditioned medium from H1792/pCR3.1 and H1792/PAPPAov. (F) Growth curve of H1792/pCR3.1 and H1792/PAPPAov cell lines.</p
MeSH term explosion and author rank improve expert recommendations
No full textInformation 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
No full textThe 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"
No full textBoth 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.
Dispelling the Myths Behind First-author Citation Counts
No full textWe 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
<i>In vivo</i> methylation analysis of the <i>Pappa</i> CGI and CGI shore.
No full text(A) The first exon of the Pappa gene is shown in relation to a conserved CGI (green box) and the P4-1 looping fragment (gray box). The location of the 12 shore CG dinucleotides investigated in this report are indicated and numbered, as are the regions covered by the two pre-made CGI pyrosequencing assays. The CGI assays each examined 5 CG dinucleotides within the island. (B) A scatterplot demonstrating a statistically significant negative correlation between 6 week MEC Pappa expression (x-axis) and shore methylation (y-axis; Pearson correlation coefficient, R, = -0.67, n = 18, p-value = 0.0023) is shown. Shore methylation values were obtained by averaging the absolute methylation percentages of the 6 significant shore sites (Sites 1, 3, 6–9) for each individual sample. A linear trend line is shown with the dotted line (slope = -7.88). (C) No correlation was observed between 6 week MEC Pappa expression (x-axis) and CGI methylation (y-axis; Pearson correlation coefficient, R, = 0.16, n = 18, p-value = 0.52). CGI methylation values were obtained by averaging the absolute methylation percentages of the 5 sites examined by the CGI-2 assay for each individual sample. A linear trend line is shown with the dotted line (slope = 0.544).</p
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