1,729,835 research outputs found
Supplemental Figure S7 from <sup>18</sup>F-FAZA PET Imaging Response Tracks the Reoxygenation of Tumors in Mice upon Treatment with the Mitochondrial Complex I Inhibitor BAY 87-2243
No full textSupplemental Figure S7. Dose-dependent effect of BAY 87-243 (1 and 9 mg/kg) on (A) 18F-FAZA uptake and (B) tumor volume for H460 xenografts of nude, murine models. For (A), *- p<0.001 for 2-way ANOVA of vehicle vs. BAY 87-2243, n=6 tumors and for (B) *-P<0.01 for 2-way ANOVA of vehicle vs. BAY 87-2243, n=6 tumors.</p
Supplemental Figure S5 from <sup>18</sup>F-FAZA PET Imaging Response Tracks the Reoxygenation of Tumors in Mice upon Treatment with the Mitochondrial Complex I Inhibitor BAY 87-2243
No full textSupplemental Figure S5. Bar graphs showing effect of BAY 87-2243 on RT-PCR expression profiles for (A) SLC16A, (B) ITGB1, (C) IGF-2 and (D) TK-1</p
Supplemental Figure S1 from <sup>18</sup>F-FAZA PET Imaging Response Tracks the Reoxygenation of Tumors in Mice upon Treatment with the Mitochondrial Complex I Inhibitor BAY 87-2243
No full textSupplemental Figure S1. Graph showing no effect of BAY 87-2243 on weight of nude mice carrying either H460 or PC3 tumor xenografts. Similar results for mice with 786-O xenografts (data not shown).</p
Supplemental Figure S6 from <sup>18</sup>F-FAZA PET Imaging Response Tracks the Reoxygenation of Tumors in Mice upon Treatment with the Mitochondrial Complex I Inhibitor BAY 87-2243
No full textSupplemental Figure S6. Bar graphs showing effect of BAY 87-2243 on RT-PCR expression profiles for (A) PDK-1, (B) GLUT-1, (C) GLUT-3 and (D) HK-2</p
Union Pacific (UP) 2243
No full textA photograph print showing Union Pacific (UP) 2243, 2-8-2, on passenger train No. 354, eastbound near Archer, WY, 2 coaches and a caboose converted from a boxcar, 45 mph
Lithium in NGC 2243 and NGC 104
Get PDFAims. Our aim was to determine the initial Li content of two clusters of similar metallicity but very different ages, the old open cluster NGC 2243 and the metal-rich globular cluster NGC 104. Methods. We compared the lithium abundances derived for a large sample of stars (from the turn-off to the red giant branch) in each cluster. For NGC 2243 the Li abundances are from the catalogues released by the Gaia-ESO Public Spectroscopic Survey, while for NGC 104 we measured the Li abundance using FLAMES/GIRAFFE spectra, which include archival data and new observations. We took the initial Li of NGC 2243 to be the lithium measured in stars on the hot side of the Li dip. We used the difference between the initial abundances and the post first dredge-up Li values of NGC 2243, and by adding this amount to the post first dredge-up stars of NGC 104 we were able to infer the initial Li of this cluster. Moreover, we compared our observational results to the predictions of theoretical stellar models for the difference between the initial Li abundance and that after the first dredge-up. Results. The initial lithium content of NGC 2243 was found to be A(Li)i = 2.85 ± 0.09 dex by taking the average Li abundance measured from the five hottest stars with the highest lithium abundance. This value is 1.69 dex higher than the lithium abundance derived in post first dredge-up stars. By adding this number to the lithium abundance derived in the post first dredge-up stars in NGC 104, we infer a lower limit of its initial lithium content of A(Li)i = 2.34 ± 0.13 dex. Stellar models predict similar values. Therefore, our result offers important insights for further theoretical developments
Inclusion list of 2243 substances
No full textInclusion list of 2243 substances (compound name, chemical formula, category, CAS number and molecular weight) used during the suspect screening of substances present in paper/board materials liable to migrate into food
Lithium in NGC 2243 and NGC 104
No full textAims. Our aim was to determine the initial Li content of two clusters of similar metallicity but very different ages, the old open cluster NGC 2243 and the metal-rich globular cluster NGC 104.
Methods. We compared the lithium abundances derived for a large sample of stars (from the turn-off to the red giant branch) in each cluster. For NGC 2243 the Li abundances are from the catalogues released by the Gaia-ESO Public Spectroscopic Survey, while for NGC 104 we measured the Li abundance using FLAMES/GIRAFFE spectra, which include archival data and new observations. We took the initial Li of NGC 2243 to be the lithium measured in stars on the hot side of the Li dip. We used the difference between the initial abundances and the post first dredge-up Li values of NGC 2243, and by adding this amount to the post first dredge-up stars of NGC 104 we were able to infer the initial Li of this cluster. Moreover, we compared our observational results to the predictions of theoretical stellar models for the difference between the initial Li abundance and that after the first dredge-up.
Results. The initial lithium content of NGC 2243 was found to be A(Li)i = 2.85 ± 0.09 dex by taking the average Li abundance measured from the five hottest stars with the highest lithium abundance. This value is 1.69 dex higher than the lithium abundance derived in post first dredge-up stars. By adding this number to the lithium abundance derived in the post first dredge-up stars in NGC 104, we infer a lower limit of its initial lithium content of A(Li)i = 2.34 ± 0.13 dex. Stellar models predict similar values. Therefore, our result offers important insights for further theoretical developments
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
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