8 research outputs found

    Vegetation and Climate Change during the Last Deglaciation in the Great Khingan Mountain, Northeastern China

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    <div><p>The Great Khingan Mountain range, Northeast China, is located on the northern limit of modern East Asian Summer Monsoon (EASM) and thus highly sensitive to the extension of the EASM from glacial to interglacial modes. Here, we present a high-resolution pollen record covering the last glacial maximum and the early Holocene from a closed crater Lake Moon to reconstruct vegetation history during the glacial-interglacial transition and thus register the evolution of the EASM during the last deglaciation. The vegetation history has gone through distinct changes from subalpine meadow in the last glacial maximum to dry steppe dominated by <i>Artemisia</i> from 20.3 to 17.4 ka BP, subalpine meadow dominated by Cyperaceae and <i>Artemisia</i> between 17.4 and 14.4 ka BP, and forest steppe dominated by <i>Betula</i> and <i>Artemisia</i> after 14.4 ka BP. The pollen-based temperature index demonstrates a gradual warming trend started at around 20.3 ka BP with interruptions of several brief events. Two cold conditions occurred around at 17.2–16.6 ka BP and 12.8–11.8 ka BP, temporally correlating to the Henrich 1 and the Younger Dryas events respectively, 1and abrupt warming events occurred around at 14.4 ka BP and 11.8 ka BP, probably relevant to the beginning of the Bølling-Allerød stages and the Holocene. The pollen-based moisture proxy shows distinct drought condition during the last glacial maximum (20.3–18.0 ka BP) and the Younger Dryas. The climate history based on pollen record of Lake Moon suggests that the regional temperature variability was coherent with the classical climate in the North Atlantic, implying the dominance of the high latitude processes on the EASM evolution from the Last Glacial Maximum (LGM) to early Holocene. The local humidity variability was influenced by the EASM limitedly before the Bølling-Allerød warming, which is mainly controlled by the summer rainfall due to the EASM front covering the Northeast China after that.</p></div

    Simplified pollen percentage and concentration diagram of Lake Moon.

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    <p>Exaggeration (×10) is indicated by light-colored shading (adapted from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146261#pone.0146261.g002" target="_blank">Fig 2</a> of [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146261#pone.0146261.ref029" target="_blank">29</a>]).</p

    Comparison of pollen records with the δ<sup>18</sup>O records from Greenland ice core and Chinese stalagmite.

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    <p>(a) The δ<sup>18</sup>O records of the Greenland ice cores [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146261#pone.0146261.ref047" target="_blank">47</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146261#pone.0146261.ref048" target="_blank">48</a>] (the light blue line is NGRIP δ<sup>18</sup>O profile, the dark blue line is GRIP δ<sup>18</sup>O profile), (b) the common logarithm transformation of pollen-based Temperature index (to outweigh strong variations caused by percentage maxima of <i>Betula</i> and Cyperaceae), (c) pollen percentages of arboreal (green line), steppe (orange line) and meadow (blue line) taxa, (d) pollen-based Moisture index, (e) the stalagmite δ<sup>18</sup>O records from Hulu cave [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146261#pone.0146261.ref010" target="_blank">10</a>].</p

    Locations of study site and other paleoclimate records in the East Asia.

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    <p>(map modified from NASA; <a href="http://earthobservatory.nasa.gov/" target="_blank">http://earthobservatory.nasa.gov/</a>) Locations of Lake Moon, Baikal (52°05′N, 105°52′E), Suigetsu (35°35′N, 135°53′E) and Mikata (35°33′N, 135°54′E), and Hulu (32°30′N, 119°10′E) and Maboroshi (34°39′N, 133°13′E) caves with atmospheric circulation in summer. Black arrows show seasonal dominant wind vectors and the red arrow points to the location of coring point of Lake Moon.</p

    Averaged monthly precipitation (bars) and air temperatures (circles) in Aershan during AD 1982–2011.

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    <p>Averaged monthly precipitation (bars) and air temperatures (circles) in Aershan during AD 1982–2011.</p

    Age-depth plot of the Moon Lake sequence.

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    <p>The calibrated AMS <sup>14</sup>C dates using CALIB 6.01 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146261#pone.0146261.ref026" target="_blank">26</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146261#pone.0146261.ref027" target="_blank">27</a>] are shown with 2 sigma error bar (adapted from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146261#pone.0146261.g002" target="_blank">Fig 2</a> of [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146261#pone.0146261.ref022" target="_blank">22</a>]).</p

    PCA ordination of nine pollen taxa with percentages >5% in any sample of pollen assemblages from Lake Moon.

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    <p>PCA ordination of nine pollen taxa with percentages >5% in any sample of pollen assemblages from Lake Moon.</p

    Assessment of progression of pulmonary fibrosis based on metabonomics and analysis of intestinal microbiota

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    The main purpose of this study was to explore the changes of biomarkers in different developmental stages of bleomycin-induced pulmonary fibrosis (PF) in rats via comprehensive pathophysiology, UPLC-QTOF/MS metabonomic technology, and 16S rRNA gene sequencing of intestinal microbiota. The rats were randomly divided into normal control and 1-, 2- and 4-week model group. The rat model of PF was established by one-time intratracheal instillation of bleomycin. The levels of inflammatory and fibrosis-related factors such as hydroxyproline (HYP), type III procollagen (COL-III), type IV collagen (COL-IV), hyaluronidase (HA), laminin (LN), interleukin (IL)-1β, IL-6, malondialdehyde (MDA) increased and superoxide dismutase (SOD) decreased as the PF cycle progressed. In the 1-, 2- and 4-week model group, 2, 19 and 18 potential metabolic biomarkers and 3, 16 and 12 potential microbial biomarkers were detected, respectively, which were significantly correlated. Glycerophospholipid metabolism pathway was observed to be an important pathway affecting PF at 1, 2 and 4 weeks; arginine and proline metabolism pathways significantly affected PF at 2 weeks. Linoleic acid metabolism pathway exhibited clear metabolic abnormalities at 2 and 4 weeks of PF, and alpha-linolenic acid metabolism pathway significantly affected PF at 4 weeks. In this study, metabolomics technology and intestinal microbiota 16S rRNA gene sequencing were used to search for biomarkers with significant differences in each stage of pulmonary fibrosis. Finally, the variation characteristics of each stage of the disease were discussed. The hope is to provide new insights into the development of diagnostic biomarkers and potential therapeutic targets at all stages. </p
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