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  • Nature Publishing Group (NPG)  (24)
  • PANGAEA  (4)
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  • 1
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    Eddy-permitting and eddy-resolving simulations of the Fram Strait ocean dynamics with the Finite-Element Sea-Ice Ocean Model (FESOM), links to NetCDF files (2017)
    PANGAEA
    In:  Supplement to: Wekerle, Claudia; Wang, Qiang; von Appen, Wilken-Jon; Danilov, Sergey; Schourup-Kristensen, Vibe; Jung, Thomas (2017): Eddy-Resolving Simulation of the Atlantic Water Circulation in the Fram Strait With Focus on the Seasonal Cycle. Journal of Geophysical Research: Oceans, 122(11), 8385-8405, https://doi.org/10.1002/2017JC012974
    Details
    Publication Date: 2023-03-16
    Description: Eddy driven recirculation of Atlantic Water (AW) in the Fram Strait modifies the amount of heat that reaches the Arctic Ocean, but is difficult to constrain in ocean models due to very small Rossby radius there. In this study we explore the effect of resolved eddies on the AW circulation in a locally eddy-resolving simulation of the global Finite-Element-Sea ice-Ocean-Model (FESOM) integrated for the years 2000-2009, by focusing on the seasonal cycle. An eddy-permitting simulation serves as a control run. Our results suggest that resolving local eddy dynamics is critical to realistically simulate ocean dynamics in the Fram Strait. Strong eddy activity simulated by the eddy-resolving model, with peak in winter and lower values in summer, is comparable in magnitude and seasonal cycle to observations from a long-term mooring array, whereas the eddy-permitting simulation underestimates the observed magnitude. Furthermore, a strong cold bias in the central Fram Strait present in the eddy-permitting simulation is reduced due to resolved eddy dynamics, and AW transport into the Arctic Ocean is increased with possible implications for the Arctic Ocean heat budget. Given the good agreement between the eddy-resolving model and measurements, it can help filling gaps that point-wise observations inevitably leave. For example, the path of the West Spitsbergen Current offshore branch, measured in the winter months by the mooring array, is shown to continue cyclonically around the Molloy Deep in the model, representing the major AW recirculation branch in this season.
    Keywords: AWI_PhyOce; File content; File format; File name; File size; FRAM; Fram Strait; Fram-Strait; FRontiers in Arctic marine Monitoring; Physical Oceanography @ AWI; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 100 data points
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    DATA PANGAEA
  • 2
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    FESOM Arctic Ocean sea ice concentration and thickness 1995-2014, links to movies in mp4 format (2016)
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven | Supplement to: Wang, Qiang; Danilov, Sergey; Jung, Thomas; Kaleschke, Lars; Wernecke, Andreas (2016): Sea ice leads in the Arctic Ocean: Model assessment, interannual variability and trends. Geophysical Research Letters, 43(13), 7019-7027, https://doi.org/10.1002/2016GL068696
    Details
    Publication Date: 2023-03-16
    Description: Northern Hemisphere sea ice from a Finite-Element Sea-Ice Ocean Model (FESOM) 4.5 km resolution simulation carried out by researchers from Alfred Wegener Institute (AWI), Germany. Concentration is shown with color; thickness is shown with shading. A global 1 degree mesh is used, with the "Arctic Ocean" locally refined to 4.5 km. South of CAA and Fram Strait the resolution is not refined in this simulation. The animation indicates that the 4.5 km model resolution helps to represent the small scale sea ice features, although much higher resolution is required to fully resolve the ice leads. The animation is created by Michael Böttinger from DKRZ (https://www.dkrz.de).
    Keywords: Arctic; DATE/TIME; File content; File format; File size; pan-Arctic; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 8 data points
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    DATA PANGAEA
  • 3
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    Results of a Finite-Element Sea-Ice Ocean Model (FESOM) set-up to study the interannual to decadal variability in the deep-water formation rates (2013)
    PANGAEA
    In:  Supplement to: Scholz, Patrick; Lohmann, Gerrit; Wang, Qiang; Danilov, Sergey (2013): Evaluation of a Finite-Element Sea-Ice Ocean Model (FESOM) set-up to study the interannual to decadal variability in the deep-water formation rates. Ocean Dynamics, 63(4), 347-370, https://doi.org/10.1007/s10236-012-0590-0
    Details
    Publication Date: 2023-01-13
    Description: The characteristics of a global set-up of the Finite-Element Sea-Ice Ocean Model under forcing of the period 1958-2004 are presented. The model set-up is designed to study the variability in the deep-water mass formation areas and was therefore regionally better resolved in the deep-water formation areas in the Labrador Sea, Greenland Sea, Weddell Sea and Ross Sea. The sea-ice model reproduces realistic sea-ice distributions and variabilities in the sea-ice extent of both hemispheres as well as sea-ice transport that compares well with observational data. Based on a comparison between model and ocean weather ship data in the North Atlantic, we observe that the vertical structure is well captured in areas with a high resolution. In our model set-up, we are able to simulate decadal ocean variability including several salinity anomaly events and corresponding fingerprint in the vertical hydrography. The ocean state of the model set-up features pronounced variability in the Atlantic Meridional Overturning Circulation as well as the associated mixed layer depth pattern in the North Atlantic deep-water formation areas.
    Keywords: File format; File name; File size; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 32 data points
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    DATA PANGAEA
  • 4
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    Eddy kinetic energy in the Arctic Ocean from moored velocity observations (2022)
    PANGAEA
    Details
    Publication Date: 2024-04-30
    Description: Mesoscale eddies are important for many aspects of the dynamics of the Arctic Ocean. These include the maintenance of the halocline and the Atlantic Water boundary current through lateral eddy fluxes, shelf-basin exchanges, transport of biological material and sea ice, and the modification of the sea-ice distribution. Here we review what is known about the mesoscale variability and its impacts in the Arctic Ocean in the context of an Arctic Ocean responding rapidly to climate change. In addition, we present the first quantification of eddy kinetic energy (EKE) from moored observations across the entire Arctic Ocean, which we compare to output from an eddy resolving numerical model. We show that EKE is largest in the northern Nordic Seas/Fram Strait and it is also elevated along the shelfbreak of the Arctic Circumpolar Boundary Current, especially in the Beaufort Sea. In the central basins it is 100-1000 times lower. Except for the region affected by southward sea-ice export south of Fram Strait, EKE is stronger when sea-ice concentration is low compared to dense ice cover. Areas where conditions typical in the Atlantic and Pacific prevail will increase. Hence, we conclude that the future Arctic Ocean will feature more energetic mesoscale variability. This table provides (eddy) kinetic energy in the Arctic Ocean calculated from moorings and a numerical model across the entire record and averaged over certain conditions (seasons, ice concentration). The calculations are explained in the manuscript (Eddies and the distribution of eddy kinetic energy in the Arctic Ocean). The used mooring data was compiled from six different sources as listed below and identified in the table based on the Source ID.
    Keywords: 250_MOOR; 293-S1_MOOR; 293-X1_MOOR; 293-X2_MOOR; 293-X3_MOOR; 295-S2_MOOR; A01_MOOR; AK1-1_MOOR; AK2-1_MOOR; AK3-1_MOOR; AK4-1_MOOR; AK5-1_MOOR; AK6-1_MOOR; AK7-1_MOOR; Akademik Tryoshnikov; AM1-91_MOOR; AM2-91_MOOR; AO1-92_MOOR; Arctic Ocean; ARK-XIV/2; ARK-XVIII/1; ARK-XXIX/3; ARK-XXX/1.2; ARK-XXX/2, GN05; ARK-XXXI/4; ATWAIN200_MOOR; AWI_PhyOce; AWI401-1_MOOR; AWI402-1_MOOR; AWI403-1_MOOR; AWI403-2_MOOR; AWI404-1_MOOR; AWI406-1_MOOR; AWI410-2_MOOR; AWI411-2_MOOR; AWI412-4_MOOR; AWI413-4_MOOR; AWI415-1_MOOR; AWI416-1_MOOR; AWI417-1_MOOR; AWI418-1_MOOR; BaffinBay_2_MOOR; BaffinBay_MOOR; BarrowSt_81_MOOR; BarrowSt_C_MOOR; BarrowSt_N_MOOR; BarrowSt_S_MOOR; BarrowSt_SC_MOOR; BarrowSt_Ss_MOOR; BG_a_MOOR; BG_b_MOOR; BG_c_MOOR; BG_d_MOOR; BI3_MOOR; BR1_MOOR; BR2_MOOR; BR3_MOOR; BRA_MOOR; BRB_MOOR; BRG_MOOR; BRK_MOOR; BS2_MOOR; BS3_MOOR; BS4_MOOR; BS5_MOOR; BS6_MOOR; BSO1_MOOR; BSO2_MOOR; BSO3_MOOR; BSO4_MOOR; BSO5_MOOR; C1_MOOR; C2_MOOR; C3_MOOR; C4_MOOR; C5_MOOR; C6_MOOR; CA04_MOOR; CA05_MOOR; CA06_MOOR; CA07_MOOR; CA08_MOOR; CA10_MOOR; CA11_MOOR; CA12_MOOR; CA13_MOOR; CA15_MOOR; CA16_MOOR; CA20_MOOR; CM-1_MOOR; CM-2_MOOR; CS1_MOOR; CS-1A_MOOR; CS2_MOOR; CS-2A_MOOR; CS3_MOOR; CS-3A_MOOR; CS4_MOOR; CS5_MOOR; Depth, bottom/max; Depth, top/min; DEPTH, water; DS_TUBE8_MOOR; Duration; EA1_MOOR; EA2_MOOR; EA3_MOOR; EA4_MOOR; EBC_MOOR; eddies; eddy kinetic energy; Eddy kinetic energy, 2000-2010; Eddy kinetic energy, 2010-2020; Eddy kinetic energy, at depth; Eddy kinetic energy, autumn; Eddy kinetic energy, ice; Eddy kinetic energy, mean; Eddy kinetic energy, model bandpass; Eddy kinetic energy, model online; Eddy kinetic energy, no ice; Eddy kinetic energy, some ice; Eddy kinetic energy, spring; Eddy kinetic energy, summer; Eddy kinetic energy, winter; EGN-1; EGS-1; EGS1-2; EGS2-1; EGS4-1; ELEVATION; F10-1; F1-1; F11_MOOR; F11-2; F12_MOOR; F12-1; F13_MOOR; F13-1; F14_MOOR; F14-1; F15-1; F16-1; F17_MOOR; F2-1; F3-1; F4-1; F5-1; F6-1; F7-1; F8-1; F9-1; FB2b_MOOR; FB6_MOOR; First year of observation; FRAM; FRontiers in Arctic marine Monitoring; FRS782_MOOR; FSC1_MOOR; FSC2_MOOR; FSC3_MOOR; FSC4_MOOR; GS-3_2_MOOR; HG-IV-S-1; High-frequency kinetic energy; HSNE60_MOOR; HudsonBay_MOOR; HudsonStrait_MOOR; I1_MOOR; I2_MOOR; I3_MOOR; IdF1-1; IdF2-1; IdF3-1; IdF4-1; ISWRIG_MOOR; Karasik-2015; KS02_MOOR; KS04_MOOR; KS06_MOOR; KS08_MOOR; KS10_MOOR; KS12_MOOR; KS14_MOOR; L97; LA97/2; Lance; Last year of observation; LATITUDE; LM3_MOOR; LONGITUDE; Low-frequency kinetic energy; M11_MOOR; M12_MOOR; M13_MOOR; M14_MOOR; M15_MOOR; M16_MOOR; M3_MOOR; M5_MOOR; M6_MOOR; M9a_MOOR; MA2B_MOOR; MB1B_MOOR; MB2B_MOOR; MB4B_MOOR; Mean kinetic energy; MOOR; Mooring; Mooring (long time); MOORY; N198_2_MOOR; N198_MOOR; N525_MOOR; N541_MOOR; NABOS_2015_AK1-1, NABOS_2018_AK1-1; NABOS_2015_AK2-1, NABOS_2018_AK2-1; NABOS_2015_AK3-1, NABOS_2018_AK3-1; NABOS_2015_AK4-1, NABOS_2018_AK4-1; NABOS_2015_AK5-1, NABOS_2018_AK5-1; NABOS_2015_AK6-1,NABOS_2018_AK6-1; NABOS_2015_AK7-1, NABOS_2018_AK7-1; NABOS, AT2015; NABOS 2015; Nansen-2015; North Greenland Sea; NPEO_MOOR; NWNA_MOOR; NWNB_MOOR; NWNC_MOOR; NWND_MOOR; NWNE_MOOR; NWNF_MOOR; NWNG_MOOR; NWSB_MOOR; NWSD_MOOR; NWSE_2_MOOR; NWSE_MOOR; OLIK-1_MOOR; OSL2a_MOOR; OSL2f_MOOR; Physical Oceanography @ AWI; Polarstern; PS100; PS100/039-2, PS114_25-1,ARKR02-01; PS100/045-1, PS114_29-2; PS100/047-1, PS114_40-2; PS100/053-1, PS114_36-1; PS100/073-1, PS109_20-1; PS100/106-1, PS114_23-2; PS100/142-1, PS109_139-1; PS100/180-2, PS109_111-1; PS100/181-1, PS109_112-1; PS100/182-1, PS109_113-1; PS100/183-1, PS109_114-1; PS109; PS109_133-1, PS114_52-1; PS109_138-2, PS114_53-1; PS109_148-1, PS114_60-2; PS114; PS52; PS62; PS94; PS99/070-1, PS107_3-1; PS99.2; R071_MOOR; R1-1; R2-1; R290_MOOR; R3-1; R333_MOOR; R356_MOOR; R4-1; R5-1; Reference/source; SS-5_MOOR; StA_MOOR; Station label; Stor_MOOR; Total kinetic energy; V-319_MOOR; Velocity, east; Velocity, north; Vilk_MOOR; WBC_MOOR; WG1_MOOR; WG15_MOOR; WG4_MOOR; Wunsch-NN1_MOOR; Wunsch-NN2_MOOR; Y1_MOOR; Y2_MOOR; YP_MOOR
    Type: Dataset
    Format: text/tab-separated-values, 4806 data points
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    DATA PANGAEA
  • 5
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    Prediction of new thermodynamically stable aluminum oxides (2015)
    Nature Publishing Group (NPG)
    Details
    Publication Date: 2015-04-02
    Description: Recently, it has been shown that under pressure, unexpected and counterintuitive chemical compounds become stable. Laser shock experiments (A. Rode, unpublished) on alumina (Al2O3) have shown non-equilibrium decomposition of alumina with the formation of free Al and a mysterious transparent phase. Inspired by these observations, we have explored the possibility of the formation of new chemical compounds in the system Al-O. Using the variable-composition structure prediction algorithm USPEX, in addition to the well-known Al2O3, we have found two extraordinary compounds Al4O7 and AlO2 to be thermodynamically stable in the pressure ranges 330-443 GPa and above 332 GPa, respectively. Both of these compounds at the same time contain oxide O2− and peroxide O22− ions, and both are insulating. Peroxo-groups are responsible for gap states, which significantly reduce the electronic band gap of both Al4O7 and AlO2. Scientific Reports 5 doi: 10.1038/srep09518
    Electronic ISSN: 2045-2322
    Topics: Natural Sciences in General
    Published by Nature Publishing Group (NPG)
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  • 6
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    Fluorescence-Activated Cell Sorting Analysis of Heterotypic Cell-in-Cell Structures (2015)
    Nature Publishing Group (NPG)
    Details
    Publication Date: 2015-04-28
    Description: Cell-in-cell structures (CICs), characterized by the presence of one or more viable cells inside another one, were recently found important player in development, immune homeostasis and tumorigenesis etc. Incompatible with ever-increasing interests on this unique phenomenon, reliable methods available for high throughput quantification and systemic investigation are lacking. Here, we report a flow cytometry-based method for rapid analysis and sorting of heterotypic CICs formed between lymphocytes and tumor cells. In this method, cells were labeled with fluorescent dyes for fluorescence-activated cell sorting (FACS) by flow cytometry, conditions for reducing cell doublets were optimized such that high purity (〉95%) of CICs could be achieved. By taking advantage of this method, we analyzed CICs formation between different cell pairs, and found that factors from both internalized effector cells and engulfing target cells affect heterotypic CICs formation. Thus, flow cytometry-based FACS analysis would serve as a high throughput method to promote systemic researches on CICs. Scientific Reports 5 doi: 10.1038/srep09588
    Electronic ISSN: 2045-2322
    Topics: Natural Sciences in General
    Published by Nature Publishing Group (NPG)
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  • 7
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    PI3Kβ Inhibitor TGX221 Selectively Inhibits Renal Cell Carcinoma Cells with Both VHL and SETD2 mutations and Links Multiple Pathways (2015)
    Nature Publishing Group (NPG)
    Details
    Publication Date: 2015-04-09
    Description: We aimed to exploit novel compounds with high selectivity to clear cell renal cell carcinoma (ccRCC) with common mutations. Using the GDSC databases, we searched for compounds with high selectivity for ccRCC with VHL and/or SETD2 mutations. Clinical impact and gene interactions were analysed using TCGA database. In vitro and in vivo studies were performed to validate the inhibitory effects of the compound. We identified the selective PI3Kβ inhibitor TGX221 as a selective inhibitor for ccRCC with both VHL and SETD2 mutations. TGX221 also targeted cancer cells with CDKN2A and PTEN mutations. Changes in PTEN and CDKN2A gene sets were associated with worsened prognosis of ccRCC. TGX221 substantially and selectively inhibited the down stream products of VHL, SETD2, and PTEN in ccRCC cells with VHL and SETD2 mutations. TGX221 also exhibited significant selectivity in inhibiting cell motility and tumourigenesis of ccRCC cells with VHL and SETD2 mutations. TGX221 is a novel inhibitor with high selectivity for ccRCC with VHL and SETD2 mutations. It also targeted PTEN and CDKN2A mutations. How those genes were associated with PI3Kβ warranted further investigations. Scientific Reports 5 doi: 10.1038/srep09465
    Electronic ISSN: 2045-2322
    Topics: Natural Sciences in General
    Published by Nature Publishing Group (NPG)
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  • 8
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    Genome sequence of cultivated Upland cotton (Gossypium hirsutum TM-1) provides insights into genome evolution (2015)
    Nature Publishing Group (NPG)
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    Publication Date: 2015-05-13
    Description: Nature Biotechnology 33, 524 (2015). doi:10.1038/nbt.3208 Authors: Fuguang Li, Guangyi Fan, Cairui Lu, Guanghui Xiao, Changsong Zou, Russell J Kohel, Zhiying Ma, Haihong Shang, Xiongfeng Ma, Jianyong Wu, Xinming Liang, Gai Huang, Richard G Percy, Kun Liu, Weihua Yang, Wenbin Chen, Xiongming Du, Chengcheng Shi, Youlu Yuan, Wuwei Ye, Xin Liu, Xueyan Zhang, Weiqing Liu, Hengling Wei, Shoujun Wei, Guodong Huang, Xianlong Zhang, Shuijin Zhu, He Zhang, Fengming Sun, Xingfen Wang, Jie Liang, Jiahao Wang, Qiang He, Leihuan Huang, Jun Wang, Jinjie Cui, Guoli Song, Kunbo Wang, Xun Xu, John Z Yu, Yuxian Zhu & Shuxun Yu
    Print ISSN: 1087-0156
    Electronic ISSN: 1546-1696
    Topics: Biology , Process Engineering, Biotechnology, Nutrition Technology
    Published by Nature Publishing Group (NPG)
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  • 9
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    Genome sequence and genetic diversity of the common carp, Cyprinus carpio (2014)
    Nature Publishing Group (NPG)
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    Publication Date: 2014-10-30
    Description: Nature Genetics 46, 1212 (2014). doi:10.1038/ng.3098 Authors: Peng Xu, Xiaofeng Zhang, Xumin Wang, Jiongtang Li, Guiming Liu, Youyi Kuang, Jian Xu, Xianhu Zheng, Lufeng Ren, Guoliang Wang, Yan Zhang, Linhe Huo, Zixia Zhao, Dingchen Cao, Cuiyun Lu, Chao Li, Yi Zhou, Zhanjiang Liu, Zhonghua Fan, Guangle Shan, Xingang Li, Shuangxiu Wu, Lipu Song, Guangyuan Hou, Yanliang Jiang, Zsigmond Jeney, Dan Yu, Li Wang, Changjun Shao, Lai Song, Jing Sun, Peifeng Ji, Jian Wang, Qiang Li, Liming Xu, Fanyue Sun, Jianxin Feng, Chenghui Wang, Shaolin Wang, Baosen Wang, Yan Li, Yaping Zhu, Wei Xue, Lan Zhao, Jintu Wang, Ying Gu, Weihua Lv, Kejing Wu, Jingfa Xiao, Jiayan Wu, Zhang Zhang, Jun Yu & Xiaowen Sun
    Print ISSN: 1061-4036
    Electronic ISSN: 1546-1718
    Topics: Biology , Medicine
    Published by Nature Publishing Group (NPG)
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  • 10
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    A human-specific AS3MT isoform and BORCS7 are molecular risk factors in the 10q24.32 schizophrenia-associated locus (2016)
    Nature Publishing Group (NPG)
    Details
    Publication Date: 2016-06-08
    Description: Nature Medicine 22, 649 (2016). doi:10.1038/nm.4096 Authors: Ming Li, Andrew E Jaffe, Richard E Straub, Ran Tao, Joo Heon Shin, Yanhong Wang, Qiang Chen, Chao Li, Yankai Jia, Kazutaka Ohi, Brady J Maher, Nicholas J Brandon, Alan Cross, Joshua G Chenoweth, Daniel J Hoeppner, Huijun Wei, Thomas M Hyde, Ronald McKay, Joel E Kleinman & Daniel R Weinberger
    Print ISSN: 1078-8956
    Electronic ISSN: 1546-170X
    Topics: Biology , Medicine
    Published by Nature Publishing Group (NPG)
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