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  • 1
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    Lake ice phenology in the Northern Hemisphere extracted from SMMR, SSM/I and SSMIS data from 1979 to 2020 (2021)
    PANGAEA
    Details
    Publication Date: 2023-04-15
    Description: This dataset contains ice phenology for 56 lakes in the Northern Hemisphere from 1979 to 2019. The ice phenology was extracted from 3.125 km 37 GHz H-polarized evening brightness temperature data from Scanning Multi-channel Microwave Radiometer (SMMR), Special Sensor Microwave Image (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) data in the Calibrated Enhanced Resolution Brightness Temperature (CETB) dataset. According to the differences in the brightness temperature between lake ice and open water, a threshold algorithm based on Moving t test method was applied to determine the lake ice status for the pixels 6.25 km away from the lake shore, and the ice phenology dates for each lake were then extracted. For the overlapping lake ice phenology results extracted from multiple satellite, the results from the satellite with the highest utilization were prioritized. The lake ice phenology dataset provides valuable information about the changes in the lakes with seasonal ice cover in the past four decades.
    Keywords: Alakol; Amadjuak; Athabasca; Ayakkum; Baikal; Baker; Balkhash; Bosten; Bratsk Reservoir; Calculated; Caspian Sea; Date; Dubawnt; Duration, number of days; Ebi; Erie; Event label; Great Bear; Great Slave; Hulun; Huron; Ice coverage, maximum; Ilmen; Kasba; Khanka; Khar Us; Khovsgol; Khyargas; Kremenchuk Reservoir; Kuybyshev Reservoir; Ladoga; La Grande 3 Reservoir; Lake_01_Great_Bear; Lake_02_Great_Slave; Lake_03_Dubawnt; Lake_04_Baker; Lake_05_Kasba; Lake_06_Athabasca; Lake_07_Netilling; Lake_08_Amadjuak; Lake_09_Winnipegosis; Lake_10_Manitoba; Lake_11_Winnipeg; Lake_12_Woods; Lake_13_La_Grande_3_Reservoir; Lake_14_Saint_Jean; Lake_15_Superior; Lake_16_Michigan; Lake_17_Huron; Lake_18_Erie; Lake_19_Ontario; Lake_20_Vanern; Lake_21_Kremenchuk_Reservoir; Lake_22_Onega; Lake_23_Ladoga; Lake_24_Peipsi; Lake_25_Ilmen; Lake_26_Rybinsk_Reservoir; Lake_27_Kuybyshev_Reservoir; Lake_28_Tsimlyanskoye_Reservoir; Lake_29_Caspian_Sea; Lake_30_Zeyskoye_Reservoir; Lake_31_Khanka; Lake_32_Bratsk_Reservoir; Lake_33_Baikal; Lake_34_Khovsgol; Lake_35_Uvs; Lake_36_Khyargas; Lake_37_Khar_Us; Lake_38_Zaysan; Lake_39_Sasykkol; Lake_40_Alakol; Lake_41_Balkhash; Lake_42_Qapshaghay_Bogeni_Reservoir; Lake_43_Ulungur; Lake_44_Ebi; Lake_45_Bosten; Lake_46_Ayakkum; Lake_47_Qinghai; Lake_48_Ngoring; Lake_49_Ma_pang_yung_tso; Lake_50_Zhari_Namco; Lake_51_Tangra; Lake_52_Siling; Lake_53_Nam; Lake_54_Hulun; Lake_55_Large_Aral_Sea; Lake_56_Sarygamysh; lake ice phenology; Large Aral Sea; Latitude of event; Longitude of event; Manitoba; Ma-p'ang yung-ts'o; Michigan; MULT; Multiple investigations; Nam; Netilling; Ngoring; Onega; Ontario; Optional event label; Passive microwave radiometer; Peipsi; Qapshaghay Bogeni Reservoir; Qinghai; remote sensing; Rybinsk Reservoir; Saint-Jean; Sarygamysh; Sasykkol; Siling; SMMR; SSM/I and SSMIS; Superior; Tangra; Tsimlyanskoye Reservoir; Ulungur; Uvs; Vanern; Winnipeg; Winnipegosis; Woods; Years; Zaysan; Zeyskoye Reservoir; Zhari Namco
    Type: Dataset
    Format: text/tab-separated-values, 14019 data points
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  • 2
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    Lake ice phenology in the Northern Hemisphere extracted from SMMR, SSM/I and SSMIS data from 1979 to 2021 (2022)
    PANGAEA
    Details
    Publication Date: 2023-04-15
    Description: This dataset contains ice phenology for 56 lakes in the Northern Hemisphere from 1979 to 2021. The ice phenology was extracted from 3.125 km 37 GHz H-polarized evening brightness temperature data from Scanning Multi-channel Microwave Radiometer (SMMR), Special Sensor Microwave Image (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) data in the Calibrated Enhanced Resolution Brightness Temperature (CETB) dataset.
    Keywords: Alakol; Amadjuak; Athabasca; Ayakkum; Baikal; Baker; Balkhash; Bosten; Bratsk Reservoir; Calculated; Caspian Sea; Date; Dubawnt; Duration, number of days; Ebi; Erie; Event label; Great Bear; Great Slave; Hulun; Huron; Ice coverage, maximum; Ilmen; Kasba; Khanka; Khar Us; Khovsgol; Khyargas; Kremenchuk Reservoir; Kuybyshev Reservoir; Ladoga; La Grande 3 Reservoir; Lake_01_Great_Bear; Lake_02_Great_Slave; Lake_03_Dubawnt; Lake_04_Baker; Lake_05_Kasba; Lake_06_Athabasca; Lake_07_Netilling; Lake_08_Amadjuak; Lake_09_Winnipegosis; Lake_10_Manitoba; Lake_11_Winnipeg; Lake_12_Woods; Lake_13_La_Grande_3_Reservoir; Lake_14_Saint_Jean; Lake_15_Superior; Lake_16_Michigan; Lake_17_Huron; Lake_18_Erie; Lake_19_Ontario; Lake_20_Vanern; Lake_21_Kremenchuk_Reservoir; Lake_22_Onega; Lake_23_Ladoga; Lake_24_Peipsi; Lake_25_Ilmen; Lake_26_Rybinsk_Reservoir; Lake_27_Kuybyshev_Reservoir; Lake_28_Tsimlyanskoye_Reservoir; Lake_29_Caspian_Sea; Lake_30_Zeyskoye_Reservoir; Lake_31_Khanka; Lake_32_Bratsk_Reservoir; Lake_33_Baikal; Lake_34_Khovsgol; Lake_35_Uvs; Lake_36_Khyargas; Lake_37_Khar_Us; Lake_38_Zaysan; Lake_39_Sasykkol; Lake_40_Alakol; Lake_41_Balkhash; Lake_42_Qapshaghay_Bogeni_Reservoir; Lake_43_Ulungur; Lake_44_Ebi; Lake_45_Bosten; Lake_46_Ayakkum; Lake_47_Qinghai; Lake_48_Ngoring; Lake_49_Ma_pang_yung_tso; Lake_50_Zhari_Namco; Lake_51_Tangra; Lake_52_Siling; Lake_53_Nam; Lake_54_Hulun; Lake_55_Large_Aral_Sea; Lake_56_Sarygamysh; lake ice phenology; Large Aral Sea; Latitude of event; Longitude of event; Manitoba; Ma-p'ang yung-ts'o; Michigan; MULT; Multiple investigations; Nam; Netilling; Ngoring; Onega; Ontario; Optional event label; Passive microwave radiometer; Peipsi; Qapshaghay Bogeni Reservoir; Qinghai; remote sensing; Rybinsk Reservoir; Saint-Jean; Sarygamysh; Sasykkol; Siling; SMMR; SSM/I and SSMIS; Superior; Tangra; Tsimlyanskoye Reservoir; Ulungur; Uncertainty; Uvs; Vanern; Winnipeg; Winnipegosis; Woods; Years; Zaysan; Zeyskoye Reservoir; Zhari Namco
    Type: Dataset
    Format: text/tab-separated-values, 22450 data points
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    DATA PANGAEA
  • 3
    Electronic Resource
    Electronic Resource
    Artificial Neural Network Method for Predicting the Specificity of GalNAc-transferase (1997)
    Springer
    The protein journal 16 (1997), S. 689-700 
    Details
    ISSN: 1573-4943
    Keywords: Artificial neural network ; Kohonen's self-organization model ; nonapeptide ; O-glycosylation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract The specificity of GalNAc-transferase is consistent with the existence of an extended site composed of nine subsites, denoted by R4, R3, R2, R1, R0, R1′, R2′, R3′, and R4′, where the acceptor at R0 is either Ser or Thr to which the reducing monosaccharide is anchored. To predict whether a peptide will react with the enzyme to form a Ser- or Thr-conjugated glycopeptide, a neural network method—Kohonen's self-organization model is proposed in this paper. Three hundred five oligopeptides are chosen for the training site, with another 30 oligopeptides for the test set. Because of its high correct prediction rate (26/30=86.7%) and stronger fault-tolerant ability, it is expected that the neural network method can be used as a technique for predicting O-glycosylation and designing effective inhibitors of GalNAc-transferase. It might also be useful for targeting drugs to specific sites in the body and for enzyme replacement therapy for the treatment of genetic disorders.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1026306520790
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    Paper (German National Licenses)
    Fulltext
  • 4
    Electronic Resource
    Electronic Resource
    Prediction of β-Turns (1998)
    Springer
    The protein journal 17 (1998), S. 363-376 
    Details
    ISSN: 1573-4943
    Keywords: Artificial neural network ; Kohonen's self-organization model ; β-turns
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract Kohonen's self-organization model, a neural network model, is applied to predict the β-turns in proteins. There are 455 β-turn tetrapeptides and 3807 non-β-turn tetrapeptides in the training database. The rates of correct prediction for the 110 β-turn tetrapeptides and 30,229 non-β-turn tetrapeptides in the testing database are 81.8% and 90.7%, respectively. The high quality of prediction of neural network model implies that the residue-coupled effect along a polypeptide chain is important for the formation of reversal turns, such as β-turns, during the process of protein folding.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022559300504
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    Paper (German National Licenses)
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  • 5
    Electronic Resource
    Electronic Resource
    On robust stability of a class of polynomial families (1993)
    Springer
    Applied mathematics and mechanics 14 (1993), S. 1095-1102 
    Details
    ISSN: 1573-2754
    Keywords: value mapping ; H-stability ; H-equivalence
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Mathematics , Physics
    Notes: Abstract In this paper, we discuss the robust stability of a class of polynomial families more general than the interval polynomial family and diamond polynomial family. We prove that the Hurwitz stability of some special cases of this class of polynomial families can be determined by checking finite polynomials. We also give an example to illustrate that it is not always possible to determine the Hurwitz stability of all this class of polynomial families by checking finite polynomials.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02456647
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  • 6
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    Theoretical Study of Antiferrodistortive Phase Transition in Strontium Titanate (2010)
    American Institute of Physics
    Details
    Publication Date: 2010-04-01
    Print ISSN: 1674-0068
    Electronic ISSN: 2327-2244
    Topics: Chemistry and Pharmacology , Physics
    Published by American Institute of Physics on behalf of Chinese Physical Society.
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  • 7
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    Hypoxic tumor microenvironment activates GLI2 via HIF-1α and TGF-β2 to promote chemoresistance in colorectal cancer (2018)
    National Academy of Sciences
    Details
    Publication Date: 2018-06-11
    Description: Colorectal cancer patients often relapse after chemotherapy, owing to the survival of stem or progenitor cells referred to as cancer stem cells (CSCs). Although tumor stromal factors are known to contribute to chemoresistance, it remains not fully understood how CSCs in the hypoxic tumor microenvironment escape the chemotherapy. Here, we report that hypoxia-inducible factor (HIF-1α) and cancer-associated fibroblasts (CAFs)-secreted TGF-β2 converge to activate the expression of hedgehog transcription factor GLI2 in CSCs, resulting in increased stemness/dedifferentiation and intrinsic resistance to chemotherapy. Genetic or small-molecule inhibitor-based ablation of HIF-1α/TGF-β2−mediated GLI2 signaling effectively reversed the chemoresistance caused by the tumor microenvironment. Importantly, high expression levels of HIF-1α/TGF-β2/GLI2 correlated robustly with the patient relapse following chemotherapy, highlighting a potential biomarker and therapeutic target for chemoresistance in colorectal cancer. Our study thus uncovers a molecular mechanism by which hypoxic colorectal tumor microenvironment promotes cancer cell stemness and resistance to chemotherapy and suggests a potentially targeted treatment approach to mitigating chemoresistance.
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
    Published by National Academy of Sciences
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  • 8
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    Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction (2020)
    Wiley
    Details
    Publication Date: 2020-08-19
    Print ISSN: 1932-1864
    Electronic ISSN: 1932-1872
    Topics: Mathematics
    Published by Wiley
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  • 9
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    Optimization of nitric oxide donors for investigating biofilm dispersal response in Pseudomonas aeruginosa clinical isolates (2020)
    Springer
    Details
    Publication Date: 2020-08-31
    Description: Abstract Pseudomonas aeruginosa biofilms contribute heavily to chronic lung infection in cystic fibrosis patients, leading to morbidity and mortality. Nitric oxide (NO) has been shown to disperse P. aeruginosa biofilms in vitro, ex vivo and in clinical trials as a promising anti-biofilm agent. Traditional NO donors such as sodium nitroprusside (SNP) have been extensively employed in different studies. However, the dosage of SNP in different studies was not consistent, ranging from 500 nM to 500 μM. SNP is light sensitive and produces cyanide, which may lead to data misinterpretation and inaccurate predictions of dispersal responses in clinical settings. New NO donors and NO delivery methods have therefore been explored. Here we assessed 7 NO donors using P. aeruginosa PAO1 and determined that SNP and Spermine NONOate (S150) successfully reduced 〉 60% biomass within 24 and 2 h, respectively. While neither dosage posed toxicity towards bacterial cells, chemiluminescence assays showed that SNP only released NO upon light exposure in M9 media and S150 delivered much higher performance spontaneously. S150 was then tested on 13 different cystic fibrosis P. aeruginosa (CF-PA) isolates; most CF-PA biofilms were significantly dispersed by 250 μM S150. Our work therefore discovered a commercially available NO donor S150, which disperses CF-PA biofilms efficiently within a short period of time and without releasing cyanide, as an alternative of SNP in clinical trials in the future. Key points • S150 performs the best in dispersing P. aeruginosa biofilms among 7 NO donors. • SNP only releases NO in the presence of light, while S150 releases NO spontaneously. • S150 successfully disperses biofilms formed by P. aeruginosa cystic fibrosis clinical isolates.
    Print ISSN: 0175-7598
    Electronic ISSN: 1432-0614
    Topics: Biology , Process Engineering, Biotechnology, Nutrition Technology
    Published by Springer
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  • 10
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    PEGylated Multi-Walled Carbon Nanotubes for Encapsulation and Sustained Release of Oxaliplatin (2012)
    Springer
    Details
    Publication Date: 2012-09-20
    Print ISSN: 0724-8741
    Electronic ISSN: 1573-904X
    Topics: Chemistry and Pharmacology
    Published by Springer
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