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  1. Article ; Online: Unravel a neuroactive sHA sulfation pattern with neurogenesis activity by a library of defined oligosaccharides.

    Yao, Wang / Chen, Man / Dou, Xiaodong / Jin, Hongwei / Zhang, Xiao / Zhu, Yong / Sha, Meng / Liu, Zhenming / Meng, Xiangbao / Zhang, Liangren / Zhu, Shigong / Li, Zhongjun

    European journal of medicinal chemistry

    2018  Volume 163, Page(s) 583–596

    Abstract: Sulfated hyaluronic acid (sHA) is chemically synthetic mimetic of glycosaminoglycan (GAG ... of chondroitin sulfate (CS), sHA bears much higher molecular weight and is nearly free of other proteoglycan contaminants ... These properties make sHA a better bioscaffold to build safer and more functionalized material. However, chemical ...

    Abstract Sulfated hyaluronic acid (sHA) is chemically synthetic mimetic of glycosaminoglycan (GAG) presenting promising biological functions. Specific sulfation pattern, termed as sulfation code plays critical roles in regulating the binding mode between GAG and proteins. As a structural analogue of chondroitin sulfate (CS), sHA bears much higher molecular weight and is nearly free of other proteoglycan contaminants. These properties make sHA a better bioscaffold to build safer and more functionalized material. However, chemical sulfonation process on naked HA polysaccharide produces random sulfation patterns which makes it difficult in disclosing the SAR. Herein, we utilized sHA and CS oligosaccharides with defined sulfation pattern to unravel the SAR between sHA and neurogenesis. We demonstrate sHA tetrasaccharide bearing 6-O-sulfation (sHA-6S) but not other sulfation patterns bind to growth factors at nanomolar range and promote the neurite outgrowth of rat E18 hippocampal neurons in vitro. Furthermore, synthetic sHA polysaccharide enriched in 6-O-sulfation also promote the hippocampal neurite outgrowth in vitro. Our work provides an effective method to disclose the bioactive sulfation pattern of sHA. Our results indicate that a specific sHA sulfation pattern could direct important physiological processes and open the way for the application of sHA-6S in neuroscience and medicine.
    MeSH term(s) Animals ; Biomimetic Materials/chemistry ; Chondroitin Sulfates ; Glycosaminoglycans/chemistry ; Hippocampus/cytology ; Hyaluronic Acid/chemistry ; Hyaluronic Acid/pharmacology ; Neurogenesis/drug effects ; Neurons/ultrastructure ; Oligosaccharides ; Protein Binding ; Rats ; Sulfates/chemistry
    Chemical Substances Glycosaminoglycans ; Oligosaccharides ; Sulfates ; Hyaluronic Acid (9004-61-9) ; Chondroitin Sulfates (9007-28-7)
    Language English
    Publishing date 2018-12-04
    Publishing country France
    Document type Journal Article
    ZDB-ID 188597-2
    ISSN 1768-3254 ; 0009-4374 ; 0223-5234
    ISSN (online) 1768-3254
    ISSN 0009-4374 ; 0223-5234
    DOI 10.1016/j.ejmech.2018.12.004
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Application of the ReNuMa model in the Sha He river watershed: tools for watershed environmental management.

    Sha, Jian / Liu, Min / Wang, Dong / Swaney, Dennis P / Wang, Yuqiu

    Journal of environmental management

    2013  Volume 124, Page(s) 40–50

    Abstract: ... for examining the sensitivity and uncertainty of the model estimates were assessed for the Sha He River (SHR ...

    Abstract Models and related analytical methods are critical tools for use in modern watershed management. A modeling approach for quantifying the source apportionment of dissolved nitrogen (DN) and associated tools for examining the sensitivity and uncertainty of the model estimates were assessed for the Sha He River (SHR) watershed in China. The Regional Nutrient Management model (ReNuMa) was used to infer the primary sources of DN in the SHR watershed. This model is based on the Generalized Watershed Loading Functions (GWLF) and the Net Anthropogenic Nutrient Input (NANI) framework, modified to improve the characterization of subsurface hydrology and septic system loads. Hydrochemical processes of the SHR watershed, including streamflow, DN load fluxes, and corresponding DN concentration responses, were simulated following calibrations against observations of streamflow and DN fluxes. Uncertainty analyses were conducted with a Monte Carlo analysis to vary model parameters for assessing the associated variations in model outputs. The model performed accurately at the watershed scale and provided estimates of monthly streamflows and nutrient loads as well as DN source apportionments. The simulations identified the dominant contribution of agricultural land use and significant monthly variations. These results provide valuable support for science-based watershed management decisions and indicate the utility of ReNuMa for such applications.
    MeSH term(s) Calibration ; China ; Climate Change ; Conservation of Natural Resources ; Decision Making ; Hydrology ; Models, Theoretical ; Monte Carlo Method ; Nitrogen/analysis ; Uncertainty
    Chemical Substances Nitrogen (N762921K75)
    Language English
    Publishing date 2013-07-30
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 184882-3
    ISSN 1095-8630 ; 0301-4797
    ISSN (online) 1095-8630
    ISSN 0301-4797
    DOI 10.1016/j.jenvman.2013.03.030
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Application of the ReNuMa model in the Sha He river watershed: Tools for watershed environmental management

    Sha, J. / Liu, M. / Wang, D. / Swaney, D. P. / Wang, Y.

    Journal of environmental management

    2013  Volume -, Issue 124, Page(s) 40

    Language English
    Document type Article
    ZDB-ID 184882-3
    ISSN 0301-4797
    Database Current Contents Nutrition, Environment, Agriculture

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  4. Article: Application of the ReNuMa model in the Sha He river watershed: Tools for watershed environmental management

    Sha, Jian / Liu, Min / Wang, Dong / Swaney, Dennis P / Wang, Yuqiu

    Journal of environmental management. 2013 July 30, v. 124

    2013  

    Abstract: ... for examining the sensitivity and uncertainty of the model estimates were assessed for the Sha He River (SHR ...

    Abstract Models and related analytical methods are critical tools for use in modern watershed management. A modeling approach for quantifying the source apportionment of dissolved nitrogen (DN) and associated tools for examining the sensitivity and uncertainty of the model estimates were assessed for the Sha He River (SHR) watershed in China. The Regional Nutrient Management model (ReNuMa) was used to infer the primary sources of DN in the SHR watershed. This model is based on the Generalized Watershed Loading Functions (GWLF) and the Net Anthropogenic Nutrient Input (NANI) framework, modified to improve the characterization of subsurface hydrology and septic system loads. Hydrochemical processes of the SHR watershed, including streamflow, DN load fluxes, and corresponding DN concentration responses, were simulated following calibrations against observations of streamflow and DN fluxes. Uncertainty analyses were conducted with a Monte Carlo analysis to vary model parameters for assessing the associated variations in model outputs. The model performed accurately at the watershed scale and provided estimates of monthly streamflows and nutrient loads as well as DN source apportionments. The simulations identified the dominant contribution of agricultural land use and significant monthly variations. These results provide valuable support for science-based watershed management decisions and indicate the utility of ReNuMa for such applications.
    Keywords Monte Carlo method ; agricultural land ; analytical methods ; environmental management ; land use ; model uncertainty ; models ; nitrogen ; nutrient management ; pollution load ; rivers ; stream flow ; uncertainty analysis ; watershed management ; watersheds ; China
    Language English
    Dates of publication 2013-0730
    Size p. 40-50.
    Publishing place Elsevier Ltd
    Document type Article
    ZDB-ID 184882-3
    ISSN 1095-8630 ; 0301-4797
    ISSN (online) 1095-8630
    ISSN 0301-4797
    DOI 10.1016/j.jenvman.2013.03.030
    Database NAL-Catalogue (AGRICOLA)

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  5. Book ; Online: The essential role of language in survey research

    Mandy Sha / Tim Gabel

    (RTI Press Publication ; BK-0023-2004)

    2020  

    Series title RTI Press Publication ; BK-0023-2004
    Keywords SOC024000 ; LAN004000 ; JHBC ; GTC ; Survey research
    Language English
    Size 1 electronic resource (290 pages)
    Publisher RTI Press/RTI International
    Publishing place Research Triangle Park, NC
    Document type Book ; Online
    Note English
    HBZ-ID HT030375354
    ISBN 9781934831236 ; 1934831239
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  6. Book ; Online: Imagination and Science in Romanticism

    Sha, Richard C.

    2018  

    Keywords Literary theory
    Language 0|e
    Size 1 electronic resource (344 pages)
    Publisher Johns Hopkins University Press
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT021612433
    ISBN 9781421441245 ; 1421441241
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  7. Book: Sha bing hua ji

    Sha, Bing

    1982  

    Language Chinese
    Size chiefly ill
    Edition Di 1 ban
    Publisher Ren min mei shu chu ban she
    Publishing place Bei jing
    Document type Book
    Database Former special subject collection: coastal and deep sea fishing

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  8. Book ; Online: Radiocarbon ages and diatom abundance in cores GA306-BC4 and GA306-GC4 obtained during Galathea 3 cruise, West Greenland, supplementary data to: Sha, Longbin; Jiang, Hui; Knudsen, Karen Luise (2012): Diatom evidence of climatic change in Holsteinsborg Dyb, west of Greenland, during the last 1200 years. The Holocene, 22(3), 347-358

    Sha, Longbin / Jiang, Hui / Knudsen, Karen Luise

    2012  

    Abstract: Diatom assemblages from Holsteinsborg Dyb on the West Greenland shelf were analysed with high temporal resolution for the last 1200 years. A high degree of consistency between changes in frequency of selected diatom species and instrumental data from the ...

    Abstract Diatom assemblages from Holsteinsborg Dyb on the West Greenland shelf were analysed with high temporal resolution for the last 1200 years. A high degree of consistency between changes in frequency of selected diatom species and instrumental data from the same area during the last 70 years confirms the reliability of diatoms (particularly sea-ice species and warm-water species) for the study of palaeoceanographic changes in this area. A general cooling trend with some fluctuations is marked by an increase in sea-ice species throughout the last 1200 years. A relatively warm period with increased influence of Atlantic water masses of the Irminger Current (IC) is found at AD 750-1330, although with some oceanographic variability after AD 1000. A pronounced oceanographic shift occurred at AD 1330, corresponding in time to the transition from the so-called 'Medieval Warm Period' (MWP) to the 'Little Ice Age' (LIA). The LIA cold episode is characterized by three intervals with particularly cold sea-surface conditions at AD 1330-1350, AD 1400-1575 and AD 1660-1710 as a result of variable influence of Polar waters in the area. During the last 70 years, two relatively warm periods and one cold period (the early 1960s to mid-1990s) are indicated by changes in the diatom components. Our study demonstrates that sedimentary records on the West Greenland shelf provide valuable palaeoenvironment data that confirm a linkage between local and large-scale North Atlantic oceanographic and atmospheric oscillations.
    Language English
    Dates of publication 2012-9999
    Size Online-Ressource
    Publisher PANGAEA - Data Publisher for Earth & Environmental Science
    Publishing place Bremen/Bremerhaven
    Document type Book ; Online
    Note This dataset is supplement to doi:10.1177/0959683611423684
    DOI 10.1594/PANGAEA.783985
    Database Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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  9. Article ; Online: Predictive models for short-term load forecasting in the UK's electrical grid.

    Sha'aban, Yusuf A

    PloS one

    2024  Volume 19, Issue 4, Page(s) e0297267

    Abstract: There are global efforts to deploy Electric Vehicles (EVs) because of the role they promise to play in energy transition. These efforts underscore the e-mobility paradigm, representing an interplay between renewable energy resources, smart technologies, ... ...

    Abstract There are global efforts to deploy Electric Vehicles (EVs) because of the role they promise to play in energy transition. These efforts underscore the e-mobility paradigm, representing an interplay between renewable energy resources, smart technologies, and networked transportation. However, there are concerns that these initiatives could burden the electricity grid due to increased demand. Hence, the need for accurate short-term load forecasting is pivotal for the efficient planning, operation, and control of the grid and associated power systems. This study presents robust models for forecasting half-hourly and hourly loads in the UK's power system. The work leverages machine learning techniques such as Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR) to develop robust prediction models using the net imports dataset from 2010 to 2020. The models were evaluated based on metrics like Root Mean Square Error (RMSE), Mean Absolute Prediction Error (MAPE), Mean Absolute Deviation (MAD), and the Correlation of Determination (R2). For half-hourly forecasts, SVR performed best with an R-value of 99.85%, followed closely by GPR and ANN. But, for hourly forecasts, ANN led with an R-value of 99.71%. The findings affirm the reliability and precision of machine learning methods in short-term load forecasting, particularly highlighting the superior accuracy of the SVR model for half-hourly forecasts and the ANN model for hourly forecasts.
    MeSH term(s) Reproducibility of Results ; Benchmarking ; Computer Systems ; Electricity ; United Kingdom ; Forecasting
    Language English
    Publishing date 2024-04-04
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0297267
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: A Graph Partition-Based Large-Scale Distribution Network Reconfiguration Method.

    Sha, Yuanxia

    publication RETRACTED

    Computational intelligence and neuroscience

    2022  Volume 2022, Page(s) 3169065

    Abstract: This article focuses on the analysis of large-scale distribution network reconstruction fused with graph theory and graph partitioning algorithms. Graph theory and graph segmentation algorithms have been rushed by many researchers in the fields of ... ...

    Abstract This article focuses on the analysis of large-scale distribution network reconstruction fused with graph theory and graph partitioning algorithms. Graph theory and graph segmentation algorithms have been rushed by many researchers in the fields of medicine, drone, and neural network. It is a newcomer in the field of computer vision, which can not only realize the division in color but also divide it by image data. The distribution network is also indispensable for new energy, electric machines, but the traditional distribution network has many problems, such as not suitable for distributed power access and excessive network loss. To improve the performance of distribution networks and reduce network losses, this paper A multi-division model for distribution network construction and reconstruction is established, and a graph theory-based division algorithm method is proposed to effectively solve the problem of feeder-to-feeder reconstruction during large-scale distribution in distribution networks. Through its superconductivity phenomenon and the characteristics of clustering algorithm division, this paper uses formulas to show its division principle and gives examples of various distribution network reconstruction algorithms to explore which method of improvement can improve the performance of the distribution network and reduce network losses. The number of iterations is also strictly considered, and the value is taken after multiple iterations to reduce the error. Through the distribution network calculation example, the network loss reduction value is obtained, and the distribution network fault repair model is exemplified. The picture is used to briefly describe the process of distribution network reconstruction and find that the faults of the distribution network can be quickly located and isolated through the FTU, and quickly repaired. Finally, in order to reduce the network loss, reduce the load of power flow calculation, and solve the problem of local optimization, a JA-BE-JA optimization algorithm based on large-scale distribution network reconfiguration is proposed. The mixed sampling method is preferred to test the number of divisions in the four states, and the parameters are selected to test the performance of the improved annealing simulation algorithm, and the conclusion is drawn as follows: the improved graph segmentation algorithm has strong robustness, can avoid local optimization of graph data, and can reduce network loss. Compared with traditional distribution network reconstruction methods, the network loss can be reduced to 454.3 KW, which can be optimized by 10.68% compared with the initial network loss.
    MeSH term(s) Algorithms ; Cluster Analysis ; Computer Simulation ; Neural Networks, Computer
    Language English
    Publishing date 2022-03-14
    Publishing country United States
    Document type Journal Article ; Retracted Publication
    ZDB-ID 2388208-6
    ISSN 1687-5273 ; 1687-5273
    ISSN (online) 1687-5273
    ISSN 1687-5273
    DOI 10.1155/2022/3169065
    Database MEDical Literature Analysis and Retrieval System OnLINE

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