Dataset Identification:
Resource Abstract:
- description: Establishing a predictive science is a timely approach for nanotechnology-based enterprises wishing to avoid
the problems faced by the chemical industry, where only a few hundred of the ca. 50,000 industrial chemicals have undergone
toxicity testing, making it very challenging to control their toxicological impact in the environment. There is also growing
recognition in Europe and Asia that a paradigm shift in toxicology is required to deal with anthropogenic activity. The UC-CEIN
proposes to conduct predictive toxicological sciencefor engineered nanomaterials (NMs)through the founding of the Center for
Environmental Implications of Nanotechnology (CEIN) at UC Los Angeles (UCLA) in partnership with UC Santa Barbara (UCSB),
UC Davis (UCD), UC Riverside (UCR), Columbia University (New York), University of Texas (El Paso, TX), Nanyang Technological
University (NTU, Singapore), the Molecular Foundry at Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National
Laboratory (LLNL), Sandia National Laboratory (SNL), the University of Bremen (Germany), University College Dublin (UCD, Ireland),
and the Universitat Rovira i Virgili (URV, Spain). This Center unites a highly integrated, multidisciplinary, synergistic
team with the skill set to solve the complexities of environmental science, eco-toxicity, materials science, nanotechnology,
biological mechanisms of injury, and the environmental fate and transport of NMs.The goal of the Center is to develop a broad-based
model of predictive toxicologypremised on quantitative structure-a ctivity relationships (QSARs) and NM injury paradigms at
the biological level. This predictive scientific modelwill consider: (i) the NMs most likely to come into contact with the
environment; (ii) their distribution in the environment; (iii) representative environmental life forms serving as early sentinels
to monitor the spread and bio-accumulation of hazardous NMs; (iv) biological screening assays allowing QSARs to be developed
based on the bio-physicochemical properties of NMs; (v) High throughput screening (HTS) of a combinatorial NM library; and
(vi) a self-learning computational system providing a framework for predictive risk analysis. These research activities will
be combined with educational programs informing the public, future generations of scientists, public agencies, and industrial
stakeholders of the importance of safe implementation of nanotechnology in the environment. The overall impact will be to
reduce uncertainty about the possible consequences of NMs in the environment, while at the same time providing guidelines
for their safe design to prevent environmental hazards.; abstract: Establishing a predictive science is a timely approach
for nanotechnology-based enterprises wishing to avoid the problems faced by the chemical industry, where only a few hundred
of the ca. 50,000 industrial chemicals have undergone toxicity testing, making it very challenging to control their toxicological
impact in the environment. There is also growing recognition in Europe and Asia that a paradigm shift in toxicology is required
to deal with anthropogenic activity. The UC-CEIN proposes to conduct predictive toxicological sciencefor engineered nanomaterials
(NMs)through the founding of the Center for Environmental Implications of Nanotechnology (CEIN) at UC Los Angeles (UCLA) in
partnership with UC Santa Barbara (UCSB), UC Davis (UCD), UC Riverside (UCR), Columbia University (New York), University of
Texas (El Paso, TX), Nanyang Technological University (NTU, Singapore), the Molecular Foundry at Lawrence Berkeley National
Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), Sandia National Laboratory (SNL), the University of Bremen
(Germany), University College Dublin (UCD, Ireland), and the Universitat Rovira i Virgili (URV, Spain). This Center unites
a highly integrated, multidisciplinary, synergistic team with the skill set to solve the complexities of environmental science,
eco-toxicity, materials science, nanotechnology, biological mechanisms of injury, and the environmental fate and transport
of NMs.The goal of the Center is to develop a broad-based model of predictive toxicologypremised on quantitative structure-a
ctivity relationships (QSARs) and NM injury paradigms at the biological level. This predictive scientific modelwill consider:
(i) the NMs most likely to come into contact with the environment; (ii) their distribution in the environment; (iii) representative
environmental life forms serving as early sentinels to monitor the spread and bio-accumulation of hazardous NMs; (iv) biological
screening assays allowing QSARs to be developed based on the bio-physicochemical properties of NMs; (v) High throughput screening
(HTS) of a combinatorial NM library; and (vi) a self-learning computational system providing a framework for predictive risk
analysis. These research activities will be combined with educational programs informing the public, future generations of
scientists, public agencies, and industrial stakeholders of the importance of safe implementation of nanotechnology in the
environment. The overall impact will be to reduce uncertainty about the possible consequences of NMs in the environment, while
at the same time providing guidelines for their safe design to prevent environmental hazards.
Citation
- Title University of California €” Center for Environmental Implications of Nanotechnology (UC €”CEIN).
-
- creation Date
2017-03-08T15:51:04.612887
Resource language:
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Metadata data stamp:
2018-08-06T22:32:43Z
Resource Maintenance Information
- maintenance or update frequency:
- notes: This metadata record was generated by an xslt transformation from a dc metadata record; Transform by Stephen M. Richard, based
on a transform by Damian Ulbricht. Run on 2018-08-06T22:32:43Z
Metadata contact
-
pointOfContact
- organisation Name
CINERGI Metadata catalog
-
- Contact information
-
-
- Address
-
- electronic Mail Address cinergi@sdsc.edu
Metadata language
eng
Metadata character set encoding:
utf8
Metadata standard for this record:
ISO 19139 Geographic Information - Metadata - Implementation Specification
standard version:
2007
Metadata record identifier:
urn:dciso:metadataabout:4c1472cb-0b6e-4c2e-ac6a-7aa7bc4b3dea
Metadata record format is ISO19139 XML (MD_Metadata)