Nanoinformatics for environmental health and biomedicine
As an interdisciplinary field consisting mainly of nanotechnology and data science, nanoinformatics has significantly advanced over the last decade, playing an increasingly important role in research and development in nanomedicine and environmental health impact assessment of nanomaterials. In addition, efforts in nanoinformatics research have provided in a multitude of tools and resources that are being made available through nanoinformatics cyberinfrastructures and web platforms. However, much of the current research and advances in nanoinformatics are not documented in dedicated resources and, given the interdisciplinary nature of nanoinformatics, are dispersed throughout a wide range of sources and journals. As a consequence, researchers and practitioners in other fields of nanotechnology have been at a disadvantage not having easy access to the most recent resources and tools provided by the nanoinformatics research community. Accordingly, this Thematic Series is devoted to bring together the state-of-the-art in nanoinformatics with a particular focus on the latest related developments and applications for environmental health and biomedicine.
- Full Research Paper
- Published 13 Apr 2015
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Figure 1: Overview of the release and environmental distribution of nanomaterials (RedNano) simulation tool a...
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Figure 2: Transport processes in MendNano. Green dashed lines represent intermedia transport processes, blue ...
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Figure 3: Lifecycle tracking of ENMs. The various lines represent the paths for which transfer coefficients q...
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Figure 4: Sankey diagram depicting the flows of different ENMs from production and use, through technical com...
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Figure 5: Example of the global distribution of the release rates of TiO2 into water.
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Figure 6: Workflow for assessing the environmental distribution of ENMs. ITP: intermedia transport processes,...
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Figure 7: Examples of MendNano web-based graphical user interface for scenario building showing inputs of soi...
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Figure 8: Examples of graphical representations of MendNano simulation results depicting concentration profil...
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Figure 9: Effect of release scenario on temporal dynamics of TiO2 media concentrations in Los Angeles. TiO2 r...
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Figure 10: Effect of dry deposition on the reduction of TiO2 concentrations in air and soil (postcessation of ...
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Figure 11: Effect of rain scavenging on TiO2 concentration in air, water, and soil in Los Angeles as a functio...
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Figure 12: Estimated CeO2 release rates for 12 selected countries.
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Figure 13: Predicted compartmental concentrations for CeO2 in 12 selected countries at the end of a 1 year sim...
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Figure 14: Apportionment of environmental release rates of selected ENMs to specific compartments in the Los A...
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- Full Research Paper
- Published 20 Jul 2015
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Figure 1: Data processing for model development.
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Figure 2: Chemical structures used to calculate the surface properties.
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Figure 3: Zebrafish mortality at 120 hpf following exposure to: (A) ZnO NPs with and (B) without surface modi...
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Figure 4: Individual variance for each of the principal components (PCs). Black dots represent the accumulate...
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Figure 5: Clustering analysis based on Euclidian distance for ZnO NPs partitioned into 3 clusters. Shown on t...
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Figure 6: Kriging estimation contour map for embryonic zebrafish exposed to 250 mg/L of each type of zinc oxi...
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- Full Research Paper
- Published 21 Jul 2015
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Figure 1: Example nanomaterial types supported by caNanoLab. Pictured is a subset of supported nanomaterials,...
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Figure 2: Sample search. Users can search for samples by keyword, name, point of contact, or feature. Followi...
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Figure 3: Example nanoparticle composition in caNanoLab of a triazine dendrimer with paclitaxel. Composition ...
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Figure 4: Example Nanoparticle Characterization in caNanoLab of a Dendrimer. Characterization information cap...
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Figure 5: caNanoLab sample information by publication. List and active links to all curated data for a given ...
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Figure 6: caNanoLab data submission and search workflow. A graphic available upon login that illustrates the ...
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Figure 7: caNanoLab MyWorkspace screen.
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Figure 8: caNanoLab protocol submission screen.
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Figure 9: caNanoLab sample composition submission screen.
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Figure 10: caNanoLab sample characterization submission screen – techniques and instruments.
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Figure 11: caNanoLab sample characterization submission screen – data and conditions.
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Figure 12: caNanoLab data availability metrics table. The first and middle columns list data supported and rec...
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Figure 13: caNanoLab sample publication submission screen. Information for PubMed articles is auto-populated b...
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- Full Research Paper
- Published 27 Jul 2015
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Figure 1: Screenshot illustrating free text search finding ontology annotated database entries (e.g. protocol...
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Figure 2: Top level substance API documentation. The “GET /substance” call is used to retrieve or search a li...
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Figure 3: Screenshot showing a nanomaterial entry (a gold nanoparticle with the name G15.AC) and its componen...
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Figure 4: Experimental data JSON example.
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Figure 5: Physico-chemical and toxicity data from the NanoWiki data set.
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Figure 6: Compound, substance and study search API documentation.
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Figure 7: Outline of the data model: Substances are characterised by their “composition” and are identified b...
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Figure 8: Data upload web page of the database system showing support for two file formats.
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Figure 9: Bundle API documentation at http://enanomapper.github.io/API. A bundle is a REST resource, allowing...
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Figure 10: Screenshot of the bundle view with the Protein Corona data set. In addition to the Substance API, w...
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Figure 11: Physicochemical data for multi-walled carbon nanotubes. The screenshot illustrates the data model a...
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Figure 12: Toxicity data for multi-walled carbon nanotubes. The repeated dose toxicity (inhalation) is shown i...
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Figure 13: Screenshot showing the results of a chemical similarity query (octyl amine, SMILES CCCCCCCCN) with ...
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Figure 14: Screenshot showing query results in the NanoWiki data set for particle sizes between 50 and 60 nm. ...
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Figure 15: Pie chart created with d3.js and ambit.js in a web page showing that the NanoWiki and Protein Coron...
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Figure 16: API call in ambit.js code.
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Figure 17: Histogram of nanomaterial sizes created with d3.js and ambit.js.
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Figure 18: Scatter plot of nanomaterial zeta potentials against the nanomaterial sizes, also created with d3.j...
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Figure 19: Screenshot of the Jaqpot Quattro modelling web services API, compatible with the eNanoMapper API. A...
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Figure 20: Conjoiner API: modelling-oriented information can be extracted from bundles of experimental data. D...
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Figure 21: Example of a PMML document.
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Figure 22: JPDI-compliant web services can be seamlessly incorporated into the eNanoMapper framework. The clie...
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Figure 23: Algorithm API that allows to consume as well as register new algorithms (following the JPDI specifi...
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Figure 24: A JPDI request for training.
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Figure 25: A model returned by JPDI service in JSON format.
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Figure 26: An example of a JSON prediction request.
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Figure 27: Screenshot of the descriptors calculated with quantum mechanics MOPAC web service.
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- Full Research Paper
- Published 28 Jul 2015
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Figure 1: Workflow for visual data exploration of soil bacteria susceptible to MNP treatments.
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Figure 2: Bipartite graph for MNP-bacteria interrelationships at order level. Soil bacteria taxa identified f...
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Figure 3: Bipartite graph for bacterial taxon → MNP treatment at order level. Soil bacteria taxa identified f...
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Figure 4: Bipartite graph for MNP treatment → bacterial taxon at order level. Soil bacteria taxa identified f...
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Figure 5: Bipartite graphs for MNP-bacteria interrelationships at genus levels. At genus level, soil bacteria...
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Figure 6: Bipartite graphs for MNP-bacteria interrelationships at family levels. Soil bacteria taxa identifie...
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Figure 7: Bipartite graphs for MNP-bacteria interrelationships at (a). class, and (b). phylum levels. Soil ba...
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Figure 8: Contribution biplots generated by log-ratio analyses for taxonomic levels from OTU to phylum. The t...
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Figure 9: Contribution biplot for phylum level with Gemmatimonadetes removed. The treatments are labelled as ...
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Figure 10: Distance correlation between taxonomic levels from OTU to phylum using (a) log-ratio (LR) distance ...
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Figure 11: Clusters of treatments obtained via hierarchical clustering based on their L1 distances calculated ...
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Figure 12: Nonmetric multidimensional scaling (NMDS) for OTU level (stress = 14.85%). The treatments are label...
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Figure 13: Simplified nonmetric multidimensional scaling (NMDS) for taxonomic levels from OTU to phylum. The g...
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- Full Research Paper
- Published 31 Jul 2015
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Figure 1: NEDD for brain cancer research across the disciplines.
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Figure 2: Publication trend of top 10 countries in 1990–2014.
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Figure 3: Scatter plot of international collaboration index (ICI) for top 10 countries in (a) 1990–1999; (b) ...
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Figure 4: Publications of the top 12 institutions in 1990–2014.
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Figure 5: Collaboration activity of the top 12 institutions.
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Figure 6: Co-author network of the top 20 authors.
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Figure 7: The author activities of top 20 authors in NEDD for brain cancer.
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- Full Research Paper
- Published 21 Aug 2015
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Figure 1: Number of available products over time (since 2007) in each major category and in the Health and Fi...
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Figure 2: (a) Claimed composition of nanomaterials listed in the CPI, grouped into five major categories: not...
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Figure 3: Major nanomaterial composition groups over time. Carbon = carbonaceous nanomaterials (carbon black,...
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Figure 4: Major nanomaterial composition pairs in consumer products. Carbonaceous nanomaterials (carbon black...
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Figure 5: Locations of nanomaterials in consumer products for which a nanomaterial composition has been ident...
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Figure 6: Expected benefits of incorporating nanomaterial additives into consumer products.
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Figure 7: Potential exposure pathways from the expected normal use of consumer products, grouped by major nan...
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Figure 8: Distribution of products into the “How much we know” categories.
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Figure 9: Nanotechnology survey answers on how respondents have used the CPI in the past and how they might u...
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- Full Research Paper
- Published 25 Aug 2015
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Figure 1: Proposed fields of application of engineered nanomaterials (ENMs) according to the publications in ...
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Figure 2: Information in the NanoE-Tox database for different types of engineered nanomaterials (ENMs): (a) n...
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Figure 3: Evolution of nanoecotoxicological information about eight different nanomaterials according to the ...
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Figure 4: NanoE-Tox database: available data on characterisation of ENMs. Pristine (a) and environment-specif...
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Figure 5: Types of test organisms used for evaluation of biological effects of selected ENMs in NanoE-Tox dat...
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Figure 6: NanoE-Tox database: toxicity of selected nanoparticles to different organisms (data filtered by key...
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Figure 7: Classification of selected nanoparticles according to European Union CLP legislation based on their...
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- Commentary
- Published 04 Sep 2015
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Figure 1: Common steps in nanocuration. The steps commonly included in nanocuration workflows are illustrated...
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Figure 2: Stakeholder responses regarding sourcing Data. Stakeholder responses to questions related to sourci...
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Figure 3: Stakeholder responses regarding data entry and review. Stakeholder responses to questions related t...
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Figure 4: Stakeholder responses regarding creation and revision. Stakeholder responses to questions related t...
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Figure 5: Stakeholder responses regarding working with other organizations. Stakeholder responses to question...
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Figure 6: Scientific and nanomaterial curation challenges. Nanomaterial curation challenges expand on curatio...
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- Full Research Paper
- Published 11 Sep 2015
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Figure 1: Decision tree for both 10-fold and leave-one-out cross-validation J48 classifier of the first, seco...
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Figure 2: Decision tree for 10-fold cross-validation J48 classifier for the fourth analysis including the mol...
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Figure 3: Simplified workflow diagram for the method used in this study.
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- Full Research Paper
- Published 05 Oct 2015
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Figure 1: A schematic illustration of the links between ISA-TAB-Nano files. Biological or material samples ar...
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Figure 2: Schematic overview of the steps carried out by the Python program for converting Excel (“xls”) base...
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Figure 3: Upload options for loading the suitable version of the “Toy Dataset” (Supporting Information File 3) into the nanoDMS online dat...
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Figure 4: Confirmation that the “Toy Dataset” (Supporting Information File 3) was successfully uploaded: no error messages were generated ...
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Figure 5: A summary of the in vitro cell-based assay toy data in the “Toy Dataset” (Supporting Information File 3) generated via the nanoD...
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Figure 6: A summary of the physicochemical assay toy data recorded in the “Toy Dataset” (Supporting Information File 3), generated via the...
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Figure 7: Retrieving the “Toy Dataset” (Supporting Information File 3) via searching for "oxidative stress" data in the nanoDMS system.
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