Silica-coated upconversion lanthanide nanoparticles: The effect of crystal design on morphology, structure and optical properties

• Uliana Kostiv,
• Miroslav Šlouf,
• Hana Macková,
• Alexander Zhigunov,
• Hana Engstová,
• Katarína Smolková,
• Petr Ježek and
• Daniel Horák

Beilstein J. Nanotechnol. 2015, 6, 2290–2299, doi:10.3762/bjnano.6.235

• number and diameter of the particles, respectively. The ED patterns were processed with ProcessDiffraction software [29] and compared with the diffraction patterns of known NaYF4 crystal structures calculated with PowderCell software [30] or downloaded from crystallographic databases. The hydrodynamic

An ISA-TAB-Nano based data collection framework to support data-driven modelling of nanotoxicology

• Richard L. Marchese Robinson,
• Mark T. D. Cronin,
• Andrea-Nicole Richarz and
• Robert Rallo

Beilstein J. Nanotechnol. 2015, 6, 1978–1999, doi:10.3762/bjnano.6.202

• weaknesses of the resources are discussed along with possible future developments. Keywords: databases; ISA-TAB-Nano; nanoinformatics; nanotoxicology; quantitative structure–activity relationship (QSAR); Introduction Nanotechnology, which may be considered the design and application of engineered

• standardised, electronic format that facilitates meaningful exchange of information between different researchers, submission to (web-based) searchable databases, integration with other electronic data resources and analysis via appropriate (modelling) software [9][16][17][18]. This could entail directly

• populating files based on a standardised format or direct entry of data into searchable databases using a (web-based) data entry tool [19], followed via data export/exchange in a standardised format. However, in contrast to directly populating standardised, structured files (such as spreadsheets), direct

Predicting cytotoxicity of PAMAM dendrimers using molecular descriptors

• David E. Jones,
• Hamidreza Ghandehari and
• Julio C. Facelli

Beilstein J. Nanotechnol. 2015, 6, 1886–1896, doi:10.3762/bjnano.6.192

• viability of seven different cell lines [4]. Sayes and Ivanov used machine learning to predict the induced cellular membrane damage of immortalized human lung epithelial cells caused by metal oxide nanomaterials [5]. As discussed in a previous paper [6], there are a very limited number of databases

Framework for automatic information extraction from research papers on nanocrystal devices

• Thaer M. Dieb,
• Masaharu Yoshioka,
• Shinjiro Hara and
• Marcus C. Newton

Beilstein J. Nanotechnol. 2015, 6, 1872–1882, doi:10.3762/bjnano.6.190

• research papers containing experimental results have been published. Because it is a very time-consuming task to read through all related papers, several research efforts have been conducted in the nanoinformatics research domain. This includes the construction of databases for sharing the experimental

Nanocuration workflows: Establishing best practices for identifying, inputting, and sharing data to inform decisions on nanomaterials

• Christina M. Powers,
• Karmann A. Mills,
• Stephanie A. Morris,
• Fred Klaessig,
• Sharon Gaheen,
• Nastassja Lewinski and
• Christine Ogilvie Hendren

Beilstein J. Nanotechnol. 2015, 6, 1860–1871, doi:10.3762/bjnano.6.189

• information in journal articles with information from other sources (e.g., searching for the paper in other databases) (Figure 2), since this approach provides a valuable source of supplemental data (see Supporting Information File 1 for details). When using sources other than peer-reviewed articles

NanoE-Tox: New and in-depth database concerning ecotoxicity of nanomaterials

• Katre Juganson,
• Angela Ivask,
• Irina Blinova,
• Monika Mortimer and
• Anne Kahru

Beilstein J. Nanotechnol. 2015, 6, 1788–1804, doi:10.3762/bjnano.6.183

• ENMs to their physico-chemical properties and reveal the data gaps, the existing data have to be carefully collected and analysed. One increasingly popular approach in systematically collecting and organising available data on nanomaterials is creating databases. In 2012, Hristozov et al. emphasised

• that the available data on nanomaterials in environmental, health and safety databases and online chemical databases were very scarce [14]. Recently, a databases working group was established in the framework of European Union NanoSafety Cluster [15] which highlights the importance of development of in

• -depth databases on ENMs. In addition, nanotoxicity-related databases are developed and supported at national level in EU. For instance, in Germany an application-based nanomaterial database, which includes information on potential toxicological effects of ENMs, has been created in the DaNa project [16

Nanotechnology in the real world: Redeveloping the nanomaterial consumer products inventory

• Marina E. Vance,
• Todd Kuiken,
• Eric P. Vejerano,
• Sean P. McGinnis,
• Michael F. Hochella Jr.,
• David Rejeski and
• Matthew S. Hull

Beilstein J. Nanotechnol. 2015, 6, 1769–1780, doi:10.3762/bjnano.6.181

• stakeholders and to develop tools for its most effective use [12]. Databases such as the CPI offer information useful and relevant to a variety of stakeholders who are interested in a) understanding which consumer products incorporate nanotechnology and b) developing strategies, tools, and policies that may be

The eNanoMapper database for nanomaterial safety information

• Nina Jeliazkova,
• Charalampos Chomenidis,
• Philip Doganis,
• Bengt Fadeel,
• Roland Grafström,
• Barry Hardy,
• Janna Hastings,
• Markus Hegi,
• Vedrin Jeliazkov,
• Nikolay Kochev,
• Pekka Kohonen,
• Cristian R. Munteanu,
• Haralambos Sarimveis,
• Bart Smeets,
• Pantelis Sopasakis,
• Georgia Tsiliki,
• David Vorgrimmler and
• Egon Willighagen

Beilstein J. Nanotechnol. 2015, 6, 1609–1634, doi:10.3762/bjnano.6.165

• ENM data and an extensive review of existing nano-related data models, databases, and nanomaterials-related entries in chemical and toxicogenomic databases. This paper continues with a focus on the database functionality exposed through its application programming interface (API), and its use in

• expectations and (inter)national standards. This usually translates into a set of available study summaries (rarely raw data) for a given ENM. The inclusion of links to product databases could also be considered (e.g., whether the nanomaterial occurs in nature, whether it is emitted by cars or is present in

• to replicates or similar experiments). The framework should allow for the addition of information based on the outcomes of the predictive toxicology models, including the biological role of the ENM, clearance, accumulation, and pathway information (e.g., WikiPathways entries [4]). Existing databases

How decision analysis can further nanoinformatics

• Matthew E. Bates,
• Sabrina Larkin,
• Jeffrey M. Keisler and
• Igor Linkov

Beilstein J. Nanotechnol. 2015, 6, 1594–1600, doi:10.3762/bjnano.6.162

• VOI in nanoinformatics efforts. Databases can be expanded to include uncertainties for criteria other than hazards (e.g., cost or performance), providing a foundation in the data for the VOI. This is important because research activities that quantify or reduce uncertainty about environmental concerns

• further research. A series of next steps can also be explored for including WOE in nanoinformatics efforts. When data is added to nanoinformatics databases, additional quantitative and qualitative metrics (e.g., data statistical significance, precision, applicability, soundness, completeness, uncertainty

• . Conclusion Recent discussions from the Nanotechnology Knowledge Infrastructure have heralded the creation of a communication portal for the various nanotechnology databases and tools. The tremendous amount of data that would be available via that portal would necessitate not only the bottom-up accumulation

Experiences in supporting the structured collection of cancer nanotechnology data using caNanoLab

• Stephanie A. Morris,
• Sharon Gaheen,
• Michal Lijowski,
• Mervi Heiskanen and
• Juli Klemm

Beilstein J. Nanotechnol. 2015, 6, 1580–1593, doi:10.3762/bjnano.6.161

• ; cancer research; databases; nanomaterials; nanomedicine; Introduction The U.S. annual report to the nation on the state of cancer indicates a steady decline in overall mortality rates, with increases in incidence for many cancers [1]. Internationally, cancer incidence paints a more dramatic picture in

• ]. Databases such as dbGaP have provided investigators access to hundreds of genomics studies, resulting in three times that number of publications and scientific advances in the genetic basis of disease [8]. Unlike genomics, nanotechnology data management systems, which are at relatively early stages of

• address the needs of different communities. The task of creating relevant databases for nanotechnology risk assessment, manufacturing, characterizations, and literature data is being taken on globally by government, academic, and regulatory organizations. To date, there are approximately 38 databases at

Influence of surface chemical properties on the toxicity of engineered zinc oxide nanoparticles to embryonic zebrafish

• Zitao Zhou,
• Jino Son,
• Bryan Harper,
• Zheng Zhou and
• Stacey Harper

Beilstein J. Nanotechnol. 2015, 6, 1568–1579, doi:10.3762/bjnano.6.160

• NPs drive toxicity. This work has shown that large databases of similar NPs with varying surface features studied under identical experimental design protocols, are invaluable in the development of models of nanoparticle-biological interactions. We have shown that intrinsic features of NPs

Using natural language processing techniques to inform research on nanotechnology

• Nastassja A. Lewinski and
• Bridget T. McInnes

Beilstein J. Nanotechnol. 2015, 6, 1439–1449, doi:10.3762/bjnano.6.149

• language processing methods are applied, and (2) experimental data to which data modeling methods, such as those used in HDAT and NanoMiner, are applied [5][6]. Despite being a largely overlooked area of informatics, several reviews have been published that list the different databases and tools currently

• separates NLP applications from other data processing systems is their use of knowledge about human language [12]. Many of the NLP applications utilize literature retrieved from databases. Information retrieval, document classification, and pattern matching methods are often utilized to ensure that the

• , and environmental risk assessment. Methods This review was limited to the English language literature included in two databases, PubMED and Web of Science [22][23]. The searches were conducted on February 12, 2015. For the search term (nano* AND “natural language processing”), Web of Science retrieved

Simulation tool for assessing the release and environmental distribution of nanomaterials

• Haoyang Haven Liu,
• Muhammad Bilal,
• Anastasiya Lazareva,
• Arturo Keller and
• Yoram Cohen

Beilstein J. Nanotechnol. 2015, 6, 938–951, doi:10.3762/bjnano.6.97

• region(s) of interest (see section, Databases). The mass balance equations (Supporting Information File 1, Equations S1–S4) are then solved to determine the average ENM release rates to the environmental compartments (i.e., air, water, and soil). Mass “flows” of ENMs among the various compartments can be

• distribution (lower subplot). It is noted that such information can be utilized to convert MendNano reported ENM mass concentrations to surface area concentration [35][36] given the knowledge of the primary particle size. Databases The parameter database contains material properties, geographical, and

• ), compiled from various published studies [17], public databases [40], and market research [5], and estimated based on economic indicators [41]). Use cases for assessing multimedia distribution of ENMs The integrated RedNano simulation tool is suitable for a variety of assessments regarding the environmental

Molecular biology approaches in bioadhesion research

• Marcelo Rodrigues,
• Birgit Lengerer,
• Thomas Ostermann and
• Peter Ladurner

Beilstein J. Nanotechnol. 2014, 5, 983–993, doi:10.3762/bjnano.5.112

• facility 2.1 BLAST – basic local alignment search tool Basic local alignment search tool (BLAST) is a software package to query sequence databases for homologues [31]. Statistical information helps to determine the significance of every alignment. BLAST is widely used to analyze sequencing data and to find