Visualization of Multidimensional Data for Nanomaterial Characterization

For the purpose of visualization, several techniques can be used to handle multidimensional data, such as parallel coordinates, heat-maps, projection, and clustering methods. This chapter presents a case study in the field of nanotoxicology and in particular of how large multidimensional data can be visually represented using structure-activity relationship (SAR) approach based on parallel coordinates. The case study includes data sets of 18 engineered nanomaterials (ENMs), each of which was characterized in terms of its physicochemical properties such as particle size, size distribution, surface area, morphology, metal content, reactivity, and free radical generation. In addition, a range of toxicity tests were conducted for the same panel of ENMs to determine their acute in vitro toxicity. More information on parallel coordinate method is discussed in the chapter, so as to give the reader sufficient knowledge to understand the case study.

[1]  T. Moreno,et al.  The physicochemical characterisation of microscopic airborne particles in south Wales: a review of the locations and methodologies. , 2006, The Science of the total environment.

[2]  Jerzy Leszczynski,et al.  Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. , 2011, Nature nanotechnology.

[3]  Alfred Inselberg,et al.  The plane with parallel coordinates , 1985, The Visual Computer.

[4]  Andrew Worth,et al.  Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential. , 2013, Toxicology.

[5]  B. Hoogendoorn,et al.  Airborne Particles in Swansea, UK: Their Collection and Characterization , 2010, Journal of toxicology and environmental health. Part A.

[6]  Mark Bradley,et al.  Differential pro-inflammatory effects of metal oxide nanoparticles and their soluble ions in vitro and in vivo; zinc and copper nanoparticles, but not their ions, recruit eosinophils to the lungs , 2012, Nanotoxicology.

[7]  T. Xia,et al.  Understanding biophysicochemical interactions at the nano-bio interface. , 2009, Nature materials.

[8]  K. Rock,et al.  Silica crystals and aluminum salts activate the NALP3 inflammasome through phagosomal destabilization , 2008, Nature Immunology.

[9]  Alexander Tropsha,et al.  Exploring quantitative nanostructure-activity relationships (QNAR) modeling as a tool for predicting biological effects of manufactured nanoparticles. , 2011, Combinatorial chemistry & high throughput screening.

[10]  X. Wang,et al.  Multidimensional Visualization of Principal Component Scores for Process Historical Data Analysis , 2004 .

[11]  Jerzy Leszczynski,et al.  Advancing risk assessment of engineered nanomaterials: application of computational approaches. , 2012, Advanced drug delivery reviews.

[12]  T. Puzyn,et al.  Toward the development of "nano-QSARs": advances and challenges. , 2009, Small.

[13]  Danuta Leszczynska,et al.  Chapter 10:Nano-QSAR: Advances and Challenges , 2012 .

[14]  Adriele Prina-Mello,et al.  Length-dependent pathogenic effects of nickel nanowires in the lungs and the peritoneal cavity , 2012, Nanotoxicology.

[15]  E. Wegman.  Hyperdimensional Data Analysis Using Parallel Coordinates , 1990 .

[16]  Alfred Inselberg.  Visualization and data mining of high-dimensional data , 2002 .

[17]  Xue Z. Wang,et al.  Multidimensional visualisation for process historical data analysis: a comparative study with multivariate statistical process control , 2005 .

[18]  Alfred Inselberg,et al.  Convexity algorithms in parallel coordinates , 1987, JACM.

[19]  Francesco Stellacci,et al.  Surface-structure-regulated cell-membrane penetration by monolayer-protected nanoparticles. , 2008, Nature materials.

[20]  Lutz Mädler,et al.  Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation. , 2012, ACS nano.

[21]  R. Weissleder,et al.  Modeling biological activities of nanoparticles. , 2012, Nano letters.

[22]  K. Donaldson,et al.  Principal component and causal analysis of structural and acute in vitro toxicity data for nanoparticles , 2014, Nanotoxicology.

[23]  Craig A. Poland,et al.  Carbon nanotubes introduced into the abdominal cavity of mice show asbestos-like pathogenicity in a pilot study. , 2008, Nature nanotechnology.

[24]  Wei Wang,et al.  Cytotoxicity induced by engineered silver nanocrystallites is dependent on surface coatings and cell types. , 2012, Langmuir : the ACS journal of surfaces and colloids.

[25]  Alfred Inselberg.  Visual Data Mining with Parallel Coordinates , 1998 .

[26]  A. Tropsha,et al.  Quantitative nanostructure-activity relationship modeling. , 2010, ACS nano.

[27]  X. Wang,et al.  Historical data analysis based on plots of independent and parallel coordinates and statistical control limits , 2006 .

[28]  Craig A. Poland,et al.  Asbestos, carbon nanotubes and the pleural mesothelium: a review of the hypothesis regarding the role of long fibre retention in the parietal pleura, inflammation and mesothelioma , 2010, Particle and Fibre Toxicology.

[29]  Andrew P Worth,et al.  A theoretical framework for predicting the oxidative stress potential of oxide nanoparticles , 2011, Nanotoxicology.

[30]  Jerzy Leszczynski,et al.  Nanomaterials – the Next Great Challenge for Qsar Modelers , 2009, Recent Advances in QSAR Studies.

[31]  T. Xia,et al.  Development of structure-activity relationship for metal oxide nanoparticles. , 2013, Nanoscale.

[32]  I. Ivanov,et al.  Comparative Study of Predictive Computational Models for Nanoparticle‐Induced Cytotoxicity , 2010, Risk analysis : an official publication of the Society for Risk Analysis.

[33]  A. Worth,et al.  Computational methods to predict the reactivity of nanoparticles through structure–property relationships , 2010, Expert opinion on drug delivery.

[34]  C. Oksel,et al.  Current situation on the availability of nanostructure–biological activity data , 2015, SAR and QSAR in environmental research.

[35]  Anna M. Wise,et al.  Sulfidation of silver nanoparticles decreases Escherichia coli growth inhibition. , 2012, Environmental science & technology.

[36]  Andrew P. Worth,et al.  QSAR modeling of nanomaterials. , 2011, Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology.