Bilal M. Khan

Nanomaterials Environmental Impact Assessment (CEIN)

 

A data-driven modeling platform based on Bayesian network (BN) was developed for qualitatively and quantitatively assessing the potential environmental impact of ENMs. BN structure was designed based on domain knowledge of toxicological and transport behaviors of ENMs, relating their physicochemical properties, environmental distribution, exposure concentration, and relevant hazard (or toxicity) information. The conditional probability tables for the BN was populated using data from experimental and computational modeling results of ENM toxicity and exposure levels. The modeling platform was deployed as a web application via custom designed user interface adhering to standard web application principles (i.e., MVC), which enables rapid online expert survey and elicitation.

(Model solver: Java, PHP; Web application: PHP, JavaScript, MySQL)


ToxNano: A Toolkit for Toxicity Data Analysis of Engineered Nanomaterials (CEIN)

 

An integrated online toolkit (ToxNano) was created for predictive ENMs toxicology via data-driven models to mine toxicity data from published studies and evaluate ENMs toxicity. ToxNano includes a set of advanced models and computational tools based on machine learning/data mining approaches for:

• Knowledge Discovery and quantitative structure-activity relationships (QSAR) development for high content bioactivity data
• Tiered approaches to correlate toxicity metrics with qualitative and quantitative information
• Identification of the parameters that can be used for predictive toxicology
• Evaluation of the body of evidence w.r.t. ENM bioactivity
• High Throughput Screening (HTS) data integration with CEIN Data Management System and advanced techniques for HTS data analysis

ToxNano facilitated the development of toxicity QSARs for a wide range of ENMs including, metal, metal-oxides, and QDs, as well as various surface modified ENMs

(Model solvers: Java, MATLAB, R, Netica, PHP; Web application: PHP, JavaScript, MySQL)


NanoDatabank: A Flexible Database Management System for Nanomaterials

 

NanoDatabank, is a flexible data management system that provides for classification and storage of various ENMs relevant data types. NanoDatabank currently contains data sets on more than about 400 ENM types, and more than 1000 investigations regarding ENM toxicity (including metal oxides, quantum dots, CNTs and more), F&T and ENM characterization. NanoDatabank supports nanoinformatics tools/simulators by providing (a) accessibility to data sets by various simulators and data processing tools, (b) ability to upload raw data and perform various data processing functions, and (c) an intelligent datasets query system. A unique feature of the NanoDatabank is a dynamically built taxonomy/ontology and storage of ENM information/data with various data access/security levels to allow and promote safe data sharing and storage. In addition, reliability (i.e. clarity regarding exactly what is being reported and trustworthiness/reproducibility) and relevance (i.e. usefulness for a particular purpose) of information is stored in NanoDatabank as metadata along with compressed associated information. To address the issues of data sharing and integration, NanoDatabank uses a range of data converters/utilities to integrate the information among computational tools as part of nanoinformatics platform (nanoinfo.org) for various scenarios such as life cycle assessment of the release of nanomaterials, multimedia exposure analysis of ENMs, QSARs and data driven models for the evaluation of toxicity of ENMs.

(Development environment: PHP, JavaScript, MySQL, KnockoutJS)

Life Cycle Environmental Assessment For The Release Of Nanomaterials (CEIN)

 

A generalized web-based modeling platform of the life cycle environmental assessment for the release of ENMs (LearNano) was built to estimate the ENMs release rates to the environment by tracking the mass of ENM from production, through the various technical compartments (i.e., waste water treatment, septic systems, waste incineration), to the eventual ENM release to different environmental compartments.

(Model solver: Java, C++, PHP; Web application: PHP, JavaScript, MySQL)


Rapid assessment of Multimedia Environmental Distribution of Nanomaterials (CEIN)

 

A BN model was developed to enable rapid assessment of the environmental multimedia mass distribution of ENMs utilizing mechanistic models for the estimation of emissions and multimedia environmental distribution of ENMs. The simulation data was generated using design of experiment techniques (CCD and FFD) for BN model development and validation. The BN is capable of providing reasonable real time estimates of ENMs concentrations based on the data for wide ranges of parameters. BN model is suited for “what if” first tier analyses to provide estimations of potential exposure concentrations, impact of ENMs release rates and various other related parameters. BN also provides the causal-effect relationships between the parameters and resulting ENM concentrations in order to visually investigate their variations and their impact on ENMs concentrations. The modeling framework has been implemented as a web-based modeling system, which assists users in rapidly assessing ENMs exposure concentrations by specifying relevant ENMs properties, geographical and meteorological parameters (i.e., regions, temperature, wind speed, rain, etc.), and source emissions, as well as visualizing the results.

(Model Solver: Netica; Web application: PHP, JavaScript)

Conditional dependence assessment and association rule mining of zebrafish phenotypes (CEIN)

 

A meta-analysis was conducted for the assembly and generalization of ENMs impact on zebrafish and understanding relationships between ENMs properties and Embryonic Zebrafish (EZ) toxicity. Using 7 different types of ENMs (metal, metal oxide, cellulose, dendrimer, carbon, semiconductor, polymeric) as a model system, 1,147 samples from the nanomaterial biological interactions (NBI) knowledgebase were extracted followed by predictive model development to relate EZ metric to the ENM physicochemical and experimental parameters. The EZ metric was integrated using 21 phenotypes including zebrafish 24 and 120 hours post fertilization (HPF) mortality. A range of clustering techniques (i.e., SOM, hierarchical) and association rule mining techniques were developed to assess the relationships and interdependence of zebrafish phenotypes. The association rule mining and other clustering approaches demonstrated that the the olfactory regions (such as eye, snout, jaw) were strongly correlated with each other and heart had stronger correlations with olfactory regions as well as other phenotypes (especially yolk sac edema, curved axis, trunk malfunctioning, touch response, circulation, caudal fin, otic vessicle). Overall, the present work suggests that information derived from literature data mining can provide guidance regarding key ENM attributes (e.g., core properties, surface properties and experimental settings) that should be characterized and reported in EZ toxicity assessment studies. In addition, the present study suggests that the assessment of conditional dependences of zebrafish phenotypes provides useful information on phenotype ranking when integrating them for evaluating ENM toxicity.

(Model Solver: Netica, R, MATLAB)

Inferring the Most Probable Maps of Underground Utilities (University of Leeds, 2013)

 

An approach for automated creation of revised maps of buried underground utilities was developed by integrating the knowledge extracted from sensors raw data and available statutory records. The combination of statutory records with the hypotheses from sensors was for initial estimation of what might be found underground and roughly where. Data fusion techniques were applied to integrate information from multiple sources followed by Bayesian model development for 2D/3D map (re)construction. The maps were (re)constructed using automated image segmentation techniques for hypotheses extraction and Bayesian classification techniques for segment-manhole connections. The project was funded by Mapping the Underworld (MTU) which is a major initiative in the UK, focused on addressing social, environmental and economic consequences raised from the inability to locate buried underground utilities (such as pipes and cables) by developing multi-sensor mobile device.

(Model Solver: MATLAB)

Cooperative Vehicular Ad hoc Network for safer driving (University of Bradford, 2011)

 

A game theoretic approach was applied for safer driving using efficient route selection for vehicles especially Emergency Vehicles (EV). A probabilistic route selection mechanism was designed by conditioning on density, number of junctions and number of traffic lights. The vehicle route clearance was incorporated in network simulations by enabling the vehicles connected on the road to share warning message. The level of cooperation by other vehicles in clearing the route was calculated by employing an optimization algorithm called Expectation Maximization (EM) algorithm. An important criterion in safer driving was to assess the level of cooperation by drivers connected on the road depending on their distance from EV, distance from closest junctions, direction, speed and network connectivity strength (signal to noise ratio). Using these features, the cooperation level quantified using EM was used for the distribution of credit among contributing drivers. The credit distribution was implemented using game theoretic concept called the Shapley Value. The technique was proposed to implement a safer and efficient driving system and incorporate cooperative behavior among contributing drivers which could help improve emergency services in terms of improved route selection and vehicle-to-vehicle communication.

(Model Solver: Network Simulator, NCTUns (C++))

Tracesaver: A mobile network service tracker and user centric data analyser (Metricell, 2011)

 

A mobile appplication (Tracesaver) was developed to pinpoint service area problems for network operators and to use signal coverage facts to drive necessary investment or improvements by network operators. Tracesaver brings Quality of Experience (QoE) from smart phone users and user centric information which when used in conjunction with technical data from network operators helps in better understanding of the customer faced issue/fault and helps in quick and productive/localized rectification efforts. Tracesaver monitors and reports in locations where there is no coverage or data service and this type of data can be used to report serious problematic locations to network operators. Tracesaver is capable of performing intelligent traffic analysis completely transparently to record the "no signal and poor quality" service provided by the operator.

(Model Solver: NetBeans (Java))