Satish Chikkagoudar

Comparative hazard analysis and toxicological modeling of diverse nanomaterials using the embryonic zebrafish (EZ) metric of toxicity

Abstract The integration of rapid assays, large datasets, informatics, and modeling can overcome current barriers in understanding nanomaterial structure–toxicity relationships by providing a weight-of-the-evidence mechanism to generate hazard rankings for nanomaterials. Here, we present the use of a rapid, low-cost assay to perform screening-level toxicity evaluations of nanomaterials in vivo. Calculated EZ Metric scores, a combined measure of morbidity and mortality in developing embryonic zebrafish, were established at realistic exposure levels and used to develop a hazard ranking of diverse nanomaterial toxicity. Hazard ranking and clustering analysis of 68 diverse nanomaterials revealed distinct patterns of toxicity related to both the core composition and outermost surface chemistry of nanomaterials. The resulting clusters guided the development of a surface chemistry-based model of gold nanoparticle toxicity. Our findings suggest that risk assessments based on the size and core composition of nanomaterials alone may be wholly inappropriate, especially when considering complex engineered nanomaterials. Research should continue to focus on methodologies for determining nanomaterial hazard based on multiple sub-lethal responses following realistic, low-dose exposures, thus increasing the availability of quantitative measures of nanomaterial hazard to support the development of nanoparticle structure–activity relationships.

Disentangling the Lexicons of Disaster Response in Twitter

People around the world use social media platforms such as Twitter to express their opinion and share activities about various aspects of daily life. In the same way social media changes communication in daily life, it also is transforming the way individuals communicate during disasters and emergencies. Because emergency officials have come to rely on social media to communicate alerts and updates, they must learn how users communicate disaster related content on social media. We used a novel information-theoretic unsupervised learning tool, CorEx, to extract and characterize highly relevant content used by the public on Twitter during known emergencies, such as fires, explosions, and hurricanes. Using the resulting analysis, authorities may be able to score social media content and prioritize their attention toward those messages most likely to be related to the disaster.

Physicochemical signatures of nanoparticle-dependent complement activation

Nanoparticles are potentially powerful therapeutic tools that have the capacity to target drug payloads and imaging agents. However, some nanoparticles can activate complement, a branch of the innate immune system, and cause adverse side-effects. Recently, we employed an in vitro hemolysis assay to measure the serum complement activity of perfluorocarbon nanoparticles that differed by size, surface charge, and surface chemistry, quantifying the nanoparticle-dependent complement activity using a metric called Residual Hemolytic Activity (RHA). In the present work, we have used a decision tree learning algorithm to derive the rules for estimating nanoparticle-dependent complement response based on the data generated from the hemolytic assay studies. Our results indicate that physicochemical properties of nanoparticles, namely, size, polydispersity index, zeta potential, and mole percentage of the active surface ligand of a nanoparticle, can serve as good descriptors for prediction of nanoparticle-dependent complement activation in the decision tree modeling framework.

Impact of network activity levels on the performance of passive network service dependency discovery

Network services often depend on other services distributed throughout a network to function correctly. If a service fails, is disrupted, or is degraded, it is likely to impair other services. The web of dependencies can be surprisingly complex-especially within a large enterprise network-and evolve over time. Acquiring, maintaining, and understanding dependency knowledge is critical for many network management and cyber defense activities, such as cyber mission mapping. While automation can improve situation awareness for network operators and cyber practitioners, poor detection performance reduces their confidence and can complicate their roles. In this paper, we study the effects of network activity levels on the detection performance of passive network-based service dependency discovery methods. The performance of all methods except for one were inconsistent with respect to network activity levels. Our proposed cross-correlation method was particularly robust to the influence of network activity. The proposed experimental treatment will further advance a more scientific evaluation of methods and provide a foundation to determine their operational boundaries.

Automating network node behavior characterization by mining communication patterns

Enterprise networks of scale are complex, dynamic computing environments that respond to evolving business objectives and requirements. Characterizing system behaviors in these environments is essential for network management and cybersecurity operations. Characterization of systems communication is typical and is supported using network flow information (Net-Flow). Related work has characterized behavior using theoretical graph metrics; results are often difficult to interpret by enterprise staff. We propose a different approach, where flow information is mapped to sets of tags that contextualize the data in terms of network principals and enterprise concepts. Frequent patterns are then extracted and are expressed as behaviors. Behaviors can be compared, identifying systems expressing similar behaviors. We evaluate the approach using two case studies.

Towards Real-Time High Performance Computing for Power Grid Analysis

Real-time computing has traditionally been considered largely in the context of single-processor and embedded systems, and indeed, the terms real-time computing, embedded systems, and control systems are often mentioned in closely related contexts. However, real-time computing in the context of multinode systems, specifically high-performance, cluster-computing systems, remains relatively unexplored. Imposing realtime constraints on a parallel (cluster) computing environment introduces a variety of challenges with respect to the formal verification of the systems timing properties. In this paper, we give a motivating example to demonstrate the need for such a system- an application to estimate the electromechanical states of the power grid- and we introduce an formal method for performing verification of certain temporal properties within a system of parallel processes. We describe our work towards a full real-time implementation of the target application- namely, our progress towards extracting a key mathematical kernel from the application, the formal process by which we analyze the intricate timing behavior of the processes on the cluster, as well as timing measurements taken on our test cluster to demonstrate use of these concepts.

Annotating the structure and components of a nanoparticle formulation using computable string expressions

Nanoparticle formulations that are being developed and tested for various medical applications are typically multi-component systems that vary in their structure, chemical composition, and function. It is difficult to compare and understand the differences between the structural and chemical descriptions of hundreds and thousands of nanoparticle formulations found in text documents. We have developed a string nomenclature to create computable string expressions that identify and enumerate the different high-level types of material parts of a nanoparticle formulation and represent the spatial order of their connectivity to each other. The string expressions are intended to be used as IDs, along with terms that describe a nanoparticle formulation and its material parts, in data sharing documents and nanomaterial research databases. The strings can be parsed and represented as a directed acyclic graph. The nodes of the graph can be used to display the string ID, name and other text descriptions of the nanoparticle formulation or its material part, while the edges represent the connectivity between the material parts with respect to the whole nanoparticle formulation. The different patterns in the string expressions can be searched for and used to compare the structure and chemical components of different nanoparticle formulations. The proposed string nomenclature is extensible and can be applied along with ontology terms to annotate the complete description of nanoparticles formulations.

A Formal Model for Real-Time Parallel Computation

The imposition of real-time constraints on a parallel computing environment- specifically high-performance, cluster-computing systems- introduces a variety of challenges with respect to the formal verification of the systems timing properties. In this paper, we briefly motivate the need for such a system, and we introduce an automaton-based method for performing such formal verification. We define the concept of a consistent parallel timing system: a hybrid system consisting of a set of timed automata (specifically, timed Buchi automata as well as a timed variant of standard finite automata), intended to model the timing properties of a well-behaved real-time parallel system. Finally, we give a brief case study to demonstrate the concepts in the paper: a parallel matrix multiplication kernel which operates within provable upper time bounds. We give the algorithm used, a corresponding consistent parallel timing system, and empirical results showing that the system operates under the specified timing constraints.

Automated detection of anomalous shipping manifests to identify illicit trade

We describe an approach to analyzing anomalies in trade data based on the identification of cluster outliers. The approach uses unsupervised machine learning methods to discover semantically coherent clusters of shipping records in large collections of trade data. Trade data with cluster annotations are then used as input to a supervised machine learning algorithm to train and evaluate a classification model capable of identifying members of each cluster. The evaluation of this classification model provides an assessment of cluster coherence. Outliers are identified for each cluster by measuring the Euclidean distance from each member of the cluster to the cluster centroid, and then selecting a percentile threshold to identify shipping records with extreme distances from the cluster centroid. We describe a specific application of this approach to a dataset of 2.36M records for containerized shipments, with specific reference to the detection of anomalies potentially related to nuclear smuggling. Results show that this approach succeeds in finding semantically coherent clusters of shipping records, and identifying outliers that may help facilitate the detection of illicit trade.