Identifying Sample Provenance From SEM/EDS Automated Particle Analysis via Few-Shot Learning Coupled With Similarity Graph Clustering.

Automated particle analysis (APA) provides a vast amount of compositional data via energy-dispersive X-ray spectroscopy along with size and shape data via scanning electron microscopy for individual particles in a sample. In many instances, APA data are leveraged to support identification of the source of a sample based on the detection of particles of a specific composition. Often, the particles that provide context make up a minuscule portion of the sample. Additionally, the interpretation of complex samples can be difficult due to the diversity of compositions both in the mixture and within a particle. In this work, we demonstrate a method to compute and cluster similarity graphs that describe inter-particle relationships within a sample using a multi-modal few-shot learning neural network. As a proof-of-concept, we show that samples known to have been exposed to gunshot residue can be distinguished from samples occasionally mistaken for gunshot residue. Our workflow builds upon standard APA techniques and data processing methods to unveil additional information in a readily interpretable and quantitatively comparable format.

A novel visualization technique for electric power grid analytics.

The application of information visualization holds tremendous promise for the electric power industry, but its potential has so far not been sufficiently exploited by the visualization community. Prior work on visualizing electric power systems has been limited to depicting raw or processed information on top of a geographic layout. Little effort has been devoted to visualizing the physics of the power grids, which ultimately determines the condition and stability of the electricity infrastructure. Based on this assessment, we developed a novel visualization system prototype, GreenGrid, to explore the planning and monitoring of the North American Electricity Infrastructure. The paper discusses the rationale underlying the GreenGrid design, describes its implementation and performance details, and assesses its strengths and weaknesses against the current geographic-based power grid visualization. We also present a case study using GreenGrid to analyze the information collected moments before the last major electric blackout in the Western United States and Canada, and a usability study to evaluate the practical significance of our design in simulated real-life situations. Our result indicates that many of the disturbance characteristics can be readily identified with the proper form of visualization.

BioGraphE: high-performance bionetwork analysis using the Biological Graph Environment.

BACKGROUND: Graphs and networks are common analysis representations for biological systems. Many traditional graph algorithms such as k-clique, k-coloring, and subgraph matching have great potential as analysis techniques for newly available data in biology. Yet, as the amount of genomic and bionetwork information rapidly grows, scientists need advanced new computational strategies and tools for dealing with the complexities of the bionetwork analysis and the volume of the data. RESULTS: We introduce a computational framework for graph analysis called the Biological Graph Environment (BioGraphE), which provides a general, scalable integration platform for connecting graph problems in biology to optimized computational solvers and high-performance systems. This framework enables biology researchers and computational scientists to identify and deploy network analysis applications and to easily connect them to efficient and powerful computational software and hardware that are specifically designed and tuned to solve complex graph problems. In our particular application of BioGraphE to support network analysis in genome biology, we investigate the use of a Boolean satisfiability solver known as Survey Propagation as a core computational solver executing on standard high-performance parallel systems, as well as multi-threaded architectures. CONCLUSION: In our application of BioGraphE to conduct bionetwork analysis of homology networks, we found that BioGraphE and a custom, parallel implementation of the Survey Propagation SAT solver were capable of solving very large bionetwork problems at high rates of execution on different high-performance computing platforms.

Graph signatures for visual analytics.

We present a visual analytics technique to explore graphs using the concept of a data signature. A data signature, in our context, is a multidimensional vector that captures the local topology information surrounding each graph node. Signature vectors extracted from a graph are projected onto a low-dimensional scatterplot through the use of scaling. The resultant scatterplot, which reflects the similarities of the vectors, allows analysts to examine the graph structures and their corresponding real-life interpretations through repeated use of brushing and linking between the two visualizations. The interpretation of the graph structures is based on the outcomes of multiple participatory analysis sessions with intelligence analysts conducted by the authors at the Pacific Northwest National Laboratory. The paper first uses three public domain data sets with either well-known or obvious features to explain the rationale of our design and illustrate its results. More advanced examples are then used in a customized usability study to evaluate the effectiveness and efficiency of our approach. The study results reveal not only the limitations and weaknesses of the traditional approach based solely on graph visualization, but also the advantages and strengths of our signature-guided approach presented in the paper.

Generating graphs for visual analytics through interactive sketching.

We introduce an interactive graph generator, GreenSketch, designed to facilitate the creation of descriptive graphs required for different visual analytics tasks. The human-centric design approach of GreenSketch enables users to master the creation process without specific training or prior knowledge of graph model theory. The customized user interface encourages users to gain insight into the connection between the compact matrix representation and the topology of a graph layout when they sketch their graphs. Both the human-enforced and machine-generated randomnesses supported by GreenSketch provide the flexibility needed to address the uncertainty factor in many analytical tasks. This paper describes more than two dozen examples that cover a wide variety of graph creations from a single line of nodes to a real-life small-world network that describes a snapshot of telephone connections. While the discussion focuses mainly on the design of GreenSketch, we include a case study that applies the technology in a visual analytics environment and a usability study that evaluates the strengths and weaknesses of our design approach.

A space-filling visualization technique for multivariate small-world graphs.

We introduce an information visualization technique, known as GreenCurve, for large multivariate sparse graphs that exhibit small-world properties. Our fractal-based design approach uses spatial cues to approximate the node connections and thus eliminates the links between the nodes in the visualization. The paper describes a robust algorithm to order the neighboring nodes of a large sparse graph by solving the Fiedler vector of its graph Laplacian, and then fold the graph nodes into a space-filling fractal curve based on the Fiedler vector. The result is a highly compact visualization that gives a succinct overview of the graph with guaranteed visibility of every graph node. GreenCurve is designed with the power grid infrastructure in mind. It is intended for use in conjunction with other visualization techniques to support electric power grid operations. The research and development of GreenCurve was conducted in collaboration with domain experts who understand the challenges and possibilities intrinsic to the power grid infrastructure. The paper reports a case study on applying GreenCurve to a power grid problem and presents a usability study to evaluate the design claims that we set forth.

High prevalence of ascorbate deficiency in an Australian peritoneal dialysis population.

BACKGROUND: An adequate total body pool of ascorbate is essential for optimum health in humans. Requirements for ascorbate are increased in peritoneal dialysis (PD) patients most likely due to a combination of poor nutrition and increased dialysate losses. METHODS: We measured serum ascorbate levels in 45 clinically stable PD patients to assess the prevalence of ascorbate insufficiency (level between 2 and 4 mg/L) and deficiency (level <2 mg/L). We also assessed the efficacy of subsequent supplementation and patients' adherence to the prescribed supplementation. All patients were advised on commencement of dialysis to take a multivitamin tablet containing 100-120 mg ascorbate. RESULTS: Eighteen (41%) PD patients were regularly taking ascorbate-containing multivitamins, while 27 (59%) patients did not take ascorbate supplements. Serum ascorbate levels ranged from <0.2 to 41 mg/L, with wide variations in serum ascorbate at any given intake level. Ascorbate deficiency was present in 1/3 of the current PD population (44% of patients not taking supplements and in 16% of those on supplements), although none of the patients demonstrated clinical manifestations of scurvy. Targeted supplementation of ascorbate insufficient patients increased the median serum ascorbate level from 1.7 mg/L (IQR 1.2-2.2) to 22.5 mg/L (IQR 16.7-32.9). CONCLUSION: Our results show that, in PD patients, ascorbate deficiency is common and can readily be identified with serum ascorbate measurements. Oral supplements in the form of inexpensive multivitamin preparations restore adequate serum ascorbate levels in the majority of these patients. We therefore suggest measurement of ascorbate levels in all PD patients at the commencement of dialysis with a target level in the normal range (4-14 mg/L).

Infliximab and nephrotic syndrome.

Infliximab is a chimeric tumor necrosis factor-alpha (TNF-alpha) monoclonal antibody, which has been used extensively in patients with rheumatoid arthritis and inflammatory bowel disease. It also appears to be effective in other conditions such as psoriasis and ankylosing spondylitis. The major side effect of infliximab is infection. Renal complications are uncommon and not well recognized. This report describes a probable case of infliximab-induced membranous nephropathy.