Engineering topics

Sego, Landon H.

Publications and source records attributed to Sego, Landon H..

4 records

DOE OSTI2017

Protein abundances can distinguish between naturally-occurring and laboratory strains of Yersinia pestis , the causative agent of plague

Adaptive processes in bacterial species can occur rapidly in laboratory culture, leading to genetic divergence between naturally occurring and laboratory-adapted strains. Differentiating wild and closely-related laboratory strains is clearly important for biodefense and bioforensics; however, DNA sequence data alone has thus far not provided a clear signature, perhaps due to lack of understanding of how diverse genome changes lead to adapted phenotypes. Protein abundance profiles from mass spectrometry-based proteomics analyses are a molecular measure of phenotype. Proteomics data contains sufficient information that powerful statistical methods can uncover signatures that distinguish wild strains of Yersinia pestis from laboratory-adapted strains.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

System and method of designing models in a feedback loop

A method and system for designing models is disclosed. The method includes selecting a plurality of models for modeling a common event of interest. The method further includes aggregating the results of the models and analyzing each model compared to the aggregate result to obtain comparative information. The method also includes providing the information back to the plurality of models to design more accurate models through a feedback loop.

42 ENGINEERING↗

DOE OSTI2015

Social Sensor Analytics: Making Sense of Network Models in Social Media

Social networks can be thought of as noisy sensor networks mapping real world information to the web. Owing to the extensive body of literature in sensor network analysis, this work sought to apply several novel and traditional methods in sensor network analysis for the purposes of efficiently interrogating social media data streams from raw data. We carefully revisit our definition of a social media signal from previous work both in terms of time-varying features within the data and the networked nature of the medium. Further, we detail our analysis of global patterns in Twitter over the months of November 2013 and June 2014, detect and categorize events, and illustrate how these analyses can be used to inform graph-based models of Twitter, namely using a recent network influence model called PhySense: similar to PageRank but tuned to behavioral analysis by leveraging a sociologically inspired probabilistic model. We ultimately identify forms of information dissemination via analysis of time series and dynamic graph spectra and corroborate these findings through manual investigation of the data as a requisite step in modeling the diffusion process with PhySense. We hope to sufficiently characterize global behavior in a medium such as Twitter as a means of learning global model parameters one may use to predict or simulate behavior on a large scale. We have made our time series and dynamic graph analytical code available via a GitHub repository https://github.com/cpatdowling/salsa and our data are available upon request.

event detection↗

DOE OSTI2014

Tessera: Open source software for accelerated data science

Extracting useful, actionable information from data can be a formidable challenge for the safeguards, nonproliferation, and arms control verification communities. Data scientists are often on the “front-lines” of making sense of complex and large datasets. They require flexible tools that make it easy to rapidly reformat large datasets, interactively explore and visualize data, develop statistical algorithms, and validate their approaches—and they need to perform these activities with minimal lines of code. Existing commercial software solutions often lack extensibility and the flexibility required to address the nuances of the demanding and dynamic environments where data scientists work. To address this need, Pacific Northwest National Laboratory developed Tessera, an open source software suite designed to enable data scientists to interactively perform their craft at the terabyte scale. Tessera automatically manages the complicated tasks of distributed storage and computation, empowering data scientists to do what they do best: tackling critical research and mission objectives by deriving insight from data. We illustrate the use of Tessera with an example analysis of computer network data.

Sego, Landon H.↗