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Sego, Landon H.

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

8 records
All sourcesNASA NTRSDOE OSTI
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.↗
DOE OSTI2014

DNDO Analysis Cell Concept

The Domestic Nuclear Detection Office (DNDO) has a mission of implementing rad/nuc interdiction capabilities for a managed and coordinated response to threats, integration of federal nuclear forensics programs, and coordinating the development of the global nuclear detection and reporting architecture. In the process of executing this mission, DNDO has generated substantial information, data, technical results, operational workflows and analytical tools. The effective utilization of these resources is an overarching goal of the organization. After nearly a decade of performing work, DNDO faces a challenge in capitalizing on the large amount of data, reports, processes, tools, and people. As new work is being planned, managers and researchers need to have an understating of what information has been collected, what tools are available, the collaborations which can be utilized to propel the work forward, processes to plan and execute, and how to present conclusions and results that can assist the government in making decisions. This type of challenge can be met through the use of a series of organized and connected elements which form a broader structure (cell) that promotes cross utilization of elements such that they can be tailored (analyzed) to fit the context of the problem to be solved. The development of an analysis cell for DNDO will address the challenges of utilizing existing elements, identifying gaps, annually reporting the performance of rad/nuc interdiction instrumentation, and planning the execution of future work.

Pagh, Richard T.↗
DOE OSTI2014

Visual Sample Plan Version 7.0 User's Guide

User's guide for VSP 7.0 This user's guide describes Visual Sample Plan (VSP) Version 7.0 and provides instructions for using the software. VSP selects the appropriate number and location of environmental samples to ensure that the results of statistical tests performed to provide input to risk decisions have the required confidence and performance. VSP Version 7.0 provides sample-size equations or algorithms needed by specific statistical tests appropriate for specific environmental sampling objectives. It also provides data quality assessment and statistical analysis functions to support evaluation of the data and determine whether the data support decisions regarding sites suspected of contamination. The easy-to-use program is highly visual and graphic. VSP runs on personal computers with Microsoft Windows operating systems (XP, Vista, Windows 7, and Windows 8). Designed primarily for project managers and users without expertise in statistics, VSP is applicable to two- and three-dimensional populations to be sampled (e.g., rooms and buildings, surface soil, a defined layer of subsurface soil, water bodies, and other similar applications) for studies of environmental quality. VSP is also applicable for designing sampling plans for assessing chem/rad/bio threat and hazard identification within rooms and buildings, and for designing geophysical surveys for unexploded ordnance (UXO) identification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Application of the Combined Judgmental and Randomly Placed Sampling Method

In the wake of the 2001 anthrax incidents in the United States, there has been considerable discussion about the role of judgmental and probabilistic sampling strategies, particularly in the context of clearance sampling. The Combined Judgmental and Random (CJR) methodology provides a statistical methodology that allows practitioners to leverage information from both judgmental and randomly selected sample locations and then quantify the confidence in negative results. The objective of the CJR sampling design is to demonstrate, with high probability, that a high percentage of the decision area (or population) is acceptable, given that none of the samples are unacceptable. The purpose of this document is to assist practitioners to appropriately use the CJR methodology and to implement this sampling design in Visual Sample Plan (VSP). We begin by defining some fundamental terms. A decision area is the geographical or spatial area (indoor or outdoor) about which practitioners wish to draw a conclusion on the basis of sampling and other relevant information. Decision areas typically include all or part of a building, but they may be any contaminated (or potentially contaminated) area for which characterization or clearance is required. In this report, a statistical sampling approach refers to any sampling approach based on a mathematical or statistical model that provides confidence or probability statements about the decision area. Statistical sampling approaches typically involve taking samples at random locations, or at the nodes of a randomly located grid or lattice that is superimposed on the decision area. The term probabilistic sampling is often used to describe statistical sampling designs that exclusively use randomly located samples. This terminology implies that probabilistic statements can only be made based upon the results of randomly placed samples. However, the CJR method utilizes a Bayesian model that gives rise to a formal probabilistic statement regarding the acceptability of the decision area based upon the information obtained from both judgmental and random samples. Judgmental samples play a vital role in the characterization and clearance process because they include the informed prior belief and expert knowledge of personnel involved in the sampling process.

97 MATHEMATICS AND COMPUTING↗