PNNL

PNNL Panels

Ignite Session: Diversity, Equity, and Inclusion in National Security

Co-Facilitator: Karen Taylor

A panel discusses diversity and inclusion in national security. Panelists from across the community will share strategies and opportunities available to enhance diversity, equity, and inclusion (DEI) and useful resources.

PNNL Presentations

A Critical Analysis Of Disease Forecasting: Considerations For Disease Types, Locations, And Operational Readiness

Presenter: Samantha Erwin

Disease forecasting is important to providing both situational awareness and highlighting strategies to mitigate the impact of infectious disease outbreaks. Limited work has been done to comprehensively compare data-driven models across multiple diseases or locations with varying modes of transmission. The researchers implemented a method to harmonize disparate data to a common spatial and temporal resolution to facilitate the usability and scalability of the platform. This work is on-going and will ultimately lay the foundation of the disease forecasting methods for future chemical and biological threats.

What Is Normal? Profiling Volatile and Non-Volatile Components of Healthy Human Breath

Presenter: Brooke Kaiser

Identification of biomarkers of infection or exposure in exhaled breath is a critical step in making breath-based diagnostics a reality. The goal is to help establish a baseline profile of normal healthy human breath biomarkers with defined measures of statistical confidence and improve our understanding of the metabolic pathways that generate these breath features. This research creates a foundational understanding of the intra- and inter-individual variation in breath of normal humans, which will set the stage for more rapid and defensible breath diagnostics and fieldable device development.

Development of Field Forward Diagnostics for Detecting DNA Modifying Enzymes Using Commercial Nanopore Sequencing Instruments

Presenter: Jennifer Mobberley

There is a need for portable and scalable diagnostics that can be performed by non-experts for detecting nucleic acid damage in the field, clinic or in a low-resourced laboratories. The research team is developing an assay to detect enzymatic activity on nucleic acid targets, using a portable commercial off-the-shelf (COTS) nanopore platform, the Oxford Nanopore Technologies (ONT) MinION, that is already used in several laboratories. This method enables specific, sensitive, and accurate enzyme detection and quantitation outside of a laboratory and into resource-limited environments for use by military personnel and first responders.

PNNL Posters

Direct Detection Of Peptides Following Enzymatic Cleavage Using a Field-Portable Nanopore Sequencing Device

Presenters: Fanny Chu, Sarah Jenson, Kai-For Mo, Matthew Turner, Kristin Engbrecht, David Wunschel

The research team developed a novel, field-portable strategy for enzyme activity detection using a nanopore sequencing device via direct detection of bait peptides as enzymatic cleavage products. This novel approach differentiated cleaved and uncleaved forms of peptide substrates for targeted enzyme detection. This approach can be applied as a rapid, field-portable tool for targeted enzyme activity detection to meet point-of-care diagnostic needs, such as biomarker detection in the clinic, and detect bacterial virulence activity.

Mixed Reality Enhanced Chemical Detection for Color-Changing Paper Using Machine Learning On Microsoft HoloLens

Presenters: Martin Pratt, Samuel Dixon, Melissa Swift, Jonathan Forman, Angela Melville, Carolyn Cramer, Lauren Charles

The team developed a prototype machine learning (ML), color-differentiating algorithm and deployed it on a standalone Microsoft HoloLens (HL2). The advantage of applying this ML algorithm provides the warfighter with a consistent approach for analyzing and interpreting color-changing paper indicative chemical tests under varying conditions.

Machine Learning Techniques For Threat Classification On Ion Mobility Spectroscopy: A Survey Of Methods

Presenters: John Cooper, Prativa Hartnett, Gabe Anderson, Robert Finedore, Samuel Dixon, Carolyn Cramer, Lauren Charles

The team performed experiments with different machine learning architectures on a class imbalanced, ion mobility spectroscopy (IMS) dataset of limited size. They used models with varying levels of complexity to classify chemical threats in real-time to serve as an early warning system for warfighters in the field.

Discovery Of Non-Canonical Protein Targets Of Chemicals Of Concern Using Activity-Based Protein Profiling (ABPP)

Presenters: Vivian Lin, Agne Sveistyte, Lydia Griggs, Gerard Lomas, Sankar Krishnamoorthy, Katherine Schultz, William Nelson, Jordan Smith, Stephen Callister, Aaron Wright

Acute exposures to known toxic chemicals, such as nerve agents and pharmaceutical-based agents, lead to well-documented adverse health effects. Yet significant efforts are still needed to achieve a comprehensive molecular level understanding of how these chemicals impact human biology. The team designed and synthesized 13 probes that mimic the structures and/or reactivity of organophosphates, benzodiazepines, ϒ-aminobutyric acid (GABA)-A receptor antagonists, fentanyl, ketamine, dexmedetomidine, and sevoflurane for activity-based protein profiling (ABPP) of mammalian tissue lysates. Further analysis of these data will help characterize the potential molecular mechanisms that these chemicals of concern impact human health and may provide insight into the biological pathways and markers that can be targeted for future broad-spectrum medical countermeasure development.

Facile Incorporation Of Non-canonical Amino Acids Into Structural Modeling

Presenters: Doo Nam Kim, John Cort, Richard Overstreet, Katherine Grahman

The team has computationally designed peptides whose canonical amino acids are exhaustively replaced with non-canonical amino acids (ncAAs) and experimentally verifying how much and how well ncAAs can replace canonical amino acids without compromising the existing stable secondary structure, using circular dichroism and NMR spectroscopy to characterize their secondary structure and stability.

Non-Canonical Amino Acid Parameterization Engine For Charmm Potentials

Presenters: Richard Overstreet, Dennis Thomas, John Cort, Katherine Grahman

Non-canonical or non-proteinogenic amino acids (ncAAs) present both opportunities and challenges for chemical and biological defense. In the absence of empirical data, modeling through scoring functions or molecular dynamics potentials, such as CHARMM, is commonly employed. The team introduce a software package to generate CHARMM compatible parameters for ncAAs from quantum density functional theory (DFT) calculations. The goal of this software is to reduce user intervention and analysis as much as feasible to efficiently generate potential parameters.

Portable And Convenient Identification Of Enzymatic Activity By Direct Detection Of Cleaved Peptides Using A Nanopore Sequencing Device

Presenters: Matthew Turner, Fanny Chu, Kristin Engbrecht, Jennifer Mobberley, Kai-For Mo, David Wunschel

Rapid, sensitive, and specific detection of enzyme cleavage of proteins in a portable device can bolster diagnostic capabilities in the field. The team developed a strategy to detect enzyme activity by using a commercially available nanopore sequencing device from Oxford Nanopore Technologies to differentiate the translocation current signatures between cleaved and uncleaved peptide substrates.