Biosecurity and Public Health

Los Alamos, NM, United States

Biosecurity and Public Health

Los Alamos, NM, United States
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News Article | April 26, 2017
Site: www.rdmag.com

A new computer modeling study from Los Alamos National Laboratory is aimed at making epidemiological models more accessible and useful for public-health collaborators and improving disease-related decision making. "In a real-world outbreak, the time is often too short and the data too limited to build a really accurate model to map disease progression or guide public-health decisions," said Ashlynn R. Daughton, a graduate research assistant at Los Alamos and doctoral student at University of Colorado, Boulder. She is lead author on a paper out last week in Scientific Reports, a Nature journal. "Our aim is to use existing models with low computational requirements first to explore disease-control measures and second to develop a platform for public-health collaborators to use and provide feedback on models," she said. The research draws on Los Alamos' expertise in computational modeling and health sciences and contributes to the Laboratory's national security mission by protecting against biological threats. Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a highly subjective process that involves both surveillance and expert opinion. Epidemiological modeling can fill gaps in the decision-making process, she says, by using available data to provide quantitative estimates of outbreak trajectories--determining where the infection is going, and how fast, so medical supplies and staff can be deployed for maximum effect. But if the tool requires unavailable data or overwhelms the capabilities of the health system, it won't be operationally useful. Collaboration between the modeling community and public-health policy community enables effective deployment, but the modeling resources need to be connected more strongly with the health community. Such collaboration is rare, as Daughton describes it, resulting in a scarcity of models that truly meet the needs of the public-health community. Simple, traditional models group people into categories based on their disease status (for example, SIR for Susceptible, Infected or Recovered). "For this initial work, we use a SIR model, modified to include a control measure, to explore many possible disease progression paths. The SIR model was chosen because it is the simplest and requires minimal computational resources," the paper notes. Other models are called agent-based, meaning they identify agents, often akin to an "individual," and map each agent's potential interactions during a day (for example, an individual might go to school, go to work, and interact with other members of the household). The model then extrapolates how each interaction could spread the disease. Because these are high-resolution models requiring significant expertise and computing power, as well as large quantities of data, they require resources beyond the reach of an average health department. For this study, using the simpler SIR model, the team explored outbreaks of measles, norovirus and influenza to show the feasibility of its use and describe a research agenda to further promote interactions between decision makers and the modeling community. "Unlike standard epidemiological models that are disease and location specific and not transferrable or generalizable, this model is disease and location agnostic and can be used at a much higher level for planning purposes regardless of the specific control measure," said Alina Deshpande, group leader of the Biosecurity and Public Health group at Los Alamos and principal investigator on the project. Overall, the team determined, there is a clear need in the field to better understand outbreak parameters, underlying model assumptions, and the ways that these apply to real-world scenarios. The authors conclude, "We thus propose thoughtful validation of SIR models as an important next step. Such a validation would accomplish several things. It would (1) validate the counterfactual approach, (2) provide additional data to describe when compartmental models are appropriate approximations of real world outbreaks and (3) provide data to describe situations where the compartmental models do not match real world outbreaks and should not be used for decision support."


News Article | April 25, 2017
Site: www.sciencedaily.com

A new computer modeling study from Los Alamos National Laboratory is aimed at making epidemiological models more accessible and useful for public-health collaborators and improving disease-related decision making. "In a real-world outbreak, the time is often too short and the data too limited to build a really accurate model to map disease progression or guide public-health decisions," said Ashlynn R. Daughton, a graduate research assistant at Los Alamos and doctoral student at University of Colorado, Boulder. She is lead author on a paper out last week in Scientific Reports, a Nature journal. "Our aim is to use existing models with low computational requirements first to explore disease-control measures and second to develop a platform for public-health collaborators to use and provide feedback on models," she said. The research draws on Los Alamos' expertise in computational modeling and health sciences and contributes to the Laboratory's national security mission by protecting against biological threats. Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a highly subjective process that involves both surveillance and expert opinion. Epidemiological modeling can fill gaps in the decision-making process, she says, by using available data to provide quantitative estimates of outbreak trajectories -- determining where the infection is going, and how fast, so medical supplies and staff can be deployed for maximum effect. But if the tool requires unavailable data or overwhelms the capabilities of the health system, it won't be operationally useful. Collaboration between the modeling community and public-health policy community enables effective deployment, but the modeling resources need to be connected more strongly with the health community. Such collaboration is rare, as Daughton describes it, resulting in a scarcity of models that truly meet the needs of the public-health community. Simple, traditional models group people into categories based on their disease status (for example, SIR for Susceptible, Infected or Recovered). "For this initial work, we use a SIR model, modified to include a control measure, to explore many possible disease progression paths. The SIR model was chosen because it is the simplest and requires minimal computational resources," the paper notes. Other models are called agent-based, meaning they identify agents, often akin to an "individual," and map each agent's potential interactions during a day (for example, an individual might go to school, go to work, and interact with other members of the household). The model then extrapolates how each interaction could spread the disease. Because these are high-resolution models requiring significant expertise and computing power, as well as large quantities of data, they require resources beyond the reach of an average health department. For this study, using the simpler SIR model, the team explored outbreaks of measles, norovirus and influenza to show the feasibility of its use and describe a research agenda to further promote interactions between decision makers and the modeling community. "Unlike standard epidemiological models that are disease and location specific and not transferrable or generalizable, this model is disease and location agnostic and can be used at a much higher level for planning purposes regardless of the specific control measure," said Alina Deshpande, group leader of the Biosecurity and Public Health group at Los Alamos and principal investigator on the project. Overall, the team determined, there is a clear need in the field to better understand outbreak parameters, underlying model assumptions, and the ways that these apply to real-world scenarios. The authors conclude, "We thus propose thoughtful validation of SIR models as an important next step. Such a validation would accomplish several things. It would (1) validate the counterfactual approach, (2) provide additional data to describe when compartmental models are appropriate approximations of real world outbreaks and (3) provide data to describe situations where the compartmental models do not match real world outbreaks and should not be used for decision support."


News Article | April 25, 2017
Site: phys.org

"In a real-world outbreak, the time is often too short and the data too limited to build a really accurate model to map disease progression or guide public-health decisions," said Ashlynn R. Daughton, a graduate research assistant at Los Alamos and doctoral student at University of Colorado, Boulder. She is lead author on a paper out last week in Scientific Reports, a Nature journal. "Our aim is to use existing models with low computational requirements first to explore disease-control measures and second to develop a platform for public-health collaborators to use and provide feedback on models," she said. The research draws on Los Alamos' expertise in computational modeling and health sciences and contributes to the Laboratory's national security mission by protecting against biological threats. Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a highly subjective process that involves both surveillance and expert opinion. Epidemiological modeling can fill gaps in the decision-making process, she says, by using available data to provide quantitative estimates of outbreak trajectories—determining where the infection is going, and how fast, so medical supplies and staff can be deployed for maximum effect. But if the tool requires unavailable data or overwhelms the capabilities of the health system, it won't be operationally useful. Collaboration between the modeling community and public-health policy community enables effective deployment, but the modeling resources need to be connected more strongly with the health community. Such collaboration is rare, as Daughton describes it, resulting in a scarcity of models that truly meet the needs of the public-health community. Simple, traditional models group people into categories based on their disease status (for example, SIR for Susceptible, Infected or Recovered). "For this initial work, we use a SIR model, modified to include a control measure, to explore many possible disease progression paths. The SIR model was chosen because it is the simplest and requires minimal computational resources," the paper notes. Other models are called agent-based, meaning they identify agents, often akin to an "individual," and map each agent's potential interactions during a day (for example, an individual might go to school, go to work, and interact with other members of the household). The model then extrapolates how each interaction could spread the disease. Because these are high-resolution models requiring significant expertise and computing power, as well as large quantities of data, they require resources beyond the reach of an average health department. For this study, using the simpler SIR model, the team explored outbreaks of measles, norovirus and influenza to show the feasibility of its use and describe a research agenda to further promote interactions between decision makers and the modeling community. "Unlike standard epidemiological models that are disease and location specific and not transferrable or generalizable, this model is disease and location agnostic and can be used at a much higher level for planning purposes regardless of the specific control measure," said Alina Deshpande, group leader of the Biosecurity and Public Health group at Los Alamos and principal investigator on the project. Overall, the team determined, there is a clear need in the field to better understand outbreak parameters, underlying model assumptions, and the ways that these apply to real-world scenarios. The authors conclude, "We thus propose thoughtful validation of SIR models as an important next step. Such a validation would accomplish several things. It would (1) validate the counterfactual approach, (2) provide additional data to describe when compartmental models are appropriate approximations of real world outbreaks and (3) provide data to describe situations where the compartmental models do not match real world outbreaks and should not be used for decision support." The paper: "An approach to and web-based tool for infectious disease outbreak intervention analysis," authors Ashlynn R. Daughton, Nicholas Generous, Reid Priedhorsky and Alina Deshpande, in Scientific Reports. Explore further: Study uses social media, internet to forecast disease outbreaks More information: Ashlynn R. Daughton et al, An approach to and web-based tool for infectious disease outbreak intervention analysis, Scientific Reports (2017). DOI: 10.1038/srep46076


News Article | November 29, 2016
Site: www.eurekalert.org

LOS ALAMOS, N.M., Nov. 29, 2016 -- A new bioinformatics platform called Empowering the Development of Genomics Expertise (EDGE) will help democratize the genomics revolution by allowing users with limited bioinformatics expertise to quickly analyze and interpret genomic sequence data. Researchers at Los Alamos National Laboratory and their collaborators at the Naval Medical Research Center developed EDGE, which is described in a paper recently published in Nucleic Acids Research. "We realized that while next-generation sequencing instruments are becoming more widespread and more accessible to the average biologist or physician, the bioinformatics tools required to process and analyze the data were not as user-friendly or accessible," said Patrick Chain, of Los Alamos' Biosecurity and Public Health group and EDGE team lead. "Given the large number of applications where sequencing is now used, a robust bioinformatics platform that encapsulates a broad array of algorithms is required to help address questions a researcher may have. We sought to develop a web-based environment where non-bioinformatics experts could easily select what pipelines they need and rapidly obtain results and interact with their data." Stopping the spread of disease--from naturally occurring or manmade threats -- requires an in-depth understanding of pathogens and how they work. To this end, the ability to characterize organisms through accurately and rapidly comparing genomic data is an important part of Los Alamos' national security mission. Technology advancements have fueled the development of new sequencing applications and will flood current databases with raw data. A number of factors limit the use of these data, including the large number of associated software and hardware dependencies and the detailed expertise required to perform this analysis. To address these issues, Chain and his team have developed an intuitive web-based environment with a wide assortment of integrated and pioneering bioinformatics tools in pre-configured workflows, all of which can be readily applied to isolate genome sequencing projects or metagenomics projects. EDGE is a user-friendly and open-source platform that integrates hundreds of cutting-edge tools and helps reduce data analysis times from days or weeks to minutes or hours. The workflows in EDGE, along with its ease of use, provide novice next-generation sequencing users with the ability to perform many complex analyses with only a few mouse clicks. This bioinformatics platform is described as an initial attempt at empowering the development of genomics dxpertise, as its name suggests, for a wide range of applications in microbial research. EDGE has already helped streamline data analysis for groups in Thailand, Georgia, Peru, South Korea, Gabon, Uganda, Egypt and Cambodia, as well as within several government laboratories in the United States. The paper "Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform" was published in Nucleic Acids Research in partnership with the Defense Threat Reduction Agency, the Naval Medical Research Center-Frederick and the Henry M. Jackson Foundation. Patrick Chain earned his master's of science in microbial genomics from McMaster University and his doctoral degree in molecular microbiology and molecular genetics at Michigan State University. He is currently leading the Bioinformatics and Analytics Team and the Metagenomics Program within the Biosecurity and Public Health group at Los Alamos National Laboratory. His background is in microbial ecology, evolution, genomics and bioinformatics, having spent the past 20 years using genomics to study various microbial systems, including the human microbiome, other environmental metagenomic communities, various isolate microbes or single cells, including bacterial and viral pathogens as well as fungal, algal, plant and animal systems. He currently leads a team of researchers whose charge is to devise novel methods, algorithms and strategies for the biological interpretation of massively parallel sequencing data. Los Alamos National Laboratory, a multidisciplinary research institution engaged in strategic science on behalf of national security, is operated by Los Alamos National Security, LLC, a team composed of Bechtel National, the University of California, BWXT Government Group, and URS, an AECOM company, for the Department of Energy's National Nuclear Security Administration. Los Alamos enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.


News Article | December 19, 2016
Site: www.scientificcomputing.com

A new bioinformatics platform called Empowering the Development of Genomics Expertise (EDGE) will help democratize the genomics revolution by allowing users with limited bioinformatics expertise to quickly analyze and interpret genomic sequence data. Researchers at Los Alamos National Laboratory and their collaborators at the Naval Medical Research Center developed EDGE, which is described in a paper recently published in Nucleic Acids Research. “We realized that while next-generation sequencing instruments are becoming more widespread and more accessible to the average biologist or physician, the bioinformatics tools required to process and analyze the data were not as user-friendly or accessible,” said Patrick Chain, of Los Alamos’ Biosecurity and Public Health group and EDGE team lead. “Given the large number of applications where sequencing is now used, a robust bioinformatics platform that encapsulates a broad array of algorithms is required to help address questions a researcher may have. We sought to develop a web-based environment where non-bioinformatics experts could easily select what pipelines they need and rapidly obtain results and interact with their data.” Stopping the spread of disease—from naturally occurring or manmade threats—requires an in-depth understanding of pathogens and how they work. To this end, the ability to characterize organisms through accurately and rapidly comparing genomic data is an important part of Los Alamos’ national security mission. Technology advancements have fueled the development of new sequencing applications and will flood current databases with raw data. A number of factors limit the use of these data, including the large number of associated software and hardware dependencies and the detailed expertise required to perform this analysis. To address these issues, Chain and his team have developed an intuitive web-based environment with a wide assortment of integrated and pioneering bioinformatics tools in pre-configured workflows, all of which can be readily applied to isolate genome sequencing projects or metagenomics projects. EDGE is a user-friendly and open-source platform that integrates hundreds of cutting-edge tools and helps reduce data analysis times from days or weeks to minutes or hours. The workflows in EDGE, along with its ease of use, provide novice next-generation sequencing users with the ability to perform many complex analyses with only a few mouse clicks. This bioinformatics platform is described as an initial attempt at empowering the development of genomics expertise, as its name suggests, for a wide range of applications in microbial research. EDGE has already helped streamline data analysis for groups in Thailand, Georgia, Peru, South Korea, Gabon, Uganda, Egypt and Cambodia, as well as within several government laboratories in the United States. The paper “Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform” was published in Nucleic Acids Research in partnership with the Defense Threat Reduction Agency, the Naval Medical Research Center-Frederick and the Henry M. Jackson Foundation.


Nagy A.,Biosecurity and Public Health | Steinbruck A.,Los Alamos National Laboratory | Gao J.,Biosecurity and Public Health | Doggett N.,Biosecurity and Public Health | And 2 more authors.
ACS Nano | Year: 2012

The growing potential of quantum dots (QDs) in applications as diverse as biomedicine and energy has provoked much dialogue about their conceivable impact on human health and the environment at large. Consequently, there has been an urgent need to understand their interaction with biological systems. Parameters such as size, composition, surface charge, and functionalization can be modified in ways to either enhance biocompatibility or reduce their deleterious effects. In the current study, we simultaneously compared the impact of size, charge, and functionalization alone or in combination on biological responses using primary normal human bronchial epithelial cells. Using a suite of cellular end points and gene expression analysis, we determined the biological impact of each of these properties. Our results suggest that positively charged QDs are significantly more cytotoxic compared to negative QDs. Furthermore, while QDs functionalized with long ligands were found to be more cytotoxic than those functionalized with short ligands, negative QDs functionalized with long ligands also demonstrated size-dependent cytotoxicity. We conclude that QD-elicited cytotoxicity is not a function of a single property but a combination of factors. The mechanism of toxicity was found to be independent of reactive oxygen species formation, as cellular viability could not be rescued in the presence of the antioxidant n-acetyl cysteine. Further exploring these responses at the molecular level, we found that the relatively benign negative QDs increased gene expression of proinflammatory cytokines and those associated with DNA damage, while the highly toxic positive QDs induced changes in genes associated with mitochondrial function. In an attempt to tentatively "rank" the contribution of each property in the observed QD-induced responses, we concluded that QD charge and ligand length, and to a lesser extent, size, are key factors that should be considered when engineering nanomaterials with minimal bioimpact (charge > functionalization > size). © 2012 American Chemical Society.


News Article | November 29, 2016
Site: phys.org

"We realized that while next-generation sequencing instruments are becoming more widespread and more accessible to the average biologist or physician, the bioinformatics tools required to process and analyze the data were not as user-friendly or accessible," said Patrick Chain, of Los Alamos' Biosecurity and Public Health group and EDGE team lead. "Given the large number of applications where sequencing is now used, a robust bioinformatics platform that encapsulates a broad array of algorithms is required to help address questions a researcher may have. We sought to develop a web-based environment where non-bioinformatics experts could easily select what pipelines they need and rapidly obtain results and interact with their data." Stopping the spread of disease—from naturally occurring or manmade threats—requires an in-depth understanding of pathogens and how they work. To this end, the ability to characterize organisms through accurately and rapidly comparing genomic data is an important part of Los Alamos' national security mission. Technology advancements have fueled the development of new sequencing applications and will flood current databases with raw data. A number of factors limit the use of these data, including the large number of associated software and hardware dependencies and the detailed expertise required to perform this analysis. To address these issues, Chain and his team have developed an intuitive web-based environment with a wide assortment of integrated and pioneering bioinformatics tools in pre-configured workflows, all of which can be readily applied to isolate genome sequencing projects or metagenomics projects. EDGE is a user-friendly and open-source platform that integrates hundreds of cutting-edge tools and helps reduce data analysis times from days or weeks to minutes or hours. The workflows in EDGE, along with its ease of use, provide novice next-generation sequencing users with the ability to perform many complex analyses with only a few mouse clicks. This bioinformatics platform is described as an initial attempt at empowering the development of genomics dxpertise, as its name suggests, for a wide range of applications in microbial research. Explore further: Furthering data analysis of next-generation sequencing to facilitate research More information: Enabling the democratization of the genomics revolution with a fully integrated web-based bioinformatics platform, OUP accepted manuscript, Nucleic Acids Research (2016). DOI: 10.1093/nar/gkw1027

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