TY - BOOK AU - National Academies of Sciences, Engineering, and Medicine TI - The Chemistry of Microbiomes: Proceedings of a Seminar Series SN - DO - 10.17226/24751 PY - 2017 UR - https://nap.nationalacademies.org/catalog/24751/the-chemistry-of-microbiomes-proceedings-of-a-seminar-series PB - The National Academies Press CY - Washington, DC LA - English KW - Math, Chemistry, and Physics AB - The 21st century has witnessed a complete revolution in the understanding and description of bacteria in eco- systems and microbial assemblages, and how they are regulated by complex interactions among microbes, hosts, and environments. The human organism is no longer considered a monolithic assembly of tissues, but is instead a true ecosystem composed of human cells, bacteria, fungi, algae, and viruses. As such, humans are not unlike other complex ecosystems containing microbial assemblages observed in the marine and earth environments. They all share a basic functional principle: Chemical communication is the universal language that allows such groups to properly function together. These chemical networks regulate interactions like metabolic exchange, antibiosis and symbiosis, and communication. The National Academies of Sciences, Engineering, and Medicine’s Chemical Sciences Roundtable organized a series of four seminars in the autumn of 2016 to explore the current advances, opportunities, and challenges toward unveiling this “chemical dark matter” and its role in the regulation and function of different ecosystems. The first three focused on specific ecosystems—earth, marine, and human—and the last on all microbiome systems. This publication summarizes the presentations and discussions from the seminars. ER - TY - BOOK AU - National Academies of Sciences, Engineering, and Medicine A2 - Linda Casola TI - Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop SN - DO - 10.17226/24900 PY - 2017 UR - https://nap.nationalacademies.org/catalog/24900/challenges-in-machine-generation-of-analytic-products-from-multi-source-data PB - The National Academies Press CY - Washington, DC LA - English KW - Math, Chemistry, and Physics KW - Surveys and Statistics AB - The Intelligence Community Studies Board of the National Academies of Sciences, Engineering, and Medicine convened a workshop on August 9-10, 2017 to examine challenges in machine generation of analytic products from multi-source data. Workshop speakers and participants discussed research challenges related to machine-based methods for generating analytic products and for automating the evaluation of these products, with special attention to learning from small data, using multi-source data, adversarial learning, and understanding the human-machine relationship. This publication summarizes the presentations and discussions from the workshop. ER - TY - BOOK AU - National Academies of Sciences, Engineering, and Medicine TI - Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions SN - DO - 10.17226/23670 PY - 2017 UR - https://nap.nationalacademies.org/catalog/23670/strengthening-data-science-methods-for-department-of-defense-personnel-and-readiness-missions PB - The National Academies Press CY - Washington, DC LA - English KW - Math, Chemistry, and Physics KW - Conflict and Security Issues KW - Surveys and Statistics AB - The Office of the Under Secretary of Defense (Personnel & Readiness), referred to throughout this report as P&R, is responsible for the total force management of all Department of Defense (DoD) components including the recruitment, readiness, and retention of personnel. Its work and policies are supported by a number of organizations both within DoD, including the Defense Manpower Data Center (DMDC), and externally, including the federally funded research and development centers (FFRDCs) that work for DoD. P&R must be able to answer questions for the Secretary of Defense such as how to recruit people with an aptitude for and interest in various specialties and along particular career tracks and how to assess on an ongoing basis service members' career satisfaction and their ability to meet new challenges. P&R must also address larger-scale questions, such as how the current realignment of forces to the Asia-Pacific area and other regions will affect recruitment, readiness, and retention. While DoD makes use of large-scale data and mathematical analysis in intelligence, surveillance, reconnaissance, and elsewhere—exploiting techniques such as complex network analysis, machine learning, streaming social media analysis, and anomaly detection—these skills and capabilities have not been applied as well to the personnel and readiness enterprise. Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions offers and roadmap and implementation plan for the integration of data analysis in support of decisions within the purview of P&R. ER - TY - BOOK AU - National Academies of Sciences, Engineering, and Medicine TI - A Proposed Framework for Identifying Potential Biodefense Vulnerabilities Posed by Synthetic Biology: Interim Report SN - DO - 10.17226/24832 PY - 2017 UR - https://nap.nationalacademies.org/catalog/24832/a-proposed-framework-for-identifying-potential-biodefense-vulnerabilities-posed-by-synthetic-biology PB - The National Academies Press CY - Washington, DC LA - English KW - Conflict and Security Issues KW - Biology and Life Sciences KW - Math, Chemistry, and Physics AB - Building on an increasingly sophisticated understanding of naturally occurring biological processes, researchers have developed technologies to predictably modify or create organisms or biological components. This research, known collectively as synthetic biology, is being pursued for a variety of purposes, from reducing the burden of disease to improving agricultural yields to remediating pollution. While synthetic biology is being pursued primarily for beneficial and legitimate purposes, it is possible to imagine malicious uses that could threaten human health or military readiness and performance. Making informed decisions about how to address such concerns requires a comprehensive, realistic assessment. To this end, the U.S. Department of Defense, working with other agencies involved in biodefense, asked the National Academies of Sciences, Engineering, and Medicine to develop a framework to guide an assessment of the security concerns related to advances in synthetic biology, to assess the level of concern warranted for various advances and identify areas of vulnerability, and to prioritize options to address these vulnerabilities. This interim report proposes a framework for identifying and prioritizing potential areas of concern associated with synthetic biology—a tool to aid the consideration of concerns related to synthetic biology. The framework describes categories of synthetic biology technologies and applications—such as genome editing, directed evolution, and automated biological design—and provides a set of initial questions to guide the assessment of concern related to these technologies and applications. ER - TY - BOOK AU - National Academies of Sciences, Engineering, and Medicine A2 - Ben A. Wender TI - Refining the Concept of Scientific Inference When Working with Big Data: Proceedings of a Workshop SN - DO - 10.17226/24654 PY - 2017 UR - https://nap.nationalacademies.org/catalog/24654/refining-the-concept-of-scientific-inference-when-working-with-big-data PB - The National Academies Press CY - Washington, DC LA - English KW - Math, Chemistry, and Physics KW - Engineering and Technology KW - Policy for Science and Technology KW - Surveys and Statistics AB - The concept of utilizing big data to enable scientific discovery has generated tremendous excitement and investment from both private and public sectors over the past decade, and expectations continue to grow. Using big data analytics to identify complex patterns hidden inside volumes of data that have never been combined could accelerate the rate of scientific discovery and lead to the development of beneficial technologies and products. However, producing actionable scientific knowledge from such large, complex data sets requires statistical models that produce reliable inferences (NRC, 2013). Without careful consideration of the suitability of both available data and the statistical models applied, analysis of big data may result in misleading correlations and false discoveries, which can potentially undermine confidence in scientific research if the results are not reproducible. In June 2016 the National Academies of Sciences, Engineering, and Medicine convened a workshop to examine critical challenges and opportunities in performing scientific inference reliably when working with big data. Participants explored new methodologic developments that hold significant promise and potential research program areas for the future. This publication summarizes the presentations and discussions from the workshop. ER -