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We explore the idea that key information about plant and soil diversity can largely be hidden in low-variance features, comparing to extensive ground truth at a highly diverse and well-studied California field site.
Importance of nuclear data to bias changed significantly when including diverse measurement sets. LLNL pulsed sphere measurements provided information for 9 Be nuclear data above 2 MeV and subcritical benchmarks provided information for 240 Pu nuclear data between 100 keV and 10 MeV. Pulsed sphere leakage spectra and neutron noise observables are differently sensitive to nuclear data compared to critical benchmarks. Leakage spectra are sensitive to nuclear data above 5 MeV. Count rate and Feynman Y are more sensitive to nuclear data. A change in bias can help evaluators identify discrepant nuclear data. In the future, EUCLID plans to perform RAFIEKI analysis with additional benchmark and measurement sets.
Background: Microbial ecologists often employ methods from classical community ecology to analyze microbial community diversity. However, these methods have limitations because microbial communities differ from macro-organismal communities in key ways. This study sought to quantify microbial diversity using methods that are better suited for data spanning multiple domains of life and dimensions of diversity. Diversity profiles are one novel, promising way to analyze microbial datasets. Diversity profiles encompass many other indices, provide effective numbers of diversity (mathematical generalizations of previous indices that better convey the magnitude of differences in diversity), and can incorporate taxa similarity information. To explore whether these profiles change interpretations of microbial datasets, diversity profiles were calculated for four microbial datasets from different environments spanning all domains of life as well as viruses. Both similarity-based profiles that incorporated phylogenetic relatedness and naïve (not similarity-based) profiles were calculated. Simulated datasets were used to examine the robustness of diversity profiles to varying phylogenetic topology and community composition. Results: Diversity profiles provided insights into microbial datasets that were not detectable with classical univariate diversity metrics. For all datasets analyzed, there were key distinctions between calculations that incorporated phylogenetic diversity as a measure of taxa similarity and naïve calculations. The profiles also provided information about the effects of rare species on diversity calculations. Additionally, diversity profiles were used to examine thousands of simulated microbial communities, showing that similarity-based and naïve diversity profiles only agreed approximately 50% of the time in their classification of which sample was most diverse. This is a strong argument for incorporating similarity information and calculating diversity with a range of emphases on rare and abundant species when quantifying microbial community diversity. Conclusions: For many datasets, diversity profiles provided a different view of microbial community diversity compared to analyses that did not take into account taxa similarity information, effective diversity, or multiple diversity metrics. These findings are a valuable contribution to data analysis methodology in microbial ecology.
Recent years have witnessed the development of single-molecule localization microscopy as a generic tool for sampling diverse biologically relevant information at the super-resolution level. While current approaches often rely on the target-specific alteration of the point spread function to encode the multidimensional contents of single fluorophores, the details of the point spread function in an unmodified microscope already contain rich information. Here we introduce a data-driven approach in which artificial neural networks are trained to make a direct link between an experimental point spread function image and its underlying, multidimensional parameters, and compare results with alternative approaches based on maximum likelihood estimation. To demonstrate this concept in real systems, we decipher in fixed cells both the colors and the axial positions of single molecules in regular localization microscopy data.
The determination of the nature, distribution, and origin of organic material in carbonaceous chondrites is fundamental to understanding early solar nebular conditions and the origin of life. Using a variety of extraction techniques, followed by detailed chemical analysis, an extensive suite of organic compounds has been identified in carbonaceous chondrites. These data have provided key information on the diversity and isotopic composition of the organic component in chondrites. However, one disadvantage of extraction techniques is that all information regarding the spatial distribution of the organics on a fine scale is lost. This is especially important for the insoluble macromolecular carbon, which constitutes approximately 70% of the carbon in carbonaceous chondrites such as Murchison. The distribution and mineralogical associations may provide important constraints on the possible origins of the carbonaceous material. Our previous studies of the CV3 chondrites Allende and Vigarano have demonstrated that energy filtered transmission electron microscopy (EFTEM), combined with high resolution TEM (HRTEM) are powerful tools for the in situ characterization of insoluble organic matter in carbonaceous chondrites. In this study, we have used SEM and TEM techniques to characterize the matrix mineralogy of the CV3 chondrite ALH 84028 and examine the distribution and mineralogical associations of carbon. We are especially interested in establishing whether the occurrence of poorly graphitized carbon (PGC), observed in Allende matrix olivines, is common to all oxidized CV3 chondrites or is a unique feature of Allende.
Transformative science often occurs at the boundaries of different disciplines. Making interdisciplinary science data, software and documentation discoverable and accessible is essential to enabling transformative science. However, connecting this diverse and heterogeneous information is often a challenge due to several factors including the dispersed and sometimes isolated nature of data and the semantic differences between topical areas. NASA’s Science Discovery Engine (SDE) has developed several approaches to tackling these challenges. The SDE is a unified, insightful search experience that enables discovery of NASA’s open science data across five topical areas: astrophysics, biological and physical sciences, Earth science, heliophysics and planetary science. In this presentation, we will discuss our efforts to develop a systematic scientific curation workflow to integrate diverse content into a single search environment. We will also share lessons learned from our work to create a metadata crosswalk across the five disciplines.
Investigations within Security Operation Centers (SOCs) are tedious as they rely on manual efforts to query diverse data sources, overlay related logs, correlate the data into information, and then document results in a ticketing system. Security Orchestration, Automation, and Response (SOAR) tools are a relatively new technology that promise, with appropriate configuration, to collect, filter, and display needed diverse information; automate many of the common tasks that unnecessarily require SOC analysts’ time; facilitate SOC collaboration; and, in doing so, improve both efficiency and consistency of SOCs. There has been no prior research to test SOAR tools in practice; hence, understanding and evaluation of their effect is nascent and needed. Here, in this paper, we design and administer the first hands-on user study of SOAR tools, involving 24 participants and six commercial SOAR tools. Our contributions include the experimental design, itemizing six characteristics of SOAR tools, and a methodology for testing them. We describe configuration of a cyber range test environment, including network, user, and threat emulation; a full SOC tool suite; and creation of artifacts allowing multiple representative investigation scenarios to permit testing. We present the first research results on SOAR tools. Concisely, our findings are that: per-SOC SOAR configuration is extremely important; SOAR tools increase efficiency and reduce context switching, although with potentially decreased ticketing accuracy/completeness; user preference is slightly negatively correlated with their performance with the tool; internet dependence varies widely among SOAR tools; and balance of automation with assisting decision making is preferred by senior participants. We deliver a public user- and tool-anonymized and -obfuscated version of the data.
The Multi-Source Signatures for Nuclear Programs project, part of Pacific Northwest National Laboratory’s (PNNL) Signature Discovery Initiative, seeks to computationally capture expert assessment of multi-type information such as text, sensor output, imagery, or audio/video files, to assess nuclear activities through a series of Bayesian network (BN) models. These models incorporate knowledge from a diverse range of information sources in order to help assess a country’s nuclear activities. The models span engineering topic areas, state-level indicators, and facility-specific characteristics. To illustrate the development, calibration, and use of BN models for multi-source assessment, we present a model that predicts a country’s likelihood to participate in the international nuclear nonproliferation regime. We validate this model by examining the extent to which the model assists non-experts arrive at conclusions similar to those provided by nuclear proliferation experts. We also describe the PNNL-developed software used throughout the lifecycle of the Bayesian network model development.
Mechanochromic photonic materials present promising prospects for enhancing diverse facets of human life. Cholesteric liquid crystal elastomers (CLCEs) present immediate mechanochromic properties, allowing their utility in various fields, including camouflages, textiles, and anticounterfeiting. Nonetheless, it is essential to provide differentiated color information corresponding to diverse types and degrees of deformation for practical use in real life. Herein, the mechanochromic responses of CLCEs are investigated using their optical rotation under compression and uniaxial stretching. The CLCEs exhibit the same reflection colors indistinguishable for compression and uniaxial stretching due to their contracted pitches. In contrast, the linearly polarized light passing through deformed CLCEs can be modulated by optical rotation, resulting in distinct transmission colors that can be discerned through mechanical deformations. A visual signaling system with color information derived from optical rotation is demonstrated. By comprehending and manipulating the mechanoresponsive behavior of CLCEs, these materials can be advanced into innovative solutions with enhanced functionality and performance, thereby broadening their applications for human life enhancement.
Mechanochromic photonic materials present promising prospects for enhancing diverse facets of human life. Cholesteric liquid crystal elastomers (CLCEs) present immediate mechanochromic properties, allowing their utility in various fields, including camouflages, textiles, and anticounterfeiting. Nonetheless, it is essential to provide differentiated color information corresponding to diverse types and degrees of deformation for practical use in real life. Herein, we investigate the mechanochromic responses of CLCEs using their optical rotation under compression and uniaxial stretching. The CLCEs exhibit the same reflection colors indistinguishable for compression and uniaxial stretching due to their contracted pitches. In contrast, the linearly polarized light passing through deformed CLCEs can be modulated by optical rotation, resulting in distinct transmission colors that can be discerned through mechanical deformations. We demonstrate a visual signaling system with color information derived from optical rotation. By comprehending and manipulating the mechanoresponsive behavior of CLCEs, we can advance these materials into innovative solutions with enhanced functionality and performance, thereby broadening their applications for human life enhancement.
A collection of 47 bacteria isolated from the mucilage of aerial roots of energy sorghum is available at the Great Lakes Bioenergy Research Center, Michigan State University, Michigan, USA. We enriched bacteria with putative plant-beneficial phenotypes and included information on phenotypic diversity, taxonomy, and whole genome sequences.
In this paper we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.
This paper represents a progress report on HyLite (Hypermedia Library technology): a research and development activity to produce a versatile system as part of NASA's technology thrusts in automation, information sciences, and communications. HyLite can be used as a system or tool to facilitate the creation and maintenance of large distributed electronic libraries. The contents of such a library may be software components, hardware parts or designs, scientific data sets or databases, configuration management information, etc. Proliferation of computer use has made the diversity and quantity of information too large for any single user to sort, process, and utilize effectively. In response to this information deluge, we have created HyLite to enable the user to process relevant information into a more efficient organization for presentation, retrieval, and readability. To accomplish this end, we have incorporated various AI techniques into the HyLite hypermedia engine to facilitate parameters and properties of the system. The proposed techniques include intelligent searching tools for the libraries, intelligent retrievals, and navigational assistance based on user histories. HyLite itself is based on an earlier project, the Encyclopedia of Software Components (ESC) which used hypermedia to facilitate and encourage software reuse.
Yeasts are ubiquitous in temperate forests. While this broad habitat is well-defined, the yeasts inhabiting it and their life cycles, niches, and contributions to ecosystem functioning are less understood. Yeasts are present on nearly all sampled substrates in temperate forests worldwide. They associate with soils, macroorganisms, and other habitats and no doubt contribute to broader ecosystem-wide processes. Researchers have gathered information leading to hypotheses about yeasts' niches and their life cycles based on physiological observations in the laboratory as well as genomic analyses, but the challenge remains to test these hypotheses in the forests themselves. Here, we summarize the habitat and global patterns of yeast diversity, give some information on a handful of well-studied temperate forest yeast genera, discuss the various strategies to isolate forest yeasts, and explain temperate forest yeasts' contributions to biotechnology. Here, we close with a summary of the many future directions and outstanding questions facing researchers in temperate forest yeast ecology. Yeasts present an exciting opportunity to better understand the hidden world of microbial ecology in this threatened and global habitat.
We present a new approach to distributing processed telemetry data among spacecraft flight controllers within the control centers at NASA's Johnson Space Center. This approach facilitates the development of application programs which integrate spacecraft-telemetered data and ground-based synthesized data, then distributes this information to flight controllers for analysis and decision-making. The new approach combines various distributed computing models into one hybrid distributed computing model. The model employs both client-server and peer-to-peer distributed computing models cooperating to provide users with information throughout a diverse operations environment. Specifically, it provides an attractive foundation upon which we are building critical real-time monitoring and control applications, while simultaneously lending itself to peripheral applications in playback operations, mission preparations, flight controller training, and program development and verification. We have realized the hybrid distributed computing model through an information sharing protocol. We shall describe the motivations that inspired us to create this protocol, along with a brief conceptual description of the distributed computing models it employs. We describe the protocol design in more detail, discussing many of the program design considerations and techniques we have adopted. Finally, we describe how this model is especially suitable for supporting the implementation of distributed expert system applications.
Subsurface environments contain a large proportion of planetary microbial biomass and harbor diverse communities responsible for mediating biogeochemical cycles important to groundwater used by human society for consumption, irrigation, agriculture and industry. Within the saturated zone, capillary fringe and vadose zones, microorganisms can reside in two distinct phases (planktonic or biofilm), and significant differences in community composition, structure and activity between free-living and attached communities are commonly accepted. However, largely due to sampling constraints and the challenges of working with solid substrata, the contribution of each phase to subsurface processes is largely unresolved. Here, we synthesize current information on the diversity and activity of shallow freshwater subsurface habitats, discuss the challenges associated with sampling planktonic and biofilm communities across spatial, temporal and geological gradients, and discuss how biofilms may be constrained within shallow terrestrial subsurface aquifers. We suggest that merging traditional activity measurements and sequencing/-omics technologies with hydrological parameters important to sediment biofilm assembly and stability will help delineate key system parameters. Ultimately, integration will enhance our understanding of shallow subsurface ecophysiology in terms of bulk-flow through porous media and distinguish the respective activities of sessile microbial communities from more transient planktonic communities to ecosystem service and maintenance.
Letters of Agreement (LOAs) are text-based air traffic control documents that contain procedures and actions agreed upon by the different parties, typically two or more FAA facilities, that are subject to an agreement. The documents contain among other things generic constraints, which are explicit and implicit combinations of procedures that limit a flight’s trajectory and affects pilot actions. For example, a controller may be required, to assign a specific altitude to an aircraft crossing the boundary between two airspaces. Although LOA generic constraints directly impact the trajectory of an aircraft, they are not currently available in a digital form that can be used for (or directly ingested into automated) flight planning. Instead, the constraints are manually input into an onboard or ground based system. LOA documents are primarily stored at a controlling facility and the generic constraints are implemented by experienced air traffic controllers and pilots primarily using voice instructions. This increases the workload of the controllers, likelihood of error (e.g., due to noisy communication) and makes it impractical for implementation with unmanned aircraft. Therefore, steps must be taken to make existing constraints machine interpretable to enable e.g., automated handoffs which in turn would reduce controller workload. With recent advances in natural language processing, especially the rise in digitization of text documents (e.g., medical documents) and automated extraction of information therein, it is now possible to extract flight specific constraints from LOAs. The goal of this work is to digitize named entities through a combination of natural language processing tasks: named entity disambiguation, toponym resolution, and numeric parsing to extract general constraint components contained within LOAs, herein referred to as Entity Enhancement (EE). Starting with a small list of named entities (e.g., ARTCC, Tower, Altitude and Speed), EE can extract the named entities while simultaneously converting the string-based output into a digital format using an ensemble of processes like rule-based gazetteers and syntactic-lexical patterns. The digital format contains a diverse set of information based on the entity label in question, ranging from standardized facility names to units of measure (e.g., feet) and other numeric information. Upon validating our approach using a truth dataset, we show an overall F1-Score of 0.71 for the extraction process. Looking beyond entity enhancement, we are also working towards the goal of completely digitizing the general constraints by performing EE and fitting them into a standardized exchange model (XM) such as the Aeronautical Information Exchange Model (AIXM). This will allow for easy distribution and dissemination of LOA constraints to air users, better searchability within documents, and enable ingestion into automated flight planning. Finally, we show a preliminary version of the proposed XM architecture and demonstrate how the model can be populated from the EE output.