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Usage-Based Discovery of Earth Observations

Most providers of Earth Observation data enable search via dataset characteristics, (e.g., quantity being measured, instrument, location, and time). However, the Earth Science Information Partners Federation (ESIP) is attempting to improve dataset discovery by capturing information on how datasets are used. At a usage-based discovery hackfest at the July 2020 ESIP Summer Meeting, participants pooled their collective skills to implement a prototype based on the connections between datasets and data usage, which can reveal unanticipated patterns in dataset connectivity that are not apparent through traditional approaches. A survey of natural hazards websites, focusing on hydrology-related hazards such as floods and hydrology datasets such as rainfall, illustrated the difficulty of gleaning which dataset was actually used and how it was accessed. This difficulty highlights the need for greater transparency on dataset usage in website design. An application wireframe for a usage-based discovery database was conceptualized as a tool to help applications data specialists rapidly assemble a fit-for-purpose website as part of a disaster response, where customer feedback was integral to the manner in which the website would be developed and delivered. Capturing connections among datasets and instances of usage, either for research or applications, could help users identify datasets with a proven track record of utility or those that have been supplanted by more accurate and(or) precise representations. It may also help Earth Observation providers maximize returns on investments in data services. Social discovery, i.e, following discovery paths laid down by previous users of the data, could be a critical tool for improving the timeliness and applicability of dataset delivery at times and in circumstances where practitioners don’t have the capacity to evaluate the deluge of datasets that are returned by today’s search tools.

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DOE OSTI2010

Computational Materials Science and Chemistry: Accelerating Discovery and Innovation through Simulation-Based Engineering and Science

This report is based on a SC Workshop on Computational Materials Science and Chemistry for Innovation on July 26-27, 2010, to assess the potential of state-of-the-art computer simulations to accelerate understanding and discovery in materials science and chemistry, with a focus on potential impacts in energy technologies and innovation. The urgent demand for new energy technologies has greatly exceeded the capabilities of today's materials and chemical processes. To convert sunlight to fuel, efficiently store energy, or enable a new generation of energy production and utilization technologies requires the development of new materials and processes of unprecedented functionality and performance. New materials and processes are critical pacing elements for progress in advanced energy systems and virtually all industrial technologies. Over the past two decades, the United States has developed and deployed the world's most powerful collection of tools for the synthesis, processing, characterization, and simulation and modeling of materials and chemical systems at the nanoscale, dimensions of a few atoms to a few hundred atoms across. These tools, which include world-leading x-ray and neutron sources, nanoscale science facilities, and high-performance computers, provide an unprecedented view of the atomic-scale structure and dynamics of materials and the molecular-scale basis of chemical processes. For the first time in history, we are able to synthesize, characterize, and model materials and chemical behavior at the length scale where this behavior is controlled. This ability is transformational for the discovery process and, as a result, confers a significant competitive advantage. Perhaps the most spectacular increase in capability has been demonstrated in high performance computing. Over the past decade, computational power has increased by a factor of a million due to advances in hardware and software. This rate of improvement, which shows no sign of abating, has enabled the development of computer simulations and models of unprecedented fidelity. We are at the threshold of a new era where the integrated synthesis, characterization, and modeling of complex materials and chemical processes will transform our ability to understand and design new materials and chemistries with predictive power. In turn, this predictive capability will transform technological innovation by accelerating the development and deployment of new materials and processes in products and manufacturing. Harnessing the potential of computational science and engineering for the discovery and development of materials and chemical processes is essential to maintaining leadership in these foundational fields that underpin energy technologies and industrial competitiveness. Capitalizing on the opportunities presented by simulation-based engineering and science in materials and chemistry will require an integration of experimental capabilities with theoretical and computational modeling; the development of a robust and sustainable infrastructure to support the development and deployment of advanced computational models; and the assembly of a community of scientists and engineers to implement this integration and infrastructure. This community must extend to industry, where incorporating predictive materials science and chemistry into design tools can accelerate the product development cycle and drive economic competitiveness. The confluence of new theories, new materials synthesis capabilities, and new computer platforms has created an unprecedented opportunity to implement a "materials-by-design" paradigm with wide-ranging benefits in technological innovation and scientific discovery. The Workshop on Computational Materials Science and Chemistry for Innovation was convened in Bethesda, Maryland, on July 26-27, 2010. Sponsored by the Department of Energy (DOE) Offices of Advanced Scientific Computing Research and Basic Energy Sciences, the workshop brought together 160 experts in materials science, chemistry, and computational science representing more than 65 universities, laboratories, and industries, and four agencies. The workshop examined seven foundational challenge areas in materials science and chemistry: materials for extreme conditions, self-assembly, light harvesting, chemical reactions, designer fluids, thin films and interfaces, and electronic structure. Each of these challenge areas is critical to the development of advanced energy systems, and each can be accelerated by the integrated application of predictive capability with theory and experiment. The workshop concluded that emerging capabilities in predictive modeling and simulation have the potential to revolutionize the development of new materials and chemical processes. Coupled with world-leading materials characterization and nanoscale science facilities, this predictive capability provides the foundation for an innovation ecosystem that can accelerate the discovery, development, and deployment of new technologies, including advanced energy systems. Delivering on the promise of this innovation ecosystem requires the following: Integration of synthesis, processing, characterization, theory, and simulation and modeling. Many of the newly established Energy Frontier Research Centers and Energy Hubs are exploiting this integration. Achieving/strengthening predictive capability in foundational challenge areas. Predictive capability in the seven foundational challenge areas described in this report is critical to the development of advanced energy technologies. Developing validated computational approaches that span vast differences in time and length scales. This fundamental computational challenge crosscuts all of the foundational challenge areas. Similarly challenging is coupling of analytical data from multiple instruments and techniques that are required to link these length and time scales. Experimental validation and quantification of uncertainty in simulation and modeling. Uncertainty quantification becomes increasingly challenging as simulations become more complex. Robust and sustainable computational infrastructure, including software and applications. For modeling and simulation, software equals infrastructure. To validate the computational tools, software is critical infrastructure that effectively translates huge arrays of experimental data into useful scientific understanding. An integrated approach for managing this infrastructure is essential. Efficient transfer and incorporation of simulation-based engineering and science in industry. Strategies for bridging the gap between research and industrial applications and for widespread industry adoption of integrated computational materials engineering are needed.

36 MATERIALS SCIENCE↗

DOE OSTI2023

A New Drug Discovery Platform: Application to DNA Polymerase Eta and Apurinic/Apyrimidinic Endonuclease 1

The ability to quickly discover reliable hits from screening and rapidly convert them into lead compounds, which can be verified in functional assays, is central to drug discovery. The expedited validation of novel targets and the identification of modulators to advance to preclinical studies can significantly increase drug development success. Our SaXPyTM (“SAR by X-ray Poses Quickly”) platform, which is applicable to any X-ray crystallography-enabled drug target, couples the established methods of protein X-ray crystallography and fragment-based drug discovery (FBDD) with advanced computational and medicinal chemistry to deliver small molecule modulators or targeted protein degradation ligands in a short timeframe. Our approach, especially for elusive or “undruggable” targets, allows for (i) hit generation; (ii) the mapping of protein–ligand interactions; (iii) the assessment of target ligandability; (iv) the discovery of novel and potential allosteric binding sites; and (v) hit-to-lead execution. These advances inform chemical tractability and downstream biology and generate novel intellectual property. We describe here the application of SaXPy in the discovery and development of DNA damage response inhibitors against DNA polymerase eta (Pol η or POLH) and apurinic/apyrimidinic endonuclease 1 (APE1 or APEX1). Notably, our SaXPy platform allowed us to solve the first crystal structures of these proteins bound to small molecules and to discover novel binding sites for each target.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Hierarchical Gaussian process-based Bayesian optimization for materials discovery in high entropy alloy spaces

Bayesian optimization (BO) is a powerful and data-efficient method for iterative materials discovery and design, particularly valuable when prior knowledge is limited, underlying functional relationships are complex or unknown, and the cost of querying the materials space is significant. Traditional BO methodologies typically utilize conventional Gaussian Processes (cGPs) to model the relationships between material inputs and properties, as well as correlations within the input space. However, cGP-BO approaches often fall short in multi-objective optimization scenarios, where they are unable to fully exploit correlations between distinct material properties. Leveraging these correlations can significantly enhance the discovery process, as information about one property can inform and improve predictions about others. Here, this study addresses this limitation by employing advanced kernel structures to capture and model multi-dimensional property correlations through multi-task (MTGPs) or deep Gaussian Processes (DGPs), thus accelerating the discovery process. We demonstrate the effectiveness of MTGP-BO and DGP-BO in rapidly and robustly solving complex materials design challenges that occur within the context of complex multi-objective optimization over FCC FeCrNiCoCu high entropy alloy (HEA) spaces, where traditional cGP-BO approaches fail. Furthermore, we highlight how the differential costs associated with querying various material properties can be strategically leveraged to make the materials discovery process more cost-efficient.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Mass Spectrometry-Based Approaches for Clinical Biomarker Discovery in Traumatic Brain Injury

Purpose of Review: Precision treatments to address the multifaceted pathophysiology of traumatic brain injury (TBI) are desperately needed, which has led to the intense study of fluid-based protein biomarkers in TBI. Mass Spectrometry (MS) is increasingly being applied to biomarker discovery and quantification in neurological disease to explore the proteome, allowing for more flexibility in biomarker discovery than commonly encountered antibody-based assays. In this narrative review, we will provide specific examples of how MS technology has advanced translational research in traumatic brain injury (TBI) focusing on clinical studies, and looking ahead to promising emerging applications of MS to the field of Neurocritical Care. Recent Findings: Proteomic biomarker discovery using MS technology in human subjects has included the full range of injury severity in TBI, though critically ill patients can offer more options to biofluids given the need for invasive monitoring. Blood, urine, cerebrospinal fluid, brain specimens, and cerebral extracellular fluid have all been sources for analysis. Emerging evidence suggests there are distinct proteomic profiles in radiographic TBI subtypes, and that biomarkers may be used to distinguish patients sustaining TBI from healthy controls. Metabolomics may offer a window into the perturbations of ongoing cerebral insults in critically ill patients after severe TBI. Summary: Emerging MS technologies may offer biomarker discovery and validation opportunities not afforded by conventional means due to its ability to handle the complexities associated with the proteome. In conclusion, while MS techniques are relatively early in development in the neurosciences space, the potential applications to TBI and neurocritical care are likely to accelerate in the coming decade.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2011

Discovery of the calcium, indium, tin, and platinum isotopes

Currently, twenty-four calcium, thirty-eight indium, thirty-eight tin, and thirty-nine platinum isotopes have been observed and the discovery of these isotopes is discussed here. For each isotope a brief synopsis of the first refereed publication, including the production and identification method, is presented. - Highlights: Documentation of the discovery of all calcium, indium, tin and platinum isotopes. {yields} Summary of author, journal, year, place and country of discovery for each isotope. {yields} Brief description of discovery history of each isotope.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2010

The discovery and development of proteomic safety biomarkers for the detection of drug-induced liver toxicity

Biomarkers are biometric measurements that provide critical quantitative information about the biological condition of the animal or individual being tested. In drug safety studies, established toxicity biomarkers are used along with other conventional study data to determine dose-limiting organ toxicity, and to define species sensitivity for new chemical entities intended for possible use as human medicines. A continuing goal of drug safety scientists in the pharmaceutical industry is to discover and develop better trans-species biomarkers that can be used to determine target organ toxicities for preclinical species in short-term studies at dose levels that are some multiple of the intended human dose and again later in full development for monitoring clinical trials at lower therapeutic doses. Of particular value are early, predictive, noninvasive biomarkers that have in vitro, in vivo, and clinical transferability. Such translational biomarkers bridge animal testing used in preclinical science and human studies that are part of subsequent clinical testing. Although suitable for in vivo preclinical regulatory studies, conventional hepatic safety biomarkers are basically confirmatory markers because they signal organ toxicity after some pathological damage has occurred, and are therefore not well-suited for short-term, predictive screening assays early in the discovery-to-development progression of new chemical entities (NCEs) available in limited quantities. Efforts between regulatory agencies and the pharmaceutical industry are underway for the coordinated discovery, qualification, verification and validation of early predictive toxicity biomarkers. Early predictive safety biomarkers are those that are detectable and quantifiable prior to the onset of irreversible tissue injury and which are associated with a mechanism of action relevant to a specific type of potential hepatic injury. Potential drug toxicity biomarkers are typically endogenous macromolecules in biological fluids with varying immunoreactivity which can present bioanalytical challenges when first discovered. The potential success of these efforts is greatly enhanced by recent advances in two closely linked technologies, toxicoproteomics and targeted, quantitative mass spectrometry. This review focuses on the examination of the current status of these technologies as they relate to the discovery and development of novel preclinical biomarkers of hepatotoxicity. A critical assessment of the current literature reveals two distinct lines of safety biomarker investigation, (1) peripheral fluid biomarkers of organ toxicity and (2) tissue or cell-based toxicity signatures. Improved peripheral fluid biomarkers should allow the sensitive detection of potential organ toxicity prior to the onset of overt organ pathology. Advancements in tissue or cell-based toxicity biomarkers will provide sensitive in vitro or ex vivo screening systems based on toxicity pathway markers. An examination of the current practices in clinical pathology and the critical evaluation of some recently proposed biomarker candidates in comparison to the desired characteristics of an ideal toxicity biomarker lead this author to conclude that a combination of selected biomarkers will be more informative if not predictive of potential animal organ toxicity than any single biomarker, new or old. For the practical assessment of combinations of conventional and/or novel toxicity biomarkers in rodent and large animal preclinical species, mass spectrometry has emerged as the premier analytical tool compared to specific immunoassays or functional assays. Selected and multiple reaction monitoring mass spectrometry applications make it possible for this same basic technology to be used in the progressive stages of biomarker discovery, development, and more importantly, routine study applications without the use of specific antibody reagents. This technology combined with other 'omics' technologies can provide added selectivity and sensitivity in preclinical drug safety testing.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multimolecular and Solvent-Inclusive Collective Variables

The typically rugged nature of molecular free energy landscapes can frustrate eﬃcient sampling of the thermodynamically relevant phase space due to the presence of high free energy barriers. Enhanced sampling techniques can improve phase space exploration by accelerating sampling along particular collective variables (CVs). A number of techniques exist for data-driven discovery of CVs parameterizing the important large scale motions of the system. A challenge to CV discovery is learning CVs invariant to symmetries of the molecular system, frequently rigid translation, rigid rotation, and permutational relabeling of identical particles. Of these, permutational invariance has proved a persistent challenge in frustrating the data-driven discovery of multi-molecular CVs in systems of self-assembling particles and solvent-inclusive CVs for solvated systems. In this work, we integrate Permutation Invariant Vector (PIV) featurizations with autoencoding neural networks to learn nonlinear CVs invariant to translation, rotation, and permutation, and perform interleaved rounds of CV discovery and enhanced sampling to iteratively expand sampling of configurational phase space and obtain converged CVs and free energy landscapes. Here, we demonstrate the Permutationally Invariant Network for Enhanced Sampling (PINES) approach in applications to the self-assembly of a 13-atom Argon cluster, association/dissociation of a NaCl ion pair in water, and hydrophobic collapse of a C 45 H 92 n-pentatetracontane polymer chain. We make the approach freely available as a new module within the PLUMED2 enhanced sampling libraries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

On-the-fly closed-loop materials discovery via Bayesian active learning

Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Rapid discovery and evolution of nanosensors containing fluorogenic amino acids

Binding-activated optical sensors are powerful tools for imaging, diagnostics, and biomolecular sensing. However, biosensor discovery is slow and requires tedious steps in rational design, screening, and characterization. Here we report on a platform that streamlines biosensor discovery and unlocks directed nanosensor evolution through genetically encodable fluorogenic amino acids (FgAAs). Building on the classical knowledge-based semisynthetic approach, we engineer ~15 kDa nanosensors that recognize specific proteins, peptides, and small molecules with up to 100-fold fluorescence increases and subsecond kinetics, allowing real-time and wash-free target sensing and live-cell bioimaging. An optimized genetic code expansion chemistry with FgAAs further enables rapid (~3 h) ribosomal nanosensor discovery via the cell-free translation of hundreds of candidates in parallel and directed nanosensor evolution with improved variant-specific sensitivities (up to ~250-fold) for SARS-CoV-2 antigens. Altogether, this platform could accelerate the discovery of fluorogenic nanosensors and pave the way to modify proteins with other non-standard functionalities for diverse applications.

Biosensors↗

DOE OSTI2022

Discovery of chalcogenides structures and compositions using mixed fluxes

Advancements in many modern technologies rely on the continuous need for materials discovery. However, the design of synthesis routes leading to new and targeted solid-state materials requires understanding of reactivity patterns. Advances in synthesis science are necessary to increase efficiency and accelerate materials discovery. We present a highly effective methodology for the rational discovery of materials using high-temperature solutions or fluxes having tunable solubility. This methodology facilitates product selection by projecting the free-energy landscape into real synthetic variables: temperature and flux ratio. We demonstrate the effectiveness of this technique by synthesizing compounds in the chalcogenide system of A(Ba)-Cu-Q(O) (Q = S or Se; A = Na, K or Rb) using mixed AOH/AX (A = Li, Na, K or Rb; X = Cl or I) fluxes. We present 30 unreported compounds or compositions, including more than ten unique structural types, by systematically varying the temperature and flux ratios without requiring changing the proportions of starting materials. Additionally, we found that the structural dimensionality of the compounds decreases with increasing reactant solubility and temperature. This methodology serves as an effective general strategy for the rational discovery of inorganic solids.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX)

In this paper, we present peptide-pair encoding (PPE), a general-purpose probabilistic segmentation of protein sequences into commonly occurring variable-length sub-sequences. The idea of PPE segmentation is inspired by the byte-pair encoding (BPE) text compression algorithm, which has recently gained popularity in subword neural machine translation. We modify this algorithm by adding a sampling framework allowing for multiple ways of segmenting a sequence. PPE segmentation steps can be learned over a large set of protein sequences (Swiss-Prot) or even a domain-specific dataset and then applied to a set of unseen sequences. This representation can be widely used as the input to any downstream machine learning tasks in protein bioinformatics. In particular, here, we introduce this representation through protein motif discovery and protein sequence embedding. (i) DiMotif: we present DiMotif as an alignment-free discriminative motif discovery method and evaluate the method for finding protein motifs in three different settings: (1) comparison of DiMotif with two existing approaches on 20 distinct motif discovery problems which are experimentally verified, (2) classification-based approach for the motifs extracted for integrins, integrin-binding proteins, and biofilm formation, and (3) in sequence pattern searching for nuclear localization signal. The DiMotif, in general, obtained high recall scores, while having a comparable F1 score with other methods in the discovery of experimentally verified motifs. Having high recall suggests that the DiMotif can be used for short-list creation for further experimental investigations on motifs. In the classification-based evaluation, the extracted motifs could reliably detect the integrins, integrin-binding, and biofilm formation-related proteins on a reserved set of sequences with high F1 scores. (ii) ProtVecX: we extend k-mer based protein vector (ProtVec) embedding to variablelength protein embedding using PPE sub-sequences. We show that the new method of embedding can marginally outperform ProtVec in enzyme prediction as well as toxin prediction tasks. In addition, we conclude that the embeddings are beneficial in protein classification tasks when they are combined with raw amino acids k-mer features.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Discovery of hydrogen storage molecules using large language models and machine learning

Accelerating the discovery of new molecules with targeted properties is a central challenge in molecular design. In this contribution, we present an AI-driven molecular discovery framework that integrates Large Language Models (LLMs) for generative molecular design with Machine Learning (ML)-based screening to identify novel Liquid Organic Hydrogen Carrier (LOHC) candidates. Using the developed framework, LOHC molecules were systematically generated, evaluated, and refined iteratively, combining LLM-guided molecular generation and ML-predicted hydrogenation enthalpies (Δ H ), under physicochemical property constraints such as optimal melting points (MP), desired hydrogen storage capacity (wt% H 2 ), and synthetic accessibility (SA) scores. This approach enabled the discovery of 42 new LOHC candidates in two distinct campaigns, one seeded with experimentally known and another with previously computationally identified LOHCs, respectively. Although we began with different numbers of starting molecules (31 vs . 7 seed molecules), both runs yielded a comparable number of viable candidates, suggesting an influence of chemically intuitive seed molecule selection for success. Selected LOHC molecules, such as 3-methyl pyridine, 1-ethylnapthalene, 1,1-diphenylethane, and benzofuran, were experimentally tested and compared with benchmark LOHCs (toluene and 9-ethylcarbazole) for hydrogenation using a series of commercial supported metal catalysts. The order of conversion into fully hydrogenated products at 200 °C was 3-methyl pyridine (100%) > 9-ethyl carbazole (86.4%) > 2,3-benzofuran (74%) > 1,1-diphenylethane (66.9%) > 1-ethylnapthalene (66.7%) > toluene (57%), further validating the AI-guided molecular design. This study demonstrates promise of LLM-driven molecular design in conjunction with ML-based screening for accelerated discovery and design of molecules.

Harb, Hassan [Argonne National Laboratory (ANL), A↗

DOE OSTI2010

NEW COMPLETENESS METHODS FOR ESTIMATING EXOPLANET DISCOVERIES BY DIRECT DETECTION

We report on new methods for evaluating realistic observing programs that search stars for planets by direct imaging, where observations are selected from an optimized star list and stars can be observed multiple times. We show how these methods bring critical insight into the design of the mission and its instruments. These methods provide an estimate of the outcome of the observing program: the probability distribution of discoveries (detection and/or characterization) and an estimate of the occurrence rate of planets ({eta}). We show that these parameters can be accurately estimated from a single mission simulation, without the need for a complete Monte Carlo mission simulation, and we prove the accuracy of this new approach. Our methods provide tools to define a mission for a particular science goal; for example, a mission can be defined by the expected number of discoveries and its confidence level. We detail how an optimized star list can be built and how successive observations can be selected. Our approach also provides other critical mission attributes, such as the number of stars expected to be searched and the probability of zero discoveries. Because these attributes depend strongly on the mission scale (telescope diameter, observing capabilities and constraints, mission lifetime, etc.), our methods are directly applicable to the design of such future missions and provide guidance to the mission and instrument design based on scientific performance. We illustrate our new methods with practical calculations and exploratory design reference missions for the James Webb Space Telescope (JWST) operating with a distant starshade to reduce scattered and diffracted starlight on the focal plane. We estimate that five habitable Earth-mass planets would be discovered and characterized with spectroscopy, with a probability of zero discoveries of 0.004, assuming a small fraction of JWST observing time (7%), {eta} = 0.3, and 70 observing visits, limited by starshade fuel.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

SERENDIPITOUS DISCOVERY OF A MASSIVE cD GALAXY AT z = 1.096: IMPLICATIONS FOR THE EARLY FORMATION AND LATE EVOLUTION OF cD GALAXIES

We have made a serendipitous discovery of a massive ({approx}5 Multiplication-Sign 10{sup 11} M{sub Sun }) cD galaxy at z = 1.096 in a candidate-rich cluster in the Hubble Ultra Deep Field (HUDF) area of GOODS-South. This brightest cluster galaxy (BCG) is the most distant cD galaxy confirmed to date. Ultra-deep HST/WFC3 images reveal an extended envelope starting from {approx}10 kpc and reaching {approx}70 kpc in radius along the semimajor axis. The spectral energy distributions indicate that both its inner component and outer envelope are composed of an old, passively evolving (specific star formation rate <10{sup -4} Gyr{sup -1}) stellar population. The cD galaxy lies on the same mass-size relation as the bulk of quiescent galaxies at similar redshifts. The cD galaxy has a higher stellar mass surface density ({approx}M{sub *}/R{sub 50}{sup 2}) but a similar velocity dispersion ({approx}{radical}(M{sub *}/R{sub 50})) to those of more massive, nearby cDs. If the cD galaxy is one of the progenitors of today's more massive cDs, its size (R{sub 50}) and stellar mass have had to increase on average by factors of 3.4 {+-} 1.1 and 3.3 {+-} 1.3 over the past {approx}8 Gyr, respectively. Such increases in size and stellar mass without being accompanied by significant increases in velocity dispersion are consistent with evolutionary scenarios driven by both major and minor dissipationless (dry) mergers. If such cD envelopes originate from dry mergers, our discovery of even one example proves that some BCGs entered the dry merger phase at epochs earlier than z = 1. Our data match theoretical models which predict that the continuance of dry mergers at z < 1 can result in structures similar to those of massive cD galaxies seen today. Moreover, our discovery is a surprise given that the extreme depth of the HUDF is essential to reveal such an extended cD envelope at z > 1 and, yet, the HUDF covers only a minuscule region of sky ({approx}3.1 Multiplication-Sign 10{sup -8}). Adding that cDs are rare, our serendipitous discovery hints that such cDs may be more common than expected, perhaps even ubiquitous. Images reaching HUDF depths of more area (especially with cluster BCGs at z > 1) are needed to confirm this conjecture.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2013

Discovery potential of xenon-based neutrinoless double beta decay experiments in light of small angular scale CMB observations

The South Pole Telescope (SPT) has probed an expanded angular range of the CMB temperature power spectrum. Their recent analysis of the latest cosmological data prefers nonzero neutrino masses, with Σm{sub ν} = (0.32±0.11) eV. This result, if confirmed by the upcoming Planck data, has deep implications on the discovery of the nature of neutrinos. In particular, the values of the effective neutrino mass m{sub ββ} involved in neutrinoless double beta decay (ββ0ν) are severely constrained for both the direct and inverse hierarchy, making a discovery much more likely. In this paper, we focus in xenon-based ββ0ν experiments, on the double grounds of their good performance and the suitability of the technology to large-mass scaling. We show that the current generation, with effective masses in the range of 100 kg and conceivable exposures in the range of 500 kg·year, could already have a sizeable opportunity to observe ββ0ν events, and their combined discovery potential is quite large. The next generation, with an exposure in the range of 10 ton·year, would have a much more enhanced sensitivity, in particular due to the very low specific background that all the xenon technologies (liquid xenon, high-pressure xenon and xenon dissolved in liquid scintillator) can achieve. In addition, a high-pressure xenon gas TPC also features superb energy resolution. We show that such detector can fully explore the range of allowed effective Majorana masses, thus making a discovery very likely.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2014

DISCOVERY OF TWO RARE RIGIDLY ROTATING MAGNETOSPHERE STARS IN THE APOGEE SURVEY

The Apache Point Observatory Galactic Evolution Experiment (APOGEE)—one of the Sloan Digital Sky Survey III programs—is using near-infrared (NIR) spectra of ∼100,000 red giant branch star candidates to study the structure of the Milky Way. In the course of the survey, APOGEE also acquires spectra of hot field stars to serve as telluric calibrators for the primary science targets. We report the serendipitous discovery of two rare, fast-rotating B-stars of the σ Ori E type among those blue field stars observed during the first year of APOGEE operations. Both of the discovered stars display the spectroscopic signatures of rigidly rotating magnetospheres (RRM) common to this class of highly magnetized (B ∼ 10 kGauss) stars, increasing the number of known RRM stars by ∼10%. One (HD 345439) is a main-sequence B-star with unusually strong He absorption (similar to σ Ori E), while the other (HD 23478) fits a ''He-normal'' B3IV classification. We combine the APOGEE discovery spectra with other optical and NIR spectra of these two stars, and of σ Ori E itself, to show how NIR spectroscopy can be a uniquely powerful tool for discovering more of these rare objects, which may show little/no RRM signatures in their optical spectra. We discuss the potential for further discovery of σ Ori E type stars, as well as the implications of our discoveries for the population of these objects and insights into their origin and evolution.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2011

Bayesian multiple-instance motif discovery with BAMBI: inference of recombinase and transcription factor binding sites

Finding conserved motifs in genomic sequences represents one of essential bioinformatic problems. However, achieving high discovery performance without imposing substantial auxiliary constraints on possible motif features remains a key algorithmic challenge. This work describes BAMBI—a sequential Monte Carlo motifidentification algorithm, which is based on a position weight matrix model that does not require additional constraints and is able to estimate such motif properties as length, logo, number of instances and their locations solely on the basis of primary nucleotide sequence data. Furthermore, should biologically meaningful information about motif attributes be available, BAMBI takes advantage of this knowledge to further refine the discovery results. In practical applications, we show that the proposed approach can be used to find sites of such diverse DNA-binding molecules as the cAMP receptor protein (CRP) and Din-family site-specific serine recombinases. Results obtained by BAMBI in these and other settings demonstrate better statistical performance than any of the four widely-used profile-based motif discovery methods: MEME, BioProspector with BioOptimizer, SeSiMCMC and Motif Sampler as measured by the nucleotide-level correlation coefficient. Additionally, in the case of Din-family recombinase target site discovery, the BAMBI-inferred motif is found to be the only one functionally accurate from the underlying biochemical mechanism standpoint. C++and Matlab code is available at http://www.ee.columbia.edu/guido/ BAMBI or http://genomics.lbl.gov/BAMBI/.

59 BASIC BIOLOGICAL SCIENCES↗