Materials by numbers: Computations as tools of discovery

PubMed Central

Landman, Uzi

2005-01-01

Current issues pertaining to theoretical simulations of materials, with a focus on systems of nanometer-scale dimensions, are discussed. The use of atomistic simulations as high-resolution numerical experiments, enabling and guiding formulation and testing of analytic theoretical descriptions, is demonstrated through studies of the generation and breakup of nanojets, which have led to the derivation of a stochastic hydrodynamic description. Subsequently, I illustrate the use of computations and simulations as tools of discovery, with examples that include the self-organized formation of nanowires, the surprising nanocatalytic activity of small aggregates of gold that, in the bulk form, is notorious for being chemically inert, and the emergence of rotating electron molecules in two-dimensional quantum dots. I conclude with a brief discussion of some key challenges in nanomaterials simulations. PMID:15870210

Computational design of molecules for an all-quinone redox flow battery† †Electronic supplementary information (ESI) available: The list of computationally predicted candidate quinone molecules with interesting redox properties. See DOI: 10.1039/c4sc03030c Click here for additional data file.

PubMed Central

Er, Süleyman; Suh, Changwon; Marshak, Michael P.

2015-01-01

Inspired by the electron transfer properties of quinones in biological systems, we recently showed that quinones are also very promising electroactive materials for stationary energy storage applications. Due to the practically infinite chemical space of organic molecules, the discovery of additional quinones or other redox-active organic molecules for energy storage applications is an open field of inquiry. Here, we introduce a high-throughput computational screening approach that we applied to an accelerated study of a total of 1710 quinone (Q) and hydroquinone (QH2) (i.e., two-electron two-proton) redox couples. We identified the promising candidates for both the negative and positive sides of organic-based aqueous flow batteries, thus enabling an all-quinone battery. To further aid the development of additional interesting electroactive small molecules we also provide emerging quantitative structure-property relationships. PMID:29560173

Computational drug discovery

PubMed Central

Ou-Yang, Si-sheng; Lu, Jun-yan; Kong, Xiang-qian; Liang, Zhong-jie; Luo, Cheng; Jiang, Hualiang

2012-01-01

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field. PMID:22922346

Quantum chemical approaches in structure-based virtual screening and lead optimization

NASA Astrophysics Data System (ADS)

Cavasotto, Claudio N.; Adler, Natalia S.; Aucar, Maria G.

2018-05-01

Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.

Computer-aided drug discovery.

PubMed

Bajorath, Jürgen

2015-01-01

Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.

Building Cognition: The Construction of Computational Representations for Scientific Discovery

ERIC Educational Resources Information Center

Chandrasekharan, Sanjay; Nersessian, Nancy J.

2015-01-01

Novel computational representations, such as simulation models of complex systems and video games for scientific discovery (Foldit, EteRNA etc.), are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a…

Enantioselective photochemistry via Lewis acid catalyzed triplet energy transfer

PubMed Central

Blum, Travis R.; Miller, Zachary D.; Bates, Desiree M.; Guzei, Ilia A.; Yoon, Tehshik P.

2017-01-01

Relatively few catalytic systems are able to control the stereochemistry of electronically excited organic intermediates. Here we report the discovery that a chiral Lewis acid complex can catalyze triplet energy transfer from an electronically excited photosensitizer. This strategy is applied to asymmetric [2+2] photocycloadditions of 2′-hydroxychalcones using tris(bipyridyl) ruthenium(II) as a sensitizer. A variety of electrochemical, computational, and spectroscopic data rule out substrate activation via photoinduced electron transfer and instead support a mechanism in which Lewis acid coordination dramatically lowers the triplet energy of the chalcone substrate. We expect that this approach will enable chemists to more broadly apply their detailed understanding of chiral Lewis acid catalysis to stereocontrol in reactions of electronically excited states. PMID:27980203

Novel tools for accelerated materials discovery in the AFLOWLIB.ORG repository: breakthroughs and challenges in the mapping of the materials genome

NASA Astrophysics Data System (ADS)

Buongiorno Nardelli, Marco

2015-03-01

High-Throughput Quantum-Mechanics computation of materials properties by ab initio methods has become the foundation of an effective approach to materials design, discovery and characterization. This data driven approach to materials science currently presents the most promising path to the development of advanced technological materials that could solve or mitigate important social and economic challenges of the 21st century. In particular, the rapid proliferation of computational data on materials properties presents the possibility to complement and extend materials property databases where the experimental data is lacking and difficult to obtain. Enhanced repositories such as AFLOWLIB, open novel opportunities for structure discovery and optimization, including uncovering of unsuspected compounds, metastable structures and correlations between various properties. The practical realization of these opportunities depends on the the design effcient algorithms for electronic structure simulations of realistic material systems, the systematic compilation and classification of the generated data, and its presentation in easily accessed form to the materials science community, the primary mission of the AFLOW consortium. This work was supported by ONR-MURI under Contract N00014-13-1-0635 and the Duke University Center for Materials Genomics.

A computational study on the electronic and nonlinear optical properties of graphyne subunit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bahat, Mehmet, E-mail: bahat@gazi.edu.tr; Güney, Merve Nurhan, E-mail: merveng87@gmail.com; Özbay, Akif, E-mail: aozbay@gazi.edu.tr

2016-03-25

After discovery of graphene, it has been considered as basic material for the future nanoelectronic devices. Graphyne is a two- dimensional carbon allotropes as graphene which expected that its electronic properties is potentialy superior to graphene. The compound C{sub 24}H{sub 12} (tribenzocyclyne; TBC) is a substructure of graphyne. The electronic, and nonlinear optical properties of the C{sub 24}H{sub 12} and its some fluoro derivatives were calculated. The calculated properties are electric dipole moment, the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) energies, polarizability and first hyperpolarizability. All calculations were performed at the B3LYP/6-31+G(d,p) level.

12 CFR 1209.29 - Discovery.

Code of Federal Regulations, 2014 CFR

2014-01-01

... the timely, cost-effective management of document discovery (including, if applicable, electronically... discovery plan shall specify the form of electronic productions, if any. Documents are to be produced in... proceeding under this part may obtain document discovery by serving upon any other party in the proceeding a...

12 CFR 1209.29 - Discovery.

Code of Federal Regulations, 2012 CFR

2012-01-01

... the timely, cost-effective management of document discovery (including, if applicable, electronically... discovery plan shall specify the form of electronic productions, if any. Documents are to be produced in... proceeding under this part may obtain document discovery by serving upon any other party in the proceeding a...

12 CFR 1209.29 - Discovery.

Code of Federal Regulations, 2013 CFR

2013-01-01

... the timely, cost-effective management of document discovery (including, if applicable, electronically... discovery plan shall specify the form of electronic productions, if any. Documents are to be produced in... proceeding under this part may obtain document discovery by serving upon any other party in the proceeding a...

Computer-Aided Drug Discovery: Molecular Docking of Diminazene Ligands to DNA Minor Groove

ERIC Educational Resources Information Center

Kholod, Yana; Hoag, Erin; Muratore, Katlynn; Kosenkov, Dmytro

2018-01-01

The reported project-based laboratory unit introduces upper-division undergraduate students to the basics of computer-aided drug discovery as a part of a computational chemistry laboratory course. The students learn to perform model binding of organic molecules (ligands) to the DNA minor groove with computer-aided drug discovery (CADD) tools. The…

Impact of computational structure-based methods on drug discovery.

PubMed

Reynolds, Charles H

2014-01-01

Structure-based drug design has become an indispensible tool in drug discovery. The emergence of structure-based design is due to gains in structural biology that have provided exponential growth in the number of protein crystal structures, new computational algorithms and approaches for modeling protein-ligand interactions, and the tremendous growth of raw computer power in the last 30 years. Computer modeling and simulation have made major contributions to the discovery of many groundbreaking drugs in recent years. Examples are presented that highlight the evolution of computational structure-based design methodology, and the impact of that methodology on drug discovery.

Computational design of molecules for an all-quinone redox flow battery.

PubMed

Er, Süleyman; Suh, Changwon; Marshak, Michael P; Aspuru-Guzik, Alán

2015-02-01

Inspired by the electron transfer properties of quinones in biological systems, we recently showed that quinones are also very promising electroactive materials for stationary energy storage applications. Due to the practically infinite chemical space of organic molecules, the discovery of additional quinones or other redox-active organic molecules for energy storage applications is an open field of inquiry. Here, we introduce a high-throughput computational screening approach that we applied to an accelerated study of a total of 1710 quinone (Q) and hydroquinone (QH 2 ) ( i.e. , two-electron two-proton) redox couples. We identified the promising candidates for both the negative and positive sides of organic-based aqueous flow batteries, thus enabling an all-quinone battery. To further aid the development of additional interesting electroactive small molecules we also provide emerging quantitative structure-property relationships.

Computational chemistry at Janssen

NASA Astrophysics Data System (ADS)

van Vlijmen, Herman; Desjarlais, Renee L.; Mirzadegan, Tara

2017-03-01

Computer-aided drug discovery activities at Janssen are carried out by scientists in the Computational Chemistry group of the Discovery Sciences organization. This perspective gives an overview of the organizational and operational structure, the science, internal and external collaborations, and the impact of the group on Drug Discovery at Janssen.

Ubiquitous Computing Services Discovery and Execution Using a Novel Intelligent Web Services Algorithm

PubMed Central

Choi, Okkyung; Han, SangYong

2007-01-01

Ubiquitous Computing makes it possible to determine in real time the location and situations of service requesters in a web service environment as it enables access to computers at any time and in any place. Though research on various aspects of ubiquitous commerce is progressing at enterprises and research centers, both domestically and overseas, analysis of a customer's personal preferences based on semantic web and rule based services using semantics is not currently being conducted. This paper proposes a Ubiquitous Computing Services System that enables a rule based search as well as semantics based search to support the fact that the electronic space and the physical space can be combined into one and the real time search for web services and the construction of efficient web services thus become possible.

Computer-aided drug discovery research at a global contract research organization

NASA Astrophysics Data System (ADS)

Kitchen, Douglas B.

2017-03-01

Computer-aided drug discovery started at Albany Molecular Research, Inc in 1997. Over nearly 20 years the role of cheminformatics and computational chemistry has grown throughout the pharmaceutical industry and at AMRI. This paper will describe the infrastructure and roles of CADD throughout drug discovery and some of the lessons learned regarding the success of several methods. Various contributions provided by computational chemistry and cheminformatics in chemical library design, hit triage, hit-to-lead and lead optimization are discussed. Some frequently used computational chemistry techniques are described. The ways in which they may contribute to discovery projects are presented based on a few examples from recent publications.

Computer-aided drug discovery research at a global contract research organization.

PubMed

Kitchen, Douglas B

2017-03-01

Computer-aided drug discovery started at Albany Molecular Research, Inc in 1997. Over nearly 20 years the role of cheminformatics and computational chemistry has grown throughout the pharmaceutical industry and at AMRI. This paper will describe the infrastructure and roles of CADD throughout drug discovery and some of the lessons learned regarding the success of several methods. Various contributions provided by computational chemistry and cheminformatics in chemical library design, hit triage, hit-to-lead and lead optimization are discussed. Some frequently used computational chemistry techniques are described. The ways in which they may contribute to discovery projects are presented based on a few examples from recent publications.

Computational methods in drug discovery

PubMed Central

Leelananda, Sumudu P

2016-01-01

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed. PMID:28144341

Computational methods in drug discovery.

PubMed

Leelananda, Sumudu P; Lindert, Steffen

2016-01-01

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Parallel Density-Based Clustering for Discovery of Ionospheric Phenomena

NASA Astrophysics Data System (ADS)

Pankratius, V.; Gowanlock, M.; Blair, D. M.

2015-12-01

Ionospheric total electron content maps derived from global networks of dual-frequency GPS receivers can reveal a plethora of ionospheric features in real-time and are key to space weather studies and natural hazard monitoring. However, growing data volumes from expanding sensor networks are making manual exploratory studies challenging. As the community is heading towards Big Data ionospheric science, automation and Computer-Aided Discovery become indispensable tools for scientists. One problem of machine learning methods is that they require domain-specific adaptations in order to be effective and useful for scientists. Addressing this problem, our Computer-Aided Discovery approach allows scientists to express various physical models as well as perturbation ranges for parameters. The search space is explored through an automated system and parallel processing of batched workloads, which finds corresponding matches and similarities in empirical data. We discuss density-based clustering as a particular method we employ in this process. Specifically, we adapt Density-Based Spatial Clustering of Applications with Noise (DBSCAN). This algorithm groups geospatial data points based on density. Clusters of points can be of arbitrary shape, and the number of clusters is not predetermined by the algorithm; only two input parameters need to be specified: (1) a distance threshold, (2) a minimum number of points within that threshold. We discuss an implementation of DBSCAN for batched workloads that is amenable to parallelization on manycore architectures such as Intel's Xeon Phi accelerator with 60+ general-purpose cores. This manycore parallelization can cluster large volumes of ionospheric total electronic content data quickly. Potential applications for cluster detection include the visualization, tracing, and examination of traveling ionospheric disturbances or other propagating phenomena. Acknowledgments. We acknowledge support from NSF ACI-1442997 (PI V. Pankratius).

Discovery and Development of ATP-Competitive mTOR Inhibitors Using Computational Approaches.

PubMed

Luo, Yao; Wang, Ling

2017-11-16

The mammalian target of rapamycin (mTOR) is a central controller of cell growth, proliferation, metabolism, and angiogenesis. This protein is an attractive target for new anticancer drug development. Significant progress has been made in hit discovery, lead optimization, drug candidate development and determination of the three-dimensional (3D) structure of mTOR. Computational methods have been applied to accelerate the discovery and development of mTOR inhibitors helping to model the structure of mTOR, screen compound databases, uncover structure-activity relationship (SAR) and optimize the hits, mine the privileged fragments and design focused libraries. Besides, computational approaches were also applied to study protein-ligand interactions mechanisms and in natural product-driven drug discovery. Herein, we survey the most recent progress on the application of computational approaches to advance the discovery and development of compounds targeting mTOR. Future directions in the discovery of new mTOR inhibitors using computational methods are also discussed. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

STS-48 MS Brown on OV-103's aft flight deck poses for ESC photo

NASA Technical Reports Server (NTRS)

1991-01-01

STS-48 Mission Specialist (MS) Mark N. Brown looks away from the portable laptop computer screen to pose for an Electronic Still Camera (ESC) photo on the aft flight deck of the earth-orbiting Discovery, Orbiter Vehicle (OV) 103. Brown was working at the payload station before the interruption. Crewmembers were testing the ESC as part of Development Test Objective (DTO) 648, Electronic Still Photography. The digital image was stored on a removable hard disk or small optical disk, and could be converted to a format suitable for downlink transmission. The ESC is making its initial appearance on this Space Shuttle mission.

Building Cognition: The Construction of Computational Representations for Scientific Discovery.

PubMed

Chandrasekharan, Sanjay; Nersessian, Nancy J

2015-11-01

Novel computational representations, such as simulation models of complex systems and video games for scientific discovery (Foldit, EteRNA etc.), are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a theoretical analysis of the cognitive roles such representations play, based on an ethnographic study of the building of computational models in a systems biology laboratory. Specifically, we focus on a case of model-building by an engineer that led to a remarkable discovery in basic bioscience. Accounting for such discoveries requires a distributed cognition (DC) analysis, as DC focuses on the roles played by external representations in cognitive processes. However, DC analyses by and large have not examined scientific discovery, and they mostly focus on memory offloading, particularly how the use of existing external representations changes the nature of cognitive tasks. In contrast, we study discovery processes and argue that discoveries emerge from the processes of building the computational representation. The building process integrates manipulations in imagination and in the representation, creating a coupled cognitive system of model and modeler, where the model is incorporated into the modeler's imagination. This account extends DC significantly, and we present some of the theoretical and application implications of this extended account. Copyright © 2014 Cognitive Science Society, Inc.

Combined use of computational chemistry and chemoinformatics methods for chemical discovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sugimoto, Manabu, E-mail: sugimoto@kumamoto-u.ac.jp; Institute for Molecular Science, 38 Nishigo-Naka, Myodaiji, Okazaki 444-8585; CREST, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012

2015-12-31

Data analysis on numerical data by the computational chemistry calculations is carried out to obtain knowledge information of molecules. A molecular database is developed to systematically store chemical, electronic-structure, and knowledge-based information. The database is used to find molecules related to a keyword of “cancer”. Then the electronic-structure calculations are performed to quantitatively evaluate quantum chemical similarity of the molecules. Among the 377 compounds registered in the database, 24 molecules are found to be “cancer”-related. This set of molecules includes both carcinogens and anticancer drugs. The quantum chemical similarity analysis, which is carried out by using numerical results of themore » density-functional theory calculations, shows that, when some energy spectra are referred to, carcinogens are reasonably distinguished from the anticancer drugs. Therefore these spectral properties are considered of as important measures for classification.« less

Computational modeling in melanoma for novel drug discovery.

PubMed

Pennisi, Marzio; Russo, Giulia; Di Salvatore, Valentina; Candido, Saverio; Libra, Massimo; Pappalardo, Francesco

2016-06-01

There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches. This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials. Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.

Novel opportunities for computational biology and sociology in drug discovery

PubMed Central

Yao, Lixia

2009-01-01

Drug discovery today is impossible without sophisticated modeling and computation. In this review we touch on previous advances in computational biology and by tracing the steps involved in pharmaceutical development, we explore a range of novel, high value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry ties for scientific and human benefit. Attention to these opportunities could promise punctuated advance, and will complement the well-established computational work on which drug discovery currently relies. PMID:19674801

Ultralow Thermal Conductivity in Full Heusler Semiconductors.

PubMed

He, Jiangang; Amsler, Maximilian; Xia, Yi; Naghavi, S Shahab; Hegde, Vinay I; Hao, Shiqiang; Goedecker, Stefan; Ozoliņš, Vidvuds; Wolverton, Chris

2016-07-22

Semiconducting half and, to a lesser extent, full Heusler compounds are promising thermoelectric materials due to their compelling electronic properties with large power factors. However, intrinsically high thermal conductivity resulting in a limited thermoelectric efficiency has so far impeded their widespread use in practical applications. Here, we report the computational discovery of a class of hitherto unknown stable semiconducting full Heusler compounds with ten valence electrons (X_{2}YZ, X=Ca, Sr, and Ba; Y=Au and Hg; Z=Sn, Pb, As, Sb, and Bi) through high-throughput ab initio screening. These new compounds exhibit ultralow lattice thermal conductivity κ_{L} close to the theoretical minimum due to strong anharmonic rattling of the heavy noble metals, while preserving high power factors, thus resulting in excellent phonon-glass electron-crystal materials.

Connecting the virtual world of computers to the real world of medicinal chemistry.

PubMed

Glen, Robert C

2011-03-01

Drug discovery involves the simultaneous optimization of chemical and biological properties, usually in a single small molecule, which modulates one of nature's most complex systems: the balance between human health and disease. The increased use of computer-aided methods is having a significant impact on all aspects of the drug-discovery and development process and with improved methods and ever faster computers, computer-aided molecular design will be ever more central to the discovery process.

The ``Missing Compounds'' affair in functionality-driven material discovery

NASA Astrophysics Data System (ADS)

Zunger, Alex

2014-03-01

In the paradigm of ``data-driven discovery,'' underlying one of the leading streams of the Material Genome Initiative (MGI), one attempts to compute high-throughput style as many of the properties of as many of the N (about 10**5- 10**6) compounds listed in databases of previously known compounds. One then inspects the ensuing Big Data, searching for useful trends. The alternative and complimentary paradigm of ``functionality-directed search and optimization'' used here, searches instead for the n much smaller than N configurations and compositions that have the desired value of the target functionality. Examples include the use of genetic and other search methods that optimize the structure or identity of atoms on lattice sites, using atomistic electronic structure (such as first-principles) approaches in search of a given electronic property. This addresses a few of the bottlenecks that have faced the alternative, data-driven/high throughput/Big Data philosophy: (i) When the configuration space is theoretically of infinite size, building a complete data base as in data-driven discovery is impossible, yet searching for the optimum functionality, is still a well-posed problem. (ii) The configuration space that we explore might include artificially grown, kinetically stabilized systems (such as 2D layer stacks; superlattices; colloidal nanostructures; Fullerenes) that are not listed in compound databases (used by data-driven approaches), (iii) a large fraction of chemically plausible compounds have not been experimentally synthesized, so in the data-driven approach these are often skipped. In our approach we search explicitly for such ``Missing Compounds''. It is likely that many interesting material properties will be found in cases (i)-(iii) that elude high throughput searches based on databases encapsulating existing knowledge. I will illustrate (a) Functionality-driven discovery of topological insulators and valley-split quantum-computer semiconductors, as well as (b) Use of ``first principles thermodynamics'' to discern which of the previously ``missing compounds'' should, in fact exist and in which structure. Synthesis efforts by Poeppelmeier group at NU realized 20 never-before-made half-Heusler compounds out of the 20 predicted ones, in our predicted space groups. This type of theory-led experimental search of designed materials with target functionalities may shorten the current process of discovery of interesting functional materials. Supported by DOE ,Office of Science, Energy Frontier Research Center for Inverse Design

Electron Correlation in Oxygen Vacancy in SrTiO3

NASA Astrophysics Data System (ADS)

Lin, Chungwei; Demkov, Alexander A.

2014-03-01

Oxygen vacancies are an important type of defect in transition metal oxides. In SrTiO3 they are believed to be the main donors in an otherwise intrinsic crystal. At the same time, a relatively deep gap state associated with the vacancy is widely reported. To explain this inconsistency we investigate the effect of electron correlation in an oxygen vacancy (OV) in SrTiO3. When taking correlation into account, we find that the OV-induced localized level can at most trap one electron, while the second electron occupies the conduction band. Our results offer a natural explanation of how the OV in SrTiO3 can produce a deep in-gap level (about 1 eV below the conduction band bottom) in photoemission, and at the same time be an electron donor. Our analysis implies an OV in SrTiO3 should be fundamentally regarded as a magnetic impurity, whose deep level is always partially occupied due to the strong Coulomb repulsion. An OV-based Anderson impurity model is derived, and its implications are discussed. This work was supported by Scientific Discovery through Advanced Computing (SciDAC) program funded by U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research and Basic Energy Sciences under award number DESC0008877.

Cloud computing approaches to accelerate drug discovery value chain.

PubMed

Garg, Vibhav; Arora, Suchir; Gupta, Chitra

2011-12-01

Continued advancements in the area of technology have helped high throughput screening (HTS) evolve from a linear to parallel approach by performing system level screening. Advanced experimental methods used for HTS at various steps of drug discovery (i.e. target identification, target validation, lead identification and lead validation) can generate data of the order of terabytes. As a consequence, there is pressing need to store, manage, mine and analyze this data to identify informational tags. This need is again posing challenges to computer scientists to offer the matching hardware and software infrastructure, while managing the varying degree of desired computational power. Therefore, the potential of "On-Demand Hardware" and "Software as a Service (SAAS)" delivery mechanisms cannot be denied. This on-demand computing, largely referred to as Cloud Computing, is now transforming the drug discovery research. Also, integration of Cloud computing with parallel computing is certainly expanding its footprint in the life sciences community. The speed, efficiency and cost effectiveness have made cloud computing a 'good to have tool' for researchers, providing them significant flexibility, allowing them to focus on the 'what' of science and not the 'how'. Once reached to its maturity, Discovery-Cloud would fit best to manage drug discovery and clinical development data, generated using advanced HTS techniques, hence supporting the vision of personalized medicine.

Computational methods for a three-dimensional model of the petroleum-discovery process

USGS Publications Warehouse

Schuenemeyer, J.H.; Bawiec, W.J.; Drew, L.J.

1980-01-01

A discovery-process model devised by Drew, Schuenemeyer, and Root can be used to predict the amount of petroleum to be discovered in a basin from some future level of exploratory effort: the predictions are based on historical drilling and discovery data. Because marginal costs of discovery and production are a function of field size, the model can be used to make estimates of future discoveries within deposit size classes. The modeling approach is a geometric one in which the area searched is a function of the size and shape of the targets being sought. A high correlation is assumed between the surface-projection area of the fields and the volume of petroleum. To predict how much oil remains to be found, the area searched must be computed, and the basin size and discovery efficiency must be estimated. The basin is assumed to be explored randomly rather than by pattern drilling. The model may be used to compute independent estimates of future oil at different depth intervals for a play involving multiple producing horizons. We have written FORTRAN computer programs that are used with Drew, Schuenemeyer, and Root's model to merge the discovery and drilling information and perform the necessary computations to estimate undiscovered petroleum. These program may be modified easily for the estimation of remaining quantities of commodities other than petroleum. ?? 1980.

A new approach to the rationale discovery of polymeric biomaterials

PubMed Central

Kohn, Joachim; Welsh, William J.; Knight, Doyle

2007-01-01

This paper attempts to illustrate both the need for new approaches to biomaterials discovery as well as the significant promise inherent in the use of combinatorial and computational design strategies. The key observation of this Leading Opinion Paper is that the biomaterials community has been slow to embrace advanced biomaterials discovery tools such as combinatorial methods, high throughput experimentation, and computational modeling in spite of the significant promise shown by these discovery tools in materials science, medicinal chemistry and the pharmaceutical industry. It seems that the complexity of living cells and their interactions with biomaterials has been a conceptual as well as a practical barrier to the use of advanced discovery tools in biomaterials science. However, with the continued increase in computer power, the goal of predicting the biological response of cells in contact with biomaterials surfaces is within reach. Once combinatorial synthesis, high throughput experimentation, and computational modeling are integrated into the biomaterials discovery process, a significant acceleration is possible in the pace of development of improved medical implants, tissue regeneration scaffolds, and gene/drug delivery systems. PMID:17644176

Computational neuropharmacology: dynamical approaches in drug discovery.

PubMed

Aradi, Ildiko; Erdi, Péter

2006-05-01

Computational approaches that adopt dynamical models are widely accepted in basic and clinical neuroscience research as indispensable tools with which to understand normal and pathological neuronal mechanisms. Although computer-aided techniques have been used in pharmaceutical research (e.g. in structure- and ligand-based drug design), the power of dynamical models has not yet been exploited in drug discovery. We suggest that dynamical system theory and computational neuroscience--integrated with well-established, conventional molecular and electrophysiological methods--offer a broad perspective in drug discovery and in the search for novel targets and strategies for the treatment of neurological and psychiatric diseases.

Using Just-in-Time Information to Support Scientific Discovery Learning in a Computer-Based Simulation

ERIC Educational Resources Information Center

Hulshof, Casper D.; de Jong, Ton

2006-01-01

Students encounter many obstacles during scientific discovery learning with computer-based simulations. It is hypothesized that an effective type of support, that does not interfere with the scientific discovery learning process, should be delivered on a "just-in-time" base. This study explores the effect of facilitating access to…

Computational biology for cardiovascular biomarker discovery.

PubMed

Azuaje, Francisco; Devaux, Yvan; Wagner, Daniel

2009-07-01

Computational biology is essential in the process of translating biological knowledge into clinical practice, as well as in the understanding of biological phenomena based on the resources and technologies originating from the clinical environment. One such key contribution of computational biology is the discovery of biomarkers for predicting clinical outcomes using 'omic' information. This process involves the predictive modelling and integration of different types of data and knowledge for screening, diagnostic or prognostic purposes. Moreover, this requires the design and combination of different methodologies based on statistical analysis and machine learning. This article introduces key computational approaches and applications to biomarker discovery based on different types of 'omic' data. Although we emphasize applications in cardiovascular research, the computational requirements and advances discussed here are also relevant to other domains. We will start by introducing some of the contributions of computational biology to translational research, followed by an overview of methods and technologies used for the identification of biomarkers with predictive or classification value. The main types of 'omic' approaches to biomarker discovery will be presented with specific examples from cardiovascular research. This will include a review of computational methodologies for single-source and integrative data applications. Major computational methods for model evaluation will be described together with recommendations for reporting models and results. We will present recent advances in cardiovascular biomarker discovery based on the combination of gene expression and functional network analyses. The review will conclude with a discussion of key challenges for computational biology, including perspectives from the biosciences and clinical areas.

Practice-Based Knowledge Discovery for Comparative Effectiveness Research: An Organizing Framework

PubMed Central

Lucero, Robert J.; Bakken, Suzanne

2014-01-01

Electronic health information systems can increase the ability of health-care organizations to investigate the effects of clinical interventions. The authors present an organizing framework that integrates outcomes and informatics research paradigms to guide knowledge discovery in electronic clinical databases. They illustrate its application using the example of hospital acquired pressure ulcers (HAPU). The Knowledge Discovery through Informatics for Comparative Effectiveness Research (KDI-CER) framework was conceived as a heuristic to conceptualize study designs and address potential methodological limitations imposed by using a single research perspective. Advances in informatics research can play a complementary role in advancing the field of outcomes research including CER. The KDI-CER framework can be used to facilitate knowledge discovery from routinely collected electronic clinical data. PMID:25278645

A novel in silico approach to drug discovery via computational intelligence.

PubMed

Hecht, David; Fogel, Gary B

2009-04-01

A computational intelligence drug discovery platform is introduced as an innovative technology designed to accelerate high-throughput drug screening for generalized protein-targeted drug discovery. This technology results in collections of novel small molecule compounds that bind to protein targets as well as details on predicted binding modes and molecular interactions. The approach was tested on dihydrofolate reductase (DHFR) for novel antimalarial drug discovery; however, the methods developed can be applied broadly in early stage drug discovery and development. For this purpose, an initial fragment library was defined, and an automated fragment assembly algorithm was generated. These were combined with a computational intelligence screening tool for prescreening of compounds relative to DHFR inhibition. The entire method was assayed relative to spaces of known DHFR inhibitors and with chemical feasibility in mind, leading to experimental validation in future studies.

Towards prediction of correlated material properties using quantum Monte Carlo methods

NASA Astrophysics Data System (ADS)

Wagner, Lucas

Correlated electron systems offer a richness of physics far beyond noninteracting systems. If we would like to pursue the dream of designer correlated materials, or, even to set a more modest goal, to explain in detail the properties and effective physics of known materials, then accurate simulation methods are required. Using modern computational resources, quantum Monte Carlo (QMC) techniques offer a way to directly simulate electron correlations. I will show some recent results on a few extremely challenging materials including the metal-insulator transition of VO2, the ground state of the doped cuprates, and the pressure dependence of magnetic properties in FeSe. By using a relatively simple implementation of QMC, at least some properties of these materials can be described truly from first principles, without any adjustable parameters. Using the QMC platform, we have developed a way of systematically deriving effective lattice models from the simulation. This procedure is particularly attractive for correlated electron systems because the QMC methods treat the one-body and many-body components of the wave function and Hamiltonian on completely equal footing. I will show some examples of using this downfolding technique and the high accuracy of QMC to connect our intuitive ideas about interacting electron systems with high fidelity simulations. The work in this presentation was supported in part by NSF DMR 1206242, the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program under Award Number FG02-12ER46875, and the Center for Emergent Superconductivity, Department of Energy Frontier Research Center under Grant No. DEAC0298CH1088. Computing resources were provided by a Blue Waters Illinois grant and INCITE PhotSuper and SuperMatSim allocations.

Novel opportunities for computational biology and sociology in drug discovery☆

PubMed Central

Yao, Lixia; Evans, James A.; Rzhetsky, Andrey

2013-01-01

Current drug discovery is impossible without sophisticated modeling and computation. In this review we outline previous advances in computational biology and, by tracing the steps involved in pharmaceutical development, explore a range of novel, high-value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy–industry links for scientific and human benefit. Attention to these opportunities could promise punctuated advance and will complement the well-established computational work on which drug discovery currently relies. PMID:20349528

Collection and Retention Procedures for Electronically Stored Information (ESI) Collected Using E-Discovery Tools

EPA Pesticide Factsheets

This procedure is designed to support the collection of potentially responsive information using automated E-Discovery tools that rely on keywords, key phrases, index queries, or other technological assistance to retrieve Electronically Stored Information

Computer-Aided Drug Design in Epigenetics

NASA Astrophysics Data System (ADS)

Lu, Wenchao; Zhang, Rukang; Jiang, Hao; Zhang, Huimin; Luo, Cheng

2018-03-01

Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field.

Computer-Aided Drug Design in Epigenetics

PubMed Central

Lu, Wenchao; Zhang, Rukang; Jiang, Hao; Zhang, Huimin; Luo, Cheng

2018-01-01

Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation, and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field. PMID:29594101

COMPUTER-AIDED DRUG DISCOVERY AND DEVELOPMENT (CADDD): in silico-chemico-biological approach

PubMed Central

Kapetanovic, I.M.

2008-01-01

It is generally recognized that drug discovery and development are very time and resources consuming processes. There is an ever growing effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. In biomedical arena, computer-aided or in silico design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues. Commonly used computational approaches include ligand-based drug design (pharmacophore, a 3-D spatial arrangement of chemical features essential for biological activity), structure-based drug design (drug-target docking), and quantitative structure-activity and quantitative structure-property relationships. Regulatory agencies as well as pharmaceutical industry are actively involved in development of computational tools that will improve effectiveness and efficiency of drug discovery and development process, decrease use of animals, and increase predictability. It is expected that the power of CADDD will grow as the technology continues to evolve. PMID:17229415

Enabling drug discovery project decisions with integrated computational chemistry and informatics

NASA Astrophysics Data System (ADS)

Tsui, Vickie; Ortwine, Daniel F.; Blaney, Jeffrey M.

2017-03-01

Computational chemistry/informatics scientists and software engineers in Genentech Small Molecule Drug Discovery collaborate with experimental scientists in a therapeutic project-centric environment. Our mission is to enable and improve pre-clinical drug discovery design and decisions. Our goal is to deliver timely data, analysis, and modeling to our therapeutic project teams using best-in-class software tools. We describe our strategy, the organization of our group, and our approaches to reach this goal. We conclude with a summary of the interdisciplinary skills required for computational scientists and recommendations for their training.

Thermodynamic Routes to Novel Metastable Nitrogen-Rich Nitrides

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Wenhao; Holder, Aaron; Orvañanos, Bernardo

Compared to oxides, the nitrides are relatively unexplored, making them a promising chemical space for novel materials discovery. Of particular interest are nitrogen-rich nitrides, which often possess useful semiconducting properties for electronic and optoelectronic applications. However, such nitrogen-rich compounds are generally metastable, and the lack of a guiding theory for their synthesis has limited their exploration. Here, we review the remarkable metastability of observed nitrides, and examine the thermodynamics of how reactive nitrogen precursors can stabilize metastable nitrogen-rich compositions during materials synthesis. We map these thermodynamic strategies onto a predictive computational search, training a data-mined ionic substitution algorithm specifically formore » nitride discovery, which we combine with grand-canonical DFT-SCAN phase stability calculations to compute stabilizing nitrogen chemical potentials. We identify several new nitrogen-rich binary nitrides for experimental investigation, notably the transition metal nitrides Mn3N4, Cr3N4, V3N4, and Nb3N5, the main group nitride SbN, and the pernitrides FeN2, CrN2, and Cu2N2. By formulating rational thermodynamic routes to metastable compounds, we expand the search space for functional technological materials beyond equilibrium phases and compositions.« less

Thermodynamic Routes to Novel Metastable Nitrogen-Rich Nitrides

DOE PAGES

Sun, Wenhao; Holder, Aaron; Orvañanos, Bernardo; ...

2017-07-17

Compared to oxides, the nitrides are relatively unexplored, making them a promising chemical space for novel materials discovery. Of particular interest are nitrogen-rich nitrides, which often possess useful semiconducting properties for electronic and optoelectronic applications. However, such nitrogen-rich compounds are generally metastable, and the lack of a guiding theory for their synthesis has limited their exploration. Here, we review the remarkable metastability of observed nitrides, and examine the thermodynamics of how reactive nitrogen precursors can stabilize metastable nitrogen-rich compositions during materials synthesis. We map these thermodynamic strategies onto a predictive computational search, training a data-mined ionic substitution algorithm specifically formore » nitride discovery, which we combine with grand-canonical DFT-SCAN phase stability calculations to compute stabilizing nitrogen chemical potentials. We identify several new nitrogen-rich binary nitrides for experimental investigation, notably the transition metal nitrides Mn3N4, Cr3N4, V3N4, and Nb3N5, the main group nitride SbN, and the pernitrides FeN2, CrN2, and Cu2N2. By formulating rational thermodynamic routes to metastable compounds, we expand the search space for functional technological materials beyond equilibrium phases and compositions.« less

Federated Tensor Factorization for Computational Phenotyping

PubMed Central

Kim, Yejin; Sun, Jimeng; Yu, Hwanjo; Jiang, Xiaoqian

2017-01-01

Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospitals, in which direct patient-level data sharing is not possible (e.g., due to institutional policies). In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data. We developed secure data harmonization and federated computation procedures based on alternating direction method of multipliers (ADMM). Using this method, the multiple hospitals iteratively update tensors and transfer secure summarized information to a central server, and the server aggregates the information to generate phenotypes. We demonstrated with real medical datasets that our method resembles the centralized training model (based on combined datasets) in terms of accuracy and phenotypes discovery while respecting privacy. PMID:29071165

Computational predictions of energy materials using density functional theory

NASA Astrophysics Data System (ADS)

Jain, Anubhav; Shin, Yongwoo; Persson, Kristin A.

2016-01-01

In the search for new functional materials, quantum mechanics is an exciting starting point. The fundamental laws that govern the behaviour of electrons have the possibility, at the other end of the scale, to predict the performance of a material for a targeted application. In some cases, this is achievable using density functional theory (DFT). In this Review, we highlight DFT studies predicting energy-related materials that were subsequently confirmed experimentally. The attributes and limitations of DFT for the computational design of materials for lithium-ion batteries, hydrogen production and storage materials, superconductors, photovoltaics and thermoelectric materials are discussed. In the future, we expect that the accuracy of DFT-based methods will continue to improve and that growth in computing power will enable millions of materials to be virtually screened for specific applications. Thus, these examples represent a first glimpse of what may become a routine and integral step in materials discovery.

Cascaded spintronic logic with low-dimensional carbon

NASA Astrophysics Data System (ADS)

Friedman, Joseph S.; Girdhar, Anuj; Gelfand, Ryan M.; Memik, Gokhan; Mohseni, Hooman; Taflove, Allen; Wessels, Bruce W.; Leburton, Jean-Pierre; Sahakian, Alan V.

2017-06-01

Remarkable breakthroughs have established the functionality of graphene and carbon nanotube transistors as replacements to silicon in conventional computing structures, and numerous spintronic logic gates have been presented. However, an efficient cascaded logic structure that exploits electron spin has not yet been demonstrated. In this work, we introduce and analyse a cascaded spintronic computing system composed solely of low-dimensional carbon materials. We propose a spintronic switch based on the recent discovery of negative magnetoresistance in graphene nanoribbons, and demonstrate its feasibility through tight-binding calculations of the band structure. Covalently connected carbon nanotubes create magnetic fields through graphene nanoribbons, cascading logic gates through incoherent spintronic switching. The exceptional material properties of carbon materials permit Terahertz operation and two orders of magnitude decrease in power-delay product compared to cutting-edge microprocessors. We hope to inspire the fabrication of these cascaded logic circuits to stimulate a transformative generation of energy-efficient computing.

A resource management architecture based on complex network theory in cloud computing federation

NASA Astrophysics Data System (ADS)

Zhang, Zehua; Zhang, Xuejie

2011-10-01

Cloud Computing Federation is a main trend of Cloud Computing. Resource Management has significant effect on the design, realization, and efficiency of Cloud Computing Federation. Cloud Computing Federation has the typical characteristic of the Complex System, therefore, we propose a resource management architecture based on complex network theory for Cloud Computing Federation (abbreviated as RMABC) in this paper, with the detailed design of the resource discovery and resource announcement mechanisms. Compare with the existing resource management mechanisms in distributed computing systems, a Task Manager in RMABC can use the historical information and current state data get from other Task Managers for the evolution of the complex network which is composed of Task Managers, thus has the advantages in resource discovery speed, fault tolerance and adaptive ability. The result of the model experiment confirmed the advantage of RMABC in resource discovery performance.

Nexus Between Protein–Ligand Affinity Rank-Ordering, Biophysical Approaches, and Drug Discovery

PubMed Central

2013-01-01

The confluence of computational and biophysical methods to accurately rank-order the binding affinities of small molecules and determine structures of macromolecular complexes is a potentially transformative advance in the work flow of drug discovery. This viewpoint explores the impact that advanced computational methods may have on the efficacy of small molecule drug discovery and optimization, particularly with respect to emerging fragment-based methods. PMID:24900579

Teach-Discover-Treat (TDT): Collaborative Computational Drug Discovery for Neglected Diseases

PubMed Central

Jansen, Johanna M.; Cornell, Wendy; Tseng, Y. Jane; Amaro, Rommie E.

2012-01-01

Teach – Discover – Treat (TDT) is an initiative to promote the development and sharing of computational tools solicited through a competition with the aim to impact education and collaborative drug discovery for neglected diseases. Collaboration, multidisciplinary integration, and innovation are essential for successful drug discovery. This requires a workforce that is trained in state-of-the-art workflows and equipped with the ability to collaborate on platforms that are accessible and free. The TDT competition solicits high quality computational workflows for neglected disease targets, using freely available, open access tools. PMID:23085175

Content-Based Discovery for Web Map Service using Support Vector Machine and User Relevance Feedback

PubMed Central

Cheng, Xiaoqiang; Qi, Kunlun; Zheng, Jie; You, Lan; Wu, Huayi

2016-01-01

Many discovery methods for geographic information services have been proposed. There are approaches for finding and matching geographic information services, methods for constructing geographic information service classification schemes, and automatic geographic information discovery. Overall, the efficiency of the geographic information discovery keeps improving., There are however, still two problems in Web Map Service (WMS) discovery that must be solved. Mismatches between the graphic contents of a WMS and the semantic descriptions in the metadata make discovery difficult for human users. End-users and computers comprehend WMSs differently creating semantic gaps in human-computer interactions. To address these problems, we propose an improved query process for WMSs based on the graphic contents of WMS layers, combining Support Vector Machine (SVM) and user relevance feedback. Our experiments demonstrate that the proposed method can improve the accuracy and efficiency of WMS discovery. PMID:27861505

Content-Based Discovery for Web Map Service using Support Vector Machine and User Relevance Feedback.

PubMed

Hu, Kai; Gui, Zhipeng; Cheng, Xiaoqiang; Qi, Kunlun; Zheng, Jie; You, Lan; Wu, Huayi

2016-01-01

Many discovery methods for geographic information services have been proposed. There are approaches for finding and matching geographic information services, methods for constructing geographic information service classification schemes, and automatic geographic information discovery. Overall, the efficiency of the geographic information discovery keeps improving., There are however, still two problems in Web Map Service (WMS) discovery that must be solved. Mismatches between the graphic contents of a WMS and the semantic descriptions in the metadata make discovery difficult for human users. End-users and computers comprehend WMSs differently creating semantic gaps in human-computer interactions. To address these problems, we propose an improved query process for WMSs based on the graphic contents of WMS layers, combining Support Vector Machine (SVM) and user relevance feedback. Our experiments demonstrate that the proposed method can improve the accuracy and efficiency of WMS discovery.

78 FR 37261 - Self-Regulatory Organizations; Financial Industry Regulatory Authority, Inc.; Notice of Filing of...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-20

... Change Relating to Amendments to the Discovery Guide Used in Customer Arbitration Proceedings June 13... Discovery Guide (``Guide'') used in customer arbitration proceedings to provide general guidance on electronic discovery (``e-discovery'') issues and product cases and to clarify the existing provision...

Support for Learning with Computer Simulations: Giving Hints, Supporting Learning Processes, and Providing Hypotheses.

ERIC Educational Resources Information Center

Njoo, Melanie; de Jong, Ton

This paper contains the results of a study on the importance of discovery learning using computer simulations. The purpose of the study was to identify what constitutes discovery learning and to assess the effects of instructional support measures. College students were observed working with an assignment and a computer simulation in the domain of…

Computationally driven drug discovery meeting-3 - Verona (Italy): 4 - 6th of March 2014.

PubMed

Costantino, Gabriele

2014-12-01

The following article reports on the results and the outcome of a meeting organised at the Aptuit Auditorium in Verona (Italy), which highlighted the current applications of state-of-the-art computational science to drug design in Italy. The meeting, which had > 100 people in attendance, consisted of over 40 presentations and included keynote lectures given by world-renowned speakers. The topics included in the meeting are areas related to ligand and structure-based ligand design and library design and screening; it also provided discussion pertaining to chemometrics. The meeting also stressed the importance of public-private collaboration and reviewed the different approaches to computationally driven drug discovery taken within academia and industry. The meeting helped define the current position of state-of-the-art computational drug discovery in Italy, pointing out criticalities and assets. This kind of focused meeting is important in the sense that it lends the opportunity of a restricted yet representative community of fellow professionals to deeply discuss the current methodological approaches and provide future perspectives for computationally driven drug discovery.

Discovery Systems

NASA Technical Reports Server (NTRS)

Pell, Barney

2003-01-01

A viewgraph presentation on NASA's Discovery Systems Project is given. The topics of discussion include: 1) NASA's Computing Information and Communications Technology Program; 2) Discovery Systems Program; and 3) Ideas for Information Integration Using the Web.

100 years of elementary particles [Beam Line, vol. 27, issue 1, Spring 1997

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pais, Abraham; Weinberg, Steven; Quigg, Chris

1997-04-01

This issue of Beam Line commemorates the 100th anniversary of the April 30, 1897 report of the discovery of the electron by J.J. Thomson and the ensuing discovery of other subatomic particles. In the first three articles, theorists Abraham Pais, Steven Weinberg, and Chris Quigg provide their perspectives on the discoveries of elementary particles as well as the implications and future directions resulting from these discoveries. In the following three articles, Michael Riordan, Wolfgang Panofsky, and Virginia Trimble apply our knowledge about elementary particles to high-energy research, electronics technology, and understanding the origin and evolution of our Universe.

100 years of Elementary Particles [Beam Line, vol. 27, issue 1, Spring 1997

DOE R&D Accomplishments Database

Pais, Abraham; Weinberg, Steven; Quigg, Chris; Riordan, Michael; Panofsky, Wolfgang K. H.; Trimble, Virginia

1997-04-01

This issue of Beam Line commemorates the 100th anniversary of the April 30, 1897 report of the discovery of the electron by J.J. Thomson and the ensuing discovery of other subatomic particles. In the first three articles, theorists Abraham Pais, Steven Weinberg, and Chris Quigg provide their perspectives on the discoveries of elementary particles as well as the implications and future directions resulting from these discoveries. In the following three articles, Michael Riordan, Wolfgang Panofsky, and Virginia Trimble apply our knowledge about elementary particles to high-energy research, electronics technology, and understanding the origin and evolution of our Universe.

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery.

PubMed

Thomford, Nicholas Ekow; Senthebane, Dimakatso Alice; Rowe, Arielle; Munro, Daniella; Seele, Palesa; Maroyi, Alfred; Dzobo, Kevin

2018-05-25

The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.

Plasticity and Kinky Chemistry of Carbon Nanotubes

NASA Technical Reports Server (NTRS)

Srivastava, Deepak; Dzegilenko, Fedor

2000-01-01

Since their discovery in 1991, carbon nanotubes have been the subject of intense research interest based on early predictions of their unique mechanical, electronic, and chemical properties. Materials with the predicted unique properties of carbon nanotubes are of great interest for use in future generations of aerospace vehicles. For their structural properties, carbon nanotubes could be used as reinforcing fibers in ultralight multifunctional composites. For their electronic properties, carbon nanotubes offer the potential of very high-speed, low-power computing elements, high-density data storage, and unique sensors. In a continuing effort to model and predict the properties of carbon nanotubes, Ames accomplished three significant results during FY99. First, accurate values of the nanomechanics and plasticity of carbon nanotubes based on quantum molecular dynamics simulations were computed. Second, the concept of mechanical deformation catalyzed-kinky-chemistry as a means to control local chemistry of nanotubes was discovered. Third, the ease of nano-indentation of silicon surfaces with carbon nanotubes was established. The elastic response and plastic failure mechanisms of single-wall nanotubes were investigated by means of quantum molecular dynamics simulations.

Computational Methods in Drug Discovery

PubMed Central

Sliwoski, Gregory; Kothiwale, Sandeepkumar; Meiler, Jens

2014-01-01

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature. PMID:24381236

Contributions of computational chemistry and biophysical techniques to fragment-based drug discovery.

PubMed

Gozalbes, Rafael; Carbajo, Rodrigo J; Pineda-Lucena, Antonio

2010-01-01

In the last decade, fragment-based drug discovery (FBDD) has evolved from a novel approach in the search of new hits to a valuable alternative to the high-throughput screening (HTS) campaigns of many pharmaceutical companies. The increasing relevance of FBDD in the drug discovery universe has been concomitant with an implementation of the biophysical techniques used for the detection of weak inhibitors, e.g. NMR, X-ray crystallography or surface plasmon resonance (SPR). At the same time, computational approaches have also been progressively incorporated into the FBDD process and nowadays several computational tools are available. These stretch from the filtering of huge chemical databases in order to build fragment-focused libraries comprising compounds with adequate physicochemical properties, to more evolved models based on different in silico methods such as docking, pharmacophore modelling, QSAR and virtual screening. In this paper we will review the parallel evolution and complementarities of biophysical techniques and computational methods, providing some representative examples of drug discovery success stories by using FBDD.

The Unseen Companion of HD 114762

NASA Astrophysics Data System (ADS)

Latham, David W.

2014-01-01

I have told the story of the discovery of the unseen companion of HD114762 (Latham et al. 1989, Nature, 389, 38-40) in a recent publication (Latham 2012, New Astronomy Reviews 56, 16-18). The discovery was enabled by a happy combination of some thinking outside the box by Tsevi Mazeh at Tel Aviv University and the development of new technology for measuring stellar spectra at the Harvard-Smithsonian Center for Astrophysics. Tsevi's unconventional idea was that giant exoplanets might be found much closer to their host stars than Jupiter and Saturn are to the Sun, well inside the snow line. Our instrument was a high-resolution echelle spectrograph optimized for measuring radial velocities of stars similar to the Sun. The key technological developments were an intensified Reticon photon-counting detector under computer control combined with sophisticated analysis of the digital spectra. The detector signal-processing electronics eliminated persistence, which had plagued other intensified systems. This allowed bright Th-Ar calibration exposures before and after every stellar observation, which in turn enabled careful correction for spectrograph drifts. We built three of these systems for telescopes in Massachusetts and Arizona and christened them the "CfA Digital Speedometers". The discovery of HD 114762-b was serendipitous, but not accidental.

Particle swarm optimization with recombination and dynamic linkage discovery.

PubMed

Chen, Ying-Ping; Peng, Wen-Chih; Jian, Ming-Chung

2007-12-01

In this paper, we try to improve the performance of the particle swarm optimizer by incorporating the linkage concept, which is an essential mechanism in genetic algorithms, and design a new linkage identification technique called dynamic linkage discovery to address the linkage problem in real-parameter optimization problems. Dynamic linkage discovery is a costless and effective linkage recognition technique that adapts the linkage configuration by employing only the selection operator without extra judging criteria irrelevant to the objective function. Moreover, a recombination operator that utilizes the discovered linkage configuration to promote the cooperation of particle swarm optimizer and dynamic linkage discovery is accordingly developed. By integrating the particle swarm optimizer, dynamic linkage discovery, and recombination operator, we propose a new hybridization of optimization methodologies called particle swarm optimization with recombination and dynamic linkage discovery (PSO-RDL). In order to study the capability of PSO-RDL, numerical experiments were conducted on a set of benchmark functions as well as on an important real-world application. The benchmark functions used in this paper were proposed in the 2005 Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. The experimental results on the benchmark functions indicate that PSO-RDL can provide a level of performance comparable to that given by other advanced optimization techniques. In addition to the benchmark, PSO-RDL was also used to solve the economic dispatch (ED) problem for power systems, which is a real-world problem and highly constrained. The results indicate that PSO-RDL can successfully solve the ED problem for the three-unit power system and obtain the currently known best solution for the 40-unit system.

A View of the Science Education Research Literature: Scientific Discovery Learning with Computer Simulations.

ERIC Educational Resources Information Center

Robinson, William R.

2000-01-01

Describes a review of research that addresses the effectiveness of simulations in promoting scientific discovery learning and the problems that learners may encounter when using discovery learning. (WRM)

Joint density-functional theory for energetics and spectroscopy in complex aqueous and nonaqueous solvents

NASA Astrophysics Data System (ADS)

Gunceler, Deniz

Solvents are of great importance in many technological applications, but are difficult to study using standard, off-the-shelf ab initio electronic structure methods. This is because a single configuration of molecular positions in the solvent (a "snapshot" of the fluid) is not necessarily representative of the thermodynamic average. To obtain any thermodynamic averages (e.g. free energies), the phase space of the solvent must be sampled, typically using molecular dynamics. This greatly increases the computational cost involved in studying solvated systems. Joint density-functional theory has made its mark by being a computationally efficient yet rigorous theory by which to study solvation. It replaces the need for thermodynamic sampling with an effective continuum description of the solvent environment that is in-principle exact, computationally efficient and intuitive (easier to interpret). It has been very successful in aqueous systems, with potential applications in (among others) energy materials discovery, catalysis and surface science. In this dissertation, we develop accurate and fast joint density functional theories for complex, non-aqueous solvent enviroments, including organic solvents and room temperature ionic liquids, as well as new methods for calculating electron excitation spectra in such systems. These theories are then applied to a range of physical problems, from dendrite formation in lithium-metal batteries to the optical spectra of solvated ions.

TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records.

PubMed

Lin, Frank Po-Yen; Pokorny, Adrian; Teng, Christina; Epstein, Richard J

2017-07-31

Vast amounts of clinically relevant text-based variables lie undiscovered and unexploited in electronic medical records (EMR). To exploit this untapped resource, and thus facilitate the discovery of informative covariates from unstructured clinical narratives, we have built a novel computational pipeline termed Text-based Exploratory Pattern Analyser for Prognosticator and Associator discovery (TEPAPA). This pipeline combines semantic-free natural language processing (NLP), regular expression induction, and statistical association testing to identify conserved text patterns associated with outcome variables of clinical interest. When we applied TEPAPA to a cohort of head and neck squamous cell carcinoma patients, plausible concepts known to be correlated with human papilloma virus (HPV) status were identified from the EMR text, including site of primary disease, tumour stage, pathologic characteristics, and treatment modalities. Similarly, correlates of other variables (including gender, nodal status, recurrent disease, smoking and alcohol status) were also reliably recovered. Using highly-associated patterns as covariates, a patient's HPV status was classifiable using a bootstrap analysis with a mean area under the ROC curve of 0.861, suggesting its predictive utility in supporting EMR-based phenotyping tasks. These data support using this integrative approach to efficiently identify disease-associated factors from unstructured EMR narratives, and thus to efficiently generate testable hypotheses.

Machine learning properties of binary wurtzite superlattices

DOE PAGES

Pilania, G.; Liu, X. -Y.

2018-01-12

The burgeoning paradigm of high-throughput computations and materials informatics brings new opportunities in terms of targeted materials design and discovery. The discovery process can be significantly accelerated and streamlined if one can learn effectively from available knowledge and past data to predict materials properties efficiently. Indeed, a very active area in materials science research is to develop machine learning based methods that can deliver automated and cross-validated predictive models using either already available materials data or new data generated in a targeted manner. In the present paper, we show that fast and accurate predictions of a wide range of propertiesmore » of binary wurtzite superlattices, formed by a diverse set of chemistries, can be made by employing state-of-the-art statistical learning methods trained on quantum mechanical computations in combination with a judiciously chosen numerical representation to encode materials’ similarity. These surrogate learning models then allow for efficient screening of vast chemical spaces by providing instant predictions of the targeted properties. Moreover, the models can be systematically improved in an adaptive manner, incorporate properties computed at different levels of fidelities and are naturally amenable to inverse materials design strategies. Finally, while the learning approach to make predictions for a wide range of properties (including structural, elastic and electronic properties) is demonstrated here for a specific example set containing more than 1200 binary wurtzite superlattices, the adopted framework is equally applicable to other classes of materials as well.« less

Computational Discovery of Two Lead Free Halide Double Perovskites with Band Gaps in the Visible Range: Cs2BiAgCl6 and Cs2BiAgBr6

NASA Astrophysics Data System (ADS)

Filip, Marina; Volonakis, George; Haghighirad, Amir Abbas; Hillman, Samuel; Sakai, Nobuya; Wenger, Bernard; Snaith, Henry; Giustino, Feliciano

The perovskite solar cell is emerging as one of the most promising solution processable photovoltaic technologies, with an efficiency that now exceeds the performance of thin-film silicon devices. This performance is exclusively due to the optimum optoelectronic properties of the prototypical methylammonium lead-iodide perovskite (MAPI). However, the presence of lead in MAPI, and its problematic stability in ambient conditions poses concerns for its potential environmental impact. These concerns are motivating the search for novel non-toxic halide perovskites with similar optoelectronic properties to MAPI. In this work we will present the computational search for the homovalent and the heterovalent replacement of Pb in lead-halide perovskites. This search has lead to the computational discovery and experimental synthesis of two stable lead-free halide double perovskites based on Bi and Ag: Cs2BiAgCl6 and Cs2BiAgBr6. These new compounds are highly stable, they are semiconducting and absorb light in the visible range. In this talk we will present the electronic and optical properties of Cs2BiAgCl6 and Cs2BiAgBr6 calculated within DFT and GW and discuss the stability and formability of the entire Cs2BB'X6 family of semiconductors (B = Bi, Sb, B = Cu, Ag, Au, X = Cl, Br, I). This work was supported by the and the Leverhulme Trust (RL-2012-001).

Machine learning properties of binary wurtzite superlattices

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pilania, G.; Liu, X. -Y.

The burgeoning paradigm of high-throughput computations and materials informatics brings new opportunities in terms of targeted materials design and discovery. The discovery process can be significantly accelerated and streamlined if one can learn effectively from available knowledge and past data to predict materials properties efficiently. Indeed, a very active area in materials science research is to develop machine learning based methods that can deliver automated and cross-validated predictive models using either already available materials data or new data generated in a targeted manner. In the present paper, we show that fast and accurate predictions of a wide range of propertiesmore » of binary wurtzite superlattices, formed by a diverse set of chemistries, can be made by employing state-of-the-art statistical learning methods trained on quantum mechanical computations in combination with a judiciously chosen numerical representation to encode materials’ similarity. These surrogate learning models then allow for efficient screening of vast chemical spaces by providing instant predictions of the targeted properties. Moreover, the models can be systematically improved in an adaptive manner, incorporate properties computed at different levels of fidelities and are naturally amenable to inverse materials design strategies. Finally, while the learning approach to make predictions for a wide range of properties (including structural, elastic and electronic properties) is demonstrated here for a specific example set containing more than 1200 binary wurtzite superlattices, the adopted framework is equally applicable to other classes of materials as well.« less

Multiscale atomistic simulation of metal-oxygen surface interactions: Methodological development, theoretical investigation, and correlation with experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Judith C.

The purpose of this grant is to develop the multi-scale theoretical methods to describe the nanoscale oxidation of metal thin films, as the PI (Yang) extensive previous experience in the experimental elucidation of the initial stages of Cu oxidation by primarily in situ transmission electron microscopy methods. Through the use and development of computational tools at varying length (and time) scales, from atomistic quantum mechanical calculation, force field mesoscale simulations, to large scale Kinetic Monte Carlo (KMC) modeling, the fundamental underpinings of the initial stages of Cu oxidation have been elucidated. The development of computational modeling tools allows for acceleratedmore » materials discovery. The theoretical tools developed from this program impact a wide range of technologies that depend on surface reactions, including corrosion, catalysis, and nanomaterials fabrication.« less

Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data.

PubMed

Lasko, Thomas A; Denny, Joshua C; Levy, Mia A

2013-01-01

Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels), and where to look for them (by specifying the input variables). While appropriate for individual tasks, this approach scales poorly and misses the patterns that we don't think to look for. Unsupervised feature learning overcomes these limitations by identifying patterns (or features) that collectively form a compact and expressive representation of the source data, with no need for expert input or labeled examples. Its rising popularity is driven by new deep learning methods, which have produced high-profile successes on difficult standardized problems of object recognition in images. Here we introduce its use for phenotype discovery in clinical data. This use is challenging because the largest source of clinical data - Electronic Medical Records - typically contains noisy, sparse, and irregularly timed observations, rendering them poor substrates for deep learning methods. Our approach couples dirty clinical data to deep learning architecture via longitudinal probability densities inferred using Gaussian process regression. From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals we produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished (0.97 AUC) the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task. The unsupervised features were as accurate as gold-standard features engineered by an expert with complete knowledge of the domain, the classification task, and the class labels. Our findings demonstrate the potential for achieving computational phenotype discovery at population scale. We expect such data-driven phenotypes to expose unknown disease variants and subtypes and to provide rich targets for genetic association studies.

Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data

PubMed Central

Lasko, Thomas A.; Denny, Joshua C.; Levy, Mia A.

2013-01-01

Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels), and where to look for them (by specifying the input variables). While appropriate for individual tasks, this approach scales poorly and misses the patterns that we don’t think to look for. Unsupervised feature learning overcomes these limitations by identifying patterns (or features) that collectively form a compact and expressive representation of the source data, with no need for expert input or labeled examples. Its rising popularity is driven by new deep learning methods, which have produced high-profile successes on difficult standardized problems of object recognition in images. Here we introduce its use for phenotype discovery in clinical data. This use is challenging because the largest source of clinical data – Electronic Medical Records – typically contains noisy, sparse, and irregularly timed observations, rendering them poor substrates for deep learning methods. Our approach couples dirty clinical data to deep learning architecture via longitudinal probability densities inferred using Gaussian process regression. From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals we produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished (0.97 AUC) the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task. The unsupervised features were as accurate as gold-standard features engineered by an expert with complete knowledge of the domain, the classification task, and the class labels. Our findings demonstrate the potential for achieving computational phenotype discovery at population scale. We expect such data-driven phenotypes to expose unknown disease variants and subtypes and to provide rich targets for genetic association studies. PMID:23826094

Component architecture in drug discovery informatics.

PubMed

Smith, Peter M

2002-05-01

This paper reviews the characteristics of a new model of computing that has been spurred on by the Internet, known as Netcentric computing. Developments in this model led to distributed component architectures, which, although not new ideas, are now realizable with modern tools such as Enterprise Java. The application of this approach to scientific computing, particularly in pharmaceutical discovery research, is discussed and highlighted by a particular case involving the management of biological assay data.

KENNEDY SPACE CENTER, FLA. - In the Orbiter Processing Facility, Stephanie Stilson, NASA vehicle manager for Discovery, stands in front of a leading edge on the wing of Discovery. She is being filmed for a special feature on the KSC Web about the recent Orbiter Major Modification period on Discovery, which included inspection, modifications and reservicing of most systems onboard, plus installation of a Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.” The orbiter is now being prepared for eventual launch on a future mission.

NASA Image and Video Library

2004-01-22

KENNEDY SPACE CENTER, FLA. - In the Orbiter Processing Facility, Stephanie Stilson, NASA vehicle manager for Discovery, stands in front of a leading edge on the wing of Discovery. She is being filmed for a special feature on the KSC Web about the recent Orbiter Major Modification period on Discovery, which included inspection, modifications and reservicing of most systems onboard, plus installation of a Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.” The orbiter is now being prepared for eventual launch on a future mission.

Software Infrastructure for Computer-aided Drug Discovery and Development, a Practical Example with Guidelines.

PubMed

Moretti, Loris; Sartori, Luca

2016-09-01

In the field of Computer-Aided Drug Discovery and Development (CADDD) the proper software infrastructure is essential for everyday investigations. The creation of such an environment should be carefully planned and implemented with certain features in order to be productive and efficient. Here we describe a solution to integrate standard computational services into a functional unit that empowers modelling applications for drug discovery. This system allows users with various level of expertise to run in silico experiments automatically and without the burden of file formatting for different software, managing the actual computation, keeping track of the activities and graphical rendering of the structural outcomes. To showcase the potential of this approach, performances of five different docking programs on an Hiv-1 protease test set are presented. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Did I Tell You That? Ethical Issues Related to Using Computational Methods to Discover Non-Disclosed Patient Characteristics.

PubMed

Cato, Kenrick D; Bockting, Walter; Larson, Elaine

2016-07-01

Widespread availability of electronic health records coupled with sophisticated statistical methods offer great potential for a variety of applications for health and disease surveillance, developing predictive models and advancing decision support for clinicians. However, use of "big data" mining and discovery techniques has also raised ethical issues such as how to balance privacy and autonomy with the wider public benefits of data sharing. Furthermore, electronic data are being increasingly used to identify individual characteristics, which can be useful for clinical prediction and management, but were not previously disclosed to a clinician. This process in computer parlance is called electronic phenotyping, and has a number of ethical implications. Using the Belmont Report's principles of respect for persons, beneficence, and justice as a framework, we examined the ethical issues posed by electronic phenotyping. Ethical issues identified include the ability of the patient to consent for the use of their information, the ability to suppress pediatric information, ensuring that the potential benefits justify the risks of harm to patients, and acknowledging that the clinician's biases or stereotypes, conscious or unintended, may become a factor in the therapeutic interaction. We illustrate these issues with two vignettes, using the person characteristic of gender minority status (i.e., transgender identity) and health history characteristic of substance abuse. Data mining has the potential to uncover patient characteristics previously obscured, which can provide clinicians with beneficial clinical information. Hence, ethical guidelines must be updated to ensure that electronic phenotyping supports the principles of respect for persons, beneficence, and justice. © The Author(s) 2016.

Corpus of High School Academic Texts (COHAT): Data-Driven, Computer Assisted Discovery in Learning Academic English

ERIC Educational Resources Information Center

Bohát, Róbert; Rödlingová, Beata; Horáková, Nina

2015-01-01

Corpus of High School Academic Texts (COHAT), currently of 150,000+ words, aims to make academic language instruction a more data-driven and student-centered discovery learning as a special type of Computer-Assisted Language Learning (CALL), emphasizing students' critical thinking and metacognition. Since 2013, high school English as an additional…

The Goal Specificity Effect on Strategy Use and Instructional Efficiency during Computer-Based Scientific Discovery Learning

ERIC Educational Resources Information Center

Kunsting, Josef; Wirth, Joachim; Paas, Fred

2011-01-01

Using a computer-based scientific discovery learning environment on buoyancy in fluids we investigated the "effects of goal specificity" (nonspecific goals vs. specific goals) for two goal types (problem solving goals vs. learning goals) on "strategy use" and "instructional efficiency". Our empirical findings close an important research gap,…

A bioinformatics knowledge discovery in text application for grid computing

PubMed Central

Castellano, Marcello; Mastronardi, Giuseppe; Bellotti, Roberto; Tarricone, Gianfranco

2009-01-01

Background A fundamental activity in biomedical research is Knowledge Discovery which has the ability to search through large amounts of biomedical information such as documents and data. High performance computational infrastructures, such as Grid technologies, are emerging as a possible infrastructure to tackle the intensive use of Information and Communication resources in life science. The goal of this work was to develop a software middleware solution in order to exploit the many knowledge discovery applications on scalable and distributed computing systems to achieve intensive use of ICT resources. Methods The development of a grid application for Knowledge Discovery in Text using a middleware solution based methodology is presented. The system must be able to: perform a user application model, process the jobs with the aim of creating many parallel jobs to distribute on the computational nodes. Finally, the system must be aware of the computational resources available, their status and must be able to monitor the execution of parallel jobs. These operative requirements lead to design a middleware to be specialized using user application modules. It included a graphical user interface in order to access to a node search system, a load balancing system and a transfer optimizer to reduce communication costs. Results A middleware solution prototype and the performance evaluation of it in terms of the speed-up factor is shown. It was written in JAVA on Globus Toolkit 4 to build the grid infrastructure based on GNU/Linux computer grid nodes. A test was carried out and the results are shown for the named entity recognition search of symptoms and pathologies. The search was applied to a collection of 5,000 scientific documents taken from PubMed. Conclusion In this paper we discuss the development of a grid application based on a middleware solution. It has been tested on a knowledge discovery in text process to extract new and useful information about symptoms and pathologies from a large collection of unstructured scientific documents. As an example a computation of Knowledge Discovery in Database was applied on the output produced by the KDT user module to extract new knowledge about symptom and pathology bio-entities. PMID:19534749

A bioinformatics knowledge discovery in text application for grid computing.

PubMed

Castellano, Marcello; Mastronardi, Giuseppe; Bellotti, Roberto; Tarricone, Gianfranco

2009-06-16

A fundamental activity in biomedical research is Knowledge Discovery which has the ability to search through large amounts of biomedical information such as documents and data. High performance computational infrastructures, such as Grid technologies, are emerging as a possible infrastructure to tackle the intensive use of Information and Communication resources in life science. The goal of this work was to develop a software middleware solution in order to exploit the many knowledge discovery applications on scalable and distributed computing systems to achieve intensive use of ICT resources. The development of a grid application for Knowledge Discovery in Text using a middleware solution based methodology is presented. The system must be able to: perform a user application model, process the jobs with the aim of creating many parallel jobs to distribute on the computational nodes. Finally, the system must be aware of the computational resources available, their status and must be able to monitor the execution of parallel jobs. These operative requirements lead to design a middleware to be specialized using user application modules. It included a graphical user interface in order to access to a node search system, a load balancing system and a transfer optimizer to reduce communication costs. A middleware solution prototype and the performance evaluation of it in terms of the speed-up factor is shown. It was written in JAVA on Globus Toolkit 4 to build the grid infrastructure based on GNU/Linux computer grid nodes. A test was carried out and the results are shown for the named entity recognition search of symptoms and pathologies. The search was applied to a collection of 5,000 scientific documents taken from PubMed. In this paper we discuss the development of a grid application based on a middleware solution. It has been tested on a knowledge discovery in text process to extract new and useful information about symptoms and pathologies from a large collection of unstructured scientific documents. As an example a computation of Knowledge Discovery in Database was applied on the output produced by the KDT user module to extract new knowledge about symptom and pathology bio-entities.

Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field.

PubMed

Wójcikowski, Maciej; Zielenkiewicz, Piotr; Siedlecki, Pawel

2015-01-01

There has been huge progress in the open cheminformatics field in both methods and software development. Unfortunately, there has been little effort to unite those methods and software into one package. We here describe the Open Drug Discovery Toolkit (ODDT), which aims to fulfill the need for comprehensive and open source drug discovery software. The Open Drug Discovery Toolkit was developed as a free and open source tool for both computer aided drug discovery (CADD) developers and researchers. ODDT reimplements many state-of-the-art methods, such as machine learning scoring functions (RF-Score and NNScore) and wraps other external software to ease the process of developing CADD pipelines. ODDT is an out-of-the-box solution designed to be easily customizable and extensible. Therefore, users are strongly encouraged to extend it and develop new methods. We here present three use cases for ODDT in common tasks in computer-aided drug discovery. Open Drug Discovery Toolkit is released on a permissive 3-clause BSD license for both academic and industrial use. ODDT's source code, additional examples and documentation are available on GitHub (https://github.com/oddt/oddt).

Discovering chemistry with an ab initio nanoreactor

DOE PAGES

Wang, Lee-Ping; Titov, Alexey; McGibbon, Robert; ...

2014-11-02

Chemical understanding is driven by the experimental discovery of new compounds and reactivity, and is supported by theory and computation that provides detailed physical insight. While theoretical and computational studies have generally focused on specific processes or mechanistic hypotheses, recent methodological and computational advances harken the advent of their principal role in discovery. Here we report the development and application of the ab initio nanoreactor – a highly accelerated, first-principles molecular dynamics simulation of chemical reactions that discovers new molecules and mechanisms without preordained reaction coordinates or elementary steps. Using the nanoreactor we show new pathways for glycine synthesis frommore » primitive compounds proposed to exist on the early Earth, providing new insight into the classic Urey-Miller experiment. Ultimately, these results highlight the emergence of theoretical and computational chemistry as a tool for discovery in addition to its traditional role of interpreting experimental findings.« less

Discovering chemistry with an ab initio nanoreactor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Lee-Ping; Titov, Alexey; McGibbon, Robert

Chemical understanding is driven by the experimental discovery of new compounds and reactivity, and is supported by theory and computation that provides detailed physical insight. While theoretical and computational studies have generally focused on specific processes or mechanistic hypotheses, recent methodological and computational advances harken the advent of their principal role in discovery. Here we report the development and application of the ab initio nanoreactor – a highly accelerated, first-principles molecular dynamics simulation of chemical reactions that discovers new molecules and mechanisms without preordained reaction coordinates or elementary steps. Using the nanoreactor we show new pathways for glycine synthesis frommore » primitive compounds proposed to exist on the early Earth, providing new insight into the classic Urey-Miller experiment. Ultimately, these results highlight the emergence of theoretical and computational chemistry as a tool for discovery in addition to its traditional role of interpreting experimental findings.« less

Discovery of abnormal lithium-storage sites in molybdenum dioxide electrodes

PubMed Central

Shon, Jeong Kuk; Lee, Hyo Sug; Park, Gwi Ok; Yoon, Jeongbae; Park, Eunjun; Park, Gyeong Su; Kong, Soo Sung; Jin, Mingshi; Choi, Jae-Man; Chang, Hyuk; Doo, Seokgwang; Kim, Ji Man; Yoon, Won-Sub; Pak, Chanho; Kim, Hansu; Stucky, Galen D.

2016-01-01

Developing electrode materials with high-energy densities is important for the development of lithium-ion batteries. Here, we demonstrate a mesoporous molybdenum dioxide material with abnormal lithium-storage sites, which exhibits a discharge capacity of 1,814 mAh g−1 for the first cycle, more than twice its theoretical value, and maintains its initial capacity after 50 cycles. Contrary to previous reports, we find that a mechanism for the high and reversible lithium-storage capacity of the mesoporous molybdenum dioxide electrode is not based on a conversion reaction. Insight into the electrochemical results, obtained by in situ X-ray absorption, scanning transmission electron microscopy analysis combined with electron energy loss spectroscopy and computational modelling indicates that the nanoscale pore engineering of this transition metal oxide enables an unexpected electrochemical mass storage reaction mechanism, and may provide a strategy for the design of cation storage materials for battery systems. PMID:27001935

STS-26 MS Nelson adjusts ADSF power cable on Discovery's middeck

NASA Technical Reports Server (NTRS)

1988-01-01

STS-26 Mission Specialist (MS) George D. Nelson adjusts power cable on automated directional solidification furnace (ADSF) support electronics package. ADSF is located in forward (starboard side) lockers on Discovery's, Orbiter Vehicle (OV) 103's, middeck. ADSF consists of the furnace container (left) and the control electronics container (right). An Air National Guard, Houston, Texas, decal appears on middeck locker above ADSF.

KENNEDY SPACE CENTER, FLA. - Stephanie Stilson, NASA vehicle manager for Discovery, is being filmed for a special feature on the KSC Web about the recent Orbiter Major Modification period, which included inspection, modifications and reservicing of most systems onboard Discovery, plus installation of a Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.” The orbiter is now being prepared for eventual launch on a future mission.

NASA Image and Video Library

2004-01-22

KENNEDY SPACE CENTER, FLA. - Stephanie Stilson, NASA vehicle manager for Discovery, is being filmed for a special feature on the KSC Web about the recent Orbiter Major Modification period, which included inspection, modifications and reservicing of most systems onboard Discovery, plus installation of a Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.” The orbiter is now being prepared for eventual launch on a future mission.

Integration of lyoplate based flow cytometry and computational analysis for standardized immunological biomarker discovery.

PubMed

Villanova, Federica; Di Meglio, Paola; Inokuma, Margaret; Aghaeepour, Nima; Perucha, Esperanza; Mollon, Jennifer; Nomura, Laurel; Hernandez-Fuentes, Maria; Cope, Andrew; Prevost, A Toby; Heck, Susanne; Maino, Vernon; Lord, Graham; Brinkman, Ryan R; Nestle, Frank O

2013-01-01

Discovery of novel immune biomarkers for monitoring of disease prognosis and response to therapy in immune-mediated inflammatory diseases is an important unmet clinical need. Here, we establish a novel framework for immunological biomarker discovery, comparing a conventional (liquid) flow cytometry platform (CFP) and a unique lyoplate-based flow cytometry platform (LFP) in combination with advanced computational data analysis. We demonstrate that LFP had higher sensitivity compared to CFP, with increased detection of cytokines (IFN-γ and IL-10) and activation markers (Foxp3 and CD25). Fluorescent intensity of cells stained with lyophilized antibodies was increased compared to cells stained with liquid antibodies. LFP, using a plate loader, allowed medium-throughput processing of samples with comparable intra- and inter-assay variability between platforms. Automated computational analysis identified novel immunophenotypes that were not detected with manual analysis. Our results establish a new flow cytometry platform for standardized and rapid immunological biomarker discovery with wide application to immune-mediated diseases.

Integration of Lyoplate Based Flow Cytometry and Computational Analysis for Standardized Immunological Biomarker Discovery

PubMed Central

Villanova, Federica; Di Meglio, Paola; Inokuma, Margaret; Aghaeepour, Nima; Perucha, Esperanza; Mollon, Jennifer; Nomura, Laurel; Hernandez-Fuentes, Maria; Cope, Andrew; Prevost, A. Toby; Heck, Susanne; Maino, Vernon; Lord, Graham; Brinkman, Ryan R.; Nestle, Frank O.

2013-01-01

Discovery of novel immune biomarkers for monitoring of disease prognosis and response to therapy in immune-mediated inflammatory diseases is an important unmet clinical need. Here, we establish a novel framework for immunological biomarker discovery, comparing a conventional (liquid) flow cytometry platform (CFP) and a unique lyoplate-based flow cytometry platform (LFP) in combination with advanced computational data analysis. We demonstrate that LFP had higher sensitivity compared to CFP, with increased detection of cytokines (IFN-γ and IL-10) and activation markers (Foxp3 and CD25). Fluorescent intensity of cells stained with lyophilized antibodies was increased compared to cells stained with liquid antibodies. LFP, using a plate loader, allowed medium-throughput processing of samples with comparable intra- and inter-assay variability between platforms. Automated computational analysis identified novel immunophenotypes that were not detected with manual analysis. Our results establish a new flow cytometry platform for standardized and rapid immunological biomarker discovery with wide application to immune-mediated diseases. PMID:23843942

Non-Adiabatic Molecular Dynamics Methods for Materials Discovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Furche, Filipp; Parker, Shane M.; Muuronen, Mikko J.

2017-04-04

The flow of radiative energy in light-driven materials such as photosensitizer dyes or photocatalysts is governed by non-adiabatic transitions between electronic states and cannot be described within the Born-Oppenheimer approximation commonly used in electronic structure theory. The non-adiabatic molecular dynamics (NAMD) methods based on Tully surface hopping and time-dependent density functional theory developed in this project have greatly extended the range of molecular materials that can be tackled by NAMD simulations. New algorithms to compute molecular excited state and response properties efficiently were developed. Fundamental limitations of common non-linear response methods were discovered and characterized. Methods for accurate computations ofmore » vibronic spectra of materials such as black absorbers were developed and applied. It was shown that open-shell TDDFT methods capture bond breaking in NAMD simulations, a longstanding challenge for single-reference molecular dynamics simulations. The methods developed in this project were applied to study the photodissociation of acetaldehyde and revealed that non-adiabatic effects are experimentally observable in fragment kinetic energy distributions. Finally, the project enabled the first detailed NAMD simulations of photocatalytic water oxidation by titania nanoclusters, uncovering the mechanism of this fundamentally important reaction for fuel generation and storage.« less

Computational functional genomics-based approaches in analgesic drug discovery and repurposing.

PubMed

Lippmann, Catharina; Kringel, Dario; Ultsch, Alfred; Lötsch, Jörn

2018-06-01

Persistent pain is a major healthcare problem affecting a fifth of adults worldwide with still limited treatment options. The search for new analgesics increasingly includes the novel research area of functional genomics, which combines data derived from various processes related to DNA sequence, gene expression or protein function and uses advanced methods of data mining and knowledge discovery with the goal of understanding the relationship between the genome and the phenotype. Its use in drug discovery and repurposing for analgesic indications has so far been performed using knowledge discovery in gene function and drug target-related databases; next-generation sequencing; and functional proteomics-based approaches. Here, we discuss recent efforts in functional genomics-based approaches to analgesic drug discovery and repurposing and highlight the potential of computational functional genomics in this field including a demonstration of the workflow using a novel R library 'dbtORA'.

The Impact of Students' Exploration Strategies on Discovery Learning Using Computer-Based Simulations

ERIC Educational Resources Information Center

Dalgarno, Barney; Kennedy, Gregor; Bennett, Sue

2014-01-01

Discovery-based learning designs incorporating active exploration are common within instructional software. However, researchers have highlighted empirical evidence showing that "pure" discovery learning is of limited value and strategies which reduce complexity and provide guidance to learners are important if potential learning…

High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials

DOE PAGES

Petousis, Ioannis; Mrdjenovich, David; Ballouz, Eric; ...

2017-01-31

Dielectrics are an important class of materials that are ubiquitous in modern electronic applications. Even though their properties are important for the performance of devices, the number of compounds with known dielectric constant is on the order of a few hundred. Here, we use Density Functional Perturbation Theory as a way to screen for the dielectric constant and refractive index of materials in a fast and computationally efficient way. Our results constitute the largest dielectric tensors database to date, containing 1,056 compounds. Details regarding the computational methodology and technical validation are presented along with the format of our publicly availablemore » data. In addition, we integrate our dataset with the Materials Project allowing users easy access to material properties. Finally, we explain how our dataset and calculation methodology can be used in the search for novel dielectric compounds.« less

High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials

PubMed Central

Petousis, Ioannis; Mrdjenovich, David; Ballouz, Eric; Liu, Miao; Winston, Donald; Chen, Wei; Graf, Tanja; Schladt, Thomas D.; Persson, Kristin A.; Prinz, Fritz B.

2017-01-01

Dielectrics are an important class of materials that are ubiquitous in modern electronic applications. Even though their properties are important for the performance of devices, the number of compounds with known dielectric constant is on the order of a few hundred. Here, we use Density Functional Perturbation Theory as a way to screen for the dielectric constant and refractive index of materials in a fast and computationally efficient way. Our results constitute the largest dielectric tensors database to date, containing 1,056 compounds. Details regarding the computational methodology and technical validation are presented along with the format of our publicly available data. In addition, we integrate our dataset with the Materials Project allowing users easy access to material properties. Finally, we explain how our dataset and calculation methodology can be used in the search for novel dielectric compounds. PMID:28140408

High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials.

PubMed

Petousis, Ioannis; Mrdjenovich, David; Ballouz, Eric; Liu, Miao; Winston, Donald; Chen, Wei; Graf, Tanja; Schladt, Thomas D; Persson, Kristin A; Prinz, Fritz B

2017-01-31

Dielectrics are an important class of materials that are ubiquitous in modern electronic applications. Even though their properties are important for the performance of devices, the number of compounds with known dielectric constant is on the order of a few hundred. Here, we use Density Functional Perturbation Theory as a way to screen for the dielectric constant and refractive index of materials in a fast and computationally efficient way. Our results constitute the largest dielectric tensors database to date, containing 1,056 compounds. Details regarding the computational methodology and technical validation are presented along with the format of our publicly available data. In addition, we integrate our dataset with the Materials Project allowing users easy access to material properties. Finally, we explain how our dataset and calculation methodology can be used in the search for novel dielectric compounds.

Merging Electronic Health Record Data and Genomics for Cardiovascular Research

PubMed Central

Hall, Jennifer L.; Ryan, John J.; Bray, Bruce E.; Brown, Candice; Lanfear, David; Newby, L. Kristin; Relling, Mary V.; Risch, Neil J.; Roden, Dan M.; Shaw, Stanley Y.; Tcheng, James E.; Tenenbaum, Jessica; Wang, Thomas N.; Weintraub, William S.

2017-01-01

The process of scientific discovery is rapidly evolving. The funding climate has influenced a favorable shift in scientific discovery toward the use of existing resources such as the electronic health record. The electronic health record enables long-term outlooks on human health and disease, in conjunction with multidimensional phenotypes that include laboratory data, images, vital signs, and other clinical information. Initial work has confirmed the utility of the electronic health record for understanding mechanisms and patterns of variability in disease susceptibility, disease evolution, and drug responses. The addition of biobanks and genomic data to the information contained in the electronic health record has been demonstrated. The purpose of this statement is to discuss the current challenges in and the potential for merging electronic health record data and genomics for cardiovascular research. PMID:26976545

Knowledge Discovery as an Aid to Organizational Creativity.

ERIC Educational Resources Information Center

Siau, Keng

2000-01-01

This article presents the concept of knowledge discovery, a process of searching for associations in large volumes of computer data, as an aid to creativity. It then discusses the various techniques in knowledge discovery. Mednick's associative theory of creative thought serves as the theoretical foundation for this research. (Contains…

Distributed Drug Discovery: Advancing Chemical Education through Contextualized Combinatorial Solid-Phase Organic Laboratories

ERIC Educational Resources Information Center

Scott, William L.; Denton, Ryan E.; Marrs, Kathleen A.; Durrant, Jacob D.; Samaritoni, J. Geno; Abraham, Milata M.; Brown, Stephen P.; Carnahan, Jon M.; Fischer, Lindsey G.; Glos, Courtney E.; Sempsrott, Peter J.; O'Donnell, Martin J.

2015-01-01

The Distributed Drug Discovery (D3) program trains students in three drug discovery disciplines (synthesis, computational analysis, and biological screening) while addressing the important challenge of discovering drug leads for neglected diseases. This article focuses on implementation of the synthesis component in the second-semester…

Scenario Educational Software: Design and Development of Discovery Learning.

ERIC Educational Resources Information Center

Keegan, Mark

This book shows how and why the computer is so well suited to producing discovery learning environments. An examination of the literature outlines four basic modes of instruction: didactic, Socratic, inquiry, and discovery. Research from the fields of education, psychology, and physiology is presented to demonstrate the many strengths of…

XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem: Mid-year report FY17 Q2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moreland, Kenneth D.; Pugmire, David; Rogers, David

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressingmore » four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.« less

XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem: Year-end report FY17.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moreland, Kenneth D.; Pugmire, David; Rogers, David

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressingmore » four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.« less

XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem. Mid-year report FY16 Q2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moreland, Kenneth D.; Sewell, Christopher; Childs, Hank

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressingmore » four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.« less

XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem: Year-end report FY15 Q4.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moreland, Kenneth D.; Sewell, Christopher; Childs, Hank

The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressingmore » four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.« less

Changing paradigm from one target one ligand towards multi target directed ligand design for key drug targets of Alzheimer disease: An important role of Insilco methods in multi target directed ligands design.

PubMed

Kumar, Akhil; Tiwari, Ashish; Sharma, Ashok

2018-03-15

Alzheimer disease (AD) is now considered as a multifactorial neurodegenerative disorder and rapidly increasing to an alarming situation and causing higher death rate. One target one ligand hypothesis is not able to provide complete solution of AD due to multifactorial nature of disease and one target one drug seems to fail to provide better treatment against AD. Moreover, current available treatments are limited and most of the upcoming treatments under clinical trials are based on modulating single target. So the current AD drug discovery research shifting towards new approach for better solution that simultaneously modulate more than one targets in the neurodegenerative cascade. This can be achieved by network pharmacology, multi-modal therapies, multifaceted, and/or the more recently proposed term "multi-targeted designed drugs. Drug discovery project is tedious, costly and long term project. Moreover, multi target AD drug discovery added extra challenges such as good binding affinity of ligands for multiple targets, optimal ADME/T properties, no/less off target side effect and crossing of the blood brain barrier. These hurdles may be addressed by insilico methods for efficient solution in less time and cost as computational methods successfully applied to single target drug discovery project. Here we are summarizing some of the most prominent and computationally explored single target against AD and further we discussed successful example of dual or multiple inhibitors for same targets. Moreover we focused on ligand and structure based computational approach to design MTDL against AD. However is not an easy task to balance dual activity in a single molecule but computational approach such as virtual screening docking, QSAR, simulation and free energy are useful in future MTDLs drug discovery alone or in combination with fragment based method. However, rational and logical implementations of computational drug designing methods are capable of assisting AD drug discovery and play an important role in optimizing multi-target drug discovery. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond

DOE PAGES

Mannodi-Kanakkithodi, Arun; Chandrasekaran, Anand; Kim, Chiho; ...

2017-12-19

The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational methodologies, data descriptors, and machine learning. Polymers have long suffered from a lack of data on electronic, mechanical, and dielectric properties across large chemical spaces, causing a stagnation in the set of suitable candidates for various applications. Extensive efforts over the last few years have seen the fruitful application of MGI principles toward the accelerated discovery of attractive polymer dielectrics for capacitive energy storage. Here,more » we review these efforts, highlighting the importance of computational data generation and screening, targeted synthesis and characterization, polymer fingerprinting and machine-learning prediction models, and the creation of an online knowledgebase to guide ongoing and future polymer discovery and design. We lay special emphasis on the fingerprinting of polymers in terms of their genome or constituent atomic and molecular fragments, an idea that pays homage to the pioneers of the human genome project who identified the basic building blocks of the human DNA. As a result, by scoping the polymer genome, we present an essential roadmap for the design of polymer dielectrics, and provide future perspectives and directions for expansions to other polymer subclasses and properties.« less

Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mannodi-Kanakkithodi, Arun; Chandrasekaran, Anand; Kim, Chiho

The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational methodologies, data descriptors, and machine learning. Polymers have long suffered from a lack of data on electronic, mechanical, and dielectric properties across large chemical spaces, causing a stagnation in the set of suitable candidates for various applications. Extensive efforts over the last few years have seen the fruitful application of MGI principles toward the accelerated discovery of attractive polymer dielectrics for capacitive energy storage. Here,more » we review these efforts, highlighting the importance of computational data generation and screening, targeted synthesis and characterization, polymer fingerprinting and machine-learning prediction models, and the creation of an online knowledgebase to guide ongoing and future polymer discovery and design. We lay special emphasis on the fingerprinting of polymers in terms of their genome or constituent atomic and molecular fragments, an idea that pays homage to the pioneers of the human genome project who identified the basic building blocks of the human DNA. As a result, by scoping the polymer genome, we present an essential roadmap for the design of polymer dielectrics, and provide future perspectives and directions for expansions to other polymer subclasses and properties.« less

False Discovery Control in Large-Scale Spatial Multiple Testing

PubMed Central

Sun, Wenguang; Reich, Brian J.; Cai, T. Tony; Guindani, Michele; Schwartzman, Armin

2014-01-01

Summary This article develops a unified theoretical and computational framework for false discovery control in multiple testing of spatial signals. We consider both point-wise and cluster-wise spatial analyses, and derive oracle procedures which optimally control the false discovery rate, false discovery exceedance and false cluster rate, respectively. A data-driven finite approximation strategy is developed to mimic the oracle procedures on a continuous spatial domain. Our multiple testing procedures are asymptotically valid and can be effectively implemented using Bayesian computational algorithms for analysis of large spatial data sets. Numerical results show that the proposed procedures lead to more accurate error control and better power performance than conventional methods. We demonstrate our methods for analyzing the time trends in tropospheric ozone in eastern US. PMID:25642138

Using Innovative Tools to Teach Computer Application to Business Students--A Hawthorne Effect or Successful Implementation Here to Stay

ERIC Educational Resources Information Center

Khan, Zeenath Reza

2014-01-01

A year after the primary study that tested the impact of introducing blended learning and guided discovery to help teach computer application to business students, this paper looks into the continued success of using guided discovery and blended learning with learning management system in and out of classrooms to enhance student learning.…

Advancing Efficient All-Electron Electronic Structure Methods Based on Numeric Atom-Centered Orbitals for Energy Related Materials

NASA Astrophysics Data System (ADS)

Blum, Volker

This talk describes recent advances of a general, efficient, accurate all-electron electronic theory approach based on numeric atom-centered orbitals; emphasis is placed on developments related to materials for energy conversion and their discovery. For total energies and electron band structures, we show that the overall accuracy is on par with the best benchmark quality codes for materials, but scalable to large system sizes (1,000s of atoms) and amenable to both periodic and non-periodic simulations. A recent localized resolution-of-identity approach for the Coulomb operator enables O (N) hybrid functional based descriptions of the electronic structure of non-periodic and periodic systems, shown for supercell sizes up to 1,000 atoms; the same approach yields accurate results for many-body perturbation theory as well. For molecular systems, we also show how many-body perturbation theory for charged and neutral quasiparticle excitation energies can be efficiently yet accurately applied using basis sets of computationally manageable size. Finally, the talk highlights applications to the electronic structure of hybrid organic-inorganic perovskite materials, as well as to graphene-based substrates for possible future transition metal compound based electrocatalyst materials. All methods described here are part of the FHI-aims code. VB gratefully acknowledges contributions by numerous collaborators at Duke University, Fritz Haber Institute Berlin, TU Munich, USTC Hefei, Aalto University, and many others around the globe.

Complete-active-space second-order perturbation theory (CASPT2//CASSCF) study of the dissociative electron attachment in canonical DNA nucleobases caused by low-energy electrons (0-3 eV)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Francés-Monerris, Antonio; Segarra-Martí, Javier; Merchán, Manuela

Low-energy (0-3 eV) ballistic electrons originated during the irradiation of biological material can interact with DNA/RNA nucleobases yielding transient-anion species which undergo decompositions. Since the discovery that these reactions can eventually lead to strand breaking of the DNA chains, great efforts have been dedicated to their study. The main fragmentation at the 0-3 eV energy range is the ejection of a hydrogen atom from the specific nitrogen positions. In the present study, the methodological approach introduced in a previous work on uracil [I. González-Ramírez et al., J. Chem. Theory Comput. 8, 2769-2776 (2012)] is employed to study the DNA canonicalmore » nucleobases fragmentations of N–H bonds induced by low-energy electrons. The approach is based on minimum energy path and linear interpolation of internal coordinates computations along the N–H dissociation channels carried out at the complete-active-space self-consistent field//complete-active-space second-order perturbation theory level. On the basis of the calculated theoretical quantities, new assignations for the adenine and cytosine anion yield curves are provided. In addition, the π{sub 1}{sup −} and π{sub 2}{sup −} states of the pyrimidine nucleobases are expected to produce the temporary anions at electron energies close to 1 and 2 eV, respectively. Finally, the present theoretical results do not allow to discard neither the dipole-bound nor the valence-bound mechanisms in the range of energies explored, suggesting that both possibilities may coexist in the experiments carried out with the isolated nucleobases.« less

Complete-active-space second-order perturbation theory (CASPT2//CASSCF) study of the dissociative electron attachment in canonical DNA nucleobases caused by low-energy electrons (0-3 eV).

PubMed

Francés-Monerris, Antonio; Segarra-Martí, Javier; Merchán, Manuela; Roca-Sanjuán, Daniel

2015-12-07

Low-energy (0-3 eV) ballistic electrons originated during the irradiation of biological material can interact with DNA/RNA nucleobases yielding transient-anion species which undergo decompositions. Since the discovery that these reactions can eventually lead to strand breaking of the DNA chains, great efforts have been dedicated to their study. The main fragmentation at the 0-3 eV energy range is the ejection of a hydrogen atom from the specific nitrogen positions. In the present study, the methodological approach introduced in a previous work on uracil [I. González-Ramírez et al., J. Chem. Theory Comput. 8, 2769-2776 (2012)] is employed to study the DNA canonical nucleobases fragmentations of N-H bonds induced by low-energy electrons. The approach is based on minimum energy path and linear interpolation of internal coordinates computations along the N-H dissociation channels carried out at the complete-active-space self-consistent field//complete-active-space second-order perturbation theory level. On the basis of the calculated theoretical quantities, new assignations for the adenine and cytosine anion yield curves are provided. In addition, the π1 (-) and π2 (-) states of the pyrimidine nucleobases are expected to produce the temporary anions at electron energies close to 1 and 2 eV, respectively. Finally, the present theoretical results do not allow to discard neither the dipole-bound nor the valence-bound mechanisms in the range of energies explored, suggesting that both possibilities may coexist in the experiments carried out with the isolated nucleobases.

Complete-active-space second-order perturbation theory (CASPT2//CASSCF) study of the dissociative electron attachment in canonical DNA nucleobases caused by low-energy electrons (0-3 eV)

NASA Astrophysics Data System (ADS)

Francés-Monerris, Antonio; Segarra-Martí, Javier; Merchán, Manuela; Roca-Sanjuán, Daniel

2015-12-01

Low-energy (0-3 eV) ballistic electrons originated during the irradiation of biological material can interact with DNA/RNA nucleobases yielding transient-anion species which undergo decompositions. Since the discovery that these reactions can eventually lead to strand breaking of the DNA chains, great efforts have been dedicated to their study. The main fragmentation at the 0-3 eV energy range is the ejection of a hydrogen atom from the specific nitrogen positions. In the present study, the methodological approach introduced in a previous work on uracil [I. González-Ramírez et al., J. Chem. Theory Comput. 8, 2769-2776 (2012)] is employed to study the DNA canonical nucleobases fragmentations of N-H bonds induced by low-energy electrons. The approach is based on minimum energy path and linear interpolation of internal coordinates computations along the N-H dissociation channels carried out at the complete-active-space self-consistent field//complete-active-space second-order perturbation theory level. On the basis of the calculated theoretical quantities, new assignations for the adenine and cytosine anion yield curves are provided. In addition, the π1- and π2- states of the pyrimidine nucleobases are expected to produce the temporary anions at electron energies close to 1 and 2 eV, respectively. Finally, the present theoretical results do not allow to discard neither the dipole-bound nor the valence-bound mechanisms in the range of energies explored, suggesting that both possibilities may coexist in the experiments carried out with the isolated nucleobases.

Did I Tell You That? Ethical Issues Related to Using Computational Methods to Discover Non-Disclosed Patient Characteristics

PubMed Central

Cato, Kenrick D; Bockting, Walter; Larson, Elaine

2016-01-01

Background Widespread availability of large data sets through warehousing of electronic health records coupled with increasingly sophisticated information technology and related statistical methods offer great potential for a variety of applications for health and disease surveillance, developing predictive models and advancing decision support for clinicians. However, use of such ‘big data’ mining and discovery techniques has also raised ethical issues such as how to balance privacy and autonomy with the wider public benefits of data sharing. More specifically, electronic data are being increasingly used to identify individual characteristics which can be useful for clinical prediction and management, but that were not previously disclosed to a clinician. This process in computer parlance is called electronic phenotyping, and has a number of ethical implications. Approach Using the Belmont Report’s principles of respect for persons, beneficence, and justice as a framework, we examined the ethical issues posed by electronic phenotyping. Findings Ethical issues identified include the ability of the patient to consent for the use of their information, the ability to suppress pediatric information, ensuring that the potential benefits justify the risks of harm to patients, and acknowledging that the clinician’s biases or stereotypes, conscious or unintended, may also become a factor in the therapeutic interaction. We illustrate these issues with two vignettes, using the person characteristic of gender minority status (i.e., transgender identity) and the health history characteristic of substance abuse. Conclusion Big data mining has the potential to uncover patient characteristics previously obscured which can provide clinicians with beneficial clinical information. Hence, ethical guidelines must be updated to ensure that electronic phenotyping supports the principles of respect for persons, beneficence, and justice. PMID:27534587

Maximizing Academic Library Collections: Measuring Changes in Use Patterns Owing to EBSCO Discovery Service

ERIC Educational Resources Information Center

Calvert, Kristin

2015-01-01

Despite the prevalence of academic libraries adopting web-scale discovery tools, few studies have quantified their effect on the use of library collections. This study measures the impact that EBSCO Discovery Service has had on use of library resources through circulation statistics, use of electronic resources, and interlibrary loan requests.…

Computational Discovery of Materials Using the Firefly Algorithm

NASA Astrophysics Data System (ADS)

Avendaño-Franco, Guillermo; Romero, Aldo

Our current ability to model physical phenomena accurately, the increase computational power and better algorithms are the driving forces behind the computational discovery and design of novel materials, allowing for virtual characterization before their realization in the laboratory. We present the implementation of a novel firefly algorithm, a population-based algorithm for global optimization for searching the structure/composition space. This novel computation-intensive approach naturally take advantage of concurrency, targeted exploration and still keeping enough diversity. We apply the new method in both periodic and non-periodic structures and we present the implementation challenges and solutions to improve efficiency. The implementation makes use of computational materials databases and network analysis to optimize the search and get insights about the geometric structure of local minima on the energy landscape. The method has been implemented in our software PyChemia, an open-source package for materials discovery. We acknowledge the support of DMREF-NSF 1434897 and the Donors of the American Chemical Society Petroleum Research Fund for partial support of this research under Contract 54075-ND10.

Pulsar discovery by global volunteer computing.

PubMed

Knispel, B; Allen, B; Cordes, J M; Deneva, J S; Anderson, D; Aulbert, C; Bhat, N D R; Bock, O; Bogdanov, S; Brazier, A; Camilo, F; Champion, D J; Chatterjee, S; Crawford, F; Demorest, P B; Fehrmann, H; Freire, P C C; Gonzalez, M E; Hammer, D; Hessels, J W T; Jenet, F A; Kasian, L; Kaspi, V M; Kramer, M; Lazarus, P; van Leeuwen, J; Lorimer, D R; Lyne, A G; Machenschalk, B; McLaughlin, M A; Messenger, C; Nice, D J; Papa, M A; Pletsch, H J; Prix, R; Ransom, S M; Siemens, X; Stairs, I H; Stappers, B W; Stovall, K; Venkataraman, A

2010-09-10

Einstein@Home aggregates the computer power of hundreds of thousands of volunteers from 192 countries to mine large data sets. It has now found a 40.8-hertz isolated pulsar in radio survey data from the Arecibo Observatory taken in February 2007. Additional timing observations indicate that this pulsar is likely a disrupted recycled pulsar. PSR J2007+2722's pulse profile is remarkably wide with emission over almost the entire spin period; the pulsar likely has closely aligned magnetic and spin axes. The massive computing power provided by volunteers should enable many more such discoveries.

Tunable assembly of amyloid-forming peptides into nanosheets as a retrovirus carrier.

PubMed

Dai, Bin; Li, Dan; Xi, Wenhui; Luo, Fang; Zhang, Xiang; Zou, Man; Cao, Mi; Hu, Jun; Wang, Wenyuan; Wei, Guanghong; Zhang, Yi; Liu, Cong

2015-03-10

Using and engineering amyloid as nanomaterials are blossoming trends in bionanotechnology. Here, we show our discovery of an amyloid structure, termed "amyloid-like nanosheet," formed by a key amyloid-forming segment of Alzheimer's Aβ. Combining multiple biophysical and computational approaches, we proposed a structural model for the nanosheet that is formed by stacking the amyloid fibril spines perpendicular to the fibril axis. We further used the nanosheet for laboratorial retroviral transduction enhancement and directly visualized the presence of virus on the nanosheet surface by electron microscopy. Furthermore, based on our structural model, we designed nanosheet-forming peptides with different functionalities, elucidating the potential of rational design for amyloid-based materials with novel architecture and function.

Successful applications of computer aided drug discovery: moving drugs from concept to the clinic.

PubMed

Talele, Tanaji T; Khedkar, Santosh A; Rigby, Alan C

2010-01-01

Drug discovery and development is an interdisciplinary, expensive and time-consuming process. Scientific advancements during the past two decades have changed the way pharmaceutical research generate novel bioactive molecules. Advances in computational techniques and in parallel hardware support have enabled in silico methods, and in particular structure-based drug design method, to speed up new target selection through the identification of hits to the optimization of lead compounds in the drug discovery process. This review is focused on the clinical status of experimental drugs that were discovered and/or optimized using computer-aided drug design. We have provided a historical account detailing the development of 12 small molecules (Captopril, Dorzolamide, Saquinavir, Zanamivir, Oseltamivir, Aliskiren, Boceprevir, Nolatrexed, TMI-005, LY-517717, Rupintrivir and NVP-AUY922) that are in clinical trial or have become approved for therapeutic use.

The importance of employing computational resources for the automation of drug discovery.

PubMed

Rosales-Hernández, Martha Cecilia; Correa-Basurto, José

2015-03-01

The application of computational tools to drug discovery helps researchers to design and evaluate new drugs swiftly with a reduce economic resources. To discover new potential drugs, computational chemistry incorporates automatization for obtaining biological data such as adsorption, distribution, metabolism, excretion and toxicity (ADMET), as well as drug mechanisms of action. This editorial looks at examples of these computational tools, including docking, molecular dynamics simulation, virtual screening, quantum chemistry, quantitative structural activity relationship, principal component analysis and drug screening workflow systems. The authors then provide their perspectives on the importance of these techniques for drug discovery. Computational tools help researchers to design and discover new drugs for the treatment of several human diseases without side effects, thus allowing for the evaluation of millions of compounds with a reduced cost in both time and economic resources. The problem is that operating each program is difficult; one is required to use several programs and understand each of the properties being tested. In the future, it is possible that a single computer and software program will be capable of evaluating the complete properties (mechanisms of action and ADMET properties) of ligands. It is also possible that after submitting one target, this computer-software will be capable of suggesting potential compounds along with ways to synthesize them, and presenting biological models for testing.

Computer-aided drug design at Boehringer Ingelheim

NASA Astrophysics Data System (ADS)

Muegge, Ingo; Bergner, Andreas; Kriegl, Jan M.

2017-03-01

Computer-Aided Drug Design (CADD) is an integral part of the drug discovery endeavor at Boehringer Ingelheim (BI). CADD contributes to the evaluation of new therapeutic concepts, identifies small molecule starting points for drug discovery, and develops strategies for optimizing hit and lead compounds. The CADD scientists at BI benefit from the global use and development of both software platforms and computational services. A number of computational techniques developed in-house have significantly changed the way early drug discovery is carried out at BI. In particular, virtual screening in vast chemical spaces, which can be accessed by combinatorial chemistry, has added a new option for the identification of hits in many projects. Recently, a new framework has been implemented allowing fast, interactive predictions of relevant on and off target endpoints and other optimization parameters. In addition to the introduction of this new framework at BI, CADD has been focusing on the enablement of medicinal chemists to independently perform an increasing amount of molecular modeling and design work. This is made possible through the deployment of MOE as a global modeling platform, allowing computational and medicinal chemists to freely share ideas and modeling results. Furthermore, a central communication layer called the computational chemistry framework provides broad access to predictive models and other computational services.

Using the iPlant collaborative discovery environment.

PubMed

Oliver, Shannon L; Lenards, Andrew J; Barthelson, Roger A; Merchant, Nirav; McKay, Sheldon J

2013-06-01

The iPlant Collaborative is an academic consortium whose mission is to develop an informatics and social infrastructure to address the "grand challenges" in plant biology. Its cyberinfrastructure supports the computational needs of the research community and facilitates solving major challenges in plant science. The Discovery Environment provides a powerful and rich graphical interface to the iPlant Collaborative cyberinfrastructure by creating an accessible virtual workbench that enables all levels of expertise, ranging from students to traditional biology researchers and computational experts, to explore, analyze, and share their data. By providing access to iPlant's robust data-management system and high-performance computing resources, the Discovery Environment also creates a unified space in which researchers can access scalable tools. Researchers can use available Applications (Apps) to execute analyses on their data, as well as customize or integrate their own tools to better meet the specific needs of their research. These Apps can also be used in workflows that automate more complicated analyses. This module describes how to use the main features of the Discovery Environment, using bioinformatics workflows for high-throughput sequence data as examples. © 2013 by John Wiley & Sons, Inc.

Theory Versus Experiment: The Case of the Positron

NASA Astrophysics Data System (ADS)

Leone, Matteo

The history of positron discovery is an interesting case-study of complex relationship between theory and experiment, and therefore could promote understanding of a key issue on the nature of science (NoS) within a learning environment. As it is well known we had indeed a theory, P.A.M. Dirac's theory of the anti-electron (1931), before the beginning of the experiments leading to the experimental discovery of the positive electron (Anderson 1932). Yet, this case is not merely an instance of successful corroboration of a theoretical prediction since, as it will be shown, the man who made the discovery, Anderson, actually did not know from the start what to look for.

Functional materials discovery using energy-structure-function maps

NASA Astrophysics Data System (ADS)

Pulido, Angeles; Chen, Linjiang; Kaczorowski, Tomasz; Holden, Daniel; Little, Marc A.; Chong, Samantha Y.; Slater, Benjamin J.; McMahon, David P.; Bonillo, Baltasar; Stackhouse, Chloe J.; Stephenson, Andrew; Kane, Christopher M.; Clowes, Rob; Hasell, Tom; Cooper, Andrew I.; Day, Graeme M.

2017-03-01

Molecular crystals cannot be designed in the same manner as macroscopic objects, because they do not assemble according to simple, intuitive rules. Their structures result from the balance of many weak interactions, rather than from the strong and predictable bonding patterns found in metal-organic frameworks and covalent organic frameworks. Hence, design strategies that assume a topology or other structural blueprint will often fail. Here we combine computational crystal structure prediction and property prediction to build energy-structure-function maps that describe the possible structures and properties that are available to a candidate molecule. Using these maps, we identify a highly porous solid, which has the lowest density reported for a molecular crystal so far. Both the structure of the crystal and its physical properties, such as methane storage capacity and guest-molecule selectivity, are predicted using the molecular structure as the only input. More generally, energy-structure-function maps could be used to guide the experimental discovery of materials with any target function that can be calculated from predicted crystal structures, such as electronic structure or mechanical properties.

Functional materials discovery using energy-structure-function maps.

PubMed

Pulido, Angeles; Chen, Linjiang; Kaczorowski, Tomasz; Holden, Daniel; Little, Marc A; Chong, Samantha Y; Slater, Benjamin J; McMahon, David P; Bonillo, Baltasar; Stackhouse, Chloe J; Stephenson, Andrew; Kane, Christopher M; Clowes, Rob; Hasell, Tom; Cooper, Andrew I; Day, Graeme M

2017-03-30

Molecular crystals cannot be designed in the same manner as macroscopic objects, because they do not assemble according to simple, intuitive rules. Their structures result from the balance of many weak interactions, rather than from the strong and predictable bonding patterns found in metal-organic frameworks and covalent organic frameworks. Hence, design strategies that assume a topology or other structural blueprint will often fail. Here we combine computational crystal structure prediction and property prediction to build energy-structure-function maps that describe the possible structures and properties that are available to a candidate molecule. Using these maps, we identify a highly porous solid, which has the lowest density reported for a molecular crystal so far. Both the structure of the crystal and its physical properties, such as methane storage capacity and guest-molecule selectivity, are predicted using the molecular structure as the only input. More generally, energy-structure-function maps could be used to guide the experimental discovery of materials with any target function that can be calculated from predicted crystal structures, such as electronic structure or mechanical properties.

From computational discovery to experimental characterization of a high hole mobility organic crystal

PubMed Central

Sokolov, Anatoliy N.; Atahan-Evrenk, Sule; Mondal, Rajib; Akkerman, Hylke B.; Sánchez-Carrera, Roel S.; Granados-Focil, Sergio; Schrier, Joshua; Mannsfeld, Stefan C.B.; Zoombelt, Arjan P.; Bao, Zhenan; Aspuru-Guzik, Alán

2011-01-01

For organic semiconductors to find ubiquitous electronics applications, the development of new materials with high mobility and air stability is critical. Despite the versatility of carbon, exploratory chemical synthesis in the vast chemical space can be hindered by synthetic and characterization difficulties. Here we show that in silico screening of novel derivatives of the dinaphtho[2,3-b:2′,3′-f]thieno[3,2-b]thiophene semiconductor with high hole mobility and air stability can lead to the discovery of a new high-performance semiconductor. On the basis of estimates from the Marcus theory of charge transfer rates, we identified a novel compound expected to demonstrate a theoretic twofold improvement in mobility over the parent molecule. Synthetic and electrical characterization of the compound is reported with single-crystal field-effect transistors, showing a remarkable saturation and linear mobility of 12.3 and 16 cm2 V−1 s−1, respectively. This is one of the very few organic semiconductors with mobility greater than 10 cm2 V−1 s−1 reported to date. PMID:21847111

Computational Tools for Allosteric Drug Discovery: Site Identification and Focus Library Design.

PubMed

Huang, Wenkang; Nussinov, Ruth; Zhang, Jian

2017-01-01

Allostery is an intrinsic phenomenon of biological macromolecules involving regulation and/or signal transduction induced by a ligand binding to an allosteric site distinct from a molecule's active site. Allosteric drugs are currently receiving increased attention in drug discovery because drugs that target allosteric sites can provide important advantages over the corresponding orthosteric drugs including specific subtype selectivity within receptor families. Consequently, targeting allosteric sites, instead of orthosteric sites, can reduce drug-related side effects and toxicity. On the down side, allosteric drug discovery can be more challenging than traditional orthosteric drug discovery due to difficulties associated with determining the locations of allosteric sites and designing drugs based on these sites and the need for the allosteric effects to propagate through the structure, reach the ligand binding site and elicit a conformational change. In this study, we present computational tools ranging from the identification of potential allosteric sites to the design of "allosteric-like" modulator libraries. These tools may be particularly useful for allosteric drug discovery.

An efficient and accurate framework for calculating lattice thermal conductivity of solids: AFLOW—AAPL Automatic Anharmonic Phonon Library

NASA Astrophysics Data System (ADS)

Plata, Jose J.; Nath, Pinku; Usanmaz, Demet; Carrete, Jesús; Toher, Cormac; de Jong, Maarten; Asta, Mark; Fornari, Marco; Nardelli, Marco Buongiorno; Curtarolo, Stefano

2017-10-01

One of the most accurate approaches for calculating lattice thermal conductivity, , is solving the Boltzmann transport equation starting from third-order anharmonic force constants. In addition to the underlying approximations of ab-initio parameterization, two main challenges are associated with this path: high computational costs and lack of automation in the frameworks using this methodology, which affect the discovery rate of novel materials with ad-hoc properties. Here, the Automatic Anharmonic Phonon Library (AAPL) is presented. It efficiently computes interatomic force constants by making effective use of crystal symmetry analysis, it solves the Boltzmann transport equation to obtain , and allows a fully integrated operation with minimum user intervention, a rational addition to the current high-throughput accelerated materials development framework AFLOW. An "experiment vs. theory" study of the approach is shown, comparing accuracy and speed with respect to other available packages, and for materials characterized by strong electron localization and correlation. Combining AAPL with the pseudo-hybrid functional ACBN0 is possible to improve accuracy without increasing computational requirements.

The Evolution of Discovery Systems in Academic Libraries: A Case Study at the University of Houston Libraries

ERIC Educational Resources Information Center

Guajardo, Richard; Brett, Kelsey; Young, Frederick

2017-01-01

For the past several years academic libraries have been adopting discovery systems to provide a search experience that reflects user expectations and improves access to electronic resources. University of Houston Libraries has kept pace with this evolving trend by pursuing various discovery options; these include an open-source tool, a federated…

Contributing, Exchanging and Linking for Learning: Supporting Web Co-Discovery in One-to-One Environments

ERIC Educational Resources Information Center

Liu, Chen-Chung; Don, Ping-Hsing; Chung, Chen-Wei; Lin, Shao-Jun; Chen, Gwo-Dong; Liu, Baw-Jhiune

2010-01-01

While Web discovery is usually undertaken as a solitary activity, Web co-discovery may transform Web learning activities from the isolated individual search process into interactive and collaborative knowledge exploration. Recent studies have proposed Web co-search environments on a single computer, supported by multiple one-to-one technologies.…

Merging Electronic Health Record Data and Genomics for Cardiovascular Research: A Science Advisory From the American Heart Association.

PubMed

Hall, Jennifer L; Ryan, John J; Bray, Bruce E; Brown, Candice; Lanfear, David; Newby, L Kristin; Relling, Mary V; Risch, Neil J; Roden, Dan M; Shaw, Stanley Y; Tcheng, James E; Tenenbaum, Jessica; Wang, Thomas N; Weintraub, William S

2016-04-01

The process of scientific discovery is rapidly evolving. The funding climate has influenced a favorable shift in scientific discovery toward the use of existing resources such as the electronic health record. The electronic health record enables long-term outlooks on human health and disease, in conjunction with multidimensional phenotypes that include laboratory data, images, vital signs, and other clinical information. Initial work has confirmed the utility of the electronic health record for understanding mechanisms and patterns of variability in disease susceptibility, disease evolution, and drug responses. The addition of biobanks and genomic data to the information contained in the electronic health record has been demonstrated. The purpose of this statement is to discuss the current challenges in and the potential for merging electronic health record data and genomics for cardiovascular research. © 2016 American Heart Association, Inc.

An algorithm of discovering signatures from DNA databases on a computer cluster.

PubMed

Lee, Hsiao Ping; Sheu, Tzu-Fang

2014-10-05

Signatures are short sequences that are unique and not similar to any other sequence in a database that can be used as the basis to identify different species. Even though several signature discovery algorithms have been proposed in the past, these algorithms require the entirety of databases to be loaded in the memory, thus restricting the amount of data that they can process. It makes those algorithms unable to process databases with large amounts of data. Also, those algorithms use sequential models and have slower discovery speeds, meaning that the efficiency can be improved. In this research, we are debuting the utilization of a divide-and-conquer strategy in signature discovery and have proposed a parallel signature discovery algorithm on a computer cluster. The algorithm applies the divide-and-conquer strategy to solve the problem posed to the existing algorithms where they are unable to process large databases and uses a parallel computing mechanism to effectively improve the efficiency of signature discovery. Even when run with just the memory of regular personal computers, the algorithm can still process large databases such as the human whole-genome EST database which were previously unable to be processed by the existing algorithms. The algorithm proposed in this research is not limited by the amount of usable memory and can rapidly find signatures in large databases, making it useful in applications such as Next Generation Sequencing and other large database analysis and processing. The implementation of the proposed algorithm is available at http://www.cs.pu.edu.tw/~fang/DDCSDPrograms/DDCSD.htm.

Tools, techniques, organisation and culture of the CADD group at Sygnature Discovery.

PubMed

St-Gallay, Steve A; Sambrook-Smith, Colin P

2017-03-01

Computer-aided drug design encompasses a wide variety of tools and techniques, and can be implemented with a range of organisational structures and focus in different organisations. Here we outline the computational chemistry skills within Sygnature Discovery, along with the software and hardware at our disposal, and briefly discuss the methods that are not employed and why. The goal of the group is to provide support for design and analysis in order to improve the quality of compounds synthesised and reduce the timelines of drug discovery projects, and we reveal how this is achieved at Sygnature. Impact on medicinal chemistry is vital to demonstrating the value of computational chemistry, and we discuss the approaches taken to influence the list of compounds for synthesis, and how we recognise success. Finally we touch on some of the areas being developed within the team in order to provide further value to the projects and clients.

Tools, techniques, organisation and culture of the CADD group at Sygnature Discovery

NASA Astrophysics Data System (ADS)

St-Gallay, Steve A.; Sambrook-Smith, Colin P.

2017-03-01

Computer-aided drug design encompasses a wide variety of tools and techniques, and can be implemented with a range of organisational structures and focus in different organisations. Here we outline the computational chemistry skills within Sygnature Discovery, along with the software and hardware at our disposal, and briefly discuss the methods that are not employed and why. The goal of the group is to provide support for design and analysis in order to improve the quality of compounds synthesised and reduce the timelines of drug discovery projects, and we reveal how this is achieved at Sygnature. Impact on medicinal chemistry is vital to demonstrating the value of computational chemistry, and we discuss the approaches taken to influence the list of compounds for synthesis, and how we recognise success. Finally we touch on some of the areas being developed within the team in order to provide further value to the projects and clients.

The role of fragment-based and computational methods in polypharmacology.

PubMed

Bottegoni, Giovanni; Favia, Angelo D; Recanatini, Maurizio; Cavalli, Andrea

2012-01-01

Polypharmacology-based strategies are gaining increased attention as a novel approach to obtaining potentially innovative medicines for multifactorial diseases. However, some within the pharmaceutical community have resisted these strategies because they can be resource-hungry in the early stages of the drug discovery process. Here, we report on fragment-based and computational methods that might accelerate and optimize the discovery of multitarget drugs. In particular, we illustrate that fragment-based approaches can be particularly suited for polypharmacology, owing to the inherent promiscuous nature of fragments. In parallel, we explain how computer-assisted protocols can provide invaluable insights into how to unveil compounds theoretically able to bind to more than one protein. Furthermore, several pragmatic aspects related to the use of these approaches are covered, thus offering the reader practical insights on multitarget-oriented drug discovery projects. Copyright © 2011 Elsevier Ltd. All rights reserved.

Toward Routine Automatic Pathway Discovery from On-line Scientific Text Abstracts.

PubMed

Ng; Wong

1999-01-01

We are entering a new era of research where the latest scientific discoveries are often first reported online and are readily accessible by scientists worldwide. This rapid electronic dissemination of research breakthroughs has greatly accelerated the current pace in genomics and proteomics research. The race to the discovery of a gene or a drug has now become increasingly dependent on how quickly a scientist can scan through voluminous amount of information available online to construct the relevant picture (such as protein-protein interaction pathways) as it takes shape amongst the rapidly expanding pool of globally accessible biological data (e.g. GENBANK) and scientific literature (e.g. MEDLINE). We describe a prototype system for automatic pathway discovery from on-line text abstracts, combining technologies that (1) retrieve research abstracts from online sources, (2) extract relevant information from the free texts, and (3) present the extracted information graphically and intuitively. Our work demonstrates that this framework allows us to routinely scan online scientific literature for automatic discovery of knowledge, giving modern scientists the necessary competitive edge in managing the information explosion in this electronic age.

77 FR 45345 - DOE/Advanced Scientific Computing Advisory Committee

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-31

... Recompetition results for Scientific Discovery through Advanced Computing (SciDAC) applications Co-design Public... DEPARTMENT OF ENERGY DOE/Advanced Scientific Computing Advisory Committee AGENCY: Office of... the Advanced Scientific Computing Advisory Committee (ASCAC). The Federal Advisory Committee Act (Pub...

Protein-Ligand Interaction Detection with a Novel Method of Transient Induced Molecular Electronic Spectroscopy (TIMES): Experimental and Theoretical Studies.

PubMed

Zhang, Tiantian; Wei, Tao; Han, Yuanyuan; Ma, Heng; Samieegohar, Mohammadreza; Chen, Ping-Wei; Lian, Ian; Lo, Yu-Hwa

2016-11-23

Protein-ligand interaction detection without disturbances (e.g., surface immobilization, fluorescent labeling, and crystallization) presents a key question in protein chemistry and drug discovery. The emergent technology of transient induced molecular electronic spectroscopy (TIMES), which incorporates a unique design of microfluidic platform and integrated sensing electrodes, is designed to operate in a label-free and immobilization-free manner to provide crucial information for protein-ligand interactions in relevant physiological conditions. Through experiments and theoretical simulations, we demonstrate that the TIMES technique actually detects protein-ligand binding through signals generated by surface electric polarization. The accuracy and sensitivity of experiments were demonstrated by precise measurements of dissociation constant of lysozyme and N -acetyl-d-glucosamine (NAG) ligand and its trimer, NAG 3 . Computational fluid dynamics (CFD) computation is performed to demonstrate that the surface's electric polarization signal originates from the induced image charges during the transition state of surface mass transport, which is governed by the overall effects of protein concentration, hydraulic forces, and surface fouling due to protein adsorption. Hybrid atomistic molecular dynamics (MD) simulations and free energy computation show that ligand binding affects lysozyme structure and stability, producing different adsorption orientation and surface polarization to give the characteristic TIMES signals. Although the current work is focused on protein-ligand interactions, the TIMES method is a general technique that can be applied to study signals from reactions between many kinds of molecules.

A Survey on Data Storage and Information Discovery in the WSANs-Based Edge Computing Systems

PubMed Central

Liang, Junbin; Liu, Renping; Ni, Wei; Li, Yin; Li, Ran; Ma, Wenpeng; Qi, Chuanda

2018-01-01

In the post-Cloud era, the proliferation of Internet of Things (IoT) has pushed the horizon of Edge computing, which is a new computing paradigm with data processed at the edge of the network. As the important systems of Edge computing, wireless sensor and actuator networks (WSANs) play an important role in collecting and processing the sensing data from the surrounding environment as well as taking actions on the events happening in the environment. In WSANs, in-network data storage and information discovery schemes with high energy efficiency, high load balance and low latency are needed because of the limited resources of the sensor nodes and the real-time requirement of some specific applications, such as putting out a big fire in a forest. In this article, the existing schemes of WSANs on data storage and information discovery are surveyed with detailed analysis on their advancements and shortcomings, and possible solutions are proposed on how to achieve high efficiency, good load balance, and perfect real-time performances at the same time, hoping that it can provide a good reference for the future research of the WSANs-based Edge computing systems. PMID:29439442

A Survey on Data Storage and Information Discovery in the WSANs-Based Edge Computing Systems.

PubMed

Ma, Xingpo; Liang, Junbin; Liu, Renping; Ni, Wei; Li, Yin; Li, Ran; Ma, Wenpeng; Qi, Chuanda

2018-02-10

In the post-Cloud era, the proliferation of Internet of Things (IoT) has pushed the horizon of Edge computing, which is a new computing paradigm with data are processed at the edge of the network. As the important systems of Edge computing, wireless sensor and actuator networks (WSANs) play an important role in collecting and processing the sensing data from the surrounding environment as well as taking actions on the events happening in the environment. In WSANs, in-network data storage and information discovery schemes with high energy efficiency, high load balance and low latency are needed because of the limited resources of the sensor nodes and the real-time requirement of some specific applications, such as putting out a big fire in a forest. In this article, the existing schemes of WSANs on data storage and information discovery are surveyed with detailed analysis on their advancements and shortcomings, and possible solutions are proposed on how to achieve high efficiency, good load balance, and perfect real-time performances at the same time, hoping that it can provide a good reference for the future research of the WSANs-based Edge computing systems.

Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery.

PubMed

Heifetz, Alexander; Southey, Michelle; Morao, Inaki; Townsend-Nicholson, Andrea; Bodkin, Mike J

2018-01-01

GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects.

In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm

PubMed Central

Chung, Yongchul G.; Gómez-Gualdrón, Diego A.; Li, Peng; Leperi, Karson T.; Deria, Pravas; Zhang, Hongda; Vermeulen, Nicolaas A.; Stoddart, J. Fraser; You, Fengqi; Hupp, Joseph T.; Farha, Omar K.; Snurr, Randall Q.

2016-01-01

Discovery of new adsorbent materials with a high CO2 working capacity could help reduce CO2 emissions from newly commissioned power plants using precombustion carbon capture. High-throughput computational screening efforts can accelerate the discovery of new adsorbents but sometimes require significant computational resources to explore the large space of possible materials. We report the in silico discovery of high-performing adsorbents for precombustion CO2 capture by applying a genetic algorithm to efficiently search a large database of metal-organic frameworks (MOFs) for top candidates. High-performing MOFs identified from the in silico search were synthesized and activated and show a high CO2 working capacity and a high CO2/H2 selectivity. One of the synthesized MOFs shows a higher CO2 working capacity than any MOF reported in the literature under the operating conditions investigated here. PMID:27757420

CADD medicine: design is the potion that can cure my disease

NASA Astrophysics Data System (ADS)

Manas, Eric S.; Green, Darren V. S.

2017-03-01

The acronym "CADD" is often used interchangeably to refer to "Computer Aided Drug Discovery" and "Computer Aided Drug Design". While the former definition implies the use of a computer to impact one or more aspects of discovering a drug, in this paper we contend that computational chemists are most effective when they enable teams to apply true design principles as they strive to create medicines to treat human disease. We argue that teams must bring to bear multiple sub-disciplines of computational chemistry in an integrated manner in order to utilize these principles to address the multi-objective nature of the drug discovery problem. Impact, resourcing principles, and future directions for the field are also discussed, including areas of future opportunity as well as a cautionary note about hype and hubris.

The Role of the Virtual Astronomical Observatory in the Era of Big Data

NASA Astrophysics Data System (ADS)

Berriman, G. B.; Hanisch, R. J.; Lazio, T. J.

2013-01-01

The Virtual Observatory (VO) is realizing global electronic integration of astronomy data. The rapid growth in the size and complexity of data sets is transforming the computing landscape in astronomy. One of the long-term goals of the U.S. VO project, the Virtual Astronomical Observatory (VAO), is development of an information backbone that responds to this growth. Such a backbone will, when complete, provide innovative mechanisms for fast discovery of, and access to, massive data sets, and services that enable distributed storage, publication processing of large datasets. All these services will be built so that new projects can incorporate them as part of their data management and processing plans. Services under development to date include a general purpose indexing scheme for fast access to data sets, a cross-comparison engine that operate on catalogs of 1 billion records or more, and an interface for managing distributed data sets and connecting them to data discovery and analysis tools. The VAO advises projects on technology solutions for their data access and processing needs, and recently advised the Sagan Workshop on using cloud computing to support hands-on data analysis sessions for 150+ participants. Acknowledgements: The Virtual Astronomical Observatory (VAO) is managed by the VAO, LLC, a non-profit company established as a partnership of the Associated Universities, Inc. and the Association of Universities for Research in Astronomy, Inc. The VAO is sponsored by the National Science Foundation and the National Aeronautics and Space Administration.

Some experiences and opportunities for big data in translational research.

PubMed

Chute, Christopher G; Ullman-Cullere, Mollie; Wood, Grant M; Lin, Simon M; He, Min; Pathak, Jyotishman

2013-10-01

Health care has become increasingly information intensive. The advent of genomic data, integrated into patient care, significantly accelerates the complexity and amount of clinical data. Translational research in the present day increasingly embraces new biomedical discovery in this data-intensive world, thus entering the domain of "big data." The Electronic Medical Records and Genomics consortium has taught us many lessons, while simultaneously advances in commodity computing methods enable the academic community to affordably manage and process big data. Although great promise can emerge from the adoption of big data methods and philosophy, the heterogeneity and complexity of clinical data, in particular, pose additional challenges for big data inferencing and clinical application. However, the ultimate comparability and consistency of heterogeneous clinical information sources can be enhanced by existing and emerging data standards, which promise to bring order to clinical data chaos. Meaningful Use data standards in particular have already simplified the task of identifying clinical phenotyping patterns in electronic health records.

Some experiences and opportunities for big data in translational research

PubMed Central

Chute, Christopher G.; Ullman-Cullere, Mollie; Wood, Grant M.; Lin, Simon M.; He, Min; Pathak, Jyotishman

2014-01-01

Health care has become increasingly information intensive. The advent of genomic data, integrated into patient care, significantly accelerates the complexity and amount of clinical data. Translational research in the present day increasingly embraces new biomedical discovery in this data-intensive world, thus entering the domain of “big data.” The Electronic Medical Records and Genomics consortium has taught us many lessons, while simultaneously advances in commodity computing methods enable the academic community to affordably manage and process big data. Although great promise can emerge from the adoption of big data methods and philosophy, the heterogeneity and complexity of clinical data, in particular, pose additional challenges for big data inferencing and clinical application. However, the ultimate comparability and consistency of heterogeneous clinical information sources can be enhanced by existing and emerging data standards, which promise to bring order to clinical data chaos. Meaningful Use data standards in particular have already simplified the task of identifying clinical phenotyping patterns in electronic health records. PMID:24008998

What To Do, Instead of Counterproductive, Standardized Curricula and Testing.

ERIC Educational Resources Information Center

Keegan, Mark

1993-01-01

Presents evidence against the use of standardized curricula and testing and in favor of discovery-based instruction. Describes a successful experience with a relatively new form of discovery-based instruction, the scenario educational computer software. (AEF)

ESA is hot on the trail of Geminga

NASA Astrophysics Data System (ADS)

XMM-Newton image of Geminga showing the discovery of the twi hi-res Size hi-res: 68 kb Credits: ESA XMM-Newton image of Geminga showing the discovery of the twin tails This image was captured by the EPIC camera on board the satellite. The motion of Geminga across the sky is indicated, showing that the tails are trailing the neutron star. The scale bar corresponds to a distance of 1.5 million million kilometres at the distance of Geminga. Computer models of the shock wave created by Geminga hi-res Size hi-res: 522 kb Credits: Patrizia Caraveo Computer models of the shockwave created by Geminga Computer models of the shockwave created by Geminga show that the best matches to the data occur if the neutron star is travelling virtually across our line of sight. These correspond to the inclinations of less than 30 degrees. A neutron star measures only 20-30 kilometres across and is the dense remnant of an exploded star. Geminga is one of the closest to Earth, at a distance of about 500 light-years. Most neutron stars emit radio emissions, appearing to pulsate like a lighthouse, but Geminga is 'radio-quiet'. It does, however, emit huge quantities of pulsating gamma rays making it one of the brightest gamma-ray sources in the sky. Geminga is the only example of a successfully identified gamma-ray source from which astronomers have gained significant knowledge. It is 350 000 years old and ploughs through space at 120 kilometres per second. Its route creates a shockwave that compresses the gas of the interstellar medium and its naturally embedded magnetic field by a factor of four. Patrizia Caraveo, Instituto di Astrofisica Spaziale e Fisica Cosmica, Milano, Italy, and her colleagues (at CESR, France, ESO and MPE, Germany) have calculated that the tails are produced because highly energetic electrons become trapped in this enhanced magnetic field. As the electrons spiral inside the magnetic field, they emit the X-rays seen by XMM-Newton. The electrons themselves are created close to the neutron star. Geminga’s breathless rotation rate - once every quarter of a second - creates an extraordinary environment in which electrons and positrons, their antimatter counterparts, can be accelerated to extraordinarily high energies. At such energies, they become powerful high-energy gamma-ray producers. Astronomers had assumed that all the electrons would be converted into gamma rays. However, the discovery of the tails proves that some do find escape routes from the maelstrom. “It is astonishing that such energetic electrons succeed in escaping to create these tails,” says Caraveo, “The tail electrons have an energy very near to the maximum energy achievable in the environment of Geminga.” The tails themselves are the bright edges of the three-dimensional shockwave sculpted by Geminga. Such shockwaves are a bit like the wake of a ship travelling across the ocean. Using a computer model, the team has estimated that Geminga is travelling almost directly across our line of sight. Studies of Geminga could not be more important. The majority of known gamma-ray sources in the Universe have yet to be identified with known classes of celestial objects. Some astronomers believe that a sizeable fraction of them may be Geminga-like radio-quiet neutron stars. Certainly, the family of radio-quiet neutron stars, discovered through their X-ray emission, is continuously growing. Currently, about a dozen objects are known but only Geminga has a pair of tails! Note for editors During the search to track down this elusive celestial object, a co-author on the paper, Giovanni Bignami, named Geminga almost 30 years ago. He was Principal Investigator of XMM-Newton's EPIC camera from 1987 to1997 and is now Director of the Centre d'Etude Spatiale des Rayonnements (CESR). Geminga was first glimpsed as a mysterious source of gamma rays, coming from somewhere in the constellation Gemini by NASA's SAS-2 spacecraft in 1973. While searching to pin down its exact location and nature, Bignami named it Geminga because it was a 'Gemini gamma-ray source'. As an astronomer in Milan, Italy, he was also aware that in his native dialect 'gh'è minga' means 'it is not there', which he found amusing. It was also remarkably apt, for it was not until 1993 that he succeeded in finally 'seeing' and therefore pinpointing Geminga, using optical wavelengths. While it lacked radio emissions, the pulsating X-ray and gamma-ray emissions meant Geminga could only be a new class of radio-quiet neutron star. The original paper was published yesterday, 24 July 2003, on Science Express, a feature of Science Online.

Calculation of protein-ligand binding affinities.

PubMed

Gilson, Michael K; Zhou, Huan-Xiang

2007-01-01

Accurate methods of computing the affinity of a small molecule with a protein are needed to speed the discovery of new medications and biological probes. This paper reviews physics-based models of binding, beginning with a summary of the changes in potential energy, solvation energy, and configurational entropy that influence affinity, and a theoretical overview to frame the discussion of specific computational approaches. Important advances are reported in modeling protein-ligand energetics, such as the incorporation of electronic polarization and the use of quantum mechanical methods. Recent calculations suggest that changes in configurational entropy strongly oppose binding and must be included if accurate affinities are to be obtained. The linear interaction energy (LIE) and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) methods are analyzed, as are free energy pathway methods, which show promise and may be ready for more extensive testing. Ultimately, major improvements in modeling accuracy will likely require advances on multiple fronts, as well as continued validation against experiment.

Expressive electronic journal writing: freedom of communication for survivors of acquired brain injury.

PubMed

Fraas, Michael; Balz, Magdalen A

2008-03-01

In addition to the impaired ability to effectively communicate, adults with acquired brain injury (ABI) also experience high incidences of depression, social isolation, and decreased quality of life. Expressive writing programs have been shown to be effective in alleviating these concomitant impairments in other populations including incarcerated inmates (Lane, Writing as a road to self-discovery, F & W, Cincinnati 1993). In addition, computer applications such as email have been suggested as an effective means of improving communication and social isolation in adults with brain injury (Sohlberg et al. [2003]. Brain Injury, 17(7), 609-629). This investigation examines the effects of on-line expressive journal writing on the communication, emotional status, social integration and quality of life of individuals with brain injury.

Studying Scientific Discovery by Computer Simulation.

DTIC Science & Technology

1983-03-30

Mendel’s laws of inheritance, the law of Gay- Lussac for gaseous reactions, tile law of Dulong and Petit, the derivation of atomic weights by Avogadro...neceseary mid identify by block number) scientific discovery -ittri sic properties physical laws extensive terms data-driven heuristics intensive...terms theory-driven heuristics conservation laws 20. ABSTRACT (Continue on revere. side It necessary and identify by block number) Scientific discovery

Sign use and cognition in automated scientific discovery: are computers only special kinds of signs?

NASA Astrophysics Data System (ADS)

Giza, Piotr

2018-04-01

James Fetzer criticizes the computational paradigm, prevailing in cognitive science by questioning, what he takes to be, its most elementary ingredient: that cognition is computation across representations. He argues that if cognition is taken to be a purposive, meaningful, algorithmic problem solving activity, then computers are incapable of cognition. Instead, they appear to be signs of a special kind, that can facilitate computation. He proposes the conception of minds as semiotic systems as an alternative paradigm for understanding mental phenomena, one that seems to overcome the difficulties of computationalism. Now, I argue, that with computer systems dealing with scientific discovery, the matter is not so simple as that. The alleged superiority of humans using signs to stand for something other over computers being merely "physical symbol systems" or "automatic formal systems" is only easy to establish in everyday life, but becomes far from obvious when scientific discovery is at stake. In science, as opposed to everyday life, the meaning of symbols is, apart from very low-level experimental investigations, defined implicitly by the way the symbols are used in explanatory theories or experimental laws relevant to the field, and in consequence, human and machine discoverers are much more on a par. Moreover, the great practical success of the genetic programming method and recent attempts to apply it to automatic generation of cognitive theories seem to show, that computer systems are capable of very efficient problem solving activity in science, which is neither purposive nor meaningful, nor algorithmic. This, I think, undermines Fetzer's argument that computer systems are incapable of cognition because computation across representations is bound to be a purposive, meaningful, algorithmic problem solving activity.

KENNEDY SPACE CENTER, FLA. - NASA Vehicle Manager for Discovery, Stephanie Stilson poses for a photo after working with a KSC Web team who were filming a special feature for the KSC Web. Stilson explained her role in the recent Orbiter Major Modification period, which included inspection, modifications and reservicing of most systems onboard. The work on Discovery also included the installation of a Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.” The orbiter is now being prepared for eventual launch on a future mission.

NASA Image and Video Library

2004-01-22

KENNEDY SPACE CENTER, FLA. - NASA Vehicle Manager for Discovery, Stephanie Stilson poses for a photo after working with a KSC Web team who were filming a special feature for the KSC Web. Stilson explained her role in the recent Orbiter Major Modification period, which included inspection, modifications and reservicing of most systems onboard. The work on Discovery also included the installation of a Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.” The orbiter is now being prepared for eventual launch on a future mission.

KENNEDY SPACE CENTER, FLA. - Standing on a workstand (at left) in the Orbiter Processing Facility is Stephanie Stilson, NASA vehicle manager for Discovery. She is being filmed for a special feature on the KSC Web about the recent Orbiter Major Modification period on Discovery, which included inspection, modifications and reservicing of most systems onboard, plus installation of a Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.” The orbiter is now being prepared for eventual launch on a future mission.

NASA Image and Video Library

2004-01-22

KENNEDY SPACE CENTER, FLA. - Standing on a workstand (at left) in the Orbiter Processing Facility is Stephanie Stilson, NASA vehicle manager for Discovery. She is being filmed for a special feature on the KSC Web about the recent Orbiter Major Modification period on Discovery, which included inspection, modifications and reservicing of most systems onboard, plus installation of a Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.” The orbiter is now being prepared for eventual launch on a future mission.

KENNEDY SPACE CENTER, FLA. - During power-up of the orbiter Discovery in the Orbiter Processing Facility, a technician moves a switch. Discovery has been undergoing Orbiter Major Modifications in the past year, ranging from wiring, control panels and black boxes to gaseous and fluid systems tubing and components. These systems were deserviced, disassembled, inspected, modified, reassembled, checked out and reserviced, as were most other systems onboard. The work includes the installation of the Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.”

NASA Image and Video Library

2003-08-27

KENNEDY SPACE CENTER, FLA. - During power-up of the orbiter Discovery in the Orbiter Processing Facility, a technician moves a switch. Discovery has been undergoing Orbiter Major Modifications in the past year, ranging from wiring, control panels and black boxes to gaseous and fluid systems tubing and components. These systems were deserviced, disassembled, inspected, modified, reassembled, checked out and reserviced, as were most other systems onboard. The work includes the installation of the Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.”

KENNEDY SPACE CENTER, FLA. - During power-up of the orbiter Discovery in the Orbiter Processing Facility, a technician turns on a switch. Discovery has been undergoing Orbiter Major Modifications in the past year, ranging from wiring, control panels and black boxes to gaseous and fluid systems tubing and components. These systems were deserviced, disassembled, inspected, modified, reassembled, checked out and reserviced, as were most other systems onboard. The work includes the installation of the Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.”

NASA Image and Video Library

2003-08-27

KENNEDY SPACE CENTER, FLA. - During power-up of the orbiter Discovery in the Orbiter Processing Facility, a technician turns on a switch. Discovery has been undergoing Orbiter Major Modifications in the past year, ranging from wiring, control panels and black boxes to gaseous and fluid systems tubing and components. These systems were deserviced, disassembled, inspected, modified, reassembled, checked out and reserviced, as were most other systems onboard. The work includes the installation of the Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.”

2014 Annual Report - Argonne Leadership Computing Facility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Collins, James R.; Papka, Michael E.; Cerny, Beth A.

The Argonne Leadership Computing Facility provides supercomputing capabilities to the scientific and engineering community to advance fundamental discovery and understanding in a broad range of disciplines.

2015 Annual Report - Argonne Leadership Computing Facility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Collins, James R.; Papka, Michael E.; Cerny, Beth A.

The Argonne Leadership Computing Facility provides supercomputing capabilities to the scientific and engineering community to advance fundamental discovery and understanding in a broad range of disciplines.

Medicinal chemistry in drug discovery in big pharma: past, present and future.

PubMed

Campbell, Ian B; Macdonald, Simon J F; Procopiou, Panayiotis A

2018-02-01

The changes in synthetic and medicinal chemistry and related drug discovery science as practiced in big pharma over the past few decades are described. These have been predominantly driven by wider changes in society namely the computer, internet and globalisation. Thoughts about the future of medicinal chemistry are also discussed including sharing the risks and costs of drug discovery and the future of outsourcing. The continuing impact of access to substantial computing power and big data, the use of algorithms in data analysis and drug design are also presented. The next generation of medicinal chemists will communicate in ways that reflect social media and the results of constantly being connected to each other and data. Copyright © 2017. Published by Elsevier Ltd.

The discovery of the causes of leprosy: A computational analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Corruble, V.; Ganascia, J.G.

1996-12-31

The role played by the inductive inference has been studied extensively in the field of Scientific Discovery. The work presented here tackles the problem of induction in medical research. The discovery of the causes of leprosy is analyzed and simulated using computational means. An inductive algorithm is proposed, which is successful in simulating some essential steps in the progress of the understanding of the disease. It also allows us to simulate the false reasoning of previous centuries through the introduction of some medical a priori inherited form archaic medicine. Corroborating previous research, this problem illustrates the importance of the socialmore » and cultural environment on the way the inductive inference is performed in medicine.« less

Center for Center for Technology for Advanced Scientific Component Software (TASCS)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kostadin, Damevski

A resounding success of the Scientific Discovery through Advanced Computing (SciDAC) program is that high-performance computational science is now universally recognized as a critical aspect of scientific discovery [71], complementing both theoretical and experimental research. As scientific communities prepare to exploit unprecedented computing capabilities of emerging leadership-class machines for multi-model simulations at the extreme scale [72], it is more important than ever to address the technical and social challenges of geographically distributed teams that combine expertise in domain science, applied mathematics, and computer science to build robust and flexible codes that can incorporate changes over time. The Center for Technologymore » for Advanced Scientific Component Software (TASCS)1 tackles these these issues by exploiting component-based software development to facilitate collaborative high-performance scientific computing.« less

Transitsearch Workshop

NASA Technical Reports Server (NTRS)

Castellano, T.

2004-01-01

The discovery of more than 100 planets around nearby solar-like stars that surpass Jupiter in size yet orbit their stars more quickly than Mercury has heralded a new era in astronomy. These enigmatic 'Hot-Jupiters' are large enough and close enough to their parent stars that their 'transits' can be captured by astronomers equipped with a small computer controlled telescope and a quality electronic CCD camera. The planet reveals its presence through the periodic decrease in brightness as it passes (or transits) in front of the star as seen from Earth. The first known transiting extrasolar planet HD 209458b, in the constellation Pegasus, has been the subject of hundreds of scientific papers since its discovery in 1999. The transit of 8th magnitude HD 209458 has been observed by at least a dozen non-professional astronomers using telescopes as small as 4 inches in aperture. Using equipment already in hand, and armed with target lists, transit time predictions, observing techniques and software procedures developed by astronomers at NASA's Ames Research Center and the University of California at Santa Cruz, non-professional astronomers can contribute significantly to the study of extrasolar planets by carefully measuring the brightness of stars with known Hot-Jupiters. In this way, we may resume (after a two century interruption!) the tradition of planetary discoveries by amateur astronomers begun with William Herschel's 1787 discovery of the 'solar' planet Uranus. In the few years transitsearch has been in existence, investigators Tim Castellano (NASA Ames) and Greg Laughlin (UCSC) have written articles for Sky and Telescope and Astronomy magazines, have been featured in stories by the Reuters News Service, Nature magazine, Science magazine, Space.com, the American Institute of Physics and others and received several hundred thousand total hits on their website www.transitsearch,org.

A fortran program for Monte Carlo simulation of oil-field discovery sequences

USGS Publications Warehouse

Bohling, Geoffrey C.; Davis, J.C.

1993-01-01

We have developed a program for performing Monte Carlo simulation of oil-field discovery histories. A synthetic parent population of fields is generated as a finite sample from a distribution of specified form. The discovery sequence then is simulated by sampling without replacement from this parent population in accordance with a probabilistic discovery process model. The program computes a chi-squared deviation between synthetic and actual discovery sequences as a function of the parameters of the discovery process model, the number of fields in the parent population, and the distributional parameters of the parent population. The program employs the three-parameter log gamma model for the distribution of field sizes and employs a two-parameter discovery process model, allowing the simulation of a wide range of scenarios. ?? 1993.

Hit discovery and hit-to-lead approaches.

PubMed

Keseru, György M; Makara, Gergely M

2006-08-01

Hit discovery technologies range from traditional high-throughput screening to affinity selection of large libraries, fragment-based techniques and computer-aided de novo design, many of which have been extensively reviewed. Development of quality leads using hit confirmation and hit-to-lead approaches present their own challenges, depending on the hit discovery method used to identify the initial hits. In this paper, we summarize common industry practices adopted to tackle hit-to-lead challenges and review how the advantages and drawbacks of different hit discovery techniques could affect the various issues hit-to-lead groups face.

Anniversary Paper: Image processing and manipulation through the pages of Medical Physics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Armato, Samuel G. III; Ginneken, Bram van; Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, Room Q0S.459, 3584 CX Utrecht

The language of radiology has gradually evolved from ''the film'' (the foundation of radiology since Wilhelm Roentgen's 1895 discovery of x-rays) to ''the image,'' an electronic manifestation of a radiologic examination that exists within the bits and bytes of a computer. Rather than simply storing and displaying radiologic images in a static manner, the computational power of the computer may be used to enhance a radiologist's ability to visually extract information from the image through image processing and image manipulation algorithms. Image processing tools provide a broad spectrum of opportunities for image enhancement. Gray-level manipulations such as histogram equalization, spatialmore » alterations such as geometric distortion correction, preprocessing operations such as edge enhancement, and enhanced radiography techniques such as temporal subtraction provide powerful methods to improve the diagnostic quality of an image or to enhance structures of interest within an image. Furthermore, these image processing algorithms provide the building blocks of more advanced computer vision methods. The prominent role of medical physicists and the AAPM in the advancement of medical image processing methods, and in the establishment of the ''image'' as the fundamental entity in radiology and radiation oncology, has been captured in 35 volumes of Medical Physics.« less

Topological materials discovery using electron filling constraints

NASA Astrophysics Data System (ADS)

Chen, Ru; Po, Hoi Chun; Neaton, Jeffrey B.; Vishwanath, Ashvin

2018-01-01

Nodal semimetals are classes of topological materials that have nodal-point or nodal-line Fermi surfaces, which give them novel transport and topological properties. Despite being highly sought after, there are currently very few experimental realizations, and identifying new materials candidates has mainly relied on exhaustive database searches. Here we show how recent studies on the interplay between electron filling and nonsymmorphic space-group symmetries can guide the search for filling-enforced nodal semimetals. We recast the previously derived constraints on the allowed band-insulator fillings in any space group into a new form, which enables effective screening of materials candidates based solely on their space group, electron count in the formula unit, and multiplicity of the formula unit. This criterion greatly reduces the computation load for discovering topological materials in a database of previously synthesized compounds. As a demonstration, we focus on a few selected nonsymmorphic space groups which are predicted to host filling-enforced Dirac semimetals. Of the more than 30,000 entires listed, our filling criterion alone eliminates 96% of the entries before they are passed on for further analysis. We discover a handful of candidates from this guided search; among them, the monoclinic crystal Ca2Pt2Ga is particularly promising.

Computational Chemistry in the Pharmaceutical Industry: From Childhood to Adolescence.

PubMed

Hillisch, Alexander; Heinrich, Nikolaus; Wild, Hanno

2015-12-01

Computational chemistry within the pharmaceutical industry has grown into a field that proactively contributes to many aspects of drug design, including target selection and lead identification and optimization. While methodological advancements have been key to this development, organizational developments have been crucial to our success as well. In particular, the interaction between computational and medicinal chemistry and the integration of computational chemistry into the entire drug discovery process have been invaluable. Over the past ten years we have shaped and developed a highly efficient computational chemistry group for small-molecule drug discovery at Bayer HealthCare that has significantly impacted the clinical development pipeline. In this article we describe the setup and tasks of the computational group and discuss external collaborations. We explain what we have found to be the most valuable and productive methods and discuss future directions for computational chemistry method development. We share this information with the hope of igniting interesting discussions around this topic. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

PREFACE: Spin Electronics

NASA Astrophysics Data System (ADS)

Dieny, B.; Sousa, R.; Prejbeanu, L.

2007-04-01

Conventional electronics has in the past ignored the spin on the electron, however things began to change in 1988 with the discovery of giant magnetoresistance in metallic thin film stacks which led to the development of a new research area, so called spin-electronics. In the last 10 years, spin-electronics has achieved a number of breakthroughs from the point of view of both basic science and application. Materials research has led to several major discoveries: very large tunnel magnetoresistance effects in tunnel junctions with crystalline barriers due to a new spin-filtering mechanism associated with the spin-dependent symmetry of the electron wave functions new magnetic tunnelling barriers leading to spin-dependent tunnelling barrier heights and acting as spin-filters magnetic semiconductors with increasingly high ordering temperature. New phenomena have been predicted and observed: the possibility of acting on the magnetization of a magnetic nanostructure with a spin-polarized current. This effect, due to a transfer of angular momentum between the spin polarized conduction electrons and the local magnetization, can be viewed as the reciprocal of giant or tunnel magnetoresistance. It can be used to switch the magnetization of a magnetic nanostructure or to generate steady magnetic excitations in the system. the possibility of generating and manipulating spin current without charge current by creating non-equilibrium local accumulation of spin up or spin down electrons. The range of applications of spin electronics materials and phenomena is expanding: the first devices based on giant magnetoresistance were the magnetoresistive read-heads for computer disk drives. These heads, introduced in 1998 with current-in plane spin-valves, have evolved towards low resistance tunnel magnetoresistice heads in 2005. Besides magnetic recording technology, these very sensitive magnetoresistive sensors are finding applications in other areas, in particular in biology. magnetic tunnel junctions were introduced as memory elements in new types of non-volatile magnetic memories (MRAM). A first 4Mbit product was launched by Freescale in July 2006. Future generations of memories are being developed by academic groups or companies. the combination of magnetic elements with CMOS components opens a whole new paradigm in hybrid electronic components which can change the common conception of the architecture of complex electronic components with a much tighter integration of logic and memory. the steady magnetic excitations stimulated by spin-transfer might be used in a variety of microwave components provided the output power can be increased. Intense research and development efforts are being aimed at increasing this power by the synchronization of oscillators. The articles compiled in this special issue of Journal of Physics: Condensed Matter, devoted to spin electronics, review these recent developments. All the contributors are greatly acknowledged.

Computer-Aided Drug Discovery Approaches against the Tropical Infectious Diseases Malaria, Tuberculosis, Trypanosomiasis, and Leishmaniasis.

PubMed

Njogu, Peter M; Guantai, Eric M; Pavadai, Elumalai; Chibale, Kelly

2016-01-08

Despite the tremendous improvement in overall global health heralded by the adoption of the Millennium Declaration in the year 2000, tropical infections remain a major health problem in the developing world. Recent estimates indicate that the major tropical infectious diseases, namely, malaria, tuberculosis, trypanosomiasis, and leishmaniasis, account for more than 2.2 million deaths and a loss of approximately 85 million disability-adjusted life years annually. The crucial role of chemotherapy in curtailing the deleterious health and economic impacts of these infections has invigorated the search for new drugs against tropical infectious diseases. The research efforts have involved increased application of computational technologies in mainstream drug discovery programs at the hit identification, hit-to-lead, and lead optimization stages. This review highlights various computer-aided drug discovery approaches that have been utilized in efforts to identify novel antimalarial, antitubercular, antitrypanosomal, and antileishmanial agents. The focus is largely on developments over the past 5 years (2010-2014).

Computational medicinal chemistry in fragment-based drug discovery: what, how and when.

PubMed

Rabal, Obdulia; Urbano-Cuadrado, Manuel; Oyarzabal, Julen

2011-01-01

The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure-activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario - what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.

An introduction to web scale discovery systems.

PubMed

Hoy, Matthew B

2012-01-01

This article explores the basic principles of web-scale discovery systems and how they are being implemented in libraries. "Web scale discovery" refers to a class of products that index a vast number of resources in a wide variety formats and allow users to search for content in the physical collection, print and electronic journal collections, and other resources from a single search box. Search results are displayed in a manner similar to Internet searches, in a relevance ranked list with links to online content. The advantages and disadvantages of these systems are discussed, and a list of popular discovery products is provided. A list of library websites with discovery systems currently implemented is also provided.

Towards a privacy preserving cohort discovery framework for clinical research networks.

PubMed

Yuan, Jiawei; Malin, Bradley; Modave, François; Guo, Yi; Hogan, William R; Shenkman, Elizabeth; Bian, Jiang

2017-02-01

The last few years have witnessed an increasing number of clinical research networks (CRNs) focused on building large collections of data from electronic health records (EHRs), claims, and patient-reported outcomes (PROs). Many of these CRNs provide a service for the discovery of research cohorts with various health conditions, which is especially useful for rare diseases. Supporting patient privacy can enhance the scalability and efficiency of such processes; however, current practice mainly relies on policy, such as guidelines defined in the Health Insurance Portability and Accountability Act (HIPAA), which are insufficient for CRNs (e.g., HIPAA does not require encryption of data - which can mitigate insider threats). By combining policy with privacy enhancing technologies we can enhance the trustworthiness of CRNs. The goal of this research is to determine if searchable encryption can instill privacy in CRNs without sacrificing their usability. We developed a technique, implemented in working software to enable privacy-preserving cohort discovery (PPCD) services in large distributed CRNs based on elliptic curve cryptography (ECC). This technique also incorporates a block indexing strategy to improve the performance (in terms of computational running time) of PPCD. We evaluated the PPCD service with three real cohort definitions: (1) elderly cervical cancer patients who underwent radical hysterectomy, (2) oropharyngeal and tongue cancer patients who underwent robotic transoral surgery, and (3) female breast cancer patients who underwent mastectomy) with varied query complexity. These definitions were tested in an encrypted database of 7.1 million records derived from the publically available Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS). We assessed the performance of the PPCD service in terms of (1) accuracy in cohort discovery, (2) computational running time, and (3) privacy afforded to the underlying records during PPCD. The empirical results indicate that the proposed PPCD can execute cohort discovery queries in a reasonable amount of time, with query runtime in the range of 165-262s for the 3 use cases, with zero compromise in accuracy. We further show that the search performance is practical because it supports a highly parallelized design for secure evaluation over encrypted records. Additionally, our security analysis shows that the proposed construction is resilient to standard adversaries. PPCD services can be designed for clinical research networks. The security construction presented in this work specifically achieves high privacy guarantees by preventing both threats originating from within and beyond the network. Copyright © 2016 Elsevier Inc. All rights reserved.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crabtree, George; Glotzer, Sharon; McCurdy, Bill

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. Newmore » 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.« less

Analysing and Rationalising Molecular and Materials Databases Using Machine-Learning

NASA Astrophysics Data System (ADS)

de, Sandip; Ceriotti, Michele

Computational materials design promises to greatly accelerate the process of discovering new or more performant materials. Several collaborative efforts are contributing to this goal by building databases of structures, containing between thousands and millions of distinct hypothetical compounds, whose properties are computed by high-throughput electronic-structure calculations. The complexity and sheer amount of information has made manual exploration, interpretation and maintenance of these databases a formidable challenge, making it necessary to resort to automatic analysis tools. Here we will demonstrate how, starting from a measure of (dis)similarity between database items built from a combination of local environment descriptors, it is possible to apply hierarchical clustering algorithms, as well as dimensionality reduction methods such as sketchmap, to analyse, classify and interpret trends in molecular and materials databases, as well as to detect inconsistencies and errors. Thanks to the agnostic and flexible nature of the underlying metric, we will show how our framework can be applied transparently to different kinds of systems ranging from organic molecules and oligopeptides to inorganic crystal structures as well as molecular crystals. Funded by National Center for Computational Design and Discovery of Novel Materials (MARVEL) and Swiss National Science Foundation.

Density functional theory in the solid state

PubMed Central

Hasnip, Philip J.; Refson, Keith; Probert, Matt I. J.; Yates, Jonathan R.; Clark, Stewart J.; Pickard, Chris J.

2014-01-01

Density functional theory (DFT) has been used in many fields of the physical sciences, but none so successfully as in the solid state. From its origins in condensed matter physics, it has expanded into materials science, high-pressure physics and mineralogy, solid-state chemistry and more, powering entire computational subdisciplines. Modern DFT simulation codes can calculate a vast range of structural, chemical, optical, spectroscopic, elastic, vibrational and thermodynamic phenomena. The ability to predict structure–property relationships has revolutionized experimental fields, such as vibrational and solid-state NMR spectroscopy, where it is the primary method to analyse and interpret experimental spectra. In semiconductor physics, great progress has been made in the electronic structure of bulk and defect states despite the severe challenges presented by the description of excited states. Studies are no longer restricted to known crystallographic structures. DFT is increasingly used as an exploratory tool for materials discovery and computational experiments, culminating in ex nihilo crystal structure prediction, which addresses the long-standing difficult problem of how to predict crystal structure polymorphs from nothing but a specified chemical composition. We present an overview of the capabilities of solid-state DFT simulations in all of these topics, illustrated with recent examples using the CASTEP computer program. PMID:24516184

Systematic review: the applications of nanotechnology in gastroenterology.

PubMed

Brakmane, G; Winslet, M; Seifalian, A M

2012-08-01

Over the past 30 years, nanotechnology has evolved dramatically. It has captured the interest of variety of fields from computing and electronics to biology and medicine. Recent discoveries have made invaluable changes to future prospects in nanomedicine; and introduced the concept of theranostics. This term offers a patient specific 'two in one' modality that comprises of diagnostic and therapeutic tools. Not only nanotechnology has shown great impact on improvements in drug delivery and imaging techniques, but also there have been several ground-breaking discoveries in regenerative medicine. Gastroenterology invites multidisciplinary approach owing to high complexity of gastrointestinal (GI) system; it includes physicians, surgeons, radiologists, pharmacologists and many more. In this article, we concentrate on current developments in nano-gastroenterology. Literature search was performed using Web of Science and Pubmed search engines with terms--nanotechnology, nanomedicine and gastroenterology. Article search was concentrated on developments since 2005. We have described original and innovative approaches in gastrointestinal drug delivery, inflammatory disease and cancer-target treatments. Here, we have reviewed advances in GI imaging using nanoparticles as fluorescent contrast, and their potential for site-specific targeting. This review has also depicted various approaches and novel discoveries in GI regenerative medicine using nanomaterials for scaffold designs and induced pluripotent stem cells as cell source. Developments in nanotechnology have opened new range of possibilities to help our patients. This includes novel drug delivery vehicles, diagnostic tools for early and targeted disease detection and nanocomposite materials for tissue constructs to overcome cosmetic or physical disabilities. © 2012 Blackwell Publishing Ltd.

Phase transformation in tantalum under extreme laser deformation

DOE PAGES

Lu, C. -H.; Hahn, E. N.; Remington, B. A.; ...

2015-10-19

The structural and mechanical response of metals is intimately connected to phase transformations. For instance, the product of a phase transformation (martensite) is responsible for the extraordinary range of strength and toughness of steel, making it a versatile and important structural material. Although abundant in metals and alloys, the discovery of new phase transformations is not currently a common event and often requires a mix of experimentation, predictive computations, and luck. High-energy pulsed lasers enable the exploration of extreme pressures and temperatures, where such discoveries may lie. The formation of a hexagonal (omega) phase was observed in recovered monocrystalline body-centeredmore » cubic tantalum of four crystallographic orientations subjected to an extreme regime of pressure, temperature, and strain-rate. This was accomplished using high-energy pulsed lasers. The omega phase and twinning were identified by transmission electron microscopy at 70 GPa (determined by a corresponding VISAR experiment). It is proposed that the shear stresses generated by the uniaxial strain state of shock compression play an essential role in the transformation. In conclusion, molecular dynamics simulations show the transformation of small nodules from body-centered cubic to a hexagonal close-packed structure under the same stress state (pressure and shear).« less

Phase Transformation in Tantalum under Extreme Laser Deformation

PubMed Central

Lu, C.-H.; Hahn, E. N.; Remington, B. A.; Maddox, B. R.; Bringa, E. M.; Meyers, M. A.

2015-01-01

The structural and mechanical response of metals is intimately connected to phase transformations. For instance, the product of a phase transformation (martensite) is responsible for the extraordinary range of strength and toughness of steel, making it a versatile and important structural material. Although abundant in metals and alloys, the discovery of new phase transformations is not currently a common event and often requires a mix of experimentation, predictive computations, and luck. High-energy pulsed lasers enable the exploration of extreme pressures and temperatures, where such discoveries may lie. The formation of a hexagonal (omega) phase was observed in recovered monocrystalline body-centered cubic tantalum of four crystallographic orientations subjected to an extreme regime of pressure, temperature, and strain-rate. This was accomplished using high-energy pulsed lasers. The omega phase and twinning were identified by transmission electron microscopy at 70 GPa (determined by a corresponding VISAR experiment). It is proposed that the shear stresses generated by the uniaxial strain state of shock compression play an essential role in the transformation. Molecular dynamics simulations show the transformation of small nodules from body-centered cubic to a hexagonal close-packed structure under the same stress state (pressure and shear). PMID:26478106

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Li-Jen

The project has accomplished the following achievements including the goals outlined in the original proposal. Generation and measurements of Debye-scale electron holes in laboratory: We have generated by beam injections electron solitary waves in the LAPD experiments. The measurements were made possible by the fabrication of the state-of-the-art microprobes at UCLA to measure Debye-scale electric fields [Chiang et al., 2011]. We obtained a result that challenged the state of knowledge about electron hole generation. We found that the electron holes were not due to two-stream instability, but generated by a current-driven instability that also generated whistler-mode waves [Lefebvre et al.,more » 2011, 2010b]. Most of the grant supported a young research scientist Bertrand Lefebvre who led the dissemination of the laboratory experimental results. In addition to two publications, our work relevant to the laboratory experiments on electron holes has resulted in 7 invited talks [Chen, 2007, 2009; Pickett et al., 2009a; Lefebvre et al., 2010a; Pickett et al., 2010; Chen et al., 2011c, b] (including those given by the co-I Jolene Pickett) and 2 contributed talks [Lefebvre et al., 2009b, a]. Discovery of elecctron phase-space-hole structure in the reconnection electron layer: Our theoretical analyses and simulations under this project led to the discovery of an inversion electric field layer whose phase-space signature is an electron hole within the electron diffusion layer in 2D anti-parallel reconnection [Chen et al., 2011a]. We carried out particle tracing studies to understand the electron orbits that result in the phase-space hole structure. Most importantly, we showed that the current density in the electron layer is limited in collisionless reconnection with negligible guide field by the cyclotron turning of meandering electrons. Comparison of electrostatic solitary waves in current layers observed by Cluster and in LAPD: We compared the ESWs observed in a supersubstorm by the Cluster spacecraft and those measured in LAPD. One of the similarities in the characteristics of ESWs observed in space and in LAPD is that the time duration tends to be approximately the inverse of the electron plasma frequency [Pickett et al., 2009b]. Discovery of suprathermal electron bursts inside a series of magnetic islands: Our effort in examining the roles of ESWs in reconnection current layers resulted in the serendipitous discovery that was published in Nature Physics. In earth’s magnetosphere, we observed through the measurements from the four Cluster spacecraft, a series of magnetic islands and suprathermal electron bursts within the islands. The islands were identified to be effectively acceleration sites for electrons [Chen et al., 2008, 2009].« less

Applications and advances in electronic-nose technologies

Treesearch

A. D. Wilson; M. Baietto

2009-01-01

Electronic-nose devices have received considerable attention in the field of sensor technology during the past twenty years, largely due to the discovery of numerous applications derived from research in diverse fields of applied sciences. Recent applications of electronic nose technologies have come through advances in sensor design, material improvements, software...

Exploring the Role of Receptor Flexibility in Structure-Based Drug Discovery

PubMed Central

Feixas, Ferran; Lindert, Steffen; Sinko, William; McCammon, J. Andrew

2015-01-01

The proper understanding of biomolecular recognition mechanisms that take place in a drug target is of paramount importance to improve the efficiency of drug discovery and development. The intrinsic dynamic character of proteins has a strong influence on biomolecular recognition mechanisms and models such as conformational selection have been widely used to account for this dynamic association process. However, conformational changes occurring in the receptor prior and upon association with other molecules are diverse and not obvious to predict when only a few structures of the receptor are available. In view of the prominent role of protein flexibility in ligand binding and its implications for drug discovery, it is of great interest to identify receptor conformations that play a major role in biomolecular recognition before starting rational drug design efforts. In this review, we discuss a number of recent advances in computer-aided drug discovery techniques that have been proposed to incorporate receptor flexibility into structure-based drug design. The allowance for receptor flexibility provided by computational techniques such as molecular dynamics simulations or enhanced sampling techniques helps to improve the accuracy of methods used to estimate binding affinities and, thus, such methods can contribute to the discovery of novel drug leads. PMID:24332165

Data Resources for the Computer-Guided Discovery of Bioactive Natural Products.

PubMed

Chen, Ya; de Bruyn Kops, Christina; Kirchmair, Johannes

2017-09-25

Natural products from plants, animals, marine life, fungi, bacteria, and other organisms are an important resource for modern drug discovery. Their biological relevance and structural diversity make natural products good starting points for drug design. Natural product-based drug discovery can benefit greatly from computational approaches, which are a valuable precursor or supplementary method to in vitro testing. We present an overview of 25 virtual and 31 physical natural product libraries that are useful for applications in cheminformatics, in particular virtual screening. The overview includes detailed information about each library, the extent of its structural information, and the overlap between different sources of natural products. In terms of chemical structures, there is a large overlap between freely available and commercial virtual natural product libraries. Of particular interest for drug discovery is that at least ten percent of known natural products are readily purchasable and many more natural products and derivatives are available through on-demand sourcing, extraction and synthesis services. Many of the readily purchasable natural products are of small size and hence of relevance to fragment-based drug discovery. There are also an increasing number of macrocyclic natural products and derivatives becoming available for screening.

Applied metabolomics in drug discovery.

PubMed

Cuperlovic-Culf, M; Culf, A S

2016-08-01

The metabolic profile is a direct signature of phenotype and biochemical activity following any perturbation. Metabolites are small molecules present in a biological system including natural products as well as drugs and their metabolism by-products depending on the biological system studied. Metabolomics can provide activity information about possible novel drugs and drug scaffolds, indicate interesting targets for drug development and suggest binding partners of compounds. Furthermore, metabolomics can be used for the discovery of novel natural products and in drug development. Metabolomics can enhance the discovery and testing of new drugs and provide insight into the on- and off-target effects of drugs. This review focuses primarily on the application of metabolomics in the discovery of active drugs from natural products and the analysis of chemical libraries and the computational analysis of metabolic networks. Metabolomics methodology, both experimental and analytical is fast developing. At the same time, databases of compounds are ever growing with the inclusion of more molecular and spectral information. An increasing number of systems are being represented by very detailed metabolic network models. Combining these experimental and computational tools with high throughput drug testing and drug discovery techniques can provide new promising compounds and leads.

An overview of aldehyde oxidase: an enzyme of emerging importance in novel drug discovery.

PubMed

Rashidi, Mohammad-Reza; Soltani, Somaieh

2017-03-01

Given the rising trend in medicinal chemistry strategy to reduce cytochrome P450-dependent metabolism, aldehyde oxidase (AOX) has recently gained increased attention in drug discovery programs and the number of drug candidates that are metabolized by AOX is steadily growing. Areas covered: Despite the emerging importance of AOX in drug discovery, there are certain major recognized problems associated with AOX-mediated metabolism of drugs. Intra- and inter-species variations in AOX activity, the lack of reliable and predictive animal models using the common experimental animals, and failure in the predictions of in vivo metabolic activity of AOX using traditional in vitro methods are among these issues that are covered in this article. A comprehensive review of computational human AOX (hAOX) related studies are also provided. Expert opinion: Following the recent progress in the stem cell field, the authors recommend the application of organoids technology as an effective tool to solve the fundamental problems associated with the evaluation of AOX in drug discovery. The recent success in resolving the hAOX crystal structure can too be another valuable data source for the study of AOX-catalyzed metabolism of new drug candidates, using computer-aided drug discovery methods.

The Emergence of Organizing Structure in Conceptual Representation

ERIC Educational Resources Information Center

Lake, Brenden M.; Lawrence, Neil D.; Tenenbaum, Joshua B.

2018-01-01

Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form--where form could be a tree, ring, chain, grid, etc. (Kemp & Tenenbaum, 2008). Although this approach…

20 Discoveries that Shaped Our Lives: Century of the Sciences.

ERIC Educational Resources Information Center

Judson, Horace Freeland

1984-01-01

Describes (in separate articles) 20 developments in science, technology, and medicine that were made during the twentieth century and had significant impact on society. They include discoveries related to intelligence tests, plastics, aviation, antibiotics, genetics, evolution, birth control, computers, transistors, DNA, lasers, statistics,…

Data Science and Optimal Learning for Material Discovery and Design

Science.gov Websites

in computation and experimental techniques generating vast arrays of data, without a clear link to experimental and computational data, designing new materials and impacting computational models. This meeting computational and experimental data (c) Analysis of data from probes such as light sources, as well as other

A computer model of context-dependent perception in a very simple world

NASA Astrophysics Data System (ADS)

Lara-Dammer, Francisco; Hofstadter, Douglas R.; Goldstone, Robert L.

2017-11-01

We propose the foundations of a computer model of scientific discovery that takes into account certain psychological aspects of human observation of the world. To this end, we simulate two main components of such a system. The first is a dynamic microworld in which physical events take place, and the second is an observer that visually perceives entities and events in the microworld. For reason of space, this paper focuses only on the starting phase of discovery, which is the relatively simple visual inputs of objects and collisions.

Structure-based drug design: aiming for a perfect fit

PubMed Central

van Montfort, Rob L.M.; Workman, Paul

2017-01-01

Knowledge of the three-dimensional structure of therapeutically relevant targets has informed drug discovery since the first protein structures were determined using X-ray crystallography in the 1950s and 1960s. In this editorial we provide a brief overview of the powerful impact of structure-based drug design (SBDD), which has its roots in computational and structural biology, with major contributions from both academia and industry. We describe advances in the application of SBDD for integral membrane protein targets that have traditionally proved very challenging. We emphasize the major progress made in fragment-based approaches for which success has been exemplified by over 30 clinical drug candidates and importantly three FDA-approved drugs in oncology. We summarize the articles in this issue that provide an excellent snapshot of the current state of the field of SBDD and fragment-based drug design and which offer key insights into exciting new developments, such as the X-ray free-electron laser technology, cryo-electron microscopy, open science approaches and targeted protein degradation. We stress the value of SBDD in the design of high-quality chemical tools that are used to interrogate biology and disease pathology, and to inform target validation. We emphasize the need to maintain the scientific rigour that has been traditionally associated with structural biology and extend this to other methods used in drug discovery. This is particularly important because the quality and robustness of any form of contributory data determines its usefulness in accelerating drug design, and therefore ultimately in providing patient benefit. PMID:29118091

Selected Topics in CVD Diamond Research

NASA Astrophysics Data System (ADS)

Koizumi, Satoshi; Nebel, Christoph E.; Nesladek, Milos

2006-10-01

Since the discovery of Chemical Vapor Deposition (CVD) diamond growth in 1976, the steady scientific progress often resulted in surprising new discoveries and breakthroughs. This brought us to the idea to publish the special issue Selected Topics in CVD Diamond Research in physica status solidi (a), reflecting such advancements and interesting results at the leading edge of diamond research.The present issue summarizes this progress in the CVD diamond field by selecting contributions from several areas such as superconductivity, super-excitonic radiation, quantum computing, bio-functionalization, surface electronic properties, the nature of phosphorus doping, transport properties in high energy detectors, CVD growth and properties of nanocrystalline diamond. In all these directions CVD diamond appears to be very competitive in comparison with other semiconducting materials.As Editors of this special issue, we must admit that the selection is biased by our opinion. Nonetheless, we are sure that each contribution introduces new ideas and results which will improve the understanding of the current level of physics and chemistry of this attractive wide-bandgap semiconductor and which will help to bring it closer to applications.All submissions were invited based on the contributions of the authors to their specific research field. The Feature Articles have the format of topical reviews to give the reader a comprehensive summary. Partially, however, they are written in research paper style to report new results of ongoing research.We hope that this issue will attract the attention of a broad community of scientists and engineers, and that it will facilitate the utilization of diamond in electronic applications and technologies of the future.

Clinical decision support alert malfunctions: analysis and empirically derived taxonomy.

PubMed

Wright, Adam; Ai, Angela; Ash, Joan; Wiesen, Jane F; Hickman, Thu-Trang T; Aaron, Skye; McEvoy, Dustin; Borkowsky, Shane; Dissanayake, Pavithra I; Embi, Peter; Galanter, William; Harper, Jeremy; Kassakian, Steve Z; Ramoni, Rachel; Schreiber, Richard; Sirajuddin, Anwar; Bates, David W; Sittig, Dean F

2018-05-01

To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions. We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions. We analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common. Across organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS. CDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.

Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats

NASA Astrophysics Data System (ADS)

Ghiringhelli, Luca M.; Carbogno, Christian; Levchenko, Sergey; Mohamed, Fawzi; Huhs, Georg; Lüders, Martin; Oliveira, Micael; Scheffler, Matthias

2017-11-01

With big-data driven materials research, the new paradigm of materials science, sharing and wide accessibility of data are becoming crucial aspects. Obviously, a prerequisite for data exchange and big-data analytics is standardization, which means using consistent and unique conventions for, e.g., units, zero base lines, and file formats. There are two main strategies to achieve this goal. One accepts the heterogeneous nature of the community, which comprises scientists from physics, chemistry, bio-physics, and materials science, by complying with the diverse ecosystem of computer codes and thus develops "converters" for the input and output files of all important codes. These converters then translate the data of each code into a standardized, code-independent format. The other strategy is to provide standardized open libraries that code developers can adopt for shaping their inputs, outputs, and restart files, directly into the same code-independent format. In this perspective paper, we present both strategies and argue that they can and should be regarded as complementary, if not even synergetic. The represented appropriate format and conventions were agreed upon by two teams, the Electronic Structure Library (ESL) of the European Center for Atomic and Molecular Computations (CECAM) and the NOvel MAterials Discovery (NOMAD) Laboratory, a European Centre of Excellence (CoE). A key element of this work is the definition of hierarchical metadata describing state-of-the-art electronic-structure calculations.

IDEAS.

ERIC Educational Resources Information Center

Duncan, David R.; Litwiller, Bonnie H.

1986-01-01

Practice on computation is provided using new and novel situations, with discovery of number patterns a bonus of the computation. Activities with disguised addition and multiplication tables for whole numbers and integers are presented for varying levels. (MNS)

The Greatest Mathematical Discovery?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bailey, David H.; Borwein, Jonathan M.

2010-05-12

What mathematical discovery more than 1500 years ago: (1) Is one of the greatest, if not the greatest, single discovery in the field of mathematics? (2) Involved three subtle ideas that eluded the greatest minds of antiquity, even geniuses such as Archimedes? (3) Was fiercely resisted in Europe for hundreds of years after its discovery? (4) Even today, in historical treatments of mathematics, is often dismissed with scant mention, or else is ascribed to the wrong source? Answer: Our modern system of positional decimal notation with zero, together with the basic arithmetic computational schemes, which were discovered in India aboutmore » 500 CE.« less

Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases

PubMed Central

Wooden, Benjamin; Goossens, Nicolas; Hoshida, Yujin; Friedman, Scott L.

2016-01-01

Technologies such as genome sequencing, gene expression profiling, proteomic and metabolomic analyses, electronic medical records, and patient-reported health information have produced large amounts of data, from various populations, cell types, and disorders (big data). However, these data must be integrated and analyzed if they are to produce models or concepts about physiologic function or mechanisms of pathogenesis. Many of these data are available to the public, allowing researchers anywhere to search for markers of specific biologic processes or therapeutic targets for specific diseases or patient types. We review recent advances in the fields of computational and systems biology, and highlight opportunities for researchers to use big data sets in the fields of gastroenterology and hepatology, to complement traditional means of diagnostic and therapeutic discovery. PMID:27773806

Community structure in networks

NASA Astrophysics Data System (ADS)

Newman, Mark

2004-03-01

Many networked systems, including physical, biological, social, and technological networks, appear to contain ``communities'' -- groups of nodes within which connections are dense, but between which they are sparser. The ability to find such communities in an automated fashion could be of considerable use. Communities in a web graph for instance might correspond to sets of web sites dealing with related topics, while communities in a biochemical network or an electronic circuit might correspond to functional units of some kind. We present a number of new methods for community discovery, including methods based on ``betweenness'' measures and methods based on modularity optimization. We also give examples of applications of these methods to both computer-generated and real-world network data, and show how our techniques can be used to shed light on the sometimes dauntingly complex structure of networked systems.

Senary refractory high-entropy alloy HfNbTaTiVZr

DOE PAGES

Gao, Michael C.; Zhang, B.; Yang, S.; ...

2015-09-03

Discovery of new single-phase high-entropy alloys (HEAs) is important to understand HEA formation mechanisms. The present study reports computational design and experimental validation of a senary HEA, HfNbTaTiVZr, in a body-centered cubic structure. The phase diagrams and thermodynamic properties of this senary system were modeled using the CALPHAD method. Its atomic structure and diffusion constants were studied using ab initio molecular dynamics simulations. Here, the microstructure of the as-cast HfNbTaTiVZr alloy was studied using X-ray diffraction and scanning electron microscopy, and the microsegregation in the as-cast state was found to qualitatively agree with the solidification predictions from CALPHAD. Supported bymore » both simulation and experimental results, the HEA formation rules are discussed.« less

Discovery Orbiter Major Modifications

NASA Image and Video Library

2003-08-27

During power-up of the orbiter Discovery in the Orbiter Processing Facility, a technician moves a circuit reset on the cockpit console. Discovery has been undergoing Orbiter Major Modifications in the past year, ranging from wiring, control panels and black boxes to gaseous and fluid systems tubing and components. These systems were deserviced, disassembled, inspected, modified, reassembled, checked out and reserviced, as were most other systems onboard. The work includes the installation of the Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.”

Detection of Chorus Elements and other Wave Signatures Using Geometric Computational Techniques in the Van Allen radiation belts

NASA Astrophysics Data System (ADS)

Sengupta, A.; Kletzing, C.; Howk, R.; Kurth, W. S.

2017-12-01

An important goal of the Van Allen Probes mission is to understand wave particle interactions that can energize relativistic electron in the Earth's Van Allen radiation belts. The EMFISIS instrumentation suite provides measurements of wave electric and magnetic fields of wave features such as chorus that participate in these interactions. Geometric signal processing discovers structural relationships, e.g. connectivity across ridge-like features in chorus elements to reveal properties such as dominant angles of the element (frequency sweep rate) and integrated power along the a given chorus element. These techniques disambiguate these wave features against background hiss-like chorus. This enables autonomous discovery of chorus elements across the large volumes of EMFISIS data. At the scale of individual or overlapping chorus elements, topological pattern recognition techniques enable interpretation of chorus microstructure by discovering connectivity and other geometric features within the wave signature of a single chorus element or between overlapping chorus elements. Thus chorus wave features can be quantified and studied at multiple scales of spectral geometry using geometric signal processing techniques. We present recently developed computational techniques that exploit spectral geometry of chorus elements and whistlers to enable large-scale automated discovery, detection and statistical analysis of these events over EMFISIS data. Specifically, we present different case studies across a diverse portfolio of chorus elements and discuss the performance of our algorithms regarding precision of detection as well as interpretation of chorus microstructure. We also provide large-scale statistical analysis on the distribution of dominant sweep rates and other properties of the detected chorus elements.

Don't Hit that "Delete" Button!

ERIC Educational Resources Information Center

O'Hanlon, Charlene

2009-01-01

On Dec. 1, 2006, the once ambiguous role of e-mails in court cases became much more clear. On that day, the Federal Rules of Civil Procedure (FRCP), which govern federal civil litigation, were amended to establish standards for the discovery of electronically stored information, now known as e-discovery. Many corporations began moving quickly to…

KENNEDY SPACE CENTER, FLA. - During power-up of the orbiter Discovery in the Orbiter Processing Facility, a technician (left) looks at the circuit breaker lights in the cabin. Discovery has been undergoing Orbiter Major Modifications in the past year, ranging from wiring, control panels and black boxes to gaseous and fluid systems tubing and components. These systems were deserviced, disassembled, inspected, modified, reassembled, checked out and reserviced, as were most other systems onboard. The work includes the installation of the Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.”

NASA Image and Video Library

2003-08-27

KENNEDY SPACE CENTER, FLA. - During power-up of the orbiter Discovery in the Orbiter Processing Facility, a technician (left) looks at the circuit breaker lights in the cabin. Discovery has been undergoing Orbiter Major Modifications in the past year, ranging from wiring, control panels and black boxes to gaseous and fluid systems tubing and components. These systems were deserviced, disassembled, inspected, modified, reassembled, checked out and reserviced, as were most other systems onboard. The work includes the installation of the Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.”

Inference and Discovery in an Exploratory Laboratory. Technical Report No. 10.

ERIC Educational Resources Information Center

Shute, Valerie; And Others

This paper describes the results of a study done as part of a research program investigating the use of computer-based laboratories to support self-paced discovery learning in related to microeconomics, electricity, and light refraction. Program objectives include maximizing the laboratories' effectiveness in helping students learn content…

Astronaut Jan Davis monitors Commercial Protein Crystal Growth experiment

NASA Image and Video Library

1994-02-03

STS060-21-031 (3-11 Feb 1994) --- Using a lap top computer, astronaut N. Jan Davis monitors systems for the Commercial Protein Crystal Growth (CPCG) experiment onboard the Space Shuttle Discovery. Davis joined four other NASA astronauts and a Russian cosmonaut for eight days in space aboard Discovery.

Current status and future prospects for enabling chemistry technology in the drug discovery process.

PubMed

Djuric, Stevan W; Hutchins, Charles W; Talaty, Nari N

2016-01-01

This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of "dangerous" reagents. Also featured are advances in the "computer-assisted drug design" area and the expanding application of novel mass spectrometry-based techniques to a wide range of drug discovery activities.

How Effective Is Instructional Support for Learning with Computer Simulations?

ERIC Educational Resources Information Center

Eckhardt, Marc; Urhahne, Detlef; Conrad, Olaf; Harms, Ute

2013-01-01

The study examined the effects of two different instructional interventions as support for scientific discovery learning using computer simulations. In two well-known categories of difficulty, data interpretation and self-regulation, instructional interventions for learning with computer simulations on the topic "ecosystem water" were developed…

Computational Design and Discovery of Ni-Based Alloys and Coatings: Thermodynamic Approaches Validated by Experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Zi-Kui; Gleeson, Brian; Shang, Shunli

This project developed computational tools that can complement and support experimental efforts in order to enable discovery and more efficient development of Ni-base structural materials and coatings. The project goal was reached through an integrated computation-predictive and experimental-validation approach, including first-principles calculations, thermodynamic CALPHAD (CALculation of PHAse Diagram), and experimental investigations on compositions relevant to Ni-base superalloys and coatings in terms of oxide layer growth and microstructure stabilities. The developed description included composition ranges typical for coating alloys and, hence, allow for prediction of thermodynamic properties for these material systems. The calculation of phase compositions, phase fraction, and phase stabilities,more » which are directly related to properties such as ductility and strength, was a valuable contribution, along with the collection of computational tools that are required to meet the increasing demands for strong, ductile and environmentally-protective coatings. Specifically, a suitable thermodynamic description for the Ni-Al-Cr-Co-Si-Hf-Y system was developed for bulk alloy and coating compositions. Experiments were performed to validate and refine the thermodynamics from the CALPHAD modeling approach. Additionally, alloys produced using predictions from the current computational models were studied in terms of their oxidation performance. Finally, results obtained from experiments aided in the development of a thermodynamic modeling automation tool called ESPEI/pycalphad - for more rapid discovery and development of new materials.« less

12 CFR 1209.30 - Request for document discovery from parties.

Code of Federal Regulations, 2013 CFR

2013-01-01

..., photocopying (as specified in paragraph (b)(4) of this section) or electronic processing (as specified in... under the Freedom of Information Act, 5 U.S.C. 552. (5) Electronic processing. In the event that any... the presiding officer under § 1209.29, costs associated with the processing of such electronic...

12 CFR 1209.30 - Request for document discovery from parties.

Code of Federal Regulations, 2012 CFR

2012-01-01

..., photocopying (as specified in paragraph (b)(4) of this section) or electronic processing (as specified in... under the Freedom of Information Act, 5 U.S.C. 552. (5) Electronic processing. In the event that any... the presiding officer under § 1209.29, costs associated with the processing of such electronic...

12 CFR 1209.30 - Request for document discovery from parties.

Code of Federal Regulations, 2014 CFR

2014-01-01

..., photocopying (as specified in paragraph (b)(4) of this section) or electronic processing (as specified in... under the Freedom of Information Act, 5 U.S.C. 552. (5) Electronic processing. In the event that any... the presiding officer under § 1209.29, costs associated with the processing of such electronic...

Rational design of an enzyme mutant for anti-cocaine therapeutics

NASA Astrophysics Data System (ADS)

Zheng, Fang; Zhan, Chang-Guo

2008-09-01

(-)-Cocaine is a widely abused drug and there is no available anti-cocaine therapeutic. The disastrous medical and social consequences of cocaine addiction have made the development of an effective pharmacological treatment a high priority. An ideal anti-cocaine medication would be to accelerate (-)-cocaine metabolism producing biologically inactive metabolites. The main metabolic pathway of cocaine in body is the hydrolysis at its benzoyl ester group. Reviewed in this article is the state-of-the-art computational design of high-activity mutants of human butyrylcholinesterase (BChE) against (-)-cocaine. The computational design of BChE mutants have been based on not only the structure of the enzyme, but also the detailed catalytic mechanisms for BChE-catalyzed hydrolysis of (-)-cocaine and (+)-cocaine. Computational studies of the detailed catalytic mechanisms and the structure-and-mechanism-based computational design have been carried out through the combined use of a variety of state-of-the-art techniques of molecular modeling. By using the computational insights into the catalytic mechanisms, a recently developed unique computational design strategy based on the simulation of the rate-determining transition state has been employed to design high-activity mutants of human BChE for hydrolysis of (-)-cocaine, leading to the exciting discovery of BChE mutants with a considerably improved catalytic efficiency against (-)-cocaine. One of the discovered BChE mutants (i.e., A199S/S287G/A328W/Y332G) has a ˜456-fold improved catalytic efficiency against (-)-cocaine. The encouraging outcome of the computational design and discovery effort demonstrates that the unique computational design approach based on the transition-state simulation is promising for rational enzyme redesign and drug discovery.

Dissimilatory Reduction of Extracellular Electron Acceptors in Anaerobic Respiration

PubMed Central

Richter, Katrin; Schicklberger, Marcus

2012-01-01

An extension of the respiratory chain to the cell surface is necessary to reduce extracellular electron acceptors like ferric iron or manganese oxides. In the past few years, more and more compounds were revealed to be reduced at the surface of the outer membrane of Gram-negative bacteria, and the list does not seem to have an end so far. Shewanella as well as Geobacter strains are model organisms to discover the biochemistry that enables the dissimilatory reduction of extracellular electron acceptors. In both cases, c-type cytochromes are essential electron-transferring proteins. They make the journey of respiratory electrons from the cytoplasmic membrane through periplasm and over the outer membrane possible. Outer membrane cytochromes have the ability to catalyze the last step of the respiratory chains. Still, recent discoveries provided evidence that they are accompanied by further factors that allow or at least facilitate extracellular reduction. This review gives a condensed overview of our current knowledge of extracellular respiration, highlights recent discoveries, and discusses critically the influence of different strategies for terminal electron transfer reactions. PMID:22179232

OpenZika: An IBM World Community Grid Project to Accelerate Zika Virus Drug Discovery

PubMed Central

Perryman, Alexander L.; Horta Andrade, Carolina

2016-01-01

The Zika virus outbreak in the Americas has caused global concern. To help accelerate this fight against Zika, we launched the OpenZika project. OpenZika is an IBM World Community Grid Project that uses distributed computing on millions of computers and Android devices to run docking experiments, in order to dock tens of millions of drug-like compounds against crystal structures and homology models of Zika proteins (and other related flavivirus targets). This will enable the identification of new candidates that can then be tested in vitro, to advance the discovery and development of new antiviral drugs against the Zika virus. The docking data is being made openly accessible so that all members of the global research community can use it to further advance drug discovery studies against Zika and other related flaviviruses. PMID:27764115

OpenZika: An IBM World Community Grid Project to Accelerate Zika Virus Drug Discovery.

PubMed

Ekins, Sean; Perryman, Alexander L; Horta Andrade, Carolina

2016-10-01

The Zika virus outbreak in the Americas has caused global concern. To help accelerate this fight against Zika, we launched the OpenZika project. OpenZika is an IBM World Community Grid Project that uses distributed computing on millions of computers and Android devices to run docking experiments, in order to dock tens of millions of drug-like compounds against crystal structures and homology models of Zika proteins (and other related flavivirus targets). This will enable the identification of new candidates that can then be tested in vitro, to advance the discovery and development of new antiviral drugs against the Zika virus. The docking data is being made openly accessible so that all members of the global research community can use it to further advance drug discovery studies against Zika and other related flaviviruses.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klitsner, Tom

The recent Executive Order creating the National Strategic Computing Initiative (NSCI) recognizes the value of high performance computing for economic competitiveness and scientific discovery and commits to accelerate delivery of exascale computing. The HPC programs at Sandia –the NNSA ASC program and Sandia’s Institutional HPC Program– are focused on ensuring that Sandia has the resources necessary to deliver computation in the national interest.

Network-based approaches to climate knowledge discovery

NASA Astrophysics Data System (ADS)

Budich, Reinhard; Nyberg, Per; Weigel, Tobias

2011-11-01

Climate Knowledge Discovery Workshop; Hamburg, Germany, 30 March to 1 April 2011 Do complex networks combined with semantic Web technologies offer the next generation of solutions in climate science? To address this question, a first Climate Knowledge Discovery (CKD) Workshop, hosted by the German Climate Computing Center (Deutsches Klimarechenzentrum (DKRZ)), brought together climate and computer scientists from major American and European laboratories, data centers, and universities, as well as representatives from industry, the broader academic community, and the semantic Web communities. The participants, representing six countries, were concerned with large-scale Earth system modeling and computational data analysis. The motivation for the meeting was the growing problem that climate scientists generate data faster than it can be interpreted and the need to prepare for further exponential data increases. Current analysis approaches are focused primarily on traditional methods, which are best suited for large-scale phenomena and coarse-resolution data sets. The workshop focused on the open discussion of ideas and technologies to provide the next generation of solutions to cope with the increasing data volumes in climate science.

Computer-Aided Discovery Tools for Volcano Deformation Studies with InSAR and GPS

NASA Astrophysics Data System (ADS)

Pankratius, V.; Pilewskie, J.; Rude, C. M.; Li, J. D.; Gowanlock, M.; Bechor, N.; Herring, T.; Wauthier, C.

2016-12-01

We present a Computer-Aided Discovery approach that facilitates the cloud-scalable fusion of different data sources, such as GPS time series and Interferometric Synthetic Aperture Radar (InSAR), for the purpose of identifying the expansion centers and deformation styles of volcanoes. The tools currently developed at MIT allow the definition of alternatives for data processing pipelines that use various analysis algorithms. The Computer-Aided Discovery system automatically generates algorithmic and parameter variants to help researchers explore multidimensional data processing search spaces efficiently. We present first application examples of this technique using GPS data on volcanoes on the Aleutian Islands and work in progress on combined GPS and InSAR data in Hawaii. In the model search context, we also illustrate work in progress combining time series Principal Component Analysis with InSAR augmentation to constrain the space of possible model explanations on current empirical data sets and achieve a better identification of deformation patterns. This work is supported by NASA AIST-NNX15AG84G and NSF ACI-1442997 (PI: V. Pankratius).

Role of Open Source Tools and Resources in Virtual Screening for Drug Discovery.

PubMed

Karthikeyan, Muthukumarasamy; Vyas, Renu

2015-01-01

Advancement in chemoinformatics research in parallel with availability of high performance computing platform has made handling of large scale multi-dimensional scientific data for high throughput drug discovery easier. In this study we have explored publicly available molecular databases with the help of open-source based integrated in-house molecular informatics tools for virtual screening. The virtual screening literature for past decade has been extensively investigated and thoroughly analyzed to reveal interesting patterns with respect to the drug, target, scaffold and disease space. The review also focuses on the integrated chemoinformatics tools that are capable of harvesting chemical data from textual literature information and transform them into truly computable chemical structures, identification of unique fragments and scaffolds from a class of compounds, automatic generation of focused virtual libraries, computation of molecular descriptors for structure-activity relationship studies, application of conventional filters used in lead discovery along with in-house developed exhaustive PTC (Pharmacophore, Toxicophores and Chemophores) filters and machine learning tools for the design of potential disease specific inhibitors. A case study on kinase inhibitors is provided as an example.

X-ray generation using carbon nanotubes

NASA Astrophysics Data System (ADS)

Parmee, Richard J.; Collins, Clare M.; Milne, William I.; Cole, Matthew T.

2015-01-01

Since the discovery of X-rays over a century ago the techniques applied to the engineering of X-ray sources have remained relatively unchanged. From the inception of thermionic electron sources, which, due to simplicity of fabrication, remain central to almost all X-ray applications, there have been few fundamental technological advances. However, with the emergence of ever more demanding medical and inspection techniques, including computed tomography and tomosynthesis, security inspection, high throughput manufacturing and radiotherapy, has resulted in a considerable level of interest in the development of new fabrication methods. The use of conventional thermionic sources is limited by their slow temporal response and large physical size. In response, field electron emission has emerged as a promising alternative means of deriving a highly controllable electron beam of a well-defined distribution. When coupled to the burgeoning field of nanomaterials, and in particular, carbon nanotubes, such systems present a unique technological opportunity. This review provides a summary of the current state-of-the-art in carbon nanotube-based field emission X-ray sources. We detail the various fabrication techniques and functional advantages associated with their use, including the ability to produce ever smaller electron beam assembles, shaped cathodes, enhanced temporal stability and emergent fast-switching pulsed sources. We conclude with an overview of some of the commercial progress made towards the realisation of an innovative and disruptive technology.

Towards a complete Fermi surface in underdoped high Tc superconductors

NASA Astrophysics Data System (ADS)

Harrison, Neil

The discovery of magnetic quantum oscillations in underdoped high Tc superconductors raised many questions, and initiated a quest to understand the origin of the Fermi surface the like of which had not been seen since the very first discovery of quantum oscillations in elemental bismuth. While studies of the Fermi surface of materials are today mostly assisted by computer codes for calculating the electronic band structure, this was not the case in the underdoped high Tc materials. The Fermi surface was shown to reconstructed into small pockets, yet there was no hint of a viable order parameter. Crucial clues to understanding the origin of the Fermi surface were provided by the small value of the observed Fermi surface cross-section, the negative Hall coefficient and the small electronic heat capacity at high magnetic fields. We also know that the magnetic fields were likely to be too weak to destroy the pseudogap and that vortex pinning effects could be seen to persist to high magnetic fields at low temperatures. I will show that the Fermi surface that appears to fit best with the experimental observations is a small electron pocket formed by connecting the nodal `Fermi arcs' seen in photoemission experiments, corresponding to a density-wave state with two different orthogonal ordering vectors. The existence of such order has subsequently been detected by x-ray scattering experiments, thereby strengthening the case for charge ordering being responsible for reconstructing the Fermi surface. I will discuss new efforts to understand the relationship between the charge ordering and the pseudogap state, discussing the fate of the quasiparticles in the antinodal region and the dimensionality of the Fermi surface. The author acknowledges contributions from Suchitra Sebastian, Brad Ramshaw, Mun Chan, Yu-Te Hsu, Mate Hartstein, Gil Lonzarich, Beng Tan, Arkady Shekhter, Fedor Balakirev, Ross McDonald, Jon Betts, Moaz Altarawneh, Zengwei Zhu, Chuck Mielke, James Day, Doug Bonn, Ruixing Liang, Walter Hardy. Supported by BES ``Science of 100 tesla'' program.

Epistemic Gameplay and Discovery in Computational Model-Based Inquiry Activities

ERIC Educational Resources Information Center

Wilkerson, Michelle Hoda; Shareff, Rebecca; Laina, Vasiliki; Gravel, Brian

2018-01-01

In computational modeling activities, learners are expected to discover the inner workings of scientific and mathematical systems: First elaborating their understandings of a given system through constructing a computer model, then "debugging" that knowledge by testing and refining the model. While such activities have been shown to…

The Experimental Mathematician: The Pleasure of Discovery and the Role of Proof

ERIC Educational Resources Information Center

Borwein, Jonathan M.

2005-01-01

The emergence of powerful mathematical computing environments, the growing availability of correspondingly powerful (multi-processor) computers and the pervasive presence of the Internet allow for mathematicians, students and teachers, to proceed heuristically and "quasi-inductively." We may increasingly use symbolic and numeric computation,…

MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development | Office of Cancer Genomics

Cancer.gov

Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology.

Current status and future prospects for enabling chemistry technology in the drug discovery process

PubMed Central

Djuric, Stevan W.; Hutchins, Charles W.; Talaty, Nari N.

2016-01-01

This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of “dangerous” reagents. Also featured are advances in the “computer-assisted drug design” area and the expanding application of novel mass spectrometry-based techniques to a wide range of drug discovery activities. PMID:27781094

Giant magnetic anisotropy of heavy p-elements on high-symmetry substrates: a new paradigm for supported nanostructures

NASA Astrophysics Data System (ADS)

Pang, Rui; Deng, Bei; Shi, Xingqiang; Zheng, Xiaohong

2018-04-01

Nanostructures with giant magnetic anisotropy energies (MAEs) are desired in designing miniaturized magnetic storage and quantum computing devices. Previous works focused mainly on materials or elements with d electrons. Here, by taking Bi–X(X = In, Tl, Ge, Sn, Pb) adsorbed on nitrogenized divacancy of graphene and Bi atoms adsorbed on MgO(100) as examples, through ab initio and model calculations, we propose that special p-element dimers and single-adatoms on symmetry-matched substrates possess giant atomic MAEs of 72–200 meV, and has room temperature structural stability. The huge MAEs originate from the p-orbital degeneracy around the Fermi level in a symmetry-matched surface ligand field and the lifting of this degeneracy when spin–orbit interaction (SOI) is taken into account. Especially, we developed a simplified quantum mechanical model for the design principles of giant MAEs of supported magnetic adatoms and dimers. Thus, our discoveries and mechanisms provide a new paradigm to design giant atomic MAE of p electrons in supported nanostructures.

The Biomedical Resource Ontology (BRO) to Enable Resource Discovery in Clinical and Translational Research

PubMed Central

Tenenbaum, Jessica D.; Whetzel, Patricia L.; Anderson, Kent; Borromeo, Charles D.; Dinov, Ivo D.; Gabriel, Davera; Kirschner, Beth; Mirel, Barbara; Morris, Tim; Noy, Natasha; Nyulas, Csongor; Rubenson, David; Saxman, Paul R.; Singh, Harpreet; Whelan, Nancy; Wright, Zach; Athey, Brian D.; Becich, Michael J.; Ginsburg, Geoffrey S.; Musen, Mark A.; Smith, Kevin A.; Tarantal, Alice F.; Rubin, Daniel L; Lyster, Peter

2010-01-01

The biomedical research community relies on a diverse set of resources, both within their own institutions and at other research centers. In addition, an increasing number of shared electronic resources have been developed. Without effective means to locate and query these resources, it is challenging, if not impossible, for investigators to be aware of the myriad resources available, or to effectively perform resource discovery when the need arises. In this paper, we describe the development and use of the Biomedical Resource Ontology (BRO) to enable semantic annotation and discovery of biomedical resources. We also describe the Resource Discovery System (RDS) which is a federated, inter-institutional pilot project that uses the BRO to facilitate resource discovery on the Internet. Through the RDS framework and its associated Biositemaps infrastructure, the BRO facilitates semantic search and discovery of biomedical resources, breaking down barriers and streamlining scientific research that will improve human health. PMID:20955817

Essential Skills and Knowledge for Troubleshooting E-Resources Access Issues in a Web-Scale Discovery Environment

ERIC Educational Resources Information Center

Carter, Sunshine; Traill, Stacie

2017-01-01

Electronic resource access troubleshooting is familiar work in most libraries. The added complexity introduced when a library implements a web-scale discovery service, however, creates a strong need for well-organized, rigorous training to enable troubleshooting staff to provide the best service possible. This article outlines strategies, tools,…

Regiospecific Epoxidation of Carvone: A Discovery-Oriented Experiment for Understanding the Selectivity and Mechanism of Epoxidation Reactions

ERIC Educational Resources Information Center

Mak, Kendrew K. W.; Lai, Y. M.; Siu, Yuk-Hong

2006-01-01

This article describes a discovery-oriented experiment for demonstrating the selectivity of two epoxidation reactions. Peroxy acids and alkaline H[subscript 2]O[subscript 2] are two commonly used reagents for alkene epoxidation. The former react preferentially with electron-rich alkenes while the latter works better with alpha,beta-unsaturated…

VirusDetect: An automated pipeline for efficient virus discovery using deep sequencing of small RNAs

USDA-ARS?s Scientific Manuscript database

Accurate detection of viruses in plants and animals is critical for agriculture production and human health. Deep sequencing and assembly of virus-derived siRNAs has proven to be a highly efficient approach for virus discovery. However, to date no computational tools specifically designed for both k...

Discovery '84: Technology for Disabled Persons. Conference Papers (Chicago, Illinois, October 1-3, 1984).

ERIC Educational Resources Information Center

Smith, Christopher, Ed.

Thirty-nine papers from the conference "Discovery '84: Technology for Disabled Persons" are presented. The conference was intended to provide an overview of the areas in which technological advances have been made, including the applications of computers and other related products and services. Conference presenters represented fields of…

Triple Scheme of Learning Support Design for Scientific Discovery Learning Based on Computer Simulation: Experimental Research

ERIC Educational Resources Information Center

Zhang, Jianwei; Chen, Qi; Sun, Yanquing; Reid, David J.

2004-01-01

Learning support studies involving simulation-based scientific discovery learning have tended to adopt an ad hoc strategies-oriented approach in which the support strategies are typically pre-specified according to learners' difficulties in particular activities. This article proposes a more integrated approach, a triple scheme for learning…

The Double-Edged Sword of Pedagogy: Instruction Limits Spontaneous Exploration and Discovery

ERIC Educational Resources Information Center

Bonawitz, Elizabeth; Shafto, Patrick; Gweon, Hyowon; Goodman, Noah D.; Spelke, Elizabeth; Schulz, Laura

2011-01-01

Motivated by computational analyses, we look at how teaching affects exploration and discovery. In Experiment 1, we investigated children's exploratory play after an adult pedagogically demonstrated a function of a toy, after an interrupted pedagogical demonstration, after a naive adult demonstrated the function, and at baseline. Preschoolers in…

Applications and Methods Utilizing the Simple Semantic Web Architecture and Protocol (SSWAP) for Bioinformatics Resource Discovery and Disparate Data and Service Integration

USDA-ARS?s Scientific Manuscript database

Scientific data integration and computational service discovery are challenges for the bioinformatic community. This process is made more difficult by the separate and independent construction of biological databases, which makes the exchange of scientific data between information resources difficu...

Teaching Tip: Using Rapid Game Prototyping for Exploring Requirements Discovery and Modeling

ERIC Educational Resources Information Center

Dalal, Nikunj

2012-01-01

We describe the use of rapid game prototyping as a pedagogic technique to experientially explore and learn requirements discovery, modeling, and specification in systems analysis and design courses. Students have a natural interest in gaming that transcends age, gender, and background. Rapid digital game creation is used to build computer games…

Simulation with quantum mechanics/molecular mechanics for drug discovery.

PubMed

Barbault, Florent; Maurel, François

2015-10-01

Biological macromolecules, such as proteins or nucleic acids, are (still) molecules and thus they follow the same chemical rules that any simple molecule follows, even if their size generally renders accurate studies unhelpful. However, in the context of drug discovery, a detailed analysis of ligand association is required for understanding or predicting their interactions and hybrid quantum mechanics/molecular mechanics (QM/MM) computations are relevant tools to help elucidate this process. In this review, the authors explore the use of QM/MM for drug discovery. After a brief description of the molecular mechanics (MM) technique, the authors describe the subtractive and additive techniques for QM/MM computations. The authors then present several application cases in topics involved in drug discovery. QM/MM have been widely employed during the last decades to study chemical processes such as enzyme-inhibitor interactions. However, despite the enthusiasm around this area, plain MM simulations may be more meaningful than QM/MM. To obtain reliable results, the authors suggest fixing several keystone parameters according to the underlying chemistry of each studied system.

Simulation with quantum mechanics/molecular mechanics for drug discovery.

PubMed

Barbault, Florent; Maurel, François

2015-08-08

Biological macromolecules, such as proteins or nucleic acids, are (still) molecules and thus they follow the same chemical rules that any simple molecule follows, even if their size generally renders accurate studies unhelpful. However, in the context of drug discovery, a detailed analysis of ligand association is required for understanding or predicting their interactions and hybrid quantum mechanics/molecular mechanics (QM/MM) computations are relevant tools to help elucidate this process. Areas covered: In this review, the authors explore the use of QM/MM for drug discovery. After a brief description of the molecular mechanics (MM) technique, the authors describe the subtractive and additive techniques for QM/MM computations. The authors then present several application cases in topics involved in drug discovery. Expert opinion: QM/MM have been widely employed during the last decades to study chemical processes such as enzyme-inhibitor interactions. However, despite the enthusiasm around this area, plain MM simulations may be more meaningful than QM/MM. To obtain reliable results, the authors suggest fixing several keystone parameters according to the underlying chemistry of each studied system.

Bioinformatics in translational drug discovery.

PubMed

Wooller, Sarah K; Benstead-Hume, Graeme; Chen, Xiangrong; Ali, Yusuf; Pearl, Frances M G

2017-08-31

Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications. © 2017 The Author(s).

Designer drugs: the evolving science of drug discovery.

PubMed

Wanke, L A; DuBose, R F

1998-07-01

Drug discovery and design are fundamental to drug development. Until recently, most drugs were discovered through random screening or developed through molecular modification. New technologies are revolutionizing this phase of drug development. Rational drug design, using powerful computers and computational chemistry and employing X-ray crystallography, nuclear magnetic resonance spectroscopy, and three-dimensional quantitative structure activity relationship analysis, is creating highly specific, biologically active molecules by virtual reality modeling. Sophisticated screening technologies are eliminating all but the most active lead compounds. These new technologies promise more efficacious, safe, and cost-effective medications, while minimizing drug development time and maximizing profits.

Computational modeling approaches to quantitative structure-binding kinetics relationships in drug discovery.

PubMed

De Benedetti, Pier G; Fanelli, Francesca

2018-03-21

Simple comparative correlation analyses and quantitative structure-kinetics relationship (QSKR) models highlight the interplay of kinetic rates and binding affinity as an essential feature in drug design and discovery. The choice of the molecular series, and their structural variations, used in QSKR modeling is fundamental to understanding the mechanistic implications of ligand and/or drug-target binding and/or unbinding processes. Here, we discuss the implications of linear correlations between kinetic rates and binding affinity constants and the relevance of the computational approaches to QSKR modeling. Copyright © 2018 Elsevier Ltd. All rights reserved.

Computational Discovery of New Materials Under Pressure

NASA Astrophysics Data System (ADS)

Zurek, Eva

The pressure variable opens the door towards the synthesis of materials with unique properties, ie. superconductivity, hydrogen storage media, high-energy density and superhard materials, to name a few. Indeed, recently superconductivity has been observed below 203 K and 103 K in samples of compressed sulfur dihydride and phosphine, respectively. Under pressure elements that would not normally combine may form stable compounds, or may mix in novel proportions. As a result using our chemical intuition developed at 1 atm to theoretically predict stable phases is bound to fail. In order to enable our search for superconducting hydrogen-rich systems under pressure, we have developed XtalOpt, an open-source evolutionary algorithm for crystal structure prediction. New advances in XtalOpt that enable the prediction of unit cells with greater complexity will be described. XtalOpt has been employed to find the most stable structures of hydrides with unique stoichiometries under pressure. The electronic structure and bonding of the predicted phases has been analyzed by detailed first-principles calculations based on density functional theory. The results of our computational experiments are helping us to build chemical and physical intuition for compressed solids.

High Energy Cosmic Electrons: Messengers from Nearby Cosmic Ray Sources or Dark Matter?

NASA Technical Reports Server (NTRS)

Moiseev, Alexander

2011-01-01

This slide presentation reviews the recent discoveries by the Large Area Telescope (LAT) and the Gamma-ray Burst Monitor (GBM) on board the Fermi Gamma-Ray Telescope in reference to high energy cosmic electrons, and whether their source is cosmic rays or dark matter. Specific interest is devoted to Cosmic Ray electrons anisotropy,

Connecting Print and Electronic Titles: An Integrated Approach at the University of Nebraska-Lincoln

ERIC Educational Resources Information Center

Wolfe, Judith; Konecky, Joan Latta; Boden, Dana W. R.

2011-01-01

Libraries make heavy investments in electronic resources, with many of these resources reflecting title changes, bundled subsets, or content changes of formerly print material. These changes can distance the electronic format from its print origins, creating discovery and access issues. A task force was formed to explore the enhancement of catalog…

Multiscale computing.

PubMed

Kobayashi, M; Irino, T; Sweldens, W

2001-10-23

Multiscale computing (MSC) involves the computation, manipulation, and analysis of information at different resolution levels. Widespread use of MSC algorithms and the discovery of important relationships between different approaches to implementation were catalyzed, in part, by the recent interest in wavelets. We present two examples that demonstrate how MSC can help scientists understand complex data. The first is from acoustical signal processing and the second is from computer graphics.

View of the shuttle orbiter Discovery's payload bay during RMS checkout

NASA Image and Video Library

1997-02-12

S82-E-5014 (12 Feb. 1997) --- Space Shuttle Discovery's Remote Manipulator System (RMS) gets a preliminary workout in preparation for a busy work load later in the week. The crewmembers are preparing for a scheduled Extravehicular Activity (EVA) with the Hubble Space Telescope (HST), which will be pulled into the Space Shuttle Discovery's cargo bay with the aid of the Remote Manipulator System (RMS). A series of EVA's will be required to properly service the giant telescope. This view was taken with an Electronic Still Camera (ESC).

Equation of motion coupled cluster methods for electron attachment and ionization potential in fullerenes C60 and C70

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bhaskaran-Nair, Kiran; Kowalski, Karol; Moreno, Juana

Discovery of fullerenes has opened a entirely new chapter in chemistry due to their wide range of properties which holds exciting applications in numerous disciplines of science. The Nobel Prize in Chemistry 1996 was awarded jointly to Robert F. Curl Jr., Sir Harold W. Kroto and Richard E. Smalley in recoginition for their discovery of this new carbon allotrope. In this letter we are reporting ionization potential and electron attachment studies on fullerenes (C60 and C70) obtained with novel parallel implementation of the EA-EOM-CCSD and IP-EOM-CCSD methods in NWChem program package.

Advancing Drug Discovery through Enhanced Free Energy Calculations.

PubMed

Abel, Robert; Wang, Lingle; Harder, Edward D; Berne, B J; Friesner, Richard A

2017-07-18

A principal goal of drug discovery project is to design molecules that can tightly and selectively bind to the target protein receptor. Accurate prediction of protein-ligand binding free energies is therefore of central importance in computational chemistry and computer aided drug design. Multiple recent improvements in computing power, classical force field accuracy, enhanced sampling methods, and simulation setup have enabled accurate and reliable calculations of protein-ligands binding free energies, and position free energy calculations to play a guiding role in small molecule drug discovery. In this Account, we outline the relevant methodological advances, including the REST2 (Replica Exchange with Solute Temperting) enhanced sampling, the incorporation of REST2 sampling with convential FEP (Free Energy Perturbation) through FEP/REST, the OPLS3 force field, and the advanced simulation setup that constitute our FEP+ approach, followed by the presentation of extensive comparisons with experiment, demonstrating sufficient accuracy in potency prediction (better than 1 kcal/mol) to substantially impact lead optimization campaigns. The limitations of the current FEP+ implementation and best practices in drug discovery applications are also discussed followed by the future methodology development plans to address those limitations. We then report results from a recent drug discovery project, in which several thousand FEP+ calculations were successfully deployed to simultaneously optimize potency, selectivity, and solubility, illustrating the power of the approach to solve challenging drug design problems. The capabilities of free energy calculations to accurately predict potency and selectivity have led to the advance of ongoing drug discovery projects, in challenging situations where alternative approaches would have great difficulties. The ability to effectively carry out projects evaluating tens of thousands, or hundreds of thousands, of proposed drug candidates, is potentially transformative in enabling hard to drug targets to be attacked, and in facilitating the development of superior compounds, in various dimensions, for a wide range of targets. More effective integration of FEP+ calculations into the drug discovery process will ensure that the results are deployed in an optimal fashion for yielding the best possible compounds entering the clinic; this is where the greatest payoff is in the exploitation of computer driven design capabilities. A key conclusion from the work described is the surprisingly robust and accurate results that are attainable within the conventional classical simulation, fixed charge paradigm. No doubt there are individual cases that would benefit from a more sophisticated energy model or dynamical treatment, and properties other than protein-ligand binding energies may be more sensitive to these approximations. We conclude that an inflection point in the ability of MD simulations to impact drug discovery has now been attained, due to the confluence of hardware and software development along with the formulation of "good enough" theoretical methods and models.

A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks.

PubMed

Kajita, Seiji; Ohba, Nobuko; Jinnouchi, Ryosuke; Asahi, Ryoji

2017-12-05

Material informatics (MI) is a promising approach to liberate us from the time-consuming Edisonian (trial and error) process for material discoveries, driven by machine-learning algorithms. Several descriptors, which are encoded material features to feed computers, were proposed in the last few decades. Especially to solid systems, however, their insufficient representations of three dimensionality of field quantities such as electron distributions and local potentials have critically hindered broad and practical successes of the solid-state MI. We develop a simple, generic 3D voxel descriptor that compacts any field quantities, in such a suitable way to implement convolutional neural networks (CNNs). We examine the 3D voxel descriptor encoded from the electron distribution by a regression test with 680 oxides data. The present scheme outperforms other existing descriptors in the prediction of Hartree energies that are significantly relevant to the long-wavelength distribution of the valence electrons. The results indicate that this scheme can forecast any functionals of field quantities just by learning sufficient amount of data, if there is an explicit correlation between the target properties and field quantities. This 3D descriptor opens a way to import prominent CNNs-based algorithms of supervised, semi-supervised and reinforcement learnings into the solid-state MI.

Practical Applications of Cosmic Ray Science: Spacecraft, Aircraft, Ground Based Computation and Control Systems and Human Health and Safety

NASA Technical Reports Server (NTRS)

Atwell, William; Koontz, Steve; Normand, Eugene

2012-01-01

In this paper we review the discovery of cosmic ray effects on the performance and reliability of microelectronic systems as well as on human health and safety, as well as the development of the engineering and health science tools used to evaluate and mitigate cosmic ray effects in earth surface, atmospheric flight, and space flight environments. Three twentieth century technological developments, 1) high altitude commercial and military aircraft; 2) manned and unmanned spacecraft; and 3) increasingly complex and sensitive solid state micro-electronics systems, have driven an ongoing evolution of basic cosmic ray science into a set of practical engineering tools (e.g. ground based test methods as well as high energy particle transport and reaction codes) needed to design, test, and verify the safety and reliability of modern complex electronic systems as well as effects on human health and safety. The effects of primary cosmic ray particles, and secondary particle showers produced by nuclear reactions with spacecraft materials, can determine the design and verification processes (as well as the total dollar cost) for manned and unmanned spacecraft avionics systems. Similar considerations apply to commercial and military aircraft operating at high latitudes and altitudes near the atmospheric Pfotzer maximum. Even ground based computational and controls systems can be negatively affected by secondary particle showers at the Earth's surface, especially if the net target area of the sensitive electronic system components is large. Accumulation of both primary cosmic ray and secondary cosmic ray induced particle shower radiation dose is an important health and safety consideration for commercial or military air crews operating at high altitude/latitude and is also one of the most important factors presently limiting manned space flight operations beyond low-Earth orbit (LEO).

Opposites Attract: Organic Charge Transfer Salts

ERIC Educational Resources Information Center

van de Wouw, Heidi L.; Chamorro, Juan; Quintero, Michael; Klausen, Rebekka S.

2015-01-01

A laboratory experiment is described that introduces second-year undergraduate organic chemistry students to organic electronic materials. The discovery of metallic conductivity in the charge transfer salt tetrathiafulvalene tetracyanoquinodimethane (TTF-TCNQ) is a landmark result in the history of organic electronics. The charge transfer…

Cancer in silico drug discovery: a systems biology tool for identifying candidate drugs to target specific molecular tumor subtypes.

PubMed

San Lucas, F Anthony; Fowler, Jerry; Chang, Kyle; Kopetz, Scott; Vilar, Eduardo; Scheet, Paul

2014-12-01

Large-scale cancer datasets such as The Cancer Genome Atlas (TCGA) allow researchers to profile tumors based on a wide range of clinical and molecular characteristics. Subsequently, TCGA-derived gene expression profiles can be analyzed with the Connectivity Map (CMap) to find candidate drugs to target tumors with specific clinical phenotypes or molecular characteristics. This represents a powerful computational approach for candidate drug identification, but due to the complexity of TCGA and technology differences between CMap and TCGA experiments, such analyses are challenging to conduct and reproduce. We present Cancer in silico Drug Discovery (CiDD; scheet.org/software), a computational drug discovery platform that addresses these challenges. CiDD integrates data from TCGA, CMap, and Cancer Cell Line Encyclopedia (CCLE) to perform computational drug discovery experiments, generating hypotheses for the following three general problems: (i) determining whether specific clinical phenotypes or molecular characteristics are associated with unique gene expression signatures; (ii) finding candidate drugs to repress these expression signatures; and (iii) identifying cell lines that resemble the tumors being studied for subsequent in vitro experiments. The primary input to CiDD is a clinical or molecular characteristic. The output is a biologically annotated list of candidate drugs and a list of cell lines for in vitro experimentation. We applied CiDD to identify candidate drugs to treat colorectal cancers harboring mutations in BRAF. CiDD identified EGFR and proteasome inhibitors, while proposing five cell lines for in vitro testing. CiDD facilitates phenotype-driven, systematic drug discovery based on clinical and molecular data from TCGA. ©2014 American Association for Cancer Research.

Commentary: Crowdsourcing, Foldit, and Scientific Discovery Games

ERIC Educational Resources Information Center

Parslow, Graham R.

2013-01-01

The web has created new possibilities for collaboration that fit under the terms crowdsourcing and human-based computation. Crowdsourcing applies when a task or problem is outsourced to an undefined public rather than a specific body. Human-based computation refers to ways that humans and computers can work together to solve problems. These two…

Going Solo: Creative Ideas for the One-Computer Classroom.

ERIC Educational Resources Information Center

DuBois, Jeanine

1998-01-01

A teacher who became computer literate by playing with one over the summer, describes how even just one computer in the classroom can help differentiate curriculum, be used for individualized instruction, augment resource materials, access the World Wide Web for the latest discoveries, assist visual learners, and create new student and teacher…

Computational Physics' Greatest Hits

NASA Astrophysics Data System (ADS)

Bug, Amy

2011-03-01

The digital computer, has worked its way so effectively into our profession that now, roughly 65 years after its invention, it is virtually impossible to find a field of experimental or theoretical physics unaided by computational innovation. It is tough to think of another device about which one can make that claim. In the session ``What is computational physics?'' speakers will distinguish computation within the field of computational physics from this ubiquitous importance across all subfields of physics. This talk will recap the invited session ``Great Advances...Past, Present and Future'' in which five dramatic areas of discovery (five of our ``greatest hits'') are chronicled: The physics of many-boson systems via Path Integral Monte Carlo, the thermodynamic behavior of a huge number of diverse systems via Monte Carlo Methods, the discovery of new pharmaceutical agents via molecular dynamics, predictive simulations of global climate change via detailed, cross-disciplinary earth system models, and an understanding of the formation of the first structures in our universe via galaxy formation simulations. The talk will also identify ``greatest hits'' in our field from the teaching and research perspectives of other members of DCOMP, including its Executive Committee.

Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics

PubMed Central

Brusniak, Mi-Youn; Bodenmiller, Bernd; Campbell, David; Cooke, Kelly; Eddes, James; Garbutt, Andrew; Lau, Hollis; Letarte, Simon; Mueller, Lukas N; Sharma, Vagisha; Vitek, Olga; Zhang, Ning; Aebersold, Ruedi; Watts, Julian D

2008-01-01

Background Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics. Results We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling. Conclusion The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field. PMID:19087345

Molecular dynamics-driven drug discovery: leaping forward with confidence.

PubMed

Ganesan, Aravindhan; Coote, Michelle L; Barakat, Khaled

2017-02-01

Given the significant time and financial costs of developing a commercial drug, it remains important to constantly reform the drug discovery pipeline with novel technologies that can narrow the candidates down to the most promising lead compounds for clinical testing. The past decade has witnessed tremendous growth in computational capabilities that enable in silico approaches to expedite drug discovery processes. Molecular dynamics (MD) has become a particularly important tool in drug design and discovery. From classical MD methods to more sophisticated hybrid classical/quantum mechanical (QM) approaches, MD simulations are now able to offer extraordinary insights into ligand-receptor interactions. In this review, we discuss how the applications of MD approaches are significantly transforming current drug discovery and development efforts. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computing elastic anisotropy to discover gum-metal-like structural alloys

NASA Astrophysics Data System (ADS)

Winter, I. S.; de Jong, M.; Asta, M.; Chrzan, D. C.

2017-08-01

The computer aided discovery of structural alloys is a burgeoning but still challenging area of research. A primary challenge in the field is to identify computable screening parameters that embody key structural alloy properties. Here, an elastic anisotropy parameter that captures a material's susceptibility to solute solution strengthening is identified. The parameter has many applications in the discovery and optimization of structural materials. As a first example, the parameter is used to identify alloys that might display the super elasticity, super strength, and high ductility of the class of TiNb alloys known as gum metals. In addition, it is noted that the parameter can be used to screen candidate alloys for shape memory response, and potentially aid in the optimization of the mechanical properties of high-entropy alloys.

Drug repurposing: translational pharmacology, chemistry, computers and the clinic.

PubMed

Issa, Naiem T; Byers, Stephen W; Dakshanamurthy, Sivanesan

2013-01-01

The process of discovering a pharmacological compound that elicits a desired clinical effect with minimal side effects is a challenge. Prior to the advent of high-performance computing and large-scale screening technologies, drug discovery was largely a serendipitous endeavor, as in the case of thalidomide for erythema nodosum leprosum or cancer drugs in general derived from flora located in far-reaching geographic locations. More recently, de novo drug discovery has become a more rationalized process where drug-target-effect hypotheses are formulated on the basis of already known compounds/protein targets and their structures. Although this approach is hypothesis-driven, the actual success has been very low, contributing to the soaring costs of research and development as well as the diminished pharmaceutical pipeline in the United States. In this review, we discuss the evolution in computational pharmacology as the next generation of successful drug discovery and implementation in the clinic where high-performance computing (HPC) is used to generate and validate drug-target-effect hypotheses completely in silico. The use of HPC would decrease development time and errors while increasing productivity prior to in vitro, animal and human testing. We highlight approaches in chemoinformatics, bioinformatics as well as network biopharmacology to illustrate potential avenues from which to design clinically efficacious drugs. We further discuss the implications of combining these approaches into an integrative methodology for high-accuracy computational predictions within the context of drug repositioning for the efficient streamlining of currently approved drugs back into clinical trials for possible new indications.

Glenn in his sleep rack on the Discovery's middeck

NASA Image and Video Library

1998-10-29

STS095-E-5032 (10-29-98) --- During Flight Day 1 activties, U.S. Sen. John H. Glenn Jr. takes part in his assigned medical studies as a payload specialist onboard the Space Shuttle Discovery. A flashlight floats in front of Sen. Glenn. The photo was made with an electronic still camera (ESC) at ll:48:35 GMT, Oct. 29.

PANSAT satellite deployment from STS-95 Discovery's payload bay

NASA Image and Video Library

1998-10-30

STS095-E-5040 (30 Oct. 1998) --- PANSAT, a nonrecoverable satellite developed by the Naval Postgraduate School (NPS) in Monterey, California, is deployed from the cargo bay of the Earth-orbiting Space Shuttle Discovery. The small ball-shaped payload is basically a tiny telecommunications satellite. The photo was recorded with an electronic still camera (ESC) at 1:49:13 GMT, Oct. 30.

AutoDrug: fully automated macromolecular crystallography workflows for fragment-based drug discovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tsai, Yingssu; Stanford University, 333 Campus Drive, Mudd Building, Stanford, CA 94305-5080; McPhillips, Scott E.

New software has been developed for automating the experimental and data-processing stages of fragment-based drug discovery at a macromolecular crystallography beamline. A new workflow-automation framework orchestrates beamline-control and data-analysis software while organizing results from multiple samples. AutoDrug is software based upon the scientific workflow paradigm that integrates the Stanford Synchrotron Radiation Lightsource macromolecular crystallography beamlines and third-party processing software to automate the crystallography steps of the fragment-based drug-discovery process. AutoDrug screens a cassette of fragment-soaked crystals, selects crystals for data collection based on screening results and user-specified criteria and determines optimal data-collection strategies. It then collects and processes diffraction data,more » performs molecular replacement using provided models and detects electron density that is likely to arise from bound fragments. All processes are fully automated, i.e. are performed without user interaction or supervision. Samples can be screened in groups corresponding to particular proteins, crystal forms and/or soaking conditions. A single AutoDrug run is only limited by the capacity of the sample-storage dewar at the beamline: currently 288 samples. AutoDrug was developed in conjunction with RestFlow, a new scientific workflow-automation framework. RestFlow simplifies the design of AutoDrug by managing the flow of data and the organization of results and by orchestrating the execution of computational pipeline steps. It also simplifies the execution and interaction of third-party programs and the beamline-control system. Modeling AutoDrug as a scientific workflow enables multiple variants that meet the requirements of different user groups to be developed and supported. A workflow tailored to mimic the crystallography stages comprising the drug-discovery pipeline of CoCrystal Discovery Inc. has been deployed and successfully demonstrated. This workflow was run once on the same 96 samples that the group had examined manually and the workflow cycled successfully through all of the samples, collected data from the same samples that were selected manually and located the same peaks of unmodeled density in the resulting difference Fourier maps.« less

Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases.

PubMed

Wooden, Benjamin; Goossens, Nicolas; Hoshida, Yujin; Friedman, Scott L

2017-01-01

Technologies such as genome sequencing, gene expression profiling, proteomic and metabolomic analyses, electronic medical records, and patient-reported health information have produced large amounts of data from various populations, cell types, and disorders (big data). However, these data must be integrated and analyzed if they are to produce models or concepts about physiological function or mechanisms of pathogenesis. Many of these data are available to the public, allowing researchers anywhere to search for markers of specific biological processes or therapeutic targets for specific diseases or patient types. We review recent advances in the fields of computational and systems biology and highlight opportunities for researchers to use big data sets in the fields of gastroenterology and hepatology to complement traditional means of diagnostic and therapeutic discovery. Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.

The self-organizing fractal theory as a universal discovery method: the phenomenon of life.

PubMed

Kurakin, Alexei

2011-03-29

A universal discovery method potentially applicable to all disciplines studying organizational phenomena has been developed. This method takes advantage of a new form of global symmetry, namely, scale-invariance of self-organizational dynamics of energy/matter at all levels of organizational hierarchy, from elementary particles through cells and organisms to the Universe as a whole. The method is based on an alternative conceptualization of physical reality postulating that the energy/matter comprising the Universe is far from equilibrium, that it exists as a flow, and that it develops via self-organization in accordance with the empirical laws of nonequilibrium thermodynamics. It is postulated that the energy/matter flowing through and comprising the Universe evolves as a multiscale, self-similar structure-process, i.e., as a self-organizing fractal. This means that certain organizational structures and processes are scale-invariant and are reproduced at all levels of the organizational hierarchy. Being a form of symmetry, scale-invariance naturally lends itself to a new discovery method that allows for the deduction of missing information by comparing scale-invariant organizational patterns across different levels of the organizational hierarchy.An application of the new discovery method to life sciences reveals that moving electrons represent a keystone physical force (flux) that powers, animates, informs, and binds all living structures-processes into a planetary-wide, multiscale system of electron flow/circulation, and that all living organisms and their larger-scale organizations emerge to function as electron transport networks that are supported by and, at the same time, support the flow of electrons down the Earth's redox gradient maintained along the core-mantle-crust-ocean-atmosphere axis of the planet. The presented findings lead to a radically new perspective on the nature and origin of life, suggesting that living matter is an organizational state/phase of nonliving matter and a natural consequence of the evolution and self-organization of nonliving matter.The presented paradigm opens doors for explosive advances in many disciplines, by uniting them within a single conceptual framework and providing a discovery method that allows for the systematic generation of knowledge through comparison and complementation of empirical data across different sciences and disciplines.

The self-organizing fractal theory as a universal discovery method: the phenomenon of life

PubMed Central

2011-01-01

A universal discovery method potentially applicable to all disciplines studying organizational phenomena has been developed. This method takes advantage of a new form of global symmetry, namely, scale-invariance of self-organizational dynamics of energy/matter at all levels of organizational hierarchy, from elementary particles through cells and organisms to the Universe as a whole. The method is based on an alternative conceptualization of physical reality postulating that the energy/matter comprising the Universe is far from equilibrium, that it exists as a flow, and that it develops via self-organization in accordance with the empirical laws of nonequilibrium thermodynamics. It is postulated that the energy/matter flowing through and comprising the Universe evolves as a multiscale, self-similar structure-process, i.e., as a self-organizing fractal. This means that certain organizational structures and processes are scale-invariant and are reproduced at all levels of the organizational hierarchy. Being a form of symmetry, scale-invariance naturally lends itself to a new discovery method that allows for the deduction of missing information by comparing scale-invariant organizational patterns across different levels of the organizational hierarchy. An application of the new discovery method to life sciences reveals that moving electrons represent a keystone physical force (flux) that powers, animates, informs, and binds all living structures-processes into a planetary-wide, multiscale system of electron flow/circulation, and that all living organisms and their larger-scale organizations emerge to function as electron transport networks that are supported by and, at the same time, support the flow of electrons down the Earth's redox gradient maintained along the core-mantle-crust-ocean-atmosphere axis of the planet. The presented findings lead to a radically new perspective on the nature and origin of life, suggesting that living matter is an organizational state/phase of nonliving matter and a natural consequence of the evolution and self-organization of nonliving matter. The presented paradigm opens doors for explosive advances in many disciplines, by uniting them within a single conceptual framework and providing a discovery method that allows for the systematic generation of knowledge through comparison and complementation of empirical data across different sciences and disciplines. PMID:21447162

In Silico Discovery of a Substituted 6-Methoxy-quinalidine with Leishmanicidal Activity in Leishmania infantum.

PubMed

Stevanović, Strahinja; Perdih, Andrej; Senćanski, Milan; Glišić, Sanja; Duarte, Margarida; Tomás, Ana M; Sena, Filipa V; Sousa, Filipe M; Pereira, Manuela M; Solmajer, Tom

2018-03-27

There is an urgent need for the discovery of new antileishmanial drugs with a new mechanism of action. Type 2 NADH dehydrogenase from Leishmania infantum ( Li NDH2) is an enzyme of the parasite's respiratory system, which catalyzes the electron transfer from NADH to ubiquinone without coupled proton pumping. In previous studies of the related NADH: ubiquinone oxidoreductase crystal structure from Saccharomyces cerevisiae , two ubiquinone-binding sites (UQ I and UQ II ) were identified and shown to play an important role in the NDH-2-catalyzed oxidoreduction reaction. Based on the available structural data, we developed a three-dimensional structural model of Li NDH2 using homology detection methods and performed an in silico virtual screening campaign to search for potential inhibitors targeting the Li NDH2 ubiquinone-binding site 1-UQ I . Selected compounds displaying favorable properties in the computational screening experiments were assayed for inhibitory activity in the structurally similar recombinant NDH-2 from S. aureus and leishmanicidal activity was determined in the wild-type axenic amastigotes and promastigotes of L. infantum . The identified compound, a substituted 6-methoxy-quinalidine, showed promising nanomolar leishmanicidal activity on wild-type axenic promastigotes and amastigotes of L. infantum and the potential for further development.

Web service discovery among large service pools utilising semantic similarity and clustering

NASA Astrophysics Data System (ADS)

Chen, Fuzan; Li, Minqiang; Wu, Harris; Xie, Lingli

2017-03-01

With the rapid development of electronic business, Web services have attracted much attention in recent years. Enterprises can combine individual Web services to provide new value-added services. An emerging challenge is the timely discovery of close matches to service requests among large service pools. In this study, we first define a new semantic similarity measure combining functional similarity and process similarity. We then present a service discovery mechanism that utilises the new semantic similarity measure for service matching. All the published Web services are pre-grouped into functional clusters prior to the matching process. For a user's service request, the discovery mechanism first identifies matching services clusters and then identifies the best matching Web services within these matching clusters. Experimental results show that the proposed semantic discovery mechanism performs better than a conventional lexical similarity-based mechanism.

Virtual Observatories, Data Mining, and Astroinformatics

NASA Astrophysics Data System (ADS)

Borne, Kirk

The historical, current, and future trends in knowledge discovery from data in astronomy are presented here. The story begins with a brief history of data gathering and data organization. A description of the development ofnew information science technologies for astronomical discovery is then presented. Among these are e-Science and the virtual observatory, with its data discovery, access, display, and integration protocols; astroinformatics and data mining for exploratory data analysis, information extraction, and knowledge discovery from distributed data collections; new sky surveys' databases, including rich multivariate observational parameter sets for large numbers of objects; and the emerging discipline of data-oriented astronomical research, called astroinformatics. Astroinformatics is described as the fourth paradigm of astronomical research, following the three traditional research methodologies: observation, theory, and computation/modeling. Astroinformatics research areas include machine learning, data mining, visualization, statistics, semantic science, and scientific data management.Each of these areas is now an active research discipline, with significantscience-enabling applications in astronomy. Research challenges and sample research scenarios are presented in these areas, in addition to sample algorithms for data-oriented research. These information science technologies enable scientific knowledge discovery from the increasingly large and complex data collections in astronomy. The education and training of the modern astronomy student must consequently include skill development in these areas, whose practitioners have traditionally been limited to applied mathematicians, computer scientists, and statisticians. Modern astronomical researchers must cross these traditional discipline boundaries, thereby borrowing the best of breed methodologies from multiple disciplines. In the era of large sky surveys and numerous large telescopes, the potential for astronomical discovery is equally large, and so the data-oriented research methods, algorithms, and techniques that are presented here will enable the greatest discovery potential from the ever-growing data and information resources in astronomy.

Discovery and optimization of p38 inhibitors via computer-assisted drug design.

PubMed

Goldberg, Daniel R; Hao, Ming-Hong; Qian, Kevin C; Swinamer, Alan D; Gao, Donghong A; Xiong, Zhaoming; Sarko, Chris; Berry, Angela; Lord, John; Magolda, Ronald L; Fadra, Tazmeen; Kroe, Rachel R; Kukulka, Alison; Madwed, Jeffrey B; Martin, Leslie; Pargellis, Christopher; Skow, Donna; Song, Jinhua J; Tan, Zhulin; Torcellini, Carol A; Zimmitti, Clare S; Yee, Nathan K; Moss, Neil

2007-08-23

Integration of computational methods, X-ray crystallography, and structure-activity relationships will be disclosed, which lead to a new class of p38 inhibitors that bind to p38 MAP kinase in a Phe out conformation.

How Formal Methods Impels Discovery: A Short History of an Air Traffic Management Project

NASA Technical Reports Server (NTRS)

Butler, Ricky W.; Hagen, George; Maddalon, Jeffrey M.; Munoz, Cesar A.; Narkawicz, Anthony; Dowek, Gilles

2010-01-01

In this paper we describe a process of algorithmic discovery that was driven by our goal of achieving complete, mechanically verified algorithms that compute conflict prevention bands for use in en route air traffic management. The algorithms were originally defined in the PVS specification language and subsequently have been implemented in Java and C++. We do not present the proofs in this paper: instead, we describe the process of discovery and the key ideas that enabled the final formal proof of correctness

Genomics and transcriptomics in drug discovery.

PubMed

Dopazo, Joaquin

2014-02-01

The popularization of genomic high-throughput technologies is causing a revolution in biomedical research and, particularly, is transforming the field of drug discovery. Systems biology offers a framework to understand the extensive human genetic heterogeneity revealed by genomic sequencing in the context of the network of functional, regulatory and physical protein-drug interactions. Thus, approaches to find biomarkers and therapeutic targets will have to take into account the complex system nature of the relationships of the proteins with the disease. Pharmaceutical companies will have to reorient their drug discovery strategies considering the human genetic heterogeneity. Consequently, modeling and computational data analysis will have an increasingly important role in drug discovery. Copyright © 2013 Elsevier Ltd. All rights reserved.

The future of crystallography in drug discovery

PubMed Central

Zheng, Heping; Hou, Jing; Zimmerman, Matthew D; Wlodawer, Alexander; Minor, Wladek

2014-01-01

Introduction X-ray crystallography plays an important role in structure-based drug design (SBDD), and accurate analysis of crystal structures of target macromolecules and macromolecule–ligand complexes is critical at all stages. However, whereas there has been significant progress in improving methods of structural biology, particularly in X-ray crystallography, corresponding progress in the development of computational methods (such as in silico high-throughput screening) is still on the horizon. Crystal structures can be overinterpreted and thus bias hypotheses and follow-up experiments. As in any experimental science, the models of macromolecular structures derived from X-ray diffraction data have their limitations, which need to be critically evaluated and well understood for structure-based drug discovery. Areas covered This review describes how the validity, accuracy and precision of a protein or nucleic acid structure determined by X-ray crystallography can be evaluated from three different perspectives: i) the nature of the diffraction experiment; ii) the interpretation of an electron density map; and iii) the interpretation of the structural model in terms of function and mechanism. The strategies to optimally exploit a macromolecular structure are also discussed in the context of ‘Big Data’ analysis, biochemical experimental design and structure-based drug discovery. Expert opinion Although X-ray crystallography is one of the most detailed ‘microscopes’ available today for examining macromolecular structures, the authors would like to re-emphasize that such structures are only simplified models of the target macromolecules. The authors also wish to reinforce the idea that a structure should not be thought of as a set of precise coordinates but rather as a framework for generating hypotheses to be explored. Numerous biochemical and biophysical experiments, including new diffraction experiments, can and should be performed to verify or falsify these hypotheses. X-ray crystallography will find its future application in drug discovery by the development of specific tools that would allow realistic interpretation of the outcome coordinates and/or support testing of these hypotheses. PMID:24372145

Overview of the SAMPL5 host–guest challenge: Are we doing better?

PubMed Central

Yin, Jian; Henriksen, Niel M.; Slochower, David R.; Shirts, Michael R.; Chiu, Michael W.; Mobley, David L.; Gilson, Michael K.

2016-01-01

The ability to computationally predict protein-small molecule binding affinities with high accuracy would accelerate drug discovery and reduce its cost by eliminating rounds of trial-and-error synthesis and experimental evaluation of candidate ligands. As academic and industrial groups work toward this capability, there is an ongoing need for datasets that can be used to rigorously test new computational methods. Although protein–ligand data are clearly important for this purpose, their size and complexity make it difficult to obtain well-converged results and to troubleshoot computational methods. Host–guest systems offer a valuable alternative class of test cases, as they exemplify noncovalent molecular recognition but are far smaller and simpler. As a consequence, host–guest systems have been part of the prior two rounds of SAMPL prediction exercises, and they also figure in the present SAMPL5 round. In addition to being blinded, and thus avoiding biases that may arise in retrospective studies, the SAMPL challenges have the merit of focusing multiple researchers on a common set of molecular systems, so that methods may be compared and ideas exchanged. The present paper provides an overview of the host–guest component of SAMPL5, which centers on three different hosts, two octa-acids and a glycoluril-based molecular clip, and two different sets of guest molecules, in aqueous solution. A range of methods were applied, including electronic structure calculations with implicit solvent models; methods that combine empirical force fields with implicit solvent models; and explicit solvent free energy simulations. The most reliable methods tend to fall in the latter class, consistent with results in prior SAMPL rounds, but the level of accuracy is still below that sought for reliable computer-aided drug design. Advances in force field accuracy, modeling of protonation equilibria, electronic structure methods, and solvent models, hold promise for future improvements. PMID:27658802

Overview of the SAMPL5 host-guest challenge: Are we doing better?

PubMed

Yin, Jian; Henriksen, Niel M; Slochower, David R; Shirts, Michael R; Chiu, Michael W; Mobley, David L; Gilson, Michael K

2017-01-01

The ability to computationally predict protein-small molecule binding affinities with high accuracy would accelerate drug discovery and reduce its cost by eliminating rounds of trial-and-error synthesis and experimental evaluation of candidate ligands. As academic and industrial groups work toward this capability, there is an ongoing need for datasets that can be used to rigorously test new computational methods. Although protein-ligand data are clearly important for this purpose, their size and complexity make it difficult to obtain well-converged results and to troubleshoot computational methods. Host-guest systems offer a valuable alternative class of test cases, as they exemplify noncovalent molecular recognition but are far smaller and simpler. As a consequence, host-guest systems have been part of the prior two rounds of SAMPL prediction exercises, and they also figure in the present SAMPL5 round. In addition to being blinded, and thus avoiding biases that may arise in retrospective studies, the SAMPL challenges have the merit of focusing multiple researchers on a common set of molecular systems, so that methods may be compared and ideas exchanged. The present paper provides an overview of the host-guest component of SAMPL5, which centers on three different hosts, two octa-acids and a glycoluril-based molecular clip, and two different sets of guest molecules, in aqueous solution. A range of methods were applied, including electronic structure calculations with implicit solvent models; methods that combine empirical force fields with implicit solvent models; and explicit solvent free energy simulations. The most reliable methods tend to fall in the latter class, consistent with results in prior SAMPL rounds, but the level of accuracy is still below that sought for reliable computer-aided drug design. Advances in force field accuracy, modeling of protonation equilibria, electronic structure methods, and solvent models, hold promise for future improvements.

Bacteriorhodopsin as an electronic conduction medium for biomolecular electronics.

PubMed

Jin, Yongdong; Honig, Tal; Ron, Izhar; Friedman, Noga; Sheves, Mordechai; Cahen, David

2008-11-01

Interfacing functional proteins with solid supports for device applications is a promising route to possible applications in bio-electronics, -sensors, and -optics. Various possible applications of bacteriorhodopsin (bR) have been explored and reviewed since the discovery of bR. This tutorial review discusses bR as a medium for biomolecular optoelectronics, emphasizing ways in which it can be interfaced, especially as a thin film, solid-state current-carrying electronic element.

Computer aided drug discovery of highly ligand efficient, low molecular weight imidazopyridine analogs as FLT3 inhibitors

PubMed Central

Frett, Brendan; McConnell, Nick; Smith, Catherine C.; Wang, Yuanxiang; Shah, Neil P.; Li, Hong-yu

2015-01-01

The FLT3 kinase represents an attractive target to effectively treat AML. Unfortunately, no FLT3 targeted therapeutic is currently approved. In line with our continued interests in treating kinase related disease for anti-FLT3 mutant activity, we utilized pioneering synthetic methodology in combination with computer aided drug discovery and identified low molecular weight, highly ligand efficient, FLT3 kinase inhibitors. Compounds were analyzed for biochemical inhibition, their ability to selectively inhibit cell proliferation, for FLT3 mutant activity, and preliminary aqueous solubility. Validated hits were discovered that can serve as starting platforms for lead candidates. PMID:25765758

Outside-In Systems Pharmacology Combines Innovative Computational Methods With High-Throughput Whole Vertebrate Studies.

PubMed

Schulthess, Pascal; van Wijk, Rob C; Krekels, Elke H J; Yates, James W T; Spaink, Herman P; van der Graaf, Piet H

2018-04-25

To advance the systems approach in pharmacology, experimental models and computational methods need to be integrated from early drug discovery onward. Here, we propose outside-in model development, a model identification technique to understand and predict the dynamics of a system without requiring prior biological and/or pharmacological knowledge. The advanced data required could be obtained by whole vertebrate, high-throughput, low-resource dose-exposure-effect experimentation with the zebrafish larva. Combinations of these innovative techniques could improve early drug discovery. © 2018 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Biophysical Discovery through the Lens of a Computational Microscope

NASA Astrophysics Data System (ADS)

Amaro, Rommie

With exascale computing power on the horizon, improvements in the underlying algorithms and available structural experimental data are enabling new paradigms for chemical discovery. My work has provided key insights for the systematic incorporation of structural information resulting from state-of-the-art biophysical simulations into protocols for inhibitor and drug discovery. We have shown that many disease targets have druggable pockets that are otherwise ``hidden'' in high resolution x-ray structures, and that this is a common theme across a wide range of targets in different disease areas. We continue to push the limits of computational biophysical modeling by expanding the time and length scales accessible to molecular simulation. My sights are set on, ultimately, the development of detailed physical models of cells, as the fundamental unit of life, and two recent achievements highlight our efforts in this arena. First is the development of a molecular and Brownian dynamics multi-scale modeling framework, which allows us to investigate drug binding kinetics in addition to thermodynamics. In parallel, we have made significant progress developing new tools to extend molecular structure to cellular environments. Collectively, these achievements are enabling the investigation of the chemical and biophysical nature of cells at unprecedented scales.

Discovery of Boolean metabolic networks: integer linear programming based approach.

PubMed

Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing

2018-04-11

Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".

Computers for the Disabled. Proceedings of the Discovery '83 Conference (Minneapolis, Minnesota, September 12-14, 1983).

ERIC Educational Resources Information Center

Roehl, Janet E., Ed.

These proceedings provide 30 papers from a conference to highlight a frontier area in serving the disabled--microcomputers--which blends two disciplines, vocational rehabilitation and special education. Keynote addresses are "High Tech/High Touch: Making Good on the Promise" (Fenderson); "Curbcuts and Computers: Providing Access to Computers and…

Constructing a Foundational Platform Driven by Japan's K Supercomputer for Next-Generation Drug Design.

PubMed

Brown, J B; Nakatsui, Masahiko; Okuno, Yasushi

2014-12-01

The cost of pharmaceutical R&D has risen enormously, both worldwide and in Japan. However, Japan faces a particularly difficult situation in that its population is aging rapidly, and the cost of pharmaceutical R&D affects not only the industry but the entire medical system as well. To attempt to reduce costs, the newly launched K supercomputer is available for big data drug discovery and structural simulation-based drug discovery. We have implemented both primary (direct) and secondary (infrastructure, data processing) methods for the two types of drug discovery, custom tailored to maximally use the 88 128 compute nodes/CPUs of K, and evaluated the implementations. We present two types of results. In the first, we executed the virtual screening of nearly 19 billion compound-protein interactions, and calculated the accuracy of predictions against publicly available experimental data. In the second investigation, we implemented a very computationally intensive binding free energy algorithm, and found that comparison of our binding free energies was considerably accurate when validated against another type of publicly available experimental data. The common feature of both result types is the scale at which computations were executed. The frameworks presented in this article provide prospectives and applications that, while tuned to the computing resources available in Japan, are equally applicable to any equivalent large-scale infrastructure provided elsewhere. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Technology: Catalyst for Enhancing Chemical Education for Pre-service Teachers

NASA Astrophysics Data System (ADS)

Kumar, Vinay; Bedell, Julia Yang; Seed, Allen H.

1999-05-01

A DOE/KYEPSCoR-funded project enabled us to introduce a new curricular initiative aimed at improving the chemical education of pre-service elementary teachers. The new curriculum was developed in collaboration with the School of Education faculty. A new course for the pre-service teachers, "Discovering Chemistry with Lab" (CHE 105), was developed. The integrated lecture and lab course covers basic principles of chemistry and their applications in daily life. The course promotes reasoning and problem-solving skills and utilizes hands-on, discovery/guided-inquiry, and cooperative learning approaches. This paper describes the implementation of technology (computer-interfacing and simulation experiments) in the lab. Results of two assessment surveys conducted in the laboratory are also discussed. The key features of the lab course are eight new experiments, including four computer-interfacing/simulation experiments involving the use of Macintosh Power PCs, temperature and pH probes, and a serial box interface, and use of household materials. Several experiments and the midterm and final lab practical exams emphasize the discovery/guided-inquiry approach. The results of pre- and post-surveys showed very significant positive changes in students' attitude toward the relevancy of chemistry, use of technology (computers) in elementary school classrooms, and designing and teaching discovery-based units. Most students indicated that they would be very interested (52%) or interested (36%) in using computers in their science teaching.

Joint the Center for Applied Scientific Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gamblin, Todd; Bremer, Timo; Van Essen, Brian

The Center for Applied Scientific Computing serves as Livermore Lab’s window to the broader computer science, computational physics, applied mathematics, and data science research communities. In collaboration with academic, industrial, and other government laboratory partners, we conduct world-class scientific research and development on problems critical to national security. CASC applies the power of high-performance computing and the efficiency of modern computational methods to the realms of stockpile stewardship, cyber and energy security, and knowledge discovery for intelligence applications.

Quantitative structure-activity relationship: promising advances in drug discovery platforms.

PubMed

Wang, Tao; Wu, Mian-Bin; Lin, Jian-Ping; Yang, Li-Rong

2015-12-01

Quantitative structure-activity relationship (QSAR) modeling is one of the most popular computer-aided tools employed in medicinal chemistry for drug discovery and lead optimization. It is especially powerful in the absence of 3D structures of specific drug targets. QSAR methods have been shown to draw public attention since they were first introduced. In this review, the authors provide a brief discussion of the basic principles of QSAR, model development and model validation. They also highlight the current applications of QSAR in different fields, particularly in virtual screening, rational drug design and multi-target QSAR. Finally, in view of recent controversies, the authors detail the challenges faced by QSAR modeling and the relevant solutions. The aim of this review is to show how QSAR modeling can be applied in novel drug discovery, design and lead optimization. QSAR should intentionally be used as a powerful tool for fragment-based drug design platforms in the field of drug discovery and design. Although there have been an increasing number of experimentally determined protein structures in recent years, a great number of protein structures cannot be easily obtained (i.e., membrane transport proteins and G-protein coupled receptors). Fragment-based drug discovery, such as QSAR, could be applied further and have a significant role in dealing with these problems. Moreover, along with the development of computer software and hardware, it is believed that QSAR will be increasingly important.

J. J. Thomson and the Discovery of the Electron

NASA Astrophysics Data System (ADS)

Squires, Gordon

1997-04-01

Joseph John Thomson (1856-1940) was appointed to the Chair of Experimental Physics in the Cavendish Laboratory at the University of Cambridge at the age of 28 and started research on the conduction of electricity through gases at low pressure. He studied the properties of the rays emanating from the cathode in a gas discharge by deflecting them with electric and magnetic fields, and in 1897 announced that they were negatively charged particles about 2000 times lighter than hydrogen atoms, the lightest particles then known. Further, the mass of the particles was the same, irrespective of the nature of the gas in the discharge tube and the material of the cathode. He concluded that he had found a new particle, a universal constituent of matter. The discovery of the particle, subsequently called the electron, was one of the most significant events in the history of science. The talk will give a brief account of Thomson's career and the experiments leading to the discovery.

The iPlant collaborative: cyberinfrastructure for enabling data to discovery for the life sciences

USDA-ARS?s Scientific Manuscript database

The iPlant Collaborative provides life science research communities access to comprehensive, scalable, and cohesive computational infrastructure for data management; identify management; collaboration tools; and cloud, high-performance, high-throughput computing. iPlant provides training, learning m...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Earl, James A.

From 1948 until 1963, cloud chambers were carried to the top of the atmosphere by balloons. From these flights, which were begun by Edward P. Ney at the University of Minnesota, came the following results: discovery of heavy cosmic ray nuclei, development of scintillation and cherenkov detectors, discovery of cosmic ray electrons, and studies of solar proton events. The history of that era is illustrated here by cloud chamber photographs of primary cosmic rays.

Advances in synthetic peptides reagent discovery

NASA Astrophysics Data System (ADS)

Adams, Bryn L.; Sarkes, Deborah A.; Finch, Amethist S.; Stratis-Cullum, Dimitra N.

2013-05-01

Bacterial display technology offers a number of advantages over competing display technologies (e.g, phage) for the rapid discovery and development of peptides with interaction targeted to materials ranging from biological hazards through inorganic metals. We have previously shown that discovery of synthetic peptide reagents utilizing bacterial display technology is relatively simple and rapid to make laboratory automation possible. This included extensive study of the protective antigen system of Bacillus anthracis, including development of discovery, characterization, and computational biology capabilities for in-silico optimization. Although the benefits towards CBD goals are evident, the impact is far-reaching due to our ability to understand and harness peptide interactions that are ultimately extendable to the hybrid biomaterials of the future. In this paper, we describe advances in peptide discovery including, new target systems (e.g. non-biological materials), advanced library development and clone analysis including integrated reporting.

Modeling & Informatics at Vertex Pharmaceuticals Incorporated: our philosophy for sustained impact

NASA Astrophysics Data System (ADS)

McGaughey, Georgia; Patrick Walters, W.

2017-03-01

Molecular modelers and informaticians have the unique opportunity to integrate cross-functional data using a myriad of tools, methods and visuals to generate information. Using their drug discovery expertise, information is transformed to knowledge that impacts drug discovery. These insights are often times formulated locally and then applied more broadly, which influence the discovery of new medicines. This is particularly true in an organization where the members are exposed to projects throughout an organization, such as in the case of the global Modeling & Informatics group at Vertex Pharmaceuticals. From its inception, Vertex has been a leader in the development and use of computational methods for drug discovery. In this paper, we describe the Modeling & Informatics group at Vertex and the underlying philosophy, which has driven this team to sustain impact on the discovery of first-in-class transformative medicines.

Beginning to manage drug discovery and development knowledge.

PubMed

Sumner-Smith, M

2001-05-01

Knowledge management approaches and technologies are beginning to be implemented by the pharmaceutical industry in support of new drug discovery and development processes aimed at greater efficiencies and effectiveness. This trend coincides with moves to reduce paper, coordinate larger teams with more diverse skills that are distributed around the globe, and to comply with regulatory requirements for electronic submissions and the associated maintenance of electronic records. Concurrently, the available technologies have implemented web-based architectures with a greater range of collaborative tools and personalization through portal approaches. However, successful application of knowledge management methods depends on effective cultural change management, as well as proper architectural design to match the organizational and work processes within a company.

Fishing the Fermi sea

DOE Office of Scientific and Technical Information (OSTI.GOV)

Canfield, P.

2008-03-01

Sophocles had it right, the Rolling Stones made a friendly amendment and Linus Pauling detailed the conceptual mechanism for finding novel materials that will define and revolutionize the future. Within the field of solid-state physics, the discovery of remarkable phases and transitions is often tightly coupled to the design, discovery and growth of novel materials. The past several decades of work in the field of correlated electron physics - that is, the study of materials in which the interactions are sufficiently strong that conventional single-electron theories don't apply - can be described by a list of materials that have definedmore » new extremes, be it extremes of temperature, field, pressure, complexity or, even better, simplicity.« less

Data-driven discovery of new Dirac semimetal materials

NASA Astrophysics Data System (ADS)

Yan, Qimin; Chen, Ru; Neaton, Jeffrey

In recent years, a significant amount of materials property data from high-throughput computations based on density functional theory (DFT) and the application of database technologies have enabled the rise of data-driven materials discovery. In this work, we initiate the extension of the data-driven materials discovery framework to the realm of topological semimetal materials and to accelerate the discovery of novel Dirac semimetals. We implement current available and develop new workflows to data-mine the Materials Project database for novel Dirac semimetals with desirable band structures and symmetry protected topological properties. This data-driven effort relies on the successful development of several automatic data generation and analysis tools, including a workflow for the automatic identification of topological invariants and pattern recognition techniques to find specific features in a massive number of computed band structures. Utilizing this approach, we successfully identified more than 15 novel Dirac point and Dirac nodal line systems that have not been theoretically predicted or experimentally identified. This work is supported by the Materials Project Predictive Modeling Center through the U.S. Department of Energy, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, under Contract No. DE-AC02-05CH11231.

Getting the Most out of PubChem for Virtual Screening

PubMed Central

Kim, Sunghwan

2016-01-01

Introduction With the emergence of the “big data” era, the biomedical research community has great interest in exploiting publicly available chemical information for drug discovery. PubChem is an example of public databases that provide a large amount of chemical information free of charge. Areas covered This article provides an overview of how PubChem’s data, tools, and services can be used for virtual screening and reviews recent publications that discuss important aspects of exploiting PubChem for drug discovery. Expert opinion PubChem offers comprehensive chemical information useful for drug discovery. It also provides multiple programmatic access routes, which are essential to build automated virtual screening pipelines that exploit PubChem data. In addition, PubChemRDF allows users to download PubChem data and load them into a local computing facility, facilitating data integration between PubChem and other resources. PubChem resources have been used in many studies for developing bioactivity and toxicity prediction models, discovering polypharmacologic (multi-target) ligands, and identifying new macromolecule targets of compounds (for drug-repurposing or off-target side effect prediction). These studies demonstrate the usefulness of PubChem as a key resource for computer-aided drug discovery and related area. PMID:27454129

Large scale analysis of the mutational landscape in HT-SELEX improves aptamer discovery

PubMed Central

Hoinka, Jan; Berezhnoy, Alexey; Dao, Phuong; Sauna, Zuben E.; Gilboa, Eli; Przytycka, Teresa M.

2015-01-01

High-Throughput (HT) SELEX combines SELEX (Systematic Evolution of Ligands by EXponential Enrichment), a method for aptamer discovery, with massively parallel sequencing technologies. This emerging technology provides data for a global analysis of the selection process and for simultaneous discovery of a large number of candidates but currently lacks dedicated computational approaches for their analysis. To close this gap, we developed novel in-silico methods to analyze HT-SELEX data and utilized them to study the emergence of polymerase errors during HT-SELEX. Rather than considering these errors as a nuisance, we demonstrated their utility for guiding aptamer discovery. Our approach builds on two main advancements in aptamer analysis: AptaMut—a novel technique allowing for the identification of polymerase errors conferring an improved binding affinity relative to the ‘parent’ sequence and AptaCluster—an aptamer clustering algorithm which is to our best knowledge, the only currently available tool capable of efficiently clustering entire aptamer pools. We applied these methods to an HT-SELEX experiment developing aptamers against Interleukin 10 receptor alpha chain (IL-10RA) and experimentally confirmed our predictions thus validating our computational methods. PMID:25870409

Video mining using combinations of unsupervised and supervised learning techniques

NASA Astrophysics Data System (ADS)

Divakaran, Ajay; Miyahara, Koji; Peker, Kadir A.; Radhakrishnan, Regunathan; Xiong, Ziyou

2003-12-01

We discuss the meaning and significance of the video mining problem, and present our work on some aspects of video mining. A simple definition of video mining is unsupervised discovery of patterns in audio-visual content. Such purely unsupervised discovery is readily applicable to video surveillance as well as to consumer video browsing applications. We interpret video mining as content-adaptive or "blind" content processing, in which the first stage is content characterization and the second stage is event discovery based on the characterization obtained in stage 1. We discuss the target applications and find that using a purely unsupervised approach are too computationally complex to be implemented on our product platform. We then describe various combinations of unsupervised and supervised learning techniques that help discover patterns that are useful to the end-user of the application. We target consumer video browsing applications such as commercial message detection, sports highlights extraction etc. We employ both audio and video features. We find that supervised audio classification combined with unsupervised unusual event discovery enables accurate supervised detection of desired events. Our techniques are computationally simple and robust to common variations in production styles etc.

Astronaut James Newman works with computers and GPS

NASA Image and Video Library

1993-09-20

STS051-16-028 (12-22 Sept 1993) --- On Discovery's middeck, astronaut James H. Newman, mission specialist, works with an array of computers, including one devoted to Global Positioning System (GPS) operations, a general portable onboard computer displaying a tracking map, a portable audio data modem and another payload and general support computer. Newman was joined by four other NASA astronauts for almost ten full days in space.

Computational Science: A Research Methodology for the 21st Century

NASA Astrophysics Data System (ADS)

Orbach, Raymond L.

2004-03-01

Computational simulation - a means of scientific discovery that employs computer systems to simulate a physical system according to laws derived from theory and experiment - has attained peer status with theory and experiment. Important advances in basic science are accomplished by a new "sociology" for ultrascale scientific computing capability (USSCC), a fusion of sustained advances in scientific models, mathematical algorithms, computer architecture, and scientific software engineering. Expansion of current capabilities by factors of 100 - 1000 open up new vistas for scientific discovery: long term climatic variability and change, macroscopic material design from correlated behavior at the nanoscale, design and optimization of magnetic confinement fusion reactors, strong interactions on a computational lattice through quantum chromodynamics, and stellar explosions and element production. The "virtual prototype," made possible by this expansion, can markedly reduce time-to-market for industrial applications such as jet engines and safer, more fuel efficient cleaner cars. In order to develop USSCC, the National Energy Research Scientific Computing Center (NERSC) announced the competition "Innovative and Novel Computational Impact on Theory and Experiment" (INCITE), with no requirement for current DOE sponsorship. Fifty nine proposals for grand challenge scientific problems were submitted for a small number of awards. The successful grants, and their preliminary progress, will be described.

76 FR 36951 - In the Matter of Shiming U.S., Inc., Si Mei Te Food Ltd. (f/k/a China Discovery Acquisition Corp...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-23

... Electronics, Inc.; Order of Suspension of Trading June 21, 2011. It appears to the Securities and Exchange... information concerning the securities of SJ Electronics, Inc. because it has not filed any periodic reports...

Ocular Tolerance of Contemporary Electronic Display Devices.

PubMed

Clark, Andrew J; Yang, Paul; Khaderi, Khizer R; Moshfeghi, Andrew A

2018-05-01

Electronic displays have become an integral part of life in the developed world since the revolution of mobile computing a decade ago. With the release of multiple consumer-grade virtual reality (VR) and augmented reality (AR) products in the past 2 years utilizing head-mounted displays (HMDs), as well as the development of low-cost, smartphone-based HMDs, the ability to intimately interact with electronic screens is greater than ever. VR/AR HMDs also place the display at much closer ocular proximity than traditional electronic devices while also isolating the user from the ambient environment to create a "closed" system between the user's eyes and the display. Whether the increased interaction with these devices places the user's retina at higher risk of damage is currently unclear. Herein, the authors review the discovery of photochemical damage of the retina from visible light as well as summarize relevant clinical and preclinical data regarding the influence of modern display devices on retinal health. Multiple preclinical studies have been performed with modern light-emitting diode technology demonstrating damage to the retina at modest exposure levels, particularly from blue-light wavelengths. Unfortunately, high-quality in-human studies are lacking, and the small clinical investigations performed to date have failed to keep pace with the rapid evolutions in display technology. Clinical investigations assessing the effect of HMDs on human retinal function are also yet to be performed. From the available data, modern consumer electronic displays do not appear to pose any acute risk to vision with average use; however, future studies with well-defined clinical outcomes and illuminance metrics are needed to better understand the long-term risks of cumulative exposure to electronic displays in general and with "closed" VR/AR HMDs in particular. [Ophthalmic Surg Lasers Imaging Retina. 2018;49:346-354.]. Copyright 2018, SLACK Incorporated.

Information Science Research: The Search for the Nature of Information.

ERIC Educational Resources Information Center

Kochen, Manfred

1984-01-01

High-level scientific research in the information sciences is illustrated by sampling of recent discoveries involving adaptive information processing strategies, computer and information systems, centroid scaling, economic growth of computer and communication industries, and information flow in biological systems. Relationship of information…

Accelerating the discovery of space-time patterns of infectious diseases using parallel computing.

PubMed

Hohl, Alexander; Delmelle, Eric; Tang, Wenwu; Casas, Irene

2016-11-01

Infectious diseases have complex transmission cycles, and effective public health responses require the ability to monitor outbreaks in a timely manner. Space-time statistics facilitate the discovery of disease dynamics including rate of spread and seasonal cyclic patterns, but are computationally demanding, especially for datasets of increasing size, diversity and availability. High-performance computing reduces the effort required to identify these patterns, however heterogeneity in the data must be accounted for. We develop an adaptive space-time domain decomposition approach for parallel computation of the space-time kernel density. We apply our methodology to individual reported dengue cases from 2010 to 2011 in the city of Cali, Colombia. The parallel implementation reaches significant speedup compared to sequential counterparts. Density values are visualized in an interactive 3D environment, which facilitates the identification and communication of uneven space-time distribution of disease events. Our framework has the potential to enhance the timely monitoring of infectious diseases. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computational discovery of extremal microstructure families

PubMed Central

Chen, Desai; Skouras, Mélina; Zhu, Bo; Matusik, Wojciech

2018-01-01

Modern fabrication techniques, such as additive manufacturing, can be used to create materials with complex custom internal structures. These engineered materials exhibit a much broader range of bulk properties than their base materials and are typically referred to as metamaterials or microstructures. Although metamaterials with extraordinary properties have many applications, designing them is very difficult and is generally done by hand. We propose a computational approach to discover families of microstructures with extremal macroscale properties automatically. Using efficient simulation and sampling techniques, we compute the space of mechanical properties covered by physically realizable microstructures. Our system then clusters microstructures with common topologies into families. Parameterized templates are eventually extracted from families to generate new microstructure designs. We demonstrate these capabilities on the computational design of mechanical metamaterials and present five auxetic microstructure families with extremal elastic material properties. Our study opens the way for the completely automated discovery of extremal microstructures across multiple domains of physics, including applications reliant on thermal, electrical, and magnetic properties. PMID:29376124

A systematic approach to novel virus discovery in emerging infectious disease outbreaks.

PubMed

Sridhar, Siddharth; To, Kelvin K W; Chan, Jasper F W; Lau, Susanna K P; Woo, Patrick C Y; Yuen, Kwok-Yung

2015-05-01

The discovery of novel viruses is of great importance to human health-both in the setting of emerging infectious disease outbreaks and in disease syndromes of unknown etiology. Despite the recent proliferation of many efficient virus discovery methods, careful selection of a combination of methods is important to demonstrate a novel virus, its clinical associations, and its relevance in a timely manner. The identification of a patient or an outbreak with distinctive clinical features and negative routine microbiological workup is often the starting point for virus hunting. This review appraises the roles of culture, electron microscopy, and nucleic acid detection-based methods in optimizing virus discovery. Cell culture is generally slow but may yield viable virus. Although the choice of cell line often involves trial and error, it may be guided by the clinical syndrome. Electron microscopy is insensitive but fast, and may provide morphological clues to choice of cell line or consensus primers for nucleic acid detection. Consensus primer PCR can be used to detect viruses that are closely related to known virus families. Random primer amplification and high-throughput sequencing can catch any virus genome but cannot yield an infectious virion for testing Koch postulates. A systematic approach that incorporates carefully chosen combinations of virus detection techniques is required for successful virus discovery. Copyright © 2015 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

Discovery Learning, Representation, and Explanation within a Computer-Based Simulation: Finding the Right Mix

ERIC Educational Resources Information Center

Rieber, Lloyd P.; Tzeng, Shyh-Chii; Tribble, Kelly

2004-01-01

The purpose of this research was to explore how adult users interact and learn during an interactive computer-based simulation supplemented with brief multimedia explanations of the content. A total of 52 college students interacted with a computer-based simulation of Newton's laws of motion in which they had control over the motion of a simple…

KSC-2009-2100

NASA Image and Video Library

2009-03-15

CAPE CANAVERAL, Fla. – In Firing Room 4 of the Launch Control Center at NASA's Kennedy Space Center in Florida, Flow Director for space shuttle Discovery Stephanie Stilson, Assistant Launch Director Pete Nickolenko and Shuttle Launch Director Mike Leinbach check the computers for follow-up images of the launch of space shuttle Discovery on the STS-119 mission. Launch was on time at 7:43 p.m. EDT. The STS-119 mission is the 28th to the space station and Discovery's 36th flight. Discovery will deliver the final pair of power-generating solar array wings and the S6 truss segment. Installation of S6 will signal the station's readiness to house a six-member crew for conducting increased science. Photo credit: NASA/Kim Shiflett

A unified approach to computational drug discovery.

PubMed

Tseng, Chih-Yuan; Tuszynski, Jack

2015-11-01

It has been reported that a slowdown in the development of new medical therapies is affecting clinical outcomes. The FDA has thus initiated the Critical Path Initiative project investigating better approaches. We review the current strategies in drug discovery and focus on the advantages of the maximum entropy method being introduced in this area. The maximum entropy principle is derived from statistical thermodynamics and has been demonstrated to be an inductive inference tool. We propose a unified method to drug discovery that hinges on robust information processing using entropic inductive inference. Increasingly, applications of maximum entropy in drug discovery employ this unified approach and demonstrate the usefulness of the concept in the area of pharmaceutical sciences. Copyright © 2015. Published by Elsevier Ltd.

Large-scale high-throughput computer-aided discovery of advanced materials using cloud computing

NASA Astrophysics Data System (ADS)

Bazhirov, Timur; Mohammadi, Mohammad; Ding, Kevin; Barabash, Sergey

Recent advances in cloud computing made it possible to access large-scale computational resources completely on-demand in a rapid and efficient manner. When combined with high fidelity simulations, they serve as an alternative pathway to enable computational discovery and design of new materials through large-scale high-throughput screening. Here, we present a case study for a cloud platform implemented at Exabyte Inc. We perform calculations to screen lightweight ternary alloys for thermodynamic stability. Due to the lack of experimental data for most such systems, we rely on theoretical approaches based on first-principle pseudopotential density functional theory. We calculate the formation energies for a set of ternary compounds approximated by special quasirandom structures. During an example run we were able to scale to 10,656 CPUs within 7 minutes from the start, and obtain results for 296 compounds within 38 hours. The results indicate that the ultimate formation enthalpy of ternary systems can be negative for some of lightweight alloys, including Li and Mg compounds. We conclude that compared to traditional capital-intensive approach that requires in on-premises hardware resources, cloud computing is agile and cost-effective, yet scalable and delivers similar performance.

Predicting future discoveries from current scientific literature.

PubMed

Petrič, Ingrid; Cestnik, Bojan

2014-01-01

Knowledge discovery in biomedicine is a time-consuming process starting from the basic research, through preclinical testing, towards possible clinical applications. Crossing of conceptual boundaries is often needed for groundbreaking biomedical research that generates highly inventive discoveries. We demonstrate the ability of a creative literature mining method to advance valuable new discoveries based on rare ideas from existing literature. When emerging ideas from scientific literature are put together as fragments of knowledge in a systematic way, they may lead to original, sometimes surprising, research findings. If enough scientific evidence is already published for the association of such findings, they can be considered as scientific hypotheses. In this chapter, we describe a method for the computer-aided generation of such hypotheses based on the existing scientific literature. Our literature-based discovery of NF-kappaB with its possible connections to autism was recently approved by scientific community, which confirms the ability of our literature mining methodology to accelerate future discoveries based on rare ideas from existing literature.

Computer Conferencing and Electronic Messaging. Conference Proceedings (Guelph, Ontario, Canada, January 22-23, 1985).

ERIC Educational Resources Information Center

Guelph Univ. (Ontario).

This 21-paper collection examines various issues in electronic networking and conferencing with computers, including design issues, conferencing in education, electronic messaging, computer conferencing applications, social issues of computer conferencing, and distributed computer conferencing. In addition to a keynote address, "Computer…

Service Discovery Oriented Management System Construction Method

NASA Astrophysics Data System (ADS)

Li, Huawei; Ren, Ying

2017-10-01

In order to solve the problem that there is no uniform method for design service quality management system in large-scale complex service environment, this paper proposes a distributed service-oriented discovery management system construction method. Three measurement functions are proposed to compute nearest neighbor user similarity at different levels. At present in view of the low efficiency of service quality management systems, three solutions are proposed to improve the efficiency of the system. Finally, the key technologies of distributed service quality management system based on service discovery are summarized through the factor addition and subtraction of quantitative experiment.

New approaches to structure-based discovery of dengue protease inhibitors.

PubMed

Tomlinson, S M; Malmstrom, R D; Watowich, S J

2009-06-01

Dengue virus (DENV), a member of the family Flaviviridae, presents a tremendous threat to global health since an estimated 2.5 billion people worldwide are at risk for epidemic transmission. DENV infections are primarily restricted to sub-tropical and tropical regions; however, there is concern that the virus will spread into new regions including the United States. There are no approved antiviral drugs or vaccines to combat dengue infection, although DENV vaccines have entered Phase 3 clinical trials. Drug discovery and development efforts against DENV and other viral pathogens must overcome specificity, efficacy, safety, and resistance challenges before the shortage of licensed drugs to treat viral infections can be relieved. Current drug discovery methods are largely inefficient and thus relatively ineffective at tackling the growing threat to public health presented by emerging and remerging viral pathogens. This review discusses current and newly implemented structure-based computational efforts to discover antivirals that target the DENV NS3 protease, although it is clear that these computational tools can be applied to most disease targets.

Feedback-Driven Dynamic Invariant Discovery

NASA Technical Reports Server (NTRS)

Zhang, Lingming; Yang, Guowei; Rungta, Neha S.; Person, Suzette; Khurshid, Sarfraz

2014-01-01

Program invariants can help software developers identify program properties that must be preserved as the software evolves, however, formulating correct invariants can be challenging. In this work, we introduce iDiscovery, a technique which leverages symbolic execution to improve the quality of dynamically discovered invariants computed by Daikon. Candidate invariants generated by Daikon are synthesized into assertions and instrumented onto the program. The instrumented code is executed symbolically to generate new test cases that are fed back to Daikon to help further re ne the set of candidate invariants. This feedback loop is executed until a x-point is reached. To mitigate the cost of symbolic execution, we present optimizations to prune the symbolic state space and to reduce the complexity of the generated path conditions. We also leverage recent advances in constraint solution reuse techniques to avoid computing results for the same constraints across iterations. Experimental results show that iDiscovery converges to a set of higher quality invariants compared to the initial set of candidate invariants in a small number of iterations.

Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations.

PubMed

Cournia, Zoe; Allen, Bryce; Sherman, Woody

2017-12-26

Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.

OPENING REMARKS: SciDAC: Scientific Discovery through Advanced Computing

NASA Astrophysics Data System (ADS)

Strayer, Michael

2005-01-01

Good morning. Welcome to SciDAC 2005 and San Francisco. SciDAC is all about computational science and scientific discovery. In a large sense, computational science characterizes SciDAC and its intent is change. It transforms both our approach and our understanding of science. It opens new doors and crosses traditional boundaries while seeking discovery. In terms of twentieth century methodologies, computational science may be said to be transformational. There are a number of examples to this point. First are the sciences that encompass climate modeling. The application of computational science has in essence created the field of climate modeling. This community is now international in scope and has provided precision results that are challenging our understanding of our environment. A second example is that of lattice quantum chromodynamics. Lattice QCD, while adding precision and insight to our fundamental understanding of strong interaction dynamics, has transformed our approach to particle and nuclear science. The individual investigator approach has evolved to teams of scientists from different disciplines working side-by-side towards a common goal. SciDAC is also undergoing a transformation. This meeting is a prime example. Last year it was a small programmatic meeting tracking progress in SciDAC. This year, we have a major computational science meeting with a variety of disciplines and enabling technologies represented. SciDAC 2005 should position itself as a new corner stone for Computational Science and its impact on science. As we look to the immediate future, FY2006 will bring a new cycle to SciDAC. Most of the program elements of SciDAC will be re-competed in FY2006. The re-competition will involve new instruments for computational science, new approaches for collaboration, as well as new disciplines. There will be new opportunities for virtual experiments in carbon sequestration, fusion, and nuclear power and nuclear waste, as well as collaborations with industry and virtual prototyping. New instruments of collaboration will include institutes and centers while summer schools, workshops and outreach will invite new talent and expertise. Computational science adds new dimensions to science and its practice. Disciplines of fusion, accelerator science, and combustion are poised to blur the boundaries between pure and applied science. As we open the door into FY2006 we shall see a landscape of new scientific challenges: in biology, chemistry, materials, and astrophysics to name a few. The enabling technologies of SciDAC have been transformational as drivers of change. Planning for major new software systems assumes a base line employing Common Component Architectures and this has become a household word for new software projects. While grid algorithms and mesh refinement software have transformed applications software, data management and visualization have transformed our understanding of science from data. The Gordon Bell prize now seems to be dominated by computational science and solvers developed by TOPS ISIC. The priorities of the Office of Science in the Department of Energy are clear. The 20 year facilities plan is driven by new science. High performance computing is placed amongst the two highest priorities. Moore's law says that by the end of the next cycle of SciDAC we shall have peta-flop computers. The challenges of petascale computing are enormous. These and the associated computational science are the highest priorities for computing within the Office of Science. Our effort in Leadership Class computing is just a first step towards this goal. Clearly, computational science at this scale will face enormous challenges and possibilities. Performance evaluation and prediction will be critical to unraveling the needed software technologies. We must not lose sight of our overarching goal—that of scientific discovery. Science does not stand still and the landscape of science discovery and computing holds immense promise. In this environment, I believe it is necessary to institute a system of science based performance metrics to help quantify our progress towards science goals and scientific computing. As a final comment I would like to reaffirm that the shifting landscapes of science will force changes to our computational sciences, and leave you with the quote from Richard Hamming, 'The purpose of computing is insight, not numbers'.

Software and resources for computational medicinal chemistry

PubMed Central

Liao, Chenzhong; Sitzmann, Markus; Pugliese, Angelo; Nicklaus, Marc C

2011-01-01

Computer-aided drug design plays a vital role in drug discovery and development and has become an indispensable tool in the pharmaceutical industry. Computational medicinal chemists can take advantage of all kinds of software and resources in the computer-aided drug design field for the purposes of discovering and optimizing biologically active compounds. This article reviews software and other resources related to computer-aided drug design approaches, putting particular emphasis on structure-based drug design, ligand-based drug design, chemical databases and chemoinformatics tools. PMID:21707404

NASA Aerodynamics Program Annual Report 1991

DTIC Science & Technology

1992-04-01

results have been compared relation effort on an AH-1G Cobr : helicopter v- ,,odeI wind tunnel data at different has been completed. Computational...The computational studies cant discovery . Preliminary water-channel have shown the trapped vortex to be a viable and wind-tunnel tests have shown the

Brain-Congruent Instruction: Does the Computer Make It Feasible?

ERIC Educational Resources Information Center

Stewart, William J.

1984-01-01

Based on the premise that computers could translate brain research findings into classroom practice, this article presents discoveries concerning human brain development, organization, and operation, and describes brain activity monitoring devices, brain function and structure variables, and a procedure for monitoring and analyzing brain activity…

Teaching Molecular Biology with Microcomputers.

ERIC Educational Resources Information Center

Reiss, Rebecca; Jameson, David

1984-01-01

Describes a series of computer programs that use simulation and gaming techniques to present the basic principles of the central dogma of molecular genetics, mutation, and the genetic code. A history of discoveries in molecular biology is presented and the evolution of these computer assisted instructional programs is described. (MBR)

Computer Programming: A Medium for Teaching Problem Solving.

ERIC Educational Resources Information Center

Casey, Patrick J.

1997-01-01

Argues that including computer programming in the curriculum as a medium for instruction is a feasible alternative for teaching problem solving. Discusses the nature of problem solving; the problem-solving elements of discovery, motivation, practical learning situations and flexibility which are inherent in programming; capabilities of computer…

High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bouchard, Kristofer E.; Aimone, James B.; Chun, Miyoung

A lack of coherent plans to analyze, manage, and understand data threatens the various opportunities offered by new neuro-technologies. High-performance computing will allow exploratory analysis of massive datasets stored in standardized formats, hosted in open repositories, and integrated with simulations.

High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination

DOE PAGES

Bouchard, Kristofer E.; Aimone, James B.; Chun, Miyoung; ...

2016-11-01

A lack of coherent plans to analyze, manage, and understand data threatens the various opportunities offered by new neuro-technologies. High-performance computing will allow exploratory analysis of massive datasets stored in standardized formats, hosted in open repositories, and integrated with simulations.

Micro-CAI in Education: Some Considerations.

ERIC Educational Resources Information Center

Majsterek, David

This paper focuses on the applications which best suit the microcomputer in an educational setting with emphasis on adapting effective pedagogical practice to the computer's programability and delivery capabilities. Discovery learning and "being told" are identified as two types of computer assisted instruction (CAI) and sample uses of…

Computer aided drug discovery of highly ligand efficient, low molecular weight imidazopyridine analogs as FLT3 inhibitors.

PubMed

Frett, Brendan; McConnell, Nick; Smith, Catherine C; Wang, Yuanxiang; Shah, Neil P; Li, Hong-yu

2015-04-13

The FLT3 kinase represents an attractive target to effectively treat AML. Unfortunately, no FLT3 targeted therapeutic is currently approved. In line with our continued interests in treating kinase related disease for anti-FLT3 mutant activity, we utilized pioneering synthetic methodology in combination with computer aided drug discovery and identified low molecular weight, highly ligand efficient, FLT3 kinase inhibitors. Compounds were analyzed for biochemical inhibition, their ability to selectively inhibit cell proliferation, for FLT3 mutant activity, and preliminary aqueous solubility. Validated hits were discovered that can serve as starting platforms for lead candidates. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Photochemically Switching Diamidocarbene Spin States Leads to Reversible Büchner Ring Expansions.

PubMed

Perera, Tharushi A; Reinheimer, Eric W; Hudnall, Todd W

2017-10-18

The discovery of thermal and photochemical control by Woodward and Hoffmann revolutionized how we understand chemical reactivity. Similarly, we now describe the first example of a carbene that exhibits differing thermal and photochemical reactivity. When a singlet ground-state N,N'-diamidocarbene 1 was photolyzed at 380 nm, excitation to a triplet state was observed. The triplet-state electronic structure was characteristic of the expected biradical σ 1 p π 1 spin configuration according to a combination of spectroscopic and computational methods. Surprisingly, the triplet state of 1 was found to engage a series of arenes in thermally reversible Büchner ring expansion reactions, marking the first examples where both cyclopropanation and ring expansion of arenes were rendered reversible. Not only are these photochemical reactions different from the known thermal chemistry of 1, but the reversibility enabled us to perform the first examples of photochemically induced arene exchange/expansion reactions at a single carbon center.

Structure and Bonding in Heme-Nitrosyl Complexes and Implications for Biology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lehnert, Nicolai; Scheidt, W. Robert; Wolf, Matthew W.

This review summarizes our current understanding of the geometric and electronic structures of ferrous and ferric heme–nitrosyls, which are of key importance for the biological functions and transformations of NO. In-depth correlations are made between these properties and the reactivities of these species. Here, a focus is put on the discoveries that have been made in the last 10 years, but previous findings are also included as necessary. Besides this, ferrous heme–nitroxyl complexes are also considered, which have become of increasing interest recently due to their roles as intermediates in NO and multiheme nitrite reductases, and because of the potentialmore » role of HNO as a signaling molecule in mammals. In recent years, computational methods have received more attention as a means of investigating enzyme reaction mechanisms, and some important findings from these theoretical studies are also highlighted in this chapter.« less

"Scientific peep show": the human body in contemporary science museums.

PubMed

Canadelli, Elena

2011-01-01

The essay focuses on the discourse about the human body developed by contemporary science museums with educational and instructive purposes directed at the general public. These museums aim mostly at mediating concepts such as health and prevention. The current scenario is linked with two examples of past museums: the popular anatomical museums which emerged during the 19th century and the health museums thrived between 1910 and 1940. On the museological path about the human body self-care we went from the emotionally involving anatomical Venuses to the inexpressive Transparent Man, from anatomical specimens of ill organs and deformed subjects to the mechanical and electronic models of the healthy body. Today the body is made transparent by the new medical diagnostics and by the latest discoveries of endoscopy. The way museums and science centers presently display the human body involves computers, 3D animation, digital technologies, hands-on models of large size human parts.

Identification and design principles of low hole effective mass p-type transparent conducting oxides

PubMed Central

Hautier, Geoffroy; Miglio, Anna; Ceder, Gerbrand; Rignanese, Gian-Marco; Gonze, Xavier

2013-01-01

The development of high-performance transparent conducting oxides is critical to many technologies from transparent electronics to solar cells. Whereas n-type transparent conducting oxides are present in many devices, their p-type counterparts are not largely commercialized, as they exhibit much lower carrier mobilities due to the large hole effective masses of most oxides. Here we conduct a high-throughput computational search on thousands of binary and ternary oxides and identify several highly promising compounds displaying exceptionally low hole effective masses (up to an order of magnitude lower than state-of-the-art p-type transparent conducting oxides), as well as wide band gaps. In addition to the discovery of specific compounds, the chemical rationalization of our findings opens new directions, beyond current Cu-based chemistries, for the design and development of future p-type transparent conducting oxides. PMID:23939205

Dreaming in plastic

NASA Astrophysics Data System (ADS)

Korzhov, Marianna; Andelman, David; Shikler, Rafi

2008-07-01

Plastic is one of the most versatile materials available. It is cheap, flexible and easy to process, and as a result it is all around us - from our computer keyboards to the soles of our shoes. One of its most common applications is as an insulating coating for electric wires; indeed, plastic is well known for its insulating characteristics. It came as something of a surprise, therefore, when in the late 1970s a new generation of plastics was discovered that displayed exactly the opposite behaviour - the ability to conduct electricity. In fact, plastics can be made with a whole range of conductivities - there are polymer materials that behave like semiconductors and there are those that can conduct as well as metals. This discovery sparked a revolution in the electronics community, and three decades of research effort is now yielding a range of stunning new applications for this ubiquitous material.

Computer-Aided Drug Design Applied to Marine Drug Discovery: Meridianins as Alzheimer's Disease Therapeutic Agents.

PubMed

Llorach-Pares, Laura; Nonell-Canals, Alfons; Sanchez-Martinez, Melchor; Avila, Conxita

2017-11-27

Computer-aided drug discovery/design (CADD) techniques allow the identification of natural products that are capable of modulating protein functions in pathogenesis-related pathways, constituting one of the most promising lines followed in drug discovery. In this paper, we computationally evaluated and reported the inhibitory activity found in meridianins A-G, a group of marine indole alkaloids isolated from the marine tunicate Aplidium , against various protein kinases involved in Alzheimer's disease (AD), a neurodegenerative pathology characterized by the presence of neurofibrillary tangles (NFT). Balance splitting between tau kinase and phosphate activities caused tau hyperphosphorylation and, thereby, its aggregation and NTF formation. Inhibition of specific kinases involved in its phosphorylation pathway could be one of the key strategies to reverse tau hyperphosphorylation and would represent an approach to develop drugs to palliate AD symptoms. Meridianins bind to the adenosine triphosphate (ATP) binding site of certain protein kinases, acting as ATP competitive inhibitors. These compounds show very promising scaffolds to design new drugs against AD, which could act over tau protein kinases Glycogen synthetase kinase-3 Beta (GSK3β) and Casein kinase 1 delta (CK1δ, CK1D or KC1D), and dual specificity kinases as dual specificity tyrosine phosphorylation regulated kinase 1 (DYRK1A) and cdc2-like kinases (CLK1). This work is aimed to highlight the role of CADD techniques in marine drug discovery and to provide precise information regarding the binding mode and strength of meridianins against several protein kinases that could help in the future development of anti-AD drugs.

Systematic approach for simultaneously correcting the band-gap and p - d separation errors of common cation III-V or II-VI binaries in density functional theory calculations within a local density approximation

DOE PAGES

Wang, Jianwei; Zhang, Yong; Wang, Lin-Wang

2015-07-31

We propose a systematic approach that can empirically correct three major errors typically found in a density functional theory (DFT) calculation within the local density approximation (LDA) simultaneously for a set of common cation binary semiconductors, such as III-V compounds, (Ga or In)X with X = N,P,As,Sb, and II-VI compounds, (Zn or Cd)X, with X = O,S,Se,Te. By correcting (1) the binary band gaps at high-symmetry points , L, X, (2) the separation of p-and d-orbital-derived valence bands, and (3) conduction band effective masses to experimental values and doing so simultaneously for common cation binaries, the resulting DFT-LDA-based quasi-first-principles methodmore » can be used to predict the electronic structure of complex materials involving multiple binaries with comparable accuracy but much less computational cost than a GW level theory. This approach provides an efficient way to evaluate the electronic structures and other material properties of complex systems, much needed for material discovery and design.« less

Habituation based synaptic plasticity and organismic learning in a quantum perovskite.

PubMed

Zuo, Fan; Panda, Priyadarshini; Kotiuga, Michele; Li, Jiarui; Kang, Mingu; Mazzoli, Claudio; Zhou, Hua; Barbour, Andi; Wilkins, Stuart; Narayanan, Badri; Cherukara, Mathew; Zhang, Zhen; Sankaranarayanan, Subramanian K R S; Comin, Riccardo; Rabe, Karin M; Roy, Kaushik; Ramanathan, Shriram

2017-08-14

A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making. This behavior, known as habituation, is universal among all forms of life with a central nervous system, and is also observed in single-cell organisms that do not possess a brain. Here, we report the discovery of habituation-based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by first-principles theory, synchrotron investigations, ab initio molecular dynamics simulations, and in situ environmental breathing studies. We implement a learning algorithm inspired by the conductance relaxation behavior of perovskites that naturally incorporates habituation, and demonstrate learning to forget: a key feature of animal and human brains. Incorporating this elementary skill in learning boosts the capability of neural computing in a sequential, dynamic environment.Habituation is a learning mechanism that enables control over forgetting and learning. Zuo, Panda et al., demonstrate adaptive synaptic plasticity in SmNiO 3 perovskites to address catastrophic forgetting in a dynamic learning environment via hydrogen-induced electron localization.

Systematic approach for simultaneously correcting the band-gap and p -d separation errors of common cation III-V or II-VI binaries in density functional theory calculations within a local density approximation

NASA Astrophysics Data System (ADS)

Wang, Jianwei; Zhang, Yong; Wang, Lin-Wang

2015-07-01

We propose a systematic approach that can empirically correct three major errors typically found in a density functional theory (DFT) calculation within the local density approximation (LDA) simultaneously for a set of common cation binary semiconductors, such as III-V compounds, (Ga or In)X with X =N ,P ,As ,Sb , and II-VI compounds, (Zn or Cd)X , with X =O ,S ,Se ,Te . By correcting (1) the binary band gaps at high-symmetry points Γ , L , X , (2) the separation of p -and d -orbital-derived valence bands, and (3) conduction band effective masses to experimental values and doing so simultaneously for common cation binaries, the resulting DFT-LDA-based quasi-first-principles method can be used to predict the electronic structure of complex materials involving multiple binaries with comparable accuracy but much less computational cost than a GW level theory. This approach provides an efficient way to evaluate the electronic structures and other material properties of complex systems, much needed for material discovery and design.

The impact of pharmacophore modeling in drug design.

PubMed

Guner, Osman F

2005-07-01

With the reliable use of computer simulations in scientific research, it is possible to achieve significant increases in productivity as well as a reduction in research costs compared with experimental approaches. For example, computer-simulation can substantially enchance productivity by focusing the scientist to better, more informed choices, while also driving the 'fail-early' concept to result in a significant reduction in cost. Pharmacophore modeling is a reliable computer-aided design tool used in the discovery of new classes of compounds for a given therapeutic category. This commentary will briefly review the benefits and applications of this technology in drug discovery and design, and will also highlight its historical evolution. The two most commonly used approaches for pharmacophore model development will be discussed, and several examples of how this technology was successfully applied to identify new potent leads will be provided. The article concludes with a brief outline of the controversial issue of patentability of pharmacophore models.

Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.

PubMed

Lampa, Samuel; Alvarsson, Jonathan; Spjuth, Ola

2016-01-01

Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.

Discovery of the Kalman filter as a practical tool for aerospace and industry

NASA Technical Reports Server (NTRS)

Mcgee, L. A.; Schmidt, S. F.

1985-01-01

The sequence of events which led the researchers at Ames Research Center to the early discovery of the Kalman filter shortly after its introduction into the literature is recounted. The scientific breakthroughs and reformulations that were necessary to transform Kalman's work into a useful tool for a specific aerospace application are described. The resulting extended Kalman filter, as it is now known, is often still referred to simply as the Kalman filter. As the filter's use gained in popularity in the scientific community, the problems of implementation on small spaceborne and airborne computers led to a square-root formulation of the filter to overcome numerical difficulties associated with computer word length. The work that led to this new formulation is also discussed, including the first airborne computer implementation and flight test. Since then the applications of the extended and square-root formulations of the Kalman filter have grown rapidly throughout the aerospace industry.

Early phase drug discovery: cheminformatics and computational techniques in identifying lead series.

PubMed

Duffy, Bryan C; Zhu, Lei; Decornez, Hélène; Kitchen, Douglas B

2012-09-15

Early drug discovery processes rely on hit finding procedures followed by extensive experimental confirmation in order to select high priority hit series which then undergo further scrutiny in hit-to-lead studies. The experimental cost and the risk associated with poor selection of lead series can be greatly reduced by the use of many different computational and cheminformatic techniques to sort and prioritize compounds. We describe the steps in typical hit identification and hit-to-lead programs and then describe how cheminformatic analysis assists this process. In particular, scaffold analysis, clustering and property calculations assist in the design of high-throughput screening libraries, the early analysis of hits and then organizing compounds into series for their progression from hits to leads. Additionally, these computational tools can be used in virtual screening to design hit-finding libraries and as procedures to help with early SAR exploration. Copyright © 2012 Elsevier Ltd. All rights reserved.

Atmospheric neutrinos and discovery of neutrino oscillations

PubMed Central

Kajita, Takaaki

2010-01-01

Neutrino oscillation was discovered through studies of neutrinos produced by cosmic-ray interactions in the atmosphere. These neutrinos are called atmospheric neutrinos. They are produced as decay products in hadronic showers resulting from collisions of cosmic rays with nuclei in the atmosphere. Electron-neutrinos and muon-neutrinos are produced mainly by the decay chain of charged pions to muons to electrons. Atmospheric neutrino experiments observed zenith-angle and energy dependent deficit of muon-neutrino events. Neutrino oscillations between muon-neutrinos and tau-neutrinos explain these data well. Neutrino oscillations imply that neutrinos have small but non-zero masses. The small neutrino masses have profound implications to our understanding of elementary particle physics and the Universe. This article discusses the experimental discovery of neutrino oscillations. PMID:20431258

Role of surface defects on the formation of the 2-dimensional electron gas at polar interfaces

NASA Astrophysics Data System (ADS)

Artacho, Emilio; Aguado-Puente, Pablo

2014-03-01

The discovery of a 2-dimensional electron gas (2DEG) at the interface between two insulators, LaAlO3 and SrTiO3, has fuelled a great research activity on this and similar systems in the last years. The electronic reconstruction model, typically invoked to explain the formation of the 2DEG, while being intuitive and successful on predicting fundamental aspects of this phenomenon like the critical thickness of LaAlO3, fails to explain many other experimental observations. Oxygen vacancies, on the other hand, are known to dramatically affect the physical behaviour of this system, but their role at the atomic level is far from well understood. Here we perform ab initio simulations in order to assess whether the formation of oxygen vacancies at the surface of the polar material can account for various recent experimental results that defy the current theoretical understanding of these interfaces. We simulate SrTiO3/LaAlO3 slabs with various concentrations of surface oxygen vacancies and analyze the role of the defects on the formation of the metallic interface, their electrostatic coupling with the 2DEG and the interplay with the different instabilities of the materials involved. Financial support from Spanish MINECO under grant FIS2012-37549-C05-01. Computational resources provided by the Red Espñola de Supercomputación and DIPC.

Band Gap Engineering of Titania Systems Purposed for Photocatalytic Activity

NASA Astrophysics Data System (ADS)

Thurston, Cameron

Ab initio computer aided design drastically increases candidate population for highly specified material discovery and selection. These simulations, carried out through a first-principles computational approach, accurately extrapolate material properties and behavior. Titanium Dioxide (TiO2 ) is one such material that stands to gain a great deal from the use of these simulations. In its anatase form, titania (TiO2 ) has been found to exhibit a band gap nearing 3.2 eV. If titania is to become a viable alternative to other contemporary photoactive materials exhibiting band gaps better suited for the solar spectrum, then the band gap must be subsequently reduced. To lower the energy needed for electronic excitation, both transition metals and non-metals have been extensively researched and are currently viable candidates for the continued reduction of titania's band gap. The introduction of multicomponent atomic doping introduces new energy bands which tend to both reduce the band gap and recombination loss. Ta-N, Nb-N, V-N, Cr-N, Mo-N, and W-N substitutions were studied in titania and subsequent energy and band gap calculations show a favorable band gap reduction in the case of passivated systems.

The Computer's Debt to Science.

ERIC Educational Resources Information Center

Branscomb, Lewis M.

1984-01-01

Discusses discoveries and applications of science that have enabled the computer industry to introduce new technology each year and produce 25 percent more for the customer at constant cost. Potential limits to progress, disc storage technology, programming and end-user interface, and designing for ease of use are considered. Glossary is included.…

Individual Differences in Learning from an Intelligent Discovery World: Smithtown.

ERIC Educational Resources Information Center

Shute, Valerie J.

"Smithtown" is an intelligent computer program designed to enhance an individual's scientific inquiry skills as well as to provide an environment for learning principles of basic microeconomics. It was hypothesized that intelligent computer instruction on applying effective interrogative skills (e.g., changing one variable at a time…

Forrester types on laptop computer in the orbiter middeck

NASA Image and Video Library

2001-08-13

STS105-E-5158 (13 August 2001) --- Astronaut Patrick G. Forrester, STS-105 mission specialist, inputs data on a laptop computer on the mid deck of the Space Shuttle Discovery, which is currently docked to the International Space Station (ISS). The image was recorded with a digital still camera.

Ontology-Driven Discovery of Scientific Computational Entities

ERIC Educational Resources Information Center

Brazier, Pearl W.

2010-01-01

Many geoscientists use modern computational resources, such as software applications, Web services, scientific workflows and datasets that are readily available on the Internet, to support their research and many common tasks. These resources are often shared via human contact and sometimes stored in data portals; however, they are not necessarily…

Visualising "Junk" DNA through Bioinformatics

ERIC Educational Resources Information Center

Elwess, Nancy L.; Latourelle, Sandra M.; Cauthorn, Olivia

2005-01-01

One of the hottest areas of science today is the field in which biology, information technology,and computer science are merged into a single discipline called bioinformatics. This field enables the discovery and analysis of biological data, including nucleotide and amino acid sequences that are easily accessed through the use of computers. As…

The 'Biologically-Inspired Computing' Column

NASA Technical Reports Server (NTRS)

Hinchey, Mike

2006-01-01

The field of Biology changed dramatically in 1953, with the determination by Francis Crick and James Dewey Watson of the double helix structure of DNA. This discovery changed Biology for ever, allowing the sequencing of the human genome, and the emergence of a "new Biology" focused on DNA, genes, proteins, data, and search. Computational Biology and Bioinformatics heavily rely on computing to facilitate research into life and development. Simultaneously, an understanding of the biology of living organisms indicates a parallel with computing systems: molecules in living cells interact, grow, and transform according to the "program" dictated by DNA. Moreover, paradigms of Computing are emerging based on modelling and developing computer-based systems exploiting ideas that are observed in nature. This includes building into computer systems self-management and self-governance mechanisms that are inspired by the human body's autonomic nervous system, modelling evolutionary systems analogous to colonies of ants or other insects, and developing highly-efficient and highly-complex distributed systems from large numbers of (often quite simple) largely homogeneous components to reflect the behaviour of flocks of birds, swarms of bees, herds of animals, or schools of fish. This new field of "Biologically-Inspired Computing", often known in other incarnations by other names, such as: Autonomic Computing, Pervasive Computing, Organic Computing, Biomimetics, and Artificial Life, amongst others, is poised at the intersection of Computer Science, Engineering, Mathematics, and the Life Sciences. Successes have been reported in the fields of drug discovery, data communications, computer animation, control and command, exploration systems for space, undersea, and harsh environments, to name but a few, and augur much promise for future progress.

Einstein@home discovery of four young gamma-ray pulsars in Fermi LAT data

DOE PAGES

Pletsch, Holger J.; Guillemot, L.; Allen, B.; ...

2013-11-26

Here, we report the discovery of four gamma-ray pulsars, detected in computing-intensive blind searches of data from the Fermi Large Area Telescope (LAT). The pulsars were found using a novel search approach, combining volunteer distributed computing via Einstein@Home and methods originally developed in gravitational-wave astronomy. The pulsars PSRs J0554+3107, J1422–6138, J1522–5735, and J1932+1916 are young and energetic, with characteristic ages between 35 and 56 kyr and spin-down powers in the range 6 × 10 34—10 36 erg s –1. They are located in the Galactic plane and have rotation rates of less than 10 Hz, among which the 2.1 Hzmore » spin frequency of PSR J0554+3107 is the slowest of any known gamma-ray pulsar. For two of the new pulsars, we find supernova remnants coincident on the sky and discuss the plausibility of such associations. Deep radio follow-up observations found no pulsations, suggesting that all four pulsars are radio-quiet as viewed from Earth. These discoveries, the first gamma-ray pulsars found by volunteer computing, motivate continued blind pulsar searches of the many other unidentified LAT gamma-ray sources.« less

EINSTEIN@HOME DISCOVERY OF FOUR YOUNG GAMMA-RAY PULSARS IN FERMI LAT DATA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pletsch, H. J.; Allen, B.; Aulbert, C.

2013-12-10

We report the discovery of four gamma-ray pulsars, detected in computing-intensive blind searches of data from the Fermi Large Area Telescope (LAT). The pulsars were found using a novel search approach, combining volunteer distributed computing via Einstein@Home and methods originally developed in gravitational-wave astronomy. The pulsars PSRs J0554+3107, J1422–6138, J1522–5735, and J1932+1916 are young and energetic, with characteristic ages between 35 and 56 kyr and spin-down powers in the range 6 × 10{sup 34}—10{sup 36} erg s{sup –1}. They are located in the Galactic plane and have rotation rates of less than 10 Hz, among which the 2.1 Hz spin frequency of PSR J0554+3107 ismore » the slowest of any known gamma-ray pulsar. For two of the new pulsars, we find supernova remnants coincident on the sky and discuss the plausibility of such associations. Deep radio follow-up observations found no pulsations, suggesting that all four pulsars are radio-quiet as viewed from Earth. These discoveries, the first gamma-ray pulsars found by volunteer computing, motivate continued blind pulsar searches of the many other unidentified LAT gamma-ray sources.« less

Hot-spot analysis for drug discovery targeting protein-protein interactions.

PubMed

Rosell, Mireia; Fernández-Recio, Juan

2018-04-01

Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.

THE EINSTEIN-HOME SEARCH FOR RADIO PULSARS AND PSR J2007+2722 DISCOVERY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Allen, B.; Knispel, B.; Aulbert, C.

Einstein-Home aggregates the computer power of hundreds of thousands of volunteers from 193 countries, to search for new neutron stars using data from electromagnetic and gravitational-wave detectors. This paper presents a detailed description of the search for new radio pulsars using Pulsar ALFA survey data from the Arecibo Observatory. The enormous computing power allows this search to cover a new region of parameter space; it can detect pulsars in binary systems with orbital periods as short as 11 minutes. We also describe the first Einstein-Home discovery, the 40.8 Hz isolated pulsar PSR J2007+2722, and provide a full timing model. PSRmore » J2007+2722's pulse profile is remarkably wide with emission over almost the entire spin period. This neutron star is most likely a disrupted recycled pulsar, about as old as its characteristic spin-down age of 404 Myr. However, there is a small chance that it was born recently, with a low magnetic field. If so, upper limits on the X-ray flux suggest but cannot prove that PSR J2007+2722 is at least {approx}100 kyr old. In the future, we expect that the massive computing power provided by volunteers should enable many additional radio pulsar discoveries.« less

STS-41 MS Shepherd uses DTO 1206 portable computer on OV-103's middeck

NASA Image and Video Library

1990-10-10

STS-41 Mission Specialist (MS) William M. Shepherd uses Detailed Test Objective (DTO) Space Station Cursor Control Device Evaluation MACINTOSH portable computer on the middeck of Discovery, Orbiter Vehicle (OV) 103. The computer is velcroed to forward lockers MF71C and MF71E. Surrounding Shepherd are checklists, the field sequential (FS) crew cabin camera, and a lighting fixture.

Exploring Natural Products from the Biodiversity of Pakistan for Computational Drug Discovery Studies: Collection, Optimization, Design and Development of A Chemical Database (ChemDP).

PubMed

Mirza, Shaher Bano; Bokhari, Habib; Fatmi, Muhammad Qaiser

2015-01-01

Pakistan possesses a rich and vast source of natural products (NPs). Some of these secondary metabolites have been identified as potent therapeutic agents. However, the medicinal usage of most of these compounds has not yet been fully explored. The discoveries for new scaffolds of NPs as inhibitors of certain enzymes or receptors using advanced computational drug discovery approaches are also limited due to the unavailability of accurate 3D structures of NPs. An organized database incorporating all relevant information, therefore, can facilitate to explore the medicinal importance of the metabolites from Pakistani Biodiversity. The Chemical Database of Pakistan (ChemDP; release 01) is a fully-referenced, evolving, web-based, virtual database which has been designed and developed to introduce natural products (NPs) and their derivatives from the biodiversity of Pakistan to Global scientific communities. The prime aim is to provide quality structures of compounds with relevant information for computer-aided drug discovery studies. For this purpose, over 1000 NPs have been identified from more than 400 published articles, for which 2D and 3D molecular structures have been generated with a special focus on their stereochemistry, where applicable. The PM7 semiempirical quantum chemistry method has been used to energy optimize the 3D structure of NPs. The 2D and 3D structures can be downloaded as .sdf, .mol, .sybyl, .mol2, and .pdb files - readable formats by many chemoinformatics/bioinformatics software packages. Each entry in ChemDP contains over 100 data fields representing various molecular, biological, physico-chemical and pharmacological properties, which have been properly documented in the database for end users. These pieces of information have been either manually extracted from the literatures or computationally calculated using various computational tools. Cross referencing to a major data repository i.e. ChemSpider has been made available for overlapping compounds. An android application of ChemDP is available at its website. The ChemDP is freely accessible at www.chemdp.com.

The Discovery of Extrasolar Planets by Backyard Astronomers

NASA Technical Reports Server (NTRS)

Castellano, Tim; Laughlin, Greg; DeVincenzi, D. (Technical Monitor)

2002-01-01

The discovery since 1995 of more than 80 planets around nearby solar-like stars and the photometric measurement of a transit of the jovian mass planet orbiting the solar-like star HD 209458 (producing a more than 1% drop in brightness that lasts 3 hours) has heralded a new era in astronomy. It has now been demonstrated that small telescopes equipped with sensitive and stable electronic detectors can produce fundamental scientific discoveries regarding the frequency and nature of planets outside the solar system. The modest equipment requirements for the discovery of extrasolar planetary transits of jovian mass planets in short period orbits around solar-like stars are fulfilled by commercial small aperture telescopes and CCD (charge coupled device) imagers common among amateur astronomers. With equipment already in hand and armed with target lists, observing techniques and software procedures developed by scientists at NASA's Ames Research Center and the University of California at Santa Cruz, non-professional astronomers can contribute significantly to the discovery and study of planets around others stars. In this way, we may resume (after a two century interruption!) the tradition of planet discoveries by amateur astronomers begun with William Herschel's 1787 discovery of the 'solar' planet Uranus.

Electron Diffraction Using Transmission Electron Microscopy

PubMed Central

Bendersky, Leonid A.; Gayle, Frank W.

2001-01-01

Electron diffraction via the transmission electron microscope is a powerful method for characterizing the structure of materials, including perfect crystals and defect structures. The advantages of electron diffraction over other methods, e.g., x-ray or neutron, arise from the extremely short wavelength (≈2 pm), the strong atomic scattering, and the ability to examine tiny volumes of matter (≈10 nm3). The NIST Materials Science and Engineering Laboratory has a history of discovery and characterization of new structures through electron diffraction, alone or in combination with other diffraction methods. This paper provides a survey of some of this work enabled through electron microscopy. PMID:27500060

Structural Studies of Amorphous Materials by Fluctuation Electron Microscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Treacy, Michael M. J.

Fluctuation Electron Microscopy (FEM) is a technique that examines the fluctuations in electron scattering across a uniformly thin amorphous sample. The statistics of the intensity fluctuations, mean and variance, reveal any underlying medium-range order present in the structure. The goals of this project were: (1) To determine the fundamentals of the scattering physics that gives rise to the variance signal in fluctuation electron microscopy (FEM); (2) To use these discoveries to find ways to quantify FEM; (3) To apply the FEM method to interesting and technologically important families of amorphous materials, particularly those with important applications in energy-related processes. Excellent progress was made in items (1) and (2). In stage (3) we did not examine the metamict zircons, as proposed. Instead, we examined films of polycrystalline and amorphous semi-conducting diamond. Significant accomplishments are: (1) A Reverse Monte Carlo procedure was successfully implemented to invert FEM data into a structural model. This is computer-intensive, but it demonstrated that diffraction and FEM data from amorphous silicon are most consistent with a paracrystallite model. This means that there is more diamond-like topology present in amorphous silicon than is predicted by the continuous random network model. (2) There is significant displacement decoherence arising in diffraction from amorphous silicon and carbon. The samples are being bombarded by the electron beam and atoms do not stay still while being irradiated – much more than was formerly understood. The atom motions cause the destructive and constructive interferences in the diffraction pattern to fluctuate with time, and it is the time-averaged speckle that is being measured. The variance is reduced by a factor m, 4 ≤ m ≤ 1000, relative to that predicted by kinematical scattering theory. (3) Speckle intensity obeys a gamma distribution, where the mean intensitymore » $$ \\overline{I}\\ $$ and m are the two parameters governing the shape of the gamma distribution profile. m is determined by the illumination spatial coherence, which is normally very high, and mostly by the displacement decoherence within the sample. (4) Amorphous materials are more affected by the electron beam than are crystalline materials. Different samples exhibit different disruptibility, as measured by the effective values of m that fit the data. (5) Understanding the origin of the displacement decoherence better should lead to efficient methods for computing the observed variance from amorphous materials.« less

Trapped buckyball turns up the amp

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hellemans, A.

1997-02-21

Since their discovery in 1985, the soccer-ball shaped molecules of 60 or more carbon atoms known as fullerenes have displayed a dazzling variety of tricks, coaxed by researchers to become superconductors at low temperature, light and carbon ion beam emittors. Two European researchers have added something to this list: they have created an electronic-mechanical amplifier from a single buckyball. This article describes the circumstances of the discovery and future directions of the research.

PANSAT satellite deployment from STS-95 Discovery's payload bay

NASA Image and Video Library

1998-10-30

STS095-E-5041 (30 Oct. 1998) --- PANSAT, a nonrecoverable satellite developed by the Naval Postgraduate School (NPS) in Monterey, California, is silhouetted against a sunglint effect on ocean waters below, following its deployment from the cargo bay of the Earth-orbiting Space Shuttle Discovery. The small ball-shaped payload is basically a tiny telecommunications satellite. The photo was recorded with an electronic still camera (ESC) at 1:49:33 GMT, Oct. 30.

Discovery of replicating circular RNAs by RNA-seq and computational algorithms.

PubMed

Zhang, Zhixiang; Qi, Shuishui; Tang, Nan; Zhang, Xinxin; Chen, Shanshan; Zhu, Pengfei; Ma, Lin; Cheng, Jinping; Xu, Yun; Lu, Meiguang; Wang, Hongqing; Ding, Shou-Wei; Li, Shifang; Wu, Qingfa

2014-12-01

Replicating circular RNAs are independent plant pathogens known as viroids, or act to modulate the pathogenesis of plant and animal viruses as their satellite RNAs. The rate of discovery of these subviral pathogens was low over the past 40 years because the classical approaches are technical demanding and time-consuming. We previously described an approach for homology-independent discovery of replicating circular RNAs by analysing the total small RNA populations from samples of diseased tissues with a computational program known as progressive filtering of overlapping small RNAs (PFOR). However, PFOR written in PERL language is extremely slow and is unable to discover those subviral pathogens that do not trigger in vivo accumulation of extensively overlapping small RNAs. Moreover, PFOR is yet to identify a new viroid capable of initiating independent infection. Here we report the development of PFOR2 that adopted parallel programming in the C++ language and was 3 to 8 times faster than PFOR. A new computational program was further developed and incorporated into PFOR2 to allow the identification of circular RNAs by deep sequencing of long RNAs instead of small RNAs. PFOR2 analysis of the small RNA libraries from grapevine and apple plants led to the discovery of Grapevine latent viroid (GLVd) and Apple hammerhead viroid-like RNA (AHVd-like RNA), respectively. GLVd was proposed as a new species in the genus Apscaviroid, because it contained the typical structural elements found in this group of viroids and initiated independent infection in grapevine seedlings. AHVd-like RNA encoded a biologically active hammerhead ribozyme in both polarities, and was not specifically associated with any of the viruses found in apple plants. We propose that these computational algorithms have the potential to discover novel circular RNAs in plants, invertebrates and vertebrates regardless of whether they replicate and/or induce the in vivo accumulation of small RNAs.

Discovery of Replicating Circular RNAs by RNA-Seq and Computational Algorithms

PubMed Central

Tang, Nan; Zhang, Xinxin; Chen, Shanshan; Zhu, Pengfei; Ma, Lin; Cheng, Jinping; Xu, Yun; Lu, Meiguang; Wang, Hongqing; Ding, Shou-Wei; Li, Shifang; Wu, Qingfa

2014-01-01

Replicating circular RNAs are independent plant pathogens known as viroids, or act to modulate the pathogenesis of plant and animal viruses as their satellite RNAs. The rate of discovery of these subviral pathogens was low over the past 40 years because the classical approaches are technical demanding and time-consuming. We previously described an approach for homology-independent discovery of replicating circular RNAs by analysing the total small RNA populations from samples of diseased tissues with a computational program known as progressive filtering of overlapping small RNAs (PFOR). However, PFOR written in PERL language is extremely slow and is unable to discover those subviral pathogens that do not trigger in vivo accumulation of extensively overlapping small RNAs. Moreover, PFOR is yet to identify a new viroid capable of initiating independent infection. Here we report the development of PFOR2 that adopted parallel programming in the C++ language and was 3 to 8 times faster than PFOR. A new computational program was further developed and incorporated into PFOR2 to allow the identification of circular RNAs by deep sequencing of long RNAs instead of small RNAs. PFOR2 analysis of the small RNA libraries from grapevine and apple plants led to the discovery of Grapevine latent viroid (GLVd) and Apple hammerhead viroid-like RNA (AHVd-like RNA), respectively. GLVd was proposed as a new species in the genus Apscaviroid, because it contained the typical structural elements found in this group of viroids and initiated independent infection in grapevine seedlings. AHVd-like RNA encoded a biologically active hammerhead ribozyme in both polarities, and was not specifically associated with any of the viruses found in apple plants. We propose that these computational algorithms have the potential to discover novel circular RNAs in plants, invertebrates and vertebrates regardless of whether they replicate and/or induce the in vivo accumulation of small RNAs. PMID:25503469

Achieving Operational Adaptability: Capacity Building Needs to Become a Warfighting Function

DTIC Science & Technology

2010-04-26

platypus effect as described by David Green in The Serendipity Machine: A Voyage of Discovery Through the Unexpected World of Computers. Early in...the 18th century, the discovery of the platypus challenged the categories of animal life recognized and utilized by scientists in Europe. Scientists...resisted changing their categories for years. At first, they believed the platypus was a fabrication. Later, they resisted change since they were

Molecular Docking and Drug Discovery in β-Adrenergic Receptors.

PubMed

Vilar, Santiago; Sobarzo-Sanchez, Eduardo; Santana, Lourdes; Uriarte, Eugenio

2017-01-01

Evolution in computer engineering, availability of increasing amounts of data and the development of new and fast docking algorithms and software have led to improved molecular simulations with crucial applications in virtual high-throughput screening and drug discovery. Moreover, analysis of protein-ligand recognition through molecular docking has become a valuable tool in drug design. In this review, we focus on the applicability of molecular docking on a particular class of G protein-coupled receptors: the β-adrenergic receptors, which are relevant targets in clinic for the treatment of asthma and cardiovascular diseases. We describe the binding site in β-adrenergic receptors to understand key factors in ligand recognition along with the proteins activation process. Moreover, we focus on the discovery of new lead compounds that bind the receptors, on the evaluation of virtual screening using the active/ inactive binding site states, and on the structural optimization of known families of binders to improve β-adrenergic affinity. We also discussed strengths and challenges related to the applicability of molecular docking in β-adrenergic receptors. Molecular docking is a valuable technique in computational chemistry to deeply analyze ligand recognition and has led to important breakthroughs in drug discovery and design in the field of β-adrenergic receptors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Reflective Random Indexing and indirect inference: a scalable method for discovery of implicit connections.

PubMed

Cohen, Trevor; Schvaneveldt, Roger; Widdows, Dominic

2010-04-01

The discovery of implicit connections between terms that do not occur together in any scientific document underlies the model of literature-based knowledge discovery first proposed by Swanson. Corpus-derived statistical models of semantic distance such as Latent Semantic Analysis (LSA) have been evaluated previously as methods for the discovery of such implicit connections. However, LSA in particular is dependent on a computationally demanding method of dimension reduction as a means to obtain meaningful indirect inference, limiting its ability to scale to large text corpora. In this paper, we evaluate the ability of Random Indexing (RI), a scalable distributional model of word associations, to draw meaningful implicit relationships between terms in general and biomedical language. Proponents of this method have achieved comparable performance to LSA on several cognitive tasks while using a simpler and less computationally demanding method of dimension reduction than LSA employs. In this paper, we demonstrate that the original implementation of RI is ineffective at inferring meaningful indirect connections, and evaluate Reflective Random Indexing (RRI), an iterative variant of the method that is better able to perform indirect inference. RRI is shown to lead to more clearly related indirect connections and to outperform existing RI implementations in the prediction of future direct co-occurrence in the MEDLINE corpus. 2009 Elsevier Inc. All rights reserved.

ACFIS: a web server for fragment-based drug discovery

PubMed Central

Hao, Ge-Fei; Jiang, Wen; Ye, Yuan-Nong; Wu, Feng-Xu; Zhu, Xiao-Lei; Guo, Feng-Biao; Yang, Guang-Fu

2016-01-01

In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown ‘chemical space’ to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for ‘chemical space’, which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/. PMID:27150808

Towards Robot Scientists for autonomous scientific discovery

PubMed Central

2010-01-01

We review the main components of autonomous scientific discovery, and how they lead to the concept of a Robot Scientist. This is a system which uses techniques from artificial intelligence to automate all aspects of the scientific discovery process: it generates hypotheses from a computer model of the domain, designs experiments to test these hypotheses, runs the physical experiments using robotic systems, analyses and interprets the resulting data, and repeats the cycle. We describe our two prototype Robot Scientists: Adam and Eve. Adam has recently proven the potential of such systems by identifying twelve genes responsible for catalysing specific reactions in the metabolic pathways of the yeast Saccharomyces cerevisiae. This work has been formally recorded in great detail using logic. We argue that the reporting of science needs to become fully formalised and that Robot Scientists can help achieve this. This will make scientific information more reproducible and reusable, and promote the integration of computers in scientific reasoning. We believe the greater automation of both the physical and intellectual aspects of scientific investigations to be essential to the future of science. Greater automation improves the accuracy and reliability of experiments, increases the pace of discovery and, in common with conventional laboratory automation, removes tedious and repetitive tasks from the human scientist. PMID:20119518

Towards Robot Scientists for autonomous scientific discovery.

PubMed

Sparkes, Andrew; Aubrey, Wayne; Byrne, Emma; Clare, Amanda; Khan, Muhammed N; Liakata, Maria; Markham, Magdalena; Rowland, Jem; Soldatova, Larisa N; Whelan, Kenneth E; Young, Michael; King, Ross D

2010-01-04

We review the main components of autonomous scientific discovery, and how they lead to the concept of a Robot Scientist. This is a system which uses techniques from artificial intelligence to automate all aspects of the scientific discovery process: it generates hypotheses from a computer model of the domain, designs experiments to test these hypotheses, runs the physical experiments using robotic systems, analyses and interprets the resulting data, and repeats the cycle. We describe our two prototype Robot Scientists: Adam and Eve. Adam has recently proven the potential of such systems by identifying twelve genes responsible for catalysing specific reactions in the metabolic pathways of the yeast Saccharomyces cerevisiae. This work has been formally recorded in great detail using logic. We argue that the reporting of science needs to become fully formalised and that Robot Scientists can help achieve this. This will make scientific information more reproducible and reusable, and promote the integration of computers in scientific reasoning. We believe the greater automation of both the physical and intellectual aspects of scientific investigations to be essential to the future of science. Greater automation improves the accuracy and reliability of experiments, increases the pace of discovery and, in common with conventional laboratory automation, removes tedious and repetitive tasks from the human scientist.

ACFIS: a web server for fragment-based drug discovery.

PubMed

Hao, Ge-Fei; Jiang, Wen; Ye, Yuan-Nong; Wu, Feng-Xu; Zhu, Xiao-Lei; Guo, Feng-Biao; Yang, Guang-Fu

2016-07-08

In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown 'chemical space' to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for 'chemical space', which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Fragment-based drug discovery and molecular docking in drug design.

PubMed

Wang, Tao; Wu, Mian-Bin; Chen, Zheng-Jie; Chen, Hua; Lin, Jian-Ping; Yang, Li-Rong

2015-01-01

Fragment-based drug discovery (FBDD) has caused a revolution in the process of drug discovery and design, with many FBDD leads being developed into clinical trials or approved in the past few years. Compared with traditional high-throughput screening, it displays obvious advantages such as efficiently covering chemical space, achieving higher hit rates, and so forth. In this review, we focus on the most recent developments of FBDD for improving drug discovery, illustrating the process and the importance of FBDD. In particular, the computational strategies applied in the process of FBDD and molecular-docking programs are highlighted elaborately. In most cases, docking is used for predicting the ligand-receptor interaction modes and hit identification by structurebased virtual screening. The successful cases of typical significance and the hits identified most recently are discussed.

Virtual drug discovery: beyond computational chemistry?

PubMed

Gilardoni, Francois; Arvanites, Anthony C

2010-02-01

This editorial looks at how a fully integrated structure that performs all aspects in the drug discovery process, under one company, is slowly disappearing. The steps in the drug discovery paradigm have been slowly increasing toward virtuality or outsourcing at various phases of product development in a company's candidate pipeline. Each step in the process, such as target identification and validation and medicinal chemistry, can be managed by scientific teams within a 'virtual' company. Pharmaceutical companies to biotechnology start-ups have been quick in adopting this new research and development business strategy in order to gain flexibility, access the best technologies and technical expertise, and decrease product developmental costs. In today's financial climate, the term virtual drug discovery has an organizational meaning. It represents the next evolutionary step in outsourcing drug development.

Enabling the Discovery of Gravitational Radiation

NASA Astrophysics Data System (ADS)

Isaacson, Richard

2017-01-01

The discovery of gravitational radiation was announced with the publication of the results of a physics experiment involving over a thousand participants. This was preceded by a century of theoretical work, involving a similarly large group of physicists, mathematicians, and computer scientists. This huge effort was enabled by a substantial commitment of resources, both public and private, to develop the different strands of this complex research enterprise, and to build a community of scientists to carry it out. In the excitement following the discovery, the role of key enablers of this success has not always been adequately recognized in popular accounts. In this talk, I will try to call attention to a few of the key ingredients that proved crucial to enabling the successful discovery of gravitational waves, and the opening of a new field of science.

Modelling and enhanced molecular dynamics to steer structure-based drug discovery.

PubMed

Kalyaanamoorthy, Subha; Chen, Yi-Ping Phoebe

2014-05-01

The ever-increasing gap between the availabilities of the genome sequences and the crystal structures of proteins remains one of the significant challenges to the modern drug discovery efforts. The knowledge of structure-dynamics-functionalities of proteins is important in order to understand several key aspects of structure-based drug discovery, such as drug-protein interactions, drug binding and unbinding mechanisms and protein-protein interactions. This review presents a brief overview on the different state of the art computational approaches that are applied for protein structure modelling and molecular dynamics simulations of biological systems. We give an essence of how different enhanced sampling molecular dynamics approaches, together with regular molecular dynamics methods, assist in steering the structure based drug discovery processes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Electronic structure of the high-temperature oxide superconductors

NASA Astrophysics Data System (ADS)

Pickett, Warren E.

1989-04-01

Since the discovery of superconductivity above 30 K by Bednorz and Müller in the La copper oxide system, the critical temperature has been raised to 90 K in YBa2Cu3O7 and to 110 and 125 K in Bi-based and Tl-based copper oxides, respectively. In the two years since this Nobel-prize-winning discovery, a large number of electronic structure calculations have been carried out as a first step in understanding the electronic properties of these materials. In this paper these calculations (mostly of the density-functional type) are gathered and reviewed, and their results are compared with the relevant experimental data. The picture that emerges is one in which the important electronic states are dominated by the copper d and oxygen p orbitals, with strong hybridization between them. Photon, electron, and positron spectroscopies provide important information about the electronic states, and comparison with electronic structure calculations indicates that, while many features can be interpreted in terms of existing calculations, self-energy corrections ("correlations") are important for a more detailed understanding. The antiferromagnetism that occurs in some regions of the phase diagram poses a particularly challenging problem for any detailed theory. The study of structural stability, lattice dynamics, and electron-phonon coupling in the copper oxides is also discussed. Finally, a brief review is given of the attempts so far to identify interaction constants appropriate for a model Hamiltonian treatment of many-body interactions in these materials.

Intense Relativistic Electron Beam Investigations

DTIC Science & Technology

1979-04-01

facility early it. the second year of the contract. An extensive X-ray radiation survey using TLD dosimeters indicated the need for some additional... Dosimeter for 105 to 107 Roentgen Range," Analytical Chemistry 28(10), 1580-2 (1956). 9. "Search and Discovery -- Update on free- electron lasers and...AFOSR-TR-7-3t f FINAL REPORT TO THE AIR FORCE OFFICE OF SCIENTIFIC RESEARCH 00• on INTENSE RELATIVISTIC ELECTRON BEAM INVESTIGAZIONS AFOSR Contract

Electronic structure descriptor for the discovery of narrow-band red-emitting phosphors

DOE PAGES

Wang, Zhenbin; Chu, Iek -Heng; Zhou, Fei; ...

2016-05-09

Narrow-band red-emitting phosphors are a critical component of phosphor-converted light-emitting diodes for highly efficient illumination-grade lighting. In this work, we report the discovery of a quantitative descriptor for narrow-band Eu 2+-activated emission identified through a comparison of the electronic structures of known narrow-band and broad-band phosphors. We find that a narrow emission bandwidth is characterized by a large splitting of more than 0.1 eV between the two highest Eu 2+ 4 f 7 bands. By incorporating this descriptor in a high-throughput first-principles screening of 2259 nitride compounds, we identify five promising new nitride hosts for Eu 2+-activated red-emitting phosphors thatmore » are predicted to exhibit good chemical stability, thermal quenching resistance, and quantum efficiency, as well as narrow-band emission. Lastly, our findings provide important insights into the emission characteristics of rare-earth activators in phosphor hosts and a general strategy to the discovery of phosphors with a desired emission peak and bandwidth.« less

Electronic structure descriptor for the discovery of narrow-band red-emitting phosphors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Zhenbin; Chu, Iek -Heng; Zhou, Fei

Narrow-band red-emitting phosphors are a critical component of phosphor-converted light-emitting diodes for highly efficient illumination-grade lighting. In this work, we report the discovery of a quantitative descriptor for narrow-band Eu 2+-activated emission identified through a comparison of the electronic structures of known narrow-band and broad-band phosphors. We find that a narrow emission bandwidth is characterized by a large splitting of more than 0.1 eV between the two highest Eu 2+ 4 f 7 bands. By incorporating this descriptor in a high-throughput first-principles screening of 2259 nitride compounds, we identify five promising new nitride hosts for Eu 2+-activated red-emitting phosphors thatmore » are predicted to exhibit good chemical stability, thermal quenching resistance, and quantum efficiency, as well as narrow-band emission. Lastly, our findings provide important insights into the emission characteristics of rare-earth activators in phosphor hosts and a general strategy to the discovery of phosphors with a desired emission peak and bandwidth.« less

cudaMap: a GPU accelerated program for gene expression connectivity mapping.

PubMed

McArt, Darragh G; Bankhead, Peter; Dunne, Philip D; Salto-Tellez, Manuel; Hamilton, Peter; Zhang, Shu-Dong

2013-10-11

Modern cancer research often involves large datasets and the use of sophisticated statistical techniques. Together these add a heavy computational load to the analysis, which is often coupled with issues surrounding data accessibility. Connectivity mapping is an advanced bioinformatic and computational technique dedicated to therapeutics discovery and drug re-purposing around differential gene expression analysis. On a normal desktop PC, it is common for the connectivity mapping task with a single gene signature to take > 2h to complete using sscMap, a popular Java application that runs on standard CPUs (Central Processing Units). Here, we describe new software, cudaMap, which has been implemented using CUDA C/C++ to harness the computational power of NVIDIA GPUs (Graphics Processing Units) to greatly reduce processing times for connectivity mapping. cudaMap can identify candidate therapeutics from the same signature in just over thirty seconds when using an NVIDIA Tesla C2050 GPU. Results from the analysis of multiple gene signatures, which would previously have taken several days, can now be obtained in as little as 10 minutes, greatly facilitating candidate therapeutics discovery with high throughput. We are able to demonstrate dramatic speed differentials between GPU assisted performance and CPU executions as the computational load increases for high accuracy evaluation of statistical significance. Emerging 'omics' technologies are constantly increasing the volume of data and information to be processed in all areas of biomedical research. Embracing the multicore functionality of GPUs represents a major avenue of local accelerated computing. cudaMap will make a strong contribution in the discovery of candidate therapeutics by enabling speedy execution of heavy duty connectivity mapping tasks, which are increasingly required in modern cancer research. cudaMap is open source and can be freely downloaded from http://purl.oclc.org/NET/cudaMap.

Preface: SciDAC 2005

NASA Astrophysics Data System (ADS)

Mezzacappa, Anthony

2005-01-01

On 26-30 June 2005 at the Grand Hyatt on Union Square in San Francisco several hundred computational scientists from around the world came together for what can certainly be described as a celebration of computational science. Scientists from the SciDAC Program and scientists from other agencies and nations were joined by applied mathematicians and computer scientists to highlight the many successes in the past year where computation has led to scientific discovery in a variety of fields: lattice quantum chromodynamics, accelerator modeling, chemistry, biology, materials science, Earth and climate science, astrophysics, and combustion and fusion energy science. Also highlighted were the advances in numerical methods and computer science, and the multidisciplinary collaboration cutting across science, mathematics, and computer science that enabled these discoveries. The SciDAC Program was conceived and funded by the US Department of Energy Office of Science. It is the Office of Science's premier computational science program founded on what is arguably the perfect formula: the priority and focus is science and scientific discovery, with the understanding that the full arsenal of `enabling technologies' in applied mathematics and computer science must be brought to bear if we are to have any hope of attacking and ultimately solving today's computational Grand Challenge problems. The SciDAC Program has been in existence for four years, and many of the computational scientists funded by this program will tell you that the program has given them the hope of addressing their scientific problems in full realism for the very first time. Many of these scientists will also tell you that SciDAC has also fundamentally changed the way they do computational science. We begin this volume with one of DOE's great traditions, and core missions: energy research. As we will see, computation has been seminal to the critical advances that have been made in this arena. Of course, to understand our world, whether it is to understand its very nature or to understand it so as to control it for practical application, will require explorations on all of its scales. Computational science has been no less an important tool in this arena than it has been in the arena of energy research. From explorations of quantum chromodynamics, the fundamental theory that describes how quarks make up the protons and neutrons of which we are composed, to explorations of the complex biomolecules that are the building blocks of life, to explorations of some of the most violent phenomena in our universe and of the Universe itself, computation has provided not only significant insight, but often the only means by which we have been able to explore these complex, multicomponent systems and by which we have been able to achieve scientific discovery and understanding. While our ultimate target remains scientific discovery, it certainly can be said that at a fundamental level the world is mathematical. Equations ultimately govern the evolution of the systems of interest to us, be they physical, chemical, or biological systems. The development and choice of discretizations of these underlying equations is often a critical deciding factor in whether or not one is able to model such systems stably, faithfully, and practically, and in turn, the algorithms to solve the resultant discrete equations are the complementary, critical ingredient in the recipe to model the natural world. The use of parallel computing platforms, especially at the TeraScale, and the trend toward even larger numbers of processors, continue to present significant challenges in the development and implementation of these algorithms. Computational scientists often speak of their `workflows'. A workflow, as the name suggests, is the sum total of all complex and interlocking tasks, from simulation set up, execution, and I/O, to visualization and scientific discovery, through which the advancement in our understanding of the natural world is realized. For the computational scientist, enabling such workflows presents myriad, signiflcant challenges, and it is computer scientists that are called upon at such times to address these challenges. Simulations are currently generating data at the staggering rate of tens of TeraBytes per simulation, over the course of days. In the next few years, these data generation rates are expected to climb exponentially to hundreds of TeraBytes per simulation, performed over the course of months. The output, management, movement, analysis, and visualization of these data will be our key to unlocking the scientific discoveries buried within the data. And there is no hope of generating such data to begin with, or of scientific discovery, without stable computing platforms and a sufficiently high and sustained performance of scientific applications codes on them. Thus, scientific discovery in the realm of computational science at the TeraScale and beyond will occur at the intersection of science, applied mathematics, and computer science. The SciDAC Program was constructed to mirror this reality, and the pages that follow are a testament to the efficacy of such an approach. We would like to acknowledge the individuals on whose talents and efforts the success of SciDAC 2005 was based. Special thanks go to Betsy Riley for her work on the SciDAC 2005 Web site and meeting agenda, for lining up our corporate sponsors, for coordinating all media communications, and for her efforts in processing the proceedings contributions, to Sherry Hempfling for coordinating the overall SciDAC 2005 meeting planning, for handling a significant share of its associated communications, and for coordinating with the ORNL Conference Center and Grand Hyatt, to Angela Harris for producing many of the documents and records on which our meeting planning was based and for her efforts in coordinating with ORNL Graphics Services, to Angie Beach of the ORNL Conference Center for her efforts in procurement and setting up and executing the contracts with the hotel, and to John Bui and John Smith for their superb wireless networking and A/V set up and support. We are grateful for the relentless efforts of all of these individuals, their remarkable talents, and for the joy of working with them during this past year. They were the cornerstones of SciDAC 2005. Thanks also go to Kymba A'Hearn and Patty Boyd for on-site registration, Brittany Hagen for administrative support, Bruce Johnston for netcast support, Tim Jones for help with the proceedings and Web site, Sherry Lamb for housing and registration, Cindy Lathum for Web site design, Carolyn Peters for on-site registration, and Dami Rich for graphic design. And we would like to express our appreciation to the Oak Ridge National Laboratory, especially Jeff Nichols, the Argonne National Laboratory, the Lawrence Berkeley National Laboratory, and to our corporate sponsors, Cray, IBM, Intel, and SGI, for their support. We would like to extend special thanks also to our plenary speakers, technical speakers, poster presenters, and panelists for all of their efforts on behalf of SciDAC 2005 and for their remarkable achievements and contributions. We would like to express our deep appreciation to Lali Chatterjee, Graham Douglas and Margaret Smith of Institute of Physics Publishing, who worked tirelessly in order to provide us with this finished volume within two months, which is nothing short of miraculous. Finally, we wish to express our heartfelt thanks to Michael Strayer, SciDAC Director, whose vision it was to focus SciDAC 2005 on scientific discovery, around which all of the excitement we experienced revolved, and to our DOE SciDAC program managers, especially Fred Johnson, for their support, input, and help throughout.

77 FR 50726 - Software Requirement Specifications for Digital Computer Software and Complex Electronics Used in...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-22

... Computer Software and Complex Electronics Used in Safety Systems of Nuclear Power Plants AGENCY: Nuclear...-1209, ``Software Requirement Specifications for Digital Computer Software and Complex Electronics used... Electronics Engineers (ANSI/IEEE) Standard 830-1998, ``IEEE Recommended Practice for Software Requirements...

Deep learning-based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms

PubMed Central

Xu, Min; Chai, Xiaoqi; Muthakana, Hariank; Liang, Xiaodan; Yang, Ge; Zeev-Ben-Mordehai, Tzviya; Xing, Eric P.

2017-01-01

Abstract Motivation: Cellular Electron CryoTomography (CECT) enables 3D visualization of cellular organization at near-native state and in sub-molecular resolution, making it a powerful tool for analyzing structures of macromolecular complexes and their spatial organizations inside single cells. However, high degree of structural complexity together with practical imaging limitations makes the systematic de novo discovery of structures within cells challenging. It would likely require averaging and classifying millions of subtomograms potentially containing hundreds of highly heterogeneous structural classes. Although it is no longer difficult to acquire CECT data containing such amount of subtomograms due to advances in data acquisition automation, existing computational approaches have very limited scalability or discrimination ability, making them incapable of processing such amount of data. Results: To complement existing approaches, in this article we propose a new approach for subdividing subtomograms into smaller but relatively homogeneous subsets. The structures in these subsets can then be separately recovered using existing computation intensive methods. Our approach is based on supervised structural feature extraction using deep learning, in combination with unsupervised clustering and reference-free classification. Our experiments show that, compared with existing unsupervised rotation invariant feature and pose-normalization based approaches, our new approach achieves significant improvements in both discrimination ability and scalability. More importantly, our new approach is able to discover new structural classes and recover structures that do not exist in training data. Availability and Implementation: Source code freely available at http://www.cs.cmu.edu/∼mxu1/software. Contact: mxu1@cs.cmu.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28881965

The discovery reach of CP violation in neutrino oscillation with non-standard interaction effects

NASA Astrophysics Data System (ADS)

Rahman, Zini; Dasgupta, Arnab; Adhikari, Rathin

2015-06-01

We have studied the CP violation discovery reach in a neutrino oscillation experiment with superbeam, neutrino factory and monoenergetic neutrino beam from the electron capture process. For NSI satisfying model-dependent bound for shorter baselines (like CERN-Fréjus set-up) there is insignificant effect of NSI on the the discovery reach of CP violation due to δ. Particularly, for the superbeam and neutrino factory we have also considered relatively longer baselines for which there could be significant NSI effects on CP violation discovery reach for higher allowed values of NSI. For the monoenergetic beam only shorter baselines are considered to study CP violation with different nuclei as neutrino sources. Interestingly for non-standard interactions—{{\\varepsilon }eμ } and {{\\varepsilon }eτ } of neutrinos with matter during propagation in longer baselines in the superbeam, there is the possibility of better discovery reach of CP violation than that with only Standard Model interactions of neutrinos with matter. For complex NSI we have shown the CP violation discovery reach in the plane of Dirac phase δ and NSI phase {{φ }ij}. The CP violation due to some values of δ remain unobservable with present and near future experimental facilities in the superbeam and neutrino factory. However, in the presence of some ranges of off-diagonal NSI phase values there are some possibilities of discovering total CP violation for any {{δ }CP} value even at 5σ confidence level for neutrino factory. Our analysis indicates that for some values of NSI phases total CP violation may not be at all observable for any values of δ. Combination of shorter and longer baselines could indicate in some cases the presence of NSI. However, in general for NSIs ≲ 1 the CP violation discovery reach is better in neutrino factory set-ups. Using a neutrino beam from the electron capture process for nuclei 50110Sn and 152Yb, we have shown the discovery reach of CP violation in a neutrino oscillation experiment. Particularly for 50110Sn nuclei CP violation could be found for about 51% of the possible δ values for a baseline of 130 km with boost factor γ =500. Although the nuclei 152Yb is technically more feasible for the production of a mono-energetic beam, it is found to be unsuitable in obtaining good discovery reach of CP violation.

Recent trends in spin-resolved photoelectron spectroscopy

NASA Astrophysics Data System (ADS)

Okuda, Taichi

2017-12-01

Since the discovery of the Rashba effect on crystal surfaces and also the discovery of topological insulators, spin- and angle-resolved photoelectron spectroscopy (SARPES) has become more and more important, as the technique can measure directly the electronic band structure of materials with spin resolution. In the same way that the discovery of high-Tc superconductors promoted the development of high-resolution angle-resolved photoelectron spectroscopy, the discovery of this new class of materials has stimulated the development of new SARPES apparatus with new functions and higher resolution, such as spin vector analysis, ten times higher energy and angular resolution than conventional SARPES, multichannel spin detection, and so on. In addition, the utilization of vacuum ultra violet lasers also opens a pathway to the realization of novel SARPES measurements. In this review, such recent trends in SARPES techniques and measurements will be overviewed.

Discovery of naked charm particles and lifetime differences among charm species using nuclear emulsion techniques innovated in Japan

PubMed Central

NIU, Kiyoshi

2008-01-01

This is a historical review of the discovery of naked charm particles and lifetime differences among charm species. These discoveries in the field of cosmic-ray physics were made by the innovation of nuclear emulsion techniques in Japan. A pair of naked charm particles was discovered in 1971 in a cosmic-ray interaction, three years prior to the discovery of the hidden charm particle, J/Ψ, in western countries. Lifetime differences between charged and neutral charm particles were pointed out in 1975, which were later re-confirmed by the collaborative Experiment E531 at Fermilab. Japanese physicists led by K.Niu made essential contributions to it with improved emulsion techniques, complemented by electronic detectors. This review also discusses the discovery of artificially produced naked charm particles by us in an accelerator experiment at Fermilab in 1975 and of multiple-pair productions of charm particles in a single interaction in 1987 by the collaborative Experiment WA75 at CERN. PMID:18941283

Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point.

PubMed

Rodrigues, Tiago

2017-11-15

Natural products (NPs) present a privileged source of inspiration for chemical probe and drug design. Despite the biological pre-validation of the underlying molecular architectures and their relevance in drug discovery, the poor accessibility to NPs, complexity of the synthetic routes and scarce knowledge of their macromolecular counterparts in phenotypic screens still hinder their broader exploration. Cheminformatics algorithms now provide a powerful means of circumventing the abovementioned challenges and unlocking the full potential of NPs in a drug discovery context. Herein, I discuss recent advances in the computer-assisted design of NP mimics and how artificial intelligence may accelerate future NP-inspired molecular medicine.

Modern drug discovery technologies: opportunities and challenges in lead discovery.

PubMed

Guido, Rafael V C; Oliva, Glaucius; Andricopulo, Adriano D

2011-12-01

The identification of promising hits and the generation of high quality leads are crucial steps in the early stages of drug discovery projects. The definition and assessment of both chemical and biological space have revitalized the screening process model and emphasized the importance of exploring the intrinsic complementary nature of classical and modern methods in drug research. In this context, the widespread use of combinatorial chemistry and sophisticated screening methods for the discovery of lead compounds has created a large demand for small organic molecules that act on specific drug targets. Modern drug discovery involves the employment of a wide variety of technologies and expertise in multidisciplinary research teams. The synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds emphasize the importance of the integration of advanced technologies in drug discovery programs. These technologies (VS, HTS, SBDD, LBDD, QSAR, and so on) are complementary in the sense that they have mutual goals, thereby the combination of both empirical and in silico efforts is feasible at many different levels of lead optimization and new chemical entity (NCE) discovery. This paper provides a brief perspective on the evolution and use of key drug design technologies, highlighting opportunities and challenges.

42 CFR 1005.7 - Discovery.

Code of Federal Regulations, 2011 CFR

2011-10-01

... data stored in an electronic data storage system will be produced in a form accessible to the... information, reports, answers, records, accounts, papers and other data and documentary evidence. Nothing...

42 CFR 1005.7 - Discovery.

Code of Federal Regulations, 2013 CFR

2013-10-01

... data stored in an electronic data storage system will be produced in a form accessible to the... information, reports, answers, records, accounts, papers and other data and documentary evidence. Nothing...

42 CFR 3.516 - Discovery.

Code of Federal Regulations, 2014 CFR

2014-10-01

... data stored in an electronic data storage system must be produced in a form accessible to the... information, reports, answers, records, accounts, papers and other data and documentary evidence. Nothing...

42 CFR 1005.7 - Discovery.

Code of Federal Regulations, 2014 CFR

2014-10-01

... data stored in an electronic data storage system will be produced in a form accessible to the... information, reports, answers, records, accounts, papers and other data and documentary evidence. Nothing...

42 CFR 3.516 - Discovery.

Code of Federal Regulations, 2011 CFR

2011-10-01

... data stored in an electronic data storage system must be produced in a form accessible to the... information, reports, answers, records, accounts, papers and other data and documentary evidence. Nothing...

42 CFR 1005.7 - Discovery.

Code of Federal Regulations, 2012 CFR

2012-10-01

... data stored in an electronic data storage system will be produced in a form accessible to the... information, reports, answers, records, accounts, papers and other data and documentary evidence. Nothing...

42 CFR 3.516 - Discovery.

Code of Federal Regulations, 2013 CFR

2013-10-01

... data stored in an electronic data storage system must be produced in a form accessible to the... information, reports, answers, records, accounts, papers and other data and documentary evidence. Nothing...

42 CFR 3.516 - Discovery.

Code of Federal Regulations, 2012 CFR

2012-10-01

... data stored in an electronic data storage system must be produced in a form accessible to the... information, reports, answers, records, accounts, papers and other data and documentary evidence. Nothing...

Patient care in a technological age.

PubMed

Dragon, Natalie

2006-07-01

In this electronically wired world of the 21 st century, the health care system has tapped into technology available at the touch of a button. Scientific discoveries, high-tech equipment, electronic medical records, Smarticards, and long distance diagnosis using telehealth technology have all been embraced. But Natalie Dragon asks, what are the implications for nurses and the outcomes on patient care?

Designing for Discovery: Interactive Multimedia Learning Environments at Bank Street College. Technical Report No. 15.

ERIC Educational Resources Information Center

Wilson, Kathleen; Tally, William

This report discusses "multimedia" instruction as it applies to successful learning environments at Bank Street College of Education (New York), ranging from pre-electronic to electronic. In four of the interviews detailed, a Bank Street College professor, researcher, and two Bank Street School for Children teachers offer different perspectives…

SemaTyP: a knowledge graph based literature mining method for drug discovery.

PubMed

Sang, Shengtian; Yang, Zhihao; Wang, Lei; Liu, Xiaoxia; Lin, Hongfei; Wang, Jian

2018-05-30

Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.

Robustness of disaggregate oil and gas discovery forecasting models

USGS Publications Warehouse

Attanasi, E.D.; Schuenemeyer, J.H.

1989-01-01

The trend in forecasting oil and gas discoveries has been to develop and use models that allow forecasts of the size distribution of future discoveries. From such forecasts, exploration and development costs can more readily be computed. Two classes of these forecasting models are the Arps-Roberts type models and the 'creaming method' models. This paper examines the robustness of the forecasts made by these models when the historical data on which the models are based have been subject to economic upheavals or when historical discovery data are aggregated from areas having widely differing economic structures. Model performance is examined in the context of forecasting discoveries for offshore Texas State and Federal areas. The analysis shows how the model forecasts are limited by information contained in the historical discovery data. Because the Arps-Roberts type models require more regularity in discovery sequence than the creaming models, prior information had to be introduced into the Arps-Roberts models to accommodate the influence of economic changes. The creaming methods captured the overall decline in discovery size but did not easily allow introduction of exogenous information to compensate for incomplete historical data. Moreover, the predictive log normal distribution associated with the creaming model methods appears to understate the importance of the potential contribution of small fields. ?? 1989.

Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery

PubMed Central

Ekins, Sean; Freundlich, Joel S.; Choi, Inhee; Sarker, Malabika; Talcott, Carolyn

2010-01-01

We are witnessing the growing menace of both increasing cases of drug-sensitive and drug-resistant Mycobacterium tuberculosis strains and the challenge to produce the first new tuberculosis (TB) drug in well over 40 years. The TB community, having invested in extensive high-throughput screening efforts, is faced with the question of how to optimally leverage this data in order to move from a hit to a lead to a clinical candidate and potentially a new drug. Complementing this approach, yet conducted on a much smaller scale, cheminformatic techniques have been leveraged and are herein reviewed. We suggest these computational approaches should be more optimally integrated in a workflow with experimental approaches to accelerate TB drug discovery. PMID:21129975

DOE High Performance Computing Operational Review (HPCOR): Enabling Data-Driven Scientific Discovery at HPC Facilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gerber, Richard; Allcock, William; Beggio, Chris

2014-10-17

U.S. Department of Energy (DOE) High Performance Computing (HPC) facilities are on the verge of a paradigm shift in the way they deliver systems and services to science and engineering teams. Research projects are producing a wide variety of data at unprecedented scale and level of complexity, with community-specific services that are part of the data collection and analysis workflow. On June 18-19, 2014 representatives from six DOE HPC centers met in Oakland, CA at the DOE High Performance Operational Review (HPCOR) to discuss how they can best provide facilities and services to enable large-scale data-driven scientific discovery at themore » DOE national laboratories. The report contains findings from that review.« less

Data Mining Citizen Science Results

NASA Astrophysics Data System (ADS)

Borne, K. D.

2012-12-01

Scientific discovery from big data is enabled through multiple channels, including data mining (through the application of machine learning algorithms) and human computation (commonly implemented through citizen science tasks). We will describe the results of new data mining experiments on the results from citizen science activities. Discovering patterns, trends, and anomalies in data are among the powerful contributions of citizen science. Establishing scientific algorithms that can subsequently re-discover the same types of patterns, trends, and anomalies in automatic data processing pipelines will ultimately result from the transformation of those human algorithms into computer algorithms, which can then be applied to much larger data collections. Scientific discovery from big data is thus greatly amplified through the marriage of data mining with citizen science.

Perspective on computational and structural aspects of kinase discovery from IPK2014.

PubMed

Martin, Eric; Knapp, Stefan; Engh, Richard A; Moebitz, Henrik; Varin, Thibault; Roux, Benoit; Meiler, Jens; Berdini, Valerio; Baumann, Alexander; Vieth, Michal

2015-10-01

Recent advances in understanding the activity and selectivity of kinase inhibitors and their relationships to protein structure are presented. Conformational selection in kinases is studied from empirical, data-driven and simulation approaches. Ligand binding and its affinity are, in many cases, determined by the predetermined active and inactive conformation of kinases. Binding affinity and selectivity predictions highlight the current state of the art and advances in computational chemistry as it applies to kinase inhibitor discovery. Kinome wide inhibitor profiling and cell panel profiling lead to a better understanding of selectivity and allow for target validation and patient tailoring hypotheses. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases. Copyright © 2015 Elsevier B.V. All rights reserved.

A Simple and Resource-efficient Setup for the Computer-aided Drug Design Laboratory.

PubMed

Moretti, Loris; Sartori, Luca

2016-10-01

Undertaking modelling investigations for Computer-Aided Drug Design (CADD) requires a proper environment. In principle, this could be done on a single computer, but the reality of a drug discovery program requires robustness and high-throughput computing (HTC) to efficiently support the research. Therefore, a more capable alternative is needed but its implementation has no widespread solution. Here, the realization of such a computing facility is discussed, from general layout to technical details all aspects are covered. © 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Nonlinear Optical Properties of High-Temperature Organic Structures

NASA Astrophysics Data System (ADS)

Shi, Rui-Fang

In this thesis, we report the discovery of a new class of electro-optic organic structures, 1,8-naphthoylene -benzimidazoles, developed with computer aided molecular design combined with actual syntheses. These structures are similar to polyimide repeat units and possess high thermal, chemical and photo stabilities. Thermal analysis shows that the new class retains its linear and nonlinear optical properties well above 300^circ C in both pure forms and guest/host polyimide systems. Importantly, side group substitutions not only increase the second-order optical responses but also enhance the thermal stability. The origin of the relatively large second order optical responses of the new class is revealed by quantum many-electron calculations that explicitly take electron -electron correlations into consideration. Contour diagrams indicate that electrons are decreased on the benzimidazole -donor-substituted side and increased on the naphthoylene side upon virtual excitations, illustrating the fact that the naphthoylene group acts both as an electron acceptor and a pi-bridge that provides the necessary electron delocalization. Results for most structures show that the most dominant virtual excitation process to beta_{ijk}(-omega _3;omega_1,omega_2) involves the ground (S_0) and first excited (S_1) pi -electronic states. Importantly, increasing the electron donor strength increase the electric dipole moments and transition moments, therefore second order optical responses are enhanced. Interestingly, it is found that the position of a donor group in the new class has a significant effect on second order optical responses. DC-induced second harmonic generation (DCSHG) dispersion measurements characterize the nonlinear optical properties of the new class, using both nanosecond and picosecond tunable laser sources ranging from 1400 to 2148 nm in wavelength. Comparison between theory and experiment demonstrates that there is good agreement between them over a wide nonresonant photon energy region, illustrating the great success of our understanding of the nonlinear optical responses even in these relatively complicated organic structures. In addition, it is found that these chromophores have large beta_{ijk }: for SY177 in solution with 1,4-dioxane, mu_{x}beta_ {x}(-2omega;omega,omega) + < gamma(-2omega;omega,omega,0) > 5kT = 418 times 10 ^{-48} esu and beta _{x}(-2omega;omega,omega ) 92 times 10^ {-30} esu at hbaromega = 0.65 eV. For SY215 in solution with CH _2Cl_2, mu_ {x}beta_{x}(-2omega; omega,omega) + < gamma( -2omega;omega,omega,0) > 5kT = 1468 times 10^{-48} esu and beta_{x}(-2omega; omega,omega) = 268 times 10^{-30} esu at hbaromega = 0.65 eV. The discovery and characterization of the new high temperature class represents a critical step in the development of new materials that are suitable for practical device applications. Work is underway to optimize these structures and incorporate them into waveguide devices.

From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

PubMed

Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

2017-11-01

Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

Exploring Cells from the Inside out: New Tools for the Classroom

ERIC Educational Resources Information Center

Minogue, James; Jones, Gail; Broadwell, Bethany; Oppewal, Tom

2006-01-01

After the first observation of life under the microscope, it took two centuries of research before the "cell theory" was established. Luckily, today's teachers can take advantage of computer technology and speed up the discovery process in their classrooms. This article describes how computer-based instructional programs can be used to engage…

Knowledge Discovery in Chess Using an Aesthetics Approach

ERIC Educational Resources Information Center

Iqbal, Azlan

2012-01-01

Computational aesthetics is a relatively new subfield of artificial intelligence (AI). It includes research that enables computers to "recognize" (and evaluate) beauty in various domains such as visual art, music, and games. Aside from the benefit this gives to humans in terms of creating and appreciating art in these domains, there are perhaps…

Subgroup Discovery with User Interaction Data: An Empirically Guided Approach to Improving Intelligent Tutoring Systems

ERIC Educational Resources Information Center

Poitras, Eric G.; Lajoie, Susanne P.; Doleck, Tenzin; Jarrell, Amanda

2016-01-01

Learner modeling, a challenging and complex endeavor, is an important and oft-studied research theme in computer-supported education. From this perspective, Educational Data Mining (EDM) research has focused on modeling and comprehending various dimensions of learning in computer-based learning environments (CBLE). Researchers and designers are…

Fostering Recursive Thinking in Combinatorics through the Use of Manipulatives and Computing Technology.

ERIC Educational Resources Information Center

Abramovich, Sergei; Pieper, Anne

1996-01-01

Describes the use of manipulatives for solving simple combinatorial problems which can lead to the discovery of recurrence relations for permutations and combinations. Numerical evidence and visual imagery generated by a computer spreadsheet through modeling these relations can enable students to experience the ease and power of combinatorial…

Computer-Aided Experiment Planning toward Causal Discovery in Neuroscience.

PubMed

Matiasz, Nicholas J; Wood, Justin; Wang, Wei; Silva, Alcino J; Hsu, William

2017-01-01

Computers help neuroscientists to analyze experimental results by automating the application of statistics; however, computer-aided experiment planning is far less common, due to a lack of similar quantitative formalisms for systematically assessing evidence and uncertainty. While ontologies and other Semantic Web resources help neuroscientists to assimilate required domain knowledge, experiment planning requires not only ontological but also epistemological (e.g., methodological) information regarding how knowledge was obtained. Here, we outline how epistemological principles and graphical representations of causality can be used to formalize experiment planning toward causal discovery. We outline two complementary approaches to experiment planning: one that quantifies evidence per the principles of convergence and consistency, and another that quantifies uncertainty using logical representations of constraints on causal structure. These approaches operationalize experiment planning as the search for an experiment that either maximizes evidence or minimizes uncertainty. Despite work in laboratory automation, humans must still plan experiments and will likely continue to do so for some time. There is thus a great need for experiment-planning frameworks that are not only amenable to machine computation but also useful as aids in human reasoning.

Earth observations taken from Discovery during STS-85 mission - Typhoon Winnie

NASA Image and Video Library

1997-08-13

S85-E-5071 (13 August 1997) --- The STS-85 crew members aboard the Space Shuttle Discovery downlinked this oblique, Electronic Still Camera (ESC) view of the Super Typhoon Winnie about halfway between New Guinea and Japan in the Pacific Ocean late evening, August 13, 1997. Maximum sustained winds of 105 knots, gusts up to 130 knots. This photo was taken 14 1/2 hours after STS085-E-5069 was recorded with the same ESC.

21 CFR 17.23 - Discovery.

Code of Federal Regulations, 2011 CFR

2011-04-01

... requested data stored in an electronic data storage system must be produced in a form readily accessible to... includes information, reports, answers, records, accounts, papers and other data and documentary evidence...

42 CFR 93.512 - Discovery.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., accounts, papers, tangible items, and other data and documentary evidence. This subpart does not require the creation of any document. However, requested data stored in an electronic data storage system must...

42 CFR 93.512 - Discovery.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., accounts, papers, tangible items, and other data and documentary evidence. This subpart does not require the creation of any document. However, requested data stored in an electronic data storage system must...

21 CFR 17.23 - Discovery.

Code of Federal Regulations, 2012 CFR

2012-04-01

... requested data stored in an electronic data storage system must be produced in a form readily accessible to... includes information, reports, answers, records, accounts, papers and other data and documentary evidence...

42 CFR 93.512 - Discovery.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., accounts, papers, tangible items, and other data and documentary evidence. This subpart does not require the creation of any document. However, requested data stored in an electronic data storage system must...

21 CFR 17.23 - Discovery.

Code of Federal Regulations, 2014 CFR

2014-04-01

... requested data stored in an electronic data storage system must be produced in a form readily accessible to... includes information, reports, answers, records, accounts, papers and other data and documentary evidence...

21 CFR 17.23 - Discovery.

Code of Federal Regulations, 2013 CFR

2013-04-01

... requested data stored in an electronic data storage system must be produced in a form readily accessible to... includes information, reports, answers, records, accounts, papers and other data and documentary evidence...

42 CFR 93.512 - Discovery.

Code of Federal Regulations, 2012 CFR

2012-10-01

..., accounts, papers, tangible items, and other data and documentary evidence. This subpart does not require the creation of any document. However, requested data stored in an electronic data storage system must...

Polar Hydra Data Analysis

NASA Technical Reports Server (NTRS)

Scudder, J. D.; Hall, Van Allen

1998-01-01

The science activities are: 1) Hydra is still operating successfully on orbit. 2) A large amount of analysis and discovery has occurred with the Hydra ground data processing this past year. 3) Full interdetector calibration has been implemented and documented. This intercalibration was necessitated by the incorrect installation of bias resistors in the pre-acceleration stage to the electron channeltrons. This had the effect of making the counting efficiency for electrons energy dependent as well as channeltron specific. The nature of the error had no impact on the ion detection efficiency since they have a different bias arrangement. This intercalibration is so effective, that the electron and ion moment densities are routinely produced with a level of agreement better than 20%. 4) The data processing routinely removes glint in the sensors and produces public energy time spectrograms on the web overnight. 6) Routine, but more intensive computer processing codes are operational that determine for electrons and ions, the density, the flow vector, the pressure tensor and the heat flux by numerical integration. These codes use the magnetic field to sustain the quality of their output. To gain access to this high quality magnetic field within our data stream we have monitored Russell's web page for zero levels and timing files (since his data acquisition is not telemetry synchronous) and have a local reconstruction of B for our use. We have also detected a routine anomaly in the magnetometer data stream that we have documented to Chris Russell and developed an editing algorithm to intercept these "hits" and remove them from the geophysical analysis.

In-situ Manipulation and Imaging of Switchable Two-dimensional Electron Gas at Oxide Heterointerfaces

DTIC Science & Technology

2016-11-30

AFRL-AFOSR-JP-TR-2017-0016 In-situ Manipulation and Imaging of Switchable Two-dimensional Electron Gas at Oxide Heterointerfaces CHANG BEOM EOM...Imaging of Switchable Two-dimensional Electron Gas at Oxide Heterointerfaces 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA2386-15-1-4046 5c.  PROGRAM...NOTES 14. ABSTRACT The recent discovery of a two-dimensional electron gas (2DEG) at the interface between insulating perovskite oxides SrTiO3 and LaAlO3

Low-Pt-Content Anode Catalyst for Direct Methanol Fuel Cells

NASA Technical Reports Server (NTRS)

Narayanan, Sekharipuram; Whitacre, Jay

2008-01-01

Combinatorial experiments have led to the discovery that a nanophase alloy of Pt, Ru, Ni, and Zr is effective as an anode catalyst material for direct methanol fuel cells. This discovery has practical significance in that the electronic current densities achievable by use of this alloy are comparable or larger than those obtained by use of prior Pt/Ru catalyst alloys containing greater amounts of Pt. Heretofore, the high cost of Pt has impeded the commercialization of direct methanol fuel cells. By making it possible to obtain a given level of performance at reduced Pt content (and, hence, lower cost), the discovery may lead to reduction of the economic impediment to commercialization.

Bill Parsons with Discovery Processing Team

NASA Image and Video Library

2003-08-29

Mark McGee (right) shows the bead blasting completed on the rudder speed brake on orbiter Discovery to Shuttle Program Manager Bill Parsons (center). McGee is manager, Orbiter Processing Facility, with United Space Alliance. At left is Mark Nappi, deputy associate program manager, ground operations, USA. The work was part of Orbiter Major Modifications (OMM) that were recently completed on Discovery. The OMM work ranged from wiring, control panels and black boxes to gaseous and fluid systems tubing and components. These systems were deserviced, disassembled, inspected, modified, reassembled, checked out and reserviced, as were most other systems onboard. The work included the installation of the Multifunction Electronic Display Subsystem (MEDS) - a state-of-the-art “glass cockpit.”

The web server of IBM's Bioinformatics and Pattern Discovery group.

PubMed

Huynh, Tien; Rigoutsos, Isidore; Parida, Laxmi; Platt, Daniel; Shibuya, Tetsuo

2003-07-01

We herein present and discuss the services and content which are available on the web server of IBM's Bioinformatics and Pattern Discovery group. The server is operational around the clock and provides access to a variety of methods that have been published by the group's members and collaborators. The available tools correspond to applications ranging from the discovery of patterns in streams of events and the computation of multiple sequence alignments, to the discovery of genes in nucleic acid sequences and the interactive annotation of amino acid sequences. Additionally, annotations for more than 70 archaeal, bacterial, eukaryotic and viral genomes are available on-line and can be searched interactively. The tools and code bundles can be accessed beginning at http://cbcsrv.watson.ibm.com/Tspd.html whereas the genomics annotations are available at http://cbcsrv.watson.ibm.com/Annotations/.

The web server of IBM's Bioinformatics and Pattern Discovery group

PubMed Central

Huynh, Tien; Rigoutsos, Isidore; Parida, Laxmi; Platt, Daniel; Shibuya, Tetsuo

2003-01-01

We herein present and discuss the services and content which are available on the web server of IBM's Bioinformatics and Pattern Discovery group. The server is operational around the clock and provides access to a variety of methods that have been published by the group's members and collaborators. The available tools correspond to applications ranging from the discovery of patterns in streams of events and the computation of multiple sequence alignments, to the discovery of genes in nucleic acid sequences and the interactive annotation of amino acid sequences. Additionally, annotations for more than 70 archaeal, bacterial, eukaryotic and viral genomes are available on-line and can be searched interactively. The tools and code bundles can be accessed beginning at http://cbcsrv.watson.ibm.com/Tspd.html whereas the genomics annotations are available at http://cbcsrv.watson.ibm.com/Annotations/. PMID:12824385

CRTS SNhunt: The First Five Years of Supernova Discoveries

NASA Astrophysics Data System (ADS)

Howerton, Stanley C.

2017-01-01

CRTS SNhunt: The First Five Years of Supernova Discoveries is a compilation of all supernova and supernova-like discoveries from the first five operational years of the supernova search SNhunt which is one project in the larger Catalina Real-Time Transient Survey (CRTS). SNhunt is perhaps one of the last traditional large-scale searches in which a person compares an image with a reference frame. This kind of search is time consuming as there is not a computer to narrow down the possibilities. The only help is a subtraction frame which shows differences between the two images. Images came from the Catalina Sky Survey, Mount Lemmon Survey, and Siding Spring Survey. Most of the discoveries were by the author. For many, a confirmation or a follow-up image is included. Where possible, a light curve was also created.

42 CFR 495.368 - Combating fraud and abuse.

Code of Federal Regulations, 2010 CFR

2010-10-01

... (CONTINUED) STANDARDS AND CERTIFICATION STANDARDS FOR THE ELECTRONIC HEALTH RECORD TECHNOLOGY INCENTIVE... identified as an overpayment regardless of recoupment from such providers, within 60 days of discovery of the...

Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites.

PubMed

Covington, Brett C; McLean, John A; Bachmann, Brian O

2017-01-04

Covering: 2000 to 2016The labor-intensive process of microbial natural product discovery is contingent upon identifying discrete secondary metabolites of interest within complex biological extracts, which contain inventories of all extractable small molecules produced by an organism or consortium. Historically, compound isolation prioritization has been driven by observed biological activity and/or relative metabolite abundance and followed by dereplication via accurate mass analysis. Decades of discovery using variants of these methods has generated the natural pharmacopeia but also contributes to recent high rediscovery rates. However, genomic sequencing reveals substantial untapped potential in previously mined organisms, and can provide useful prescience of potentially new secondary metabolites that ultimately enables isolation. Recently, advances in comparative metabolomics analyses have been coupled to secondary metabolic predictions to accelerate bioactivity and abundance-independent discovery work flows. In this review we will discuss the various analytical and computational techniques that enable MS-based metabolomic applications to natural product discovery and discuss the future prospects for comparative metabolomics in natural product discovery.

Neptune: a bioinformatics tool for rapid discovery of genomic variation in bacterial populations

PubMed Central

Marinier, Eric; Zaheer, Rahat; Berry, Chrystal; Weedmark, Kelly A.; Domaratzki, Michael; Mabon, Philip; Knox, Natalie C.; Reimer, Aleisha R.; Graham, Morag R.; Chui, Linda; Patterson-Fortin, Laura; Zhang, Jian; Pagotto, Franco; Farber, Jeff; Mahony, Jim; Seyer, Karine; Bekal, Sadjia; Tremblay, Cécile; Isaac-Renton, Judy; Prystajecky, Natalie; Chen, Jessica; Slade, Peter

2017-01-01

Abstract The ready availability of vast amounts of genomic sequence data has created the need to rethink comparative genomics algorithms using ‘big data’ approaches. Neptune is an efficient system for rapidly locating differentially abundant genomic content in bacterial populations using an exact k-mer matching strategy, while accommodating k-mer mismatches. Neptune’s loci discovery process identifies sequences that are sufficiently common to a group of target sequences and sufficiently absent from non-targets using probabilistic models. Neptune uses parallel computing to efficiently identify and extract these loci from draft genome assemblies without requiring multiple sequence alignments or other computationally expensive comparative sequence analyses. Tests on simulated and real datasets showed that Neptune rapidly identifies regions that are both sensitive and specific. We demonstrate that this system can identify trait-specific loci from different bacterial lineages. Neptune is broadly applicable for comparative bacterial analyses, yet will particularly benefit pathogenomic applications, owing to efficient and sensitive discovery of differentially abundant genomic loci. The software is available for download at: http://github.com/phac-nml/neptune. PMID:29048594

The Emergence of Organizing Structure in Conceptual Representation.

PubMed

Lake, Brenden M; Lawrence, Neil D; Tenenbaum, Joshua B

2018-06-01

Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form-where form could be a tree, ring, chain, grid, etc. (Kemp & Tenenbaum, 2008). Although this approach can learn intuitive organizations, including a tree for animals and a ring for the color circle, it assumes a strong inductive bias that considers only these particular forms, and each form is explicitly provided as initial knowledge. Here we introduce a new computational model of how organizing structure can be discovered, utilizing a broad hypothesis space with a preference for sparse connectivity. Given that the inductive bias is more general, the model's initial knowledge shows little qualitative resemblance to some of the discoveries it supports. As a consequence, the model can also learn complex structures for domains that lack intuitive description, as well as predict human property induction judgments without explicit structural forms. By allowing form to emerge from sparsity, our approach clarifies how both the richness and flexibility of human conceptual organization can coexist. Copyright © 2018 Cognitive Science Society, Inc.

Advances in the treatment of explicit water molecules in docking and binding free energy calculations.

PubMed

Hu, Xiao; Maffucci, Irene; Contini, Alessandro

2018-05-13

The inclusion of direct effects mediated by water during the ligand-receptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions. In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered. Here, we analyse software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules. Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Real-Time Discovery Services over Large, Heterogeneous and Complex Healthcare Datasets Using Schema-Less, Column-Oriented Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Begoli, Edmon; Dunning, Ted; Charlie, Frasure

We present a service platform for schema-leess exploration of data and discovery of patient-related statistics from healthcare data sets. The architecture of this platform is motivated by the need for fast, schema-less, and flexible approaches to SQL-based exploration and discovery of information embedded in the common, heterogeneously structured healthcare data sets and supporting components (electronic health records, practice management systems, etc.) The motivating use cases described in the paper are clinical trials candidate discovery, and a treatment effectiveness analysis. Following the use cases, we discuss the key features and software architecture of the platform, the underlying core components (Apache Parquet,more » Drill, the web services server), and the runtime profiles and performance characteristics of the platform. We conclude by showing dramatic speedup with some approaches, and the performance tradeoffs and limitations of others.« less

Network-based drug discovery by integrating systems biology and computational technologies

PubMed Central

Leung, Elaine L.; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua

2013-01-01

Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple ‘-omics’ databases. The newly developed algorithm- or network-based computational models can tightly integrate ‘-omics’ databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various ‘-omics’ platforms and computational tools would accelerate development of network-based drug discovery and network medicine. PMID:22877768

Web-based services for drug design and discovery.

PubMed

Frey, Jeremy G; Bird, Colin L

2011-09-01

Reviews of the development of drug discovery through the 20(th) century recognised the importance of chemistry and increasingly bioinformatics, but had relatively little to say about the importance of computing and networked computing in particular. However, the design and discovery of new drugs is arguably the most significant single application of bioinformatics and cheminformatics to have benefitted from the increases in the range and power of the computational techniques since the emergence of the World Wide Web, commonly now referred to as simply 'the Web'. Web services have enabled researchers to access shared resources and to deploy standardized calculations in their search for new drugs. This article first considers the fundamental principles of Web services and workflows, and then explores the facilities and resources that have evolved to meet the specific needs of chem- and bio-informatics. This strategy leads to a more detailed examination of the basic components that characterise molecules and the essential predictive techniques, followed by a discussion of the emerging networked services that transcend the basic provisions, and the growing trend towards embracing modern techniques, in particular the Semantic Web. In the opinion of the authors, the issues that require community action are: increasing the amount of chemical data available for open access; validating the data as provided; and developing more efficient links between the worlds of cheminformatics and bioinformatics. The goal is to create ever better drug design services.

Discovery of novel bacterial toxins by genomics and computational biology.

PubMed

Doxey, Andrew C; Mansfield, Michael J; Montecucco, Cesare

2018-06-01

Hundreds and hundreds of bacterial protein toxins are presently known. Traditionally, toxin identification begins with pathological studies of bacterial infectious disease. Following identification and cultivation of a bacterial pathogen, the protein toxin is purified from the culture medium and its pathogenic activity is studied using the methods of biochemistry and structural biology, cell biology, tissue and organ biology, and appropriate animal models, supplemented by bioimaging techniques. The ongoing and explosive development of high-throughput DNA sequencing and bioinformatic approaches have set in motion a revolution in many fields of biology, including microbiology. One consequence is that genes encoding novel bacterial toxins can be identified by bioinformatic and computational methods based on previous knowledge accumulated from studies of the biology and pathology of thousands of known bacterial protein toxins. Starting from the paradigmatic cases of diphtheria toxin, tetanus and botulinum neurotoxins, this review discusses traditional experimental approaches as well as bioinformatics and genomics-driven approaches that facilitate the discovery of novel bacterial toxins. We discuss recent work on the identification of novel botulinum-like toxins from genera such as Weissella, Chryseobacterium, and Enteroccocus, and the implications of these computationally identified toxins in the field. Finally, we discuss the promise of metagenomics in the discovery of novel toxins and their ecological niches, and present data suggesting the existence of uncharacterized, botulinum-like toxin genes in insect gut metagenomes. Copyright © 2018. Published by Elsevier Ltd.

Consolidating WLCG topology and configuration in the Computing Resource Information Catalogue

DOE PAGES

Alandes, Maria; Andreeva, Julia; Anisenkov, Alexey; ...

2017-10-01

Here, the Worldwide LHC Computing Grid infrastructure links about 200 participating computing centres affiliated with several partner projects. It is built by integrating heterogeneous computer and storage resources in diverse data centres all over the world and provides CPU and storage capacity to the LHC experiments to perform data processing and physics analysis. In order to be used by the experiments, these distributed resources should be well described, which implies easy service discovery and detailed description of service configuration. Currently this information is scattered over multiple generic information sources like GOCDB, OIM, BDII and experiment-specific information systems. Such a modelmore » does not allow to validate topology and configuration information easily. Moreover, information in various sources is not always consistent. Finally, the evolution of computing technologies introduces new challenges. Experiments are more and more relying on opportunistic resources, which by their nature are more dynamic and should also be well described in the WLCG information system. This contribution describes the new WLCG configuration service CRIC (Computing Resource Information Catalogue) which collects information from various information providers, performs validation and provides a consistent set of UIs and APIs to the LHC VOs for service discovery and usage configuration. The main requirements for CRIC are simplicity, agility and robustness. CRIC should be able to be quickly adapted to new types of computing resources, new information sources, and allow for new data structures to be implemented easily following the evolution of the computing models and operations of the experiments.« less

Consolidating WLCG topology and configuration in the Computing Resource Information Catalogue

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alandes, Maria; Andreeva, Julia; Anisenkov, Alexey

Here, the Worldwide LHC Computing Grid infrastructure links about 200 participating computing centres affiliated with several partner projects. It is built by integrating heterogeneous computer and storage resources in diverse data centres all over the world and provides CPU and storage capacity to the LHC experiments to perform data processing and physics analysis. In order to be used by the experiments, these distributed resources should be well described, which implies easy service discovery and detailed description of service configuration. Currently this information is scattered over multiple generic information sources like GOCDB, OIM, BDII and experiment-specific information systems. Such a modelmore » does not allow to validate topology and configuration information easily. Moreover, information in various sources is not always consistent. Finally, the evolution of computing technologies introduces new challenges. Experiments are more and more relying on opportunistic resources, which by their nature are more dynamic and should also be well described in the WLCG information system. This contribution describes the new WLCG configuration service CRIC (Computing Resource Information Catalogue) which collects information from various information providers, performs validation and provides a consistent set of UIs and APIs to the LHC VOs for service discovery and usage configuration. The main requirements for CRIC are simplicity, agility and robustness. CRIC should be able to be quickly adapted to new types of computing resources, new information sources, and allow for new data structures to be implemented easily following the evolution of the computing models and operations of the experiments.« less

Consolidating WLCG topology and configuration in the Computing Resource Information Catalogue

NASA Astrophysics Data System (ADS)

Alandes, Maria; Andreeva, Julia; Anisenkov, Alexey; Bagliesi, Giuseppe; Belforte, Stephano; Campana, Simone; Dimou, Maria; Flix, Jose; Forti, Alessandra; di Girolamo, A.; Karavakis, Edward; Lammel, Stephan; Litmaath, Maarten; Sciaba, Andrea; Valassi, Andrea

2017-10-01

The Worldwide LHC Computing Grid infrastructure links about 200 participating computing centres affiliated with several partner projects. It is built by integrating heterogeneous computer and storage resources in diverse data centres all over the world and provides CPU and storage capacity to the LHC experiments to perform data processing and physics analysis. In order to be used by the experiments, these distributed resources should be well described, which implies easy service discovery and detailed description of service configuration. Currently this information is scattered over multiple generic information sources like GOCDB, OIM, BDII and experiment-specific information systems. Such a model does not allow to validate topology and configuration information easily. Moreover, information in various sources is not always consistent. Finally, the evolution of computing technologies introduces new challenges. Experiments are more and more relying on opportunistic resources, which by their nature are more dynamic and should also be well described in the WLCG information system. This contribution describes the new WLCG configuration service CRIC (Computing Resource Information Catalogue) which collects information from various information providers, performs validation and provides a consistent set of UIs and APIs to the LHC VOs for service discovery and usage configuration. The main requirements for CRIC are simplicity, agility and robustness. CRIC should be able to be quickly adapted to new types of computing resources, new information sources, and allow for new data structures to be implemented easily following the evolution of the computing models and operations of the experiments.

The case for electron re-acceleration at galaxy cluster shocks

NASA Astrophysics Data System (ADS)

van Weeren, Reinout J.; Andrade-Santos, Felipe; Dawson, William A.; Golovich, Nathan; Lal, Dharam V.; Kang, Hyesung; Ryu, Dongsu; Brìggen, Marcus; Ogrean, Georgiana A.; Forman, William R.; Jones, Christine; Placco, Vinicius M.; Santucci, Rafael M.; Wittman, David; Jee, M. James; Kraft, Ralph P.; Sobral, David; Stroe, Andra; Fogarty, Kevin

2017-01-01

On the largest scales, the Universe consists of voids and filaments making up the cosmic web. Galaxy clusters are located at the knots in this web, at the intersection of filaments. Clusters grow through accretion from these large-scale filaments and by mergers with other clusters and groups. In a growing number of galaxy clusters, elongated Mpc-sized radio sources have been found1,2 . Also known as radio relics, these regions of diffuse radio emission are thought to trace relativistic electrons in the intracluster plasma accelerated by low-Mach-number shocks generated by cluster-cluster merger events 3 . A long-standing problem is how low-Mach-number shocks can accelerate electrons so efficiently to explain the observed radio relics. Here, we report the discovery of a direct connection between a radio relic and a radio galaxy in the merging galaxy cluster Abell 3411-3412 by combining radio, X-ray and optical observations. This discovery indicates that fossil relativistic electrons from active galactic nuclei are re-accelerated at cluster shocks. It also implies that radio galaxies play an important role in governing the non-thermal component of the intracluster medium in merging clusters.

The case for electron re-acceleration at galaxy cluster shocks

DOE Office of Scientific and Technical Information (OSTI.GOV)

van Weeren, Reinout J.; Andrade-Santos, Felipe; Dawson, William A.

On the largest scales, the Universe consists of voids and filaments making up the cosmic web. Galaxy clusters are located at the knots in this web, at the intersection of filaments. Clusters grow through accretion from these large-scale filaments and by mergers with other clusters and groups. In a growing number of galaxy clusters, elongated Mpc-sized radio sources have been found. Also known as radio relics, these regions of diffuse radio emission are thought to trace relativistic electrons in the intracluster plasma accelerated by low-Mach-number shocks generated by cluster–cluster merger events. A long-standing problem is how low-Mach-number shocks can acceleratemore » electrons so efficiently to explain the observed radio relics. Here, we report the discovery of a direct connection between a radio relic and a radio galaxy in the merging galaxy cluster Abell 3411–3412 by combining radio, X-ray and optical observations. This discovery indicates that fossil relativistic electrons from active galactic nuclei are re-accelerated at cluster shocks. Lastly, it also implies that radio galaxies play an important role in governing the non-thermal component of the intracluster medium in merging clusters.« less

The case for electron re-acceleration at galaxy cluster shocks

DOE PAGES

van Weeren, Reinout J.; Andrade-Santos, Felipe; Dawson, William A.; ...

2017-01-04

On the largest scales, the Universe consists of voids and filaments making up the cosmic web. Galaxy clusters are located at the knots in this web, at the intersection of filaments. Clusters grow through accretion from these large-scale filaments and by mergers with other clusters and groups. In a growing number of galaxy clusters, elongated Mpc-sized radio sources have been found. Also known as radio relics, these regions of diffuse radio emission are thought to trace relativistic electrons in the intracluster plasma accelerated by low-Mach-number shocks generated by cluster–cluster merger events. A long-standing problem is how low-Mach-number shocks can acceleratemore » electrons so efficiently to explain the observed radio relics. Here, we report the discovery of a direct connection between a radio relic and a radio galaxy in the merging galaxy cluster Abell 3411–3412 by combining radio, X-ray and optical observations. This discovery indicates that fossil relativistic electrons from active galactic nuclei are re-accelerated at cluster shocks. Lastly, it also implies that radio galaxies play an important role in governing the non-thermal component of the intracluster medium in merging clusters.« less

76 FR 49753 - Privacy Act of 1974; System of Records

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-11

... Defense. DHA 14 System name: Computer/Electronics Accommodations Program for People with Disabilities... with ``Computer/Electronic Accommodations Program.'' System location: Delete entry and replace with ``Computer/Electronic Accommodations Program, Skyline 5, Suite 302, 5111 Leesburg Pike, Falls Church, VA...

Poem Generator: A Comparative Quantitative Evaluation of a Microworlds-Based Learning Approach for Teaching English

ERIC Educational Resources Information Center

Jenkins, Craig

2015-01-01

This paper is a comparative quantitative evaluation of an approach to teaching poetry in the subject domain of English that employs a "guided discovery" pedagogy using computer-based microworlds. It uses a quasi-experimental design in order to measure performance gains in computational thinking and poetic thinking following a…

Integrated computational materials engineering: Tools, simulations and new applications

DOE PAGES

Madison, Jonathan D.

2016-03-30

Here, Integrated Computational Materials Engineering (ICME) is a relatively new methodology full of tremendous potential to revolutionize how science, engineering and manufacturing work together. ICME was motivated by the desire to derive greater understanding throughout each portion of the development life cycle of materials, while simultaneously reducing the time between discovery to implementation [1,2].

Visual Reasoning in Computational Environment: A Case of Graph Sketching

ERIC Educational Resources Information Center

Leung, Allen; Chan, King Wah

2004-01-01

This paper reports the case of a form six (grade 12) Hong Kong student's exploration of graph sketching in a computational environment. In particular, the student summarized his discovery in the form of two empirical laws. The student was interviewed and the interviewed data were used to map out a possible path of his visual reasoning. Critical…

Bio-TDS: bioscience query tool discovery system.

PubMed

Gnimpieba, Etienne Z; VanDiermen, Menno S; Gustafson, Shayla M; Conn, Bill; Lushbough, Carol M

2017-01-04

Bioinformatics and computational biology play a critical role in bioscience and biomedical research. As researchers design their experimental projects, one major challenge is to find the most relevant bioinformatics toolkits that will lead to new knowledge discovery from their data. The Bio-TDS (Bioscience Query Tool Discovery Systems, http://biotds.org/) has been developed to assist researchers in retrieving the most applicable analytic tools by allowing them to formulate their questions as free text. The Bio-TDS is a flexible retrieval system that affords users from multiple bioscience domains (e.g. genomic, proteomic, bio-imaging) the ability to query over 12 000 analytic tool descriptions integrated from well-established, community repositories. One of the primary components of the Bio-TDS is the ontology and natural language processing workflow for annotation, curation, query processing, and evaluation. The Bio-TDS's scientific impact was evaluated using sample questions posed by researchers retrieved from Biostars, a site focusing on BIOLOGICAL DATA ANALYSIS: The Bio-TDS was compared to five similar bioscience analytic tool retrieval systems with the Bio-TDS outperforming the others in terms of relevance and completeness. The Bio-TDS offers researchers the capacity to associate their bioscience question with the most relevant computational toolsets required for the data analysis in their knowledge discovery process. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Distributed Drug Discovery, Part 2: Global Rehearsal of Alkylating Agents for the Synthesis of Resin-Bound Unnatural Amino Acids and Virtual D3 Catalog Construction

PubMed Central

2008-01-01

Distributed Drug Discovery (D3) proposes solving large drug discovery problems by breaking them into smaller units for processing at multiple sites. A key component of the synthetic and computational stages of D3 is the global rehearsal of prospective reagents and their subsequent use in the creation of virtual catalogs of molecules accessible by simple, inexpensive combinatorial chemistry. The first section of this article documents the feasibility of the synthetic component of Distributed Drug Discovery. Twenty-four alkylating agents were rehearsed in the United States, Poland, Russia, and Spain, for their utility in the synthesis of resin-bound unnatural amino acids 1, key intermediates in many combinatorial chemistry procedures. This global reagent rehearsal, coupled to virtual library generation, increases the likelihood that any member of that virtual library can be made. It facilitates the realistic integration of worldwide virtual D3 catalog computational analysis with synthesis. The second part of this article describes the creation of the first virtual D3 catalog. It reports the enumeration of 24 416 acylated unnatural amino acids 5, assembled from lists of either rehearsed or well-precedented alkylating and acylating reagents, and describes how the resulting catalog can be freely accessed, searched, and downloaded by the scientific community. PMID:19105725

Molecular Dynamics Simulations and Kinetic Measurements to Estimate and Predict Protein-Ligand Residence Times.

PubMed

Mollica, Luca; Theret, Isabelle; Antoine, Mathias; Perron-Sierra, Françoise; Charton, Yves; Fourquez, Jean-Marie; Wierzbicki, Michel; Boutin, Jean A; Ferry, Gilles; Decherchi, Sergio; Bottegoni, Giovanni; Ducrot, Pierre; Cavalli, Andrea

2016-08-11

Ligand-target residence time is emerging as a key drug discovery parameter because it can reliably predict drug efficacy in vivo. Experimental approaches to binding and unbinding kinetics are nowadays available, but we still lack reliable computational tools for predicting kinetics and residence time. Most attempts have been based on brute-force molecular dynamics (MD) simulations, which are CPU-demanding and not yet particularly accurate. We recently reported a new scaled-MD-based protocol, which showed potential for residence time prediction in drug discovery. Here, we further challenged our procedure's predictive ability by applying our methodology to a series of glucokinase activators that could be useful for treating type 2 diabetes mellitus. We combined scaled MD with experimental kinetics measurements and X-ray crystallography, promptly checking the protocol's reliability by directly comparing computational predictions and experimental measures. The good agreement highlights the potential of our scaled-MD-based approach as an innovative method for computationally estimating and predicting drug residence times.

PANORAMA: An approach to performance modeling and diagnosis of extreme-scale workflows

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deelman, Ewa; Carothers, Christopher; Mandal, Anirban

Here we report that computational science is well established as the third pillar of scientific discovery and is on par with experimentation and theory. However, as we move closer toward the ability to execute exascale calculations and process the ensuing extreme-scale amounts of data produced by both experiments and computations alike, the complexity of managing the compute and data analysis tasks has grown beyond the capabilities of domain scientists. Therefore, workflow management systems are absolutely necessary to ensure current and future scientific discoveries. A key research question for these workflow management systems concerns the performance optimization of complex calculation andmore » data analysis tasks. The central contribution of this article is a description of the PANORAMA approach for modeling and diagnosing the run-time performance of complex scientific workflows. This approach integrates extreme-scale systems testbed experimentation, structured analytical modeling, and parallel systems simulation into a comprehensive workflow framework called Pegasus for understanding and improving the overall performance of complex scientific workflows.« less

Cloud Infrastructures for In Silico Drug Discovery: Economic and Practical Aspects

PubMed Central

Clematis, Andrea; Quarati, Alfonso; Cesini, Daniele; Milanesi, Luciano; Merelli, Ivan

2013-01-01

Cloud computing opens new perspectives for small-medium biotechnology laboratories that need to perform bioinformatics analysis in a flexible and effective way. This seems particularly true for hybrid clouds that couple the scalability offered by general-purpose public clouds with the greater control and ad hoc customizations supplied by the private ones. A hybrid cloud broker, acting as an intermediary between users and public providers, can support customers in the selection of the most suitable offers, optionally adding the provisioning of dedicated services with higher levels of quality. This paper analyses some economic and practical aspects of exploiting cloud computing in a real research scenario for the in silico drug discovery in terms of requirements, costs, and computational load based on the number of expected users. In particular, our work is aimed at supporting both the researchers and the cloud broker delivering an IaaS cloud infrastructure for biotechnology laboratories exposing different levels of nonfunctional requirements. PMID:24106693

QSAR Methods.

PubMed

Gini, Giuseppina

2016-01-01

In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.

PANORAMA: An approach to performance modeling and diagnosis of extreme-scale workflows

DOE PAGES

Deelman, Ewa; Carothers, Christopher; Mandal, Anirban; ...

2015-07-14

Here we report that computational science is well established as the third pillar of scientific discovery and is on par with experimentation and theory. However, as we move closer toward the ability to execute exascale calculations and process the ensuing extreme-scale amounts of data produced by both experiments and computations alike, the complexity of managing the compute and data analysis tasks has grown beyond the capabilities of domain scientists. Therefore, workflow management systems are absolutely necessary to ensure current and future scientific discoveries. A key research question for these workflow management systems concerns the performance optimization of complex calculation andmore » data analysis tasks. The central contribution of this article is a description of the PANORAMA approach for modeling and diagnosing the run-time performance of complex scientific workflows. This approach integrates extreme-scale systems testbed experimentation, structured analytical modeling, and parallel systems simulation into a comprehensive workflow framework called Pegasus for understanding and improving the overall performance of complex scientific workflows.« less

Computer-aided discovery of a metal-organic framework with superior oxygen uptake.

PubMed

Moghadam, Peyman Z; Islamoglu, Timur; Goswami, Subhadip; Exley, Jason; Fantham, Marcus; Kaminski, Clemens F; Snurr, Randall Q; Farha, Omar K; Fairen-Jimenez, David

2018-04-11

Current advances in materials science have resulted in the rapid emergence of thousands of functional adsorbent materials in recent years. This clearly creates multiple opportunities for their potential application, but it also creates the following challenge: how does one identify the most promising structures, among the thousands of possibilities, for a particular application? Here, we present a case of computer-aided material discovery, in which we complete the full cycle from computational screening of metal-organic framework materials for oxygen storage, to identification, synthesis and measurement of oxygen adsorption in the top-ranked structure. We introduce an interactive visualization concept to analyze over 1000 unique structure-property plots in five dimensions and delimit the relationships between structural properties and oxygen adsorption performance at different pressures for 2932 already-synthesized structures. We also report a world-record holding material for oxygen storage, UMCM-152, which delivers 22.5% more oxygen than the best known material to date, to the best of our knowledge.

Ensemble-based docking: From hit discovery to metabolism and toxicity predictions.

PubMed

Evangelista, Wilfredo; Weir, Rebecca L; Ellingson, Sally R; Harris, Jason B; Kapoor, Karan; Smith, Jeremy C; Baudry, Jerome

2016-10-15

This paper describes and illustrates the use of ensemble-based docking, i.e., using a collection of protein structures in docking calculations for hit discovery, the exploration of biochemical pathways and toxicity prediction of drug candidates. We describe the computational engineering work necessary to enable large ensemble docking campaigns on supercomputers. We show examples where ensemble-based docking has significantly increased the number and the diversity of validated drug candidates. Finally, we illustrate how ensemble-based docking can be extended beyond hit discovery and toward providing a structural basis for the prediction of metabolism and off-target binding relevant to pre-clinical and clinical trials. Copyright © 2016 Elsevier Ltd. All rights reserved.

Effects of Relativity Lead to"Warp Speed" Computations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vay, J.-L.

A scientist at Lawrence Berkeley National Laboratory has discovered that a previously unnoticed consequence of Einstein's special theory of relativity can lead to speedup of computer calculations by orders of magnitude when applied to the computer modeling of a certain class of physical systems. This new finding offers the possibility of tackling some problems in a much shorter time and with far more precision than was possible before, as well as studying some configurations in every detail for the first time. The basis of Einstein's theory is the principle of relativity, which states that the laws of physics are themore » same for all observers, whether the 'observer' is a turtle 'racing' with a rabbit, or a beam of particles moving at near light speed. From the invariance of the laws of physics, one may be tempted to infer that the complexity of a system is independent of the motion of the observer, and consequently, a computer simulation will require the same number of mathematical operations, independently of the reference frame that is used for the calculation. Length contraction and time dilation are well known consequences of the special theory of relativity which lead to very counterintuitive effects. An alien observing human activity through a telescope in a spaceship traveling in the Vicinity of the earth near the speed of light would see everything flattened in the direction of propagation of its spaceship (for him, the earth would have the shape of a pancake), while all motions on earth would appear extremely slow, slowed almost to a standstill. Conversely, a space scientist observing the alien through a telescope based on earth would see a flattened alien almost to a standstill in a flattened spaceship. Meanwhile, an astronaut sitting in a spaceship moving at some lower velocity than the alien spaceship with regard to earth might see both the alien spaceship and the earth flattened in the same proportion and the motion unfolding in each of them at the same speed. Let us now assume that each protagonist (the alien, the space scientist and the astronaut) is to run a computer simulation describing the motion of all of them in a single calculation. In order to model a physical system on a computer, scientists often divide space and time into small chunks. Since the computer must calculated some things for each chunk, having a large system containing numerous small chunks translates to long calculations requiring many computational steps on supercomputers. Let us assume that each protagonist of our intergalactic story uses the space and time slicing as described and chooses to perform the calculation in its own frame of reference. For the alien and the space scientist, the slicing of space and time results in an exceedingly large number of chunks, due to the wide disparity of spatial and time scales needed to describe both their own environment and motion together with the other extremely flattened environment and slowed motion. Since the disparity of scales is reduced for the astronaut, who is traveling at an intermediate velocity, the number of computer operations needed to complete the calculation in his frame of reference will be significantly lower, possibly by many orders of magnitude. Analogously, the new discovery at Lawrence Berkeley National Laboratory shows that there exists a frame of reference minimizing the number of computational operations needed for studying the interaction of beams of particles or light (lasers) interacting at, or near, light speed with other particles or with surrounding structures. Speedups ranging from ten to a million times or more are predicted for the modeling of beams interacting with electron clouds, such as those in the upcoming Large Hadron Collider 'atom smasher' accelerator at CERN (Switzerland), and in free electron lasers and tabletop laser wakefield accelerators. The discovery has surprised many physicists and was received initially with much skepticism. It sounded too much like a 'free lunch'. Yet, the demonstration of a speedup of a stunning one thousand times in a test simulation of a particle beam interacting with a background of electrons (see image), has proven that the effect is real and can be applied successfully, at least to some problems. Work is being actively pursued at Berkeley Lab and elsewhere to validate the feasibility of the method for a wider range of applications, as well as to apply the already successful method to more problems, where it might help getting better understanding of some processes and eventually lead to new findings.« less

77 FR 32996 - Certain Handheld Electronic Computing Devices, Related Software, and Components Thereof...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-04

... INTERNATIONAL TRADE COMMISSION [Investigation No. 337-TA-769] Certain Handheld Electronic Computing Devices, Related Software, and Components Thereof; Termination of the Investigation Based on... electronic computing devices, related software, and components thereof by reason of infringement of certain...

Computational genomic identification and functional reconstitution of plant natural product biosynthetic pathways

PubMed Central

2016-01-01

Covering: 2003 to 2016 The last decade has seen the first major discoveries regarding the genomic basis of plant natural product biosynthetic pathways. Four key computationally driven strategies have been developed to identify such pathways, which make use of physical clustering, co-expression, evolutionary co-occurrence and epigenomic co-regulation of the genes involved in producing a plant natural product. Here, we discuss how these approaches can be used for the discovery of plant biosynthetic pathways encoded by both chromosomally clustered and non-clustered genes. Additionally, we will discuss opportunities to prioritize plant gene clusters for experimental characterization, and end with a forward-looking perspective on how synthetic biology technologies will allow effective functional reconstitution of candidate pathways using a variety of genetic systems. PMID:27321668

Metrics and the effective computational scientist: process, quality and communication.

PubMed

Baldwin, Eric T

2012-09-01

Recent treatments of computational knowledge worker productivity have focused upon the value the discipline brings to drug discovery using positive anecdotes. While this big picture approach provides important validation of the contributions of these knowledge workers, the impact accounts do not provide the granular detail that can help individuals and teams perform better. I suggest balancing the impact-focus with quantitative measures that can inform the development of scientists. Measuring the quality of work, analyzing and improving processes, and the critical evaluation of communication can provide immediate performance feedback. The introduction of quantitative measures can complement the longer term reporting of impacts on drug discovery. These metric data can document effectiveness trends and can provide a stronger foundation for the impact dialogue. Copyright © 2012 Elsevier Ltd. All rights reserved.

Innovative computer-aided methods for the discovery of new kinase ligands.

PubMed

Abuhammad, Areej; Taha, Mutasem

2016-04-01

Recent evidence points to significant roles played by protein kinases in cell signaling and cellular proliferation. Faulty protein kinases are involved in cancer, diabetes and chronic inflammation. Efforts are continuously carried out to discover new inhibitors for selected protein kinases. In this review, we discuss two new computer-aided methodologies we developed to mine virtual databases for new bioactive compounds. One method is ligand-based exploration of the pharmacophoric space of inhibitors of any particular biotarget followed by quantitative structure-activity relationship-based selection of the best pharmacophore(s). The second approach is structure-based assuming that potent ligands come into contact with binding site spots distinct from those contacted by weakly potent ligands. Both approaches yield pharmacophores useful as 3D search queries for the discovery of new bioactive (kinase) inhibitors.

Classical Cosmology Through Animation Stories

NASA Astrophysics Data System (ADS)

Mijic, Milan; Kang, E. Y. E.; Longson, T.; State LA SciVi Project, Cal

2010-05-01

Computer animations are a powerful tool for explanation and communication of ideas, especially to a younger generation. Our team completed a three part sequence of short, computer animated stories about the insight and discoveries that lead to the understanding of the overall structure of the universe. Our principal characters are Immanuel Kant, Henrietta Leavitt, and Edwin Hubble. We utilized animations to model and visualize the physical concepts behind each discovery and to recreate the characters, locations, and flavor of the time. The animations vary in length from 6 to 11 minutes. The instructors or presenters may wish to utilize them separately or together. The animations may be used for learning classical cosmology in a visual way in GE astronomy courses, in pre-college science classes, or in public science education setting.

Purposive discovery of operations

NASA Technical Reports Server (NTRS)

Sims, Michael H.; Bresina, John L.

1992-01-01

The Generate, Prune & Prove (GPP) methodology for discovering definitions of mathematical operators is introduced. GPP is a task within the IL exploration discovery system. We developed GPP for use in the discovery of mathematical operators with a wider class of representations than was possible with the previous methods by Lenat and by Shen. GPP utilizes the purpose for which an operator is created to prune the possible definitions. The relevant search spaces are immense and there exists insufficient information for a complete evaluation of the purpose constraint, so it is necessary to perform a partial evaluation of the purpose (i.e., pruning) constraint. The constraint is first transformed so that it is operational with respect to the partial information, and then it is applied to examples in order to test the generated candidates for an operator's definition. In the GPP process, once a candidate definition survives this empirical prune, it is passed on to a theorem prover for formal verification. We describe the application of this methodology to the (re)discovery of the definition of multiplication for Conway numbers, a discovery which is difficult for human mathematicians. We successfully model this discovery process utilizing information which was reasonably available at the time of Conway's original discovery. As part of this discovery process, we reduce the size of the search space from a computationally intractable size to 3468 elements.

Modern approaches to accelerate discovery of new antischistosomal drugs.

PubMed

Neves, Bruno Junior; Muratov, Eugene; Machado, Renato Beilner; Andrade, Carolina Horta; Cravo, Pedro Vitor Lemos

2016-06-01

The almost exclusive use of only praziquantel for the treatment of schistosomiasis has raised concerns about the possible emergence of drug-resistant schistosomes. Consequently, there is an urgent need for new antischistosomal drugs. The identification of leads and the generation of high quality data are crucial steps in the early stages of schistosome drug discovery projects. Herein, the authors focus on the current developments in antischistosomal lead discovery, specifically referring to the use of automated in vitro target-based and whole-organism screens and virtual screening of chemical databases. They highlight the strengths and pitfalls of each of the above-mentioned approaches, and suggest possible roadmaps towards the integration of several strategies, which may contribute for optimizing research outputs and led to more successful and cost-effective drug discovery endeavors. Increasing partnerships and access to funding for drug discovery have strengthened the battle against schistosomiasis in recent years. However, the authors believe this battle also includes innovative strategies to overcome scientific challenges. In this context, significant advances of in vitro screening as well as computer-aided drug discovery have contributed to increase the success rate and reduce the costs of drug discovery campaigns. Although some of these approaches were already used in current antischistosomal lead discovery pipelines, the integration of these strategies in a solid workflow should allow the production of new treatments for schistosomiasis in the near future.

Electron-Poor Polar Intermetallics: Complex Structures, Novel Clusters, and Intriguing Bonding with Pronounced Electron Delocalization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, Qisheng; Miller, Gordon J.

Intermetallic compounds represent an extensive pool of candidates for energy related applications stemming from magnetic, electric, optic, caloric, and catalytic properties. The discovery of novel intermetallic compounds can enhance understanding of the chemical principles that govern structural stability and chemical bonding as well as finding new applications. Valence electron-poor polar intermetallics with valence electron concentrations (VECs) between 2.0 and 3.0 e –/atom show a plethora of unprecedented and fascinating structural motifs and bonding features. Furthermore, establishing simple structure-bonding-property relationships is especially challenging for this compound class because commonly accepted valence electron counting rules are inappropriate.

Electron-Poor Polar Intermetallics: Complex Structures, Novel Clusters, and Intriguing Bonding with Pronounced Electron Delocalization

DOE PAGES

Lin, Qisheng; Miller, Gordon J.

2017-12-18

Intermetallic compounds represent an extensive pool of candidates for energy related applications stemming from magnetic, electric, optic, caloric, and catalytic properties. The discovery of novel intermetallic compounds can enhance understanding of the chemical principles that govern structural stability and chemical bonding as well as finding new applications. Valence electron-poor polar intermetallics with valence electron concentrations (VECs) between 2.0 and 3.0 e –/atom show a plethora of unprecedented and fascinating structural motifs and bonding features. Furthermore, establishing simple structure-bonding-property relationships is especially challenging for this compound class because commonly accepted valence electron counting rules are inappropriate.

The iPlant Collaborative: Cyberinfrastructure for Enabling Data to Discovery for the Life Sciences.

PubMed

Merchant, Nirav; Lyons, Eric; Goff, Stephen; Vaughn, Matthew; Ware, Doreen; Micklos, David; Antin, Parker

2016-01-01

The iPlant Collaborative provides life science research communities access to comprehensive, scalable, and cohesive computational infrastructure for data management; identity management; collaboration tools; and cloud, high-performance, high-throughput computing. iPlant provides training, learning material, and best practice resources to help all researchers make the best use of their data, expand their computational skill set, and effectively manage their data and computation when working as distributed teams. iPlant's platform permits researchers to easily deposit and share their data and deploy new computational tools and analysis workflows, allowing the broader community to easily use and reuse those data and computational analyses.

Electronic Publishing.

ERIC Educational Resources Information Center

Lancaster, F. W.

1989-01-01

Describes various stages involved in the applications of electronic media to the publishing industry. Highlights include computer typesetting, or photocomposition; machine-readable databases; the distribution of publications in electronic form; computer conferencing and electronic mail; collaborative authorship; hypertext; hypermedia publications;…

Using directed information for influence discovery in interconnected dynamical systems

NASA Astrophysics Data System (ADS)

Rao, Arvind; Hero, Alfred O.; States, David J.; Engel, James Douglas

2008-08-01

Structure discovery in non-linear dynamical systems is an important and challenging problem that arises in various applications such as computational neuroscience, econometrics, and biological network discovery. Each of these systems have multiple interacting variables and the key problem is the inference of the underlying structure of the systems (which variables are connected to which others) based on the output observations (such as multiple time trajectories of the variables). Since such applications demand the inference of directed relationships among variables in these non-linear systems, current methods that have a linear assumption on structure or yield undirected variable dependencies are insufficient. Hence, in this work, we present a methodology for structure discovery using an information-theoretic metric called directed time information (DTI). Using both synthetic dynamical systems as well as true biological datasets (kidney development and T-cell data), we demonstrate the utility of DTI in such problems.

Virtual Screening with AutoDock: Theory and Practice

PubMed Central

Cosconati, Sandro; Forli, Stefano; Perryman, Alex L.; Harris, Rodney; Goodsell, David S.; Olson, Arthur J.

2011-01-01

Importance to the field Virtual screening is a computer-based technique for identifying promising compounds to bind to a target molecule of known structure. Given the rapidly increasing number of protein and nucleic acid structures, virtual screening continues to grow as an effective method for the discovery of new inhibitors and drug molecules. Areas covered in this review We describe virtual screening methods that are available in the AutoDock suite of programs, and several of our successes in using AutoDock virtual screening in pharmaceutical lead discovery. What the reader will gain A general overview of the challenges of virtual screening is presented, along with the tools available in the AutoDock suite of programs for addressing these challenges. Take home message Virtual screening is an effective tool for the discovery of compounds for use as leads in drug discovery, and the free, open source program AutoDock is an effective tool for virtual screening. PMID:21532931

Discovery of anaerobic lithoheterotrophic haloarchaea, ubiquitous in hypersaline habitats

PubMed Central

Sorokin, Dimitry Y; Messina, Enzo; Smedile, Francesco; Roman, Pawel; Damsté, Jaap S Sinninghe; Ciordia, Sergio; Mena, Maria Carmen; Ferrer, Manuel; Golyshin, Peter N; Kublanov, Ilya V; Samarov, Nazar I; Toshchakov, Stepan V; La Cono, Violetta; Yakimov, Michail M

2017-01-01

Hypersaline anoxic habitats harbour numerous novel uncultured archaea whose metabolic and ecological roles remain to be elucidated. Until recently, it was believed that energy generation via dissimilatory reduction of sulfur compounds is not functional at salt saturation conditions. Recent discovery of the strictly anaerobic acetotrophic Halanaeroarchaeum compels to change both this assumption and the traditional view on haloarchaea as aerobic heterotrophs. Here we report on isolation and characterization of a novel group of strictly anaerobic lithoheterotrophic haloarchaea, which we propose to classify as a new genus Halodesulfurarchaeum. Members of this previously unknown physiological group are capable of utilising formate or hydrogen as electron donors and elemental sulfur, thiosulfate or dimethylsulfoxide as electron acceptors. Using genome-wide proteomic analysis we have detected the full set of enzymes required for anaerobic respiration and analysed their substrate-specific expression. Such advanced metabolic plasticity and type of respiration, never seen before in haloarchaea, empower the wide distribution of Halodesulfurarchaeum in hypersaline inland lakes, solar salterns, lagoons and deep submarine anoxic brines. The discovery of this novel functional group of sulfur-respiring haloarchaea strengthens the evidence of their possible role in biogeochemical sulfur cycling linked to the terminal anaerobic carbon mineralisation in so far overlooked hypersaline anoxic habitats. PMID:28106880

Assessment of composite motif discovery methods.

PubMed

Klepper, Kjetil; Sandve, Geir K; Abul, Osman; Johansen, Jostein; Drablos, Finn

2008-02-26

Computational discovery of regulatory elements is an important area of bioinformatics research and more than a hundred motif discovery methods have been published. Traditionally, most of these methods have addressed the problem of single motif discovery - discovering binding motifs for individual transcription factors. In higher organisms, however, transcription factors usually act in combination with nearby bound factors to induce specific regulatory behaviours. Hence, recent focus has shifted from single motifs to the discovery of sets of motifs bound by multiple cooperating transcription factors, so called composite motifs or cis-regulatory modules. Given the large number and diversity of methods available, independent assessment of methods becomes important. Although there have been several benchmark studies of single motif discovery, no similar studies have previously been conducted concerning composite motif discovery. We have developed a benchmarking framework for composite motif discovery and used it to evaluate the performance of eight published module discovery tools. Benchmark datasets were constructed based on real genomic sequences containing experimentally verified regulatory modules, and the module discovery programs were asked to predict both the locations of these modules and to specify the single motifs involved. To aid the programs in their search, we provided position weight matrices corresponding to the binding motifs of the transcription factors involved. In addition, selections of decoy matrices were mixed with the genuine matrices on one dataset to test the response of programs to varying levels of noise. Although some of the methods tested tended to score somewhat better than others overall, there were still large variations between individual datasets and no single method performed consistently better than the rest in all situations. The variation in performance on individual datasets also shows that the new benchmark datasets represents a suitable variety of challenges to most methods for module discovery.

Method and system for knowledge discovery using non-linear statistical analysis and a 1st and 2nd tier computer program

DOEpatents

Hively, Lee M [Philadelphia, TN

2011-07-12

The invention relates to a method and apparatus for simultaneously processing different sources of test data into informational data and then processing different categories of informational data into knowledge-based data. The knowledge-based data can then be communicated between nodes in a system of multiple computers according to rules for a type of complex, hierarchical computer system modeled on a human brain.

Discovery informatics in biological and biomedical sciences: research challenges and opportunities.

PubMed

Honavar, Vasant

2015-01-01

New discoveries in biological, biomedical and health sciences are increasingly being driven by our ability to acquire, share, integrate and analyze, and construct and simulate predictive models of biological systems. While much attention has focused on automating routine aspects of management and analysis of "big data", realizing the full potential of "big data" to accelerate discovery calls for automating many other aspects of the scientific process that have so far largely resisted automation: identifying gaps in the current state of knowledge; generating and prioritizing questions; designing studies; designing, prioritizing, planning, and executing experiments; interpreting results; forming hypotheses; drawing conclusions; replicating studies; validating claims; documenting studies; communicating results; reviewing results; and integrating results into the larger body of knowledge in a discipline. Against this background, the PSB workshop on Discovery Informatics in Biological and Biomedical Sciences explores the opportunities and challenges of automating discovery or assisting humans in discovery through advances (i) Understanding, formalization, and information processing accounts of, the entire scientific process; (ii) Design, development, and evaluation of the computational artifacts (representations, processes) that embody such understanding; and (iii) Application of the resulting artifacts and systems to advance science (by augmenting individual or collective human efforts, or by fully automating science).

Synthesis of Pyridine– and Pyrazine–BF 3 Complexes and Their Characterization in Solution and Solid State

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chénard, Etienne; Sutrisno, Andre; Zhu, Lingyang

2016-03-31

Following the discovery of the redox-active 1,4- bis-BF 3-quinoxaline complex, we undertook a structure- activity study with the objective to understand the active nature of the quinoxaline complex. Through systematic synthesis and characterization, we have compared complexes prepared from pyridine and pyrazine derivatives, as heterocyclic core analogues. This paper reports the structural requirements that give rise to the electrochemical features of the 1,4-bis-BF 3-quinoxaline adduct. Using solution and solidstate NMR spectroscopy, the role of aromatic ring fusion and nitrogen incorporation in bonding and electronics was elucidated. We establish the boron atom location and its interaction with its environment from 1Dmore » and 2D solution NMR, X-ray diffraction analysis, and 11B solid-state NMR experiments. Crystallographic analysis of single crystals helped to correlate the boron geometry with 11B quadrupolar coupling constant (CQ) and asymmetry parameter (ηQ), extracted from 11B solid-state NMR spectra. Additionally, computations based on density functional theory were performed to predict electrochemical behavior of the BF 3-heteroaromatic complexes. We then experimentally measured electrochemical potential using cyclic voltammetry and found that the redox potentials and CQ values are similarly affected by electronic changes in the complexes.« less

Preface - Access to Knowledge Revisited

PubMed Central

Humphreys, Betsy L.

2016-01-01

Summary Objective To review and update the Preface to the 1998 Yearbook of Medical Informatics, which had as its Special Topic “Health Informatics and the Internet”. Method Assessment of the accuracy of predictions made in 1998 and consideration of key developments in informatics since that time. Results Predictions made in 1998 were generally accurate regarding reduced dependence on keyboards, expansion of multimedia, medical data privacy policy development, impact of molecular biology on knowledge and treatment of neoplasms, and use of imaging and informatics to advance understanding of brain structure and function. Key developments since 1998 include the huge increase in publicly available electronic information; acknowledgement by leaders in government and science of the importance of biomedical informatics to societal goals for health, health care, and scientific discovery; the influence of the public in promoting clinical research transparency and free access to government-funded research results; the long-awaited arrival of electronic health records; and the “Cloud” as a 21st century reformulation of contracting out the computer center. Conclusions There are many challenging and important problems that deserve the attention of the informatics community. Informatics researchers will be best served by embracing a very broad definition of medical informatics and by promoting public understanding of the field. PMID:27199193

Selective Screening of High Temperature Superconductors by Resonant Eddy Current Analysis

DTIC Science & Technology

1990-11-01

observable electronic parameters are both stable and well defined. Further, if the circuit possesses a resonance , then it has well characterized parameters and...Engineers Construction Engineering Research Laboratory - AD-A230 194 Selective Screening of High Temperature Superconductors by Resonant Eddy Current...electrical systems or electronic components from the effects of unwanted electromagnetic energy. With the discovery of High Transition Critical Temperature

Jefferson Lab Science: Present and Future

DOE PAGES

McKeown, Robert D.

2015-02-12

The Continuous Electron Beam Accelerator Facility (CEBAF) and associated experimental equipment at Jefferson Lab comprise a unique facility for experimental nuclear physics. Furthermore, this facility is presently being upgraded, which will enable a new experimental program with substantial discovery potential to address important topics in nuclear, hadronic, and electroweak physics. Further in the future, it is envisioned that the Laboratory will evolve into an electron-ion colliding beam facility.

Discovery and follow up of asteroids

NASA Technical Reports Server (NTRS)

Bowell, E.; Chernykh, N. S.; Marsden, B. G.

1989-01-01

After a summary of the increasing activity in steroid discovery during the past few years, the importance of carefully thought out observing strategy is discussed, in particular with regard to target selection, observing frequency, and the time distribution of observations. Problems of cataloging and orbit linkage are outlined, inasmuch as they affect individual observers and orbit computers, as well as the work of the Minor Planet Center. There is some discussion of appropriate two-way communication between observers and the Minor Planet Center.

First-principles data-driven discovery of transition metal oxides for artificial photosynthesis

NASA Astrophysics Data System (ADS)

Yan, Qimin

We develop a first-principles data-driven approach for rapid identification of transition metal oxide (TMO) light absorbers and photocatalysts for artificial photosynthesis using the Materials Project. Initially focusing on Cr, V, and Mn-based ternary TMOs in the database, we design a broadly-applicable multiple-layer screening workflow automating density functional theory (DFT) and hybrid functional calculations of bulk and surface electronic and magnetic structures. We further assess the electrochemical stability of TMOs in aqueous environments from computed Pourbaix diagrams. Several promising earth-abundant low band-gap TMO compounds with desirable band edge energies and electrochemical stability are identified by our computational efforts and then synergistically evaluated using high-throughput synthesis and photoelectrochemical screening techniques by our experimental collaborators at Caltech. Our joint theory-experiment effort has successfully identified new earth-abundant copper and manganese vanadate complex oxides that meet highly demanding requirements for photoanodes, substantially expanding the known space of such materials. By integrating theory and experiment, we validate our approach and develop important new insights into structure-property relationships for TMOs for oxygen evolution photocatalysts, paving the way for use of first-principles data-driven techniques in future applications. This work is supported by the Materials Project Predictive Modeling Center and the Joint Center for Artificial Photosynthesis through the U.S. Department of Energy, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, under Contract No. DE-AC02-05CH11231. Computational resources also provided by the Department of Energy through the National Energy Supercomputing Center.

77 FR 27078 - Certain Electronic Devices, Including Mobile Phones and Tablet Computers, and Components Thereof...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-08

... Phones and Tablet Computers, and Components Thereof; Notice of Receipt of Complaint; Solicitation of... entitled Certain Electronic Devices, Including Mobile Phones and Tablet Computers, and Components Thereof... the United States after importation of certain electronic devices, including mobile phones and tablet...

77 FR 66729 - National Oil and Hazardous Substances Pollution Contingency Plan; Revision To Increase Public...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-07

... technology, to include computer telecommunications or other electronic means, that the lead agency is... assess the capacity and resources of the public to utilize and maintain an electronic- or computer... the technology, to include computer telecommunications or other electronic means, that the lead agency...

Information and Communicative Technology--Computers as Research Tools

ERIC Educational Resources Information Center

Sarsani, Mahender Reddy

2007-01-01

The emergence of "the electronic age,/electronic cottages/the electronic world" has affected the whole world; particularly the emergence of computers has penetrated everyone's life to a remarkable degree. They are being used in various fields including education. Recent advances, especially in the area of computer technology have…

Statistical detection of EEG synchrony using empirical bayesian inference.

PubMed

Singh, Archana K; Asoh, Hideki; Takeda, Yuji; Phillips, Steven

2015-01-01

There is growing interest in understanding how the brain utilizes synchronized oscillatory activity to integrate information across functionally connected regions. Computing phase-locking values (PLV) between EEG signals is a popular method for quantifying such synchronizations and elucidating their role in cognitive tasks. However, high-dimensionality in PLV data incurs a serious multiple testing problem. Standard multiple testing methods in neuroimaging research (e.g., false discovery rate, FDR) suffer severe loss of power, because they fail to exploit complex dependence structure between hypotheses that vary in spectral, temporal and spatial dimension. Previously, we showed that a hierarchical FDR and optimal discovery procedures could be effectively applied for PLV analysis to provide better power than FDR. In this article, we revisit the multiple comparison problem from a new Empirical Bayes perspective and propose the application of the local FDR method (locFDR; Efron, 2001) for PLV synchrony analysis to compute FDR as a posterior probability that an observed statistic belongs to a null hypothesis. We demonstrate the application of Efron's Empirical Bayes approach for PLV synchrony analysis for the first time. We use simulations to validate the specificity and sensitivity of locFDR and a real EEG dataset from a visual search study for experimental validation. We also compare locFDR with hierarchical FDR and optimal discovery procedures in both simulation and experimental analyses. Our simulation results showed that the locFDR can effectively control false positives without compromising on the power of PLV synchrony inference. Our results from the application locFDR on experiment data detected more significant discoveries than our previously proposed methods whereas the standard FDR method failed to detect any significant discoveries.

Improved accuracy of supervised CRM discovery with interpolated Markov models and cross-species comparison

PubMed Central

Kazemian, Majid; Zhu, Qiyun; Halfon, Marc S.; Sinha, Saurabh

2011-01-01

Despite recent advances in experimental approaches for identifying transcriptional cis-regulatory modules (CRMs, ‘enhancers’), direct empirical discovery of CRMs for all genes in all cell types and environmental conditions is likely to remain an elusive goal. Effective methods for computational CRM discovery are thus a critically needed complement to empirical approaches. However, existing computational methods that search for clusters of putative binding sites are ineffective if the relevant TFs and/or their binding specificities are unknown. Here, we provide a significantly improved method for ‘motif-blind’ CRM discovery that does not depend on knowledge or accurate prediction of TF-binding motifs and is effective when limited knowledge of functional CRMs is available to ‘supervise’ the search. We propose a new statistical method, based on ‘Interpolated Markov Models’, for motif-blind, genome-wide CRM discovery. It captures the statistical profile of variable length words in known CRMs of a regulatory network and finds candidate CRMs that match this profile. The method also uses orthologs of the known CRMs from closely related genomes. We perform in silico evaluation of predicted CRMs by assessing whether their neighboring genes are enriched for the expected expression patterns. This assessment uses a novel statistical test that extends the widely used Hypergeometric test of gene set enrichment to account for variability in intergenic lengths. We find that the new CRM prediction method is superior to existing methods. Finally, we experimentally validate 12 new CRM predictions by examining their regulatory activity in vivo in Drosophila; 10 of the tested CRMs were found to be functional, while 6 of the top 7 predictions showed the expected activity patterns. We make our program available as downloadable source code, and as a plugin for a genome browser installed on our servers. PMID:21821659