The Centrality of Engagement in Higher Education

ERIC Educational Resources Information Center

Fitzgerald, Hiram E.; Bruns, Karen; Sonka, Steven T.; Furco, Andrew; Swanson, Louis

2016-01-01

The centrality of engagement is critical to the success of higher education in the future. Engagement is essential to most effectively achieving the overall purpose of the university, which is focused on the knowledge enterprise. Today's engagement is scholarly, is an aspect of learning and discovery, and enhances society and higher education.…

The Earth's Core: How Does It Work? Perspectives in Science. Number 1.

ERIC Educational Resources Information Center

Carnegie Institution of Washington, Washington, DC.

Various research studies designed to enhance knowledge about the earth's core are discussed. Areas addressed include: (1) the discovery of the earth's core; (2) experimental approaches used in studying the earth's core (including shock-wave experiments and experiments at high static pressures), the search for the core's light elements, the…

Knowledge Discovery from Databases: An Introductory Review.

ERIC Educational Resources Information Center

Vickery, Brian

1997-01-01

Introduces new procedures being used to extract knowledge from databases and discusses rationales for developing knowledge discovery methods. Methods are described for such techniques as classification, clustering, and the detection of deviations from pre-established norms. Examines potential uses of knowledge discovery in the information field.…

The relation between prior knowledge and students' collaborative discovery learning processes

NASA Astrophysics Data System (ADS)

Gijlers, Hannie; de Jong, Ton

2005-03-01

In this study we investigate how prior knowledge influences knowledge development during collaborative discovery learning. Fifteen dyads of students (pre-university education, 15-16 years old) worked on a discovery learning task in the physics field of kinematics. The (face-to-face) communication between students was recorded and the interaction with the environment was logged. Based on students' individual judgments of the truth-value and testability of a series of domain-specific propositions, a detailed description of the knowledge configuration for each dyad was created before they entered the learning environment. Qualitative analyses of two dialogues illustrated that prior knowledge influences the discovery learning processes, and knowledge development in a pair of students. Assessments of student and dyad definitional (domain-specific) knowledge, generic (mathematical and graph) knowledge, and generic (discovery) skills were related to the students' dialogue in different discovery learning processes. Results show that a high level of definitional prior knowledge is positively related to the proportion of communication regarding the interpretation of results. Heterogeneity with respect to generic prior knowledge was positively related to the number of utterances made in the discovery process categories hypotheses generation and experimentation. Results of the qualitative analyses indicated that collaboration between extremely heterogeneous dyads is difficult when the high achiever is not willing to scaffold information and work in the low achiever's zone of proximal development.

Genomics as knowledge enterprise: Implementing an electronic research habitat at the Biopolis Experimental Therapeutics Center.

PubMed

Mitchell, Wayne; Breen, Colin; Entzeroth, Michael

2008-03-01

The Experimental Therapeutics Center (ETC) has been established at Biopolis to advance translational research by bridging the gap between discovery science and commercialization. We describe the Electronic Research Habitat at ETC, a comprehensive hardware and software infrastructure designed to effectively manage terabyte data flows and storage, increase back office efficiency, enhance the scientific work experience, and satisfy rigorous regulatory and legal requirements. Our habitat design is secure, scalable and robust, and it strives to embody the core values of the knowledge-based workplace, thus contributing to the strategic goal of building a "knowledge economy" in the context of Singapore's on-going biotechnology initiative.

Information Fusion for Natural and Man-Made Disasters

DTIC Science & Technology

2007-01-31

comprehensively large, and metaphysically accurate model of situations, through which specific tasks such as situation assessment, knowledge discovery , or the...significance” is always context specific. Event discovery is a very important element of the HLF process, which can lead to knowledge discovery about...expected, given the current state of knowledge . Examples of such behavior may include discovery of a new aggregate or situation, a specific pattern of

Emergence of Chinese drug discovery research: impact of hit and lead identification.

PubMed

Zhou, Caihong; Zhou, Yan; Wang, Jia; Zhu, Yue; Deng, Jiejie; Wang, Ming-Wei

2015-03-01

The identification of hits and the generation of viable leads is an early and yet crucial step in drug discovery. In the West, the main players of drug discovery are pharmaceutical and biotechnology companies, while in China, academic institutions remain central in the field of drug discovery. There has been a tremendous amount of investment from the public as well as private sectors to support infrastructure buildup and expertise consolidation relative to drug discovery and development in the past two decades. A large-scale compound library has been established in China, and a series of high-impact discoveries of lead compounds have been made by integrating information obtained from different technology-based strategies. Natural products are a major source in China's drug discovery efforts. Knowledge has been enhanced via disruptive breakthroughs such as the discovery of Boc5 as a nonpeptidic agonist of glucagon-like peptide 1 receptor (GLP-1R), one of the class B G protein-coupled receptors (GPCRs). Most of the original hit identification and lead generation were carried out by academic institutions, including universities and specialized research institutes. The Chinese pharmaceutical industry is gradually transforming itself from manufacturing low-end generics and active pharmaceutical ingredients to inventing new drugs. © 2014 Society for Laboratory Automation and Screening.

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…

Advances in Knowledge Discovery and Data Mining 21st Pacific Asia Conference, PAKDD 2017 Held in Jeju, South Korea, May 23 26, 2017. Proceedings Part I, Part II.

DTIC Science & Technology

2017-06-27

From - To) 05-27-2017 Final 17-03-2017 - 15-03-2018 4. TITLE AND SUBTITLE Sa. CONTRACT NUMBER FA2386-17-1-0102 Advances in Knowledge Discovery and...Springer; Switzerland. 14. ABSTRACT The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference...in the areas of knowledge discovery and data mining (KDD). We had three keynote speeches, delivered by Sang Cha from Seoul National University

The Relation between Prior Knowledge and Students' Collaborative Discovery Learning Processes

ERIC Educational Resources Information Center

Gijlers, Hannie; de Jong, Ton

2005-01-01

In this study we investigate how prior knowledge influences knowledge development during collaborative discovery learning. Fifteen dyads of students (pre-university education, 15-16 years old) worked on a discovery learning task in the physics field of kinematics. The (face-to-face) communication between students was recorded and the interaction…

Semantic MEDLINE for Discovery Browsing: Using Semantic Predications and the Literature-Based Discovery Paradigm to Elucidate a Mechanism for the Obesity Paradox

PubMed Central

Cairelli, Michael J.; Miller, Christopher M.; Fiszman, Marcelo; Workman, T. Elizabeth; Rindflesch, Thomas C.

2013-01-01

Applying the principles of literature-based discovery (LBD), we elucidate the paradox that obesity is beneficial in critical care despite contributing to disease generally. Our approach enhances a previous extension to LBD, called “discovery browsing,” and is implemented using Semantic MEDLINE, which summarizes the results of a PubMed search into an interactive graph of semantic predications. The methodology allows a user to construct argumentation underpinning an answer to a biomedical question by engaging the user in an iterative process between system output and user knowledge. Components of the Semantic MEDLINE output graph identified as “interesting” by the user both contribute to subsequent searches and are constructed into a logical chain of relationships constituting an explanatory network in answer to the initial question. Based on this methodology we suggest that phthalates leached from plastic in critical care interventions activate PPAR gamma, which is anti-inflammatory and abundant in obese patients. PMID:24551329

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.

A New Student Performance Analysing System Using Knowledge Discovery in Higher Educational Databases

ERIC Educational Resources Information Center

Guruler, Huseyin; Istanbullu, Ayhan; Karahasan, Mehmet

2010-01-01

Knowledge discovery is a wide ranged process including data mining, which is used to find out meaningful and useful patterns in large amounts of data. In order to explore the factors having impact on the success of university students, knowledge discovery software, called MUSKUP, has been developed and tested on student data. In this system a…

Knowledge Discovery in Databases.

ERIC Educational Resources Information Center

Norton, M. Jay

1999-01-01

Knowledge discovery in databases (KDD) revolves around the investigation and creation of knowledge, processes, algorithms, and mechanisms for retrieving knowledge from data collections. The article is an introductory overview of KDD. The rationale and environment of its development and applications are discussed. Issues related to database design…

XGR software for enhanced interpretation of genomic summary data, illustrated by application to immunological traits.

PubMed

Fang, Hai; Knezevic, Bogdan; Burnham, Katie L; Knight, Julian C

2016-12-13

Biological interpretation of genomic summary data such as those resulting from genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is one of the major bottlenecks in medical genomics research, calling for efficient and integrative tools to resolve this problem. We introduce eXploring Genomic Relations (XGR), an open source tool designed for enhanced interpretation of genomic summary data enabling downstream knowledge discovery. Targeting users of varying computational skills, XGR utilises prior biological knowledge and relationships in a highly integrated but easily accessible way to make user-input genomic summary datasets more interpretable. We show how by incorporating ontology, annotation, and systems biology network-driven approaches, XGR generates more informative results than conventional analyses. We apply XGR to GWAS and eQTL summary data to explore the genomic landscape of the activated innate immune response and common immunological diseases. We provide genomic evidence for a disease taxonomy supporting the concept of a disease spectrum from autoimmune to autoinflammatory disorders. We also show how XGR can define SNP-modulated gene networks and pathways that are shared and distinct between diseases, how it achieves functional, phenotypic and epigenomic annotations of genes and variants, and how it enables exploring annotation-based relationships between genetic variants. XGR provides a single integrated solution to enhance interpretation of genomic summary data for downstream biological discovery. XGR is released as both an R package and a web-app, freely available at http://galahad.well.ox.ac.uk/XGR .

Systems and methods for knowledge discovery in spatial data

DOEpatents

Obradovic, Zoran; Fiez, Timothy E.; Vucetic, Slobodan; Lazarevic, Aleksandar; Pokrajac, Dragoljub; Hoskinson, Reed L.

2005-03-08

Systems and methods are provided for knowledge discovery in spatial data as well as to systems and methods for optimizing recipes used in spatial environments such as may be found in precision agriculture. A spatial data analysis and modeling module is provided which allows users to interactively and flexibly analyze and mine spatial data. The spatial data analysis and modeling module applies spatial data mining algorithms through a number of steps. The data loading and generation module obtains or generates spatial data and allows for basic partitioning. The inspection module provides basic statistical analysis. The preprocessing module smoothes and cleans the data and allows for basic manipulation of the data. The partitioning module provides for more advanced data partitioning. The prediction module applies regression and classification algorithms on the spatial data. The integration module enhances prediction methods by combining and integrating models. The recommendation module provides the user with site-specific recommendations as to how to optimize a recipe for a spatial environment such as a fertilizer recipe for an agricultural field.

A Knowledge Discovery framework for Planetary Defense

NASA Astrophysics Data System (ADS)

Jiang, Y.; Yang, C. P.; Li, Y.; Yu, M.; Bambacus, M.; Seery, B.; Barbee, B.

2016-12-01

Planetary Defense, a project funded by NASA Goddard and the NSF, is a multi-faceted effort focused on the mitigation of Near Earth Object (NEO) threats to our planet. Currently, there exists a dispersion of information concerning NEO's amongst different organizations and scientists, leading to a lack of a coherent system of information to be used for efficient NEO mitigation. In this paper, a planetary defense knowledge discovery engine is proposed to better assist the development and integration of a NEO responding system. Specifically, we have implemented an organized information framework by two means: 1) the development of a semantic knowledge base, which provides a structure for relevant information. It has been developed by the implementation of web crawling and natural language processing techniques, which allows us to collect and store the most relevant structured information on a regular basis. 2) the development of a knowledge discovery engine, which allows for the efficient retrieval of information from our knowledge base. The knowledge discovery engine has been built on the top of Elasticsearch, an open source full-text search engine, as well as cutting-edge machine learning ranking and recommendation algorithms. This proposed framework is expected to advance the knowledge discovery and innovation in planetary science domain.

Knowledge Discovery from Biomedical Ontologies in Cross Domains.

PubMed

Shen, Feichen; Lee, Yugyung

2016-01-01

In recent years, there is an increasing demand for sharing and integration of medical data in biomedical research. In order to improve a health care system, it is required to support the integration of data by facilitating semantic interoperability systems and practices. Semantic interoperability is difficult to achieve in these systems as the conceptual models underlying datasets are not fully exploited. In this paper, we propose a semantic framework, called Medical Knowledge Discovery and Data Mining (MedKDD), that aims to build a topic hierarchy and serve the semantic interoperability between different ontologies. For the purpose, we fully focus on the discovery of semantic patterns about the association of relations in the heterogeneous information network representing different types of objects and relationships in multiple biological ontologies and the creation of a topic hierarchy through the analysis of the discovered patterns. These patterns are used to cluster heterogeneous information networks into a set of smaller topic graphs in a hierarchical manner and then to conduct cross domain knowledge discovery from the multiple biological ontologies. Thus, patterns made a greater contribution in the knowledge discovery across multiple ontologies. We have demonstrated the cross domain knowledge discovery in the MedKDD framework using a case study with 9 primary biological ontologies from Bio2RDF and compared it with the cross domain query processing approach, namely SLAP. We have confirmed the effectiveness of the MedKDD framework in knowledge discovery from multiple medical ontologies.

Knowledge Discovery from Biomedical Ontologies in Cross Domains

PubMed Central

Shen, Feichen; Lee, Yugyung

2016-01-01

In recent years, there is an increasing demand for sharing and integration of medical data in biomedical research. In order to improve a health care system, it is required to support the integration of data by facilitating semantic interoperability systems and practices. Semantic interoperability is difficult to achieve in these systems as the conceptual models underlying datasets are not fully exploited. In this paper, we propose a semantic framework, called Medical Knowledge Discovery and Data Mining (MedKDD), that aims to build a topic hierarchy and serve the semantic interoperability between different ontologies. For the purpose, we fully focus on the discovery of semantic patterns about the association of relations in the heterogeneous information network representing different types of objects and relationships in multiple biological ontologies and the creation of a topic hierarchy through the analysis of the discovered patterns. These patterns are used to cluster heterogeneous information networks into a set of smaller topic graphs in a hierarchical manner and then to conduct cross domain knowledge discovery from the multiple biological ontologies. Thus, patterns made a greater contribution in the knowledge discovery across multiple ontologies. We have demonstrated the cross domain knowledge discovery in the MedKDD framework using a case study with 9 primary biological ontologies from Bio2RDF and compared it with the cross domain query processing approach, namely SLAP. We have confirmed the effectiveness of the MedKDD framework in knowledge discovery from multiple medical ontologies. PMID:27548262

Knowledge discovery with classification rules in a cardiovascular dataset.

PubMed

Podgorelec, Vili; Kokol, Peter; Stiglic, Milojka Molan; Hericko, Marjan; Rozman, Ivan

2005-12-01

In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients. A case study is presented that shows the use of AREX for the classification of patients and for discovering possible new medical knowledge from the dataset. The defined knowledge discovery loop comprises a medical expert's assessment of induced rules to drive the evolution of rule sets towards more appropriate solutions. The final result is the discovery of a possible new medical knowledge in the field of pediatric cardiology.

Progress in Biomedical Knowledge Discovery: A 25-year Retrospective

PubMed Central

Sacchi, L.

2016-01-01

Summary Objectives We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning. Methods We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated. Results A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992-2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992. Conclusions Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains. Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data. PMID:27488403

Progress in Biomedical Knowledge Discovery: A 25-year Retrospective.

PubMed

Sacchi, L; Holmes, J H

2016-08-02

We sought to explore, via a systematic review of the literature, the state of the art of knowledge discovery in biomedical databases as it existed in 1992, and then now, 25 years later, mainly focused on supervised learning. We performed a rigorous systematic search of PubMed and latent Dirichlet allocation to identify themes in the literature and trends in the science of knowledge discovery in and between time periods and compare these trends. We restricted the result set using a bracket of five years previous, such that the 1992 result set was restricted to articles published between 1987 and 1992, and the 2015 set between 2011 and 2015. This was to reflect the current literature available at the time to researchers and others at the target dates of 1992 and 2015. The search term was framed as: Knowledge Discovery OR Data Mining OR Pattern Discovery OR Pattern Recognition, Automated. A total 538 and 18,172 documents were retrieved for 1992 and 2015, respectively. The number and type of data sources increased dramatically over the observation period, primarily due to the advent of electronic clinical systems. The period 1992- 2015 saw the emergence of new areas of research in knowledge discovery, and the refinement and application of machine learning approaches that were nascent or unknown in 1992. Over the 25 years of the observation period, we identified numerous developments that impacted the science of knowledge discovery, including the availability of new forms of data, new machine learning algorithms, and new application domains. Through a bibliometric analysis we examine the striking changes in the availability of highly heterogeneous data resources, the evolution of new algorithmic approaches to knowledge discovery, and we consider from legal, social, and political perspectives possible explanations of the growth of the field. Finally, we reflect on the achievements of the past 25 years to consider what the next 25 years will bring with regard to the availability of even more complex data and to the methods that could be, and are being now developed for the discovery of new knowledge in biomedical data.

The Evolution of Drug Development in Schizophrenia

PubMed Central

Carpenter, William T; Koenig, James I

2008-01-01

Schizophrenia is a disease syndrome with major public health implications. The primary advance in pharmacotherapeutics was in 1952 with the introduction of antipsychotic medications (ie, chlorpromazine, dopamine D2 antagonism). Barriers to progress have been substantial, but many will be subject to rapid change based on current knowledge. There are attractive psychopathology indications for drug discovery (eg, impaired cognition and negative symptoms), and drugs with efficacy in these domains may have application across a number of disease classes. These pathologies are observed prior to psychosis raising the possibility of very early intervention and secondary prevention. Success in drug discovery for cognition and negative symptom pathologies may bring forth issues in ethics as the potential for enhancing normal function is explored. PMID:18046305

Scientific discovery as a combinatorial optimisation problem: How best to navigate the landscape of possible experiments?

PubMed Central

Kell, Douglas B

2012-01-01

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the ‘best’ experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. PMID:22252984

Scientific discovery as a combinatorial optimisation problem: how best to navigate the landscape of possible experiments?

PubMed

Kell, Douglas B

2012-03-01

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a 'landscape' representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems 'hard', but as such these are to be seen as combinatorial optimisation problems that are best attacked by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the 'best' experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. Copyright © 2012 WILEY Periodicals, Inc.

Communication in Collaborative Discovery Learning

ERIC Educational Resources Information Center

Saab, Nadira; van Joolingen, Wouter R.; van Hout-Wolters, Bernadette H. A. M.

2005-01-01

Background: Constructivist approaches to learning focus on learning environments in which students have the opportunity to construct knowledge themselves, and negotiate this knowledge with others. "Discovery learning" and "collaborative learning" are examples of learning contexts that cater for knowledge construction processes. We introduce a…

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

Active Storage with Analytics Capabilities and I/O Runtime System for Petascale Systems

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

Choudhary, Alok

Computational scientists must understand results from experimental, observational and computational simulation generated data to gain insights and perform knowledge discovery. As systems approach the petascale range, problems that were unimaginable a few years ago are within reach. With the increasing volume and complexity of data produced by ultra-scale simulations and high-throughput experiments, understanding the science is largely hampered by the lack of comprehensive I/O, storage, acceleration of data manipulation, analysis, and mining tools. Scientists require techniques, tools and infrastructure to facilitate better understanding of their data, in particular the ability to effectively perform complex data analysis, statistical analysis and knowledgemore » discovery. The goal of this work is to enable more effective analysis of scientific datasets through the integration of enhancements in the I/O stack, from active storage support at the file system layer to MPI-IO and high-level I/O library layers. We propose to provide software components to accelerate data analytics, mining, I/O, and knowledge discovery for large-scale scientific applications, thereby increasing productivity of both scientists and the systems. Our approaches include 1) design the interfaces in high-level I/O libraries, such as parallel netCDF, for applications to activate data mining operations at the lower I/O layers; 2) Enhance MPI-IO runtime systems to incorporate the functionality developed as a part of the runtime system design; 3) Develop parallel data mining programs as part of runtime library for server-side file system in PVFS file system; and 4) Prototype an active storage cluster, which will utilize multicore CPUs, GPUs, and FPGAs to carry out the data mining workload.« less

Mississippi State University Center for Air Sea Technology. FY93 and FY 94 Research Program in Navy Ocean Modeling and Prediction

DTIC Science & Technology

1994-09-30

relational versus object oriented DBMS, knowledge discovery, data models, rnetadata, data filtering, clustering techniques, and synthetic data. A secondary...The first was the investigation of Al/ES Lapplications (knowledge discovery, data mining, and clustering ). Here CAST collabo.rated with Dr. Fred Petry...knowledge discovery system based on clustering techniques; implemented an on-line data browser to the DBMS; completed preliminary efforts to apply object

Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research.

PubMed

Girardi, Dominic; Küng, Josef; Kleiser, Raimund; Sonnberger, Michael; Csillag, Doris; Trenkler, Johannes; Holzinger, Andreas

2016-09-01

Established process models for knowledge discovery find the domain-expert in a customer-like and supervising role. In the field of biomedical research, it is necessary to move the domain-experts into the center of this process with far-reaching consequences for both their research output and the process itself. In this paper, we revise the established process models for knowledge discovery and propose a new process model for domain-expert-driven interactive knowledge discovery. Furthermore, we present a research infrastructure which is adapted to this new process model and demonstrate how the domain-expert can be deeply integrated even into the highly complex data-mining process and data-exploration tasks. We evaluated this approach in the medical domain for the case of cerebral aneurysms research.

Research to knowledge: promoting the training of physician-scientists in the biology of pregnancy.

PubMed

Sadovsky, Yoel; Caughey, Aaron B; DiVito, Michelle; D'Alton, Mary E; Murtha, Amy P

2018-01-01

Common disorders of pregnancy, such as preeclampsia, preterm birth, and fetal growth abnormalities, continue to challenge perinatal biologists seeking insights into disease pathogenesis that will result in better diagnosis, therapy, and disease prevention. These challenges have recently been intensified with discoveries that associate gestational diseases with long-term maternal and neonatal outcomes. Whereas modern high-throughput investigative tools enable scientists and clinicians to noninvasively probe the maternal-fetal genome, epigenome, and other analytes, their implications for clinical medicine remain uncertain. Bridging these knowledge gaps depends on strengthening the existing pool of scientists with expertise in basic, translational, and clinical tools to address pertinent questions in the biology of pregnancy. Although PhD researchers are critical in this quest, physician-scientists would facilitate the inquiry by bringing together clinical challenges and investigative tools, promoting a culture of intellectual curiosity among clinical providers, and helping transform discoveries into relevant knowledge and clinical solutions. Uncertainties related to future administration of health care, federal support for research, attrition of physician-scientists, and an inadequate supply of new scholars may jeopardize our ability to address these challenges. New initiatives are necessary to attract current scholars and future generations of researchers seeking expertise in the scientific method and to support them, through mentorship and guidance, in pursuing a career that combines scientific investigation with clinical medicine. These efforts will promote breadth and depth of inquiry into the biology of pregnancy and enhance the pace of translation of scientific discoveries into better medicine and disease prevention. Copyright © 2017 Elsevier Inc. All rights reserved.

Knowledge Discovery in Textual Documentation: Qualitative and Quantitative Analyses.

ERIC Educational Resources Information Center

Loh, Stanley; De Oliveira, Jose Palazzo M.; Gastal, Fabio Leite

2001-01-01

Presents an application of knowledge discovery in texts (KDT) concerning medical records of a psychiatric hospital. The approach helps physicians to extract knowledge about patients and diseases that may be used for epidemiological studies, for training professionals, and to support physicians to diagnose and evaluate diseases. (Author/AEF)

Joining Forces: The Chemical Biology-Medicinal Chemistry Continuum.

PubMed

Plowright, Alleyn T; Ottmann, Christian; Arkin, Michelle; Auberson, Yves P; Timmerman, Henk; Waldmann, Herbert

2017-09-21

The scientific advances being made across all disciplines are creating ever-increasing opportunities to enhance our knowledge of biological systems and how they relate to human disease. One of the central driving forces in discovering new medicines is medicinal chemistry, where the design and synthesis of novel compounds has led to multiple drugs. Chemical biology, sitting at the interface of many disciplines, has now emerged as a major contributor to the understanding of biological systems and is becoming an integral part of drug discovery. Bringing chemistry and biology much closer and blurring the boundaries between disciplines is creating new opportunities to probe and understand biology; both disciplines play key roles and need to join forces and work together effectively to synergize their impact. The power of chemical biology will then reach its full potential and drive innovation, leading to the discovery of transformative medicines to treat patients. Advances in cancer biology and drug discovery highlight this potential. Copyright © 2017 Elsevier Ltd. All rights reserved.

The OceanLink Project

NASA Astrophysics Data System (ADS)

Narock, T.; Arko, R. A.; Carbotte, S. M.; Chandler, C. L.; Cheatham, M.; Finin, T.; Hitzler, P.; Krisnadhi, A.; Raymond, L. M.; Shepherd, A.; Wiebe, P. H.

2014-12-01

A wide spectrum of maturing methods and tools, collectively characterized as the Semantic Web, is helping to vastly improve the dissemination of scientific research. Creating semantic integration requires input from both domain and cyberinfrastructure scientists. OceanLink, an NSF EarthCube Building Block, is demonstrating semantic technologies through the integration of geoscience data repositories, library holdings, conference abstracts, and funded research awards. Meeting project objectives involves applying semantic technologies to support data representation, discovery, sharing and integration. Our semantic cyberinfrastructure components include ontology design patterns, Linked Data collections, semantic provenance, and associated services to enhance data and knowledge discovery, interoperation, and integration. We discuss how these components are integrated, the continued automated and semi-automated creation of semantic metadata, and techniques we have developed to integrate ontologies, link resources, and preserve provenance and attribution.

Mosquito repellents: An insight into the chronological perspectives and novel discoveries.

PubMed

Islam, Johirul; Zaman, Kamaruz; Duarah, Sanjukta; Raju, Pakalapati Srinivas; Chattopadhyay, Pronobesh

2017-03-01

Mosquito being the major medically important arthropod vector; requires utmost attention to reduce the sufferings and economic consequences of those living in the endemic regions. This is only possible by minimising the human-mosquito contact by an absolute preventing measure. However, unfortunately, such absolute measures are yet to be developed despite enormous efforts and huge investments worldwide. In the absence of vaccines for number of mosquito-borne diseases, repellents could be an attractive option for both military personal and civilians to minimise the risk of contacting different mosquito-borne diseases. However, to achieve this golden goal, the detailed knowledge of a particular repellent is must, including its mode of repellency and other relevant informations. Here, in the present article, an effort has been made to convey the best and latest information on repellents in order to enhance the knowledge of scientific community. The review offers an overview on mosquito repellents, the novel discoveries, and areas in need of attention such as novel repellent formulations and their future prospective. Copyright © 2016 Elsevier B.V. All rights reserved.

QuarkNet: Benefits for Teachers, Their Students and Physicists

NASA Astrophysics Data System (ADS)

Bardeen, Marjorie

2017-01-01

The QuarkNet Collaboration has forged nontraditional relationships among particle physicists, high school teachers and their students. QuarkNet centers are located at 50 + universities and labs across the U.S. and Puerto Rico. We provide professional development for teachers and create opportunities for teachers and students to engage in particle physics data investigations and join research teams. Students develop scientific knowledge and habits of mind by working alongside scientists to make sense of the world using authentic experimental data. Our program is based a classroom vision where teaching strategies emulate closely the way scientists build knowledge through inquiry. We look at how student engagement in research and masterclasses develops an understanding about the process of scientific discovery and science using current scientific data. We also look at ways and to what extent teachers provide scientific discovery and science practices for students and how QuarkNet contributes to the professionalism of participating teachers. Also, we describe success factors that enhance local center programs and describe important benefits of the program that flow to university faculty. Funded by the National Science Foundation and the US Department of Energy.

A collaborative filtering-based approach to biomedical knowledge discovery.

PubMed

Lever, Jake; Gakkhar, Sitanshu; Gottlieb, Michael; Rashnavadi, Tahereh; Lin, Santina; Siu, Celia; Smith, Maia; Jones, Martin R; Krzywinski, Martin; Jones, Steven J M; Wren, Jonathan

2018-02-15

The increase in publication rates makes it challenging for an individual researcher to stay abreast of all relevant research in order to find novel research hypotheses. Literature-based discovery methods make use of knowledge graphs built using text mining and can infer future associations between biomedical concepts that will likely occur in new publications. These predictions are a valuable resource for researchers to explore a research topic. Current methods for prediction are based on the local structure of the knowledge graph. A method that uses global knowledge from across the knowledge graph needs to be developed in order to make knowledge discovery a frequently used tool by researchers. We propose an approach based on the singular value decomposition (SVD) that is able to combine data from across the knowledge graph through a reduced representation. Using cooccurrence data extracted from published literature, we show that SVD performs better than the leading methods for scoring discoveries. We also show the diminishing predictive power of knowledge discovery as we compare our predictions with real associations that appear further into the future. Finally, we examine the strengths and weaknesses of the SVD approach against another well-performing system using several predicted associations. All code and results files for this analysis can be accessed at https://github.com/jakelever/knowledgediscovery. sjones@bcgsc.ca. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Reproducibility of results in preclinical studies: a perspective from the bone field.

PubMed

Manolagas, Stavros C; Kronenberg, Henry M

2014-10-01

The biomedical research enterprise-and the public support for it-is predicated on the belief that discoveries and the conclusions drawn from them can be trusted to build a body of knowledge which will be used to improve human health. As in all other areas of scientific inquiry, knowledge and understanding grow by layering new discoveries upon earlier ones. The process self-corrects and distills knowledge by discarding false ideas and unsubstantiated claims. Although self-correction is inexorable in the long-term, in recent years biomedical scientists and the public alike have become alarmed and deeply troubled by the fact that many published results cannot be reproduced. The chorus of concern reached a high pitch with a recent commentary from the NIH Director, Francis S. Collins, and Principal Deputy Director, Lawrence A. Tabak, and their announcement of specific plans to enhance reproducibility of preclinical research that relies on animal models. In this invited perspective, we highlight the magnitude of the problem across biomedical fields and address the relevance of these concerns to the field of bone and mineral metabolism. We also suggest how our specialty journals, our scientific organizations, and our community of bone and mineral researchers can help to overcome this troubling trend. © 2014 American Society for Bone and Mineral Research.

Building Faculty Capacity through the Learning Sciences

ERIC Educational Resources Information Center

Moy, Elizabeth; O'Sullivan, Gerard; Terlecki, Melissa; Jernstedt, Christian

2014-01-01

Discoveries in the learning sciences (especially in neuroscience) have yielded a rich and growing body of knowledge about how students learn, yet this knowledge is only half of the story. The other half is "know how," i.e. the application of this knowledge. For faculty members, that means applying the discoveries of the learning sciences…

Application of Data Mining and Knowledge Discovery Techniques to Enhance Binary Target Detection and Decision-Making for Compromised Visual Images

DTIC Science & Technology

2004-11-01

affords exciting opportunities in target detection. The input signal may be a sum of sine waves, it could be an auditory signal, or possibly a visual...rendering of a scene. Since image processing is an area in which the original data are stationary in some sense ( auditory signals suffer from...11 Example 1 of SR - Identification of a Subliminal Signal below a Threshold .......................... 13 Example 2 of SR

The Physics of Thermoelectric Energy Conversion

NASA Astrophysics Data System (ADS)

Goldsmid, H. Julian

2017-04-01

This book outlines the principles of thermoelectric generation and refrigeration from the discovery of the Seebeck and Peltier effects in the 19th century through the introduction of semiconductor thermoelements in the mid-20th century to the more recent development of nanostructured materials. The conditions for favourable electronic properties are discussed. The methods for selecting materials with a low lattice thermal conductivity are outlined and the ways in which the scattering of phonons can be enhanced are described. The book is aimed at readers without specialised knowledge.

Usalpharma: A Cloud-Based Architecture to Support Quality Assurance Training Processes in Health Area Using Virtual Worlds

PubMed Central

García-Peñalvo, Francisco J.; Pérez-Blanco, Jonás Samuel; Martín-Suárez, Ana

2014-01-01

This paper discusses how cloud-based architectures can extend and enhance the functionality of the training environments based on virtual worlds and how, from this cloud perspective, we can provide support to analysis of training processes in the area of health, specifically in the field of training processes in quality assurance for pharmaceutical laboratories, presenting a tool for data retrieval and analysis that allows facing the knowledge discovery in the happenings inside the virtual worlds. PMID:24778593

The center for causal discovery of biomedical knowledge from big data

PubMed Central

Bahar, Ivet; Becich, Michael J; Benos, Panayiotis V; Berg, Jeremy; Espino, Jeremy U; Glymour, Clark; Jacobson, Rebecca Crowley; Kienholz, Michelle; Lee, Adrian V; Lu, Xinghua; Scheines, Richard

2015-01-01

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers. PMID:26138794

Mentor-mentee Relationship: A Win-Win Contract In Graduate Medical Education.

PubMed

Toklu, Hale Z; Fuller, Jacklyn C

2017-12-05

Scholarly activities (i.e., the discovery of new knowledge; development of new technologies, methods, materials, or uses; integration of knowledge leading to new understanding) are intended to measure the quality and quantity of dissemination of knowledge. A successful mentorship program is necessary during residency to help residents achieve the six core competencies (patient care, medical knowledge, practice-based learning and improvement, systems-based practice, professionalism, interpersonal and communication skills) required by the Accreditation Council for Graduate Medical Education (ACGME). The role of the mentor in this process is pivotal in the advancement of the residents' knowledge about evidence-based medicine. With this process, while mentees become more self-regulated, exhibit confidence in their performance, and demonstrate more insight and aptitude in their jobs, mentors also achieve elevated higher self-esteem, enhanced leadership skills, and personal gratification. As such, we may conclude that mentoring is a two-sided relationship; i.e., a 'win-win' style of commitment between the mentor and mentee. Hence, both parties will eventually advance academically, as well as professionally.

The Effect of Rules and Discovery in the Retention and Retrieval of Braille Inkprint Letter Pairs.

ERIC Educational Resources Information Center

Nagengast, Daniel L.; And Others

The effects of rule knowledge were investigated using Braille inkprint pairs. Both recognition and recall were studied in three groups of subjects: rule knowledge, rule discovery, and no rule. Two hypotheses were tested: (1) that the group exposed to the rule would score better than would a discovery group and a control group; and (2) that all…

Knowledge-Based Topic Model for Unsupervised Object Discovery and Localization.

PubMed

Niu, Zhenxing; Hua, Gang; Wang, Le; Gao, Xinbo

Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.

Personal discovery in diabetes self-management: Discovering cause and effect using self-monitoring data

PubMed Central

Mamykina, Lena; Heitkemper, Elizabeth M.; Smaldone, Arlene M.; Kukafka, Rita; Cole-Lewis, Heather J.; Davidson, Patricia G.; Mynatt, Elizabeth D.; Cassells, Andrea; Tobin, Jonathan N.; Hripcsak, George

2017-01-01

Objective To outline new design directions for informatics solutions that facilitate personal discovery with self-monitoring data. We investigate this question in the context of chronic disease self-management with the focus on type 2 diabetes. Materials and methods We conducted an observational qualitative study of discovery with personal data among adults attending a diabetes self-management education (DSME) program that utilized a discovery-based curriculum. The study included observations of class sessions, and interviews and focus groups with the educator and attendees of the program (n = 14). Results The main discovery in diabetes self-management evolved around discovering patterns of association between characteristics of individuals’ activities and changes in their blood glucose levels that the participants referred to as “cause and effect”. This discovery empowered individuals to actively engage in self-management and provided a desired flexibility in selection of personalized self-management strategies. We show that discovery of cause and effect involves four essential phases: (1) feature selection, (2) hypothesis generation, (3) feature evaluation, and (4) goal specification. Further, we identify opportunities to support discovery at each stage with informatics and data visualization solutions by providing assistance with: (1) active manipulation of collected data (e.g., grouping, filtering and side-by-side inspection), (2) hypotheses formulation (e.g., using natural language statements or constructing visual queries), (3) inference evaluation (e.g., through aggregation and visual comparison, and statistical analysis of associations), and (4) translation of discoveries into actionable goals (e.g., tailored selection from computable knowledge sources of effective diabetes self-management behaviors). Discussion The study suggests that discovery of cause and effect in diabetes can be a powerful approach to helping individuals to improve their self-management strategies, and that self-monitoring data can serve as a driving engine for personal discovery that may lead to sustainable behavior changes. Conclusions Enabling personal discovery is a promising new approach to enhancing chronic disease self-management with informatics interventions. PMID:28974460

Personal discovery in diabetes self-management: Discovering cause and effect using self-monitoring data.

PubMed

Mamykina, Lena; Heitkemper, Elizabeth M; Smaldone, Arlene M; Kukafka, Rita; Cole-Lewis, Heather J; Davidson, Patricia G; Mynatt, Elizabeth D; Cassells, Andrea; Tobin, Jonathan N; Hripcsak, George

2017-12-01

To outline new design directions for informatics solutions that facilitate personal discovery with self-monitoring data. We investigate this question in the context of chronic disease self-management with the focus on type 2 diabetes. We conducted an observational qualitative study of discovery with personal data among adults attending a diabetes self-management education (DSME) program that utilized a discovery-based curriculum. The study included observations of class sessions, and interviews and focus groups with the educator and attendees of the program (n = 14). The main discovery in diabetes self-management evolved around discovering patterns of association between characteristics of individuals' activities and changes in their blood glucose levels that the participants referred to as "cause and effect". This discovery empowered individuals to actively engage in self-management and provided a desired flexibility in selection of personalized self-management strategies. We show that discovery of cause and effect involves four essential phases: (1) feature selection, (2) hypothesis generation, (3) feature evaluation, and (4) goal specification. Further, we identify opportunities to support discovery at each stage with informatics and data visualization solutions by providing assistance with: (1) active manipulation of collected data (e.g., grouping, filtering and side-by-side inspection), (2) hypotheses formulation (e.g., using natural language statements or constructing visual queries), (3) inference evaluation (e.g., through aggregation and visual comparison, and statistical analysis of associations), and (4) translation of discoveries into actionable goals (e.g., tailored selection from computable knowledge sources of effective diabetes self-management behaviors). The study suggests that discovery of cause and effect in diabetes can be a powerful approach to helping individuals to improve their self-management strategies, and that self-monitoring data can serve as a driving engine for personal discovery that may lead to sustainable behavior changes. Enabling personal discovery is a promising new approach to enhancing chronic disease self-management with informatics interventions. Copyright © 2017 Elsevier Inc. All rights reserved.

Concept Formation in Scientific Knowledge Discovery from a Constructivist View

NASA Astrophysics Data System (ADS)

Peng, Wei; Gero, John S.

The central goal of scientific knowledge discovery is to learn cause-effect relationships among natural phenomena presented as variables and the consequences their interactions. Scientific knowledge is normally expressed as scientific taxonomies and qualitative and quantitative laws [1]. This type of knowledge represents intrinsic regularities of the observed phenomena that can be used to explain and predict behaviors of the phenomena. It is a generalization that is abstracted and externalized from a set of contexts and applicable to a broader scope. Scientific knowledge is a type of third-person knowledge, i.e., knowledge that independent of a specific enquirer. Artificial intelligence approaches, particularly data mining algorithms that are used to identify meaningful patterns from large data sets, are approaches that aim to facilitate the knowledge discovery process [2]. A broad spectrum of algorithms has been developed in addressing classification, associative learning, and clustering problems. However, their linkages to people who use them have not been adequately explored. Issues in relation to supporting the interpretation of the patterns, the application of prior knowledge to the data mining process and addressing user interactions remain challenges for building knowledge discovery tools [3]. As a consequence, scientists rely on their experience to formulate problems, evaluate hypotheses, reason about untraceable factors and derive new problems. This type of knowledge which they have developed during their career is called “first-person” knowledge. The formation of scientific knowledge (third-person knowledge) is highly influenced by the enquirer’s first-person knowledge construct, which is a result of his or her interactions with the environment. There have been attempts to craft automatic knowledge discovery tools but these systems are limited in their capabilities to handle the dynamics of personal experience. There are now trends in developing approaches to assist scientists applying their expertise to model formation, simulation, and prediction in various domains [4], [5]. On the other hand, first-person knowledge becomes third-person theory only if it proves general by evidence and is acknowledged by a scientific community. Researchers start to focus on building interactive cooperation platforms [1] to accommodate different views into the knowledge discovery process. There are some fundamental questions in relation to scientific knowledge development. What aremajor components for knowledge construction and how do people construct their knowledge? How is this personal construct assimilated and accommodated into a scientific paradigm? How can one design a computational system to facilitate these processes? This chapter does not attempt to answer all these questions but serves as a basis to foster thinking along this line. A brief literature review about how people develop their knowledge is carried out through a constructivist view. A hydrological modeling scenario is presented to elucidate the approach.

Concept Formation in Scientific Knowledge Discovery from a Constructivist View

NASA Astrophysics Data System (ADS)

Peng, Wei; Gero, John S.

The central goal of scientific knowledge discovery is to learn cause-effect relationships among natural phenomena presented as variables and the consequences their interactions. Scientific knowledge is normally expressed as scientific taxonomies and qualitative and quantitative laws [1]. This type of knowledge represents intrinsic regularities of the observed phenomena that can be used to explain and predict behaviors of the phenomena. It is a generalization that is abstracted and externalized from a set of contexts and applicable to a broader scope. Scientific knowledge is a type of third-person knowledge, i.e., knowledge that independent of a specific enquirer. Artificial intelligence approaches, particularly data mining algorithms that are used to identify meaningful patterns from large data sets, are approaches that aim to facilitate the knowledge discovery process [2]. A broad spectrum of algorithms has been developed in addressing classification, associative learning, and clustering problems. However, their linkages to people who use them have not been adequately explored. Issues in relation to supporting the interpretation of the patterns, the application of prior knowledge to the data mining process and addressing user interactions remain challenges for building knowledge discovery tools [3]. As a consequence, scientists rely on their experience to formulate problems, evaluate hypotheses, reason about untraceable factors and derive new problems. This type of knowledge which they have developed during their career is called "first-person" knowledge. The formation of scientific knowledge (third-person knowledge) is highly influenced by the enquirer's first-person knowledge construct, which is a result of his or her interactions with the environment. There have been attempts to craft automatic knowledge discovery tools but these systems are limited in their capabilities to handle the dynamics of personal experience. There are now trends in developing approaches to assist scientists applying their expertise to model formation, simulation, and prediction in various domains [4], [5]. On the other hand, first-person knowledge becomes third-person theory only if it proves general by evidence and is acknowledged by a scientific community. Researchers start to focus on building interactive cooperation platforms [1] to accommodate different views into the knowledge discovery process. There are some fundamental questions in relation to scientific knowledge development. What aremajor components for knowledge construction and how do people construct their knowledge? How is this personal construct assimilated and accommodated into a scientific paradigm? How can one design a computational system to facilitate these processes? This chapter does not attempt to answer all these questions but serves as a basis to foster thinking along this line. A brief literature review about how people develop their knowledge is carried out through a constructivist view. A hydrological modeling scenario is presented to elucidate the approach.

Knowledge Discovery and Data Mining: An Overview

NASA Technical Reports Server (NTRS)

Fayyad, U.

1995-01-01

The process of knowledge discovery and data mining is the process of information extraction from very large databases. Its importance is described along with several techniques and considerations for selecting the most appropriate technique for extracting information from a particular data set.

12 CFR 263.53 - Discovery depositions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 12 Banks and Banking 4 2014-01-01 2014-01-01 false Discovery depositions. 263.53 Section 263.53... Discovery depositions. (a) In general. In addition to the discovery permitted in subpart A of this part, limited discovery by means of depositions shall be allowed for individuals with knowledge of facts...

12 CFR 263.53 - Discovery depositions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 12 Banks and Banking 4 2012-01-01 2012-01-01 false Discovery depositions. 263.53 Section 263.53... Discovery depositions. (a) In general. In addition to the discovery permitted in subpart A of this part, limited discovery by means of depositions shall be allowed for individuals with knowledge of facts...

An integrative model for in-silico clinical-genomics discovery science.

PubMed

Lussier, Yves A; Sarkar, Indra Nell; Cantor, Michael

2002-01-01

Human Genome discovery research has set the pace for Post-Genomic Discovery Research. While post-genomic fields focused at the molecular level are intensively pursued, little effort is being deployed in the later stages of molecular medicine discovery research, such as clinical-genomics. The objective of this study is to demonstrate the relevance and significance of integrating mainstream clinical informatics decision support systems to current bioinformatics genomic discovery science. This paper is a feasibility study of an original model enabling novel "in-silico" clinical-genomic discovery science and that demonstrates its feasibility. This model is designed to mediate queries among clinical and genomic knowledge bases with relevant bioinformatic analytic tools (e.g. gene clustering). Briefly, trait-disease-gene relationships were successfully illustrated using QMR, OMIM, SNOMED-RT, GeneCluster and TreeView. The analyses were visualized as two-dimensional dendrograms of clinical observations clustered around genes. To our knowledge, this is the first study using knowledge bases of clinical decision support systems for genomic discovery. Although this study is a proof of principle, it provides a framework for the development of clinical decision-support-system driven, high-throughput clinical-genomic technologies which could potentially unveil significant high-level functions of genes.

Conceptualization of an R&D Based Learning-to-Innovate Model for Science Education

NASA Astrophysics Data System (ADS)

Lai, Oiki Sylvia

The purpose of this research was to conceptualize an R & D based learning-to-innovate (LTI) model. The problem to be addressed was the lack of a theoretical L TI model, which would inform science pedagogy. The absorptive capacity (ACAP) lens was adopted to untangle the R & D LTI phenomenon into four learning processes: problem-solving via knowledge acquisition, incremental improvement via knowledge participation, scientific discovery via knowledge creation, and product design via knowledge productivity. The four knowledge factors were the latent factors and each factor had seven manifest elements as measured variables. The key objectives of the non experimental quantitative survey were to measure the relative importance of the identified elements and to explore the underlining structure of the variables. A questionnaire had been prepared, and was administered to more than 155 R & D professionals from four sectors - business, academic, government, and nonprofit. The results showed that every identified element was important to the R & D professionals, in terms of improving the related type of innovation. The most important elements were highlighted to serve as building blocks for elaboration. In search for patterns of the data matrix, exploratory factor analysis (EF A) was performed. Principal component analysis was the first phase of EF A to extract factors; while maximum likelihood estimation (MLE) was used to estimate the model. EF A yielded the finding of two aspects in each kind of knowledge. Logical names were assigned to represent the nature of the subsets: problem and knowledge under knowledge acquisition, planning and participation under knowledge participation, exploration and discovery under knowledge creation, and construction and invention under knowledge productivity. These two constructs, within each kind of knowledge, added structure to the vague R & D based LTI model. The research questions and hypotheses testing were addressed using correlation analysis. The alternative hypotheses that there were positive relationships between knowledge factors and their corresponding types of innovation were accepted. In-depth study of each process is recommended in both research and application. Experimental tests are needed, in order to ultimately present the LTI model to enhance the scientific knowledge absorptive capacity of the learners to facilitate their innovation performance.

Data Mining.

ERIC Educational Resources Information Center

Benoit, Gerald

2002-01-01

Discusses data mining (DM) and knowledge discovery in databases (KDD), taking the view that KDD is the larger view of the entire process, with DM emphasizing the cleaning, warehousing, mining, and visualization of knowledge discovery in databases. Highlights include algorithms; users; the Internet; text mining; and information extraction.…

Knowledge discovery about quality of life changes of spinal cord injury patients: clustering based on rules by states.

PubMed

Gibert, Karina; García-Rudolph, Alejandro; Curcoll, Lluïsa; Soler, Dolors; Pla, Laura; Tormos, José María

2009-01-01

In this paper, an integral Knowledge Discovery Methodology, named Clustering based on rules by States, which incorporates artificial intelligence (AI) and statistical methods as well as interpretation-oriented tools, is used for extracting knowledge patterns about the evolution over time of the Quality of Life (QoL) of patients with Spinal Cord Injury. The methodology incorporates the interaction with experts as a crucial element with the clustering methodology to guarantee usefulness of the results. Four typical patterns are discovered by taking into account prior expert knowledge. Several hypotheses are elaborated about the reasons for psychological distress or decreases in QoL of patients over time. The knowledge discovery from data (KDD) approach turns out, once again, to be a suitable formal framework for handling multidimensional complexity of the health domains.

Improved accuracy of supervised CRM discovery with interpolated Markov models and cross-species comparison.

PubMed

Kazemian, Majid; Zhu, Qiyun; Halfon, Marc S; Sinha, Saurabh

2011-12-01

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. © The Author(s) 2011. Published by Oxford University Press.

Chemical Informatics and the Drug Discovery Knowledge Pyramid

PubMed Central

Lushington, Gerald H.; Dong, Yinghua; Theertham, Bhargav

2012-01-01

The magnitude of the challenges in preclinical drug discovery is evident in the large amount of capital invested in such efforts in pursuit of a small static number of eventually successful marketable therapeutics. An explosion in the availability of potentially drug-like compounds and chemical biology data on these molecules can provide us with the means to improve the eventual success rates for compounds being considered at the preclinical level, but only if the community is able to access available information in an efficient and meaningful way. Thus, chemical database resources are critical to any serious drug discovery effort. This paper explores the basic principles underlying the development and implementation of chemical databases, and examines key issues of how molecular information may be encoded within these databases so as to enhance the likelihood that users will be able to extract meaningful information from data queries. In addition to a broad survey of conventional data representation and query strategies, key enabling technologies such as new context-sensitive chemical similarity measures and chemical cartridges are examined, with recommendations on how such resources may be integrated into a practical database environment. PMID:23782037

Resource Discovery within the Networked "Hybrid" Library.

ERIC Educational Resources Information Center

Leigh, Sally-Anne

This paper focuses on the development, adoption, and integration of resource discovery, knowledge management, and/or knowledge sharing interfaces such as interactive portals, and the use of the library's World Wide Web presence to increase the availability and usability of information services. The introduction addresses changes in library…

A biological compression model and its applications.

PubMed

Cao, Minh Duc; Dix, Trevor I; Allison, Lloyd

2011-01-01

A biological compression model, expert model, is presented which is superior to existing compression algorithms in both compression performance and speed. The model is able to compress whole eukaryotic genomes. Most importantly, the model provides a framework for knowledge discovery from biological data. It can be used for repeat element discovery, sequence alignment and phylogenetic analysis. We demonstrate that the model can handle statistically biased sequences and distantly related sequences where conventional knowledge discovery tools often fail.

Enabling the hypothesis-driven prioritization of ligand candidates in big databases: Screenlamp and its application to GPCR inhibitor discovery for invasive species control

NASA Astrophysics Data System (ADS)

Raschka, Sebastian; Scott, Anne M.; Liu, Nan; Gunturu, Santosh; Huertas, Mar; Li, Weiming; Kuhn, Leslie A.

2018-03-01

While the advantage of screening vast databases of molecules to cover greater molecular diversity is often mentioned, in reality, only a few studies have been published demonstrating inhibitor discovery by screening more than a million compounds for features that mimic a known three-dimensional (3D) ligand. Two factors contribute: the general difficulty of discovering potent inhibitors, and the lack of free, user-friendly software to incorporate project-specific knowledge and user hypotheses into 3D ligand-based screening. The Screenlamp modular toolkit presented here was developed with these needs in mind. We show Screenlamp's ability to screen more than 12 million commercially available molecules and identify potent in vivo inhibitors of a G protein-coupled bile acid receptor within the first year of a discovery project. This pheromone receptor governs sea lamprey reproductive behavior, and to our knowledge, this project is the first to establish the efficacy of computational screening in discovering lead compounds for aquatic invasive species control. Significant enhancement in activity came from selecting compounds based on one of the hypotheses: that matching two distal oxygen groups in the 3D structure of the pheromone is crucial for activity. Six of the 15 most active compounds met these criteria. A second hypothesis—that presence of an alkyl sulfate side chain results in high activity—identified another 6 compounds in the top 10, demonstrating the significant benefits of hypothesis-driven screening.

KNODWAT: A scientific framework application for testing knowledge discovery methods for the biomedical domain

PubMed Central

2013-01-01

Background Professionals in the biomedical domain are confronted with an increasing mass of data. Developing methods to assist professional end users in the field of Knowledge Discovery to identify, extract, visualize and understand useful information from these huge amounts of data is a huge challenge. However, there are so many diverse methods and methodologies available, that for biomedical researchers who are inexperienced in the use of even relatively popular knowledge discovery methods, it can be very difficult to select the most appropriate method for their particular research problem. Results A web application, called KNODWAT (KNOwledge Discovery With Advanced Techniques) has been developed, using Java on Spring framework 3.1. and following a user-centered approach. The software runs on Java 1.6 and above and requires a web server such as Apache Tomcat and a database server such as the MySQL Server. For frontend functionality and styling, Twitter Bootstrap was used as well as jQuery for interactive user interface operations. Conclusions The framework presented is user-centric, highly extensible and flexible. Since it enables methods for testing using existing data to assess suitability and performance, it is especially suitable for inexperienced biomedical researchers, new to the field of knowledge discovery and data mining. For testing purposes two algorithms, CART and C4.5 were implemented using the WEKA data mining framework. PMID:23763826

KNODWAT: a scientific framework application for testing knowledge discovery methods for the biomedical domain.

PubMed

Holzinger, Andreas; Zupan, Mario

2013-06-13

Professionals in the biomedical domain are confronted with an increasing mass of data. Developing methods to assist professional end users in the field of Knowledge Discovery to identify, extract, visualize and understand useful information from these huge amounts of data is a huge challenge. However, there are so many diverse methods and methodologies available, that for biomedical researchers who are inexperienced in the use of even relatively popular knowledge discovery methods, it can be very difficult to select the most appropriate method for their particular research problem. A web application, called KNODWAT (KNOwledge Discovery With Advanced Techniques) has been developed, using Java on Spring framework 3.1. and following a user-centered approach. The software runs on Java 1.6 and above and requires a web server such as Apache Tomcat and a database server such as the MySQL Server. For frontend functionality and styling, Twitter Bootstrap was used as well as jQuery for interactive user interface operations. The framework presented is user-centric, highly extensible and flexible. Since it enables methods for testing using existing data to assess suitability and performance, it is especially suitable for inexperienced biomedical researchers, new to the field of knowledge discovery and data mining. For testing purposes two algorithms, CART and C4.5 were implemented using the WEKA data mining framework.

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

PubMed Central

2010-01-01

Background The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. Results In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. Conclusion High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data. PMID:20122245

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships.

PubMed

Seok, Junhee; Kaushal, Amit; Davis, Ronald W; Xiao, Wenzhong

2010-01-18

The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions. In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification. High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.

Form-Focused Discovery Activities in English Classes

ERIC Educational Resources Information Center

Ogeyik, Muhlise Cosgun

2011-01-01

Form-focused discovery activities allow language learners to grasp various aspects of a target language by contributing implicit knowledge by using discovered explicit knowledge. Moreover, such activities can assist learners to perceive and discover the features of their language input. In foreign language teaching environments, they can be used…

Targeting Super-Enhancers for Disease Treatment and Diagnosis.

PubMed

Shin, Ha Youn

2018-05-10

The transcriptional regulation of genes determines the fate of animal cell differentiation and subsequent organ development. With the recent progress in genome-wide technologies, the genomic landscapes of enhancers have been broadly explored in mammalian genomes, which led to the discovery of novel specific subsets of enhancers, termed superenhancers. Super-enhancers are large clusters of enhancers covering the long region of regulatory DNA and are densely occupied by transcription factors, active histone marks, and co-activators. Accumulating evidence points to the critical role that super-enhancers play in cell type-specific development and differentiation, as well as in the development of various diseases. Here, I provide a comprehensive description of the optimal approach for identifying functional units of superenhancers and their unique chromatin features in normal development and in diseases, including cancers. I also review the recent updated knowledge on novel approaches of targeting super-enhancers for the treatment of specific diseases, such as small-molecule inhibitors and potential gene therapy. This review will provide perspectives on using superenhancers as biomarkers to develop novel disease diagnostic tools and establish new directions in clinical therapeutic strategies.

75 FR 66766 - NIAID Blue Ribbon Panel Meeting on Adjuvant Discovery and Development

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-29

..., identifies gaps in knowledge and capabilities, and defines NIAID's goals for the continued discovery... DEPARTMENT OF HEALTH AND HUMAN SERVICES NIAID Blue Ribbon Panel Meeting on Adjuvant Discovery and... agenda for the discovery, development and clinical evaluation of adjuvants for use with preventive...

12 CFR 263.53 - Discovery depositions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 3 2011-01-01 2011-01-01 false Discovery depositions. 263.53 Section 263.53... depositions. (a) In general. In addition to the discovery permitted in subpart A of this part, limited discovery by means of depositions shall be allowed for individuals with knowledge of facts material to the...

12 CFR 19.170 - Discovery depositions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 1 2010-01-01 2010-01-01 false Discovery depositions. 19.170 Section 19.170... PROCEDURE Discovery Depositions and Subpoenas § 19.170 Discovery depositions. (a) General rule. In any... deposition of an expert, or of a person, including another party, who has direct knowledge of matters that...

12 CFR 19.170 - Discovery depositions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 1 2011-01-01 2011-01-01 false Discovery depositions. 19.170 Section 19.170... PROCEDURE Discovery Depositions and Subpoenas § 19.170 Discovery depositions. (a) General rule. In any... deposition of an expert, or of a person, including another party, who has direct knowledge of matters that...

12 CFR 263.53 - Discovery depositions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 12 Banks and Banking 3 2010-01-01 2010-01-01 false Discovery depositions. 263.53 Section 263.53... depositions. (a) In general. In addition to the discovery permitted in subpart A of this part, limited discovery by means of depositions shall be allowed for individuals with knowledge of facts material to the...

BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation

PubMed Central

2011-01-01

We present BioGraph, a data integration and data mining platform for the exploration and discovery of biomedical information. The platform offers prioritizations of putative disease genes, supported by functional hypotheses. We show that BioGraph can retrospectively confirm recently discovered disease genes and identify potential susceptibility genes, outperforming existing technologies, without requiring prior domain knowledge. Additionally, BioGraph allows for generic biomedical applications beyond gene discovery. BioGraph is accessible at http://www.biograph.be. PMID:21696594

Mentor-mentee Relationship: A Win-Win Contract In Graduate Medical Education

PubMed Central

Fuller, Jacklyn C

2017-01-01

Scholarly activities (i.e., the discovery of new knowledge; development of new technologies, methods, materials, or uses; integration of knowledge leading to new understanding) are intended to measure the quality and quantity of dissemination of knowledge. A successful mentorship program is necessary during residency to help residents achieve the six core competencies (patient care, medical knowledge, practice-based learning and improvement, systems-based practice, professionalism, interpersonal and communication skills) required by the Accreditation Council for Graduate Medical Education (ACGME). The role of the mentor in this process is pivotal in the advancement of the residents’ knowledge about evidence-based medicine. With this process, while mentees become more self-regulated, exhibit confidence in their performance, and demonstrate more insight and aptitude in their jobs, mentors also achieve elevated higher self-esteem, enhanced leadership skills, and personal gratification. As such, we may conclude that mentoring is a two-sided relationship; i.e., a 'win-win' style of commitment between the mentor and mentee. Hence, both parties will eventually advance academically, as well as professionally. PMID:29435394

Medical knowledge discovery and management.

PubMed

Prior, Fred

2009-05-01

Although the volume of medical information is growing rapidly, the ability to rapidly convert this data into "actionable insights" and new medical knowledge is lagging far behind. The first step in the knowledge discovery process is data management and integration, which logically can be accomplished through the application of data warehouse technologies. A key insight that arises from efforts in biosurveillance and the global scope of military medicine is that information must be integrated over both time (longitudinal health records) and space (spatial localization of health-related events). Once data are compiled and integrated it is essential to encode the semantics and relationships among data elements through the use of ontologies and semantic web technologies to convert data into knowledge. Medical images form a special class of health-related information. Traditionally knowledge has been extracted from images by human observation and encoded via controlled terminologies. This approach is rapidly being replaced by quantitative analyses that more reliably support knowledge extraction. The goals of knowledge discovery are the improvement of both the timeliness and accuracy of medical decision making and the identification of new procedures and therapies.

Taking stock of current societal, political and academic stakeholders in the Canadian healthcare knowledge translation agenda

PubMed Central

Newton, Mandi S; Scott-Findlay, Shannon

2007-01-01

Background In the past 15 years, knowledge translation in healthcare has emerged as a multifaceted and complex agenda. Theoretical and polemical discussions, the development of a science to study and measure the effects of translating research evidence into healthcare, and the role of key stakeholders including academe, healthcare decision-makers, the public, and government funding bodies have brought scholarly, organizational, social, and political dimensions to the agenda. Objective This paper discusses the current knowledge translation agenda in Canadian healthcare and how elements in this agenda shape the discovery and translation of health knowledge. Discussion The current knowledge translation agenda in Canadian healthcare involves the influence of values, priorities, and people; stakes which greatly shape the discovery of research knowledge and how it is or is not instituted in healthcare delivery. As this agenda continues to take shape and direction, ensuring that it is accountable for its influences is essential and should be at the forefront of concern to the Canadian public and healthcare community. This transparency will allow for scrutiny, debate, and improvements in health knowledge discovery and health services delivery. PMID:17916256

Concept of operations for knowledge discovery from Big Data across enterprise data warehouses

NASA Astrophysics Data System (ADS)

Sukumar, Sreenivas R.; Olama, Mohammed M.; McNair, Allen W.; Nutaro, James J.

2013-05-01

The success of data-driven business in government, science, and private industry is driving the need for seamless integration of intra and inter-enterprise data sources to extract knowledge nuggets in the form of correlations, trends, patterns and behaviors previously not discovered due to physical and logical separation of datasets. Today, as volume, velocity, variety and complexity of enterprise data keeps increasing, the next generation analysts are facing several challenges in the knowledge extraction process. Towards addressing these challenges, data-driven organizations that rely on the success of their analysts have to make investment decisions for sustainable data/information systems and knowledge discovery. Options that organizations are considering are newer storage/analysis architectures, better analysis machines, redesigned analysis algorithms, collaborative knowledge management tools, and query builders amongst many others. In this paper, we present a concept of operations for enabling knowledge discovery that data-driven organizations can leverage towards making their investment decisions. We base our recommendations on the experience gained from integrating multi-agency enterprise data warehouses at the Oak Ridge National Laboratory to design the foundation of future knowledge nurturing data-system architectures.

The center for causal discovery of biomedical knowledge from big data.

PubMed

Cooper, Gregory F; Bahar, Ivet; Becich, Michael J; Benos, Panayiotis V; Berg, Jeremy; Espino, Jeremy U; Glymour, Clark; Jacobson, Rebecca Crowley; Kienholz, Michelle; Lee, Adrian V; Lu, Xinghua; Scheines, Richard

2015-11-01

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.All rights reserved. For Permissions, please email: journals.permissions@oup.com.

How does non-formal marine education affect student attitude and knowledge? A case study using SCDNR's Discovery program

NASA Astrophysics Data System (ADS)

McGovern, Mary Francis

Non-formal environmental education provides students the opportunity to learn in ways that would not be possible in a traditional classroom setting. Outdoor learning allows students to make connections to their environment and helps to foster an appreciation for nature. This type of education can be interdisciplinary---students not only develop skills in science, but also in mathematics, social studies, technology, and critical thinking. This case study focuses on a non-formal marine education program, the South Carolina Department of Natural Resources' (SCDNR) Discovery vessel based program. The Discovery curriculum was evaluated to determine impact on student knowledge about and attitude toward the estuary. Students from two South Carolina coastal counties who attended the boat program during fall 2014 were asked to complete a brief survey before, immediately after, and two weeks following the program. The results of this study indicate that both student knowledge about and attitude significantly improved after completion of the Discovery vessel based program. Knowledge and attitude scores demonstrated a positive correlation.

Design and study of piracetam-like nootropics, controversial members of the problematic class of cognition-enhancing drugs.

PubMed

Gualtieri, Fulvio; Manetti, Dina; Romanelli, Maria Novella; Ghelardini, Carla

2002-01-01

Cognition enhancers are drugs able to facilitate attentional abilities and acquisition, storage and retrieval of information, and to attenuate the impairment of cognitive functions associated with head traumas, stroke, age and age-related pathologies. Development of cognition enhancers is still a difficult task because of complexity of the brain functions, poor predictivity of animal tests and lengthy and expensive clinical trials. After the early serendipitous discovery of first generation cognition enhancers, current research is based on a variety of working hypotheses, derived from the progress of knowledge in the neurobiopathology of cognitive processes. Among other classes of drugs, piracetam-like cognition enhancers (nootropics) have never reached general acceptance, in spite of their excellent tolerability and safety. In the present review, after a general discussion of the problems connected with the design and development of cognition enhancers, the class is examined in more detail. Reasons for the problems encountered by nootropics, compounds therapeutically available and those in development, their structure activity relationships and mechanisms of action are discussed. Recent developments which hopefully will lead to a revival of the class are reviewed.

Analysis student self efficacy in terms of using Discovery Learning model with SAVI approach

NASA Astrophysics Data System (ADS)

Sahara, Rifki; Mardiyana, S., Dewi Retno Sari

2017-12-01

Often students are unable to prove their academic achievement optimally according to their abilities. One reason is that they often feel unsure that they are capable of completing the tasks assigned to them. For students, such beliefs are necessary. The term belief has called self efficacy. Self efficacy is not something that has brought about by birth or something with permanent quality of an individual, but is the result of cognitive processes, the meaning one's self efficacy will be stimulated through learning activities. Self efficacy has developed and enhanced by a learning model that can stimulate students to foster confidence in their capabilities. One of them is by using Discovery Learning model with SAVI approach. Discovery Learning model with SAVI approach is one of learning models that involves the active participation of students in exploring and discovering their own knowledge and using it in problem solving by utilizing all the sensory devices they have. This naturalistic qualitative research aims to analyze student self efficacy in terms of use the Discovery Learning model with SAVI approach. The subjects of this study are 30 students focused on eight students who have high, medium, and low self efficacy obtained through purposive sampling technique. The data analysis of this research used three stages, that were reducing, displaying, and getting conclusion of the data. Based on the results of data analysis, it was concluded that the self efficacy appeared dominantly on the learning by using Discovery Learning model with SAVI approach is magnitude dimension.

Behavioral studies on anxiety and depression in a drug discovery environment: keys to a successful future.

PubMed

Bouwknecht, J Adriaan

2015-04-15

The review describes a personal journey through 25 years of animal research with a focus on the contribution of rodent models for anxiety and depression to the development of new medicines in a drug discovery environment. Several classic acute models for mood disorders are briefly described as well as chronic stress and disease-induction models. The paper highlights a variety of factors that influence the quality and consistency of behavioral data in a laboratory setting. The importance of meta-analysis techniques for study validation (tolerance interval) and assay sensitivity (Monte Carlo modeling) are demonstrated by examples that use historic data. It is essential for successful discovery of new potential drugs to maintain a high level of control in animal research and to bridge knowledge across in silico modeling, and in vitro and in vivo assays. Today, drug discovery is a highly dynamic environment in search of new types of treatments and new animal models which should be guided by enhanced two-way translation between bench and bed. Although productivity has been disappointing in the search of new and better medicines in psychiatry over the past decades, there has been and will always be an important role for in vivo models in-between preclinical discovery and clinical development. The right balance between good science and proper judgment versus a decent level of innovation, assay development and two-way translation will open the doors to a very bright future. Copyright © 2014 Elsevier B.V. All rights reserved.

The role of collaborative ontology development in the knowledge negotiation process

NASA Astrophysics Data System (ADS)

Rivera, Norma

Interdisciplinary research (IDR) collaboration can be defined as the process of integrating experts' knowledge, perspectives, and resources to advance scientific discovery. The flourishing of more complex research problems, together with the growth of scientific and technical knowledge has resulted in the need for researchers from diverse fields to provide different expertise and points of view to tackle these problems. These collaborations, however, introduce a new set of "culture" barriers as participating experts are trained to communicate in discipline-specific languages, theories, and research practices. We propose that building a common knowledge base for research using ontology development techniques can provide a starting point for interdisciplinary knowledge exchange, negotiation, and integration. The goal of this work is to extend ontology development techniques to support the knowledge negotiation process in IDR groups. Towards this goal, this work presents a methodology that extends previous work in collaborative ontology development and integrates learning strategies and tools to enhance interdisciplinary research practices. We evaluate the effectiveness of applying such methodology in three different scenarios that cover educational and research settings. The results of this evaluation confirm that integrating learning strategies can, in fact, be advantageous to overall collaborative practices in IDR groups.

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.

Students and Teacher Academic Evaluation Perceptions: Methodology to Construct a Representation Based on Actionable Knowledge Discovery Framework

ERIC Educational Resources Information Center

Molina, Otilia Alejandro; Ratté, Sylvie

2017-01-01

This research introduces a method to construct a unified representation of teachers and students perspectives based on the actionable knowledge discovery (AKD) and delivery framework. The representation is constructed using two models: one obtained from student evaluations and the other obtained from teachers' reflections about their teaching…

Application of Knowledge Discovery in Databases Methodologies for Predictive Models for Pregnancy Adverse Events

ERIC Educational Resources Information Center

Taft, Laritza M.

2010-01-01

In its report "To Err is Human", The Institute of Medicine recommended the implementation of internal and external voluntary and mandatory automatic reporting systems to increase detection of adverse events. Knowledge Discovery in Databases (KDD) allows the detection of patterns and trends that would be hidden or less detectable if analyzed by…

Knowledge Discovery Process: Case Study of RNAV Adherence of Radar Track Data

NASA Technical Reports Server (NTRS)

Matthews, Bryan

2018-01-01

This talk is an introduction to the knowledge discovery process, beginning with: identifying the problem, choosing data sources, matching the appropriate machine learning tools, and reviewing the results. The overview will be given in the context of an ongoing study that is assessing RNAV adherence of commercial aircraft in the national airspace.

A Virtual Bioinformatics Knowledge Environment for Early Cancer Detection

NASA Technical Reports Server (NTRS)

Crichton, Daniel; Srivastava, Sudhir; Johnsey, Donald

2003-01-01

Discovery of disease biomarkers for cancer is a leading focus of early detection. The National Cancer Institute created a network of collaborating institutions focused on the discovery and validation of cancer biomarkers called the Early Detection Research Network (EDRN). Informatics plays a key role in enabling a virtual knowledge environment that provides scientists real time access to distributed data sets located at research institutions across the nation. The distributed and heterogeneous nature of the collaboration makes data sharing across institutions very difficult. EDRN has developed a comprehensive informatics effort focused on developing a national infrastructure enabling seamless access, sharing and discovery of science data resources across all EDRN sites. This paper will discuss the EDRN knowledge system architecture, its objectives and its accomplishments.

Empowering Accelerated Personal, Professional and Scholarly Discovery among Information Seekers: An Educational Vision

ERIC Educational Resources Information Center

Harmon, Glynn

2013-01-01

The term discovery applies herein to the successful outcome of inquiry in which a significant personal, professional or scholarly breakthrough or insight occurs, and which is individually or socially acknowledged as a key contribution to knowledge. Since discoveries culminate at fixed points in time, discoveries can serve as an outcome metric for…

A Framework of Knowledge Integration and Discovery for Supporting Pharmacogenomics Target Predication of Adverse Drug Events: A Case Study of Drug-Induced Long QT Syndrome.

PubMed

Jiang, Guoqian; Wang, Chen; Zhu, Qian; Chute, Christopher G

2013-01-01

Knowledge-driven text mining is becoming an important research area for identifying pharmacogenomics target genes. However, few of such studies have been focused on the pharmacogenomics targets of adverse drug events (ADEs). The objective of the present study is to build a framework of knowledge integration and discovery that aims to support pharmacogenomics target predication of ADEs. We integrate a semantically annotated literature corpus Semantic MEDLINE with a semantically coded ADE knowledgebase known as ADEpedia using a semantic web based framework. We developed a knowledge discovery approach combining a network analysis of a protein-protein interaction (PPI) network and a gene functional classification approach. We performed a case study of drug-induced long QT syndrome for demonstrating the usefulness of the framework in predicting potential pharmacogenomics targets of ADEs.

Genes Contributing to Genetic Variation of Muscling in Sheep

PubMed Central

Tellam, Ross L.; Cockett, Noelle E.; Vuocolo, Tony; Bidwell, Christopher A.

2012-01-01

Selective breeding programs aiming to increase the productivity and profitability of the sheep meat industry use elite, progeny tested sires. The broad genetic traits of primary interest in the progeny of these sires include skeletal muscle yield, fat content, eating quality, and reproductive efficiency. Natural mutations in sheep that enhance muscling have been identified, while a number of genome scans have identified and confirmed quantitative trait loci (QTL) for skeletal muscle traits. The detailed phenotypic characteristics of sheep carrying these mutations or QTL affecting skeletal muscle show a number of common biological themes, particularly changes in developmental growth trajectories, alterations of whole animal morphology, and a shift toward fast twitch glycolytic fibers. The genetic, developmental, and biochemical mechanisms underpinning the actions of some of these genetic variants are described. This review critically assesses this research area, identifies gaps in knowledge, and highlights mechanistic linkages between genetic polymorphisms and skeletal muscle phenotypic changes. This knowledge may aid the discovery of new causal genetic variants and in some cases lead to the development of biochemical and immunological strategies aimed at enhancing skeletal muscle. PMID:22952470

'Big Data' Collaboration: Exploring, Recording and Sharing Enterprise Knowledge

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

Sukumar, Sreenivas R; Ferrell, Regina Kay

2013-01-01

As data sources and data size proliferate, knowledge discovery from "Big Data" is starting to pose several challenges. In this paper, we address a specific challenge in the practice of enterprise knowledge management while extracting actionable nuggets from diverse data sources of seemingly-related information. In particular, we address the challenge of archiving knowledge gained through collaboration, dissemination and visualization as part of the data analysis, inference and decision-making lifecycle. We motivate the implementation of an enterprise data-discovery and knowledge recorder tool, called SEEKER based on real world case-study. We demonstrate SEEKER capturing schema and data-element relationships, tracking the data elementsmore » of value based on the queries and the analytical artifacts that are being created by analysts as they use the data. We show how the tool serves as digital record of institutional domain knowledge and a documentation for the evolution of data elements, queries and schemas over time. As a knowledge management service, a tool like SEEKER saves enterprise resources and time by avoiding analytic silos, expediting the process of multi-source data integration and intelligently documenting discoveries from fellow analysts.« less

Accounting for reciprocal host-microbiome interactions in experimental science.

PubMed

Stappenbeck, Thaddeus S; Virgin, Herbert W

2016-06-09

Mammals are defined by their metagenome, a combination of host and microbiome genes. This knowledge presents opportunities to further basic biology with translation to human diseases. However, the now-documented influence of the metagenome on experimental results and the reproducibility of in vivo mammalian models present new challenges. Here we provide the scientific basis for calling on all investigators, editors and funding agencies to embrace changes that will enhance reproducible and interpretable experiments by accounting for metagenomic effects. Implementation of new reporting and experimental design principles will improve experimental work, speed discovery and translation, and properly use substantial investments in biomedical research.

Fragment-based hit identification: thinking in 3D.

PubMed

Morley, Andrew D; Pugliese, Angelo; Birchall, Kristian; Bower, Justin; Brennan, Paul; Brown, Nathan; Chapman, Tim; Drysdale, Martin; Gilbert, Ian H; Hoelder, Swen; Jordan, Allan; Ley, Steven V; Merritt, Andy; Miller, David; Swarbrick, Martin E; Wyatt, Paul G

2013-12-01

The identification of high-quality hits during the early phases of drug discovery is essential if projects are to have a realistic chance of progressing into clinical development and delivering marketed drugs. As the pharmaceutical industry goes through unprecedented change, there are increasing opportunities to collaborate via pre-competitive networks to marshal multifunctional resources and knowledge to drive impactful, innovative science. The 3D Fragment Consortium is developing fragment-screening libraries with enhanced 3D characteristics and evaluating their effect on the quality of fragment-based hit identification (FBHI) projects. Copyright © 2013 Elsevier Ltd. All rights reserved.

New genetic discoveries and primary immune deficiencies.

PubMed

Hernandez-Trujillo, Vivian

2014-04-01

The field of immunology has undergone recent discoveries of genetic causes for many primary immunodeficiency diseases (PIDD). The ever-expanding knowledge has led to increased understanding behind the pathophysiology of these diseases. Since these diseases are rare, the patients are frequently misdiagnosed early in the presentation of their illnesses. The identification of new genes has increased our opportunities for recognizing and making the diagnosis in patients with PIDD before they succumb to infections that may result secondary to their PIDD. Some mutations lead to a variety of presentations of severe combined immunodeficiency (SCID). The myriad and ever-growing genetic mutations which lead to SCID phenotypes have been identified in recent years. Other mutations associated with some genetic syndromes have associated immunodeficiency and are important for making the diagnosis of primary immunodeficiency in patients with some syndromes, who may otherwise be missed within the larger context of their syndromes. A variety of mutations also lead to increased susceptibility to infections due to particular organisms. These patterns of infections due to specific organisms are important keys in properly identifying the part of the immune system which is affected in these patients. This review will discuss recent genetic discoveries that enhance our understanding of these complex diseases.

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.

Bioenergy Knowledge Discovery Framework Fact Sheet

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

None

The Bioenergy Knowledge Discovery Framework (KDF) supports the development of a sustainable bioenergy industry by providing access to a variety of data sets, publications, and collaboration and mapping tools that support bioenergy research, analysis, and decision making. In the KDF, users can search for information, contribute data, and use the tools and map interface to synthesize, analyze, and visualize information in a spatially integrated manner.

Teachers' Journal Club: Bridging between the Dynamics of Biological Discoveries and Biology Teachers

ERIC Educational Resources Information Center

Brill, Gilat; Falk, Hedda; Yarden, Anat

2003-01-01

Since biology is one of the most dynamic research fields within the natural sciences, the gap between the accumulated knowledge in biology and the knowledge that is taught in schools, increases rapidly with time. Our long-term objective is to develop means to bridge between the dynamics of biological discoveries and the biology teachers and…

Pollen--tiny and ephemeral but not forgotten: New ideas on their ecology and evolution.

PubMed

Williams, Joseph H; Mazer, Susan J

2016-03-01

Ecologists and evolutionary biologists have been interested in the functional biology of pollen since the discovery in the 1800s that pollen grains encompass tiny plants (male gametophytes) that develop and produce sperm cells. After the discovery of double fertilization in flowering plants, botanists in the early 1900s were quick to explore the effects of temperature and maternal nutrients on pollen performance, while evolutionary biologists began studying the nature of haploid selection and pollen competition. A series of technical and theoretic developments have subsequently, but usually separately, expanded our knowledge of the nature of pollen performance and how it evolves. Today, there is a tremendous diversity of interests that touch on pollen performance, ranging from the ecological setting on the stigma, structural and physiological aspects of pollen germination and tube growth, the form of pollen competition and its role in sexual selection in plants, virus transmission, mating system evolution, and inbreeding depression. Given the explosion of technical knowledge of pollen cell biology, computer modeling, and new methods to deal with diversity in a phylogenetic context, we are now more than ever poised for a new era of research that includes complex functional traits that limit or enhance the evolution of these deceptively simple organisms. © 2016 Botanical Society of America.

The Scientific Foundation for Personal Genomics: Recommendations from a National Institutes of Health–Centers for Disease Control and Prevention Multidisciplinary Workshop

PubMed Central

Khoury, Muin J.; McBride, Colleen M.; Schully, Sheri D.; Ioannidis, John P. A.; Feero, W. Gregory; Janssens, A. Cecile J. W.; Gwinn, Marta; Simons-Morton, Denise G.; Bernhardt, Jay M.; Cargill, Michele; Chanock, Stephen J.; Church, George M.; Coates, Ralph J.; Collins, Francis S.; Croyle, Robert T.; Davis, Barry R.; Downing, Gregory J.; DuRoss, Amy; Friedman, Susan; Gail, Mitchell H.; Ginsburg, Geoffrey S.; Green, Robert C.; Greene, Mark H.; Greenland, Philip; Gulcher, Jeffrey R.; Hsu, Andro; Hudson, Kathy L.; Kardia, Sharon L. R.; Kimmel, Paul L.; Lauer, Michael S.; Miller, Amy M.; Offit, Kenneth; Ransohoff, David F.; Roberts, J. Scott; Rasooly, Rebekah S.; Stefansson, Kari; Terry, Sharon F.; Teutsch, Steven M.; Trepanier, Angela; Wanke, Kay L.; Witte, John S.; Xu, Jianfeng

2010-01-01

The increasing availability of personal genomic tests has led to discussions about the validity and utility of such tests and the balance of benefits and harms. A multidisciplinary workshop was convened by the National Institutes of Health and the Centers for Disease Control and Prevention to review the scientific foundation for using personal genomics in risk assessment and disease prevention and to develop recommendations for targeted research. The clinical validity and utility of personal genomics is a moving target with rapidly developing discoveries but little translation research to close the gap between discoveries and health impact. Workshop participants made recommendations in five domains: (1) developing and applying scientific standards for assessing personal genomic tests; (2) developing and applying a multidisciplinary research agenda, including observational studies and clinical trials to fill knowledge gaps in clinical validity and utility; (3) enhancing credible knowledge synthesis and information dissemination to clinicians and consumers; (4) linking scientific findings to evidence-based recommendations for use of personal genomics; and (5) assessing how the concept of personal utility can affect health benefits, costs, and risks by developing appropriate metrics for evaluation. To fulfill the promise of personal genomics, a rigorous multidisciplinary research agenda is needed. PMID:19617843

Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data

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

McDermott, Jason E.; Wang, Jing; Mitchell, Hugh D.

2013-01-01

The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities both for purely statistical and expert knowledge-based approaches and would benefit from improved integration of the two. Areas covered In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges thatmore » have been encountered. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. Expert opinion Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to biomarker discovery and characterization are key to future success in the biomarker field. We will describe our recommendations of possible approaches to this problem including metrics for the evaluation of biomarkers.« 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 evolution of colorectal cancer genetics—Part 1: from discovery to practice

PubMed Central

Schlussel, Andrew T.; Gagliano, Ronald A.; Eggerding, Faye; Donlon, Timothy; Berenberg, Jeffrey; Lynch, Henry T.

2014-01-01

Colorectal cancer (CRC) is an increasing burden on our society. Identifying those who are at the greatest risk and improving triage for treatment will have the greatest impact on healthcare. CRC is a prime paradigm for cancer genetics: the majority of disease results from stages of progression lending itself to prevention by early detection of the pre-disease (neoplastic) state. Approximately 10% represent well defined hereditary cancer syndromes. Hereditary CRC has the added benefit that many are slow growing and family members are armed with the knowledge of potential risk of associated carcinomas and empowerment to reduce the disease burden. This knowledge provides the indication for early endoscopic and/or surgical intervention for prevention or treatment of an entire family cohort. The molecular basis of CRC allows enhanced characterization of carcinomas, leading to targeted therapies. PMID:25276405

Project-focused activity and knowledge tracker: a unified data analysis, collaboration, and workflow tool for medicinal chemistry project teams.

PubMed

Brodney, Marian D; Brosius, Arthur D; Gregory, Tracy; Heck, Steven D; Klug-McLeod, Jacquelyn L; Poss, Christopher S

2009-12-01

Advances in the field of drug discovery have brought an explosion in the quantity of data available to medicinal chemists and other project team members. New strategies and systems are needed to help these scientists to efficiently gather, organize, analyze, annotate, and share data about potential new drug molecules of interest to their project teams. Herein we describe a suite of integrated services and end-user applications that facilitate these activities throughout the medicinal chemistry design cycle. The Automated Data Presentation (ADP) and Virtual Compound Profiler (VCP) processes automate the gathering, organization, and storage of real and virtual molecules, respectively, and associated data. The Project-Focused Activity and Knowledge Tracker (PFAKT) provides a unified data analysis and collaboration environment, enhancing decision-making, improving team communication, and increasing efficiency.

The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data.

PubMed

Margolis, Ronald; Derr, Leslie; Dunn, Michelle; Huerta, Michael; Larkin, Jennie; Sheehan, Jerry; Guyer, Mark; Green, Eric D

2014-01-01

Biomedical research has and will continue to generate large amounts of data (termed 'big data') in many formats and at all levels. Consequently, there is an increasing need to better understand and mine the data to further knowledge and foster new discovery. The National Institutes of Health (NIH) has initiated a Big Data to Knowledge (BD2K) initiative to maximize the use of biomedical big data. BD2K seeks to better define how to extract value from the data, both for the individual investigator and the overall research community, create the analytic tools needed to enhance utility of the data, provide the next generation of trained personnel, and develop data science concepts and tools that can be made available to all stakeholders. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Learning in the context of distribution drift

DTIC Science & Technology

2017-05-09

published in the leading data mining journal, Data Mining and Knowledge Discovery (Webb et. al., 2016)1. We have shown that the previous qualitative...learner Low-bias learner Aggregated classifier Figure 7: Architecture for learning fr m streaming data in th co text of variable or unknown...Learning limited dependence Bayesian classifiers, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD

A Bioinformatic Approach to Inter Functional Interactions within Protein Sequences

DTIC Science & Technology

2009-02-23

AFOSR/AOARD Reference Number: USAFAOGA07: FA4869-07-1-4050 AFOSR/AOARD Program Manager : Hiroshi Motoda, Ph.D. Period of...Conference on Knowledge Discovery and Data Mining.) In a separate study we have applied our approaches to the problem of whole genome alignment. We have...SIGKDD Conference on Knowledge Discovery and Data Mining Attached. Interactions: Please list: (a) Participation/presentations at meetings

k-neighborhood Decentralization: A Comprehensive Solution to Index the UMLS for Large Scale Knowledge Discovery

PubMed Central

Xiang, Yang; Lu, Kewei; James, Stephen L.; Borlawsky, Tara B.; Huang, Kun; Payne, Philip R.O.

2011-01-01

The Unified Medical Language System (UMLS) is the largest thesaurus in the biomedical informatics domain. Previous works have shown that knowledge constructs comprised of transitively-associated UMLS concepts are effective for discovering potentially novel biomedical hypotheses. However, the extremely large size of the UMLS becomes a major challenge for these applications. To address this problem, we designed a k-neighborhood Decentralization Labeling Scheme (kDLS) for the UMLS, and the corresponding method to effectively evaluate the kDLS indexing results. kDLS provides a comprehensive solution for indexing the UMLS for very efficient large scale knowledge discovery. We demonstrated that it is highly effective to use kDLS paths to prioritize disease-gene relations across the whole genome, with extremely high fold-enrichment values. To our knowledge, this is the first indexing scheme capable of supporting efficient large scale knowledge discovery on the UMLS as a whole. Our expectation is that kDLS will become a vital engine for retrieving information and generating hypotheses from the UMLS for future medical informatics applications. PMID:22154838

k-Neighborhood decentralization: a comprehensive solution to index the UMLS for large scale knowledge discovery.

PubMed

Xiang, Yang; Lu, Kewei; James, Stephen L; Borlawsky, Tara B; Huang, Kun; Payne, Philip R O

2012-04-01

The Unified Medical Language System (UMLS) is the largest thesaurus in the biomedical informatics domain. Previous works have shown that knowledge constructs comprised of transitively-associated UMLS concepts are effective for discovering potentially novel biomedical hypotheses. However, the extremely large size of the UMLS becomes a major challenge for these applications. To address this problem, we designed a k-neighborhood Decentralization Labeling Scheme (kDLS) for the UMLS, and the corresponding method to effectively evaluate the kDLS indexing results. kDLS provides a comprehensive solution for indexing the UMLS for very efficient large scale knowledge discovery. We demonstrated that it is highly effective to use kDLS paths to prioritize disease-gene relations across the whole genome, with extremely high fold-enrichment values. To our knowledge, this is the first indexing scheme capable of supporting efficient large scale knowledge discovery on the UMLS as a whole. Our expectation is that kDLS will become a vital engine for retrieving information and generating hypotheses from the UMLS for future medical informatics applications. Copyright © 2011 Elsevier Inc. All rights reserved.

Concept of Operations for Collaboration and Discovery from Big Data Across Enterprise Data Warehouses

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

Olama, Mohammed M; Nutaro, James J; Sukumar, Sreenivas R

2013-01-01

The success of data-driven business in government, science, and private industry is driving the need for seamless integration of intra and inter-enterprise data sources to extract knowledge nuggets in the form of correlations, trends, patterns and behaviors previously not discovered due to physical and logical separation of datasets. Today, as volume, velocity, variety and complexity of enterprise data keeps increasing, the next generation analysts are facing several challenges in the knowledge extraction process. Towards addressing these challenges, data-driven organizations that rely on the success of their analysts have to make investment decisions for sustainable data/information systems and knowledge discovery. Optionsmore » that organizations are considering are newer storage/analysis architectures, better analysis machines, redesigned analysis algorithms, collaborative knowledge management tools, and query builders amongst many others. In this paper, we present a concept of operations for enabling knowledge discovery that data-driven organizations can leverage towards making their investment decisions. We base our recommendations on the experience gained from integrating multi-agency enterprise data warehouses at the Oak Ridge National Laboratory to design the foundation of future knowledge nurturing data-system architectures.« less

Knowledge Discovery and Data Mining in Iran's Climatic Researches

NASA Astrophysics Data System (ADS)

Karimi, Mostafa

2013-04-01

Advances in measurement technology and data collection is the database gets larger. Large databases require powerful tools for analysis data. Iterative process of acquiring knowledge from information obtained from data processing is done in various forms in all scientific fields. However, when the data volume large, and many of the problems the Traditional methods cannot respond. in the recent years, use of databases in various scientific fields, especially atmospheric databases in climatology expanded. in addition, increases in the amount of data generated by the climate models is a challenge for analysis of it for extraction of hidden pattern and knowledge. The approach to this problem has been made in recent years uses the process of knowledge discovery and data mining techniques with the use of the concepts of machine learning, artificial intelligence and expert (professional) systems is overall performance. Data manning is analytically process for manning in massive volume data. The ultimate goal of data mining is access to information and finally knowledge. climatology is a part of science that uses variety and massive volume data. Goal of the climate data manning is Achieve to information from variety and massive atmospheric and non-atmospheric data. in fact, Knowledge Discovery performs these activities in a logical and predetermined and almost automatic process. The goal of this research is study of uses knowledge Discovery and data mining technique in Iranian climate research. For Achieve This goal, study content (descriptive) analysis and classify base method and issue. The result shown that in climatic research of Iran most clustering, k-means and wards applied and in terms of issues precipitation and atmospheric circulation patterns most introduced. Although several studies in geography and climate issues with statistical techniques such as clustering and pattern extraction is done, Due to the nature of statistics and data mining, but cannot say for internal climate studies in data mining and knowledge discovery techniques are used. However, it is necessary to use the KDD Approach and DM techniques in the climatic studies, specific interpreter of climate modeling result.

Knowledge Retrieval Solutions.

ERIC Educational Resources Information Center

Khan, Kamran

1998-01-01

Excalibur RetrievalWare offers true knowledge retrieval solutions. Its fundamental technologies, Adaptive Pattern Recognition Processing and Semantic Networks, have capabilities for knowledge discovery and knowledge management of full-text, structured and visual information. The software delivers a combination of accuracy, extensibility,…

Knowledge extraction from evolving spiking neural networks with rank order population coding.

PubMed

Soltic, Snjezana; Kasabov, Nikola

2010-12-01

This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.

Flood AI: An Intelligent Systems for Discovery and Communication of Disaster Knowledge

NASA Astrophysics Data System (ADS)

Demir, I.; Sermet, M. Y.

2017-12-01

Communities are not immune from extreme events or natural disasters that can lead to large-scale consequences for the nation and public. Improving resilience to better prepare, plan, recover, and adapt to disasters is critical to reduce the impacts of extreme events. The National Research Council (NRC) report discusses the topic of how to increase resilience to extreme events through a vision of resilient nation in the year 2030. The report highlights the importance of data, information, gaps and knowledge challenges that needs to be addressed, and suggests every individual to access the risk and vulnerability information to make their communities more resilient. This project presents an intelligent system, Flood AI, for flooding to improve societal preparedness by providing a knowledge engine using voice recognition, artificial intelligence, and natural language processing based on a generalized ontology for disasters with a primary focus on flooding. The knowledge engine utilizes the flood ontology and concepts to connect user input to relevant knowledge discovery channels on flooding by developing a data acquisition and processing framework utilizing environmental observations, forecast models, and knowledge bases. Communication channels of the framework includes web-based systems, agent-based chat bots, smartphone applications, automated web workflows, and smart home devices, opening the knowledge discovery for flooding to many unique use cases.

Informing child welfare policy and practice: using knowledge discovery and data mining technology via a dynamic Web site.

PubMed

Duncan, Dean F; Kum, Hye-Chung; Weigensberg, Elizabeth Caplick; Flair, Kimberly A; Stewart, C Joy

2008-11-01

Proper management and implementation of an effective child welfare agency requires the constant use of information about the experiences and outcomes of children involved in the system, emphasizing the need for comprehensive, timely, and accurate data. In the past 20 years, there have been many advances in technology that can maximize the potential of administrative data to promote better evaluation and management in the field of child welfare. Specifically, this article discusses the use of knowledge discovery and data mining (KDD), which makes it possible to create longitudinal data files from administrative data sources, extract valuable knowledge, and make the information available via a user-friendly public Web site. This article demonstrates a successful project in North Carolina where knowledge discovery and data mining technology was used to develop a comprehensive set of child welfare outcomes available through a public Web site to facilitate information sharing of child welfare data to improve policy and practice.

Covalent docking of large libraries for the discovery of chemical probes.

PubMed

London, Nir; Miller, Rand M; Krishnan, Shyam; Uchida, Kenji; Irwin, John J; Eidam, Oliv; Gibold, Lucie; Cimermančič, Peter; Bonnet, Richard; Shoichet, Brian K; Taunton, Jack

2014-12-01

Chemical probes that form a covalent bond with a protein target often show enhanced selectivity, potency and utility for biological studies. Despite these advantages, protein-reactive compounds are usually avoided in high-throughput screening campaigns. Here we describe a general method (DOCKovalent) for screening large virtual libraries of electrophilic small molecules. We apply this method prospectively to discover reversible covalent fragments that target distinct protein nucleophiles, including the catalytic serine of AmpC β-lactamase and noncatalytic cysteines in RSK2, MSK1 and JAK3 kinases. We identify submicromolar to low-nanomolar hits with high ligand efficiency, cellular activity and selectivity, including what are to our knowledge the first reported reversible covalent inhibitors of JAK3. Crystal structures of inhibitor complexes with AmpC and RSK2 confirm the docking predictions and guide further optimization. As covalent virtual screening may have broad utility for the rapid discovery of chemical probes, we have made the method freely available through an automated web server (http://covalent.docking.org/).

A platform for discovery: The University of Pennsylvania Integrated Neurodegenerative Disease Biobank

PubMed Central

Toledo, Jon B.; Van Deerlin, Vivianna M.; Lee, Edward B.; Suh, EunRan; Baek, Young; Robinson, John L.; Xie, Sharon X.; McBride, Jennifer; Wood, Elisabeth M.; Schuck, Theresa; Irwin, David J.; Gross, Rachel G.; Hurtig, Howard; McCluskey, Leo; Elman, Lauren; Karlawish, Jason; Schellenberg, Gerard; Chen-Plotkin, Alice; Wolk, David; Grossman, Murray; Arnold, Steven E.; Shaw, Leslie M.; Lee, Virginia M.-Y.; Trojanowski, John Q.

2014-01-01

Neurodegenerative diseases (NDs) are defined by the accumulation of abnormal protein deposits in the central nervous system (CNS), and only neuropathological examination enables a definitive diagnosis. Brain banks and their associated scientific programs have shaped the actual knowledge of NDs, identifying and characterizing the CNS deposits that define new diseases, formulating staging schemes, and establishing correlations between neuropathological changes and clinical features. However, brain banks have evolved to accommodate the banking of biofluids as well as DNA and RNA samples. Moreover, the value of biobanks is greatly enhanced if they link all the multidimensional clinical and laboratory information of each case, which is accomplished, optimally, using systematic and standardized operating procedures, and in the framework of multidisciplinary teams with the support of a flexible and user-friendly database system that facilitates the sharing of information of all the teams in the network. We describe a biobanking system that is a platform for discovery research at the Center for Neurodegenerative Disease Research at the University of Pennsylvania. PMID:23978324

Does Discovery-Based Instruction Enhance Learning?

ERIC Educational Resources Information Center

Alfieri, Louis; Brooks, Patricia J.; Aldrich, Naomi J.; Tenenbaum, Harriet R.

2011-01-01

Discovery learning approaches to education have recently come under scrutiny (Tobias & Duffy, 2009), with many studies indicating limitations to discovery learning practices. Therefore, 2 meta-analyses were conducted using a sample of 164 studies: The 1st examined the effects of unassisted discovery learning versus explicit instruction, and the…

A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

DOE PAGES

Wang, Jing; Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; ...

2013-01-01

Background . The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective . To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods . The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expertmore » knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results . The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions . Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.« less

A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

PubMed Central

Wang, Jing; Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; Varnum, Susan M.; Brown, Joseph N.; Riensche, Roderick M.; Adkins, Joshua N.; Jacobs, Jon M.; Hoidal, John R.; Scholand, Mary Beth; Pounds, Joel G.; Blackburn, Michael R.; Rodland, Karin D.; McDermott, Jason E.

2013-01-01

Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification. PMID:24223463

A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification

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

Wang, Jing; Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.

2013-10-01

Background. The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. Objective. To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. Methods. The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integratedmore » into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. Results. The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. Conclusions. Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.« less

Policy forum. Data, privacy, and the greater good.

PubMed

Horvitz, Eric; Mulligan, Deirdre

2015-07-17

Large-scale aggregate analyses of anonymized data can yield valuable results and insights that address public health challenges and provide new avenues for scientific discovery. These methods can extend our knowledge and provide new tools for enhancing health and wellbeing. However, they raise questions about how to best address potential threats to privacy while reaping benefits for individuals and to society as a whole. The use of machine learning to make leaps across informational and social contexts to infer health conditions and risks from nonmedical data provides representative scenarios for reflections on directions with balancing innovation and regulation. Copyright © 2015, American Association for the Advancement of Science.

Global health and the global economic crisis.

PubMed

Benatar, Solomon R; Gill, Stephen; Bakker, Isabella

2011-04-01

Although the resources and knowledge for achieving improved global health exist, a new, critical paradigm on health as an aspect of human development, human security, and human rights is needed. Such a shift is required to sufficiently modify and credibly reduce the present dominance of perverse market forces on global health. New scientific discoveries can make wide-ranging contributions to improved health; however, improved global health depends on achieving greater social justice, economic redistribution, and enhanced democratization of production, caring social institutions for essential health care, education, and other public goods. As with the quest for an HIV vaccine, the challenge of improved global health requires an ambitious multidisciplinary research program.

On the Growth of Scientific Knowledge: Yeast Biology as a Case Study

PubMed Central

He, Xionglei; Zhang, Jianzhi

2009-01-01

The tempo and mode of human knowledge expansion is an enduring yet poorly understood topic. Through a temporal network analysis of three decades of discoveries of protein interactions and genetic interactions in baker's yeast, we show that the growth of scientific knowledge is exponential over time and that important subjects tend to be studied earlier. However, expansions of different domains of knowledge are highly heterogeneous and episodic such that the temporal turnover of knowledge hubs is much greater than expected by chance. Familiar subjects are preferentially studied over new subjects, leading to a reduced pace of innovation. While research is increasingly done in teams, the number of discoveries per researcher is greater in smaller teams. These findings reveal collective human behaviors in scientific research and help design better strategies in future knowledge exploration. PMID:19300476

On the growth of scientific knowledge: yeast biology as a case study.

PubMed

He, Xionglei; Zhang, Jianzhi

2009-03-01

The tempo and mode of human knowledge expansion is an enduring yet poorly understood topic. Through a temporal network analysis of three decades of discoveries of protein interactions and genetic interactions in baker's yeast, we show that the growth of scientific knowledge is exponential over time and that important subjects tend to be studied earlier. However, expansions of different domains of knowledge are highly heterogeneous and episodic such that the temporal turnover of knowledge hubs is much greater than expected by chance. Familiar subjects are preferentially studied over new subjects, leading to a reduced pace of innovation. While research is increasingly done in teams, the number of discoveries per researcher is greater in smaller teams. These findings reveal collective human behaviors in scientific research and help design better strategies in future knowledge exploration.

Good surgeon: A search for meaning.

PubMed

Akopov, Andrey L; Artioukh, Dmitri Y

2017-01-01

The art and philosophy of surgery are not as often discussed as scientific discoveries and technological advances in the modern era of surgery. Although these are difficult to teach and pass on to the next generations of surgeons they are no less important for training good surgeons and maintaining their high standards. The authors of this review and opinion article tried to define what being a good surgeon really means and to look into the subject by analysing the essential conditions for being a good surgeon and the qualities that such a specialist should possess. In addition to a strong theoretic knowledge and practical skills and among the several described professional and personal characteristics, a good surgeon is expected to have common sense. It enables a surgeon to make a sound practical judgment independent of specialized medical knowledge and training. The possible ways of developing and/or enhancing common sense during surgical training and subsequent practice require separate analysis.

Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective

PubMed Central

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

2017-01-01

Genes causally involved in human insensitivity to pain provide a unique molecular source of studying the pathophysiology of pain and the development of novel analgesic drugs. The increasing availability of “big data” enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 20 genes causally involved in human hereditary insensitivity to pain with the knowledge about the functions of thousands of genes. An integrated computational analysis proposed that among the functions of this set of genes, the processes related to nervous system development and to ceramide and sphingosine signaling pathways are particularly important. This is in line with earlier suggestions to use these pathways as therapeutic target in pain. Following identification of the biological processes characterizing hereditary insensitivity to pain, the biological processes were used for a similarity analysis with the functions of n = 4,834 database-queried drugs. Using emergent self-organizing maps, a cluster of n = 22 drugs was identified sharing important functional features with hereditary insensitivity to pain. Several members of this cluster had been implicated in pain in preclinical experiments. Thus, the present concept of machine-learned knowledge discovery for pain research provides biologically plausible results and seems to be suitable for drug discovery by identifying a narrow choice of repurposing candidates, demonstrating that contemporary machine-learned methods offer innovative approaches to knowledge discovery from available evidence. PMID:28848388

Applying Knowledge Discovery in Databases in Public Health Data Set: Challenges and Concerns

PubMed Central

Volrathongchia, Kanittha

2003-01-01

In attempting to apply Knowledge Discovery in Databases (KDD) to generate a predictive model from a health care dataset that is currently available to the public, the first step is to pre-process the data to overcome the challenges of missing data, redundant observations, and records containing inaccurate data. This study will demonstrate how to use simple pre-processing methods to improve the quality of input data. PMID:14728545

Big, Deep, and Smart Data in Scanning Probe Microscopy

DOE PAGES

Kalinin, Sergei V.; Strelcov, Evgheni; Belianinov, Alex; ...

2016-09-27

Scanning probe microscopy techniques open the door to nanoscience and nanotechnology by enabling imaging and manipulation of structure and functionality of matter on nanometer and atomic scales. We analyze the discovery process by SPM in terms of information flow from tip-surface junction to the knowledge adoption by scientific community. Furthermore, we discuss the challenges and opportunities offered by merging of SPM and advanced data mining, visual analytics, and knowledge discovery technologies.

Exploiting Early Intent Recognition for Competitive Advantage

DTIC Science & Technology

2009-01-01

basketball [Bhan- dari et al., 1997; Jug et al., 2003], and Robocup soccer sim- ulations [Riley and Veloso, 2000; 2002; Kuhlmann et al., 2006] and non...actions (e.g. before, after, around). Jug et al. [2003] used a similar framework for offline basketball game analysis. More recently, Hess et al...and K. Ramanujam. Advanced Scout: Data mining and knowledge discovery in NBA data. Data Mining and Knowledge Discovery, 1(1):121–125, 1997. [Chang

An Alternative Time for Telling: When Conceptual Instruction Prior to Exploration Improves Mathematical Knowledge

ERIC Educational Resources Information Center

Fyfe, Emily R.; DeCaro, Marci S.; Rittle-Johnson, Bethany

2013-01-01

An emerging consensus suggests that guided discovery, which combines discovery and instruction, is a more effective educational approach than either one in isolation. The goal of this study was to examine two specific forms of guided discovery, testing whether conceptual instruction should precede or follow exploratory problem solving. In both…

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.…

Knowledge Management in Higher Education: A Knowledge Repository Approach

ERIC Educational Resources Information Center

Wedman, John; Wang, Feng-Kwei

2005-01-01

One might expect higher education, where the discovery and dissemination of new and useful knowledge is vital, to be among the first to implement knowledge management practices. Surprisingly, higher education has been slow to implement knowledge management practices (Townley, 2003). This article describes an ongoing research and development effort…

Crowdsourcing Knowledge Discovery and Innovations in Medicine

PubMed Central

2014-01-01

Clinicians face difficult treatment decisions in contexts that are not well addressed by available evidence as formulated based on research. The digitization of medicine provides an opportunity for clinicians to collaborate with researchers and data scientists on solutions to previously ambiguous and seemingly insolvable questions. But these groups tend to work in isolated environments, and do not communicate or interact effectively. Clinicians are typically buried in the weeds and exigencies of daily practice such that they do not recognize or act on ways to improve knowledge discovery. Researchers may not be able to identify the gaps in clinical knowledge. For data scientists, the main challenge is discerning what is relevant in a domain that is both unfamiliar and complex. Each type of domain expert can contribute skills unavailable to the other groups. “Health hackathons” and “data marathons”, in which diverse participants work together, can leverage the current ready availability of digital data to discover new knowledge. Utilizing the complementary skills and expertise of these talented, but functionally divided groups, innovations are formulated at the systems level. As a result, the knowledge discovery process is simultaneously democratized and improved, real problems are solved, cross-disciplinary collaboration is supported, and innovations are enabled. PMID:25239002

Crowdsourcing knowledge discovery and innovations in medicine.

PubMed

Celi, Leo Anthony; Ippolito, Andrea; Montgomery, Robert A; Moses, Christopher; Stone, David J

2014-09-19

Clinicians face difficult treatment decisions in contexts that are not well addressed by available evidence as formulated based on research. The digitization of medicine provides an opportunity for clinicians to collaborate with researchers and data scientists on solutions to previously ambiguous and seemingly insolvable questions. But these groups tend to work in isolated environments, and do not communicate or interact effectively. Clinicians are typically buried in the weeds and exigencies of daily practice such that they do not recognize or act on ways to improve knowledge discovery. Researchers may not be able to identify the gaps in clinical knowledge. For data scientists, the main challenge is discerning what is relevant in a domain that is both unfamiliar and complex. Each type of domain expert can contribute skills unavailable to the other groups. "Health hackathons" and "data marathons", in which diverse participants work together, can leverage the current ready availability of digital data to discover new knowledge. Utilizing the complementary skills and expertise of these talented, but functionally divided groups, innovations are formulated at the systems level. As a result, the knowledge discovery process is simultaneously democratized and improved, real problems are solved, cross-disciplinary collaboration is supported, and innovations are enabled.

Empirical study using network of semantically related associations in bridging the knowledge gap.

PubMed

Abedi, Vida; Yeasin, Mohammed; Zand, Ramin

2014-11-27

The data overload has created a new set of challenges in finding meaningful and relevant information with minimal cognitive effort. However designing robust and scalable knowledge discovery systems remains a challenge. Recent innovations in the (biological) literature mining tools have opened new avenues to understand the confluence of various diseases, genes, risk factors as well as biological processes in bridging the gaps between the massive amounts of scientific data and harvesting useful knowledge. In this paper, we highlight some of the findings using a text analytics tool, called ARIANA--Adaptive Robust and Integrative Analysis for finding Novel Associations. Empirical study using ARIANA reveals knowledge discovery instances that illustrate the efficacy of such tool. For example, ARIANA can capture the connection between the drug hexamethonium and pulmonary inflammation and fibrosis that caused the tragic death of a healthy volunteer in a 2001 John Hopkins asthma study, even though the abstract of the study was not part of the semantic model. An integrated system, such as ARIANA, could assist the human expert in exploratory literature search by bringing forward hidden associations, promoting data reuse and knowledge discovery as well as stimulating interdisciplinary projects by connecting information across the disciplines.

The Service Environment for Enhanced Knowledge and Research (SEEKR) Framework

NASA Astrophysics Data System (ADS)

King, T. A.; Walker, R. J.; Weigel, R. S.; Narock, T. W.; McGuire, R. E.; Candey, R. M.

2011-12-01

The Service Environment for Enhanced Knowledge and Research (SEEKR) Framework is a configurable service oriented framework to enable the discovery, access and analysis of data shared in a community. The SEEKR framework integrates many existing independent services through the use of web technologies and standard metadata. Services are hosted on systems by using an application server and are callable by using REpresentational State Transfer (REST) protocols. Messages and metadata are transferred with eXtensible Markup Language (XML) encoding which conform to a published XML schema. Space Physics Archive Search and Extract (SPASE) metadata is central to utilizing the services. Resources (data, documents, software, etc.) are described with SPASE and the associated Resource Identifier is used to access and exchange resources. The configurable options for the service can be set by using a web interface. Services are packaged as web application resource (WAR) files for direct deployment on application services such as Tomcat or Jetty. We discuss the composition of the SEEKR framework, how new services can be integrated and the steps necessary to deploying the framework. The SEEKR Framework emerged from NASA's Virtual Magnetospheric Observatory (VMO) and other systems and we present an overview of these systems from a SEEKR Framework perspective.

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.

Effective knowledge management in translational medicine.

PubMed

Szalma, Sándor; Koka, Venkata; Khasanova, Tatiana; Perakslis, Eric D

2010-07-19

The growing consensus that most valuable data source for biomedical discoveries is derived from human samples is clearly reflected in the growing number of translational medicine and translational sciences departments across pharma as well as academic and government supported initiatives such as Clinical and Translational Science Awards (CTSA) in the US and the Seventh Framework Programme (FP7) of EU with emphasis on translating research for human health. The pharmaceutical companies of Johnson and Johnson have established translational and biomarker departments and implemented an effective knowledge management framework including building a data warehouse and the associated data mining applications. The implemented resource is built from open source systems such as i2b2 and GenePattern. The system has been deployed across multiple therapeutic areas within the pharmaceutical companies of Johnson and Johnsons and being used actively to integrate and mine internal and public data to support drug discovery and development decisions such as indication selection and trial design in a translational medicine setting. Our results show that the established system allows scientist to quickly re-validate hypotheses or generate new ones with the use of an intuitive graphical interface. The implemented resource can serve as the basis of precompetitive sharing and mining of studies involving samples from human subjects thus enhancing our understanding of human biology and pathophysiology and ultimately leading to more effective treatment of diseases which represent unmet medical needs.

Data mining in pharma sector: benefits.

PubMed

Ranjan, Jayanthi

2009-01-01

The amount of data getting generated in any sector at present is enormous. The information flow in the pharma industry is huge. Pharma firms are progressing into increased technology-enabled products and services. Data mining, which is knowledge discovery from large sets of data, helps pharma firms to discover patterns in improving the quality of drug discovery and delivery methods. The paper aims to present how data mining is useful in the pharma industry, how its techniques can yield good results in pharma sector, and to show how data mining can really enhance in making decisions using pharmaceutical data. This conceptual paper is written based on secondary study, research and observations from magazines, reports and notes. The author has listed the types of patterns that can be discovered using data mining in pharma data. The paper shows how data mining is useful in the pharma industry and how its techniques can yield good results in pharma sector. Although much work can be produced for discovering knowledge in pharma data using data mining, the paper is limited to conceptualizing the ideas and view points at this stage; future work may include applying data mining techniques to pharma data based on primary research using the available, famous significant data mining tools. Research papers and conceptual papers related to data mining in Pharma industry are rare; this is the motivation for the paper.

Comprehensive Analysis of MILE Gene Expression Data Set Advances Discovery of Leukaemia Type and Subtype Biomarkers.

PubMed

Labaj, Wojciech; Papiez, Anna; Polanski, Andrzej; Polanska, Joanna

2017-03-01

Large collections of data in studies on cancer such as leukaemia provoke the necessity of applying tailored analysis algorithms to ensure supreme information extraction. In this work, a custom-fit pipeline is demonstrated for thorough investigation of the voluminous MILE gene expression data set. Three analyses are accomplished, each for gaining a deeper understanding of the processes underlying leukaemia types and subtypes. First, the main disease groups are tested for differential expression against the healthy control as in a standard case-control study. Here, the basic knowledge on molecular mechanisms is confirmed quantitatively and by literature references. Second, pairwise comparison testing is performed for juxtaposing the main leukaemia types among each other. In this case by means of the Dice coefficient similarity measure the general relations are pointed out. Moreover, lists of candidate main leukaemia group biomarkers are proposed. Finally, with this approach being successful, the third analysis provides insight into all of the studied subtypes, followed by the emergence of four leukaemia subtype biomarkers. In addition, the class enhanced DEG signature obtained on the basis of novel pipeline processing leads to significantly better classification power of multi-class data classifiers. The developed methodology consisting of batch effect adjustment, adaptive noise and feature filtration coupled with adequate statistical testing and biomarker definition proves to be an effective approach towards knowledge discovery in high-throughput molecular biology experiments.

Text-based discovery in biomedicine: the architecture of the DAD-system.

PubMed

Weeber, M; Klein, H; Aronson, A R; Mork, J G; de Jong-van den Berg, L T; Vos, R

2000-01-01

Current scientific research takes place in highly specialized contexts with poor communication between disciplines as a likely consequence. Knowledge from one discipline may be useful for the other without researchers knowing it. As scientific publications are a condensation of this knowledge, literature-based discovery tools may help the individual scientist to explore new useful domains. We report on the development of the DAD-system, a concept-based Natural Language Processing system for PubMed citations that provides the biomedical researcher such a tool. We describe the general architecture and illustrate its operation by a simulation of a well-known text-based discovery: The favorable effects of fish oil on patients suffering from Raynaud's disease [1].

Which are the greatest recent discoveries and the greatest future challenges in nutrition?

PubMed

Katan, M B; Boekschoten, M V; Connor, W E; Mensink, R P; Seidell, J; Vessby, B; Willett, W

2009-01-01

Nutrition science aims to create new knowledge, but scientists rarely sit back to reflect on what nutrition research has achieved in recent decades. We report the outcome of a 1-day symposium at which the audience was asked to vote on the greatest discoveries in nutrition since 1976 and on the greatest challenges for the coming 30 years. Most of the 128 participants were Dutch scientists working in nutrition or related biomedical and public health fields. Candidate discoveries and challenges were nominated by five invited speakers and by members of the audience. Ballot forms were then prepared on which participants selected one discovery and one challenge. A total of 15 discoveries and 14 challenges were nominated. The audience elected Folic acid prevents birth defects as the greatest discovery in nutrition science since 1976. Controlling obesity and insulin resistance through activity and diet was elected as the greatest challenge for the coming 30 years. This selection was probably biased by the interests and knowledge of the speakers and the audience. For the present review, we therefore added 12 discoveries from the period 1976 to 2006 that we judged worthy of consideration, but that had not been nominated at the meeting. The meeting did not represent an objective selection process, but it did demonstrate that the past 30 years have yielded major new discoveries in nutrition and health.

Translating three states of knowledge--discovery, invention, and innovation

PubMed Central

2010-01-01

Background Knowledge Translation (KT) has historically focused on the proper use of knowledge in healthcare delivery. A knowledge base has been created through empirical research and resides in scholarly literature. Some knowledge is amenable to direct application by stakeholders who are engaged during or after the research process, as shown by the Knowledge to Action (KTA) model. Other knowledge requires multiple transformations before achieving utility for end users. For example, conceptual knowledge generated through science or engineering may become embodied as a technology-based invention through development methods. The invention may then be integrated within an innovative device or service through production methods. To what extent is KT relevant to these transformations? How might the KTA model accommodate these additional development and production activities while preserving the KT concepts? Discussion Stakeholders adopt and use knowledge that has perceived utility, such as a solution to a problem. Achieving a technology-based solution involves three methods that generate knowledge in three states, analogous to the three classic states of matter. Research activity generates discoveries that are intangible and highly malleable like a gas; development activity transforms discoveries into inventions that are moderately tangible yet still malleable like a liquid; and production activity transforms inventions into innovations that are tangible and immutable like a solid. The paper demonstrates how the KTA model can accommodate all three types of activity and address all three states of knowledge. Linking the three activities in one model also illustrates the importance of engaging the relevant stakeholders prior to initiating any knowledge-related activities. Summary Science and engineering focused on technology-based devices or services change the state of knowledge through three successive activities. Achieving knowledge implementation requires methods that accommodate these three activities and knowledge states. Accomplishing beneficial societal impacts from technology-based knowledge involves the successful progression through all three activities, and the effective communication of each successive knowledge state to the relevant stakeholders. The KTA model appears suitable for structuring and linking these processes. PMID:20205873

Distributed data mining on grids: services, tools, and applications.

PubMed

Cannataro, Mario; Congiusta, Antonio; Pugliese, Andrea; Talia, Domenico; Trunfio, Paolo

2004-12-01

Data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science, and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems. The grid can play a significant role in providing an effective computational support for distributed knowledge discovery applications. For the development of data mining applications on grids we designed a system called Knowledge Grid. This paper describes the Knowledge Grid framework and presents the toolset provided by the Knowledge Grid for implementing distributed knowledge discovery. The paper discusses how to design and implement data mining applications by using the Knowledge Grid tools starting from searching grid resources, composing software and data components, and executing the resulting data mining process on a grid. Some performance results are also discussed.

Knowledge Discovery/A Collaborative Approach, an Innovative Solution

NASA Technical Reports Server (NTRS)

Fitts, Mary A.

2009-01-01

Collaboration between Medical Informatics and Healthcare Systems (MIHCS) at NASA/Johnson Space Center (JSC) and the Texas Medical Center (TMC) Library was established to investigate technologies for facilitating knowledge discovery across multiple life sciences research disciplines in multiple repositories. After reviewing 14 potential Enterprise Search System (ESS) solutions, Collexis was determined to best meet the expressed needs. A three month pilot evaluation of Collexis produced positive reports from multiple scientists across 12 research disciplines. The joint venture and a pilot-phased approach achieved the desired results without the high cost of purchasing software, hardware or additional resources to conduct the task. Medical research is highly compartmentalized by discipline, e.g. cardiology, immunology, neurology. The medical research community at large, as well as at JSC, recognizes the need for cross-referencing relevant information to generate best evidence. Cross-discipline collaboration at JSC is specifically required to close knowledge gaps affecting space exploration. To facilitate knowledge discovery across these communities, MIHCS combined expertise with the TMC library and found Collexis to best fit the needs of our researchers including:

Plant defense compounds: systems approaches to metabolic analysis.

PubMed

Kliebenstein, Daniel J

2012-01-01

Systems biology attempts to answer biological questions by integrating across diverse genomic data sets. With the increasing ability to conduct genomics experiments, this integrative approach is being rapidly applied across numerous biological research communities. One of these research communities investigates how plants utilize secondary metabolites or defense metabolites to defend against attack by pathogens and other biotic organisms. This use of systems biology to integrate across transcriptomics, metabolomics, and genomics is significantly enhancing the rate of discovery of genes, metabolites, and bioactivities for plant defense compounds as well as extending our knowledge of how these compounds are regulated. Plant defense compounds are also providing a unique proving platform to develop new approaches that enhance the ability to conduct systems biology with existing and previously unforseen genomics data sets. This review attempts to illustrate both how systems biology is helping the study of plant defense compounds and vice versa.

Big, Deep, and Smart Data in Scanning Probe Microscopy.

PubMed

Kalinin, Sergei V; Strelcov, Evgheni; Belianinov, Alex; Somnath, Suhas; Vasudevan, Rama K; Lingerfelt, Eric J; Archibald, Richard K; Chen, Chaomei; Proksch, Roger; Laanait, Nouamane; Jesse, Stephen

2016-09-27

Scanning probe microscopy (SPM) techniques have opened the door to nanoscience and nanotechnology by enabling imaging and manipulation of the structure and functionality of matter at nanometer and atomic scales. Here, we analyze the scientific discovery process in SPM by following the information flow from the tip-surface junction, to knowledge adoption by the wider scientific community. We further discuss the challenges and opportunities offered by merging SPM with advanced data mining, visual analytics, and knowledge discovery technologies.

Building Scalable Knowledge Graphs for Earth Science

NASA Technical Reports Server (NTRS)

Ramachandran, Rahul; Maskey, Manil; Gatlin, Patrick; Zhang, Jia; Duan, Xiaoyi; Miller, J. J.; Bugbee, Kaylin; Christopher, Sundar; Freitag, Brian

2017-01-01

Knowledge Graphs link key entities in a specific domain with other entities via relationships. From these relationships, researchers can query knowledge graphs for probabilistic recommendations to infer new knowledge. Scientific papers are an untapped resource which knowledge graphs could leverage to accelerate research discovery. Goal: Develop an end-to-end (semi) automated methodology for constructing Knowledge Graphs for Earth Science.

Genetic discoveries and nursing implications for complex disease prevention and management.

PubMed

Frazier, Lorraine; Meininger, Janet; Halsey Lea, Dale; Boerwinkle, Eric

2004-01-01

The purpose of this article is to examine the management of patients with complex diseases, in light of recent genetic discoveries, and to explore how these genetic discoveries will impact nursing practice and nursing research. The nursing science processes discussed are not comprehensive of all nursing practice but, instead, are concentrated in areas where genetics will have the greatest influence. Advances in genetic science will revolutionize our approach to patients and to health care in the prevention, diagnosis, and treatment of disease, raising many issues for nursing research and practice. As the scope of genetics expands to encompass multifactorial disease processes, a continuing reexamination of the knowledge base is required for nursing practice, with incorporation of genetic knowledge into the repertoire of every nurse, and with advanced knowledge for nurses who select specialty roles in the genetics area. This article explores the impact of this revolution on nursing science and practice as well as the opportunities for nursing science and practice to participate fully in this revolution. Because of the high proportion of the population at risk for complex diseases and because nurses are occupied every day in the prevention, assessment, treatment, and therapeutic intervention of patients with such diseases in practice and research, there is great opportunity for nurses to improve health care through the application (nursing practice) and discovery (nursing research) of genetic knowledge.

Medical data mining: knowledge discovery in a clinical data warehouse.

PubMed Central

Prather, J. C.; Lobach, D. F.; Goodwin, L. K.; Hales, J. W.; Hage, M. L.; Hammond, W. E.

1997-01-01

Clinical databases have accumulated large quantities of information about patients and their medical conditions. Relationships and patterns within this data could provide new medical knowledge. Unfortunately, few methodologies have been developed and applied to discover this hidden knowledge. In this study, the techniques of data mining (also known as Knowledge Discovery in Databases) were used to search for relationships in a large clinical database. Specifically, data accumulated on 3,902 obstetrical patients were evaluated for factors potentially contributing to preterm birth using exploratory factor analysis. Three factors were identified by the investigators for further exploration. This paper describes the processes involved in mining a clinical database including data warehousing, data query and cleaning, and data analysis. PMID:9357597

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).

Cooperative knowledge evolution: a construction-integration approach to knowledge discovery in medicine.

PubMed

Schmalhofer, F J; Tschaitschian, B

1998-11-01

In this paper, we perform a cognitive analysis of knowledge discovery processes. As a result of this analysis, the construction-integration theory is proposed as a general framework for developing cooperative knowledge evolution systems. We thus suggest that for the acquisition of new domain knowledge in medicine, one should first construct pluralistic views on a given topic which may contain inconsistencies as well as redundancies. Only thereafter does this knowledge become consolidated into a situation-specific circumscription and the early inconsistencies become eliminated. As a proof for the viability of such knowledge acquisition processes in medicine, we present the IDEAS system, which can be used for the intelligent documentation of adverse events in clinical studies. This system provides a better documentation of the side-effects of medical drugs. Thereby, knowledge evolution occurs by achieving consistent explanations in increasingly larger contexts (i.e., more cases and more pharmaceutical substrates). Finally, it is shown how prototypes, model-based approaches and cooperative knowledge evolution systems can be distinguished as different classes of knowledge-based systems.

Building Better Decision-Support by Using Knowledge Discovery.

ERIC Educational Resources Information Center

Jurisica, Igor

2000-01-01

Discusses knowledge-based decision-support systems that use artificial intelligence approaches. Addresses the issue of how to create an effective case-based reasoning system for complex and evolving domains, focusing on automated methods for system optimization and domain knowledge evolution that can supplement knowledge acquired from domain…

Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data

PubMed Central

McDermott, Jason E.; Wang, Jing; Mitchell, Hugh; Webb-Robertson, Bobbie-Jo; Hafen, Ryan; Ramey, John; Rodland, Karin D.

2012-01-01

Introduction The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful molecular signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities for more sophisticated approaches to integrating purely statistical and expert knowledge-based approaches. Areas covered In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered in deriving valid and useful signatures of disease. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. Expert opinion Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to identify predictive signatures of disease are key to future success in the biomarker field. We will describe our recommendations for possible approaches to this problem including metrics for the evaluation of biomarkers. PMID:23335946

Scholar in Residence: an innovative application of the scholarship of engagement.

PubMed

Jacelon, Cynthia S; Donoghue, Linda Carey; Breslin, Eileen

2010-01-01

Universities are expected to engage with communities for the benefit of both. Based on the definition of scholarship advanced by Boyer in the 1990s, inclusive of the scholarship of discovery, integration, teaching, and application, the School of Nursing and Jewish Geriatric Services, Inc., have instituted a unique collaboration entitled the Scholar in Residence. Unlike traditional agreements between schools of nursing and agencies to provide clinical experience and educate students, this agreement is designed to build scholarship for both university and agency. Outcomes include building opportunities for faculty and staff scholarship at the agency, enhancing the integration of knowledge into practice, intensifying opportunities for the sharing of knowledge by providing opportunities for students to work with faculty and staff on individual projects, and enriching the application of knowledge by providing opportunities for faculty clinical practice and consultation. The Scholar in Residence is a model of collaboration between the university and the community that reflects the mission of the university and provides value to the community agency through strategic engagement of both entities. Copyright 2010 Elsevier Inc. All rights reserved.

Population Sciences, Translational Research and the Opportunities and Challenges for Genomics to Reduce the Burden of Cancer in the 21st Century

PubMed Central

Khoury, Muin J.; Clauser, Steven B.; Freedman, Andrew N.; Gillanders, Elizabeth M.; Glasgow, Russ E.; Klein, William M. P.; Schully, Sheri D.

2011-01-01

Advances in genomics and related fields are promising tools for risk assessment, early detection, and targeted therapies across the entire cancer care continuum. In this commentary, we submit that this promise cannot be fulfilled without an enhanced translational genomics research agenda firmly rooted in the population sciences. Population sciences include multiple disciplines that are needed throughout the translational research continuum. For example, epidemiologic studies are needed not only to accelerate genomic discoveries and new biological insights into cancer etiology and pathogenesis, but to characterize and critically evaluate these discoveries in well defined populations for their potential for cancer prediction, prevention and response to treatments. Behavioral, social and communication sciences are needed to explore genomic-modulated responses to old and new behavioral interventions, adherence to therapies, decision-making across the continuum, and effective use in health care. Implementation science, health services, outcomes research, comparative effectiveness research and regulatory science are needed for moving validated genomic applications into practice and for measuring their effectiveness, cost effectiveness and unintended consequences. Knowledge synthesis, evidence reviews and economic modeling of the effects of promising genomic applications will facilitate policy decisions, and evidence-based recommendations. Several independent and multidisciplinary panels have recently made specific recommendations for enhanced research and policy infrastructure to inform clinical and population research for moving genomic innovations into the cancer care continuum. An enhanced translational genomics and population sciences agenda is urgently needed to fulfill the promise of genomics in reducing the burden of cancer. PMID:21795499

Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances

PubMed Central

Lionta, Evanthia; Spyrou, George; Vassilatis, Demetrios K.; Cournia, Zoe

2014-01-01

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process. PMID:25262799

Did Zika Virus Mutate to Cause Severe Outbreaks?

PubMed

Rossi, Shannan L; Ebel, Gregory D; Shan, Chao; Shi, Pei-Yong; Vasilakis, Nikos

2018-06-11

Zika virus (ZIKV) has challenged the assumed knowledge regarding the pathobiology of flaviviruses. Despite causing sporadic and mild disease in the 50 years since its discovery, Zika virus has now caused multiple outbreaks in dozens of countries worldwide. Moreover, the disease severity in recent outbreaks, with neurological disease in adult and devastating congenital malformations in fetuses, was not previously seen. One hypothesis is that the virus has acquired mutations that have increased its virulence. Indeed, mutations in other arboviruses, such as West Nile virus (WNV), chikungunya virus (CHIKV), and Venezuelan equine encephalitis virus (VEEV), have enhanced outbreaks. This possibility, as well as alternative hypotheses, are explored here. Copyright © 2018 Elsevier Ltd. All rights reserved.

Global Health and the Global Economic Crisis

PubMed Central

Gill, Stephen; Bakker, Isabella

2011-01-01

Although the resources and knowledge for achieving improved global health exist, a new, critical paradigm on health as an aspect of human development, human security, and human rights is needed. Such a shift is required to sufficiently modify and credibly reduce the present dominance of perverse market forces on global health. New scientific discoveries can make wide-ranging contributions to improved health; however, improved global health depends on achieving greater social justice, economic redistribution, and enhanced democratization of production, caring social institutions for essential health care, education, and other public goods. As with the quest for an HIV vaccine, the challenge of improved global health requires an ambitious multidisciplinary research program. PMID:21330597

To ontologise or not to ontologise: An information model for a geospatial knowledge infrastructure

NASA Astrophysics Data System (ADS)

Stock, Kristin; Stojanovic, Tim; Reitsma, Femke; Ou, Yang; Bishr, Mohamed; Ortmann, Jens; Robertson, Anne

2012-08-01

A geospatial knowledge infrastructure consists of a set of interoperable components, including software, information, hardware, procedures and standards, that work together to support advanced discovery and creation of geoscientific resources, including publications, data sets and web services. The focus of the work presented is the development of such an infrastructure for resource discovery. Advanced resource discovery is intended to support scientists in finding resources that meet their needs, and focuses on representing the semantic details of the scientific resources, including the detailed aspects of the science that led to the resource being created. This paper describes an information model for a geospatial knowledge infrastructure that uses ontologies to represent these semantic details, including knowledge about domain concepts, the scientific elements of the resource (analysis methods, theories and scientific processes) and web services. This semantic information can be used to enable more intelligent search over scientific resources, and to support new ways to infer and visualise scientific knowledge. The work describes the requirements for semantic support of a knowledge infrastructure, and analyses the different options for information storage based on the twin goals of semantic richness and syntactic interoperability to allow communication between different infrastructures. Such interoperability is achieved by the use of open standards, and the architecture of the knowledge infrastructure adopts such standards, particularly from the geospatial community. The paper then describes an information model that uses a range of different types of ontologies, explaining those ontologies and their content. The information model was successfully implemented in a working geospatial knowledge infrastructure, but the evaluation identified some issues in creating the ontologies.

Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes.

PubMed

Hassani-Pak, Keywan; Rawlings, Christopher

2017-06-13

Genetics and "omics" studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future.

Nearing saturation of cancer driver gene discovery.

PubMed

Hsiehchen, David; Hsieh, Antony

2018-06-15

Extensive sequencing efforts of cancer genomes such as The Cancer Genome Atlas (TCGA) have been undertaken to uncover bona fide cancer driver genes which has enhanced our understanding of cancer and revealed therapeutic targets. However, the number of driver gene mutations is bounded, indicating that there must be a point when further sequencing efforts will be excessive. We found that there was a significant positive correlation between sample size and identified driver gene mutations across 33 cancers sequenced by the TCGA, which is expected if additional sequencing is still leading to the identification of more driver genes. However, the rate of new cancer driver genes being discovered with larger samples is declining rapidly. Our analysis provides a general guide for determining which cancer types would likely benefit from additional sequencing efforts, particularly those with relatively high rates of cancer driver gene discovery. Our results argue that past strategies of indiscriminately sequencing as many specimens as possible for all cancer types is becoming inefficient. In addition, without significant investments into applying our knowledge of cancer genomes, we risk sequencing more cancer genomes for the sake of sequencing rather than meaningful patient benefit.

Academic College of Emergency Experts in India's INDO-US Joint Working Group and OPUS12 Foundation Consensus Statement on Creating A Coordinated, Multi-Disciplinary, Patient-Centered, Global Point-of-Care Biomarker Discovery Network.

PubMed

Stawicki, Stanislaw P; Stoltzfus, Jill C; Aggarwal, Praveen; Bhoi, Sanjeev; Bhatt, Shashi; Kalra, O P; Bhalla, Ashish; Hoey, Brian A; Galwankar, Sagar C; Paladino, Lorenzo; Papadimos, Thomas J

2014-07-01

Biomarker science brings great promise to clinical medicine. This is especially true in the era of technology miniaturization, rapid dissemination of knowledge, and point-of-care (POC) implementation of novel diagnostics. Despite this tremendous progress, the journey from a candidate biomarker to a scientifically validated biomarker continues to be an arduous one. In addition to substantial financial resources, biomarker research requires considerable expertise and a multidisciplinary approach. Investigational designs must also be taken into account, with the randomized controlled trial remaining the "gold standard". The authors present a condensed overview of biomarker science and associated investigational methods, followed by specific examples from clinical areas where biomarker development and/or implementation resulted in tangible enhancements in patient care. This manuscript also serves as a call to arms for the establishment of a truly global, well-coordinated infrastructure dedicated to biomarker research and development, with focus on delivery of the latest discoveries directly to the patient via point-of-care technology.

Emerging concepts in biomarker discovery; The US-Japan workshop on immunological molecular markers in oncology

PubMed Central

Tahara, Hideaki; Sato, Marimo; Thurin, Magdalena; Wang, Ena; Butterfield, Lisa H; Disis, Mary L; Fox, Bernard A; Lee, Peter P; Khleif, Samir N; Wigginton, Jon M; Ambs, Stefan; Akutsu, Yasunori; Chaussabel, Damien; Doki, Yuichiro; Eremin, Oleg; Fridman, Wolf Hervé; Hirohashi, Yoshihiko; Imai, Kohzoh; Jacobson, James; Jinushi, Masahisa; Kanamoto, Akira; Kashani-Sabet, Mohammed; Kato, Kazunori; Kawakami, Yutaka; Kirkwood, John M; Kleen, Thomas O; Lehmann, Paul V; Liotta, Lance; Lotze, Michael T; Maio, Michele; Malyguine, Anatoli; Masucci, Giuseppe; Matsubara, Hisahiro; Mayrand-Chung, Shawmarie; Nakamura, Kiminori; Nishikawa, Hiroyoshi; Palucka, A Karolina; Petricoin, Emanuel F; Pos, Zoltan; Ribas, Antoni; Rivoltini, Licia; Sato, Noriyuki; Shiku, Hiroshi; Slingluff, Craig L; Streicher, Howard; Stroncek, David F; Takeuchi, Hiroya; Toyota, Minoru; Wada, Hisashi; Wu, Xifeng; Wulfkuhle, Julia; Yaguchi, Tomonori; Zeskind, Benjamin; Zhao, Yingdong; Zocca, Mai-Britt; Marincola, Francesco M

2009-01-01

Supported by the Office of International Affairs, National Cancer Institute (NCI), the "US-Japan Workshop on Immunological Biomarkers in Oncology" was held in March 2009. The workshop was related to a task force launched by the International Society for the Biological Therapy of Cancer (iSBTc) and the United States Food and Drug Administration (FDA) to identify strategies for biomarker discovery and validation in the field of biotherapy. The effort will culminate on October 28th 2009 in the "iSBTc-FDA-NCI Workshop on Prognostic and Predictive Immunologic Biomarkers in Cancer", which will be held in Washington DC in association with the Annual Meeting. The purposes of the US-Japan workshop were a) to discuss novel approaches to enhance the discovery of predictive and/or prognostic markers in cancer immunotherapy; b) to define the state of the science in biomarker discovery and validation. The participation of Japanese and US scientists provided the opportunity to identify shared or discordant themes across the distinct immune genetic background and the diverse prevalence of disease between the two Nations. Converging concepts were identified: enhanced knowledge of interferon-related pathways was found to be central to the understanding of immune-mediated tissue-specific destruction (TSD) of which tumor rejection is a representative facet. Although the expression of interferon-stimulated genes (ISGs) likely mediates the inflammatory process leading to tumor rejection, it is insufficient by itself and the associated mechanisms need to be identified. It is likely that adaptive immune responses play a broader role in tumor rejection than those strictly related to their antigen-specificity; likely, their primary role is to trigger an acute and tissue-specific inflammatory response at the tumor site that leads to rejection upon recruitment of additional innate and adaptive immune mechanisms. Other candidate systemic and/or tissue-specific biomarkers were recognized that might be added to the list of known entities applicable in immunotherapy trials. The need for a systematic approach to biomarker discovery that takes advantage of powerful high-throughput technologies was recognized; it was clear from the current state of the science that immunotherapy is still in a discovery phase and only a few of the current biomarkers warrant extensive validation. It was, finally, clear that, while current technologies have almost limitless potential, inadequate study design, limited standardization and cross-validation among laboratories and suboptimal comparability of data remain major road blocks. The institution of an interactive consortium for high throughput molecular monitoring of clinical trials with voluntary participation might provide cost-effective solutions. PMID:19534815

Plant Enhancers: A Call for Discovery.

PubMed

Weber, Blaise; Zicola, Johan; Oka, Rurika; Stam, Maike

2016-11-01

Higher eukaryotes typically contain many different cell types, displaying different cellular functions that are influenced by biotic and abiotic cues. The different functions are characterized by specific gene expression patterns mediated by regulatory sequences such as transcriptional enhancers. Recent genome-wide approaches have identified thousands of enhancers in animals, reviving interest in enhancers in gene regulation. Although the regulatory roles of plant enhancers are as crucial as those in animals, genome-wide approaches have only very recently been applied to plants. Here we review characteristics of enhancers at the DNA and chromatin level in plants and other species, their similarities and differences, and techniques widely used for genome-wide discovery of enhancers in animal systems that can be implemented in plants. Copyright © 2016 Elsevier Ltd. All rights reserved.

Great Originals of Modern Physics

ERIC Educational Resources Information Center

Decker, Fred W.

1972-01-01

European travel can provide an intimate view of the implements and locales of great discoveries in physics for the knowledgeable traveler. The four museums at Cambridge, London, Remscheid-Lennep, and Munich display a full range of discovery apparatus in modern physics as outlined here. (Author/TS)

Dulse on the Distaff Side.

ERIC Educational Resources Information Center

MacKenzie, Marion

1983-01-01

Scientific research leading to the discovery of female plants of the red alga Palmaria plamata (dulse) is described. This discovery has not only advanced knowledge of marine organisms and taxonomic relationships but also has practical implications. The complete life cycle of this organism is included. (JN)

43 CFR 4.1132 - Scope of discovery.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature, custody... persons having knowledge of any discoverable matter. (b) It is not ground for objection that information...

43 CFR 4.1132 - Scope of discovery.

Code of Federal Regulations, 2012 CFR

2012-10-01

..., the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature, custody... persons having knowledge of any discoverable matter. (b) It is not ground for objection that information...

43 CFR 4.1132 - Scope of discovery.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature, custody... persons having knowledge of any discoverable matter. (b) It is not ground for objection that information...

43 CFR 4.1132 - Scope of discovery.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature, custody... persons having knowledge of any discoverable matter. (b) It is not ground for objection that information...

Scientific Knowledge Discovery in Complex Semantic Networks of Geophysical Systems

NASA Astrophysics Data System (ADS)

Fox, P.

2012-04-01

The vast majority of explorations of the Earth's systems are limited in their ability to effectively explore the most important (often most difficult) problems because they are forced to interconnect at the data-element, or syntactic, level rather than at a higher scientific, or semantic, level. Recent successes in the application of complex network theory and algorithms to climate data, raise expectations that more general graph-based approaches offer the opportunity for new discoveries. In the past ~ 5 years in the natural sciences there has substantial progress in providing both specialists and non-specialists the ability to describe in machine readable form, geophysical quantities and relations among them in meaningful and natural ways, effectively breaking the prior syntax barrier. The corresponding open-world semantics and reasoning provide higher-level interconnections. That is, semantics provided around the data structures, using semantically-equipped tools, and semantically aware interfaces between science application components allowing for discovery at the knowledge level. More recently, formal semantic approaches to continuous and aggregate physical processes are beginning to show promise and are soon likely to be ready to apply to geoscientific systems. To illustrate these opportunities, this presentation presents two application examples featuring domain vocabulary (ontology) and property relations (named and typed edges in the graphs). First, a climate knowledge discovery pilot encoding and exploration of CMIP5 catalog information with the eventual goal to encode and explore CMIP5 data. Second, a multi-stakeholder knowledge network for integrated assessments in marine ecosystems, where the data is highly inter-disciplinary.

78 FR 69363 - Lake Tahoe Basin Management Unit, California, Heavenly Mountain Resort Epic Discovery Project

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-19

... DEPARTMENT OF AGRICULTURE Forest Service Lake Tahoe Basin Management Unit, California, Heavenly Mountain Resort Epic Discovery Project AGENCY: Lake Tahoe Basin Management Unit, Forest Service, USDA...: The Epic Discovery Project is intended to enhance summer activities in response to the USDA Forest...

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.

The Importance of Earth Observations and Data Collaboration within Environmental Intelligence Supporting Arctic Research

NASA Technical Reports Server (NTRS)

Casas, Joseph

2017-01-01

Within the IARPC Collaboration Team activities of 2016, Arctic in-situ and remote earth observations advanced topics such as :1) exploring the role for new and innovative autonomous observing technologies in the Arctic; 2) advancing catalytic national and international community based observing efforts in support of the National Strategy for the Arctic Region; and 3) enhancing the use of discovery tools for observing system collaboration such as the U.S. National Oceanic and Atmospheric Administration (NOAA) Arctic Environmental Response Management Application (ERMA) and the U.S. National Aeronautics and Space Administration (NASA) Arctic Collaborative Environment (ACE) project geo reference visualization decision support and exploitation internet based tools. Critical to the success of these earth observations for both in-situ and remote systems is the emerging of new and innovative data collection technologies and comprehensive modeling as well as enhanced communications and cyber infrastructure capabilities which effectively assimilate and dissemination many environmental intelligence products in a timely manner. The Arctic Collaborative Environment (ACE) project is well positioned to greatly enhance user capabilities for accessing, organizing, visualizing, sharing and producing collaborative knowledge for the Arctic.

From Information Center to Discovery System: Next Step for Libraries?

ERIC Educational Resources Information Center

Marcum, James W.

2001-01-01

Proposes a discovery system model to guide technology integration in academic libraries that fuses organizational learning, systems learning, and knowledge creation techniques with constructivist learning practices to suggest possible future directions for digital libraries. Topics include accessing visual and continuous media; information…

Foreword to "The Secret of Childhood."

ERIC Educational Resources Information Center

Stephenson, Margaret E.

2000-01-01

Discusses the basic discoveries of Montessori's Casa dei Bambini. Considers principles of Montessori's organizing theory: the absorbent mind, the unfolding nature of life, the spiritual embryo, self-construction, acquisition of culture, creativity of life, repetition of exercise, freedom within limits, children's discovery of knowledge, the secret…

Cosmic Discovery

NASA Astrophysics Data System (ADS)

Harwit, Martin

1984-04-01

In the remarkable opening section of this book, a well-known Cornell astronomer gives precise thumbnail histories of the 43 basic cosmic discoveries - stars, planets, novae, pulsars, comets, gamma-ray bursts, and the like - that form the core of our knowledge of the universe. Many of them, he points out, were made accidentally and outside the mainstream of astronomical research and funding. This observation leads him to speculate on how many more major phenomena there might be and how they might be most effectively sought out in afield now dominated by large instruments and complex investigative modes and observational conditions. The book also examines discovery in terms of its political, financial, and sociological context - the role of new technologies and of industry and the military in revealing new knowledge; and methods of funding, of peer review, and of allotting time on our largest telescopes. It concludes with specific recommendations for organizing astronomy in ways that will best lead to the discovery of the many - at least sixty - phenomena that Harwit estimates are still waiting to be found.

The discovery of medicines for rare diseases

PubMed Central

Swinney, David C; Xia, Shuangluo

2015-01-01

There is a pressing need for new medicines (new molecular entities; NMEs) for rare diseases as few of the 6800 rare diseases (according to the NIH) have approved treatments. Drug discovery strategies for the 102 orphan NMEs approved by the US FDA between 1999 and 2012 were analyzed to learn from past success: 46 NMEs were first in class; 51 were followers; and five were imaging agents. First-in-class medicines were discovered with phenotypic assays (15), target-based approaches (12) and biologic strategies (18). Identification of genetic causes in areas with more basic and translational research such as cancer and in-born errors in metabolism contributed to success regardless of discovery strategy. In conclusion, greater knowledge increases the chance of success and empirical solutions can be effective when knowledge is incomplete. PMID:25068983

The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery

PubMed Central

2014-01-01

The Semanticscience Integrated Ontology (SIO) is an ontology to facilitate biomedical knowledge discovery. SIO features a simple upper level comprised of essential types and relations for the rich description of arbitrary (real, hypothesized, virtual, fictional) objects, processes and their attributes. SIO specifies simple design patterns to describe and associate qualities, capabilities, functions, quantities, and informational entities including textual, geometrical, and mathematical entities, and provides specific extensions in the domains of chemistry, biology, biochemistry, and bioinformatics. SIO provides an ontological foundation for the Bio2RDF linked data for the life sciences project and is used for semantic integration and discovery for SADI-based semantic web services. SIO is freely available to all users under a creative commons by attribution license. See website for further information: http://sio.semanticscience.org. PMID:24602174

Knowledge discovery from structured mammography reports using inductive logic programming.

PubMed

Burnside, Elizabeth S; Davis, Jesse; Costa, Victor Santos; Dutra, Inês de Castro; Kahn, Charles E; Fine, Jason; Page, David

2005-01-01

The development of large mammography databases provides an opportunity for knowledge discovery and data mining techniques to recognize patterns not previously appreciated. Using a database from a breast imaging practice containing patient risk factors, imaging findings, and biopsy results, we tested whether inductive logic programming (ILP) could discover interesting hypotheses that could subsequently be tested and validated. The ILP algorithm discovered two hypotheses from the data that were 1) judged as interesting by a subspecialty trained mammographer and 2) validated by analysis of the data itself.

A Metadata based Knowledge Discovery Methodology for Seeding Translational Research.

PubMed

Kothari, Cartik R; Payne, Philip R O

2015-01-01

In this paper, we present a semantic, metadata based knowledge discovery methodology for identifying teams of researchers from diverse backgrounds who can collaborate on interdisciplinary research projects: projects in areas that have been identified as high-impact areas at The Ohio State University. This methodology involves the semantic annotation of keywords and the postulation of semantic metrics to improve the efficiency of the path exploration algorithm as well as to rank the results. Results indicate that our methodology can discover groups of experts from diverse areas who can collaborate on translational research projects.

Direction discovery: A science enrichment program for high school students.

PubMed

Sikes, Suzanne S; Schwartz-Bloom, Rochelle D

2009-03-01

Launch into education about pharmacology (LEAP) is an inquiry-based science enrichment program designed to enhance competence in biology and chemistry and foster interest in science careers especially among under-represented minorities. The study of how drugs work, how they enter cells, alter body chemistry, and exit the body engages students to conceptualize fundamental precepts in biology, chemistry, and math. Students complete an intensive three-week course in the fundamentals of pharmacology during the summer followed by a mentored research component during the school year. Following a 5E learning paradigm, the summer course captures student interest by introducing controversial topics in pharmacology and provides a framework that guides them to explore topics in greater detail. The 5E learning cycle is recapitulated as students extend their knowledge to design and to test an original research question in pharmacology. LEAP students demonstrated significant gains in biology and chemistry knowledge and interests in pursuing science. Several students earned honors for the presentation of their research in regional and state science fairs. Success of the LEAP model in its initial 2 years argues that coupling college-level coursework of interest to teens with an authentic research experience enhances high school student success in and enthusiasm for science. Copyright © 2009 International Union of Biochemistry and Molecular Biology, Inc.

ASIS 2000: Knowledge Innovations: Celebrating Our Heritage, Designing Our Future. Proceedings of the ASIS Annual Meeting (63rd, Chicago, Illinois, November 12-16, 2000). Volume 37.

ERIC Educational Resources Information Center

Kraft, Donald H., Ed.

The 2000 ASIS (American Society for Information Science) conference explored knowledge innovation. The tracks in the conference program included knowledge discovery, capture, and creation; classification and representation; information retrieval; knowledge dissemination; and social, behavioral, ethical, and legal aspects. This proceedings is…

Application of plant cell and tissue culture for the production of phytochemicals in medicinal plants.

PubMed

Pant, Bijaya

2014-01-01

Approximately 80% of the world inhabitants depend on the medicinal plants in the form of traditional formulations for their primary health care system well as in the treatment of a number of diseases since the ancient time. Many commercially used drugs have come from the information of indigenous knowledge of plants and their folk uses. Linking of the indigenous knowledge of medicinal plants to modern research activities provides a new reliable approach, for the discovery of novel drugs much more effectively than with random collection. Increase in population and increasing demand of plant products along with illegal trade are causing depletion of medicinal plants and many are threatened in natural habitat. Plant tissue culture technique has proved potential alternative for the production of desirable bioactive components from plants, to produce the enough amounts of plant material that is needed and for the conservation of threatened species. Different plant tissue culture systems have been extensively studied to improve and enhance the production of plant chemicals in various medicinal plants.

Possible Brain Mechanisms of Creativity.

PubMed

Heilman, Kenneth M

2016-06-01

Creativity is the new discovery, understanding, development and expression of orderly and meaningful relationships. Creativity has three major stages: preparation, the development (nature and nurture) of critical knowledge and skills; innovation, the development of a creative solution; and creative production. Successful preparation requires a basic level of general intelligence and domain specific knowledge and skills and highly creative people may have anatomic alterations of specific neocortical regions. Innovation requires disengagement and divergent thinking primarily mediated by frontal networks. Creative people are often risk-takers and novelty seekers, behaviors that activate their ventral striatal reward system. Innovation also requires associative and convergent thinking, activities that are dependent on the integration of highly distributed networks. People are often most creative when they are in mental states associated with reduced levels of brain norepinephrine, which may enhance the communication between distributed networks. We, however, need to learn more about the brain mechanisms of creativity. Published by Oxford University Press 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Evaluating the Science of Discovery in Complex Health Systems

ERIC Educational Resources Information Center

Norman, Cameron D.; Best, Allan; Mortimer, Sharon; Huerta, Timothy; Buchan, Alison

2011-01-01

Complex health problems such as chronic disease or pandemics require knowledge that transcends disciplinary boundaries to generate solutions. Such transdisciplinary discovery requires researchers to work and collaborate across boundaries, combining elements of basic and applied science. At the same time, calls for more interdisciplinary health…

29 CFR 18.14 - Scope of discovery.

Code of Federal Regulations, 2014 CFR

2014-07-01

... administrative law judge in accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the... things and the identity and location of persons having knowledge of any discoverable matter. (b) It is...

49 CFR 386.38 - Scope of discovery.

Code of Federal Regulations, 2011 CFR

2011-10-01

... accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature... location of persons having knowledge of any discoverable matter. (b) It is not ground for objection that...

49 CFR 386.38 - Scope of discovery.

Code of Federal Regulations, 2012 CFR

2012-10-01

... accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature... location of persons having knowledge of any discoverable matter. (b) It is not ground for objection that...

29 CFR 18.14 - Scope of discovery.

Code of Federal Regulations, 2012 CFR

2012-07-01

... administrative law judge in accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the... things and the identity and location of persons having knowledge of any discoverable matter. (b) It is...

49 CFR 386.38 - Scope of discovery.

Code of Federal Regulations, 2013 CFR

2013-10-01

... accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature... location of persons having knowledge of any discoverable matter. (b) It is not ground for objection that...

29 CFR 18.14 - Scope of discovery.

Code of Federal Regulations, 2011 CFR

2011-07-01

... administrative law judge in accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the... things and the identity and location of persons having knowledge of any discoverable matter. (b) It is...

29 CFR 18.14 - Scope of discovery.

Code of Federal Regulations, 2013 CFR

2013-07-01

... administrative law judge in accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the... things and the identity and location of persons having knowledge of any discoverable matter. (b) It is...

49 CFR 386.38 - Scope of discovery.

Code of Federal Regulations, 2014 CFR

2014-10-01

... accordance with these rules, the parties may obtain discovery regarding any matter, not privileged, which is relevant to the subject matter involved in the proceeding, including the existence, description, nature... location of persons having knowledge of any discoverable matter. (b) It is not ground for objection that...

ESIP's Earth Science Knowledge Graph (ESKG) Testbed Project: An Automatic Approach to Building Interdisciplinary Earth Science Knowledge Graphs to Improve Data Discovery

NASA Astrophysics Data System (ADS)

McGibbney, L. J.; Jiang, Y.; Burgess, A. B.

2017-12-01

Big Earth observation data have been produced, archived and made available online, but discovering the right data in a manner that precisely and efficiently satisfies user needs presents a significant challenge to the Earth Science (ES) community. An emerging trend in information retrieval community is to utilize knowledge graphs to assist users in quickly finding desired information from across knowledge sources. This is particularly prevalent within the fields of social media and complex multimodal information processing to name but a few, however building a domain-specific knowledge graph is labour-intensive and hard to keep up-to-date. In this work, we update our progress on the Earth Science Knowledge Graph (ESKG) project; an ESIP-funded testbed project which provides an automatic approach to building a dynamic knowledge graph for ES to improve interdisciplinary data discovery by leveraging implicit, latent existing knowledge present within across several U.S Federal Agencies e.g. NASA, NOAA and USGS. ESKG strengthens ties between observations and user communities by: 1) developing a knowledge graph derived from various sources e.g. Web pages, Web Services, etc. via natural language processing and knowledge extraction techniques; 2) allowing users to traverse, explore, query, reason and navigate ES data via knowledge graph interaction. ESKG has the potential to revolutionize the way in which ES communities interact with ES data in the open world through the entity, spatial and temporal linkages and characteristics that make it up. This project enables the advancement of ESIP collaboration areas including both Discovery and Semantic Technologies by putting graph information right at our fingertips in an interactive, modern manner and reducing the efforts to constructing ontology. To demonstrate the ESKG concept, we will demonstrate use of our framework across NASA JPL's PO.DAAC, NOAA's Earth Observation Requirements Evaluation System (EORES) and various USGS systems.

Trends in Modern Drug Discovery.

PubMed

Eder, Jörg; Herrling, Paul L

2016-01-01

Drugs discovered by the pharmaceutical industry over the past 100 years have dramatically changed the practice of medicine and impacted on many aspects of our culture. For many years, drug discovery was a target- and mechanism-agnostic approach that was based on ethnobotanical knowledge often fueled by serendipity. With the advent of modern molecular biology methods and based on knowledge of the human genome, drug discovery has now largely changed into a hypothesis-driven target-based approach, a development which was paralleled by significant environmental changes in the pharmaceutical industry. Laboratories became increasingly computerized and automated, and geographically dispersed research sites are now more and more clustered into large centers to capture technological and biological synergies. Today, academia, the regulatory agencies, and the pharmaceutical industry all contribute to drug discovery, and, in order to translate the basic science into new medical treatments for unmet medical needs, pharmaceutical companies have to have a critical mass of excellent scientists working in many therapeutic fields, disciplines, and technologies. The imperative for the pharmaceutical industry to discover breakthrough medicines is matched by the increasing numbers of first-in-class drugs approved in recent years and reflects the impact of modern drug discovery approaches, technologies, and genomics.

Reuniting Virtue and Knowledge

ERIC Educational Resources Information Center

Culham, Tom

2015-01-01

Einstein held that intuition is more important than rational inquiry as a source of discovery. Further, he explicitly and implicitly linked the heart, the sacred, devotion and intuitive knowledge. The raison d'être of universities is the advance of knowledge; however, they have primarily focused on developing student's skills in working with…

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

Lummen, Tom T. A.; Leung, J.; Kumar, Amit

The design of new or enhanced functionality in materials is traditionally viewed as requiring the discovery of new chemical compositions through synthesis. Large property enhancements may however also be hidden within already well-known materials, when their structural symmetry is deviated from equilibrium through a small local strain or field. Here, the discovery of enhanced material properties associated with a new metastable phase of monoclinic symmetry within bulk KNbO3 is reported. This phase is found to coexist with the nominal orthorhombic phase at room temperature, and is both induced by and stabilized with local strains generated by a network of ferroelectricmore » domain walls. While the local microstructural shear strain involved is only approximate to 0.017%, the concurrent symmetry reduction results in an optical second harmonic generation response that is over 550% higher at room temperature. Moreover, the meandering walls of the low-symmetry domains also exhibit enhanced electrical conductivity on the order of 1 S m(-1). This discovery reveals a potential new route to local engineering of significant property enhancements and conductivity through symmetry lowering in ferroelectric crystals.« less

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

Lummen, Tom T. A.; Leung, J.; Kumar, Amit

The design of new or enhanced functionality in materials is traditionally viewed as requiring the discovery of new chemical compositions through synthesis. Large property enhancements may however also be hidden within already well-known materials, when their structural symmetry is deviated from equilibrium through a small local strain or field. Here, the discovery of enhanced material properties associated with a new metastable phase of monoclinic symmetry within bulk KNbO 3 is reported. This phase is found to coexist with the nominal orthorhombic phase at room temperature, and is both induced by and stabilized with local strains generated by a network ofmore » ferroelectric domain walls. While the local microstructural shear strain involved is only ≈0.017%, the concurrent symmetry reduction results in an optical second harmonic generation response that is over 550% higher at room temperature. Moreover, the meandering walls of the low-symmetry domains also exhibit enhanced electrical conductivity on the order of 1 S m -1. In conclusion, this discovery reveals a potential new route to local engineering of significant property enhancements and conductivity through symmetry lowering in ferroelectric crystals.« less

How music training enhances working memory: a cerebrocerebellar blending mechanism that can lead equally to scientific discovery and therapeutic efficacy in neurological disorders.

PubMed

Vandervert, Larry

2015-01-01

Following in the vein of studies that concluded that music training resulted in plastic changes in Einstein's cerebral cortex, controlled research has shown that music training (1) enhances central executive attentional processes in working memory, and (2) has also been shown to be of significant therapeutic value in neurological disorders. Within this framework of music training-induced enhancement of central executive attentional processes, the purpose of this article is to argue that: (1) The foundational basis of the central executive begins in infancy as attentional control during the establishment of working memory, (2) In accordance with Akshoomoff, Courchesne and Townsend's and Leggio and Molinari's cerebellar sequence detection and prediction models, the rigors of volitional control demands of music training can enhance voluntary manipulation of information in thought and movement, (3) The music training-enhanced blending of cerebellar internal models in working memory as can be experienced as intuition in scientific discovery (as Einstein often indicated) or, equally, as moments of therapeutic advancement toward goals in the development of voluntary control in neurological disorders, and (4) The blending of internal models as in (3) thus provides a mechanism by which music training enhances central executive processes in working memory that can lead to scientific discovery and improved therapeutic outcomes in neurological disorders. Within the framework of Leggio and Molinari's cerebellar sequence detection model, it is determined that intuitive steps forward that occur in both scientific discovery and during therapy in those with neurological disorders operate according to the same mechanism of adaptive error-driven blending of cerebellar internal models. It is concluded that the entire framework of the central executive structure of working memory is a product of the cerebrocerebellar system which can, through the learning of internal models, incorporate the multi-dimensional rigor and volitional-control demands of music training and, thereby, enhance voluntary control. It is further concluded that this cerebrocerebellar view of the music training-induced enhancement of central executive control in working memory provides a needed mechanism to explain both the highest level of scientific discovery and the efficacy of music training in the remediation of neurological impairments.

Integrative Systems Biology for Data Driven Knowledge Discovery

PubMed Central

Greene, Casey S.; Troyanskaya, Olga G.

2015-01-01

Integrative systems biology is an approach that brings together diverse high throughput experiments and databases to gain new insights into biological processes or systems at molecular through physiological levels. These approaches rely on diverse high-throughput experimental techniques that generate heterogeneous data by assaying varying aspects of complex biological processes. Computational approaches are necessary to provide an integrative view of these experimental results and enable data-driven knowledge discovery. Hypotheses generated from these approaches can direct definitive molecular experiments in a cost effective manner. Using integrative systems biology approaches, we can leverage existing biological knowledge and large-scale data to improve our understanding of yet unknown components of a system of interest and how its malfunction leads to disease. PMID:21044756

Automated Knowledge Discovery from Simulators

NASA Technical Reports Server (NTRS)

Burl, Michael C.; DeCoste, D.; Enke, B. L.; Mazzoni, D.; Merline, W. J.; Scharenbroich, L.

2006-01-01

In this paper, we explore one aspect of knowledge discovery from simulators, the landscape characterization problem, where the aim is to identify regions in the input/ parameter/model space that lead to a particular output behavior. Large-scale numerical simulators are in widespread use by scientists and engineers across a range of government agencies, academia, and industry; in many cases, simulators provide the only means to examine processes that are infeasible or impossible to study otherwise. However, the cost of simulation studies can be quite high, both in terms of the time and computational resources required to conduct the trials and the manpower needed to sift through the resulting output. Thus, there is strong motivation to develop automated methods that enable more efficient knowledge extraction.

18 CFR 385.402 - Scope of discovery (Rule 402).

Code of Federal Regulations, 2010 CFR

2010-04-01

... 18 Conservation of Power and Water Resources 1 2010-04-01 2010-04-01 false Scope of discovery (Rule 402). 385.402 Section 385.402 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY... persons having any knowledge of any discoverable matter. It is not ground for objection that the...

Doors to Discovery[TM]. What Works Clearinghouse Intervention Report

ERIC Educational Resources Information Center

What Works Clearinghouse, 2013

2013-01-01

"Doors to Discovery"]TM] is a preschool literacy curriculum that uses eight thematic units of activities to help children build fundamental early literacy skills in oral language, phonological awareness, concepts of print, alphabet knowledge, writing, and comprehension. The eight thematic units cover topics such as nature, friendship,…

78 FR 12933 - Proceedings Before the Commodity Futures Trading Commission

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-26

... proceedings. These new amendments also provide that Judgment Officers may conduct sua sponte discovery in... discovery; (4) sound risk management practices; and (5) other public interest considerations. The amendments... representative capacity, it was done with full power and authority to do so; (C) To the best of his knowledge...

76 FR 64803 - Rules of Adjudication and Enforcement

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-19

...) is also amended to clarify the limits on discovery when the Commission orders the ALJ to consider the... that the complainant identify, to the best of its knowledge, the ``like or directly competitive... the taking of discovery by the parties shall be at the discretion of the presiding ALJ. The ITCTLA...

78 FR 63253 - Davidson Kempner Capital Management LLC; Notice of Application

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-23

... employees of the Adviser other than the Contributor have any knowledge of the Contribution prior to its discovery by the Adviser on November 2, 2011. The Contribution was discovered by the Adviser's compliance... names of employees. After discovery of the Contribution, the Adviser and Contributor obtained the...

Hypothesis driven drug design: improving quality and effectiveness of the design-make-test-analyse cycle.

PubMed

Plowright, Alleyn T; Johnstone, Craig; Kihlberg, Jan; Pettersson, Jonas; Robb, Graeme; Thompson, Richard A

2012-01-01

In drug discovery, the central process of constructing and testing hypotheses, carefully conducting experiments and analysing the associated data for new findings and information is known as the design-make-test-analyse cycle. Each step relies heavily on the inputs and outputs of the other three components. In this article we report our efforts to improve and integrate all parts to enable smooth and rapid flow of high quality ideas. Key improvements include enhancing multi-disciplinary input into 'Design', increasing the use of knowledge and reducing cycle times in 'Make', providing parallel sets of relevant data within ten working days in 'Test' and maximising the learning in 'Analyse'. Copyright © 2011 Elsevier Ltd. All rights reserved.

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.

Bridging the Gap in Neurotherapeutic Discovery and Development: The Role of the National Institute of Neurological Disorders and Stroke in Translational Neuroscience.

PubMed

Mott, Meghan; Koroshetz, Walter

2015-07-01

The mission of the National Institute of Neurological Disorders and Stroke (NINDS) is to seek fundamental knowledge about the brain and nervous system and to use that knowledge to reduce the burden of neurological disease. NINDS supports early- and late-stage therapy development funding programs to accelerate preclinical discovery and the development of new therapeutic interventions for neurological disorders. The NINDS Office of Translational Research facilitates and funds the movement of discoveries from the laboratory to patients. Its grantees include academics, often with partnerships with the private sector, as well as small businesses, which, by Congressional mandate, receive > 3% of the NINDS budget for small business innovation research. This article provides an overview of NINDS-funded therapy development programs offered by the NINDS Office of Translational Research.

Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases

PubMed Central

Frijters, Raoul; van Vugt, Marianne; Smeets, Ruben; van Schaik, René; de Vlieg, Jacob; Alkema, Wynand

2010-01-01

The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also well-suited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs. PMID:20885778

Interdisciplinary Laboratory Course Facilitating Knowledge Integration, Mutualistic Teaming, and Original Discovery.

PubMed

Full, Robert J; Dudley, Robert; Koehl, M A R; Libby, Thomas; Schwab, Cheryl

2015-11-01

Experiencing the thrill of an original scientific discovery can be transformative to students unsure about becoming a scientist, yet few courses offer authentic research experiences. Increasingly, cutting-edge discoveries require an interdisciplinary approach not offered in current departmental-based courses. Here, we describe a one-semester, learning laboratory course on organismal biomechanics offered at our large research university that enables interdisciplinary teams of students from biology and engineering to grow intellectually, collaborate effectively, and make original discoveries. To attain this goal, we avoid traditional "cookbook" laboratories by training 20 students to use a dozen research stations. Teams of five students rotate to a new station each week where a professor, graduate student, and/or team member assists in the use of equipment, guides students through stages of critical thinking, encourages interdisciplinary collaboration, and moves them toward authentic discovery. Weekly discussion sections that involve the entire class offer exchange of discipline-specific knowledge, advice on experimental design, methods of collecting and analyzing data, a statistics primer, and best practices for writing and presenting scientific papers. The building of skills in concert with weekly guided inquiry facilitates original discovery via a final research project that can be presented at a national meeting or published in a scientific journal. © The Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

Emergent Low-Symmetry Phases and Large Property Enhancements in Ferroelectric KNbO 3 Bulk Crystals [Emergent Low-Symmetry Phases with Large Property Enhancement in Ferroelectric KNbO 3 Bulk Crystals

DOE PAGES

Lummen, Tom T. A.; Leung, J.; Kumar, Amit; ...

2017-06-19

The design of new or enhanced functionality in materials is traditionally viewed as requiring the discovery of new chemical compositions through synthesis. Large property enhancements may however also be hidden within already well-known materials, when their structural symmetry is deviated from equilibrium through a small local strain or field. Here, the discovery of enhanced material properties associated with a new metastable phase of monoclinic symmetry within bulk KNbO 3 is reported. This phase is found to coexist with the nominal orthorhombic phase at room temperature, and is both induced by and stabilized with local strains generated by a network ofmore » ferroelectric domain walls. While the local microstructural shear strain involved is only ≈0.017%, the concurrent symmetry reduction results in an optical second harmonic generation response that is over 550% higher at room temperature. Moreover, the meandering walls of the low-symmetry domains also exhibit enhanced electrical conductivity on the order of 1 S m -1. In conclusion, this discovery reveals a potential new route to local engineering of significant property enhancements and conductivity through symmetry lowering in ferroelectric crystals.« less

Asymmetric threat data mining and knowledge discovery

NASA Astrophysics Data System (ADS)

Gilmore, John F.; Pagels, Michael A.; Palk, Justin

2001-03-01

Asymmetric threats differ from the conventional force-on- force military encounters that the Defense Department has historically been trained to engage. Terrorism by its nature is now an operational activity that is neither easily detected or countered as its very existence depends on small covert attacks exploiting the element of surprise. But terrorism does have defined forms, motivations, tactics and organizational structure. Exploiting a terrorism taxonomy provides the opportunity to discover and assess knowledge of terrorist operations. This paper describes the Asymmetric Threat Terrorist Assessment, Countering, and Knowledge (ATTACK) system. ATTACK has been developed to (a) data mine open source intelligence (OSINT) information from web-based newspaper sources, video news web casts, and actual terrorist web sites, (b) evaluate this information against a terrorism taxonomy, (c) exploit country/region specific social, economic, political, and religious knowledge, and (d) discover and predict potential terrorist activities and association links. Details of the asymmetric threat structure and the ATTACK system architecture are presented with results of an actual terrorist data mining and knowledge discovery test case shown.

Interoperability between biomedical ontologies through relation expansion, upper-level ontologies and automatic reasoning.

PubMed

Hoehndorf, Robert; Dumontier, Michel; Oellrich, Anika; Rebholz-Schuhmann, Dietrich; Schofield, Paul N; Gkoutos, Georgios V

2011-01-01

Researchers design ontologies as a means to accurately annotate and integrate experimental data across heterogeneous and disparate data- and knowledge bases. Formal ontologies make the semantics of terms and relations explicit such that automated reasoning can be used to verify the consistency of knowledge. However, many biomedical ontologies do not sufficiently formalize the semantics of their relations and are therefore limited with respect to automated reasoning for large scale data integration and knowledge discovery. We describe a method to improve automated reasoning over biomedical ontologies and identify several thousand contradictory class definitions. Our approach aligns terms in biomedical ontologies with foundational classes in a top-level ontology and formalizes composite relations as class expressions. We describe the semi-automated repair of contradictions and demonstrate expressive queries over interoperable ontologies. Our work forms an important cornerstone for data integration, automatic inference and knowledge discovery based on formal representations of knowledge. Our results and analysis software are available at http://bioonto.de/pmwiki.php/Main/ReasonableOntologies.

Conceptual dissonance: evaluating the efficacy of natural language processing techniques for validating translational knowledge constructs.

PubMed

Payne, Philip R O; Kwok, Alan; Dhaval, Rakesh; Borlawsky, Tara B

2009-03-01

The conduct of large-scale translational studies presents significant challenges related to the storage, management and analysis of integrative data sets. Ideally, the application of methodologies such as conceptual knowledge discovery in databases (CKDD) provides a means for moving beyond intuitive hypothesis discovery and testing in such data sets, and towards the high-throughput generation and evaluation of knowledge-anchored relationships between complex bio-molecular and phenotypic variables. However, the induction of such high-throughput hypotheses is non-trivial, and requires correspondingly high-throughput validation methodologies. In this manuscript, we describe an evaluation of the efficacy of a natural language processing-based approach to validating such hypotheses. As part of this evaluation, we will examine a phenomenon that we have labeled as "Conceptual Dissonance" in which conceptual knowledge derived from two or more sources of comparable scope and granularity cannot be readily integrated or compared using conventional methods and automated tools.

Oak Ridge Graph Analytics for Medical Innovation (ORiGAMI)

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

Roberts, Larry W.; Lee, Sangkeun

2016-01-01

In this era of data-driven decisions and discovery where Big Data is producing Bigger Data, data scientists at the Oak Ridge National Laboratory are leveraging unique leadership infrastructure (e.g., Urika XA and Urika GD appliances) to develop scalable algorithms for semantic, logical and statistical reasoning with Big Data (i.e., data stored in databases as well as unstructured data in documents). ORiGAMI is a next-generation knowledge-discovery framework that is: (a) knowledge nurturing (i.e., evolves seamlessly with newer knowledge and data), (b) smart and curious (i.e. using information-foraging and reasoning algorithms to digest content) and (c) synergistic (i.e., interfaces computers with whatmore » they do best to help subject-matter-experts do their best. ORiGAMI has been demonstrated using the National Library of Medicine's SEMANTIC MEDLINE (archive of medical knowledge since 1994).« less

Use of a Drosophila Genome-Wide Conserved Sequence Database to Identify Functionally Related cis-Regulatory Enhancers

PubMed Central

Brody, Thomas; Yavatkar, Amarendra S; Kuzin, Alexander; Kundu, Mukta; Tyson, Leonard J; Ross, Jermaine; Lin, Tzu-Yang; Lee, Chi-Hon; Awasaki, Takeshi; Lee, Tzumin; Odenwald, Ward F

2012-01-01

Background: Phylogenetic footprinting has revealed that cis-regulatory enhancers consist of conserved DNA sequence clusters (CSCs). Currently, there is no systematic approach for enhancer discovery and analysis that takes full-advantage of the sequence information within enhancer CSCs. Results: We have generated a Drosophila genome-wide database of conserved DNA consisting of >100,000 CSCs derived from EvoPrints spanning over 90% of the genome. cis-Decoder database search and alignment algorithms enable the discovery of functionally related enhancers. The program first identifies conserved repeat elements within an input enhancer and then searches the database for CSCs that score highly against the input CSC. Scoring is based on shared repeats as well as uniquely shared matches, and includes measures of the balance of shared elements, a diagnostic that has proven to be useful in predicting cis-regulatory function. To demonstrate the utility of these tools, a temporally-restricted CNS neuroblast enhancer was used to identify other functionally related enhancers and analyze their structural organization. Conclusions: cis-Decoder reveals that co-regulating enhancers consist of combinations of overlapping shared sequence elements, providing insights into the mode of integration of multiple regulating transcription factors. The database and accompanying algorithms should prove useful in the discovery and analysis of enhancers involved in any developmental process. Developmental Dynamics 241:169–189, 2012. © 2011 Wiley Periodicals, Inc. Key findings A genome-wide catalog of Drosophila conserved DNA sequence clusters. cis-Decoder discovers functionally related enhancers. Functionally related enhancers share balanced sequence element copy numbers. Many enhancers function during multiple phases of development. PMID:22174086

Knowledge discovery from data and Monte-Carlo DEA to evaluate technical efficiency of mental health care in small health areas

PubMed Central

García-Alonso, Carlos; Pérez-Naranjo, Leonor

2009-01-01

Introduction Knowledge management, based on information transfer between experts and analysts, is crucial for the validity and usability of data envelopment analysis (DEA). Aim To design and develop a methodology: i) to assess technical efficiency of small health areas (SHA) in an uncertainty environment, and ii) to transfer information between experts and operational models, in both directions, for improving expert’s knowledge. Method A procedure derived from knowledge discovery from data (KDD) is used to select, interpret and weigh DEA inputs and outputs. Based on KDD results, an expert-driven Monte-Carlo DEA model has been designed to assess the technical efficiency of SHA in Andalusia. Results In terms of probability, SHA 29 is the most efficient being, on the contrary, SHA 22 very inefficient. 73% of analysed SHA have a probability of being efficient (Pe) >0.9 and 18% <0.5. Conclusions Expert knowledge is necessary to design and validate any operational model. KDD techniques make the transfer of information from experts to any operational model easy and results obtained from the latter improve expert’s knowledge.

18 CFR 385.403 - Methods of discovery; general provisions (Rule 403).

Code of Federal Regulations, 2010 CFR

2010-04-01

... 18 Conservation of Power and Water Resources 1 2010-04-01 2010-04-01 false Methods of discovery; general provisions (Rule 403). 385.403 Section 385.403 Conservation of Power and Water Resources FEDERAL... the response is true and accurate to the best of that person's knowledge, information, and belief...

Evaluation Techniques for the Sandy Point Discovery Center, Great Bay National Estuarine Research Reserve.

ERIC Educational Resources Information Center

Heffernan, Bernadette M.

1998-01-01

Describes work done to provide staff of the Sandy Point Discovery Center with methods for evaluating exhibits and interpretive programming. Quantitative and qualitative evaluation measures were designed to assess the program's objective of estuary education. Pretest-posttest questionnaires and interviews are used to measure subjects' knowledge and…

The Prehistory of Discovery: Precursors of Representational Change in Solving Gear System Problems.

ERIC Educational Resources Information Center

Dixon, James A.; Bangert, Ashley S.

2002-01-01

This study investigated whether the process of representational change undergoes developmental change or different processes occupy different niches in the course of knowledge acquisition. Subjects--college, third-, and sixth-grade students--solved gear system problems over two sessions. Findings indicated that for all grades, discovery of the…

40 CFR 300.300 - Phase I-Discovery or notification.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 27 2010-07-01 2010-07-01 false Phase I-Discovery or notification. 300.300 Section 300.300 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SUPERFUND... person in charge of a vessel or a facility shall, as soon as he or she has knowledge of any discharge...

Comparative study on drug safety surveillance between medical students of Malaysia and Nigeria.

PubMed

Abubakar, Abdullahi Rabiu; Ismail, Salwani; Rahman, Nor Iza A; Haque, Mainul

2015-01-01

Internationally, there is a remarkable achievement in the areas of drug discovery, drug design, and clinical trials. New and efficient drug formulation techniques are widely available which have led to success in treatment of several diseases. Despite these achievements, large number of patients continue to experience adverse drug reactions (ADRs), and majority of them are yet to be on record. The purpose of this survey is to compare knowledge, attitude, and practice with respect to ADRs and pharmacovigilance (PV) between medical students of Malaysia and Nigeria and to determine if there is a relationship between their knowledge and practice. A cross-sectional, questionnaire-based survey involving year IV and year V medical students of the Department of Medicine, Universiti Sultan Zainal Abidin and Bayero University Kano was carried out. The questionnaire which comprised 25 questions on knowledge, attitude, and practice was adopted, modified, validated, and administered to them. The response was analyzed using SPSS version 20. The response rate from each country was 74%. There was a statistically significant difference in mean knowledge and practice score on ADRs and PV between medical students of Malaysia and Nigeria, both at P<0.000. No significance difference in attitude was observed at P=0.389. Also, a statistically significant relationship was recorded between their knowledge and practice (r=0.229, P=0.001), although the relationship was weak. Nigerian medical students have better knowledge and practice than those of Malaysia, although they need improvement. Imparting knowledge of ADRs and PV among medical students will upgrade their practice and enhance health care delivery services in the future.

Serendipity: Accidental Discoveries in Science

NASA Astrophysics Data System (ADS)

Roberts, Royston M.

1989-06-01

Many of the things discovered by accident are important in our everyday lives: Teflon, Velcro, nylon, x-rays, penicillin, safety glass, sugar substitutes, and polyethylene and other plastics. And we owe a debt to accident for some of our deepest scientific knowledge, including Newton's theory of gravitation, the Big Bang theory of Creation, and the discovery of DNA. Even the Rosetta Stone, the Dead Sea Scrolls, and the ruins of Pompeii came to light through chance. This book tells the fascinating stories of these and other discoveries and reveals how the inquisitive human mind turns accident into discovery. Written for the layman, yet scientifically accurate, this illuminating collection of anecdotes portrays invention and discovery as quintessentially human acts, due in part to curiosity, perserverance, and luck.

Closed-Loop Multitarget Optimization for Discovery of New Emulsion Polymerization Recipes

PubMed Central

2015-01-01

Self-optimization of chemical reactions enables faster optimization of reaction conditions or discovery of molecules with required target properties. The technology of self-optimization has been expanded to discovery of new process recipes for manufacture of complex functional products. A new machine-learning algorithm, specifically designed for multiobjective target optimization with an explicit aim to minimize the number of “expensive” experiments, guides the discovery process. This “black-box” approach assumes no a priori knowledge of chemical system and hence particularly suited to rapid development of processes to manufacture specialist low-volume, high-value products. The approach was demonstrated in discovery of process recipes for a semibatch emulsion copolymerization, targeting a specific particle size and full conversion. PMID:26435638

Key aspects of the Novartis compound collection enhancement project for the compilation of a comprehensive chemogenomics drug discovery screening collection.

PubMed

Jacoby, Edgar; Schuffenhauer, Ansgar; Popov, Maxim; Azzaoui, Kamal; Havill, Benjamin; Schopfer, Ulrich; Engeloch, Caroline; Stanek, Jaroslav; Acklin, Pierre; Rigollier, Pascal; Stoll, Friederike; Koch, Guido; Meier, Peter; Orain, David; Giger, Rudolph; Hinrichs, Jürgen; Malagu, Karine; Zimmermann, Jürg; Roth, Hans-Joerg

2005-01-01

The NIBR (Novartis Institutes for BioMedical Research) compound collection enrichment and enhancement project integrates corporate internal combinatorial compound synthesis and external compound acquisition activities in order to build up a comprehensive screening collection for a modern drug discovery organization. The main purpose of the screening collection is to supply the Novartis drug discovery pipeline with hit-to-lead compounds for today's and the future's portfolio of drug discovery programs, and to provide tool compounds for the chemogenomics investigation of novel biological pathways and circuits. As such, it integrates designed focused and diversity-based compound sets from the synthetic and natural paradigms able to cope with druggable and currently deemed undruggable targets and molecular interaction modes. Herein, we will summarize together with new trends published in the literature, scientific challenges faced and key approaches taken at NIBR to match the chemical and biological spaces.

From the EBM pyramid to the Greek temple: a new conceptual approach to Guidelines as implementation tools in mental health.

PubMed

Salvador-Carulla, L; Lukersmith, S; Sullivan, W

2017-04-01

Guideline methods to develop recommendations dedicate most effort around organising discovery and corroboration knowledge following the evidence-based medicine (EBM) framework. Guidelines typically use a single dimension of information, and generally discard contextual evidence and formal expert knowledge and consumer's experiences in the process. In recognition of the limitations of guidelines in complex cases, complex interventions and systems research, there has been significant effort to develop new tools, guides, resources and structures to use alongside EBM methods of guideline development. In addition to these advances, a new framework based on the philosophy of science is required. Guidelines should be defined as implementation decision support tools for improving the decision-making process in real-world practice and not only as a procedure to optimise the knowledge base of scientific discovery and corroboration. A shift from the model of the EBM pyramid of corroboration of evidence to the use of broader multi-domain perspective graphically depicted as 'Greek temple' could be considered. This model takes into account the different stages of scientific knowledge (discovery, corroboration and implementation), the sources of knowledge relevant to guideline development (experimental, observational, contextual, expert-based and experiential); their underlying inference mechanisms (deduction, induction, abduction, means-end inferences) and a more precise definition of evidence and related terms. The applicability of this broader approach is presented for the development of the Canadian Consensus Guidelines for the Primary Care of People with Developmental Disabilities.

Applying data mining techniques to medical time series: an empirical case study in electroencephalography and stabilometry.

PubMed

Anguera, A; Barreiro, J M; Lara, J A; Lizcano, D

2016-01-01

One of the major challenges in the medical domain today is how to exploit the huge amount of data that this field generates. To do this, approaches are required that are capable of discovering knowledge that is useful for decision making in the medical field. Time series are data types that are common in the medical domain and require specialized analysis techniques and tools, especially if the information of interest to specialists is concentrated within particular time series regions, known as events. This research followed the steps specified by the so-called knowledge discovery in databases (KDD) process to discover knowledge from medical time series derived from stabilometric (396 series) and electroencephalographic (200) patient electronic health records (EHR). The view offered in the paper is based on the experience gathered as part of the VIIP project. Knowledge discovery in medical time series has a number of difficulties and implications that are highlighted by illustrating the application of several techniques that cover the entire KDD process through two case studies. This paper illustrates the application of different knowledge discovery techniques for the purposes of classification within the above domains. The accuracy of this application for the two classes considered in each case is 99.86% and 98.11% for epilepsy diagnosis in the electroencephalography (EEG) domain and 99.4% and 99.1% for early-age sports talent classification in the stabilometry domain. The KDD techniques achieve better results than other traditional neural network-based classification techniques.

Teaching Evidence-Based Approaches to Suicide Risk Assessment and Prevention that Enhance Psychiatric Training

PubMed Central

Zisook, Sidney; Anzia, Joan; Atri, Ashutosh; Baroni, Argelinda; Clayton, Paula; Haller, Ellen; Lomax, Jim; Mann, J. John; Oquendo, Maria A.; Pato, Michele; Perez-Rodriguez, M. Mercedes; Prabhakar, Deepak; Sen, Srijan; Thrall, Grace; Yaseen, Zimri S.

2012-01-01

This report describes one in a series of National Institute of Health (NIH) supported conferences aimed at enhancing the ability of leaders of psychiatry residency training to teach research literacy and produce both clinician-scholars and physician-scientists in their home programs. Most psychiatry training directors would not consider themselves research scholars or even well-schooled in evidence based practice. Yet they are the front line educators to prepare tomorrow’s psychiatrists to keep up with, critically evaluate, and in some cases actually participate in the discovery of new and emerging psychiatric knowledge. This annual conference is meant to help psychiatry training directors become more enthusiastic, knowledgeable and pedagogically prepared to create research-friendly environments at their home institutions, so that more trainees will, in turn, become research literate, practice evidence-based psychiatry, and enter research fellowships and careers. The overall design of each year’s meeting is a series of plenary sessions introducing participants to new information pertaining to the core theme of that year’s meeting, integrated with highly interactive small group teaching sessions designed to consolidate knowledge and provide pragmatic teaching tools appropriate for residents at various levels of training. The theme of each meeting, selected to be a compelling and contemporary clinical problem, serves as a vehicle to capture training directors’ attention while teaching relevant brain science, research literacy and effective pedagogy. This report describes the content and assessment of the 2011 annual pre-meeting, “Evidence-based Approaches to Suicide Risk Assessment and Prevention: Insights from the Neurosciences and Behavioral Sciences for use in Psychiatry Residency Training.” PMID:22995449

Equation Discovery for Model Identification in Respiratory Mechanics of the Mechanically Ventilated Human Lung

NASA Astrophysics Data System (ADS)

Ganzert, Steven; Guttmann, Josef; Steinmann, Daniel; Kramer, Stefan

Lung protective ventilation strategies reduce the risk of ventilator associated lung injury. To develop such strategies, knowledge about mechanical properties of the mechanically ventilated human lung is essential. This study was designed to develop an equation discovery system to identify mathematical models of the respiratory system in time-series data obtained from mechanically ventilated patients. Two techniques were combined: (i) the usage of declarative bias to reduce search space complexity and inherently providing the processing of background knowledge. (ii) A newly developed heuristic for traversing the hypothesis space with a greedy, randomized strategy analogical to the GSAT algorithm. In 96.8% of all runs the applied equation discovery system was capable to detect the well-established equation of motion model of the respiratory system in the provided data. We see the potential of this semi-automatic approach to detect more complex mathematical descriptions of the respiratory system from respiratory data.

Antisense oligonucleotide technologies in drug discovery.

PubMed

Aboul-Fadl, Tarek

2006-09-01

The principle of antisense oligonucleotide (AS-OD) technologies is based on the specific inhibition of unwanted gene expression by blocking mRNA activity. It has long appeared to be an ideal strategy to leverage new genomic knowledge for drug discovery and development. In recent years, AS-OD technologies have been widely used as potent and promising tools for this purpose. There is a rapid increase in the number of antisense molecules progressing in clinical trials. AS-OD technologies provide a simple and efficient approach for drug discovery and development and are expected to become a reality in the near future. This editorial describes the established and emerging AS-OD technologies in drug discovery.

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.

State of the Art: Response Assessment in Lung Cancer in the Era of Genomic Medicine

PubMed Central

Hatabu, Hiroto; Johnson, Bruce E.; McLoud, Theresa C.

2014-01-01

Tumor response assessment has been a foundation for advances in cancer therapy. Recent discoveries of effective targeted therapy for specific genomic abnormalities in lung cancer and their clinical application have brought revolutionary advances in lung cancer therapy and transformed the oncologist’s approach to patients with lung cancer. Because imaging is a major method of response assessment in lung cancer both in clinical trials and practice, radiologists must understand the genomic alterations in lung cancer and the rapidly evolving therapeutic approaches to effectively communicate with oncology colleagues and maintain the key role in lung cancer care. This article describes the origin and importance of tumor response assessment, presents the recent genomic discoveries in lung cancer and therapies directed against these genomic changes, and describes how these discoveries affect the radiology community. The authors then summarize the conventional Response Evaluation Criteria in Solid Tumors and World Health Organization guidelines, which continue to be the major determinants of trial endpoints, and describe their limitations particularly in an era of genomic-based therapy. More advanced imaging techniques for lung cancer response assessment are presented, including computed tomography tumor volume and perfusion, dynamic contrast material–enhanced and diffusion-weighted magnetic resonance imaging, and positron emission tomography with fluorine 18 fluorodeoxyglucose and novel tracers. State-of-art knowledge of lung cancer biology, treatment, and imaging will help the radiology community to remain effective contributors to the personalized care of lung cancer patients. © RSNA, 2014 PMID:24661292

A network model of knowledge accumulation through diffusion and upgrade

NASA Astrophysics Data System (ADS)

Zhuang, Enyu; Chen, Guanrong; Feng, Gang

2011-07-01

In this paper, we introduce a model to describe knowledge accumulation through knowledge diffusion and knowledge upgrade in a multi-agent network. Here, knowledge diffusion refers to the distribution of existing knowledge in the network, while knowledge upgrade means the discovery of new knowledge. It is found that the population of the network and the number of each agent’s neighbors affect the speed of knowledge accumulation. Four different policies for updating the neighboring agents are thus proposed, and their influence on the speed of knowledge accumulation and the topology evolution of the network are also studied.

Interfaith Education: An Islamic Perspective

ERIC Educational Resources Information Center

Pallavicini, Yahya Sergio Yahe

2016-01-01

According to a teaching of the Prophet Muhammad, "the quest for knowledge is the duty of each Muslim, male or female", where knowledge is meant as the discovery of the real value of things and of oneself in relationship with the world in which God has placed us. This universal dimension of knowledge is in fact a wealth of wisdom of the…

Improving drug safety: From adverse drug reaction knowledge discovery to clinical implementation.

PubMed

Tan, Yuxiang; Hu, Yong; Liu, Xiaoxiao; Yin, Zhinan; Chen, Xue-Wen; Liu, Mei

2016-11-01

Adverse drug reactions (ADRs) are a major public health concern, causing over 100,000 fatalities in the United States every year with an annual cost of $136 billion. Early detection and accurate prediction of ADRs is thus vital for drug development and patient safety. Multiple scientific disciplines, namely pharmacology, pharmacovigilance, and pharmacoinformatics, have been addressing the ADR problem from different perspectives. With the same goal of improving drug safety, this article summarizes and links the research efforts in the multiple disciplines into a single framework from comprehensive understanding of the interactions between drugs and biological system and the identification of genetic and phenotypic predispositions of patients susceptible to higher ADR risks and finally to the current state of implementation of medication-related decision support systems. We start by describing available computational resources for building drug-target interaction networks with biological annotations, which provides a fundamental knowledge for ADR prediction. Databases are classified by functions to help users in selection. Post-marketing surveillance is then introduced where data-driven approach can not only enhance the prediction accuracy of ADRs but also enables the discovery of genetic and phenotypic risk factors of ADRs. Understanding genetic risk factors for ADR requires well organized patient genetics information and analysis by pharmacogenomic approaches. Finally, current state of clinical decision support systems is presented and described how clinicians can be assisted with the integrated knowledgebase to minimize the risk of ADR. This review ends with a discussion of existing challenges in each of disciplines with potential solutions and future directions. Copyright © 2016 Elsevier Inc. All rights reserved.

77 FR 75459 - Self-Regulatory Organizations; BATS Exchange, Inc.; Notice of Filing of a Proposed Rule Change To...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-20

... both sides would participate in an Exchange Auction, this proposed change would aid in price discovery... auction price. This proposed change would aid in price discovery and help to reduce the likelihood of... Sell Shares and, therefore, a User would never have complete knowledge of liquidity available on both...

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,…

Revealing Significant Relations between Chemical/Biological Features and Activity: Associative Classification Mining for Drug Discovery

ERIC Educational Resources Information Center

Yu, Pulan

2012-01-01

Classification, clustering and association mining are major tasks of data mining and have been widely used for knowledge discovery. Associative classification mining, the combination of both association rule mining and classification, has emerged as an indispensable way to support decision making and scientific research. In particular, it offers a…

Mothers' Initial Discovery of Childhood Disability: Exploring Maternal Identification of Developmental Issues in Young Children

ERIC Educational Resources Information Center

Silbersack, Elionora W.

2014-01-01

The purpose of this qualitative study was to expand the scarce information available on how mothers first observe their children's early development, assess potential problems, and then come to recognize their concerns. In-depth knowledge about mothers' perspectives on the discovery process can help social workers to promote identification of…

76 FR 36320 - Rules of Practice in Proceedings Relative to False Representation and Lottery Orders

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-22

... officers. 952.18 Evidence. 952.19 Subpoenas. 952.20 Witness fees. 952.21 Discovery. 952.22 Transcript. 952..., motions, proposed orders, and other documents for the record. Discovery need not be filed except as may be... witnesses, that the statement correctly states the witness's opinion or knowledge concerning the matters in...

Making the Long Tail Visible: Social Networking Sites and Independent Music Discovery

ERIC Educational Resources Information Center

Gaffney, Michael; Rafferty, Pauline

2009-01-01

Purpose: The purpose of this paper is to investigate users' knowledge and use of social networking sites and folksonomies to discover if social tagging and folksonomies, within the area of independent music, aid in its information retrieval and discovery. The sites examined in this project are MySpace, Lastfm, Pandora and Allmusic. In addition,…

Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data

PubMed Central

2016-01-01

Observations of individual organisms (data) can be combined with expert ecological knowledge of species, especially causal knowledge, to model and extract from flower–visiting data useful information about behavioral interactions between insect and plant organisms, such as nectar foraging and pollen transfer. We describe and evaluate a method to elicit and represent such expert causal knowledge of behavioral ecology, and discuss the potential for wider application of this method to the design of knowledge-based systems for knowledge discovery in biodiversity and ecosystem informatics. PMID:27851814

Discovery learning model with geogebra assisted for improvement mathematical visual thinking ability

NASA Astrophysics Data System (ADS)

Juandi, D.; Priatna, N.

2018-05-01

The main goal of this study is to improve the mathematical visual thinking ability of high school student through implementation the Discovery Learning Model with Geogebra Assisted. This objective can be achieved through study used quasi-experimental method, with non-random pretest-posttest control design. The sample subject of this research consist of 62 senior school student grade XI in one of school in Bandung district. The required data will be collected through documentation, observation, written tests, interviews, daily journals, and student worksheets. The results of this study are: 1) Improvement students Mathematical Visual Thinking Ability who obtain learning with applied the Discovery Learning Model with Geogebra assisted is significantly higher than students who obtain conventional learning; 2) There is a difference in the improvement of students’ Mathematical Visual Thinking ability between groups based on prior knowledge mathematical abilities (high, medium, and low) who obtained the treatment. 3) The Mathematical Visual Thinking Ability improvement of the high group is significantly higher than in the medium and low groups. 4) The quality of improvement ability of high and low prior knowledge is moderate category, in while the quality of improvement ability in the high category achieved by student with medium prior knowledge.

Systematic identification of latent disease-gene associations from PubMed articles.

PubMed

Zhang, Yuji; Shen, Feichen; Mojarad, Majid Rastegar; Li, Dingcheng; Liu, Sijia; Tao, Cui; Yu, Yue; Liu, Hongfang

2018-01-01

Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research.

No wisdom in the crowd: genome annotation in the era of big data - current status and future prospects.

PubMed

Danchin, Antoine; Ouzounis, Christos; Tokuyasu, Taku; Zucker, Jean-Daniel

2018-07-01

Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from 'the sequence tells the structure tells the function' fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader. © 2018 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

Systematic identification of latent disease-gene associations from PubMed articles

PubMed Central

Mojarad, Majid Rastegar; Li, Dingcheng; Liu, Sijia; Tao, Cui; Yu, Yue; Liu, Hongfang

2018-01-01

Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research. PMID:29373609

Temporal data mining for the quality assessment of hemodialysis services.

PubMed

Bellazzi, Riccardo; Larizza, Cristiana; Magni, Paolo; Bellazzi, Roberto

2005-05-01

This paper describes the temporal data mining aspects of a research project that deals with the definition of methods and tools for the assessment of the clinical performance of hemodialysis (HD) services, on the basis of the time series automatically collected during hemodialysis sessions. Intelligent data analysis and temporal data mining techniques are applied to gain insight and to discover knowledge on the causes of unsatisfactory clinical results. In particular, two new methods for association rule discovery and temporal rule discovery are applied to the time series. Such methods exploit several pre-processing techniques, comprising data reduction, multi-scale filtering and temporal abstractions. We have analyzed the data of more than 5800 dialysis sessions coming from 43 different patients monitored for 19 months. The qualitative rules associating the outcome parameters and the measured variables were examined by the domain experts, which were able to distinguish between rules confirming available background knowledge and unexpected but plausible rules. The new methods proposed in the paper are suitable tools for knowledge discovery in clinical time series. Their use in the context of an auditing system for dialysis management helped clinicians to improve their understanding of the patients' behavior.

Protein crystallography and drug discovery: recollections of knowledge exchange between academia and industry

PubMed Central

2017-01-01

The development of structure-guided drug discovery is a story of knowledge exchange where new ideas originate from all parts of the research ecosystem. Dorothy Crowfoot Hodgkin obtained insulin from Boots Pure Drug Company in the 1930s and insulin crystallization was optimized in the company Novo in the 1950s, allowing the structure to be determined at Oxford University. The structure of renin was developed in academia, on this occasion in London, in response to a need to develop antihypertensives in pharma. The idea of a dimeric aspartic protease came from an international academic team and was discovered in HIV; it eventually led to new HIV antivirals being developed in industry. Structure-guided fragment-based discovery was developed in large pharma and biotechs, but has been exploited in academia for the development of new inhibitors targeting protein–protein interactions and also antimicrobials to combat mycobacterial infections such as tuberculosis. These observations provide a strong argument against the so-called ‘linear model’, where ideas flow only in one direction from academic institutions to industry. Structure-guided drug discovery is a story of applications of protein crystallography and knowledge exhange between academia and industry that has led to new drug approvals for cancer and other common medical conditions by the Food and Drug Administration in the USA, as well as hope for the treatment of rare genetic diseases and infectious diseases that are a particular challenge in the developing world. PMID:28875019

Choosing experiments to accelerate collective discovery

PubMed Central

Rzhetsky, Andrey; Foster, Jacob G.; Foster, Ian T.

2015-01-01

A scientist’s choice of research problem affects his or her personal career trajectory. Scientists’ combined choices affect the direction and efficiency of scientific discovery as a whole. In this paper, we infer preferences that shape problem selection from patterns of published findings and then quantify their efficiency. We represent research problems as links between scientific entities in a knowledge network. We then build a generative model of discovery informed by qualitative research on scientific problem selection. We map salient features from this literature to key network properties: an entity’s importance corresponds to its degree centrality, and a problem’s difficulty corresponds to the network distance it spans. Drawing on millions of papers and patents published over 30 years, we use this model to infer the typical research strategy used to explore chemical relationships in biomedicine. This strategy generates conservative research choices focused on building up knowledge around important molecules. These choices become more conservative over time. The observed strategy is efficient for initial exploration of the network and supports scientific careers that require steady output, but is inefficient for science as a whole. Through supercomputer experiments on a sample of the network, we study thousands of alternatives and identify strategies much more efficient at exploring mature knowledge networks. We find that increased risk-taking and the publication of experimental failures would substantially improve the speed of discovery. We consider institutional shifts in grant making, evaluation, and publication that would help realize these efficiencies. PMID:26554009

Knowledge Discovery, Integration and Communication for Extreme Weather and Flood Resilience Using Artificial Intelligence: Flood AI Alpha

NASA Astrophysics Data System (ADS)

Demir, I.; Sermet, M. Y.

2016-12-01

Nobody is immune from extreme events or natural hazards that can lead to large-scale consequences for the nation and public. One of the solutions to reduce the impacts of extreme events is to invest in improving resilience with the ability to better prepare, plan, recover, and adapt to disasters. The National Research Council (NRC) report discusses the topic of how to increase resilience to extreme events through a vision of resilient nation in the year 2030. The report highlights the importance of data, information, gaps and knowledge challenges that needs to be addressed, and suggests every individual to access the risk and vulnerability information to make their communities more resilient. This abstracts presents our project on developing a resilience framework for flooding to improve societal preparedness with objectives; (a) develop a generalized ontology for extreme events with primary focus on flooding; (b) develop a knowledge engine with voice recognition, artificial intelligence, natural language processing, and inference engine. The knowledge engine will utilize the flood ontology and concepts to connect user input to relevant knowledge discovery outputs on flooding; (c) develop a data acquisition and processing framework from existing environmental observations, forecast models, and social networks. The system will utilize the framework, capabilities and user base of the Iowa Flood Information System (IFIS) to populate and test the system; (d) develop a communication framework to support user interaction and delivery of information to users. The interaction and delivery channels will include voice and text input via web-based system (e.g. IFIS), agent-based bots (e.g. Microsoft Skype, Facebook Messenger), smartphone and augmented reality applications (e.g. smart assistant), and automated web workflows (e.g. IFTTT, CloudWork) to open the knowledge discovery for flooding to thousands of community extensible web workflows.

Understanding drug targets: no such thing as bad news.

PubMed

Roberts, Ruth A

2018-05-24

How can small-to-medium pharma and biotech companies enhance the chances of running a successful drug project and maximise the return on a limited number of assets? Having a full appreciation of the safety risks associated with proposed drug targets is a crucial element in understanding the unwanted side-effects that might stop a project in its tracks. Having this information is necessary to complement knowledge about the probable efficacy of a future drug. However, the lack of data-rich insight into drug-target safety is one of the major causes of drug-project failure today. Conducting comprehensive target-safety reviews early in the drug discovery process enables project teams to make the right decisions about which drug targets to take forward. Copyright © 2018 Elsevier Ltd. All rights reserved.

Revealing chemical processes and kinetics of drug action within single living cells via plasmonic Raman probes.

PubMed

Li, Shan-Shan; Guan, Qi-Yuan; Meng, Gang; Chang, Xiao-Feng; Wei, Ji-Wu; Wang, Peng; Kang, Bin; Xu, Jing-Juan; Chen, Hong-Yuan

2017-05-23

Better understanding the drug action within cells may extend our knowledge on drug action mechanisms and promote new drugs discovery. Herein, we studied the processes of drug induced chemical changes on proteins and nucleic acids in human breast adenocarcinoma (MCF-7) cells via time-resolved plasmonic-enhanced Raman spectroscopy (PERS) in combination with principal component analysis (PCA). Using three popular chemotherapy drugs (fluorouracil, cisplatin and camptothecin) as models, chemical changes during drug action process were clearly discriminated. Reaction kinetics related to protein denaturation, conformational modification, DNA damage and their associated biomolecular events were calculated. Through rate constants and reaction delay times, the different action modes of these drugs could be distinguished. These results may provide vital insights into understanding the chemical reactions associated with drug-cell interactions.

Improved Access to NSF Funded Ocean Research Data

NASA Astrophysics Data System (ADS)

Chandler, C. L.; Groman, R. C.; Kinkade, D.; Shepherd, A.; Rauch, S.; Allison, M. D.; Gegg, S. R.; Wiebe, P. H.; Glover, D. M.

2015-12-01

Data from NSF-funded, hypothesis-driven research comprise an essential part of the research results upon which we base our knowledge and improved understanding of the impacts of climate change. Initially funded in 2006, the Biological and Chemical Oceanography Data Management Office (BCO-DMO) works with marine scientists to ensure that data from NSF-funded ocean research programs are fully documented and freely available for future use. BCO-DMO works in partnership with information technology professionals, other marine data repositories and national data archive centers to ensure long-term preservation of these valuable environmental research data. Data contributed to BCO-DMO by the original investigators are enhanced with sufficient discipline-specific documentation and published in a variety of standards-compliant forms designed to enable discovery and support accurate re-use.

Bioanalysis-related highlights from the 2011 AAPS National Biotechnology Conference.

PubMed

Crisino, Rebecca M; Dulanto, Beatriz

2011-08-01

The American Association of Pharmaceutical Scientists is a dynamic international forum for the exchange of knowledge among scientists to enhance their contributions to drug development. The annual National Biotechnology Conference, conducted and organized by the American Association of Pharmaceutical Scientists, is a forum dedicated to advancements in science and technology related to discovery, development and manufacture of medical biotechnology products. The 2011 National Biotechnology Conference meeting convened in San Francisco, CA, USA on 16-18 May. Over 300 abstracts were submitted and approximately 50 sessions examined topics pertaining to advances in drug development, emerging analytical technologies, bioanalysis-related issues, biosimilar therapies, updates on global regulatory documents and expectations, and other topics. The focus of this article is to highlight key developments relevant to immunogenicity and pharmacokinetic drug concentration bioanalysis.

Assessing the Diversity of Rodent-Borne Viruses: Exploring of High-Throughput Sequencing and Classical Amplification/Sequencing Approaches.

PubMed

Drewes, Stephan; Straková, Petra; Drexler, Jan F; Jacob, Jens; Ulrich, Rainer G

2017-01-01

Rodents are distributed throughout the world and interact with humans in many ways. They provide vital ecosystem services, some species are useful models in biomedical research and some are held as pet animals. However, many rodent species can have adverse effects such as damage to crops and stored produce, and they are of health concern because of the transmission of pathogens to humans and livestock. The first rodent viruses were discovered by isolation approaches and resulted in break-through knowledge in immunology, molecular and cell biology, and cancer research. In addition to rodent-specific viruses, rodent-borne viruses are causing a large number of zoonotic diseases. Most prominent examples are reemerging outbreaks of human hemorrhagic fever disease cases caused by arena- and hantaviruses. In addition, rodents are reservoirs for vector-borne pathogens, such as tick-borne encephalitis virus and Borrelia spp., and may carry human pathogenic agents, but likely are not involved in their transmission to human. In our days, next-generation sequencing or high-throughput sequencing (HTS) is revolutionizing the speed of the discovery of novel viruses, but other molecular approaches, such as generic RT-PCR/PCR and rolling circle amplification techniques, contribute significantly to the rapidly ongoing process. However, the current knowledge still represents only the tip of the iceberg, when comparing the known human viruses to those known for rodents, the mammalian taxon with the largest species number. The diagnostic potential of HTS-based metagenomic approaches is illustrated by their use in the discovery and complete genome determination of novel borna- and adenoviruses as causative disease agents in squirrels. In conclusion, HTS, in combination with conventional RT-PCR/PCR-based approaches, resulted in a drastically increased knowledge of the diversity of rodent viruses. Future improvements of the used workflows, including bioinformatics analysis, will further enhance our knowledge and preparedness in case of the emergence of novel viruses. Classical virological and additional molecular approaches are needed for genome annotation and functional characterization of novel viruses, discovered by these technologies, and evaluation of their zoonotic potential. © 2017 Elsevier Inc. All rights reserved.

Applying knowledge-anchored hypothesis discovery methods to advance clinical and translational research: the OAMiner project

PubMed Central

Jackson, Rebecca D; Best, Thomas M; Borlawsky, Tara B; Lai, Albert M; James, Stephen; Gurcan, Metin N

2012-01-01

The conduct of clinical and translational research regularly involves the use of a variety of heterogeneous and large-scale data resources. Scalable methods for the integrative analysis of such resources, particularly when attempting to leverage computable domain knowledge in order to generate actionable hypotheses in a high-throughput manner, remain an open area of research. In this report, we describe both a generalizable design pattern for such integrative knowledge-anchored hypothesis discovery operations and our experience in applying that design pattern in the experimental context of a set of driving research questions related to the publicly available Osteoarthritis Initiative data repository. We believe that this ‘test bed’ project and the lessons learned during its execution are both generalizable and representative of common clinical and translational research paradigms. PMID:22647689

Integrative Sparse K-Means With Overlapping Group Lasso in Genomic Applications for Disease Subtype Discovery

PubMed Central

Huo, Zhiguang; Tseng, George

2017-01-01

Cancer subtypes discovery is the first step to deliver personalized medicine to cancer patients. With the accumulation of massive multi-level omics datasets and established biological knowledge databases, omics data integration with incorporation of rich existing biological knowledge is essential for deciphering a biological mechanism behind the complex diseases. In this manuscript, we propose an integrative sparse K-means (is-K means) approach to discover disease subtypes with the guidance of prior biological knowledge via sparse overlapping group lasso. An algorithm using an alternating direction method of multiplier (ADMM) will be applied for fast optimization. Simulation and three real applications in breast cancer and leukemia will be used to compare is-K means with existing methods and demonstrate its superior clustering accuracy, feature selection, functional annotation of detected molecular features and computing efficiency. PMID:28959370

Integrative Sparse K-Means With Overlapping Group Lasso in Genomic Applications for Disease Subtype Discovery.

PubMed

Huo, Zhiguang; Tseng, George

2017-06-01

Cancer subtypes discovery is the first step to deliver personalized medicine to cancer patients. With the accumulation of massive multi-level omics datasets and established biological knowledge databases, omics data integration with incorporation of rich existing biological knowledge is essential for deciphering a biological mechanism behind the complex diseases. In this manuscript, we propose an integrative sparse K -means (is- K means) approach to discover disease subtypes with the guidance of prior biological knowledge via sparse overlapping group lasso. An algorithm using an alternating direction method of multiplier (ADMM) will be applied for fast optimization. Simulation and three real applications in breast cancer and leukemia will be used to compare is- K means with existing methods and demonstrate its superior clustering accuracy, feature selection, functional annotation of detected molecular features and computing efficiency.

How can knowledge discovery methods uncover spatio-temporal patterns in environmental data?

NASA Astrophysics Data System (ADS)

Wachowicz, Monica

2000-04-01

This paper proposes the integration of KDD, GVis and STDB as a long-term strategy, which will allow users to apply knowledge discovery methods for uncovering spatio-temporal patterns in environmental data. The main goal is to combine innovative techniques and associated tools for exploring very large environmental data sets in order to arrive at valid, novel, potentially useful, and ultimately understandable spatio-temporal patterns. The GeoInsight approach is described using the principles and key developments in the research domains of KDD, GVis, and STDB. The GeoInsight approach aims at the integration of these research domains in order to provide tools for performing information retrieval, exploration, analysis, and visualization. The result is a knowledge-based design, which involves visual thinking (perceptual-cognitive process) and automated information processing (computer-analytical process).

A Semantic Lexicon-Based Approach for Sense Disambiguation and Its WWW Application

NASA Astrophysics Data System (ADS)

di Lecce, Vincenzo; Calabrese, Marco; Soldo, Domenico

This work proposes a basic framework for resolving sense disambiguation through the use of Semantic Lexicon, a machine readable dictionary managing both word senses and lexico-semantic relations. More specifically, polysemous ambiguity characterizing Web documents is discussed. The adopted Semantic Lexicon is WordNet, a lexical knowledge-base of English words widely adopted in many research studies referring to knowledge discovery. The proposed approach extends recent works on knowledge discovery by focusing on the sense disambiguation aspect. By exploiting the structure of WordNet database, lexico-semantic features are used to resolve the inherent sense ambiguity of written text with particular reference to HTML resources. The obtained results may be extended to generic hypertextual repositories as well. Experiments show that polysemy reduction can be used to hint about the meaning of specific senses in given contexts.

Development of National Map ontologies for organization and orchestration of hydrologic observations

NASA Astrophysics Data System (ADS)

Lieberman, J. E.

2014-12-01

Feature layers in the National Map program (TNM) are a fundamental context for much of the data collection and analysis conducted by the USGS and other governmental and nongovernmental organizations. Their computational usefulness, though, has been constrained by the lack of formal relationships besides superposition between TNM layers, as well as limited means of representing how TNM datasets relate to additional attributes, datasets, and activities. In the field of Geospatial Information Science, there has been a growing recognition of the value of semantic representation and technology for addressing these limitations, particularly in the face of burgeoning information volume and heterogeneity. Fundamental to this approach is the development of formal ontologies for concepts related to that information that can be processed computationally to enhance creation and discovery of new geospatial knowledge. They offer a means of making much of the presently innate knowledge about relationships in and between TNM features accessible for machine processing and distributed computation.A full and comprehensive ontology of all knowledge represented by TNM features is still impractical. The work reported here involves elaboration and integration of a number of small ontology design patterns (ODP's) that represent limited, discrete, but commonly accepted and broadly applicable physical theories for the behavior of TNM features representing surface water bodies and landscape surfaces and the connections between them. These ontology components are validated through use in applications for discovery and aggregation of water science observational data associated with National Hydrography Data features, features from the National Elevation Dataset (NED) and Water Boundary Dataset (WBD) that constrain water occurrence in the continental US. These applications emphasize workflows which are difficult or impossible to automate using existing data structures. Evaluation of the usefulness of the developed ontology components includes both solicitation of feedback on prototype applications, and provision of a query / mediation service for feature-linked data to facilitate development of additional third-party applications.

State of the Art in Tumor Antigen and Biomarker Discovery

PubMed Central

Even-Desrumeaux, Klervi; Baty, Daniel; Chames, Patrick

2011-01-01

Our knowledge of tumor immunology has resulted in multiple approaches for the treatment of cancer. However, a gap between research of new tumors markers and development of immunotherapy has been established and very few markers exist that can be used for treatment. The challenge is now to discover new targets for active and passive immunotherapy. This review aims at describing recent advances in biomarkers and tumor antigen discovery in terms of antigen nature and localization, and is highlighting the most recent approaches used for their discovery including “omics” technology. PMID:24212823

Discovering and Articulating What Is Not yet Known: Using Action Learning and Grounded Theory as a Knowledge Management Strategy

ERIC Educational Resources Information Center

Pauleen, David J.; Corbitt, Brian; Yoong, Pak

2007-01-01

Purpose: To provide a conceptual model for the discovery and articulation of emergent organizational knowledge, particularly knowledge that develops when people work with new technologies. Design/methodology/approach: The model is based on two widely accepted research methods--action learning and grounded theory--and is illustrated using a case…

Cache-Cache Comparison for Supporting Meaningful Learning

ERIC Educational Resources Information Center

Wang, Jingyun; Fujino, Seiji

2015-01-01

The paper presents a meaningful discovery learning environment called "cache-cache comparison" for a personalized learning support system. The processing of seeking hidden relations or concepts in "cache-cache comparison" is intended to encourage learners to actively locate new knowledge in their knowledge framework and check…

From Wisdom to Innocence: Passing on the Knowledge of the Night Sky

NASA Technical Reports Server (NTRS)

Shope, R.

1996-01-01

Memorable learning can happen when the whole family shares the thrill of discovery together. The fascination of the night sky presents a perfect opportunity for gifted parents and children to experience the tradition of passing on knowledge from generation to generation.

Ontology-guided data preparation for discovering genotype-phenotype relationships.

PubMed

Coulet, Adrien; Smaïl-Tabbone, Malika; Benlian, Pascale; Napoli, Amedeo; Devignes, Marie-Dominique

2008-04-25

Complexity and amount of post-genomic data constitute two major factors limiting the application of Knowledge Discovery in Databases (KDD) methods in life sciences. Bio-ontologies may nowadays play key roles in knowledge discovery in life science providing semantics to data and to extracted units, by taking advantage of the progress of Semantic Web technologies concerning the understanding and availability of tools for knowledge representation, extraction, and reasoning. This paper presents a method that exploits bio-ontologies for guiding data selection within the preparation step of the KDD process. We propose three scenarios in which domain knowledge and ontology elements such as subsumption, properties, class descriptions, are taken into account for data selection, before the data mining step. Each of these scenarios is illustrated within a case-study relative to the search of genotype-phenotype relationships in a familial hypercholesterolemia dataset. The guiding of data selection based on domain knowledge is analysed and shows a direct influence on the volume and significance of the data mining results. The method proposed in this paper is an efficient alternative to numerical methods for data selection based on domain knowledge. In turn, the results of this study may be reused in ontology modelling and data integration.

A framework for interval-valued information system

NASA Astrophysics Data System (ADS)

Yin, Yunfei; Gong, Guanghong; Han, Liang

2012-09-01

Interval-valued information system is used to transform the conventional dataset into the interval-valued form. To conduct the interval-valued data mining, we conduct two investigations: (1) construct the interval-valued information system, and (2) conduct the interval-valued knowledge discovery. In constructing the interval-valued information system, we first make the paired attributes in the database discovered, and then, make them stored in the neighbour locations in a common database and regard them as 'one' new field. In conducting the interval-valued knowledge discovery, we utilise some related priori knowledge and regard the priori knowledge as the control objectives; and design an approximate closed-loop control mining system. On the implemented experimental platform (prototype), we conduct the corresponding experiments and compare the proposed algorithms with several typical algorithms, such as the Apriori algorithm, the FP-growth algorithm and the CLOSE+ algorithm. The experimental results show that the interval-valued information system method is more effective than the conventional algorithms in discovering interval-valued patterns.

Knowledge Discovery in Variant Databases Using Inductive Logic Programming

PubMed Central

Nguyen, Hoan; Luu, Tien-Dao; Poch, Olivier; Thompson, Julie D.

2013-01-01

Understanding the effects of genetic variation on the phenotype of an individual is a major goal of biomedical research, especially for the development of diagnostics and effective therapeutic solutions. In this work, we describe the use of a recent knowledge discovery from database (KDD) approach using inductive logic programming (ILP) to automatically extract knowledge about human monogenic diseases. We extracted background knowledge from MSV3d, a database of all human missense variants mapped to 3D protein structure. In this study, we identified 8,117 mutations in 805 proteins with known three-dimensional structures that were known to be involved in human monogenic disease. Our results help to improve our understanding of the relationships between structural, functional or evolutionary features and deleterious mutations. Our inferred rules can also be applied to predict the impact of any single amino acid replacement on the function of a protein. The interpretable rules are available at http://decrypthon.igbmc.fr/kd4v/. PMID:23589683

Knowledge discovery in variant databases using inductive logic programming.

PubMed

Nguyen, Hoan; Luu, Tien-Dao; Poch, Olivier; Thompson, Julie D

2013-01-01

Understanding the effects of genetic variation on the phenotype of an individual is a major goal of biomedical research, especially for the development of diagnostics and effective therapeutic solutions. In this work, we describe the use of a recent knowledge discovery from database (KDD) approach using inductive logic programming (ILP) to automatically extract knowledge about human monogenic diseases. We extracted background knowledge from MSV3d, a database of all human missense variants mapped to 3D protein structure. In this study, we identified 8,117 mutations in 805 proteins with known three-dimensional structures that were known to be involved in human monogenic disease. Our results help to improve our understanding of the relationships between structural, functional or evolutionary features and deleterious mutations. Our inferred rules can also be applied to predict the impact of any single amino acid replacement on the function of a protein. The interpretable rules are available at http://decrypthon.igbmc.fr/kd4v/.

Analytic Steering: Inserting Context into the Information Dialog

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

Bohn, Shawn J.; Calapristi, Augustin J.; Brown, Shyretha D.

2011-10-23

An analyst’s intrinsic domain knowledge is a primary asset in almost any analysis task. Unstructured text analysis systems that apply un-supervised content analysis approaches can be more effective if they can leverage this domain knowledge in a manner that augments the information discovery process without obfuscating new or unexpected content. Current unsupervised approaches rely upon the prowess of the analyst to submit the right queries or observe generalized document and term relationships from ranked or visual results. We propose a new approach which allows the user to control or steer the analytic view within the unsupervised space. This process ismore » controlled through the data characterization process via user supplied context in the form of a collection of key terms. We show that steering with an appropriate choice of key terms can provide better relevance to the analytic domain and still enable the analyst to uncover un-expected relationships; this paper discusses cases where various analytic steering approaches can provide enhanced analysis results and cases where analytic steering can have a negative impact on the analysis process.« less

Hackathons as a means of accelerating scientific discoveries and knowledge transfer.

PubMed

Ghouila, Amel; Siwo, Geoffrey Henry; Entfellner, Jean-Baka Domelevo; Panji, Sumir; Button-Simons, Katrina A; Davis, Sage Zenon; Fadlelmola, Faisal M; Ferdig, Michael T; Mulder, Nicola

2018-05-01

Scientific research plays a key role in the advancement of human knowledge and pursuit of solutions to important societal challenges. Typically, research occurs within specific institutions where data are generated and subsequently analyzed. Although collaborative science bringing together multiple institutions is now common, in such collaborations the analytical processing of the data is often performed by individual researchers within the team, with only limited internal oversight and critical analysis of the workflow prior to publication. Here, we show how hackathons can be a means of enhancing collaborative science by enabling peer review before results of analyses are published by cross-validating the design of studies or underlying data sets and by driving reproducibility of scientific analyses. Traditionally, in data analysis processes, data generators and bioinformaticians are divided and do not collaborate on analyzing the data. Hackathons are a good strategy to build bridges over the traditional divide and are potentially a great agile extension to the more structured collaborations between multiple investigators and institutions. © 2018 Ghouila et al.; Published by Cold Spring Harbor Laboratory Press.

Discovery and Observations of a Stem-Boring Weevil (Myrmex sp.) a Potentially Useful Biocontrol of Mistletoe

Treesearch

J. D. Solomon; L. Newsome; T. H. Filer

1984-01-01

A stem-boring weevil obtained from infested clusters of mistletoe was subsequently reared and identified as Myrmex sp. To our knowledge its discovery in Mississippi is the easternmost record of mistletoe-feeding Myrmex, previously recorded only from the West and Southwest. Based on current studies, the weevil overwinters as larvae in tunnels within mistletoe stems....

The importance of Leonhard Euler's discoveries in the field of shipbuilding for the scientific evolution of academician A. N. Krylov

NASA Astrophysics Data System (ADS)

Sharkov, N. A.; Sharkova, O. A.

2018-05-01

The paper identifies the importance of the Leonhard Euler's discoveries in the field of shipbuilding for the scientific evolution of academician A. N. Krylov and for the modern knowledge in survivability and safety of ships. The works by Leonard Euler "Marine Science" and "The Moon Motion New Theory" are discussed.

41. DISCOVERY, SEARCH, AND COMMUNICATION OF TEXTUAL KNOWLEDGE RESOURCES IN DISTRIBUTED SYSTEMS a. Discovering and Utilizing Knowledge Sources for Metasearch Knowledge Systems

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

Zamora, Antonio

Advanced Natural Language Processing Tools for Web Information Retrieval, Content Analysis, and Synthesis. The goal of this SBIR was to implement and evaluate several advanced Natural Language Processing (NLP) tools and techniques to enhance the precision and relevance of search results by analyzing and augmenting search queries and by helping to organize the search output obtained from heterogeneous databases and web pages containing textual information of interest to DOE and the scientific-technical user communities in general. The SBIR investigated 1) the incorporation of spelling checkers in search applications, 2) identification of significant phrases and concepts using a combination of linguisticmore » and statistical techniques, and 3) enhancement of the query interface and search retrieval results through the use of semantic resources, such as thesauri. A search program with a flexible query interface was developed to search reference databases with the objective of enhancing search results from web queries or queries of specialized search systems such as DOE's Information Bridge. The DOE ETDE/INIS Joint Thesaurus was processed to create a searchable database. Term frequencies and term co-occurrences were used to enhance the web information retrieval by providing algorithmically-derived objective criteria to organize relevant documents into clusters containing significant terms. A thesaurus provides an authoritative overview and classification of a field of knowledge. By organizing the results of a search using the thesaurus terminology, the output is more meaningful than when the results are just organized based on the terms that co-occur in the retrieved documents, some of which may not be significant. An attempt was made to take advantage of the hierarchy provided by broader and narrower terms, as well as other field-specific information in the thesauri. The search program uses linguistic morphological routines to find relevant entries regardless of whether terms are stored in singular or plural form. Implementation of additional inflectional morphology processes for verbs can enhance retrieval further, but this has to be balanced by the possibility of broadening the results too much. In addition to the DOE energy thesaurus, other sources of specialized organized knowledge such as the Medical Subject Headings (MeSH), the Unified Medical Language System (UMLS), and Wikipedia were investigated. The supporting role of the NLP thesaurus search program was enhanced by incorporating spelling aid and a part-of-speech tagger to cope with misspellings in the queries and to determine the grammatical roles of the query words and identify nouns for special processing. To improve precision, multiple modes of searching were implemented including Boolean operators, and field-specific searches. Programs to convert a thesaurus or reference file into searchable support files can be deployed easily, and the resulting files are immediately searchable to produce relevance-ranked results with builtin spelling aid, morphological processing, and advanced search logic. Demonstration systems were built for several databases, including the DOE energy thesaurus.« less

Cost-Benefit Analysis of Confidentiality Policies for Advanced Knowledge Management Systems

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

May, D

Knowledge Discovery (KD) processes can create new information within a Knowledge Management (KM) system. In many domains, including government, this new information must be secured against unauthorized disclosure. Applying an appropriate confidentiality policy achieves this. However, it is not evident which confidentiality policy to apply, especially when the goals of sharing and disseminating knowledge have to be balanced with the requirements to secure knowledge. This work proposes to solve this problem by developing a cost-benefit analysis technique for examining the tradeoffs between securing and sharing discovered knowledge.

Comparative study on drug safety surveillance between medical students of Malaysia and Nigeria

PubMed Central

Abubakar, Abdullahi Rabiu; Ismail, Salwani; Rahman, Nor Iza A; Haque, Mainul

2015-01-01

Background Internationally, there is a remarkable achievement in the areas of drug discovery, drug design, and clinical trials. New and efficient drug formulation techniques are widely available which have led to success in treatment of several diseases. Despite these achievements, large number of patients continue to experience adverse drug reactions (ADRs), and majority of them are yet to be on record. Objectives The purpose of this survey is to compare knowledge, attitude, and practice with respect to ADRs and pharmacovigilance (PV) between medical students of Malaysia and Nigeria and to determine if there is a relationship between their knowledge and practice. Method A cross-sectional, questionnaire-based survey involving year IV and year V medical students of the Department of Medicine, Universiti Sultan Zainal Abidin and Bayero University Kano was carried out. The questionnaire which comprised 25 questions on knowledge, attitude, and practice was adopted, modified, validated, and administered to them. The response was analyzed using SPSS version 20. Results The response rate from each country was 74%. There was a statistically significant difference in mean knowledge and practice score on ADRs and PV between medical students of Malaysia and Nigeria, both at P<0.000. No significance difference in attitude was observed at P=0.389. Also, a statistically significant relationship was recorded between their knowledge and practice (r=0.229, P=0.001), although the relationship was weak. Conclusion Nigerian medical students have better knowledge and practice than those of Malaysia, although they need improvement. Imparting knowledge of ADRs and PV among medical students will upgrade their practice and enhance health care delivery services in the future. PMID:26170680

Standardized plant disease evaluations will enhance resistance gene discovery

USDA-ARS?s Scientific Manuscript database

Gene discovery and marker development using DNA-based tools require plant populations with well documented phenotypes. If dissimilar phenotype evaluation methods or data scoring techniques are employed with different crops, or at different labs for the same crops, then data mining for genetic marker...

Integrative annotation and knowledge discovery of kinase post-translational modifications and cancer-associated mutations through federated protein ontologies and resources.

PubMed

Huang, Liang-Chin; Ross, Karen E; Baffi, Timothy R; Drabkin, Harold; Kochut, Krzysztof J; Ruan, Zheng; D'Eustachio, Peter; McSkimming, Daniel; Arighi, Cecilia; Chen, Chuming; Natale, Darren A; Smith, Cynthia; Gaudet, Pascale; Newton, Alexandra C; Wu, Cathy; Kannan, Natarajan

2018-04-25

Many bioinformatics resources with unique perspectives on the protein landscape are currently available. However, generating new knowledge from these resources requires interoperable workflows that support cross-resource queries. In this study, we employ federated queries linking information from the Protein Kinase Ontology, iPTMnet, Protein Ontology, neXtProt, and the Mouse Genome Informatics to identify key knowledge gaps in the functional coverage of the human kinome and prioritize understudied kinases, cancer variants and post-translational modifications (PTMs) for functional studies. We identify 32 functional domains enriched in cancer variants and PTMs and generate mechanistic hypotheses on overlapping variant and PTM sites by aggregating information at the residue, protein, pathway and species level from these resources. We experimentally test the hypothesis that S768 phosphorylation in the C-helix of EGFR is inhibitory by showing that oncogenic variants altering S768 phosphorylation increase basal EGFR activity. In contrast, oncogenic variants altering conserved phosphorylation sites in the 'hydrophobic motif' of PKCβII (S660F and S660C) are loss-of-function in that they reduce kinase activity and enhance membrane translocation. Our studies provide a framework for integrative, consistent, and reproducible annotation of the cancer kinomes.

Discovery of the leinamycin family of natural products by mining actinobacterial genomes

PubMed Central

Xu, Zhengren; Guo, Zhikai; Hindra; Ma, Ming; Zhou, Hao; Gansemans, Yannick; Zhu, Xiangcheng; Huang, Yong; Zhao, Li-Xing; Jiang, Yi; Cheng, Jinhua; Van Nieuwerburgh, Filip; Suh, Joo-Won; Duan, Yanwen

2017-01-01

Nature’s ability to generate diverse natural products from simple building blocks has inspired combinatorial biosynthesis. The knowledge-based approach to combinatorial biosynthesis has allowed the production of designer analogs by rational metabolic pathway engineering. While successful, structural alterations are limited, with designer analogs often produced in compromised titers. The discovery-based approach to combinatorial biosynthesis complements the knowledge-based approach by exploring the vast combinatorial biosynthesis repertoire found in Nature. Here we showcase the discovery-based approach to combinatorial biosynthesis by targeting the domain of unknown function and cysteine lyase domain (DUF–SH) didomain, specific for sulfur incorporation from the leinamycin (LNM) biosynthetic machinery, to discover the LNM family of natural products. By mining bacterial genomes from public databases and the actinomycetes strain collection at The Scripps Research Institute, we discovered 49 potential producers that could be grouped into 18 distinct clades based on phylogenetic analysis of the DUF–SH didomains. Further analysis of the representative genomes from each of the clades identified 28 lnm-type gene clusters. Structural diversities encoded by the LNM-type biosynthetic machineries were predicted based on bioinformatics and confirmed by in vitro characterization of selected adenylation proteins and isolation and structural elucidation of the guangnanmycins and weishanmycins. These findings demonstrate the power of the discovery-based approach to combinatorial biosynthesis for natural product discovery and structural diversity and highlight Nature’s rich biosynthetic repertoire. Comparative analysis of the LNM-type biosynthetic machineries provides outstanding opportunities to dissect Nature’s biosynthetic strategies and apply these findings to combinatorial biosynthesis for natural product discovery and structural diversity. PMID:29229819

Discovery of the leinamycin family of natural products by mining actinobacterial genomes.

PubMed

Pan, Guohui; Xu, Zhengren; Guo, Zhikai; Hindra; Ma, Ming; Yang, Dong; Zhou, Hao; Gansemans, Yannick; Zhu, Xiangcheng; Huang, Yong; Zhao, Li-Xing; Jiang, Yi; Cheng, Jinhua; Van Nieuwerburgh, Filip; Suh, Joo-Won; Duan, Yanwen; Shen, Ben

2017-12-26

Nature's ability to generate diverse natural products from simple building blocks has inspired combinatorial biosynthesis. The knowledge-based approach to combinatorial biosynthesis has allowed the production of designer analogs by rational metabolic pathway engineering. While successful, structural alterations are limited, with designer analogs often produced in compromised titers. The discovery-based approach to combinatorial biosynthesis complements the knowledge-based approach by exploring the vast combinatorial biosynthesis repertoire found in Nature. Here we showcase the discovery-based approach to combinatorial biosynthesis by targeting the domain of unknown function and cysteine lyase domain (DUF-SH) didomain, specific for sulfur incorporation from the leinamycin (LNM) biosynthetic machinery, to discover the LNM family of natural products. By mining bacterial genomes from public databases and the actinomycetes strain collection at The Scripps Research Institute, we discovered 49 potential producers that could be grouped into 18 distinct clades based on phylogenetic analysis of the DUF-SH didomains. Further analysis of the representative genomes from each of the clades identified 28 lnm -type gene clusters. Structural diversities encoded by the LNM-type biosynthetic machineries were predicted based on bioinformatics and confirmed by in vitro characterization of selected adenylation proteins and isolation and structural elucidation of the guangnanmycins and weishanmycins. These findings demonstrate the power of the discovery-based approach to combinatorial biosynthesis for natural product discovery and structural diversity and highlight Nature's rich biosynthetic repertoire. Comparative analysis of the LNM-type biosynthetic machineries provides outstanding opportunities to dissect Nature's biosynthetic strategies and apply these findings to combinatorial biosynthesis for natural product discovery and structural diversity.

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.

Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system.

PubMed

Tudor, Catalina O; Ross, Karen E; Li, Gang; Vijay-Shanker, K; Wu, Cathy H; Arighi, Cecilia N

2015-01-01

Protein phosphorylation is a reversible post-translational modification where a protein kinase adds a phosphate group to a protein, potentially regulating its function, localization and/or activity. Phosphorylation can affect protein-protein interactions (PPIs), abolishing interaction with previous binding partners or enabling new interactions. Extracting phosphorylation information coupled with PPI information from the scientific literature will facilitate the creation of phosphorylation interaction networks of kinases, substrates and interacting partners, toward knowledge discovery of functional outcomes of protein phosphorylation. Increasingly, PPI databases are interested in capturing the phosphorylation state of interacting partners. We have previously developed the eFIP (Extracting Functional Impact of Phosphorylation) text mining system, which identifies phosphorylated proteins and phosphorylation-dependent PPIs. In this work, we present several enhancements for the eFIP system: (i) text mining for full-length articles from the PubMed Central open-access collection; (ii) the integration of the RLIMS-P 2.0 system for the extraction of phosphorylation events with kinase, substrate and site information; (iii) the extension of the PPI module with new trigger words/phrases describing interactions and (iv) the addition of the iSimp tool for sentence simplification to aid in the matching of syntactic patterns. We enhance the website functionality to: (i) support searches based on protein roles (kinases, substrates, interacting partners) or using keywords; (ii) link protein entities to their corresponding UniProt identifiers if mapped and (iii) support visual exploration of phosphorylation interaction networks using Cytoscape. The evaluation of eFIP on full-length articles achieved 92.4% precision, 76.5% recall and 83.7% F-measure on 100 article sections. To demonstrate eFIP for knowledge extraction and discovery, we constructed phosphorylation-dependent interaction networks involving 14-3-3 proteins identified from cancer-related versus diabetes-related articles. Comparison of the phosphorylation interaction network of kinases, phosphoproteins and interactants obtained from eFIP searches, along with enrichment analysis of the protein set, revealed several shared interactions, highlighting common pathways discussed in the context of both diseases. © The Author(s) 2015. Published by Oxford University Press.

Balancing the risks and the benefits.

PubMed

Klopack

2000-04-01

Pharmaceutical research organizations can benefit from outsourcing discovery activities that are not core competencies of the organization. The core competencies for a discovery operation are the expertise and systems that give the organization an advantage over its competition. Successful outsourcing ventures result in cost reduction, increased operation efficiency and optimization of resource allocation. While there are pitfalls to outsourcing, including poor partner selection and inadequate implementation, outsourcing can be a powerful tool for enhancing drug discovery operations.

Ensuring the Quality of Outreach: The Critical Role of Evaluating Individual and Collective Initiatives and Performance

ERIC Educational Resources Information Center

Lynton, Ernest A.

2016-01-01

New knowledge is created in the course of the application of outreach. Each complex problem in the real world is likely to have unique aspects and thus it requires some modification of standard approaches. Hence, each engagement in outreach is likely to have an element of inquiry and discovery, leading to new knowledge. The flow of knowledge is in…

Concepts of formal concept analysis

NASA Astrophysics Data System (ADS)

Žáček, Martin; Homola, Dan; Miarka, Rostislav

2017-07-01

The aim of this article is apply of Formal Concept Analysis on concept of world. Formal concept analysis (FCA) as a methodology of data analysis, information management and knowledge representation has potential to be applied to a verity of linguistic problems. FCA is mathematical theory for concepts and concept hierarchies that reflects an understanding of concept. Formal concept analysis explicitly formalizes extension and intension of a concept, their mutual relationships. A distinguishing feature of FCA is an inherent integration of three components of conceptual processing of data and knowledge, namely, the discovery and reasoning with concepts in data, discovery and reasoning with dependencies in data, and visualization of data, concepts, and dependencies with folding/unfolding capabilities.

Using a computer-based simulation with an artificial intelligence component and discovery learning to formulate training needs for a new technology

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

Hillis, D.R.

A computer-based simulation with an artificial intelligence component and discovery learning was investigated as a method to formulate training needs for new or unfamiliar technologies. Specifically, the study examined if this simulation method would provide for the recognition of applications and knowledge/skills which would be the basis for establishing training needs. The study also examined the effect of field-dependence/independence on recognition of applications and knowledge/skills. A pretest-posttest control group experimental design involving fifty-eight college students from an industrial technology program was used. The study concluded that the simulation was effective in developing recognition of applications and the knowledge/skills for amore » new or unfamiliar technology. And, the simulation's effectiveness for providing this recognition was not limited by an individual's field-dependence/independence.« less

Semi-automated knowledge discovery: identifying and profiling human trafficking

NASA Astrophysics Data System (ADS)

Poelmans, Jonas; Elzinga, Paul; Ignatov, Dmitry I.; Kuznetsov, Sergei O.

2012-11-01

We propose an iterative and human-centred knowledge discovery methodology based on formal concept analysis. The proposed approach recognizes the important role of the domain expert in mining real-world enterprise applications and makes use of specific domain knowledge, including human intelligence and domain-specific constraints. Our approach was empirically validated at the Amsterdam-Amstelland police to identify suspects and victims of human trafficking in 266,157 suspicious activity reports. Based on guidelines of the Attorney Generals of the Netherlands, we first defined multiple early warning indicators that were used to index the police reports. Using concept lattices, we revealed numerous unknown human trafficking and loverboy suspects. In-depth investigation by the police resulted in a confirmation of their involvement in illegal activities resulting in actual arrestments been made. Our human-centred approach was embedded into operational policing practice and is now successfully used on a daily basis to cope with the vastly growing amount of unstructured information.

The role of indirect evidence and traditional ecological knowledge in the discovery and description of new ape and monkey species since 1980.

PubMed

Rossi, Lorenzo; Gippoliti, Spartaco; Angelici, Francesco Maria

2018-06-04

Although empirical data are necessary to describe new species, their discoveries can be guided from the survey of the so-called circumstantial evidence (that indirectly determines the existence or nonexistence of a fact). Yet this type of evidence, generally linked to traditional ecological knowledge (TEK), is often disputed by field biologists due to its uncertain nature and, on account of that, generally untapped by them. To verify this behavior and the utility of circumstantial evidence, we reviewed the existing literature about the species of apes and monkeys described or rediscovered since January 1, 1980 and submitted a poll to the authors. The results show that circumstantial evidence has proved to be useful in 40.5% of the examined cases and point to the possibility that its use could speed up the process at the heart of the discovery and description of new species, an essential step for conservation purposes.

CONSTRUCTING KNOWLEDGE FROM MULTIVARIATE SPATIOTEMPORAL DATA: INTEGRATING GEOGRAPHIC VISUALIZATION WITH KNOWLEDGE DISCOVERY IN DATABASE METHODS. (R825195)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

Dewey: How to Make It Work for You

ERIC Educational Resources Information Center

Panzer, Michael

2013-01-01

As knowledge brokers, librarians are living in interesting times for themselves and libraries. It causes them to wonder sometimes if the traditional tools like the Dewey Decimal Classification (DDC) system can cope with the onslaught of information. The categories provided do not always seem adequate for the knowledge-discovery habits of…

78 FR 29071 - Assessment of Mediation and Arbitration Procedures

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-17

... proceeding. Program participants in the new arbitration program will have prior knowledge of the issues to be... final rules, all parties opting into the arbitration program will have full prior knowledge that these... including discovery, the submission of evidence, and the treatment of confidential information, and the...

Streamlining the Discovery, Evaluation, and Integration of Data, Models, and Decision Support Systems: a Big Picture View

EPA Science Inventory

21st century environmental problems are wicked and require holistic systems thinking and solutions that integrate social and economic knowledge with knowledge of the environment. Computer-based technologies are fundamental to our ability to research and understand the relevant sy...

Teaching Practice: A Perspective on Inter-Text and Prior Knowledge

ERIC Educational Resources Information Center

Costley, Kevin C.; West, Howard G.

2012-01-01

The use of teaching practices that involve intertextual relationship discovery in today's elementary classrooms is increasingly essential to the success of young learners of reading. Teachers must constantly strive to expand their perspective of how to incorporate the dialogue included in prior knowledge assessment. Teachers must also consider how…

Globalization of Knowledge Discovery and Information Retrieval in Teaching and Learning

ERIC Educational Resources Information Center

Zaidel, Mark; Guerrero, Osiris

2008-01-01

Developments in communication and information technologies in the last decade have had a significant impact on instructional and learning activities. For many students and educators, the Internet became the significant medium for sharing instruction, learning and communication. Access to knowledge beyond boundaries and cultures has an impact on…

An Evaluation of Text Mining Tools as Applied to Selected Scientific and Engineering Literature.

ERIC Educational Resources Information Center

Trybula, Walter J.; Wyllys, Ronald E.

2000-01-01

Addresses an approach to the discovery of scientific knowledge through an examination of data mining and text mining techniques. Presents the results of experiments that investigated knowledge acquisition from a selected set of technical documents by domain experts. (Contains 15 references.) (Author/LRW)

Vocational Education Institutions' Role in National Innovation

ERIC Educational Resources Information Center

Moodie, Gavin

2006-01-01

This article distinguishes research--the discovery of new knowledge--from innovation, which is understood to be the transformation of practice in a community or the incorporation of existing knowledge into economic activity. From a survey of roles served by vocational education institutions in a number of OECD countries the paper argues that…

Building more realistic reservoir optimization models using data mining - A case study of Shelbyville Reservoir

NASA Astrophysics Data System (ADS)

Hejazi, Mohamad I.; Cai, Ximing

2011-06-01

In this paper, we promote a novel approach to develop reservoir operation routines by learning from historical hydrologic information and reservoir operations. The proposed framework involves a knowledge discovery step to learn the real drivers of reservoir decision making and to subsequently build a more realistic (enhanced) model formulation using stochastic dynamic programming (SDP). The enhanced SDP model is compared to two classic SDP formulations using Lake Shelbyville, a reservoir on the Kaskaskia River in Illinois, as a case study. From a data mining procedure with monthly data, the past month's inflow ( Qt-1 ), current month's inflow ( Qt), past month's release ( Rt-1 ), and past month's Palmer drought severity index ( PDSIt-1 ) are identified as important state variables in the enhanced SDP model for Shelbyville Reservoir. When compared to a weekly enhanced SDP model of the same case study, a different set of state variables and constraints are extracted. Thus different time scales for the model require different information. We demonstrate that adding additional state variables improves the solution by shifting the Pareto front as expected while using new constraints and the correct objective function can significantly reduce the difference between derived policies and historical practices. The study indicates that the monthly enhanced SDP model resembles historical records more closely and yet provides lower expected average annual costs than either of the two classic formulations (25.4% and 4.5% reductions, respectively). The weekly enhanced SDP model is compared to the monthly enhanced SDP, and it shows that acquiring the correct temporal scale is crucial to model reservoir operation for particular objectives.

Exploring relation types for literature-based discovery.

PubMed

Preiss, Judita; Stevenson, Mark; Gaizauskas, Robert

2015-09-01

Literature-based discovery (LBD) aims to identify "hidden knowledge" in the medical literature by: (1) analyzing documents to identify pairs of explicitly related concepts (terms), then (2) hypothesizing novel relations between pairs of unrelated concepts that are implicitly related via a shared concept to which both are explicitly related. Many LBD approaches use simple techniques to identify semantically weak relations between concepts, for example, document co-occurrence. These generate huge numbers of hypotheses, difficult for humans to assess. More complex techniques rely on linguistic analysis, for example, shallow parsing, to identify semantically stronger relations. Such approaches generate fewer hypotheses, but may miss hidden knowledge. The authors investigate this trade-off in detail, comparing techniques for identifying related concepts to discover which are most suitable for LBD. A generic LBD system that can utilize a range of relation types was developed. Experiments were carried out comparing a number of techniques for identifying relations. Two approaches were used for evaluation: replication of existing discoveries and the "time slicing" approach.(1) RESULTS: Previous LBD discoveries could be replicated using relations based either on document co-occurrence or linguistic analysis. Using relations based on linguistic analysis generated many fewer hypotheses, but a significantly greater proportion of them were candidates for hidden knowledge. The use of linguistic analysis-based relations improves accuracy of LBD without overly damaging coverage. LBD systems often generate huge numbers of hypotheses, which are infeasible to manually review. Improving their accuracy has the potential to make these systems significantly more usable. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

The National Heart, Lung, and Blood Institute Small Business Program

PubMed Central

Marek, Kurt W.

2016-01-01

SUMMARY Small companies working to develop products in the cardiovascular space face numerous challenges, from regulatory, intellectual property, and reimbursement barriers to securing funds to keep the lights on and reach the next development milestone. Most small companies that spin out from universities have the scientific knowledge, but product development expertise and business acumen are also needed to be successful. Other challenges include reduced interest in early stage technologies (Pharma & Biotech 2015 in Review, EP Vantage) and limited deal flow for cardiovascular products (Gormley B., Wall Street Journal, September 15, 2014). The NHLBI small business program is a comprehensive ecosystem designed to address these critical challenges and to provide resources and expertise to assist early stage companies developing cardiovascular and other products within the institute’s mission. This article describes steps that NHLBI has taken to enhance our small business program to more effectively translate basic discoveries into commercial products to benefit patients and public health, including enhancing internal expertise and developing non-financial resources to assist small businesses as they develop their products and seek private sector investment and partnership. PMID:28580435

The National Heart, Lung, and Blood Institute Small Business Program: A Comprehensive Ecosystem for Biomedical Product Development.

PubMed

Marek, Kurt W

2016-12-01

Small companies working to develop products in the cardiovascular space face numerous challenges, from regulatory, intellectual property, and reimbursement barriers to securing funds to keep the lights on and reach the next development milestone. Most small companies that spin out from universities have the scientific knowledge, but product development expertise and business acumen are also needed to be successful. Other challenges include reduced interest in early stage technologies (Pharma & Biotech 2015 in Review, EP Vantage) and limited deal flow for cardiovascular products (Gormley B., Wall Street Journal, September 15, 2014). The NHLBI small business program is a comprehensive ecosystem designed to address these critical challenges and to provide resources and expertise to assist early stage companies developing cardiovascular and other products within the institute's mission. This article describes steps that NHLBI has taken to enhance our small business program to more effectively translate basic discoveries into commercial products to benefit patients and public health, including enhancing internal expertise and developing non-financial resources to assist small businesses as they develop their products and seek private sector investment and partnership.

An Analysis Pipeline with Statistical and Visualization-Guided Knowledge Discovery for Michigan-Style Learning Classifier Systems

PubMed Central

Urbanowicz, Ryan J.; Granizo-Mackenzie, Ambrose; Moore, Jason H.

2014-01-01

Michigan-style learning classifier systems (M-LCSs) represent an adaptive and powerful class of evolutionary algorithms which distribute the learned solution over a sizable population of rules. However their application to complex real world data mining problems, such as genetic association studies, has been limited. Traditional knowledge discovery strategies for M-LCS rule populations involve sorting and manual rule inspection. While this approach may be sufficient for simpler problems, the confounding influence of noise and the need to discriminate between predictive and non-predictive attributes calls for additional strategies. Additionally, tests of significance must be adapted to M-LCS analyses in order to make them a viable option within fields that require such analyses to assess confidence. In this work we introduce an M-LCS analysis pipeline that combines uniquely applied visualizations with objective statistical evaluation for the identification of predictive attributes, and reliable rule generalizations in noisy single-step data mining problems. This work considers an alternative paradigm for knowledge discovery in M-LCSs, shifting the focus from individual rules to a global, population-wide perspective. We demonstrate the efficacy of this pipeline applied to the identification of epistasis (i.e., attribute interaction) and heterogeneity in noisy simulated genetic association data. PMID:25431544

DataHub: Knowledge-based data management for data discovery

NASA Astrophysics Data System (ADS)

Handley, Thomas H.; Li, Y. Philip

1993-08-01

Currently available database technology is largely designed for business data-processing applications, and seems inadequate for scientific applications. The research described in this paper, the DataHub, will address the issues associated with this shortfall in technology utilization and development. The DataHub development is addressing the key issues in scientific data management of scientific database models and resource sharing in a geographically distributed, multi-disciplinary, science research environment. Thus, the DataHub will be a server between the data suppliers and data consumers to facilitate data exchanges, to assist science data analysis, and to provide as systematic approach for science data management. More specifically, the DataHub's objectives are to provide support for (1) exploratory data analysis (i.e., data driven analysis); (2) data transformations; (3) data semantics capture and usage; analysis-related knowledge capture and usage; and (5) data discovery, ingestion, and extraction. Applying technologies that vary from deductive databases, semantic data models, data discovery, knowledge representation and inferencing, exploratory data analysis techniques and modern man-machine interfaces, DataHub will provide a prototype, integrated environement to support research scientists' needs in multiple disciplines (i.e. oceanography, geology, and atmospheric) while addressing the more general science data management issues. Additionally, the DataHub will provide data management services to exploratory data analysis applications such as LinkWinds and NCSA's XIMAGE.

Standardized Plant Disease Evaluations will Enhance Resistance Gene Discovery

USDA-ARS?s Scientific Manuscript database

Gene discovery and marker development using DNA based tools require plant populations with well-documented phenotypes. Related crops such as apples and pears may share a number of genes, for example resistance to common diseases, and data mining in one crop may reveal genes for the other. However, u...

Scientific Discoveries the Year I Was Born

ERIC Educational Resources Information Center

Cherif, Abour

2012-01-01

The author has successfully used a learning activity titled "The Year I Was Born" to motivate students to conduct historical research and present key scientific discoveries from their birth year. The activity promotes writing, helps students enhance their scientific literacy, and also improves their attitude toward the learning of science. As one…

Constructing a Graph Database for Semantic Literature-Based Discovery.

PubMed

Hristovski, Dimitar; Kastrin, Andrej; Dinevski, Dejan; Rindflesch, Thomas C

2015-01-01

Literature-based discovery (LBD) generates discoveries, or hypotheses, by combining what is already known in the literature. Potential discoveries have the form of relations between biomedical concepts; for example, a drug may be determined to treat a disease other than the one for which it was intended. LBD views the knowledge in a domain as a network; a set of concepts along with the relations between them. As a starting point, we used SemMedDB, a database of semantic relations between biomedical concepts extracted with SemRep from Medline. SemMedDB is distributed as a MySQL relational database, which has some problems when dealing with network data. We transformed and uploaded SemMedDB into the Neo4j graph database, and implemented the basic LBD discovery algorithms with the Cypher query language. We conclude that storing the data needed for semantic LBD is more natural in a graph database. Also, implementing LBD discovery algorithms is conceptually simpler with a graph query language when compared with standard SQL.

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.

Aflatoxin control--how a regulatory agency managed risk from an unavoidable natural toxicant in food and feed.

PubMed

Park, D L; Stoloff, L

1989-04-01

The control by the Food and Drug Administration (FDA) of aflatoxin, a relatively recently discovered, unavoidable natural contaminant produced by specific molds that invade a number of basic food and feedstuffs, provides an example of the varying forces that affect risk assessment and management by a regulatory Agency. This is the story of how the FDA responded to the initial discovery of a potential carcinogenic hazard to humans in a domestic commodity, to the developing information concerning the nature of the hazard, to the economic and political pressures that are created by the impact of natural forces on regulatory controls, and to the restraints of laws within which the Agency must work. This story covers four periods: the years of discovery and action decisions on the basis of meager knowledge and the fear of cancer; the years of tinkering on paper with the regulatory process, the years of digestion of the accumulating knowledge, and the application of that knowledge to actions forced by natural events; and an audit of the current status of knowledge about the hazard from aflatoxin, and proposals for regulatory control based on that knowledge.

In Situ Teaching: Fusing Labs & Lectures in Undergraduate Science Courses to Enhance Immersion in Scientific Research

PubMed Central

Round, Jennifer; Lom, Barbara

2015-01-01

Undergraduate courses in the life sciences at most colleges and universities are traditionally composed of two or three weekly sessions in a classroom supplemented with a weekly three-hour session in a laboratory. We have found that many undergraduates can have difficulty making connections and/or transferring knowledge between lab activities and lecture material. Consequently, we are actively developing ways to decrease the physical and intellectual divides between lecture and lab to help students make more direct links between what they learn in the classroom and what they learn in the lab. In this article we discuss our experiences teaching fused laboratory biology courses that intentionally blurred the distinctions between lab and lecture to provide undergraduates with immersive experiences in science that promote discovery and understanding. PMID:26240531

Convection in Cool Stars, as Seen Through Kepler's Eyes

NASA Astrophysics Data System (ADS)

Bastien, Fabienne A.

2015-01-01

Stellar surface processes represent a fundamental limit to the detection of extrasolar planets with the currently most heavily-used techniques. As such, considerable effort has gone into trying to mitigate the impact of these processes on planet detection, with most studies focusing on magnetic spots. Meanwhile, high-precision photometric planet surveys like CoRoT and Kepler have unveiled a wide variety of stellar variability at previously inaccessible levels. We demonstrate that these newly revealed variations are not solely magnetically driven but also trace surface convection through light curve ``flicker.'' We show that ``flicker'' not only yields a simple measurement of surface gravity with a precision of ˜0.1 dex, but it may also improve our knowledge of planet properties, enhance radial velocity planet detection and discovery, and provide new insights into stellar evolution.

Translational genomics for plant breeding with the genome sequence explosion.

PubMed

Kang, Yang Jae; Lee, Taeyoung; Lee, Jayern; Shim, Sangrea; Jeong, Haneul; Satyawan, Dani; Kim, Moon Young; Lee, Suk-Ha

2016-04-01

The use of next-generation sequencers and advanced genotyping technologies has propelled the field of plant genomics in model crops and plants and enhanced the discovery of hidden bridges between genotypes and phenotypes. The newly generated reference sequences of unstudied minor plants can be annotated by the knowledge of model plants via translational genomics approaches. Here, we reviewed the strategies of translational genomics and suggested perspectives on the current databases of genomic resources and the database structures of translated information on the new genome. As a draft picture of phenotypic annotation, translational genomics on newly sequenced plants will provide valuable assistance for breeders and researchers who are interested in genetic studies. © 2015 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

Application of theoretical methods to increase succinate production in engineered strains.

PubMed

Valderrama-Gomez, M A; Kreitmayer, D; Wolf, S; Marin-Sanguino, A; Kremling, A

2017-04-01

Computational methods have enabled the discovery of non-intuitive strategies to enhance the production of a variety of target molecules. In the case of succinate production, reviews covering the topic have not yet analyzed the impact and future potential that such methods may have. In this work, we review the application of computational methods to the production of succinic acid. We found that while a total of 26 theoretical studies were published between 2002 and 2016, only 10 studies reported the successful experimental implementation of any kind of theoretical knowledge. None of the experimental studies reported an exact application of the computational predictions. However, the combination of computational analysis with complementary strategies, such as directed evolution and comparative genome analysis, serves as a proof of concept and demonstrates that successful metabolic engineering can be guided by rational computational methods.

Kv7 channels are upregulated during striatal neuron development and promote maturation of human iPSC-derived neurons.

PubMed

Telezhkin, Vsevolod; Straccia, Marco; Yarova, Polina; Pardo, Monica; Yung, Sun; Vinh, Ngoc-Nga; Hancock, Jane M; Barriga, Gerardo Garcia-Diaz; Brown, David A; Rosser, Anne E; Brown, Jonathan T; Canals, Josep M; Randall, Andrew D; Allen, Nicholas D; Kemp, Paul J

2018-05-24

Kv7 channels determine the resting membrane potential of neurons and regulate their excitability. Even though dysfunction of Kv7 channels has been linked to several debilitating childhood neuronal disorders, the ontogeny of the constituent genes, which encode Kv7 channels (KNCQ), and expression of their subunits have been largely unexplored. Here, we show that developmentally regulated expression of specific KCNQ mRNA and Kv7 channel subunits in mouse and human striatum is crucial to the functional maturation of mouse striatal neurons and human-induced pluripotent stem cell-derived neurons. This demonstrates their pivotal role in normal development and maturation, the knowledge of which can now be harnessed to synchronise and accelerate neuronal differentiation of stem cell-derived neurons, enhancing their utility for disease modelling and drug discovery.

Postgenomic strategies in antibacterial drug discovery.

PubMed

Brötz-Oesterhelt, Heike; Sass, Peter

2010-10-01

During the last decade the field of antibacterial drug discovery has changed in many aspects including bacterial organisms of primary interest, discovery strategies applied and pharmaceutical companies involved. Target-based high-throughput screening had been disappointingly unsuccessful for antibiotic research. Understanding of this lack of success has increased substantially and the lessons learned refer to characteristics of targets, screening libraries and screening strategies. The 'genomics' approach was replaced by a diverse array of discovery strategies, for example, searching for new natural product leads among previously abandoned compounds or new microbial sources, screening for synthetic inhibitors by targeted approaches including structure-based design and analyses of focused libraries and designing resistance-breaking properties into antibiotics of established classes. Furthermore, alternative treatment options are being pursued including anti-virulence strategies and immunotherapeutic approaches. This article summarizes the lessons learned from the genomics era and describes discovery strategies resulting from that knowledge.

Priority of discovery in the life sciences

PubMed Central

Vale, Ronald D; Hyman, Anthony A

2016-01-01

The job of a scientist is to make a discovery and then communicate this new knowledge to others. For a scientist to be successful, he or she needs to be able to claim credit or priority for discoveries throughout their career. However, despite being fundamental to the reward system of science, the principles for establishing the "priority of discovery" are rarely discussed. Here we break down priority into two steps: disclosure, in which the discovery is released to the world-wide community; and validation, in which other scientists assess the accuracy, quality and importance of the work. Currently, in biology, disclosure and an initial validation are combined in a journal publication. Here, we discuss the advantages of separating these steps into disclosure via a preprint, and validation via a combination of peer review at a journal and additional evaluation by the wider scientific community. PMID:27310529

Enhancing knowledge discovery from cancer genomics data with Galaxy

PubMed Central

Albuquerque, Marco A.; Grande, Bruno M.; Ritch, Elie J.; Pararajalingam, Prasath; Jessa, Selin; Krzywinski, Martin; Grewal, Jasleen K.; Shah, Sohrab P.; Boutros, Paul C.

2017-01-01

Abstract The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on the genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise. To address this, we have produced a collection of Galaxy tools that represent many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We developed new methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability and summarized their runtimes on multiple cloud service providers. Some tools represent extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate these to achieve data integration and visualizations are demonstrated on a cohort of 96 diffuse large B-cell lymphomas and enabled the discovery of multiple candidate lymphoma-related genes. Our toolkit is available from our GitHub repository as Galaxy tool and dependency definitions and has been deployed using virtualization on multiple platforms including Docker. PMID:28327945

Enhancing knowledge discovery from cancer genomics data with Galaxy.

PubMed

Albuquerque, Marco A; Grande, Bruno M; Ritch, Elie J; Pararajalingam, Prasath; Jessa, Selin; Krzywinski, Martin; Grewal, Jasleen K; Shah, Sohrab P; Boutros, Paul C; Morin, Ryan D

2017-05-01

The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on the genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise. To address this, we have produced a collection of Galaxy tools that represent many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We developed new methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability and summarized their runtimes on multiple cloud service providers. Some tools represent extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate these to achieve data integration and visualizations are demonstrated on a cohort of 96 diffuse large B-cell lymphomas and enabled the discovery of multiple candidate lymphoma-related genes. Our toolkit is available from our GitHub repository as Galaxy tool and dependency definitions and has been deployed using virtualization on multiple platforms including Docker. © The Author 2017. Published by Oxford University Press.

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.

The top quark (20 years after the discovery)

DOE PAGES

Boos, Eduard; Brandt, Oleg; Denisov, Dmitri; ...

2015-09-10

On the twentieth anniversary of the observation of the top quark, we trace our understanding of this heaviest of all known particles from the prediction of its existence, through the searches and discovery, to the current knowledge of its production mechanisms and properties. We also discuss the central role of the top quark in the Standard Model and the windows that it opens for seeking new physics beyond the Standard Model.

Facilitating knowledge discovery and visualization through mining contextual data from published studies: lessons from JournalMap

USDA-ARS?s Scientific Manuscript database

Valuable information on the location and context of ecological studies are locked up in publications in myriad formats that are not easily machine readable. This presents significant challenges to building geographic-based tools to search for and visualize sources of ecological knowledge. JournalMap...

77 FR 11345 - Harmonization of Compliance Obligations for Registered Investment Companies Required To Register...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-24

... his or her knowledge and belief, the information contained in the document is accurate and complete. The first item in the certification required by SEC Form N-CSR is: ``Based on my knowledge, this..., competitiveness and financial integrity of futures markets; (3) price discovery; (4) sound risk management...

Incremental Knowledge Discovery in Social Media

ERIC Educational Resources Information Center

Tang, Xuning

2013-01-01

In light of the prosperity of online social media, Web users are shifting from data consumers to data producers. To catch the pulse of this rapidly changing world, it is critical to transform online social media data to information and to knowledge. This dissertation centers on the issue of modeling the dynamics of user communities, trending…

Effects of Students' Prior Knowledge on Scientific Reasoning in Density.

ERIC Educational Resources Information Center

Yang, Il-Ho; Kwon, Yong-Ju; Kim, Young-Shin; Jang, Myoung-Duk; Jeong, Jin-Woo; Park, Kuk-Tae

2002-01-01

Investigates the effects of students' prior knowledge on the scientific reasoning processes of performing the task of controlling variables with computer simulation and identifies a number of problems that students encounter in scientific discovery. Involves (n=27) 5th grade students and (n=33) 7th grade students. Indicates that students' prior…

The genomic applications in practice and prevention network.

PubMed

Khoury, Muin J; Feero, W Gregory; Reyes, Michele; Citrin, Toby; Freedman, Andrew; Leonard, Debra; Burke, Wylie; Coates, Ralph; Croyle, Robert T; Edwards, Karen; Kardia, Sharon; McBride, Colleen; Manolio, Teri; Randhawa, Gurvaneet; Rasooly, Rebekah; St Pierre, Jeannette; Terry, Sharon

2009-07-01

The authors describe the rationale and initial development of a new collaborative initiative, the Genomic Applications in Practice and Prevention Network. The network convened by the Centers for Disease Control and Prevention and the National Institutes of Health includes multiple stakeholders from academia, government, health care, public health, industry and consumers. The premise of Genomic Applications in Practice and Prevention Network is that there is an unaddressed chasm between gene discoveries and demonstration of their clinical validity and utility. This chasm is due to the lack of readily accessible information about the utility of most genomic applications and the lack of necessary knowledge by consumers and providers to implement what is known. The mission of Genomic Applications in Practice and Prevention Network is to accelerate and streamline the effective integration of validated genomic knowledge into the practice of medicine and public health, by empowering and sponsoring research, evaluating research findings, and disseminating high quality information on candidate genomic applications in practice and prevention. Genomic Applications in Practice and Prevention Network will develop a process that links ongoing collection of information on candidate genomic applications to four crucial domains: (1) knowledge synthesis and dissemination for new and existing technologies, and the identification of knowledge gaps, (2) a robust evidence-based recommendation development process, (3) translation research to evaluate validity, utility and impact in the real world and how to disseminate and implement recommended genomic applications, and (4) programs to enhance practice, education, and surveillance.

LiverAtlas: a unique integrated knowledge database for systems-level research of liver and hepatic disease.

PubMed

Zhang, Yanqiong; Yang, Chunyuan; Wang, Shaochuang; Chen, Tao; Li, Mansheng; Wang, Xue; Li, Dongsheng; Wang, Kang; Ma, Jie; Wu, Songfeng; Zhang, Xueli; Zhu, Yunping; Wu, Jinsheng; He, Fuchu

2013-09-01

A large amount of liver-related physiological and pathological data exist in publicly available biological and bibliographic databases, which are usually far from comprehensive or integrated. Data collection, integration and mining processes pose a great challenge to scientific researchers and clinicians interested in the liver. To address these problems, we constructed LiverAtlas (http://liveratlas.hupo.org.cn), a comprehensive resource of biomedical knowledge related to the liver and various hepatic diseases by incorporating 53 databases. In the present version, LiverAtlas covers data on liver-related genomics, transcriptomics, proteomics, metabolomics and hepatic diseases. Additionally, LiverAtlas provides a wealth of manually curated information, relevant literature citations and cross-references to other databases. Importantly, an expert-confirmed Human Liver Disease Ontology, including relevant information for 227 types of hepatic disease, has been constructed and is used to annotate LiverAtlas data. Furthermore, we have demonstrated two examples of applying LiverAtlas data to identify candidate markers for hepatocellular carcinoma (HCC) at the systems level and to develop a systems biology-based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC differential diagnosis. LiverAtlas is the most comprehensive liver and hepatic disease resource, which helps biologists and clinicians to analyse their data at the systems level and will contribute much to the biomarker discovery and diagnostic performance enhancement for liver diseases. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Advances in the understanding and use of the genomic base of microbial secondary metabolite biosynthesis for the discovery of new natural products.

PubMed

McAlpine, James B

2009-03-27

Over the past decade major changes have occurred in the access to genome sequences that encode the enzymes responsible for the biosynthesis of secondary metabolites, knowledge of how those sequences translate into the final structure of the metabolite, and the ability to alter the sequence to obtain predicted products via both homologous and heterologous expression. Novel genera have been discovered leading to new chemotypes, but more surprisingly several instances have been uncovered where the apparently general rules of modular translation have not applied. Several new biosynthetic pathways have been unearthed, and our general knowledge grows rapidly. This review aims to highlight some of the more striking discoveries and advances of the decade.

Text mining patents for biomedical knowledge.

PubMed

Rodriguez-Esteban, Raul; Bundschus, Markus

2016-06-01

Biomedical text mining of scientific knowledge bases, such as Medline, has received much attention in recent years. Given that text mining is able to automatically extract biomedical facts that revolve around entities such as genes, proteins, and drugs, from unstructured text sources, it is seen as a major enabler to foster biomedical research and drug discovery. In contrast to the biomedical literature, research into the mining of biomedical patents has not reached the same level of maturity. Here, we review existing work and highlight the associated technical challenges that emerge from automatically extracting facts from patents. We conclude by outlining potential future directions in this domain that could help drive biomedical research and drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

The combined approach of strain discovery and the inbred line technique for improving control of Delia radicum with Heterorhabditis bacteriophora

USDA-ARS?s Scientific Manuscript database

Entomopathogenic nematodes are potent biocontrol agents but their efficacy can be compromised under unfavorable environmental conditions such as cold temperatures. Discovery of new nematode species or strains that are adapted to local conditions is one approach that can be used to enhance efficacy. ...

CREB and the discovery of cognitive enhancers.

PubMed

Scott, Roderick; Bourtchuladze, Rusiko; Gossweiler, Scott; Dubnau, Josh; Tully, Tim

2002-01-01

In the past few years, a series of molecular-genetic, biochemical, cellular and behavioral studies in fruit flies, sea slugs and mice have confirmed a long-standing notion that long-term memory formation depends on the synthesis of new proteins. Experiments focused on the cAMP-responsive transcription factor, CREB, have established that neural activity-induced regulation of gene transcription promotes a synaptic growth process that strengthens the connections among active neurons. This process constitutes a physical basis for the engram--and CREB is a "molecular switch" to produce the engram. Helicon Therapeutics has been formed to identify drug compounds that enhance memory formation via augmentation of CREB biochemistry. Candidate compounds have been identified from a high throughput cell-based screen and are being evaluated in animal models of memory formation. A gene discovery program also seeks to identify new genes, which function downstream of CREB during memory formation, as a source for new drug discoveries in the future. Together, these drug and gene discovery efforts promise new class of pharmaceutical therapies for the treatment of various forms of cognitive dysfunction.

Periodic array-based substrates for surface-enhanced infrared spectroscopy

NASA Astrophysics Data System (ADS)

Mayerhöfer, Thomas G.; Popp, Jürgen

2018-01-01

At the beginning of the 1980s, the first reports of surface-enhanced infrared spectroscopy (SEIRS) surfaced. Probably due to signal-enhancement factors of only 101 to 103, which are modest compared to those of surface-enhanced Raman spectroscopy (SERS), SEIRS did not reach the same significance up to date. However, taking the compared to Raman scattering much larger cross-sections of infrared absorptions and the enhancement factors together, SEIRS reaches about the same sensitivity for molecular species on a surface in terms of the cross-sections as SERS and, due to the complementary nature of both techniques, can valuably augment information gained by SERS. For the first 20 years since its discovery, SEIRS relied completely on metal island films, fabricated by either vapor or electrochemical deposition. The resulting films showed a strong variance concerning their structure, which was essentially random. Therefore, the increase in the corresponding signal-enhancement factors of these structures stagnated in the last years. In the very same years, however, the development of periodic array-based substrates helped SEIRS to gather momentum. This development was supported by technological progress concerning electromagnetic field solvers, which help to understand plasmonic properties and allow targeted design. In addition, the strong progress concerning modern fabrication methods allowed to implement these designs into practice. The aim of this contribution is to critically review the development of these engineered surfaces for SEIRS, to compare the different approaches with regard to their performance where possible, and report further gain of knowledge around and in relation to these structures.

A bilateral integrative health-care knowledge service mechanism based on 'MedGrid'.

PubMed

Liu, Chao; Jiang, Zuhua; Zhen, Lu; Su, Hai

2008-04-01

Current health-care organizations are encountering impression of paucity of medical knowledge. This paper classifies medical knowledge with new scopes. The discovery of health-care 'knowledge flow' initiates a bilateral integrative health-care knowledge service, and we make medical knowledge 'flow' around and gain comprehensive effectiveness through six operations (such as knowledge refreshing...). Seizing the active demand of Chinese health-care revolution, this paper presents 'MedGrid', which is a platform with medical ontology and knowledge contents service. Each level and detailed contents are described on MedGrid info-structure. Moreover, a new diagnosis and treatment mechanism are formed by technically connecting with electronic health-care records (EHRs).

78 FR 35826 - Unfair Competitive Advantages; Enhancement of the Formal Complaint Process

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-14

... Postal Service to the time and expense of the discovery process. The Commission anticipates that allowing.... 1739] Unfair Competitive Advantages; Enhancement of the Formal Complaint Process AGENCY: Postal... enhance the formal complaint process in cases involving alleged violations of a law that prohibits the...

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.

Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

PubMed Central

Bell, Lindsey; Chowdhary, Rajesh; Liu, Jun S.; Niu, Xufeng; Zhang, Jinfeng

2011-01-01

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs. PMID:21738677

32 CFR 34.2 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... increasing knowledge or understanding in science and engineering. Applied research is defined as efforts that attempt to determine and exploit the potential of scientific discoveries or improvements in technology...

76 FR 4452 - Privacy Act of 1974; Report of Modified or Altered System of Records

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-25

... Disease Control and Prevention (CDC) for more complete knowledge of the disease/condition in the following... the light of future discoveries and proven associations so that relevant data collected at the time of... professional staff at the Centers for Disease Control and Prevention (CDC) for more complete knowledge of the...

Trying to Teach Well: A Story of Small Discoveries

ERIC Educational Resources Information Center

Lewis, P. J.

2004-01-01

''Stories do not simply contain knowledge, they are themselves the knowledge'' (Jackson (In: K. Eagan, H. McEwan (Eds.), Narrative in Teaching, Learning and Research, Teacher College Press, New York, 1995, p. 5)). How can we teach well? Perhaps we can find answers through our stories from the classroom. It is through our stories that we make sense…

Knowledge Representation and Data Mining of Neuronal Morphologies Using Neuroinformatics Tools and Formal Ontologies

ERIC Educational Resources Information Center

Polavaram, Sridevi

2016-01-01

Neuroscience can greatly benefit from using novel methods in computer science and informatics, which enable knowledge discovery in unexpected ways. Currently one of the biggest challenges in Neuroscience is to map the functional circuitry of the brain. The applications of this goal range from understanding structural reorganization of neurons to…

Knowledge Translation versus Knowledge Integration: A "Funder's" Perspective

ERIC Educational Resources Information Center

Kerner, Jon F.

2006-01-01

Each year, billions of US tax dollars are spent on basic discovery, intervention development, and efficacy research, while hundreds of billions of US tax dollars are also spent on health service delivery programs. However, little is spent on or known about how best to ensure that the lessons learned from science inform and improve the quality of…

The Assessment of Self-Directed Learning Readiness in Medical Education

ERIC Educational Resources Information Center

Monroe, Katherine Swint

2014-01-01

The rapid pace of scientific discovery has catalyzed the need for medical students to be able to find and assess new information. The knowledge required for physicians' skillful practice will change of the course of their careers, and, to keep up, they must be able to recognized their deficiencies, search for new knowledge, and critically evaluate…

EPA Web Taxonomy

EPA Pesticide Factsheets

EPA's Web Taxonomy is a faceted hierarchical vocabulary used to tag web pages with terms from a controlled vocabulary. Tagging enables search and discovery of EPA's Web based information assests. EPA's Web Taxonomy is being provided in Simple Knowledge Organization System (SKOS) format. SKOS is a standard for sharing and linking knowledge organization systems that promises to make Federal terminology resources more interoperable.

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.

Knowledge discovery by accuracy maximization

PubMed Central

Cacciatore, Stefano; Luchinat, Claudio; Tenori, Leonardo

2014-01-01

Here we describe KODAMA (knowledge discovery by accuracy maximization), an unsupervised and semisupervised learning algorithm that performs feature extraction from noisy and high-dimensional data. Unlike other data mining methods, the peculiarity of KODAMA is that it is driven by an integrated procedure of cross-validation of the results. The discovery of a local manifold’s topology is led by a classifier through a Monte Carlo procedure of maximization of cross-validated predictive accuracy. Briefly, our approach differs from previous methods in that it has an integrated procedure of validation of the results. In this way, the method ensures the highest robustness of the obtained solution. This robustness is demonstrated on experimental datasets of gene expression and metabolomics, where KODAMA compares favorably with other existing feature extraction methods. KODAMA is then applied to an astronomical dataset, revealing unexpected features. Interesting and not easily predictable features are also found in the analysis of the State of the Union speeches by American presidents: KODAMA reveals an abrupt linguistic transition sharply separating all post-Reagan from all pre-Reagan speeches. The transition occurs during Reagan’s presidency and not from its beginning. PMID:24706821

The Knowledge-Integrated Network Biomarkers Discovery for Major Adverse Cardiac Events

PubMed Central

Jin, Guangxu; Zhou, Xiaobo; Wang, Honghui; Zhao, Hong; Cui, Kemi; Zhang, Xiang-Sun; Chen, Luonan; Hazen, Stanley L.; Li, King; Wong, Stephen T. C.

2010-01-01

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein–protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein–protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction. PMID:18665624

Big data to smart data in Alzheimer's disease: The brain health modeling initiative to foster actionable knowledge.

PubMed

Geerts, Hugo; Dacks, Penny A; Devanarayan, Viswanath; Haas, Magali; Khachaturian, Zaven S; Gordon, Mark Forrest; Maudsley, Stuart; Romero, Klaus; Stephenson, Diane

2016-09-01

Massive investment and technological advances in the collection of extensive and longitudinal information on thousands of Alzheimer patients results in large amounts of data. These "big-data" databases can potentially advance CNS research and drug development. However, although necessary, they are not sufficient, and we posit that they must be matched with analytical methods that go beyond retrospective data-driven associations with various clinical phenotypes. Although these empirically derived associations can generate novel and useful hypotheses, they need to be organically integrated in a quantitative understanding of the pathology that can be actionable for drug discovery and development. We argue that mechanism-based modeling and simulation approaches, where existing domain knowledge is formally integrated using complexity science and quantitative systems pharmacology can be combined with data-driven analytics to generate predictive actionable knowledge for drug discovery programs, target validation, and optimization of clinical development. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

A knowledge discovery object model API for Java

PubMed Central

Zuyderduyn, Scott D; Jones, Steven JM

2003-01-01

Background Biological data resources have become heterogeneous and derive from multiple sources. This introduces challenges in the management and utilization of this data in software development. Although efforts are underway to create a standard format for the transmission and storage of biological data, this objective has yet to be fully realized. Results This work describes an application programming interface (API) that provides a framework for developing an effective biological knowledge ontology for Java-based software projects. The API provides a robust framework for the data acquisition and management needs of an ontology implementation. In addition, the API contains classes to assist in creating GUIs to represent this data visually. Conclusions The Knowledge Discovery Object Model (KDOM) API is particularly useful for medium to large applications, or for a number of smaller software projects with common characteristics or objectives. KDOM can be coupled effectively with other biologically relevant APIs and classes. Source code, libraries, documentation and examples are available at . PMID:14583100

National Synchrotron Light Source II

ScienceCinema

Hill, John; Dooryhee, Eric; Wilkins, Stuart; Miller, Lisa; Chu, Yong

2018-01-16

NSLS-II is a synchrotron light source helping researchers explore solutions to the grand energy challenges faced by the nation, and open up new regimes of scientific discovery that will pave the way to discoveries in physics, chemistry, and biology — advances that will ultimately enhance national security and help drive the development of abundant, safe, and clean energy technologies.

Mathematical modeling for novel cancer drug discovery and development.

PubMed

Zhang, Ping; Brusic, Vladimir

2014-10-01

Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments. This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment. Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.

Lifeomics leads the age of grand discoveries.

PubMed

He, Fuchu

2013-03-01

When our knowledge of a field accumulates to a certain level, we are bound to see the rise of one or more great scientists. They will make a series of grand discoveries/breakthroughs and push the discipline into an 'age of grand discoveries'. Mathematics, geography, physics and chemistry have all experienced their ages of grand discoveries; and in life sciences, the age of grand discoveries has appeared countless times since the 16th century. Thanks to the ever-changing development of molecular biology over the past 50 years, contemporary life science is once again approaching its breaking point and the trigger for this is most likely to be 'lifeomics'. At the end of the 20th century, genomics wrote out the 'script of life'; proteomics decoded the script; and RNAomics, glycomics and metabolomics came into bloom. These 'omics', with their unique epistemology and methodology, quickly became the thrust of life sciences, pushing the discipline to new high. Lifeomics, which encompasses all omics, has taken shape and is now signalling the dawn of a new era, the age of grand discoveries.

Recent advances in inkjet dispensing technologies: applications in drug discovery.

PubMed

Zhu, Xiangcheng; Zheng, Qiang; Yang, Hu; Cai, Jin; Huang, Lei; Duan, Yanwen; Xu, Zhinan; Cen, Peilin

2012-09-01

Inkjet dispensing technology is a promising fabrication methodology widely applied in drug discovery. The automated programmable characteristics and high-throughput efficiency makes this approach potentially very useful in miniaturizing the design patterns for assays and drug screening. Various custom-made inkjet dispensing systems as well as specialized bio-ink and substrates have been developed and applied to fulfill the increasing demands of basic drug discovery studies. The incorporation of other modern technologies has further exploited the potential of inkjet dispensing technology in drug discovery and development. This paper reviews and discusses the recent developments and practical applications of inkjet dispensing technology in several areas of drug discovery and development including fundamental assays of cells and proteins, microarrays, biosensors, tissue engineering, basic biological and pharmaceutical studies. Progression in a number of areas of research including biomaterials, inkjet mechanical systems and modern analytical techniques as well as the exploration and accumulation of profound biological knowledge has enabled different inkjet dispensing technologies to be developed and adapted for high-throughput pattern fabrication and miniaturization. This in turn presents a great opportunity to propel inkjet dispensing technology into drug 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.

From Residency to Lifelong Learning.

PubMed

Brandt, Keith

2015-11-01

The residency training experience is the perfect environment for learning. The university/institution patient population provides a never-ending supply of patients with unique management challenges. Resources abound that allow the discovery of knowledge about similar situations. Senior teachers provide counseling and help direct appropriate care. Periodic testing and evaluations identify deficiencies, which can be corrected with future study. What happens, however, when the resident graduates? Do they possess all the knowledge they'll need for the rest of their career? Will medical discovery stand still limiting the need for future study? If initial certification establishes that the physician has the skills and knowledge to function as an independent physician and surgeon, how do we assure the public that plastic surgeons will practice lifelong learning and remain safe throughout their career? Enter Maintenance of Certification (MOC). In an ideal world, MOC would provide many of the same tools as residency training: identification of gaps in knowledge, resources to correct those deficiencies, overall assessment of knowledge, feedback about communication skills and professionalism, and methods to evaluate and improve one's practice. This article discusses the need; for education and self-assessment that extends beyond residency training and a commitment to lifelong learning. The American Board of Plastic Surgery MOC program is described to demonstrate how it helps the diplomate reach the goal of continuous practice improvement.

Semantically-enabled Knowledge Discovery in the Deep Carbon Observatory

NASA Astrophysics Data System (ADS)

Wang, H.; Chen, Y.; Ma, X.; Erickson, J. S.; West, P.; Fox, P. A.

2013-12-01

The Deep Carbon Observatory (DCO) is a decadal effort aimed at transforming scientific and public understanding of carbon in the complex deep earth system from the perspectives of Deep Energy, Deep Life, Extreme Physics and Chemistry, and Reservoirs and Fluxes. Over the course of the decade DCO scientific activities will generate a massive volume of data across a variety of disciplines, presenting significant challenges in terms of data integration, management, analysis and visualization, and ultimately limiting the ability of scientists across disciplines to make insights and unlock new knowledge. The DCO Data Science Team (DCO-DS) is applying Semantic Web methodologies to construct a knowledge representation focused on the DCO Earth science disciplines, and use it together with other technologies (e.g. natural language processing and data mining) to create a more expressive representation of the distributed corpus of DCO artifacts including datasets, metadata, instruments, sensors, platforms, deployments, researchers, organizations, funding agencies, grants and various awards. The embodiment of this knowledge representation is the DCO Data Science Infrastructure, in which unique entities within the DCO domain and the relations between them are recognized and explicitly identified. The DCO-DS Infrastructure will serve as a platform for more efficient and reliable searching, discovery, access, and publication of information and knowledge for the DCO scientific community and beyond.

KT-HAK-KUP transporters in major terrestrial photosynthetic organisms: A twenty years tale.

PubMed

Santa-María, Guillermo E; Oliferuk, Sonia; Moriconi, Jorge I

2018-04-21

Since their discovery, twenty years ago, KT-HAK-KUP transporters have become a keystone to understand how alkali cation fluxes are controlled in major land-dwelling photosynthetic organisms. In this review we focus on their discovery, phylogeny, and functions, as well as the regulation of its canonical member, AtHAK5. We also address issues related to structure-function studies, and the technological possibilities opened up by recent findings. Available evidence suggests that this family of transporters underwent an early divergence into major groups following the conquest of land by embryophytes. KT-HAK-KUPs are necessary to accomplish several major developmental and growth processes, as well as to ensure plant responses to environmental injuries. Although the primary function of these transporters is to mediate potassium (K + ) fluxes, some of them can also mediate sodium (Na + ) and cesium (Cs + ) transport, and contribute to maintenance of K + (and Na + ) homeostasis in different plant tissues. In addition, there is evidence for a role of some members of this family in auxin movement and in adenylate cyclase activity. Recent research, focusing on the regulation of the canonical member of this family, AtHAK5, revealed the existence of a complex network that involves transcriptional and post-transcriptional phenomena which control the enhancement of AtHAK5-mediated K + uptake when Arabidopsis thaliana plants are faced with low K + supply. In spite of the formidable advances made since their discovery, important subjects remain to be elucidated to gain a more complete knowledge of the roles and regulation of KT-HAK-KUPs, as well as to improve their use for innovative procedures in crop breeding. Copyright © 2018 Elsevier GmbH. All rights reserved.

Scaffold Repurposing of Old Drugs Towards New Cancer Drug Discovery.

PubMed

Chen, Haijun; Wu, Jianlei; Gao, Yu; Chen, Haiying; Zhou, Jia

2016-01-01

As commented by the Nobelist James Black that "The most fruitful basis of the discovery of a new drug is to start with an old drug", drug repurposing represents an attractive drug discovery strategy. Despite the success of several repurposed drugs on the market, the ultimate therapeutic potential of a large number of non-cancer drugs is hindered during their repositioning due to various issues including the limited efficacy and intellectual property. With the increasing knowledge about the pharmacological properties and newly identified targets, the scaffolds of the old drugs emerge as a great treasure-trove towards new cancer drug discovery. In this review, we summarize the recent advances in the development of novel small molecules for cancer therapy by scaffold repurposing with highlighted examples. The relevant strategies, advantages, challenges and future research directions associated with this approach are also discussed.

Why Quantify Uncertainty in Ecosystem Studies: Obligation versus Discovery Tool?

NASA Astrophysics Data System (ADS)

Harmon, M. E.

2016-12-01

There are multiple motivations for quantifying uncertainty in ecosystem studies. One is as an obligation; the other is as a tool useful in moving ecosystem science toward discovery. While reporting uncertainty should become a routine expectation, a more convincing motivation involves discovery. By clarifying what is known and to what degree it is known, uncertainty analyses can point the way toward improvements in measurements, sampling designs, and models. While some of these improvements (e.g., better sampling designs) may lead to incremental gains, those involving models (particularly model selection) may require large gains in knowledge. To be fully harnessed as a discovery tool, attitudes toward uncertainty may have to change: rather than viewing uncertainty as a negative assessment of what was done, it should be viewed as positive, helpful assessment of what remains to be done.

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.

Mapping the Landscape, Journeying Together: The Gold Foundation's Model for Research-Based Advocacy in Humanism in Medicine.

PubMed

Gaufberg, Elizabeth

2017-12-01

Mapping the Landscape, Journeying Together (MTL) is an initiative of the Arnold P. Gold Foundation Research Institute. The MTL initiative awards teams with a grant to complete a rigorous review of the literature on a topic related to humanism in health care. Teams may then seek a discovery or advocacy grant to fill in gaps in knowledge or to make or advocate for change. In this Commentary, the author reveals the MTL journey through the metaphor of cartography. She describes the initial development of a road map, as well as the MTL community's experience of navigation, discovery, and exploration. MTL participants are not only incrementally adding to a complex body of knowledge but also actively cultivating a robust community of practice.

Microbial Dark Matter Investigations: How Microbial Studies Transform Biological Knowledge and Empirically Sketch a Logic of Scientific Discovery

PubMed Central

Bernard, Guillaume; Pathmanathan, Jananan S; Lannes, Romain; Lopez, Philippe; Bapteste, Eric

2018-01-01

Abstract Microbes are the oldest and most widespread, phylogenetically and metabolically diverse life forms on Earth. However, they have been discovered only 334 years ago, and their diversity started to become seriously investigated even later. For these reasons, microbial studies that unveil novel microbial lineages and processes affecting or involving microbes deeply (and repeatedly) transform knowledge in biology. Considering the quantitative prevalence of taxonomically and functionally unassigned sequences in environmental genomics data sets, and that of uncultured microbes on the planet, we propose that unraveling the microbial dark matter should be identified as a central priority for biologists. Based on former empirical findings of microbial studies, we sketch a logic of discovery with the potential to further highlight the microbial unknowns. PMID:29420719

Conference report: Bioanalysis highlights from the 2012 American Association of Pharmaceutical Scientists National Biotechnology Conference.

PubMed

Crisino, Rebecca M; Geist, Brian; Li, Jian

2012-09-01

The American Association of Pharmaceutical Scientists (AAPS) is an international forum for the exchange of knowledge among scientists to enhance their contributions to drug development. The annual National Biotechnology Conference, organized by the AAPS on 21-23 May 2012 in San Diego, CA, USA, brings together experts from various disciplines representing private industry, academia and governing institutions dedicated toward advancing the scientific and technological progress related to discovery, development and manufacture of medical biotechnology products. Over 300 scientific poster presentations and approximately 50 oral presentation and discussion sessions examined a breadth of topics pertaining to biotechnology drug development, such as the advancement of vaccines and biosimilars, emerging and innovative technologies, nonclinical and clinical bioanalysis, and regulatory updates. This conference report highlights the existing challenges with ligand-binding assays, emerging challenges, innovative integration of various technology platforms and applicable regulatory considerations as they relate to immunogenicity and pharmacokinetic bioanalytical assessments.

Clinical Pharmacology & Therapeutics: Past, Present and Future

PubMed Central

Waldman, SA; Terzic, A

2016-01-01

Clinical Pharmacology & Therapeutics (CPT), the definitive and timely source for advances in human therapeutics, transcends the drug discovery, development, regulation and utilization continuum to catalyze, evolve and disseminate discipline-transformative knowledge. Prioritized themes and multidisciplinary content drive the science and practice of clinical pharmacology, offering a trusted point of reference. An authoritative herald across global communities, CPT is a timeless information vehicle at the vanguard of discovery, translation and application ushering therapeutic innovation into modern health care. PMID:28194770

Order priors for Bayesian network discovery with an application to malware phylogeny

DOE PAGES

Oyen, Diane; Anderson, Blake; Sentz, Kari; ...

2017-09-15

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

Order priors for Bayesian network discovery with an application to malware phylogeny

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

Oyen, Diane; Anderson, Blake; Sentz, Kari

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges)more » in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.« less

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.

Knowledge discovery and system biology in molecular medicine: an application on neurodegenerative diseases.

PubMed

Fattore, Matteo; Arrigo, Patrizio

2005-01-01

The possibility to study an organism in terms of system theory has been proposed in the past, but only the advancement of molecular biology techniques allow us to investigate the dynamical properties of a biological system in a more quantitative and rational way than before . These new techniques can gave only the basic level view of an organisms functionality. The comprehension of its dynamical behaviour depends on the possibility to perform a multiple level analysis. Functional genomics has stimulated the interest in the investigation the dynamical behaviour of an organism as a whole. These activities are commonly known as System Biology, and its interests ranges from molecules to organs. One of the more promising applications is the 'disease modeling'. The use of experimental models is a common procedure in pharmacological and clinical researches; today this approach is supported by 'in silico' predictive methods. This investigation can be improved by a combination of experimental and computational tools. The Machine Learning (ML) tools are able to process different heterogeneous data sources, taking into account this peculiarity, they could be fruitfully applied to support a multilevel data processing (molecular, cellular and morphological) that is the prerequisite for the formal model design; these techniques can allow us to extract the knowledge for mathematical model development. The aim of our work is the development and implementation of a system that combines ML and dynamical models simulations. The program is addressed to the virtual analysis of the pathways involved in neurodegenerative diseases. These pathologies are multifactorial diseases and the relevance of the different factors has not yet been well elucidated. This is a very complex task; in order to test the integrative approach our program has been limited to the analysis of the effects of a specific protein, the Cyclin dependent kinase 5 (CDK5) which relies on the induction of neuronal apoptosis. The system has a modular structure centred on a textual knowledge discovery approach. The text mining is the only way to enhance the capability to extract ,from multiple data sources, the information required for the dynamical simulator. The user may access the publically available modules through the following site: http://biocomp.ge.ismac.cnr.it.

The History of the Discovery of Blood Circulation: Unrecognized Contributions of Ayurveda Masters

ERIC Educational Resources Information Center

Patwardhan, Kishor

2012-01-01

Ayurveda, the native healthcare system of India, is a rich resource of well-documented ancient medical knowledge. Although the roots of this knowledge date back to the Vedic and post-Vedic eras, it is generally believed that a dedicated branch for healthcare was gradually established approximately between 400 BCE and 200 CE. Probably because the…

Creating a Ten-Year Science and Innovation Framework for the UK: A Perspective Based on US Experience

ERIC Educational Resources Information Center

Crawley, Edward F.; Greenwald, Suzanne B.

2006-01-01

The sustainability of a competitive, national economy depends largely on the ability of companies to deliver innovative knowledge-intensive goods and services to the market. These are the ultimate outputs of a scientific knowledge system. Ideas flow from the critical, identifiable phases of (a) the discovery, (b) the development, (c) the…

[The discovery of blood circulation: revolution or revision?].

PubMed

Crignon, Claire

2011-01-01

The discovery of the principle of blood circulation by William Harvey is generally considered as one of the major events of the "scientific revolution" of the 17th century. This paper reconsiders the question by taking in account the way Harvey's discovery was discussed by some contemporary philosophers and physicians, in particular Fontenelle, who insisted on the necessity of redefining methods and principles of medical knowledge, basing themselves on the revival of anatomy and physiology, and of its consequences on the way it permits to think about the human nature. This return allows us to consider the opportunity of substituting the kuhnian scheme of "structure of scientific revolutions" for the bachelardian concept of "refonte".

Antibacterial Drug Discovery: Some Assembly Required.

PubMed

Tommasi, Rubén; Iyer, Ramkumar; Miller, Alita A

2018-05-11

Our limited understanding of the molecular basis for compound entry into and efflux out of Gram-negative bacteria is now recognized as a key bottleneck for the rational discovery of novel antibacterial compounds. Traditional, large-scale biochemical or target-agnostic phenotypic antibacterial screening efforts have, as a result, not been very fruitful. A main driver of this knowledge gap has been the historical lack of predictive cellular assays, tools, and models that provide structure-activity relationships to inform optimization of compound accumulation. A variety of recent approaches has recently been described to address this conundrum. This Perspective explores these approaches and considers ways in which their integration could successfully redirect antibacterial drug discovery efforts.

The Self-Discovery Programme for Children with Special Educational Needs in Mainstream Primary and Secondary Schools: An Exploratory Study

ERIC Educational Resources Information Center

Cullen-Powell, Lesley; Barlow, Julie; Bagh, Jagrup

2005-01-01

This exploratory study concerns the Self-Discovery Programme (SDP) designed for children with emotional and behavioural difficulties in mainstream schools. The aim of the SDP is to provide children with a range of practical relaxation skills that may enhance emotional wellbeing, increase self-awareness and promote self-regulatory behaviour.…

From scientific discovery to cures: bright stars within a galaxy.

PubMed

Williams, R Sanders; Lotia, Samad; Holloway, Alisha K; Pico, Alexander R

2015-09-24

We propose that data mining and network analysis utilizing public databases can identify and quantify relationships between scientific discoveries and major advances in medicine (cures). Further development of such approaches could help to increase public understanding and governmental support for life science research and could enhance decision making in the quest for cures. Copyright © 2015 Elsevier Inc. All rights reserved.

Knowledge discovery in traditional Chinese medicine: state of the art and perspectives.

PubMed

Feng, Yi; Wu, Zhaohui; Zhou, Xuezhong; Zhou, Zhongmei; Fan, Weiyu

2006-11-01

As a complementary medical system to Western medicine, traditional Chinese medicine (TCM) provides a unique theoretical and practical approach to the treatment of diseases over thousands of years. Confronted with the increasing popularity of TCM and the huge volume of TCM data, historically accumulated and recently obtained, there is an urgent need to explore these resources effectively by the techniques of knowledge discovery in database (KDD). This paper aims at providing an overview of recent KDD studies in TCM field. A literature search was conducted in both English and Chinese publications, and major studies of knowledge discovery in TCM (KDTCM) reported in these materials were identified. Based on an introduction to the state of the art of TCM data resources, a review of four subfields of KDTCM research was presented, including KDD for the research of Chinese medical formula, KDD for the research of Chinese herbal medicine, KDD for TCM syndrome research, and KDD for TCM clinical diagnosis. Furthermore, the current state and main problems in each subfield were summarized based on a discussion of existing studies, and future directions for each subfield were also proposed accordingly. A series of KDD methods are used in existing KDTCM researches, ranging from conventional frequent itemset mining to state of the art latent structure model. Considerable interesting discoveries are obtained by these methods, such as novel TCM paired drugs discovered by frequent itemset analysis, functional community of related genes discovered under syndrome perspective by text mining, the high proportion of toxic plants in the botanical family Ranunculaceae disclosed by statistical analysis, the association between M-cholinoceptor blocking drug and Solanaceae revealed by association rule mining, etc. It is particularly inspiring to see some studies connecting TCM with biomedicine, which provide a novel top-down view for functional genomics research. However, further developments of KDD methods are still expected to better adapt to the features of TCM. Existing studies demonstrate that KDTCM is effective in obtaining medical discoveries. However, much more work needs to be done in order to discover real diamonds from TCM domain. The usage and development of KDTCM in the future will substantially contribute to the TCM community, as well as modern life science.

75 FR 71005 - American Education Week, 2010

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-22

... maintain our Nation's role as the world's engine of discovery and innovation, my Administration is.... Our Nation's schools can give students the tools, skills, and knowledge to participate fully in our...

75 FR 14608 - Statement of Organization, Functions, and Delegations of Authority

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-26

... situations; (5) helps to develop and encourage innovation throughout the spectrum from scientific discovery... transparency and accountability of CDC extramural research programs; (5) provides oversight of knowledge...

Knowledge discovery based on experiential learning corporate culture management

NASA Astrophysics Data System (ADS)

Tu, Kai-Jan

2014-10-01

A good corporate culture based on humanistic theory can make the enterprise's management very effective, all enterprise's members have strong cohesion and centripetal force. With experiential learning model, the enterprise can establish an enthusiastic learning spirit corporate culture, have innovation ability to gain the positive knowledge growth effect, and to meet the fierce global marketing competition. A case study on Trend's corporate culture can offer the proof of industry knowledge growth rate equation as the contribution to experiential learning corporate culture management.

Data Mining and Knowledge Discovery tools for exploiting big Earth-Observation data

NASA Astrophysics Data System (ADS)

Espinoza Molina, D.; Datcu, M.

2015-04-01

The continuous increase in the size of the archives and in the variety and complexity of Earth-Observation (EO) sensors require new methodologies and tools that allow the end-user to access a large image repository, to extract and to infer knowledge about the patterns hidden in the images, to retrieve dynamically a collection of relevant images, and to support the creation of emerging applications (e.g.: change detection, global monitoring, disaster and risk management, image time series, etc.). In this context, we are concerned with providing a platform for data mining and knowledge discovery content from EO archives. The platform's goal is to implement a communication channel between Payload Ground Segments and the end-user who receives the content of the data coded in an understandable format associated with semantics that is ready for immediate exploitation. It will provide the user with automated tools to explore and understand the content of highly complex images archives. The challenge lies in the extraction of meaningful information and understanding observations of large extended areas, over long periods of time, with a broad variety of EO imaging sensors in synergy with other related measurements and data. The platform is composed of several components such as 1.) ingestion of EO images and related data providing basic features for image analysis, 2.) query engine based on metadata, semantics and image content, 3.) data mining and knowledge discovery tools for supporting the interpretation and understanding of image content, 4.) semantic definition of the image content via machine learning methods. All these components are integrated and supported by a relational database management system, ensuring the integrity and consistency of Terabytes of Earth Observation data.

Generalized lessons about sequence learning from the study of the serial reaction time task

PubMed Central

Schwarb, Hillary; Schumacher, Eric H.

2012-01-01

Over the last 20 years researchers have used the serial reaction time (SRT) task to investigate the nature of spatial sequence learning. They have used the task to identify the locus of spatial sequence learning, identify situations that enhance and those that impair learning, and identify the important cognitive processes that facilitate this type of learning. Although controversies remain, the SRT task has been integral in enhancing our understanding of implicit sequence learning. It is important, however, to ask what, if anything, the discoveries made using the SRT task tell us about implicit learning more generally. This review analyzes the state of the current spatial SRT sequence learning literature highlighting the stimulus-response rule hypothesis of sequence learning which we believe provides a unifying account of discrepant SRT data. It also challenges researchers to use the vast body of knowledge acquired with the SRT task to understand other implicit learning literatures too often ignored in the context of this particular task. This broad perspective will make it possible to identify congruences among data acquired using various different tasks that will allow us to generalize about the nature of implicit learning. PMID:22723815

Training scientists as future industry leaders: teaching translational science from an industry executive’s perspective

PubMed Central

Lee, Gloria; Kranzler, Jay D; Ramasamy, Ravichandran; Gold-von Simson, Gabrielle

2018-01-01

PhDs and post-doctoral biomedical graduates, in greater numbers, are choosing industry based careers. However, most scientists do not have formal training in business strategies and venture creation and may find senior management positions untenable. To fill this training gap, “Biotechnology Industry: Structure and Strategy” was offered at New York University School of Medicine (NYUSOM). The course focuses on the business aspects of translational medicine and research translation and incorporates the practice of business case discussions, mock negotiation, and direct interactions into the didactic. The goal is to teach scientists at an early career stage how to create solutions, whether at the molecular level or via the creation of devices or software, to benefit those with disease. In doing so, young, talented scientists can develop a congruent mindset with biotechnology/industry executives. Our data demonstrates that the course enhances students’ knowledge of the biotechnology industry. In turn, these learned skills may further encourage scientists to seek leadership positions in the field. Implementation of similar courses and educational programs will enhance scientists’ training and inspire them to become innovative leaders in the discovery and development of therapeutics. PMID:29657853

Training scientists as future industry leaders: teaching translational science from an industry executive's perspective.

PubMed

Lee, Gloria; Kranzler, Jay D; Ramasamy, Ravichandran; Gold-von Simson, Gabrielle

2018-01-01

PhDs and post-doctoral biomedical graduates, in greater numbers, are choosing industry based careers. However, most scientists do not have formal training in business strategies and venture creation and may find senior management positions untenable. To fill this training gap, "Biotechnology Industry: Structure and Strategy" was offered at New York University School of Medicine (NYUSOM). The course focuses on the business aspects of translational medicine and research translation and incorporates the practice of business case discussions, mock negotiation, and direct interactions into the didactic. The goal is to teach scientists at an early career stage how to create solutions, whether at the molecular level or via the creation of devices or software, to benefit those with disease. In doing so, young, talented scientists can develop a congruent mindset with biotechnology/industry executives. Our data demonstrates that the course enhances students' knowledge of the biotechnology industry. In turn, these learned skills may further encourage scientists to seek leadership positions in the field. Implementation of similar courses and educational programs will enhance scientists' training and inspire them to become innovative leaders in the discovery and development of therapeutics.

A review of the source and function of microbiota in breast milk.

PubMed

Latuga, M Susan; Stuebe, Alison; Seed, Patrick C

2014-01-01

Breast milk contains a rich microbiota composed of viable skin and non-skin bacteria. The extent of the breast milk microbiota diversity has been revealed through new culture-independent studies using microbial DNA signatures. However, the extent to which the breast milk microbiota are transferred from mother to infant and the function of these breast milk microbiota for the infant are only partially understood. Here, we appraise hypotheses regarding the formation of breast milk microbiota, including retrograde infant-to-mother transfer and enteromammary trafficking, and we review current knowledge of mechanisms determining the extent of breast milk microbiota transfer from mother to infant. We highlight known functions of constituents in the breast milk microbiota-to enhance immunity, liberate nutrients, synergize with breast milk oligosaccharides to enhance intestinal barrier function, and strengthen a functional gut-brain axis. We also consider the pathophysiology of maternal mastitis with respect to a dysbiosis or abnormal shift in the breast milk microbiota. In conclusion, through a complex, highly evolved process in the early stages of discovery, mothers transfer the breast milk microbiota to their infants to impact infant growth and development. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Gene and enhancer trap tagging of vascular-expressed genes in poplar trees

Treesearch

Andrew Groover; Joseph R. Fontana; Gayle Dupper; Caiping Ma; Robert Martienssen; Steven Strauss; Richard Meilan

2004-01-01

We report a gene discovery system for poplar trees based on gene and enhancer traps. Gene and enhancer trap vectors carrying the β-glucuronidase (GUS) reporter gene were inserted into the poplar genome via Agrobacterium tumefaciens transformation, where they reveal the expression pattern of genes at or near the insertion sites. Because GUS...

NASA Reverb: Standards-Driven Earth Science Data and Service Discovery

NASA Astrophysics Data System (ADS)

Cechini, M. F.; Mitchell, A.; Pilone, D.

2011-12-01

NASA's Earth Observing System Data and Information System (EOSDIS) is a core capability in NASA's Earth Science Data Systems Program. NASA's EOS ClearingHOuse (ECHO) is a metadata catalog for the EOSDIS, providing a centralized catalog of data products and registry of related data services. Working closely with the EOSDIS community, the ECHO team identified a need to develop the next generation EOS data and service discovery tool. This development effort relied on the following principles: + Metadata Driven User Interface - Users should be presented with data and service discovery capabilities based on dynamic processing of metadata describing the targeted data. + Integrated Data & Service Discovery - Users should be able to discovery data and associated data services that facilitate their research objectives. + Leverage Common Standards - Users should be able to discover and invoke services that utilize common interface standards. Metadata plays a vital role facilitating data discovery and access. As data providers enhance their metadata, more advanced search capabilities become available enriching a user's search experience. Maturing metadata formats such as ISO 19115 provide the necessary depth of metadata that facilitates advanced data discovery capabilities. Data discovery and access is not limited to simply the retrieval of data granules, but is growing into the more complex discovery of data services. These services include, but are not limited to, services facilitating additional data discovery, subsetting, reformatting, and re-projecting. The discovery and invocation of these data services is made significantly simpler through the use of consistent and interoperable standards. By utilizing an adopted standard, developing standard-specific adapters can be utilized to communicate with multiple services implementing a specific protocol. The emergence of metadata standards such as ISO 19119 plays a similarly important role in discovery as the 19115 standard. After a yearlong design, development, and testing process, the ECHO team successfully released "Reverb - The Next Generation Earth Science Discovery Tool." Reverb relies heavily on the information contained in dataset and granule metadata, such as ISO 19115, to provide a dynamic experience to users based on identified search facet values extracted from science metadata. Such an approach allows users to perform cross-dataset correlation and searches, discovering additional data that they may not previously have been aware of. In addition to data discovery, Reverb users may discover services associated with their data of interest. When services utilize supported standards and/or protocols, Reverb can facilitate the invocation of both synchronous and asynchronous data processing services. This greatly enhances a users ability to discover data of interest and accomplish their research goals. Extrapolating on the current movement towards interoperable standards and an increase in available services, data service invocation and chaining will become a natural part of data discovery. Reverb is one example of a discovery tool that provides a mechanism for transforming the earth science data discovery paradigm.

Featured Article: Genotation: Actionable knowledge for the scientific reader

PubMed Central

Willis, Ethan; Sakauye, Mark; Jose, Rony; Chen, Hao; Davis, Robert L

2016-01-01

We present an article viewer application that allows a scientific reader to easily discover and share knowledge by linking genomics-related concepts to knowledge of disparate biomedical databases. High-throughput data streams generated by technical advancements have contributed to scientific knowledge discovery at an unprecedented rate. Biomedical Informaticists have created a diverse set of databases to store and retrieve the discovered knowledge. The diversity and abundance of such resources present biomedical researchers a challenge with knowledge discovery. These challenges highlight a need for a better informatics solution. We use a text mining algorithm, Genomine, to identify gene symbols from the text of a journal article. The identified symbols are supplemented with information from the GenoDB knowledgebase. Self-updating GenoDB contains information from NCBI Gene, Clinvar, Medgen, dbSNP, KEGG, PharmGKB, Uniprot, and Hugo Gene databases. The journal viewer is a web application accessible via a web browser. The features described herein are accessible on www.genotation.org. The Genomine algorithm identifies gene symbols with an accuracy shown by .65 F-Score. GenoDB currently contains information regarding 59,905 gene symbols, 5633 drug–gene relationships, 5981 gene–disease relationships, and 713 pathways. This application provides scientific readers with actionable knowledge related to concepts of a manuscript. The reader will be able to save and share supplements to be visualized in a graphical manner. This provides convenient access to details of complex biological phenomena, enabling biomedical researchers to generate novel hypothesis to further our knowledge in human health. This manuscript presents a novel application that integrates genomic, proteomic, and pharmacogenomic information to supplement content of a biomedical manuscript and enable readers to automatically discover actionable knowledge. PMID:26900164

Featured Article: Genotation: Actionable knowledge for the scientific reader.

PubMed

Nagahawatte, Panduka; Willis, Ethan; Sakauye, Mark; Jose, Rony; Chen, Hao; Davis, Robert L

2016-06-01

We present an article viewer application that allows a scientific reader to easily discover and share knowledge by linking genomics-related concepts to knowledge of disparate biomedical databases. High-throughput data streams generated by technical advancements have contributed to scientific knowledge discovery at an unprecedented rate. Biomedical Informaticists have created a diverse set of databases to store and retrieve the discovered knowledge. The diversity and abundance of such resources present biomedical researchers a challenge with knowledge discovery. These challenges highlight a need for a better informatics solution. We use a text mining algorithm, Genomine, to identify gene symbols from the text of a journal article. The identified symbols are supplemented with information from the GenoDB knowledgebase. Self-updating GenoDB contains information from NCBI Gene, Clinvar, Medgen, dbSNP, KEGG, PharmGKB, Uniprot, and Hugo Gene databases. The journal viewer is a web application accessible via a web browser. The features described herein are accessible on www.genotation.org The Genomine algorithm identifies gene symbols with an accuracy shown by .65 F-Score. GenoDB currently contains information regarding 59,905 gene symbols, 5633 drug-gene relationships, 5981 gene-disease relationships, and 713 pathways. This application provides scientific readers with actionable knowledge related to concepts of a manuscript. The reader will be able to save and share supplements to be visualized in a graphical manner. This provides convenient access to details of complex biological phenomena, enabling biomedical researchers to generate novel hypothesis to further our knowledge in human health. This manuscript presents a novel application that integrates genomic, proteomic, and pharmacogenomic information to supplement content of a biomedical manuscript and enable readers to automatically discover actionable knowledge. © 2016 by the Society for Experimental Biology and Medicine.

Designing and Developing a NASA Research Projects Knowledge Base and Implementing Knowledge Management and Discovery Techniques

NASA Astrophysics Data System (ADS)

Dabiru, L.; O'Hara, C. G.; Shaw, D.; Katragadda, S.; Anderson, D.; Kim, S.; Shrestha, B.; Aanstoos, J.; Frisbie, T.; Policelli, F.; Keblawi, N.

2006-12-01

The Research Project Knowledge Base (RPKB) is currently being designed and will be implemented in a manner that is fully compatible and interoperable with enterprise architecture tools developed to support NASA's Applied Sciences Program. Through user needs assessment, collaboration with Stennis Space Center, Goddard Space Flight Center, and NASA's DEVELOP Staff personnel insight to information needs for the RPKB were gathered from across NASA scientific communities of practice. To enable efficient, consistent, standard, structured, and managed data entry and research results compilation a prototype RPKB has been designed and fully integrated with the existing NASA Earth Science Systems Components database. The RPKB will compile research project and keyword information of relevance to the six major science focus areas, 12 national applications, and the Global Change Master Directory (GCMD). The RPKB will include information about projects awarded from NASA research solicitations, project investigator information, research publications, NASA data products employed, and model or decision support tools used or developed as well as new data product information. The RPKB will be developed in a multi-tier architecture that will include a SQL Server relational database backend, middleware, and front end client interfaces for data entry. The purpose of this project is to intelligently harvest the results of research sponsored by the NASA Applied Sciences Program and related research program results. We present various approaches for a wide spectrum of knowledge discovery of research results, publications, projects, etc. from the NASA Systems Components database and global information systems and show how this is implemented in SQL Server database. The application of knowledge discovery is useful for intelligent query answering and multiple-layered database construction. Using advanced EA tools such as the Earth Science Architecture Tool (ESAT), RPKB will enable NASA and partner agencies to efficiently identify the significant results for new experiment directions and principle investigators to formulate experiment directions for new proposals.

Consistent visualizations of changing knowledge

PubMed Central

Tipney, Hannah J.; Schuyler, Ronald P.; Hunter, Lawrence

2009-01-01

Networks are increasingly used in biology to represent complex data in uncomplicated symbolic form. However, as biological knowledge is continually evolving, so must those networks representing this knowledge. Capturing and presenting this type of knowledge change over time is particularly challenging due to the intimate manner in which researchers customize those networks they come into contact with. The effective visualization of this knowledge is important as it creates insight into complex systems and stimulates hypothesis generation and biological discovery. Here we highlight how the retention of user customizations, and the collection and visualization of knowledge associated provenance supports effective and productive network exploration. We also present an extension of the Hanalyzer system, ReOrient, which supports network exploration and analysis in the presence of knowledge change. PMID:21347184

Social Ontology Documentation for Knowledge Externalization

NASA Astrophysics Data System (ADS)

Aranda-Corral, Gonzalo A.; Borrego-Díaz, Joaquín; Jiménez-Mavillard, Antonio

Knowledge externalization and organization is a major challenge that companies must face. Also, they have to ask whether is possible to enhance its management. Mechanical processing of information represents a chance to carry out these tasks, as well as to turn intangible knowledge assets into real assets. Machine-readable knowledge provides a basis to enhance knowledge management. A promising approach is the empowering of Knowledge Externalization by the community (users, employees). In this paper, a social semantic tool (called OntoxicWiki) for enhancing the quality of knowledge is presented.

Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery

PubMed Central

Lounnas, Valère; Ritschel, Tina; Kelder, Jan; McGuire, Ross; Bywater, Robert P.; Foloppe, Nicolas

2013-01-01

The past decade has witnessed a paradigm shift in preclinical drug discovery with structure-based drug design (SBDD) making a comeback while high-throughput screening (HTS) methods have continued to generate disappointing results. There is a deficit of information between identified hits and the many criteria that must be fulfilled in parallel to convert them into preclinical candidates that have a real chance to become a drug. This gap can be bridged by investigating the interactions between the ligands and their receptors. Accurate calculations of the free energy of binding are still elusive; however progresses were made with respect to how one may deal with the versatile role of water. A corpus of knowledge combining X-ray structures, bioinformatics and molecular modeling techniques now allows drug designers to routinely produce receptor homology models of increasing quality. These models serve as a basis to establish and validate efficient rationales used to tailor and/or screen virtual libraries with enhanced chances of obtaining hits. Many case reports of successful SBDD show how synergy can be gained from the combined use of several techniques. The role of SBDD with respect to two different classes of widely investigated pharmaceutical targets: (a) protein kinases (PK) and (b) G-protein coupled receptors (GPCR) is discussed. Throughout these examples prototypical situations covering the current possibilities and limitations of SBDD are presented. PMID:24688704

Mining manufacturing data for discovery of high productivity process characteristics.

PubMed

Charaniya, Salim; Le, Huong; Rangwala, Huzefa; Mills, Keri; Johnson, Kevin; Karypis, George; Hu, Wei-Shou

2010-06-01

Modern manufacturing facilities for bioproducts are highly automated with advanced process monitoring and data archiving systems. The time dynamics of hundreds of process parameters and outcome variables over a large number of production runs are archived in the data warehouse. This vast amount of data is a vital resource to comprehend the complex characteristics of bioprocesses and enhance production robustness. Cell culture process data from 108 'trains' comprising production as well as inoculum bioreactors from Genentech's manufacturing facility were investigated. Each run constitutes over one-hundred on-line and off-line temporal parameters. A kernel-based approach combined with a maximum margin-based support vector regression algorithm was used to integrate all the process parameters and develop predictive models for a key cell culture performance parameter. The model was also used to identify and rank process parameters according to their relevance in predicting process outcome. Evaluation of cell culture stage-specific models indicates that production performance can be reliably predicted days prior to harvest. Strong associations between several temporal parameters at various manufacturing stages and final process outcome were uncovered. This model-based data mining represents an important step forward in establishing a process data-driven knowledge discovery in bioprocesses. Implementation of this methodology on the manufacturing floor can facilitate a real-time decision making process and thereby improve the robustness of large scale bioprocesses. 2010 Elsevier B.V. All rights reserved.

Perceptual learning modules in mathematics: enhancing students' pattern recognition, structure extraction, and fluency.

PubMed

Kellman, Philip J; Massey, Christine M; Son, Ji Y

2010-04-01

Learning in educational settings emphasizes declarative and procedural knowledge. Studies of expertise, however, point to other crucial components of learning, especially improvements produced by experience in the extraction of information: perceptual learning (PL). We suggest that such improvements characterize both simple sensory and complex cognitive, even symbolic, tasks through common processes of discovery and selection. We apply these ideas in the form of perceptual learning modules (PLMs) to mathematics learning. We tested three PLMs, each emphasizing different aspects of complex task performance, in middle and high school mathematics. In the MultiRep PLM, practice in matching function information across multiple representations improved students' abilities to generate correct graphs and equations from word problems. In the Algebraic Transformations PLM, practice in seeing equation structure across transformations (but not solving equations) led to dramatic improvements in the speed of equation solving. In the Linear Measurement PLM, interactive trials involving extraction of information about units and lengths produced successful transfer to novel measurement problems and fraction problem solving. Taken together, these results suggest (a) that PL techniques have the potential to address crucial, neglected dimensions of learning, including discovery and fluent processing of relations; (b) PL effects apply even to complex tasks that involve symbolic processing; and (c) appropriately designed PL technology can produce rapid and enduring advances in learning. Copyright © 2009 Cognitive Science Society, Inc.

Sugar Transporters in Plants: New Insights and Discoveries.

PubMed

Julius, Benjamin T; Leach, Kristen A; Tran, Thu M; Mertz, Rachel A; Braun, David M

2017-09-01

Carbohydrate partitioning is the process of carbon assimilation and distribution from source tissues, such as leaves, to sink tissues, such as stems, roots and seeds. Sucrose, the primary carbohydrate transported long distance in many plant species, is loaded into the phloem and unloaded into distal sink tissues. However, many factors, both genetic and environmental, influence sucrose metabolism and transport. Therefore, understanding the function and regulation of sugar transporters and sucrose metabolic enzymes is key to improving agriculture. In this review, we highlight recent findings that (i) address the path of phloem loading of sucrose in rice and maize leaves; (ii) discuss the phloem unloading pathways in stems and roots and the sugar transporters putatively involved; (iii) describe how heat and drought stress impact carbohydrate partitioning and phloem transport; (iv) shed light on how plant pathogens hijack sugar transporters to obtain carbohydrates for pathogen survival, and how the plant employs sugar transporters to defend against pathogens; and (v) discuss novel roles for sugar transporters in plant biology. These exciting discoveries and insights provide valuable knowledge that will ultimately help mitigate the impending societal challenges due to global climate change and a growing population by improving crop yield and enhancing renewable energy production. © The Author 2017. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.

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

,

The reports included in this report are for project activities that occurred from October 2011 through September 2012. These reports describe in detail the discoveries, achievements, and challenges encountered by our talented and enthusiastic principal investigators (PIs). Many of the reports describe R&D efforts that were “successful” in their pursuits and resulted in a positive outcome or technology realization. As we’ve stated before, and continue to stress, in some cases the result is a “negative” finding, for instance a technology is currently impractical or out of reach. This can often be viewed erroneously as a “failure,” but is actually amore » valid outcome in the pursuit of high-risk research, which often leads to unforeseen new paths of discovery. Either result advances our knowledge and increases our ability to identify solutions and/or likewise avoid costly paths not appropriate for the challenges presented. The SDRD program continues to provide an unfettered mechanism for innovation and development that returns multifold to the NNSS mission. Overall the program is a strong R&D innovation engine, benefited by an enhanced mission, committed resources, and sound competitiveness to yield maximum benefit. The 23 projects described exemplify the creativity and ability of a diverse scientific and engineering talent base. The efforts also showcase an impressive capability and resource that can be brought to find solutions to a broad array of technology needs and applications relevant to the NNSS mission and national security.« less

Strongly coupled quark-gluon plasma in heavy ion collisions

NASA Astrophysics Data System (ADS)

Shuryak, Edward

2017-07-01

A decade ago, a brief summary of the field of the relativistic heavy ion physics could be formulated as the discovery of strongly coupled quark-gluon plasma, sQGP for short, a near-perfect fluid with surprisingly large entropy-density-to-viscosity ratio. Since 2010, the LHC heavy ion program added excellent new data and discoveries. Significant theoretical efforts have been made to understand these phenomena. Now there is a need to consolidate what we have learned and formulate a list of issues to be studied next. Studies of angular correlations of two and more secondaries reveal higher harmonics of flow, identified as the sound waves induced by the initial state perturbations. As in cosmology, detailed measurements and calculations of these correlations helped to make our knowledge of the explosion much more quantitative. In particular, their damping had quantified the viscosity. Other kinetic coefficients—the heavy-quark diffusion constants and the jet quenching parameters—also show enhancements near the critical point T ≈Tc. Since densities of QGP quarks and gluons strongly decrease at this point, these facts indicate large role of nonperturbative mechanisms, e.g., scattering on monopoles. New studies of the p p and p A collisions at high multiplicities reveal collective explosions similar to those in heavy ion A A collisions. These "smallest drops of the sQGP" revived debates about the initial out-of-equilibrium stage of the collisions and mechanisms of subsequent equilibration.

Evaluation of solar electric propulsion technologies for discovery class missions

NASA Technical Reports Server (NTRS)

Oh, David Y.

2005-01-01

A detailed study examines the potential benefits that advanced electric propulsion (EP) technologies offer to the cost-capped missions in NASA's Discovery program. The study looks at potential cost and performance benefits provided by three EP technologies that are currently in development: NASA's Evolutionary Xenon Thruster (NEXT), an Enhanced NSTAR system, and a Low Power Hall effect thruster. These systems are analyzed on three straw man Discovery class missions and their performance is compared to a state of the art system using the NSTAR ion thruster. An electric propulsion subsystem cost model is used to conduct a cost-benefit analysis for each option. The results show that each proposed technology offers a different degree of performance and/or cost benefit for Discovery class missions.

Clinical Pharmacology & Therapeutics: Past, Present, and Future.

PubMed

Waldman, S A; Terzic, A

2017-03-01

Clinical Pharmacology & Therapeutics (CPT), the definitive and timely source for advances in human therapeutics, transcends the drug discovery, development, regulation, and utilization continuum to catalyze, evolve, and disseminate discipline-transformative knowledge. Prioritized themes and multidisciplinary content drive the science and practice of clinical pharmacology, offering a trusted point of reference. An authoritative herald across global communities, CPT is a timeless information vehicle at the vanguard of discovery, translation, and application ushering therapeutic innovation into modern healthcare. © 2017 American Society for Clinical Pharmacology and Therapeutics.

Development of Scientific Approach Based on Discovery Learning Module

NASA Astrophysics Data System (ADS)

Ellizar, E.; Hardeli, H.; Beltris, S.; Suharni, R.

2018-04-01

Scientific Approach is a learning process, designed to make the students actively construct their own knowledge through stages of scientific method. The scientific approach in learning process can be done by using learning modules. One of the learning model is discovery based learning. Discovery learning is a learning model for the valuable things in learning through various activities, such as observation, experience, and reasoning. In fact, the students’ activity to construct their own knowledge were not optimal. It’s because the available learning modules were not in line with the scientific approach. The purpose of this study was to develop a scientific approach discovery based learning module on Acid Based, also on electrolyte and non-electrolyte solution. The developing process of this chemistry modules use the Plomp Model with three main stages. The stages are preliminary research, prototyping stage, and the assessment stage. The subject of this research was the 10th and 11th Grade of Senior High School students (SMAN 2 Padang). Validation were tested by the experts of Chemistry lecturers and teachers. Practicality of these modules had been tested through questionnaire. The effectiveness had been tested through experimental procedure by comparing student achievement between experiment and control groups. Based on the findings, it can be concluded that the developed scientific approach discovery based learning module significantly improve the students’ learning in Acid-based and Electrolyte solution. The result of the data analysis indicated that the chemistry module was valid in content, construct, and presentation. Chemistry module also has a good practicality level and also accordance with the available time. This chemistry module was also effective, because it can help the students to understand the content of the learning material. That’s proved by the result of learning student. Based on the result can conclude that chemistry module based on discovery learning and scientific approach in electrolyte and non-electrolyte solution and Acid Based for the 10th and 11th grade of senior high school students were valid, practice, and effective.

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

75 FR 5299 - Office of Special Education and Rehabilitative Services; Overview Information; National Institute...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-02

... planned outputs are expected to contribute to advances in knowledge, improvements in policy and practice... of accomplishments (e.g., new or improved tools, methods, discoveries, standards, interventions...

The Circle of Apollonius: A Discovery Activity.

ERIC Educational Resources Information Center

Cain, Ralph W.

1994-01-01

Presents an activity using simple constructions and a knowledge of proportions to discover that the sets of points generated by the described procedures are circles. Presents a proof of the result. (Author/MKR)

Psyche: State of Knowledge from Ground-Based Observations

NASA Astrophysics Data System (ADS)

Reddy, V.; Shepard, M. K.; Takir, D.; Sanchez, J. A.; Richardson, J.; Emery, J. P.; Taylor, P. A.

2017-07-01

We present results from a multi-year campaign to characterize asteroid (16) Psyche, the target of NASA Discovery mission. Our results suggest that Psyche is covered with exogenic carbonaceous impactor similar to Vesta.

BONSAI Garden: Parallel knowledge discovery system for amino acid sequences

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

Shoudai, T.; Miyano, S.; Shinohara, A.

1995-12-31

We have developed a machine discovery system BON-SAI which receives positive and negative examples as inputs and produces as a hypothesis a pair of a decision tree over regular patterns and an alphabet indexing. This system has succeeded in discovering reasonable knowledge on transmembrane domain sequences and signal peptide sequences by computer experiments. However, when several kinds of sequences axe mixed in the data, it does not seem reasonable for a single BONSAI system to find a hypothesis of a reasonably small size with high accuracy. For this purpose, we have designed a system BONSAI Garden, in which several BONSAI`smore » and a program called Gardener run over a network in parallel, to partition the data into some number of classes together with hypotheses explaining these classes accurately.« less

Key Relation Extraction from Biomedical Publications.

PubMed

Huang, Lan; Wang, Ye; Gong, Leiguang; Kulikowski, Casimir; Bai, Tian

2017-01-01

Within the large body of biomedical knowledge, recent findings and discoveries are most often presented as research articles. Their number has been increasing sharply since the turn of the century, presenting ever-growing challenges for search and discovery of knowledge and information related to specific topics of interest, even with the help of advanced online search tools. This is especially true when the goal of a search is to find or discover key relations between important concepts or topic words. We have developed an innovative method for extracting key relations between concepts from abstracts of articles. The method focuses on relations between keywords or topic words in the articles. Early experiments with the method on PubMed publications have shown promising results in searching and discovering keywords and their relationships that are strongly related to the main topic of an article.

Ethical, legal, and social issues in the translation of genomics into health care.

PubMed

Badzek, Laurie; Henaghan, Mark; Turner, Martha; Monsen, Rita

2013-03-01

The rapid continuous feed of new information from scientific discoveries related to the human genome makes translation and incorporation of information into the clinical setting difficult and creates ethical, legal, and social challenges for providers. This article overviews some of the legal and ethical foundations that guide our response to current complex issues in health care associated with the impact of scientific discoveries related to the human genome. Overlapping ethical, legal, and social implications impact nurses and other healthcare professionals as they seek to identify and translate into practice important information related to new genomic scientific knowledge. Ethical and legal foundations such as professional codes, human dignity, and human rights provide the framework for understanding highly complex genomic issues. Ethical, legal, and social concerns of the health provider in the translation of genomic knowledge into practice including minimizing harms, maximizing benefits, transparency, confidentiality, and informed consent are described. Additionally, nursing professional competencies related to ethical, legal, and social issues in the translation of genomics into health care are discussed. Ethical, legal, and social considerations in new genomic discovery necessitate that healthcare professionals have knowledge and competence to respond to complex genomic issues and provide appropriate information and care to patients, families, and communities. Understanding the ethical, legal, and social issues in the translation of genomic information into practice is essential to provide patients, families, and communities with competent, safe, effective health care. © 2013 Sigma Theta Tau International.

PCM-SABRE: a platform for benchmarking and comparing outcome prediction methods in precision cancer medicine.

PubMed

Eyal-Altman, Noah; Last, Mark; Rubin, Eitan

2017-01-17

Numerous publications attempt to predict cancer survival outcome from gene expression data using machine-learning methods. A direct comparison of these works is challenging for the following reasons: (1) inconsistent measures used to evaluate the performance of different models, and (2) incomplete specification of critical stages in the process of knowledge discovery. There is a need for a platform that would allow researchers to replicate previous works and to test the impact of changes in the knowledge discovery process on the accuracy of the induced models. We developed the PCM-SABRE platform, which supports the entire knowledge discovery process for cancer outcome analysis. PCM-SABRE was developed using KNIME. By using PCM-SABRE to reproduce the results of previously published works on breast cancer survival, we define a baseline for evaluating future attempts to predict cancer outcome with machine learning. We used PCM-SABRE to replicate previous work that describe predictive models of breast cancer recurrence, and tested the performance of all possible combinations of feature selection methods and data mining algorithms that was used in either of the works. We reconstructed the work of Chou et al. observing similar trends - superior performance of Probabilistic Neural Network (PNN) and logistic regression (LR) algorithms and inconclusive impact of feature pre-selection with the decision tree algorithm on subsequent analysis. PCM-SABRE is a software tool that provides an intuitive environment for rapid development of predictive models in cancer precision medicine.

NASA's Discovery Program

NASA Astrophysics Data System (ADS)

Kicza, Mary; Bruegge, Richard Vorder

1995-01-01

NASA's Discovery Program represents an new era in planetary exploration. Discovery's primary goal: to maintain U.S. scientific leadership in planetary research by conducting a series of highly focused, cost effective missions to answer critical questions in solar system science. The Program will stimulate the development of innovative management approaches by encouraging new teaming arrangements among industry, universities and the government. The program encourages the prudent use of new technologies to enable/enhance science return and to reduce life cycle cost, and it supports the transfer of these technologies to the private sector for secondary applications. The Near-Earth Asteroid Rendezvous and Mars Pathfinder missions have been selected as the first two Discovery missions. Both will be launched in 1996. Subsequent, competitively selected missions will be conceived and proposed to NASA by teams of scientists and engineers from industry, academia, and government organizations. This paper summarizes the status of Discovery Program planning.

Discovery of pyridine-based agrochemicals by using Intermediate Derivatization Methods.

PubMed

Guan, Ai-Ying; Liu, Chang-Ling; Sun, Xu-Feng; Xie, Yong; Wang, Ming-An

2016-02-01

Pyridine-based compounds have been playing a crucial role as agrochemicals or pesticides including fungicides, insecticides/acaricides and herbicides, etc. Since most of the agrochemicals listed in the Pesticide Manual were discovered through screening programs that relied on trial-and-error testing and new agrochemical discovery is not benefiting as much from the in silico new chemical compound identification/discovery techniques used in pharmaceutical research, it has become more important to find new methods to enhance the efficiency of discovering novel lead compounds in the agrochemical field to shorten the time of research phases in order to meet changing market requirements. In this review, we selected 18 representative known agrochemicals containing a pyridine moiety and extrapolate their discovery from the perspective of Intermediate Derivatization Methods in the hope that this approach will have greater appeal to researchers engaged in the discovery of agrochemicals and/or pharmaceuticals. Copyright © 2015 Elsevier Ltd. All rights reserved.

Discovery of novel drugs for promising targets.

PubMed

Martell, Robert E; Brooks, David G; Wang, Yan; Wilcoxen, Keith

2013-09-01

Once a promising drug target is identified, the steps to actually discover and optimize a drug are diverse and challenging. The goal of this study was to provide a road map to navigate drug discovery. Review general steps for drug discovery and provide illustrating references. A number of approaches are available to enhance and accelerate target identification and validation. Consideration of a variety of potential mechanisms of action of potential drugs can guide discovery efforts. The hit to lead stage may involve techniques such as high-throughput screening, fragment-based screening, and structure-based design, with informatics playing an ever-increasing role. Biologically relevant screening models are discussed, including cell lines, 3-dimensional culture, and in vivo screening. The process of enabling human studies for an investigational drug is also discussed. Drug discovery is a complex process that has significantly evolved in recent years. © 2013 Elsevier HS Journals, Inc. All rights reserved.

Lysergic acid diethylamide: a drug of ‘use’?

PubMed Central

Das, Saibal; Barnwal, Preeti; Ramasamy, Anand; Sen, Sumalya; Mondal, Somnath

2016-01-01

Lysergic acid diethylamide (LSD), described as a classical hallucinogen, began its journey from the middle of the last century following an accidental discovery. Since then, it was used as a popular and notorious substance of abuse in various parts of the world. Its beneficial role as an adjunct to psychotherapy was much unknown, until some ‘benevolent’ experiments were carried out over time to explore some of its potential uses. But, many of its effects were unclear and seemed to be a psychedelic enigma. In this review article, we have described the receptor pharmacology, mechanism of action, effects and adverse effects of LSD on the normal body system. We have also highlighted its addictive potentials and the chances of developing tolerance. We have assimilated some of the interesting therapeutic uses of this drug, such as an antianxiety agent, a creativity enhancer, a suggestibility enhancer, and a performance enhancer. We have also described LSD to be successfully used in drug and alcohol dependence, and as a part of psychedelic peak therapy in terminally ill patients. The relevant chronological history and literature in the light of present knowledge and scenarios have been discussed. Based on available evidence, LSD could be tried therapeutically in certain specific conditions under controlled settings. But as we mention, due to all the safety concerns, the use of this nonaddictive ‘entheogen’ in actual practice warrants a lot of expertise, caution, cooperation and ethical considerations. PMID:27354909

Lysergic acid diethylamide: a drug of 'use'?

PubMed

Das, Saibal; Barnwal, Preeti; Ramasamy, Anand; Sen, Sumalya; Mondal, Somnath

2016-06-01

Lysergic acid diethylamide (LSD), described as a classical hallucinogen, began its journey from the middle of the last century following an accidental discovery. Since then, it was used as a popular and notorious substance of abuse in various parts of the world. Its beneficial role as an adjunct to psychotherapy was much unknown, until some 'benevolent' experiments were carried out over time to explore some of its potential uses. But, many of its effects were unclear and seemed to be a psychedelic enigma. In this review article, we have described the receptor pharmacology, mechanism of action, effects and adverse effects of LSD on the normal body system. We have also highlighted its addictive potentials and the chances of developing tolerance. We have assimilated some of the interesting therapeutic uses of this drug, such as an antianxiety agent, a creativity enhancer, a suggestibility enhancer, and a performance enhancer. We have also described LSD to be successfully used in drug and alcohol dependence, and as a part of psychedelic peak therapy in terminally ill patients. The relevant chronological history and literature in the light of present knowledge and scenarios have been discussed. Based on available evidence, LSD could be tried therapeutically in certain specific conditions under controlled settings. But as we mention, due to all the safety concerns, the use of this nonaddictive 'entheogen' in actual practice warrants a lot of expertise, caution, cooperation and ethical considerations.

Mathematical Basis of Knowledge Discovery and Autonomous Intelligent Architectures. Task #2: Rapid Knowledge Fusion in the Scalable Infosphere

DTIC Science & Technology

2005-12-14

66 4.5.1. K now ledge V isualization by V irtual Reality ...interoperability between market participants (players) in a semantic manner are needed; " War avoidance operations such as peace-keeping, peace...and analysis , situation prediction, etc. The current report sums up the obtained results. It is organized in the following way. Section 2 introduces

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.…

A renaissance of neural networks in drug discovery.

PubMed

Baskin, Igor I; Winkler, David; Tetko, Igor V

2016-08-01

Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the most appropriate approach. In this review, the authors discuss traditional and newly emerging neural network approaches to drug discovery. Their focus is on backpropagation neural networks and their variants, self-organizing maps and associated methods, and a relatively new technique, deep learning. The most important technical issues are discussed including overfitting and its prevention through regularization, ensemble and multitask modeling, model interpretation, and estimation of applicability domain. Different aspects of using neural networks in drug discovery are considered: building structure-activity models with respect to various targets; predicting drug selectivity, toxicity profiles, ADMET and physicochemical properties; characteristics of drug-delivery systems and virtual screening. Neural networks continue to grow in importance for drug discovery. Recent developments in deep learning suggests further improvements may be gained in the analysis of large chemical data sets. It's anticipated that neural networks will be more widely used in drug discovery in the future, and applied in non-traditional areas such as drug delivery systems, biologically compatible materials, and regenerative medicine.

Potential biological targets for bioassay development in drug discovery of Sturge-Weber syndrome.

PubMed

Mohammadipanah, Fatemeh; Salimi, Fatemeh

2018-02-01

Sturge-Weber Syndrome (SWS) is a neurocutaneous disease with clinical manifestations including ocular (glaucoma), cutaneous (port-wine birthmark), neurologic (seizures), and vascular problems. Molecular mechanisms of SWS pathogenesis are initiated by the somatic mutation in GNAQ. Therefore, no definite treatments exist for SWS and treatment options only mitigate the intensity of its clinical manifestations. Biological assay design for drug discovery against this syndrome demands comprehensive knowledge on mechanisms which are involved in its pathogenesis. By analysis of the interrelated molecular targets of SWS, some in vitro bioassay systems can be allotted for drug screening against its progression. Development of such platforms of bioassay can bring along the implementation of high-throughput screening of natural or synthetic compounds in drug discovery programs. Regarding the fact that study of molecular targets and their integration in biological assay design can facilitate the process of effective drug discovery; some potential biological targets and their respective biological assay for SWS drug discovery are propounded in this review. For this purpose, some biological targets for SWS drug discovery such as acetylcholinesterase, alkaline phosphatase, GABAergic receptors, Hypoxia-Inducible Factor (HIF)-1α and 2α are suggested. © 2017 John Wiley & Sons A/S.

Science education as an exercise in foreign affairs

NASA Astrophysics Data System (ADS)

Cobern, William W.

1995-07-01

In Kuhnian terms, science education has been a process of inducting students into the reigning paradigms of science. In 1985, Duschl noted that science education had not kept pace with developments in the history and philosophy of science. The claim of certainty for scientific knowledge which science educators grounded in positivist philosophy was rendered untenable years ago and it turns out that social and cultural factors surrounding discovery may be at least as important as the justification of knowledge. Capitalizing on these new developments, Duschl, Hamilton, and Grandy (1990) wrote a compelling argument for the need to have a joint research effort in science education involving the philosophy and history of science along with cognitive psychology. However, the issue of discovery compels the research community go one step further. If the science education community has been guilty of neglecting historical and philosophical issues in science, let it not now be guilty of ignoring sociological issues in science. A collaborative view ought also to include the sociological study of cultural milieu in which scientific ideas arise. In other words, an external sociological perspective on science. The logic of discovery from a sociological point of view implies that conceptual change can also be viewed from a sociological perspective.

Ontology-Based Search of Genomic Metadata.

PubMed

Fernandez, Javier D; Lenzerini, Maurizio; Masseroli, Marco; Venco, Francesco; Ceri, Stefano

2016-01-01

The Encyclopedia of DNA Elements (ENCODE) is a huge and still expanding public repository of more than 4,000 experiments and 25,000 data files, assembled by a large international consortium since 2007; unknown biological knowledge can be extracted from these huge and largely unexplored data, leading to data-driven genomic, transcriptomic, and epigenomic discoveries. Yet, search of relevant datasets for knowledge discovery is limitedly supported: metadata describing ENCODE datasets are quite simple and incomplete, and not described by a coherent underlying ontology. Here, we show how to overcome this limitation, by adopting an ENCODE metadata searching approach which uses high-quality ontological knowledge and state-of-the-art indexing technologies. Specifically, we developed S.O.S. GeM (http://www.bioinformatics.deib.polimi.it/SOSGeM/), a system supporting effective semantic search and retrieval of ENCODE datasets. First, we constructed a Semantic Knowledge Base by starting with concepts extracted from ENCODE metadata, matched to and expanded on biomedical ontologies integrated in the well-established Unified Medical Language System. We prove that this inference method is sound and complete. Then, we leveraged the Semantic Knowledge Base to semantically search ENCODE data from arbitrary biologists' queries. This allows correctly finding more datasets than those extracted by a purely syntactic search, as supported by the other available systems. We empirically show the relevance of found datasets to the biologists' queries.

Choosing experiments to accelerate collective discovery

DOE PAGES

Rzhetsky, Andrey; Foster, Jacob G.; Foster, Ian T.; ...

2015-11-24

Scientists perform a tiny subset of all possible experiments. What characterizes the experiments they choose? What are the consequences of those choices for the pace of scientific discovery? We model scientific knowledge as a network and science as a sequence of experiments designed to gradually uncover it. By analyzing millions of biomedical articles published over 30 y, we find that biomedical scientists pursue conservative research strategies exploring the local neighborhood of central, important molecules. Although such strategies probably serve scientific careers, we show that they slow scientific advance, especially in mature fields, where more risk and less redundant experimentation wouldmore » accelerate discovery of the network. Lastly, we also consider institutional arrangements that could help science pursue these more efficient strategies.« less

The A-Z of Zika drug discovery.

PubMed

Mottin, Melina; Borba, Joyce V V B; Braga, Rodolpho C; Torres, Pedro H M; Martini, Matheus C; Proenca-Modena, Jose Luiz; Judice, Carla C; Costa, Fabio T M; Ekins, Sean; Perryman, Alexander L; Andrade, Carolina Horta

2018-06-20

Despite the recent outbreak of Zika virus (ZIKV), there are still no approved treatments, and early-stage compounds are probably many years away from approval. A comprehensive A-Z review of the recent advances in ZIKV drug discovery efforts is presented, highlighting drug repositioning and computationally guided compounds, including discovered viral and host cell inhibitors. Promising ZIKV molecular targets are also described and discussed, as well as targets belonging to the host cell, as new opportunities for ZIKV drug discovery. All this knowledge is not only crucial to advancing the fight against the Zika virus and other flaviviruses but also helps us prepare for the next emerging virus outbreak to which we will have to respond. Copyright © 2018. Published by Elsevier Ltd.

Gamma-Ray Bursts: An Overview of Recent Observational Progress

NASA Astrophysics Data System (ADS)

McKay, T. A.; Akerlof, C.; Kehoe, B.; Pawl, A.; Balsano, R.; Bloch, J.; Casperson, D.; Fletcher, S.; Gisler, G.; Hills, J.; Szymanski, J.; Wren, J.; Marshall, S.; Lee, B.

Gamma-ray bursts have, since their discovery in 1969, been the archetypal astrophysical mystery. Despite the detection of thousands of events, our knowledge of the origin and nature of GRBs remained minimal for nearly 30 years. Progress in understanding gamma-ray bursts has undergone explosive growth since the observation in 1997 of the first optical afterglow of a burst. The discovery of afterglows was followed in 1999 by the first simultaneous optical detection of a GRB. These discoveries constitute the beginning of a new field, the multiwavelength study of GRBs. We review here some highlights of what we have learned over the last two years, and look ahead towards an observational program likely to settle most of the remaining GRB questions.

Semantics-enabled service discovery framework in the SIMDAT pharma grid.

PubMed

Qu, Cangtao; Zimmermann, Falk; Kumpf, Kai; Kamuzinzi, Richard; Ledent, Valérie; Herzog, Robert

2008-03-01

We present the design and implementation of a semantics-enabled service discovery framework in the data Grids for process and product development using numerical simulation and knowledge discovery (SIMDAT) Pharma Grid, an industry-oriented Grid environment for integrating thousands of Grid-enabled biological data services and analysis services. The framework consists of three major components: the Web ontology language (OWL)-description logic (DL)-based biological domain ontology, OWL Web service ontology (OWL-S)-based service annotation, and semantic matchmaker based on the ontology reasoning. Built upon the framework, workflow technologies are extensively exploited in the SIMDAT to assist biologists in (semi)automatically performing in silico experiments. We present a typical usage scenario through the case study of a biological workflow: IXodus.

Knowledge Discovery from Vibration Measurements

PubMed Central

Li, Jian; Wang, Daoyao

2014-01-01

The framework as well as the particular algorithms of pattern recognition process is widely adopted in structural health monitoring (SHM). However, as a part of the overall process of knowledge discovery from data bases (KDD), the results of pattern recognition are only changes and patterns of changes of data features. In this paper, based on the similarity between KDD and SHM and considering the particularity of SHM problems, a four-step framework of SHM is proposed which extends the final goal of SHM from detecting damages to extracting knowledge to facilitate decision making. The purposes and proper methods of each step of this framework are discussed. To demonstrate the proposed SHM framework, a specific SHM method which is composed by the second order structural parameter identification, statistical control chart analysis, and system reliability analysis is then presented. To examine the performance of this SHM method, real sensor data measured from a lab size steel bridge model structure are used. The developed four-step framework of SHM has the potential to clarify the process of SHM to facilitate the further development of SHM techniques. PMID:24574933

How to revive breakthrough innovation in the pharmaceutical industry.

PubMed

Munos, Bernard H; Chin, William W

2011-06-29

Over the past 20 years, pharmaceutical companies have implemented conservative management practices to improve the predictability of therapeutics discovery and success rates of drug candidates. This approach has often yielded compounds that are only marginally better than existing therapies, yet require larger, longer, and more complex trials. To fund them, companies have shifted resources away from drug discovery to late clinical development; this has hurt innovation and amplified the crisis brought by the expiration of patents on many best-selling drugs. Here, we argue that more breakthrough therapeutics will reach patients only if the industry ceases to pursue "safe" incremental innovation, re-engages in high-risk discovery research, and adopts collaborative innovation models that allow sharing of knowledge and costs among collaborators.

Towards building a disease-phenotype knowledge base: extracting disease-manifestation relationship from literature

PubMed Central

Xu, Rong; Li, Li; Wang, QuanQiu

2013-01-01

Motivation: Systems approaches to studying phenotypic relationships among diseases are emerging as an active area of research for both novel disease gene discovery and drug repurposing. Currently, systematic study of disease phenotypic relationships on a phenome-wide scale is limited because large-scale machine-understandable disease–phenotype relationship knowledge bases are often unavailable. Here, we present an automatic approach to extract disease–manifestation (D-M) pairs (one specific type of disease–phenotype relationship) from the wide body of published biomedical literature. Data and Methods: Our method leverages external knowledge and limits the amount of human effort required. For the text corpus, we used 119 085 682 MEDLINE sentences (21 354 075 citations). First, we used D-M pairs from existing biomedical ontologies as prior knowledge to automatically discover D-M–specific syntactic patterns. We then extracted additional pairs from MEDLINE using the learned patterns. Finally, we analysed correlations between disease manifestations and disease-associated genes and drugs to demonstrate the potential of this newly created knowledge base in disease gene discovery and drug repurposing. Results: In total, we extracted 121 359 unique D-M pairs with a high precision of 0.924. Among the extracted pairs, 120 419 (99.2%) have not been captured in existing structured knowledge sources. We have shown that disease manifestations correlate positively with both disease-associated genes and drug treatments. Conclusions: The main contribution of our study is the creation of a large-scale and accurate D-M phenotype relationship knowledge base. This unique knowledge base, when combined with existing phenotypic, genetic and proteomic datasets, can have profound implications in our deeper understanding of disease etiology and in rapid drug repurposing. Availability: http://nlp.case.edu/public/data/DMPatternUMLS/ Contact: rxx@case.edu PMID:23828786

A role for physicians in ethnopharmacology and drug discovery.

PubMed

Raza, Mohsin

2006-04-06

Ethnopharmacology investigations classically involved traditional healers, botanists, anthropologists, chemists and pharmacologists. The role of some groups of researchers but not of physician has been highlighted and well defined in ethnopharmacological investigations. Historical data shows that discovery of several important modern drugs of herbal origin owe to the medical knowledge and clinical expertise of physicians. Current trends indicate negligible role of physicians in ethnopharmacological studies. Rising cost of modern drug development is attributed to the lack of classical ethnopharmacological approach. Physicians can play multiple roles in the ethnopharmacological studies to facilitate drug discovery as well as to rescue authentic traditional knowledge of use of medicinal plants. These include: (1) Ethnopharmacological field work which involves interviewing healers, interpreting traditional terminologies into their modern counterparts, examining patients consuming herbal remedies and identifying the disease for which an herbal remedy is used. (2) Interpretation of signs and symptoms mentioned in ancient texts and suggesting proper use of old traditional remedies in the light of modern medicine. (3) Clinical studies on herbs and their interaction with modern medicines. (4) Advising pharmacologists to carryout laboratory studies on herbs observed during field studies. (5) Work in collaboration with local healers to strengthen traditional system of medicine in a community. In conclusion, physician's involvement in ethnopharmacological studies will lead to more reliable information on traditional use of medicinal plants both from field and ancient texts, more focused and cheaper natural product based drug discovery, as well as bridge the gap between traditional and modern medicine.

Discovery: Under the Microscope at Kennedy Space Center

NASA Technical Reports Server (NTRS)

Howard, Philip M.

2013-01-01

The National Aeronautics & Space Administration (NASA) is known for discovery, exploration, and advancement of knowledge. Since the days of Leeuwenhoek, microscopy has been at the forefront of discovery and knowledge. No truer is that statement than today at Kennedy Space Center (KSC), where microscopy plays a major role in contamination identification and is an integral part of failure analysis. Space exploration involves flight hardware undergoing rigorous "visually clean" inspections at every step of processing. The unknown contaminants that are discovered on these inspections can directly impact the mission by decreasing performance of sensors and scientific detectors on spacecraft and satellites, acting as micrometeorites, damaging critical sealing surfaces, and causing hazards to the crew of manned missions. This talk will discuss how microscopy has played a major role in all aspects of space port operations at KSC. Case studies will highlight years of analysis at the Materials Science Division including facility and payload contamination for the Navigation Signal Timing and Ranging Global Positioning Satellites (NA VST AR GPS) missions, quality control monitoring of monomethyl hydrazine fuel procurement for launch vehicle operations, Shuttle Solids Rocket Booster (SRB) foam processing failure analysis, and Space Shuttle Main Engine Cut-off (ECO) flight sensor anomaly analysis. What I hope to share with my fellow microscopists is some of the excitement of microscopy and how its discoveries has led to hardware processing, that has helped enable the successful launch of vehicles and space flight missions here at Kennedy Space Center.

On the Limitations of Biological Knowledge

PubMed Central

Dougherty, Edward R; Shmulevich, Ilya

2012-01-01

Scientific knowledge is grounded in a particular epistemology and, owing to the requirements of that epistemology, possesses limitations. Some limitations are intrinsic, in the sense that they depend inherently on the nature of scientific knowledge; others are contingent, depending on the present state of knowledge, including technology. Understanding limitations facilitates scientific research because one can then recognize when one is confronted by a limitation, as opposed to simply being unable to solve a problem within the existing bounds of possibility. In the hope that the role of limiting factors can be brought more clearly into focus and discussed, we consider several sources of limitation as they apply to biological knowledge: mathematical complexity, experimental constraints, validation, knowledge discovery, and human intellectual capacity. PMID:23633917

Reproduction, physiology and biochemistry

USDA-ARS?s Scientific Manuscript database

This chapter summarizes fundamental knowledge and recent discoveries about the reproduction, physiology and biochemistry of plant-parasitic nematodes. Various types of reproduction are reviewed, including sexual reproduction and mitotic and meiotic parthenogenesis. Although much is known about the p...

77 FR 20491 - National Cancer Control Month, 2012

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-05

... discoveries. During National Cancer Control Month, we remember those we have lost, support Americans fighting... combatting cancer. We owe the knowledge we have gained and the lives we have saved to the countless doctors...

Biochemistry

USDA-ARS?s Scientific Manuscript database

Part of the framework for effective control or management of cyst nematodes depends upon the detailed understanding of their biology. This chapter summarizes fundamental knowledge and recent discoveries about the biochemistry of cyst nematodes, particularly areas related to lipids, carbohydrates and...

Radioactive Dating: A Method for Geochronology.

ERIC Educational Resources Information Center

Rowe, M. W.

1985-01-01

Gives historical background on the discovery of natural radiation and discusses various techniques for using knowledge of radiochemistry in geochronological studies. Indicates that of these radioactive techniques, Potassium-40/Argon-40 dating is used most often. (JN)

48 CFR 31.205-18 - Independent research and development and bid and proposal costs.

Code of Federal Regulations, 2012 CFR

2012-10-01

... determine and exploit the potential of scientific discoveries or improvements in technology, materials... systematic use, under whatever name, of scientific and technical knowledge in the design, development, test...

48 CFR 31.205-18 - Independent research and development and bid and proposal costs.

Code of Federal Regulations, 2013 CFR

2013-10-01

... determine and exploit the potential of scientific discoveries or improvements in technology, materials... systematic use, under whatever name, of scientific and technical knowledge in the design, development, test...

48 CFR 31.205-18 - Independent research and development and bid and proposal costs.

Code of Federal Regulations, 2011 CFR

2011-10-01

... determine and exploit the potential of scientific discoveries or improvements in technology, materials... systematic use, under whatever name, of scientific and technical knowledge in the design, development, test...

48 CFR 31.205-18 - Independent research and development and bid and proposal costs.

Code of Federal Regulations, 2014 CFR

2014-10-01

... determine and exploit the potential of scientific discoveries or improvements in technology, materials... systematic use, under whatever name, of scientific and technical knowledge in the design, development, test...

78 FR 37522 - Request for Information on Pilots to Inform the Creation of Potential New Manufacturing...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-21

... knowledge transfer, technology transition, and technology diffusion steps, along with numerous... promising research discoveries and ideas for advanced, high-value-added products and processes with existing...

A self-updating road map of The Cancer Genome Atlas.

PubMed

Robbins, David E; Grüneberg, Alexander; Deus, Helena F; Tanik, Murat M; Almeida, Jonas S

2013-05-15

Since 2011, The Cancer Genome Atlas' (TCGA) files have been accessible through HTTP from a public site, creating entirely new possibilities for cancer informatics by enhancing data discovery and retrieval. Significantly, these enhancements enable the reporting of analysis results that can be fully traced to and reproduced using their source data. However, to realize this possibility, a continually updated road map of files in the TCGA is required. Creation of such a road map represents a significant data modeling challenge, due to the size and fluidity of this resource: each of the 33 cancer types is instantiated in only partially overlapping sets of analytical platforms, while the number of data files available doubles approximately every 7 months. We developed an engine to index and annotate the TCGA files, relying exclusively on third-generation web technologies (Web 3.0). Specifically, this engine uses JavaScript in conjunction with the World Wide Web Consortium's (W3C) Resource Description Framework (RDF), and SPARQL, the query language for RDF, to capture metadata of files in the TCGA open-access HTTP directory. The resulting index may be queried using SPARQL, and enables file-level provenance annotations as well as discovery of arbitrary subsets of files, based on their metadata, using web standard languages. In turn, these abilities enhance the reproducibility and distribution of novel results delivered as elements of a web-based computational ecosystem. The development of the TCGA Roadmap engine was found to provide specific clues about how biomedical big data initiatives should be exposed as public resources for exploratory analysis, data mining and reproducible research. These specific design elements align with the concept of knowledge reengineering and represent a sharp departure from top-down approaches in grid initiatives such as CaBIG. They also present a much more interoperable and reproducible alternative to the still pervasive use of data portals. A prepared dashboard, including links to source code and a SPARQL endpoint, is available at http://bit.ly/TCGARoadmap. A video tutorial is available at http://bit.ly/TCGARoadmapTutorial. robbinsd@uab.edu.

A self-updating road map of The Cancer Genome Atlas

PubMed Central

Robbins, David E.; Grüneberg, Alexander; Deus, Helena F.; Tanik, Murat M.; Almeida, Jonas S.

2013-01-01

Motivation: Since 2011, The Cancer Genome Atlas’ (TCGA) files have been accessible through HTTP from a public site, creating entirely new possibilities for cancer informatics by enhancing data discovery and retrieval. Significantly, these enhancements enable the reporting of analysis results that can be fully traced to and reproduced using their source data. However, to realize this possibility, a continually updated road map of files in the TCGA is required. Creation of such a road map represents a significant data modeling challenge, due to the size and fluidity of this resource: each of the 33 cancer types is instantiated in only partially overlapping sets of analytical platforms, while the number of data files available doubles approximately every 7 months. Results: We developed an engine to index and annotate the TCGA files, relying exclusively on third-generation web technologies (Web 3.0). Specifically, this engine uses JavaScript in conjunction with the World Wide Web Consortium’s (W3C) Resource Description Framework (RDF), and SPARQL, the query language for RDF, to capture metadata of files in the TCGA open-access HTTP directory. The resulting index may be queried using SPARQL, and enables file-level provenance annotations as well as discovery of arbitrary subsets of files, based on their metadata, using web standard languages. In turn, these abilities enhance the reproducibility and distribution of novel results delivered as elements of a web-based computational ecosystem. The development of the TCGA Roadmap engine was found to provide specific clues about how biomedical big data initiatives should be exposed as public resources for exploratory analysis, data mining and reproducible research. These specific design elements align with the concept of knowledge reengineering and represent a sharp departure from top-down approaches in grid initiatives such as CaBIG. They also present a much more interoperable and reproducible alternative to the still pervasive use of data portals. Availability: A prepared dashboard, including links to source code and a SPARQL endpoint, is available at http://bit.ly/TCGARoadmap. A video tutorial is available at http://bit.ly/TCGARoadmapTutorial. Contact: robbinsd@uab.edu PMID:23595662

Handling knowledge via Concept Maps: a space weather use case

NASA Astrophysics Data System (ADS)

Messerotti, Mauro; Fox, Peter

Concept Maps (Cmaps) are powerful means for knowledge coding in graphical form. As flexible software tools exist to manipulate the knowledge embedded in Cmaps in machine-readable form, such complex entities are suitable candidates not only for the representation of ontologies and semantics in Virtual Observatory (VO) architectures, but also for knowledge handling and knowledge discovery. In this work, we present a use case relevant to space weather applications and we elaborate on its possible implementation and adavanced use in Semantic Virtual Observatories dedicated to Sun-Earth Connections. This analysis was carried out in the framework of the Electronic Geophysical Year (eGY) and represents an achievement synergized by the eGY Virtual Observatories Working Group.

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.

Artificial intelligence techniques for monitoring dangerous infections.

PubMed

Lamma, Evelina; Mello, Paola; Nanetti, Anna; Riguzzi, Fabrizio; Storari, Sergio; Valastro, Gianfranco

2006-01-01

The monitoring and detection of nosocomial infections is a very important problem arising in hospitals. A hospital-acquired or nosocomial infection is a disease that develops after admission into the hospital and it is the consequence of a treatment, not necessarily a surgical one, performed by the medical staff. Nosocomial infections are dangerous because they are caused by bacteria which have dangerous (critical) resistance to antibiotics. This problem is very serious all over the world. In Italy, almost 5-8% of the patients admitted into hospitals develop this kind of infection. In order to reduce this figure, policies for controlling infections should be adopted by medical practitioners. In order to support them in this complex task, we have developed a system, called MERCURIO, capable of managing different aspects of the problem. The objectives of this system are the validation of microbiological data and the creation of a real time epidemiological information system. The system is useful for laboratory physicians, because it supports them in the execution of the microbiological analyses; for clinicians, because it supports them in the definition of the prophylaxis, of the most suitable antibi-otic therapy and in monitoring patients' infections; and for epidemiologists, because it allows them to identify outbreaks and to study infection dynamics. In order to achieve these objectives, we have adopted expert system and data mining techniques. We have also integrated a statistical module that monitors the diffusion of nosocomial infections over time in the hospital, and that strictly interacts with the knowledge based module. Data mining techniques have been used for improving the system knowledge base. The knowledge discovery process is not antithetic, but complementary to the one based on manual knowledge elicitation. In order to verify the reliability of the tasks performed by MERCURIO and the usefulness of the knowledge discovery approach, we performed a test based on a dataset of real infection events. In the validation task MERCURIO achieved an accuracy of 98.5%, a sensitivity of 98.5% and a specificity of 99%. In the therapy suggestion task, MERCURIO achieved very high accuracy and specificity as well. The executed test provided many insights to experts, too (we discovered some of their mistakes). The knowledge discovery approach was very effective in validating part of the MERCURIO knowledge base, and also in extending it with new validation rules, confirmed by interviewed microbiologists and specific to the hospital laboratory under consideration.

Residency research requirements and the CanMEDS-FM scholar role

PubMed Central

Koo, Jonathan; Bains, Jason; Collins, Marisa B.; Dharamsi, Shafik

2012-01-01

Abstract Objective To explore the perspectives of family medicine residents and recent family medicine graduates on the research requirements and other CanMEDS scholar competencies in family practice residency training. Design Semistructured focus groups and individual interviews. Setting Family practice residency program at the University of British Columbia in Vancouver. Participants Convenience sample of 6 second-year family medicine residents and 6 family physicians who had graduated from the University of British Columbia family practice residency program within the previous 5 years. Methods Two focus groups with residents and individual interviews with each of the 6 recently graduated physicians. All interviews were audiotaped, transcribed, and analyzed for thematic content. Main findings Three themes emerged that captured key issues around research requirements in family practice training: 1) relating the scholar role to family practice, 2) realizing that scholarship is more than simply the creation or discovery of new knowledge, and 3) addressing barriers to integrating research into a clinical career. Conclusion Creation of new medical knowledge is just one aspect of the CanMEDS scholar role, and more attention should be paid to the other competencies, including teaching, enhancing professional activities through ongoing learning, critical appraisal of information, and learning how to better contribute to the dissemination, application, and translation of knowledge. Research is valued as important, but opinions still vary as to whether a formal research study should be required in residency. Completion of residency research projects is viewed as somewhat rewarding, but with an equivocal effect on future research intentions. PMID:22859631

MachineProse: an Ontological Framework for Scientific Assertions

PubMed Central

Dinakarpandian, Deendayal; Lee, Yugyung; Vishwanath, Kartik; Lingambhotla, Rohini

2006-01-01

Objective: The idea of testing a hypothesis is central to the practice of biomedical research. However, the results of testing a hypothesis are published mainly in the form of prose articles. Encoding the results as scientific assertions that are both human and machine readable would greatly enhance the synergistic growth and dissemination of knowledge. Design: We have developed MachineProse (MP), an ontological framework for the concise specification of scientific assertions. MP is based on the idea of an assertion constituting a fundamental unit of knowledge. This is in contrast to current approaches that use discrete concept terms from domain ontologies for annotation and assertions are only inferred heuristically. Measurements: We use illustrative examples to highlight the advantages of MP over the use of the Medical Subject Headings (MeSH) system and keywords in indexing scientific articles. Results: We show how MP makes it possible to carry out semantic annotation of publications that is machine readable and allows for precise search capabilities. In addition, when used by itself, MP serves as a knowledge repository for emerging discoveries. A prototype for proof of concept has been developed that demonstrates the feasibility and novel benefits of MP. As part of the MP framework, we have created an ontology of relationship types with about 100 terms optimized for the representation of scientific assertions. Conclusion: MachineProse is a novel semantic framework that we believe may be used to summarize research findings, annotate biomedical publications, and support sophisticated searches. PMID:16357355

Acts of Discovery: Using Collaborative Research to Mobilize and Generate Knowledge about Visual Arts Teaching Practice

ERIC Educational Resources Information Center

Mitchell, Donna Mathewson

2014-01-01

Visual arts teachers engage in complex work on a daily basis. This work is informed by practical knowledge that is rarely examined or drawn on in research or in the development of policy. Focusing on the work of secondary visual arts teachers, this article reports on a research program conducted in a regional area of New South Wales, Australia.…

New Initiatives for Electronic Scholarly Publishing: Academic Information Sources on the Internet

DTIC Science & Technology

2003-04-01

organizations/countries to participate in the development of the global knowledge base. The paper will outline the strategic factors that impact on -the...society proceedings, and the successful creation of more specialised journals, reflecting the fragmentation of knowledge into more specialised...Guedon (2001) - establishing "priority" over a particular scientific discovery or advance, and of "packing" current communication into an indexed and

An interactive visualization tool for mobile objects

NASA Astrophysics Data System (ADS)

Kobayashi, Tetsuo

Recent advancements in mobile devices---such as Global Positioning System (GPS), cellular phones, car navigation system, and radio-frequency identification (RFID)---have greatly influenced the nature and volume of data about individual-based movement in space and time. Due to the prevalence of mobile devices, vast amounts of mobile objects data are being produced and stored in databases, overwhelming the capacity of traditional spatial analytical methods. There is a growing need for discovering unexpected patterns, trends, and relationships that are hidden in the massive mobile objects data. Geographic visualization (GVis) and knowledge discovery in databases (KDD) are two major research fields that are associated with knowledge discovery and construction. Their major research challenges are the integration of GVis and KDD, enhancing the ability to handle large volume mobile objects data, and high interactivity between the computer and users of GVis and KDD tools. This dissertation proposes a visualization toolkit to enable highly interactive visual data exploration for mobile objects datasets. Vector algebraic representation and online analytical processing (OLAP) are utilized for managing and querying the mobile object data to accomplish high interactivity of the visualization tool. In addition, reconstructing trajectories at user-defined levels of temporal granularity with time aggregation methods allows exploration of the individual objects at different levels of movement generality. At a given level of generality, individual paths can be combined into synthetic summary paths based on three similarity measures, namely, locational similarity, directional similarity, and geometric similarity functions. A visualization toolkit based on the space-time cube concept exploits these functionalities to create a user-interactive environment for exploring mobile objects data. Furthermore, the characteristics of visualized trajectories are exported to be utilized for data mining, which leads to the integration of GVis and KDD. Case studies using three movement datasets (personal travel data survey in Lexington, Kentucky, wild chicken movement data in Thailand, and self-tracking data in Utah) demonstrate the potential of the system to extract meaningful patterns from the otherwise difficult to comprehend collections of space-time trajectories.

Do quality improvement collaboratives' educational components match the dominant learning style preferences of the participants?

PubMed

Weggelaar-Jansen, Anne Marie; van Wijngaarden, Jeroen; Slaghuis, Sarah-Sue

2015-06-20

Quality improvement collaboratives are used to improve healthcare by various organizations. Despite their popularity literature shows mixed results on their effectiveness. A quality improvement collaborative can be seen as a temporary learning organization in which knowledge about improvement themes and methods is exchanged. In this research we studied: Does the learning approach of a quality improvement collaborative match the learning styles preferences of the individual participants and how does that affect the learning process of participants? This research used a mixed methods design combining a validated learning style questionnaire with data collected in the tradition of action research methodology to study two Dutch quality improvement collaboratives. The questionnaire is based on the learning style model of Ruijters and Simons, distinguishing five learning style preferences: Acquisition of knowledge, Apperception from others, Discovery of new insights, Exercising in fictitious situations and Participation with others. The most preferred learning styles of the participants were Discovery and Participation. The learning style Acquisition was moderately preferred and Apperception and Exercising were least preferred. The educational components of the quality improvement collaboratives studied (national conferences, half-day learning sessions, faculty site visits and use of an online tool) were predominantly associated with the learning styles Acquisition and Apperception. We observed a decrease in attendance to the learning activities and non-conformance with the standardized set goals and approaches. We conclude that the participants' satisfaction with the offered learning approach changed over time. The lacking match between these learning style preferences and the learning approach in the educational components of the quality improvement collaboratives studied might be the reason why the participants felt they did not gain new insights and therefore ceased their participation in the collaborative. This study provides guidance for future organisers and participants of quality improvement collaboratives about which learning approaches will best suit the participants and enhance improvement work.

Relational Network for Knowledge Discovery through Heterogeneous Biomedical and Clinical Features

PubMed Central

Chen, Huaidong; Chen, Wei; Liu, Chenglin; Zhang, Le; Su, Jing; Zhou, Xiaobo

2016-01-01

Biomedical big data, as a whole, covers numerous features, while each dataset specifically delineates part of them. “Full feature spectrum” knowledge discovery across heterogeneous data sources remains a major challenge. We developed a method called bootstrapping for unified feature association measurement (BUFAM) for pairwise association analysis, and relational dependency network (RDN) modeling for global module detection on features across breast cancer cohorts. Discovered knowledge was cross-validated using data from Wake Forest Baptist Medical Center’s electronic medical records and annotated with BioCarta signaling signatures. The clinical potential of the discovered modules was exhibited by stratifying patients for drug responses. A series of discovered associations provided new insights into breast cancer, such as the effects of patient’s cultural background on preferences for surgical procedure. We also discovered two groups of highly associated features, the HER2 and the ER modules, each of which described how phenotypes were associated with molecular signatures, diagnostic features, and clinical decisions. The discovered “ER module”, which was dominated by cancer immunity, was used as an example for patient stratification and prediction of drug responses to tamoxifen and chemotherapy. BUFAM-derived RDN modeling demonstrated unique ability to discover clinically meaningful and actionable knowledge across highly heterogeneous biomedical big data sets. PMID:27427091

Integrating Genomic Data Sets for Knowledge Discovery: An Informed Approach to Management of Captive Endangered Species.

PubMed

Irizarry, Kristopher J L; Bryant, Doug; Kalish, Jordan; Eng, Curtis; Schmidt, Peggy L; Barrett, Gini; Barr, Margaret C

2016-01-01

Many endangered captive populations exhibit reduced genetic diversity resulting in health issues that impact reproductive fitness and quality of life. Numerous cost effective genomic sequencing and genotyping technologies provide unparalleled opportunity for incorporating genomics knowledge in management of endangered species. Genomic data, such as sequence data, transcriptome data, and genotyping data, provide critical information about a captive population that, when leveraged correctly, can be utilized to maximize population genetic variation while simultaneously reducing unintended introduction or propagation of undesirable phenotypes. Current approaches aimed at managing endangered captive populations utilize species survival plans (SSPs) that rely upon mean kinship estimates to maximize genetic diversity while simultaneously avoiding artificial selection in the breeding program. However, as genomic resources increase for each endangered species, the potential knowledge available for management also increases. Unlike model organisms in which considerable scientific resources are used to experimentally validate genotype-phenotype relationships, endangered species typically lack the necessary sample sizes and economic resources required for such studies. Even so, in the absence of experimentally verified genetic discoveries, genomics data still provides value. In fact, bioinformatics and comparative genomics approaches offer mechanisms for translating these raw genomics data sets into integrated knowledge that enable an informed approach to endangered species management.

Integrating Genomic Data Sets for Knowledge Discovery: An Informed Approach to Management of Captive Endangered Species

PubMed Central

Irizarry, Kristopher J. L.; Bryant, Doug; Kalish, Jordan; Eng, Curtis; Schmidt, Peggy L.; Barrett, Gini; Barr, Margaret C.

2016-01-01

Many endangered captive populations exhibit reduced genetic diversity resulting in health issues that impact reproductive fitness and quality of life. Numerous cost effective genomic sequencing and genotyping technologies provide unparalleled opportunity for incorporating genomics knowledge in management of endangered species. Genomic data, such as sequence data, transcriptome data, and genotyping data, provide critical information about a captive population that, when leveraged correctly, can be utilized to maximize population genetic variation while simultaneously reducing unintended introduction or propagation of undesirable phenotypes. Current approaches aimed at managing endangered captive populations utilize species survival plans (SSPs) that rely upon mean kinship estimates to maximize genetic diversity while simultaneously avoiding artificial selection in the breeding program. However, as genomic resources increase for each endangered species, the potential knowledge available for management also increases. Unlike model organisms in which considerable scientific resources are used to experimentally validate genotype-phenotype relationships, endangered species typically lack the necessary sample sizes and economic resources required for such studies. Even so, in the absence of experimentally verified genetic discoveries, genomics data still provides value. In fact, bioinformatics and comparative genomics approaches offer mechanisms for translating these raw genomics data sets into integrated knowledge that enable an informed approach to endangered species management. PMID:27376076

Relational Network for Knowledge Discovery through Heterogeneous Biomedical and Clinical Features

NASA Astrophysics Data System (ADS)

Chen, Huaidong; Chen, Wei; Liu, Chenglin; Zhang, Le; Su, Jing; Zhou, Xiaobo

2016-07-01

Biomedical big data, as a whole, covers numerous features, while each dataset specifically delineates part of them. “Full feature spectrum” knowledge discovery across heterogeneous data sources remains a major challenge. We developed a method called bootstrapping for unified feature association measurement (BUFAM) for pairwise association analysis, and relational dependency network (RDN) modeling for global module detection on features across breast cancer cohorts. Discovered knowledge was cross-validated using data from Wake Forest Baptist Medical Center’s electronic medical records and annotated with BioCarta signaling signatures. The clinical potential of the discovered modules was exhibited by stratifying patients for drug responses. A series of discovered associations provided new insights into breast cancer, such as the effects of patient’s cultural background on preferences for surgical procedure. We also discovered two groups of highly associated features, the HER2 and the ER modules, each of which described how phenotypes were associated with molecular signatures, diagnostic features, and clinical decisions. The discovered “ER module”, which was dominated by cancer immunity, was used as an example for patient stratification and prediction of drug responses to tamoxifen and chemotherapy. BUFAM-derived RDN modeling demonstrated unique ability to discover clinically meaningful and actionable knowledge across highly heterogeneous biomedical big data sets.

Bench-to-bedside review: Future novel diagnostics for sepsis - a systems biology approach

PubMed Central

2013-01-01

The early, accurate diagnosis and risk stratification of sepsis remains an important challenge in the critically ill. Since traditional biomarker strategies have not yielded a gold standard marker for sepsis, focus is shifting towards novel strategies that improve assessment capabilities. The combination of technological advancements and information generated through the human genome project positions systems biology at the forefront of biomarker discovery. While previously available, developments in the technologies focusing on DNA, gene expression, gene regulatory mechanisms, protein and metabolite discovery have made these tools more feasible to implement and less costly, and they have taken on an enhanced capacity such that they are ripe for utilization as tools to advance our knowledge and clinical research. Medicine is in a genome-level era that can leverage the assessment of thousands of molecular signals beyond simply measuring selected circulating proteins. Genomics is the study of the entire complement of genetic material of an individual. Epigenetics is the regulation of gene activity by reversible modifications of the DNA. Transcriptomics is the quantification of the relative levels of messenger RNA for a large number of genes in specific cells or tissues to measure differences in the expression levels of different genes, and the utilization of patterns of differential gene expression to characterize different biological states of a tissue. Proteomics is the large-scale study of proteins. Metabolomics is the study of the small molecule profiles that are the terminal downstream products of the genome and consists of the total complement of all low-molecular-weight molecules that cellular processes leave behind. Taken together, these individual fields of study may be linked during a systems biology approach. There remains a valuable opportunity to deploy these technologies further in human research. The techniques described in this paper not only have the potential to increase the spectrum of diagnostic and prognostic biomarkers in sepsis, but they may also enable the discovery of new disease pathways. This may in turn lead us to improved therapeutic targets. The objective of this paper is to provide an overview and basic framework for clinicians and clinical researchers to better understand the 'omics technologies' to enhance further use of these valuable tools. PMID:24093155

Paintbrush of Discovery: Using Java Applets to Enhance Mathematics Education

ERIC Educational Resources Information Center

Eason, Ray; Heath, Garrett

2004-01-01

This article addresses the enhancement of the learning environment by using Java applets in the mathematics classroom. Currently, the first year mathematics program at the United States Military Academy involves one semester of modeling with discrete dynamical systems (DDS). Several faculty members from the Academy have integrated Java applets…

Constructing a Cross-Domain Resource Inventory: Key Components and Results of the EarthCube CINERGI Project.

NASA Astrophysics Data System (ADS)

Zaslavsky, I.; Richard, S. M.; Malik, T.; Hsu, L.; Gupta, A.; Grethe, J. S.; Valentine, D. W., Jr.; Lehnert, K. A.; Bermudez, L. E.; Ozyurt, I. B.; Whitenack, T.; Schachne, A.; Giliarini, A.

2015-12-01

While many geoscience-related repositories and data discovery portals exist, finding information about available resources remains a pervasive problem, especially when searching across multiple domains and catalogs. Inconsistent and incomplete metadata descriptions, disparate access protocols and semantic differences across domains, and troves of unstructured or poorly structured information which is hard to discover and use are major hindrances toward discovery, while metadata compilation and curation remain manual and time-consuming. We report on methodology, main results and lessons learned from an ongoing effort to develop a geoscience-wide catalog of information resources, with consistent metadata descriptions, traceable provenance, and automated metadata enhancement. Developing such a catalog is the central goal of CINERGI (Community Inventory of EarthCube Resources for Geoscience Interoperability), an EarthCube building block project (earthcube.org/group/cinergi). The key novel technical contributions of the projects include: a) development of a metadata enhancement pipeline and a set of document enhancers to automatically improve various aspects of metadata descriptions, including keyword assignment and definition of spatial extents; b) Community Resource Viewers: online applications for crowdsourcing community resource registry development, curation and search, and channeling metadata to the unified CINERGI inventory, c) metadata provenance, validation and annotation services, d) user interfaces for advanced resource discovery; and e) geoscience-wide ontology and machine learning to support automated semantic tagging and faceted search across domains. We demonstrate these CINERGI components in three types of user scenarios: (1) improving existing metadata descriptions maintained by government and academic data facilities, (2) supporting work of several EarthCube Research Coordination Network projects in assembling information resources for their domains, and (3) enhancing the inventory and the underlying ontology to address several complicated data discovery use cases in hydrology, geochemistry, sedimentology, and critical zone science. Support from the US National Science Foundation under award ICER-1343816 is gratefully acknowledged.

76 FR 44015 - Thirteenth International Paul-Ehrlich-Seminar: Allergen Products for Diagnosis and Therapy...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-22

... be provided on a space-available basis. If you need special accommodations due to a disability... dissemination of knowledge on the discovery, development, evaluation, and utilization of medicines and related...

Astronomy in the Age of Columbus.

ERIC Educational Resources Information Center

Gingerich, Owen

1992-01-01

Presents an historical perspective of astronomy. Discusses how Columbus' discovery of America demonstrated the incompleteness of the ancient knowledge of the world and paved the way for unorthodox astronomical ideas, including the sun-centered cosmology of Copernicus. (MCO)

Transfer Learning of Classification Rules for Biomarker Discovery and Verification from Molecular Profiling Studies

PubMed Central

Ganchev, Philip; Malehorn, David; Bigbee, William L.; Gopalakrishnan, Vanathi

2013-01-01

We present a novel framework for integrative biomarker discovery from related but separate data sets created in biomarker profiling studies. The framework takes prior knowledge in the form of interpretable, modular rules, and uses them during the learning of rules on a new data set. The framework consists of two methods of transfer of knowledge from source to target data: transfer of whole rules and transfer of rule structures. We evaluated the methods on three pairs of data sets: one genomic and two proteomic. We used standard measures of classification performance and three novel measures of amount of transfer. Preliminary evaluation shows that whole-rule transfer improves classification performance over using the target data alone, especially when there is more source data than target data. It also improves performance over using the union of the data sets. PMID:21571094

Collaboration versus outsourcing: the need to think outside the box.

PubMed

Robertson, Graeme M; Mayr, Lorenz M

2011-12-01

As has been widely reviewed elsewhere, the pharmaceutical industry is experiencing an 'innovation deficit' as evidenced by the decline in new chemical entity output. This decline, compounded by increased costs and regulatory requirements highlights the need to significantly revise strategic options across the drug-discovery spectrum. Within such revision(s), much of the focus has been on outsourcing to reduce, or at least contain, costs, but if the underlying predominance of 'closed collaborations' is not challenged to allow better use of combined knowledge and, thus, move towards a more genuine collaborative process then a 'numbers only' approach will not bring medium-to-long-term survival. There are many problems to confront in evolving new sustainable strategies, a real need to think differently exists and should to be cultivated. This article reviews current outsourcing and collaboration strategies to provide a perspective on how great knowledge sharing could help revise the drug-discovery process.

Genalogical approaches to ethical implications of informational assimilative integrated discovery systems (AIDS) in business

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

Pharhizgar, K.D.; Lunce, S.E.

1994-12-31

Development of knowledge-based technological acquisition techniques and customers` information profiles are known as assimilative integrated discovery systems (AIDS) in modern organizations. These systems have access through processing to both deep and broad domains of information in modern societies. Through these systems organizations and individuals can predict future trend probabilities and events concerning their customers. AIDSs are new techniques which produce new information which informants can use without the help of the knowledge sources because of the existence of highly sophisticated computerized networks. This paper has analyzed the danger and side effects of misuse of information through the illegal, unethical andmore » immoral access to the data-base in an integrated and assimilative information system as described above. Cognivistic mapping, pragmatistic informational design gathering, and holistic classifiable and distributive techniques are potentially abusive systems whose outputs can be easily misused by businesses when researching the firm`s customers.« less

Overlooked habitat of a vulnerable gorgonian revealed in the Mediterranean and Eastern Atlantic by ecological niche modelling

PubMed Central

Boavida, Joana; Assis, Jorge; Silva, Inga; Serrão, Ester A.

2016-01-01

Factors shaping the distribution of mesophotic octocorals (30–200 m depth) remain poorly understood, potentially leaving overlooked coral areas, particularly near their bathymetric and geographic distributional limits. Yet, detailed knowledge about habitat requirements is crucial for conservation of sensitive gorgonians. Here we use Ecological Niche Modelling (ENM) relating thirteen environmental predictors and a highly comprehensive presence dataset, enhanced by SCUBA diving surveys, to investigate the suitable habitat of an important structuring species, Paramuricea clavata, throughout its distribution (Mediterranean and adjacent Atlantic). Models showed that temperature (11.5–25.5 °C) and slope are the most important predictors carving the niche of P. clavata. Prediction throughout the full distribution (TSS 0.9) included known locations of P. clavata alongside with previously unknown or unreported sites along the coast of Portugal and Africa, including seamounts. These predictions increase the understanding of the potential distribution for the northern Mediterranean and indicate suitable hard bottom areas down to >150 m depth. Poorly sampled habitats with predicted presence along Algeria, Alboran Sea and adjacent Atlantic coasts encourage further investigation. We propose that surveys of target areas from the predicted distribution map, together with local expert knowledge, may lead to discoveries of new P. clavata sites and identify priority conservation areas. PMID:27841263

A Python Geospatial Language Toolkit

NASA Astrophysics Data System (ADS)

Fillmore, D.; Pletzer, A.; Galloy, M.

2012-12-01

The volume and scope of geospatial data archives, such as collections of satellite remote sensing or climate model products, has been rapidly increasing and will continue to do so in the near future. The recently launched (October 2011) Suomi National Polar-orbiting Partnership satellite (NPP) for instance, is the first of a new generation of Earth observation platforms that will monitor the atmosphere, oceans, and ecosystems, and its suite of instruments will generate several terabytes each day in the form of multi-spectral images and derived datasets. Full exploitation of such data for scientific analysis and decision support applications has become a major computational challenge. Geophysical data exploration and knowledge discovery could benefit, in particular, from intelligent mechanisms for extracting and manipulating subsets of data relevant to the problem of interest. Potential developments include enhanced support for natural language queries and directives to geospatial datasets. The translation of natural language (that is, human spoken or written phrases) into complex but unambiguous objects and actions can be based on a context, or knowledge domain, that represents the underlying geospatial concepts. This poster describes a prototype Python module that maps English phrases onto basic geospatial objects and operations. This module, along with the associated computational geometry methods, enables the resolution of natural language directives that include geographic regions of arbitrary shape and complexity.

Paediatric Virology as a new educational initiative: An interview with Nobelist Professor of Virology Harald zur Hausen.

PubMed

Mammas, Ioannis N; Spandidos, Demetrios A

2017-10-01

Born in Gelsenkirchen-Buer in Germany on March 11th, 1936, Professor Harald zur Hausen, Emeritus Professor of Virology at the University of Freiburg and 2008 Nobel Prize Laureate in Physiology or Medicine for his discovery of human papillomavirus (HPV), which causes cervical cancer, believes that good knowledge of virological methods and diagnostic possibilities are an asset for all young paediatricians. Professor zur Hausen considers that the creation of an educational platform on Paediatric Virology is definitely very beneficial for young paediatricians, as this will greatly enhance their knowledge in the field of Virology. He very actively advocates the vaccination of boys for the eradication of HPV infection and emphasises that male HPV vaccination should be included into the current vaccination programmes. He would have certainly considered Dr George N. Papanicolaou (Kyme, Island of Euboea, Greece, 1883 - Miami, Florida, USA, 1962) as an excellent candidate for the Nobel Prize, stating that the contribution of Dr Papanicolaou did not find sufficient recognition in the past. In the context of the 3rd Workshop on Paediatric Virology, which will be held in Athens, Greece, on October 7th, 2017, Professor zur Hausen will give his plenary lecture on 'Paediatric Virology and Oncology: Virus persistence and the important first years of life'.

Paediatric Virology as a new educational initiative: An interview with Nobelist Professor of Virology Harald zur Hausen

PubMed Central

Mammas, Ioannis N.; Spandidos, Demetrios A.

2017-01-01

Born in Gelsenkirchen-Buer in Germany on March 11th, 1936, Professor Harald zur Hausen, Emeritus Professor of Virology at the University of Freiburg and 2008 Nobel Prize Laureate in Physiology or Medicine for his discovery of human papillomavirus (HPV), which causes cervical cancer, believes that good knowledge of virological methods and diagnostic possibilities are an asset for all young paediatricians. Professor zur Hausen considers that the creation of an educational platform on Paediatric Virology is definitely very beneficial for young paediatricians, as this will greatly enhance their knowledge in the field of Virology. He very actively advocates the vaccination of boys for the eradication of HPV infection and emphasises that male HPV vaccination should be included into the current vaccination programmes. He would have certainly considered Dr George N. Papanicolaou (Kyme, Island of Euboea, Greece, 1883 - Miami, Florida, USA, 1962) as an excellent candidate for the Nobel Prize, stating that the contribution of Dr Papanicolaou did not find sufficient recognition in the past. In the context of the 3rd Workshop on Paediatric Virology, which will be held in Athens, Greece, on October 7th, 2017, Professor zur Hausen will give his plenary lecture on ‘Paediatric Virology and Oncology: Virus persistence and the important first years of life’. PMID:29042913

Outlier analysis of functional genomic profiles enriches for oncology targets and enables precision medicine.

PubMed

Zhu, Zhou; Ihle, Nathan T; Rejto, Paul A; Zarrinkar, Patrick P

2016-06-13

Genome-scale functional genomic screens across large cell line panels provide a rich resource for discovering tumor vulnerabilities that can lead to the next generation of targeted therapies. Their data analysis typically has focused on identifying genes whose knockdown enhances response in various pre-defined genetic contexts, which are limited by biological complexities as well as the incompleteness of our knowledge. We thus introduce a complementary data mining strategy to identify genes with exceptional sensitivity in subsets, or outlier groups, of cell lines, allowing an unbiased analysis without any a priori assumption about the underlying biology of dependency. Genes with outlier features are strongly and specifically enriched with those known to be associated with cancer and relevant biological processes, despite no a priori knowledge being used to drive the analysis. Identification of exceptional responders (outliers) may not lead only to new candidates for therapeutic intervention, but also tumor indications and response biomarkers for companion precision medicine strategies. Several tumor suppressors have an outlier sensitivity pattern, supporting and generalizing the notion that tumor suppressors can play context-dependent oncogenic roles. The novel application of outlier analysis described here demonstrates a systematic and data-driven analytical strategy to decipher large-scale functional genomic data for oncology target and precision medicine discoveries.

Specifications of insilicoML 1.0: a multilevel biophysical model description language.

PubMed

Asai, Yoshiyuki; Suzuki, Yasuyuki; Kido, Yoshiyuki; Oka, Hideki; Heien, Eric; Nakanishi, Masao; Urai, Takahito; Hagihara, Kenichi; Kurachi, Yoshihisa; Nomura, Taishin

2008-12-01

An extensible markup language format, insilicoML (ISML), version 0.1, describing multi-level biophysical models has been developed and available in the public domain. ISML is fully compatible with CellML 1.0, a model description standard developed by the IUPS Physiome Project, for enhancing knowledge integration and model sharing. This article illustrates the new specifications of ISML 1.0 that largely extend the capability of ISML 0.1. ISML 1.0 can describe various types of mathematical models, including ordinary/partial differential/difference equations representing the dynamics of physiological functions and the geometry of living organisms underlying the functions. ISML 1.0 describes a model using a set of functional elements (modules) each of which can specify mathematical expressions of the functions. Structural and logical relationships between any two modules are specified by edges, which allow modular, hierarchical, and/or network representations of the model. The role of edge-relationships is enriched by key words in order for use in constructing a physiological ontology. The ontology is further improved by the traceability of history of the model's development and by linking between different ISML models stored in the model's database using meta-information. ISML 1.0 is designed to operate with a model database and integrated environments for model development and simulations for knowledge integration and discovery.

Renal injury in neonates: use of "omics" for developing precision medicine in neonatology.

PubMed

Joshi, Mandar S; Montgomery, Kelsey A; Giannone, Peter J; Bauer, John A; Hanna, Mina H

2017-01-01

Preterm birth is associated with increased risks of morbidity and mortality along with increased healthcare costs. Advances in medicine have enhanced survival for preterm infants but the overall incidence of major morbidities has changed very little. Abnormal renal development is an important consequence of premature birth. Acute kidney injury (AKI) in the neonatal period is multifactorial and may increase lifetime risk of chronic kidney disease.Traditional biomarkers in newborns suffer from considerable confounders, limiting their use for early identification of AKI. There is a need to develop novel biomarkers that can identify, in real time, the evolution of renal dysfunction in an early diagnostic, monitoring and prognostic fashion. Use of "omics", particularly metabolomics, may provide valuable information regarding functional pathways underlying AKI and prediction of clinical outcomes.The emerging knowledge generated by the application of "omics" (genomics, proteomics, metabolomics) in neonatology provides new insights that can help to identify markers of early diagnosis, disease progression, and identify new therapeutic targets. Additionally, omics will have major implications in the field of personalized healthcare in the future. Here, we will review the current knowledge of different omics technologies in neonatal-perinatal medicine including biomarker discovery, defining as yet unrecognized biologic therapeutic targets, and linking of omics to relevant standard indices and long-term outcomes.

The WISECARE Project and the impact of information technology on nursing knowledge.

PubMed

Sermeus, W; Hoy, D; Jodrell, N; Hyslop, A; Gypen, T; Kinnunen, J; Mantas, J; Delesie, L; Tansley, J; Hofdijk, J

1997-01-01

The European Union retained the WISECARE project "Work flow Information Systems for European nursing CARE" for funding. The project focuses on the use of telematics technology for clinical and resource management in oncology care in hospitals. This paper outlines the impact of introducing this kind of advanced nursing informatics application on the management of nursing knowledge. Three shift in knowledge management that will get high attention in WISECARE, are identified. The first is the shift from knowledge dissemination to knowledge sharing. The second is the shift from individual knowledge to organisational knowledge. The third is the shift from deductive, prescriptive knowledge as seen in guidelines, protocols to more inductive, experience based knowledge. The paper emphasizes that the real impact of information technology is not in the automation of existing processes but on the discovery of new ways of organisation and living.

Molecular biology and immunology of head and neck cancer.

PubMed

Guo, Theresa; Califano, Joseph A

2015-07-01

In recent years, our knowledge and understanding of head and neck squamous cell carcinoma (HNSCC) has expanded dramatically. New high-throughput sequencing technologies have accelerated these discoveries since the first reports of whole-exome sequencing of HNSCC tumors in 2011. In addition, the discovery of human papillomavirus in relationship with oropharyngeal squamous cell carcinoma has shifted our molecular understanding of the disease. New investigation into the role of immune evasion in HNSCC has also led to potential novel therapies based on immune-specific systemic therapies. Copyright © 2015 Elsevier Inc. All rights reserved.

Compound annotation with real time cellular activity profiles to improve drug discovery.

PubMed

Fang, Ye

2016-01-01

In the past decade, a range of innovative strategies have been developed to improve the productivity of pharmaceutical research and development. In particular, compound annotation, combined with informatics, has provided unprecedented opportunities for drug discovery. In this review, a literature search from 2000 to 2015 was conducted to provide an overview of the compound annotation approaches currently used in drug discovery. Based on this, a framework related to a compound annotation approach using real-time cellular activity profiles for probe, drug, and biology discovery is proposed. Compound annotation with chemical structure, drug-like properties, bioactivities, genome-wide effects, clinical phenotypes, and textural abstracts has received significant attention in early drug discovery. However, these annotations are mostly associated with endpoint results. Advances in assay techniques have made it possible to obtain real-time cellular activity profiles of drug molecules under different phenotypes, so it is possible to generate compound annotation with real-time cellular activity profiles. Combining compound annotation with informatics, such as similarity analysis, presents a good opportunity to improve the rate of discovery of novel drugs and probes, and enhance our understanding of the underlying biology.

Isolation, enzyme-bound structure and antibacterial activity of platencin A[subscript 1] from Streptomyces platensis

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

Singh, Sheo B.; Ondeyka, John G.; Herath, Kithsiri B.

Natural products continue to serve as one of the best sources for discovery of antibacterial agents as exemplified by the recent discoveries of platensimycin and platencin. Chemical modifications as well as discovery of congeners are the main sources for gaining knowledge of structure-activity relationship of natural products. Screening for congeners in the extracts of the fermentation broths of Streptomyces platensis led to the isolation of platencin A{sub 1}, a hydroxy congener of platencin. The hydroxylation of the tricyclic enone moiety negatively affected the antibacterial activity and appears to be consistent with the hydrophobic binding pocket of the FabF. Isolation, structure,more » enzyme-bound structure and activity of platencin A{sub 1} and two other congeners have been described.« less

Binding thermodynamics discriminates fragments from druglike compounds: a thermodynamic description of fragment-based drug discovery.

PubMed

Williams, Glyn; Ferenczy, György G; Ulander, Johan; Keserű, György M

2017-04-01

Small is beautiful - reducing the size and complexity of chemical starting points for drug design allows better sampling of chemical space, reveals the most energetically important interactions within protein-binding sites and can lead to improvements in the physicochemical properties of the final drug. The impact of fragment-based drug discovery (FBDD) on recent drug discovery projects and our improved knowledge of the structural and thermodynamic details of ligand binding has prompted us to explore the relationships between ligand-binding thermodynamics and FBDD. Information on binding thermodynamics can give insights into the contributions to protein-ligand interactions and could therefore be used to prioritise compounds with a high degree of specificity in forming key interactions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Basics of Antibody Phage Display Technology.

PubMed

Ledsgaard, Line; Kilstrup, Mogens; Karatt-Vellatt, Aneesh; McCafferty, John; Laustsen, Andreas H

2018-06-09

Antibody discovery has become increasingly important in almost all areas of modern medicine. Different antibody discovery approaches exist, but one that has gained increasing interest in the field of toxinology and antivenom research is phage display technology. In this review, the lifecycle of the M13 phage and the basics of phage display technology are presented together with important factors influencing the success rates of phage display experiments. Moreover, the pros and cons of different antigen display methods and the use of naïve versus immunized phage display antibody libraries is discussed, and selected examples from the field of antivenom research are highlighted. This review thus provides in-depth knowledge on the principles and use of phage display technology with a special focus on discovery of antibodies that target animal toxins.

Prediction of intracellular exposure bridges the gap between target- and cell-based drug discovery

PubMed Central

Gordon, Laurie J.; Wayne, Gareth J.; Almqvist, Helena; Axelsson, Hanna; Seashore-Ludlow, Brinton; Treyer, Andrea; Lundbäck, Thomas; West, Andy; Hann, Michael M.; Artursson, Per

2017-01-01

Inadequate target exposure is a major cause of high attrition in drug discovery. Here, we show that a label-free method for quantifying the intracellular bioavailability (Fic) of drug molecules predicts drug access to intracellular targets and hence, pharmacological effect. We determined Fic in multiple cellular assays and cell types representing different targets from a number of therapeutic areas, including cancer, inflammation, and dementia. Both cytosolic targets and targets localized in subcellular compartments were investigated. Fic gives insights on membrane-permeable compounds in terms of cellular potency and intracellular target engagement, compared with biochemical potency measurements alone. Knowledge of the amount of drug that is locally available to bind intracellular targets provides a powerful tool for compound selection in early drug discovery. PMID:28701380

Label-assisted mass spectrometry for the acceleration of reaction discovery and optimization

NASA Astrophysics Data System (ADS)

Cabrera-Pardo, Jaime R.; Chai, David I.; Liu, Song; Mrksich, Milan; Kozmin, Sergey A.

2013-05-01

The identification of new reactions expands our knowledge of chemical reactivity and enables new synthetic applications. Accelerating the pace of this discovery process remains challenging. We describe a highly effective and simple platform for screening a large number of potential chemical reactions in order to discover and optimize previously unknown catalytic transformations, thereby revealing new chemical reactivity. Our strategy is based on labelling one of the reactants with a polyaromatic chemical tag, which selectively undergoes a photoionization/desorption process upon laser irradiation, without the assistance of an external matrix, and enables rapid mass spectrometric detection of any products originating from such labelled reactants in complex reaction mixtures without any chromatographic separation. This method was successfully used for high-throughput discovery and subsequent optimization of two previously unknown benzannulation reactions.

100 years poliovirus: from discovery to eradication. A meeting report.

PubMed

Skern, Tim

2010-09-01

Just over hundred years ago, Karl Landsteiner and Erwin Popper identified a virus, later termed poliovirus, as the causative agent of poliomyelitis. This groundbreaking discovery simultaneously provided the basis for the measures that today prevent the outbreaks of the terrible epidemics caused by poliovirus. In 1988, the WHO started its eradication program to eliminate the virus from the planet. The symposium celebrated the discovery of poliovirus and discussed our current state of knowledge of poliovirus biology. Prospects for the eradication program were evaluated, with particular emphasis being placed on why certain countries still have not succeeding in interrupting wild-type transmission of poliovirus. Discussion also centred on the role of inactivated poliovirus vaccines in the eradication program and the maintenance of a poliovirus-free world, whenever this goal should be achieved.