Studying Professional Knowledge Use in Practice Using Multimedia Scenarios Delivered Online

ERIC Educational Resources Information Center

Herbst, Patricio; Chazan, Daniel

2015-01-01

We describe how multimedia scenarios delivered online can be used in instruments for the study of professional knowledge. Based on our work in the study of the knowledge and rationality involved in mathematics teaching, we describe how the study of professional knowledge writ large can benefit from the capacity to represent know-how using…

Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs

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

Zhang, Baichuan; Choudhury, Sutanay; Al-Hasan, Mohammad

2016-02-01

Estimating the confidence for a link is a critical task for Knowledge Graph construction. Link prediction, or predicting the likelihood of a link in a knowledge graph based on prior state is a key research direction within this area. We propose a Latent Feature Embedding based link recommendation model for prediction task and utilize Bayesian Personalized Ranking based optimization technique for learning models for each predicate. Experimental results on large-scale knowledge bases such as YAGO2 show that our approach achieves substantially higher performance than several state-of-art approaches. Furthermore, we also study the performance of the link prediction algorithm in termsmore » of topological properties of the Knowledge Graph and present a linear regression model to reason about its expected level of accuracy.« less

Reasoning Mind Genie 2: An Intelligent Tutoring System as a Vehicle for International Transfer of Instructional Methods in Mathematics

ERIC Educational Resources Information Center

Khachatryan, George A.; Romashov, Andrey V.; Khachatryan, Alexander R.; Gaudino, Steven J.; Khachatryan, Julia M.; Guarian, Konstantin R.; Yufa, Nataliya V.

2014-01-01

Effective mathematics teachers have a large body of professional knowledge, which is largely undocumented and shared by teachers working in a given country's education system. The volume and cultural nature of this knowledge make it particularly challenging to share curricula and instructional methods between countries. Thus, approaches based on…

Enrichment assessment of multiple virtual screening strategies for Toll-like receptor 8 agonists based on a maximal unbiased benchmarking data set.

PubMed

Pei, Fen; Jin, Hongwei; Zhou, Xin; Xia, Jie; Sun, Lidan; Liu, Zhenming; Zhang, Liangren

2015-11-01

Toll-like receptor 8 agonists, which activate adaptive immune responses by inducing robust production of T-helper 1-polarizing cytokines, are promising candidates for vaccine adjuvants. As the binding site of toll-like receptor 8 is large and highly flexible, virtual screening by individual method has inevitable limitations; thus, a comprehensive comparison of different methods may provide insights into seeking effective strategy for the discovery of novel toll-like receptor 8 agonists. In this study, the performance of knowledge-based pharmacophore, shape-based 3D screening, and combined strategies was assessed against a maximum unbiased benchmarking data set containing 13 actives and 1302 decoys specialized for toll-like receptor 8 agonists. Prior structure-activity relationship knowledge was involved in knowledge-based pharmacophore generation, and a set of antagonists was innovatively used to verify the selectivity of the selected knowledge-based pharmacophore. The benchmarking data set was generated from our recently developed 'mubd-decoymaker' protocol. The enrichment assessment demonstrated a considerable performance through our selected three-layer virtual screening strategy: knowledge-based pharmacophore (Phar1) screening, shape-based 3D similarity search (Q4_combo), and then a Gold docking screening. This virtual screening strategy could be further employed to perform large-scale database screening and to discover novel toll-like receptor 8 agonists. © 2015 John Wiley & Sons A/S.

A Discretization Algorithm for Meteorological Data and its Parallelization Based on Hadoop

NASA Astrophysics Data System (ADS)

Liu, Chao; Jin, Wen; Yu, Yuting; Qiu, Taorong; Bai, Xiaoming; Zou, Shuilong

2017-10-01

In view of the large amount of meteorological observation data, the property is more and the attribute values are continuous values, the correlation between the elements is the need for the application of meteorological data, this paper is devoted to solving the problem of how to better discretize large meteorological data to more effectively dig out the hidden knowledge in meteorological data and research on the improvement of discretization algorithm for large scale data, in order to achieve data in the large meteorological data discretization for the follow-up to better provide knowledge to provide protection, a discretization algorithm based on information entropy and inconsistency of meteorological attributes is proposed and the algorithm is parallelized under Hadoop platform. Finally, the comparison test validates the effectiveness of the proposed algorithm for discretization in the area of meteorological large data.

HSTDEK: Developing a methodology for construction of large-scale, multi-use knowledge bases

NASA Technical Reports Server (NTRS)

Freeman, Michael S.

1987-01-01

The primary research objectives of the Hubble Space Telescope Design/Engineering Knowledgebase (HSTDEK) are to develop a methodology for constructing and maintaining large scale knowledge bases which can be used to support multiple applications. To insure the validity of its results, this research is being persued in the context of a real world system, the Hubble Space Telescope. The HSTDEK objectives are described in detail. The history and motivation of the project are briefly described. The technical challenges faced by the project are outlined.

PLAN-IT: Knowledge-Based Mission Sequencing

NASA Astrophysics Data System (ADS)

Biefeld, Eric W.

1987-02-01

Mission sequencing consumes a large amount of time and manpower during a space exploration effort. Plan-It is a knowledge-based approach to assist in mission sequencing. Plan-It uses a combined frame and blackboard architecture. This paper reports on the approach implemented by Plan-It and the current applications of Plan-It for sequencing at NASA.

Knowledge based systems for intelligent robotics

NASA Technical Reports Server (NTRS)

Rajaram, N. S.

1982-01-01

It is pointed out that the construction of large space platforms, such as space stations, has to be carried out in the outer space environment. As it is extremely expensive to support human workers in space for large periods, the only feasible solution appears to be related to the development and deployment of highly capable robots for most of the tasks. Robots for space applications will have to possess characteristics which are very different from those needed by robots in industry. The present investigation is concerned with the needs of space robotics and the technologies which can be of assistance to meet these needs, giving particular attention to knowledge bases. 'Intelligent' robots are required for the solution of arising problems. The collection of facts and rules needed for accomplishing such solutions form the 'knowledge base' of the system.

Computer Assisted Multi-Center Creation of Medical Knowledge Bases

PubMed Central

Giuse, Nunzia Bettinsoli; Giuse, Dario A.; Miller, Randolph A.

1988-01-01

Computer programs which support different aspects of medical care have been developed in recent years. Their capabilities range from diagnosis to medical imaging, and include hospital management systems and therapy prescription. In spite of their diversity these systems have one commonality: their reliance on a large body of medical knowledge in computer-readable form. This knowledge enables such programs to draw inferences, validate hypotheses, and in general to perform their intended task. As has been clear to developers of such systems, however, the creation and maintenance of medical knowledge bases are very expensive. Practical and economical difficulties encountered during this long-term process have discouraged most attempts. This paper discusses knowledge base creation and maintenance, with special emphasis on medical applications. We first describe the methods currently used and their limitations. We then present our recent work on developing tools and methodologies which will assist in the process of creating a medical knowledge base. We focus, in particular, on the possibility of multi-center creation of the knowledge base.

Drug knowledge bases and their applications in biomedical informatics research.

PubMed

Zhu, Yongjun; Elemento, Olivier; Pathak, Jyotishman; Wang, Fei

2018-01-03

Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many drug knowledge bases have been constructed. They range from simple ones with specific focuses to comprehensive ones that contain information on almost every aspect of a drug. These curated drug knowledge bases have made significant contributions to the development of efficient and effective health information technologies for better health-care service delivery. Understanding and comparing existing drug knowledge bases and how they are applied in various biomedical studies will help us recognize the state of the art and design better knowledge bases in the future. In addition, researchers can get insights on novel applications of the drug knowledge bases through a review of successful use cases. In this study, we provide a review of existing popular drug knowledge bases and their applications in drug-related studies. We discuss challenges in constructing and using drug knowledge bases as well as future research directions toward a better ecosystem of drug knowledge bases. © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Knowledge-based machine indexing from natural language text: Knowledge base design, development, and maintenance

NASA Technical Reports Server (NTRS)

Genuardi, Michael T.

1993-01-01

One strategy for machine-aided indexing (MAI) is to provide a concept-level analysis of the textual elements of documents or document abstracts. In such systems, natural-language phrases are analyzed in order to identify and classify concepts related to a particular subject domain. The overall performance of these MAI systems is largely dependent on the quality and comprehensiveness of their knowledge bases. These knowledge bases function to (1) define the relations between a controlled indexing vocabulary and natural language expressions; (2) provide a simple mechanism for disambiguation and the determination of relevancy; and (3) allow the extension of concept-hierarchical structure to all elements of the knowledge file. After a brief description of the NASA Machine-Aided Indexing system, concerns related to the development and maintenance of MAI knowledge bases are discussed. Particular emphasis is given to statistically-based text analysis tools designed to aid the knowledge base developer. One such tool, the Knowledge Base Building (KBB) program, presents the domain expert with a well-filtered list of synonyms and conceptually-related phrases for each thesaurus concept. Another tool, the Knowledge Base Maintenance (KBM) program, functions to identify areas of the knowledge base affected by changes in the conceptual domain (for example, the addition of a new thesaurus term). An alternate use of the KBM as an aid in thesaurus construction is also discussed.

Space shuttle main engine anomaly data and inductive knowledge based systems: Automated corporate expertise

NASA Technical Reports Server (NTRS)

Modesitt, Kenneth L.

1987-01-01

Progress is reported on the development of SCOTTY, an expert knowledge-based system to automate the analysis procedure following test firings of the Space Shuttle Main Engine (SSME). The integration of a large-scale relational data base system, a computer graphics interface for experts and end-user engineers, potential extension of the system to flight engines, application of the system for training of newly-hired engineers, technology transfer to other engines, and the essential qualities of good software engineering practices for building expert knowledge-based systems are among the topics discussed.

Globalisation, Transnational Academic Mobility and the Chinese Knowledge Diaspora: An Australian Case Study

ERIC Educational Resources Information Center

Yang, Rui; Welch, Anthony R.

2010-01-01

The master discourses of economic globalisation and the knowledge economy each cite knowledge diasporas as vital "trans-national human capital". Based on a case study of a major Australian university, this article examines the potential to deploy China's large and highly-skilled diaspora in the service of Chinese and Australian…

Engineering youth service system infrastructure: Hawaii's continued efforts at large-scale implementation through knowledge management strategies.

PubMed

Nakamura, Brad J; Mueller, Charles W; Higa-McMillan, Charmaine; Okamura, Kelsie H; Chang, Jaime P; Slavin, Lesley; Shimabukuro, Scott

2014-01-01

Hawaii's Child and Adolescent Mental Health Division provides a unique illustration of a youth public mental health system with a long and successful history of large-scale quality improvement initiatives. Many advances are linked to flexibly organizing and applying knowledge gained from the scientific literature and move beyond installing a limited number of brand-named treatment approaches that might be directly relevant only to a small handful of system youth. This article takes a knowledge-to-action perspective and outlines five knowledge management strategies currently under way in Hawaii. Each strategy represents one component of a larger coordinated effort at engineering a service system focused on delivering both brand-named treatment approaches and complimentary strategies informed by the evidence base. The five knowledge management examples are (a) a set of modular-based professional training activities for currently practicing therapists, (b) an outreach initiative for supporting youth evidence-based practices training at Hawaii's mental health-related professional programs, (c) an effort to increase consumer knowledge of and demand for youth evidence-based practices, (d) a practice and progress agency performance feedback system, and (e) a sampling of system-level research studies focused on understanding treatment as usual. We end by outlining a small set of lessons learned and a longer term vision for embedding these efforts into the system's infrastructure.

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

Segmentation of medical images using explicit anatomical knowledge

NASA Astrophysics Data System (ADS)

Wilson, Laurie S.; Brown, Stephen; Brown, Matthew S.; Young, Jeanne; Li, Rongxin; Luo, Suhuai; Brandt, Lee

1999-07-01

Knowledge-based image segmentation is defined in terms of the separation of image analysis procedures and representation of knowledge. Such architecture is particularly suitable for medical image segmentation, because of the large amount of structured domain knowledge. A general methodology for the application of knowledge-based methods to medical image segmentation is described. This includes frames for knowledge representation, fuzzy logic for anatomical variations, and a strategy for determining the order of segmentation from the modal specification. This method has been applied to three separate problems, 3D thoracic CT, chest X-rays and CT angiography. The application of the same methodology to such a range of applications suggests a major role in medical imaging for segmentation methods incorporating representation of anatomical knowledge.

Experiments in Knowledge Refinement for a Large Rule-Based System

DTIC Science & Technology

1993-08-01

empirical analysis to refine expert system knowledge bases. Aritificial Intelligence , 22:23-48, 1984. *! ...The Addison- Weslev series in artificial intelligence . Addison-Weslev. Reading, Massachusetts. 1981. Cooke, 1991: ttoger M. Cooke. Experts in...ment for classification systems. Artificial Intelligence , 35:197-226, 1988. 14 Overall, we believe that it will be possible to build a heuristic system

KAT: A Flexible XML-based Knowledge Authoring Environment

PubMed Central

Hulse, Nathan C.; Rocha, Roberto A.; Del Fiol, Guilherme; Bradshaw, Richard L.; Hanna, Timothy P.; Roemer, Lorrie K.

2005-01-01

As part of an enterprise effort to develop new clinical information systems at Intermountain Health Care, the authors have built a knowledge authoring tool that facilitates the development and refinement of medical knowledge content. At present, users of the application can compose order sets and an assortment of other structured clinical knowledge documents based on XML schemas. The flexible nature of the application allows the immediate authoring of new types of documents once an appropriate XML schema and accompanying Web form have been developed and stored in a shared repository. The need for a knowledge acquisition tool stems largely from the desire for medical practitioners to be able to write their own content for use within clinical applications. We hypothesize that medical knowledge content for clinical use can be successfully created and maintained through XML-based document frameworks containing structured and coded knowledge. PMID:15802477

Translating Knowledge on Poverty to Humanize Care: Benefits and Synergies of Community Engagement with the Arts

ERIC Educational Resources Information Center

Lévesque, Martine Cécile; Dupéré, Sophie; Morin, Nathalie; Côté, Johanne; Roberge, Nancy; Laurin, Isabelle; Charbonneau, Anne; Loignon, Christine; Bedos, Christophe

2015-01-01

The knowledge translation movement in health has led to the production of vast amounts of knowledge tools aimed at broadening clinicians' evidence base and improving the quality and efficacy of their practices. However important, these tools, largely oriented towards biomedical and technological aspects of care, are of limited potential for…

Awareness Development Across Perspectives Tool (ADAPT)

DTIC Science & Technology

2010-10-01

individualist and collectivist cultures are described and linked in the generic knowledge base, and the specific cultural aspects and how they relate to...effort focuses on making a tool based on (1) knowledge developed within diverse scientific disciplines (e.g. cultural anthropology, social psychology...psychological operations, humanitarian missions) is performed in a large variety of locations and cultures (e.g., Africa, Asia), requiring a diversity

Chapter 1: Biomedical knowledge integration.

PubMed

Payne, Philip R O

2012-01-01

The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems.

Chapter 1: Biomedical Knowledge Integration

PubMed Central

Payne, Philip R. O.

2012-01-01

The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems. PMID:23300416

Improved knowledge diffusion model based on the collaboration hypernetwork

NASA Astrophysics Data System (ADS)

Wang, Jiang-Pan; Guo, Qiang; Yang, Guang-Yong; Liu, Jian-Guo

2015-06-01

The process for absorbing knowledge becomes an essential element for innovation in firms and in adapting to changes in the competitive environment. In this paper, we present an improved knowledge diffusion hypernetwork (IKDH) model based on the idea that knowledge will spread from the target node to all its neighbors in terms of the hyperedge and knowledge stock. We apply the average knowledge stock V(t) , the variable σ2(t) , and the variance coefficient c(t) to evaluate the performance of knowledge diffusion. By analyzing different knowledge diffusion ways, selection ways of the highly knowledgeable nodes, hypernetwork sizes and hypernetwork structures for the performance of knowledge diffusion, results show that the diffusion speed of IKDH model is 3.64 times faster than that of traditional knowledge diffusion (TKDH) model. Besides, it is three times faster to diffuse knowledge by randomly selecting "expert" nodes than that by selecting large-hyperdegree nodes as "expert" nodes. Furthermore, either the closer network structure or smaller network size results in the faster knowledge diffusion.

Using CLIPS in the domain of knowledge-based massively parallel programming

NASA Technical Reports Server (NTRS)

Dvorak, Jiri J.

1994-01-01

The Program Development Environment (PDE) is a tool for massively parallel programming of distributed-memory architectures. Adopting a knowledge-based approach, the PDE eliminates the complexity introduced by parallel hardware with distributed memory and offers complete transparency in respect of parallelism exploitation. The knowledge-based part of the PDE is realized in CLIPS. Its principal task is to find an efficient parallel realization of the application specified by the user in a comfortable, abstract, domain-oriented formalism. A large collection of fine-grain parallel algorithmic skeletons, represented as COOL objects in a tree hierarchy, contains the algorithmic knowledge. A hybrid knowledge base with rule modules and procedural parts, encoding expertise about application domain, parallel programming, software engineering, and parallel hardware, enables a high degree of automation in the software development process. In this paper, important aspects of the implementation of the PDE using CLIPS and COOL are shown, including the embedding of CLIPS with C++-based parts of the PDE. The appropriateness of the chosen approach and of the CLIPS language for knowledge-based software engineering are discussed.

GARN: Sampling RNA 3D Structure Space with Game Theory and Knowledge-Based Scoring Strategies.

PubMed

Boudard, Mélanie; Bernauer, Julie; Barth, Dominique; Cohen, Johanne; Denise, Alain

2015-01-01

Cellular processes involve large numbers of RNA molecules. The functions of these RNA molecules and their binding to molecular machines are highly dependent on their 3D structures. One of the key challenges in RNA structure prediction and modeling is predicting the spatial arrangement of the various structural elements of RNA. As RNA folding is generally hierarchical, methods involving coarse-grained models hold great promise for this purpose. We present here a novel coarse-grained method for sampling, based on game theory and knowledge-based potentials. This strategy, GARN (Game Algorithm for RNa sampling), is often much faster than previously described techniques and generates large sets of solutions closely resembling the native structure. GARN is thus a suitable starting point for the molecular modeling of large RNAs, particularly those with experimental constraints. GARN is available from: http://garn.lri.fr/.

Using Topdown Conceptual Analysis To Accelerate The Learning Of New Domains For Knowledge Engineers & Domain Experts

NASA Astrophysics Data System (ADS)

Xuan, Albert L.; Shinghal, Rajjan

1989-03-01

As the need for knowledge-based systems increases, an increasing number of domain experts are becoming interested in taking more active part in the building of knowledge-based systems. However, such a domain expert often must deal with a large number of unfamiliar terms concepts, facts, procedures and principles based on different approaches and schools of thought. He (for brevity, we shall use masculine pronouns for both genders) may need the help of a knowledge engineer (KE) in building the knowledge-based system but may encounter a number of problems. For instance, much of the early interaction between him and the knowl edge engineer may be spent in educating each other about their seperate kinds of expertise. Since the knowledge engineer will usually be ignorant of the knowledge domain while the domain expert (DE) will have little knowledge about knowledge-based systems, a great deal of time will be wasted on these issues ad the DE and the KE train each other to the point where a fruitful interaction can occur. In some situations, it may not even be possible for the DE to find a suitable KE to work with because he has no time to train the latter in his domain. This will engender the need for the DE to be more knowledgeable about knowledge-based systems and for the KE to find methods and techniques which will allow them to learn new domains as fast as they can. In any event, it is likely that the process of building knowledge-based systems will be smooth, er and more efficient if the domain expert is knowledgeable about the methods and techniques of knowledge-based systems building.

Ontological modelling of knowledge management for human-machine integrated design of ultra-precision grinding machine

NASA Astrophysics Data System (ADS)

Hong, Haibo; Yin, Yuehong; Chen, Xing

2016-11-01

Despite the rapid development of computer science and information technology, an efficient human-machine integrated enterprise information system for designing complex mechatronic products is still not fully accomplished, partly because of the inharmonious communication among collaborators. Therefore, one challenge in human-machine integration is how to establish an appropriate knowledge management (KM) model to support integration and sharing of heterogeneous product knowledge. Aiming at the diversity of design knowledge, this article proposes an ontology-based model to reach an unambiguous and normative representation of knowledge. First, an ontology-based human-machine integrated design framework is described, then corresponding ontologies and sub-ontologies are established according to different purposes and scopes. Second, a similarity calculation-based ontology integration method composed of ontology mapping and ontology merging is introduced. The ontology searching-based knowledge sharing method is then developed. Finally, a case of human-machine integrated design of a large ultra-precision grinding machine is used to demonstrate the effectiveness of the method.

Capturing flight system test engineering expertise: Lessons learned

NASA Technical Reports Server (NTRS)

Woerner, Irene Wong

1991-01-01

Within a few years, JPL will be challenged by the most active mission set in history. Concurrently, flight systems are increasingly more complex. Presently, the knowledge to conduct integration and test of spacecraft and large instruments is held by a few key people, each with many years of experience. JPL is in danger of losing a significant amount of this critical expertise, through retirement, during a period when demand for this expertise is rapidly increasing. The most critical issue at hand is to collect and retain this expertise and develop tools that would ensure the ability to successfully perform the integration and test of future spacecraft and large instruments. The proposed solution was to capture and codity a subset of existing knowledge, and to utilize this captured expertise in knowledge-based systems. First year results and activities planned for the second year of this on-going effort are described. Topics discussed include lessons learned in knowledge acquisition and elicitation techniques, life-cycle paradigms, and rapid prototyping of a knowledge-based advisor (Spacecraft Test Assistant) and a hypermedia browser (Test Engineering Browser). The prototype Spacecraft Test Assistant supports a subset of integration and test activities for flight systems. Browser is a hypermedia tool that allows users easy perusal of spacecraft test topics. A knowledge acquisition tool called ConceptFinder which was developed to search through large volumes of data for related concepts is also described and is modified to semi-automate the process of creating hypertext links.

KBGIS-II: A knowledge-based geographic information system

NASA Technical Reports Server (NTRS)

Smith, Terence; Peuquet, Donna; Menon, Sudhakar; Agarwal, Pankaj

1986-01-01

The architecture and working of a recently implemented Knowledge-Based Geographic Information System (KBGIS-II), designed to satisfy several general criteria for the GIS, is described. The system has four major functions including query-answering, learning and editing. The main query finds constrained locations for spatial objects that are describable in a predicate-calculus based spatial object language. The main search procedures include a family of constraint-satisfaction procedures that use a spatial object knowledge base to search efficiently for complex spatial objects in large, multilayered spatial data bases. These data bases are represented in quadtree form. The search strategy is designed to reduce the computational cost of search in the average case. The learning capabilities of the system include the addition of new locations of complex spatial objects to the knowledge base as queries are answered, and the ability to learn inductively definitions of new spatial objects from examples. The new definitions are added to the knowledge base by the system. The system is performing all its designated tasks successfully. Future reports will relate performance characteristics of the system.

The BBN Knowledge Acquisition Project

DTIC Science & Technology

1988-09-01

Optation 17 S . Large-Scale Revisions of Knowledge Bases 19 5.1 The Macro and Structure Editor 19 5.2 Developing Macro Editing Procedre 20 5.2.1 Macro...consistency checking foreach style of representaton easier and mote efficient, so that knowledge engineers and subject matter expert s can work together to... s ~Ta OIM-iJC ~Va Coetnin: MILECSXCT 8.1.3.1 Local Comumand Menus Anytime ICREM ais~laying a view of a particular kind of knowledge , the State Winsdow

Evaluation and experimentation with duck management strategies

USGS Publications Warehouse

Nichols, J.D.; Johnson, F.A.

1989-01-01

Our knowledge of the effects of hunting regulations on duck populations has been based largely on retrospective studies of historical data. We have reached the limits of what can be learned in this way. Future knowledge gains will likely come about only through experimentation and adaptive management.

Framework Support For Knowledge-Based Software Development

NASA Astrophysics Data System (ADS)

Huseth, Steve

1988-03-01

The advent of personal engineering workstations has brought substantial information processing power to the individual programmer. Advanced tools and environment capabilities supporting the software lifecycle are just beginning to become generally available. However, many of these tools are addressing only part of the software development problem by focusing on rapid construction of self-contained programs by a small group of talented engineers. Additional capabilities are required to support the development of large programming systems where a high degree of coordination and communication is required among large numbers of software engineers, hardware engineers, and managers. A major player in realizing these capabilities is the framework supporting the software development environment. In this paper we discuss our research toward a Knowledge-Based Software Assistant (KBSA) framework. We propose the development of an advanced framework containing a distributed knowledge base that can support the data representation needs of tools, provide environmental support for the formalization and control of the software development process, and offer a highly interactive and consistent user interface.

Integrated Database And Knowledge Base For Genomic Prospective Cohort Study In Tohoku Medical Megabank Toward Personalized Prevention And Medicine.

PubMed

Ogishima, Soichi; Takai, Takako; Shimokawa, Kazuro; Nagaie, Satoshi; Tanaka, Hiroshi; Nakaya, Jun

2015-01-01

The Tohoku Medical Megabank project is a national project to revitalization of the disaster area in the Tohoku region by the Great East Japan Earthquake, and have conducted large-scale prospective genome-cohort study. Along with prospective genome-cohort study, we have developed integrated database and knowledge base which will be key database for realizing personalized prevention and medicine.

Improving Urban Teachers' Assessment Literacy through Synergistic Individualized Tutoring and Self-Reflection

ERIC Educational Resources Information Center

Murphy Odo, Dennis

2015-01-01

Assessment literacy entails understanding and proper use of assessments based on knowledge of theoretical and philosophical foundations of the measurement of students' learning (Volante & Fazio, 2007). It includes knowledge of formative and summative assessment, classroom and large-scale assessment, key psychometric concepts (Deluca &…

An application of object-oriented knowledge representation to engineering expert systems

NASA Technical Reports Server (NTRS)

Logie, D. S.; Kamil, H.; Umaretiya, J. R.

1990-01-01

The paper describes an object-oriented knowledge representation and its application to engineering expert systems. The object-oriented approach promotes efficient handling of the problem data by allowing knowledge to be encapsulated in objects and organized by defining relationships between the objects. An Object Representation Language (ORL) was implemented as a tool for building and manipulating the object base. Rule-based knowledge representation is then used to simulate engineering design reasoning. Using a common object base, very large expert systems can be developed, comprised of small, individually processed, rule sets. The integration of these two schemes makes it easier to develop practical engineering expert systems. The general approach to applying this technology to the domain of the finite element analysis, design, and optimization of aerospace structures is discussed.

Computer integrated documentation

NASA Technical Reports Server (NTRS)

Boy, Guy

1991-01-01

The main technical issues of the Computer Integrated Documentation (CID) project are presented. The problem of automation of documents management and maintenance is analyzed both from an artificial intelligence viewpoint and from a human factors viewpoint. Possible technologies for CID are reviewed: conventional approaches to indexing and information retrieval; hypertext; and knowledge based systems. A particular effort was made to provide an appropriate representation for contextual knowledge. This representation is used to generate context on hypertext links. Thus, indexing in CID is context sensitive. The implementation of the current version of CID is described. It includes a hypertext data base, a knowledge based management and maintenance system, and a user interface. A series is also presented of theoretical considerations as navigation in hyperspace, acquisition of indexing knowledge, generation and maintenance of a large documentation, and relation to other work.

Introducing the Big Knowledge to Use (BK2U) challenge.

PubMed

Perl, Yehoshua; Geller, James; Halper, Michael; Ochs, Christopher; Zheng, Ling; Kapusnik-Uner, Joan

2017-01-01

The purpose of the Big Data to Knowledge initiative is to develop methods for discovering new knowledge from large amounts of data. However, if the resulting knowledge is so large that it resists comprehension, referred to here as Big Knowledge (BK), how can it be used properly and creatively? We call this secondary challenge, Big Knowledge to Use. Without a high-level mental representation of the kinds of knowledge in a BK knowledgebase, effective or innovative use of the knowledge may be limited. We describe summarization and visualization techniques that capture the big picture of a BK knowledgebase, possibly created from Big Data. In this research, we distinguish between assertion BK and rule-based BK (rule BK) and demonstrate the usefulness of summarization and visualization techniques of assertion BK for clinical phenotyping. As an example, we illustrate how a summary of many intracranial bleeding concepts can improve phenotyping, compared to the traditional approach. We also demonstrate the usefulness of summarization and visualization techniques of rule BK for drug-drug interaction discovery. © 2016 New York Academy of Sciences.

Knowledge as a Service at the Point of Care.

PubMed

Shellum, Jane L; Freimuth, Robert R; Peters, Steve G; Nishimura, Rick A; Chaudhry, Rajeev; Demuth, Steve J; Knopp, Amy L; Miksch, Timothy A; Milliner, Dawn S

2016-01-01

An electronic health record (EHR) can assist the delivery of high-quality patient care, in part by providing the capability for a broad range of clinical decision support, including contextual references (e.g., Infobuttons), alerts and reminders, order sets, and dashboards. All of these decision support tools are based on clinical knowledge; unfortunately, the mechanisms for managing rules, order sets, Infobuttons, and dashboards are often unrelated, making it difficult to coordinate the application of clinical knowledge to various components of the clinical workflow. Additional complexity is encountered when updating enterprise-wide knowledge bases and delivering the content through multiple modalities to different consumers. We present the experience of Mayo Clinic as a case study to examine the requirements and implementation challenges related to knowledge management across a large, multi-site medical center. The lessons learned through the development of our knowledge management and delivery platform will help inform the future development of interoperable knowledge resources.

Knowledge as a Service at the Point of Care

PubMed Central

Shellum, Jane L.; Freimuth, Robert R.; Peters, Steve G.; Nishimura, Rick A.; Chaudhry, Rajeev; Demuth, Steve J.; Knopp, Amy L.; Miksch, Timothy A.; Milliner, Dawn S.

2016-01-01

An electronic health record (EHR) can assist the delivery of high-quality patient care, in part by providing the capability for a broad range of clinical decision support, including contextual references (e.g., Infobuttons), alerts and reminders, order sets, and dashboards. All of these decision support tools are based on clinical knowledge; unfortunately, the mechanisms for managing rules, order sets, Infobuttons, and dashboards are often unrelated, making it difficult to coordinate the application of clinical knowledge to various components of the clinical workflow. Additional complexity is encountered when updating enterprise-wide knowledge bases and delivering the content through multiple modalities to different consumers. We present the experience of Mayo Clinic as a case study to examine the requirements and implementation challenges related to knowledge management across a large, multi-site medical center. The lessons learned through the development of our knowledge management and delivery platform will help inform the future development of interoperable knowledge resources. PMID:28269911

Combining knowledge discovery from databases (KDD) and case-based reasoning (CBR) to support diagnosis of medical images

NASA Astrophysics Data System (ADS)

Stranieri, Andrew; Yearwood, John; Pham, Binh

1999-07-01

The development of data warehouses for the storage and analysis of very large corpora of medical image data represents a significant trend in health care and research. Amongst other benefits, the trend toward warehousing enables the use of techniques for automatically discovering knowledge from large and distributed databases. In this paper, we present an application design for knowledge discovery from databases (KDD) techniques that enhance the performance of the problem solving strategy known as case- based reasoning (CBR) for the diagnosis of radiological images. The problem of diagnosing the abnormality of the cervical spine is used to illustrate the method. The design of a case-based medical image diagnostic support system has three essential characteristics. The first is a case representation that comprises textual descriptions of the image, visual features that are known to be useful for indexing images, and additional visual features to be discovered by data mining many existing images. The second characteristic of the approach presented here involves the development of a case base that comprises an optimal number and distribution of cases. The third characteristic involves the automatic discovery, using KDD techniques, of adaptation knowledge to enhance the performance of the case based reasoner. Together, the three characteristics of our approach can overcome real time efficiency obstacles that otherwise mitigate against the use of CBR to the domain of medical image analysis.

Seeking and Sharing Knowledge Using Social Media in an Organization: The Impact of Social Influence, Organization Structure and Social Capital

ERIC Educational Resources Information Center

Schutz, Douglas M.

2013-01-01

The prolific use of social media tools such as blogs and wikis is leading several organizations to adopt these tools. However, success of social media depends on its use by employees to share and seek knowledge. Based on a unique data set obtained from a large multi-national corporation, I examined three different aspects of knowledge seeking and…

Nurses experience of using scientific knowledge in clinical practice: a grounded theory study.

PubMed

Renolen, Åste; Hjälmhult, Esther

2015-12-01

Guidelines recommend the use of evidence-based practice in nursing. Nurses are expected to give patients care and treatment based on the best knowledge available. They may have knowledge and positive attitudes, but this does not mean that they are basing their work on evidence-based practice. Knowledge is still lacking about what is needed to successfully implement evidence-based practice. The aim of this study was to gain more knowledge about what nurses perceive as the most important challenge in implementing evidence-based practice and to explain how they act to face and overcome this challenge. We used classical grounded theory methodology and collected data through four focus groups and one individual interview in different geographical locations in one large hospital trust in Norway. Fourteen registered clinical practice nurses participated. We analysed the data in accordance with grounded theory, using the constant comparative method. Contextual balancing of knowledge emerged as the core category and explains how the nurses dealt with their main concern, how to determine what types of knowledge they could trust. The nurses' main strategies were an inquiring approach, examining knowledge and maintaining control while taking care of patients. They combined their own experienced-based knowledge and the guidelines of evidence-based practice with a sense of control in the actual situation. The grounded theory contextual balancing of knowledge may help us to understand how nurses detect what types of knowledge they can trust in clinical practice. The nurses needed to rely on what they did, and they seemed to rely on their own experience rather than on research. © 2015 Nordic College of Caring Science.

KAM (Knowledge Acquisition Module): A tool to simplify the knowledge acquisition process

NASA Technical Reports Server (NTRS)

Gettig, Gary A.

1988-01-01

Analysts, knowledge engineers and information specialists are faced with increasing volumes of time-sensitive data in text form, either as free text or highly structured text records. Rapid access to the relevant data in these sources is essential. However, due to the volume and organization of the contents, and limitations of human memory and association, frequently: (1) important information is not located in time; (2) reams of irrelevant data are searched; and (3) interesting or critical associations are missed due to physical or temporal gaps involved in working with large files. The Knowledge Acquisition Module (KAM) is a microcomputer-based expert system designed to assist knowledge engineers, analysts, and other specialists in extracting useful knowledge from large volumes of digitized text and text-based files. KAM formulates non-explicit, ambiguous, or vague relations, rules, and facts into a manageable and consistent formal code. A library of system rules or heuristics is maintained to control the extraction of rules, relations, assertions, and other patterns from the text. These heuristics can be added, deleted or customized by the user. The user can further control the extraction process with optional topic specifications. This allows the user to cluster extracts based on specific topics. Because KAM formalizes diverse knowledge, it can be used by a variety of expert systems and automated reasoning applications. KAM can also perform important roles in computer-assisted training and skill development. Current research efforts include the applicability of neural networks to aid in the extraction process and the conversion of these extracts into standard formats.

Margin based ontology sparse vector learning algorithm and applied in biology science.

PubMed

Gao, Wei; Qudair Baig, Abdul; Ali, Haidar; Sajjad, Wasim; Reza Farahani, Mohammad

2017-01-01

In biology field, the ontology application relates to a large amount of genetic information and chemical information of molecular structure, which makes knowledge of ontology concepts convey much information. Therefore, in mathematical notation, the dimension of vector which corresponds to the ontology concept is often very large, and thus improves the higher requirements of ontology algorithm. Under this background, we consider the designing of ontology sparse vector algorithm and application in biology. In this paper, using knowledge of marginal likelihood and marginal distribution, the optimized strategy of marginal based ontology sparse vector learning algorithm is presented. Finally, the new algorithm is applied to gene ontology and plant ontology to verify its efficiency.

Relating indices of knowledge structure coherence and accuracy to skill-based performance: Is there utility in using a combination of indices?

PubMed

Schuelke, Matthew J; Day, Eric Anthony; McEntire, Lauren E; Boatman, Jazmine Espejo; Wang, Xiaoqian; Kowollik, Vanessa; Boatman, Paul R

2009-07-01

The authors examined the relative criterion-related validity of knowledge structure coherence and two accuracy-based indices (closeness and correlation) as well as the utility of using a combination of knowledge structure indices in the prediction of skill acquisition and transfer. Findings from an aggregation of 5 independent samples (N = 958) whose participants underwent training on a complex computer simulation indicated that coherence and the accuracy-based indices yielded comparable zero-order predictive validities. Support for the incremental validity of using a combination of indices was mixed; the most, albeit small, gain came in pairing coherence and closeness when predicting transfer. After controlling for baseline skill, general mental ability, and declarative knowledge, only coherence explained a statistically significant amount of unique variance in transfer. Overall, the results suggested that the different indices largely overlap in their representation of knowledge organization, but that coherence better reflects adaptable aspects of knowledge organization important to skill transfer.

Orbital transfer vehicle launch operations study: Automated technology knowledge base, volume 4

NASA Technical Reports Server (NTRS)

1986-01-01

A simplified retrieval strategy for compiling automation-related bibliographies from NASA/RECON is presented. Two subsets of NASA Thesaurus subject terms were extracted: a primary list, which is used to obtain an initial set of citations; and a secondary list, which is used to limit or further specify a large initial set of citations. These subject term lists are presented in Appendix A as the Automated Technology Knowledge Base (ATKB) Thesaurus.

Speech recognition: Acoustic phonetic and lexical knowledge representation

NASA Astrophysics Data System (ADS)

Zue, V. W.

1983-02-01

The purpose of this program is to develop a speech data base facility under which the acoustic characteristics of speech sounds in various contexts can be studied conveniently; investigate the phonological properties of a large lexicon of, say 10,000 words, and determine to what extent the phontactic constraints can be utilized in speech recognition; study the acoustic cues that are used to mark work boundaries; develop a test bed in the form of a large-vocabulary, IWR system to study the interactions of acoustic, phonetic and lexical knowledge; and develop a limited continuous speech recognition system with the goal of recognizing any English word from its spelling in order to assess the interactions of higher-level knowledge sources.

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.

Measuring the Interest of German Students in Agriculture: The Role of Knowledge, Nature Experience, Disgust, and Gender

ERIC Educational Resources Information Center

Bickel, Malte; Strack, Micha; Bögeholz, Susanne

2015-01-01

Modern knowledge-based societies, especially their younger members, have largely lost their bonds to farming. However, learning about agriculture and its interrelations with environmental issues may be facilitated by students' individual interests in agriculture. To date, an adequate instrument to investigate agricultural interests has been…

A Master Key to Workforce Skills Development

ERIC Educational Resources Information Center

Association of Canadian Community Colleges, 2006

2006-01-01

Canadian society is undergoing a significant transformation, largely in response to the forces of globalization and the development of the knowledge/information economy. The key to the economic and social well being of Canada's diverse communities lies in the knowledge-and-skills base of its citizens. Canada must design policies and programs which…

Mega-Universities and Knowledge Media: Technology Strategies for Higher Education.

ERIC Educational Resources Information Center

Daniel, John S

This book explores the essentials of distance education and reviews issues facing large open universities (mega-universities) worldwide. It uses examples from industry and the knowledge media, to show how technology-based learning can be made attractive both to students and to institutions. The book's eight chapters, including 10 figures and 5…

Unveiling health attitudes and creating good-for-you foods: the genomics metaphor, consumer innovative web-based technologies.

PubMed

Moskowitz, H R; German, J B; Saguy, I S

2005-01-01

This article presents an integrated analysis of three emerging knowledge bases in the nutrition and consumer products industries, and how they may effect the food industry. These knowledge bases produce new vistas for corporate product development, especially with respect to those foods that are positioned as 'good for you.' Couched within the current thinking of state-of-the-art knowledge and information, this article highlights how today's thinking about accelerated product development can be introduced into the food and health industries to complement these three research areas. The 3 knowledge bases are: the genomics revolution, which has opened new insights into understanding the interactions of personal needs of individual consumers with nutritionally relevant components of the foods; the investigation of food choice by scientific studies; the development of large scale databases (mega-studies) about the consumer mind. These knowledge bases, combined with new methods to understand the consumer through research, make possible a more focused development. The confluence of trends outlined in this article provides the corporation with the beginnings of a new path to a knowledge-based, principles-grounded product-development system. The approaches hold the potential to create foods based upon people's nutritional requirements combined with their individual preferences. Integrating these emerging knowledge areas with new consumer research techniques may well reshape how the food industry develops new products to satisfy consumer needs and wants.

An evidential approach to problem solving when a large number of knowledge systems is available

NASA Technical Reports Server (NTRS)

Dekorvin, Andre

1989-01-01

Some recent problems are no longer formulated in terms of imprecise facts, missing data or inadequate measuring devices. Instead, questions pertaining to knowledge and information itself arise and can be phrased independently of any particular area of knowledge. The problem considered in the present work is how to model a problem solver that is trying to find the answer to some query. The problem solver has access to a large number of knowledge systems that specialize in diverse features. In this context, feature means an indicator of what the possibilities for the answer are. The knowledge systems should not be accessed more than once, in order to have truly independent sources of information. Moreover, these systems are allowed to run in parallel. Since access might be expensive, it is necessary to construct a management policy for accessing these knowledge systems. To help in the access policy, some control knowledge systems are available. Control knowledge systems have knowledge about the performance parameters status of the knowledge systems. In order to carry out the double goal of estimating what units to access and to answer the given query, diverse pieces of evidence must be fused. The Dempster-Shafer Theory of Evidence is used to pool the knowledge bases.

A Large-Scale Inquiry-Based Astronomy Intervention Project: Impact on Students' Content Knowledge Performance and Views of Their High School Science Classroom

ERIC Educational Resources Information Center

Fitzgerald, Michael; McKinnon, David H.; Danaia, Lena; Deehan, James

2016-01-01

In this paper, we present the results from a study of the impact on students involved in a large-scale inquiry-based astronomical high school education intervention in Australia. Students in this intervention were led through an educational design allowing them to undertake an investigative approach to understanding the lifecycle of stars more…

A knowledge engineering approach to recognizing and extracting sequences of nucleic acids from scientific literature.

PubMed

García-Remesal, Miguel; Maojo, Victor; Crespo, José

2010-01-01

In this paper we present a knowledge engineering approach to automatically recognize and extract genetic sequences from scientific articles. To carry out this task, we use a preliminary recognizer based on a finite state machine to extract all candidate DNA/RNA sequences. The latter are then fed into a knowledge-based system that automatically discards false positives and refines noisy and incorrectly merged sequences. We created the knowledge base by manually analyzing different manuscripts containing genetic sequences. Our approach was evaluated using a test set of 211 full-text articles in PDF format containing 3134 genetic sequences. For such set, we achieved 87.76% precision and 97.70% recall respectively. This method can facilitate different research tasks. These include text mining, information extraction, and information retrieval research dealing with large collections of documents containing genetic sequences.

Large system change challenges: addressing complex critical issues in linked physical and social domains

NASA Astrophysics Data System (ADS)

Waddell, Steve; Cornell, Sarah; Hsueh, Joe; Ozer, Ceren; McLachlan, Milla; Birney, Anna

2015-04-01

Most action to address contemporary complex challenges, including the urgent issues of global sustainability, occurs piecemeal and without meaningful guidance from leading complex change knowledge and methods. The potential benefit of using such knowledge is greater efficacy of effort and investment. However, this knowledge and its associated tools and methods are under-utilized because understanding about them is low, fragmented between diverse knowledge traditions, and often requires shifts in mindsets and skills from expert-led to participant-based action. We have been engaged in diverse action-oriented research efforts in Large System Change for sustainability. For us, "large" systems can be characterized as large-scale systems - up to global - with many components, of many kinds (physical, biological, institutional, cultural/conceptual), operating at multiple levels, driven by multiple forces, and presenting major challenges for people involved. We see change of such systems as complex challenges, in contrast with simple or complicated problems, or chaotic situations. In other words, issues and sub-systems have unclear boundaries, interact with each other, and are often contradictory; dynamics are non-linear; issues are not "controllable", and "solutions" are "emergent" and often paradoxical. Since choices are opportunity-, power- and value-driven, these social, institutional and cultural factors need to be made explicit in any actionable theory of change. Our emerging network is sharing and building a knowledge base of experience, heuristics, and theories of change from multiple disciplines and practice domains. We will present our views on focal issues for the development of the field of large system change, which include processes of goal-setting and alignment; leverage of systemic transitions and transformation; and the role of choice in influencing critical change processes, when only some sub-systems or levels of the system behave in purposeful ways, while others are undeniably and unavoidably deterministic.

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

Electronic access to scientific nursing knowledge: the Virginia Henderson International Nursing Library.

PubMed

Graves, J R

2001-02-01

To inform oncology nurses about the electronic knowledge resources offered by the Sigma Theta Tau International Virginia Henderson International Nursing Library. Published articles and research studies. Clinical nursing research dissemination has been seriously affected by publication bias. The Virginia Henderson International Nursing Library has introduced both a new publishing paradigm for research and a new knowledge indexing strategy for improving electronic access to research knowledge (findings). The ability of oncology nursing to evolve, as an evidence-based practice, is largely dependent on access to research findings.

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

Awareness, knowledge, and attitude of dentistry students in Kerman towards evidence-based dentistry

PubMed Central

Sarani, Arezoo; Sarani, Melika; Abdar, Mohammad Esmaeli; Abdar, Zahra Esmaeili

2016-01-01

Introduction Evidence-based care helps dentists provide quality dental services to patients, and such care is based on the use of reliable information about treatment and patient care from a large number of papers, books, and published textbooks. This study aimed to determine the knowledge, awareness, and attitude of dentistry students towards evidence-based dentistry. Methods In this cross-sectional study, all dentistry students who were studying in their sixth semester and higher in the Kerman School of Dentistry (n = 73) were studied. The data were analyzed using SPSS version 17 and the independent-samples t-tests and the ANOVA test. Results The means of the students’ knowledge, awareness, and attitude scores were 29.2 ± 10.8, 29.9 ± 8.12 and 44.5 ± 5.3, respectively. Among demographic variables, only the number of semesters showed a significant difference with knowledge, awareness, and attitude of dentistry students toward evidence-based dentistry (p = 0.001). Conclusion According to the results of this study, knowledge and awareness of dentistry students at Kerman University of Medical Sciences towards evidence-based dentistry were average and have a neutral attitude. Thus, providing necessary training in this regard will cause promoting the knowledge, awareness, and improved attitudes of dentistry students. PMID:27382446

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.

D and D Knowledge Management Information Tool - 2012 - 12106

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

Upadhyay, H.; Lagos, L.; Quintero, W.

2012-07-01

Deactivation and decommissioning (D and D) work is a high priority activity across the Department of Energy (DOE) complex. Subject matter specialists (SMS) associated with the different ALARA (As-Low-As-Reasonably-Achievable) Centers, DOE sites, Energy Facility Contractors Group (EFCOG) and the D and D community have gained extensive knowledge and experience over the years in the cleanup of the legacy waste from the Manhattan Project. To prevent the D and D knowledge and expertise from being lost over time from the evolving and aging workforce, DOE and the Applied Research Center (ARC) at Florida International University (FIU) proposed to capture and maintainmore » this valuable information in a universally available and easily usable system. D and D KM-IT provides single point access to all D and D related activities through its knowledge base. It is a community driven system. D and D KM-IT makes D and D knowledge available to the people who need it at the time they need it and in a readily usable format. It uses the World Wide Web as the primary source for content in addition to information collected from subject matter specialists and the D and D community. It brings information in real time through web based custom search processes and its dynamic knowledge repository. Future developments include developing a document library, providing D and D information access on mobile devices for the Technology module and Hotline, and coordinating multiple subject matter specialists to support the Hotline. The goal is to deploy a high-end sophisticated and secured system to serve as a single large knowledge base for all the D and D activities. The system consolidates a large amount of information available on the web and presents it to users in the simplest way possible. (authors)« less

Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

PubMed Central

Mikut, Ralf

2017-01-01

Many automatically analyzable scientific questions are well-posed and a variety of information about expected outcomes is available a priori. Although often neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and by direct information about the ambiguity inherent in the extracted data. We present a new concept that increases the result quality awareness of image analysis operators by estimating and distributing the degree of uncertainty involved in their output based on prior knowledge. This allows the use of simple processing operators that are suitable for analyzing large-scale spatiotemporal (3D+t) microscopy images without compromising result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it to enhance the result quality of various processing operators. These concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. The functionality of the proposed approach is further validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. The generality of the concept makes it applicable to practically any field with processing strategies that are arranged as linear pipelines. The automated analysis of terabyte-scale microscopy data will especially benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout. PMID:29095927

AI in medicine on its way from knowledge-intensive to data-intensive systems.

PubMed

Horn, W

2001-08-01

The last 20 years of research and development in the field of artificial intelligence in medicine (AIM) show a path from knowledge-intensive systems, which try to capture the essential knowledge of experts in a knowledge-based system, to data-intensive systems available today. Nowadays enormous amounts of information is accessible electronically. Large datasets are collected continuously monitoring physiological parameters of patients. Knowledge-based systems are needed to make use of all these data available and to help us to cope with the information explosion. In addition, temporal data analysis and intelligent information visualization can help us to get a summarized view of the change over time of clinical parameters. Integrating AIM modules into the daily-routine software environment of our care providers gives us a great chance for maintaining and improving quality of care.

Genomics Portals: integrative web-platform for mining genomics data.

PubMed

Shinde, Kaustubh; Phatak, Mukta; Johannes, Freudenberg M; Chen, Jing; Li, Qian; Vineet, Joshi K; Hu, Zhen; Ghosh, Krishnendu; Meller, Jaroslaw; Medvedovic, Mario

2010-01-13

A large amount of experimental data generated by modern high-throughput technologies is available through various public repositories. Our knowledge about molecular interaction networks, functional biological pathways and transcriptional regulatory modules is rapidly expanding, and is being organized in lists of functionally related genes. Jointly, these two sources of information hold a tremendous potential for gaining new insights into functioning of living systems. Genomics Portals platform integrates access to an extensive knowledge base and a large database of human, mouse, and rat genomics data with basic analytical visualization tools. It provides the context for analyzing and interpreting new experimental data and the tool for effective mining of a large number of publicly available genomics datasets stored in the back-end databases. The uniqueness of this platform lies in the volume and the diversity of genomics data that can be accessed and analyzed (gene expression, ChIP-chip, ChIP-seq, epigenomics, computationally predicted binding sites, etc), and the integration with an extensive knowledge base that can be used in such analysis. The integrated access to primary genomics data, functional knowledge and analytical tools makes Genomics Portals platform a unique tool for interpreting results of new genomics experiments and for mining the vast amount of data stored in the Genomics Portals backend databases. Genomics Portals can be accessed and used freely at http://GenomicsPortals.org.

Genomics Portals: integrative web-platform for mining genomics data

PubMed Central

2010-01-01

Background A large amount of experimental data generated by modern high-throughput technologies is available through various public repositories. Our knowledge about molecular interaction networks, functional biological pathways and transcriptional regulatory modules is rapidly expanding, and is being organized in lists of functionally related genes. Jointly, these two sources of information hold a tremendous potential for gaining new insights into functioning of living systems. Results Genomics Portals platform integrates access to an extensive knowledge base and a large database of human, mouse, and rat genomics data with basic analytical visualization tools. It provides the context for analyzing and interpreting new experimental data and the tool for effective mining of a large number of publicly available genomics datasets stored in the back-end databases. The uniqueness of this platform lies in the volume and the diversity of genomics data that can be accessed and analyzed (gene expression, ChIP-chip, ChIP-seq, epigenomics, computationally predicted binding sites, etc), and the integration with an extensive knowledge base that can be used in such analysis. Conclusion The integrated access to primary genomics data, functional knowledge and analytical tools makes Genomics Portals platform a unique tool for interpreting results of new genomics experiments and for mining the vast amount of data stored in the Genomics Portals backend databases. Genomics Portals can be accessed and used freely at http://GenomicsPortals.org. PMID:20070909

Pediatric and adolescent gynecology learned via a Web-based computerized case series.

PubMed

De Silva, Nirupama K; Dietrich, Jennifer E; Young, Amy E

2010-04-01

To increase resident knowledge in pediatric and adolescent gynecology via a Web-based self-tutorial. Prospective cohort involving 11 third- and fourth-year residents in a large university program. Residents were asked to complete a Web-based teaching series of cases involving common topics of pediatric and adolescent gynecology (PAG). A pretest and a posttest were completed to assess knowledge gained. Residents were asked to give feedback regarding improvements to the Web-based series for future case development. University-affiliated residency program in a major metropolitan area. Resident physicians in the Department of Obstetrics and Gynecology. Introduction of a Web-based teaching series to enhance resident education. Improvement of resident knowledge in PAG. All residents improved their knowledge in PAG after reviewing the series of cases. The pretest group mean score was 50%. The posttest group score was 69% (P < .05). All (100%) of participants said that this tool was an effective way to improve resident knowledge in PAG. A computer-based self-tutorial in pediatric and adolescent gynecology is a feasible and satisfactory teaching adjunct to PAG. Copyright 2010 North American Society for Pediatric and Adolescent Gynecology. Published by Elsevier Inc. All rights reserved.

Explaining Authors' Contribution to Pivotal Artifacts during Mass Collaboration in the Wikipedia's Knowledge Base

ERIC Educational Resources Information Center

Halatchliyski, Iassen; Moskaliuk, Johannes; Kimmerle, Joachim; Cress, Ulrike

2014-01-01

This article discusses the relevance of large-scale mass collaboration for computer-supported collaborative learning (CSCL) research, adhering to a theoretical perspective that views collective knowledge both as substance and as participatory activity. In an empirical study using the German Wikipedia as a data source, we explored collective…

A Fresh Look at Spanish Scientific Publishing in the Framework of International Standards

ERIC Educational Resources Information Center

Kindelan, Paz

2009-01-01

Research has become a key element in the knowledge-based society with its role of producing and disseminating results. In this context, scientific publishing becomes the means by which research activity and knowledge production are circulated to the scientific community and society at large. However, there are factors influencing the system of…

Automated Session-Quality Assessment for Human Tutoring Based on Expert Ratings of Tutoring Success

ERIC Educational Resources Information Center

Nye, Benjamin D.; Morrison, Donald M.; Samei, Borhan

2015-01-01

Archived transcripts from tens of millions of online human tutoring sessions potentially contain important knowledge about how online tutors help, or fail to help, students learn. However, without ways of automatically analyzing these large corpora, any knowledge in this data will remain buried. One way to approach this issue is to train an…

In Rhode Island, Building a bRIdge to the Knowledge Economy

ERIC Educational Resources Information Center

Leonard, Adam

2012-01-01

In 2008, Rhode Island was in the early stages of refocusing its economic development efforts on transitioning to a knowledge-based economy. This move would require an educated workforce, largely deemed the responsibility of the state's 11 public and private institutions of higher education. For a state with slightly over a million residents and…

Faculty Consulting in Natural Sciences and Engineering: Between Formal and Informal Knowledge Transfer

ERIC Educational Resources Information Center

Amara, Nabil; Landry, Rejean; Halilem, Norrin

2013-01-01

Academic consulting is a form of knowledge and technology transfer largely under-documented and under-studied that raises ethical and resources allocation issues. Based on a survey of 2,590 Canadian researchers in engineering and natural sciences, this paper explores three forms of academic consulting: (1) paid consulting; (2) unpaid consulting…

When Mind, Heart, and Hands Meet: Communication Design and Designers

ERIC Educational Resources Information Center

Cheung, Ming

2012-01-01

Hong Kong's transformation from a manufacturing to a knowledge-based economy has prompted the local government to promote the city as a regional design center. The 2008 Policy Address delivered by Hong Kong's Chief Executive calls for the creation of a large pool of creative and knowledgeable talent. The government recognizes that, in addition to…

Learning to Teach Reading: A Theory-Practice Approach to Psychology Teaching in University Teacher Education

ERIC Educational Resources Information Center

Drechsel, Barbara; Breunig, Katharina; Thurn, Daniela; Basten, Johanna

2014-01-01

The report portrays a theory-practice psychology course on reading education in a German teacher education programme. Having completed a theoretical course phase that is largely based on knowledge from cognitive and educational psychology, pre-service student-teachers applied their acquired knowledge by working with a fifth-grader in five…

Graphical explanation in an expert system for Space Station Freedom rack integration

NASA Technical Reports Server (NTRS)

Craig, F. G.; Cutts, D. E.; Fennel, T. R.; Purves, B.

1990-01-01

The rationale and methodology used to incorporate graphics into explanations provided by an expert system for Space Station Freedom rack integration is examined. The rack integration task is typical of a class of constraint satisfaction problems for large programs where expertise from several areas is required. Graphically oriented approaches are used to explain the conclusions made by the system, the knowledge base content, and even at more abstract levels the control strategies employed by the system. The implemented architecture combines hypermedia and inference engine capabilities. The advantages of this architecture include: closer integration of user interface, explanation system, and knowledge base; the ability to embed links to deeper knowledge underlying the compiled knowledge used in the knowledge base; and allowing for more direct control of explanation depth and duration by the user. The graphical techniques employed range from simple statis presentation of schematics to dynamic creation of a series of pictures presented motion picture style. User models control the type, amount, and order of information presented.

Employing Model-Based Reasoning in Interdisciplinary Research Teams: Evidence-Based Practices for Integrating Knowledge Across Systems

NASA Astrophysics Data System (ADS)

Pennington, D. D.; Vincent, S.

2017-12-01

The NSF-funded project "Employing Model-Based Reasoning in Socio-Environmental Synthesis (EMBeRS)" has developed a generic model for exchanging knowledge across disciplines that is based on findings from the cognitive, learning, social, and organizational sciences addressing teamwork in complex problem solving situations. Two ten-day summer workshops for PhD students from large, NSF-funded interdisciplinary projects working on a variety of water issues were conducted in 2016 and 2017, testing the model by collecting a variety of data, including surveys, interviews, audio/video recordings, material artifacts and documents, and photographs. This presentation will introduce the EMBeRS model, the design of workshop activities based on the model, and results from surveys and interviews with the participating students. Findings suggest that this approach is very effective for developing a shared, integrated research vision across disciplines, compared with activities typically provided by most large research projects, and that students believe the skills developed in the EMBeRS workshops are unique and highly desireable.

KBGIS-2: A knowledge-based geographic information system

NASA Technical Reports Server (NTRS)

Smith, T.; Peuquet, D.; Menon, S.; Agarwal, P.

1986-01-01

The architecture and working of a recently implemented knowledge-based geographic information system (KBGIS-2) that was designed to satisfy several general criteria for the geographic information system are described. The system has four major functions that include query-answering, learning, and editing. The main query finds constrained locations for spatial objects that are describable in a predicate-calculus based spatial objects language. The main search procedures include a family of constraint-satisfaction procedures that use a spatial object knowledge base to search efficiently for complex spatial objects in large, multilayered spatial data bases. These data bases are represented in quadtree form. The search strategy is designed to reduce the computational cost of search in the average case. The learning capabilities of the system include the addition of new locations of complex spatial objects to the knowledge base as queries are answered, and the ability to learn inductively definitions of new spatial objects from examples. The new definitions are added to the knowledge base by the system. The system is currently performing all its designated tasks successfully, although currently implemented on inadequate hardware. Future reports will detail the performance characteristics of the system, and various new extensions are planned in order to enhance the power of KBGIS-2.

Blocking the benefit of group-based HIV-prevention efforts during adolescence: the problem of HIV-related stigma.

PubMed

Barker, David H; Swenson, Rebecca R; Brown, Larry K; Stanton, Bonita F; Vanable, Peter A; Carey, Michael P; Valois, Robert F; Diclemente, Ralph J; Salazar, Laura F; Romer, Daniel

2012-04-01

HIV-related stigma has been shown to impede HIV-antibody testing and safer sexual practices in adults. Less is known about its effects on prevention programs among at-risk youth. This study examined the longitudinal relationships between HIV-stigma and HIV-knowledge following completion of a validated group-based intervention. Data were provided by 1,654 African-American adolescents who participated in a large multi-city prevention trial (Project iMPACCS). Participants were randomly assigned to an empirically-validated skill-based intervention or a general health promotion control group. Both stigma and knowledge were assessed at baseline and post-intervention. Results suggested that adolescents participating in the intervention showed improvements in knowledge and decreases in stigma when compared to controls. Improvements in stigma appeared to be partly driven by improvements in knowledge. Higher baseline stigma was shown to reduce gains in knowledge in both the treatment and control groups. Results suggest that HIV-stigma can interfere with how youth identify with and internalize messages from group-based prevention trials.

RelFinder: Revealing Relationships in RDF Knowledge Bases

NASA Astrophysics Data System (ADS)

Heim, Philipp; Hellmann, Sebastian; Lehmann, Jens; Lohmann, Steffen; Stegemann, Timo

The Semantic Web has recently seen a rise of large knowledge bases (such as DBpedia) that are freely accessible via SPARQL endpoints. The structured representation of the contained information opens up new possibilities in the way it can be accessed and queried. In this paper, we present an approach that extracts a graph covering relationships between two objects of interest. We show an interactive visualization of this graph that supports the systematic analysis of the found relationships by providing highlighting, previewing, and filtering features.

Expert systems and simulation models; Proceedings of the Seminar, Tucson, AZ, November 18, 19, 1985

NASA Technical Reports Server (NTRS)

1986-01-01

The seminar presents papers on modeling and simulation methodology, artificial intelligence and expert systems, environments for simulation/expert system development, and methodology for simulation/expert system development. Particular attention is given to simulation modeling concepts and their representation, modular hierarchical model specification, knowledge representation, and rule-based diagnostic expert system development. Other topics include the combination of symbolic and discrete event simulation, real time inferencing, and the management of large knowledge-based simulation projects.

Development of a GIService based on spatial data mining for location choice of convenience stores in Taipei City

NASA Astrophysics Data System (ADS)

Jung, Chinte; Sun, Chih-Hong

2006-10-01

Motivated by the increasing accessibility of technology, more and more spatial data are being made digitally available. How to extract the valuable knowledge from these large (spatial) databases is becoming increasingly important to businesses, as well. It is essential to be able to analyze and utilize these large datasets, convert them into useful knowledge, and transmit them through GIS-enabled instruments and the Internet, conveying the key information to business decision-makers effectively and benefiting business entities. In this research, we combine the techniques of GIS, spatial decision support system (SDSS), spatial data mining (SDM), and ArcGIS Server to achieve the following goals: (1) integrate databases from spatial and non-spatial datasets about the locations of businesses in Taipei, Taiwan; (2) use the association rules, one of the SDM methods, to extract the knowledge from the integrated databases; and (3) develop a Web-based SDSS GIService as a location-selection tool for business by the product of ArcGIS Server.

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.

The usefulness of science knowledge for parents of hearing-impaired children.

PubMed

Shauli, Sophie; Baram-Tsabari, Ayelet

2018-04-01

Hearing-impaired children's chances of integrating into hearing society largely depend on their parents, who need to learn vast amounts of science knowledge in the field of hearing. This study characterized the role played by science knowledge in the lives of nonscientists faced with science-related decisions by examining the interactions between general science knowledge, contextual science knowledge in the field of hearing, and parents' advocacy knowledge and attitudes. Based on six semi-structured interviews and 115 questionnaires completed by parents of hearing-impaired children, contextual science knowledge emerged as the only predictor for having slightly better advocacy attitudes and knowledge (5.5% explained variance). Although general science knowledge was the best predictor of contextual knowledge (14% of explained variance), it was not a direct predictor of advocacy knowledge and attitudes. Science knowledge plays some role in the lives of hearing-impaired families, even if they do not list it as a resource for successful rehabilitation.

Who Really Wants an Ambitious Large-Scale Restoration of the Seine Estuary? A Strategic Analysis of a Science-Policy Interface Locked in a Stalemate.

PubMed

Coreau, Audrey; Narcy, Jean-Baptiste; Lumbroso, Sarah

2018-05-01

The development of ecosystem knowledge is an essential condition for effective environmental management but using available knowledge to solve environmental controversies is still difficult in "real" situations. This paper explores the conditions under which ecological knowledge could contribute to the environmental strategies and actions of stakeholders at science-policy interface. Ecological restoration of the Seine estuary is an example of an environmental issue whose overall management has run into difficulties despite the production of a large amount of knowledge by a dedicated organization, GIP Seine Aval. Thanks to an action-research project, based on a futures study, we analyze the reasons of these difficulties and help the GIP Seine Aval adopt a robust strategy to overcome them. According to our results, most local stakeholders involved in the large-scale restoration project emphasize the need for a clear divide between knowledge production and environmental action. This kind of divide may be strategic in a context where the robustness of environmental decisions is strongly depending on the mobilization of "neutral" scientific knowledge. But in our case study, this rather blocks action because some powerful stakeholders continuously ask for more knowledge before taking action. The construction and analysis of possible future scenarios has led to three alternative strategies being identified to counter this stalemate situation: (1) to circumvent difficulties by creating indirect links between knowledge and actions; (2) to use knowledge to sustain advocacy for the interests of each and every stakeholder; (3) to involve citizens in decisions about knowledge production and use, so that environmental issues weight more on the local political agenda.

Knowledge and decision-making for labour analgesia of Australian primiparous women.

PubMed

Raynes-Greenow, Camille H; Roberts, Christine L; McCaffery, Kirsten; Clarke, Judith

2007-06-01

to assess and investigate knowledge of labour pain management options and decision-making among primiparous women. a semi-structured guide was used in focus groups to gather pregnant women's knowledge concerning labour analgesia. Attitudes to labour and pain relief, knowledge of pain relief, trustworthiness of knowledge sources, and plans and expectations for labour pain relief were investigated. a major tertiary obstetric hospital in metropolitan Sydney, Australia. twenty five primiparous women, who were 25 weeks or more gestation, and planning a vaginal birth. although women considered themselves knowledgeable, they were unable to describe labour analgesic risks or benefits. There was a large discrepancy between perception and actual knowledge. The main source of knowledge was anecdotal information. Late in pregnancy was considered the ideal time to be given information about labour analgesia. Women described their labour pain relief plans as flexible in relation to their labour circumstances; however, most women wanted to take an active role in decision-making. the large discrepancy between perceived knowledge and actual knowledge of the likely consequences of labour analgesia suggests that women rely too heavily on anecdotal information. clinicians should be aware that some women overestimate their knowledge and understanding of analgesic options, which is often based on anecdotal information. Standardised labour analgesia information at an appropriate time in their pregnancy may benefit some women and assist health-care providers and women to practice shared decision-making.

Graph-based real-time fault diagnostics

NASA Technical Reports Server (NTRS)

Padalkar, S.; Karsai, G.; Sztipanovits, J.

1988-01-01

A real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components.

NASA's online machine aided indexing system

NASA Technical Reports Server (NTRS)

Silvester, June P.; Genuardi, Michael T.; Klingbiel, Paul H.

1993-01-01

This report describes the NASA Lexical Dictionary, a machine aided indexing system used online at the National Aeronautics and Space Administration's Center for Aerospace Information (CASI). This system is comprised of a text processor that is based on the computational, non-syntactic analysis of input text, and an extensive 'knowledge base' that serves to recognize and translate text-extracted concepts. The structure and function of the various NLD system components are described in detail. Methods used for the development of the knowledge base are discussed. Particular attention is given to a statistically-based text analysis program that provides the knowledge base developer with a list of concept-specific phrases extracted from large textual corpora. Production and quality benefits resulting from the integration of machine aided indexing at CASI are discussed along with a number of secondary applications of NLD-derived systems including on-line spell checking and machine aided lexicography.

Riverine settlement in the evolution of prehistoric land-use systems in the Middle Rio Grande Valley, New Mexico

Treesearch

Joseph A. Tainter; Bonnie Bagley Tainter

1996-01-01

Ecosystem management should be based on the fullest possible knowledge of ecological structures and processes. In prehistoric North America, the involvement of Indian populations in ecosystem processes ranged from inadvertent alteration of the distribution and abundance of species to large-scale management of landscapes. The knowledge needed to manage ecosystems today...

Development of Bilingual Phonological Awareness in Spanish-Speaking English Language Learners: The Roles of Vocabulary, Letter Knowledge, and Prior Phonological Awareness

ERIC Educational Resources Information Center

Anthony, Jason L.; Solari, Emily J.; Williams, Jeffrey M.; Schoger, Kimberly D.; Zhang, Zhou; Branum-Martin, Lee; Francis, David J.

2009-01-01

Theories concerning the development of phonological awareness place special emphasis on lexical and orthographic knowledge. Given the large degree of variability in preschool classrooms that house Spanish-speaking English language learners (ELL), this study controlled for classroom effects by removing classroom means and covariances based on 158…

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…

Modelling Situation Awareness Information for Naval Decision Support Design

DTIC Science & Technology

2003-10-01

Modelling Situation Awareness Information for Naval Decision Support Design Dr.-Ing. Bernhard Doering, Dipl.-Ing. Gert Doerfel, Dipl.-Ing... knowledge -based user interfaces. For developing such interfaces information of the three different SA levels which operators need in performing their...large scale on situation awareness of operators which is defined as the state of operator knowledge about the external environment resulting from

Development of an Educational Video to Improve HIV-Related Knowledge, Attitudes and Prevention among Company Workers in Ecuador

ERIC Educational Resources Information Center

del Carmen Cabezas, María; Fornasini, Marco; Barmettler, David; Ortuño, Diego; Borja, Teresa; Albert, Adelin

2015-01-01

Objective: To develop and assess an innovative educational video package for improving HIV knowledge, attitudes and practices among company workers in Ecuador. Methods: The design and development of the HIV prevention educational video was based on the results of a large-scale survey conducted in 115 companies (commerce, manufacturing and real…

Relations between Mathematical Knowledge for Teaching, Mathematics Instructional Quality, and Student Achievement in the Context of the "Responsive Classroom (RC)" Approach

ERIC Educational Resources Information Center

Ottmar, Erin R.; Rimm-Kaufman, Sara E.; Larsen, Ross; Merritt, Eileen G.

2011-01-01

Despite over thirty years of theoretically based research investigating "how" teacher mathematical knowledge and instructional practice relate to student learning, it is still largely unclear how these constructs are related, and policy makers and practitioners are still situated in a context with insufficient data to make decisions. Thus, there…

An overview of expert systems. [artificial intelligence

NASA Technical Reports Server (NTRS)

Gevarter, W. B.

1982-01-01

An expert system is defined and its basic structure is discussed. The knowledge base, the inference engine, and uses of expert systems are discussed. Architecture is considered, including choice of solution direction, reasoning in the presence of uncertainty, searching small and large search spaces, handling large search spaces by transforming them and by developing alternative or additional spaces, and dealing with time. Existing expert systems are reviewed. Tools for building such systems, construction, and knowledge acquisition and learning are discussed. Centers of research and funding sources are listed. The state-of-the-art, current problems, required research, and future trends are summarized.

Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications

NASA Astrophysics Data System (ADS)

Liu, Jia; Liu, Longli; Xue, Yong; Dong, Jing; Hu, Yingcui; Hill, Richard; Guang, Jie; Li, Chi

2017-01-01

Workflow for remote sensing quantitative retrieval is the ;bridge; between Grid services and Grid-enabled application of remote sensing quantitative retrieval. Workflow averts low-level implementation details of the Grid and hence enables users to focus on higher levels of application. The workflow for remote sensing quantitative retrieval plays an important role in remote sensing Grid and Cloud computing services, which can support the modelling, construction and implementation of large-scale complicated applications of remote sensing science. The validation of workflow is important in order to support the large-scale sophisticated scientific computation processes with enhanced performance and to minimize potential waste of time and resources. To research the semantic correctness of user-defined workflows, in this paper, we propose a workflow validation method based on tacit knowledge research in the remote sensing domain. We first discuss the remote sensing model and metadata. Through detailed analysis, we then discuss the method of extracting the domain tacit knowledge and expressing the knowledge with ontology. Additionally, we construct the domain ontology with Protégé. Through our experimental study, we verify the validity of this method in two ways, namely data source consistency error validation and parameters matching error validation.

God-mother-baby: what children think they know.

PubMed

Kiessling, Florian; Perner, Josef

2014-01-01

This study tested one hundred and nine 3- to 6-year-old children on a knowledge-ignorance task about knowledge in humans (mother, baby) and God. In their responses, participants not reliably grasping that seeing leads to knowing in humans (pre-representational) were significantly influenced by own knowledge and marginally by question format. Moreover, knowledge was attributed significantly more often to mother than baby and explained by agent-based characteristics. Of participants mastering the task for humans (representational), God was largely conceived as ignorant "man in the sky" by younger and increasingly as "supernatural agent in the sky" by older children. Evidence for egocentrism and for anthropomorphizing God lends support to an anthropomorphism hypothesis. First-time evidence for an agent-based conception of others' knowledge in pre-representational children is presented. © 2013 The Authors. Child Development © 2013 Society for Research in Child Development, Inc.

Will the future of knowledge work automation transform personalized medicine?

PubMed

Naik, Gauri; Bhide, Sanika S

2014-09-01

Today, we live in a world of 'information overload' which demands high level of knowledge-based work. However, advances in computer hardware and software have opened possibilities to automate 'routine cognitive tasks' for knowledge processing. Engineering intelligent software systems that can process large data sets using unstructured commands and subtle judgments and have the ability to learn 'on the fly' are a significant step towards automation of knowledge work. The applications of this technology for high throughput genomic analysis, database updating, reporting clinically significant variants, and diagnostic imaging purposes are explored using case studies.

Towards Semantic e-Science for Traditional Chinese Medicine

PubMed Central

Chen, Huajun; Mao, Yuxin; Zheng, Xiaoqing; Cui, Meng; Feng, Yi; Deng, Shuiguang; Yin, Aining; Zhou, Chunying; Tang, Jinming; Jiang, Xiaohong; Wu, Zhaohui

2007-01-01

Background Recent advances in Web and information technologies with the increasing decentralization of organizational structures have resulted in massive amounts of information resources and domain-specific services in Traditional Chinese Medicine. The massive volume and diversity of information and services available have made it difficult to achieve seamless and interoperable e-Science for knowledge-intensive disciplines like TCM. Therefore, information integration and service coordination are two major challenges in e-Science for TCM. We still lack sophisticated approaches to integrate scientific data and services for TCM e-Science. Results We present a comprehensive approach to build dynamic and extendable e-Science applications for knowledge-intensive disciplines like TCM based on semantic and knowledge-based techniques. The semantic e-Science infrastructure for TCM supports large-scale database integration and service coordination in a virtual organization. We use domain ontologies to integrate TCM database resources and services in a semantic cyberspace and deliver a semantically superior experience including browsing, searching, querying and knowledge discovering to users. We have developed a collection of semantic-based toolkits to facilitate TCM scientists and researchers in information sharing and collaborative research. Conclusion Semantic and knowledge-based techniques are suitable to knowledge-intensive disciplines like TCM. It's possible to build on-demand e-Science system for TCM based on existing semantic and knowledge-based techniques. The presented approach in the paper integrates heterogeneous distributed TCM databases and services, and provides scientists with semantically superior experience to support collaborative research in TCM discipline. PMID:17493289

Conceptual astronomy: A novel model for teaching postsecondary science courses

NASA Astrophysics Data System (ADS)

Zeilik, Michael; Schau, Candace; Mattern, Nancy; Hall, Shannon; Teague, Kathleen W.; Bisard, Walter

1997-10-01

An innovative, conceptually based instructional model for teaching large undergraduate astronomy courses was designed, implemented, and evaluated in the Fall 1995 semester. This model was based on cognitive and educational theories of knowledge and, we believe, is applicable to other large postsecondary science courses. Major components were: (a) identification of the basic important concepts and their interrelationships that are necessary for connected understanding of astronomy in novice students; (b) use of these concepts and their interrelationships throughout the design, implementation, and evaluation stages of the model; (c) identification of students' prior knowledge and misconceptions; and (d) implementation of varied instructional strategies targeted toward encouraging conceptual understanding in students (i.e., instructional concept maps, cooperative small group work, homework assignments stressing concept application, and a conceptually based student assessment system). Evaluation included the development and use of three measures of conceptual understanding and one of attitudes toward studying astronomy. Over the semester, students showed very large increases in their understanding as assessed by a conceptually based multiple-choice measure of misconceptions, a select-and-fill-in concept map measure, and a relatedness-ratings measure. Attitudes, which were slightly positive before the course, changed slightly in a less favorable direction.

Skills, rules and knowledge in aircraft maintenance: errors in context

NASA Technical Reports Server (NTRS)

Hobbs, Alan; Williamson, Ann

2002-01-01

Automatic or skill-based behaviour is generally considered to be less prone to error than behaviour directed by conscious control. However, researchers who have applied Rasmussen's skill-rule-knowledge human error framework to accidents and incidents have sometimes found that skill-based errors appear in significant numbers. It is proposed that this is largely a reflection of the opportunities for error which workplaces present and does not indicate that skill-based behaviour is intrinsically unreliable. In the current study, 99 errors reported by 72 aircraft mechanics were examined in the light of a task analysis based on observations of the work of 25 aircraft mechanics. The task analysis identified the opportunities for error presented at various stages of maintenance work packages and by the job as a whole. Once the frequency of each error type was normalized in terms of the opportunities for error, it became apparent that skill-based performance is more reliable than rule-based performance, which is in turn more reliable than knowledge-based performance. The results reinforce the belief that industrial safety interventions designed to reduce errors would best be directed at those aspects of jobs that involve rule- and knowledge-based performance.

Child Demographics Associated with Outcomes in a Community-Based Pivotal Response Training Program

ERIC Educational Resources Information Center

Baker-Ericzen, Mary J.; Stahmer, Aubyn C.; Burns, Amelia

2007-01-01

Although knowledge about the efficacy of treatments such as pivotal response training (PRT) for children with autism is increasing, studies of large-scale effectiveness for and transportability to diverse community populations are needed. The current study provides a large-scale preliminary assessment of (a) the effectiveness of a community-based…

Back to Basics: Naked-Eye Astronomical Observation

ERIC Educational Resources Information Center

Barclay, Charles

2003-01-01

For pupils of both sexes and all ages from about six upwards, the subject of Astronomy holds many fascinations--the rapid changes in knowledge, the large resource of available IT packages and above all the beautiful pictures from Hubble and the large Earth-based telescopes. This article, however, stresses the excitement and importance of naked-eye…

Probabilistic load simulation: Code development status

NASA Astrophysics Data System (ADS)

Newell, J. F.; Ho, H.

1991-05-01

The objective of the Composite Load Spectra (CLS) project is to develop generic load models to simulate the composite load spectra that are included in space propulsion system components. The probabilistic loads thus generated are part of the probabilistic design analysis (PDA) of a space propulsion system that also includes probabilistic structural analyses, reliability, and risk evaluations. Probabilistic load simulation for space propulsion systems demands sophisticated probabilistic methodology and requires large amounts of load information and engineering data. The CLS approach is to implement a knowledge based system coupled with a probabilistic load simulation module. The knowledge base manages and furnishes load information and expertise and sets up the simulation runs. The load simulation module performs the numerical computation to generate the probabilistic loads with load information supplied from the CLS knowledge base.

Knowledge-driven computational modeling in Alzheimer's disease research: Current state and future trends.

PubMed

Geerts, Hugo; Hofmann-Apitius, Martin; Anastasio, Thomas J

2017-11-01

Neurodegenerative diseases such as Alzheimer's disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-scale omics studies ranging from genetics to imaging, a large number of processes that might be involved in AD pathology at different stages and levels have been identified. The sheer number of putative hypotheses makes it almost impossible to estimate their contribution to the clinical outcome and to develop a comprehensive view on the pathological processes driving the clinical phenotype. Traditionally, bioinformatics approaches have provided correlations and associations between processes and phenotypes. Focusing on causality, a new breed of advanced and more quantitative modeling approaches that use formalized domain expertise offer new opportunities to integrate these different modalities and outline possible paths toward new therapeutic interventions. This article reviews three different computational approaches and their possible complementarities. Process algebras, implemented using declarative programming languages such as Maude, facilitate simulation and analysis of complicated biological processes on a comprehensive but coarse-grained level. A model-driven Integration of Data and Knowledge, based on the OpenBEL platform and using reverse causative reasoning and network jump analysis, can generate mechanistic knowledge and a new, mechanism-based taxonomy of disease. Finally, Quantitative Systems Pharmacology is based on formalized implementation of domain expertise in a more fine-grained, mechanism-driven, quantitative, and predictive humanized computer model. We propose a strategy to combine the strengths of these individual approaches for developing powerful modeling methodologies that can provide actionable knowledge for rational development of preventive and therapeutic interventions. Development of these computational approaches is likely to be required for further progress in understanding and treating AD. Copyright © 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Knowledge network model of the energy consumption in discrete manufacturing system

NASA Astrophysics Data System (ADS)

Xu, Binzi; Wang, Yan; Ji, Zhicheng

2017-07-01

Discrete manufacturing system generates a large amount of data and information because of the development of information technology. Hence, a management mechanism is urgently required. In order to incorporate knowledge generated from manufacturing data and production experience, a knowledge network model of the energy consumption in the discrete manufacturing system was put forward based on knowledge network theory and multi-granularity modular ontology technology. This model could provide a standard representation for concepts, terms and their relationships, which could be understood by both human and computer. Besides, the formal description of energy consumption knowledge elements (ECKEs) in the knowledge network was also given. Finally, an application example was used to verify the feasibility of the proposed method.

Integrating Science Communication, Outreach and Awareness into the R&D Investment Framework of SKA in order to drive a knowledge economy

NASA Astrophysics Data System (ADS)

Raynard, Lorenzo; Garnier, William

2015-08-01

The emergence of the "knowledge society" has reshaped the context and content of public communication of science and technology. The construction and design of SKA and associated science research are increasingly characterised by the formation of large, multidisciplinary and multi-institute research teams. The integration of science communication into the scientific endeavour is key to building the foundations of a thriving knowledge-based economy identified by new technology investments, high-technology industries and highly skilled labour. Knowledge Economy Indicators profile, among others, the efficient and effective Management of Knowledge Assets. This presentation will explore the strategic trade and positioning of Knowledge Assets in order to drive and stimulate innovation.

Improving performance with knowledge management

NASA Astrophysics Data System (ADS)

Kim, Sangchul

2018-06-01

People and organization are unable to easily locate their experience and knowledge, so meaningful data is usually fragmented, unstructured, not up-to-date and largely incomplete. Poor knowledge management (KM) leaves a company weak to their knowledge-base - or intellectual capital - walking out of the door each year, that is minimum estimated at 10%. Knowledge management (KM) can be defined as an emerging set of organizational design and operational principles, processes, organizational structures, applications and technologies that helps knowledge workers dramatically leverage their creativity and ability to deliver business value and to reap finally a competitive advantage. Then, this paper proposed various method and software starting with an understanding of the enterprise aspect, and gave inspiration to those who wanted to use KM.

Finding gene regulatory network candidates using the gene expression knowledge base.

PubMed

Venkatesan, Aravind; Tripathi, Sushil; Sanz de Galdeano, Alejandro; Blondé, Ward; Lægreid, Astrid; Mironov, Vladimir; Kuiper, Martin

2014-12-10

Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of 'omics' data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.

Knowledge-based and model-based hybrid methodology for comprehensive waste minimization in electroplating plants

NASA Astrophysics Data System (ADS)

Luo, Keqin

1999-11-01

The electroplating industry of over 10,000 planting plants nationwide is one of the major waste generators in the industry. Large quantities of wastewater, spent solvents, spent process solutions, and sludge are the major wastes generated daily in plants, which costs the industry tremendously for waste treatment and disposal and hinders the further development of the industry. It becomes, therefore, an urgent need for the industry to identify technically most effective and economically most attractive methodologies and technologies to minimize the waste, while the production competitiveness can be still maintained. This dissertation aims at developing a novel WM methodology using artificial intelligence, fuzzy logic, and fundamental knowledge in chemical engineering, and an intelligent decision support tool. The WM methodology consists of two parts: the heuristic knowledge-based qualitative WM decision analysis and support methodology and fundamental knowledge-based quantitative process analysis methodology for waste reduction. In the former, a large number of WM strategies are represented as fuzzy rules. This becomes the main part of the knowledge base in the decision support tool, WMEP-Advisor. In the latter, various first-principles-based process dynamic models are developed. These models can characterize all three major types of operations in an electroplating plant, i.e., cleaning, rinsing, and plating. This development allows us to perform a thorough process analysis on bath efficiency, chemical consumption, wastewater generation, sludge generation, etc. Additional models are developed for quantifying drag-out and evaporation that are critical for waste reduction. The models are validated through numerous industrial experiments in a typical plating line of an industrial partner. The unique contribution of this research is that it is the first time for the electroplating industry to (i) use systematically available WM strategies, (ii) know quantitatively and accurately what is going on in each tank, and (iii) identify all WM opportunities through process improvement. This work has formed a solid foundation for the further development of powerful WM technologies for comprehensive WM in the following decade.

Do large-scale assessments measure students' ability to integrate scientific knowledge?

NASA Astrophysics Data System (ADS)

Lee, Hee-Sun

2010-03-01

Large-scale assessments are used as means to diagnose the current status of student achievement in science and compare students across schools, states, and countries. For efficiency, multiple-choice items and dichotomously-scored open-ended items are pervasively used in large-scale assessments such as Trends in International Math and Science Study (TIMSS). This study investigated how well these items measure secondary school students' ability to integrate scientific knowledge. This study collected responses of 8400 students to 116 multiple-choice and 84 open-ended items and applied an Item Response Theory analysis based on the Rasch Partial Credit Model. Results indicate that most multiple-choice items and dichotomously-scored open-ended items can be used to determine whether students have normative ideas about science topics, but cannot measure whether students integrate multiple pieces of relevant science ideas. Only when the scoring rubric is redesigned to capture subtle nuances of student open-ended responses, open-ended items become a valid and reliable tool to assess students' knowledge integration ability.

Conceptualising GP teachers' knowledge: a pedagogical content knowledge perspective.

PubMed

Cantillon, Peter; de Grave, Willem

2012-05-01

Most teacher development initiatives focus on enhancing knowledge of teaching (pedagogy), whilst largely ignoring other important features of teacher knowledge such as subject matter knowledge and awareness of the learning context. Furthermore, teachers' ability to learn from faculty development interventions is limited by their existing (often implicit) pedagogical knowledge and beliefs. Pedagogical content knowledge (PCK) represents a model of teacher knowledge incorporating what they know about subject matter, pedagogy and context. PCK can be used to explore teachers' prior knowledge and to structure faculty development programmes so that they take account of a broader range of teachers' knowledge. We set out to examine the application of a PCK model in a general practice education setting. This study is part of a larger study that employed a mixed method approach (concept mapping, phenomenological interviews and video-stimulated recall) to explore features of GP teachers' subject matter knowledge, pedagogical knowledge and knowledge of the learning environment in the context of a general practice tutorial. This paper presents data on GP teachers' pedagogical and context knowledge. There was considerable overlap between different GP teachers' knowledge and beliefs about learners and the clinical learning environment (i.e. knowledge of context). The teachers' beliefs about learners were largely based on assumptions derived from their own student experiences. There were stark differences, however, between teachers in terms of pedagogical knowledge, particularly in terms of their teaching orientations (i.e. transmission or facilitation orientation) and this was manifest in their teaching behaviours. PCK represents a useful model for conceptualising clinical teacher prior knowledge in three domains, namely subject matter, learning context and pedagogy. It can and should be used as a simple guiding framework by faculty developers to inform the design and delivery of their faculty development programmes.

Assessing the Effect of Web-Based Learning Tools on Student Understanding of Stoichiometry Using Knowledge Space Theory

ERIC Educational Resources Information Center

Arasasingham, Ramesh D.; Taagepera, Mare; Potter, Frank; Martorell, Ingrid; Lonjers, Stacy

2005-01-01

Student achievement in web-based learning tools is assessed by using in-class examination, pretests, and posttests. The study reveals that using mastering chemistry web software in large-scale instruction provides an overall benefit to introductory chemistry students.

A knowledge-based approach to improving optimization techniques in system planning

NASA Technical Reports Server (NTRS)

Momoh, J. A.; Zhang, Z. Z.

1990-01-01

A knowledge-based (KB) approach to improve mathematical programming techniques used in the system planning environment is presented. The KB system assists in selecting appropriate optimization algorithms, objective functions, constraints and parameters. The scheme is implemented by integrating symbolic computation of rules derived from operator and planner's experience and is used for generalized optimization packages. The KB optimization software package is capable of improving the overall planning process which includes correction of given violations. The method was demonstrated on a large scale power system discussed in the paper.

Demonopolizing medical knowledge.

PubMed

Arora, Sanjeev; Thornton, Karla; Komaromy, Miriam; Kalishman, Summers; Katzman, Joanna; Duhigg, Daniel

2014-01-01

In the past 100 years, there has been an explosion of medical knowledge-and in the next 50 years, more medical knowledge will be available than ever before. Regrettably, current medical practice has been unable to keep pace with this explosion of medical knowledge. Specialized medical knowledge has been confined largely to academic medical centers (i.e., teaching hospitals) and to specialists in major cities; it has been disconnected from primary care clinicians on the front lines of patient care. To bridge this disconnect, medical knowledge must be demonopolized, and a platform for collaborative practice amongst all clinicians needs to be created. A new model of health care and education delivery called Project ECHO (Extension for Community Healthcare Outcomes), developed by the first author, does just this. Using videoconferencing technology and case-based learning, ECHO's medical specialists provide training and mentoring to primary care clinicians working in rural and urban underserved areas so that the latter can deliver the best evidence-based care to patients with complex health conditions in their own communities. The ECHO model increases access to care in rural and underserved areas, and it demonopolizes specialized medical knowledge and expertise.

Knowledge management: An abstraction of knowledge base and database management systems

NASA Technical Reports Server (NTRS)

Riedesel, Joel D.

1990-01-01

Artificial intelligence application requirements demand powerful representation capabilities as well as efficiency for real-time domains. Many tools exist, the most prevalent being expert systems tools such as ART, KEE, OPS5, and CLIPS. Other tools just emerging from the research environment are truth maintenance systems for representing non-monotonic knowledge, constraint systems, object oriented programming, and qualitative reasoning. Unfortunately, as many knowledge engineers have experienced, simply applying a tool to an application requires a large amount of effort to bend the application to fit. Much work goes into supporting work to make the tool integrate effectively. A Knowledge Management Design System (KNOMAD), is described which is a collection of tools built in layers. The layered architecture provides two major benefits; the ability to flexibly apply only those tools that are necessary for an application, and the ability to keep overhead, and thus inefficiency, to a minimum. KNOMAD is designed to manage many knowledge bases in a distributed environment providing maximum flexibility and expressivity to the knowledge engineer while also providing support for efficiency.

Predicate Oriented Pattern Analysis for Biomedical Knowledge Discovery

PubMed Central

Shen, Feichen; Liu, Hongfang; Sohn, Sunghwan; Larson, David W.; Lee, Yugyung

2017-01-01

In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF, OWL). With the increasing number of semi-structured data coming into the biomedical community, data integration and knowledge discovery from heterogeneous domains become important research problem. In the application level, detection of related concepts among medical ontologies is an important goal of life science research. It is more crucial to figure out how different concepts are related within a single ontology or across multiple ontologies by analysing predicates in different knowledge bases. However, the world today is one of information explosion, and it is extremely difficult for biomedical researchers to find existing or potential predicates to perform linking among cross domain concepts without any support from schema pattern analysis. Therefore, there is a need for a mechanism to do predicate oriented pattern analysis to partition heterogeneous ontologies into closer small topics and do query generation to discover cross domain knowledge from each topic. In this paper, we present such a model that predicates oriented pattern analysis based on their close relationship and generates a similarity matrix. Based on this similarity matrix, we apply an innovated unsupervised learning algorithm to partition large data sets into smaller and closer topics and generate meaningful queries to fully discover knowledge over a set of interlinked data sources. We have implemented a prototype system named BmQGen and evaluate the proposed model with colorectal surgical cohort from the Mayo Clinic. PMID:28983419

Using a Dialogue System Based on Dialogue Maps for Computer Assisted Second Language Learning

ERIC Educational Resources Information Center

Choi, Sung-Kwon; Kwon, Oh-Woog; Kim, Young-Kil; Lee, Yunkeun

2016-01-01

In order to use dialogue systems for computer assisted second-language learning systems, one of the difficult issues in such systems is how to construct large-scale dialogue knowledge that matches the dialogue modelling of a dialogue system. This paper describes how we have accomplished the short-term construction of large-scale and…

Patient safety, quality of care, and knowledge translation in the intensive care unit.

PubMed

Needham, Dale M

2010-07-01

A large gap exists between the completion of clinical research demonstrating the benefit of new treatment interventions and improved patient outcomes resulting from implementation of these interventions as part of routine clinical practice. This gap clearly affects patient safety and quality of care. Knowledge translation is important for addressing this gap, but evaluation of the most appropriate and effective knowledge translation methods is still ongoing. Through describing one model for knowledge translation and an example of its implementation, insights can be gained into systematic methods for advancing the implementation of evidence-based interventions to improve safety, quality, and patient outcomes.

Software-engineering challenges of building and deploying reusable problem solvers.

PubMed

O'Connor, Martin J; Nyulas, Csongor; Tu, Samson; Buckeridge, David L; Okhmatovskaia, Anna; Musen, Mark A

2009-11-01

Problem solving methods (PSMs) are software components that represent and encode reusable algorithms. They can be combined with representations of domain knowledge to produce intelligent application systems. A goal of research on PSMs is to provide principled methods and tools for composing and reusing algorithms in knowledge-based systems. The ultimate objective is to produce libraries of methods that can be easily adapted for use in these systems. Despite the intuitive appeal of PSMs as conceptual building blocks, in practice, these goals are largely unmet. There are no widely available tools for building applications using PSMs and no public libraries of PSMs available for reuse. This paper analyzes some of the reasons for the lack of widespread adoptions of PSM techniques and illustrate our analysis by describing our experiences developing a complex, high-throughput software system based on PSM principles. We conclude that many fundamental principles in PSM research are useful for building knowledge-based systems. In particular, the task-method decomposition process, which provides a means for structuring knowledge-based tasks, is a powerful abstraction for building systems of analytic methods. However, despite the power of PSMs in the conceptual modeling of knowledge-based systems, software engineering challenges have been seriously underestimated. The complexity of integrating control knowledge modeled by developers using PSMs with the domain knowledge that they model using ontologies creates a barrier to widespread use of PSM-based systems. Nevertheless, the surge of recent interest in ontologies has led to the production of comprehensive domain ontologies and of robust ontology-authoring tools. These developments present new opportunities to leverage the PSM approach.

Software-engineering challenges of building and deploying reusable problem solvers

PubMed Central

O’CONNOR, MARTIN J.; NYULAS, CSONGOR; TU, SAMSON; BUCKERIDGE, DAVID L.; OKHMATOVSKAIA, ANNA; MUSEN, MARK A.

2012-01-01

Problem solving methods (PSMs) are software components that represent and encode reusable algorithms. They can be combined with representations of domain knowledge to produce intelligent application systems. A goal of research on PSMs is to provide principled methods and tools for composing and reusing algorithms in knowledge-based systems. The ultimate objective is to produce libraries of methods that can be easily adapted for use in these systems. Despite the intuitive appeal of PSMs as conceptual building blocks, in practice, these goals are largely unmet. There are no widely available tools for building applications using PSMs and no public libraries of PSMs available for reuse. This paper analyzes some of the reasons for the lack of widespread adoptions of PSM techniques and illustrate our analysis by describing our experiences developing a complex, high-throughput software system based on PSM principles. We conclude that many fundamental principles in PSM research are useful for building knowledge-based systems. In particular, the task–method decomposition process, which provides a means for structuring knowledge-based tasks, is a powerful abstraction for building systems of analytic methods. However, despite the power of PSMs in the conceptual modeling of knowledge-based systems, software engineering challenges have been seriously underestimated. The complexity of integrating control knowledge modeled by developers using PSMs with the domain knowledge that they model using ontologies creates a barrier to widespread use of PSM-based systems. Nevertheless, the surge of recent interest in ontologies has led to the production of comprehensive domain ontologies and of robust ontology-authoring tools. These developments present new opportunities to leverage the PSM approach. PMID:23565031

Building distributed rule-based systems using the AI Bus

NASA Technical Reports Server (NTRS)

Schultz, Roger D.; Stobie, Iain C.

1990-01-01

The AI Bus software architecture was designed to support the construction of large-scale, production-quality applications in areas of high technology flux, running heterogeneous distributed environments, utilizing a mix of knowledge-based and conventional components. These goals led to its current development as a layered, object-oriented library for cooperative systems. This paper describes the concepts and design of the AI Bus and its implementation status as a library of reusable and customizable objects, structured by layers from operating system interfaces up to high-level knowledge-based agents. Each agent is a semi-autonomous process with specialized expertise, and consists of a number of knowledge sources (a knowledge base and inference engine). Inter-agent communication mechanisms are based on blackboards and Actors-style acquaintances. As a conservative first implementation, we used C++ on top of Unix, and wrapped an embedded Clips with methods for the knowledge source class. This involved designing standard protocols for communication and functions which use these protocols in rules. Embedding several CLIPS objects within a single process was an unexpected problem because of global variables, whose solution involved constructing and recompiling a C++ version of CLIPS. We are currently working on a more radical approach to incorporating CLIPS, by separating out its pattern matcher, rule and fact representations and other components as true object oriented modules.

A Very Large Area Network (VLAN) knowledge-base applied to space communication problems

NASA Technical Reports Server (NTRS)

Zander, Carol S.

1988-01-01

This paper first describes a hierarchical model for very large area networks (VLAN). Space communication problems whose solution could profit by the model are discussed and then an enhanced version of this model incorporating the knowledge needed for the missile detection-destruction problem is presented. A satellite network or VLAN is a network which includes at least one satellite. Due to the complexity, a compromise between fully centralized and fully distributed network management has been adopted. Network nodes are assigned to a physically localized group, called a partition. Partitions consist of groups of cell nodes with one cell node acting as the organizer or master, called the Group Master (GM). Coordinating the group masters is a Partition Master (PM). Knowledge is also distributed hierarchically existing in at least two nodes. Each satellite node has a back-up earth node. Knowledge must be distributed in such a way so as to minimize information loss when a node fails. Thus the model is hierarchical both physically and informationally.

Predicting Mycobacterium tuberculosis Complex Clades Using Knowledge-Based Bayesian Networks

PubMed Central

Bennett, Kristin P.

2014-01-01

We develop a novel approach for incorporating expert rules into Bayesian networks for classification of Mycobacterium tuberculosis complex (MTBC) clades. The proposed knowledge-based Bayesian network (KBBN) treats sets of expert rules as prior distributions on the classes. Unlike prior knowledge-based support vector machine approaches which require rules expressed as polyhedral sets, KBBN directly incorporates the rules without any modification. KBBN uses data to refine rule-based classifiers when the rule set is incomplete or ambiguous. We develop a predictive KBBN model for 69 MTBC clades found in the SITVIT international collection. We validate the approach using two testbeds that model knowledge of the MTBC obtained from two different experts and large DNA fingerprint databases to predict MTBC genetic clades and sublineages. These models represent strains of MTBC using high-throughput biomarkers called spacer oligonucleotide types (spoligotypes), since these are routinely gathered from MTBC isolates of tuberculosis (TB) patients. Results show that incorporating rules into problems can drastically increase classification accuracy if data alone are insufficient. The SITVIT KBBN is publicly available for use on the World Wide Web. PMID:24864238

Knowledge base and sensor bus messaging service architecture for critical tsunami warning and decision-support

NASA Astrophysics Data System (ADS)

Sabeur, Z. A.; Wächter, J.; Middleton, S. E.; Zlatev, Z.; Häner, R.; Hammitzsch, M.; Loewe, P.

2012-04-01

The intelligent management of large volumes of environmental monitoring data for early tsunami warning requires the deployment of robust and scalable service oriented infrastructure that is supported by an agile knowledge-base for critical decision-support In the TRIDEC project (TRIDEC 2010-2013), a sensor observation service bus of the TRIDEC system is being developed for the advancement of complex tsunami event processing and management. Further, a dedicated TRIDEC system knowledge-base is being implemented to enable on-demand access to semantically rich OGC SWE compliant hydrodynamic observations and operationally oriented meta-information to multiple subscribers. TRIDEC decision support requires a scalable and agile real-time processing architecture which enables fast response to evolving subscribers requirements as the tsunami crisis develops. This is also achieved with the support of intelligent processing services which specialise in multi-level fusion methods with relevance feedback and deep learning. The TRIDEC knowledge base development work coupled with that of the generic sensor bus platform shall be presented to demonstrate advanced decision-support with situation awareness in context of tsunami early warning and crisis management.

Structure of the knowledge base for an expert labeling system

NASA Technical Reports Server (NTRS)

Rajaram, N. S.

1981-01-01

One of the principal objectives of the NASA AgRISTARS program is the inventory of global crop resources using remotely sensed data gathered by Land Satellites (LANDSAT). A central problem in any such crop inventory procedure is the interpretation of LANDSAT images and identification of parts of each image which are covered by a particular crop of interest. This task of labeling is largely a manual one done by trained human analysts and consequently presents obstacles to the development of totally automated crop inventory systems. However, development in knowledge engineering as well as widespread availability of inexpensive hardware and software for artificial intelligence work offers possibilities for developing expert systems for labeling of crops. Such a knowledge based approach to labeling is presented.

ECO: A Framework for Entity Co-Occurrence Exploration with Faceted Navigation

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

Halliday, K. D.

2010-08-20

Even as highly structured databases and semantic knowledge bases become more prevalent, a substantial amount of human knowledge is reported as written prose. Typical textual reports, such as news articles, contain information about entities (people, organizations, and locations) and their relationships. Automatically extracting such relationships from large text corpora is a key component of corporate and government knowledge bases. The primary goal of the ECO project is to develop a scalable framework for extracting and presenting these relationships for exploration using an easily navigable faceted user interface. ECO uses entity co-occurrence relationships to identify related entities. The system aggregates andmore » indexes information on each entity pair, allowing the user to rapidly discover and mine relational information.« less

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.

Variables Affecting Secondary School Students' Willingness to Eat Genetically Modified Food Crops

NASA Astrophysics Data System (ADS)

Maes, Jasmien; Bourgonjon, Jeroen; Gheysen, Godelieve; Valcke, Martin

2017-04-01

A large-scale cross-sectional study (N = 4002) was set up to determine Flemish secondary school students' willingness to eat genetically modified food (WTE) and to link students' WTE to previously identified key variables from research on the acceptance of genetic modification (GM). These variables include subjective and objective knowledge about genetics and biotechnology, perceived risks and benefits of GM food crops, trust in information from different sources about GM, and food neophobia. Differences between WTE-related variables based on students' grade level, educational track, and gender were analyzed. The students displayed a rather indecisive position toward GM food and scored weakly on a genetics and biotechnology knowledge test. WTE correlated most strongly with perceived benefits and subjective and objective knowledge. The results have clear implications for education, as they reiterate the need to strengthen students' scientific knowledge base and to introduce a GM-related debate at a much earlier stage in their school career.

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

Modelling students' knowledge organisation: Genealogical conceptual networks

NASA Astrophysics Data System (ADS)

Koponen, Ismo T.; Nousiainen, Maija

2018-04-01

Learning scientific knowledge is largely based on understanding what are its key concepts and how they are related. The relational structure of concepts also affects how concepts are introduced in teaching scientific knowledge. We model here how students organise their knowledge when they represent their understanding of how physics concepts are related. The model is based on assumptions that students use simple basic linking-motifs in introducing new concepts and mostly relate them to concepts that were introduced a few steps earlier, i.e. following a genealogical ordering. The resulting genealogical networks have relatively high local clustering coefficients of nodes but otherwise resemble networks obtained with an identical degree distribution of nodes but with random linking between them (i.e. the configuration-model). However, a few key nodes having a special structural role emerge and these nodes have a higher than average communicability betweenness centralities. These features agree with the empirically found properties of students' concept networks.

So what exactly is nursing knowledge?

PubMed

Clarke, L

2011-06-01

This paper aims to present a discussion about intrinsic nursing knowledge. The paper stems from the author's study of knowledge claims enshrined in nursing journal articles, books and conference speeches. It is argued that claims by academic nurses have largely depended on principles drawn from continental and not Analytic (British-American) philosophy. Thus, claims are credible only insofar as they defer propositional logic. This is problematic inasmuch as nursing is a practice-based activity usually carried out in medical settings. Transpersonal nursing models are particularly criticizable in respect of their unworldly character as are also concepts based on shallow usages of physics or mathematics. I argue that sensible measurements of the 'real world' are possible--without endorsing positivism--and that nursing requires little recourse to logically unsustainable claims. The paper concludes with an analysis of a recent review of nursing knowledge, which analysis indicates the circularity that attends many discussions on the topic. © 2011 Blackwell Publishing.

Variables Affecting Secondary School Students' Willingness to Eat Genetically Modified Food Crops

NASA Astrophysics Data System (ADS)

Maes, Jasmien; Bourgonjon, Jeroen; Gheysen, Godelieve; Valcke, Martin

2018-06-01

A large-scale cross-sectional study ( N = 4002) was set up to determine Flemish secondary school students' willingness to eat genetically modified food (WTE) and to link students' WTE to previously identified key variables from research on the acceptance of genetic modification (GM). These variables include subjective and objective knowledge about genetics and biotechnology, perceived risks and benefits of GM food crops, trust in information from different sources about GM, and food neophobia. Differences between WTE-related variables based on students' grade level, educational track, and gender were analyzed. The students displayed a rather indecisive position toward GM food and scored weakly on a genetics and biotechnology knowledge test. WTE correlated most strongly with perceived benefits and subjective and objective knowledge. The results have clear implications for education, as they reiterate the need to strengthen students' scientific knowledge base and to introduce a GM-related debate at a much earlier stage in their school career.

Case-based Reasoning for Automotive Engine Performance Tune-up

NASA Astrophysics Data System (ADS)

Vong, C. M.; Huang, H.; Wong, P. K.

2010-05-01

The automotive engine performance tune-up is greatly affected by the calibration of its electronic control unit (ECU). The ECU calibration is traditionally done by trial-and-error method. This traditional method consumes a large amount of time and money because of a large number of dynamometer tests. To resolve this problem, case based reasoning (CBR) is employed, so that an existing and effective ECU setup can be adapted to fit another similar class of engines. The adaptation procedure is done through a more sophisticated step called case-based adaptation (CBA) [1, 2]. CBA is an effective knowledge management tool, which can interactively learn the expert adaptation knowledge. The paper briefly reviews the methodologies of CBR and CBA. Then the application to ECU calibration is described via a case study. With CBR and CBA, the efficiency of calibrating an ECU can be enhanced. A prototype system has also been developed to verify the usefulness of CBR in ECU calibration.

Advanced data structures for the interpretation of image and cartographic data in geo-based information systems

NASA Technical Reports Server (NTRS)

Peuquet, D. J.

1986-01-01

A growing need to usse geographic information systems (GIS) to improve the flexibility and overall performance of very large, heterogeneous data bases was examined. The Vaster structure and the Topological Grid structure were compared to test whether such hybrid structures represent an improvement in performance. The use of artificial intelligence in a geographic/earth sciences data base context is being explored. The architecture of the Knowledge Based GIS (KBGIS) has a dual object/spatial data base and a three tier hierarchial search subsystem. Quadtree Spatial Spectra (QTSS) are derived, based on the quadtree data structure, to generate and represent spatial distribution information for large volumes of spatial data.

Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology

PubMed Central

2010-01-01

Background In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships. Results The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches. Conclusions The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms. PMID:21172053

Student Use of Academic Knowledge and Skills in Work-Based Learning

ERIC Educational Resources Information Center

Hawley, Joshua D.; Marks, Helen M.

2006-01-01

Using data from in a large Mid-western district, this study analyses the use of academic skills in work-based learning. The primary question asked in this study has to do with the impact of participating in work-based learning on the use of academic skills. Four sets of academic skills were measured using surveys (language arts, math, science, and…

Online self-administered training for post-traumatic stress disorder treatment providers: design and methods for a randomized, prospective intervention study

PubMed Central

2012-01-01

This paper presents the rationale and methods for a randomized controlled evaluation of web-based training in motivational interviewing, goal setting, and behavioral task assignment. Web-based training may be a practical and cost-effective way to address the need for large-scale mental health training in evidence-based practice; however, there is a dearth of well-controlled outcome studies of these approaches. For the current trial, 168 mental health providers treating post-traumatic stress disorder (PTSD) were assigned to web-based training plus supervision, web-based training, or training-as-usual (control). A novel standardized patient (SP) assessment was developed and implemented for objective measurement of changes in clinical skills, while on-line self-report measures were used for assessing changes in knowledge, perceived self-efficacy, and practice related to cognitive behavioral therapy (CBT) techniques. Eligible participants were all actively involved in mental health treatment of veterans with PTSD. Study methodology illustrates ways of developing training content, recruiting participants, and assessing knowledge, perceived self-efficacy, and competency-based outcomes, and demonstrates the feasibility of conducting prospective studies of training efficacy or effectiveness in large healthcare systems. PMID:22583520

Histopathologic and immunohistological evaluation of anaplastic large cell lymphoma with Epstein-Barr virus in an orangutan (Pongo pygmaeus).

PubMed

Bak, Eun-Jung; Jho, Yeonsook; Woo, Gye-Hyeong

2015-02-01

An 18-month-old female orangutan (Pongo pygmaeus) died after exhibiting fever, cough, and rapid breathing. Based on serological, virological, histopathological and immunohistochemical examination, anaplastic large cell lymphoma was confirmed. To the best of our knowledge, this is the first report of anaplastic large cell lymphoma associated with Epstein-Barr virus (EBV) in an orangutan. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Facilitating Metacognitive Processes of Academic Genre-Based Writing Using an Online Writing System

ERIC Educational Resources Information Center

Yeh, Hui-Chin

2015-01-01

Few studies have investigated how metacognitive processes foster the application of genre knowledge to students' academic writing. This is largely due to its internal and unobservable characteristics. To bridge this gap, an online writing system based on metacognition, involving the stages of planning, monitoring, evaluating, and revising, was…

Building Capacity for Work-Readiness: Bridging the Cognitive and Affective Domains

ERIC Educational Resources Information Center

Bandaranaike, Suniti; Willison, John

2015-01-01

Teaching for work-integrated learning (WIL) competency is largely directed at delivering knowledge based cognitive skills with little emphasis on affective skills. This study looks at empirical evidence of WIL students through their understanding of the cognitive and affective domains. The research is based on a validated employability framework,…

What Is Implementation Research? Rationale, Concepts, and Practices

ERIC Educational Resources Information Center

Bhattacharyya, Onil; Reeves, Scott; Zwarenstein, Merrick

2009-01-01

Despite the growing knowledge base on evidence-based practices in social work and medicine, there is a large gap between what is known and what is consistently done. Implementation research is the study of methods to promote the uptake of research findings into routine practice. In this article, we describe the rationale for implementation…

Ecosystem-based management in the lodgepole pine zone

Treesearch

Colin C. Hardy; Robert E. Keane; Catherine A. Stewart

2000-01-01

The significant geographic extent of lodgepole pine (Pinus contorta) in the interior West and the large proportion within the mixed-severity fire regime has led to efforts for more ecologically based management of lodgepole pine. New research and demonstration activities are presented that may provide knowledge and techniques to manage lodgepole pine...

Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing

PubMed Central

2013-01-01

Background A large-scale, highly accurate, machine-understandable drug-disease treatment relationship knowledge base is important for computational approaches to drug repurposing. The large body of published biomedical research articles and clinical case reports available on MEDLINE is a rich source of FDA-approved drug-disease indication as well as drug-repurposing knowledge that is crucial for applying FDA-approved drugs for new diseases. However, much of this information is buried in free text and not captured in any existing databases. The goal of this study is to extract a large number of accurate drug-disease treatment pairs from published literature. Results In this study, we developed a simple but highly accurate pattern-learning approach to extract treatment-specific drug-disease pairs from 20 million biomedical abstracts available on MEDLINE. We extracted a total of 34,305 unique drug-disease treatment pairs, the majority of which are not included in existing structured databases. Our algorithm achieved a precision of 0.904 and a recall of 0.131 in extracting all pairs, and a precision of 0.904 and a recall of 0.842 in extracting frequent pairs. In addition, we have shown that the extracted pairs strongly correlate with both drug target genes and therapeutic classes, therefore may have high potential in drug discovery. Conclusions We demonstrated that our simple pattern-learning relationship extraction algorithm is able to accurately extract many drug-disease pairs from the free text of biomedical literature that are not captured in structured databases. The large-scale, accurate, machine-understandable drug-disease treatment knowledge base that is resultant of our study, in combination with pairs from structured databases, will have high potential in computational drug repurposing tasks. PMID:23742147

Secure base representations in middle childhood across two Western cultures: Associations with parental attachment representations and maternal reports of behavior problems.

PubMed

Waters, Theodore E A; Bosmans, Guy; Vandevivere, Eva; Dujardin, Adinda; Waters, Harriet S

2015-08-01

Recent work examining the content and organization of attachment representations suggests that 1 way in which we represent the attachment relationship is in the form of a cognitive script. This work has largely focused on early childhood or adolescence/adulthood, leaving a large gap in our understanding of script-like attachment representations in the middle childhood period. We present 2 studies and provide 3 critical pieces of evidence regarding the presence of a script-like representation of the attachment relationship in middle childhood. We present evidence that a middle childhood attachment script assessment tapped a stable underlying script using samples drawn from 2 western cultures, the United States (Study 1) and Belgium (Study 2). We also found evidence suggestive of the intergenerational transmission of secure base script knowledge (Study 1) and relations between secure base script knowledge and symptoms of psychopathology in middle childhood (Study 2). The results from this investigation represent an important downward extension of the secure base script construct. (c) 2015 APA, all rights reserved).

Secure Base Representations in Middle Childhood Across Two Western Cultures: Associations with Parental Attachment Representations and Maternal Reports of Behavior Problems

PubMed Central

Waters, Theodore E. A.; Bosmans, Guy; Vandevivere, Eva; Dujardin, Adinda; Waters, Harriet S.

2015-01-01

Recent work examining the content and organization of attachment representations suggests that one way in which we represent the attachment relationship is in the form of a cognitive script. That said, this work has largely focused on early childhood or adolescence/adulthood, leaving a large gap in our understanding of script-like attachment representations in the middle childhood period. We present two studies and provide three critical pieces of evidence regarding the presence of a script-like representation of the attachment relationship in middle childhood. We present evidence that a middle childhood attachment script assessment tapped a stable underlying script using samples drawn from two western cultures, the United States (Study 1) and Belgium (Study 2). We also found evidence suggestive of the intergenerational transmission of secure base script knowledge (Study 1) and relations between secure base script knowledge and symptoms of psychopathology in middle childhood (Study 2). The results from this investigation represent an important downward extension of the secure base script construct. PMID:26147774

Racking Response of Reinforced Concrete Cut and Cover Tunnel

DOT National Transportation Integrated Search

2016-01-01

Currently, the knowledge base and quantitative data sets concerning cut and cover tunnel seismic response are scarce. In this report, a large-scale experimental program is conducted to assess: i) stiffness, capacity, and potential seismically-induced...

A knowledge-based, concept-oriented view generation system for clinical data.

PubMed

Zeng, Q; Cimino, J J

2001-04-01

Information overload is a well-known problem for clinicians who must review large amounts of data in patient records. Concept-oriented views, which organize patient data around clinical concepts such as diagnostic strategies and therapeutic goals, may offer a solution to the problem of information overload. However, although concept-oriented views are desirable, they are difficult to create and maintain. We have developed a general-purpose, knowledge-based approach to the generation of concept-oriented views and have developed a system to test our approach. The system creates concept-oriented views through automated identification of relevant patient data. The knowledge in the system is represented by both a semantic network and rules. The key relevant data identification function is accomplished by a rule-based traversal of the semantic network. This paper focuses on the design and implementation of the system; an evaluation of the system is reported separately.

Description of research interests and current work related to automating software design

NASA Technical Reports Server (NTRS)

Kaindl, Hermann

1992-01-01

Enclosed is a list of selected and recent publications. Most of these publications concern applied research in the areas of software engineering and human-computer interaction. It is felt that domain-specific knowledge plays a major role in software development. Additionally, it is believed that improvements in the general software development process (e.g., object-oriented approaches) will have to be combined with the use of large domain-specific knowledge bases.

Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries.

PubMed

Ma, Xiao H; Jia, Jia; Zhu, Feng; Xue, Ying; Li, Ze R; Chen, Yu Z

2009-05-01

Machine learning methods have been explored as ligand-based virtual screening tools for facilitating drug lead discovery. These methods predict compounds of specific pharmacodynamic, pharmacokinetic or toxicological properties based on their structure-derived structural and physicochemical properties. Increasing attention has been directed at these methods because of their capability in predicting compounds of diverse structures and complex structure-activity relationships without requiring the knowledge of target 3D structure. This article reviews current progresses in using machine learning methods for virtual screening of pharmacodynamically active compounds from large compound libraries, and analyzes and compares the reported performances of machine learning tools with those of structure-based and other ligand-based (such as pharmacophore and clustering) virtual screening methods. The feasibility to improve the performance of machine learning methods in screening large libraries is discussed.

Helping Mothers Survive Bleeding After Birth: retention of knowledge, skills, and confidence nine months after obstetric simulation-based training.

PubMed

Nelissen, Ellen; Ersdal, Hege; Mduma, Estomih; Evjen-Olsen, Bjørg; Broerse, Jacqueline; van Roosmalen, Jos; Stekelenburg, Jelle

2015-08-25

It is important to know the decay of knowledge, skills, and confidence over time to provide evidence-based guidance on timing of follow-up training. Studies addressing retention of simulation-based education reveal mixed results. The aim of this study was to measure the level of knowledge, skills, and confidence before, immediately after, and nine months after simulation-based training in obstetric care in order to understand the impact of training on these components. An educational intervention study was carried out in 2012 in a rural referral hospital in Northern Tanzania. Eighty-nine healthcare workers of different cadres were trained in "Helping Mothers Survive Bleeding After Birth", which addresses basic delivery skills including active management of third stage of labour and management of postpartum haemorrhage (PPH). Knowledge, skills, and confidence were tested before, immediately after, and nine months after training amongst 38 healthcare workers. Knowledge was tested by completing a written 26-item multiple-choice questionnaire. Skills were tested in two simulated scenarios "basic delivery" and "management of PPH". Confidence in active management of third stage of labour, management of PPH, determination of completeness of the placenta, bimanual uterine compression, and accessing advanced care was self-assessed using a written 5-item questionnaire. Mean knowledge scores increased immediately after training from 70 % to 77 %, but decreased close to pre-training levels (72 %) at nine-month follow-up (p = 0.386) (all p-levels are compared to pre-training). The mean score in basic delivery skills increased after training from 43 % to 51 %, and was 49 % after nine months (p = 0.165). Mean scores of management of PPH increased from 39 % to 51 % and were sustained at 50 % at nine months (p = 0.003). Bimanual uterine compression skills increased from 19 % before, to 43 % immediately after, to 48 % nine months after training (p = 0.000). Confidence increased immediately after training, and was largely retained at nine-month follow-up. Training resulted in an immediate increase in knowledge, skills, and confidence. While knowledge and simulated basic delivery skills decayed after nine months, confidence and simulated obstetric emergency skills were largely retained. These findings indicate a need for continuation of training. Future research should focus on the frequency and dosage of follow-up training.

Addressing the translational dilemma: dynamic knowledge representation of inflammation using agent-based modeling.

PubMed

An, Gary; Christley, Scott

2012-01-01

Given the panoply of system-level diseases that result from disordered inflammation, such as sepsis, atherosclerosis, cancer, and autoimmune disorders, understanding and characterizing the inflammatory response is a key target of biomedical research. Untangling the complex behavioral configurations associated with a process as ubiquitous as inflammation represents a prototype of the translational dilemma: the ability to translate mechanistic knowledge into effective therapeutics. A critical failure point in the current research environment is a throughput bottleneck at the level of evaluating hypotheses of mechanistic causality; these hypotheses represent the key step toward the application of knowledge for therapy development and design. Addressing the translational dilemma will require utilizing the ever-increasing power of computers and computational modeling to increase the efficiency of the scientific method in the identification and evaluation of hypotheses of mechanistic causality. More specifically, development needs to focus on facilitating the ability of non-computer trained biomedical researchers to utilize and instantiate their knowledge in dynamic computational models. This is termed "dynamic knowledge representation." Agent-based modeling is an object-oriented, discrete-event, rule-based simulation method that is well suited for biomedical dynamic knowledge representation. Agent-based modeling has been used in the study of inflammation at multiple scales. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggest that this modeling framework is well suited for addressing the translational dilemma. This review describes agent-based modeling, gives examples of its applications in the study of inflammation, and introduces a proposed general expansion of the use of modeling and simulation to augment the generation and evaluation of knowledge by the biomedical research community at large.

Artificial Intelligence In Computational Fluid Dynamics

NASA Technical Reports Server (NTRS)

Vogel, Alison Andrews

1991-01-01

Paper compares four first-generation artificial-intelligence (Al) software systems for computational fluid dynamics. Includes: Expert Cooling Fan Design System (EXFAN), PAN AIR Knowledge System (PAKS), grid-adaptation program MITOSIS, and Expert Zonal Grid Generation (EZGrid). Focuses on knowledge-based ("expert") software systems. Analyzes intended tasks, kinds of knowledge possessed, magnitude of effort required to codify knowledge, how quickly constructed, performances, and return on investment. On basis of comparison, concludes Al most successful when applied to well-formulated problems solved by classifying or selecting preenumerated solutions. In contrast, application of Al to poorly understood or poorly formulated problems generally results in long development time and large investment of effort, with no guarantee of success.

Knowledge-acquisition tools for medical knowledge-based systems.

PubMed

Lanzola, G; Quaglini, S; Stefanelli, M

1995-03-01

Knowledge-based systems (KBS) have been proposed to solve a large variety of medical problems. A strategic issue for KBS development and maintenance are the efforts required for both knowledge engineers and domain experts. The proposed solution is building efficient knowledge acquisition (KA) tools. This paper presents a set of KA tools we are developing within a European Project called GAMES II. They have been designed after the formulation of an epistemological model of medical reasoning. The main goal is that of developing a computational framework which allows knowledge engineers and domain experts to interact cooperatively in developing a medical KBS. To this aim, a set of reusable software components is highly recommended. Their design was facilitated by the development of a methodology for KBS construction. It views this process as comprising two activities: the tailoring of the epistemological model to the specific medical task to be executed and the subsequent translation of this model into a computational architecture so that the connections between computational structures and their knowledge level counterparts are maintained. The KA tools we developed are illustrated taking examples from the behavior of a KBS we are building for the management of children with acute myeloid leukemia.

The Network of Excellence 'Knowledge-based Multicomponent Materials for Durable and Safe Performance'

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

Moreno, Arnaldo

The Network of Excellence 'Knowledge-based Multicomponent Materials for Durable and Safe Performance' (KMM-NoE) consists of 36 institutional partners from 10 countries representing leading European research institutes and university departments (25), small and medium enterprises, SMEs (5) and large industry (7) in the field of knowledge-based multicomponent materials (KMM), more specifically in intermetallics, metal-ceramic composites, functionally graded materials and thin layers. The main goal of the KMM-NoE (currently funded by the European Commission) is to mobilise and concentrate the fragmented scientific potential in the KMM field to create a durable and efficient organism capable of developing leading-edge research while spreading themore » accumulated knowledge outside the Network and enhancing the technological skills of the related industries. The long-term strategic goal of the KMM-NoE is to establish a self-supporting pan-European institution in the field of knowledge-based multicomponent materials--KMM Virtual Institute (KMM-VIN). It will combine industry oriented research with educational and training activities. The KMM Virtual Institute will be founded on three main pillars: KMM European Competence Centre, KMM Integrated Post-Graduate School, KMM Mobility Programme. The KMM-NoE is coordinated by the Institute of Fundamental Technological Research (IPPT) of the Polish Academy of Sciences, Warsaw, Poland.« less

Knowledge-based simulation using object-oriented programming

NASA Technical Reports Server (NTRS)

Sidoran, Karen M.

1993-01-01

Simulations have become a powerful mechanism for understanding and modeling complex phenomena. Their results have had substantial impact on a broad range of decisions in the military, government, and industry. Because of this, new techniques are continually being explored and developed to make them even more useful, understandable, extendable, and efficient. One such area of research is the application of the knowledge-based methods of artificial intelligence (AI) to the computer simulation field. The goal of knowledge-based simulation is to facilitate building simulations of greatly increased power and comprehensibility by making use of deeper knowledge about the behavior of the simulated world. One technique for representing and manipulating knowledge that has been enhanced by the AI community is object-oriented programming. Using this technique, the entities of a discrete-event simulation can be viewed as objects in an object-oriented formulation. Knowledge can be factual (i.e., attributes of an entity) or behavioral (i.e., how the entity is to behave in certain circumstances). Rome Laboratory's Advanced Simulation Environment (RASE) was developed as a research vehicle to provide an enhanced simulation development environment for building more intelligent, interactive, flexible, and realistic simulations. This capability will support current and future battle management research and provide a test of the object-oriented paradigm for use in large scale military applications.

Accelerating preschoolers' early literacy development through classroom-based teacher-child storybook reading and explicit print referencing.

PubMed

Justice, Laura M; Kaderavek, Joan N; Fan, Xitao; Sofka, Amy; Hunt, Aileen

2009-01-01

This study examined the impact of teacher use of a print referencing style during classroom-based storybook reading sessions conducted over an academic year. Impacts on preschoolers' early literacy development were examined, focusing specifically on the domain of print knowledge. This randomized, controlled trial examined the effects of a print referencing style on 106 preschool children attending 23 classrooms serving disadvantaged preschoolers. Following random assignment, teachers in 14 classrooms used a print referencing style during 120 large-group storybook reading sessions during a 30-week period. Teachers in 9 comparison classrooms read at the same frequency and with the same storybooks but used their normal style of reading. Children whose teachers used a print referencing style showed larger gains on 3 standardized measures of print knowledge: print concept knowledge, alphabet knowledge, and name writing, with medium-sized effects. The convergence of the present findings with those of previous efficacy studies indicates that print referencing intervention can be used confidently as an approach for facilitating print knowledge in preschool-age children. Speech-language pathologists can serve an important role in supporting preschool educators as they use this evidence-based technique with pupils in their classrooms.

Advances in knowledge-based software engineering

NASA Technical Reports Server (NTRS)

Truszkowski, Walt

1991-01-01

The underlying hypothesis of this work is that a rigorous and comprehensive software reuse methodology can bring about a more effective and efficient utilization of constrained resources in the development of large-scale software systems by both government and industry. It is also believed that correct use of this type of software engineering methodology can significantly contribute to the higher levels of reliability that will be required of future operational systems. An overview and discussion of current research in the development and application of two systems that support a rigorous reuse paradigm are presented: the Knowledge-Based Software Engineering Environment (KBSEE) and the Knowledge Acquisition fo the Preservation of Tradeoffs and Underlying Rationales (KAPTUR) systems. Emphasis is on a presentation of operational scenarios which highlight the major functional capabilities of the two systems.

Research directions in large scale systems and decentralized control

NASA Technical Reports Server (NTRS)

Tenney, R. R.

1980-01-01

Control theory provides a well established framework for dealing with automatic decision problems and a set of techniques for automatic decision making which exploit special structure, but it does not deal well with complexity. The potential exists for combining control theoretic and knowledge based concepts into a unified approach. The elements of control theory are diagrammed, including modern control and large scale systems.

Ethnic use of the Tonto: geographic extension of the recreation knowledge base

Treesearch

Denver Hospodarsky; Martha Lee

1995-01-01

The recreational use of the Tonto National Forest, Arizona was investigated by using data on ethnic and racial subgroups. The Tonto is a Class 1 urban proximate forest adjoining the large, culturally diverse population of the Phoenix. An on-site survey of 524 recreating groups found sufficiently large numbers of Anglos (n=425) and Hispanics (n=82) who participated in...

Defaults, context, and knowledge: alternatives for OWL-indexed knowledge bases.

PubMed

Rector, A

2004-01-01

The new Web Ontology Language (OWL) and its Description Logic compatible sublanguage (OWL-DL) explicitly exclude defaults and exceptions, as do all logic based formalisms for ontologies. However, many biomedical applications appear to require default reasoning, at least if they are to be engineered in a maintainable way. Default reasoning has always been one of the great strengths of Frame systems such as Protégé. Resolving this conflict requires analysis of the different uses for defaults and exceptions. In some cases, alternatives can be provided within the OWL framework; in others, it appears that hybrid reasoning about a knowledge base of contingent facts built around the core ontology is necessary. Trade-offs include both human factors and the scaling of computational performance. The analysis presented here is based on the OpenGALEN experience with large scale ontologies using a formalism, GRAIL, which explicitly incorporates constructs for hybrid reasoning, numerous experiments with OWL, and initial work on combining OWL and Protégé.

Longitudinal determinants of energy levels in knowledge workers.

PubMed

Arnetz, Bengt B; Broadbridge, Carissa L; Ghosh, Samiran

2014-01-01

Increasingly, workers in the service, welfare, and health care sectors suffer adverse effects (ie, depression, burnout, etc) of "low-energy syndromes." Less is known about energy-based outcomes among knowledge workers. This study aimed to identify determinants of self-rated energy in knowledge workers and examine how these determinants change over time. In collaboration with a large union and employer federation, 317 knowledge workers in Sweden responded to the health and productivity survey three times. At each assessment, worry, satisfaction with eating habits, and work-effectiveness were predictive of energy levels; however, only work-effectiveness covaried with energy over time. This study suggests that perceived work-effectiveness is an important factor in preventing knowledge workers from experiencing "low-energy syndromes." Lifestyle factors also play a role. Therefore, multifaceted interventions for increasing energy are needed.

Knowledge-based approaches to the maintenance of a large controlled medical terminology.

PubMed Central

Cimino, J J; Clayton, P D; Hripcsak, G; Johnson, S B

1994-01-01

OBJECTIVE: Develop a knowledge-based representation for a controlled terminology of clinical information to facilitate creation, maintenance, and use of the terminology. DESIGN: The Medical Entities Dictionary (MED) is a semantic network, based on the Unified Medical Language System (UMLS), with a directed acyclic graph to represent multiple hierarchies. Terms from four hospital systems (laboratory, electrocardiography, medical records coding, and pharmacy) were added as nodes in the network. Additional knowledge about terms, added as semantic links, was used to assist in integration, harmonization, and automated classification of disparate terminologies. RESULTS: The MED contains 32,767 terms and is in active clinical use. Automated classification was successfully applied to terms for laboratory specimens, laboratory tests, and medications. One benefit of the approach has been the automated inclusion of medications into multiple pharmacologic and allergenic classes that were not present in the pharmacy system. Another benefit has been the reduction of maintenance efforts by 90%. CONCLUSION: The MED is a hybrid of terminology and knowledge. It provides domain coverage, synonymy, consistency of views, explicit relationships, and multiple classification while preventing redundancy, ambiguity (homonymy) and misclassification. PMID:7719786

Mechanical Transformation of Task Heuristics into Operational Procedures

DTIC Science & Technology

1981-04-14

Introduction A central theme of recent research in artificial intelligence is that *Intelligent task performance requires large amounts of knowledge...PLAY P1 C4] (. (LEADING (QSO)) (OR (CAN-LEAO- HEARrS (gSO)J (mEg (SUIT-OF C3) H])] C-) (FOLLOWING (QSO)) (OR [VOID (OSO) (SUIT-LED)3 [IN-SUIT C3 (SUIT...Production rules as a representation for a knowledge based consultation system. Artificial Intelligence 8:15-45, Spring, 1977. [Davis 77b] R. Davis

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.

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

School Staff Responses to Gender-Based Bullying as Moral Interpretation: An Exploratory Study

ERIC Educational Resources Information Center

Anagnostopoulos, Dorothea; Buchanan, Nicole T.; Pereira, Christine; Lichty, Lauren F.

2009-01-01

Gender-based bullying is the most common form of violence that students encounter in U.S. public schools. Several large-scale surveys reveal its consequences for students. Fewer studies examine how school staff members make sense of and respond to such violence. The authors address this knowledge gap by presenting analyses of interviews conducted…

Item Difficulty in the Evaluation of Computer-Based Instruction: An Example from Neuroanatomy

ERIC Educational Resources Information Center

Chariker, Julia H.; Naaz, Farah; Pani, John R.

2012-01-01

This article reports large item effects in a study of computer-based learning of neuroanatomy. Outcome measures of the efficiency of learning, transfer of learning, and generalization of knowledge diverged by a wide margin across test items, with certain sets of items emerging as particularly difficult to master. In addition, the outcomes of…

Scaffolded Writing and Rewriting in the Discipline: A Web-Based Reciprocal Peer Review System

ERIC Educational Resources Information Center

Cho, Kwangsu; Schunn, Christian D.

2007-01-01

This paper describes how SWoRD (scaffolded writing and rewriting in the discipline), a web-based reciprocal peer review system, supports writing practice, particularly for large content courses in which writing is considered critical but not feasibly included. To help students gain content knowledge as well as writing and reviewing skills, SWoRD…

Maximize a Team-Based Learning Gallery Walk Experience: Herding Cats Is Easier than You Think

ERIC Educational Resources Information Center

Rodenbaugh, David W.

2015-01-01

Team-based learning (TBL) is an instructional strategy that promotes small group learning and peer instruction in a large class environment. TBL is structured to include the following steps: 1) student preparation, e.g., reading/reviewing course lectures, and 2) readiness assurance testing. Preparation and foundational knowledge is assessed on an…

From science to action: Principles for undertaking environmental research that enables knowledge exchange and evidence-based decision-making.

PubMed

Cvitanovic, C; McDonald, J; Hobday, A J

2016-12-01

Effective conservation requires knowledge exchange among scientists and decision-makers to enable learning and support evidence-based decision-making. Efforts to improve knowledge exchange have been hindered by a paucity of empirically-grounded guidance to help scientists and practitioners design and implement research programs that actively facilitate knowledge exchange. To address this, we evaluated the Ningaloo Research Program (NRP), which was designed to generate new scientific knowledge to support evidence-based decisions about the management of the Ningaloo Marine Park in north-western Australia. Specifically, we evaluated (1) outcomes of the NRP, including the extent to which new knowledge informed management decisions; (2) the barriers that prevented knowledge exchange among scientists and managers; (3) the key requirements for improving knowledge exchange processes in the future; and (4) the core capacities that are required to support knowledge exchange processes. While the NRP generated expansive and multidisciplinary science outputs directly relevant to the management of the Ningaloo Marine Park, decision-makers are largely unaware of this knowledge and little has been integrated into decision-making processes. A range of barriers prevented efficient and effective knowledge exchange among scientists and decision-makers including cultural differences among the groups, institutional barriers within decision-making agencies, scientific outputs that were not translated for decision-makers and poor alignment between research design and actual knowledge needs. We identify a set of principles to be implemented routinely as part of any applied research program, including; (i) stakeholder mapping prior to the commencement of research programs to identify all stakeholders, (ii) research questions to be co-developed with stakeholders, (iii) implementation of participatory research approaches, (iv) use of a knowledge broker, and (v) tailored knowledge management systems. Finally, we articulate the individual, institutional and financial capacities that must be developed to underpin successful knowledge exchange strategies. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Prey items and predation behavior of killer whales (Orcinus orca) in Nunavut, Canada based on Inuit hunter interviews

PubMed Central

2012-01-01

Background Killer whales (Orcinus orca) are the most widely distributed cetacean, occurring in all oceans worldwide, and within ocean regions different ecotypes are defined based on prey preferences. Prey items are largely unknown in the eastern Canadian Arctic and therefore we conducted a survey of Inuit Traditional Ecological Knowledge (TEK) to provide information on the feeding ecology of killer whales. We compiled Inuit observations on killer whales and their prey items via 105 semi-directed interviews conducted in 11 eastern Nunavut communities (Kivalliq and Qikiqtaaluk regions) from 2007-2010. Results Results detail local knowledge of killer whale prey items, hunting behaviour, prey responses, distribution of predation events, and prey capture techniques. Inuit TEK and published literature agree that killer whales at times eat only certain parts of prey, particularly of large whales, that attacks on large whales entail relatively small groups of killer whales, and that they hunt cooperatively. Inuit observations suggest that there is little prey specialization beyond marine mammals and there are no definitive observations of fish in the diet. Inuit hunters and elders also documented the use of sea ice and shallow water as prey refugia. Conclusions By combining TEK and scientific approaches we provide a more holistic view of killer whale predation in the eastern Canadian Arctic relevant to management and policy. Continuing the long-term relationship between scientists and hunters will provide for successful knowledge integration and has resulted in considerable improvement in understanding of killer whale ecology relevant to management of prey species. Combining scientists and Inuit knowledge will assist in northerners adapting to the restructuring of the Arctic marine ecosystem associated with warming and loss of sea ice. PMID:22520955

Prey items and predation behavior of killer whales (Orcinus orca) in Nunavut, Canada based on Inuit hunter interviews.

PubMed

Ferguson, Steven H; Higdon, Jeff W; Westdal, Kristin H

2012-01-30

Killer whales (Orcinus orca) are the most widely distributed cetacean, occurring in all oceans worldwide, and within ocean regions different ecotypes are defined based on prey preferences. Prey items are largely unknown in the eastern Canadian Arctic and therefore we conducted a survey of Inuit Traditional Ecological Knowledge (TEK) to provide information on the feeding ecology of killer whales. We compiled Inuit observations on killer whales and their prey items via 105 semi-directed interviews conducted in 11 eastern Nunavut communities (Kivalliq and Qikiqtaaluk regions) from 2007-2010. Results detail local knowledge of killer whale prey items, hunting behaviour, prey responses, distribution of predation events, and prey capture techniques. Inuit TEK and published literature agree that killer whales at times eat only certain parts of prey, particularly of large whales, that attacks on large whales entail relatively small groups of killer whales, and that they hunt cooperatively. Inuit observations suggest that there is little prey specialization beyond marine mammals and there are no definitive observations of fish in the diet. Inuit hunters and elders also documented the use of sea ice and shallow water as prey refugia. By combining TEK and scientific approaches we provide a more holistic view of killer whale predation in the eastern Canadian Arctic relevant to management and policy. Continuing the long-term relationship between scientists and hunters will provide for successful knowledge integration and has resulted in considerable improvement in understanding of killer whale ecology relevant to management of prey species. Combining scientists and Inuit knowledge will assist in northerners adapting to the restructuring of the Arctic marine ecosystem associated with warming and loss of sea ice.

Visualizing the Topical Structure of the Medical Sciences: A Self-Organizing Map Approach

PubMed Central

Skupin, André; Biberstine, Joseph R.; Börner, Katy

2013-01-01

Background We implement a high-resolution visualization of the medical knowledge domain using the self-organizing map (SOM) method, based on a corpus of over two million publications. While self-organizing maps have been used for document visualization for some time, (1) little is known about how to deal with truly large document collections in conjunction with a large number of SOM neurons, (2) post-training geometric and semiotic transformations of the SOM tend to be limited, and (3) no user studies have been conducted with domain experts to validate the utility and readability of the resulting visualizations. Our study makes key contributions to all of these issues. Methodology Documents extracted from Medline and Scopus are analyzed on the basis of indexer-assigned MeSH terms. Initial dimensionality is reduced to include only the top 10% most frequent terms and the resulting document vectors are then used to train a large SOM consisting of over 75,000 neurons. The resulting two-dimensional model of the high-dimensional input space is then transformed into a large-format map by using geographic information system (GIS) techniques and cartographic design principles. This map is then annotated and evaluated by ten experts stemming from the biomedical and other domains. Conclusions Study results demonstrate that it is possible to transform a very large document corpus into a map that is visually engaging and conceptually stimulating to subject experts from both inside and outside of the particular knowledge domain. The challenges of dealing with a truly large corpus come to the fore and require embracing parallelization and use of supercomputing resources to solve otherwise intractable computational tasks. Among the envisaged future efforts are the creation of a highly interactive interface and the elaboration of the notion of this map of medicine acting as a base map, onto which other knowledge artifacts could be overlaid. PMID:23554924

Aggregating concept map data to investigate the knowledge of beginning CS students

NASA Astrophysics Data System (ADS)

Mühling, Andreas

2016-07-01

Concept maps have a long history in educational settings as a tool for teaching, learning, and assessing. As an assessment tool, they are predominantly used to extract the structural configuration of learners' knowledge. This article presents an investigation of the knowledge structures of a large group of beginning CS students. The investigation is based on a method that collects, aggregates, and automatically analyzes the concept maps of a group of learners as a whole, to identify common structural configurations and differences in the learners' knowledge. It shows that those students who have attended CS education in their secondary school life have, on average, configured their knowledge about typical core CS/OOP concepts differently. Also, artifacts of their particular CS curriculum are visible in their externalized knowledge. The data structures and analysis methods necessary for working with concept landscapes have been implemented as a GNU R package that is freely available.

Tools for knowledge acquisition within the NeuroScholar system and their application to anatomical tract-tracing data

PubMed Central

Burns, Gully APC; Cheng, Wei-Cheng

2006-01-01

Background Knowledge bases that summarize the published literature provide useful online references for specific areas of systems-level biology that are not otherwise supported by large-scale databases. In the field of neuroanatomy, groups of small focused teams have constructed medium size knowledge bases to summarize the literature describing tract-tracing experiments in several species. Despite years of collation and curation, these databases only provide partial coverage of the available published literature. Given that the scientists reading these papers must all generate the interpretations that would normally be entered into such a system, we attempt here to provide general-purpose annotation tools to make it easy for members of the community to contribute to the task of data collation. Results In this paper, we describe an open-source, freely available knowledge management system called 'NeuroScholar' that allows straightforward structured markup of the PDF files according to a well-designed schema to capture the essential details of this class of experiment. Although, the example worked through in this paper is quite specific to neuroanatomical connectivity, the design is freely extensible and could conceivably be used to construct local knowledge bases for other experiment types. Knowledge representations of the experiment are also directly linked to the contributing textual fragments from the original research article. Through the use of this system, not only could members of the community contribute to the collation task, but input data can be gathered for automated approaches to permit knowledge acquisition through the use of Natural Language Processing (NLP). Conclusion We present a functional, working tool to permit users to populate knowledge bases for neuroanatomical connectivity data from the literature through the use of structured questionnaires. This system is open-source, fully functional and available for download from [1]. PMID:16895608

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.

The Spacecraft Materials Selector: An Artificial Intelligence System for Preliminary Design Trade Studies, Materials Assessments, and Estimates of Environments Present

NASA Technical Reports Server (NTRS)

Pippin, H. G.; Woll, S. L. B.

2000-01-01

Institutions need ways to retain valuable information even as experienced individuals leave an organization. Modern electronic systems have enough capacity to retain large quantities of information that can mitigate the loss of experience. Performance information for long-term space applications is relatively scarce and specific information (typically held by a few individuals within a single project) is often rather narrowly distributed. Spacecraft operate under severe conditions and the consequences of hardware and/or system failures, in terms of cost, loss of information, and time required to replace the loss, are extreme. These risk factors place a premium on appropriate choice of materials and components for space applications. An expert system is a very cost-effective method for sharing valuable and scarce information about spacecraft performance. Boeing has an artificial intelligence software package, called the Boeing Expert System Tool (BEST), to construct and operate knowledge bases to selectively recall and distribute information about specific subjects. A specific knowledge base to evaluate the on-orbit performance of selected materials on spacecraft has been developed under contract to the NASA SEE program. The performance capabilities of the Spacecraft Materials Selector (SMS) knowledge base are described. The knowledge base is a backward-chaining, rule-based system. The user answers a sequence of questions, and the expert system provides estimates of optical and mechanical performance of selected materials under specific environmental conditions. The initial operating capability of the system will include data for Kapton, silverized Teflon, selected paints, silicone-based materials, and certain metals. For situations where a mission profile (launch date, orbital parameters, mission duration, spacecraft orientation) is not precisely defined, the knowledge base still attempts to provide qualitative observations about materials performance and likely exposures. Prior to the NASA contract, a knowledge base, the Spacecraft Environments Assistant (SEA,) was initially developed by Boeing to estimate the environmental factors important for a specific spacecraft mission profile. The NASA SEE program has funded specific enhancements to the capability of this knowledge base. The SEA qualitatively identifies over 25 environmental factors that may influence the performance of a spacecraft during its operational lifetime. For cases where sufficiently detailed answers are provided to questions asked by the knowledge base, atomic oxygen fluence levels, proton and/or electron fluence and dose levels, and solar exposure hours are calculated. The SMS knowledge base incorporates the previously developed SEA knowledge base. A case history for previous flight experiment will be shown as an example, and capabilities and limitations of the system will be discussed.

Learning Physics-based Models in Hydrology under the Framework of Generative Adversarial Networks

NASA Astrophysics Data System (ADS)

Karpatne, A.; Kumar, V.

2017-12-01

Generative adversarial networks (GANs), that have been highly successful in a number of applications involving large volumes of labeled and unlabeled data such as computer vision, offer huge potential for modeling the dynamics of physical processes that have been traditionally studied using simulations of physics-based models. While conventional physics-based models use labeled samples of input/output variables for model calibration (estimating the right parametric forms of relationships between variables) or data assimilation (identifying the most likely sequence of system states in dynamical systems), there is a greater opportunity to explore the full power of machine learning (ML) methods (e.g, GANs) for studying physical processes currently suffering from large knowledge gaps, e.g. ground-water flow. However, success in this endeavor requires a principled way of combining the strengths of ML methods with physics-based numerical models that are founded on a wealth of scientific knowledge. This is especially important in scientific domains like hydrology where the number of data samples is small (relative to Internet-scale applications such as image recognition where machine learning methods has found great success), and the physical relationships are complex (high-dimensional) and non-stationary. We will present a series of methods for guiding the learning of GANs using physics-based models, e.g., by using the outputs of physics-based models as input data to the generator-learner framework, and by using physics-based models as generators trained using validation data in the adversarial learning framework. These methods are being developed under the broad paradigm of theory-guided data science that we are developing to integrate scientific knowledge with data science methods for accelerating scientific discovery.

A Large-Scale Inquiry-Based Astronomy Intervention Project: Impact on Students' Content Knowledge Performance and Views of their High School Science Classroom

NASA Astrophysics Data System (ADS)

Fitzgerald, Michael; McKinnon, David H.; Danaia, Lena; Deehan, James

2016-12-01

In this paper, we present the results from a study of the impact on students involved in a large-scale inquiry-based astronomical high school education intervention in Australia. Students in this intervention were led through an educational design allowing them to undertake an investigative approach to understanding the lifecycle of stars more aligned with the `ideal' picture of school science. Through the use of two instruments, one focused on content knowledge gains and the other on student views of school science, we explore the impact of this design. Overall, students made moderate content knowledge gains although these gains were heavily dependent on the individual teacher, the number of times a teacher implemented and the depth to which an individual teacher went with the provided materials. In terms of students' views, there were significant global changes in their views of their experience of the science classroom. However, there were some areas where no change or slightly negative changes of which some were expected and some were not. From these results, we comment on the necessity of sustained long-period implementations rather than single interventions, the requirement for similarly sustained professional development and the importance of monitoring the impact of inquiry-based implementations. This is especially important as inquiry-based approaches to science are required by many new curriculum reforms, most notably in this context, the new Australian curriculum currently being rolled out.

Tritium

DTIC Science & Technology

2011-11-01

fusion energy -production processes of the particular type of reactor using a lithium (Li) blanket or related alloys such as the Pb-17Li eutectic. As such, tritium breeding is intimately connected with energy production, thermal management, radioactivity management, materials properties, and mechanical structures of any plausible future large-scale fusion power reactor. JASON is asked to examine the current state of scientific knowledge and engineering practice on the physical and chemical bases for large-scale tritium

Facilitating Naval Knowledge Flow

DTIC Science & Technology

2001-07-01

flow theory and its application to very-large enterprises such as the Navy. Without such basic understanding, one cannot expect to design effective...understanding knowledge flow? Informed by advances in knowledge-flow theory , this work can propel knowledge management toward the methods and tools...address the phenomenology of knowledge flow well, nor do we have the benefit of knowledge-flow theory and its application to very-large enterprises

Engineering large-scale agent-based systems with consensus

NASA Technical Reports Server (NTRS)

Bokma, A.; Slade, A.; Kerridge, S.; Johnson, K.

1994-01-01

The paper presents the consensus method for the development of large-scale agent-based systems. Systems can be developed as networks of knowledge based agents (KBA) which engage in a collaborative problem solving effort. The method provides a comprehensive and integrated approach to the development of this type of system. This includes a systematic analysis of user requirements as well as a structured approach to generating a system design which exhibits the desired functionality. There is a direct correspondence between system requirements and design components. The benefits of this approach are that requirements are traceable into design components and code thus facilitating verification. The use of the consensus method with two major test applications showed it to be successful and also provided valuable insight into problems typically associated with the development of large systems.

Muscle dysmorphia: current insights.

PubMed

Tod, David; Edwards, Christian; Cranswick, Ieuan

2016-01-01

Since 1997, there has been increasing research focusing on muscle dysmorphia, a condition underpinned by people's beliefs that they have insufficient muscularity, in both the Western and non-Western medical and scientific communities. Much of this empirical interest has surveyed nonclinical samples, and there is limited understanding of people with the condition beyond knowledge about their characteristics. Much of the existing knowledge about people with the condition is unsurprising and inherent in the definition of the disorder, such as dissatisfaction with muscularity and adherence to muscle-building activities. Only recently have investigators started to explore questions beyond these limited tautological findings that may give rise to substantial knowledge advances, such as the examination of masculine and feminine norms. There is limited understanding of additional topics such as etiology, prevalence, nosology, prognosis, and treatment. Further, the evidence is largely based on a small number of unstandardized case reports and descriptive studies (involving small samples), which are largely confined to Western (North American, British, and Australian) males. Although much research has been undertaken since the term "muscle dysmorphia" entered the psychiatric lexicon in 1997, there remains tremendous scope for knowledge advancement. A primary task in the short term is for investigators to examine the extent to which the condition exists among well-defined populations to help determine the justification for research funding relative to other public health issues. A greater variety of research questions and designs may contribute to a broader and more robust knowledge base than currently exists. Future work will help clinicians assist a group of people whose quality of life and health are placed at risk by their muscular preoccupation.

Muscle dysmorphia: current insights

PubMed Central

Tod, David; Edwards, Christian; Cranswick, Ieuan

2016-01-01

Since 1997, there has been increasing research focusing on muscle dysmorphia, a condition underpinned by people’s beliefs that they have insufficient muscularity, in both the Western and non-Western medical and scientific communities. Much of this empirical interest has surveyed nonclinical samples, and there is limited understanding of people with the condition beyond knowledge about their characteristics. Much of the existing knowledge about people with the condition is unsurprising and inherent in the definition of the disorder, such as dissatisfaction with muscularity and adherence to muscle-building activities. Only recently have investigators started to explore questions beyond these limited tautological findings that may give rise to substantial knowledge advances, such as the examination of masculine and feminine norms. There is limited understanding of additional topics such as etiology, prevalence, nosology, prognosis, and treatment. Further, the evidence is largely based on a small number of unstandardized case reports and descriptive studies (involving small samples), which are largely confined to Western (North American, British, and Australian) males. Although much research has been undertaken since the term “muscle dysmorphia” entered the psychiatric lexicon in 1997, there remains tremendous scope for knowledge advancement. A primary task in the short term is for investigators to examine the extent to which the condition exists among well-defined populations to help determine the justification for research funding relative to other public health issues. A greater variety of research questions and designs may contribute to a broader and more robust knowledge base than currently exists. Future work will help clinicians assist a group of people whose quality of life and health are placed at risk by their muscular preoccupation. PMID:27536165

Spotting East African mammals in open savannah from space.

PubMed

Yang, Zheng; Wang, Tiejun; Skidmore, Andrew K; de Leeuw, Jan; Said, Mohammed Y; Freer, Jim

2014-01-01

Knowledge of population dynamics is essential for managing and conserving wildlife. Traditional methods of counting wild animals such as aerial survey or ground counts not only disturb animals, but also can be labour intensive and costly. New, commercially available very high-resolution satellite images offer great potential for accurate estimates of animal abundance over large open areas. However, little research has been conducted in the area of satellite-aided wildlife census, although computer processing speeds and image analysis algorithms have vastly improved. This paper explores the possibility of detecting large animals in the open savannah of Maasai Mara National Reserve, Kenya from very high-resolution GeoEye-1 satellite images. A hybrid image classification method was employed for this specific purpose by incorporating the advantages of both pixel-based and object-based image classification approaches. This was performed in two steps: firstly, a pixel-based image classification method, i.e., artificial neural network was applied to classify potential targets with similar spectral reflectance at pixel level; and then an object-based image classification method was used to further differentiate animal targets from the surrounding landscapes through the applications of expert knowledge. As a result, the large animals in two pilot study areas were successfully detected with an average count error of 8.2%, omission error of 6.6% and commission error of 13.7%. The results of the study show for the first time that it is feasible to perform automated detection and counting of large wild animals in open savannahs from space, and therefore provide a complementary and alternative approach to the conventional wildlife survey techniques.

Enriching semantic knowledge bases for opinion mining in big data applications.

PubMed

Weichselbraun, A; Gindl, S; Scharl, A

2014-10-01

This paper presents a novel method for contextualizing and enriching large semantic knowledge bases for opinion mining with a focus on Web intelligence platforms and other high-throughput big data applications. The method is not only applicable to traditional sentiment lexicons, but also to more comprehensive, multi-dimensional affective resources such as SenticNet. It comprises the following steps: (i) identify ambiguous sentiment terms, (ii) provide context information extracted from a domain-specific training corpus, and (iii) ground this contextual information to structured background knowledge sources such as ConceptNet and WordNet. A quantitative evaluation shows a significant improvement when using an enriched version of SenticNet for polarity classification. Crowdsourced gold standard data in conjunction with a qualitative evaluation sheds light on the strengths and weaknesses of the concept grounding, and on the quality of the enrichment process.

Incorporating Semantics into Data Driven Workflows for Content Based Analysis

NASA Astrophysics Data System (ADS)

Argüello, M.; Fernandez-Prieto, M. J.

Finding meaningful associations between text elements and knowledge structures within clinical narratives in a highly verbal domain, such as psychiatry, is a challenging goal. The research presented here uses a small corpus of case histories and brings into play pre-existing knowledge, and therefore, complements other approaches that use large corpus (millions of words) and no pre-existing knowledge. The paper describes a variety of experiments for content-based analysis: Linguistic Analysis using NLP-oriented approaches, Sentiment Analysis, and Semantically Meaningful Analysis. Although it is not standard practice, the paper advocates providing automatic support to annotate the functionality as well as the data for each experiment by performing semantic annotation that uses OWL and OWL-S. Lessons learnt can be transmitted to legacy clinical databases facing the conversion of clinical narratives according to prominent Electronic Health Records standards.

Good, but Not Perfect: Parental Knowledge About Risk Factors for Skin Cancer and the Necessity of Sun Protection in Southern Germany.

PubMed

Gefeller, Olaf; Uter, Wolfgang; Pfahlberg, Annette B

2015-01-01

The level of knowledge and awareness of skin cancer risks in parents of young children is largely unknown. The Erlangen Kindergarten study, which enrolled 3,129 parents of 3- to 6-year-old children in southern Germany, addressed this. The population-based survey found an overall high level of knowledge about skin cancer risks and strong support for the necessity of sun protection but identified two areas (role of intermittent sun exposure, sun protection on cloudy summer days) offering a target for improvement in future public health campaigns. © 2015 Wiley Periodicals, Inc.

Variables that Correlate with Faculty Use of Research-Based Instructional Strategies

NASA Astrophysics Data System (ADS)

Henderson, Charles; Dancy, Melissa H.; Niewiadomska-Bugaj, Magdalena

2010-10-01

During the Fall of 2008 a web survey, designed to collect information about pedagogical knowledge and practices, was completed by a representative sample of 722 physics faculty across the United States (a 50.3% response rate). This paper examines how 20 predictor variables correlate with faculty knowledge about and use of research-based instructional strategies (RBIS). Profiles were developed for each of four faculty levels of knowledge about and use of RBIS. Logistic regression analysis was used to identify a subset of the variables that could predict group membership. Five significant predictor variables were identified. High levels of knowledge and use of RBIS were associated with the following characteristics: attendee of the physics and astronomy new faculty workshop, attendee of at least one talk or workshop related to teaching in the last two years, satisfaction with meeting instructional goals, regular reader of one or more journals related to teaching, and being female. High research productivity and large class sizes were not found to be barriers to use of at least some RBIS.

Best practice recommendations for the development, implementation, and evaluation of online knowledge translation resources in rehabilitation.

PubMed

Levac, Danielle; Glegg, Stephanie M N; Camden, Chantal; Rivard, Lisa M; Missiuna, Cheryl

2015-04-01

The knowledge-to-practice gap in rehabilitation has spurred knowledge translation (KT) initiatives aimed at promoting clinician behavior change and improving patient care. Online KT resources for physical therapists and other rehabilitation clinicians are appealing because of their potential to reach large numbers of individuals through self-paced, self-directed learning. This article proposes best practice recommendations for developing online KT resources that are designed to translate evidence into practice. Four recommendations are proposed with specific steps in the development, implementation, and evaluation process: (1) develop evidence-based, user-centered content; (2) tailor content to online format; (3) evaluate impact; and (4) share results and disseminate knowledge. Based on KT evidence and instructional design principles, concrete examples are provided along with insights gained from experiences in creating and evaluating online KT resources for physical therapists. In proposing these recommendations, the next steps for research are suggested, and others are invited to contribute to the discussion. © 2015 American Physical Therapy Association.

Evaluating Evidence-Based Practice Knowledge and Beliefs Through the e-Learning EBP Academy.

PubMed

Green, Angela; Jeffs, Debra A; Boateng, Beatrice A; Lowe, Gary R; Walden, Marlene

2017-07-01

This research examined evidence-based practice (EBP) knowledge and beliefs before and after a 3-month e-learning program was implemented to build EBP capacity at a large children's hospital. Ten clinicians completed the development, implementation, and evaluation of the e-learning education, comprising phase one. Revision and participation by 41 clinicians followed in phase two. Participants in both phases completed the EBP Beliefs and Implementation Scales preintervention, postintervention, and 6 months after postintervention. EBP beliefs and implementation increased immediately and 6 months after postintervention, with statistically significant increases in both phases. Participants in both phases applied knowledge by completing mentor-supported EBP projects. Although EBP beliefs and implementation scores increased and e-learning provided flexibility for clinician participation, challenges arose, resulting in lower-than-expected completion. Subsequent revisions resulted in hybrid education, integrating classroom and e-learning with project mentoring. This funded e-learning research contributes knowledge to the growing specialty of professional development. J Contin Educ Nurs. 2017;48(7):304-311. Copyright 2017, SLACK Incorporated.

Experimental evidence on the impact of food advertising on children's knowledge about and preferences for healthful food.

PubMed

Reisch, Lucia A; Gwozdz, Wencke; Barba, Gianvincenzo; De Henauw, Stefaan; Lascorz, Natalia; Pigeot, Iris

2013-01-01

To understand the rising prevalence of childhood obesity in affluent societies, it is necessary to take into account the growing obesity infrastructure, which over past decades has developed into an obesogenic environment. This study examines the effects of one of the constituent factors of consumer societies and a potential contributory factor to childhood obesity: commercial food communication targeted to children. Specifically, it investigates the impact of TV advertising on children's food knowledge and food preferences and correlates these findings with their weight status. Evaluations of traditional information- and education-based interventions suggest that they may not sustainably change food patterns. Based on prior consumer research, we propose five hypotheses, which we then test using a subsample from the IDEFICS study, a large-scale pan-European intervention study on childhood obesity. The results indicate that advertising has divergent effects on children's food knowledge and preferences and that food knowledge is unrelated to food preferences. This finding has important implications for both future research and public policy.

Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications

PubMed Central

Chang, Hang; Han, Ju; Zhong, Cheng; Snijders, Antoine M.; Mao, Jian-Hua

2017-01-01

The capabilities of (I) learning transferable knowledge across domains; and (II) fine-tuning the pre-learned base knowledge towards tasks with considerably smaller data scale are extremely important. Many of the existing transfer learning techniques are supervised approaches, among which deep learning has the demonstrated power of learning domain transferrable knowledge with large scale network trained on massive amounts of labeled data. However, in many biomedical tasks, both the data and the corresponding label can be very limited, where the unsupervised transfer learning capability is urgently needed. In this paper, we proposed a novel multi-scale convolutional sparse coding (MSCSC) method, that (I) automatically learns filter banks at different scales in a joint fashion with enforced scale-specificity of learned patterns; and (II) provides an unsupervised solution for learning transferable base knowledge and fine-tuning it towards target tasks. Extensive experimental evaluation of MSCSC demonstrates the effectiveness of the proposed MSCSC in both regular and transfer learning tasks in various biomedical domains. PMID:28129148

Analysis of Online Information Searching for Cardiovascular Diseases on a Consumer Health Information Portal

PubMed Central

Jadhav, Ashutosh; Sheth, Amit; Pathak, Jyotishman

2014-01-01

Since the early 2000’s, Internet usage for health information searching has increased significantly. Studying search queries can help us to understand users “information need” and how do they formulate search queries (“expression of information need”). Although cardiovascular diseases (CVD) affect a large percentage of the population, few studies have investigated how and what users search for CVD. We address this knowledge gap in the community by analyzing a large corpus of 10 million CVD related search queries from MayoClinic.com. Using UMLS MetaMap and UMLS semantic types/concepts, we developed a rule-based approach to categorize the queries into 14 health categories. We analyzed structural properties, types (keyword-based/Wh-questions/Yes-No questions) and linguistic structure of the queries. Our results show that the most searched health categories are ‘Diseases/Conditions’, ‘Vital-Sings’, ‘Symptoms’ and ‘Living-with’. CVD queries are longer and are predominantly keyword-based. This study extends our knowledge about online health information searching and provides useful insights for Web search engines and health websites. PMID:25954380

Use of a web-based education program improves nurses' knowledge of breastfeeding.

PubMed

Deloian, Barbara J; Lewin, Linda Orkin; O'Connor, Mary E

2015-01-01

To evaluate the baseline knowledge and knowledge gained of nurses, nursing students, midwives, and nurse practitioners who completed Breastfeeding Basics, an online educational program. This study reports on an anonymous evaluation of an online breastfeeding education program developed and maintained to promote evidence-based breastfeeding practice. Included in the study were 3736 nurses, 728 nurse practitioners/midwives, and 3106 nursing students from the United States who completed ≥ one pretest or posttest on the Breastfeeding Basics website between April 1999 and December 31, 2011. Baseline scores were analyzed to determine if nurses' baseline knowledge varied by selected demographic variables such as age, gender, professional level, personal or partner breastfeeding experience, and whether they were required to complete the website for a job or school requirement and to determine knowledge gaps. Pretest and posttest scores on all modules and in specific questions with low pretest scores were compared as a measure of knowledge gained. Lower median pretest scores were found in student nurses (71%), males (71%), those required to take the course (75%), and those without personal breastfeeding experience (72%). The modules with the lowest median pretest scores were Anatomy/Physiology (67%), Growth and Development of the Breastfed Infant (67%), the Breastfeeding Couple (73%), and the Term Infant with Problems (60%). Posttest scores in all modules increased significantly (p < .001). Breastfeeding Basics was used by a large number of nurses and nursing students. Gaps exist in nurses' breastfeeding knowledge. Knowledge improved in all areas based on comparison of pretest and posttest scores. © 2015 AWHONN, the Association of Women's Health, Obstetric and Neonatal Nurses.

Effects of a School-based Intervention Program on Attitude and Knowledge of Household Members Towards a Smoke-free Home: a Cluster Controlled Trial.

PubMed

Intarut, Nirun; Chongsuvivatwong, Virasakdi; McNeil, Edward

2016-01-01

A school-based smoke free home (SFH) program is useful in empowering the mother and child to reduce secondhand smoke exposure but the effects of pretesting on knowledge and attitude has been largely ignored. We aimed to test whether such a program can be effective in Southern Thailand with an additional assessment of the net effect of the pretest. A Solomon four-group design was used. Twelve rural primary schools were assigned to one of the four conditions (each with 3 schools): intervention with and without a pretest, control with and without the same pretest. The intervention was performed in the classroom and home over a period of 1 month. Outcomes were assessed at baseline and 3 months after the intervention on whether the home was smoke free and related knowledge and attitude. The intervention could lead to a smoke-free home without statistical significance. Attitude, knowledge and self-confidence on creating a smoke-free home, and self-confidence in avoidance of secondhand smoke exposure and persuading smokers to not smoke in their home were significantly improved. No pretest effect was observed. Gain in attitude, knowledge and self-confidence among family members from the brief school-based education should be enhanced by other measures.

How much cash does your company need?

PubMed

Passov, Richard

2003-11-01

In late 2001, the directors of Pfizer asked that very question. And with good reason. After its 2000 merger with rival Warner-Lambert, the New York-based pharmaceutical giant found itself sitting on a net cash position of $8 billion, which seemed extraordinarily conservative for a company whose products generated $30 billion in revenues. Most large companies with revenues that healthy would increase leverage, thereby unlocking tremendous value for shareholders. But knowledge-intensive companies like Pfizer, this author argues, are in a class apart. Because their largely intangible assets (like R&D) are highly volatile and cannot easily be valued, they are more vulnerable to financial distress than are firms with a preponderance of tangible assets. To insure against that risk, they need to maintain large positive cash balances. These companies' decisions to run large cash balances is one of the key reasons their shares sustain consistent premiums. Only by investing in their intangible assets can knowledge-based companies hope to preserve the value of those assets. A company that finds itself unable to do so because unfavorable market conditions reduce its operating cash flows will see its share price suffer almost as much as if it were to default on its debts. By the same token, with the right balance sheet, knowledge companies can profitably insure against the risk of failing to sustain value-added investments in difficult times. An optimal capital structure that calls for significant cash balances is certainly at odds with the results of a traditional capital structure analysis, the author demonstrates, but it explains the financial policies of many well-run companies, from Pfizer to Intel to ChevronTexaco.

A reusable knowledge acquisition shell: KASH

NASA Technical Reports Server (NTRS)

Westphal, Christopher; Williams, Stephen; Keech, Virginia

1991-01-01

KASH (Knowledge Acquisition SHell) is proposed to assist a knowledge engineer by providing a set of utilities for constructing knowledge acquisition sessions based on interviewing techniques. The information elicited from domain experts during the sessions is guided by a question dependency graph (QDG). The QDG defined by the knowledge engineer, consists of a series of control questions about the domain that are used to organize the knowledge of an expert. The content information supplies by the expert, in response to the questions, is represented in the form of a concept map. These maps can be constructed in a top-down or bottom-up manner by the QDG and used by KASH to generate the rules for a large class of expert system domains. Additionally, the concept maps can support the representation of temporal knowledge. The high degree of reusability encountered in the QDG and concept maps can vastly reduce the development times and costs associated with producing intelligent decision aids, training programs, and process control functions.

Tacit Beginnings Towards a Model of Scientific Thinking

NASA Astrophysics Data System (ADS)

Glass, Rory J.

2013-10-01

The purpose of this paper is to provide an examination of the role tacit knowledge plays in understanding, and to provide a model to make such knowledge identifiable. To do this I first consider the needs of society, the ubiquity of information in our world and the future demands of the science classroom. I propose the use of more implicit or tacit understandings as foundational elements for the development of student knowledge. To justify this proposition I consider a wide range of philosophical and psychological perspectives on knowledge. Then develop a Model of Scientific Knowledge, based in large part on a similar model created by Paul Ernest (Social constructivism as a philosophy of mathematics, SUNY Press, Albany, NY, 1998a; Situated cognition and the learning of mathematics, University of Oxford Department of Educational Studies, Oxford, 1998b). Finally, I consider the work that has been done by those in fields beyond education and the ways in which tacit knowledge can be used as a starting point for knowledge building.

Social Cognitive Mediators of Sociodemographic Differences in Colorectal Cancer Screening Uptake

PubMed Central

Lo, Siu Hing; Waller, Jo; Vrinten, Charlotte; Kobayashi, Lindsay; von Wagner, Christian

2015-01-01

Background. This study examined if and how sociodemographic differences in colorectal cancer (CRC) screening uptake can be explained by social cognitive factors. Methods. Face-to-face interviews were conducted with individuals aged 60–70 years (n = 1309) living in England as part of a population-based omnibus survey. Results. There were differences in screening uptake by SES, marital status, ethnicity, and age but not by gender. Perceived barriers (stand. b = −0.40, p < 0.001), social norms (stand. b = 0.33, p < 0.001), and screening knowledge (stand. b = 0.17, p < 0.001) had independent associations with uptake. SES differences in uptake were mediated through knowledge, social norms, and perceived barriers. Ethnic differences were mediated through knowledge. Differences in uptake by marital status were primarily mediated through social norms and to a lesser extent through knowledge. Age differences were largely unmediated, except for a small mediated effect via social norms. Conclusions. Sociodemographic differences in CRC screening uptake were largely mediated through social cognitive factors. Impact. Our findings suggest that multifaceted interventions might be needed to reduce socioeconomic inequalities. Ethnic differences might be reduced through improved screening knowledge. Normative interventions could emphasise screening as an activity endorsed by important others outside the immediate family to appeal to a wider audience. PMID:26504782

An American knowledge base in England - Alternate implementations of an expert system flight status monitor

NASA Technical Reports Server (NTRS)

Butler, G. F.; Graves, A. T.; Disbrow, J. D.; Duke, E. L.

1989-01-01

A joint activity between the Dryden Flight Research Facility of the NASA Ames Research Center (Ames-Dryden) and the Royal Aerospace Establishment (RAE) on knowledge-based systems has been agreed. Under the agreement, a flight status monitor knowledge base developed at Ames-Dryden has been implemented using the real-time AI (artificial intelligence) toolkit MUSE, which was developed in the UK. Here, the background to the cooperation is described and the details of the flight status monitor and a prototype MUSE implementation are presented. It is noted that the capabilities of the expert-system flight status monitor to monitor data downlinked from the flight test aircraft and to generate information on the state and health of the system for the test engineers provides increased safety during flight testing of new systems. Furthermore, the expert-system flight status monitor provides the systems engineers with ready access to the large amount of information required to describe a complex aircraft system.

The AI Bus architecture for distributed knowledge-based systems

NASA Technical Reports Server (NTRS)

Schultz, Roger D.; Stobie, Iain

1991-01-01

The AI Bus architecture is layered, distributed object oriented framework developed to support the requirements of advanced technology programs for an order of magnitude improvement in software costs. The consequent need for highly autonomous computer systems, adaptable to new technology advances over a long lifespan, led to the design of an open architecture and toolbox for building large scale, robust, production quality systems. The AI Bus accommodates a mix of knowledge based and conventional components, running on heterogeneous, distributed real world and testbed environment. The concepts and design is described of the AI Bus architecture and its current implementation status as a Unix C++ library or reusable objects. Each high level semiautonomous agent process consists of a number of knowledge sources together with interagent communication mechanisms based on shared blackboards and message passing acquaintances. Standard interfaces and protocols are followed for combining and validating subsystems. Dynamic probes or demons provide an event driven means for providing active objects with shared access to resources, and each other, while not violating their security.

[Between collegial conversation and controlled trials: hospital-based neurologists' sources of information and their practical knowledge in the treatment of stroke patients].

PubMed

Hasenbein, Uwe; Schulze, Axel; Frank, Bernd; Wallesch, Claus-Werner

2006-01-01

The application of evidence-based and standardized knowledge is an important basis for coordinated and effective clinical work. A multicenter study, which was conducted in 30 clinical neurology departments and included 99 junior and senior registrars, addressed aspects of practical knowledge of stroke treatment by open structured oral interviews which were analyzed with respect to interphysician and guideline conformity. In addition, the participants were asked to rate the usefulness of various informational sources and describe their informational sources used according to the aspects of practical knowledge addressed. The respective departmental directors were questioned about the sources of medical knowledge accessible in their clinic. In almost all hospitals, physicians had access to a library and the Internet. However, departments differed with respect to their information management (frequency and type of medical education, quality management, participation in research activities). In accordance with other studies, the knowledge sources reported most often were textbooks, journals and clinical studies. For most sources, the usefulness rankings largely corresponded to the rankings of user behavior. Both were only weak predictors for interphysician and guideline conformity. They are more closely related to the within-department homogeneity of practical knowledge than to interdepartmental and guideline conformity. Within-department homogeneity is enhanced by the use of recent, published and evidence-based sources and topical interaction with clinical colleagues. Guideline conformity was related to the use of databases and professional journals. A preference for colleagues from out-patient practices diminished both interphysician and guideline conformity. It seems there is still untapped potential for within-department knowledge management, such as medical education, implementation and use of guidelines and topical discussions, which may enhance the homogeneity and guideline conformity of the practical knowledge that clinicians apply.

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

Berra, P.B.; Chung, S.M.; Hachem, N.I.

This article presents techniques for managing a very large data/knowledge base to support multiple inference-mechanisms for logic programming. Because evaluation of goals can require accessing data from the extensional database, or EDB, in very general ways, one must often resort to indexing on all fields of the extensional database facts. This presents a formidable management problem in that the index data may be larger than the EDB itself. This problem becomes even more serious in this case of very large data/knowledge bases (hundreds of gigabytes), since considerably more hardware will be required to process and store the index data. Inmore » order to reduce the amount of index data considerably without losing generality, the authors form a surrogate file, which is a hashing transformation of the facts. Superimposed code words (SCW), concatenated code words (CCW), and transformed inverted lists (TIL) are possible structures for the surrogate file. since these transformations are quite regular and compact, the authors consider possible computer architecture for the processing of the surrogate file.« less

Health data and data governance.

PubMed

Hovenga, Evelyn J S; Grain, Heather

2013-01-01

Health is a knowledge industry, based on data collected to support care, service planning, financing and knowledge advancement. Increasingly there is a need to collect, retrieve and use health record information in an electronic format to provide greater flexibility, as this enables retrieval and display of data in multiple locations and formats irrespective of where the data were collected. Electronically maintained records require greater structure and consistency to achieve this. The use of data held in records generated in real time in clinical systems also has the potential to reduce the time it takes to gain knowledge, as there is less need to collect research specific information, this is only possible if data governance principles are applied. Connected devices and information systems are now generating huge amounts of data, as never before seen. An ability to analyse and mine very large amounts of data, "Big Data", provides policy and decision makers with new insights into varied aspects of work and information flow and operational business patterns and trends, and drives greater efficiencies, and safer and more effective health care. This enables decision makers to apply rules and guidance that have been developed based upon knowledge from many individual patient records through recognition of triggers based upon that knowledge. In clinical decision support systems information about the individual is compared to rules based upon knowledge gained from accumulated information of many to provide guidance at appropriate times in the clinical process. To achieve this the data in the individual system, and the knowledge rules must be represented in a compatible and consistent manner. This chapter describes data attributes; explains the difference between data and information; outlines the requirements for quality data; shows the relevance of health data standards; and describes how data governance impacts representation of content in systems and the use of that information.

Results, Knowledge, and Attitudes Regarding an Incentive Compensation Plan in a Hospital-Based, Academic, Employed Physician Multispecialty Group.

PubMed

Dolan, Robert W; Nesto, Richard; Ellender, Stacey; Luccessi, Christopher

Hospitals and healthcare systems are introducing incentive metrics into compensation plans that align with value-based payment methodologies. These incentive measures should be considered a practical application of the transition from volume to value and will likely replace traditional productivity-based compensation in the future. During the transition, there will be provider resistance and implementation challenges. This article examines a large multispecialty group's experience with a newly implemented incentive compensation plan including the structure of the plan, formulas for calculation of the payments, the mix of quality and productivity metrics, and metric threshold achievement. Three rounds of surveys with comments were collected to measure knowledge and attitudes regarding the plan. Lessons learned and specific recommendations for success are described. The participant's knowledge and attitudes regarding the plan are important considerations and affect morale and engagement. Significant provider dissatisfaction with the plan was found. Careful metric selection, design, and management are critical activities that will facilitate provider acceptance and support. Improvements in data collection and reporting will be needed to produce reliable metrics that can supplant traditional volume-based productivity measures.

Rethinking the lecture: the application of problem based learning methods to atypical contexts.

PubMed

Rogal, Sonya M M; Snider, Paul D

2008-05-01

Problem based learning is a teaching and learning strategy that uses a problematic stimulus as a means of motivating and directing students to develop and acquire knowledge. Problem based learning is a strategy that is typically used with small groups attending a series of sessions. This article describes the principles of problem based learning and its application in atypical contexts; large groups attending discrete, stand-alone sessions. The principles of problem based learning are based on Socratic teaching, constructivism and group facilitation. To demonstrate the application of problem based learning in an atypical setting, this article focuses on the graduate nurse intake from a teaching hospital. The groups are relatively large and meet for single day sessions. The modified applications of problem based learning to meet the needs of atypical groups are described. This article contains a step by step guide of constructing a problem based learning package for large, single session groups. Nurse educators facing similar groups will find they can modify problem based learning to suit their teaching context.

Building Community-Engaged Health Research and Discovery Infrastructure on the South Side of Chicago: Science in Service to Community Priorities

PubMed Central

Lindau, Stacy Tessler; Makelarski, Jennifer A.; Chin, Marshall H.; Desautels, Shane; Johnson, Daniel; Johnson, Waldo E.; Miller, Doriane; Peters, Susan; Robinson, Connie; Schneider, John; Thicklin, Florence; Watson, Natalie P.; Wolfe, Marcus; Whitaker, Eric

2011-01-01

Objective To describe the roles community members can and should play in, and an asset-based strategy used by Chicago’s South Side Health and Vitality Studies for, building sustainable, large-scale community health research infrastructure. The Studies are a family of research efforts aiming to produce actionable knowledge to inform health policy, programming, and investments for the region. Methods Community and university collaborators, using a consensus-based approach, developed shared theoretical perspectives, guiding principles, and a model for collaboration in 2008, which were used to inform an asset-based operational strategy. Ongoing community engagement and relationship-building support the infrastructure and research activities of the Studies. Results Key steps in the asset-based strategy include: 1) continuous community engagement and relationship building, 2) identifying community priorities, 3) identifying community assets, 4) leveraging assets, 5) conducting research, 6) sharing knowledge and 7) informing action. Examples of community member roles, and how these are informed by the Studies’ guiding principles, are provided. Conclusions Community and university collaborators, with shared vision and principles, can effectively work together to plan innovative, large-scale community-based research that serves community needs and priorities. Sustainable, effective models are needed to realize NIH’s mandate for meaningful translation of biomedical discovery into improved population health. PMID:21236295

Follow YOUR Heart: development of an evidence-based campaign empowering older women with HIV to participate in a large-scale cardiovascular disease prevention trial.

PubMed

Zanni, Markella V; Fitch, Kathleen; Rivard, Corinne; Sanchez, Laura; Douglas, Pamela S; Grinspoon, Steven; Smeaton, Laura; Currier, Judith S; Looby, Sara E

2017-03-01

Women's under-representation in HIV and cardiovascular disease (CVD) research suggests a need for novel strategies to ensure robust representation of women in HIV-associated CVD research. To elicit perspectives on CVD research participation among a community-sample of women with or at risk for HIV, and to apply acquired insights toward the development of an evidence-based campaign empowering older women with HIV to participate in a large-scale CVD prevention trial. In a community-based setting, we surveyed 40 women with or at risk for HIV about factors which might facilitate or impede engagement in CVD research. We applied insights derived from these surveys into the development of the Follow YOUR Heart campaign, educating women about HIV-associated CVD and empowering them to learn more about a multi-site HIV-associated CVD prevention trial: REPRIEVE. Endorsed best methods for learning about a CVD research study included peer-to-peer communication (54%), provider communication (46%) and video-based communication (39%). Top endorsed non-monetary reasons for participating in research related to gaining information (63%) and helping others (47%). Top endorsed reasons for not participating related to lack of knowledge about studies (29%) and lack of request to participate (29%). Based on survey results, the REPRIEVE Follow YOUR Heart campaign was developed. Interwoven campaign components (print materials, video, web presence) offer provider-based information/knowledge, peer-to-peer communication, and empowerment to learn more. Campaign components reflect women's self-identified motivations for research participation - education and altruism. Investigation of factors influencing women's participation in HIV-associated CVD research may be usefully applied to develop evidence-based strategies for enhancing women's enrollment in disease-specific large-scale trials. If proven efficacious, such strategies may enhance conduct of large-scale research studies across disciplines.

The Use of Research-Based Instructional Strategies in Introductory Physics: Where do Faculty Leave the Innovation-Decision Process?

NASA Astrophysics Data System (ADS)

Henderson, Charles; Dancy, Melissa; Niewiadomska-Bugaj, Magdalena

2013-03-01

During the Fall of 2008 a web survey was completed by a representative sample of 722 United States physics faculty. In this talk we will briefly present summary statistics to describe faculty knowledge about and use of 24 specific research-based instructional strategies (RBIS). We will then analyze the results based on a four stage model of the innovation-decision process: knowledge, trial, continuation, and high use. The largest losses occur at the continuation stage, with approximately 1/3 of faculty discontinuing use of all RBIS after trying one or more of these strategies. These results suggest that common dissemination strategies are good at creating knowledge about RBIS and motivation to try a RBIS, but more work is needed to support faculty during implementation and continued use of RBIS. Based on a logistic regression analysis, only nine of the 20 potential predictor variables measured were statistically significant when controlling for other variables. Faculty age, institutional type, and percentage of job related to teaching were not found to be correlated with knowledge or use at any stage. High research productivity and large class sizes were not found to be barriers to use of at least some RBIS. Supported by NSF #0715698.

A trial of e-simulation of sudden patient deterioration (FIRST2ACT WEB) on student learning.

PubMed

Bogossian, Fiona E; Cooper, Simon J; Cant, Robyn; Porter, Joanne; Forbes, Helen

2015-10-01

High-fidelity simulation pedagogy is of increasing importance in health professional education; however, face-to-face simulation programs are resource intensive and impractical to implement across large numbers of students. To investigate undergraduate nursing students' theoretical and applied learning in response to the e-simulation program-FIRST2ACT WEBTM, and explore predictors of virtual clinical performance. Multi-center trial of FIRST2ACT WEBTM accessible to students in five Australian universities and colleges, across 8 campuses. A population of 489 final-year nursing students in programs of study leading to license to practice. Participants proceeded through three phases: (i) pre-simulation-briefing and assessment of clinical knowledge and experience; (ii) e-simulation-three interactive e-simulation clinical scenarios which included video recordings of patients with deteriorating conditions, interactive clinical tasks, pop up responses to tasks, and timed performance; and (iii) post-simulation feedback and evaluation. Descriptive statistics were followed by bivariate analysis to detect any associations, which were further tested using standard regression analysis. Of 409 students who commenced the program (83% response rate), 367 undergraduate nursing students completed the web-based program in its entirety, yielding a completion rate of 89.7%; 38.1% of students achieved passing clinical performance across three scenarios, and the proportion achieving passing clinical knowledge increased from 78.15% pre-simulation to 91.6% post-simulation. Knowledge was the main independent predictor of clinical performance in responding to a virtual deteriorating patient R(2)=0.090, F(7, 352)=4.962, p<0.001. The use of web-based technology allows simulation activities to be accessible to a large number of participants and completion rates indicate that 'Net Generation' nursing students were highly engaged with this mode of learning. The web-based e-simulation program FIRST2ACTTM effectively enhanced knowledge, virtual clinical performance, and self-assessed knowledge, skills, confidence, and competence in final-year nursing students. Copyright © 2015 Elsevier Ltd. All rights reserved.

WE-F-BRB-00: New Developments in Knowledge-Based Treatment Planning and Automation

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

NONE

2015-06-15

Advancements in informatics in radiotherapy are opening up opportunities to improve our ability to assess treatment plans. Models on individualizing patient dose constraints from prior patient data and shape relationships have been extensively researched and are now making their way into commercial products. New developments in knowledge based treatment planning involve understanding the impact of the radiation dosimetry on the patient. Akin to radiobiology models that have driven intensity modulated radiotherapy optimization, toxicity and outcome predictions based on treatment plans and prior patient experiences may be the next step in knowledge based planning. In order to realize these predictions, itmore » is necessary to understand how the clinical information can be captured, structured and organized with ontologies and databases designed for recall. Large databases containing radiation dosimetry and outcomes present the opportunity to evaluate treatment plans against predictions of toxicity and disease response. Such evaluations can be based on dose volume histogram or even the full 3-dimensional dose distribution and its relation to the critical anatomy. This session will provide an understanding of ontologies and standard terminologies used to capture clinical knowledge into structured databases; How data can be organized and accessed to utilize the knowledge in planning; and examples of research and clinical efforts to incorporate that clinical knowledge into planning for improved care for our patients. Learning Objectives: Understand the role of standard terminologies, ontologies and data organization in oncology Understand methods to capture clinical toxicity and outcomes in a clinical setting Understand opportunities to learn from clinical data and its application to treatment planning Todd McNutt receives funding from Philips, Elekta and Toshiba for some of the work presented.« less

A knowledge-based approach to identification and adaptation in dynamical systems control

NASA Technical Reports Server (NTRS)

Glass, B. J.; Wong, C. M.

1988-01-01

Artificial intelligence techniques are applied to the problems of model form and parameter identification of large-scale dynamic systems. The object-oriented knowledge representation is discussed in the context of causal modeling and qualitative reasoning. Structured sets of rules are used for implementing qualitative component simulations, for catching qualitative discrepancies and quantitative bound violations, and for making reconfiguration and control decisions that affect the physical system. These decisions are executed by backward-chaining through a knowledge base of control action tasks. This approach was implemented for two examples: a triple quadrupole mass spectrometer and a two-phase thermal testbed. Results of tests with both of these systems demonstrate that the software replicates some or most of the functionality of a human operator, thereby reducing the need for a human-in-the-loop in the lower levels of control of these complex systems.

Genetic and molecular bases of yield-associated traits: a translational biology approach between rice and wheat.

PubMed

Valluru, Ravi; Reynolds, Matthew P; Salse, Jerome

2014-07-01

Transferring the knowledge bases between related species may assist in enlarging the yield potential of crop plants. Being cereals, rice and wheat share a high level of gene conservation; however, they differ at metabolic levels as a part of the environmental adaptation resulting in different yield capacities. This review focuses on the current understanding of genetic and molecular regulation of yield-associated traits in both crop species, highlights the similarities and differences and presents the putative knowledge gaps. We focus on the traits associated with phenology, photosynthesis, and assimilate partitioning and lodging resistance; the most important drivers of yield potential. Currently, there are large knowledge gaps in the genetic and molecular control of such major biological processes that can be filled in a translational biology approach in transferring genomics and genetics informations between rice and wheat.

Knowledge, Belief, and Science Education

NASA Astrophysics Data System (ADS)

Ferreira, Tiago Alfredo S.; El-Hani, Charbel N.; da Silva-Filho, Waldomiro José

2016-10-01

This article intends to show that the defense of "understanding" as one of the major goals of science education can be grounded on an anti-reductionist perspective on testimony as a source of knowledge. To do so, we critically revisit the discussion between Harvey Siegel and Alvin Goldman about the goals of science education, especially where it involves arguments based on the epistemology of testimony. Subsequently, we come back to a discussion between Charbel N. El-Hani and Eduardo Mortimer, on the one hand, and Michael Hoffmann, on the other, striving to strengthen the claim that rather than students' belief change, understanding should have epistemic priority as a goal of science education. Based on these two lines of discussion, we conclude that the reliance on testimony as a source of knowledge is necessary to the development of a more large and comprehensive scientific understanding by science students.

Database systems for knowledge-based discovery.

PubMed

Jagarlapudi, Sarma A R P; Kishan, K V Radha

2009-01-01

Several database systems have been developed to provide valuable information from the bench chemist to biologist, medical practitioner to pharmaceutical scientist in a structured format. The advent of information technology and computational power enhanced the ability to access large volumes of data in the form of a database where one could do compilation, searching, archiving, analysis, and finally knowledge derivation. Although, data are of variable types the tools used for database creation, searching and retrieval are similar. GVK BIO has been developing databases from publicly available scientific literature in specific areas like medicinal chemistry, clinical research, and mechanism-based toxicity so that the structured databases containing vast data could be used in several areas of research. These databases were classified as reference centric or compound centric depending on the way the database systems were designed. Integration of these databases with knowledge derivation tools would enhance the value of these systems toward better drug design and discovery.

Northeast Artificial Intelligence Consortium annual report. Volume 2. 1988. Discussing, using, and recognizing plans (NLP). Interim report, January-December 1988

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

Shapiro, S.C.; Woolf, B.

The Northeast Artificial Intelligence Consortium (NAIC) was created by the Air Force Systems Command, Rome Air Development Center, and the Office of Scientific Research. Its purpose is to conduct pertinent research in artificial intelligence and to perform activities ancillary to this research. This report describes progress that has been made in the fourth year of the existence of the NAIC on the technical research tasks undertaken at the member universities. The topics covered in general are: versatile expert system for equipment maintenance, distributed AI for communications system control, automatic photointerpretation, time-oriented problem solving, speech understanding systems, knowledge base maintenance, hardwaremore » architectures for very large systems, knowledge-based reasoning and planning, and a knowledge acquisition, assistance, and explanation system. The specific topic for this volume is the recognition of plans expressed in natural language, followed by their discussion and use.« less

Enriching semantic knowledge bases for opinion mining in big data applications

PubMed Central

Weichselbraun, A.; Gindl, S.; Scharl, A.

2014-01-01

This paper presents a novel method for contextualizing and enriching large semantic knowledge bases for opinion mining with a focus on Web intelligence platforms and other high-throughput big data applications. The method is not only applicable to traditional sentiment lexicons, but also to more comprehensive, multi-dimensional affective resources such as SenticNet. It comprises the following steps: (i) identify ambiguous sentiment terms, (ii) provide context information extracted from a domain-specific training corpus, and (iii) ground this contextual information to structured background knowledge sources such as ConceptNet and WordNet. A quantitative evaluation shows a significant improvement when using an enriched version of SenticNet for polarity classification. Crowdsourced gold standard data in conjunction with a qualitative evaluation sheds light on the strengths and weaknesses of the concept grounding, and on the quality of the enrichment process. PMID:25431524

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 19: Computer and information technology and aerospace knowledge diffusion

NASA Technical Reports Server (NTRS)

Pinelli, Thomas E.; Kennedy, John M.; Barclay, Rebecca O.; Bishop, Ann P.

1992-01-01

To remain a world leader in aerospace, the US must improve and maintain the professional competency of its engineers and scientists, increase the research and development (R&D) knowledge base, improve productivity, and maximize the integration of recent technological developments into the R&D process. How well these objectives are met, and at what cost, depends on a variety of factors, but largely on the ability of US aerospace engineers and scientists to acquire and process the results of federally funded R&D. The Federal Government's commitment to high speed computing and networking systems presupposes that computer and information technology will play a major role in the aerospace knowledge diffusion process. However, we know little about information technology needs, uses, and problems within the aerospace knowledge diffusion process. The use of computer and information technology by US aerospace engineers and scientists in academia, government, and industry is reported.

A machine independent expert system for diagnosing environmentally induced spacecraft anomalies

NASA Technical Reports Server (NTRS)

Rolincik, Mark J.

1991-01-01

A new rule-based, machine independent analytical tool for diagnosing spacecraft anomalies, the EnviroNET expert system, was developed. Expert systems provide an effective method for storing knowledge, allow computers to sift through large amounts of data pinpointing significant parts, and most importantly, use heuristics in addition to algorithms which allow approximate reasoning and inference, and the ability to attack problems not rigidly defines. The EviroNET expert system knowledge base currently contains over two hundred rules, and links to databases which include past environmental data, satellite data, and previous known anomalies. The environmental causes considered are bulk charging, single event upsets (SEU), surface charging, and total radiation dose.

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

NASA Technical Reports Server (NTRS)

Stclair, D. C.; Sabharwal, C. L.; Bond, W. E.; Hacke, Keith

1988-01-01

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

The Toxicant-Target Paradigm for Toxicity Screening – Pharmacophore Based Constraints

EPA Science Inventory

There is a compelling need to develop information for the screening and prioritization of the health and environmental effects of large numbers of man-made chemicals. Knowledge of the potential pathways for activity provides a rational basis for the preliminary evaluation of ris...

Promoting Students' Attention to Argumentative Reasoning Patterns

ERIC Educational Resources Information Center

Cavagnetto, Andy R.; Kurtz, Kenneth J.

2016-01-01

Argument-based interventions in science education have largely been motivated by the perspective that students lack knowledge of argument. Recent studies, however, suggest that contextual factors influence students' argument quality. The authors hypothesize that a key limiting factor lies in students' abilities to recognize when to employ…

Accessibility Principles for Reading Assessments

ERIC Educational Resources Information Center

Thurlow, Martha L.; Laitusis, Cara Cahalan; Dillon, Deborah R.; Cook, Linda L.; Moen, Ross E.; Abedi, Jamal; O'Brien, David G.

2009-01-01

Within the context of standards-based educational systems, states are using large scale reading assessments to help ensure that all children have the opportunity to learn essential knowledge and skills. The challenge for developers of accessible reading assessments is to develop assessments that measure only those student characteristics that are…

University Students' Views of Obesity and Weight Management Strategies

ERIC Educational Resources Information Center

Okonkwo, Ononuju; While, Alison

2010-01-01

Objective: To investigate the knowledge and views of university students regarding obesity and weight management strategies. Design: Online questionnaire-based survey of undergraduate and postgraduate university students in a large London university with a diverse student population. Method: The survey was administered online and circulated…

The emerging role of large eddy simulation in industrial practice: challenges and opportunities.

PubMed

Hutton, A G

2009-07-28

That class of methods for treating turbulence gathered under the banner of large eddy simulation is poised to enter mainstream engineering practice. There is a growing body of evidence that such methods offer a significant stretch in industrial capability over solely Reynolds-averaged Navier-Stokes (RANS)-based modelling. A key enabling development will be the adaptation of innovative processor architectures, resulting from the huge investment in the gaming industry, to engineering analysis. This promises to reduce the computational burden to practicable levels. However, there are many lessons to be learned from the history of the past three decades. These lessons should be analysed in order to inform, if not modulate, the unfolding of this next cycle in the development of industrial modelling capability. This provides the theme for this paper, which is written very much from the standpoint of the informed practitioner rather than the innovator; someone with a strong motivation to improve significantly the competence with which industrial turbulent flows are treated. It is asserted that the reliable deployment of the methodology in the industrial context will prove to be a knowledge-based discipline, as was the case with RANS-based modelling, if not more so. The community at large should collectively make great efforts to put in place that knowledge base from which best practice advice can be derived at the very start of this cycle of advancement and continue to enrich it as the cycle progresses.

A Game Based e-Learning System to Teach Artificial Intelligence in the Computer Sciences Degree

ERIC Educational Resources Information Center

de Castro-Santos, Amable; Fajardo, Waldo; Molina-Solana, Miguel

2017-01-01

Our students taking the Artificial Intelligence and Knowledge Engineering courses often encounter a large number of problems to solve which are not directly related to the subject to be learned. To solve this problem, we have developed a game based e-learning system. The elected game, that has been implemented as an e-learning system, allows to…

Computational Selection of Transcriptomics Experiments Improves Guilt-by-Association Analyses

PubMed Central

Bhat, Prajwal; Yang, Haixuan; Bögre, László; Devoto, Alessandra; Paccanaro, Alberto

2012-01-01

The Guilt-by-Association (GBA) principle, according to which genes with similar expression profiles are functionally associated, is widely applied for functional analyses using large heterogeneous collections of transcriptomics data. However, the use of such large collections could hamper GBA functional analysis for genes whose expression is condition specific. In these cases a smaller set of condition related experiments should instead be used, but identifying such functionally relevant experiments from large collections based on literature knowledge alone is an impractical task. We begin this paper by analyzing, both from a mathematical and a biological point of view, why only condition specific experiments should be used in GBA functional analysis. We are able to show that this phenomenon is independent of the functional categorization scheme and of the organisms being analyzed. We then present a semi-supervised algorithm that can select functionally relevant experiments from large collections of transcriptomics experiments. Our algorithm is able to select experiments relevant to a given GO term, MIPS FunCat term or even KEGG pathways. We extensively test our algorithm on large dataset collections for yeast and Arabidopsis. We demonstrate that: using the selected experiments there is a statistically significant improvement in correlation between genes in the functional category of interest; the selected experiments improve GBA-based gene function prediction; the effectiveness of the selected experiments increases with annotation specificity; our algorithm can be successfully applied to GBA-based pathway reconstruction. Importantly, the set of experiments selected by the algorithm reflects the existing literature knowledge about the experiments. [A MATLAB implementation of the algorithm and all the data used in this paper can be downloaded from the paper website: http://www.paccanarolab.org/papers/CorrGene/]. PMID:22879875

[The treatment of scientific knowledge in the framework of CITES].

PubMed

Lanfranchi, Marie-Pierre

2014-03-01

Access to scientific knowledge in the context of CITES is a crucial issue. The effectiveness of the text is indeed largely based on adequate scientific knowledge of CITES species. This is a major challenge: more than 30,000 species and 178 member states are involved. The issue of expertise, however, is not really addressed by the Convention. The question was left to the consideration of the COP. Therefore, the COP has created two ad hoc scientific committees: the Plants Committee and the Animals Committee, conferring upon them an ambitious mandate. The article addresses some important issues at stake which are linked to institutional questions, as well as the mixed record after twenty-five years of practice.

Knowledge based word-concept model estimation and refinement for biomedical text mining.

PubMed

Jimeno Yepes, Antonio; Berlanga, Rafael

2015-02-01

Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale biomedical text mining systems. KB-based methods do not have this limitation. In this paper, we describe a novel method to generate word-concept probabilities from a KB, which can serve as a basis for several text mining tasks. This method not only takes into account the underlying patterns within the descriptions contained in the KB but also those in texts available from large unlabeled corpora such as MEDLINE. The parameters of the model have been estimated without training data. Patterns from MEDLINE have been built using MetaMap for entity recognition and related using co-occurrences. The word-concept probabilities were evaluated on the task of word sense disambiguation (WSD). The results showed that our method obtained a higher degree of accuracy than other state-of-the-art approaches when evaluated on the MSH WSD data set. We also evaluated our method on the task of document ranking using MEDLINE citations. These results also showed an increase in performance over existing baseline retrieval approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

Design and Analysis Techniques for Concurrent Blackboard Systems. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Mcmanus, John William

1992-01-01

Blackboard systems are a natural progression of knowledge-based systems into a more powerful problem solving technique. They provide a way for several highly specialized knowledge sources to cooperate to solve large, complex problems. Blackboard systems incorporate the concepts developed by rule-based and expert systems programmers and include the ability to add conventionally coded knowledge sources. The small and specialized knowledge sources are easier to develop and test, and can be hosted on hardware specifically suited to the task that they are solving. The Formal Model for Blackboard Systems was developed to provide a consistent method for describing a blackboard system. A set of blackboard system design tools has been developed and validated for implementing systems that are expressed using the Formal Model. The tools are used to test and refine a proposed blackboard system design before the design is implemented. My research has shown that the level of independence and specialization of the knowledge sources directly affects the performance of blackboard systems. Using the design, simulation, and analysis tools, I developed a concurrent object-oriented blackboard system that is faster, more efficient, and more powerful than existing systems. The use of the design and analysis tools provided the highly specialized and independent knowledge sources required for my concurrent blackboard system to achieve its design goals.

Management of Knowledge Representation Standards Activities

NASA Technical Reports Server (NTRS)

Patil, Ramesh S.

1993-01-01

Ever since the mid-seventies, researchers have recognized that capturing knowledge is the key to building large and powerful AI systems. In the years since, we have also found that representing knowledge is difficult and time consuming. In spite of the tools developed to help with knowledge acquisition, knowledge base construction remains one of the major costs in building an Al system: For almost every system we build, a new knowledge base must be constructed from scratch. As a result, most systems remain small to medium in size. Even if we build several systems within a general area, such as medicine or electronics diagnosis, significant portions of the domain must be represented for every system we create. The cost of this duplication of effort has been high and will become prohibitive as we attempt to build larger and larger systems. To overcome this barrier we must find ways of preserving existing knowledge bases and of sharing, re-using, and building on them. This report describes the efforts undertaken over the last two years to identify the issues underlying the current difficulties in sharing and reuse, and a community wide initiative to overcome them. First, we discuss four bottlenecks to sharing and reuse, present a vision of a future in which these bottlenecks have been ameliorated, and describe the efforts of the initiative's four working groups to address these bottlenecks. We then address the supporting technology and infrastructure that is critical to enabling the vision of the future. Finally, we consider topics of longer-range interest by reviewing some of the research issues raised by our vision.

Creating the sustainable conditions for knowledge information sharing in virtual community.

PubMed

Wang, Jiangtao; Yang, Jianmei; Chen, Quan; Tsai, Sang-Bing

2016-01-01

Encyclopedias are not a new platform for the distribution of knowledge, but they have recently drawn a great deal of attention in their online iteration. Peer production in particular has emerged as a new mode of providing information with value and offering competitive advantage in information production. Large numbers of volunteers actively share their knowledge by continuously editing articles in Baidu encyclopedias. Most articles in the online communities are the cumulative and integrated products of the contributions of many coauthors. Email-based surveys and objective data mining were here used to collect analytical data. Critical mass theory is here used to analyze the characteristics of these collective actions and to explain the emergence and sustainability of these actions in the Baidu Encyclopedia communities. These results show that, based on the collective action framework, the contributors group satisfied the two key characteristics that ensure the collective action of knowledge contribution will both take place and become self-sustaining. This analysis not only facilitates the identification of collective actions related to individuals sharing knowledge in virtual communities, but also can provide an insight for other similar virtual communities' management and development.

Executable medical guidelines with Arden Syntax-Applications in dermatology and obstetrics.

PubMed

Seitinger, Alexander; Rappelsberger, Andrea; Leitich, Harald; Binder, Michael; Adlassnig, Klaus-Peter

2016-08-12

Clinical decision support systems (CDSSs) are being developed to assist physicians in processing extensive data and new knowledge based on recent scientific advances. Structured medical knowledge in the form of clinical alerts or reminder rules, decision trees or tables, clinical protocols or practice guidelines, score algorithms, and others, constitute the core of CDSSs. Several medical knowledge representation and guideline languages have been developed for the formal computerized definition of such knowledge. One of these languages is Arden Syntax for Medical Logic Systems, an International Health Level Seven (HL7) standard whose development started in 1989. Its latest version is 2.10, which was presented in 2014. In the present report we discuss Arden Syntax as a modern medical knowledge representation and processing language, and show that this language is not only well suited to define clinical alerts, reminders, and recommendations, but can also be used to implement and process computerized medical practice guidelines. This section describes how contemporary software such as Java, server software, web-services, XML, is used to implement CDSSs based on Arden Syntax. Special emphasis is given to clinical decision support (CDS) that employs practice guidelines as its clinical knowledge base. Two guideline-based applications using Arden Syntax for medical knowledge representation and processing were developed. The first is a software platform for implementing practice guidelines from dermatology. This application employs fuzzy set theory and logic to represent linguistic and propositional uncertainty in medical data, knowledge, and conclusions. The second application implements a reminder system based on clinically published standard operating procedures in obstetrics to prevent deviations from state-of-the-art care. A to-do list with necessary actions specifically tailored to the gestational week/labor/delivery is generated. Today, with the latest versions of Arden Syntax and the application of contemporary software development methods, Arden Syntax has become a powerful and versatile medical knowledge representation and processing language, well suited to implement a large range of CDSSs, including clinical-practice-guideline-based CDSSs. Moreover, such CDS is provided and can be shared as a service by different medical institutions, redefining the sharing of medical knowledge. Arden Syntax is also highly flexible and provides developers the freedom to use up-to-date software design and programming patterns for external patient data access. Copyright © 2016. Published by Elsevier B.V.

Analysis of Nursing Clinical Decision Support Requests and Strategic Plan in a Large Academic Health System.

PubMed

Whalen, Kimberly; Bavuso, Karen; Bouyer-Ferullo, Sharon; Goldsmith, Denise; Fairbanks, Amanda; Gesner, Emily; Lagor, Charles; Collins, Sarah

2016-01-01

To understand requests for nursing Clinical Decision Support (CDS) interventions at a large integrated health system undergoing vendor-based EHR implementation. In addition, to establish a process to guide both short-term implementation and long-term strategic goals to meet nursing CDS needs. We conducted an environmental scan to understand current state of nursing CDS over three months. The environmental scan consisted of a literature review and an analysis of CDS requests received from across our health system. We identified existing high priority CDS and paper-based tools used in nursing practice at our health system that guide decision-making. A total of 46 nursing CDS requests were received. Fifty-six percent (n=26) were specific to a clinical specialty; 22 percent (n=10) were focused on facilitating clinical consults in the inpatient setting. "Risk Assessments/Risk Reduction/Promotion of Healthy Habits" (n=23) was the most requested High Priority Category received for nursing CDS. A continuum of types of nursing CDS needs emerged using the Data-Information-Knowledge-Wisdom Conceptual Framework: 1) facilitating data capture, 2) meeting information needs, 3) guiding knowledge-based decision making, and 4) exposing analytics for wisdom-based clinical interpretation by the nurse. Identifying and prioritizing paper-based tools that can be modified into electronic CDS is a challenge. CDS strategy is an evolving process that relies on close collaboration and engagement with clinical sites for short-term implementation and should be incorporated into a long-term strategic plan that can be optimized and achieved overtime. The Data-Information-Knowledge-Wisdom Conceptual Framework in conjunction with the High Priority Categories established may be a useful tool to guide a strategic approach for meeting short-term nursing CDS needs and aligning with the organizational strategic plan.

Adiabatic Compression Sensitivity of AF-M315E

DTIC Science & Technology

2015-07-01

the current work is to expand the knowledge base from previous experiments completed at AFRL for AF-M315E in stainless steel U-tubes at room...addressed, to some degree, with the use of clamps and a large stainless steel plate to dissipate any major vibrations. A large preheated bath of 50:50 v/v...autocatalytic chain decomposition in the propellant. This exothermic decomposition decreases the fume -off initiation temperature of the propellant and its

Advanced techniques for the storage and use of very large, heterogeneous spatial databases

NASA Technical Reports Server (NTRS)

Peuquet, Donna J.

1987-01-01

Progress is reported in the development of a prototype knowledge-based geographic information system. The overall purpose of this project is to investigate and demonstrate the use of advanced methods in order to greatly improve the capabilities of geographic information system technology in the handling of large, multi-source collections of spatial data in an efficient manner, and to make these collections of data more accessible and usable for the Earth scientist.

Plumbing the brain drain.

PubMed

Saravia, Nancy Gore; Miranda, Juan Francisco

2004-08-01

Opportunity is the driving force of migration. Unsatisfied demands for higher education and skills, which have been created by the knowledge-based global economy, have generated unprecedented opportunities in knowledge-intensive service industries. These multi-trillion dollar industries include information, communication, finance, business, education and health. The leading industrialized nations are also the focal points of knowledge-intensive service industries and as such constitute centres of research and development activity that proactively draw in talented individuals worldwide through selective immigration policies, employment opportunities and targeted recruitment. Higher education is another major conduit of talent from less-developed countries to the centres of the knowledge-based global economy. Together career and educational opportunities drive "brain drain and recirculation". The departure of a large proportion of the most competent and innovative individuals from developing nations slows the achievement of the critical mass needed to generate the enabling context in which knowledge creation occurs. To favourably modify the asymmetric movement and distribution of global talent, developing countries must implement bold and creative strategies that are backed by national policies to: provide world-class educational opportunities, construct knowledge-based research and development industries, and sustainably finance the required investment for these strategies. Brazil, China and India have moved in this direction, offering world-class education in areas crucial to national development, such as biotechnology and information technology, paralleled by investments in research and development. As a result, only a small proportion of the most highly educated individuals migrate from these countries, and research and development opportunities employ national talent and even attract immigrants.

Knowledge Acquisition and Management for the NASA Earth Exchange (NEX)

NASA Astrophysics Data System (ADS)

Votava, P.; Michaelis, A.; Nemani, R. R.

2013-12-01

NASA Earth Exchange (NEX) is a data, computing and knowledge collaboratory that houses NASA satellite, climate and ancillary data where a focused community can come together to share modeling and analysis codes, scientific results, knowledge and expertise on a centralized platform with access to large supercomputing resources. As more and more projects are being executed on NEX, we are increasingly focusing on capturing the knowledge of the NEX users and provide mechanisms for sharing it with the community in order to facilitate reuse and accelerate research. There are many possible knowledge contributions to NEX, it can be a wiki entry on the NEX portal contributed by a developer, information extracted from a publication in an automated way, or a workflow captured during code execution on the supercomputing platform. The goal of the NEX knowledge platform is to capture and organize this information and make it easily accessible to the NEX community and beyond. The knowledge acquisition process consists of three main faucets - data and metadata, workflows and processes, and web-based information. Once the knowledge is acquired, it is processed in a number of ways ranging from custom metadata parsers to entity extraction using natural language processing techniques. The processed information is linked with existing taxonomies and aligned with internal ontology (which heavily reuses number of external ontologies). This forms a knowledge graph that can then be used to improve users' search query results as well as provide additional analytics capabilities to the NEX system. Such a knowledge graph will be an important building block in creating a dynamic knowledge base for the NEX community where knowledge is both generated and easily shared.

Entrepreneurial Regions: Do Macro-Psychological Cultural Characteristics of Regions Help Solve the “Knowledge Paradox” of Economics?

PubMed Central

Obschonka, Martin; Stuetzer, Michael; Gosling, Samuel D.; Rentfrow, Peter J.; Lamb, Michael E.; Potter, Jeff; Audretsch, David B.

2015-01-01

In recent years, modern economies have shifted away from being based on physical capital and towards being based on new knowledge (e.g., new ideas and inventions). Consequently, contemporary economic theorizing and key public policies have been based on the assumption that resources for generating knowledge (e.g., education, diversity of industries) are essential for regional economic vitality. However, policy makers and scholars have discovered that, contrary to expectations, the mere presence of, and investments in, new knowledge does not guarantee a high level of regional economic performance (e.g., high entrepreneurship rates). To date, this “knowledge paradox” has resisted resolution. We take an interdisciplinary perspective to offer a new explanation, hypothesizing that “hidden” regional culture differences serve as a crucial factor that is missing from conventional economic analyses and public policy strategies. Focusing on entrepreneurial activity, we hypothesize that the statistical relation between knowledge resources and entrepreneurial vitality (i.e., high entrepreneurship rates) in a region will depend on “hidden” regional differences in entrepreneurial culture. To capture such “hidden” regional differences, we derive measures of entrepreneurship-prone culture from two large personality datasets from the United States (N = 935,858) and Great Britain (N = 417,217). In both countries, the findings were consistent with the knowledge-culture-interaction hypothesis. A series of nine additional robustness checks underscored the robustness of these results. Naturally, these purely correlational findings cannot provide direct evidence for causal processes, but the results nonetheless yield a remarkably consistent and robust picture in the two countries. In doing so, the findings raise the idea of regional culture serving as a new causal candidate, potentially driving the knowledge paradox; such an explanation would be consistent with research on the psychological characteristics of entrepreneurs. PMID:26098674

Entrepreneurial Regions: Do Macro-Psychological Cultural Characteristics of Regions Help Solve the "Knowledge Paradox" of Economics?

PubMed

Obschonka, Martin; Stuetzer, Michael; Gosling, Samuel D; Rentfrow, Peter J; Lamb, Michael E; Potter, Jeff; Audretsch, David B

2015-01-01

In recent years, modern economies have shifted away from being based on physical capital and towards being based on new knowledge (e.g., new ideas and inventions). Consequently, contemporary economic theorizing and key public policies have been based on the assumption that resources for generating knowledge (e.g., education, diversity of industries) are essential for regional economic vitality. However, policy makers and scholars have discovered that, contrary to expectations, the mere presence of, and investments in, new knowledge does not guarantee a high level of regional economic performance (e.g., high entrepreneurship rates). To date, this "knowledge paradox" has resisted resolution. We take an interdisciplinary perspective to offer a new explanation, hypothesizing that "hidden" regional culture differences serve as a crucial factor that is missing from conventional economic analyses and public policy strategies. Focusing on entrepreneurial activity, we hypothesize that the statistical relation between knowledge resources and entrepreneurial vitality (i.e., high entrepreneurship rates) in a region will depend on "hidden" regional differences in entrepreneurial culture. To capture such "hidden" regional differences, we derive measures of entrepreneurship-prone culture from two large personality datasets from the United States (N = 935,858) and Great Britain (N = 417,217). In both countries, the findings were consistent with the knowledge-culture-interaction hypothesis. A series of nine additional robustness checks underscored the robustness of these results. Naturally, these purely correlational findings cannot provide direct evidence for causal processes, but the results nonetheless yield a remarkably consistent and robust picture in the two countries. In doing so, the findings raise the idea of regional culture serving as a new causal candidate, potentially driving the knowledge paradox; such an explanation would be consistent with research on the psychological characteristics of entrepreneurs.

An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic

NASA Astrophysics Data System (ADS)

Zhu, A.-Xing; Wang, Rongxun; Qiao, Jianping; Qin, Cheng-Zhi; Chen, Yongbo; Liu, Jing; Du, Fei; Lin, Yang; Zhu, Tongxin

2014-06-01

This paper presents an expert knowledge-based approach to landslide susceptibility mapping in an effort to overcome the deficiencies of data-driven approaches. The proposed approach consists of three generic steps: (1) extraction of knowledge on the relationship between landslide susceptibility and predisposing factors from domain experts, (2) characterization of predisposing factors using GIS techniques, and (3) prediction of landslide susceptibility under fuzzy logic. The approach was tested in two study areas in China - the Kaixian study area (about 250 km2) and the Three Gorges study area (about 4600 km2). The Kaixian study area was used to develop the approach and to evaluate its validity. The Three Gorges study area was used to test both the portability and the applicability of the developed approach for mapping landslide susceptibility over large study areas. Performance was evaluated by examining if the mean of the computed susceptibility values at landslide sites was statistically different from that of the entire study area. A z-score test was used to examine the statistical significance of the difference. The computed z for the Kaixian area was 3.70 and the corresponding p-value was less than 0.001. This suggests that the computed landslide susceptibility values are good indicators of landslide occurrences. In the Three Gorges study area, the computed z was 10.75 and the corresponding p-value was less than 0.001. In addition, we divided the susceptibility value into four levels: low (0.0-0.25), moderate (0.25-0.5), high (0.5-0.75) and very high (0.75-1.0). No landslides were found for areas of low susceptibility. Landslide density was about three times higher in areas of very high susceptibility than that in the moderate susceptibility areas, and more than twice as high as that in the high susceptibility areas. The results from the Three Gorge study area suggest that the extracted expert knowledge can be extrapolated to another study area and the developed approach can be used in large-scale projects. Results from these case studies suggest that the expert knowledge-based approach is effective in mapping landslide susceptibility and that its performance is maintained when it is moved to a new area from the model development area without changes to the knowledge base.

Embedding Open-domain Common-sense Knowledge from Text

PubMed Central

Goodwin, Travis; Harabagiu, Sanda

2017-01-01

Our ability to understand language often relies on common-sense knowledge – background information the speaker can assume is known by the reader. Similarly, our comprehension of the language used in complex domains relies on access to domain-specific knowledge. Capturing common-sense and domain-specific knowledge can be achieved by taking advantage of recent advances in open information extraction (IE) techniques and, more importantly, of knowledge embeddings, which are multi-dimensional representations of concepts and relations. Building a knowledge graph for representing common-sense knowledge in which concepts discerned from noun phrases are cast as vertices and lexicalized relations are cast as edges leads to learning the embeddings of common-sense knowledge accounting for semantic compositionality as well as implied knowledge. Common-sense knowledge is acquired from a vast collection of blogs and books as well as from WordNet. Similarly, medical knowledge is learned from two large sets of electronic health records. The evaluation results of these two forms of knowledge are promising: the same knowledge acquisition methodology based on learning knowledge embeddings works well both for common-sense knowledge and for medical knowledge Interestingly, the common-sense knowledge that we have acquired was evaluated as being less neutral than than the medical knowledge, as it often reflected the opinion of the knowledge utterer. In addition, the acquired medical knowledge was evaluated as more plausible than the common-sense knowledge, reflecting the complexity of acquiring common-sense knowledge due to the pragmatics and economicity of language. PMID:28649676

Key characteristics of knowledge transfer and exchange in healthcare: integrative literature review.

PubMed

Pentland, Duncan; Forsyth, Kirsty; Maciver, Donald; Walsh, Mike; Murray, Richard; Irvine, Linda; Sikora, Simon

2011-07-01

This paper presents the results of a review of literature relating to knowledge transfer and exchange in healthcare. Treatment, planning and policy decisions in contemporary nursing and healthcare should be based on sound evidence wherever possible, but research knowledge remains generally underused. Knowledge transfer and exchange initiatives aim to facilitate the accessibility, application and production of evidence and may provide solutions to this challenge. This review was conducted to help inform the design and implementation of knowledge transfer and exchange activities for a large healthcare organization. Databases: ASSIA, Business Source Premier, CINAHL, PsychInfo, Medline and the Cochrane Database of Systematic Reviews. An integrative literature review was carried out including an extensive literature search. English language systematic reviews, literature reviews, primary quantitative and qualitative papers and grey literature of high relevance evaluating, describing or discussing knowledge transfer or exchange activities in healthcare were included for review (January 1990-September 2009). Thirty-three papers were reviewed (four systematic reviews, nine literature reviews, one environmental scan, nine empirical studies and ten case studies). Robust research into knowledge transfer and exchange in healthcare is limited. Analysis of a wide range of evidence indicates a number of commonly featured characteristics but further evaluation of these activities would benefit their application in facilitating evidence-based practice in nursing. © 2011 The Authors. Journal of Advanced Nursing © 2011 Blackwell Publishing Ltd.

The Environmental Impacts of Boating; Proceedings of a Workshop held at Woods Hole Oceanographic Institution, Woods Hole MA USA, December 7 to 9, 1994.

DTIC Science & Technology

1998-03-01

workshop was the revelation of the greater activity and knowledge base of many state-level environmental managers than that for members of the academic...discussion and get as knowledgeable as we can. And then we can use the information to educate the public to get them concerned and help them understand...court. As I have observed from airplanes, jet skiers run in shallow water looking for animals. They stop near turtles or large sharks in shallow water

The Semi-opened Infrastructure Model (SopIM): A Frame to Set Up an Organizational Learning Process

NASA Astrophysics Data System (ADS)

Grundstein, Michel

In this paper, we introduce the "Semi-opened Infrastructure Model (SopIM)" implemented to deploy Artificial Intelligence and Knowledge-based Systems within a large industrial company. This model illustrates what could be two of the operating elements of the Model for General Knowledge Management within the Enterprise (MGKME) that are essential to set up the organizational learning process that leads people to appropriate and use concepts, methods and tools of an innovative technology: the "Ad hoc Infrastructures" element, and the "Organizational Learning Processes" element.

The Importance and Satisfaction of Collaborative Innovation for Strategic Entrepreneurship

ERIC Educational Resources Information Center

Tsai, I-Chang; Lei, Han-Sheng

2016-01-01

Building on network, learning, resource-based and real options theories, collaborative innovation through the sharing of ideas, knowledge, expertise, and opportunities can enable both small and large firms to successfully engage in strategic entrepreneurship. We use the real case of a research-oriented organization and its incubator for analysis…

Fast Spatio-Temporal Data Mining from Large Geophysical Datasets

NASA Technical Reports Server (NTRS)

Stolorz, P.; Mesrobian, E.; Muntz, R.; Santos, J. R.; Shek, E.; Yi, J.; Mechoso, C.; Farrara, J.

1995-01-01

Use of the UCLA CONQUEST (CONtent-based Querying in Space and Time) is reviewed for performance of automatic cyclone extraction and detection of spatio-temporal blocking conditions on MPP. CONQUEST is a data analysis environment for knowledge and data mining to aid in high-resolution modeling of climate modeling.

The Goal-Based Scenario Builder: Experiences with Novice Instructional Designers.

ERIC Educational Resources Information Center

Bell, Benjamin; Korcuska, Michael

Creating educational software generally requires a great deal of computer expertise, and as a result, educators lacking such knowledge have largely been excluded from the design process. Recently, researchers have been designing tools for automating some aspects of building instructional applications. These tools typically aim for generality,…

Satisfaction Analysis of Experiential Learning-Based Popular Science Education

ERIC Educational Resources Information Center

Dzan, Wei-Yuan; Tsai, Huei-Yin; Lou, Shi-Jer; Shih, Ru-Chu

2015-01-01

This study employed Kolb's experiential learning model-specific experiences, observations of reflections, abstract conceptualization, and experiment-action in activities to serve as the theoretical basis for popular science education planning. It designed the six activity themes of "Knowledge of the Ocean, Easy to Know, See the Large from the…

Evaluation Capacity Building: Can a Classroom-Based Course Make a Difference?

ERIC Educational Resources Information Center

Kaye-Tzadok, Avital; Spiro, Shimon E.

2016-01-01

Purpose: Growing emphasis on program and practice evaluation in social work education coalesces with a growing interest in evaluation capacity building (ECB) within the interdisciplinary field of evaluation. However, the literature on ECB, while recognizing the importance of imparting knowledge and skills to individuals, largely ignores the…

Racial and Socioeconomic Disparities in Nutrition Behaviors: Targeted Interventions Needed

ERIC Educational Resources Information Center

Fahlman, Mariane M.; McCaughtry, Nate; Martin, Jeffrey; Shen, Bo

2010-01-01

Objective: To compare dietary knowledge, behaviors and self-efficacy of black middle school students of low socioeconomic status with their white counterparts of higher socioeconomic status. Design: Cross-sectional, school-based survey. Setting: Large metropolitan area in the United States. Participants: Middle school students (1,208 of low…

Management of Classroom Behaviors: Perceived Readiness of Education Interns

ERIC Educational Resources Information Center

Garland, Dennis; Garland, Krista Vince; Vasquez, Eleazar, III

2013-01-01

Education students at a large research university participated in internships during their final semesters as part of their respective programs of study as a capstone experience. Qualitative and quantitative methods were used to collect data on the perceptions of interns' readiness and knowledge of evidence-based practices to manage classroom…

GROUNDWATER RECHARGE AND CHEMICAL CONTAMINANTS: CHALLENGES IN COMMUNICATING THE CONNECTIONS AND COLLISIONS OF TWO DISPARATE WORLDS

EPA Science Inventory

The existing knowledge base regarding the presence and significance of chemicals foreign to the subsurface environment is large and growing -the papers in this volume serving as recent testament. But complex questions with few answers surround the unknowns regarding the potenti...

Planning meals: Problem-solving on a real data-base

ERIC Educational Resources Information Center

Byrne, Richard

1977-01-01

Planning the menu for a dinner party, which involves problem-solving with a large body of knowledge, is used to study the daily operation of human memory. Verbal protocol analysis, a technique devised to investigate formal problem-solving, is examined theoretically and adapted for analysis of this task. (Author/MV)

Modeling Student Cognition in Digital and Nondigital Assessment Environments

ERIC Educational Resources Information Center

DiCerbo, Kristen E.; Xu, Yuning; Levy, Roy; Lai, Emily; Holland, Laura

2017-01-01

Inferences about student knowledge, skills, and attributes based on digital activity still largely come from whether students ultimately get a correct result or not. However, the ability to collect activity stream data as individuals interact with digital environments provides information about students' processes as they progress through learning…

Biological Bases for Radiation Adaptive Responses in the Lung

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

Scott, Bobby R.; Lin, Yong; Wilder, Julie

2015-03-01

Our main research objective was to determine the biological bases for low-dose, radiation-induced adaptive responses in the lung, and use the knowledge gained to produce an improved risk model for radiation-induced lung cancer that accounts for activated natural protection, genetic influences, and the role of epigenetic regulation (epiregulation). Currently, low-dose radiation risk assessment is based on the linear-no-threshold hypothesis, which now is known to be unsupported by a large volume of data.

Graph-based impact analysis as a framework for incorporating practitioner knowledge in dairy herd health management.

PubMed

Krieger, M; Schwabenbauer, E-M; Hoischen-Taubner, S; Emanuelson, U; Sundrum, A

2018-03-01

Production diseases in dairy cows are multifactorial, which means they emerge from complex interactions between many different farm variables. Variables with a large impact on production diseases can be identified for groups of farms using statistical models, but these methods cannot be used to identify highly influential variables in individual farms. This, however, is necessary for herd health planning, because farm conditions and associated health problems vary largely between farms. The aim of this study was to rank variables according to their anticipated effect on production diseases on the farm level by applying a graph-based impact analysis on 192 European organic dairy farms. Direct impacts between 13 pre-defined variables were estimated for each farm during a round-table discussion attended by practitioners, that is farmer, veterinarian and herd advisor. Indirect impacts were elaborated through graph analysis taking into account impact strengths. Across farms, factors supposedly exerting the most influence on production diseases were 'feeding', 'hygiene' and 'treatment' (direct impacts), as well as 'knowledge and skills' and 'herd health monitoring' (indirect impacts). Factors strongly influenced by production diseases were 'milk performance', 'financial resources' and 'labour capacity' (directly and indirectly). Ranking of variables on the farm level revealed considerable differences between farms in terms of their most influential and most influenced farm factors. Consequently, very different strategies may be required to reduce production diseases in these farms. The method is based on perceptions and estimations and thus prone to errors. From our point of view, however, this weakness is clearly outweighed by the ability to assess and to analyse farm-specific relationships and thus to complement general knowledge with contextual knowledge. Therefore, we conclude that graph-based impact analysis represents a promising decision support tool for herd health planning. The next steps include testing the method using more specific and problem-oriented variables as well as evaluating its effectiveness.

Letter-Sound Knowledge: Exploring Gender Differences in Children When They Start School Regarding Knowledge of Large Letters, Small Letters, Sound Large Letters, and Sound Small Letters

PubMed Central

Sigmundsson, Hermundur; Eriksen, Adrian D.; Ofteland, Greta Storm; Haga, Monika

2017-01-01

This study explored whether there is a gender difference in letter-sound knowledge when children start at school. 485 children aged 5–6 years completed assessment of letter-sound knowledge, i.e., large letters; sound of large letters; small letters; sound of small letters. The findings indicate a significant difference between girls and boys in all four factors tested in this study in favor of the girls. There are still no clear explanations to the basis of a presumed gender difference in letter-sound knowledge. That the findings have origin in neuro-biological factors cannot be excluded, however, the fact that girls probably have been exposed to more language experience/stimulation compared to boys, lends support to explanations derived from environmental aspects. PMID:28951726

Fetal well-being assessment in bovine near-term gestations: Current knowledge and future perspectives arising from comparative medicine

PubMed Central

Buczinski, Sébastien M.C.; Fecteau, Gilles; Lefebvre, Réjean C.; Smith, Lawrence C.

2007-01-01

Cloning technology is associated with multiple losses throughout pregnancy and in the neonatal period. Any maternal or fetal disease can compromise pregnancy. A paucity of data are available on bovine fetal well-being in late pregnancy; development of well-being assessment methods might augment early diagnosis of abnormal pregnancy or fetal distress, allowing early intervention. This review presents the current knowledge on fetal well-being based on bovine, ovine, equine, and human studies, as well as interesting research parameters that have been studied in other species and not yet investigated in cattle. Transabdominal ultrasonography allows for diagnosis of large placentomes and hydrallantois that frequently accompany clone pregnancies. Fetal inactivity or large hyperechoic particles imaged within the fetal annexes are associated with fetal distress or death, and should be reassessed to confirm compromised pregnancy. Measurements of different fetal parameters (thoracic aorta, metacarpal or metatarsal thickness) could be reliable tools for early detection of the large offspring syndrome commonly found in cloned calves. PMID:17334032

An automated knowledge-based textual summarization system for longitudinal, multivariate clinical data.

PubMed

Goldstein, Ayelet; Shahar, Yuval

2016-06-01

Design and implement an intelligent free-text summarization system: The system's input includes large numbers of longitudinal, multivariate, numeric and symbolic clinical raw data, collected over varying periods of time, and in different complex contexts, and a suitable medical knowledge base. The system then automatically generates a textual summary of the data. We aim to prove the feasibility of implementing such a system, and to demonstrate its potential benefits for clinicians and for enhancement of quality of care. We have designed a new, domain-independent, knowledge-based system, the CliniText system, for automated summarization in free text of longitudinal medical records of any duration, in any context. The system is composed of six components: (1) A temporal abstraction module generates all possible abstractions from the patient's raw data using a temporal-abstraction knowledge base; (2) The abductive reasoning module infers abstractions or events from the data, which were not explicitly included in the database; (3) The pruning module filters out raw or abstract data based on predefined heuristics; (4) The document structuring module organizes the remaining raw or abstract data, according to the desired format; (5) The microplanning module, groups the raw or abstract data and creates referring expressions; (6) The surface realization module, generates the text, and applies the grammar rules of the chosen language. We have performed an initial technical evaluation of the system in the cardiac intensive-care and diabetes domains. We also summarize the results of a more detailed evaluation study that we have performed in the intensive-care domain that assessed the completeness, correctness, and overall quality of the system's generated text, and its potential benefits to clinical decision making. We assessed these measures for 31 letters originally composed by clinicians, and for the same letters when generated by the CliniText system. We have successfully implemented all of the components of the CliniText system in software. We have also been able to create a comprehensive temporal-abstraction knowledge base to support its functionality, mostly in the intensive-care domain. The initial technical evaluation of the system in the cardiac intensive-care and diabetes domains has shown great promise, proving the feasibility of constructing and operating such systems. The detailed results of the evaluation in the intensive-care domain are out of scope of the current paper, and we refer the reader to a more detailed source. In all of the letters composed by clinicians, there were at least two important items per letter missed that were included by the CliniText system. The clinicians' letters got a significantly better grade in three out of four measured quality parameters, as judged by an expert; however, the variance in the quality was much higher in the clinicians' letters. In addition, three clinicians answered questions based on the discharge letter 40% faster, and answered four out of the five questions equally well or significantly better, when using the CliniText-generated letters, than when using the clinician-composed letters. Constructing a working system for automated summarization in free text of large numbers of varying periods of multivariate longitudinal clinical data is feasible. So is the construction of a large knowledge base, designed to support such a system, in a complex clinical domain, such as the intensive-care domain. The integration of the quality and functionality results suggests that the optimal discharge letter should exploit both human and machine, possibly by creating a machine-generated draft that will be polished by a human clinician. Copyright © 2016 Elsevier Inc. All rights reserved.

Adaptive interface for personalizing information seeking.

PubMed

Narayanan, S; Koppaka, Lavanya; Edala, Narasimha; Loritz, Don; Daley, Raymond

2004-12-01

An adaptive interface autonomously adjusts its display and available actions to current goals and abilities of the user by assessing user status, system task, and the context. Knowledge content adaptability is needed for knowledge acquisition and refinement tasks. In the case of knowledge content adaptability, the requirements of interface design focus on the elicitation of information from the user and the refinement of information based on patterns of interaction. In such cases, the emphasis on adaptability is on facilitating information search and knowledge discovery. In this article, we present research on adaptive interfaces that facilitates personalized information seeking from a large data warehouse. The resulting proof-of-concept system, called source recommendation system (SRS), assists users in locating and navigating data sources in the repository. Based on the initial user query and an analysis of the content of the search results, the SRS system generates a profile of the user tailored to the individual's context during information seeking. The user profiles are refined successively and are used in progressively guiding the user to the appropriate set of sources within the knowledge base. The SRS system is implemented as an Internet browser plug-in to provide a seamless and unobtrusive, personalized experience to the users during the information search process. The rationale behind our approach, system design, empirical evaluation, and implications for research on adaptive interfaces are described in this paper.

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.

Experimental Evidence on the Impact of Food Advertising on Children's Knowledge about and Preferences for Healthful Food

PubMed Central

Reisch, Lucia A.; Gwozdz, Wencke; De Henauw, Stefaan; Lascorz, Natalia; Pigeot, Iris

2013-01-01

To understand the rising prevalence of childhood obesity in affluent societies, it is necessary to take into account the growing obesity infrastructure, which over past decades has developed into an obesogenic environment. This study examines the effects of one of the constituent factors of consumer societies and a potential contributory factor to childhood obesity: commercial food communication targeted to children. Specifically, it investigates the impact of TV advertising on children's food knowledge and food preferences and correlates these findings with their weight status. Evaluations of traditional information- and education-based interventions suggest that they may not sustainably change food patterns. Based on prior consumer research, we propose five hypotheses, which we then test using a subsample from the IDEFICS study, a large-scale pan-European intervention study on childhood obesity. The results indicate that advertising has divergent effects on children's food knowledge and preferences and that food knowledge is unrelated to food preferences. This finding has important implications for both future research and public policy. PMID:23691285

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.

PANDORA: keyword-based analysis of protein sets by integration of annotation sources.

PubMed

Kaplan, Noam; Vaaknin, Avishay; Linial, Michal

2003-10-01

Recent advances in high-throughput methods and the application of computational tools for automatic classification of proteins have made it possible to carry out large-scale proteomic analyses. Biological analysis and interpretation of sets of proteins is a time-consuming undertaking carried out manually by experts. We have developed PANDORA (Protein ANnotation Diagram ORiented Analysis), a web-based tool that provides an automatic representation of the biological knowledge associated with any set of proteins. PANDORA uses a unique approach of keyword-based graphical analysis that focuses on detecting subsets of proteins that share unique biological properties and the intersections of such sets. PANDORA currently supports SwissProt keywords, NCBI Taxonomy, InterPro entries and the hierarchical classification terms from ENZYME, SCOP and GO databases. The integrated study of several annotation sources simultaneously allows a representation of biological relations of structure, function, cellular location, taxonomy, domains and motifs. PANDORA is also integrated into the ProtoNet system, thus allowing testing thousands of automatically generated clusters. We illustrate how PANDORA enhances the biological understanding of large, non-uniform sets of proteins originating from experimental and computational sources, without the need for prior biological knowledge on individual proteins.

A Knowledge-Based System for the Computer Assisted Diagnosis of Endoscopic Images

NASA Astrophysics Data System (ADS)

Kage, Andreas; Münzenmayer, Christian; Wittenberg, Thomas

Due to the actual demographic development the use of Computer-Assisted Diagnosis (CAD) systems becomes a more important part of clinical workflows and clinical decision making. Because changes on the mucosa of the esophagus can indicate the first stage of cancerous developments, there is a large interest to detect and correctly diagnose any such lesion. We present a knowledge-based system which is able to support a physician with the interpretation and diagnosis of endoscopic images of the esophagus. Our system is designed to support the physician directly during the examination of the patient, thus prodving diagnostic assistence at the point of care (POC). Based on an interactively marked region in an endoscopic image of interest, the system provides a diagnostic suggestion, based on an annotated reference image database. Furthermore, using relevant feedback mechanisms, the results can be enhanced interactively.

Information Retention and Overload in First-Time Hearing Aid Users: An Interactive Multimedia Educational Solution.

PubMed

Ferguson, Melanie; Brandreth, Marian; Brassington, William; Wharrad, Heather

2015-09-01

An educational intervention to improve knowledge of hearing aids and communication in first-time hearing aid users was assessed. This intervention was based on the concept of reusable learning objects (RLOs). A randomized controlled trial was conducted. One group received the educational intervention, and the other acted as a control group. RLOs were delivered online and through DVD for television and personal computer. Knowledge of both practical and psychosocial aspects of hearing aids and communication was assessed using a free-recall method 6 weeks postfitting. Knowledge of both practical and psychosocial issues was significantly higher in the group that received the RLOs than in the control group. Moderate to large effect sizes indicated that these differences were clinically significant. An educational intervention that supplements clinical practice results in improved knowledge in first-time hearing aid users.

Computer-Assisted Search Of Large Textual Data Bases

NASA Technical Reports Server (NTRS)

Driscoll, James R.

1995-01-01

"QA" denotes high-speed computer system for searching diverse collections of documents including (but not limited to) technical reference manuals, legal documents, medical documents, news releases, and patents. Incorporates previously available and emerging information-retrieval technology to help user intelligently and rapidly locate information found in large textual data bases. Technology includes provision for inquiries in natural language; statistical ranking of retrieved information; artificial-intelligence implementation of semantics, in which "surface level" knowledge found in text used to improve ranking of retrieved information; and relevance feedback, in which user's judgements of relevance of some retrieved documents used automatically to modify search for further information.

Technology and testing.

PubMed

Quellmalz, Edys S; Pellegrino, James W

2009-01-02

Large-scale testing of educational outcomes benefits already from technological applications that address logistics such as development, administration, and scoring of tests, as well as reporting of results. Innovative applications of technology also provide rich, authentic tasks that challenge the sorts of integrated knowledge, critical thinking, and problem solving seldom well addressed in paper-based tests. Such tasks can be used on both large-scale and classroom-based assessments. Balanced assessment systems can be developed that integrate curriculum-embedded, benchmark, and summative assessments across classroom, district, state, national, and international levels. We discuss here the potential of technology to launch a new era of integrated, learning-centered assessment systems.

A Large-Scale Internet/Computer-Based, Training Module: Dissemination of Evidence-Based Management of Postpartum Hemorrhage to Front-Line Health Care Workers

ERIC Educational Resources Information Center

Abawi, Karim; Gertiser, Lynn; Idris, Raqibat; Villar, José; Langer, Ana; Chatfield, Alison; Campana, Aldo

2017-01-01

Postpartum hemorrhage (PPH) is the leading cause of maternal mortality in most developing and low-income countries and the cause of one-quarter of maternal deaths worldwide. With appropriate and prompt care, these deaths can be prevented. With the current and rapidly developing research and worldwide access to information, a lack of knowledge of…

Development of a measure of knowledge use by stakeholders in rehabilitation technology

PubMed Central

Nobrega, Amanda R; Lane, Joseph P; Tomita, Machiko R; Usiak, Douglas J; Lockett, Michelle M

2014-01-01

Objectives: Uptake of new knowledge by diverse and diffuse stakeholders of health-care technology innovations has been a persistent challenge, as has been measurement of this uptake. This article describes the development of the Level of Knowledge Use Survey instrument, a web-based measure of self-reported knowledge use. Methods: The Level of Knowledge Use Survey instrument was developed in the context of assessing effectiveness of knowledge communication strategies in rehabilitation technology. It was validated on samples representing five stakeholder types: researchers, manufacturers, clinician–practitioners, knowledge brokers, and consumers. Its structure is broadly based on Rogers’ stages of innovation adoption. Its item generation was initially guided by Hall et al’s Levels of Use framework. Item selection was based on content validity indices computed from expert ratings (n 1 = 4; n 2 = 3). Five representative stakeholders established usability of the web version. The version included 47 items (content validity index for individual items >0.78; content validity index for a scale or set of items >0.90) in self-reporting format. Psychometrics were then established for the version. Results: Analyses of data from small (n = 69) and large (n = 215) samples using the Level of Knowledge Use Survey instrument suggested a conceptual model of four levels of knowledge use—Non-awareness, Awareness, Interest, and Use. The levels covered eight dimensions and six user action categories. The sequential nature of levels was inconclusive due to low cell frequencies. The Level of Knowledge Use Survey instrument showed adequate content validity (≈ 0.88; n = 3) and excellent test–retest reliability (1.0; n = 69). It also demonstrated good construct validity (n = 215) for differentiating among new knowledge outputs (p < 0.001) and among stakeholder types (0.001 < p ≤ 0.013). It showed strong responsiveness to change between baseline and follow-up testing (0.001 < p ≤ 0.002; n = 215). Conclusion: The Level of Knowledge Use Survey instrument is valid and reliable for measuring uptake of innovations across diffuse stakeholders of rehabilitation technologies and therefore also for tracking changes in knowledge use. PMID:26770743

IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis.

PubMed

Chang, Hsien-Tsung; Mishra, Nilamadhab; Lin, Chung-Chih

2015-01-01

The current rapid growth of Internet of Things (IoT) in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought-provoking application possibilities. The objective of the present work is to inspect the structural analysis and clustering of complex knowledge granules in an IoT big-data environment. In this work, we propose a knowledge granule analytic and clustering (KGAC) framework that explores and assembles knowledge granules from IoT big-data arrays for a business intelligence (BI) application. Our work implements neuro-fuzzy analytic architecture rather than a standard fuzzified approach to discover the complex knowledge granules. Furthermore, we implement an enhanced knowledge granule clustering (e-KGC) mechanism that is more elastic than previous techniques when assembling the tactical and explicit complex knowledge granules from IoT big-data arrays. The analysis and discussion presented here show that the proposed framework and mechanism can be implemented to extract knowledge granules from an IoT big-data array in such a way as to present knowledge of strategic value to executives and enable knowledge users to perform further BI actions.

IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis

PubMed Central

Chang, Hsien-Tsung; Mishra, Nilamadhab; Lin, Chung-Chih

2015-01-01

The current rapid growth of Internet of Things (IoT) in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought-provoking application possibilities. The objective of the present work is to inspect the structural analysis and clustering of complex knowledge granules in an IoT big-data environment. In this work, we propose a knowledge granule analytic and clustering (KGAC) framework that explores and assembles knowledge granules from IoT big-data arrays for a business intelligence (BI) application. Our work implements neuro-fuzzy analytic architecture rather than a standard fuzzified approach to discover the complex knowledge granules. Furthermore, we implement an enhanced knowledge granule clustering (e-KGC) mechanism that is more elastic than previous techniques when assembling the tactical and explicit complex knowledge granules from IoT big-data arrays. The analysis and discussion presented here show that the proposed framework and mechanism can be implemented to extract knowledge granules from an IoT big-data array in such a way as to present knowledge of strategic value to executives and enable knowledge users to perform further BI actions. PMID:26600156

WE-F-BRB-01: The Power of Ontologies and Standardized Terminologies for Capturing Clinical Knowledge

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

Gabriel, P.

2015-06-15

Advancements in informatics in radiotherapy are opening up opportunities to improve our ability to assess treatment plans. Models on individualizing patient dose constraints from prior patient data and shape relationships have been extensively researched and are now making their way into commercial products. New developments in knowledge based treatment planning involve understanding the impact of the radiation dosimetry on the patient. Akin to radiobiology models that have driven intensity modulated radiotherapy optimization, toxicity and outcome predictions based on treatment plans and prior patient experiences may be the next step in knowledge based planning. In order to realize these predictions, itmore » is necessary to understand how the clinical information can be captured, structured and organized with ontologies and databases designed for recall. Large databases containing radiation dosimetry and outcomes present the opportunity to evaluate treatment plans against predictions of toxicity and disease response. Such evaluations can be based on dose volume histogram or even the full 3-dimensional dose distribution and its relation to the critical anatomy. This session will provide an understanding of ontologies and standard terminologies used to capture clinical knowledge into structured databases; How data can be organized and accessed to utilize the knowledge in planning; and examples of research and clinical efforts to incorporate that clinical knowledge into planning for improved care for our patients. Learning Objectives: Understand the role of standard terminologies, ontologies and data organization in oncology Understand methods to capture clinical toxicity and outcomes in a clinical setting Understand opportunities to learn from clinical data and its application to treatment planning Todd McNutt receives funding from Philips, Elekta and Toshiba for some of the work presented.« less

Exploitation of biotechnology in a large company.

PubMed

Dart, E C

1989-08-31

Almost from the outset, most large companies saw the 'new biotechnology' not as a new business but as a set of very powerful techniques that, in time, would radically improve the understanding of biological systems. This new knowledge was generally seen by them as enhancing the process of invention and not as a substitute for tried and tested ways of meeting clearly identified targets. As the knowledge base grows, so the big-company response to biotechnology becomes more positive. Within ICI, biotechnology is now integrated into five bio-businesses (Pharmaceuticals, Agrochemicals, Seeds, Diagnostics and Biological Products). Within the Central Toxicology Laboratory it also contributes to the understanding of the mechanisms of toxic action of chemicals as part of assessing risk. ICI has entered two of these businesses (Seeds and Diagnostics) because it sees biotechnology making a major contribution to the profitability of each.

Development and Evaluation of a Pharmacogenomics Educational Program for Pharmacists

PubMed Central

Formea, Christine M.; Nicholson, Wayne T.; McCullough, Kristen B.; Berg, Kevin D.; Berg, Melody L.; Cunningham, Julie L.; Merten, Julianna A.; Ou, Narith N.; Stollings, Joanna L.

2013-01-01

Objectives. To evaluate hospital and outpatient pharmacists’ pharmacogenomics knowledge before and 2 months after participating in a targeted, case-based pharmacogenomics continuing education program. Design. As part of a continuing education program accredited by the Accreditation Council for Pharmacy Education (ACPE), pharmacists were provided with a fundamental pharmacogenomics education program. Evaluation. An 11-question, multiple-choice, electronic survey instrument was distributed to 272 eligible pharmacists at a single campus of a large, academic healthcare system. Pharmacists improved their pharmacogenomics test scores by 0.7 questions (pretest average 46%; posttest average 53%, p=0.0003). Conclusions. Although pharmacists demonstrated improvement, overall retention of educational goals and objectives was marginal. These results suggest that the complex topic of pharmacogenomics requires a large educational effort in order to increase pharmacists’ knowledge and comfort level with this emerging therapeutic opportunity. PMID:23459098

Development and evaluation of a pharmacogenomics educational program for pharmacists.

PubMed

Formea, Christine M; Nicholson, Wayne T; McCullough, Kristen B; Berg, Kevin D; Berg, Melody L; Cunningham, Julie L; Merten, Julianna A; Ou, Narith N; Stollings, Joanna L

2013-02-12

Objectives. To evaluate hospital and outpatient pharmacists' pharmacogenomics knowledge before and 2 months after participating in a targeted, case-based pharmacogenomics continuing education program.Design. As part of a continuing education program accredited by the Accreditation Council for Pharmacy Education (ACPE), pharmacists were provided with a fundamental pharmacogenomics education program.Evaluation. An 11-question, multiple-choice, electronic survey instrument was distributed to 272 eligible pharmacists at a single campus of a large, academic healthcare system. Pharmacists improved their pharmacogenomics test scores by 0.7 questions (pretest average 46%; posttest average 53%, p=0.0003).Conclusions. Although pharmacists demonstrated improvement, overall retention of educational goals and objectives was marginal. These results suggest that the complex topic of pharmacogenomics requires a large educational effort in order to increase pharmacists' knowledge and comfort level with this emerging therapeutic opportunity.

Health-Related Fitness Knowledge and Its Relation to Student Physical Activity Patterns at a Large U.S. Southern State University

ERIC Educational Resources Information Center

Keating, Xiaofen D.; Castro-Pinero, Jose; Centeio, Erin; Harrison, Louis, Jr.; Ramirez, Tere; Chen, Li

2010-01-01

This study examined student health-related fitness (HRF) knowledge and its relationship to physical activity (PA). The participants were undergraduate students from a large U.S. state university. HRF knowledge was assessed using a test consisting of 150 multiple choice items. Differences in HRF knowledge scores by sex, ethnicity, and years in…

Automatic computation of 2D cardiac measurements from B-mode echocardiography

NASA Astrophysics Data System (ADS)

Park, JinHyeong; Feng, Shaolei; Zhou, S. Kevin

2012-03-01

We propose a robust and fully automatic algorithm which computes the 2D echocardiography measurements recommended by America Society of Echocardiography. The algorithm employs knowledge-based imaging technologies which can learn the expert's knowledge from the training images and expert's annotation. Based on the models constructed from the learning stage, the algorithm searches initial location of the landmark points for the measurements by utilizing heart structure of left ventricle including mitral valve aortic valve. It employs the pseudo anatomic M-mode image generated by accumulating the line images in 2D parasternal long axis view along the time to refine the measurement landmark points. The experiment results with large volume of data show that the algorithm runs fast and is robust comparable to expert.

Supply Chain Engineering and the Use of a Supporting Knowledge Management Application

NASA Astrophysics Data System (ADS)

Laakmann, Frank

The future competition in markets will happen between logistics networks and no longer between enterprises. A new approach for supporting the engineering of logistics networks is developed by this research as a part of the Collaborative Research Centre (SFB) 559: "Modeling of Large Networks in Logistics" at the University of Dortmund together with the Fraunhofer-Institute of Material Flow and Logistics founded by Deutsche Forschungsgemeinschaft (DFG). Based on a reference model for logistics processes, the process chain model, a guideline for logistics engineers is developed to manage the different types of design tasks of logistics networks. The technical background of this solution is a collaborative knowledge management application. This paper will introduce how new Internet-based technologies support supply chain design projects.

Dutch home-based pre-reading intervention with children at familial risk of dyslexia.

PubMed

van Otterloo, Sandra G; van der Leij, Aryan

2009-12-01

Children (5 and 6 years old, n = 30) at familial risk of dyslexia received a home-based intervention that focused on phoneme awareness and letter knowledge in the year prior to formal reading instruction. The children were compared to a no-training at-risk control group (n = 27), which was selected a year earlier. After training, we found a small effect on a composite score of phoneme awareness (d = 0.29) and a large effect on receptive letter knowledge (d = 0.88). In first grade, however, this did not result in beneficial effects for the experimental group in word reading and spelling. Results are compared to three former intervention studies in The Netherlands and comparable studies from Denmark and Australia.

Suicide Prevention for School Communities: An Educational Initiative for Student Safety.

PubMed

Roberts, Diane Cody; Taylor, Mary Ellen; Pyle, Audrey D'Ann

2018-05-01

A knowledge gap exists in school communities regarding suicide prevention and means reduction education. The article highlights two core interrelated topics: school nurse engagement in dialogue with students' families and the implementation of an innovative, community-based suicide prevention educational program at a suburban public school district. The authors provide an overview of the public health problem of suicide for students, current student challenges, role of the school nurse in suicide prevention, and a key gap in current school nursing practice. At the request of the school counselors and principal, an innovative suicide prevention educational program was initiated as a community-based project at a large suburban public school district in Texas. The two overarching goals for this community-based collaboration are the following: school nurses will engage in frank, productive conversations with students' parents and families about suicidality concerns and increase the school community's knowledge about suicide prevention. This school community knowledge includes effective risk mitigation and means reduction strategies to better manage suicidality in students. Ultimately, this ongoing family and school community collaboration aims to prevent student deaths by suicide.

Unbiased Protein Association Study on the Public Human Proteome Reveals Biological Connections between Co-Occurring Protein Pairs

PubMed Central

2017-01-01

Mass-spectrometry-based, high-throughput proteomics experiments produce large amounts of data. While typically acquired to answer specific biological questions, these data can also be reused in orthogonal ways to reveal new biological knowledge. We here present a novel method for such orthogonal data reuse of public proteomics data. Our method elucidates biological relationships between proteins based on the co-occurrence of these proteins across human experiments in the PRIDE database. The majority of the significantly co-occurring protein pairs that were detected by our method have been successfully mapped to existing biological knowledge. The validity of our novel method is substantiated by the extremely few pairs that can be mapped to existing knowledge based on random associations between the same set of proteins. Moreover, using literature searches and the STRING database, we were able to derive meaningful biological associations for unannotated protein pairs that were detected using our method, further illustrating that as-yet unknown associations present highly interesting targets for follow-up analysis. PMID:28480704

Large-Scale 3D Printing: The Way Forward

NASA Astrophysics Data System (ADS)

Jassmi, Hamad Al; Najjar, Fady Al; Ismail Mourad, Abdel-Hamid

2018-03-01

Research on small-scale 3D printing has rapidly evolved, where numerous industrial products have been tested and successfully applied. Nonetheless, research on large-scale 3D printing, directed to large-scale applications such as construction and automotive manufacturing, yet demands a great a great deal of efforts. Large-scale 3D printing is considered an interdisciplinary topic and requires establishing a blended knowledge base from numerous research fields including structural engineering, materials science, mechatronics, software engineering, artificial intelligence and architectural engineering. This review article summarizes key topics of relevance to new research trends on large-scale 3D printing, particularly pertaining (1) technological solutions of additive construction (i.e. the 3D printers themselves), (2) materials science challenges, and (3) new design opportunities.

Recognizing 3 D Objects from 2D Images Using Structural Knowledge Base of Genetic Views

DTIC Science & Technology

1988-08-31

technical report. [BIE85] I. Biederman , "Human image understanding: Recent research and a theory", Computer Vision, Graphics, and Image Processing, vol...model bases", Technical Report 87-85, COINS Dept, University of Massachusetts, Amherst, MA 01003, August 1987 . [BUR87b) Burns, J. B. and L. J. Kitchen...34Recognition in 2D images of 3D objects from large model bases using prediction hierarchies", Proc. IJCAI-10, 1987 . [BUR891 J. B. Burns, forthcoming

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.

Monitoring and reporting attacks on education in the Democratic Republic of the Congo and Somalia.

PubMed

Bennouna, Cyril; van Boetzelaer, Elburg; Rojas, Lina; Richard, Kinyera; Karume, Gang; Nshombo, Marius; Roberts, Leslie; Boothby, Neil

2018-04-01

The United Nations' Monitoring and Reporting Mechanism is charged with documenting six grave violations against children in a time of conflict, including attacks on schools. Many of these incidents, however, remain unreported across the globe. This study explores whether or not a local knowledge base of education and child protection actors in North and South Kivu Provinces, Democratic Republic of the Congo, and in Mogadishu, Somalia, could contribute to a more complete record of attacks on education in those areas. Hundreds of semi-structured interviews were conducted with key informants across the three settings, and in total 432 attacks on education were documented. Purposive samples of these reports were verified and a large majority was confirmed. Local non-governmental organisations and education institutions were most knowledgeable about these incidents, but most never reported them to a monitoring authority. The study concludes that attack surveillance and response were largely insufficient, and recommends investing in mechanisms that utilise local knowledge to address these shortcomings. © 2018 The Author(s). Disasters © Overseas Development Institute, 2018.

The effect of a knowledge-based ergonomic intervention amongst administrators at Aga Khan University Hospital, Nairobi.

PubMed

Wanyonyi, Nancy; Frantz, Jose; Saidi, Hassan

2015-01-01

Low back pain (LBP) and neck pain are part of the common work-related musculoskeletal disorders with a large impact on the affected person. Despite having a multifactorial aetiology, ergonomic factors play a major role thus necessitating workers' education. To determine the prevalence of ergonomic-related LBP and neck pain, and describe the effect of a knowledge-based ergonomic intervention amongst administrators in Aga Khan University Hospital, Nairobi. This study applied a mixed method design utilizing a survey and two focus group discussions (FGD). A self-administered questionnaire was distributed to 208 participants through systematic sampling. A one hour knowledge-based ergonomic session founded on the survey results was thereafter administered to interested participants, followed by two FGDs a month later with purposive selection of eight participants to explore their experience of the ergonomic intervention. Quantitative data was captured and analyzed using SPSS by means of descriptive and inferential statistics, whereas thematic content analysis was used for qualitative data. Most participants were knowledgeable about ergonomic-related LBP and neck pain with a twelve month prevalence of 75.5% and 67.8% respectively. Continual ergonomic education is necessary for adherence to health-related behaviours that will preventwork-related LBP and neck pain.

Distributed Leadership in Practice: Evidence, Misconceptions and Possibilities

ERIC Educational Resources Information Center

Harris, Alma; DeFlaminis, John

2016-01-01

This article takes a contemporary look at distributed leadership in practice by drawing upon empirical evidence from a large-scale project in the USA. Initially, it considers the existing knowledge base on distributed leadership and questions some of the assertions and assumptions in recent accounts of the literature. The article also addresses…

The Impact of Collaborative and Individualized Student Response System Strategies on Learner Motivation, Metacognition, and Knowledge Transfer

ERIC Educational Resources Information Center

Jones, M. E.; Antonenko, P. D.; Greenwood, C. M.

2012-01-01

This study investigated the impact of collaborative and individualized student response system-based instruction on learner motivation, metacognition, and concept transfer in a large-enrolment undergraduate science course. Participants in the collaborative group responded to conceptual questions, discussed their responses in small groups, and…

Cross reactive cellular immune responses in chickens previously exposed to low pathogenic avian influenza

USDA-ARS?s Scientific Manuscript database

Avian influenza (AI) infection in poultry can result in high morbidity and mortality, and negatively affect international trade. Because most AI vaccines used for poultry are inactivated, our knowledge of immunity against AI is based largely on humoral immune responses. In fact, little is known abo...

Students' Perceptions of Meaningfulness in First Year Experience Courses: A Case Study

ERIC Educational Resources Information Center

Evans, Nancy J.

2012-01-01

This qualitative case study, framed by a constructivist perspective, addresses a deficit in the literature and the knowledge base of a first year experience (FYE) academic program at a large, urban university regarding freshmen perceptions of meaningfulness in their courses. Existing studies identify concepts related to meaningfulness, but do not…

A Popperian Perspective on Science Education

ERIC Educational Resources Information Center

Blackie, Margaret A. L.

2012-01-01

Despite the rigorous debate around the purposes of higher education and the associated concept of graduate attributes, science education at the tertiary level has remained largely impervious to engaging with these ideas. This may be due to the emphasis on the knowledge base of the hard science as opposed to the emphasis on the knower in the…

Team-Based Learning Exercise Efficiently Teaches Brief Intervention Skills to Medicine Residents

ERIC Educational Resources Information Center

Wamsley, Maria A.; Julian, Katherine A.; O'Sullivan, Patricia; McCance-Katz, Elinore F.; Batki, Steven L.; Satre, Derek D.; Satterfield, Jason

2013-01-01

Background: Evaluations of substance use screening and brief intervention (SBI) curricula typically focus on learner attitudes and knowledge, although effects on clinical skills are of greater interest and utility. Moreover, these curricula often require large amounts of training time and teaching resources. This study examined whether a 3-hour…

A Data Management System Integrating Web-Based Training and Randomized Trials

ERIC Educational Resources Information Center

Muroff, Jordana; Amodeo, Maryann; Larson, Mary Jo; Carey, Margaret; Loftin, Ralph D.

2011-01-01

This article describes a data management system (DMS) developed to support a large-scale randomized study of an innovative web-course that was designed to improve substance abuse counselors' knowledge and skills in applying a substance abuse treatment method (i.e., cognitive behavioral therapy; CBT). The randomized trial compared the performance…

Cross reactive immunity derived from chickens infected with H9N2 low pathogenic avian influenza against homologous and heterosubtypic challenge

USDA-ARS?s Scientific Manuscript database

Because vaccines for use in commercial poultry against avian influenza (AI) are mainly inactivated and delivered parenterally, our knowledge of protective immunity of poultry against AI is largely based on the induction of serum-neutralizing antibodies produced against a specific hemagglutinin (HA) ...

Recursive renormalization group theory based subgrid modeling

NASA Technical Reports Server (NTRS)

Zhou, YE

1991-01-01

Advancing the knowledge and understanding of turbulence theory is addressed. Specific problems to be addressed will include studies of subgrid models to understand the effects of unresolved small scale dynamics on the large scale motion which, if successful, might substantially reduce the number of degrees of freedom that need to be computed in turbulence simulation.

Planned Organizational Change; A Study in Change Dynamics.

ERIC Educational Resources Information Center

Jones, Garth N.

This study attempts to develop a broad model or concept, based largely on empirical evidence, which applies social science knowledge and methodology to the planning of change in corporations, armies, schools, hospitals, government, community groups, and other formal and informal organizations. Chapters 2,3, and 4 define and discuss the roles of…

An Exploration of Learning, the Knowledge-Based Economy, and Owner-Managers of Small Bookselling Businesses.

ERIC Educational Resources Information Center

Paige, Helen

2002-01-01

A qualitative study of six owner/managers of small Australian bookselling businesses elicited these themes: participation in learning is largely informal or incidental; interaction with information/communication technologies is less than optimal; and small business management relies on personal and business networking. Ways to develop a more…

Identifying Predictors of Achievement in the Newly Defined Information Literacy: A Neural Network Analysis

ERIC Educational Resources Information Center

Sexton, Randall; Hignite, Michael; Margavio, Thomas M.; Margavio, Geanie W.

2009-01-01

Information Literacy is a concept that evolved as a result of efforts to move technology-based instructional and research efforts beyond the concepts previously associated with "computer literacy." While computer literacy was largely a topic devoted to knowledge of hardware and software, information literacy is concerned with students' abilities…

Reframing Teacher Education for Learning Equity

ERIC Educational Resources Information Center

Fischetti, John

2018-01-01

The current models of teacher education in the Western world are still largely based upon the building of students' knowledge and skills using approaches similar to those designed for the assembly lines of the past. The prevailing model of schooling is still centered around the notion that schools are places young people go to watch their teacher…

The Changing American Child: The Perspective of Educators.

ERIC Educational Resources Information Center

Zimiles, Herbert

A study was based on retrospective descriptions obtained from interviews with a large number of teachers who have taught for over 20 years. Three areas of change in students were consistently noted in the descriptions: children today know more, are freer, and grow up more rapidly. More autonomous, and armed with greater knowledge, children emerge…

GPCR ontology: development and application of a G protein-coupled receptor pharmacology knowledge framework.

PubMed

Przydzial, Magdalena J; Bhhatarai, Barun; Koleti, Amar; Vempati, Uma; Schürer, Stephan C

2013-12-15

Novel tools need to be developed to help scientists analyze large amounts of available screening data with the goal to identify entry points for the development of novel chemical probes and drugs. As the largest class of drug targets, G protein-coupled receptors (GPCRs) remain of particular interest and are pursued by numerous academic and industrial research projects. We report the first GPCR ontology to facilitate integration and aggregation of GPCR-targeting drugs and demonstrate its application to classify and analyze a large subset of the PubChem database. The GPCR ontology, based on previously reported BioAssay Ontology, depicts available pharmacological, biochemical and physiological profiles of GPCRs and their ligands. The novelty of the GPCR ontology lies in the use of diverse experimental datasets linked by a model to formally define these concepts. Using a reasoning system, GPCR ontology offers potential for knowledge-based classification of individuals (such as small molecules) as a function of the data. The GPCR ontology is available at http://www.bioassayontology.org/bao_gpcr and the National Center for Biomedical Ontologies Web site.

Drug transport across the blood–brain barrier

PubMed Central

Pardridge, William M

2012-01-01

The blood–brain barrier (BBB) prevents the brain uptake of most pharmaceuticals. This property arises from the epithelial-like tight junctions within the brain capillary endothelium. The BBB is anatomically and functionally distinct from the blood–cerebrospinal fluid barrier at the choroid plexus. Certain small molecule drugs may cross the BBB via lipid-mediated free diffusion, providing the drug has a molecular weight <400 Da and forms <8 hydrogen bonds. These chemical properties are lacking in the majority of small molecule drugs, and all large molecule drugs. Nevertheless, drugs can be reengineered for BBB transport, based on the knowledge of the endogenous transport systems within the BBB. Small molecule drugs can be synthesized that access carrier-mediated transport (CMT) systems within the BBB. Large molecule drugs can be reengineered with molecular Trojan horse delivery systems to access receptor-mediated transport (RMT) systems within the BBB. Peptide and antisense radiopharmaceuticals are made brain-penetrating with the combined use of RMT-based delivery systems and avidin–biotin technology. Knowledge on the endogenous CMT and RMT systems expressed at the BBB enable new solutions to the problem of BBB drug transport. PMID:22929442

Co-production of knowledge: An Inuit Indigenous Knowledge perspective

NASA Astrophysics Data System (ADS)

Daniel, R.; Behe, C.

2017-12-01

A "co-production of knowledge" approach brings together different knowledge systems while building equitable and collaborative partnerships from `different ways of knowing.' Inuit Indigenous Knowledge is a systematic way of thinking applied to phenomena across biological, physical, cultural and spiritual systems; rooted with a holistic understanding of ecosystems (ICC Alaska 2016). A holistic image of Arctic environmental change is attained by bringing Indigenous Knowledge (IK) holders and scientists together through a co-production of knowledge framework. Experts from IK and science should be involved together from the inception of a project. IK should be respected as its own knowledge system and should not be translated into science. A co-production of knowledge approach is important in developing adaptation policies and practices, for sustainability and to address biodiversity conservation (Daniel et al. 2016). Co-production of knowledge is increasingly being recognized by the scientific community at-large. However, in many instances the concept is being incorrectly applied. This talk will build on the important components of co-production of knowledge from an Inuit perspective and specifically IK. In this presentation we will differentiate the co-production of knowledge from a multi-disciplinary approach or multi-evidence based decision-making. We underscore the role and value of different knowledge systems with different methodologies and the need for collaborative approaches in identifying research questions. We will also provide examples from our experiences with Indigenous communities and scientists in the Arctic. References: Inuit Circumpolar Council of Alaska. 2016. Alaskan Inuit Food Security Conceptual Framework: How to Assess the Arctic From An Inuit Perspective, 201pp. Daniel, R., C. Behe, J. Raymond-Yakoubian, E. Krummel, and S. Gearhead. Arctic Observing Summit White Paper Synthesis, Theme 6: Interfacing Indigenous Knowledge, Community-based Monitoring and Scientific Methods for Sustained Arctic Observations. http://www.arcticobservingsummit.org/sites/arcticobservingsummit.org/files/Daniel_Laing_Kielsen%20Holm_et_al-AOS2016-Theme-6-IK-CBM-Synthesis-updated-2016-04.pdf

Parameter estimation and prediction for the course of a single epidemic outbreak of a plant disease.

PubMed

Kleczkowski, A; Gilligan, C A

2007-10-22

Many epidemics of plant diseases are characterized by large variability among individual outbreaks. However, individual epidemics often follow a well-defined trajectory which is much more predictable in the short term than the ensemble (collection) of potential epidemics. In this paper, we introduce a modelling framework that allows us to deal with individual replicated outbreaks, based upon a Bayesian hierarchical analysis. Information about 'similar' replicate epidemics can be incorporated into a hierarchical model, allowing both ensemble and individual parameters to be estimated. The model is used to analyse the data from a replicated experiment involving spread of Rhizoctonia solani on radish in the presence or absence of a biocontrol agent, Trichoderma viride. The rate of primary (soil-to-plant) infection is found to be the most variable factor determining the final size of epidemics. Breakdown of biological control in some replicates results in high levels of primary infection and increased variability. The model can be used to predict new outbreaks of disease based upon knowledge from a 'library' of previous epidemics and partial information about the current outbreak. We show that forecasting improves significantly with knowledge about the history of a particular epidemic, whereas the precision of hindcasting to identify the past course of the epidemic is largely independent of detailed knowledge of the epidemic trajectory. The results have important consequences for parameter estimation, inference and prediction for emerging epidemic outbreaks.

KnowLife: a versatile approach for constructing a large knowledge graph for biomedical sciences.

PubMed

Ernst, Patrick; Siu, Amy; Weikum, Gerhard

2015-05-14

Biomedical knowledge bases (KB's) have become important assets in life sciences. Prior work on KB construction has three major limitations. First, most biomedical KBs are manually built and curated, and cannot keep up with the rate at which new findings are published. Second, for automatic information extraction (IE), the text genre of choice has been scientific publications, neglecting sources like health portals and online communities. Third, most prior work on IE has focused on the molecular level or chemogenomics only, like protein-protein interactions or gene-drug relationships, or solely address highly specific topics such as drug effects. We address these three limitations by a versatile and scalable approach to automatic KB construction. Using a small number of seed facts for distant supervision of pattern-based extraction, we harvest a huge number of facts in an automated manner without requiring any explicit training. We extend previous techniques for pattern-based IE with confidence statistics, and we combine this recall-oriented stage with logical reasoning for consistency constraint checking to achieve high precision. To our knowledge, this is the first method that uses consistency checking for biomedical relations. Our approach can be easily extended to incorporate additional relations and constraints. We ran extensive experiments not only for scientific publications, but also for encyclopedic health portals and online communities, creating different KB's based on different configurations. We assess the size and quality of each KB, in terms of number of facts and precision. The best configured KB, KnowLife, contains more than 500,000 facts at a precision of 93% for 13 relations covering genes, organs, diseases, symptoms, treatments, as well as environmental and lifestyle risk factors. KnowLife is a large knowledge base for health and life sciences, automatically constructed from different Web sources. As a unique feature, KnowLife is harvested from different text genres such as scientific publications, health portals, and online communities. Thus, it has the potential to serve as one-stop portal for a wide range of relations and use cases. To showcase the breadth and usefulness, we make the KnowLife KB accessible through the health portal (http://knowlife.mpi-inf.mpg.de).

Plumbing the brain drain.

PubMed Central

Saravia, Nancy Gore; Miranda, Juan Francisco

2004-01-01

Opportunity is the driving force of migration. Unsatisfied demands for higher education and skills, which have been created by the knowledge-based global economy, have generated unprecedented opportunities in knowledge-intensive service industries. These multi-trillion dollar industries include information, communication, finance, business, education and health. The leading industrialized nations are also the focal points of knowledge-intensive service industries and as such constitute centres of research and development activity that proactively draw in talented individuals worldwide through selective immigration policies, employment opportunities and targeted recruitment. Higher education is another major conduit of talent from less-developed countries to the centres of the knowledge-based global economy. Together career and educational opportunities drive "brain drain and recirculation". The departure of a large proportion of the most competent and innovative individuals from developing nations slows the achievement of the critical mass needed to generate the enabling context in which knowledge creation occurs. To favourably modify the asymmetric movement and distribution of global talent, developing countries must implement bold and creative strategies that are backed by national policies to: provide world-class educational opportunities, construct knowledge-based research and development industries, and sustainably finance the required investment for these strategies. Brazil, China and India have moved in this direction, offering world-class education in areas crucial to national development, such as biotechnology and information technology, paralleled by investments in research and development. As a result, only a small proportion of the most highly educated individuals migrate from these countries, and research and development opportunities employ national talent and even attract immigrants. PMID:15375451

Interprofessional education increases knowledge, promotes team building, and changes practice in the care of Parkinson's disease.

PubMed

Cohen, Elaine V; Hagestuen, Ruth; González-Ramos, Gladys; Cohen, Hillel W; Bassich, Celia; Book, Elaine; Bradley, Kathy P; Carter, Julie H; Di Minno, Mariann; Gardner, Joan; Giroux, Monique; González, Manny J; Holten, Sandra; Joseph, Ricky; Kornegay, Denise D; Simpson, Patricia A; Tomaino, Concetta M; Vandendolder, Richard P; Walde-Douglas, Maria; Wichmann, Rosemary; Morgan, John C

2016-01-01

Examine outcomes for the National Parkinson Foundation (NPF) Allied Team Training for Parkinson (ATTP), an interprofessional education (IPE) program in Parkinson's disease (PD) and team-based care for medicine, nursing, occupational, physical and music therapies, physician assistant, social work and speech-language pathology disciplines. Healthcare professionals need education in evidence-based PD practices and working effectively in teams. Few evidence-based models of IPE in PD exist. Knowledge about PD, team-based care, the role of other disciplines and attitudes towards healthcare teams were measured before and after a protocol-driven training program. Knowledge, attitudes and practice changes were again measured at 6-month post-training. Trainee results were compared to results of controls. Twenty-six NPF-ATTP trainings were held across the U.S. (2003-2013). Compared to control participants (n = 100), trainees (n = 1468) showed statistically significant posttest improvement in all major outcomes, including self-perceived (p < 0.001) and objective knowledge (p < 0.001), Understanding Role of Other Disciplines (p < 0.001), Attitudes Toward Health Care Teams Scale (p < 0.001), and the Attitudes Toward Value of Teams (p < 0.001) subscale. Despite some decline, significant improvements were largely sustained at six-month post-training. Qualitative analyses confirmed post-training practice changes. The NPF-ATTP model IPE program showed sustained positive gains in knowledge of PD, team strategies and role of other disciplines, team attitudes, and important practice improvements. Further research should examine longer-term outcomes, objectively measure practice changes and mediators, and determine impact on patient outcomes. Copyright © 2015 Elsevier Ltd. All rights reserved.

A History of U.S. Navy Airborne and Shipboard Periscope Detection Radar Design and Development

DTIC Science & Technology

2014-01-01

military applications were originally large ground-based units designed, developed, and employed by the British for detecting inbound German aircraft...evaluation (RDT&E) and the operational employment of PDR sensors has involved a rich and proud history of military endeavor. This history is embodied in...retire from the military and civilian workforce, their knowledge base, their memory, and the lessons learned become lost to subsequent generations

Biomedical discovery acceleration, with applications to craniofacial development.

PubMed

Leach, Sonia M; Tipney, Hannah; Feng, Weiguo; Baumgartner, William A; Kasliwal, Priyanka; Schuyler, Ronald P; Williams, Trevor; Spritz, Richard A; Hunter, Lawrence

2009-03-01

The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work.

Community pharmacists' knowledge of and attitudes toward oral chemotherapy.

PubMed

O'Bryant, Cindy L; Crandell, Brian C

2008-01-01

To assess community pharmacists' attitude toward and knowledge of oral chemotherapy (OC) in terms of drug indications, general dosing principles, drug interactions, adverse effects, and special handling precautions. Descriptive, nonexperimental, cross-sectional survey. Colorado, Kansas, and the southeastern United States in May and June 2005. 1,080 pharmacists in four divisions of a large community pharmacy chain. Web-based survey. Pharmacist knowledge of and attitude toward OC. 243 surveys were returned (response rate 22.5%). Overall, pharmacists answered 49.7% of knowledge questions correctly. Pharmacists were most knowledgeable about general dosing principles (69%) and least knowledgeable about adverse effects (45%) and special handling (25%) of OC. Higher scores were seen for pharmacists who dispensed a greater number of OC prescriptions. Percentages of correct responses did not vary based on years of experience or number of OC continuing pharmacy education (CPE) programs attended. On a Likert-type scale of 1 (low) to 5 (high), the average comfort in dispensing OC was 2.4. On average, pharmacists indicated that knowing about OC was important to their practice (3.7) and expressed interest in participating in additional CPE programs on OC (4.2). Of respondents, 94.7% indicated that their pharmacy did not have a counting tray devoted to cytotoxic drugs. This survey identified several areas in which pharmacists' knowledge of OC could be enhanced. Handling of OC is an area of important need, given the low number of pharmacists reporting separate counting trays for cytotoxic drugs.

Challenges and promises of integrating knowledge engineering and qualitative methods

NASA Astrophysics Data System (ADS)

Lundberg, C. Gustav; Holm, Gunilla

Our goal is to expose some of the close ties that exist between knowledge engineering (KE) and qualitative methodology (QM). Many key concepts of qualitative research, for example meaning, commonsense, understanding, and everyday life, overlap with central research concerns in artificial intelligence. These shared interests constitute a largely unexplored avenue for interdisciplinary cooperation. We compare and take some steps toward integrating two historically diverse methodologies by exploring the commonalities of KE and QM both from a substantive and a methodological/technical perspective. In the second part of this essay, we address knowledge acquisition problems and procedures. Knowledge acquisition within KE has been based primarily on cognitive psychology/science foundations, whereas knowledge acquisition within QM has a broader foundation in phenomenology, symbolic interactionism, and ethnomethodology. Our discussion and examples are interdisciplinary in nature. We do not suggest that there is a clash between the KE and QM frameworks, but rather that the lack of communication potentially may limit each framework's future development.

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.

Development of a Quality Improvement Curriculum in Physician Assistant Studies.

PubMed

Kindratt, Tiffany B; Orcutt, Venetia L

2017-06-01

The purpose of this project was to develop and evaluate a curriculum for physician assistant (PA) students addressing knowledge, skills, and attitudes (KSA) toward quality improvement (QI). Students (N = 77) completed a pretest rating their KSA. A curriculum was developed to improve KSA among didactic and clinical students. Two department-wide QI projects were developed for student participation. Students completed a posttest after completing curriculum components and changes in KSA had been measured. Postcurriculum implementation, QI knowledge, and skills increased significantly in most areas. Large improvements were seen in knowledge of Plan, Do, Study, Act models and life cycles of QI projects (p < .0001). Seven students (20%) participated in department-wide projects. Our curriculum model (1) was effective at improving students' QI knowledge and skills; (2) allowed students to participate in community-based QI projects; and (3) can be used by other PA programs looking to enhance their QI curriculum.

The DAB model of drawing processes

NASA Technical Reports Server (NTRS)

Hochhaus, Larry W.

1989-01-01

The problem of automatic drawing was investigated in two ways. First, a DAB model of drawing processes was introduced. DAB stands for three types of knowledge hypothesized to support drawing abilities, namely, Drawing Knowledge, Assimilated Knowledge, and Base Knowledge. Speculation concerning the content and character of each of these subsystems of the drawing process is introduced and the overall adequacy of the model is evaluated. Second, eight experts were each asked to understand six engineering drawings and to think aloud while doing so. It is anticipated that a concurrent protocol analysis of these interviews can be carried out in the future. Meanwhile, a general description of the videotape database is provided. In conclusion, the DAB model was praised as a worthwhile first step toward solution of a difficult problem, but was considered by and large inadequate to the challenge of automatic drawing. Suggestions for improvements on the model were made.

Meta-tools for software development and knowledge acquisition

NASA Technical Reports Server (NTRS)

Eriksson, Henrik; Musen, Mark A.

1992-01-01

The effectiveness of tools that provide support for software development is highly dependent on the match between the tools and their task. Knowledge-acquisition (KA) tools constitute a class of development tools targeted at knowledge-based systems. Generally, KA tools that are custom-tailored for particular application domains are more effective than are general KA tools that cover a large class of domains. The high cost of custom-tailoring KA tools manually has encouraged researchers to develop meta-tools for KA tools. Current research issues in meta-tools for knowledge acquisition are the specification styles, or meta-views, for target KA tools used, and the relationships between the specification entered in the meta-tool and other specifications for the target program under development. We examine different types of meta-views and meta-tools. Our current project is to provide meta-tools that produce KA tools from multiple specification sources--for instance, from a task analysis of the target application.

Joint Interactions in Large Online Knowledge Communities: The A[subscript 3]C Framework

ERIC Educational Resources Information Center

Jeong, Heisawn; Cress, Ulrike; Moskaliuk, Johannes; Kimmerle, Joachim

2017-01-01

Social interaction is crucial for understanding individual and collective processes in knowledge communities. We describe how technology has changed the way people interact in large communities. Building on this description, we propose a framework that distinguishes four types of joint interactions in online knowledge communities: Attendance,…

A concept ideation framework for medical device design.

PubMed

Hagedorn, Thomas J; Grosse, Ian R; Krishnamurty, Sundar

2015-06-01

Medical device design is a challenging process, often requiring collaboration between medical and engineering domain experts. This collaboration can be best institutionalized through systematic knowledge transfer between the two domains coupled with effective knowledge management throughout the design innovation process. Toward this goal, we present the development of a semantic framework for medical device design that unifies a large medical ontology with detailed engineering functional models along with the repository of design innovation information contained in the US Patent Database. As part of our development, existing medical, engineering, and patent document ontologies were modified and interlinked to create a comprehensive medical device innovation and design tool with appropriate properties and semantic relations to facilitate knowledge capture, enrich existing knowledge, and enable effective knowledge reuse for different scenarios. The result is a Concept Ideation Framework for Medical Device Design (CIFMeDD). Key features of the resulting framework include function-based searching and automated inter-domain reasoning to uniquely enable identification of functionally similar procedures, tools, and inventions from multiple domains based on simple semantic searches. The significance and usefulness of the resulting framework for aiding in conceptual design and innovation in the medical realm are explored via two case studies examining medical device design problems. Copyright © 2015 Elsevier Inc. All rights reserved.

Advanced techniques for the storage and use of very large, heterogeneous spatial databases. The representation of geographic knowledge: Toward a universal framework. [relations (mathematics)

NASA Technical Reports Server (NTRS)

Peuquet, Donna J.

1987-01-01

A new approach to building geographic data models that is based on the fundamental characteristics of the data is presented. An overall theoretical framework for representing geographic data is proposed. An example of utilizing this framework in a Geographic Information System (GIS) context by combining artificial intelligence techniques with recent developments in spatial data processing techniques is given. Elements of data representation discussed include hierarchical structure, separation of locational and conceptual views, and the ability to store knowledge at variable levels of completeness and precision.

Food Consumption and its impact on Cardiovascular Disease: Importance of Solutions focused on the globalized food system

PubMed Central

Anand, Sonia S.; Hawkes, Corinna; de Souza, Russell J.; Mente, Andrew; Dehghan, Mahshid; Nugent, Rachel; Zulyniak, Michael A.; Weis, Tony; Bernstein, Adam M.; Krauss, Ronald; Kromhout, Daan; Jenkins, David J.A.; Malik, Vasanti; Martinez-Gonzalez, Miguel A.; Mozafarrian, Dariush; Yusuf, Salim; Willett, Walter C.; Popkin, Barry M

2015-01-01

Major scholars in the field, based on a 3-day consensus, created an in-depth review of current knowledge on the role of diet in CVD, the changing global food system and global dietary patterns, and potential policy solutions. Evidence from different countries, age/race/ethnicity/socioeconomic groups suggest the health effects studies of foods, macronutrients, and dietary patterns on CVD appear to be far more consistent though regional knowledge gaps are highlighted. There are large gaps in knowledge about the association of macronutrients to CVD in low- and middle-income countries (LMIC), particularly linked with dietary patterns are reviewed. Our understanding of foods and macronutrients in relationship to CVD is broadly clear; however major gaps exist both in dietary pattern research and ways to change diets and food systems. Based on the current evidence, the traditional Mediterranean-type diet, including plant foods/emphasizing plant protein sources, provides a well-tested healthy dietary pattern to reduce CVD. PMID:26429085

The neuron classification problem

PubMed Central

Bota, Mihail; Swanson, Larry W.

2007-01-01

A systematic account of neuron cell types is a basic prerequisite for determining the vertebrate nervous system global wiring diagram. With comprehensive lineage and phylogenetic information unavailable, a general ontology based on structure-function taxonomy is proposed and implemented in a knowledge management system, and a prototype analysis of select regions (including retina, cerebellum, and hypothalamus) presented. The supporting Brain Architecture Knowledge Management System (BAMS) Neuron ontology is online and its user interface allows queries about terms and their definitions, classification criteria based on the original literature and “Petilla Convention” guidelines, hierarchies, and relations—with annotations documenting each ontology entry. Combined with three BAMS modules for neural regions, connections between regions and neuron types, and molecules, the Neuron ontology provides a general framework for physical descriptions and computational modeling of neural systems. The knowledge management system interacts with other web resources, is accessible in both XML and RDF/OWL, is extendible to the whole body, and awaits large-scale data population requiring community participation for timely implementation. PMID:17582506

Identifying novel drug indications through automated reasoning.

PubMed

Tari, Luis; Vo, Nguyen; Liang, Shanshan; Patel, Jagruti; Baral, Chitta; Cai, James

2012-01-01

With the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using in silico approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process. In this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications. To evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7%) are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8%) are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative) based on their molecular targets and interactions alone and has the potential to discover novel drug indications for existing drugs.

A Fast Projection-Based Algorithm for Clustering Big Data.

PubMed

Wu, Yun; He, Zhiquan; Lin, Hao; Zheng, Yufei; Zhang, Jingfen; Xu, Dong

2018-06-07

With the fast development of various techniques, more and more data have been accumulated with the unique properties of large size (tall) and high dimension (wide). The era of big data is coming. How to understand and discover new knowledge from these data has attracted more and more scholars' attention and has become the most important task in data mining. As one of the most important techniques in data mining, clustering analysis, a kind of unsupervised learning, could group a set data into objectives(clusters) that are meaningful, useful, or both. Thus, the technique has played very important role in knowledge discovery in big data. However, when facing the large-sized and high-dimensional data, most of the current clustering methods exhibited poor computational efficiency and high requirement of computational source, which will prevent us from clarifying the intrinsic properties and discovering the new knowledge behind the data. Based on this consideration, we developed a powerful clustering method, called MUFOLD-CL. The principle of the method is to project the data points to the centroid, and then to measure the similarity between any two points by calculating their projections on the centroid. The proposed method could achieve linear time complexity with respect to the sample size. Comparison with K-Means method on very large data showed that our method could produce better accuracy and require less computational time, demonstrating that the MUFOLD-CL can serve as a valuable tool, at least may play a complementary role to other existing methods, for big data clustering. Further comparisons with state-of-the-art clustering methods on smaller datasets showed that our method was fastest and achieved comparable accuracy. For the convenience of most scholars, a free soft package was constructed.

Factors shaping the evolution of electronic documentation systems

NASA Technical Reports Server (NTRS)

Dede, Christopher J.; Sullivan, Tim R.; Scace, Jacque R.

1990-01-01

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments.

Ontology-based topic clustering for online discussion data

NASA Astrophysics Data System (ADS)

Wang, Yongheng; Cao, Kening; Zhang, Xiaoming

2013-03-01

With the rapid development of online communities, mining and extracting quality knowledge from online discussions becomes very important for the industrial and marketing sector, as well as for e-commerce applications and government. Most of the existing techniques model a discussion as a social network of users represented by a user-based graph without considering the content of the discussion. In this paper we propose a new multilayered mode to analysis online discussions. The user-based and message-based representation is combined in this model. A novel frequent concept sets based clustering method is used to cluster the original online discussion network into topic space. Domain ontology is used to improve the clustering accuracy. Parallel methods are also used to make the algorithms scalable to very large data sets. Our experimental study shows that the model and algorithms are effective when analyzing large scale online discussion data.

Barriers to and Facilitators of Compliance with Clinic-Based Cervical Cancer Screening: Population-Based Cohort Study of Women Aged 23-60 Years

PubMed Central

Östensson, Ellinor; Alder, Susanna; Elfström, K. Miriam; Sundström, Karin; Zethraeus, Niklas; Arbyn, Marc; Andersson, Sonia

2015-01-01

Objective This study aims to identify possible barriers to and facilitators of cervical cancer screening by (a) estimating time and travel costs and other direct non-medical costs incurred in attending clinic-based cervical cancer screening, (b) investigating screening compliance and reasons for noncompliance, (c) determining women’s knowledge of human papillomavirus (HPV), its relationship to cervical cancer, and HPV and cervical cancer prevention, and (d) investigating correlates of HPV knowledge and screening compliance. Materials and Methods 1510 women attending the clinic-based cervical cancer screening program in Stockholm, Sweden were included. Data on sociodemographic characteristics, time and travel costs and other direct non-medical costs incurred in attending (e.g., indirect cost of time needed for the screening visit, transportation costs, child care costs, etc.), mode(s) of travel, time, distance, companion’s attendance, HPV knowledge, and screening compliance were obtained via self-administered questionnaire. Results Few respondents had low socioeconomic status. Mean total time and travel costs and direct non-medical cost per attendance, including companion (if any) were €55.6. Over half (53%) of the respondents took time off work to attend screening (mean time 147 minutes). A large portion (44%) of the respondents were noncompliant (i.e., did not attend screening within 1 year of the initial invitation), 51% of whom stated difficulties in taking time off work. 64% of all respondents knew that HPV vaccination was available; only 34% knew it was important to continue to attend screening following vaccination. Age, education, and income were the most important correlates of HPV knowledge and compliance; and additional factors associated with compliance were time off work, accompanying companion and HPV knowledge. Conclusion Time and travel costs and other direct non-medical costs for clinic-based screening can be considerable, may affect the cost-effectiveness of a screening program, and may constitute barriers to screening while HPV knowledge may facilitate compliance with screening. PMID:26011051

Challenging the collegiate rite of passage: a campus-wide social marketing media campaign to reduce binge drinking.

PubMed

Glider, P; Midyett, S J; Mills-Novoa, B; Johannessen, K; Collins, C

2001-01-01

A social marketing media campaign, based on a normative social influence model and focused on normative messages regarding binge drinking, on a large, southwestern university campus has yielded positive preliminary results of an overall 29.2 percent decrease in binge drinking rates over a three-year period. The Core Alcohol and Drug Survey and the Health Enhancement Survey provided information on student knowledge, perceptions, and behaviors regarding alcohol and binge drinking. This study represents the first in-depth research on the impact of a media approach, based on a normative social influence model, to reduce binge drinking on a large university campus and has yielded promising initial results.

Radiation risk and human space exploration.

PubMed

Schimmerling, W; Cucinotta, F A; Wilson, J W

2003-01-01

Radiation protection is essential to enable humans to live and work safely in space. Predictions about the nature and magnitude of the risks posed by space radiation are subject to very large uncertainties. Prudent use of worst-case scenarios may impose unacceptable constraints on shielding mass for spacecraft or habitats, tours of duty of crews on Space Station, and on the radius and duration of sorties on planetary surfaces. The NASA Space Radiation Health Program has been devised to develop the knowledge required to accurately predict and to efficiently manage radiation risk. The knowledge will be acquired by means of a peer-reviewed, largely ground-based and investigator-initiated, basic science research program. The NASA Strategic Plan to accomplish these objectives in a manner consistent with the high priority assigned to the protection and health maintenance of crews will be presented. Published by Elsevier Science Ltd on behalf of COSPAR.

Radiation risk and human space exploration

NASA Technical Reports Server (NTRS)

Schimmerling, W.; Cucinotta, F. A.; Wilson, J. W.

2003-01-01

Radiation protection is essential to enable humans to live and work safely in space. Predictions about the nature and magnitude of the risks posed by space radiation are subject to very large uncertainties. Prudent use of worst-case scenarios may impose unacceptable constraints on shielding mass for spacecraft or habitats, tours of duty of crews on Space Station, and on the radius and duration of sorties on planetary surfaces. The NASA Space Radiation Health Program has been devised to develop the knowledge required to accurately predict and to efficiently manage radiation risk. The knowledge will be acquired by means of a peer-reviewed, largely ground-based and investigator-initiated, basic science research program. The NASA Strategic Plan to accomplish these objectives in a manner consistent with the high priority assigned to the protection and health maintenance of crews will be presented. Published by Elsevier Science Ltd on behalf of COSPAR.

Systems survivor: a program for house staff in systems-based practice.

PubMed

Turley, Christine B; Roach, Richard; Marx, Marilyn

2007-01-01

The Systems-Based Practice competency expanded the scope of graduate medical education. Innovative approaches are needed to teach this material. We have designed and implemented a rotation in Systems-Based Practice focused on the interrelationships of patient care, clinical revenue, and the physician's role within health care systems. Experiential learning occurs during a 5-day rotation through 26 areas encompassing the clinical revenue cycle, guided by "expert" staff. Using a reversal of the TV show Survivor, house staff begin conceptually "alone" and discover they are members of a large, dedicated team. Assessment results, including a system knowledge test and course evaluations, are presented. Twenty-five residents from four clinical departments participated in Year 1. An increase in pretest to posttest knowledge scores of 14.8% (p

A CLIPS based personal computer hardware diagnostic system

NASA Technical Reports Server (NTRS)

Whitson, George M.

1991-01-01

Often the person designated to repair personal computers has little or no knowledge of how to repair a computer. Described here is a simple expert system to aid these inexperienced repair people. The first component of the system leads the repair person through a number of simple system checks such as making sure that all cables are tight and that the dip switches are set correctly. The second component of the system assists the repair person in evaluating error codes generated by the computer. The final component of the system applies a large knowledge base to attempt to identify the component of the personal computer that is malfunctioning. We have implemented and tested our design with a full system to diagnose problems for an IBM compatible system based on the 8088 chip. In our tests, the inexperienced repair people found the system very useful in diagnosing hardware problems.

Impact of a Mental Health Curriculum on Knowledge and Stigma Among High School Students: A Randomized Controlled Trial.

PubMed

Milin, Robert; Kutcher, Stanley; Lewis, Stephen P; Walker, Selena; Wei, Yifeng; Ferrill, Natasha; Armstrong, Michael A

2016-05-01

This study evaluated the effectiveness of a school-based mental health literacy intervention for adolescents on knowledge and stigma. A total of 24 high schools and 534 students in the regional area of Ottawa, Ontario, Canada participated in this randomized controlled trial. Schools were randomly assigned to either the curriculum or control condition. The curriculum was integrated into the province's grade 11 and 12 "Healthy Living" courses and was delivered by teachers. Changes in mental health knowledge and stigma were measured using pre- and posttest questionnaires. Descriptive analyses were conducted to provide sample characteristics, and multilevel modeling was used to examine study outcomes. For the curriculum condition, there was a significant change in stigma scores over time (p = .001), with positive attitudes toward mental illness increasing from pre to post. There was also a significant change in knowledge scores over time (p < .001), with knowledge scores increasing from pre to post. No significant changes in knowledge or stigma were found for participants in the control condition. A meaningful relationship was found whereby increases in knowledge significantly predicted increases in positive attitudes toward mental health (p < .001). This is the first large randomized controlled trial to demonstrate the effectiveness in mental health literacy of an integrated, manualized mental health educational resource for high school students on knowledge and stigma. Findings also support the applicability by teachers and suggest the potential for broad-based implementation of the educational curriculum in high schools. Replication and further studies are warranted. Clinical trial registration information-Impact of a Mental Health Curriculum for High School Students on Knowledge and Stigma; http://clinicaltrials.gov/; NCT02561780. Copyright © 2016 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

An Expert System toward Buiding An Earth Science Knowledge Graph

NASA Astrophysics Data System (ADS)

Zhang, J.; Duan, X.; Ramachandran, R.; Lee, T. J.; Bao, Q.; Gatlin, P. N.; Maskey, M.

2017-12-01

In this ongoing work, we aim to build foundations of Cognitive Computing for Earth Science research. The goal of our project is to develop an end-to-end automated methodology for incrementally constructing Knowledge Graphs for Earth Science (KG4ES). These knowledge graphs can then serve as the foundational components for building cognitive systems in Earth science, enabling researchers to uncover new patterns and hypotheses that are virtually impossible to identify today. In addition, this research focuses on developing mining algorithms needed to exploit these constructed knowledge graphs. As such, these graphs will free knowledge from publications that are generated in a very linear, deterministic manner, and structure knowledge in a way that users can both interact and connect with relevant pieces of information. Our major contributions are two-fold. First, we have developed an end-to-end methodology for constructing Knowledge Graphs for Earth Science (KG4ES) using existing corpus of journal papers and reports. One of the key challenges in any machine learning, especially deep learning applications, is the need for robust and large training datasets. We have developed techniques capable of automatically retraining models and incrementally building and updating KG4ES, based on ever evolving training data. We also adopt the evaluation instrument based on common research methodologies used in Earth science research, especially in Atmospheric Science. Second, we have developed an algorithm to infer new knowledge that can exploit the constructed KG4ES. In more detail, we have developed a network prediction algorithm aiming to explore and predict possible new connections in the KG4ES and aid in new knowledge discovery.

Architectural Large Constructed Environment. Modeling and Interaction Using Dynamic Simulations

NASA Astrophysics Data System (ADS)

Fiamma, P.

2011-09-01

How to use for the architectural design, the simulation coming from a large size data model? The topic is related to the phase coming usually after the acquisition of the data, during the construction of the model and especially after, when designers must have an interaction with the simulation, in order to develop and verify their idea. In the case of study, the concept of interaction includes the concept of real time "flows". The work develops contents and results that can be part of the large debate about the current connection between "architecture" and "movement". The focus of the work, is to realize a collaborative and participative virtual environment on which different specialist actors, client and final users can share knowledge, targets and constraints to better gain the aimed result. The goal is to have used a dynamic micro simulation digital resource that allows all the actors to explore the model in powerful and realistic way and to have a new type of interaction in a complex architectural scenario. On the one hand, the work represents a base of knowledge that can be implemented more and more; on the other hand the work represents a dealt to understand the large constructed architecture simulation as a way of life, a way of being in time and space. The architectural design before, and the architectural fact after, both happen in a sort of "Spatial Analysis System". The way is open to offer to this "system", knowledge and theories, that can support architectural design work for every application and scale. We think that the presented work represents a dealt to understand the large constructed architecture simulation as a way of life, a way of being in time and space. Architecture like a spatial configuration, that can be reconfigurable too through designing.

Pooling knowledge and improving safety for contracted works at a large industrial park.

PubMed

Agnello, Patrizia; Ansaldi, Silvia; Bragatto, Paolo

2015-01-01

At a large chemical park maintenance is contracted by the major companies operating the plants to many small firms. The cultural and psychological isolation of contractor workers was recognized a root cause of severe accidents in the recent years. That problem is common in chemical industry. The knowledge sharing has been assumed a good key to involve contractors and sub contractors in safety culture and contributing to injuries prevention. The selection of personal protective equipment PPE for the maintenance works has been taken as benchmark to demonstrate the adequateness of the proposed approach. To support plant operators, contractors and subcontractors in PPE discussion, a method has been developed. Its core is a knowledge-base, organized in an Ontology, as suitable for inferring decisions. By means of this tool all stakeholders have merged experience and information and find out the right PPE, to be provided, with adequate training and information package. PPE selection requires sound competencies about process and environmental hazards, including major accident, preventive and protective measures, maintenance activities. These pieces of knowledge previously fragmented among plant operators and contractors, have to be pooled, and used to find out the adequate PPE for a number of maintenance works. The PPE selection is per se important, but it is also a good chance to break the contractors' isolation and involve them in safety objectives. Thus by pooling experience and practical knowledge, the common understanding of safety issues has been strengthened.

Association of Systemic Anaplastic Large Cell Lymphoma and Active Toxoplasmosis in a Child.

PubMed

Sayyahfar, Shirin; Karimi, Abdollah; Gharib, Atoosa; Fahimzad, Alireza

2015-08-01

Anaplastic large cell lymphoma is a subset of non-Hodgkin lymphoma and an unusual disease in children. Herein we have reported a 7- year- old girl with a large necrotic skin ulcer on the chest caused by systemic form of anaplastic large-cell lymphoma and simultaneous active toxoplasmosis diagnosed by PCR on lymph node specimen. There were few reports showing a role for toxoplasma infection to cause some malignancies such as lymphoma in adults. Based to our knowledge, this has been the first report of simultaneous systemic anaplastic large cell lymphoma and active toxoplasmosis, documented by positive PCR on tissue biopsy in a child. This case report has suggested more attention to the accompanying Toxoplasma gondii infection as a probable cause of some types of lymphomas.

Immersion Education and Cognitive Strategies: Can the Obstacle Be the Advantage in a Multilingual Society?

ERIC Educational Resources Information Center

Maillat, Didier; Serra, Cecilia

2009-01-01

This paper focusses on the teaching of non-linguistic subject matters in a second or third language through bilingual education. We investigate how this specific educational framework influences the development of linguistic competence as well as disciplinary knowledge. Based on a large-scale corpus of classroom interactions collected in bilingual…

Black Females in High School: A Statistical Educational Profile

ERIC Educational Resources Information Center

Muhammad, Crystal Gafford; Dixson, Adrienne D.

2008-01-01

In life as in literature, both the mainstream public and the Black community writ large, overlook the Black female experiences, both adolescent and adult. In order to contribute to the knowledge base regarding this population, we present through our study a statistical portrait of Black females in high school. To do so, we present an analysis of…

Mental Spatial Transformations of Objects and Bodies: Different Developmental Trajectories in Children from 7 to 11 Years of Age

ERIC Educational Resources Information Center

Crescentini, Cristiano; Fabbro, Franco; Urgesi, Cosimo

2014-01-01

Despite the large body of knowledge on adults suggesting that 2 basic types of mental spatial transformation--namely, object-based and egocentric perspective transformations--are dissociable and specialized for different situations, there is much less research investigating the developmental aspects of such spatial transformation systems. Here, an…

A Large Class Engagement (LCE) Model Based on Service-Dominant Logic (SDL) and Flipped Classrooms

ERIC Educational Resources Information Center

Jarvis, Wade; Halvorson, Wade; Sadeque, Saalem; Johnston, Shannon

2014-01-01

Ensuring that university graduates are ready for their professional futures is a complex undertaking that includes, but is not limited to, the development of their professional knowledge and skills, and the provision of empowering learning experiences established through their own contributions. One way to draw these complex processes together for…

An Application of the Occupation Competence Model to Organizing Factors Associated with Return to Work.

ERIC Educational Resources Information Center

Shaw, Lynn; Polatajko, Helene

2002-01-01

A 20-year review of literature on return to work outcomes for ill or injured persons found that research is largely atheoretical and the knowledge base fragmented. The Occupational Competence Model can fill this gap by reflecting the multidimensional nature of work disability (personal, environmental, and occupational dimensions) and factors…

Unintended Consequences or Testing the Integrity of Teachers and Students.

ERIC Educational Resources Information Center

Kimmel, Ernest W.

Large-scale testing programs are generally based on the assumptions that the test-takers experience standard conditions for taking the test and that everyone will do his or her own work without having prior knowledge of specific questions. These assumptions are not necessarily true. The ways students and educators use to get around standardizing…

Moving Knowledge Around: Strategies for Fostering Equity within Educational Systems

ERIC Educational Resources Information Center

Ainscow, Mel

2012-01-01

This paper describes and analyses the work of a large scale improvement project in England in order to find more effective ways of fostering equity within education systems. The project involved an approach based on an analysis of local context, and used processes of networking and collaboration in order to make better use of available expertise.…

Education Reform as Platform for Promoting Lifelong Learning: The Case of Hong Kong

ERIC Educational Resources Information Center

Poon-McBrayer, Kim Fong

2008-01-01

The fierce competition largely brought by the globalization of a knowledge-based economy provided an impetus for the Hong Kong government to endorse massive education reforms in 2000, as proposed by the Education Commission, as the central strategy to improve and sustain workforce quality and social justice through the provision of lifelong…

RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests

ERIC Educational Resources Information Center

Khosravi, Hassan; Kitto, Kirsty; Cooper, Kendra

2017-01-01

Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests…

Mechanisms Affecting the Sustainability and Scale-up of a System-Wide Numeracy Reform

ERIC Educational Resources Information Center

Bobis, Janette

2011-01-01

With deliberate system-level reform now being acted upon around the world, both successful and unsuccessful cases provide a rich source of knowledge from which we can learn to improve large-scale reform. Research surrounding the effectiveness of a theory-based system-wide numeracy reform operating in primary schools across Australia is examined to…

What We Are Learning about Mathematics Interventions and Conducting Research on Mathematics Interventions

ERIC Educational Resources Information Center

Gersten, Russell

2016-01-01

In this commentary, the author reflects on four studies that have greatly expanded the knowledge base on effective interventions in mathematics, and he provides four rigorous experimental studies of approaches for students likely to experience difficulties learning mathematics over a large grade-level span (pre-K to 4th grade). All of the…

Mind Maps: Hot New Tools Proposed for Cyberspace Librarians.

ERIC Educational Resources Information Center

Humphreys, Nancy K.

1999-01-01

Describes how online searchers can use a software tool based on back-of-the-book indexes to assist in dealing with search engine databases compiled by spiders that crawl across the entire Internet or through large Web sites. Discusses human versus machine knowledge, conversion of indexes to mind maps or mini-thesauri, middleware, eXtensible Markup…

Music Education and the Brain: What Does It Take to Make a Change?

ERIC Educational Resources Information Center

Collins, Anita

2014-01-01

Neuroscientists have worked for over two decades to understand how the brain processes music, affects emotions, and changes brain development. Much of this research has been based on a model that compares the brain function of participants classified as musicians and nonmusicians. This body of knowledge reveals a large number of benefits from…

Revisiting Preschoolers' Living Things Concept: A Microgenetic Analysis of Conceptual Change in Basic Biology

ERIC Educational Resources Information Center

Opfer, John E.; Siegler, Robert S.

2004-01-01

Many preschoolers know that plants and animals share basic biological properties, but this knowledge does not usually lead them to conclude that plants, like animals, are living things. To resolve this seeming paradox, we hypothesized that preschoolers largely base their judgments of life status on a biological property, capacity for teleological…

Gathering and Exploring Scientific Knowledge in Pharmacovigilance

PubMed Central

Lopes, Pedro; Nunes, Tiago; Campos, David; Furlong, Laura Ines; Bauer-Mehren, Anna; Sanz, Ferran; Carrascosa, Maria Carmen; Mestres, Jordi; Kors, Jan; Singh, Bharat; van Mulligen, Erik; Van der Lei, Johan; Diallo, Gayo; Avillach, Paul; Ahlberg, Ernst; Boyer, Scott; Diaz, Carlos; Oliveira, José Luís

2013-01-01

Pharmacovigilance plays a key role in the healthcare domain through the assessment, monitoring and discovery of interactions amongst drugs and their effects in the human organism. However, technological advances in this field have been slowing down over the last decade due to miscellaneous legal, ethical and methodological constraints. Pharmaceutical companies started to realize that collaborative and integrative approaches boost current drug research and development processes. Hence, new strategies are required to connect researchers, datasets, biomedical knowledge and analysis algorithms, allowing them to fully exploit the true value behind state-of-the-art pharmacovigilance efforts. This manuscript introduces a new platform directed towards pharmacovigilance knowledge providers. This system, based on a service-oriented architecture, adopts a plugin-based approach to solve fundamental pharmacovigilance software challenges. With the wealth of collected clinical and pharmaceutical data, it is now possible to connect knowledge providers’ analysis and exploration algorithms with real data. As a result, new strategies allow a faster identification of high-risk interactions between marketed drugs and adverse events, and enable the automated uncovering of scientific evidence behind them. With this architecture, the pharmacovigilance field has a new platform to coordinate large-scale drug evaluation efforts in a unique ecosystem, publicly available at http://bioinformatics.ua.pt/euadr/. PMID:24349421

Arizona Registered Dietitians Show Gaps in Knowledge of Bean Health Benefits

PubMed Central

Thompson, Sharon V.; Dougherty, Mariah K.

2018-01-01

Registered Dietitians (RDs) promote nutrition practices and policies and can influence food consumption patterns to include nutrient dense foods such as beans. Although many evidence-based health benefits of bean consumption (e.g., cholesterol reduction, glycemic control) have been demonstrated, there is limited research on the knowledge, attitudes, and perceptions of RDs regarding the inclusion of beans in a healthy diet. To fill this existing research gap, this cross-sectional survey explored the perceptions, knowledge, and attitudes of 296 RDs in Arizona, USA, toward beans. The RDs largely held positive attitudes toward the healthfulness of beans and were aware of many health benefits. Some gaps in awareness were evident, including effect on cancer risk, intestinal health benefits, folate content, and application with celiac disease patients. RDs with greater personal bean consumption had significantly higher bean health benefit knowledge. Twenty-nine percent of the RDs did not know the meaning of ‘legume’, and over two-thirds could not define the term ‘pulse’. It is essential that RDs have up-to-date, evidence-based information regarding bean benefits to provide appropriate education to patients, clients, and the public. PMID:29316699

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis

PubMed Central

Lee, Kyubum; Kim, Byounggun; Jeon, Minji; Kim, Jihye; Tan, Aik Choon

2018-01-01

Background With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. Objective This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. Methods We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. Results The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. Conclusions In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge. PMID:29305341

Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

PubMed Central

Ikeda, Mitsuru

2017-01-01

Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and intractable processing of unstructured clinical texts. Even though most previous works have been conducted on these issues by applying semisupervised learning for the former and a word-based approach for the latter, they face with complexity in an acquisition of initial labeled data and ignorance of structured sequence of natural language. In this study, we propose automatic data labeling by distant supervision where knowledge bases are exploited to assign an entity-level relation label for each drug-event pair in texts, and then, we use patterns for characterizing ADR relation. The multiple-instance learning with expectation-maximization method is employed to estimate model parameters. The method applies transductive learning to iteratively reassign a probability of unknown drug-event pair at the training time. By investigating experiments with 50,998 discharge summaries, we evaluate our method by varying large number of parameters, that is, pattern types, pattern-weighting models, and initial and iterative weightings of relations for unlabeled data. Based on evaluations, our proposed method outperforms the word-based feature for NB-EM (iEM), MILR, and TSVM with F1 score of 11.3%, 9.3%, and 6.5% improvement, respectively. PMID:29090077

Back to basics: naked-eye astronomical observation

NASA Astrophysics Data System (ADS)

Barclay, Charles

2003-09-01

For pupils of both sexes and all ages from about six upwards, the subject of Astronomy holds many fascinations - the rapid changes in knowledge, the large resource of available IT packages and above all the beautiful pictures from Hubble and the large Earth-based telescopes. This article, however, stresses the excitement and importance of naked-eye (unaided) first-hand observation, where light pollution allows, and suggests some techniques that may be used to enthuse and introduce youngsters to the glory of the night sky without recourse to computer screens.

Web Video Event Recognition by Semantic Analysis From Ubiquitous Documents.

PubMed

Yu, Litao; Yang, Yang; Huang, Zi; Wang, Peng; Song, Jingkuan; Shen, Heng Tao

2016-12-01

In recent years, the task of event recognition from videos has attracted increasing interest in multimedia area. While most of the existing research was mainly focused on exploring visual cues to handle relatively small-granular events, it is difficult to directly analyze video content without any prior knowledge. Therefore, synthesizing both the visual and semantic analysis is a natural way for video event understanding. In this paper, we study the problem of Web video event recognition, where Web videos often describe large-granular events and carry limited textual information. Key challenges include how to accurately represent event semantics from incomplete textual information and how to effectively explore the correlation between visual and textual cues for video event understanding. We propose a novel framework to perform complex event recognition from Web videos. In order to compensate the insufficient expressive power of visual cues, we construct an event knowledge base by deeply mining semantic information from ubiquitous Web documents. This event knowledge base is capable of describing each event with comprehensive semantics. By utilizing this base, the textual cues for a video can be significantly enriched. Furthermore, we introduce a two-view adaptive regression model, which explores the intrinsic correlation between the visual and textual cues of the videos to learn reliable classifiers. Extensive experiments on two real-world video data sets show the effectiveness of our proposed framework and prove that the event knowledge base indeed helps improve the performance of Web video event recognition.

Evidence for Knowledge of the Syntax of Large Numbers in Preschoolers

ERIC Educational Resources Information Center

Barrouillet, Pierre; Thevenot, Catherine; Fayol, Michel

2010-01-01

The aim of this study was to provide evidence for knowledge of the syntax governing the verbal form of large numbers in preschoolers long before they are able to count up to these numbers. We reasoned that if such knowledge exists, it should facilitate the maintenance in short-term memory of lists of lexical primitives that constitute a number…

Towards organizing health knowledge on community-based health services.

PubMed

Akbari, Mohammad; Hu, Xia; Nie, Liqiang; Chua, Tat-Seng

2016-12-01

Online community-based health services accumulate a huge amount of unstructured health question answering (QA) records at a continuously increasing pace. The ability to organize these health QA records has been found to be effective for data access. The existing approaches for organizing information are often not applicable to health domain due to its domain nature as characterized by complex relation among entities, large vocabulary gap, and heterogeneity of users. To tackle these challenges, we propose a top-down organization scheme, which can automatically assign the unstructured health-related records into a hierarchy with prior domain knowledge. Besides automatic hierarchy prototype generation, it also enables each data instance to be associated with multiple leaf nodes and profiles each node with terminologies. Based on this scheme, we design a hierarchy-based health information retrieval system. Experiments on a real-world dataset demonstrate the effectiveness of our scheme in organizing health QA into a topic hierarchy and retrieving health QA records from the topic hierarchy.

Planning bioinformatics workflows using an expert system.

PubMed

Chen, Xiaoling; Chang, Jeffrey T

2017-04-15

Bioinformatic analyses are becoming formidably more complex due to the increasing number of steps required to process the data, as well as the proliferation of methods that can be used in each step. To alleviate this difficulty, pipelines are commonly employed. However, pipelines are typically implemented to automate a specific analysis, and thus are difficult to use for exploratory analyses requiring systematic changes to the software or parameters used. To automate the development of pipelines, we have investigated expert systems. We created the Bioinformatics ExperT SYstem (BETSY) that includes a knowledge base where the capabilities of bioinformatics software is explicitly and formally encoded. BETSY is a backwards-chaining rule-based expert system comprised of a data model that can capture the richness of biological data, and an inference engine that reasons on the knowledge base to produce workflows. Currently, the knowledge base is populated with rules to analyze microarray and next generation sequencing data. We evaluated BETSY and found that it could generate workflows that reproduce and go beyond previously published bioinformatics results. Finally, a meta-investigation of the workflows generated from the knowledge base produced a quantitative measure of the technical burden imposed by each step of bioinformatics analyses, revealing the large number of steps devoted to the pre-processing of data. In sum, an expert system approach can facilitate exploratory bioinformatic analysis by automating the development of workflows, a task that requires significant domain expertise. https://github.com/jefftc/changlab. jeffrey.t.chang@uth.tmc.edu. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Planning bioinformatics workflows using an expert system

PubMed Central

Chen, Xiaoling; Chang, Jeffrey T.

2017-01-01

Abstract Motivation: Bioinformatic analyses are becoming formidably more complex due to the increasing number of steps required to process the data, as well as the proliferation of methods that can be used in each step. To alleviate this difficulty, pipelines are commonly employed. However, pipelines are typically implemented to automate a specific analysis, and thus are difficult to use for exploratory analyses requiring systematic changes to the software or parameters used. Results: To automate the development of pipelines, we have investigated expert systems. We created the Bioinformatics ExperT SYstem (BETSY) that includes a knowledge base where the capabilities of bioinformatics software is explicitly and formally encoded. BETSY is a backwards-chaining rule-based expert system comprised of a data model that can capture the richness of biological data, and an inference engine that reasons on the knowledge base to produce workflows. Currently, the knowledge base is populated with rules to analyze microarray and next generation sequencing data. We evaluated BETSY and found that it could generate workflows that reproduce and go beyond previously published bioinformatics results. Finally, a meta-investigation of the workflows generated from the knowledge base produced a quantitative measure of the technical burden imposed by each step of bioinformatics analyses, revealing the large number of steps devoted to the pre-processing of data. In sum, an expert system approach can facilitate exploratory bioinformatic analysis by automating the development of workflows, a task that requires significant domain expertise. Availability and Implementation: https://github.com/jefftc/changlab Contact: jeffrey.t.chang@uth.tmc.edu PMID:28052928

Semi-Supervised Geographical Feature Detection

NASA Astrophysics Data System (ADS)

Yu, H.; Yu, L.; Kuo, K. S.

2016-12-01

Extraction and tracking geographical features is a fundamental requirement in many geoscience fields. However, this operation has become an increasingly challenging task for domain scientists when tackling a large amount of geoscience data. Although domain scientists may have a relatively clear definition of features, it is difficult to capture the presence of features in an accurate and efficient fashion. We propose a semi-supervised approach to address large geographical feature detection. Our approach has two main components. First, we represent a heterogeneous geoscience data in a unified high-dimensional space, which can facilitate us to evaluate the similarity of data points with respect to geolocation, time, and variable values. We characterize the data from these measures, and use a set of hash functions to parameterize the initial knowledge of the data. Second, for any user query, our approach can automatically extract the initial results based on the hash functions. To improve the accuracy of querying, our approach provides a visualization interface to display the querying results and allow users to interactively explore and refine them. The user feedback will be used to enhance our knowledge base in an iterative manner. In our implementation, we use high-performance computing techniques to accelerate the construction of hash functions. Our design facilitates a parallelization scheme for feature detection and extraction, which is a traditionally challenging problem for large-scale data. We evaluate our approach and demonstrate the effectiveness using both synthetic and real world datasets.

The assessment of the readiness of five countries to implement child maltreatment prevention programs on a large scale.

PubMed

Mikton, Christopher; Power, Mick; Raleva, Marija; Makoae, Mokhantso; Al Eissa, Majid; Cheah, Irene; Cardia, Nancy; Choo, Claire; Almuneef, Maha

2013-12-01

This study aimed to systematically assess the readiness of five countries - Brazil, the Former Yugoslav Republic of Macedonia, Malaysia, Saudi Arabia, and South Africa - to implement evidence-based child maltreatment prevention programs on a large scale. To this end, it applied a recently developed method called Readiness Assessment for the Prevention of Child Maltreatment based on two parallel 100-item instruments. The first measures the knowledge, attitudes, and beliefs concerning child maltreatment prevention of key informants; the second, completed by child maltreatment prevention experts using all available data in the country, produces a more objective assessment readiness. The instruments cover all of the main aspects of readiness including, for instance, availability of scientific data on the problem, legislation and policies, will to address the problem, and material resources. Key informant scores ranged from 31.2 (Brazil) to 45.8/100 (the Former Yugoslav Republic of Macedonia) and expert scores, from 35.2 (Brazil) to 56/100 (Malaysia). Major gaps identified in almost all countries included a lack of professionals with the skills, knowledge, and expertise to implement evidence-based child maltreatment programs and of institutions to train them; inadequate funding, infrastructure, and equipment; extreme rarity of outcome evaluations of prevention programs; and lack of national prevalence surveys of child maltreatment. In sum, the five countries are in a low to moderate state of readiness to implement evidence-based child maltreatment prevention programs on a large scale. Such an assessment of readiness - the first of its kind - allows gaps to be identified and then addressed to increase the likelihood of program success. Copyright © 2013 Elsevier Ltd. All rights reserved.

Extending TOPS: Ontology-driven Anomaly Detection and Analysis System

NASA Astrophysics Data System (ADS)

Votava, P.; Nemani, R. R.; Michaelis, A.

2010-12-01

Terrestrial Observation and Prediction System (TOPS) is a flexible modeling software system that integrates ecosystem models with frequent satellite and surface weather observations to produce ecosystem nowcasts (assessments of current conditions) and forecasts useful in natural resources management, public health and disaster management. We have been extending the Terrestrial Observation and Prediction System (TOPS) to include a capability for automated anomaly detection and analysis of both on-line (streaming) and off-line data. In order to best capture the knowledge about data hierarchies, Earth science models and implied dependencies between anomalies and occurrences of observable events such as urbanization, deforestation, or fires, we have developed an ontology to serve as a knowledge base. We can query the knowledge base and answer questions about dataset compatibilities, similarities and dependencies so that we can, for example, automatically analyze similar datasets in order to verify a given anomaly occurrence in multiple data sources. We are further extending the system to go beyond anomaly detection towards reasoning about possible causes of anomalies that are also encoded in the knowledge base as either learned or implied knowledge. This enables us to scale up the analysis by eliminating a large number of anomalies early on during the processing by either failure to verify them from other sources, or matching them directly with other observable events without having to perform an extensive and time-consuming exploration and analysis. The knowledge is captured using OWL ontology language, where connections are defined in a schema that is later extended by including specific instances of datasets and models. The information is stored using Sesame server and is accessible through both Java API and web services using SeRQL and SPARQL query languages. Inference is provided using OWLIM component integrated with Sesame.

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.

Analysis of Nursing Clinical Decision Support Requests and Strategic Plan in a Large Academic Health System

PubMed Central

Bavuso, Karen; Bouyer-Ferullo, Sharon; Goldsmith, Denise; Fairbanks, Amanda; Gesner, Emily; Lagor, Charles; Collins, Sarah

2016-01-01

Summary Objectives To understand requests for nursing Clinical Decision Support (CDS) interventions at a large integrated health system undergoing vendor-based EHR implementation. In addition, to establish a process to guide both short-term implementation and long-term strategic goals to meet nursing CDS needs. Materials and Methods We conducted an environmental scan to understand current state of nursing CDS over three months. The environmental scan consisted of a literature review and an analysis of CDS requests received from across our health system. We identified existing high priority CDS and paper-based tools used in nursing practice at our health system that guide decision-making. Results A total of 46 nursing CDS requests were received. Fifty-six percent (n=26) were specific to a clinical specialty; 22 percent (n=10) were focused on facilitating clinical consults in the inpatient setting. “Risk Assessments/Risk Reduction/Promotion of Healthy Habits” (n=23) was the most requested High Priority Category received for nursing CDS. A continuum of types of nursing CDS needs emerged using the Data-Information-Knowledge-Wisdom Conceptual Framework: 1) facilitating data capture, 2) meeting information needs, 3) guiding knowledge-based decision making, and 4) exposing analytics for wisdom-based clinical interpretation by the nurse. Conclusion Identifying and prioritizing paper-based tools that can be modified into electronic CDS is a challenge. CDS strategy is an evolving process that relies on close collaboration and engagement with clinical sites for short-term implementation and should be incorporated into a long-term strategic plan that can be optimized and achieved overtime. The Data-Information-Knowledge-Wisdom Conceptual Framework in conjunction with the High Priority Categories established may be a useful tool to guide a strategic approach for meeting short-term nursing CDS needs and aligning with the organizational strategic plan. PMID:27437036

WE-F-BRB-02: Setting the Stage for Incorporation of Toxicity Measures in Treatment Plan Assessments

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

Mayo, C.

2015-06-15

Advancements in informatics in radiotherapy are opening up opportunities to improve our ability to assess treatment plans. Models on individualizing patient dose constraints from prior patient data and shape relationships have been extensively researched and are now making their way into commercial products. New developments in knowledge based treatment planning involve understanding the impact of the radiation dosimetry on the patient. Akin to radiobiology models that have driven intensity modulated radiotherapy optimization, toxicity and outcome predictions based on treatment plans and prior patient experiences may be the next step in knowledge based planning. In order to realize these predictions, itmore » is necessary to understand how the clinical information can be captured, structured and organized with ontologies and databases designed for recall. Large databases containing radiation dosimetry and outcomes present the opportunity to evaluate treatment plans against predictions of toxicity and disease response. Such evaluations can be based on dose volume histogram or even the full 3-dimensional dose distribution and its relation to the critical anatomy. This session will provide an understanding of ontologies and standard terminologies used to capture clinical knowledge into structured databases; How data can be organized and accessed to utilize the knowledge in planning; and examples of research and clinical efforts to incorporate that clinical knowledge into planning for improved care for our patients. Learning Objectives: Understand the role of standard terminologies, ontologies and data organization in oncology Understand methods to capture clinical toxicity and outcomes in a clinical setting Understand opportunities to learn from clinical data and its application to treatment planning Todd McNutt receives funding from Philips, Elekta and Toshiba for some of the work presented.« less

WE-F-BRB-03: Inclusion of Data-Driven Risk Predictions in Radiation Treatment Planning in the Context of a Local Level Learning Health System

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

McNutt, T.

Advancements in informatics in radiotherapy are opening up opportunities to improve our ability to assess treatment plans. Models on individualizing patient dose constraints from prior patient data and shape relationships have been extensively researched and are now making their way into commercial products. New developments in knowledge based treatment planning involve understanding the impact of the radiation dosimetry on the patient. Akin to radiobiology models that have driven intensity modulated radiotherapy optimization, toxicity and outcome predictions based on treatment plans and prior patient experiences may be the next step in knowledge based planning. In order to realize these predictions, itmore » is necessary to understand how the clinical information can be captured, structured and organized with ontologies and databases designed for recall. Large databases containing radiation dosimetry and outcomes present the opportunity to evaluate treatment plans against predictions of toxicity and disease response. Such evaluations can be based on dose volume histogram or even the full 3-dimensional dose distribution and its relation to the critical anatomy. This session will provide an understanding of ontologies and standard terminologies used to capture clinical knowledge into structured databases; How data can be organized and accessed to utilize the knowledge in planning; and examples of research and clinical efforts to incorporate that clinical knowledge into planning for improved care for our patients. Learning Objectives: Understand the role of standard terminologies, ontologies and data organization in oncology Understand methods to capture clinical toxicity and outcomes in a clinical setting Understand opportunities to learn from clinical data and its application to treatment planning Todd McNutt receives funding from Philips, Elekta and Toshiba for some of the work presented.« less

GalenOWL: Ontology-based drug recommendations discovery

PubMed Central

2012-01-01

Background Identification of drug-drug and drug-diseases interactions can pose a difficult problem to cope with, as the increasingly large number of available drugs coupled with the ongoing research activities in the pharmaceutical domain, make the task of discovering relevant information difficult. Although international standards, such as the ICD-10 classification and the UNII registration, have been developed in order to enable efficient knowledge sharing, medical staff needs to be constantly updated in order to effectively discover drug interactions before prescription. The use of Semantic Web technologies has been proposed in earlier works, in order to tackle this problem. Results This work presents a semantic-enabled online service, named GalenOWL, capable of offering real time drug-drug and drug-diseases interaction discovery. For enabling this kind of service, medical information and terminology had to be translated to ontological terms and be appropriately coupled with medical knowledge of the field. International standards such as the aforementioned ICD-10 and UNII, provide the backbone of the common representation of medical data, while the medical knowledge of drug interactions is represented by a rule base which makes use of the aforementioned standards. Details of the system architecture are presented while also giving an outline of the difficulties that had to be overcome. A comparison of the developed ontology-based system with a similar system developed using a traditional business logic rule engine is performed, giving insights on the advantages and drawbacks of both implementations. Conclusions The use of Semantic Web technologies has been found to be a good match for developing drug recommendation systems. Ontologies can effectively encapsulate medical knowledge and rule-based reasoning can capture and encode the drug interactions knowledge. PMID:23256945

Promising strategies for advancement in knowledge of suicide risk factors and prevention.

PubMed

Sareen, Jitender; Isaak, Corinne; Katz, Laurence Y; Bolton, James; Enns, Murray W; Stein, Murray B

2014-09-01

Suicide is an important public health problem. Although there have been advances in our knowledge of suicide, gaps remain in knowledge about suicide risk factors and prevention. Here, we discuss research pathways that have the potential to rapidly advance knowledge in suicide risk assessment and reduction of suicide deaths over the next decade. We provide a concise overview of the methodologic approaches that have the capacity to rapidly increase knowledge and change practice, which have been successful in past work in psychiatry and other areas of medicine. We suggest three specific pathways to advance knowledge of suicide risk factors and prevention. First, analysis of large-scale epidemiologic surveys and administrative data sets can advance the understanding of suicide. Second, given the low base rate of suicide, there is a need for networks/consortia of investigators in the field of suicide prevention. Such consortia have the capacity to analyze existing epidemiologic data sets, create multi-site cohort studies of high-risk groups to increase knowledge of biological and other risk factors, and create a platform for multi-site clinical trials. Third, partnerships with policymakers and researchers would facilitate careful scientific evaluation of policies and programs aimed at reducing suicide. Suicide intervention policies are often multifaceted, expensive, and rarely evaluated. Using quasi-experimental methods or sophisticated analytic strategies such as propensity score-matching techniques, the impact of large-scale interventions on suicide can be evaluated. Furthermore, such partnerships between policymakers and researchers can lead to the design and support of prospective RCTs (e.g., cluster randomized trials, stepped wedge designs, waiting list designs) in high-risk groups (e.g., people with a history of suicide attempts, multi-axial comorbidity, and offspring of people who have died by suicide). These research pathways could lead to rapid knowledge uptake between communities and have the strong potential to reduce suicide. Copyright © 2014 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Ebola hemorrhagic fever under scope, view of knowledge, attitude and practice from rural Sudan in 2015.

PubMed

Mohamed, Mohamed M G; Shwaib, Hussam M; Fahim, Monica M; Ahmed, Elhamy A; Omer, Mawadda K; Monier, Islam A; Balla, Siham A

Ebola hemorrhagic fever (EHF) is an emerging threat to public health. The last epidemic in West Africa had a great effect on the affected communities. Timely and effective interventions were necessary in addition to community participation to control the epidemic. The knowledge, attitude and practices of vulnerable communities remain unknown, particularly in Sudan. The aim of this study was to explore the knowledge, attitude and practices of rural residents in Sudan regarding Ebola hemorrhagic fever. We conducted a cross sectional, community-based large-scale study in Al Gaziera state in rural Sudan in eight localities. In total, 1500 random adult participants were selected. The participants were assessed by a predesigned pretested questionnaire regarding their knowledge, attitude and practices regarding Ebola. Their sources of information were determined, and we assessed demographic factors as predictors of knowledge. We found poor knowledge, a fair attitude and suboptimal practices among the participants. The main sources of information were the press and media. Education was the only predictor of knowledge regarding Ebola. A lack of knowledge and suboptimal preventive practices mandates orientation and education programs to raise public awareness. Health care providers are advised to engage more in educating the community. Copyright © 2016 King Saud Bin Abdulaziz University for Health Sciences. Published by Elsevier Ltd. All rights reserved.

Using interactive family science shows to improve public knowledge on antibiotic resistance: does it work?

PubMed

Lecky, Donna M; Hawking, Meredith K D; Verlander, Neville Q; McNulty, Cliodna A M

2014-01-01

The public plays an important role in controlling the emergence and spread of antibiotic resistance. A large British survey showed that there is still public misunderstanding about microbes and antibiotics. e-Bug, a European DG Sanco sponsored project, aims to disseminate a school antibiotic and hygiene educational pack and website across Europe. Interactive science shows based on the e-Bug educational packs were developed to take the key health and hygiene messages from the e-Bug school resources to families. The science show was evaluated to assess public knowledge and understanding of antibiotics and antibiotic resistance pre and post intervention. An interactive stall comprised of a 3×2 m backing stand with background information, an interactive activity and discussions with a trained demonstrator was on display at a family holiday resort. Pre-piloted knowledge questionnaires were completed by parents and children pre and post intervention. Adult (≥19 years) baseline knowledge regarding antibiotics and antibiotic resistance was high although significant knowledge improvement was observed where baseline knowledge was low. Children's (5-11 years) knowledge around antibiotics and antibiotic resistance was significantly improved for all questions. The science show can be viewed as a success in improving parents' and children's knowledge of antibiotic use thereby highlighting the importance of educating the public through interaction.

Fuzzy logic techniques for rendezvous and docking of two geostationary satellites

NASA Technical Reports Server (NTRS)

Ortega, Guillermo

1995-01-01

Large assemblings in space require the ability to manage rendezvous and docking operations. In future these techniques will be required for the gradual build up of big telecommunication platforms in the geostationary orbit. The paper discusses the use of fuzzy logic to model and implement a control system for the docking/berthing of two satellites in geostationary orbit. The system mounted in a chaser vehicle determines the actual state of both satellites and generates torques to execute maneuvers to establish the structural latching. The paper describes the proximity operations to collocate the two satellites in the same orbital window, the fuzzy guidance and navigation of the chaser approaching the target and the final Fuzzy berthing. The fuzzy logic system represents a knowledge based controller that realizes the close loop operations autonomously replacing the conventional control algorithms. The goal is to produce smooth control actions in the proximity of the target and during the docking to avoid disturbance torques in the final assembly orbit. The knowledge of the fuzzy controller consists of a data base of rules and the definitions of the fuzzy sets. The knowledge of an experienced spacecraft controller is captured into a set of rules forming the Rules Data Base.

Theme II Joint Work Plan -2017 Collaboration and Knowledge Sharing on Large-scale Demonstration Projects

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

Zhang, Xiaoliang; Stauffer, Philip H.

This effort is designed to expedite learnings from existing and planned large demonstration projects and their associated research through effective knowledge sharing among participants in the US and China.

Semantics-informed geological maps: Conceptual modeling and knowledge encoding

NASA Astrophysics Data System (ADS)

Lombardo, Vincenzo; Piana, Fabrizio; Mimmo, Dario

2018-07-01

This paper introduces a novel, semantics-informed geologic mapping process, whose application domain is the production of a synthetic geologic map of a large administrative region. A number of approaches concerning the expression of geologic knowledge through UML schemata and ontologies have been around for more than a decade. These approaches have yielded resources that concern specific domains, such as, e.g., lithology. We develop a conceptual model that aims at building a digital encoding of several domains of geologic knowledge, in order to support the interoperability of the sources. We apply the devised terminological base to the classification of the elements of a geologic map of the Italian Western Alps and northern Apennines (Piemonte region). The digitally encoded knowledge base is a merged set of ontologies, called OntoGeonous. The encoding process identifies the objects of the semantic encoding, the geologic units, gathers the relevant information about such objects from authoritative resources, such as GeoSciML (giving priority to the application schemata reported in the INSPIRE Encoding Cookbook), and expresses the statements by means of axioms encoded in the Web Ontology Language (OWL). To support interoperability, OntoGeonous interlinks the general concepts by referring to the upper part level of ontology SWEET (developed by NASA), and imports knowledge that is already encoded in ontological format (e.g., ontology Simple Lithology). Machine-readable knowledge allows for consistency checking and for classification of the geological map data through algorithms of automatic reasoning.

Presenting an Approach for Conducting Knowledge Architecture within Large-Scale Organizations

PubMed Central

Varaee, Touraj; Habibi, Jafar; Mohaghar, Ali

2015-01-01

Knowledge architecture (KA) establishes the basic groundwork for the successful implementation of a short-term or long-term knowledge management (KM) program. An example of KA is the design of a prototype before a new vehicle is manufactured. Due to a transformation to large-scale organizations, the traditional architecture of organizations is undergoing fundamental changes. This paper explores the main strengths and weaknesses in the field of KA within large-scale organizations and provides a suitable methodology and supervising framework to overcome specific limitations. This objective was achieved by applying and updating the concepts from the Zachman information architectural framework and the information architectural methodology of enterprise architecture planning (EAP). The proposed solution may be beneficial for architects in knowledge-related areas to successfully accomplish KM within large-scale organizations. The research method is descriptive; its validity is confirmed by performing a case study and polling the opinions of KA experts. PMID:25993414

Presenting an Approach for Conducting Knowledge Architecture within Large-Scale Organizations.

PubMed

Varaee, Touraj; Habibi, Jafar; Mohaghar, Ali

2015-01-01

Knowledge architecture (KA) establishes the basic groundwork for the successful implementation of a short-term or long-term knowledge management (KM) program. An example of KA is the design of a prototype before a new vehicle is manufactured. Due to a transformation to large-scale organizations, the traditional architecture of organizations is undergoing fundamental changes. This paper explores the main strengths and weaknesses in the field of KA within large-scale organizations and provides a suitable methodology and supervising framework to overcome specific limitations. This objective was achieved by applying and updating the concepts from the Zachman information architectural framework and the information architectural methodology of enterprise architecture planning (EAP). The proposed solution may be beneficial for architects in knowledge-related areas to successfully accomplish KM within large-scale organizations. The research method is descriptive; its validity is confirmed by performing a case study and polling the opinions of KA experts.

Factors influencing the development of evidence-based practice among nurses: a self-report survey

PubMed Central

2012-01-01

Background Health authorities in several countries have decided that the health care services should be evidence-based. Recent research indicates that evidence-based practice may be more successfully implemented if the interventions overcome identified barriers. Aims The present study aimed to examine factors influencing the implementation of evidence-based practice among nurses in a large Norwegian university hospital. Methods Cross-sectional data was collected from 407 nurses during the period November 8 to December 3, 2010, using the Norwegian version of Developing Evidence-based Practice questionnaire (DEBP). The DEBP included data on various sources of information used for support in practice, on potential barriers for evidence-based practice, and on self-reported skills on managing research-based evidence. The DEBP was translated into Norwegian in accordance with standardized guidelines for translation and cultural adaptation. Results Nurses largely used experienced-based knowledge collected from their own observations, colleagues and other collaborators for support in practice. Evidence from research was seldom used. The greatest barriers were lack of time and lack of skills to find and manage research evidence. The nurse’s age, the number of years of nursing practice, and the number of years since obtaining the last health professional degree influenced the use of sources of knowledge and self-reported barriers. Self-reported skills in finding, reviewing and using different sources of evidence were positively associated with the use of research evidence and inversely related to barriers in use of research evidence. Conclusion Skills in evidence-based practice seem to reduce barriers to using research evidence and to increase use of research evidence in clinical practice. PMID:23092366

Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records

PubMed Central

Sun, Jimeng; Hu, Jianying; Luo, Dijun; Markatou, Marianthi; Wang, Fei; Edabollahi, Shahram; Steinhubl, Steven E.; Daar, Zahra; Stewart, Walter F.

2012-01-01

Background: The ability to identify the risk factors related to an adverse condition, e.g., heart failures (HF) diagnosis, is very important for improving care quality and reducing cost. Existing approaches for risk factor identification are either knowledge driven (from guidelines or literatures) or data driven (from observational data). No existing method provides a model to effectively combine expert knowledge with data driven insight for risk factor identification. Methods: We present a systematic approach to enhance known knowledge-based risk factors with additional potential risk factors derived from data. The core of our approach is a sparse regression model with regularization terms that correspond to both knowledge and data driven risk factors. Results: The approach is validated using a large dataset containing 4,644 heart failure cases and 45,981 controls. The outpatient electronic health records (EHRs) for these patients include diagnosis, medication, lab results from 2003–2010. We demonstrate that the proposed method can identify complementary risk factors that are not in the existing known factors and can better predict the onset of HF. We quantitatively compare different sets of risk factors in the context of predicting onset of HF using the performance metric, the Area Under the ROC Curve (AUC). The combined risk factors between knowledge and data significantly outperform knowledge-based risk factors alone. Furthermore, those additional risk factors are confirmed to be clinically meaningful by a cardiologist. Conclusion: We present a systematic framework for combining knowledge and data driven insights for risk factor identification. We demonstrate the power of this framework in the context of predicting onset of HF, where our approach can successfully identify intuitive and predictive risk factors beyond a set of known HF risk factors. PMID:23304365

The place of knowledge and evidence in the context of Australian general practice nursing.

PubMed

Mills, Jane; Field, John; Cant, Robyn

2009-01-01

The purpose of the study was to ascertain the place of knowledge and evidence in the context of Australian general practice nursing. General practice nursing is a rapidly developing area of specialized nursing in Australia. The provision of primary care services in Australia rests largely with medical general practitioners who employ nurses in a small business model. A statistical research design was used that included a validated instrument: the developing evidence-based practice questionnaire (Gerrish et al. 2007). A total of 1,800 Victorian practice nurses were surveyed with a return of 590 completed questionnaires, equaling a response rate of 33%. Lack of time to access knowledge for practice was a barrier for participants in this study. In-service education and training opportunities were ranked as the number one source of knowledge for general practice nurses. Experiential learning and interactions with clients, peers, medical practitioners, and specialist nurses were also considered very important sources of knowledge. Research journals were ranked much lower than experiential learning and personal interactions. Participants assessed their own skills at sourcing and translating evidence into practice knowledge as low. Younger general practice nurses were more likely than older nurses to assess themselves as competent at using the library and Internet to locate evidence. The predominantly oral culture of nursing needs to be identified and incorporated into methods for disseminating evidence from research findings in order to increase the knowledge base of Australian general practice nurses. Findings from this study will be significant for policy makers and funders of Australian nursing in general practice. The establishment of a career structure for general practice nurses that includes salaried positions for clinical nurse specialists would assist in the translation of evidence into knowledge for utilization at the point of care.

Abstract number and arithmetic in preschool children.

PubMed

Barth, Hilary; La Mont, Kristen; Lipton, Jennifer; Spelke, Elizabeth S

2005-09-27

Educated humans use language to express abstract number, applying the same number words to seven apples, whistles, or sins. Is language or education the source of numerical abstraction? Claims to the contrary must present evidence for numerical knowledge that applies to disparate entities, in people who have received no formal mathematics instruction and cannot express such knowledge in words. Here we show that preschool children can compare and add large sets of elements without counting, both within a single visual-spatial modality (arrays of dots) and across two modalities and formats (dot arrays and tone sequences). In two experiments, children viewed animations and either compared one visible array of dots to a second array or added two successive dot arrays and compared the sum to a third array. In further experiments, a dot array was replaced by a sequence of sounds, so that participants had to integrate quantity information presented aurally and visually. Children performed all tasks successfully, without resorting to guessing strategies or responding to continuous variables. Their accuracy varied with the ratio of the two quantities: a signature of large, approximate number representations in adult humans and animals. Addition was as accurate as comparison, even though children showed no relevant knowledge when presented with symbolic versions of the addition tasks. Abstract knowledge of number and addition therefore precedes, and may guide, language-based instruction in mathematics.

Effectiveness of an education intervention to strengthen nurses' readiness for evidence-based practice: A single-blind randomized controlled study.

PubMed

Saunders, Hannele; Vehviläinen-Julkunen, Katri; Stevens, Kathleen R

2016-08-01

Nurses' lack of readiness for evidence-based practice slows down the uptake, adoption, and implementation of evidence-based practice which is of international concern as it impedes attainment of the highest quality of care and best patient outcomes. There is limited evidence about the most effective approaches to strengthen nurses' readiness for evidence-based practice. To evaluate the effectiveness of an Advanced Practice Nurse-delivered education program to strengthen nurses' readiness for evidence-based practice at a university hospital. A single-blind randomized controlled trial with repeated measures design, with measures completed during spring and fall 2015, before the education program (T0), within 1week after (T1), 8weeks after (T2), and 4months after completion of education interventions (T3). One large university hospital system in Finland, consisting of 15 acute care hospitals. The required sample size, calculated by a priori power analysis and including a 20% estimated attrition rate, called for 85 nurse participants to be recruited. Nurses working in different professional nursing roles and care settings were randomly allocated into two groups: intervention (evidence-based practice education, N=43) and control (research utilization education, N=34). The nurse participants received live 4-h education sessions on the basic principles of evidence-based practice (intervention group) and on the principles of research utilization (control group). The intervention group also received a web-based interactive evidence-based practice education module with a booster mentoring intervention. Readiness for evidence-based practice data, previous experience with evidence-based practice, and participant demographics were collected using the Stevens' EBP Readiness Inventory. Nurses' confidence in employing evidence-based practice and actual evidence-based practice knowledge were lower at T0, compared with the post-education scores, specifically at T1. The improvement in the confidence or actual evidence-based practice knowledge levels did not differ between the intervention and control groups. Confidence in employing evidence-based practice was directly correlated with level of education and inversely correlated with age. Actual evidence-based practice knowledge was lowest among nurses who had no previous knowledge or experience of evidence-based practice. Both the evidence-based practice and research utilization education interventions improved nurses' confidence in employing evidence-based practice and actual evidence-based practice knowledge, strengthening their evidence-based practice readiness at least in the short-term. Most of the variation in the confidence in employing evidence-based practice and actual evidence-based practice knowledge levels was due to background factors, such as primary role and education level, which emphasize differences in educational needs between nurses with diverse backgrounds. Copyright © 2016 Elsevier Inc. All rights reserved.

A Hybrid Neuro-Fuzzy Model For Integrating Large Earth-Science Datasets

NASA Astrophysics Data System (ADS)

Porwal, A.; Carranza, J.; Hale, M.

2004-12-01

A GIS-based hybrid neuro-fuzzy approach to integration of large earth-science datasets for mineral prospectivity mapping is described. It implements a Takagi-Sugeno type fuzzy inference system in the framework of a four-layered feed-forward adaptive neural network. Each unique combination of the datasets is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent datasets. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location) is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a prospectivity map. The procedure is demonstrated by an application to regional-scale base metal prospectivity mapping in a study area located in the Aravalli metallogenic province (western India). A comparison of the hybrid neuro-fuzzy approach with pure knowledge-driven fuzzy and pure data-driven neural network approaches indicates that the former offers a superior method for integrating large earth-science datasets for predictive spatial mathematical modelling.

How can psychological theory help to promote condom use in sub-Saharan African developing countries?

PubMed

Campbell, T

1997-06-01

Condom use for HIV prevention has been very inconsistent in most sub-Saharan African countries. Studies from around the continent report that knowledge about HIV transmission is variable and seems to be related to gender, socioeconomic and educational status. There is a large body of psychological knowledge about HIV prevention which has been applied to condom promotion campaigns in developed countries. These approaches to condom promotion, based on formal theory, have not been used on a wide scale in African countries and this paper explores ways in which psychological theory might be appropriately applied in a situation of high HIV prevalence.

Reverse engineering highlights potential principles of large gene regulatory network design and learning.

PubMed

Carré, Clément; Mas, André; Krouk, Gabriel

2017-01-01

Inferring transcriptional gene regulatory networks from transcriptomic datasets is a key challenge of systems biology, with potential impacts ranging from medicine to agronomy. There are several techniques used presently to experimentally assay transcription factors to target relationships, defining important information about real gene regulatory networks connections. These techniques include classical ChIP-seq, yeast one-hybrid, or more recently, DAP-seq or target technologies. These techniques are usually used to validate algorithm predictions. Here, we developed a reverse engineering approach based on mathematical and computer simulation to evaluate the impact that this prior knowledge on gene regulatory networks may have on training machine learning algorithms. First, we developed a gene regulatory networks-simulating engine called FRANK (Fast Randomizing Algorithm for Network Knowledge) that is able to simulate large gene regulatory networks (containing 10 4 genes) with characteristics of gene regulatory networks observed in vivo. FRANK also generates stable or oscillatory gene expression directly produced by the simulated gene regulatory networks. The development of FRANK leads to important general conclusions concerning the design of large and stable gene regulatory networks harboring scale free properties (built ex nihilo). In combination with supervised (accepting prior knowledge) support vector machine algorithm we (i) address biologically oriented questions concerning our capacity to accurately reconstruct gene regulatory networks and in particular we demonstrate that prior-knowledge structure is crucial for accurate learning, and (ii) draw conclusions to inform experimental design to performed learning able to solve gene regulatory networks in the future. By demonstrating that our predictions concerning the influence of the prior-knowledge structure on support vector machine learning capacity holds true on real data ( Escherichia coli K14 network reconstruction using network and transcriptomic data), we show that the formalism used to build FRANK can to some extent be a reasonable model for gene regulatory networks in real cells.

The Impact of Electronic Knowledge-Based Nursing Content and Decision-Support on Nursing-Sensitive Patient Outcomes

DTIC Science & Technology

2014-02-01

aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information...if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE February 2014 2... Akre , et al., 2006) content and evidence-based clinical decision support (CDS) tools were embedded into the EHR of one large health care system. Since

From data to wisdom: quality improvement strategies supporting large-scale implementation of evidence-based services.

PubMed

Daleiden, Eric L; Chorpita, Bruce F

2005-04-01

The Hawaii Department of Health Child and Adolescent Mental Health Division has explored various strategies to promote widespread use of empirical evidence to improve the quality of services and outcomes for youth. This article describes a core set of clinical decisions and how several general and local evidence bases may inform those decisions. Multiple quality improvement strategies are illustrated in the context of a model that outlines four phases of evidence: data, information, knowledge, and wisdom.

AiGERM: A logic programming front end for GERM

NASA Technical Reports Server (NTRS)

Hashim, Safaa H.

1990-01-01

AiGerm (Artificially Intelligent Graphical Entity Relation Modeler) is a relational data base query and programming language front end for MCC (Mission Control Center)/STP's (Space Test Program) Germ (Graphical Entity Relational Modeling) system. It is intended as an add-on component of the Germ system to be used for navigating very large networks of information. It can also function as an expert system shell for prototyping knowledge-based systems. AiGerm provides an interface between the programming language and Germ.

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.

Expert system for computer-assisted annotation of MS/MS spectra.

PubMed

Neuhauser, Nadin; Michalski, Annette; Cox, Jürgen; Mann, Matthias

2012-11-01

An important step in mass spectrometry (MS)-based proteomics is the identification of peptides by their fragment spectra. Regardless of the identification score achieved, almost all tandem-MS (MS/MS) spectra contain remaining peaks that are not assigned by the search engine. These peaks may be explainable by human experts but the scale of modern proteomics experiments makes this impractical. In computer science, Expert Systems are a mature technology to implement a list of rules generated by interviews with practitioners. We here develop such an Expert System, making use of literature knowledge as well as a large body of high mass accuracy and pure fragmentation spectra. Interestingly, we find that even with high mass accuracy data, rule sets can quickly become too complex, leading to over-annotation. Therefore we establish a rigorous false discovery rate, calculated by random insertion of peaks from a large collection of other MS/MS spectra, and use it to develop an optimized knowledge base. This rule set correctly annotates almost all peaks of medium or high abundance. For high resolution HCD data, median intensity coverage of fragment peaks in MS/MS spectra increases from 58% by search engine annotation alone to 86%. The resulting annotation performance surpasses a human expert, especially on complex spectra such as those of larger phosphorylated peptides. Our system is also applicable to high resolution collision-induced dissociation data. It is available both as a part of MaxQuant and via a webserver that only requires an MS/MS spectrum and the corresponding peptides sequence, and which outputs publication quality, annotated MS/MS spectra (www.biochem.mpg.de/mann/tools/). It provides expert knowledge to beginners in the field of MS-based proteomics and helps advanced users to focus on unusual and possibly novel types of fragment ions.

Expert System for Computer-assisted Annotation of MS/MS Spectra*

PubMed Central

Neuhauser, Nadin; Michalski, Annette; Cox, Jürgen; Mann, Matthias

2012-01-01

An important step in mass spectrometry (MS)-based proteomics is the identification of peptides by their fragment spectra. Regardless of the identification score achieved, almost all tandem-MS (MS/MS) spectra contain remaining peaks that are not assigned by the search engine. These peaks may be explainable by human experts but the scale of modern proteomics experiments makes this impractical. In computer science, Expert Systems are a mature technology to implement a list of rules generated by interviews with practitioners. We here develop such an Expert System, making use of literature knowledge as well as a large body of high mass accuracy and pure fragmentation spectra. Interestingly, we find that even with high mass accuracy data, rule sets can quickly become too complex, leading to over-annotation. Therefore we establish a rigorous false discovery rate, calculated by random insertion of peaks from a large collection of other MS/MS spectra, and use it to develop an optimized knowledge base. This rule set correctly annotates almost all peaks of medium or high abundance. For high resolution HCD data, median intensity coverage of fragment peaks in MS/MS spectra increases from 58% by search engine annotation alone to 86%. The resulting annotation performance surpasses a human expert, especially on complex spectra such as those of larger phosphorylated peptides. Our system is also applicable to high resolution collision-induced dissociation data. It is available both as a part of MaxQuant and via a webserver that only requires an MS/MS spectrum and the corresponding peptides sequence, and which outputs publication quality, annotated MS/MS spectra (www.biochem.mpg.de/mann/tools/). It provides expert knowledge to beginners in the field of MS-based proteomics and helps advanced users to focus on unusual and possibly novel types of fragment ions. PMID:22888147

Managing Astronomy Research Data: Data Practices in the Sloan Digital Sky Survey and Large Synoptic Survey Telescope Projects

ERIC Educational Resources Information Center

Sands, Ashley Elizabeth

2017-01-01

Ground-based astronomy sky surveys are massive, decades-long investments in scientific data collection. Stakeholders expect these datasets to retain scientific value well beyond the lifetime of the sky survey. However, the necessary investments in knowledge infrastructures for managing sky survey data are not yet in place to ensure the long-term…

Using a Module-Based Laboratory to Incorporate Inquiry into a Large Cell Biology Course

ERIC Educational Resources Information Center

Howard, David R.; Miskowski, Jennifer A.

2005-01-01

Because cell biology has rapidly increased in breadth and depth, instructors are challenged not only to provide undergraduate science students with a strong, up-to-date foundation of knowledge, but also to engage them in the scientific process. To these ends, revision of the Cell Biology Lab course at the University of Wisconsin-La Crosse was…

The Economic Importance of Air Travel in High-Amenity Rural Areas

ERIC Educational Resources Information Center

Rasker, Ray; Gude, Patricia H.; Gude, Justin A.; van den Noort, Jeff

2009-01-01

The western United States offers a case study on the importance of access to large population centers and their markets, via road and air travel, for economic development. The vast distances between towns and cities in the American West can be a detriment to business, yet they also serve to attract technology and knowledge-based workers seeking to…

Applications of Artificial Intelligence to Information Search and Retrieval: The Development and Testing of an Intelligent Technical Information System.

ERIC Educational Resources Information Center

Harvey, Francis A.

This paper describes the evolution and development of an intelligent information system, i.e., a knowledge base for steel structures being undertaken as part of the Technical Information Center for Steel Structures at Lehigh University's Center of Advanced Technology for Large Structural Systems (ATLSS). The initial development of the Technical…

Marek's disease in backyard chickens, a study of pathological findings and viral loads in tumorous and non-tumorous birds

USDA-ARS?s Scientific Manuscript database

Marek’s disease (MD) is a major cause of mortality in backyard chickens. The diagnosis of MD is complex, however, and knowledge on Marek’s disease virus (MDV) in spontaneous field cases such as in backyard chickens is largely unknown. Forty backyard chickens with presumptive MD diagnosis based on hi...

DSN Network e-VLBI Calibration of Earth Orientation

NASA Technical Reports Server (NTRS)

Zhang, Liwei Dennis; Steppe, A.; Lanyi, G.; Jacobs, C.

2006-01-01

This viewgraph presentation reviews the calibration of the Earth's orientation by using the Deep Space Network (DSN) e Very Large Base Integration (VLBI). The topics include: 1) Background: TEMPO; 2) Background: UT1 Knowledge Error; 3) e-VLBI: WVSR TEMPO Overview; 4) e-VLBI: WVSR TEMPO Turnaround; 5) e-VLBI: WVSR TEMPO R&D Tests; and 6) WVSR TEMPO Test Conclusion.

Identifying Consistent Variables in a Heterogeneous Data Set: Evaluation of a Web-Based Pre-Course in Mathematics

ERIC Educational Resources Information Center

Derr, Katja

2017-01-01

E-learning has made course evaluation easier in many ways, as a multitude of learner data can be collected and related to student performance. At the same time, open learning environments can be a difficult field for evaluation, with a large variance in participants' knowledge level, learner behaviour, and commitment. In this study the…

EmbryoMiner: A new framework for interactive knowledge discovery in large-scale cell tracking data of developing embryos.

PubMed

Schott, Benjamin; Traub, Manuel; Schlagenhauf, Cornelia; Takamiya, Masanari; Antritter, Thomas; Bartschat, Andreas; Löffler, Katharina; Blessing, Denis; Otte, Jens C; Kobitski, Andrei Y; Nienhaus, G Ulrich; Strähle, Uwe; Mikut, Ralf; Stegmaier, Johannes

2018-04-01

State-of-the-art light-sheet and confocal microscopes allow recording of entire embryos in 3D and over time (3D+t) for many hours. Fluorescently labeled structures can be segmented and tracked automatically in these terabyte-scale 3D+t images, resulting in thousands of cell migration trajectories that provide detailed insights to large-scale tissue reorganization at the cellular level. Here we present EmbryoMiner, a new interactive open-source framework suitable for in-depth analyses and comparisons of entire embryos, including an extensive set of trajectory features. Starting at the whole-embryo level, the framework can be used to iteratively focus on a region of interest within the embryo, to investigate and test specific trajectory-based hypotheses and to extract quantitative features from the isolated trajectories. Thus, the new framework provides a valuable new way to quantitatively compare corresponding anatomical regions in different embryos that were manually selected based on biological prior knowledge. As a proof of concept, we analyzed 3D+t light-sheet microscopy images of zebrafish embryos, showcasing potential user applications that can be performed using the new framework.

Advanced Parkinson's or "complex phase" Parkinson's disease? Re-evaluation is needed.

PubMed

Titova, Nataliya; Martinez-Martin, Pablo; Katunina, Elena; Chaudhuri, K Ray

2017-12-01

Holistic management of Parkinson's disease, now recognised as a combined motor and nonmotor disorder, remains a key unmet need. Such management needs relatively accurate definition of the various stages of Parkinson's from early untreated to late palliative as each stage calls for personalised therapies. Management also needs to have a robust knowledge of the progression pattern and clinical heterogeneity of the presentation of Parkinson's which may manifest in a motor dominant or nonmotor dominant manner. The "advanced" stages of Parkinson's disease qualify for advanced treatments such as with continuous infusion or stereotactic surgery yet the concept of "advanced Parkinson's disease" (APD) remains controversial in spite of growing knowledge of the natural history of the motor syndrome of PD. Advanced PD is currently largely defined on the basis of consensus opinion and thus with several caveats. Nonmotor aspects of PD may also reflect advancing course of the disorder, so far not reflected in usual scale based assessments which are largely focussed on motor symptoms. In this paper, we discuss the problems with current definitions of "advanced" PD and also propose the term "complex phase" Parkinson's disease as an alternative which takes into account a multimodal symptoms and biomarker based approach in addition to patient preference.

Fuzzy Linguistic Knowledge Based Behavior Extraction for Building Energy Management Systems

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

Dumidu Wijayasekara; Milos Manic

2013-08-01

Significant portion of world energy production is consumed by building Heating, Ventilation and Air Conditioning (HVAC) units. Thus along with occupant comfort, energy efficiency is also an important factor in HVAC control. Modern buildings use advanced Multiple Input Multiple Output (MIMO) control schemes to realize these goals. However, since the performance of HVAC units is dependent on many criteria including uncertainties in weather, number of occupants, and thermal state, the performance of current state of the art systems are sub-optimal. Furthermore, because of the large number of sensors in buildings, and the high frequency of data collection, large amount ofmore » information is available. Therefore, important behavior of buildings that compromise energy efficiency or occupant comfort is difficult to identify. This paper presents an easy to use and understandable framework for identifying such behavior. The presented framework uses human understandable knowledge-base to extract important behavior of buildings and present it to users via a graphical user interface. The presented framework was tested on a building in the Pacific Northwest and was shown to be able to identify important behavior that relates to energy efficiency and occupant comfort.« less

Knowledge-based reusable software synthesis system

NASA Technical Reports Server (NTRS)

Donaldson, Cammie

1989-01-01

The Eli system, a knowledge-based reusable software synthesis system, is being developed for NASA Langley under a Phase 2 SBIR contract. Named after Eli Whitney, the inventor of interchangeable parts, Eli assists engineers of large-scale software systems in reusing components while they are composing their software specifications or designs. Eli will identify reuse potential, search for components, select component variants, and synthesize components into the developer's specifications. The Eli project began as a Phase 1 SBIR to define a reusable software synthesis methodology that integrates reusabilityinto the top-down development process and to develop an approach for an expert system to promote and accomplish reuse. The objectives of the Eli Phase 2 work are to integrate advanced technologies to automate the development of reusable components within the context of large system developments, to integrate with user development methodologies without significant changes in method or learning of special languages, and to make reuse the easiest operation to perform. Eli will try to address a number of reuse problems including developing software with reusable components, managing reusable components, identifying reusable components, and transitioning reuse technology. Eli is both a library facility for classifying, storing, and retrieving reusable components and a design environment that emphasizes, encourages, and supports reuse.

An intelligent tool for activity data collection.

PubMed

Sarkar, A M Jehad

2011-01-01

Activity recognition systems using simple and ubiquitous sensors require a large variety of real-world sensor data for not only evaluating their performance but also training the systems for better functioning. However, a tremendous amount of effort is required to setup an environment for collecting such data. For example, expertise and resources are needed to design and install the sensors, controllers, network components, and middleware just to perform basic data collections. It is therefore desirable to have a data collection method that is inexpensive, flexible, user-friendly, and capable of providing large and diverse activity datasets. In this paper, we propose an intelligent activity data collection tool which has the ability to provide such datasets inexpensively without physically deploying the testbeds. It can be used as an inexpensive and alternative technique to collect human activity data. The tool provides a set of web interfaces to create a web-based activity data collection environment. It also provides a web-based experience sampling tool to take the user's activity input. The tool generates an activity log using its activity knowledge and the user-given inputs. The activity knowledge is mined from the web. We have performed two experiments to validate the tool's performance in producing reliable datasets.

Expert system shell to reason on large amounts of data

NASA Technical Reports Server (NTRS)

Giuffrida, Gionanni

1994-01-01

The current data base management systems (DBMS's) do not provide a sophisticated environment to develop rule based expert systems applications. Some of the new DBMS's come with some sort of rule mechanism; these are active and deductive database systems. However, both of these are not featured enough to support full implementation based on rules. On the other hand, current expert system shells do not provide any link with external databases. That is, all the data are kept in the system working memory. Such working memory is maintained in main memory. For some applications the reduced size of the available working memory could represent a constraint for the development. Typically these are applications which require reasoning on huge amounts of data. All these data do not fit into the computer main memory. Moreover, in some cases these data can be already available in some database systems and continuously updated while the expert system is running. This paper proposes an architecture which employs knowledge discovering techniques to reduce the amount of data to be stored in the main memory; in this architecture a standard DBMS is coupled with a rule-based language. The data are stored into the DBMS. An interface between the two systems is responsible for inducing knowledge from the set of relations. Such induced knowledge is then transferred to the rule-based language working memory.

MO-D-BRC-03: Knowledge-Based Planning

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

Wu, Q.

Treatment planning is a central part of radiation therapy, including delineation in tumor volumes and critical organs, setting treatment goals of prescription doses to the tumor targets and tolerance doses to the critical organs, and finally generation of treatment plans to meet the treatment goals. National groups like RTOG have led the effort to standardize treatment goals of the prescription doses to the tumor targets and tolerance doses to the critical organs based on accumulated knowledge from decades of abundant clinical trial experience. The challenge for each clinical department is how to achieve or surpass these set goals within themore » time constraints of clinical practice. Using fifteen testing cases from different treatment sites such as head and neck, prostate with and without pelvic lymph nodes, SBRT spine, we will present clinically utility of advanced planning tools, including knowledge based, automatic based, and multiple criteria based tools that are clinically implemented. The objectives of this session are: Understand differences among these three advanced planning tools Provide clinical assessments on the utility of the advanced planning tools Discuss clinical challenges of treatment planning with large variations in tumor volumes and their relationships with adjacent critical organs. Ping Xia received research grant from Philips. Jackie Wu received research grant from Varian; P. Xia, Research support by Philips and Varian; Q. Wu, NIH, Varian Medical.« less

MO-D-BRC-00: In Memoriam of Jan Van De Geijn: Knowledge-Based Planning

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

NONE

Treatment planning is a central part of radiation therapy, including delineation in tumor volumes and critical organs, setting treatment goals of prescription doses to the tumor targets and tolerance doses to the critical organs, and finally generation of treatment plans to meet the treatment goals. National groups like RTOG have led the effort to standardize treatment goals of the prescription doses to the tumor targets and tolerance doses to the critical organs based on accumulated knowledge from decades of abundant clinical trial experience. The challenge for each clinical department is how to achieve or surpass these set goals within themore » time constraints of clinical practice. Using fifteen testing cases from different treatment sites such as head and neck, prostate with and without pelvic lymph nodes, SBRT spine, we will present clinically utility of advanced planning tools, including knowledge based, automatic based, and multiple criteria based tools that are clinically implemented. The objectives of this session are: Understand differences among these three advanced planning tools Provide clinical assessments on the utility of the advanced planning tools Discuss clinical challenges of treatment planning with large variations in tumor volumes and their relationships with adjacent critical organs. Ping Xia received research grant from Philips. Jackie Wu received research grant from Varian; P. Xia, Research support by Philips and Varian; Q. Wu, NIH, Varian Medical.« less

NSF's Perspective on Space Weather Research for Building Forecasting Capabilities

NASA Astrophysics Data System (ADS)

Bisi, M. M.; Pulkkinen, A. A.; Bisi, M. M.; Pulkkinen, A. A.; Webb, D. F.; Oughton, E. J.; Azeem, S. I.

2017-12-01

Space weather research at the National Science Foundation (NSF) is focused on scientific discovery and on deepening knowledge of the Sun-Geospace system. The process of maturation of knowledge base is a requirement for the development of improved space weather forecast models and for the accurate assessment of potential mitigation strategies. Progress in space weather forecasting requires advancing in-depth understanding of the underlying physical processes, developing better instrumentation and measurement techniques, and capturing the advancements in understanding in large-scale physics based models that span the entire chain of events from the Sun to the Earth. This presentation will provide an overview of current and planned programs pertaining to space weather research at NSF and discuss the recommendations of the Geospace Section portfolio review panel within the context of space weather forecasting capabilities.

A comparison of food safety knowledge among restaurant managers, by source of training and experience, in Oklahoma County, Oklahoma.

PubMed

Lynch, Robert A; Elledge, Brenda L; Griffith, Charles C; Boatright, Daniel T

2003-09-01

The annual incidence of illness related to food consumption continues to present a challenge to environmental health management. A significant fraction of cases have been attributed to consumption of food in restaurants, and as the number of meals eaten away from the home continues to rise, the potential for large-scale foodborne-disease outbreaks will continue to increase. Food handlers in retail establishments contribute to the incidence of foodborne disease; therefore, it is essential that workers and management staff have a thorough understanding of safe food practices. Since the training, certification, and experience of food service managers vary greatly, it is also likely that managers' knowledge base may differ. In the study reported here, restaurant managers were administered a survey designed to measure their understanding of basic food safety principles. The sources of training, certification, and experience were found to significantly affect the level of food safety knowledge; however, increased hours of training did not increase knowledge. In addition, the time lapsed since training did not significantly affect the level of knowledge.

Dynamics of Research Team Formation in Complex Networks

NASA Astrophysics Data System (ADS)

Sun, Caihong; Wan, Yuzi; Chen, Yu

Most organizations encourage the formation of teams to accomplish complicated tasks, and vice verse, effective teams could bring lots benefits and profits for organizations. Network structure plays an important role in forming teams. In this paper, we specifically study the dynamics of team formation in large research communities in which knowledge of individuals plays an important role on team performance and individual utility. An agent-based model is proposed, in which heterogeneous agents from research communities are described and empirically tested. Each agent has a knowledge endowment and a preference for both income and leisure. Agents provide a variable input (‘effort’) and their knowledge endowments to production. They could learn from others in their team and those who are not in their team but have private connections in community to adjust their own knowledge endowment. They are allowed to join other teams or work alone when it is welfare maximizing to do so. Various simulation experiments are conducted to examine the impacts of network topology, knowledge diffusion among community network, and team output sharing mechanisms on the dynamics of team formation.

Informatics in radiology: radiology gamuts ontology: differential diagnosis for the Semantic Web.

PubMed

Budovec, Joseph J; Lam, Cesar A; Kahn, Charles E

2014-01-01

The Semantic Web is an effort to add semantics, or "meaning," to empower automated searching and processing of Web-based information. The overarching goal of the Semantic Web is to enable users to more easily find, share, and combine information. Critical to this vision are knowledge models called ontologies, which define a set of concepts and formalize the relations between them. Ontologies have been developed to manage and exploit the large and rapidly growing volume of information in biomedical domains. In diagnostic radiology, lists of differential diagnoses of imaging observations, called gamuts, provide an important source of knowledge. The Radiology Gamuts Ontology (RGO) is a formal knowledge model of differential diagnoses in radiology that includes 1674 differential diagnoses, 19,017 terms, and 52,976 links between terms. Its knowledge is used to provide an interactive, freely available online reference of radiology gamuts ( www.gamuts.net ). A Web service allows its content to be discovered and consumed by other information systems. The RGO integrates radiologic knowledge with other biomedical ontologies as part of the Semantic Web. © RSNA, 2014.

Supporting Knowledge Transfer in IS Deployment Projects

NASA Astrophysics Data System (ADS)

Schönström, Mikael

To deploy new information systems is an expensive and complex task, and does seldom result in successful usage where the system adds strategic value to the firm (e.g. Sharma et al. 2003). It has been argued that innovation diffusion is a knowledge integration problem (Newell et al. 2000). Knowledge about business processes, deployment processes, information systems and technology are needed in a large-scale deployment of a corporate IS. These deployments can therefore to a large extent be argued to be a knowledge management (KM) problem. An effective deployment requires that knowledge about the system is effectively transferred to the target organization (Ko et al. 2005).

Social capital and knowledge sharing: effects on patient safety.

PubMed

Chang, Chia-Wen; Huang, Heng-Chiang; Chiang, Chi-Yun; Hsu, Chiu-Ping; Chang, Chia-Chen

2012-08-01

This article is a report on a study that empirically examines the influence of social capital on knowledge sharing and the impact of knowledge sharing on patient safety. Knowledge sharing is linked to many desirable managerial outcomes, including learning and problem-solving, which are essential for patient safety. Rather than studying the tangible effects of rewards, this study examines whether social capital (including social interaction, trust and shared vision) directly supports individual knowledge sharing in an organization. This cross-sectional study analysed data collected through a questionnaire survey of nurses from a major medical centre in northern Taiwan. The data were collected over a 9-month period from 2008 to 2009. The data analysis was conducted using the Partial Least Squares Graph v3.0 program to evaluate the measurement properties and the structural relationships specified in the research model. Based on a large-scale survey, empirical results indicate that Registered Nurses' perceptions of trust and shared vision have statistically significant and direct effects on knowledge sharing. In addition, knowledge sharing is significantly and positively associated with patient safety. The findings suggest that hospital administrators should foster group trust and initiate a common vision among Registered Nurses. In addition, administrators and chief knowledge officers of hospitals should encourage positive intentions towards knowledge sharing. © 2011 The Authors. Journal of Advanced Nursing © 2011 Blackwell Publishing Ltd.

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).

GAMES II Project: a general architecture for medical knowledge-based systems.

PubMed

Bruno, F; Kindler, H; Leaning, M; Moustakis, V; Scherrer, J R; Schreiber, G; Stefanelli, M

1994-10-01

GAMES II aims at developing a comprehensive and commercially viable methodology to avoid problems ordinarily occurring in KBS development. GAMES II methodology proposes to design a KBS starting from an epistemological model of medical reasoning (the Select and Test Model). The design is viewed as a process of adding symbol level information to the epistemological model. The architectural framework provided by GAMES II integrates the use of different formalisms and techniques providing a large set of tools. The user can select the most suitable one for representing a piece of knowledge after a careful analysis of its epistemological characteristics. Special attention is devoted to the tools dealing with knowledge acquisition (both manual and automatic). A panel of practicing physicians are assessing the medical value of such a framework and its related tools by using it in a practical application.

Knowledge at Work.

ERIC Educational Resources Information Center

Scribner, Sylvia

1985-01-01

Activity theory posits that culturally organized actions guide the acquisition and organization of knowledge. This theory was applied to the organization of knowledge within a large milk processing plant. The dairy was found to be organized by social knowledge, yet individuals creatively synthesized several domains of knowledge to organize their…

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 59: Japanese Technological Innovation. Implications for Large Commercial Aircraft and Knowledge Diffusion

NASA Technical Reports Server (NTRS)

Pinelli, Thomas E.; Barclay, Rebecca O.; Kotler, Mindy L.

1997-01-01

This paper explores three factors-public policy, the Japanese (national) innovation system, and knowledge-that influence technological innovation in Japan. To establish a context for the paper, we examine Japanese culture and the U.S. and Japanese patent systems in the background section. A brief history of the Japanese aircraft industry as a source of knowledge and technology for other industries is presented. Japanese and U.S. alliances and linkages in three sectors-biotechnology, semiconductors, and large commercial aircraft (LCA)-and the importation, absorption, and diffusion of knowledge and technology are examined next. The paper closes with implications for diffusing knowledge and technology, U.S. public policy, and LCA.

Portraiture in the Large Lecture: Storying One Chemistry Professor's Practical Knowledge

NASA Astrophysics Data System (ADS)

Eddleton, Jeannine E.

Practical knowledge, as defined by Freema Elbaz (1983), is a complex, practically oriented set of understandings which teachers use to actively shape and direct their work. The goal of this study is the construction of a social science portrait that illuminates the practical knowledge of a large lecture professor of general chemistry at a public research university in the southeast. This study continues Elbaz's (1981) work on practical knowledge with the incorporation of a qualitative and intentionally interventionist methodology which "blurs the boundaries of aesthetics and empiricism in an effort to capture the complexity, dynamics, and subtlety of human experience and organizational life," (Lawrence-Lightfoot & Davis, 1997). This collection of interviews, observations, writings, and reflections is designed for an eclectic audience with the intent of initiating conversation on the topic of the large lecture and is a purposeful attempt to link research and practice. Social science portraiture is uniquely suited to this intersection of researcher and researched, the perfect combination of methodology and analysis for a project that is both product and praxis. The following research questions guide the study. • Are aspects of Elbaz's practical knowledge identifiable in the research conversations conducted with a large lecture college professor? • Is practical knowledge identifiable during observations of Patricia's large lecture? Freema Elbaz conducted research conversations with Sarah, a high school classroom and writing resource teacher who conducted much of her teaching work one on one with students. Patricia's practice differs significantly from Sarah's with respect to subject matter and to scale.

Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems.

PubMed

Huang, Lifu; May, Jonathan; Pan, Xiaoman; Ji, Heng; Ren, Xiang; Han, Jiawei; Zhao, Lin; Hendler, James A

2017-03-01

The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework.

Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems

PubMed Central

Huang, Lifu; May, Jonathan; Pan, Xiaoman; Ji, Heng; Ren, Xiang; Han, Jiawei; Zhao, Lin; Hendler, James A.

2017-01-01

Abstract The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework. PMID:28328252

The application of SSADM to modelling the logical structure of proteins.

PubMed

Saldanha, J; Eccles, J

1991-10-01

A logical design that describes the overall structure of proteins, together with a more detailed design describing secondary and some supersecondary structures, has been constructed using the computer-aided software engineering (CASE) tool, Auto-mate. Auto-mate embodies the philosophy of the Structured Systems Analysis and Design Method (SSADM) which enables the logical design of computer systems. Our design will facilitate the building of large information systems, such as databases and knowledgebases in the field of protein structure, by the derivation of system requirements from our logical model prior to producing the final physical system. In addition, the study has highlighted the ease of employing SSADM as a formalism in which to conduct the transferral of concepts from an expert into a design for a knowledge-based system that can be implemented on a computer (the knowledge-engineering exercise). It has been demonstrated how SSADM techniques may be extended for the purpose of modelling the constituent Prolog rules. This facilitates the integration of the logical system design model with the derived knowledge-based system.

The online Tabloid Proteome: an annotated database of protein associations

PubMed Central

Turan, Demet; Tavernier, Jan

2018-01-01

Abstract A complete knowledge of the proteome can only be attained by determining the associations between proteins, along with the nature of these associations (e.g. physical contact in protein–protein interactions, participation in complex formation or different roles in the same pathway). Despite extensive efforts in elucidating direct protein interactions, our knowledge on the complete spectrum of protein associations remains limited. We therefore developed a new approach that detects protein associations from identifications obtained after re-processing of large-scale, public mass spectrometry-based proteomics data. Our approach infers protein association based on the co-occurrence of proteins across many different proteomics experiments, and provides information that is almost completely complementary to traditional direct protein interaction studies. We here present a web interface to query and explore the associations derived from this method, called the online Tabloid Proteome. The online Tabloid Proteome also integrates biological knowledge from several existing resources to annotate our derived protein associations. The online Tabloid Proteome is freely available through a user-friendly web interface, which provides intuitive navigation and data exploration options for the user at http://iomics.ugent.be/tabloidproteome. PMID:29040688

The Typicality Ranking Task: A New Method to Derive Typicality Judgments from Children.

PubMed

Djalal, Farah Mutiasari; Ameel, Eef; Storms, Gert

2016-01-01

An alternative method for deriving typicality judgments, applicable in young children that are not familiar with numerical values yet, is introduced, allowing researchers to study gradedness at younger ages in concept development. Contrary to the long tradition of using rating-based procedures to derive typicality judgments, we propose a method that is based on typicality ranking rather than rating, in which items are gradually sorted according to their typicality, and that requires a minimum of linguistic knowledge. The validity of the method is investigated and the method is compared to the traditional typicality rating measurement in a large empirical study with eight different semantic concepts. The results show that the typicality ranking task can be used to assess children's category knowledge and to evaluate how this knowledge evolves over time. Contrary to earlier held assumptions in studies on typicality in young children, our results also show that preference is not so much a confounding variable to be avoided, but that both variables are often significantly correlated in older children and even in adults.

The Typicality Ranking Task: A New Method to Derive Typicality Judgments from Children

PubMed Central

Ameel, Eef; Storms, Gert

2016-01-01

An alternative method for deriving typicality judgments, applicable in young children that are not familiar with numerical values yet, is introduced, allowing researchers to study gradedness at younger ages in concept development. Contrary to the long tradition of using rating-based procedures to derive typicality judgments, we propose a method that is based on typicality ranking rather than rating, in which items are gradually sorted according to their typicality, and that requires a minimum of linguistic knowledge. The validity of the method is investigated and the method is compared to the traditional typicality rating measurement in a large empirical study with eight different semantic concepts. The results show that the typicality ranking task can be used to assess children’s category knowledge and to evaluate how this knowledge evolves over time. Contrary to earlier held assumptions in studies on typicality in young children, our results also show that preference is not so much a confounding variable to be avoided, but that both variables are often significantly correlated in older children and even in adults. PMID:27322371

Knowledge-based expert systems and a proof-of-concept case study for multiple sequence alignment construction and analysis.

PubMed

Aniba, Mohamed Radhouene; Siguenza, Sophie; Friedrich, Anne; Plewniak, Frédéric; Poch, Olivier; Marchler-Bauer, Aron; Thompson, Julie Dawn

2009-01-01

The traditional approach to bioinformatics analyses relies on independent task-specific services and applications, using different input and output formats, often idiosyncratic, and frequently not designed to inter-operate. In general, such analyses were performed by experts who manually verified the results obtained at each step in the process. Today, the amount of bioinformatics information continuously being produced means that handling the various applications used to study this information presents a major data management and analysis challenge to researchers. It is now impossible to manually analyse all this information and new approaches are needed that are capable of processing the large-scale heterogeneous data in order to extract the pertinent information. We review the recent use of integrated expert systems aimed at providing more efficient knowledge extraction for bioinformatics research. A general methodology for building knowledge-based expert systems is described, focusing on the unstructured information management architecture, UIMA, which provides facilities for both data and process management. A case study involving a multiple alignment expert system prototype called AlexSys is also presented.

Knowledge-based expert systems and a proof-of-concept case study for multiple sequence alignment construction and analysis

PubMed Central

Aniba, Mohamed Radhouene; Siguenza, Sophie; Friedrich, Anne; Plewniak, Frédéric; Poch, Olivier; Marchler-Bauer, Aron

2009-01-01

The traditional approach to bioinformatics analyses relies on independent task-specific services and applications, using different input and output formats, often idiosyncratic, and frequently not designed to inter-operate. In general, such analyses were performed by experts who manually verified the results obtained at each step in the process. Today, the amount of bioinformatics information continuously being produced means that handling the various applications used to study this information presents a major data management and analysis challenge to researchers. It is now impossible to manually analyse all this information and new approaches are needed that are capable of processing the large-scale heterogeneous data in order to extract the pertinent information. We review the recent use of integrated expert systems aimed at providing more efficient knowledge extraction for bioinformatics research. A general methodology for building knowledge-based expert systems is described, focusing on the unstructured information management architecture, UIMA, which provides facilities for both data and process management. A case study involving a multiple alignment expert system prototype called AlexSys is also presented. PMID:18971242

U-Science (Invited)

NASA Astrophysics Data System (ADS)

Borne, K. D.

2009-12-01

The emergence of e-Science over the past decade as a paradigm for Internet-based science was an inevitable evolution of science that built upon the web protocols and access patterns that were prevalent at that time, including Web Services, XML-based information exchange, machine-to-machine communication, service registries, the Grid, and distributed data. We now see a major shift in web behavior patterns to social networks, user-provided content (e.g., tags and annotations), ubiquitous devices, user-centric experiences, and user-led activities. The inevitable accrual of these social networking patterns and protocols by scientists and science projects leads to U-Science as a new paradigm for online scientific research (i.e., ubiquitous, user-led, untethered, You-centered science). U-Science applications include components from semantic e-science (ontologies, taxonomies, folksonomies, tagging, annotations, and classification systems), which is much more than Web 2.0-based science (Wikis, blogs, and online environments like Second Life). Among the best examples of U-Science are Citizen Science projects, including Galaxy Zoo, Stardust@Home, Project Budburst, Volksdata, CoCoRaHS (the Community Collaborative Rain, Hail and Snow network), and projects utilizing Volunteer Geographic Information (VGI). There are also scientist-led projects for scientists that engage a wider community in building knowledge through user-provided content. Among the semantic-based U-Science projects for scientists are those that specifically enable user-based annotation of scientific results in databases. These include the Heliophysics Knowledgebase, BioDAS, WikiProteins, The Entity Describer, and eventually AstroDAS. Such collaborative tagging of scientific data addresses several petascale data challenges for scientists: how to find the most relevant data, how to reuse those data, how to integrate data from multiple sources, how to mine and discover new knowledge in large databases, how to represent and encode the new knowledge, and how to curate the discovered knowledge. This talk will address the emergence of U-Science as a type of Semantic e-Science, and will explore challenges, implementations, and results. Semantic e-Science and U-Science applications and concepts will be discussed within the context of one particular implementation (AstroDAS: Astronomy Distributed Annotation System) and its applicability to petascale science projects such as the LSST (Large Synoptic Survey Telescope), coming online within the next few years.

Knowledge information management toolkit and method

DOEpatents

Hempstead, Antoinette R.; Brown, Kenneth L.

2006-08-15

A system is provided for managing user entry and/or modification of knowledge information into a knowledge base file having an integrator support component and a data source access support component. The system includes processing circuitry, memory, a user interface, and a knowledge base toolkit. The memory communicates with the processing circuitry and is configured to store at least one knowledge base. The user interface communicates with the processing circuitry and is configured for user entry and/or modification of knowledge pieces within a knowledge base. The knowledge base toolkit is configured for converting knowledge in at least one knowledge base from a first knowledge base form into a second knowledge base form. A method is also provided.

Sources, topics and use of knowledge by coaches.

PubMed

Stoszkowski, John; Collins, Dave

2016-01-01

In recent years, the value of social learning approaches as part of the design and delivery of formalised coach development initiatives has gained credence in the literature. However, insight is currently lacking into the fundamental social dimensions that underpin coach learning. Accordingly, this study aimed to explore coaches' perceptions of their actual and preferred methods of acquiring new coaching knowledge, the types of knowledge they currently acquire and/or desire, and their application of new knowledge. Responses to an online survey, completed by practicing coaches (N = 320) in a range of sports and contexts, were analysed descriptively and inductively. Results revealed that coaches preferred, and mostly acquired, coaching knowledge from informal learning activities, especially when these permitted social interaction. Notably, however, formal coach education courses were also reported relatively frequently as a source of recent knowledge acquisition. Nevertheless, critical justification for and application of acquired knowledge was largely absent. Based on the findings, we suggest that, before social learning activities such as mentoring schemes and communities of practice are placed at the centre of formalised coach development provision, coach educators must put in place the support structures to better enable coaches to recognise and deal with the potentially mixed influences of the social milieu on coach learning, aiming to ensure that their informal development is sufficiently open-minded, reflective and critical.

Knowledge translation of research findings.

PubMed

Grimshaw, Jeremy M; Eccles, Martin P; Lavis, John N; Hill, Sophie J; Squires, Janet E

2012-05-31

One of the most consistent findings from clinical and health services research is the failure to translate research into practice and policy. As a result of these evidence-practice and policy gaps, patients fail to benefit optimally from advances in healthcare and are exposed to unnecessary risks of iatrogenic harms, and healthcare systems are exposed to unnecessary expenditure resulting in significant opportunity costs. Over the last decade, there has been increasing international policy and research attention on how to reduce the evidence-practice and policy gap. In this paper, we summarise the current concepts and evidence to guide knowledge translation activities, defined as T2 research (the translation of new clinical knowledge into improved health). We structure the article around five key questions: what should be transferred; to whom should research knowledge be transferred; by whom should research knowledge be transferred; how should research knowledge be transferred; and, with what effect should research knowledge be transferred? We suggest that the basic unit of knowledge translation should usually be up-to-date systematic reviews or other syntheses of research findings. Knowledge translators need to identify the key messages for different target audiences and to fashion these in language and knowledge translation products that are easily assimilated by different audiences. The relative importance of knowledge translation to different target audiences will vary by the type of research and appropriate endpoints of knowledge translation may vary across different stakeholder groups. There are a large number of planned knowledge translation models, derived from different disciplinary, contextual (i.e., setting), and target audience viewpoints. Most of these suggest that planned knowledge translation for healthcare professionals and consumers is more likely to be successful if the choice of knowledge translation strategy is informed by an assessment of the likely barriers and facilitators. Although our evidence on the likely effectiveness of different strategies to overcome specific barriers remains incomplete, there is a range of informative systematic reviews of interventions aimed at healthcare professionals and consumers (i.e., patients, family members, and informal carers) and of factors important to research use by policy makers. There is a substantial (if incomplete) evidence base to guide choice of knowledge translation activities targeting healthcare professionals and consumers. The evidence base on the effects of different knowledge translation approaches targeting healthcare policy makers and senior managers is much weaker but there are a profusion of innovative approaches that warrant further evaluation.

Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies.

PubMed

Samwald, Matthias; Miñarro Giménez, Jose Antonio; Boyce, Richard D; Freimuth, Robert R; Adlassnig, Klaus-Peter; Dumontier, Michel

2015-02-22

Every year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics. We developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles. Our methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners. The ontology-based framework we developed can be used to represent, organize and reason over the growing wealth of pharmacogenomic knowledge, as well as to identify errors, inconsistencies and insufficient definitions in source data sets or individual patient data. Our study highlights both advantages and potential practical issues with such an ontology-based approach.

Thematic knowledge, artifact concepts, and the left posterior temporal lobe: Where action and object semantics converge

PubMed Central

Kalénine, Solène; Buxbaum, Laurel J.

2016-01-01

Converging evidence supports the existence of functionally and neuroanatomically distinct taxonomic (similarity-based; e.g., hammer-screwdriver) and thematic (event-based; e.g., hammer-nail) semantic systems. Processing of thematic relations between objects has been shown to selectively recruit the left posterior temporoparietal cortex. Similar posterior regions have been also been shown to be critical for knowledge of relationships between actions and manipulable human-made objects (artifacts). Based on the hypothesis that thematic relationships for artifacts are based, at least in part, on action relationships, we assessed the prediction that the same regions of the left posterior temporoparietal cortex would be critical for conceptual processing of artifact-related actions and thematic relations for artifacts. To test this hypothesis, we evaluated processing of taxonomic and thematic relations for artifact and natural objects as well as artifact action knowledge (gesture recognition) abilities in a large sample of 48 stroke patients with a range of lesion foci in the left hemisphere. Like control participants, patients identified thematic relations faster than taxonomic relations for artifacts, whereas they identified taxonomic relations faster than thematic relations for natural objects. Moreover, response times for identifying thematic relations for artifacts selectively predicted performance in gesture recognition. Whole brain Voxel Based Lesion-Symptom Mapping (VLSM) analyses and Region of Interest (ROI) regression analyses further demonstrated that lesions to the left posterior temporal cortex, overlapping with LTO and visual motion area hMT+, were associated both with relatively slower response times in identifying thematic relations for artifacts and poorer artifact action knowledge in patients. These findings provide novel insights into the functional role of left posterior temporal cortex in thematic knowledge, and suggest that the close association between thematic relations for artifacts and action representations may reflect their common dependence on visual motion and manipulation information. PMID:27389801

CmapTools: A Software Environment for Knowledge Modeling and Sharing

NASA Technical Reports Server (NTRS)

Canas, Alberto J.

2004-01-01

In an ongoing collaborative effort between a group of NASA Ames scientists and researchers at the Institute for Human and Machine Cognition (IHMC) of the University of West Florida, a new version of CmapTools has been developed that enable scientists to construct knowledge models of their domain of expertise, share them with other scientists, make them available to anybody on the Internet with access to a Web browser, and peer-review other scientists models. These software tools have been successfully used at NASA to build a large-scale multimedia on Mars and in knowledge model on Habitability Assessment. The new version of the software places emphasis on greater usability for experts constructing their own knowledge models, and support for the creation of large knowledge models with large number of supporting resources in the forms of images, videos, web pages, and other media. Additionally, the software currently allows scientists to cooperate with each other in the construction, sharing and criticizing of knowledge models. Scientists collaborating from remote distances, for example researchers at the Astrobiology Institute, can concurrently manipulate the knowledge models they are viewing without having to do this at a special videoconferencing facility.

How faculty learn about and implement research-based instructional strategies: The case of Peer Instruction

NASA Astrophysics Data System (ADS)

Dancy, Melissa; Henderson, Charles; Turpen, Chandra

2016-06-01

[This paper is part of the Focused Collection on Preparing and Supporting University Physics Educators.] The lack of knowledge about how to effectively spread and sustain the use of research-based instructional strategies is currently a significant barrier to the improvement of undergraduate physics education. In this paper we address this lack of knowledge by reporting on an interview study of 35 physics faculty, of varying institution types, who were self-reported users of, former users of, or knowledgeable nonusers of the research-based instructional strategy Peer Instruction. Interview questions included in this analysis focused on the faculty's experiences, knowledge, and use of Peer Instruction, along with general questions about current and past teaching methods used by the interviewee. The primary findings include the following: (i) Faculty self-reported user status is an unreliable measure of their actual practice. (ii) Faculty generally modify specific instructional strategies and may modify out essential components. (iii) Faculty are often unaware of the essential features of an instructional strategy they claim to know about or use. (iv) Informal social interactions provide a significant communication channel in the dissemination process, in contrast to the formal avenues of workshops, papers, websites, etc., often promoted by change agents, and (v) experience with research-based strategies as a graduate student or through curriculum development work may be highly impactful. These findings indicate that educational transformation can be better facilitated by improving communication with faculty, supporting effective modification by faculty during implementation, and acknowledging and understanding the large impact of informal social interactions as a mode of dissemination.

Addressing the Common Pathway Underlying Hypertension and Diabetes in People Who Are Obese by Maximizing Health: The Ultimate Knowledge Translation Gap

PubMed Central

Dean, Elizabeth; Lomi, Constantina; Bruno, Selma; Awad, Hamzeh; O'Donoghue, Grainne

2011-01-01

In accordance with the WHO definition of health, this article examines the alarming discord between the epidemiology of hypertension, type 2 diabetes mellitus (T2DM), and obesity and the low profile of noninvasive (nondrug) compared with invasive (drug) interventions with respect to their prevention, reversal and management. Herein lies the ultimate knowledge translation gap and challenge in 21st century health care. Although lifestyle modification has long appeared in guidelines for medically managing these conditions, this evidence-based strategy is seldom implemented as rigorously as drug prescription. Biomedicine focuses largely on reducing signs and symptoms; the effects of the problem rather than the problem. This article highlights the evidence-based rationale supporting prioritizing the underlying causes and contributing factors for hypertension and T2DM, and, in turn, obesity. We argue that a primary focus on maximizing health could eliminate all three conditions, at best, or, at worst, minimize their severity, complications, and medication needs. To enable such knowledge translation and maximizing health outcome, the health care community needs to practice as an integrated team, and address barriers to effecting maximal health in all patients. Addressing the ultimate knowledge translation gap, by aligning the health care paradigm to 21st century needs, would constitute a major advance. PMID:21423684

Development and validation of an energy-balance knowledge test for fourth- and fifth-grade students.

PubMed

Chen, Senlin; Zhu, Xihe; Kang, Minsoo

2017-05-01

A valid test measuring children's energy-balance (EB) knowledge is lacking in research. This study developed and validated the energy-balance knowledge test (EBKT) for fourth and fifth grade students. The original EBKT contained 25 items but was reduced to 23 items based on pilot result and intensive expert panel discussion. De-identified data were collected from 468 fourth and fifth grade students enrolled in four schools to examine the psychometric properties of the EBKT items. The Rasch model analysis was conducted using the Winstep 3.65.0 software. Differential item functioning (DIF) analysis flagged 1 item (item #4) functioning differently between boys and girls, which was deleted. The final 22-item EBKT showed desirable model-data fit indices. The items had large variability ranging from -3.58 logit (item #10, the easiest) to 1.70 logit (item #3, the hardest). The average person ability on the test was 0.28 logit (SD = .78). Additional analyses supported known-group difference validity of the EBKT scores in capturing gender- and grade-based ability differences. The test was overall valid but could be further improved by expanding test items to discern various ability levels. For lack of a better test, researchers and practitioners may use the EBKT to assess fourth- and fifth-grade students' EB knowledge.

Popular Beliefs about the Infectivity of Water among School Children in Two Hyperendemic Schistosomiasis Areas of Brazil

PubMed Central

Gazzinelli, Maria Flávia Carvalho; Kloos, Helmut; de Cássia Marques, Rita; dos Reis, Dener Carlos; Gazzinelli, Andrea

2009-01-01

This article examines changing common knowledge of elementary school children to scientific knowledge related to the relationship between water characteristics and the transmission of schistosomiasis through health education. A review of the literature and two case studies from rural elementary schools in Brazil show how the prevailing concept of dirty and polluted water, which has operated as an epistemological obstacle for acquiring scientific knowledge, may be related to symbolic thought and cultural parameters. Through an educational intervention not commonly applied to health programs involving elementary school students in two schistosomiasis-endemic rural communities in Brazil this paper describes the difficulties researchers encountered in changing the prevailing perception that very dirty and polluted water provides optimal conditions for schistosome transmission, to the scientifically accepted view that transmission occurs most often in visually clean, although fecally contaminated water. This conceptual difficulty may be largely explained in terms of the symbolism involved in clean and dirty water and the life-giving quality of water. Based on our results, we recommend that knowledge about water-related beliefs and concepts among school children should be considered in school-based health education programs in areas of endemic schistosomiasis and possibly other intestinal infections. PMID:18599008

Infrared vehicle recognition using unsupervised feature learning based on K-feature

NASA Astrophysics Data System (ADS)

Lin, Jin; Tan, Yihua; Xia, Haijiao; Tian, Jinwen

2018-02-01

Subject to the complex battlefield environment, it is difficult to establish a complete knowledge base in practical application of vehicle recognition algorithms. The infrared vehicle recognition is always difficult and challenging, which plays an important role in remote sensing. In this paper we propose a new unsupervised feature learning method based on K-feature to recognize vehicle in infrared images. First, we use the target detection algorithm which is based on the saliency to detect the initial image. Then, the unsupervised feature learning based on K-feature, which is generated by Kmeans clustering algorithm that extracted features by learning a visual dictionary from a large number of samples without label, is calculated to suppress the false alarm and improve the accuracy. Finally, the vehicle target recognition image is finished by some post-processing. Large numbers of experiments demonstrate that the proposed method has satisfy recognition effectiveness and robustness for vehicle recognition in infrared images under complex backgrounds, and it also improve the reliability of it.

Knowledge discovery for Deep Phenotyping serious mental illness from Electronic Mental Health records.

PubMed

Jackson, Richard; Patel, Rashmi; Velupillai, Sumithra; Gkotsis, George; Hoyle, David; Stewart, Robert

2018-01-01

Background: Deep Phenotyping is the precise and comprehensive analysis of phenotypic features in which the individual components of the phenotype are observed and described. In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond what is expressed in most medical knowledge bases. The SNOMED CT nomenclature potentially offers the means to model such information at scale, yet given a sufficiently large body of clinical text collected over many years, it is difficult to identify the language that clinicians favour to express concepts. Methods: By utilising a large corpus of healthcare data, we sought to make use of semantic modelling and clustering techniques to represent the relationship between the clinical vocabulary of internationally recognised SMI symptoms and the preferred language used by clinicians within a care setting. We explore how such models can be used for discovering novel vocabulary relevant to the task of phenotyping Serious Mental Illness (SMI) with only a small amount of prior knowledge.  Results: 20 403 terms were derived and curated via a two stage methodology. The list was reduced to 557 putative concepts based on eliminating redundant information content. These were then organised into 9 distinct categories pertaining to different aspects of psychiatric assessment. 235 concepts were found to be expressions of putative clinical significance. Of these, 53 were identified having novel synonymy with existing SNOMED CT concepts. 106 had no mapping to SNOMED CT. Conclusions: We demonstrate a scalable approach to discovering new concepts of SMI symptomatology based on real-world clinical observation. Such approaches may offer the opportunity to consider broader manifestations of SMI symptomatology than is typically assessed via current diagnostic frameworks, and create the potential for enhancing nomenclatures such as SNOMED CT based on real-world expressions.

Knowledge discovery for Deep Phenotyping serious mental illness from Electronic Mental Health records

PubMed Central

Jackson, Richard; Patel, Rashmi; Velupillai, Sumithra; Gkotsis, George; Hoyle, David; Stewart, Robert

2018-01-01

Background: Deep Phenotyping is the precise and comprehensive analysis of phenotypic features in which the individual components of the phenotype are observed and described. In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond what is expressed in most medical knowledge bases. The SNOMED CT nomenclature potentially offers the means to model such information at scale, yet given a sufficiently large body of clinical text collected over many years, it is difficult to identify the language that clinicians favour to express concepts. Methods: By utilising a large corpus of healthcare data, we sought to make use of semantic modelling and clustering techniques to represent the relationship between the clinical vocabulary of internationally recognised SMI symptoms and the preferred language used by clinicians within a care setting. We explore how such models can be used for discovering novel vocabulary relevant to the task of phenotyping Serious Mental Illness (SMI) with only a small amount of prior knowledge.  Results: 20 403 terms were derived and curated via a two stage methodology. The list was reduced to 557 putative concepts based on eliminating redundant information content. These were then organised into 9 distinct categories pertaining to different aspects of psychiatric assessment. 235 concepts were found to be expressions of putative clinical significance. Of these, 53 were identified having novel synonymy with existing SNOMED CT concepts. 106 had no mapping to SNOMED CT. Conclusions: We demonstrate a scalable approach to discovering new concepts of SMI symptomatology based on real-world clinical observation. Such approaches may offer the opportunity to consider broader manifestations of SMI symptomatology than is typically assessed via current diagnostic frameworks, and create the potential for enhancing nomenclatures such as SNOMED CT based on real-world expressions. PMID:29899974

"When Words Don't Come Easily": Personal Narratives from Adolescents Experiencing Shyness as an Emotional and Behavioural Problem in the School Context

ERIC Educational Resources Information Center

Lund, Ingrid

2016-01-01

The purpose of this article is to present narratives from 15 adolescents experiencing shy behaviour as an emotional and behavioural problem in the school context in light of narrative understanding. The investigation is intended to generate knowledge about this largely under-researched phenomenon based on the personal accounts of those who are…

Surveys and questionnaires in nursing research.

PubMed

Timmins, Fiona

2015-06-17

Surveys and questionnaires are often used in nursing research to elicit the views of large groups of people to develop the nursing knowledge base. This article provides an overview of survey and questionnaire use in nursing research, clarifies the place of the questionnaire as a data collection tool in quantitative research design and provides information and advice about best practice in the development of quantitative surveys and questionnaires.

The Teacher as Co-Creator of Drama: A Phenomenological Study of the Experiences and Reflections of Irish Primary School Teachers

ERIC Educational Resources Information Center

McDonagh, Fiona; Finneran, Michael

2017-01-01

Classroom drama in the Irish primary school context remains a relatively new endeavour and is largely under-researched. The knowledge base for all aspects of teacher education should be informed by rigorous reflection on teachers' experiences in the classroom. This paper reports on a phenomenological study conducted with seven Irish primary school…

The KhoeSan Early Learning Center Pilot Project: Negotiating Power and Possibility in a South African Institute of Higher Learning

ERIC Educational Resources Information Center

De Wet, Priscilla

2011-01-01

As we search for a new paradigm in post-apartheid South Africa, the knowledge base and worldview of the KhoeSan first Indigenous peoples is largely missing. The South African government has established various mechanisms as agents for social change. Institutions of higher learning have implemented transformation programs. KhoeSan peoples, however,…

The Collision of the Adoption and Safe Families Act and Substance Abuse: Research-Based Education and Training Priorities for Child Welfare Professionals

ERIC Educational Resources Information Center

Schroeder, Julie; Lemieux, Catherine; Pogue, Rene

2008-01-01

A large body of descriptive literature demonstrates the problem of substance abuse in child welfare. The 1997 Adoption and Safe Families Act (ASFA) established time frames that make children's need for permanency the overriding priority in families involved with the child welfare system. Child welfare workers often lack proper knowledge and skill…

Errors in terrain-based model preditions caused by altered forest inventory plot locations in the Southern Appalachian Mountains, USA.

Treesearch

Huei-Jin Wang; Stephen Prisley; Philip Radtke; John Coulston

2012-01-01

Forest modeling applications that cover large geographic area can benefit from the use of widely-held knowledge about relationships between forest attributes and topographic variables. A noteworthy example involved the coupling of field survey data from the Forest Inventory Analysis (FIA) program of USDA Forest Service with digital elevation model (DEM) data in...

Use of research-based instructional strategies in introductory physics: Where do faculty leave the innovation-decision process?

NASA Astrophysics Data System (ADS)

Henderson, Charles; Dancy, Melissa; Niewiadomska-Bugaj, Magdalena

2012-12-01

During the fall of 2008 a web survey, designed to collect information about pedagogical knowledge and practices, was completed by a representative sample of 722 physics faculty across the United States (50.3% response rate). This paper presents partial results to describe how 20 potential predictor variables correlate with faculty knowledge about and use of research-based instructional strategies (RBIS). The innovation-decision process was conceived of in terms of four stages: knowledge versus no knowledge, trial versus no trial, continuation versus discontinuation, and high versus low use. The largest losses occur at the continuation stage, with approximately 1/3 of faculty discontinuing use of all RBIS after trying one or more of these strategies. Nine of the predictor variables were statistically significant for at least one of these stages when controlling for other variables. Knowledge and/or use of RBIS are significantly correlated with reading teaching-related journals, attending talks and workshops related to teaching, attending the physics and astronomy new faculty workshop, having an interest in using more RBIS, being female, being satisfied with meeting instructional goals, and having a permanent, full-time position. The types of variables that are significant at each stage vary substantially. These results suggest that common dissemination strategies are good at creating knowledge about RBIS and motivation to try a RBIS, but more work is needed to support faculty during implementation and continued use of RBIS. Also, contrary to common assumptions, faculty age, institutional type, and percentage of job related to teaching were not found to be barriers to knowledge or use at any stage. High research productivity and large class sizes were not found to be barriers to use of at least some RBIS.

Web-video-mining-supported workflow modeling for laparoscopic surgeries.

PubMed

Liu, Rui; Zhang, Xiaoli; Zhang, Hao

2016-11-01

As quality assurance is of strong concern in advanced surgeries, intelligent surgical systems are expected to have knowledge such as the knowledge of the surgical workflow model (SWM) to support their intuitive cooperation with surgeons. For generating a robust and reliable SWM, a large amount of training data is required. However, training data collected by physically recording surgery operations is often limited and data collection is time-consuming and labor-intensive, severely influencing knowledge scalability of the surgical systems. The objective of this research is to solve the knowledge scalability problem in surgical workflow modeling with a low cost and labor efficient way. A novel web-video-mining-supported surgical workflow modeling (webSWM) method is developed. A novel video quality analysis method based on topic analysis and sentiment analysis techniques is developed to select high-quality videos from abundant and noisy web videos. A statistical learning method is then used to build the workflow model based on the selected videos. To test the effectiveness of the webSWM method, 250 web videos were mined to generate a surgical workflow for the robotic cholecystectomy surgery. The generated workflow was evaluated by 4 web-retrieved videos and 4 operation-room-recorded videos, respectively. The evaluation results (video selection consistency n-index ≥0.60; surgical workflow matching degree ≥0.84) proved the effectiveness of the webSWM method in generating robust and reliable SWM knowledge by mining web videos. With the webSWM method, abundant web videos were selected and a reliable SWM was modeled in a short time with low labor cost. Satisfied performances in mining web videos and learning surgery-related knowledge show that the webSWM method is promising in scaling knowledge for intelligent surgical systems. Copyright © 2016 Elsevier B.V. All rights reserved.

T cell regulation in microgravity - The current knowledge from in vitro experiments conducted in space, parabolic flights and ground-based facilities

NASA Astrophysics Data System (ADS)

Hauschild, Swantje; Tauber, Svantje; Lauber, Beatrice; Thiel, Cora S.; Layer, Liliana E.; Ullrich, Oliver

2014-11-01

Dating back to the Apollo and Skylab missions, it has been reported that astronauts suffered from bacterial and viral infections during space flight or after returning to Earth. Blood analyses revealed strongly reduced capability of human lymphocytes to become active upon mitogenic stimulation. Since then, a large number of in vitro studies on human immune cells have been conducted in space, in parabolic flights, and in ground-based facilities. It became obvious that microgravity affects cell morphology and important cellular functions. Observed changes include cell proliferation, the cytoskeleton, signal transduction and gene expression. This review gives an overview of the current knowledge of T cell regulation under altered gravity conditions obtained by in vitro studies with special emphasis on the cell culture conditions used. We propose that future in vitro experiments should follow rigorous standardized cell culture conditions, which allows better comparison of the results obtained in different flight- and ground-based experiment platforms.

Multi-layered reasoning by means of conceptual fuzzy sets

NASA Technical Reports Server (NTRS)

Takagi, Tomohiro; Imura, Atsushi; Ushida, Hirohide; Yamaguchi, Toru

1993-01-01

The real world consists of a very large number of instances of events and continuous numeric values. On the other hand, people represent and process their knowledge in terms of abstracted concepts derived from generalization of these instances and numeric values. Logic based paradigms for knowledge representation use symbolic processing both for concept representation and inference. Their underlying assumption is that a concept can be defined precisely. However, as this assumption hardly holds for natural concepts, it follows that symbolic processing cannot deal with such concepts. Thus symbolic processing has essential problems from a practical point of view of applications in the real world. In contrast, fuzzy set theory can be viewed as a stronger and more practical notation than formal, logic based theories because it supports both symbolic processing and numeric processing, connecting the logic based world and the real world. In this paper, we propose multi-layered reasoning by using conceptual fuzzy sets (CFS). The general characteristics of CFS are discussed along with upper layer supervision and context dependent processing.

Soils of Walker Branch Watershed

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

Lietzke, D.A.

1994-03-01

The soil survey of Walker Branch Watershed (WBW) utilized the most up-to-date knowledge of soils, geology, and geohydrology in building the soils data base needed to reinterpret past research and to begin new research in the watershed. The soils of WBW were also compared with soils mapped elsewhere along Chestnut Ridge on the Oak Ridge Reservation to (1) establish whether knowledge obtained elsewhere could be used within the watershed, (2) determine whether there were any soils restricted to the watershed, and (3) evaluate geologic formation lateral variability. Soils, surficial geology, and geomorphology were mapped at a scale of 1:1,200 usingmore » a paper base map having 2-ft contour intervals. Most of the contours seemed to reasonably represent actual landform configurations, except for dense wooded areas. For example, the very large dolines or sinkholes were shown on the contour base map, but numerous smaller ones were not. In addition, small drainageways and gullies were often not shown. These often small but important features were located approximately as soil mapping progressed.« less

Using multimodal information for the segmentation of fluorescent micrographs with application to virology and microbiology.

PubMed

Held, Christian; Wenzel, Jens; Webel, Rike; Marschall, Manfred; Lang, Roland; Palmisano, Ralf; Wittenberg, Thomas

2011-01-01

In order to improve reproducibility and objectivity of fluorescence microscopy based experiments and to enable the evaluation of large datasets, flexible segmentation methods are required which are able to adapt to different stainings and cell types. This adaption is usually achieved by the manual adjustment of the segmentation methods parameters, which is time consuming and challenging for biologists with no knowledge on image processing. To avoid this, parameters of the presented methods automatically adapt to user generated ground truth to determine the best method and the optimal parameter setup. These settings can then be used for segmentation of the remaining images. As robust segmentation methods form the core of such a system, the currently used watershed transform based segmentation routine is replaced by a fast marching level set based segmentation routine which incorporates knowledge on the cell nuclei. Our evaluations reveal that incorporation of multimodal information improves segmentation quality for the presented fluorescent datasets.

Wetlands as large-scale nature-based solutions: status and future challenges for research and management

NASA Astrophysics Data System (ADS)

Thorslund, Josefin; Jarsjö, Jerker; Destouni, Georgia

2017-04-01

Wetlands are often considered as nature-based solutions that can provide a multitude of services of great social, economic and environmental value to humankind. The services may include recreation, greenhouse gas sequestration, contaminant retention, coastal protection, groundwater level and soil moisture regulation, flood regulation and biodiversity support. Changes in land-use, water use and climate can all impact wetland functions and occur at scales extending well beyond the local scale of an individual wetland. However, in practical applications, management decisions usually regard and focus on individual wetland sites and local conditions. To understand the potential usefulness and services of wetlands as larger-scale nature-based solutions, e.g. for mitigating negative impacts from large-scale change pressures, one needs to understand the combined function multiple wetlands at the relevant large scales. We here systematically investigate if and to what extent research so far has addressed the large-scale dynamics of landscape systems with multiple wetlands, which are likely to be relevant for understanding impacts of regional to global change. Our investigation regards key changes and impacts of relevance for nature-based solutions, such as large-scale nutrient and pollution retention, flow regulation and coastal protection. Although such large-scale knowledge is still limited, evidence suggests that the aggregated functions and effects of multiple wetlands in the landscape can differ considerably from those observed at individual wetlands. Such scale differences may have important implications for wetland function-effect predictability and management under large-scale change pressures and impacts, such as those of climate change.

Using Interactive Family Science Shows to Improve Public Knowledge on Antibiotic Resistance: Does It Work?

PubMed Central

Lecky, Donna M.; Hawking, Meredith K. D.; Verlander, Neville Q.; McNulty, Cliodna A. M.

2014-01-01

The public plays an important role in controlling the emergence and spread of antibiotic resistance. A large British survey showed that there is still public misunderstanding about microbes and antibiotics. e-Bug, a European DG Sanco sponsored project, aims to disseminate a school antibiotic and hygiene educational pack and website across Europe. Interactive science shows based on the e-Bug educational packs were developed to take the key health and hygiene messages from the e-Bug school resources to families. The science show was evaluated to assess public knowledge and understanding of antibiotics and antibiotic resistance pre and post intervention. An interactive stall comprised of a 3×2 m backing stand with background information, an interactive activity and discussions with a trained demonstrator was on display at a family holiday resort. Pre-piloted knowledge questionnaires were completed by parents and children pre and post intervention. Adult (≥19 years) baseline knowledge regarding antibiotics and antibiotic resistance was high although significant knowledge improvement was observed where baseline knowledge was low. Children's (5–11 years) knowledge around antibiotics and antibiotic resistance was significantly improved for all questions. The science show can be viewed as a success in improving parents' and children's knowledge of antibiotic use thereby highlighting the importance of educating the public through interaction. PMID:25162505

Space Shuttle Projects

NASA Image and Video Library

1993-10-01

Designed by the mission crew members, the STS-61 crew insignia depicts the astronaut symbol superimposed against the sky with the Earth underneath. Also seen are two circles representing the optical configuration of the Hubble Space Telescope (HST). Light is focused by reflections from a large primary mirror and a smaller secondary mirror. The light is analyzed by various instruments and, according to the crew members, brings to us on Earth knowledge about planets, stars, galaxies and other celestial objects, allowing us to better understand the complex physical processes at work in the universe. The Space Shuttle Endeavour is also represented as the fundamental tool that allows the crew to perform the first servicing of the Hubble Space Telescope so its scientific deep space mission may be extended for several years to come. The overall design of the emblem, with lines converging to a high point, is also a symbolic representation of the large-scale Earth-based effort which involves space agencies, industry, and the universities to reach goals of knowledge and perfection.

GENOME-WIDE GENETIC INTERACTION ANALYSIS OF GLAUCOMA USING EXPERT KNOWLEDGE DERIVED FROM HUMAN PHENOTYPE NETWORKS

PubMed Central

HU, TING; DARABOS, CHRISTIAN; CRICCO, MARIA E.; KONG, EMILY; MOORE, JASON H.

2014-01-01

The large volume of GWAS data poses great computational challenges for analyzing genetic interactions associated with common human diseases. We propose a computational framework for characterizing epistatic interactions among large sets of genetic attributes in GWAS data. We build the human phenotype network (HPN) and focus around a disease of interest. In this study, we use the GLAUGEN glaucoma GWAS dataset and apply the HPN as a biological knowledge-based filter to prioritize genetic variants. Then, we use the statistical epistasis network (SEN) to identify a significant connected network of pairwise epistatic interactions among the prioritized SNPs. These clearly highlight the complex genetic basis of glaucoma. Furthermore, we identify key SNPs by quantifying structural network characteristics. Through functional annotation of these key SNPs using Biofilter, a software accessing multiple publicly available human genetic data sources, we find supporting biomedical evidences linking glaucoma to an array of genetic diseases, proving our concept. We conclude by suggesting hypotheses for a better understanding of the disease. PMID:25592582

A Large-Scale Analysis of Variance in Written Language.

PubMed

Johns, Brendan T; Jamieson, Randall K

2018-01-22

The collection of very large text sources has revolutionized the study of natural language, leading to the development of several models of language learning and distributional semantics that extract sophisticated semantic representations of words based on the statistical redundancies contained within natural language (e.g., Griffiths, Steyvers, & Tenenbaum, ; Jones & Mewhort, ; Landauer & Dumais, ; Mikolov, Sutskever, Chen, Corrado, & Dean, ). The models treat knowledge as an interaction of processing mechanisms and the structure of language experience. But language experience is often treated agnostically. We report a distributional semantic analysis that shows written language in fiction books varies appreciably between books from the different genres, books from the same genre, and even books written by the same author. Given that current theories assume that word knowledge reflects an interaction between processing mechanisms and the language environment, the analysis shows the need for the field to engage in a more deliberate consideration and curation of the corpora used in computational studies of natural language processing. Copyright © 2018 Cognitive Science Society, Inc.

Region Evolution eXplorer - A tool for discovering evolution trends in ontology regions.

PubMed

Christen, Victor; Hartung, Michael; Groß, Anika

2015-01-01

A large number of life science ontologies has been developed to support different application scenarios such as gene annotation or functional analysis. The continuous accumulation of new insights and knowledge affects specific portions in ontologies and thus leads to their adaptation. Therefore, it is valuable to study which ontology parts have been extensively modified or remained unchanged. Users can monitor the evolution of an ontology to improve its further development or apply the knowledge in their applications. Here we present REX (Region Evolution eXplorer) a web-based system for exploring the evolution of ontology parts (regions). REX provides an analysis platform for currently about 1,000 versions of 16 well-known life science ontologies. Interactive workflows allow an explorative analysis of changing ontology regions and can be used to study evolution trends for long-term periods. REX is a web application providing an interactive and user-friendly interface to identify (un)stable regions in large life science ontologies. It is available at http://www.izbi.de/rex.

Metal artifact reduction for CT-based luggage screening.

PubMed

Karimi, Seemeen; Martz, Harry; Cosman, Pamela

2015-01-01

In aviation security, checked luggage is screened by computed tomography scanning. Metal objects in the bags create artifacts that degrade image quality. Though there exist metal artifact reduction (MAR) methods mainly in medical imaging literature, they require knowledge of the materials in the scan, or are outlier rejection methods. To improve and evaluate a MAR method we previously introduced, that does not require knowledge of the materials in the scan, and gives good results on data with large quantities and different kinds of metal. We describe in detail an optimization which de-emphasizes metal projections and has a constraint for beam hardening and scatter. This method isolates and reduces artifacts in an intermediate image, which is then fed to a previously published sinogram replacement method. We evaluate the algorithm for luggage data containing multiple and large metal objects. We define measures of artifact reduction, and compare this method against others in MAR literature. Metal artifacts were reduced in our test images, even for multiple and large metal objects, without much loss of structure or resolution. Our MAR method outperforms the methods with which we compared it. Our approach does not make assumptions about image content, nor does it discard metal projections.

Knowledge, attitudes and perceptions towards polio immunization among residents of two highly affected regions of Pakistan.

PubMed

Khan, Muhammad Umair; Ahmad, Akram; Aqeel, Talieha; Salman, Saad; Ibrahim, Qamer; Idrees, Jawaria; Khan, Muhammad Ubaid

2015-11-05

Despite the efforts of national and international organizations, polio has not been eradicated from Pakistan. The prevalence of polio in Pakistan is exceptional in global context. Quetta and Peshawar divisions are amongst the most affected regions hit by polio in Pakistan. This study was carried out to assess the knowledge, attitudes and perceptions towards polio immunization among residents of Quetta and Peshawar divisions in Pakistan. A descriptive, cross-sectional study involving 768 participants was conducted from August to December, 2014 in Quetta and Peshawar divisions in Pakistan. Multistage sampling technique was used to draw a sample of residents from each division. A pre-tested, self-administered questionnaire was used to collect the data from eligible participants. Descriptive and logistic regression analyses were used to express the results. A total of 38.8 % participants exhibited good knowledge about polio. Mean knowledge score of the participants was 7.35 ± 2.54 (based on 15 knowledge questions). Older age (p < 0.001), low qualification (p < 0.05), rural locality (p < 0.05) and Quetta division (p < 0.001) were significantly associated with poor knowledge of polio. A large proportion of participants displayed negative attitudes towards polio immunization (84.8 %), with a mean score of 19.19 ± 2.39 (based on 8 attitude statements). Lack of education (p < 0.001) and rural residence (p < 0.001) were significantly associated with the negative attitudes of participants towards polio immunization. False religious beliefs (39.06 %), lack of knowledge (33.7 %), fear of infertility by polio vaccines (32.16 %) and security issues (29.42 %) were reported by the participants as the main barriers towards polio immunization. The findings of this study showed poor knowledge and negative attitudes of participants towards polio immunizations. Religious beliefs and lack of knowledge about polio immunization were reported as the major barriers towards polio immunization.

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis.

PubMed

Kim, Seongsoon; Park, Donghyeon; Choi, Yonghwa; Lee, Kyubum; Kim, Byounggun; Jeon, Minji; Kim, Jihye; Tan, Aik Choon; Kang, Jaewoo

2018-01-05

With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge. ©Seongsoon Kim, Donghyeon Park, Yonghwa Choi, Kyubum Lee, Byounggun Kim, Minji Jeon, Jihye Kim, Aik Choon Tan, Jaewoo Kang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.01.2018.

A Framework for Spatial Interaction Analysis Based on Large-Scale Mobile Phone Data

PubMed Central

Li, Weifeng; Cheng, Xiaoyun; Guo, Gaohua

2014-01-01

The overall understanding of spatial interaction and the exact knowledge of its dynamic evolution are required in the urban planning and transportation planning. This study aimed to analyze the spatial interaction based on the large-scale mobile phone data. The newly arisen mass dataset required a new methodology which was compatible with its peculiar characteristics. A three-stage framework was proposed in this paper, including data preprocessing, critical activity identification, and spatial interaction measurement. The proposed framework introduced the frequent pattern mining and measured the spatial interaction by the obtained association. A case study of three communities in Shanghai was carried out as verification of proposed method and demonstration of its practical application. The spatial interaction patterns and the representative features proved the rationality of the proposed framework. PMID:25435865

Development and Two-Year Follow-Up Evaluation of a Training Workshop for the Large Preventive Positive Psychology Happy Family Kitchen Project in Hong Kong

PubMed Central

Lai, Agnes Y.; Mui, Moses W.; Wan, Alice; Stewart, Sunita M.; Yew, Carol; Lam, Tai-hing; Chan, Sophia S.

2016-01-01

Evidence-based practice and capacity-building approaches are essential for large-scale health promotion interventions. However, there are few models in the literature to guide and evaluate training of social service workers in community settings. This paper presents the development and evaluation of the “train-the-trainer” workshop (TTT) for the first large scale, community-based, family intervention projects, entitled “Happy Family Kitchen Project” (HFK) under the FAMILY project, a Hong Kong Jockey Club Initiative for a Harmonious Society. The workshop aimed to enhance social workers’ competence and performance in applying positive psychology constructs in their family interventions under HFK to improve family well-being of the community they served. The two-day TTT was developed and implemented by a multidisciplinary team in partnership with community agencies to 50 social workers (64% women). It focused on the enhancement of knowledge, attitude, and practice of five specific positive psychology themes, which were the basis for the subsequent development of the 23 family interventions for 1419 participants. Acceptability and applicability were enhanced by completing a needs assessment prior to the training. The TTT was evaluated by trainees’ reactions to the training content and design, changes in learners (trainees) and benefits to the service organizations. Focus group interviews to evaluate the workshop at three months after the training, and questionnaire survey at pre-training, immediately after, six months, one year and two years after training were conducted. There were statistically significant increases with large to moderate effect size in perceived knowledge, self-efficacy and practice after training, which sustained to 2-year follow-up. Furthermore, there were statistically significant improvements in family communication and well-being of the participants in the HFK interventions they implemented after training. This paper offers a practical example of development, implementation and model-based evaluation of training programs, which may be helpful to others seeking to develop such programs in diverse communities. PMID:26808541

Development and Two-Year Follow-Up Evaluation of a Training Workshop for the Large Preventive Positive Psychology Happy Family Kitchen Project in Hong Kong.

PubMed

Lai, Agnes Y; Mui, Moses W; Wan, Alice; Stewart, Sunita M; Yew, Carol; Lam, Tai-Hing; Chan, Sophia S

2016-01-01

Evidence-based practice and capacity-building approaches are essential for large-scale health promotion interventions. However, there are few models in the literature to guide and evaluate training of social service workers in community settings. This paper presents the development and evaluation of the "train-the-trainer" workshop (TTT) for the first large scale, community-based, family intervention projects, entitled "Happy Family Kitchen Project" (HFK) under the FAMILY project, a Hong Kong Jockey Club Initiative for a Harmonious Society. The workshop aimed to enhance social workers' competence and performance in applying positive psychology constructs in their family interventions under HFK to improve family well-being of the community they served. The two-day TTT was developed and implemented by a multidisciplinary team in partnership with community agencies to 50 social workers (64% women). It focused on the enhancement of knowledge, attitude, and practice of five specific positive psychology themes, which were the basis for the subsequent development of the 23 family interventions for 1419 participants. Acceptability and applicability were enhanced by completing a needs assessment prior to the training. The TTT was evaluated by trainees' reactions to the training content and design, changes in learners (trainees) and benefits to the service organizations. Focus group interviews to evaluate the workshop at three months after the training, and questionnaire survey at pre-training, immediately after, six months, one year and two years after training were conducted. There were statistically significant increases with large to moderate effect size in perceived knowledge, self-efficacy and practice after training, which sustained to 2-year follow-up. Furthermore, there were statistically significant improvements in family communication and well-being of the participants in the HFK interventions they implemented after training. This paper offers a practical example of development, implementation and model-based evaluation of training programs, which may be helpful to others seeking to develop such programs in diverse communities.

KoBaMIN: a knowledge-based minimization web server for protein structure refinement.

PubMed

Rodrigues, João P G L M; Levitt, Michael; Chopra, Gaurav

2012-07-01

The KoBaMIN web server provides an online interface to a simple, consistent and computationally efficient protein structure refinement protocol based on minimization of a knowledge-based potential of mean force. The server can be used to refine either a single protein structure or an ensemble of proteins starting from their unrefined coordinates in PDB format. The refinement method is particularly fast and accurate due to the underlying knowledge-based potential derived from structures deposited in the PDB; as such, the energy function implicitly includes the effects of solvent and the crystal environment. Our server allows for an optional but recommended step that optimizes stereochemistry using the MESHI software. The KoBaMIN server also allows comparison of the refined structures with a provided reference structure to assess the changes brought about by the refinement protocol. The performance of KoBaMIN has been benchmarked widely on a large set of decoys, all models generated at the seventh worldwide experiments on critical assessment of techniques for protein structure prediction (CASP7) and it was also shown to produce top-ranking predictions in the refinement category at both CASP8 and CASP9, yielding consistently good results across a broad range of model quality values. The web server is fully functional and freely available at http://csb.stanford.edu/kobamin.

Effect of railway safety education on the safety knowledge and behaviour intention of schoolchildren.

PubMed

Silla, Anne; Kallberg, Veli-Pekka

2016-04-01

This study was designed to evaluate whether railway safety lessons are effective in increasing schoolchildren's safety knowledge and behaviour intention. The railway safety education in schools included a 45-min lesson on safe behaviour in a railway environment directed at 8-11 year old schoolchildren. The lessons were held in four schools located near railway lines in Finland. The effectiveness of this measure was evaluated based on a short survey directed at pupils before the lesson (base level) and around 2-3 months later (post-lesson) based on three variables which are considered as strong determinants of actual behaviour: behaviour intention, estimated dangerousness of the behaviour, and level of knowledge on the legality of the behaviour. The results show that the change in the share of correct answers was positive regarding all questions except for one question in which the difference was not significant. Based on this we can reasonably assume that railway safety education in schools can have a positive effect for all the measured variables, although the effect is not necessarily large. The results indicate that these positive changes can have a positive effect on the frequency of trespassing (i.e. fewer unsafe crossings in the future). We can further assume that reduction in the frequency of trespassing would reduce the frequency of trespassing accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.

Eurogin 2016 Roadmap: how HPV knowledge is changing screening practice.

PubMed

Wentzensen, Nicolas; Arbyn, Marc; Berkhof, Johannes; Bower, Mark; Canfell, Karen; Einstein, Mark; Farley, Christopher; Monsonego, Joseph; Franceschi, Silvia

2017-05-15

Human papillomaviruses (HPVs) are the necessary cause of most cervical cancers, a large proportion of other anogenital cancers, and a subset of oropharyngeal cancers. The knowledge about HPV has led to development of novel HPV-based prevention strategies with important impact on clinical and public health practice. Two complementary reviews have been prepared following the 2015 Eurogin Conference to evaluate how knowledge about HPV is changing practice in HPV infection and disease control through vaccination and screening. This review focuses on screening for cervical and anal cancers in increasingly vaccinated populations. The introduction of HPV vaccines a decade ago has led to reductions in HPV infections and early cancer precursors in countries with wide vaccination coverage. Despite the high efficacy of HPV vaccines, cervical cancer screening will remain important for many decades. Many healthcare systems are considering switching to primary HPV screening, which has higher sensitivity for cervical precancers and allows extending screening intervals. We describe different approaches to implementing HPV-based screening efforts in different healthcare systems with a focus in high-income countries. While the population prevalence for other anogenital cancers is too low for population-based screening, anal cancer incidence is very high in HIV-infected men who have sex with men, warranting consideration of early detection approaches. We summarize the current evidence on HPV-based prevention of anal cancers and highlight important evidence gaps. © 2016 UICC.

Drug2Gene: an exhaustive resource to explore effectively the drug-target relation network.

PubMed

Roider, Helge G; Pavlova, Nadia; Kirov, Ivaylo; Slavov, Stoyan; Slavov, Todor; Uzunov, Zlatyo; Weiss, Bertram

2014-03-11

Information about drug-target relations is at the heart of drug discovery. There are now dozens of databases providing drug-target interaction data with varying scope, and focus. Therefore, and due to the large chemical space, the overlap of the different data sets is surprisingly small. As searching through these sources manually is cumbersome, time-consuming and error-prone, integrating all the data is highly desirable. Despite a few attempts, integration has been hampered by the diversity of descriptions of compounds, and by the fact that the reported activity values, coming from different data sets, are not always directly comparable due to usage of different metrics or data formats. We have built Drug2Gene, a knowledge base, which combines the compound/drug-gene/protein information from 19 publicly available databases. A key feature is our rigorous unification and standardization process which makes the data truly comparable on a large scale, allowing for the first time effective data mining in such a large knowledge corpus. As of version 3.2, Drug2Gene contains 4,372,290 unified relations between compounds and their targets most of which include reported bioactivity data. We extend this set with putative (i.e. homology-inferred) relations where sufficient sequence homology between proteins suggests they may bind to similar compounds. Drug2Gene provides powerful search functionalities, very flexible export procedures, and a user-friendly web interface. Drug2Gene v3.2 has become a mature and comprehensive knowledge base providing unified, standardized drug-target related information gathered from publicly available data sources. It can be used to integrate proprietary data sets with publicly available data sets. Its main goal is to be a 'one-stop shop' to identify tool compounds targeting a given gene product or for finding all known targets of a drug. Drug2Gene with its integrated data set of public compound-target relations is freely accessible without restrictions at http://www.drug2gene.com.

Large-System Transformation in Health Care: A Realist Review

PubMed Central

Best, Allan; Greenhalgh, Trisha; Lewis, Steven; Saul, Jessie E; Carroll, Simon; Bitz, Jennifer

2012-01-01

Context An evidence base that addresses issues of complexity and context is urgently needed for large-system transformation (LST) and health care reform. Fundamental conceptual and methodological challenges also must be addressed. The Saskatchewan Ministry of Health in Canada requested a six-month synthesis project to guide four major policy development and strategy initiatives focused on patient- and family-centered care, primary health care renewal, quality improvement, and surgical wait lists. The aims of the review were to analyze examples of successful and less successful transformation initiatives, to synthesize knowledge of the underlying mechanisms, to clarify the role of government, and to outline options for evaluation. Methods We used realist review, whose working assumption is that a particular intervention triggers particular mechanisms of change. Mechanisms may be more or less effective in producing their intended outcomes, depending on their interaction with various contextual factors. We explain the variations in outcome as the interplay between context and mechanisms. We nested this analytic approach in a macro framing of complex adaptive systems (CAS). Findings Our rapid realist review identified five “simple rules” of LST that were likely to enhance the success of the target initiatives: (1) blend designated leadership with distributed leadership; (2) establish feedback loops; (3) attend to history; (4) engage physicians; and (5) include patients and families. These principles play out differently in different contexts affecting human behavior (and thereby contributing to change) through a wide range of different mechanisms. Conclusions Realist review methodology can be applied in combination with a complex system lens on published literature to produce a knowledge synthesis that informs a prospective change effort in large-system transformation. A collaborative process engaging both research producers and research users contributes to local applications of universal principles and mid-range theories, as well as to a more robust knowledge base for applied research. We conclude with suggestions for the future development of synthesis and evaluation methods. PMID:22985277

Map of Life - A Dashboard for Monitoring Planetary Species Distributions

NASA Astrophysics Data System (ADS)

Jetz, W.

2016-12-01

Geographic information about biodiversity is vital for understanding the many services nature provides and their potential changes, yet remains unreliable and often insufficient. By integrating a wide range of knowledge about species distributions and their dynamics over time, Map of Life supports global biodiversity education, monitoring, research and decision-making. Built on a scalable web platform geared for large biodiversity and environmental data, Map of Life endeavors provides species range information globally and species lists for any area. With data and technology provided by NASA and Google Earth Engine, tools under development use remote sensing-based environmental layers to enable on-the-fly predictions of species distributions, range changes, and early warning signals for threatened species. The ultimate vision is a globally connected, collaborative knowledge- and tool-base for regional and local biodiversity decision-making, education, monitoring, and projection. For currently available tools, more information and to follow progress, go to MOL.org.

Occupational Health in Eastern Europe

PubMed Central

Malan, R. M.

1963-01-01

Progress may be fostered as much by spreading information as by research. The aim of this review is to add to the existing knowledge of the pattern of occupational health services in the socialist countries of Eastern Europe. The work consists of two main parts. Part I is based on official information issued by government departments or typewritten reports prepared by government officials, and relates mostly to the Union of Soviet Socialist Republics and to Czechoslovakia. Part II is largely based on direct observation, discussion, and comparison of the occupational health services in Czechoslovakia, of which I have more extensive knowledge than of the other countries of Eastern Europe. This part embodies a number of conclusions and is followed by a list of bibliographical references. Throughout the review I have endeavoured to show how problems which exist all over the world are dealt with in Eastern Europe. PMID:13932439

Knowledge acquisition is governed by striatal prediction errors.

PubMed

Pine, Alex; Sadeh, Noa; Ben-Yakov, Aya; Dudai, Yadin; Mendelsohn, Avi

2018-04-26

Discrepancies between expectations and outcomes, or prediction errors, are central to trial-and-error learning based on reward and punishment, and their neurobiological basis is well characterized. It is not known, however, whether the same principles apply to declarative memory systems, such as those supporting semantic learning. Here, we demonstrate with fMRI that the brain parametrically encodes the degree to which new factual information violates expectations based on prior knowledge and beliefs-most prominently in the ventral striatum, and cortical regions supporting declarative memory encoding. These semantic prediction errors determine the extent to which information is incorporated into long-term memory, such that learning is superior when incoming information counters strong incorrect recollections, thereby eliciting large prediction errors. Paradoxically, by the same account, strong accurate recollections are more amenable to being supplanted by misinformation, engendering false memories. These findings highlight a commonality in brain mechanisms and computational rules that govern declarative and nondeclarative learning, traditionally deemed dissociable.

Effectiveness of an Asynchronous Online Module on University Students' Understanding of the Bohr Model of the Hydrogen Atom

NASA Astrophysics Data System (ADS)

Farina, William J.; Bodzin, Alec M.

2017-12-01

Web-based learning is a growing field in education, yet empirical research into the design of high quality Web-based university science instruction is scarce. A one-week asynchronous online module on the Bohr Model of the atom was developed and implemented guided by the knowledge integration framework. The unit design aligned with three identified metaprinciples of science learning: making science accessible, making thinking visible, and promoting autonomy. Students in an introductory chemistry course at a large east coast university completed either an online module or traditional classroom instruction. Data from 99 students were analyzed and results showed significant knowledge growth in both online and traditional formats. For the online learning group, findings revealed positive student perceptions of their learning experiences, highly positive feedback for online science learning, and an interest amongst students to learn chemistry within an online environment.

Why small-scale cannabis growers stay small: five mechanisms that prevent small-scale growers from going large scale.

PubMed

Hammersvik, Eirik; Sandberg, Sveinung; Pedersen, Willy

2012-11-01

Over the past 15-20 years, domestic cultivation of cannabis has been established in a number of European countries. New techniques have made such cultivation easier; however, the bulk of growers remain small-scale. In this study, we explore the factors that prevent small-scale growers from increasing their production. The study is based on 1 year of ethnographic fieldwork and qualitative interviews conducted with 45 Norwegian cannabis growers, 10 of whom were growing on a large-scale and 35 on a small-scale. The study identifies five mechanisms that prevent small-scale indoor growers from going large-scale. First, large-scale operations involve a number of people, large sums of money, a high work-load and a high risk of detection, and thus demand a higher level of organizational skills than for small growing operations. Second, financial assets are needed to start a large 'grow-site'. Housing rent, electricity, equipment and nutrients are expensive. Third, to be able to sell large quantities of cannabis, growers need access to an illegal distribution network and knowledge of how to act according to black market norms and structures. Fourth, large-scale operations require advanced horticultural skills to maximize yield and quality, which demands greater skills and knowledge than does small-scale cultivation. Fifth, small-scale growers are often embedded in the 'cannabis culture', which emphasizes anti-commercialism, anti-violence and ecological and community values. Hence, starting up large-scale production will imply having to renegotiate or abandon these values. Going from small- to large-scale cannabis production is a demanding task-ideologically, technically, economically and personally. The many obstacles that small-scale growers face and the lack of interest and motivation for going large-scale suggest that the risk of a 'slippery slope' from small-scale to large-scale growing is limited. Possible political implications of the findings are discussed. Copyright © 2012 Elsevier B.V. All rights reserved.

The Effect of Game-Based Interventions in Rehabilitation of Diabetics: A Systematic Review and Meta-Analysis.

PubMed

Christensen, Jan; Valentiner, Laura Staun; Petersen, Rikke Juelsgaard; Langberg, Henning

2016-10-01

Game-based interventions have been proposed as a way to improve both patient adherence to physical activity (PA) and disease-related knowledge to achieve better self-management of blood glucose levels (HbA1c). The objective of this study was to systematically review the literature on the effect of game-based interventions on HbA1c, diabetes-related knowledge, and physical outcomes in rehabilitation of diabetes patients. We conducted a systematic literature search in MEDLINE, EMBASE, PEDro, Scopus, Cochrane Central Register of Controlled Trials, CINAHL, and Psych INFO in October 2014 based on a priori defined inclusion criteria: patients with diabetes (type 1 or type 2), game-based interventions, and randomized controlled trials. The database search identified 1,101 potential articles for screening, four of which were eligible for the present systematic review. Game-based interventions show no effect on HbA1c (three studies) standardized mean difference = -0.10, 95% confidence interval = [-0.33, 0.14] compared to usual care or waiting lists. Game-based interventions were superior to controls in improving health-related quality of life, muscle strength, and balance (one study). No difference was found between game-based interventions and usual care or waiting lists in terms of diabetes-related knowledge (one study). PA is important for diabetes management. The present review indicates that game-based interventions are not superior to ordinary PA in controlling HbA1c. Due to the weak methodological quality of the included studies and the very low body of evidence, the likelihood that the real effect of game-based interventions will be substantially different (i.e., large enough difference to possibly affect decision-making) is high.

The Relationship between Scientific Knowledge and Behaviour: An HIV/AIDS Case

ERIC Educational Resources Information Center

Mnguni, Lindelani; Abrie, Mia; Ebersohn, Liesel

2016-01-01

Debates on the role of scientific knowledge to affect behaviour are continuing. The theory of planned behaviour suggests that behaviour is influenced by attitudes, subjective norms and perceived behavioural control and not by knowledge. However, a large body of knowledge argues that increased HIV/AIDS-related knowledge leads to the adoption of…