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.

Climbing plants: attachment adaptations and bioinspired innovations.

PubMed

Burris, Jason N; Lenaghan, Scott C; Stewart, C Neal

2018-04-01

Climbing plants have unique adaptations to enable them to compete for sunlight, for which they invest minimal resources for vertical growth. Indeed, their stems bear relatively little weight, as they traverse their host substrates skyward. Climbers possess high tensile strength and flexibility, which allows them to utilize natural and manmade structures for support and growth. The climbing strategies of plants have intrigued scientists for centuries, yet our understanding about biochemical adaptations and their molecular undergirding is still in the early stages of research. Nonetheless, recent discoveries are promising, not only from a basic knowledge perspective, but also for bioinspired product development. Several adaptations, including nanoparticle and adhesive production will be reviewed, as well as practical translation of these adaptations to commercial applications. We will review the botanical literature on the modes of adaptation to climb, as well as specialized organs-and cellular innovations. Finally, recent molecular and biochemical data will be reviewed to assess the future needs and new directions for potential practical products that may be bioinspired by climbing plants.

System Architecture Development for Energy and Water Infrastructure Data Management and Geovisual Analytics

NASA Astrophysics Data System (ADS)

Berres, A.; Karthik, R.; Nugent, P.; Sorokine, A.; Myers, A.; Pang, H.

2017-12-01

Building an integrated data infrastructure that can meet the needs of a sustainable energy-water resource management requires a robust data management and geovisual analytics platform, capable of cross-domain scientific discovery and knowledge generation. Such a platform can facilitate the investigation of diverse complex research and policy questions for emerging priorities in Energy-Water Nexus (EWN) science areas. Using advanced data analytics, machine learning techniques, multi-dimensional statistical tools, and interactive geovisualization components, such a multi-layered federated platform is being developed, the Energy-Water Nexus Knowledge Discovery Framework (EWN-KDF). This platform utilizes several enterprise-grade software design concepts and standards such as extensible service-oriented architecture, open standard protocols, event-driven programming model, enterprise service bus, and adaptive user interfaces to provide a strategic value to the integrative computational and data infrastructure. EWN-KDF is built on the Compute and Data Environment for Science (CADES) environment in Oak Ridge National Laboratory (ORNL).

Novel innate and adaptive lymphocytes: The new players in the pathogenesis of inflammatory upper airway diseases.

PubMed

Liu, Y; Yao, Y; Wang, Z-C; Ning, Q; Liu, Z

2018-06-01

Host immunity (innate and adaptive immunity) plays essential roles in the pathogenesis of inflammatory upper airway diseases, including allergic rhinitis and chronic rhinosinusitis. Recently, the discovery of novel innate immune cells, particularly innate lymphoid cells, has renewed our view on the role of innate immunity in inflammatory upper airway diseases. Meanwhile, the identification of new subsets of T helper (Th) cells, including Th22, Th9 and follicular Th cells, and regulatory B cells in the adaptive immunity, has broadened our knowledge on the complex immune networks in inflammatory upper airway diseases. In this review, we focus on these newly identified innate and adaptive lymphocytes with their contributions to the immunological disturbance in allergic rhinitis and chronic rhinosinusitis. We further discuss the perspective for future research and potential clinical utility of regulating these novel lymphocytes for the treatment of allergic rhinitis and chronic rhinosinusitis. © 2018 John Wiley & Sons Ltd.

The semantic connectivity map: an adapting self-organising knowledge discovery method in data bases. Experience in gastro-oesophageal reflux disease.

PubMed

Buscema, Massimo; Grossi, Enzo

2008-01-01

We describe here a new mapping method able to find out connectivity traces among variables thanks to an artificial adaptive system, the Auto Contractive Map (AutoCM), able to define the strength of the associations of each variable with all the others in a dataset. After the training phase, the weights matrix of the AutoCM represents the map of the main connections between the variables. The example of gastro-oesophageal reflux disease data base is extremely useful to figure out how this new approach can help to re-design the overall structure of factors related to complex and specific diseases description.

Knowledge Discovery from Databases: An Introductory Review.

ERIC Educational Resources Information Center

Vickery, Brian

1997-01-01

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

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

NASA Astrophysics Data System (ADS)

Gijlers, Hannie; de Jong, Ton

2005-03-01

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

The Th17 Lineage: From Barrier Surfaces Homeostasis to Autoimmunity, Cancer, and HIV-1 Pathogenesis.

PubMed

Wacleche, Vanessa Sue; Landay, Alan; Routy, Jean-Pierre; Ancuta, Petronela

2017-10-19

The T helper 17 (Th17) cells represent a subset of CD4+ T-cells with unique effector functions, developmental plasticity, and stem-cell features. Th17 cells bridge innate and adaptive immunity against fungal and bacterial infections at skin and mucosal barrier surfaces. Although Th17 cells have been extensively studied in the context of autoimmunity, their role in various other pathologies is underexplored and remains an area of open investigation. This review summarizes the history of Th17 cell discovery and the current knowledge relative to the beneficial role of Th17 cells in maintaining mucosal immunity homeostasis. We further discuss the concept of Th17 pathogenicity in the context of autoimmunity, cancer, and HIV infection, and we review the most recent discoveries on molecular mechanisms regulating HIV replication/persistence in pathogenic Th17 cells. Finally, we stress the need for novel fundamental research discovery-based Th17-specific therapeutic interventions to treat pathogenic conditions associated with Th17 abnormalities, including HIV infection.

Information Fusion for Natural and Man-Made Disasters

DTIC Science & Technology

2007-01-31

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

Barrancaceae: A new green algal lineage with structural and behavioral adaptations to a fluctuating environment.

PubMed

Caisová, Lenka; Reyes, Carolina Pérez; Álamo, Virginia Cruz; Quintana, Antera Martel; Surek, Barbara; Melkonian, Michael

2015-09-01

To enhance our knowledge of the diversity of microalgae, a phycological survey of the Canary Islands (Spain) was undertaken. Here we report the discovery of a (semi)terrestrial green filamentous alga isolated from a steep volcanic canyon on La Palma. This alga is continually exposed to changing weather conditions (floods vs. droughts) and thus provides a good opportunity to investigate possible adaptations to a semiterrestrial habitat with large fluctuations of environmental parameters. We used axenic cultures, simulated flood and drought stresses and studied their effect on the life history of the alga using light, confocal laser scanning and scanning electron microscopy including fluorescent staining. Furthermore, phylogenetic analyses using rDNA sequence comparisons were performed. Three specific life-history traits that likely represent adaptations to the fluctuating environment of the canyon were observed: (1) fragmentation through "filament splitting", a unique branching mechanism not reported before in algae and initiated by formation of oblique cross walls, (2) aplanospore formation, and (3) reproduction by multiflagellate zoospores with 4-24 flagella arranged in groups of four. Phylogenetic analyses identified the alga as Barranca multiflagellata gen. et sp. nov. (Barrancaceae fam. nov., Chaetophorales, Chlorophyceae). Moreover, the Chaetophoraceae Greville, 1824 was emended and a new family, Uronemataceae (fam. nov.) erected. The discovery of Barrancaceae fam. nov. highlights the importance of investigating nonconventional habitats to explore microalgal diversity. The reproductive versatility demonstrated by Barranca suggests adaptation to a semiterrestrial habitat with large fluctuations in water availability. © 2015 Botanical Society of America.

A KNOWLEDGE DISCOVERY STRATEGY FOR RELATING SEA SURFACE TEMPERATURES TO FREQUENCIES OF TROPICAL STORMS AND GENERATING PREDICTIONS OF HURRICANES UNDER 21ST-CENTURY GLOBAL WARMING SCENARIOS

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

Race, Caitlin; Steinbach, Michael; Ganguly, Auroop R

2010-01-01

The connections among greenhouse-gas emissions scenarios, global warming, and frequencies of hurricanes or tropical cyclones are among the least understood in climate science but among the most fiercely debated in the context of adaptation decisions or mitigation policies. Here we show that a knowledge discovery strategy, which leverages observations and climate model simulations, offers the promise of developing credible projections of tropical cyclones based on sea surface temperatures (SST) in a warming environment. While this study motivates the development of new methodologies in statistics and data mining, the ability to solve challenging climate science problems with innovative combinations of traditionalmore » and state-of-the-art methods is demonstrated. Here we develop new insights, albeit in a proof-of-concept sense, on the relationship between sea surface temperatures and hurricane frequencies, and generate the most likely projections with uncertainty bounds for storm counts in the 21st-century warming environment based in turn on the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios. Our preliminary insights point to the benefits that can be achieved for climate science and impacts analysis, as well as adaptation and mitigation policies, by a solution strategy that remains tailored to the climate domain and complements physics-based climate model simulations with a combination of existing and new computational and data science approaches.« less

A New System To Support Knowledge Discovery: Telemakus.

ERIC Educational Resources Information Center

Revere, Debra; Fuller, Sherrilynne S.; Bugni, Paul F.; Martin, George M.

2003-01-01

The Telemakus System builds on the areas of concept representation, schema theory, and information visualization to enhance knowledge discovery from scientific literature. This article describes the underlying theories and an overview of a working implementation designed to enhance the knowledge discovery process through retrieval, visual and…

Knowledge Discovery as an Aid to Organizational Creativity.

ERIC Educational Resources Information Center

Siau, Keng

2000-01-01

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

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

DTIC Science & Technology

2017-06-27

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

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

ERIC Educational Resources Information Center

Gijlers, Hannie; de Jong, Ton

2005-01-01

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

Early repositioning through compound set enrichment analysis: a knowledge-recycling strategy.

PubMed

Temesi, Gergely; Bolgár, Bence; Arany, Adám; Szalai, Csaba; Antal, Péter; Mátyus, Péter

2014-04-01

Despite famous serendipitous drug repositioning success stories, systematic projects have not yet delivered the expected results. However, repositioning technologies are gaining ground in different phases of routine drug development, together with new adaptive strategies. We demonstrate the power of the compound information pool, the ever-growing heterogeneous information repertoire of approved drugs and candidates as an invaluable catalyzer in this transition. Systematic, computational utilization of this information pool for candidates in early phases is an open research problem; we propose a novel application of the enrichment analysis statistical framework for fusion of this information pool, specifically for the prediction of indications. Pharmaceutical consequences are formulated for a systematic and continuous knowledge recycling strategy, utilizing this information pool throughout the drug-discovery pipeline.

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

PubMed

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

2006-11-01

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

SemaTyP: a knowledge graph based literature mining method for drug discovery.

PubMed

Sang, Shengtian; Yang, Zhihao; Wang, Lei; Liu, Xiaoxia; Lin, Hongfei; Wang, Jian

2018-05-30

Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.

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

ERIC Educational Resources Information Center

Guruler, Huseyin; Istanbullu, Ayhan; Karahasan, Mehmet

2010-01-01

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

Knowledge Discovery in Databases.

ERIC Educational Resources Information Center

Norton, M. Jay

1999-01-01

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

Cultural adaptation, compounding vulnerabilities and conjunctures in Norse Greenland.

PubMed

Dugmore, Andrew J; McGovern, Thomas H; Vésteinsson, Orri; Arneborg, Jette; Streeter, Richard; Keller, Christian

2012-03-06

Norse Greenland has been seen as a classic case of maladaptation by an inflexible temperate zone society extending into the arctic and collapse driven by climate change. This paper, however, recognizes the successful arctic adaptation achieved in Norse Greenland and argues that, although climate change had impacts, the end of Norse settlement can only be truly understood as a complex socioenvironmental system that includes local and interregional interactions operating at different geographic and temporal scales and recognizes the cultural limits to adaptation of traditional ecological knowledge. This paper is not focused on a single discovery and its implications, an approach that can encourage monocausal and environmentally deterministic emphasis to explanation, but it is the product of sustained international interdisciplinary investigations in Greenland and the rest of the North Atlantic. It is based on data acquisitions, reinterpretation of established knowledge, and a somewhat different philosophical approach to the question of collapse. We argue that the Norse Greenlanders created a flexible and successful subsistence system that responded effectively to major environmental challenges but probably fell victim to a combination of conjunctures of large-scale historic processes and vulnerabilities created by their successful prior response to climate change. Their failure was an inability to anticipate an unknowable future, an inability to broaden their traditional ecological knowledge base, and a case of being too specialized, too small, and too isolated to be able to capitalize on and compete in the new protoworld system extending into the North Atlantic in the early 15th century.

Space Shuttle Discovery Docked to the Pressurized Mating Adapter

NASA Technical Reports Server (NTRS)

2007-01-01

Space Shuttle Discovery, docked to the Pressurized Mating Adapter (PMA-2) on the International Space Station (ISS), is featured in this image photographed by a space walker during the second session of extravehicular activity (EVA) for the STS-120 mission on October 28, 2007.

Energy-Water Nexus Knowledge Discovery Framework

NASA Astrophysics Data System (ADS)

Bhaduri, B. L.; Foster, I.; Chandola, V.; Chen, B.; Sanyal, J.; Allen, M.; McManamay, R.

2017-12-01

As demand for energy grows, the energy sector is experiencing increasing competition for water. With increasing population and changing environmental, socioeconomic scenarios, new technology and investment decisions must be made for optimized and sustainable energy-water resource management. This requires novel scientific insights into the complex interdependencies of energy-water infrastructures across multiple space and time scales. An integrated data driven modeling, analysis, and visualization capability is needed to understand, design, and develop efficient local and regional practices for the energy-water infrastructure components that can be guided with strategic (federal) policy decisions to ensure national energy resilience. To meet this need of the energy-water nexus (EWN) community, an Energy-Water Knowledge Discovery Framework (EWN-KDF) is being proposed to accomplish two objectives: Development of a robust data management and geovisual analytics platform that provides access to disparate and distributed physiographic, critical infrastructure, and socioeconomic data, along with emergent ad-hoc sensor data to provide a powerful toolkit of analysis algorithms and compute resources to empower user-guided data analysis and inquiries; and Demonstration of knowledge generation with selected illustrative use cases for the implications of climate variability for coupled land-water-energy systems through the application of state-of-the art data integration, analysis, and synthesis. Oak Ridge National Laboratory (ORNL), in partnership with Argonne National Laboratory (ANL) and researchers affiliated with the Center for International Earth Science Information Partnership (CIESIN) at Columbia University and State University of New York-Buffalo (SUNY), propose to develop this Energy-Water Knowledge Discovery Framework to generate new, critical insights regarding the complex dynamics of the EWN and its interactions with climate variability and change. An overarching objective of this project is to integrate impacts, adaptation, and vulnerability (IAV) science with emerging data science to meet the data analysis needs of the U.S. Department of Energy and partner federal agencies with respect to the EWN.

A Knowledge Discovery framework for Planetary Defense

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Translational Research 2.0: a framework for accelerating collaborative discovery.

PubMed

Asakiewicz, Chris

2014-05-01

The world wide web has revolutionized the conduct of global, cross-disciplinary research. In the life sciences, interdisciplinary approaches to problem solving and collaboration are becoming increasingly important in facilitating knowledge discovery and integration. Web 2.0 technologies promise to have a profound impact - enabling reproducibility, aiding in discovery, and accelerating and transforming medical and healthcare research across the healthcare ecosystem. However, knowledge integration and discovery require a consistent foundation upon which to operate. A foundation should be capable of addressing some of the critical issues associated with how research is conducted within the ecosystem today and how it should be conducted for the future. This article will discuss a framework for enhancing collaborative knowledge discovery across the medical and healthcare research ecosystem. A framework that could serve as a foundation upon which ecosystem stakeholders can enhance the way data, information and knowledge is created, shared and used to accelerate the translation of knowledge from one area of the ecosystem to another.

Implementing the Army NetCentric Data Strategy in a ServiceOriented Environment

DTIC Science & Technology

2009-04-23

a Data Subscriptionc c e s s Federated Search Data Search D a t a A b s t r a c t i o n Adapter Configuration Adapter Data Service D a t a S e r...across t e enterpr se.  • Patterns • Search • Status • Receive – Services • Federated   Search • Artifact Discovery • Data Discovery 17 Data Discovery

Progress in understanding the immunopathogenesis of psoriasis

PubMed Central

Mak, R.K.H.; Hundhausen, C.; Nestle, F.O.

2010-01-01

This review emphasizes how translation from bench research to clinical knowledge and vice versa has resulted in considerable progress in understanding the immunopathogenesis of psoriasis. First, the journey in understanding the pathogenic mechanisms behind psoriasis is described. The roles of different components of the adaptive and innate immune systems involved in driving the inflammatory response are explained. Discovery of new immune pathways i.e. the IL23/Th17 axis and its subsequent impact on the development of novel biological therapies is highlighted. Identification of potential targets warranting further research for future therapeutic development are also discussed. PMID:20096156

Knowledge Discovery from Biomedical Ontologies in Cross Domains.

PubMed

Shen, Feichen; Lee, Yugyung

2016-01-01

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

Knowledge Discovery from Biomedical Ontologies in Cross Domains

PubMed Central

Shen, Feichen; Lee, Yugyung

2016-01-01

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

Knowledge discovery with classification rules in a cardiovascular dataset.

PubMed

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

2005-12-01

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

Progress in Biomedical Knowledge Discovery: A 25-year Retrospective

PubMed Central

Sacchi, L.

2016-01-01

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

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

PubMed

Sacchi, L; Holmes, J H

2016-08-02

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

Communication in Collaborative Discovery Learning

ERIC Educational Resources Information Center

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

2005-01-01

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

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

PubMed Central

Lucero, Robert J.; Bakken, Suzanne

2014-01-01

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

An Adaptive Jitter Mechanism for Reactive Route Discovery in Sensor Networks

PubMed Central

Cordero, Juan Antonio; Yi, Jiazi; Clausen, Thomas

2014-01-01

This paper analyses the impact of jitter when applied to route discovery in reactive (on-demand) routing protocols. In multi-hop non-synchronized wireless networks, jitter—a small, random variation in the timing of message emission—is commonly employed, as a means to avoid collisions of simultaneous transmissions by adjacent routers over the same channel. In a reactive routing protocol for sensor and ad hoc networks, jitter is recommended during the route discovery process, specifically, during the network-wide flooding of route request messages, in order to avoid collisions. Commonly, a simple uniform jitter is recommended. Alas, this is not without drawbacks: when applying uniform jitter to the route discovery process, an effect called delay inversion is observed. This paper, first, studies and quantifies this delay inversion effect. Second, this paper proposes an adaptive jitter mechanism, designed to alleviate the delay inversion effect and thereby to reduce the route discovery overhead and (ultimately) allow the routing protocol to find more optimal paths, as compared to uniform jitter. This paper presents both analytical and simulation studies, showing that the proposed adaptive jitter can effectively decrease the cost of route discovery and increase the path quality. PMID:25111238

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

DTIC Science & Technology

1994-09-30

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

Animal models of intellectual disability: towards a translational approach

PubMed Central

Scorza, Carla A; Cavalheiro, Esper A.

2011-01-01

Intellectual disability is a prevalent form of cognitive impairment, affecting 2–3% of the general population. It is a daunting societal problem characterized by significant limitations both in intellectual functioning and in adaptive behavior as expressed in conceptual, social and practical adaptive skills. Intellectual disability is a clinically important disorder for which the etiology and pathogenesis are still poorly understood. Moreover, although tremendous progress has been made, pharmacological intervention is still currently non-existent and therapeutic strategies remain limited. Studies in humans have a very limited capacity to explain basic mechanisms of this condition. In this sense, animal models have been invaluable in intellectual disability investigation. Certainly, a great deal of the knowledge that has improved our understanding of several pathologies has derived from appropriate animal models. Moreover, to improve human health, scientific discoveries must be translated into practical applications. Translational research specifically aims at taking basic scientific discoveries and best practices to benefit the lives of people in our communities. In this context, the challenge that basic science research needs to meet is to make use of a comparative approach to benefit the most from what each animal model can tell us. Intellectual disability results from many different genetic and environmental insults. Taken together, the present review will describe several animal models of potential intellectual disability risk factors. PMID:21779723

Knowledge Discovery in Textual Documentation: Qualitative and Quantitative Analyses.

ERIC Educational Resources Information Center

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

2001-01-01

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

The discovery and development of HIV therapy: the new challenges.

PubMed

Perno, Carlo Federico

2011-01-01

The therapy of HIV infection has been dramatically improved over the years, and allowed the achievement of unexpected results. The availability of many drugs, and the knowledge of HIV related pathogenesis, helped in selecting highly effective antiviral therapies, yet today a major challenge stands, that is the selection of the best regimen(s) in clinical practice. In this frame, evidence-based medicine remains a cornerstone of modern medicine, but its structure needs to be adapted to the new challenges, made by an excess of information (not always fully reliable), by highly sophisticated statistical systems that may overlook the clinical practice despite their ability to define the statistical significance, and the limited number of independent controlled studies. The revision of the criteria of evidence-based medicine, and their adaptation to the new tools available, may allow a better contribution to the definition of the best therapy for each single patient.

A collaborative filtering-based approach to biomedical knowledge discovery.

PubMed

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

2018-02-15

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

Time for a paradigm shift in how we transfer knowledge? Making the case for translational science and public engagement

NASA Astrophysics Data System (ADS)

Orr, Barron

2015-04-01

By any measure, our efforts to protect and restore the environment have failed to keep pace with environmental change, despite extraordinary scientific advances. Clearly there is a problem in knowledge transfer, which is often blamed on limited public awareness, misunderstanding or even apathy. Whether it's moving research to practice, informing policy, or educating the public on the environmental challenges of our time, our track record is poor. A major part of our failure lies in how scientists and practitioners understand (or misunderstand) and practice knowledge transfer. What actually drives knowledge acquisition and the motivation to gain knowledge, and what does this say about the methods used for knowledge transfer? Is the problem a supply issue (deficit of knowledge) or a demand issue (personal relevance)? The false assumptions that spin out of how we conceptualize knowledge acquisition lead to investment in knowledge transfer balanced heavily in "science communication" and "awareness raising" activities that tend to be unidirectional, top-down, and rarely linked to personal interests. Successful adaptation to environmental change requires a theoretical and practical understanding of coupled natural-human systems as well as advances in bridging knowledge systems and the science-society gap. To be effective, this means a "translational science" approach that promotes the capture and integration of scientific and local knowledge, addresses the influences of scale (biophysically, socially, institutionally), encourages mutual learning among all parties, and builds capacity as part of the process. The facilitation and translation of information and meanings among stakeholders can lead to the co-production of knowledge, more informed decision making, and in a very pragmatic way, more effective use of assessments and other products of scientific discovery. The purpose of this presentation is to shed light on what underlies the majority of investment in knowledge transfer, the false assumptions that result, and the ramifications for the methods employed the vast majority of the time by the scientific community. The case for public engagement and participatory approaches will be made, followed by a brief survey of the theories, methods and tools that make engagement possible and effective. Successful adaptation to environmental change requires a much stronger link between science and society. While science communication and awareness raising are necessary, they are much more effective when coupled with robust, formative, and participatory approaches to stakeholder engagement. This is necessary for successful land-based adaptation to environmental change.

Building Faculty Capacity through the Learning Sciences

ERIC Educational Resources Information Center

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

2014-01-01

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

Investigation into the efficacy of generating synthetic pathological oscillations for domain adaptation

NASA Astrophysics Data System (ADS)

Lewis, Rory; Ellenberger, James; Williams, Colton; White, Andrew M.

2013-11-01

In the ongoing investigation of integrating Knowledge Discovery in Databases (KDD) into neuroscience, we present a paper that facilitates overcoming the two challenges preventing this integration. Pathological oscillations found in the human brain are difficult to evaluate because 1) there is often no time to learn and train off of the same distribution in the fatally sick, and 2) sinusoidal signals found in the human brain are complex and transient in nature requiring large data sets to work with which are costly and often very expensive or impossible to acquire. Overcoming these challenges in today's neuro-intensive-care unit (ICU) requires insurmountable resources. For these reasons, optimizing KDD for pathological oscillations so machine learning systems can predict neuropathological states would be of immense value. Domain adaptation, which allows a way of predicting on a separate set of data than the training data, can theoretically overcome the first challenge. However, the challenge of acquiring large data sets that show whether domain adaptation is a good candidate to test in a live neuro ICU remains a challenge. To solve this conundrum, we present a methodology for generating synthesized neuropathological oscillations for domain adaptation.

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

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

ERIC Educational Resources Information Center

Nagengast, Daniel L.; And Others

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

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

PubMed

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

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

Children Studying the Sea.

ERIC Educational Resources Information Center

Hoile, Tim

1999-01-01

Describes the Marine Discovery Center (MDC) which emphasizes conservation of the marine environment, adaptations and features of various creatures, and the discovery of marine creatures in their habitat. (CCM)

The Adam and Eve Robot Scientists for the Automated Discovery of Scientific Knowledge

NASA Astrophysics Data System (ADS)

King, Ross

A Robot Scientist is a physically implemented robotic system that applies techniques from artificial intelligence to execute cycles of automated scientific experimentation. A Robot Scientist can automatically execute cycles of hypothesis formation, selection of efficient experiments to discriminate between hypotheses, execution of experiments using laboratory automation equipment, and analysis of results. The motivation for developing Robot Scientists is to better understand science, and to make scientific research more efficient. The Robot Scientist `Adam' was the first machine to autonomously discover scientific knowledge: both form and experimentally confirm novel hypotheses. Adam worked in the domain of yeast functional genomics. The Robot Scientist `Eve' was originally developed to automate early-stage drug development, with specific application to neglected tropical disease such as malaria, African sleeping sickness, etc. We are now adapting Eve to work with on cancer. We are also teaching Eve to autonomously extract information from the scientific literature.

Concept Formation in Scientific Knowledge Discovery from a Constructivist View

NASA Astrophysics Data System (ADS)

Peng, Wei; Gero, John S.

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

Concept Formation in Scientific Knowledge Discovery from a Constructivist View

NASA Astrophysics Data System (ADS)

Peng, Wei; Gero, John S.

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

Knowledge Discovery and Data Mining: An Overview

NASA Technical Reports Server (NTRS)

Fayyad, U.

1995-01-01

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

12 CFR 263.53 - Discovery depositions.

Code of Federal Regulations, 2014 CFR

2014-01-01

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

12 CFR 263.53 - Discovery depositions.

Code of Federal Regulations, 2012 CFR

2012-01-01

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

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

PubMed

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

2002-01-01

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

Data Mining.

ERIC Educational Resources Information Center

Benoit, Gerald

2002-01-01

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

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

PubMed

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

2009-01-01

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

Role of Academic Drug Discovery in the Quest for New CNS Therapeutics.

PubMed

Yokley, Brian H; Hartman, Matthew; Slusher, Barbara S

2017-03-15

There was a greater than 50% decline in central nervous system (CNS) drug discovery and development programs by major pharmaceutical companies from 2009 to 2014. This decline was paralleled by a rise in the number of university led drug discovery centers, many in the CNS area, and a growth in the number of public-private drug discovery partnerships. Diverse operating models have emerged as the academic drug discovery centers adapt to this changing ecosystem.

A Review of Adaptive Optics Optical Coherence Tomography: Technical Advances, Scientific Applications, and the Future.

PubMed

Jonnal, Ravi S; Kocaoglu, Omer P; Zawadzki, Robert J; Liu, Zhuolin; Miller, Donald T; Werner, John S

2016-07-01

Optical coherence tomography (OCT) has enabled "virtual biopsy" of the living human retina, revolutionizing both basic retina research and clinical practice over the past 25 years. For most of those years, in parallel, adaptive optics (AO) has been used to improve the transverse resolution of ophthalmoscopes to foster in vivo study of the retina at the microscopic level. Here, we review work done over the last 15 years to combine the microscopic transverse resolution of AO with the microscopic axial resolution of OCT, building AO-OCT systems with the highest three-dimensional resolution of any existing retinal imaging modality. We surveyed the literature to identify the most influential antecedent work, important milestones in the development of AO-OCT technology, its applications that have yielded new knowledge, research areas into which it may productively expand, and nascent applications that have the potential to grow. Initial efforts focused on demonstrating three-dimensional resolution. Since then, many improvements have been made in resolution and speed, as well as other enhancements of acquisition and postprocessing techniques. Progress on these fronts has produced numerous discoveries about the anatomy, function, and optical properties of the retina. Adaptive optics OCT continues to evolve technically and to contribute to our basic and clinical knowledge of the retina. Due to its capacity to reveal cellular and microscopic detail invisible to clinical OCT systems, it is an ideal companion to those instruments and has the demonstrable potential to produce images that can guide the interpretation of clinical findings.

Identification of C3b-Binding Small-Molecule Complement Inhibitors Using Cheminformatics.

PubMed

Garcia, Brandon L; Skaff, D Andrew; Chatterjee, Arindam; Hanning, Anders; Walker, John K; Wyckoff, Gerald J; Geisbrecht, Brian V

2017-05-01

The complement system is an elegantly regulated biochemical cascade formed by the collective molecular recognition properties and proteolytic activities of more than two dozen membrane-bound or serum proteins. Complement plays diverse roles in human physiology, such as acting as a sentry against invading microorganisms, priming of the adaptive immune response, and removal of immune complexes. However, dysregulation of complement can serve as a trigger for a wide range of human diseases, which include autoimmune, inflammatory, and degenerative conditions. Despite several potential advantages of modulating complement with small-molecule inhibitors, small-molecule drugs are highly underrepresented in the current complement-directed therapeutics pipeline. In this study, we have employed a cheminformatics drug discovery approach based on the extensive structural and functional knowledge available for the central proteolytic fragment of the cascade, C3b. Using parallel in silico screening methodologies, we identified 45 small molecules that putatively bind C3b near ligand-guided functional hot spots. Surface plasmon resonance experiments resulted in the validation of seven dose-dependent C3b-binding compounds. Competition-based biochemical assays demonstrated the ability of several C3b-binding compounds to interfere with binding of the original C3b ligand that guided their discovery. In vitro assays of complement function identified a single complement inhibitory compound, termed cmp-5, and mechanistic studies of the cmp-5 inhibitory mode revealed it acts at the level of C5 activation. This study has led to the identification of a promising new class of C3b-binding small-molecule complement inhibitors and, to our knowledge, provides the first demonstration of cheminformatics-based, complement-directed drug discovery. Copyright © 2017 by The American Association of Immunologists, Inc.

Identification of C3b-binding Small Molecule Complement Inhibitors Using Cheminformatics

PubMed Central

Garcia, Brandon L.; Skaff, D. Andrew; Chatterjee, Arindam; Hanning, Anders; Walker, John K.; Wyckoff, Gerald J.; Geisbrecht, Brian V.

2017-01-01

The complement system is an elegantly regulated biochemical cascade formed by the collective molecular recognition properties and proteolytic activities of over two dozen membrane-bound or serum proteins. Complement plays diverse roles in human physiology which include acting as a sentry against invading microorganisms, priming of the adaptive immune response, and removal of immune complexes. However, dysregulation of complement can serve as a trigger for a wide range of human diseases which include autoimmune, inflammatory, and degenerative conditions. Despite several potential advantages of modulating complement with small molecule inhibitors, small molecule drugs are highly underrepresented in the current complement-directed therapeutics pipeline. In this study we have employed a cheminformatics drug discovery approach based on the extensive structural and functional knowledge available for the central proteolytic fragment of the cascade, C3b. Using parallel in silico screening methodologies we identified 45 small molecules which putatively bind C3b near ligand-guided functional hot-spots. Surface plasmon resonance experiments resulted in the validation of seven dose-dependent C3b-binding compounds. Competition-based biochemical assays demonstrated the ability of several C3b-binding compounds to interfere with binding of the original C3b ligand which guided their discovery. In vitro assays of complement function identified a single complement inhibitory compound, termed cmp-5, and mechanistic studies of the cmp-5 inhibitory mode revealed it acts at the level of C5 activation. This study has led to the identification of a promising new class of C3b-binding small molecule complement inhibitors, and to our knowledge, provides the first demonstration of cheminformatics-based complement-directed drug discovery. PMID:28298523

Retroelements and their impact on genome evolution and functioning.

PubMed

Gogvadze, Elena; Buzdin, Anton

2009-12-01

Retroelements comprise a considerable fraction of eukaryotic genomes. Since their initial discovery by Barbara McClintock in maize DNA, retroelements have been found in genomes of almost all organisms. First considered as a "junk DNA" or genomic parasites, they were shown to influence genome functioning and to promote genetic innovations. For this reason, they were suggested as an important creative force in the genome evolution and adaptation of an organism to altered environmental conditions. In this review, we summarize the up-to-date knowledge of different ways of retroelement involvement in structural and functional evolution of genes and genomes, as well as the mechanisms generated by cells to control their retrotransposition.

Knowledge Discovery for Transonic Regional-Jet Wing through Multidisciplinary Design Exploration

NASA Astrophysics Data System (ADS)

Chiba, Kazuhisa; Obayashi, Shigeru; Morino, Hiroyuki

Data mining is an important facet of solving multi-objective optimization problem. Because it is one of the effective manner to discover the design knowledge in the multi-objective optimization problem which obtains large data. In the present study, data mining has been performed for a large-scale and real-world multidisciplinary design optimization (MDO) to provide knowledge regarding the design space. The MDO among aerodynamics, structures, and aeroelasticity of the regional-jet wing was carried out using high-fidelity evaluation models on the adaptive range multi-objective genetic algorithm. As a result, nine non-dominated solutions were generated and used for tradeoff analysis among three objectives. All solutions evaluated during the evolution were analyzed for the tradeoffs and influence of design variables using a self-organizing map to extract key features of the design space. Although the MDO results showed the inverted gull-wings as non-dominated solutions, one of the key features found by data mining was the non-gull wing geometry. When this knowledge was applied to one optimum solution, the resulting design was found to have better performance compared with the original geometry designed in the conventional manner.

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

PubMed

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

2017-03-01

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

Resource Discovery within the Networked "Hybrid" Library.

ERIC Educational Resources Information Center

Leigh, Sally-Anne

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

A biological compression model and its applications.

PubMed

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

2011-01-01

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

Reasoning and Knowledge Acquisition Framework for 5G Network Analytics

PubMed Central

2017-01-01

Autonomic self-management is a key challenge for next-generation networks. This paper proposes an automated analysis framework to infer knowledge in 5G networks with the aim to understand the network status and to predict potential situations that might disrupt the network operability. The framework is based on the Endsley situational awareness model, and integrates automated capabilities for metrics discovery, pattern recognition, prediction techniques and rule-based reasoning to infer anomalous situations in the current operational context. Those situations should then be mitigated, either proactive or reactively, by a more complex decision-making process. The framework is driven by a use case methodology, where the network administrator is able to customize the knowledge inference rules and operational parameters. The proposal has also been instantiated to prove its adaptability to a real use case. To this end, a reference network traffic dataset was used to identify suspicious patterns and to predict the behavior of the monitored data volume. The preliminary results suggest a good level of accuracy on the inference of anomalous traffic volumes based on a simple configuration. PMID:29065473

Reasoning and Knowledge Acquisition Framework for 5G Network Analytics.

PubMed

Sotelo Monge, Marco Antonio; Maestre Vidal, Jorge; García Villalba, Luis Javier

2017-10-21

Autonomic self-management is a key challenge for next-generation networks. This paper proposes an automated analysis framework to infer knowledge in 5G networks with the aim to understand the network status and to predict potential situations that might disrupt the network operability. The framework is based on the Endsley situational awareness model, and integrates automated capabilities for metrics discovery, pattern recognition, prediction techniques and rule-based reasoning to infer anomalous situations in the current operational context. Those situations should then be mitigated, either proactive or reactively, by a more complex decision-making process. The framework is driven by a use case methodology, where the network administrator is able to customize the knowledge inference rules and operational parameters. The proposal has also been instantiated to prove its adaptability to a real use case. To this end, a reference network traffic dataset was used to identify suspicious patterns and to predict the behavior of the monitored data volume. The preliminary results suggest a good level of accuracy on the inference of anomalous traffic volumes based on a simple configuration.

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

PubMed Central

2013-01-01

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

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

PubMed

Holzinger, Andreas; Zupan, Mario

2013-06-13

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

Power centroid radar and its rise from the universal cybernetics duality

NASA Astrophysics Data System (ADS)

Feria, Erlan H.

2014-05-01

Power centroid radar (PC-Radar) is a fast and powerful adaptive radar scheme that naturally surfaced from the recent discovery of the time-dual for information theory which has been named "latency theory." Latency theory itself was born from the universal cybernetics duality (UC-Duality), first identified in the late 1970s, that has also delivered a time dual for thermodynamics that has been named "lingerdynamics" and anchors an emerging lifespan theory for biological systems. In this paper the rise of PC-Radar from the UC-Duality is described. The development of PC-Radar, US patented, started with Defense Advanced Research Projects Agency (DARPA) funded research on knowledge-aided (KA) adaptive radar of the last decade. The outstanding signal to interference plus noise ratio (SINR) performance of PC-Radar under severely taxing environmental disturbances will be established. More specifically, it will be seen that the SINR performance of PC-Radar, either KA or knowledgeunaided (KU), approximates that of an optimum KA radar scheme. The explanation for this remarkable result is that PC-Radar inherently arises from the UC-Duality, which advances a "first principles" duality guidance theory for the derivation of synergistic storage-space/computational-time compression solutions. Real-world synthetic aperture radar (SAR) images will be used as prior-knowledge to illustrate these results.

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

PubMed Central

2010-01-01

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

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

PubMed

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

2010-01-18

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

Form-Focused Discovery Activities in English Classes

ERIC Educational Resources Information Center

Ogeyik, Muhlise Cosgun

2011-01-01

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

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

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-29

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

12 CFR 263.53 - Discovery depositions.

Code of Federal Regulations, 2011 CFR

2011-01-01

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

12 CFR 19.170 - Discovery depositions.

Code of Federal Regulations, 2010 CFR

2010-01-01

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

12 CFR 19.170 - Discovery depositions.

Code of Federal Regulations, 2011 CFR

2011-01-01

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

12 CFR 263.53 - Discovery depositions.

Code of Federal Regulations, 2010 CFR

2010-01-01

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

BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation

PubMed Central

2011-01-01

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

Cognitive Tutoring based on Intelligent Decision Support in the PENTHA Instructional Design Model

NASA Astrophysics Data System (ADS)

dall'Acqua, Luisa

2010-06-01

The research finality of this paper is how to support Authors to develop rule driven—subject oriented, adaptable course content, meta-rules—representing the disciplinary epistemology, model of teaching, Learning Path structure, and assessment parameters—for intelligent Tutoring actions in a personalized, adaptive e-Learning environment. The focus is to instruct the student to be a decision manager for himself, able to recognize the elements of a problem, select the necessary information with the perspective of factual choices. In particular, our research intends to provide some fundamental guidelines for the definition of didactical rules and logical relations, that Authors should provide to a cognitive Tutoring system through the use of an Instructional Design method (PENTHA Model) which proposes an educational environment, able to: increase productivity and operability, create conditions for a cooperative dialogue, developing participatory research activities of knowledge, observations and discoveries, customizing the learning design in a complex and holistic vision of the learning / teaching processes.

Bacterial Adaptation to Antibiotics through Regulatory RNAs.

PubMed

Felden, Brice; Cattoir, Vincent

2018-05-01

The extensive use of antibiotics has resulted in a situation where multidrug-resistant pathogens have become a severe menace to human health worldwide. A deeper understanding of the principles used by pathogens to adapt to, respond to, and resist antibiotics would pave the road to the discovery of drugs with novel mechanisms. For bacteria, antibiotics represent clinically relevant stresses that induce protective responses. The recent implication of regulatory RNAs (small RNAs [sRNAs]) in antibiotic response and resistance in several bacterial pathogens suggests that they should be considered innovative drug targets. This minireview discusses sRNA-mediated mechanisms exploited by bacterial pathogens to fight against antibiotics. A critical discussion of the newest findings in the field is provided, with emphasis on the implication of sRNAs in major mechanisms leading to antibiotic resistance, including drug uptake, active drug efflux, drug target modifications, biofilms, cell walls, and lipopolysaccharide (LPS) biosynthesis. Of interest is the lack of knowledge about sRNAs implicated in Gram-positive compared to Gram-negative bacterial resistance. Copyright © 2018 American Society for Microbiology.

Avian influenza virus transmission to mammals.

PubMed

Herfst, S; Imai, M; Kawaoka, Y; Fouchier, R A M

2014-01-01

Influenza A viruses cause yearly epidemics and occasional pandemics. In addition, zoonotic influenza A viruses sporadically infect humans and may cause severe respiratory disease and fatalities. Fortunately, most of these viruses do not have the ability to be efficiently spread among humans via aerosols or respiratory droplets (airborne transmission) and to subsequently cause a pandemic. However, adaptation of these zoonotic viruses to humans by mutation or reassortment with human influenza A viruses may result in airborne transmissible viruses with pandemic potential. Although our knowledge of factors that affect mammalian adaptation and transmissibility of influenza viruses is still limited, we are beginning to understand some of the biological traits that drive airborne transmission of influenza viruses among mammals. Increased understanding of the determinants and mechanisms of airborne transmission may aid in assessing the risks posed by avian influenza viruses to human health, and preparedness for such risks. This chapter summarizes recent discoveries on the genetic and phenotypic traits required for avian influenza viruses to become airborne transmissible between mammals.

Medical knowledge discovery and management.

PubMed

Prior, Fred

2009-05-01

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

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

PubMed Central

Newton, Mandi S; Scott-Findlay, Shannon

2007-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2013-05-01

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

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

PubMed Central

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

2009-01-01

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

Anaerobic gut fungi: Advances in isolation, culture, and cellulolytic enzyme discovery for biofuel production.

PubMed

Haitjema, Charles H; Solomon, Kevin V; Henske, John K; Theodorou, Michael K; O'Malley, Michelle A

2014-08-01

Anaerobic gut fungi are an early branching family of fungi that are commonly found in the digestive tract of ruminants and monogastric herbivores. It is becoming increasingly clear that they are the primary colonizers of ingested plant biomass, and that they significantly contribute to the decomposition of plant biomass into fermentable sugars. As such, anaerobic fungi harbor a rich reservoir of undiscovered cellulolytic enzymes and enzyme complexes that can potentially transform the conversion of lignocellulose into bioenergy products. Despite their unique evolutionary history and cellulolytic activity, few species have been isolated and studied in great detail. As a result, their life cycle, cellular physiology, genetics, and cellulolytic metabolism remain poorly understood compared to aerobic fungi. To help address this limitation, this review briefly summarizes the current body of knowledge pertaining to anaerobic fungal biology, and describes progress made in the isolation, cultivation, molecular characterization, and long-term preservation of these microbes. We also discuss recent cellulase- and cellulosome-discovery efforts from gut fungi, and how these interesting, non-model microbes could be further adapted for biotechnology applications. © 2014 Wiley Periodicals, Inc.

A resurgence in field research is essential to better understand the diversity, ecology, and evolution of microbial eukaryotes.

PubMed

Heger, Thierry J; Edgcomb, Virginia P; Kim, Eunsoo; Lukeš, Julius; Leander, Brian S; Yubuki, Naoji

2014-01-01

The discovery and characterization of protist communities from diverse environments are crucial for understanding the overall evolutionary history of life on earth. However, major questions about the diversity, ecology, and evolutionary history of protists remain unanswered, notably because data obtained from natural protist communities, especially of heterotrophic species, remain limited. In this review, we discuss the challenges associated with "field protistology", defined here as the exploration, characterization, and interpretation of microbial eukaryotic diversity within the context of natural environments or field experiments, and provide suggestions to help fill this important gap in knowledge. We also argue that increased efforts in field studies that combine molecular and microscopical methods offer the most promising path toward (1) the discovery of new lineages that expand the tree of eukaryotes; (2) the recognition of novel evolutionary patterns and processes; (3) the untangling of ecological interactions and functions, and their roles in larger ecosystem processes; and (4) the evaluation of protist adaptations to a changing climate. © 2013 The Author(s) Journal of Eukaryotic Microbiology © 2013 International Society of Protistologists.

Towards a Web-Enabled Geovisualization and Analytics Platform for the Energy and Water Nexus

NASA Astrophysics Data System (ADS)

Sanyal, J.; Chandola, V.; Sorokine, A.; Allen, M.; Berres, A.; Pang, H.; Karthik, R.; Nugent, P.; McManamay, R.; Stewart, R.; Bhaduri, B. L.

2017-12-01

Interactive data analytics are playing an increasingly vital role in the generation of new, critical insights regarding the complex dynamics of the energy/water nexus (EWN) and its interactions with climate variability and change. Integration of impacts, adaptation, and vulnerability (IAV) science with emerging, and increasingly critical, data science capabilities offers a promising potential to meet the needs of the EWN community. To enable the exploration of pertinent research questions, a web-based geospatial visualization platform is being built that integrates a data analysis toolbox with advanced data fusion and data visualization capabilities to create a knowledge discovery framework for the EWN. The system, when fully built out, will offer several geospatial visualization capabilities including statistical visual analytics, clustering, principal-component analysis, dynamic time warping, support uncertainty visualization and the exploration of data provenance, as well as support machine learning discoveries to render diverse types of geospatial data and facilitate interactive analysis. Key components in the system architecture includes NASA's WebWorldWind, the Globus toolkit, postgresql, as well as other custom built software modules.

Pierre Bourdieu's Theory of Practice offers nurses a framework to uncover embodied knowledge of patients living with disabilities or illnesses: A discussion paper.

PubMed

Oerther, Sarah; Oerther, Daniel B

2018-04-01

To discuss how Bourdieu's theory of practice can be used by nurse researchers to better uncover the embodied knowledge of patients living with disability and illness. Bourdieu's theory of practice has been used in social and healthcare researches. This theory emphasizes that an individual's everyday practices are not always explicit and mediated by language, but instead an individual's everyday practices are often are tacit and embodied. Discussion paper. Ovid MEDLINE, CINAHL and SCOPUS were searched for concepts from Bourdieu's theory that was used to understand embodied knowledge of patients living with disability and illness. The literature search included articles from 2003 - 2017. Nurse researchers should use Bourdieu's theory of practice to uncover the embodied knowledge of patients living with disability and illness, and nurse researchers should translate these discoveries into policy recommendations and improved evidence-based best practice. The practice of nursing should incorporate an understanding of embodied knowledge to support disabled and ill patients as these patients modify "everyday practices" in the light of their disabilities and illnesses. Bourdieu's theory enriches nursing because the theory allows for consideration of both the objective and the subjective through the conceptualization of capital, habitus and field. Uncovering individuals embodied knowledge is critical to implement best practices that assist patients as they adapt to bodily changes during disability and illness. © 2017 John Wiley & Sons Ltd.

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

PubMed

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

2015-11-01

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

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

NASA Astrophysics Data System (ADS)

McGovern, Mary Francis

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

A-DaGO-Fun: an adaptable Gene Ontology semantic similarity-based functional analysis tool.

PubMed

Mazandu, Gaston K; Chimusa, Emile R; Mbiyavanga, Mamana; Mulder, Nicola J

2016-02-01

Gene Ontology (GO) semantic similarity measures are being used for biological knowledge discovery based on GO annotations by integrating biological information contained in the GO structure into data analyses. To empower users to quickly compute, manipulate and explore these measures, we introduce A-DaGO-Fun (ADaptable Gene Ontology semantic similarity-based Functional analysis). It is a portable software package integrating all known GO information content-based semantic similarity measures and relevant biological applications associated with these measures. A-DaGO-Fun has the advantage not only of handling datasets from the current high-throughput genome-wide applications, but also allowing users to choose the most relevant semantic similarity approach for their biological applications and to adapt a given module to their needs. A-DaGO-Fun is freely available to the research community at http://web.cbio.uct.ac.za/ITGOM/adagofun. It is implemented in Linux using Python under free software (GNU General Public Licence). gmazandu@cbio.uct.ac.za or Nicola.Mulder@uct.ac.za Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Acoustic Wave Guiding by Reconfigurable Tessellated Arrays

NASA Astrophysics Data System (ADS)

Zou, Chengzhe; Lynd, Danielle T.; Harne, Ryan L.

2018-01-01

The reconfiguration of origami tessellations is a prime vehicle to harness for adapting system properties governed by a structural form. While the knowledge of mechanical property changes associated with origami tessellation folding has been extensively built up, the opportunities to integrate other physics into a framework of tessellated, adaptive structures remain to be fully exploited. Acoustics appears to be a prime domain to marry with origami science. Specifically, deep technical analogies are revealed between wave-guiding properties achieved via digital methods that virtually reposition array elements and the actual repositioning of facets by folding origami-inspired tessellations. Here we capitalize on this analogy to investigate acoustic arrays established upon facet layouts of origami-inspired tessellations. We show that a concept of reconfigurable tessellated arrays may guide waves more effectively than traditional digitally phased arrays using fewer transducer elements. Moreover, we show that the refinement of tessellated arrays trends to the ideal case of classical wave radiators or receivers grounded in principles of geometrical acoustics. By linear wave physics shared among myriad scientific disciplines and across orders of magnitude in length scale, these discoveries may cultivate numerous opportunities for wave-guiding adaptive structures inspired by low-dimensional origami tessellations.

Framework for measuring adaptive knowledge-rich systems performance.

PubMed

Bushko, Renata G

2005-01-01

The universe is non repeatable in nature--most of events cannot be prestated and do not repeat themselves. The only way to create systems that are truly useful is to make them adaptive (able to reason by analogy and learn) and rich in knowledge (including common sense knowledge). Adaptive and knowledge-rich health management could get us closer to errorless health care where small incremental adjustments happen all the time preventing occurrence of an error. In the era of adaptive systems we need to have a way to evaluate their performance. Are they truly adaptive? How adaptive are they? Are they accurate enough? Are they fast enough? Are they cost effective? This chapter presents general framework for measuring adaptive knowledge-rich systems' performance and includes among others definitions of adaptiveness factor, britt (a unit of brittleness) and uso-quant (unit of usefulness of a piece of knowledge). Measuring adaptive knowledge-rich systems performance is one of the most important research areas that can have a big pay-off in healthcare now and in the future.

A Review of Adaptive Optics Optical Coherence Tomography: Technical Advances, Scientific Applications, and the Future

PubMed Central

Jonnal, Ravi S.; Kocaoglu, Omer P.; Zawadzki, Robert J.; Liu, Zhuolin; Miller, Donald T.; Werner, John S.

2016-01-01

Purpose Optical coherence tomography (OCT) has enabled “virtual biopsy” of the living human retina, revolutionizing both basic retina research and clinical practice over the past 25 years. For most of those years, in parallel, adaptive optics (AO) has been used to improve the transverse resolution of ophthalmoscopes to foster in vivo study of the retina at the microscopic level. Here, we review work done over the last 15 years to combine the microscopic transverse resolution of AO with the microscopic axial resolution of OCT, building AO-OCT systems with the highest three-dimensional resolution of any existing retinal imaging modality. Methods We surveyed the literature to identify the most influential antecedent work, important milestones in the development of AO-OCT technology, its applications that have yielded new knowledge, research areas into which it may productively expand, and nascent applications that have the potential to grow. Results Initial efforts focused on demonstrating three-dimensional resolution. Since then, many improvements have been made in resolution and speed, as well as other enhancements of acquisition and postprocessing techniques. Progress on these fronts has produced numerous discoveries about the anatomy, function, and optical properties of the retina. Conclusions Adaptive optics OCT continues to evolve technically and to contribute to our basic and clinical knowledge of the retina. Due to its capacity to reveal cellular and microscopic detail invisible to clinical OCT systems, it is an ideal companion to those instruments and has the demonstrable potential to produce images that can guide the interpretation of clinical findings. PMID:27409507

Evidence-based medicine: is it a bridge too far?

PubMed

Fernandez, Ana; Sturmberg, Joachim; Lukersmith, Sue; Madden, Rosamond; Torkfar, Ghazal; Colagiuri, Ruth; Salvador-Carulla, Luis

2015-11-06

This paper aims to describe the contextual factors that gave rise to evidence-based medicine (EBM), as well as its controversies and limitations in the current health context. Our analysis utilizes two frameworks: (1) a complex adaptive view of health that sees both health and healthcare as non-linear phenomena emerging from their different components; and (2) the unified approach to the philosophy of science that provides a new background for understanding the differences between the phases of discovery, corroboration, and implementation in science. The need for standardization, the development of clinical epidemiology, concerns about the economic sustainability of health systems and increasing numbers of clinical trials, together with the increase in the computer's ability to handle large amounts of data, have paved the way for the development of the EBM movement. It was quickly adopted on the basis of authoritative knowledge rather than evidence of its own capacity to improve the efficiency and equity of health systems. The main problem with the EBM approach is the restricted and simplistic approach to scientific knowledge, which prioritizes internal validity as the major quality of the studies to be included in clinical guidelines. As a corollary, the preferred method for generating evidence is the explanatory randomized controlled trial. This method can be useful in the phase of discovery but is inadequate in the field of implementation, which needs to incorporate additional information including expert knowledge, patients' values and the context. EBM needs to move forward and perceive health and healthcare as a complex interaction, i.e. an interconnected, non-linear phenomenon that may be better analysed using a variety of complexity science techniques.

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.

Enhancing Learning Environments through Solution-based Knowledge Discovery Tools: Forecasting for Self-Perpetuating Systemic Reform.

ERIC Educational Resources Information Center

Tsantis, Linda; Castellani, John

2001-01-01

This article explores how knowledge-discovery applications can empower educators with the information they need to provide anticipatory guidance for teaching and learning, forecast school and district needs, and find critical markers for making the best program decisions for children and youth with disabilities. Data mining for schools is…

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

ERIC Educational Resources Information Center

Molina, Otilia Alejandro; Ratté, Sylvie

2017-01-01

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

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

ERIC Educational Resources Information Center

Taft, Laritza M.

2010-01-01

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

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

NASA Technical Reports Server (NTRS)

Matthews, Bryan

2018-01-01

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

A Virtual Bioinformatics Knowledge Environment for Early Cancer Detection

NASA Technical Reports Server (NTRS)

Crichton, Daniel; Srivastava, Sudhir; Johnsey, Donald

2003-01-01

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

Genomic Enzymology: Web Tools for Leveraging Protein Family Sequence-Function Space and Genome Context to Discover Novel Functions.

PubMed

Gerlt, John A

2017-08-22

The exponentially increasing number of protein and nucleic acid sequences provides opportunities to discover novel enzymes, metabolic pathways, and metabolites/natural products, thereby adding to our knowledge of biochemistry and biology. The challenge has evolved from generating sequence information to mining the databases to integrating and leveraging the available information, i.e., the availability of "genomic enzymology" web tools. Web tools that allow identification of biosynthetic gene clusters are widely used by the natural products/synthetic biology community, thereby facilitating the discovery of novel natural products and the enzymes responsible for their biosynthesis. However, many novel enzymes with interesting mechanisms participate in uncharacterized small-molecule metabolic pathways; their discovery and functional characterization also can be accomplished by leveraging information in protein and nucleic acid databases. This Perspective focuses on two genomic enzymology web tools that assist the discovery novel metabolic pathways: (1) Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST) for generating sequence similarity networks to visualize and analyze sequence-function space in protein families and (2) Enzyme Function Initiative-Genome Neighborhood Tool (EFI-GNT) for generating genome neighborhood networks to visualize and analyze the genome context in microbial and fungal genomes. Both tools have been adapted to other applications to facilitate target selection for enzyme discovery and functional characterization. As the natural products community has demonstrated, the enzymology community needs to embrace the essential role of web tools that allow the protein and genome sequence databases to be leveraged for novel insights into enzymological problems.

Genomic Enzymology: Web Tools for Leveraging Protein Family Sequence–Function Space and Genome Context to Discover Novel Functions

PubMed Central

2017-01-01

The exponentially increasing number of protein and nucleic acid sequences provides opportunities to discover novel enzymes, metabolic pathways, and metabolites/natural products, thereby adding to our knowledge of biochemistry and biology. The challenge has evolved from generating sequence information to mining the databases to integrating and leveraging the available information, i.e., the availability of “genomic enzymology” web tools. Web tools that allow identification of biosynthetic gene clusters are widely used by the natural products/synthetic biology community, thereby facilitating the discovery of novel natural products and the enzymes responsible for their biosynthesis. However, many novel enzymes with interesting mechanisms participate in uncharacterized small-molecule metabolic pathways; their discovery and functional characterization also can be accomplished by leveraging information in protein and nucleic acid databases. This Perspective focuses on two genomic enzymology web tools that assist the discovery novel metabolic pathways: (1) Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST) for generating sequence similarity networks to visualize and analyze sequence–function space in protein families and (2) Enzyme Function Initiative-Genome Neighborhood Tool (EFI-GNT) for generating genome neighborhood networks to visualize and analyze the genome context in microbial and fungal genomes. Both tools have been adapted to other applications to facilitate target selection for enzyme discovery and functional characterization. As the natural products community has demonstrated, the enzymology community needs to embrace the essential role of web tools that allow the protein and genome sequence databases to be leveraged for novel insights into enzymological problems. PMID:28826221

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

ERIC Educational Resources Information Center

Harmon, Glynn

2013-01-01

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

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

PubMed

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

2013-01-01

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

Hooks and Shifts: A Dialectical Study of Mediated Discovery

ERIC Educational Resources Information Center

Abrahamson, Dor; Trninic, Dragan; Gutierrez, Jose F.; Huth, Jacob; Lee, Rosa G.

2011-01-01

Radical constructivists advocate discovery-based pedagogical regimes that enable students to incrementally and continuously adapt their cognitive structures to the instrumented cultural environment. Some sociocultural theorists, however, maintain that learning implies discontinuity in conceptual development, because novices must appropriate expert…

Scaling Up: Adapting a Phage-Hunting Course to Increase Participation of First-Year Students in Research

PubMed Central

Staub, Nancy L.; Poxleitner, Marianne; Braley, Amanda; Smith-Flores, Helen; Pribbenow, Christine M.; Jaworski, Leslie; Lopatto, David; Anders, Kirk R.

2016-01-01

Authentic research experiences are valuable components of effective undergraduate education. Research experiences during the first years of college are especially critical to increase persistence in science, technology, engineering, and mathematics fields. The Science Education Alliance Phage Hunters Advancing Genomics and Evolutionary Science (SEA-PHAGES) model provides a high-impact research experience to first-year students but is usually available to a limited number of students, and its implementation is costly in faculty time and laboratory space. To offer a research experience to all students taking introductory biology at Gonzaga University (n = 350/yr), we modified the traditional two-semester SEA-PHAGES course by streamlining the first-semester Phage Discovery lab and integrating the second SEA-PHAGES semester into other courses in the biology curriculum. Because most students in the introductory course are not biology majors, the Phage Discovery semester may be their only encounter with research. To discover whether students benefit from the first semester alone, we assessed the effects of the one-semester Phage Discovery course on students’ understanding of course content. Specifically, students showed improvement in knowledge of bacteriophages, lab math skills, and understanding experimental design and interpretation. They also reported learning gains and benefits comparable with other course-based research experiences. Responses to open-ended questions suggest that students experienced this course as a true undergraduate research experience. PMID:27146160

Collaborative Web-Enabled GeoAnalytics Applied to OECD Regional Data

NASA Astrophysics Data System (ADS)

Jern, Mikael

Recent advances in web-enabled graphics technologies have the potential to make a dramatic impact on developing collaborative geovisual analytics (GeoAnalytics). In this paper, tools are introduced that help establish progress initiatives at international and sub-national levels aimed at measuring and collaborating, through statistical indicators, economic, social and environmental developments and to engage both statisticians and the public in such activities. Given this global dimension of such a task, the “dream” of building a repository of progress indicators, where experts and public users can use GeoAnalytics collaborative tools to compare situations for two or more countries, regions or local communities, could be accomplished. While the benefits of GeoAnalytics tools are many, it remains a challenge to adapt these dynamic visual tools to the Internet. For example, dynamic web-enabled animation that enables statisticians to explore temporal, spatial and multivariate demographics data from multiple perspectives, discover interesting relationships, share their incremental discoveries with colleagues and finally communicate selected relevant knowledge to the public. These discoveries often emerge through the diverse backgrounds and experiences of expert domains and are precious in a creative analytics reasoning process. In this context, we introduce a demonstrator “OECD eXplorer”, a customized tool for interactively analyzing, and collaborating gained insights and discoveries based on a novel story mechanism that capture, re-use and share task-related explorative events.

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

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

Sukumar, Sreenivas R; Ferrell, Regina Kay

2013-01-01

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

Examining Challenges Related to the Production of Actionable Climate Knowledge for Adaptation Decision-Making: A Focus on Climate Knowledge System Producers

NASA Astrophysics Data System (ADS)

Ernst, K.; Preston, B. L.; Tenggren, S.; Klein, R.; Gerger-Swartling, Å.

2017-12-01

Many challenges to adaptation decision-making and action have been identified across peer-reviewed and gray literature. These challenges have primarily focused on the use of climate knowledge for adaptation decision-making, the process of adaptation decision-making, and the needs of the decision-maker. Studies on climate change knowledge systems often discuss the imperative role of climate knowledge producers in adaptation decision-making processes and stress the need for producers to engage in knowledge co-production activities and to more effectively meet decision-maker needs. While the influence of climate knowledge producers on the co-production of science for adaptation decision-making is well-recognized, hardly any research has taken a direct approach to analyzing the challenges that climate knowledge producers face when undertaking science co-production. Those challenges can influence the process of knowledge production and may hinder the creation, utilization, and dissemination of actionable knowledge for adaptation decision-making. This study involves semi-structured interviews, focus groups, and participant observations to analyze, identify, and contextualize the challenges that climate knowledge producers in Sweden face as they endeavor to create effective climate knowledge systems for multiple contexts, scales, and levels across the European Union. Preliminary findings identify complex challenges related to education, training, and support; motivation, willingness, and culture; varying levels of prioritization; professional roles and responsibilities; the type and amount of resources available; and professional incentive structures. These challenges exist at varying scales and levels across individuals, organizations, networks, institutions, and disciplines. This study suggests that the creation of actionable knowledge for adaptation decision-making is not supported across scales and levels in the climate knowledge production landscape. Additionally, enabling the production of actionable knowledge for adaptation decision-making requires multi-level effort beyond the individual level.

RHSEG and Subdue: Background and Preliminary Approach for Combining these Technologies for Enhanced Image Data Analysis, Mining and Knowledge Discovery

NASA Technical Reports Server (NTRS)

Tilton, James C.; Cook, Diane J.

2008-01-01

Under a project recently selected for funding by NASA's Science Mission Directorate under the Applied Information Systems Research (AISR) program, Tilton and Cook will design and implement the integration of the Subdue graph based knowledge discovery system, developed at the University of Texas Arlington and Washington State University, with image segmentation hierarchies produced by the RHSEG software, developed at NASA GSFC, and perform pilot demonstration studies of data analysis, mining and knowledge discovery on NASA data. Subdue represents a method for discovering substructures in structural databases. Subdue is devised for general-purpose automated discovery, concept learning, and hierarchical clustering, with or without domain knowledge. Subdue was developed by Cook and her colleague, Lawrence B. Holder. For Subdue to be effective in finding patterns in imagery data, the data must be abstracted up from the pixel domain. An appropriate abstraction of imagery data is a segmentation hierarchy: a set of several segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. The RHSEG program, a recursive approximation to a Hierarchical Segmentation approach (HSEG), can produce segmentation hierarchies quickly and effectively for a wide variety of images. RHSEG and HSEG were developed at NASA GSFC by Tilton. In this presentation we provide background on the RHSEG and Subdue technologies and present a preliminary analysis on how RHSEG and Subdue may be combined to enhance image data analysis, mining and knowledge discovery.

Predicting future discoveries from current scientific literature.

PubMed

Petrič, Ingrid; Cestnik, Bojan

2014-01-01

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

Bioenergy Knowledge Discovery Framework Fact Sheet

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

None

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

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

ERIC Educational Resources Information Center

Brill, Gilat; Falk, Hedda; Yarden, Anat

2003-01-01

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

An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework.

PubMed

Chen, Yi-An; Tripathi, Lokesh P; Mizuguchi, Kenji

2016-01-01

Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format. Database URL: http://targetmine.mizuguchilab.org. © The Author(s) 2016. Published by Oxford University Press.

An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework

PubMed Central

Chen, Yi-An; Tripathi, Lokesh P.; Mizuguchi, Kenji

2016-01-01

Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format. Database URL: http://targetmine.mizuguchilab.org PMID:26989145

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

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

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

2013-01-01

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

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.

FIR: An Effective Scheme for Extracting Useful Metadata from Social Media.

PubMed

Chen, Long-Sheng; Lin, Zue-Cheng; Chang, Jing-Rong

2015-11-01

Recently, the use of social media for health information exchange is expanding among patients, physicians, and other health care professionals. In medical areas, social media allows non-experts to access, interpret, and generate medical information for their own care and the care of others. Researchers paid much attention on social media in medical educations, patient-pharmacist communications, adverse drug reactions detection, impacts of social media on medicine and healthcare, and so on. However, relatively few papers discuss how to extract useful knowledge from a huge amount of textual comments in social media effectively. Therefore, this study aims to propose a Fuzzy adaptive resonance theory network based Information Retrieval (FIR) scheme by combining Fuzzy adaptive resonance theory (ART) network, Latent Semantic Indexing (LSI), and association rules (AR) discovery to extract knowledge from social media. In our FIR scheme, Fuzzy ART network firstly has been employed to segment comments. Next, for each customer segment, we use LSI technique to retrieve important keywords. Then, in order to make the extracted keywords understandable, association rules mining is presented to organize these extracted keywords to build metadata. These extracted useful voices of customers will be transformed into design needs by using Quality Function Deployment (QFD) for further decision making. Unlike conventional information retrieval techniques which acquire too many keywords to get key points, our FIR scheme can extract understandable metadata from social media.

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

PubMed

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

2018-06-01

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

Opportunities and Challenges for Drug Development: Public-Private Partnerships, Adaptive Designs and Big Data.

PubMed

Yildirim, Oktay; Gottwald, Matthias; Schüler, Peter; Michel, Martin C

2016-01-01

Drug development faces the double challenge of increasing costs and increasing pressure on pricing. To avoid that lack of perceived commercial perspective will leave existing medical needs unmet, pharmaceutical companies and many other stakeholders are discussing ways to improve the efficiency of drug Research and Development. Based on an international symposium organized by the Medical School of the University of Duisburg-Essen (Germany) and held in January 2016, we discuss the opportunities and challenges of three specific areas, i.e., public-private partnerships, adaptive designs and big data. Public-private partnerships come in many different forms with regard to scope, duration and type and number of participants. They range from project-specific collaborations to strategic alliances to large multi-party consortia. Each of them offers specific opportunities and faces distinct challenges. Among types of collaboration, investigator-initiated studies are becoming increasingly popular but have legal, ethical, and financial implications. Adaptive trial designs are also increasingly discussed. However, adaptive should not be used as euphemism for the repurposing of a failed trial; rather it requires carefully planning and specification before a trial starts. Adaptive licensing can be a counter-part of adaptive trial design. The use of Big Data is another opportunity to leverage existing information into knowledge useable for drug discovery and development. Respecting limitations of informed consent and privacy is a key challenge in the use of Big Data. Speakers and participants at the symposium were convinced that appropriate use of the above new options may indeed help to increase the efficiency of future drug development.

Opportunities and Challenges for Drug Development: Public–Private Partnerships, Adaptive Designs and Big Data

PubMed Central

Yildirim, Oktay; Gottwald, Matthias; Schüler, Peter; Michel, Martin C.

2016-01-01

Drug development faces the double challenge of increasing costs and increasing pressure on pricing. To avoid that lack of perceived commercial perspective will leave existing medical needs unmet, pharmaceutical companies and many other stakeholders are discussing ways to improve the efficiency of drug Research and Development. Based on an international symposium organized by the Medical School of the University of Duisburg-Essen (Germany) and held in January 2016, we discuss the opportunities and challenges of three specific areas, i.e., public–private partnerships, adaptive designs and big data. Public–private partnerships come in many different forms with regard to scope, duration and type and number of participants. They range from project-specific collaborations to strategic alliances to large multi-party consortia. Each of them offers specific opportunities and faces distinct challenges. Among types of collaboration, investigator-initiated studies are becoming increasingly popular but have legal, ethical, and financial implications. Adaptive trial designs are also increasingly discussed. However, adaptive should not be used as euphemism for the repurposing of a failed trial; rather it requires carefully planning and specification before a trial starts. Adaptive licensing can be a counter-part of adaptive trial design. The use of Big Data is another opportunity to leverage existing information into knowledge useable for drug discovery and development. Respecting limitations of informed consent and privacy is a key challenge in the use of Big Data. Speakers and participants at the symposium were convinced that appropriate use of the above new options may indeed help to increase the efficiency of future drug development. PMID:27999543

Knowledge Discovery from Posts in Online Health Communities Using Unified Medical Language System.

PubMed

Chen, Donghua; Zhang, Runtong; Liu, Kecheng; Hou, Lei

2018-06-19

Patient-reported posts in Online Health Communities (OHCs) contain various valuable information that can help establish knowledge-based online support for online patients. However, utilizing these reports to improve online patient services in the absence of appropriate medical and healthcare expert knowledge is difficult. Thus, we propose a comprehensive knowledge discovery method that is based on the Unified Medical Language System for the analysis of narrative posts in OHCs. First, we propose a domain-knowledge support framework for OHCs to provide a basis for post analysis. Second, we develop a Knowledge-Involved Topic Modeling (KI-TM) method to extract and expand explicit knowledge within the text. We propose four metrics, namely, explicit knowledge rate, latent knowledge rate, knowledge correlation rate, and perplexity, for the evaluation of the KI-TM method. Our experimental results indicate that our proposed method outperforms existing methods in terms of providing knowledge support. Our method enhances knowledge support for online patients and can help develop intelligent OHCs in the future.

CRISPR/Cas9: From Genome Engineering to Cancer Drug Discovery

PubMed Central

Luo, Ji

2016-01-01

Advances in translational research are often driven by new technologies. The advent of microarrays, next-generation sequencing, proteomics and RNA interference (RNAi) have led to breakthroughs in our understanding of the mechanisms of cancer and the discovery of new cancer drug targets. The discovery of the bacterial clustered regularly interspaced palindromic repeat (CRISPR) system and its subsequent adaptation as a tool for mammalian genome engineering has opened up new avenues for functional genomics studies. This review will focus on the utility of CRISPR in the context of cancer drug target discovery. PMID:28603775

Learning in the context of distribution drift

DTIC Science & Technology

2017-05-09

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

A Bioinformatic Approach to Inter Functional Interactions within Protein Sequences

DTIC Science & Technology

2009-02-23

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

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

PubMed Central

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

2011-01-01

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

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

PubMed

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

2012-04-01

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

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

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

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

2013-01-01

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

Knowledge Discovery and Data Mining in Iran's Climatic Researches

NASA Astrophysics Data System (ADS)

Karimi, Mostafa

2013-04-01

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

Gendered knowledge and adaptive practices: Differentiation and change in Mwanga District, Tanzania.

PubMed

Smucker, Thomas A; Wangui, Elizabeth Edna

2016-12-01

We examine the wider social knowledge domain that complements technical and environmental knowledge in enabling adaptive practices through two case studies in Tanzania. We are concerned with knowledge production that is shaped by gendered exclusion from the main thrusts of planned adaptation, in the practice of irrigation in a dryland village and the adoption of fast-maturing seed varieties in a highland village. The findings draw on data from a household survey, community workshops, and key informant interviews. The largest challenge to effective adaptation is a lack of access to the social networks and institutions that allocate resources needed for adaptation. Results demonstrate the social differentiation of local knowledge, and how it is entwined with adaptive practices that emerge in relation to gendered mechanisms of access. We conclude that community-based adaptation can learn from engaging the broader social knowledge base in evaluating priorities for coping with greater climate variability.

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

PubMed

Soltic, Snjezana; Kasabov, Nikola

2010-12-01

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

Citation Discovery Tools for Conducting Adaptive Meta-analyses to Update Systematic Reviews.

PubMed

Bae, Jong-Myon; Kim, Eun Hee

2016-03-01

The systematic review (SR) is a research methodology that aims to synthesize related evidence. Updating previously conducted SRs is necessary when new evidence has been produced, but no consensus has yet emerged on the appropriate update methodology. The authors have developed a new SR update method called 'adaptive meta-analysis' (AMA) using the 'cited by', 'similar articles', and 'related articles' citation discovery tools in the PubMed and Scopus databases. This study evaluates the usefulness of these citation discovery tools for updating SRs. Lists were constructed by applying the citation discovery tools in the two databases to the articles analyzed by a published SR. The degree of overlap between the lists and distribution of excluded results were evaluated. The articles ultimately selected for the SR update meta-analysis were found in the lists obtained from the 'cited by' and 'similar' tools in PubMed. Most of the selected articles appeared in both the 'cited by' lists in Scopus and PubMed. The Scopus 'related' tool did not identify the appropriate articles. The AMA, which involves using both citation discovery tools in PubMed, and optionally, the 'related' tool in Scopus, was found to be useful for updating an SR.

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

PubMed

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

2008-11-01

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

Host Control of Fungal Infections: Lessons from Basic Studies and Human Cohorts.

PubMed

Lionakis, Michail S; Levitz, Stuart M

2018-04-26

In the last few decades, the AIDS pandemic and the significant advances in the medical management of individuals with neoplastic and inflammatory conditions have resulted in a dramatic increase in the population of immunosuppressed patients with opportunistic, life-threatening fungal infections. The parallel development of clinically relevant mouse models of fungal disease and the discovery and characterization of several inborn errors of immune-related genes that underlie inherited human susceptibility to opportunistic mycoses have significantly expanded our understanding of the innate and adaptive immune mechanisms that protect against ubiquitous fungal exposures. This review synthesizes immunological knowledge derived from basic mouse studies and from human cohorts and provides an overview of mammalian antifungal host defenses that show promise for informing therapeutic and vaccination strategies for vulnerable patients.

Space Station Freedom Gateway to the Future

NASA Technical Reports Server (NTRS)

1992-01-01

The first inhabited outpost on the frontier of space will be a place to live, work, and discover. Experiments conducted on Freedom will advance scientific knowledge about our world, our environment, and ourselves. We will learn how to adapt to the space environment and to build and operate new spacecraft with destinations far beyond Earth, continuing the tradition of exploration that began with a journey to the Moon. What we learn from living and working on Freedom will strengthen our expertise in science and engineering, promote national research and development initiatives and inspire another generation of Americans to push forward and onward. On the eve of the 21st century, Space Station Freedom will be our gateway to the future. This material covers gateways to space, research, discovery, utilization, benefits, and NASA.

Mathematical Observations: The Genesis of Mathematical Discovery in the Classroom

ERIC Educational Resources Information Center

Beaugris, Louis M.

2013-01-01

In his "Proofs and Refutations," Lakatos identifies the "Primitive Conjecture" as the first stage in the pattern of mathematical discovery. In this article, I am interested in ways of reaching the "Primitive Conjecture" stage in an undergraduate classroom. I adapted Realistic Mathematics Education methods in an…

Political Discovery Resource Book.

ERIC Educational Resources Information Center

Political Discovery Education Collaborative for Greater Boston, MA.

This resource book for secondary students describes various aspects of federal, state, and local political processes. Originally written for use in the magnet education program "Political Discovery" in Boston, Massachusetts, the book can easily be used or adapted by teachers in any state. The first part of the book deals with the federal…

Developing integrated crop knowledge networks to advance candidate gene discovery.

PubMed

Hassani-Pak, Keywan; Castellote, Martin; Esch, Maria; Hindle, Matthew; Lysenko, Artem; Taubert, Jan; Rawlings, Christopher

2016-12-01

The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.

Multi-Protein Dynamic Combinatorial Chemistry: A Novel Strategy that Leads to Simultaneous Discovery of Subfamily-Selective Inhibitors for Nucleic Acid Demethylases FTO and ALKBH3.

PubMed

Das, Mohua; Tianming, Yang; Jinghua, Dong; Prasetya, Fransisca; Yiming, Xie; Wong, Kendra; Cheong, Adeline; Woon, Esther C Y

2018-06-19

Dynamic combinatorial chemistry (DCC) is a powerful supramolecular approach for discovering ligands for biomolecules. To date, most, if not all, biologically-templated DCC employ only a single biomolecule in directing the self-assembly process. To expand the scope and potential of DCC, herein, we developed a novel multi-protein DCC strategy which combines the discriminatory power of zwitterionic 'thermal-tag' with the sensitivity of differential scanning fluorimetry. This strategy enables the discovery of ligands against several proteins of interest concurrently. It is remarkably sensitive and could differentiate the binding of ligands to structurally-similar subfamily members, which is extremely challenging to achieve. Through this approach, we were able to simultaneously identify subfamily-selective probes against two clinically important epigenetic enzymes, FTO (7; IC₅₀ = 2.6 µM) and ALKBH3 (8; IC₅₀ = 3.7 µM). To our knowledge, this is the first report of a subfamily-selective ALKBH3 inhibitor. The developed strategy could, in principle, be adapted to a broad range of proteins, thus it shall be of widespread scientific interest. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Earliest colobine skeletons from Nakali, Kenya.

PubMed

Nakatsukasa, Masato; Mbua, Emma; Sawada, Yoshihiro; Sakai, Tetsuya; Nakaya, Hideo; Yano, Wataru; Kunimatsu, Yutaka

2010-11-01

Old World monkeys represent one of the most successful adaptive radiations of modern primates, but a sparse fossil record has limited our knowledge about the early evolution of this clade. We report the discovery of two partial skeletons of an early colobine monkey (Microcolobus) from the Nakali Formation (9.8-9.9 Ma) in Kenya that share postcranial synapomorphies with extant colobines in relation to arboreality such as mediolaterally wide distal humeral joint, globular humeral capitulum, distinctly angled zona conoidea, reduced medial trochlear keel, long medial epicondyle with weak retroflexion, narrow and tall olecranon, posteriorly dislocated fovea on the radial head, low projection of the femoral greater trochanter, wide talar head with a greater rotation, and proximodistally short cuboid and ectocuneiform. Microcolobus in Nakali clearly differs from the stem cercopithecoid Victoriapithecus regarding these features, as Victoriapithecus is postcranially similar to extant small-sized terrestrial cercopithecines. However, degeneration of the thumb, a hallmark of modern colobines, is not observed, suggesting that this was a late event in colobine evolution. This discovery contradicts the prevailing hypothesis that the forest invasion by cercopithecids first occurred in the Plio-Pleistocene, and shows that this event occurred by the late Miocene at a time when ape diversity declined. © 2010 Wiley-Liss, Inc.

SNP discovery in candidate adaptive genes using exon capture in a free-ranging alpine ungulate

Treesearch

Gretchen H. Roffler; Stephen J. Amish; Seth Smith; Ted Cosart; Marty Kardos; Michael K. Schwartz; Gordon Luikart

2016-01-01

Identification of genes underlying genomic signatures of natural selection is key to understanding adaptation to local conditions. We used targeted resequencing to identify SNP markers in 5321 candidate adaptive genes associated with known immunological, metabolic and growth functions in ovids and other ungulates. We selectively targeted 8161 exons in protein-coding...

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

PubMed Central

He, Xionglei; Zhang, Jianzhi

2009-01-01

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

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

PubMed

He, Xionglei; Zhang, Jianzhi

2009-03-01

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

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

PubMed Central

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

2017-01-01

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

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

PubMed Central

Volrathongchia, Kanittha

2003-01-01

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

Big, Deep, and Smart Data in Scanning Probe Microscopy

DOE PAGES

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

2016-09-27

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

Exploiting Early Intent Recognition for Competitive Advantage

DTIC Science & Technology

2009-01-01

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

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

ERIC Educational Resources Information Center

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

2013-01-01

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

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

ERIC Educational Resources Information Center

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

2010-01-01

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

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

USDA-ARS?s Scientific Manuscript database

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

Knowledge Management in Higher Education: A Knowledge Repository Approach

ERIC Educational Resources Information Center

Wedman, John; Wang, Feng-Kwei

2005-01-01

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

Crowdsourcing Knowledge Discovery and Innovations in Medicine

PubMed Central

2014-01-01

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

Crowdsourcing knowledge discovery and innovations in medicine.

PubMed

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

2014-09-19

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

Issues and challenges in resource management and its interaction with levels 2/3 fusion with applications to real-world problems: an annotated perspective

NASA Astrophysics Data System (ADS)

Blasch, Erik; Kadar, Ivan; Hintz, Kenneth; Biermann, Joachim; Chong, Chee-Yee; Salerno, John; Das, Subrata

2007-04-01

Resource management (or process refinement) is critical for information fusion operations in that users, sensors, and platforms need to be informed, based on mission needs, on how to collect, process, and exploit data. To meet these growing concerns, a panel session was conducted at the International Society of Information Fusion Conference in 2006 to discuss the various issues surrounding the interaction of Resource Management with Level 2/3 Situation and Threat Assessment. This paper briefly consolidates the discussion of the invited panel panelists. The common themes include: (1) Addressing the user in system management, sensor control, and knowledge based information collection (2) Determining a standard set of fusion metrics for optimization and evaluation based on the application (3) Allowing dynamic and adaptive updating to deliver timely information needs and information rates (4) Optimizing the joint objective functions at all information fusion levels based on decision-theoretic analysis (5) Providing constraints from distributed resource mission planning and scheduling; and (6) Defining L2/3 situation entity definitions for knowledge discovery, modeling, and information projection

EarthCube: Advancing Partnerships, Collaborative Platforms and Knowledge Networks in the Ocean Sciences

NASA Astrophysics Data System (ADS)

Stephen, Diggs; Lee, Allison

2014-05-01

The National Science Foundation's EarthCube initiative aims to create a community-driven data and knowledge management system that will allow for unprecedented data sharing across the geosciences. More than 2,500 participants through forums, work groups, EarthCube events, and virtual and in-person meetings have participated. The individuals that have engaged represent the core earth-system sciences of solid Earth, Atmosphere, Oceans, and Polar Sciences. EarthCube is a cornerstone of NSF's Cyberinfrastructure for the 21st Century (CIF21) initiative, whose chief objective is to develop a U.S. nationwide, sustainable, and community-based cyberinfrastructure for researchers and educators. Increasingly effective community-driven cyberinfrastructure allows global data discovery and knowledge management and achieves interoperability and data integration across scientific disciplines. There is growing convergence across scientific and technical communities on creating a networked, knowledge management system and scientific data cyberinfrastructure that integrates Earth system and human dimensions data in an open, transparent, and inclusive manner. EarthCube does not intend to replicate these efforts, but build upon them. An agile development process is underway for the development and governance of EarthCube. The agile approach was deliberately selected due to its iterative and incremental nature while promoting adaptive planning and rapid and flexible response. Such iterative deployment across a variety of EarthCube stakeholders encourages transparency, consensus, accountability, and inclusiveness.

Virtual Observatories, Data Mining, and Astroinformatics

NASA Astrophysics Data System (ADS)

Borne, Kirk

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

Designing a Semantic Bliki System to Support Different Types of Knowledge and Adaptive Learning

ERIC Educational Resources Information Center

Huang, Shiu-Li; Yang, Chia-Wei

2009-01-01

Though blogs and wikis have been used to support knowledge management and e-learning, existing blogs and wikis cannot support different types of knowledge and adaptive learning. A case in point, types of knowledge vary greatly in category and viewpoints. Additionally, adaptive learning is crucial to improving one's learning performance. This study…

Educating to the seismic risk with the community of learners

NASA Astrophysics Data System (ADS)

Miranda, Nicola

2013-04-01

The web has a lot of information available for teachers about seismology both at informal and at a scientific level. The average student, who approaches the study of seismology for the first time, lacks direct involvement in the study and it often happens that the simple informal knowledge does not result in learning and formalization. The teacher who wants to use web resources for the didactics, finds it is difficult to select information and adapt it to the school level, because of the short weekly time reserved for earth science studies. The seismologist, who gives a lecture in the school, has difficulty to understand how much of the knowledge transmitted will pass to the students. A way to solve these problems is to adopt the Community of Learners' method, creating groups of different-aged students directly involved in research activities and in the production of learning material, using websites organized for web research, production and sharing of ideas, as Jigsaw methodology suggests. The poster shown documents the experience I had with a group of students (aged from 14 to 18) in an Italian high school in Somma Vesuviana, near Naples. The method adopted is adaptable to any kind of technical-scientific issue. In this case seismology was the topic of the work group and thanks to the Community of Learners' method, all the students: -Started as apprentices, learning new things, questioning their knowledge, accessing new sources, using different channels and means of communication and debating with the others; -Moved to the role of teachers, sharing their own knowledge with the others, and explaining their own discoveries; -Became scientists specialized in something unknown to the other students, producers of new and original ideas to explain to the others who had different opinions and ideas. Thanks to the Community of Learners, they all became apprentices, teachers and scientists.

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

PubMed

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

2000-01-01

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

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

PubMed

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

2009-01-01

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

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

PubMed Central

2010-01-01

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

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

PubMed

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

2004-12-01

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

Knowledge Discovery/A Collaborative Approach, an Innovative Solution

NASA Technical Reports Server (NTRS)

Fitts, Mary A.

2009-01-01

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

Adaptive Knowledge Management of Project-Based Learning

ERIC Educational Resources Information Center

Tilchin, Oleg; Kittany, Mohamed

2016-01-01

The goal of an approach to Adaptive Knowledge Management (AKM) of project-based learning (PBL) is to intensify subject study through guiding, inducing, and facilitating development knowledge, accountability skills, and collaborative skills of students. Knowledge development is attained by knowledge acquisition, knowledge sharing, and knowledge…

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

PubMed

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

2016-09-27

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

Building Scalable Knowledge Graphs for Earth Science

NASA Technical Reports Server (NTRS)

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

2017-01-01

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

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

PubMed

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

2004-01-01

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

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

PubMed Central

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

1997-01-01

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

Discovery informatics in biological and biomedical sciences: research challenges and opportunities.

PubMed

Honavar, Vasant

2015-01-01

New discoveries in biological, biomedical and health sciences are increasingly being driven by our ability to acquire, share, integrate and analyze, and construct and simulate predictive models of biological systems. While much attention has focused on automating routine aspects of management and analysis of "big data", realizing the full potential of "big data" to accelerate discovery calls for automating many other aspects of the scientific process that have so far largely resisted automation: identifying gaps in the current state of knowledge; generating and prioritizing questions; designing studies; designing, prioritizing, planning, and executing experiments; interpreting results; forming hypotheses; drawing conclusions; replicating studies; validating claims; documenting studies; communicating results; reviewing results; and integrating results into the larger body of knowledge in a discipline. Against this background, the PSB workshop on Discovery Informatics in Biological and Biomedical Sciences explores the opportunities and challenges of automating discovery or assisting humans in discovery through advances (i) Understanding, formalization, and information processing accounts of, the entire scientific process; (ii) Design, development, and evaluation of the computational artifacts (representations, processes) that embody such understanding; and (iii) Application of the resulting artifacts and systems to advance science (by augmenting individual or collective human efforts, or by fully automating science).

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

PubMed

Schmalhofer, F J; Tschaitschian, B

1998-11-01

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

An Ensemble Approach to Building Mercer Kernels with Prior Information

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd

2005-01-01

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.

Building Better Decision-Support by Using Knowledge Discovery.

ERIC Educational Resources Information Center

Jurisica, Igor

2000-01-01

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

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

PubMed Central

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

2012-01-01

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

Wains: a pattern-seeking artificial life species.

PubMed

de Buitléir, Amy; Russell, Michael; Daly, Mark

2012-01-01

We describe the initial phase of a research project to develop an artificial life framework designed to extract knowledge from large data sets with minimal preparation or ramp-up time. In this phase, we evolved an artificial life population with a new brain architecture. The agents have sufficient intelligence to discover patterns in data and to make survival decisions based on those patterns. The species uses diploid reproduction, Hebbian learning, and Kohonen self-organizing maps, in combination with novel techniques such as using pattern-rich data as the environment and framing the data analysis as a survival problem for artificial life. The first generation of agents mastered the pattern discovery task well enough to thrive. Evolution further adapted the agents to their environment by making them a little more pessimistic, and also by making their brains more efficient.

NREL Electrochromic Window Research Wins Award

ScienceCinema

None

2017-12-09

Winners of the CO-LABS Governor's Award for High-Impact Research in Energy Efficiency, Dr. Satyen Deb at the U.S. Department of Energy's National Renewable Energy Laboratory (NREL) discovered that a small electrical charge can change the opacity of tungsten oxide from clear to tinted. He, Dr. Dane Gillaspie, and their fellow scientists at NREL then applied this knowledge to develop and transfer the technologies required to construct an electrochromic window, which can switch between clear and heavily tinted states. Electrochromic windows allow natural light in while adding tint to reduce summer heat and glare, and going clear to allow sunlight through in the winter. Broad adaptation of these windows could reduce US total energy use by four percent and reduce building cooling loads by 20%, much of this during expensive peak hours. Windows based on these discoveries are now being installed worldwide.

HIGH-EFFICIENCY AUTONOMOUS LASER ADAPTIVE OPTICS

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

Baranec, Christoph; Riddle, Reed; Tendulkar, Shriharsh

2014-07-20

As new large-scale astronomical surveys greatly increase the number of objects targeted and discoveries made, the requirement for efficient follow-up observations is crucial. Adaptive optics imaging, which compensates for the image-blurring effects of Earth's turbulent atmosphere, is essential for these surveys, but the scarcity, complexity and high demand of current systems limit their availability for following up large numbers of targets. To address this need, we have engineered and implemented Robo-AO, a fully autonomous laser adaptive optics and imaging system that routinely images over 200 objects per night with an acuity 10 times sharper at visible wavelengths than typically possible frommore » the ground. By greatly improving the angular resolution, sensitivity, and efficiency of 1-3 m class telescopes, we have eliminated a major obstacle in the follow-up of the discoveries from current and future large astronomical surveys.« less

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

NASA Astrophysics Data System (ADS)

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

2012-08-01

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

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

PubMed

Hassani-Pak, Keywan; Rawlings, Christopher

2017-06-13

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

An Adaptive Approach to Managing Knowledge Development in a Project-Based Learning Environment

ERIC Educational Resources Information Center

Tilchin, Oleg; Kittany, Mohamed

2016-01-01

In this paper we propose an adaptive approach to managing the development of students' knowledge in the comprehensive project-based learning (PBL) environment. Subject study is realized by two-stage PBL. It shapes adaptive knowledge management (KM) process and promotes the correct balance between personalized and collaborative learning. The…

Optimizing the discovery organization for innovation.

PubMed

Sams-Dodd, Frank

2005-08-01

Strategic management is the process of adapting organizational structure and management principles to fit the strategic goal of the business unit. The pharmaceutical industry has generally been expert at optimizing its organizations for drug development, but has rarely implemented different structures for the early discovery process, where the objective is innovation and the transformation of innovation into drug projects. Here, a set of strategic management methods is proposed, covering team composition, organizational structure, management principles and portfolio management, which are designed to increase the level of innovation in the early drug discovery process.

Surveillance theory applied to virus detection: a case for targeted discovery

USGS Publications Warehouse

Bogich, Tiffany L.; Anthony, Simon J.; Nichols, James D.

2013-01-01

Virus detection and mathematical modeling have gone through rapid developments in the past decade. Both offer new insights into the epidemiology of infectious disease and characterization of future risk; however, modeling has not yet been applied to designing the best surveillance strategies for viral and pathogen discovery. We review recent developments and propose methods to integrate viral and pathogen discovery and mathematical modeling through optimal surveillance theory, arguing for a more targeted approach to novel virus detection guided by the principles of adaptive management and structured decision-making.

The Z1 truss is ready to be moved into Discovery's payload bay

NASA Technical Reports Server (NTRS)

2000-01-01

Inside the Payload Changeout Room (PCR), a worker makes sure the Integrated Truss Structure Z1 is ready to be moved into the payload bay of Space Shuttle Discovery. The Z1 truss is the first of 10 that will become the backbone of the International Space Station, eventually stretching the length of a football field. Along with its companion payload, the third Pressurized Mating Adapter, the Z1 is scheduled to be launched aboard Discovery Oct. 5 at 9:38 p.m. EDT.

Space Shuttle Discovery rolls out to Launch Pad 39A for Oct. 5 launch

NASA Technical Reports Server (NTRS)

2000-01-01

As the sun crawls from below the horizon at right, Space Shuttle Discovery crawls up Launch Pad 39A and its resting spot next to the fixed service structure (FSS) (seen at left). The powerful silhouette dwarfs people and other vehicles near the FSS. Discovery is scheduled to launch Oct. 5 at 9:30 p.m. EDT on mission STS-92. Making the 100th Space Shuttle mission launched from Kennedy Space Center, Discovery will carry two pieces of hardware for the International Space Station, the Z1 truss, which is the cornerstone truss of the Station, and the third Pressurized Mating Adapter. Discovery also will be making its 28th flight into space, more than any of the other orbiters to date.

The Place of Crowdfunding in the Discovery of Scientific and Social Value of Medical Research.

PubMed

Del Savio, Lorenzo

2017-06-01

Crowdfunding is increasingly common in medical research. Some critics are concerned that by adopting crowdfunding, some researchers may sidestep the established systems of review of the social and scientific value of studies (e.g. impact on disease burden, issues of justice), especially mechanisms of expert-based review. I argue firstly that such concerns are based on a misleading picture of how research value is assessed and secondly that crowdfunding may turn out to be an useful complement of extant funding systems. I start with the idea that medical knowledge is a structured and intermediate public good and explain from this perspective that funding systems as a whole, rather than any of their parts (such as expert-based reviews) ought to be considered devices for the discovery of the social and scientific value of research. If so, we should not be concerned with whether crowdfunding bypasses expert reviews, but with whether it may constitute an improvement of extant funding systems. In the second part, I speculate that crowdfunding may ameliorate, albeit limitedly, some recalcitrant failures of funding systems, such as the sponsorship of research on neglected diseases, and smooth funding adaptations for scientific transitions. If, after trial, such hypotheses turn out to be true, crowdfunding ought to be promoted. © 2017 John Wiley & Sons Ltd.

Great Originals of Modern Physics

ERIC Educational Resources Information Center

Decker, Fred W.

1972-01-01

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

Dulse on the Distaff Side.

ERIC Educational Resources Information Center

MacKenzie, Marion

1983-01-01

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

43 CFR 4.1132 - Scope of discovery.

Code of Federal Regulations, 2014 CFR

2014-10-01

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

43 CFR 4.1132 - Scope of discovery.

Code of Federal Regulations, 2012 CFR

2012-10-01

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

43 CFR 4.1132 - Scope of discovery.

Code of Federal Regulations, 2013 CFR

2013-10-01

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

43 CFR 4.1132 - Scope of discovery.

Code of Federal Regulations, 2011 CFR

2011-10-01

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

Molecular Chaperones of Leishmania: Central Players in Many Stress-Related and -Unrelated Physiological Processes

PubMed Central

Requena, Jose M.; Montalvo, Ana M.; Fraga, Jorge

2015-01-01

Molecular chaperones are key components in the maintenance of cellular homeostasis and survival, not only during stress but also under optimal growth conditions. Folding of nascent polypeptides is supported by molecular chaperones, which avoid the formation of aggregates by preventing nonspecific interactions and aid, when necessary, the translocation of proteins to their correct intracellular localization. Furthermore, when proteins are damaged, molecular chaperones may also facilitate their refolding or, in the case of irreparable proteins, their removal by the protein degradation machinery of the cell. During their digenetic lifestyle, Leishmania parasites encounter and adapt to harsh environmental conditions, such as nutrient deficiency, hypoxia, oxidative stress, changing pH, and shifts in temperature; all these factors are potential triggers of cellular stress. We summarize here our current knowledge on the main types of molecular chaperones in Leishmania and their functions. Among them, heat shock proteins play important roles in adaptation and survival of this parasite against temperature changes associated with its passage from the poikilothermic insect vector to the warm-blooded vertebrate host. The study of structural features and the function of chaperones in Leishmania biology is providing opportunities (and challenges) for drug discovery and improving of current treatments against leishmaniasis. PMID:26167482

Targeting complement-mediated immunoregulation for cancer immunotherapy.

PubMed

Kolev, Martin; Markiewski, Maciej M

2018-06-01

Complement was initially discovered as an assembly of plasma proteins "complementing" the cytolytic activity of antibodies. However, our current knowledge places this complex system of several plasma proteins, receptors, and regulators in the center of innate immunity as a bridge between the initial innate responses and adaptive immune reactions. Consequently, complement appears to be pivotal for elimination of pathogens, not only as an early response defense, but by directing the subsequent adaptive immune response. The discovery of functional intracellular complement and its roles in cellular metabolism opened novel avenues for research and potential therapeutic implications. The recent studies demonstrating immunoregulatory functions of complement in the tumor microenvironment and the premetastatic niche shifted the paradigm on our understanding of functions of the complement system in regulating immunity. Several complement proteins, through their interaction with cells in the tumor microenvironment and in metastasis-targeted organs, contribute to modulating tumor growth, antitumor immunity, angiogenesis, and therefore, the overall progression of malignancy and, perhaps, responsiveness of cancer to different therapies. Here, we focus on recent progress in our understanding of immunostimulatory vs. immunoregulatory functions of complement and potential applications of these findings to the design of novel therapies for cancer patients. Copyright © 2018 Elsevier Ltd. All rights reserved.

The pervasive role of social learning in primate lifetime development.

PubMed

Whiten, Andrew; van de Waal, Erica

2018-01-01

In recent decades, an accelerating research effort has exploited a substantial diversity of methodologies to garner mounting evidence for social learning and culture in many species of primate. As in humans, the evidence suggests that the juvenile phases of non-human primates' lives represent a period of particular intensity in adaptive learning from others, yet the relevant research remains scattered in the literature. Accordingly, we here offer what we believe to be the first substantial collation and review of this body of work and its implications for the lifetime behavioral ecology of primates. We divide our analysis into three main phases: a first phase of learning focused on primary attachment figures, typically the mother; a second phase of selective learning from a widening array of group members, including some with expertise that the primary figures may lack; and a third phase following later dispersal, when a migrant individual encounters new ecological and social circumstances about which the existing residents possess expertise that can be learned from. Collating a diversity of discoveries about this lifetime process leads us to conclude that social learning pervades primate ontogenetic development, importantly shaping locally adaptive knowledge and skills that span multiple aspects of the behavioral repertoire.

Scientific Knowledge Discovery in Complex Semantic Networks of Geophysical Systems

NASA Astrophysics Data System (ADS)

Fox, P.

2012-04-01

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

Strategies for enhancing the effectiveness of metagenomic-based enzyme discovery in lignocellulytic microbial communities

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

DeAngelis, K.M.; Gladden, J.G.; Allgaier, M.

2010-03-01

Producing cellulosic biofuels from plant material has recently emerged as a key U.S. Department of Energy goal. For this technology to be commercially viable on a large scale, it is critical to make production cost efficient by streamlining both the deconstruction of lignocellulosic biomass and fuel production. Many natural ecosystems efficiently degrade lignocellulosic biomass and harbor enzymes that, when identified, could be used to increase the efficiency of commercial biomass deconstruction. However, ecosystems most likely to yield relevant enzymes, such as tropical rain forest soil in Puerto Rico, are often too complex for enzyme discovery using current metagenomic sequencing technologies.more » One potential strategy to overcome this problem is to selectively cultivate the microbial communities from these complex ecosystems on biomass under defined conditions, generating less complex biomass-degrading microbial populations. To test this premise, we cultivated microbes from Puerto Rican soil or green waste compost under precisely defined conditions in the presence dried ground switchgrass (Panicum virgatum L.) or lignin, respectively, as the sole carbon source. Phylogenetic profiling of the two feedstock-adapted communities using SSU rRNA gene amplicon pyrosequencing or phylogenetic microarray analysis revealed that the adapted communities were significantly simplified compared to the natural communities from which they were derived. Several members of the lignin-adapted and switchgrass-adapted consortia are related to organisms previously characterized as biomass degraders, while others were from less well-characterized phyla. The decrease in complexity of these communities make them good candidates for metagenomic sequencing and will likely enable the reconstruction of a greater number of full length genes, leading to the discovery of novel lignocellulose-degrading enzymes adapted to feedstocks and conditions of interest.« less

Beginning to manage drug discovery and development knowledge.

PubMed

Sumner-Smith, M

2001-05-01

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

Mindfulness as an organizational capability: Evidence from wildland firefighting

Treesearch

Michelle Barton; Kathleen Sutcliffe

2008-01-01

Mindful organizing has been proposed as an adaptive form for unpredictable, unknowable environments. Mindfulness induces a rich awareness of details and facilitates the discovery and correction of ill-structured contingencies so that adaptations can be made as action unfolds. Although these ideas are appealing, empirical studies examining mindfulness and its effects...

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

ERIC Educational Resources Information Center

Marcum, James W.

2001-01-01

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

Foreword to "The Secret of Childhood."

ERIC Educational Resources Information Center

Stephenson, Margaret E.

2000-01-01

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

Cosmic Discovery

NASA Astrophysics Data System (ADS)

Harwit, Martin

1984-04-01

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

The discovery of medicines for rare diseases

PubMed Central

Swinney, David C; Xia, Shuangluo

2015-01-01

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

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

PubMed Central

2014-01-01

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

A restoration genetics guide for coral reef conservation.

PubMed

Baums, Iliana B

2008-06-01

Worldwide degradation of coral reef communities has prompted a surge in restoration efforts. They proceed largely without considering genetic factors because traditionally, coral populations have been regarded as open over large areas with little potential for local adaptation. Since, biophysical and molecular studies indicated that most populations are closed over shorter time and smaller spatial scales. Thus, it is justified to re-examine the potential for site adaptation in corals. There is ample evidence for differentiated populations, inbreeding, asexual reproduction and the occurrence of ecotypes, factors that may facilitate local adaptation. Discovery of widespread local adaptation would influence coral restoration projects mainly with regard to the physical and evolutionary distance from the source wild and/or captive bred propagules may be moved without causing a loss of fitness in the restored population. Proposed causes for loss of fitness as a result of (plant) restoration efforts include founder effects, genetic swamping, inbreeding and/or outbreeding depression. Direct evidence for any of these processes is scarce in reef corals due to a lack of model species that allow for testing over multiple generations and the separation of the relative contributions of algal symbionts and their coral hosts to the overall performance of the coral colony. This gap in our knowledge may be closed by employing novel population genetic and genomics approaches. The use of molecular tools may aid managers in the selection of appropriate propagule sources, guide spatial arrangement of transplants, and help in assessing the success of coral restoration projects by tracking the performance of transplants, thereby generating important data for future coral reef conservation and restoration projects.

Knowledge discovery from structured mammography reports using inductive logic programming.

PubMed

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

2005-01-01

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

A Metadata based Knowledge Discovery Methodology for Seeding Translational Research.

PubMed

Kothari, Cartik R; Payne, Philip R O

2015-01-01

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

OBSERVATIONS OF HIERARCHICAL SOLAR-TYPE MULTIPLE STAR SYSTEMS

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

Roberts, Lewis C. Jr.; Tokovinin, Andrei; Mason, Brian D.

2015-10-15

Twenty multiple stellar systems with solar-type primaries were observed at high angular resolution using the PALM-3000 adaptive optics system at the 5 m Hale telescope. The goal was to complement the knowledge of hierarchical multiplicity in the solar neighborhood by confirming recent discoveries by the visible Robo-AO system with new near-infrared observations with PALM-3000. The physical status of most, but not all, of the new pairs is confirmed by photometry in the Ks band and new positional measurements. In addition, we resolved for the first time five close sub-systems: the known astrometric binary in HIP 17129AB, companions to the primariesmore » of HIP 33555, and HIP 118213, and the companions to the secondaries in HIP 25300 and HIP 101430. We place the components on a color–magnitude diagram and discuss each multiple system individually.« less

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

Steven Larson MD

This project funded since 1986 serves as a core project for cancer research throughout MSKCC, producing key radiotracers as well as basic knowledge about thel physics of radiation decay and imaging, for nuclear medicine applications to cancer diagnosis and therapy. In recent years this research application has broadened to include experiments intended to lead to an improved understanding of cancer biology and into the discovery and testing of new cancer drugs. Advances in immune based radiotargeting form the basis for this project. Both antibody and cellular based immune targeting methods have been explored. The multi-step targeting methodologies (MST) developed bymore » NeoRex (Seattle,Washington), have been adapted for use with positron emitting isotopes and PET allowing the quantification and optimization of targeted delivery. In addition, novel methods for radiolabeling immune T-cells with PET tracers have advanced our ability to track these cells of prolonged period of time.« less

A hybrid, auto-adaptive and rule-based multi-agent approach using evolutionary algorithms for improved searching

NASA Astrophysics Data System (ADS)

Izquierdo, Joaquín; Montalvo, Idel; Campbell, Enrique; Pérez-García, Rafael

2016-08-01

Selecting the most appropriate heuristic for solving a specific problem is not easy, for many reasons. This article focuses on one of these reasons: traditionally, the solution search process has operated in a given manner regardless of the specific problem being solved, and the process has been the same regardless of the size, complexity and domain of the problem. To cope with this situation, search processes should mould the search into areas of the search space that are meaningful for the problem. This article builds on previous work in the development of a multi-agent paradigm using techniques derived from knowledge discovery (data-mining techniques) on databases of so-far visited solutions. The aim is to improve the search mechanisms, increase computational efficiency and use rules to enrich the formulation of optimization problems, while reducing the search space and catering to realistic problems.

Automatic analysis of quantitative NMR data of pharmaceutical compound libraries.

PubMed

Liu, Xuejun; Kolpak, Michael X; Wu, Jiejun; Leo, Gregory C

2012-08-07

In drug discovery, chemical library compounds are usually dissolved in DMSO at a certain concentration and then distributed to biologists for target screening. Quantitative (1)H NMR (qNMR) is the preferred method for the determination of the actual concentrations of compounds because the relative single proton peak areas of two chemical species represent the relative molar concentrations of the two compounds, that is, the compound of interest and a calibrant. Thus, an analyte concentration can be determined using a calibration compound at a known concentration. One particularly time-consuming step in the qNMR analysis of compound libraries is the manual integration of peaks. In this report is presented an automated method for performing this task without prior knowledge of compound structures and by using an external calibration spectrum. The script for automated integration is fast and adaptable to large-scale data sets, eliminating the need for manual integration in ~80% of the cases.

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

ERIC Educational Resources Information Center

Kraft, Donald H., Ed.

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

Evaluating the Science of Discovery in Complex Health Systems

ERIC Educational Resources Information Center

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

2011-01-01

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

29 CFR 18.14 - Scope of discovery.

Code of Federal Regulations, 2014 CFR

2014-07-01

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

49 CFR 386.38 - Scope of discovery.

Code of Federal Regulations, 2011 CFR

2011-10-01

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

49 CFR 386.38 - Scope of discovery.

Code of Federal Regulations, 2012 CFR

2012-10-01

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

29 CFR 18.14 - Scope of discovery.

Code of Federal Regulations, 2012 CFR

2012-07-01

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

49 CFR 386.38 - Scope of discovery.

Code of Federal Regulations, 2013 CFR

2013-10-01

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

29 CFR 18.14 - Scope of discovery.

Code of Federal Regulations, 2011 CFR

2011-07-01

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

29 CFR 18.14 - Scope of discovery.

Code of Federal Regulations, 2013 CFR

2013-07-01

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

49 CFR 386.38 - Scope of discovery.

Code of Federal Regulations, 2014 CFR

2014-10-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Trends in Modern Drug Discovery.

PubMed

Eder, Jörg; Herrling, Paul L

2016-01-01

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

Envisioning engineering education and practice in the coming intelligence convergence era — a complex adaptive systems approach

NASA Astrophysics Data System (ADS)

Noor, Ahmed K.

2013-12-01

Some of the recent attempts for improving and transforming engineering education are reviewed. The attempts aim at providing the entry level engineers with the skills needed to address the challenges of future large-scale complex systems and projects. Some of the frontier sectors and future challenges for engineers are outlined. The major characteristics of the coming intelligence convergence era (the post-information age) are identified. These include the prevalence of smart devices and environments, the widespread applications of anticipatory computing and predictive / prescriptive analytics, as well as a symbiotic relationship between humans and machines. Devices and machines will be able to learn from, and with, humans in a natural collaborative way. The recent game changers in learnscapes (learning paradigms, technologies, platforms, spaces, and environments) that can significantly impact engineering education in the coming era are identified. Among these are open educational resources, knowledge-rich classrooms, immersive interactive 3D learning, augmented reality, reverse instruction / flipped classroom, gamification, robots in the classroom, and adaptive personalized learning. Significant transformative changes in, and mass customization of, learning are envisioned to emerge from the synergistic combination of the game changers and other technologies. The realization of the aforementioned vision requires the development of a new multidisciplinary framework of emergent engineering for relating innovation, complexity and cybernetics, within the future learning environments. The framework can be used to treat engineering education as a complex adaptive system, with dynamically interacting and communicating components (instructors, individual, small, and large groups of learners). The emergent behavior resulting from the interactions can produce progressively better, and continuously improving, learning environment. As a first step towards the realization of the vision, intelligent adaptive cyber-physical ecosystems need to be developed to facilitate collaboration between the various stakeholders of engineering education, and to accelerate the development of a skilled engineering workforce. The major components of the ecosystems include integrated knowledge discovery and exploitation facilities, blended learning and research spaces, novel ultra-intelligent software agents, multimodal and autonomous interfaces, and networked cognitive and tele-presence robots.

Care and Neurorehabilitation in the Disorder of Consciousness: A Model in Progress

PubMed Central

Dolce, Giuliano; Arcuri, Francesco; Carozzo, Simone; Cortese, Maria Daniela; Greco, Pierpaolo; Lucca, Lucia Francesca; Pignolo, Loris; Pugliese, Maria Elena

2015-01-01

The operational model and strategies developed at the Institute S. Anna-RAN to be applied in the care and neurorehabilitation of subjects with disorders of consciousness (DOC) are described. The institute units are sequentially organized to guarantee appropriate care and provide rehabilitation programs adapted to the patients' clinical condition and individual's needs at each phase of evolution during treatment in a fast turnover rate. Patients eligible of home care are monitored remotely. Transferring advanced technology to a stage of regular operation is the main mission. Responsiveness and the time windows characterized by better residual responsiveness are identified and the spontaneous/induced changes in the autonomic system functional state and biological parameters are monitored both in dedicated sessions and by means of an ambient intelligence platform acquiring large databases from traditional and innovative sensors and interfaced with knowledge management and knowledge discovery systems. Diagnosis of vegetative state/unresponsive wakefulness syndrome or minimal conscious state and early prognosis are in accordance with the current criteria. Over one thousand patients with DOC have been admitted and treated in the years 1998–2013. The model application has progressively shortened the time of hospitalization and reduced costs at unchanged quality of services. PMID:25893211

Reuniting Virtue and Knowledge

ERIC Educational Resources Information Center

Culham, Tom

2015-01-01

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

Integrative Systems Biology for Data Driven Knowledge Discovery

PubMed Central

Greene, Casey S.; Troyanskaya, Olga G.

2015-01-01

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

Automated Knowledge Discovery from Simulators

NASA Technical Reports Server (NTRS)

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

2006-01-01

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

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

Code of Federal Regulations, 2010 CFR

2010-04-01

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

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

ERIC Educational Resources Information Center

What Works Clearinghouse, 2013

2013-01-01

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

78 FR 12933 - Proceedings Before the Commodity Futures Trading Commission

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-26

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

76 FR 64803 - Rules of Adjudication and Enforcement

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-19

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

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

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-23

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

KSC00pp1289

NASA Image and Video Library

2000-09-11

KENNEDY SPACE CENTER, Fla. -- As the sun crawls from below the horizon at right, Space Shuttle Discovery crawls up Launch Pad 39A and its resting spot next to the fixed service structure (FSS) (seen at left). The powerful silhouette dwarfs people and other vehicles near the FSS. Discovery is scheduled to launch Oct. 5 at 9:30 p.m. EDT on mission STS-92. Making the 100th Space Shuttle mission launched from Kennedy Space Center, Discovery will carry two pieces of hardware for the International Space Station, the Z1 truss, which is the cornerstone truss of the Station, and the third Pressurized Mating Adapter. Discovery also will be making its 28th flight into space, more than any of the other orbiters to date

KSC-00pp1289

NASA Image and Video Library

2000-09-11

KENNEDY SPACE CENTER, Fla. -- As the sun crawls from below the horizon at right, Space Shuttle Discovery crawls up Launch Pad 39A and its resting spot next to the fixed service structure (FSS) (seen at left). The powerful silhouette dwarfs people and other vehicles near the FSS. Discovery is scheduled to launch Oct. 5 at 9:30 p.m. EDT on mission STS-92. Making the 100th Space Shuttle mission launched from Kennedy Space Center, Discovery will carry two pieces of hardware for the International Space Station, the Z1 truss, which is the cornerstone truss of the Station, and the third Pressurized Mating Adapter. Discovery also will be making its 28th flight into space, more than any of the other orbiters to date

Putting Priors in Mixture Density Mercer Kernels

NASA Technical Reports Server (NTRS)

Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd

2004-01-01

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.

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

DTIC Science & Technology

2010-04-26

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

Network-based approaches to climate knowledge discovery

NASA Astrophysics Data System (ADS)

Budich, Reinhard; Nyberg, Per; Weigel, Tobias

2011-11-01

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

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

PubMed

Mott, Meghan; Koroshetz, Walter

2015-07-01

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

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

PubMed Central

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

2010-01-01

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

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

PubMed

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

2015-11-01

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

Asymmetric threat data mining and knowledge discovery

NASA Astrophysics Data System (ADS)

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

2001-03-01

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

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

PubMed

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

2011-01-01

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

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

PubMed

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

2009-03-01

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

Oak Ridge Graph Analytics for Medical Innovation (ORiGAMI)

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

Roberts, Larry W.; Lee, Sangkeun

2016-01-01

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

How hard they hit? Perception, adaptation and public health implications of heat waves in urban and peri-urban Pakistan.

PubMed

Rauf, Sara; Bakhsh, Khuda; Abbas, Azhar; Hassan, Sarfraz; Ali, Asghar; Kächele, Harald

2017-04-01

Heat waves threaten human health given the fast changing climatic scenarios in the recent past. Adaptation to heat waves would take place when people perceive their impacts based on their knowledge. The present study examines perception level and its determinants resulting in adaptation to heat waves in Pakistan. The study used cross-sectional data from urban and peri-urban respondents of Faisalabad District. The study employs a health belief model to assess risk perception among the respondents. Logistic model is used to determine factors affecting level of knowledge, perception and adaptation to heat waves. Around 30% of peri-urban respondents have a low level of knowledge about the fatal impacts of heat waves. Risk perception of heat waves is very low among urban (57%) and peri-urban (66%) respondents. Households' knowledge on heat waves is significantly related to age, gender, education, wealth and access to health services. Determinants of perception include knowledge of heat waves, age and joint effect of marital status and knowledge while income level, family size, urban/peri-urban background, perceived barriers, perceived benefits and cues to action significantly affect adaptation to heat waves. To reduce deadly health impacts, mass awareness campaigns are needed to build perception and improve adaptation to heat waves.

KSC-00pp1296

NASA Image and Video Library

2000-09-12

KENNEDY SPACE CENTER, Fla. -- The morning sun spotlights Launch Pad 39A and Space Shuttle Discovery atop the Mobile Launcher Platform. To its left is the Rotating Service Structure in its open position, at the top of the ramp that the Shuttle must negotiate on the crawler-transporter. Above Discovery looms the 80-foot fiberglass lightning mast. At the far left is the Vehicle Assembly Building, where a Space Shuttle begins its voyage to the pad. Discovery is scheduled to launch on mission STS-92 Oct. 5 at 9:30 p.m. EDT. Making the 100th Space Shuttle mission launched from Kennedy Space Center, Discovery will carry two pieces of hardware for the International Space Station, the Z1 truss, which is the cornerstone truss of the Station, and the third Pressurized Mating Adapter. Discovery also will be making its 28th flight into space, more than any of the other orbiters to date

KSC00pp1296

NASA Image and Video Library

2000-09-12

KENNEDY SPACE CENTER, Fla. -- The morning sun spotlights Launch Pad 39A and Space Shuttle Discovery atop the Mobile Launcher Platform. To its left is the Rotating Service Structure in its open position, at the top of the ramp that the Shuttle must negotiate on the crawler-transporter. Above Discovery looms the 80-foot fiberglass lightning mast. At the far left is the Vehicle Assembly Building, where a Space Shuttle begins its voyage to the pad. Discovery is scheduled to launch on mission STS-92 Oct. 5 at 9:30 p.m. EDT. Making the 100th Space Shuttle mission launched from Kennedy Space Center, Discovery will carry two pieces of hardware for the International Space Station, the Z1 truss, which is the cornerstone truss of the Station, and the third Pressurized Mating Adapter. Discovery also will be making its 28th flight into space, more than any of the other orbiters to date

Gendered medicinal plant knowledge contributions to adaptive capacity and health sovereignty in Amazonia.

PubMed

Díaz-Reviriego, Isabel; Fernández-Llamazares, Álvaro; Salpeteur, Matthieu; Howard, Patricia L; Reyes-García, Victoria

2016-12-01

Local medical systems are key elements of social-ecological systems as they provide culturally appropriate and locally accessible health care options, especially for populations with scarce access to biomedicine. The adaptive capacity of local medical systems generally rests on two pillars: species diversity and a robust local knowledge system, both threatened by local and global environmental change. We first present a conceptual framework to guide the assessment of knowledge diversity and redundancy in local medicinal knowledge systems through a gender lens. Then, we apply this conceptual framework to our research on the local medicinal plant knowledge of the Tsimane' Amerindians. Our results suggest that Tsimane' medicinal plant knowledge is gendered and that the frequency of reported ailments and the redundancy of knowledge used to treat them are positively associated. We discuss the implications of knowledge diversity and redundancy for local knowledge systems' adaptive capacity, resilience, and health sovereignty.

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

PubMed Central

García-Alonso, Carlos; Pérez-Naranjo, Leonor

2009-01-01

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

Scaling Up: Adapting a Phage-Hunting Course to Increase Participation of First-Year Students in Research.

PubMed

Staub, Nancy L; Poxleitner, Marianne; Braley, Amanda; Smith-Flores, Helen; Pribbenow, Christine M; Jaworski, Leslie; Lopatto, David; Anders, Kirk R

2016-01-01

Authentic research experiences are valuable components of effective undergraduate education. Research experiences during the first years of college are especially critical to increase persistence in science, technology, engineering, and mathematics fields. The Science Education Alliance Phage Hunters Advancing Genomics and Evolutionary Science (SEA-PHAGES) model provides a high-impact research experience to first-year students but is usually available to a limited number of students, and its implementation is costly in faculty time and laboratory space. To offer a research experience to all students taking introductory biology at Gonzaga University (n = 350/yr), we modified the traditional two-semester SEA-PHAGES course by streamlining the first-semester Phage Discovery lab and integrating the second SEA-PHAGES semester into other courses in the biology curriculum. Because most students in the introductory course are not biology majors, the Phage Discovery semester may be their only encounter with research. To discover whether students benefit from the first semester alone, we assessed the effects of the one-semester Phage Discovery course on students' understanding of course content. Specifically, students showed improvement in knowledge of bacteriophages, lab math skills, and understanding experimental design and interpretation. They also reported learning gains and benefits comparable with other course-based research experiences. Responses to open-ended questions suggest that students experienced this course as a true undergraduate research experience. © 2016 N. L. Staub et al. CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Discovery of a novel mastrevirus and alphasatellite-like circular DNA in dragonflies (Epiprocta) from Puerto Rico.

PubMed

Rosario, Karyna; Padilla-Rodriguez, Marco; Kraberger, Simona; Stainton, Daisy; Martin, Darren P; Breitbart, Mya; Varsani, Arvind

2013-01-01

Geminiviruses have emerged as serious agricultural pathogens. Despite all the species that have been already catalogued, new molecular techniques continue to expand the diversity and geographical ranges of these single-stranded DNA viruses and their associated satellite molecules. Since all geminiviruses are insect-transmitted, examination of insect vector populations through vector-enabled metagenomics (VEM) has been recently used to investigate the diversity of geminiviruses transmitted by a specific vector in a given region. Here we used a more comprehensive adaptation of the VEM approach by surveying small circular DNA viruses found within top insect predators, specifically dragonflies (Epiprocta). This 'predator-enabled' approach is not limited to viral groups transmitted by specific vectors since dragonflies can accumulate the wide range of viruses transmitted by their diverse insect prey. Analysis of six dragonflies collected from an agricultural field in Puerto Rico culminated in the discovery of the first mastrevirus (Dragonfly-associated mastrevirus; DfasMV) and alphasatellite molecule (Dragonfly-associated alphasatellite; Dfas-alphasatellite) from the Caribbean. Since DfasMV and Dfas-alphasatellite are divergent from the limited number of sequences that have been reported from the Americas, this study unequivocally demonstrates that there have been at least two independent past introductions of both mastreviruses and alphasatellites to the New World. Overall, the use of predacious insects as sampling tools can profoundly alter our views of natural plant virus diversity and biogeography by allowing the discovery of novel geminiviruses and associated satellite molecules without a priori knowledge of the types of viruses or insect vectors in a given area. Copyright © 2012 Elsevier B.V. All rights reserved.

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

Code of Federal Regulations, 2010 CFR

2010-04-01

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

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

ERIC Educational Resources Information Center

Heffernan, Bernadette M.

1998-01-01

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

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

ERIC Educational Resources Information Center

Dixon, James A.; Bangert, Ashley S.

2002-01-01

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

40 CFR 300.300 - Phase I-Discovery or notification.

Code of Federal Regulations, 2010 CFR

2010-07-01

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

Facilitating and Learning at the Edge of Chaos: Expanding the Context of Experiential Education.

ERIC Educational Resources Information Center

Oekerman, Carl

Significant recent discoveries within a number of scientific disciplines, collectively referred to as the science of complexity, are creating a major shift in how human beings understand the complex, adaptive systems that make up the world. A complex adaptive system consists of networks of large numbers of agents that interact with each other and…

"New Skills and Abilities to Enable Me to Support My Pupils in a Forward Thinking Positive Way:" A Self-Discovery Programme for Teachers in Mainstream School

ERIC Educational Resources Information Center

Powell, Lesley; Cheshire, Anna

2008-01-01

The purpose of this study is to adapt, deliver, and pilot test the Self-discovery Programme (SDP) for teachers in mainstream school. The study used a pre-test post-test design. Quantitative data were collected by self-administered questionnaires given to teachers at two points in time: baseline (immediately pre-SDP) and immediately post-SDP.…

Serendipity: Accidental Discoveries in Science

NASA Astrophysics Data System (ADS)

Roberts, Royston M.

1989-06-01

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

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

PubMed Central

2015-01-01

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

The Development of Adaptive Expertise in Biotransport

ERIC Educational Resources Information Center

Martin, Taylor; Petrosino, Anthony J.; Rivale, Stephanie; Diller, Kenneth R.

2006-01-01

This chapter describes a model for continuous development of adaptive expertise, including growth along the dimensions of innovation and knowledge, examined in the context of a biotransport course in biomedical engineering. Students improved on both knowledge and innovation, moving along a continuum toward adaptive expertise. (Contains 5 figures.)

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

PubMed

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

2017-04-01

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

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

PubMed

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

2016-01-01

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

The Z1 truss is prepped in the PCR for transfer to Discovery's payload bay

NASA Technical Reports Server (NTRS)

2000-01-01

Inside the Payload Changeout Room (PCR), workers prepare to move the Integrated Truss Structure Z1 out of the payload canister. Once inside the PCR, workers will get ready to move the Z1 into the payload bay of Space Shuttle Discovery. The Z1 truss is the first of 10 that will become the backbone of the International Space Station, eventually stretching the length of a football field. Along with its companion payload, the third Pressurized Mating Adapter, the Z1 is scheduled to be launched aboard Discovery Oct. 5 at 9:38 p.m. EDT.

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

NASA Astrophysics Data System (ADS)

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

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

Role of vitamin D in cytotoxic T lymphocyte immunity to pathogens and cancer.

PubMed

Sarkar, Surojit; Hewison, Martin; Studzinski, George P; Li, Yan Chun; Kalia, Vandana

2016-01-01

The discovery of vitamin D receptor (VDR) expression in immune cells has opened up a new area of research into immunoregulation by vitamin D, a niche that is distinct from its classical role in skeletal health. Today, about three decades since this discovery, numerous cellular and molecular targets of vitamin D in the immune system have been delineated. Moreover, strong clinical associations between vitamin D status and the incidence/severity of many immune-regulated disorders (e.g. infectious diseases, cancers and autoimmunity) have prompted the idea of using vitamin D supplementation to manipulate disease outcome. While much is known about the effects of vitamin D on innate immune responses and helper T (T(H)) cell immunity, there has been relatively limited progress on the frontier of cytotoxic T lymphocyte (CTL) immunity--an arm of host cellular adaptive immunity that is crucial for the control of such intracellular pathogens as human immunodeficiency virus (HIV), tuberculosis (TB), malaria, and hepatitis C virus (HCV). In this review, we discuss the strong historical and clinical link between vitamin D and infectious diseases that involves cytotoxic T lymphocyte (CTL) immunity, present our current understanding as well as critical knowledge gaps in the realm of vitamin D regulation of host CTL responses, and highlight potential regulatory connections between vitamin D and effector and memory CD8 T cell differentiation events during infections.

Antisense oligonucleotide technologies in drug discovery.

PubMed

Aboul-Fadl, Tarek

2006-09-01

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

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

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

Pais, Abraham; Weinberg, Steven; Quigg, Chris

1997-04-01

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

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

DOE R&D Accomplishments Database

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

1997-04-01

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

A knowledgebase system to enhance scientific discovery: Telemakus

PubMed Central

Fuller, Sherrilynne S; Revere, Debra; Bugni, Paul F; Martin, George M

2004-01-01

Background With the rapid expansion of scientific research, the ability to effectively find or integrate new domain knowledge in the sciences is proving increasingly difficult. Efforts to improve and speed up scientific discovery are being explored on a number of fronts. However, much of this work is based on traditional search and retrieval approaches and the bibliographic citation presentation format remains unchanged. Methods Case study. Results The Telemakus KnowledgeBase System provides flexible new tools for creating knowledgebases to facilitate retrieval and review of scientific research reports. In formalizing the representation of the research methods and results of scientific reports, Telemakus offers a potential strategy to enhance the scientific discovery process. While other research has demonstrated that aggregating and analyzing research findings across domains augments knowledge discovery, the Telemakus system is unique in combining document surrogates with interactive concept maps of linked relationships across groups of research reports. Conclusion Based on how scientists conduct research and read the literature, the Telemakus KnowledgeBase System brings together three innovations in analyzing, displaying and summarizing research reports across a domain: (1) research report schema, a document surrogate of extracted research methods and findings presented in a consistent and structured schema format which mimics the research process itself and provides a high-level surrogate to facilitate searching and rapid review of retrieved documents; (2) research findings, used to index the documents, allowing searchers to request, for example, research studies which have studied the relationship between neoplasms and vitamin E; and (3) visual exploration interface of linked relationships for interactive querying of research findings across the knowledgebase and graphical displays of what is known as well as, through gaps in the map, what is yet to be tested. The rationale and system architecture are described and plans for the future are discussed. PMID:15507158

Predicting missing values in a home care database using an adaptive uncertainty rule method.

PubMed

Konias, S; Gogou, G; Bamidis, P D; Vlahavas, I; Maglaveras, N

2005-01-01

Contemporary literature illustrates an abundance of adaptive algorithms for mining association rules. However, most literature is unable to deal with the peculiarities, such as missing values and dynamic data creation, that are frequently encountered in fields like medicine. This paper proposes an uncertainty rule method that uses an adaptive threshold for filling missing values in newly added records. A new approach for mining uncertainty rules and filling missing values is proposed, which is in turn particularly suitable for dynamic databases, like the ones used in home care systems. In this study, a new data mining method named FiMV (Filling Missing Values) is illustrated based on the mined uncertainty rules. Uncertainty rules have quite a similar structure to association rules and are extracted by an algorithm proposed in previous work, namely AURG (Adaptive Uncertainty Rule Generation). The main target was to implement an appropriate method for recovering missing values in a dynamic database, where new records are continuously added, without needing to specify any kind of thresholds beforehand. The method was applied to a home care monitoring system database. Randomly, multiple missing values for each record's attributes (rate 5-20% by 5% increments) were introduced in the initial dataset. FiMV demonstrated 100% completion rates with over 90% success in each case, while usual approaches, where all records with missing values are ignored or thresholds are required, experienced significantly reduced completion and success rates. It is concluded that the proposed method is appropriate for the data-cleaning step of the Knowledge Discovery process in databases. The latter, containing much significance for the output efficiency of any data mining technique, can improve the quality of the mined information.

Extreme halophilic archaea derive from two distinct methanogen Class II lineages.

PubMed

Aouad, Monique; Taib, Najwa; Oudart, Anne; Lecocq, Michel; Gouy, Manolo; Brochier-Armanet, Céline

2018-04-20

Phylogenetic analyses of conserved core genes have disentangled most of the ancient relationships in Archaea. However, some groups remain debated, like the DPANN, a deep-branching super-phylum composed of nanosized archaea with reduced genomes. Among these, the Nanohaloarchaea require high-salt concentrations for growth. Their discovery in 2012 was significant because they represent, together with Halobacteria (a Class belonging to Euryarchaeota), the only two described lineages of extreme halophilic archaea. The phylogenetic position of Nanohaloarchaea is highly debated, being alternatively proposed as the sister-lineage of Halobacteria or a member of the DPANN super-phylum. Pinpointing the phylogenetic position of extreme halophilic archaea is important to improve our knowledge of the deep evolutionary history of Archaea and the molecular adaptive processes and evolutionary paths that allowed their emergence. Using comparative genomic approaches, we identified 258 markers carrying a reliable phylogenetic signal. By combining strategies limiting the impact of biases on phylogenetic inference, we showed that Nanohaloarchaea and Halobacteria represent two independent lines that derived from two distinct but related methanogens Class II lineages. This implies that adaptation to high salinity emerged twice independently in Archaea and indicates that their emergence within DPANN in previous studies is likely the consequence of a tree reconstruction artifact, challenging the existence of this super-phylum. Copyright © 2018. Published by Elsevier Inc.

Stage-Specific Changes in Plasmodium Metabolism Required for Differentiation and Adaptation to Different Host and Vector Environments.

PubMed

Srivastava, Anubhav; Philip, Nisha; Hughes, Katie R; Georgiou, Konstantina; MacRae, James I; Barrett, Michael P; Creek, Darren J; McConville, Malcolm J; Waters, Andrew P

2016-12-01

Malaria parasites (Plasmodium spp.) encounter markedly different (nutritional) environments during their complex life cycles in the mosquito and human hosts. Adaptation to these different host niches is associated with a dramatic rewiring of metabolism, from a highly glycolytic metabolism in the asexual blood stages to increased dependence on tricarboxylic acid (TCA) metabolism in mosquito stages. Here we have used stable isotope labelling, targeted metabolomics and reverse genetics to map stage-specific changes in Plasmodium berghei carbon metabolism and determine the functional significance of these changes on parasite survival in the blood and mosquito stages. We show that glutamine serves as the predominant input into TCA metabolism in both asexual and sexual blood stages and is important for complete male gametogenesis. Glutamine catabolism, as well as key reactions in intermediary metabolism and CoA synthesis are also essential for ookinete to oocyst transition in the mosquito. These data extend our knowledge of Plasmodium metabolism and point towards possible targets for transmission-blocking intervention strategies. Furthermore, they highlight significant metabolic differences between Plasmodium species which are not easily anticipated based on genomics or transcriptomics studies and underline the importance of integration of metabolomics data with other platforms in order to better inform drug discovery and design.

Stage-Specific Changes in Plasmodium Metabolism Required for Differentiation and Adaptation to Different Host and Vector Environments

PubMed Central

Srivastava, Anubhav; Philip, Nisha; Hughes, Katie R.; Georgiou, Konstantina; MacRae, James I.; Barrett, Michael P.; McConville, Malcolm J.

2016-01-01

Malaria parasites (Plasmodium spp.) encounter markedly different (nutritional) environments during their complex life cycles in the mosquito and human hosts. Adaptation to these different host niches is associated with a dramatic rewiring of metabolism, from a highly glycolytic metabolism in the asexual blood stages to increased dependence on tricarboxylic acid (TCA) metabolism in mosquito stages. Here we have used stable isotope labelling, targeted metabolomics and reverse genetics to map stage-specific changes in Plasmodium berghei carbon metabolism and determine the functional significance of these changes on parasite survival in the blood and mosquito stages. We show that glutamine serves as the predominant input into TCA metabolism in both asexual and sexual blood stages and is important for complete male gametogenesis. Glutamine catabolism, as well as key reactions in intermediary metabolism and CoA synthesis are also essential for ookinete to oocyst transition in the mosquito. These data extend our knowledge of Plasmodium metabolism and point towards possible targets for transmission-blocking intervention strategies. Furthermore, they highlight significant metabolic differences between Plasmodium species which are not easily anticipated based on genomics or transcriptomics studies and underline the importance of integration of metabolomics data with other platforms in order to better inform drug discovery and design. PMID:28027318

A network model of knowledge accumulation through diffusion and upgrade

NASA Astrophysics Data System (ADS)

Zhuang, Enyu; Chen, Guanrong; Feng, Gang

2011-07-01

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

Interfaith Education: An Islamic Perspective

ERIC Educational Resources Information Center

Pallavicini, Yahya Sergio Yahe

2016-01-01

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

Integrating semantic web technologies and geospatial catalog services for geospatial information discovery and processing in cyberinfrastructure

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

Yue, Peng; Gong, Jianya; Di, Liping

Abstract A geospatial catalogue service provides a network-based meta-information repository and interface for advertising and discovering shared geospatial data and services. Descriptive information (i.e., metadata) for geospatial data and services is structured and organized in catalogue services. The approaches currently available for searching and using that information are often inadequate. Semantic Web technologies show promise for better discovery methods by exploiting the underlying semantics. Such development needs special attention from the Cyberinfrastructure perspective, so that the traditional focus on discovery of and access to geospatial data can be expanded to support the increased demand for processing of geospatial information andmore » discovery of knowledge. Semantic descriptions for geospatial data, services, and geoprocessing service chains are structured, organized, and registered through extending elements in the ebXML Registry Information Model (ebRIM) of a geospatial catalogue service, which follows the interface specifications of the Open Geospatial Consortium (OGC) Catalogue Services for the Web (CSW). The process models for geoprocessing service chains, as a type of geospatial knowledge, are captured, registered, and discoverable. Semantics-enhanced discovery for geospatial data, services/service chains, and process models is described. Semantic search middleware that can support virtual data product materialization is developed for the geospatial catalogue service. The creation of such a semantics-enhanced geospatial catalogue service is important in meeting the demands for geospatial information discovery and analysis in Cyberinfrastructure.« less

Does Environmental Instability Favor the Production and Horizontal Transmission of Knowledge regarding Medicinal Plants? A Study in Southeast Brazil

PubMed Central

Soldati, Gustavo Taboada; Hanazaki, Natália; Crivos, Marta; Albuquerque, Ulysses Paulino

2015-01-01

Greater socio-environmental instability favors the individual production of knowledge because innovations are adapted to new circumstances. Furthermore, instability stimulates the horizontal transmission of knowledge because this mechanism disseminates adapted information. This study investigates the following hypothesis: Greater socio-environmental instability favors the production of knowledge (innovation) to adapt to new situations, and socio-environmental instability stimulates the horizontal transmission of knowledge, which is a mechanism that diffuses adapted information. In addition, the present study describes “how”, “when”, “from whom” and the “stimulus/context”, in which knowledge regarding medicinal plants is gained or transferred. Data were collected through semi-structured interviews from three groups that represented different levels of socio-environmental instability. Socio-environmental instability did not favor individual knowledge production or any cultural transmission modes, including vertical to horizontal, despite increasing the frequency of horizontal pathways. Vertical transmission was the most important knowledge transmission strategy in all of the groups in which mothers were the most common models (knowledge sources). Significantly, childhood was the most important learning stage, although learning also occurred throughout life. Direct teaching using language was notable as a knowledge transmission strategy. Illness was the main stimulus that triggered local learning. Learning modes about medicinal plants were influenced by the knowledge itself, particularly the dynamic uses of therapeutic resources. PMID:25992578

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

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-20

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

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

ERIC Educational Resources Information Center

Carter, Sunshine; Traill, Stacie

2017-01-01

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

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

ERIC Educational Resources Information Center

Yu, Pulan

2012-01-01

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

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

ERIC Educational Resources Information Center

Silbersack, Elionora W.

2014-01-01

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

Augmented Reality-Based Simulators as Discovery Learning Tools: An Empirical Study

ERIC Educational Resources Information Center

Ibáñez, María-Blanca; Di-Serio, Ángela; Villarán-Molina, Diego; Delgado-Kloos, Carlos

2015-01-01

This paper reports empirical evidence on having students use AR-SaBEr, a simulation tool based on augmented reality (AR), to discover the basic principles of electricity through a series of experiments. AR-SaBEr was enhanced with knowledge-based support and inquiry-based scaffolding mechanisms, which proved useful for discovery learning in…

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

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-22

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

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

ERIC Educational Resources Information Center

Gaffney, Michael; Rafferty, Pauline

2009-01-01

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

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

PubMed Central

2016-01-01

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

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

NASA Astrophysics Data System (ADS)

Juandi, D.; Priatna, N.

2018-05-01

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

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

PubMed

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

2018-01-01

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

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

PubMed

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

2018-07-01

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

Systematic identification of latent disease-gene associations from PubMed articles

PubMed Central

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

2018-01-01

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

Temporal data mining for the quality assessment of hemodialysis services.

PubMed

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

2005-05-01

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

ITMS: Individualized Teaching Material System: Adaptive Integration of Web Pages Distributed in Some Servers.

ERIC Educational Resources Information Center

Mitsuhara, Hiroyuki; Kurose, Yoshinobu; Ochi, Youji; Yano, Yoneo

The authors developed a Web-based Adaptive Educational System (Web-based AES) named ITMS (Individualized Teaching Material System). ITMS adaptively integrates knowledge on the distributed Web pages and generates individualized teaching material that has various contents. ITMS also presumes the learners' knowledge levels from the states of their…

Pedagogical Content Knowledge of Experienced Teachers in Physical Education: Functional Analysis of Adaptations

ERIC Educational Resources Information Center

Ayvazo, Shiri; Ward, Phillip

2011-01-01

Pedagogical content knowledge (PCK) is the teacher's ability to pedagogically adapt content to students of diverse abilities. In this study, we investigated how teachers' adaptations of instruction for individual students differed when teaching stronger and weaker instructional units. We used functional analysis (Hanley, Iwata, & McCord, 2003) of…

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

PubMed Central

2017-01-01

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

Choosing experiments to accelerate collective discovery

PubMed Central

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

2015-01-01

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

A frozen super-Earth orbiting a star at the bottom of the main sequence

NASA Astrophysics Data System (ADS)

Kubas, D.; Beaulieu, J. P.; Bennett, D. P.; Cassan, A.; Cole, A.; Lunine, J.; Marquette, J. B.; Dong, S.; Gould, A.; Sumi, T.; Batista, V.; Fouqué, P.; Brillant, S.; Dieters, S.; Coutures, C.; Greenhill, J.; Bond, I.; Nagayama, T.; Udalski, A.; Pompei, E.; Nürnberger, D. E. A.; Le Bouquin, J. B.

2012-04-01

Context. Microlensing is a unique method to probe low mass exoplanets beyond the snow line. However, the scientific potential of the new microlensing planet discovery is often unfulfilled due to lack of knowledge of the properties of the lens and source stars. The discovery light curve of the super Earth MOA-2007-BLG-192Lb suffers from significant degeneracies that limit what can be inferred about its physical properties. Aims: High resolution adaptive optics images allow us to solve this problem by resolving the microlensing target from all unrelated background stars, yielding the unique determination of magnified source and lens fluxes. This estimation permits the solution of our microlens model for the mass of the planet and its host and their physical projected separation. Methods: We observed the microlensing event MOA-2007-BLG-192 at high angular resolution in JHKs with the NACO adaptive optics system on the VLT while the object was still amplified by a factor 1.23 and then at baseline 18 months later. We analyzed and calibrated the NACO photometry in the standard 2MASS system in order to accurately constrain the source and the lens star fluxes. Results: We detect light from the host star of MOA-2007-BLG-192Lb, which significantly reduces the uncertainties in its characteristics as compared to earlier analyses. We find that MOA-2007-BLG-192L is most likely a very low mass late type M-dwarf (0.084-0.012+0.015 M⊙) at a distance of 660-70+100 pc orbited by a 3.2-1.8+5.2 M⊕ super-Earth at 0.66-0.22+0.51 AU. We then discuss the properties of this cold planetary system. Based on observations under ESO Prog.IDs: 279.C-5044(A) and 383-C.0495(A).

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

PubMed Central

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

2012-01-01

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

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

PubMed Central

Huo, Zhiguang; Tseng, George

2017-01-01

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

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

PubMed

Huo, Zhiguang; Tseng, George

2017-06-01

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

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

NASA Astrophysics Data System (ADS)

Wachowicz, Monica

2000-04-01

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

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

NASA Astrophysics Data System (ADS)

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

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

State of the Art in Tumor Antigen and Biomarker Discovery

PubMed Central

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

2011-01-01

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

Hypnocounseling: Ericksonian Hypnosis for Counselors.

ERIC Educational Resources Information Center

Gunnison, Hugh

1990-01-01

Describes ways in which Erickson's discoveries and thinking might be used by counselors, specifically describing hypnocounseling. Discusses how Ericksonian language patterns can be adapted by most counselors to their primary orientations and techniques. (Author/ABL)

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

ERIC Educational Resources Information Center

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

2007-01-01

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

Cache-Cache Comparison for Supporting Meaningful Learning

ERIC Educational Resources Information Center

Wang, Jingyun; Fujino, Seiji

2015-01-01

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

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

NASA Technical Reports Server (NTRS)

Shope, R.

1996-01-01

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

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

PubMed

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

2008-04-25

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

A framework for interval-valued information system

NASA Astrophysics Data System (ADS)

Yin, Yunfei; Gong, Guanghong; Han, Liang

2012-09-01

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

Knowledge Discovery in Variant Databases Using Inductive Logic Programming

PubMed Central

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

2013-01-01

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

Knowledge discovery in variant databases using inductive logic programming.

PubMed

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

2013-01-01

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

Information About a Layperson's Knowledge Supports Experts in Giving Effective and Efficient Online Advice to Laypersons

ERIC Educational Resources Information Center

Nuckles, Matthias; Wittwer, Jorg; Renkl, Alexander

2005-01-01

To give effective and efficient advice to laypersons, experts should adapt their explanations to the layperson's knowledge. However, experts often fail to consider the limited domain knowledge of laypersons. To support adaptation in asynchronous help desk communication, researchers provided computer experts with information about a layperson's…

Quantitative trait Loci analysis using the false discovery rate.

PubMed

Benjamini, Yoav; Yekutieli, Daniel

2005-10-01

False discovery rate control has become an essential tool in any study that has a very large multiplicity problem. False discovery rate-controlling procedures have also been found to be very effective in QTL analysis, ensuring reproducible results with few falsely discovered linkages and offering increased power to discover QTL, although their acceptance has been slower than in microarray analysis, for example. The reason is partly because the methodological aspects of applying the false discovery rate to QTL mapping are not well developed. Our aim in this work is to lay a solid foundation for the use of the false discovery rate in QTL mapping. We review the false discovery rate criterion, the appropriate interpretation of the FDR, and alternative formulations of the FDR that appeared in the statistical and genetics literature. We discuss important features of the FDR approach, some stemming from new developments in FDR theory and methodology, which deem it especially useful in linkage analysis. We review false discovery rate-controlling procedures--the BH, the resampling procedure, and the adaptive two-stage procedure-and discuss the validity of these procedures in single- and multiple-trait QTL mapping. Finally we argue that the control of the false discovery rate has an important role in suggesting, indicating the significance of, and confirming QTL and present guidelines for its use.

The Z1 truss is transported to Launch Pad 39A

NASA Technical Reports Server (NTRS)

2000-01-01

Before dawn, the payload canister (left) with the Integrated Truss Structure Z1 moves slowly up the crawlerway ramp on Launch Pad 39A toward Space Shuttle Discovery in the background. The canister will be lifted up the Rotating Service Structure to the Payload Changeout Room where the Z1 will be removed and transferred to Discovery's payload bay. The Z1 truss is the first of 10 that will become the backbone of the International Space Station, eventually stretching the length of a football field. Along with its companion payload, the third Pressurized Mating Adapter, the Z1 is scheduled to be launched aboard Discovery Oct. 5 at 9:38 p.m. EDT.

The Z1 truss is transported to Launch Pad 39A

NASA Technical Reports Server (NTRS)

2000-01-01

At Launch Pad 39A, the payload canister at left draws closer to the Rotating Service Structure where it will be lifted to the Payload Changeout Room. There its cargo, the Integrated Truss Structure Z1, will be removed and later transferred to Space Shuttle Discovery's payload bay. Discovery is at right, sitting atop the Mobile Launcher Platform. The Z1 truss is the first of 10 that will become the backbone of the International Space Station, eventually stretching the length of a football field. Along with its companion payload, the third Pressurized Mating Adapter, the Z1 is scheduled to be launched aboard Discovery Oct. 5 at 9:38 p.m. EDT.

Co-production of knowledge: recipe for success in land-based climate change adaptation?

NASA Astrophysics Data System (ADS)

Coninx, Ingrid; Swart, Rob

2015-04-01

After multiple failures of scientists to trigger policymakers and other relevant actors to take action when communicating research findings, the request for co-production (or co-creation) of knowledge and stakeholder involvement in climate change adaptation efforts has rapidly increased over the past few years. In particular for land-based adaptation, on-the-ground action is often met by societal resistance towards solutions proposed by scientists, by a misfit of potential solutions with the local context, leading to misunderstanding and even rejection of scientific recommendations. A fully integrative co-creation process in which both scientists and practitioners discuss climate vulnerability and possible responses, exploring perspectives and designing adaptation measures based on their own knowledge, is expected to prevent the adaptation deadlock. The apparent conviction that co-creation processes result in successful adaptation, has not yet been unambiguously empirically demonstrated, but has resulted in co-creation being one of basic principles in many new research and policy programmes. But is co-creation that brings knowledge of scientists and practitioners together always the best recipe for success in climate change adaptation? Assessing a number of actual cases, the authors have serious doubts. The paper proposes additional considerations for adaptively managing the environment that should be taken into account in the design of participatory knowledge development in which climate scientists play a role. These include the nature of the problem at stake; the values, interests and perceptions of the actors involved; the methods used to build trust, strengthen alignment and develop reciprocal relationships among scientists and practitioners; and the concreteness of the co-creation output.

Evolutionary history of barley cultivation in Europe revealed by genetic analysis of extant landraces

PubMed Central

2011-01-01

Background Understanding the evolution of cultivated barley is important for two reasons. First, the evolutionary relationships between different landraces might provide information on the spread and subsequent development of barley cultivation, including the adaptation of the crop to new environments and its response to human selection. Second, evolutionary information would enable landraces with similar traits but different genetic backgrounds to be identified, providing alternative strategies for the introduction of these traits into modern germplasm. Results The evolutionary relationships between 651 barley landraces were inferred from the genotypes for 24 microsatellites. The landraces could be divided into nine populations, each with a different geographical distribution. Comparisons with ear row number, caryopsis structure, seasonal growth habit and flowering time revealed a degree of association between population structure and phenotype, and analysis of climate variables indicated that the landraces are adapted, at least to some extent, to their environment. Human selection and/or environmental adaptation may therefore have played a role in the origin and/or maintenance of one or more of the barley landrace populations. There was also evidence that at least some of the population structure derived from geographical partitioning set up during the initial spread of barley cultivation into Europe, or reflected the later introduction of novel varieties. In particular, three closely-related populations were made up almost entirely of plants with the daylength nonresponsive version of the photoperiod response gene PPD-H1, conferring adaptation to the long annual growth season of northern Europe. These three populations probably originated in the eastern Fertile Crescent and entered Europe after the initial spread of agriculture. Conclusions The discovery of population structure, combined with knowledge of associated phenotypes and environmental adaptations, enables a rational approach to identification of landraces that might be used as sources of germplasm for breeding programs. The population structure also enables hypotheses concerning the prehistoric spread and development of agriculture to be addressed. PMID:22047039

Persistent Adaptation by Chronic Alcohol is Facilitated by Neuroimmune Activation Linked to Stress and CRF

PubMed Central

Breese, George R.; Knapp, Darin J.

2016-01-01

This review updates the conceptual basis for the association of alcohol abuse with an insidious adaptation that facilitates negative affect during withdrawal from chronic intermittent alcohol (CIA) exposure – a change that later supports sensitization of stress-induced anxiety following alcohol abstinence. The finding that a CRF1-receptor antagonist (CRF1RA) minimized CIA withdrawal-induced negative affect supported an association of alcohol withdrawal with a stress mechanism. The finding that repeated stresses or multiple CRF injections into selected brain sites prior to a single 5-day chronic alcohol (CA) exposure induced anxiety during withdrawal provided critical support for a linkage of CIA withdrawal with stress. The determination that CRF1RA injection into positive CRF-sensitive brain sites prevented CIA withdrawal-induced anxiety provided support that neural path integration maintains the persistent CIA adaptation. Based upon reports that stress increases neuroimmune function, an effort was undertaken to test whether cytokines would support the adaptation induced by stress/CA exposure. Twenty-four hours after withdrawal from CIA, cytokine mRNAs were found to be increased in cortex as well as other sites in brain. Further, repeated cytokine injections into previously identified brain sites substituted for stress and CRF induction of anxiety during CA withdrawal. Discovery that a CRF1RA prevented the brain cytokine mRNA increase induced by CA withdrawal provided critical evidence for CRF involvement in this neuroimmune induction after CA withdrawal. However, the CRF1RA did not block the stress increase in cytokine mRNA increases in controls. The latter data supported the hypothesis that distinct mechanisms linked to stress and CA withdrawal can support common neuroimmune functions within a brain site. As evidence evolves concerning neural involvement in brain neuroimmune function, a better understanding of the progressive adaptation associated with CIA exposure will advance new knowledge that could possibly lead to strategies to combat alcohol abuse. PMID:27139233

Learning the Structure of Biomedical Relationships from Unstructured Text

PubMed Central

Percha, Bethany; Altman, Russ B.

2015-01-01

The published biomedical research literature encompasses most of our understanding of how drugs interact with gene products to produce physiological responses (phenotypes). Unfortunately, this information is distributed throughout the unstructured text of over 23 million articles. The creation of structured resources that catalog the relationships between drugs and genes would accelerate the translation of basic molecular knowledge into discoveries of genomic biomarkers for drug response and prediction of unexpected drug-drug interactions. Extracting these relationships from natural language sentences on such a large scale, however, requires text mining algorithms that can recognize when different-looking statements are expressing similar ideas. Here we describe a novel algorithm, Ensemble Biclustering for Classification (EBC), that learns the structure of biomedical relationships automatically from text, overcoming differences in word choice and sentence structure. We validate EBC's performance against manually-curated sets of (1) pharmacogenomic relationships from PharmGKB and (2) drug-target relationships from DrugBank, and use it to discover new drug-gene relationships for both knowledge bases. We then apply EBC to map the complete universe of drug-gene relationships based on their descriptions in Medline, revealing unexpected structure that challenges current notions about how these relationships are expressed in text. For instance, we learn that newer experimental findings are described in consistently different ways than established knowledge, and that seemingly pure classes of relationships can exhibit interesting chimeric structure. The EBC algorithm is flexible and adaptable to a wide range of problems in biomedical text mining. PMID:26219079

Learning and Relevance in Information Retrieval: A Study in the Application of Exploration and User Knowledge to Enhance Performance

ERIC Educational Resources Information Center

Hyman, Harvey

2012-01-01

This dissertation examines the impact of exploration and learning upon eDiscovery information retrieval; it is written in three parts. Part I contains foundational concepts and background on the topics of information retrieval and eDiscovery. This part informs the reader about the research frameworks, methodologies, data collection, and…

Using Discovery Maps as a Free-Choice Learning Process Can Enhance the Effectiveness of Environmental Education in a Botanical Garden

ERIC Educational Resources Information Center

Yang, Xi; Chen, Jin

2017-01-01

Botanical gardens (BGs) are important agencies that enhance human knowledge and attitude towards flora conservation. By following free-choice learning model, we developed a "Discovery map" and distributed the map to visitors at the Xishuangbanna Tropical Botanical Garden in Yunnan, China. Visitors, who did and did not receive discovery…

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

Treesearch

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

1984-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Survival in hostile territory: the microbiota of the stomach.

PubMed

Yang, Ines; Nell, Sandra; Suerbaum, Sebastian

2013-09-01

The human stomach is a formidable barrier to orally ingested microorganisms and was long thought to be sterile. The discovery of Helicobacter pylori, a carcinogenic bacterial pathogen that infects the stomach mucosa of more than one half of all humans globally, has started a major paradigm shift in our understanding of the stomach as an ecological niche for bacteria. The special adaptations that enable H. pylori to colonize this well-protected habitat have been intensively studied over the last three decades. In contrast, our knowledge concerning bacteria other than H. pylori in the human stomach is still quite limited. However, a substantial body of evidence documents convincingly that bacteria can regularly be sampled from the stomachs of healthy adults. Commonly detected phyla include Firmicutes, Actinobacteria, Bacteroidetes, and Proteobacteria, and characteristic genera are Lactobacillus, Streptococcus, and Propionibacterium. In this review, we summarize the available literature about the gastric microbiota in humans and selected model animals, discuss the methods used in its characterization, and identify gaps in our knowledge that need to be addressed to advance our understanding of the bacterial colonization of the different layers of the gastric mucosa and its potential role in health and disease. © 2013 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

Potential of Cognitive Computing and Cognitive Systems

NASA Astrophysics Data System (ADS)

Noor, Ahmed K.

2015-01-01

Cognitive computing and cognitive technologies are game changers for future engineering systems, as well as for engineering practice and training. They are major drivers for knowledge automation work, and the creation of cognitive products with higher levels of intelligence than current smart products. This paper gives a brief review of cognitive computing and some of the cognitive engineering systems activities. The potential of cognitive technologies is outlined, along with a brief description of future cognitive environments, incorporating cognitive assistants - specialized proactive intelligent software agents designed to follow and interact with humans and other cognitive assistants across the environments. The cognitive assistants engage, individually or collectively, with humans through a combination of adaptive multimodal interfaces, and advanced visualization and navigation techniques. The realization of future cognitive environments requires the development of a cognitive innovation ecosystem for the engineering workforce. The continuously expanding major components of the ecosystem include integrated knowledge discovery and exploitation facilities (incorporating predictive and prescriptive big data analytics); novel cognitive modeling and visual simulation facilities; cognitive multimodal interfaces; and cognitive mobile and wearable devices. The ecosystem will provide timely, engaging, personalized / collaborative, learning and effective decision making. It will stimulate creativity and innovation, and prepare the participants to work in future cognitive enterprises and develop new cognitive products of increasing complexity. http://www.aee.odu.edu/cognitivecomp

Detecting Role Errors in the Gene Hierarchy of the NCI Thesaurus

PubMed Central

Min, Hua; Cohen, Barry; Halper, Michael; Oren, Marc; Perl, Yehoshua

2008-01-01

Gene terminologies are playing an increasingly important role in the ever-growing field of genomic research. While errors in large, complex terminologies are inevitable, gene terminologies are even more susceptible to them due to the rapid growth of genomic knowledge and the nature of its discovery. It is therefore very important to establish quality-assurance protocols for such genomic-knowledge repositories. Different kinds of terminologies oftentimes require auditing methodologies adapted to their particular structures. In light of this, an auditing methodology tailored to the characteristics of the NCI Thesaurus’s (NCIT’s) Gene hierarchy is presented. The Gene hierarchy is of particular interest to the NCIT’s designers due to the primary role of genomics in current cancer research. This multiphase methodology focuses on detecting role-errors, such as missing roles or roles with incorrect or incomplete target structures, occurring within that hierarchy. The methodology is based on two kinds of abstraction networks, called taxonomies, that highlight the role distribution among concepts within the IS-A (subsumption) hierarchy. These abstract views tend to highlight portions of the hierarchy having a higher concentration of errors. The errors found during an application of the methodology are reported. Hypotheses pertaining to the efficacy of our methodology are investigated. PMID:19221606

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

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

May, D

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

Problems of Primary Education Today

ERIC Educational Resources Information Center

Dubova, M. V.

2014-01-01

Primary education in Russia has failed to adapt to the needs of post-Soviet society, and is still based on rote learning and memorization instead of learning through discovery and learning to use and apply what is learned.

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-12-26

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

Identifying transcription factor functions and targets by phenotypic activation

PubMed Central

Chua, Gordon; Morris, Quaid D.; Sopko, Richelle; Robinson, Mark D.; Ryan, Owen; Chan, Esther T.; Frey, Brendan J.; Andrews, Brenda J.; Boone, Charles; Hughes, Timothy R.

2006-01-01

Mapping transcriptional regulatory networks is difficult because many transcription factors (TFs) are activated only under specific conditions. We describe a generic strategy for identifying genes and pathways induced by individual TFs that does not require knowledge of their normal activation cues. Microarray analysis of 55 yeast TFs that caused a growth phenotype when overexpressed showed that the majority caused increased transcript levels of genes in specific physiological categories, suggesting a mechanism for growth inhibition. Induced genes typically included established targets and genes with consensus promoter motifs, if known, indicating that these data are useful for identifying potential new target genes and binding sites. We identified the sequence 5′-TCACGCAA as a binding sequence for Hms1p, a TF that positively regulates pseudohyphal growth and previously had no known motif. The general strategy outlined here presents a straightforward approach to discovery of TF activities and mapping targets that could be adapted to any organism with transgenic technology. PMID:16880382

MARIKA - A model revision system using qualitative analysis of simulations. [of human orientation system

NASA Technical Reports Server (NTRS)

Groleau, Nicolas; Frainier, Richard; Colombano, Silvano; Hazelton, Lyman; Szolovits, Peter

1993-01-01

This paper describes portions of a novel system called MARIKA (Model Analysis and Revision of Implicit Key Assumptions) to automatically revise a model of the normal human orientation system. The revision is based on analysis of discrepancies between experimental results and computer simulations. The discrepancies are calculated from qualitative analysis of quantitative simulations. The experimental and simulated time series are first discretized in time segments. Each segment is then approximated by linear combinations of simple shapes. The domain theory and knowledge are represented as a constraint network. Incompatibilities detected during constraint propagation within the network yield both parameter and structural model alterations. Interestingly, MARIKA diagnosed a data set from the Massachusetts Eye and Ear Infirmary Vestibular Laboratory as abnormal though the data was tagged as normal. Published results from other laboratories confirmed the finding. These encouraging results could lead to a useful clinical vestibular tool and to a scientific discovery system for space vestibular adaptation.

Trimethylamine-N-oxide switches from stabilizing nature: A mechanistic outlook through experimental techniques and molecular dynamics simulation

NASA Astrophysics Data System (ADS)

Rani, Anjeeta; Jayaraj, Abhilash; Jayaram, B.; Pannuru, Venkatesu

2016-03-01

In adaptation biology of the discovery of the intracellular osmolytes, the osmolytes are found to play a central role in cellular homeostasis and stress response. A number of models using these molecules are now poised to address a wide range of problems in biology. Here, a combination of biophysical measurements and molecular dynamics (MD) simulation method is used to examine the effect of trimethylamine-N-oxide (TMAO) on stem bromelain (BM) structure, stability and function. From the analysis of our results, we found that TMAO destabilizes BM hydrophobic pockets and active site as a result of concerted polar and non-polar interactions which is strongly evidenced by MD simulation carried out for 250 ns. This destabilization is enthalpically favourable at higher concentrations of TMAO while entropically unfavourable. However, to the best of our knowledge, the results constitute first detailed unambiguous proof of destabilizing effect of most commonly addressed TMAO on the interactions governing stability of BM and present plausible mechanism of protein unfolding by TMAO.

Regulatory mechanisms of thiol-based redox sensors: lessons learned from structural studies on prokaryotic redox sensors.

PubMed

Lee, Sang Jae; Kim, Dong-Gyun; Lee, Kyu-Yeon; Koo, Ji Sung; Lee, Bong-Jin

2018-05-17

Oxidative stresses, such as reactive oxygen species, reactive electrophilic species, reactive nitrogen species, and reactive chlorine species, can damage cellular components, leading to cellular malfunction and death. In response to oxidative stress, bacteria have evolved redox-responsive sensors that enable them to simultaneously monitor and eradicate potential oxidative stress. Specifically, redox-sensing transcription regulators react to oxidative stress by means of modifying the thiol groups of cysteine residues, functioning as part of an efficient survival mechanism for many bacteria. In general, oxidative molecules can induce changes in the three-dimensional structures of redox sensors, which, in turn, affects the transcription of specific genes in detoxification pathways and defense mechanisms. Moreover, pathogenic bacteria utilize these redox sensors for adaptation and to evade subsequent oxidative attacks from host immune defense. For this reason, the redox sensors of pathogenic bacteria are potential antibiotic targets. Understanding the regulatory mechanisms of thiol-based redox sensors in bacteria will provide insight and knowledge into the discovery of new antibiotics.

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

NASA Astrophysics Data System (ADS)

McGaughey, Georgia; Patrick Walters, W.

2017-03-01

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

Participatory adaptive management leads to environmental learning outcomes extending beyond the sphere of science.

PubMed

Fujitani, Marie; McFall, Andrew; Randler, Christoph; Arlinghaus, Robert

2017-06-01

Resolving uncertainties in managed social-ecological systems requires adaptive experimentation at whole-ecosystem levels. However, whether participatory adaptive management fosters ecological understanding among stakeholders beyond the sphere of science is unknown. We experimentally involved members of German angling clubs ( n = 181 in workshops, n = 2483 in total) engaged in self-governance of freshwater fisheries resources in a large-scale ecological experiment of active adaptive management of fish stocking, which constitutes a controversial management practice for biodiversity and ecosystem functioning when conducted inappropriately. The collaborative ecological experiments spanned several years and manipulated fish densities in 24 lakes with two species. In parallel, we experimentally compared changes in ecological knowledge and antecedents of proenvironmental behavior in stakeholders and managers who were members of a participatory adaptive management treatment group, with those receiving only a standard lecture, relative to placebo controls. Using a within-subjects pretest-posttest control design, changes in ecological knowledge, environmental beliefs, attitudes, norms, and behavioral intentions were evaluated. Participants in adaptive management retained more knowledge of ecological topics after a period of 8 months compared to those receiving a standard lecture, both relative to controls. Involvement in adaptive management was also the only treatment that altered personal norms and beliefs related to stocking. Critically, only the stakeholders who participated in adaptive management reduced their behavioral intentions to engage in fish stocking in the future. Adaptive management is essential for robust ecological knowledge, and we show that involving stakeholders in adaptive management experiments is a powerful tool to enhance ecological literacy and build environmental capacity to move toward sustainability.

Participatory adaptive management leads to environmental learning outcomes extending beyond the sphere of science

PubMed Central

Fujitani, Marie; McFall, Andrew; Randler, Christoph; Arlinghaus, Robert

2017-01-01

Resolving uncertainties in managed social-ecological systems requires adaptive experimentation at whole-ecosystem levels. However, whether participatory adaptive management fosters ecological understanding among stakeholders beyond the sphere of science is unknown. We experimentally involved members of German angling clubs (n = 181 in workshops, n = 2483 in total) engaged in self-governance of freshwater fisheries resources in a large-scale ecological experiment of active adaptive management of fish stocking, which constitutes a controversial management practice for biodiversity and ecosystem functioning when conducted inappropriately. The collaborative ecological experiments spanned several years and manipulated fish densities in 24 lakes with two species. In parallel, we experimentally compared changes in ecological knowledge and antecedents of proenvironmental behavior in stakeholders and managers who were members of a participatory adaptive management treatment group, with those receiving only a standard lecture, relative to placebo controls. Using a within-subjects pretest-posttest control design, changes in ecological knowledge, environmental beliefs, attitudes, norms, and behavioral intentions were evaluated. Participants in adaptive management retained more knowledge of ecological topics after a period of 8 months compared to those receiving a standard lecture, both relative to controls. Involvement in adaptive management was also the only treatment that altered personal norms and beliefs related to stocking. Critically, only the stakeholders who participated in adaptive management reduced their behavioral intentions to engage in fish stocking in the future. Adaptive management is essential for robust ecological knowledge, and we show that involving stakeholders in adaptive management experiments is a powerful tool to enhance ecological literacy and build environmental capacity to move toward sustainability. PMID:28630904

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

ERIC Educational Resources Information Center

Lynton, Ernest A.

2016-01-01

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

Distinguishing Models of Professional Development: The Case of an Adaptive Model's Impact on Teachers' Knowledge, Instruction, and Student Achievement

ERIC Educational Resources Information Center

Koellner, Karen; Jacobs, Jennifer

2015-01-01

We posit that professional development (PD) models fall on a continuum from highly adaptive to highly specified, and that these constructs provide a productive way to characterize and distinguish among models. The study reported here examines the impact of an adaptive mathematics PD model on teachers' knowledge and instructional practices as well…

Concepts of formal concept analysis

NASA Astrophysics Data System (ADS)

Žáček, Martin; Homola, Dan; Miarka, Rostislav

2017-07-01

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

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

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

Hillis, D.R.

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

Semi-automated knowledge discovery: identifying and profiling human trafficking

NASA Astrophysics Data System (ADS)

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

2012-11-01

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

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.

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

PubMed

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

2018-06-04

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

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

EPA Science Inventory

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

Dewey: How to Make It Work for You

ERIC Educational Resources Information Center

Panzer, Michael

2013-01-01

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

78 FR 29071 - Assessment of Mediation and Arbitration Procedures

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-17

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

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

EPA Science Inventory

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

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

ERIC Educational Resources Information Center

Costley, Kevin C.; West, Howard G.

2012-01-01

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

Globalization of Knowledge Discovery and Information Retrieval in Teaching and Learning

ERIC Educational Resources Information Center

Zaidel, Mark; Guerrero, Osiris

2008-01-01

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

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

ERIC Educational Resources Information Center

Trybula, Walter J.; Wyllys, Ronald E.

2000-01-01

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

Vocational Education Institutions' Role in National Innovation

ERIC Educational Resources Information Center

Moodie, Gavin

2006-01-01

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

Beyond the International Year of Astronomy: The Universe Discovery Guides

NASA Astrophysics Data System (ADS)

Lawton, B.; Berendsen, M.; Gurton, S.; Smith, D.; NASA SMD Astrophysics EPO Community

2014-07-01

Developed for informal educators and their audiences, the 12 Universe Discovery Guides (UDGs, one per month) are adapted from the Discovery Guides that were developed for the International Year of Astronomy in 2009. The UDGs showcase education and public outreach resources from across more than 30 NASA astrophysics missions and programs. Via collaboration through scientist and educator partnerships, the UDGs aim to increase the impact of individual missions and programs, put their efforts into context, and extend their reach to new audiences. Each of the UDGs has a science topic, an interpretive story, a sky object to view with finding charts, hands-on activities, and connections to recent NASA science discoveries. The UDGs are modular; informal educators can take resources from the guides that they find most useful for their audiences. Attention is being given to audience needs, and field-testing is ongoing. The UDGs are available via downloadable PDFs.

Exploring relation types for literature-based discovery.

PubMed

Preiss, Judita; Stevenson, Mark; Gaizauskas, Robert

2015-09-01

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

Serology in the 21st Century: The Molecular-Level Analysis of the Serum Antibody Repertoire

PubMed Central

Wine, Yariv; Horton, Andrew P.; Ippolito, Gregory C.; Georgiou, George

2015-01-01

The ensemble of antibodies found in serum and secretions represents the key adaptive component of B-cell mediated humoral immunity. The antibody repertoire is shaped by the historical record of exposure to exogenous factors such as pathogens and vaccines, as well as by endogenous host-intrinsic factors such as genetics, self-antigens, and age. Thanks to very recent technology advancements it is now becoming possible to identify and quantify the individual antibodies comprising the serological repertoire. In parallel, the advent of high throughput methods for antigen and immunosignature discovery opens up unprecedented opportunities to transform our understanding of numerous key questions in adaptive humoral immunity, including the nature and dynamics of serological memory, the role of polyspecific antibodies in health and disease and how protective responses to infections or vaccine challenge arise. Additionally, these technologies also hold great promise for therapeutic antibody and biomarker discovery in a variety of settings PMID:26172290

Essential Neuroscience in Immunology

PubMed Central

Chavan, Sangeeta S.; Tracey, Kevin J.

2017-01-01

The field of immunology is principally focused on the molecular mechanisms by which hematopoetic cells initiate and maintain innate and adaptive immunity. That cornerstone of attention has been expanded by recent discoveries that neuronal signals occupy a critical regulatory niche in immunity. The discovery is that neuronal circuits operating reflexively regulate innate and adaptive immunity. One particularly well-characterized circuit regulating innate immunity, the inflammatory reflex, is dependent upon action potentials transmitted to the reticuloendothelial system via the vagus and splenic nerves. This field has grown significantly with identification of several other reflexes regulating discrete immune functions. As reviewed here, the delineation of these mechanisms revealed a new understanding of immunity, enabled a first in class clinical trial using bioelectronic devices to inhibit cytokines and inflammation in rheumatoid arthritis patients, and provided a mosaic view of immunity as the integration of hematopoetic and neural responses to infection and injury. PMID:28416717

Essential Neuroscience in Immunology.

PubMed

Chavan, Sangeeta S; Tracey, Kevin J

2017-05-01

The field of immunology is principally focused on the molecular mechanisms by which hematopoietic cells initiate and maintain innate and adaptive immunity. That cornerstone of attention has been expanded by recent discoveries that neuronal signals occupy a critical regulatory niche in immunity. The discovery is that neuronal circuits operating reflexively regulate innate and adaptive immunity. One particularly well-characterized circuit regulating innate immunity, the inflammatory reflex, is dependent upon action potentials transmitted to the reticuloendothelial system via the vagus and splenic nerves. This field has grown significantly with the identification of several other reflexes regulating discrete immune functions. As outlined in this review, the delineation of these mechanisms revealed a new understanding of immunity, enabled a first-in-class clinical trial using bioelectronic devices to inhibit cytokines and inflammation in rheumatoid arthritis patients, and provided a mosaic view of immunity as the integration of hematopoietic and neural responses to infection and injury. Copyright © 2017 by The American Association of Immunologists, Inc.

DataHub: Knowledge-based data management for data discovery

NASA Astrophysics Data System (ADS)

Handley, Thomas H.; Li, Y. Philip

1993-08-01

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

Analysis of Swedish Forest Owners' Information and Knowledge-Sharing Networks for Decision-Making: Insights for Climate Change Communication and Adaptation.

PubMed

André, Karin; Baird, Julia; Gerger Swartling, Åsa; Vulturius, Gregor; Plummer, Ryan

2017-06-01

To further the understanding of climate change adaptation processes, more attention needs to be paid to the various contextual factors that shape whether and how climate-related knowledge and information is received and acted upon by actors involved. This study sets out to examine the characteristics of forest owners' in Sweden, the information and knowledge-sharing networks they draw upon for decision-making, and their perceptions of climate risks, their forests' resilience, the need for adaptation, and perceived adaptive capacity. By applying the concept of ego-network analysis, the empirical data was generated by a quantitative survey distributed to 3000 private forest owners' in Sweden in 2014 with a response rate of 31%. The results show that there is a positive correlation, even though it is generally weak, between forest owner climate perceptions and (i) network features, i.e. network size and heterogeneity, and (ii) presence of certain alter groups (i.e. network members or actors). Results indicate that forest owners' social networks currently serve only a minimal function of sharing knowledge of climate change and adaptation. Moreover, considering the fairly infrequent contact between respondents and alter groups, the timing of knowledge sharing is important. In conclusion we suggest those actors that forest owners' most frequently communicate with, especially forestry experts providing advisory services (e.g. forest owner associations, companies, and authorities) have a clear role to communicate both the risks of climate change and opportunities for adaptation. Peers are valuable in connecting information about climate risks and adaptation to the actual forest property.

Analysis of Swedish Forest Owners' Information and Knowledge-Sharing Networks for Decision-Making: Insights for Climate Change Communication and Adaptation

NASA Astrophysics Data System (ADS)

André, Karin; Baird, Julia; Gerger Swartling, Åsa; Vulturius, Gregor; Plummer, Ryan

2017-06-01

To further the understanding of climate change adaptation processes, more attention needs to be paid to the various contextual factors that shape whether and how climate-related knowledge and information is received and acted upon by actors involved. This study sets out to examine the characteristics of forest owners' in Sweden, the information and knowledge-sharing networks they draw upon for decision-making, and their perceptions of climate risks, their forests' resilience, the need for adaptation, and perceived adaptive capacity. By applying the concept of ego-network analysis, the empirical data was generated by a quantitative survey distributed to 3000 private forest owners' in Sweden in 2014 with a response rate of 31%. The results show that there is a positive correlation, even though it is generally weak, between forest owner climate perceptions and (i) network features, i.e. network size and heterogeneity, and (ii) presence of certain alter groups (i.e. network members or actors). Results indicate that forest owners' social networks currently serve only a minimal function of sharing knowledge of climate change and adaptation. Moreover, considering the fairly infrequent contact between respondents and alter groups, the timing of knowledge sharing is important. In conclusion we suggest those actors that forest owners' most frequently communicate with, especially forestry experts providing advisory services (e.g. forest owner associations, companies, and authorities) have a clear role to communicate both the risks of climate change and opportunities for adaptation. Peers are valuable in connecting information about climate risks and adaptation to the actual forest property.

Discovery of a Giant Chameleon-Like Lizard (Anolis) on Hispaniola and Its Significance to Understanding Replicated Adaptive Radiations.

PubMed

Mahler, D Luke; Lambert, Shea M; Geneva, Anthony J; Ng, Julienne; Hedges, S Blair; Losos, Jonathan B; Glor, Richard E

2016-09-01

We report a new chameleon-like Anolis species from Hispaniola that is ecomorphologically similar to congeners found only on Cuba. Lizards from both clades possess short limbs and a short tail and utilize relatively narrow perches, leading us to recognize a novel example of ecomorphological matching among islands in the well-known Greater Antillean anole radiation. This discovery supports the hypothesis that the assembly of island faunas can be substantially deterministic and highlights the continued potential for basic discovery to reveal new insights in well-studied groups. Restricted to a threatened band of midelevation transitional forest near the border of the Dominican Republic and Haiti, this new species appears to be highly endangered.

A perfect launch viewed across Banana Creek

NASA Technical Reports Server (NTRS)

2000-01-01

Billows of smoke and steam surround Space Shuttle Discovery as it lifts off from Launch Pad 39A on mission STS-92 to the International Space Station. The perfect on-time liftoff occurred at 7:17 p.m. EDT, sending a crew of seven on the 100th launch in the history of the Shuttle program. Discovery carries a payload that includes the Integrated Truss Structure Z-1, first of 10 trusses that will form the backbone of the Space Station, and the third Pressurized Mating Adapter that will provide a Shuttle docking port for solar array installation on the sixth Station flight and Lab installation on the seventh Station flight. Discovery's landing is expected Oct. 22 at 2:10 p.m. EDT.

The Z1 truss is transported to Launch Pad 39A

NASA Technical Reports Server (NTRS)

2000-01-01

At Launch Pad 39A, the payload canister with the Integrated Truss Structure Z1 inside arrives at the spot under the Rotating Service Structure where the canister can be lifted to the Payload Changeout Room. There the Z1 truss will be removed and later transferred to Space Shuttle Discovery's payload bay. Discovery is at right, sitting atop the Mobile Launcher Platform. The Z1 truss is the first of 10 that will become the backbone of the International Space Station, eventually stretching the length of a football field. Along with its companion payload, the third Pressurized Mating Adapter, the Z1 is scheduled to be launched aboard Discovery Oct. 5 at 9:38 p.m. EDT.

Constructing a Graph Database for Semantic Literature-Based Discovery.

PubMed

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

2015-01-01

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

Computational methods in drug discovery

PubMed Central

Leelananda, Sumudu P

2016-01-01

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

Computational methods in drug discovery.

PubMed

Leelananda, Sumudu P; Lindert, Steffen

2016-01-01

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

Machine learning properties of binary wurtzite superlattices

DOE PAGES

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

2018-01-12

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

Machine learning properties of binary wurtzite superlattices

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

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

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

Knowledge Discovery from Climate Data using Graph-Based Methods

NASA Astrophysics Data System (ADS)

Steinhaeuser, K.

2012-04-01

Climate and Earth sciences have recently experienced a rapid transformation from a historically data-poor to a data-rich environment, thus bringing them into the realm of the Fourth Paradigm of scientific discovery - a term coined by the late Jim Gray (Hey et al. 2009), the other three being theory, experimentation and computer simulation. In particular, climate-related observations from remote sensors on satellites and weather radars, in situ sensors and sensor networks, as well as outputs of climate or Earth system models from large-scale simulations, provide terabytes of spatio-temporal data. These massive and information-rich datasets offer a significant opportunity for advancing climate science and our understanding of the global climate system, yet current analysis techniques are not able to fully realize their potential benefits. We describe a class of computational approaches, specifically from the data mining and machine learning domains, which may be novel to the climate science domain and can assist in the analysis process. Computer scientists have developed spatial and spatio-temporal analysis techniques for a number of years now, and many of them may be applicable and/or adaptable to problems in climate science. We describe a large-scale, NSF-funded project aimed at addressing climate science question using computational analysis methods; team members include computer scientists, statisticians, and climate scientists from various backgrounds. One of the major thrusts is in the development of graph-based methods, and several illustrative examples of recent work in this area will be presented.

Homo naledi and Pleistocene hominin evolution in subequatorial Africa

PubMed Central

Berger, Lee R; Hawks, John; Dirks, Paul HGM; Elliott, Marina; Roberts, Eric M

2017-01-01

New discoveries and dating of fossil remains from the Rising Star cave system, Cradle of Humankind, South Africa, have strong implications for our understanding of Pleistocene human evolution in Africa. Direct dating of Homo naledi fossils from the Dinaledi Chamber (Berger et al., 2015) shows that they were deposited between about 236 ka and 335 ka (Dirks et al., 2017), placing H. naledi in the later Middle Pleistocene. Hawks and colleagues (Hawks et al., 2017) report the discovery of a second chamber within the Rising Star system (Dirks et al., 2015) that contains H. naledi remains. Previously, only large-brained modern humans or their close relatives had been demonstrated to exist at this late time in Africa, but the fossil evidence for any hominins in subequatorial Africa was very sparse. It is now evident that a diversity of hominin lineages existed in this region, with some divergent lineages contributing DNA to living humans and at least H. naledi representing a survivor from the earliest stages of diversification within Homo. The existence of a diverse array of hominins in subequatorial comports with our present knowledge of diversity across other savanna-adapted species, as well as with palaeoclimate and paleoenvironmental data. H. naledi casts the fossil and archaeological records into a new light, as we cannot exclude that this lineage was responsible for the production of Acheulean or Middle Stone Age tool industries. DOI: http://dx.doi.org/10.7554/eLife.24234.001 PMID:28483041

The adaptive nature of culture. A cross-cultural analysis of the returns of local environmental knowledge in three indigenous societies.

PubMed

Reyes-García, Victoria; Guèze, Maximilien; Díaz-Reviriego, Isabel; Duda, Romain; Fernández-Llamazares, Álvaro; Gallois, Sandrine; Napitupulu, Lucentezza; Orta-Martínez, Martí; Pyhälä, Aili

2016-12-01

Researchers have argued that the behavioral adaptations that explain the success of our species are partially cultural, i.e., cumulative and socially transmitted. Thus, understanding the adaptive nature of culture is crucial to understand human evolution. We use a cross-cultural framework and empirical data purposely collected to test whether culturally transmitted and individually appropriated knowledge provides individual returns in terms of hunting yields and health and, by extension, to nutritional status, a proxy for individual adaptive success. Data were collected in three subsistence-oriented societies: the Tsimane' (Amazon), the Baka (Congo Basin), and the Punan (Borneo). Results suggest that variations in individual levels of local environmental knowledge relate to individual hunting returns and to self-reported health, but not to nutritional status. We argue that this paradox can be explained through the prevalence of sharing: individuals achieving higher returns to their knowledge transfer them to the rest of the population, which explains the lack of association between knowledge and nutritional status. The finding is in consonance with previous research highlighting the importance of cultural traits favoring group success, but pushes it forward by elucidating the mechanisms through which individual and group level adaptive forces interact.

The adaptive nature of culture. A cross-cultural analysis of the returns of local environmental knowledge in three indigenous societies

PubMed Central

Reyes-García, Victoria; Guèze, Maximilien; Díaz-Reviriego, Isabel; Duda, Romain; Fernández-Llamazares, Álvaro; Gallois, Sandrine; Napitupulu, Lucentezza; Orta-Martínez, Martí; Pyhälä, Aili

2016-01-01

Researchers have argued that the behavioral adaptations that explain the success of our species are partially cultural, i.e., cumulative and socially transmitted. Thus, understanding the adaptive nature of culture is crucial to understand human evolution. We use a cross-cultural framework and empirical data purposely collected to test whether culturally transmitted and individually appropriated knowledge provides individual returns in terms of hunting yields and health and, by extension, to nutritional status, a proxy for individual adaptive success. Data were collected in three subsistence-oriented societies: the Tsimane’ (Amazon), the Baka (Congo Basin), and the Punan (Borneo). Results suggest that variations in individual levels of local environmental knowledge relate to individual hunting returns and to self-reported health, but not to nutritional status. We argue that this paradox can be explained through the prevalence of sharing: individuals achieving higher returns to their knowledge transfer them to the rest of the population, which explains the lack of association between knowledge and nutritional status. The finding is in consonance with previous research highlighting the importance of cultural traits favoring group success, but pushes it forward by elucidating the mechanisms through which individual and group level adaptive forces interact. PMID:28104924

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

PubMed

Park, D L; Stoloff, L

1989-04-01

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

Rethinking Social Barriers to Effective Adaptive Management

NASA Astrophysics Data System (ADS)

West, Simon; Schultz, Lisen; Bekessy, Sarah

2016-09-01

Adaptive management is an approach to environmental management based on learning-by-doing, where complexity, uncertainty, and incomplete knowledge are acknowledged and management actions are treated as experiments. However, while adaptive management has received significant uptake in theory, it remains elusively difficult to enact in practice. Proponents have blamed social barriers and have called for social science contributions. We address this gap by adopting a qualitative approach to explore the development of an ecological monitoring program within an adaptive management framework in a public land management organization in Australia. We ask what practices are used to enact the monitoring program and how do they shape learning? We elicit a rich narrative through extensive interviews with a key individual, and analyze the narrative using thematic analysis. We discuss our results in relation to the concept of `knowledge work' and Westley's 2002) framework for interpreting the strategies of adaptive managers—`managing through, in, out and up.' We find that enacting the program is conditioned by distinct and sometimes competing logics—scientific logics prioritizing experimentation and learning, public logics emphasizing accountability and legitimacy, and corporate logics demanding efficiency and effectiveness. In this context, implementing adaptive management entails practices of translation to negotiate tensions between objective and situated knowledge, external experts and organizational staff, and collegiate and hierarchical norms. Our contribution embraces the `doing' of learning-by-doing and marks a shift from conceptualizing the social as an external barrier to adaptive management to be removed to an approach that situates adaptive management as social knowledge practice.

Rethinking Social Barriers to Effective Adaptive Management.

PubMed

West, Simon; Schultz, Lisen; Bekessy, Sarah

2016-09-01

Adaptive management is an approach to environmental management based on learning-by-doing, where complexity, uncertainty, and incomplete knowledge are acknowledged and management actions are treated as experiments. However, while adaptive management has received significant uptake in theory, it remains elusively difficult to enact in practice. Proponents have blamed social barriers and have called for social science contributions. We address this gap by adopting a qualitative approach to explore the development of an ecological monitoring program within an adaptive management framework in a public land management organization in Australia. We ask what practices are used to enact the monitoring program and how do they shape learning? We elicit a rich narrative through extensive interviews with a key individual, and analyze the narrative using thematic analysis. We discuss our results in relation to the concept of 'knowledge work' and Westley's (2002) framework for interpreting the strategies of adaptive managers-'managing through, in, out and up.' We find that enacting the program is conditioned by distinct and sometimes competing logics-scientific logics prioritizing experimentation and learning, public logics emphasizing accountability and legitimacy, and corporate logics demanding efficiency and effectiveness. In this context, implementing adaptive management entails practices of translation to negotiate tensions between objective and situated knowledge, external experts and organizational staff, and collegiate and hierarchical norms. Our contribution embraces the 'doing' of learning-by-doing and marks a shift from conceptualizing the social as an external barrier to adaptive management to be removed to an approach that situates adaptive management as social knowledge practice.

Fantastic animals as an experimental model to teach animal adaptation

PubMed Central

Guidetti, Roberto; Baraldi, Laura; Calzolai, Caterina; Pini, Lorenza; Veronesi, Paola; Pederzoli, Aurora

2007-01-01

Background Science curricula and teachers should emphasize evolution in a manner commensurate with its importance as a unifying concept in science. The concept of adaptation represents a first step to understand the results of natural selection. We settled an experimental project of alternative didactic to improve knowledge of organism adaptation. Students were involved and stimulated in learning processes by creative activities. To set adaptation in a historic frame, fossil records as evidence of past life and evolution were considered. Results The experimental project is schematized in nine phases: review of previous knowledge; lesson on fossils; lesson on fantastic animals; planning an imaginary world; creation of an imaginary animal; revision of the imaginary animals; adaptations of real animals; adaptations of fossil animals; and public exposition. A rubric to evaluate the student's performances is reported. The project involved professors and students of the University of Modena and Reggio Emilia and of the "G. Marconi" Secondary School of First Degree (Modena, Italy). Conclusion The educational objectives of the project are in line with the National Indications of the Italian Ministry of Public Instruction: knowledge of the characteristics of living beings, the meanings of the term "adaptation", the meaning of fossils, the definition of ecosystem, and the particularity of the different biomes. At the end of the project, students will be able to grasp particular adaptations of real organisms and to deduce information about the environment in which the organism evolved. This project allows students to review previous knowledge and to form their personalities. PMID:17767729

Postgenomic strategies in antibacterial drug discovery.

PubMed

Brötz-Oesterhelt, Heike; Sass, Peter

2010-10-01

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

Priority of discovery in the life sciences

PubMed Central

Vale, Ronald D; Hyman, Anthony A

2016-01-01

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

Computational biology for cardiovascular biomarker discovery.

PubMed

Azuaje, Francisco; Devaux, Yvan; Wagner, Daniel

2009-07-01

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

Out of America: tracing the genetic footprints of the global diffusion of maize.

PubMed

Mir, C; Zerjal, T; Combes, V; Dumas, F; Madur, D; Bedoya, C; Dreisigacker, S; Franco, J; Grudloyma, P; Hao, P X; Hearne, S; Jampatong, C; Laloë, D; Muthamia, Z; Nguyen, T; Prasanna, B M; Taba, S; Xie, C X; Yunus, M; Zhang, S; Warburton, M L; Charcosset, A

2013-11-01

Maize was first domesticated in a restricted valley in south-central Mexico. It was diffused throughout the Americas over thousands of years, and following the discovery of the New World by Columbus, was introduced into Europe. Trade and colonization introduced it further into all parts of the world to which it could adapt. Repeated introductions, local selection and adaptation, a highly diverse gene pool and outcrossing nature, and global trade in maize led to difficulty understanding exactly where the diversity of many of the local maize landraces originated. This is particularly true in Africa and Asia, where historical accounts are scarce or contradictory. Knowledge of post-domestication movements of maize around the world would assist in germplasm conservation and plant breeding efforts. To this end, we used SSR markers to genotype multiple individuals from hundreds of representative landraces from around the world. Applying a multidisciplinary approach combining genetic, linguistic, and historical data, we reconstructed possible patterns of maize diffusion throughout the world from American "contribution" centers, which we propose reflect the origins of maize worldwide. These results shed new light on introductions of maize into Africa and Asia. By providing a first globally comprehensive genetic characterization of landraces using markers appropriate to this evolutionary time frame, we explore the post-domestication evolutionary history of maize and highlight original diversity sources that may be tapped for plant improvement in different regions of the world.

KnowledgePuzzle: A Browsing Tool to Adapt the Web Navigation Process to the Learner's Mental Model

ERIC Educational Resources Information Center

AlAgha, Iyad

2012-01-01

This article presents KnowledgePuzzle, a browsing tool for knowledge construction from the web. It aims to adapt the structure of web content to the learner's information needs regardless of how the web content is originally delivered. Learners are provided with a meta-cognitive space (e.g., a concept mapping tool) that enables them to plan…

The top quark (20 years after the discovery)

DOE PAGES

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

2015-09-10

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

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

USDA-ARS?s Scientific Manuscript database

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

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

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-24

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

Incremental Knowledge Discovery in Social Media

ERIC Educational Resources Information Center

Tang, Xuning

2013-01-01

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

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

ERIC Educational Resources Information Center

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

2002-01-01

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

Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

PubMed

Dobchev, Dimitar; Karelson, Mati

2016-07-01

Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery. In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field. The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

Targeted discovery of glycoside hydrolases from a switchgrass-adapted compost community

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

Allgaier, M.; Reddy, A.; Park, J. I.

2009-11-15

Development of cellulosic biofuels from non-food crops is currently an area of intense research interest. Tailoring depolymerizing enzymes to particular feedstocks and pretreatment conditions is one promising avenue of research in this area. Here we added a green-waste compost inoculum to switchgrass (Panicum virgatum) and simulated thermophilic composting in a bioreactor to select for a switchgrass-adapted community and to facilitate targeted discovery of glycoside hydrolases. Small-subunit (SSU) rRNA-based community profiles revealed that the microbial community changed dramatically between the initial and switchgrass-adapted compost (SAC) with some bacterial populations being enriched over 20-fold. We obtained 225 Mbp of 454-titanium pyrosequence datamore » from the SAC community and conservatively identified 800 genes encoding glycoside hydrolase domains that were biased toward depolymerizing grass cell wall components. Of these, {approx}10% were putative cellulases mostly belonging to families GH5 and GH9. We synthesized two SAC GH9 genes with codon optimization for heterologous expression in Escherichia coli and observed activity for one on carboxymethyl cellulose. The active GH9 enzyme has a temperature optimum of 50 C and pH range of 5.5 to 8 consistent with the composting conditions applied. We demonstrate that microbial communities adapt to switchgrass decomposition using simulated composting condition and that full-length genes can be identified from complex metagenomic sequence data, synthesized and expressed resulting in active enzyme.« less

Targeted Discovery of Glycoside Hydrolases from a Switchgrass-Adapted Compost Community

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

Reddy, Amitha; Allgaier, Martin; Park, Joshua I.

2011-05-11

Development of cellulosic biofuels from non-food crops is currently an area of intense research interest. Tailoring depolymerizing enzymes to particular feedstocks and pretreatment conditions is one promising avenue of research in this area. Here we added a green-waste compost inoculum to switchgrass (Panicum virgatum) and simulated thermophilic composting in a bioreactor to select for a switchgrass-adapted community and to facilitate targeted discovery of glycoside hydrolases. Smallsubunit (SSU) rRNA-based community profiles revealed that the microbial community changed dramatically between the initial and switchgrass-adapted compost (SAC) with some bacterial populations being enriched over 20-fold. We obtained 225 Mbp of 454-titanium pyrosequence datamore » from the SAC community and conservatively identified 800 genes encoding glycoside hydrolase domains that were biased toward depolymerizing grass cell wall components. Of these, ,10percent were putative cellulasesmostly belonging to families GH5 and GH9. We synthesized two SAC GH9 genes with codon optimization for heterologous expression in Escherichia coli and observed activity for one on carboxymethyl cellulose. The active GH9 enzyme has a temperature optimum of 50uC and pH range of 5.5 to 8 consistent with the composting conditions applied. We demonstrate that microbial communities adapt to switchgrass decomposition using simulated composting condition and that full-length genes can be identified from complex metagenomic sequence data, synthesized and expressed resulting in active enzyme.« less

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

PubMed

McAlpine, James B

2009-03-27

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

Text mining patents for biomedical knowledge.

PubMed

Rodriguez-Esteban, Raul; Bundschus, Markus

2016-06-01

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

SNP discovery in candidate adaptive genes using exon capture in a free-ranging alpine ungulate

USGS Publications Warehouse

Roffler, Gretchen H.; Amish, Stephen J.; Smith, Seth; Cosart, Ted F.; Kardos, Marty; Schwartz, Michael K.; Luikart, Gordon

2016-01-01

Identification of genes underlying genomic signatures of natural selection is key to understanding adaptation to local conditions. We used targeted resequencing to identify SNP markers in 5321 candidate adaptive genes associated with known immunological, metabolic and growth functions in ovids and other ungulates. We selectively targeted 8161 exons in protein-coding and nearby 5′ and 3′ untranslated regions of chosen candidate genes. Targeted sequences were taken from bighorn sheep (Ovis canadensis) exon capture data and directly from the domestic sheep genome (Ovis aries v. 3; oviAri3). The bighorn sheep sequences used in the Dall's sheep (Ovis dalli dalli) exon capture aligned to 2350 genes on the oviAri3 genome with an average of 2 exons each. We developed a microfluidic qPCR-based SNP chip to genotype 476 Dall's sheep from locations across their range and test for patterns of selection. Using multiple corroborating approaches (lositan and bayescan), we detected 28 SNP loci potentially under selection. We additionally identified candidate loci significantly associated with latitude, longitude, precipitation and temperature, suggesting local environmental adaptation. The three methods demonstrated consistent support for natural selection on nine genes with immune and disease-regulating functions (e.g. Ovar-DRA, APC, BATF2, MAGEB18), cell regulation signalling pathways (e.g. KRIT1, PI3K, ORRC3), and respiratory health (CYSLTR1). Characterizing adaptive allele distributions from novel genetic techniques will facilitate investigation of the influence of environmental variation on local adaptation of a northern alpine ungulate throughout its range. This research demonstrated the utility of exon capture for gene-targeted SNP discovery and subsequent SNP chip genotyping using low-quality samples in a nonmodel species.

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

PubMed

Kalyaanamoorthy, Subha; Chen, Yi-Ping Phoebe

2014-05-01

The ever-increasing gap between the availabilities of the genome sequences and the crystal structures of proteins remains one of the significant challenges to the modern drug discovery efforts. The knowledge of structure-dynamics-functionalities of proteins is important in order to understand several key aspects of structure-based drug discovery, such as drug-protein interactions, drug binding and unbinding mechanisms and protein-protein interactions. This review presents a brief overview on the different state of the art computational approaches that are applied for protein structure modelling and molecular dynamics simulations of biological systems. We give an essence of how different enhanced sampling molecular dynamics approaches, together with regular molecular dynamics methods, assist in steering the structure based drug discovery processes. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

PubMed

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

2008-04-01

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

Introduction to Radar Signal and Data Processing: The Opportunity

DTIC Science & Technology

2006-09-01

SpA) Director of Analysis of Integrated Systems Group Via Tiburtina Km. 12.400 00131 Rome ITALY e.mail: afarina@selex-si.com Key words: radar...signal processing, data processing, adaptivity, space-time adaptive processing, knowledge based systems , CFAR. 1. SUMMARY This paper introduces to...the lecture series dedicated to the knowledge-based radar signal and data processing. Knowledge-based expert system (KBS) is in the realm of

Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point.

PubMed

Rodrigues, Tiago

2017-11-15

Natural products (NPs) present a privileged source of inspiration for chemical probe and drug design. Despite the biological pre-validation of the underlying molecular architectures and their relevance in drug discovery, the poor accessibility to NPs, complexity of the synthetic routes and scarce knowledge of their macromolecular counterparts in phenotypic screens still hinder their broader exploration. Cheminformatics algorithms now provide a powerful means of circumventing the abovementioned challenges and unlocking the full potential of NPs in a drug discovery context. Herein, I discuss recent advances in the computer-assisted design of NP mimics and how artificial intelligence may accelerate future NP-inspired molecular medicine.

CRISPR-Cas adaptation: insights into the mechanism of action.

PubMed

Amitai, Gil; Sorek, Rotem

2016-02-01

Since the first demonstration that CRISPR-Cas systems provide bacteria and archaea with adaptive immunity against phages and plasmids, numerous studies have yielded key insights into the molecular mechanisms governing how these systems attack and degrade foreign DNA. However, the molecular mechanisms underlying the adaptation stage, in which new immunological memory is formed, have until recently represented a major unresolved question. In this Progress article, we discuss recent discoveries that have shown both how foreign DNA is identified by the CRISPR-Cas adaptation machinery and the molecular basis for its integration into the chromosome to form an immunological memory. Furthermore, we describe the roles of each of the specific CRISPR-Cas components that are involved in memory formation, and consider current models for their evolutionary origin.

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

PubMed Central

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

2011-01-01

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

Seafloor Science and Remotely Operated Vehicle (SSROV) Day Camp: A Week-Long, Hands-On STEM Summer Camp

NASA Astrophysics Data System (ADS)

Wheat, C. G.; Fournier, T.; Monahan, K.; Paul, C.

2015-12-01

RETINA (Robotic Exploration Technologies IN Astrobiology) has developed a program geared towards stimulating our youth with innovative and relevant hands-on learning modules under a STEM umbrella. Given the breadth of potential science and engineering topics that excite children, the RETINA Program focuses on interactive participation in the design and development of simple robotic and sensor systems, providing a range of challenges to engage students through project-based learning (PBL). Thus, young students experience scientific discovery through the use and understanding of technology. This groundwork serves as the foundation for SSROV Camp, a week-long, summer day camp for 6th-8th grade students. The camp is centered on the sensors and platforms that guide seafloor exploration and discovery and builds upon the notion that transformative discoveries in the deep sea result from either sampling new environments or making new measurements with sensors adapted to this extreme environment. These technical and scientific needs are folded into the curriculum. Each of the first four days of the camp includes four team-based, hands-on technical challenges, communication among peer groups, and competition. The fifth day includes additional activities, culminating in camper-led presentations to describe a planned mission based on a given geologic setting. Presentations include hypotheses, operational requirements and expected data products. SSROV Camp was initiated last summer for three sessions, two in Monterey, CA and one in Oxford, MS. Campers from both regions grasped key elements of the program, based on written responses to questions before and after the camp. On average, 32% of the pre-test questions were answered correctly compared with 80% of the post-test questions. Additional confirmation of gains in campers' knowledge, skills, and critical thinking on environmental issues and engineering problems were apparent during the "jeopardy" competition, nightly homework, and mission presentations. On the basis of this successful effort, we hope to expand to other towns.

Adaptivity and Autonomy Development in a Learning Personalization Process

ERIC Educational Resources Information Center

Verpoorten, D.

2009-01-01

Within the iClass (Integrated Project 507922) and Enhanced Learning Experience and Knowledge Transfer (ELEKTRA; Specific Targeted Research or Innovation Project 027986) European projects, the author was requested to harness his pedagogical knowledge to the production of educational adaptive systems. The article identifies and documents the…

Knowledge-light adaptation approaches in case-based reasoning for radiotherapy treatment planning.

PubMed

Petrovic, Sanja; Khussainova, Gulmira; Jagannathan, Rupa

2016-03-01

Radiotherapy treatment planning aims at delivering a sufficient radiation dose to cancerous tumour cells while sparing healthy organs in the tumour-surrounding area. It is a time-consuming trial-and-error process that requires the expertise of a group of medical experts including oncologists and medical physicists and can take from 2 to 3h to a few days. Our objective is to improve the performance of our previously built case-based reasoning (CBR) system for brain tumour radiotherapy treatment planning. In this system, a treatment plan for a new patient is retrieved from a case base containing patient cases treated in the past and their treatment plans. However, this system does not perform any adaptation, which is needed to account for any difference between the new and retrieved cases. Generally, the adaptation phase is considered to be intrinsically knowledge-intensive and domain-dependent. Therefore, an adaptation often requires a large amount of domain-specific knowledge, which can be difficult to acquire and often is not readily available. In this study, we investigate approaches to adaptation that do not require much domain knowledge, referred to as knowledge-light adaptation. We developed two adaptation approaches: adaptation based on machine-learning tools and adaptation-guided retrieval. They were used to adapt the beam number and beam angles suggested in the retrieved case. Two machine-learning tools, neural networks and naive Bayes classifier, were used in the adaptation to learn how the difference in attribute values between the retrieved and new cases affects the output of these two cases. The adaptation-guided retrieval takes into consideration not only the similarity between the new and retrieved cases, but also how to adapt the retrieved case. The research was carried out in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. All experiments were performed using real-world brain cancer patient cases treated with three-dimensional (3D)-conformal radiotherapy. Neural networks-based adaptation improved the success rate of the CBR system with no adaptation by 12%. However, naive Bayes classifier did not improve the current retrieval results as it did not consider the interplay among attributes. The adaptation-guided retrieval of the case for beam number improved the success rate of the CBR system by 29%. However, it did not demonstrate good performance for the beam angle adaptation. Its success rate was 29% versus 39% when no adaptation was performed. The obtained empirical results demonstrate that the proposed adaptation methods improve the performance of the existing CBR system in recommending the number of beams to use. However, we also conclude that to be effective, the proposed adaptation of beam angles requires a large number of relevant cases in the case base. Copyright © 2016 Elsevier B.V. All rights reserved.

Metagenomics as a Tool for Enzyme Discovery: Hydrolytic Enzymes from Marine-Related Metagenomes.

PubMed

Popovic, Ana; Tchigvintsev, Anatoly; Tran, Hai; Chernikova, Tatyana N; Golyshina, Olga V; Yakimov, Michail M; Golyshin, Peter N; Yakunin, Alexander F

2015-01-01

This chapter discusses metagenomics and its application for enzyme discovery, with a focus on hydrolytic enzymes from marine metagenomic libraries. With less than one percent of culturable microorganisms in the environment, metagenomics, or the collective study of community genetics, has opened up a rich pool of uncharacterized metabolic pathways, enzymes, and adaptations. This great untapped pool of genes provides the particularly exciting potential to mine for new biochemical activities or novel enzymes with activities tailored to peculiar sets of environmental conditions. Metagenomes also represent a huge reservoir of novel enzymes for applications in biocatalysis, biofuels, and bioremediation. Here we present the results of enzyme discovery for four enzyme activities, of particular industrial or environmental interest, including esterase/lipase, glycosyl hydrolase, protease and dehalogenase.

A User-Centered Approach to Adaptive Hypertext Based on an Information Relevance Model

NASA Technical Reports Server (NTRS)

Mathe, Nathalie; Chen, James

1994-01-01

Rapid and effective to information in large electronic documentation systems can be facilitated if information relevant in an individual user's content can be automatically supplied to this user. However most of this knowledge on contextual relevance is not found within the contents of documents, it is rather established incrementally by users during information access. We propose a new model for interactively learning contextual relevance during information retrieval, and incrementally adapting retrieved information to individual user profiles. The model, called a relevance network, records the relevance of references based on user feedback for specific queries and user profiles. It also generalizes such knowledge to later derive relevant references for similar queries and profiles. The relevance network lets users filter information by context of relevance. Compared to other approaches, it does not require any prior knowledge nor training. More importantly, our approach to adaptivity is user-centered. It facilitates acceptance and understanding by users by giving them shared control over the adaptation without disturbing their primary task. Users easily control when to adapt and when to use the adapted system. Lastly, the model is independent of the particular application used to access information, and supports sharing of adaptations among users.

A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system.

PubMed

Chrysafiadi, Konstantina; Virvou, Maria

2013-12-01

In this paper a knowledge representation approach of an adaptive and/or personalized tutoring system is presented. The domain knowledge should be represented in a more realistic way in order to allow the adaptive and/or personalized tutoring system to deliver the learning material to each individual learner dynamically taking into account her/his learning needs and her/his different learning pace. To succeed this, the domain knowledge representation has to depict the possible increase or decrease of the learner's knowledge. Considering that the domain concepts that constitute the learning material are not independent from each other, the knowledge representation approach has to allow the system to recognize either the domain concepts that are already partly or completely known for a learner, or the domain concepts that s/he has forgotten, taking into account the learner's knowledge level of the related concepts. In other words, the system should be informed about the knowledge dependencies that exist among the domain concepts of the learning material, as well as the strength on impact of each domain concept on others. Fuzzy Cognitive Maps (FCMs) seem to be an ideal way for representing graphically this kind of information. The suggested knowledge representation approach has been implemented in an e-learning adaptive system for teaching computer programming. The particular system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus and was compared with a corresponding system, in which the domain knowledge was represented using the most common used technique of network of concepts. The results of the evaluation were very encouraging.

32 CFR 34.2 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

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

e-IQ and IQ knowledge mining for generalized LDA

NASA Astrophysics Data System (ADS)

Jenkins, Jeffrey; van Bergem, Rutger; Sweet, Charles; Vietsch, Eveline; Szu, Harold

2015-05-01

How can the human brain uncover patterns, associations and features in real-time, real-world data? There must be a general strategy used to transform raw signals into useful features, but representing this generalization in the context of our information extraction tool set is lacking. In contrast to Big Data (BD), Large Data Analysis (LDA) has become a reachable multi-disciplinary goal in recent years due in part to high performance computers and algorithm development, as well as the availability of large data sets. However, the experience of Machine Learning (ML) and information communities has not been generalized into an intuitive framework that is useful to researchers across disciplines. The data exploration phase of data mining is a prime example of this unspoken, ad-hoc nature of ML - the Computer Scientist works with a Subject Matter Expert (SME) to understand the data, and then build tools (i.e. classifiers, etc.) which can benefit the SME and the rest of the researchers in that field. We ask, why is there not a tool to represent information in a meaningful way to the researcher asking the question? Meaning is subjective and contextual across disciplines, so to ensure robustness, we draw examples from several disciplines and propose a generalized LDA framework for independent data understanding of heterogeneous sources which contribute to Knowledge Discovery in Databases (KDD). Then, we explore the concept of adaptive Information resolution through a 6W unsupervised learning methodology feedback system. In this paper, we will describe the general process of man-machine interaction in terms of an asymmetric directed graph theory (digging for embedded knowledge), and model the inverse machine-man feedback (digging for tacit knowledge) as an ANN unsupervised learning methodology. Finally, we propose a collective learning framework which utilizes a 6W semantic topology to organize heterogeneous knowledge and diffuse information to entities within a society in a personalized way.

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

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-25

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

Trying to Teach Well: A Story of Small Discoveries

ERIC Educational Resources Information Center

Lewis, P. J.

2004-01-01

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

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

ERIC Educational Resources Information Center

Polavaram, Sridevi

2016-01-01

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

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

ERIC Educational Resources Information Center

Kerner, Jon F.

2006-01-01

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

The Assessment of Self-Directed Learning Readiness in Medical Education

ERIC Educational Resources Information Center

Monroe, Katherine Swint

2014-01-01

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

EPA Web Taxonomy

EPA Pesticide Factsheets

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

The Emergence of Organizing Structure in Conceptual Representation.

PubMed

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

2018-06-01

Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form-where form could be a tree, ring, chain, grid, etc. (Kemp & Tenenbaum, 2008). Although this approach can learn intuitive organizations, including a tree for animals and a ring for the color circle, it assumes a strong inductive bias that considers only these particular forms, and each form is explicitly provided as initial knowledge. Here we introduce a new computational model of how organizing structure can be discovered, utilizing a broad hypothesis space with a preference for sparse connectivity. Given that the inductive bias is more general, the model's initial knowledge shows little qualitative resemblance to some of the discoveries it supports. As a consequence, the model can also learn complex structures for domains that lack intuitive description, as well as predict human property induction judgments without explicit structural forms. By allowing form to emerge from sparsity, our approach clarifies how both the richness and flexibility of human conceptual organization can coexist. Copyright © 2018 Cognitive Science Society, Inc.

Knowledge discovery by accuracy maximization

PubMed Central

Cacciatore, Stefano; Luchinat, Claudio; Tenori, Leonardo

2014-01-01

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

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

PubMed Central

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

2010-01-01

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

Brazilian transcultural adaptation of an instrument on physicians' knowledge and attitudes towards dementia

PubMed Central

Jacinto, Alessandro Ferrari; de Oliveira, Erika Correa; Citero, Vanessa de Albuquerque

2015-01-01

Objective The aim of this study was to obtain a Brazilian transcultural adaptation of an instrument developed in the United Kingdom for assessing the knowledge and attitudes towards dementia by physicians. Methods The "Knowledge Quiz" (KQ) contains 14 items on epidemiology, diagnosis and management of dementia, while the "Attitude Quiz" contains 10 sentences about physicians' thoughts on the management of demented patients. The Quizzes were translated, back-translated and the resultant version applied to five physicians. Results The transcultural equivalence process was performed and four items of the KQ needed adapting to the Brazilian context. After changes suggested by a panel of specialists, the final version was applied to another five physicians and the transcultural equivalence considered adequate. Conclusion The Brazilian version of the instrument was successfully transculturally adapted for future validation and application in Brazil. PMID:29213968

Social E-Learning in Topolor: A Case Study

ERIC Educational Resources Information Center

Shi, Lei; Al Qudah, Dana; Cristea, Alexandra I.

2013-01-01

Social e-learning is a process through which learners achieve their learning goals via social interactions with each other by sharing knowledge, skills, abilities and educational materials. Adaptive e-learning enables adaptation and personalization of the learning process, based on learner needs, knowledge, preferences and other characteristics.…

Dynamic Courseware Generation on the WWW.

ERIC Educational Resources Information Center

Vassileva, Julita; Deters, Ralph

1998-01-01

The Dynamic Courseware Generator (DCG), which runs on a Web server, was developed for the authoring of adaptive computer-assisted learning courses. It generates an individual course according to the learner's goals and previous knowledge, and dynamically adapts the course according to the learner's success in knowledge acquisition. The tool may be…

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

PubMed

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

2016-09-01

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

A knowledge discovery object model API for Java

PubMed Central

Zuyderduyn, Scott D; Jones, Steven JM

2003-01-01

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

Explicit and implicit motor learning in children with unilateral cerebral palsy.

PubMed

van der Kamp, John; Steenbergen, Bert; Masters, Rich S W

2017-07-30

The current study aimed to investigate the capacity for explicit and implicit learning in children with unilateral cerebral palsy. Children with left and right unilateral cerebral palsy and typically developing children shuffled disks toward a target. A prism-adaptation design was implemented, consisting of pre-exposure, prism exposure, and post-exposure phases. Half of the participants were instructed about the function of the prism glasses, while the other half were not. For each trial, the distance between the target and the shuffled disk was determined. Explicit learning was indicated by the rate of adaptation during the prism exposure phase, whereas implicit learning was indicated by the magnitude of the negative after-effect at the start of the post-exposure phase. Results No significant effects were revealed between typically developing participants and participants with unilateral cerebral palsy. Comparison of participants with left and right unilateral cerebral palsy demonstrated that participants with right unilateral cerebral palsy had a significantly lower rate of adaptation than participants with left unilateral cerebral palsy, but only when no instructions were provided. The magnitude of the negative after-effects did not differ significantly between participants with right and left unilateral cerebral palsy. The capacity for explicit motor learning is reduced among individuals with right unilateral cerebral palsy when accumulation of declarative knowledge is unguided (i.e., discovery learning). In contrast, the capacity for implicit learning appears to remain intact among individuals with left as well as right unilateral cerebral palsy. Implications for rehabilitation Implicit motor learning interventions are recommended for individuals with cerebral palsy, particularly for individuals with right unilateral cerebral palsy Explicit motor learning interventions for individual with cerebral palsy - if used - best consist of singular verbal instruction.

Application of Knowledge-Based Techniques to Tracking Function

DTIC Science & Technology

2006-09-01

38394041 42434445 46474849 505152 53545556 57585960 616263 646566 676869 707172 737475 7677 7879 8081 8283 8485 8687 8889 9091 9293 9495 969798 99100...Knowledge-based applications to adaptive space-time processing. Volume I: Summary”, AFRL-SN-TR-2001-146 Vol. I (of Vol. VI ), Final Technical Report, July...2001-146 Vol. IV (of Vol. VI ), Final Technical Report, July 2001. [53] C. Morgan, L. Moyer, “Knowledge-based applications to adaptive space-time

Teaching for adaptive expertise in biomedical engineering ethics.

PubMed

Martin, Taylor; Rayne, Karen; Kemp, Nate J; Hart, Jack; Diller, Kenneth R

2005-04-01

This paper considers an approach to teaching ethics in bioengineering based on the How People Learn (HPL) framework. Curricula based on this framework have been effective in mathematics and science instruction from the kindergarten to the college levels. This framework is well suited to teaching bioengineering ethics because it helps learners develop "adaptive expertise". Adaptive expertise refers to the ability to use knowledge and experience in a domain to learn in unanticipated situations. It differs from routine expertise, which requires using knowledge appropriately to solve routine problems. Adaptive expertise is an important educational objective for bioengineers because the regulations and knowledge base in the discipline are likely to change significantly over the course of their careers. This study compares the performance of undergraduate bioengineering students who learned about ethics for stem cell research using the HPL method of instruction to the performance of students who learned following a standard lecture sequence. Both groups learned the factual material equally well, but the HPL group was more prepared to act adaptively when presented with a novel situation.

New Genomic Insights into “Entotheonella” Symbionts in Theonella swinhoei: Mixotrophy, Anaerobic Adaptation, Resilience, and Interaction

PubMed Central

Liu, Fang; Li, Jinlong; Feng, Guofang; Li, Zhiyong

2016-01-01

“Entotheonella” (phylum “Tectomicrobia”) is a filamentous symbiont that produces almost all known bioactive compounds derived from the Lithistida sponge Theonella swinhoei. In contrast to the comprehensive knowledge of its secondary metabolism, knowledge of its lifestyle, resilience, and interaction with the sponge host and other symbionts remains rudimentary. In this study, we obtained two “Entotheonella” genomes from T. swinhoei from the South China Sea through metagenome binning, and used a RASTtk pipeline to achieve better genome annotation. The high average nucleotide index values suggested they were the same phylotypes as the two “Entotheonella” phylotypes from T. swinhoei from the Japan Sea. Genomic features related to utilization of various carbon sources, peptidase secretion, CO2 fixation, sulfate reduction, anaerobic respiration, and denitrification indicated the mixotrophic nature of “Entotheonella.” The endospore-forming potential along with metal- and antibiotic resistance indicated “Entotheonella” was highly resilient to harsh conditions. The potential for endospore formation also explained the widespread distribution of “Entotheonella” to some extent. The discovery of Type II (general secretion pathway proteins and the Widespread Colonization Island) and Type VI secretion systems in “Entotheonella” suggested it could secrete extracellular hydrolases, form tight adhesion, act against phagocytes, and kill other prokaryotes. Overall, the newly discovered genomic features suggest “Entotheonella” is a highly competitive member of the symbiotic community of T. swinhoei. PMID:27610106

The full translational spectrum of prevention science: facilitating the transfer of knowledge to practices and policies that prevent behavioral health problems.

PubMed

Fishbein, Diana H; Ridenour, Ty A; Stahl, Mindy; Sussman, Steve

2016-03-01

A broad-span, six-stage translational prevention model is presented, extending from the basic sciences-taking a multi-level systems approach, including the neurobiological sciences-through to globalization. The application of a very wide perspective of translation research from basic scientific discovery to international policy change promises to elicit sustainable, population-level reductions in behavioral health disorders. To illustrate the conceptualization and actualization of a program of translational prevention research, we walk through each stage of research to practice and policy using an exemplar, callous-unemotional (CU) traits. Basic science has identified neurobiological, psychophysiological, behavioral, contextual, and experiential differences in this subgroup, and yet, these findings have not been applied to the development of more targeted intervention. As a result, there are currently no programs considered especially effective for CU traits, likely because they do not specifically target underlying mechanisms. To prevent/reduce the prevalence of conduct disorder, it is critical that we transfer existing knowledge to subsequent translational stages, including intervention development, implementation, and scaling. And eventually, once resulting programs have been rigorously evaluated, replicated, and adapted across cultural, ethnic, and gender groups, there is potential to institutionalize them as well as call attention to the special needs of this population. In this paper, we begin to consider what resources and changes in research perspectives are needed to move along this translational spectrum.

The Formative Method for Adapting Psychotherapy (FMAP): A community-based developmental approach to culturally adapting therapy

PubMed Central

Hwang, Wei-Chin

2010-01-01

How do we culturally adapt psychotherapy for ethnic minorities? Although there has been growing interest in doing so, few therapy adaptation frameworks have been developed. The majority of these frameworks take a top-down theoretical approach to adapting psychotherapy. The purpose of this paper is to introduce a community-based developmental approach to modifying psychotherapy for ethnic minorities. The Formative Method for Adapting Psychotherapy (FMAP) is a bottom-up approach that involves collaborating with consumers to generate and support ideas for therapy adaptation. It involves 5-phases that target developing, testing, and reformulating therapy modifications. These phases include: (a) generating knowledge and collaborating with stakeholders (b) integrating generated information with theory and empirical and clinical knowledge, (c) reviewing the initial culturally adapted clinical intervention with stakeholders and revising the culturally adapted intervention, (d) testing the culturally adapted intervention, and (e) finalizing the culturally adapted intervention. Application of the FMAP is illustrated using examples from a study adapting psychotherapy for Chinese Americans, but can also be readily applied to modify therapy for other ethnic groups. PMID:20625458

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

PubMed

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

2015-03-01

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

Mathematical modeling for novel cancer drug discovery and development.

PubMed

Zhang, Ping; Brusic, Vladimir

2014-10-01

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

Lifeomics leads the age of grand discoveries.

PubMed

He, Fuchu

2013-03-01

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

Method and system for knowledge discovery using non-linear statistical analysis and a 1st and 2nd tier computer program

DOEpatents

Hively, Lee M [Philadelphia, TN

2011-07-12

The invention relates to a method and apparatus for simultaneously processing different sources of test data into informational data and then processing different categories of informational data into knowledge-based data. The knowledge-based data can then be communicated between nodes in a system of multiple computers according to rules for a type of complex, hierarchical computer system modeled on a human brain.

From Residency to Lifelong Learning.

PubMed

Brandt, Keith

2015-11-01

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

Semantically-enabled Knowledge Discovery in the Deep Carbon Observatory

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

The Study and Design of Adaptive Learning System Based on Fuzzy Set Theory

NASA Astrophysics Data System (ADS)

Jia, Bing; Zhong, Shaochun; Zheng, Tianyang; Liu, Zhiyong

Adaptive learning is an effective way to improve the learning outcomes, that is, the selection of learning content and presentation should be adapted to each learner's learning context, learning levels and learning ability. Adaptive Learning System (ALS) can provide effective support for adaptive learning. This paper proposes a new ALS based on fuzzy set theory. It can effectively estimate the learner's knowledge level by test according to learner's target. Then take the factors of learner's cognitive ability and preference into consideration to achieve self-organization and push plan of knowledge. This paper focuses on the design and implementation of domain model and user model in ALS. Experiments confirmed that the system providing adaptive content can effectively help learners to memory the content and improve their comprehension.

Multiple testing with discrete data: Proportion of true null hypotheses and two adaptive FDR procedures.

PubMed

Chen, Xiongzhi; Doerge, Rebecca W; Heyse, Joseph F

2018-05-11

We consider multiple testing with false discovery rate (FDR) control when p values have discrete and heterogeneous null distributions. We propose a new estimator of the proportion of true null hypotheses and demonstrate that it is less upwardly biased than Storey's estimator and two other estimators. The new estimator induces two adaptive procedures, that is, an adaptive Benjamini-Hochberg (BH) procedure and an adaptive Benjamini-Hochberg-Heyse (BHH) procedure. We prove that the adaptive BH (aBH) procedure is conservative nonasymptotically. Through simulation studies, we show that these procedures are usually more powerful than their nonadaptive counterparts and that the adaptive BHH procedure is usually more powerful than the aBH procedure and a procedure based on randomized p-value. The adaptive procedures are applied to a study of HIV vaccine efficacy, where they identify more differentially polymorphic positions than the BH procedure at the same FDR level. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Beyond adaptive-critic creative learning for intelligent mobile robots

NASA Astrophysics Data System (ADS)

Liao, Xiaoqun; Cao, Ming; Hall, Ernest L.

2001-10-01

Intelligent industrial and mobile robots may be considered proven technology in structured environments. Teach programming and supervised learning methods permit solutions to a variety of applications. However, we believe that to extend the operation of these machines to more unstructured environments requires a new learning method. Both unsupervised learning and reinforcement learning are potential candidates for these new tasks. The adaptive critic method has been shown to provide useful approximations or even optimal control policies to non-linear systems. The purpose of this paper is to explore the use of new learning methods that goes beyond the adaptive critic method for unstructured environments. The adaptive critic is a form of reinforcement learning. A critic element provides only high level grading corrections to a cognition module that controls the action module. In the proposed system the critic's grades are modeled and forecasted, so that an anticipated set of sub-grades are available to the cognition model. The forecasting grades are interpolated and are available on the time scale needed by the action model. The success of the system is highly dependent on the accuracy of the forecasted grades and adaptability of the action module. Examples from the guidance of a mobile robot are provided to illustrate the method for simple line following and for the more complex navigation and control in an unstructured environment. The theory presented that is beyond the adaptive critic may be called creative theory. Creative theory is a form of learning that models the highest level of human learning - imagination. The application of the creative theory appears to not only be to mobile robots but also to many other forms of human endeavor such as educational learning and business forecasting. Reinforcement learning such as the adaptive critic may be applied to known problems to aid in the discovery of their solutions. The significance of creative theory is that it permits the discovery of the unknown problems, ones that are not yet recognized but may be critical to survival or success.

Scaffold Repurposing of Old Drugs Towards New Cancer Drug Discovery.

PubMed

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

2016-01-01

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

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

NASA Astrophysics Data System (ADS)

Harmon, M. E.

2016-12-01

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

Metrics and the effective computational scientist: process, quality and communication.

PubMed

Baldwin, Eric T

2012-09-01

Recent treatments of computational knowledge worker productivity have focused upon the value the discipline brings to drug discovery using positive anecdotes. While this big picture approach provides important validation of the contributions of these knowledge workers, the impact accounts do not provide the granular detail that can help individuals and teams perform better. I suggest balancing the impact-focus with quantitative measures that can inform the development of scientists. Measuring the quality of work, analyzing and improving processes, and the critical evaluation of communication can provide immediate performance feedback. The introduction of quantitative measures can complement the longer term reporting of impacts on drug discovery. These metric data can document effectiveness trends and can provide a stronger foundation for the impact dialogue. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

PubMed

Gaufberg, Elizabeth

2017-12-01

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

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

PubMed Central

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

2018-01-01

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

Rule-based mechanisms of learning for intelligent adaptive flight control

NASA Technical Reports Server (NTRS)

Handelman, David A.; Stengel, Robert F.

1990-01-01

How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

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

ERIC Educational Resources Information Center

Kochen, Manfred

1984-01-01

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

Remarks to Eighth Annual State of Modeling and Simulation

DTIC Science & Technology

1999-06-04

organization, training as well as materiel Discovery vice Verification Tolerance for Surprise Free play Red Team Iterative Process Push to failure...Account for responsive & innovative future adversaries – free play , adaptive strategies and tactics by professional red teams • Address C2 issues & human

The future of biotechnology testing in the next decade: a perspective.

PubMed

Johnson, D E

2001-01-01

The power of genomics in transforming the biotechnology industry will be evident in all areas of drug discovery and development. This will be particularly true in safety evaluation, where the field will be forced to adapt to new schemes of e-R&D and e-business in general. Toxicologists will be required to create information-rich, real-time systems that can be used for decisions in the earliest phases of discovery and throughout development. The largest growth area in specific product types will be those that can move genomics information from the laboratory to the clinic the fastest.

KSC-00pp1357

NASA Image and Video Library

2000-09-13

Inside the Payload Changeout Room (PCR), workers check the controls on movement of the Integrated Truss Structure Z1 behind them into the PCR from the payload canister. Once sealed inside the PCR, workers will get ready to move the Z1 into the payload bay of Space Shuttle Discovery. The Z1 truss is the first of 10 that will become the backbone of the International Space Station, eventually stretching the length of a football field. Along with its companion payload, the third Pressurized Mating Adapter, the Z1 is scheduled to be launched aboard Discovery Oct. 5 at 9:38 p.m. EDT

KSC-00pp0917

NASA Image and Video Library

2000-07-12

In the Orbiter Processing Facility bay 1, STS-92 crew members, along with Boeing workers, look closely at the tools they will be using on their mission. The crew comprises Commander Brian Duffy, Pilot Pam Melroy and Mission Specialists Koichi Wakata, Leroy Chiao, Jeff Wisoff, Michael Lopez-Alegria and Bill McArthur. STS-92 is scheduled to launch Oct. 5 on Shuttle Discovery from Launch Pad 39A on the fifth flight to the International Space Station. Discovery will carry the Integrated Truss Structure (ITS) Z1, Pressurized Mating Adapter 3, Ku-band Communications System, and Control Moment Gyros (CMGs)

Clinical Pharmacology & Therapeutics: Past, Present and Future

PubMed Central

Waldman, SA; Terzic, A

2016-01-01

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

The hypothalamic-pituitary-thyroid axis and biological rhythms: The discovery of TSH's unexpected role using animal models.

PubMed

Ikegami, Keisuke; Yoshimura, Takashi

2017-10-01

Thyroid hormones (TH) are important for development, growth, and metabolism. It is also clear that the synthesis and secretion of TH are regulated by the hypothalamic-pituitary-thyroid (HPT) axis. Animal models have helped advance our understanding of the roles and regulatory mechanisms of TH. The animals' bodies develop through coordinated timing of cell division and differentiation. Studies of frog metamorphosis led to the discovery of TH and their role in development. However, to adapt to rhythmic environmental changes, animals also developed various endocrine rhythms. Studies of rodents clarified the neural and molecular mechanisms underlying the circadian regulation of the HPT axis. Moreover, birds have a sophisticated seasonal adaptation mechanism, and recent studies of quail revealed unexpected roles for thyroid-stimulating hormone (TSH) and TH in the seasonal regulation of reproduction. Interestingly, this mechanism is conserved in mammals. Thus, we review how animal studies have shaped our general understanding of the HPT axis in relation to biological rhythms. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

DOE PAGES

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

2017-09-15

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

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

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

Oyen, Diane; Anderson, Blake; Sentz, Kari

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

Bio-TDS: bioscience query tool discovery system.

PubMed

Gnimpieba, Etienne Z; VanDiermen, Menno S; Gustafson, Shayla M; Conn, Bill; Lushbough, Carol M

2017-01-04

Bioinformatics and computational biology play a critical role in bioscience and biomedical research. As researchers design their experimental projects, one major challenge is to find the most relevant bioinformatics toolkits that will lead to new knowledge discovery from their data. The Bio-TDS (Bioscience Query Tool Discovery Systems, http://biotds.org/) has been developed to assist researchers in retrieving the most applicable analytic tools by allowing them to formulate their questions as free text. The Bio-TDS is a flexible retrieval system that affords users from multiple bioscience domains (e.g. genomic, proteomic, bio-imaging) the ability to query over 12 000 analytic tool descriptions integrated from well-established, community repositories. One of the primary components of the Bio-TDS is the ontology and natural language processing workflow for annotation, curation, query processing, and evaluation. The Bio-TDS's scientific impact was evaluated using sample questions posed by researchers retrieved from Biostars, a site focusing on BIOLOGICAL DATA ANALYSIS: The Bio-TDS was compared to five similar bioscience analytic tool retrieval systems with the Bio-TDS outperforming the others in terms of relevance and completeness. The Bio-TDS offers researchers the capacity to associate their bioscience question with the most relevant computational toolsets required for the data analysis in their knowledge discovery process. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

A pilot randomized controlled trial to decrease adaptation difficulties in chinese new immigrants to Hong Kong.

PubMed

Yu, Xiaonan; Stewart, Sunita M; Chui, Jolian P L; Ho, Joy L Y; Li, Anthony C H; Lam, Tai Hing

2014-01-01

Immigration occurs globally, and immigrants are vulnerable to the development of adaptation difficulties. Little evidence is available for effective programs to enhance immigrant adaptation outside of the West. This pilot randomized controlled trial tested the effectiveness of two interventions used to decrease adaptation difficulties by (a) providing knowledge of resources that are relevant to the Hong Kong context or (b) enhancing personal resilience in immigrants to Hong Kong from Mainland China. A total of 220 participants were randomly assigned to three conditions: information, resilience, or control arms. They completed measures on adaptation difficulties, knowledge, and personal resilience at baseline, immediately after the intervention (postintervention), and at a 3-month follow-up. The information intervention resulted in higher increases postintervention in knowledge than did the other two arms. The resilience intervention reported greater increases in personal resilience than did the control arm at both postintervention and 3 months later; it also reported greater increases than the information arm did at the 3-month follow-up. Although both interventions reported greater decreases in adaptation difficulties than the control arm did at postintervention and 3 months later, no significant differences were found when they were compared with each other at both time points. Both programs had high acceptability and were feasible to implement in the community. Change in knowledge had no significant mediation effect on adaption difficulties, but change in personal resilience from baseline to postintervention mediated the effect of the intervention on the outcome of adaptation difficulties at the 3-month follow-up. These findings indicate evidence for benefits of the information and resilience interventions, and they inform further development of our programs. Copyright © 2013. Published by Elsevier Ltd.

Improved knowledge retention among clinical pharmacy students using an anthropology classroom assessment technique.

PubMed

Whitley, Heather P; Parton, Jason M

2014-09-15

To adapt a classroom assessment technique (CAT) from an anthropology course to a diabetes module in a clinical pharmacy skills laboratory and to determine student knowledge retention from baseline. Diabetes item stems, focused on module objectives, replaced anthropology terms. Answer choices, coded to Bloom's Taxonomy, were expanded to include higher-order thinking. Students completed the online 5-item probe 4 times: prelaboratory lecture, postlaboratory, and at 6 months and 12 months after laboratory. Statistical analyses utilized a single factor, repeated measures design using rank transformations of means with a Mann-Whitney-Wilcoxon test. The CAT revealed a significant increase in knowledge from prelaboratory compared to all postlaboratory measurements (p<0.0001). Significant knowledge retention was maintained with basic terms, but declined with complex terms between 6 and 12 months. The anthropology assessment tool was effectively adapted using Bloom's Taxonomy as a guide and, when used repeatedly, demonstrated knowledge retention. Minimal time was devoted to application of the probe making it an easily adaptable CAT.

Adaptive Search through Constraint Violations

DTIC Science & Technology

1990-01-01

procedural) knowledge? Different methodologies are used to investigate these questions: Psychological experiments, computer simulations, historical studies...learns control knowledge through adaptive search. Unlike most other psychological models of skill acquisition, HS is a model of analytical, or...Newzll, 1986; VanLehn, in press). Psychological models of skill acquisition employ different problem solving mechanisms (forward search, backward

Learning to Critique and Adapt Science Curriculum Materials: Examining the Development of Preservice Elementary Teachers' Pedagogical Content Knowledge

ERIC Educational Resources Information Center

Beyer, Carrie J.; Davis, Elizabeth A.

2012-01-01

Teachers often engage in curricular planning by critiquing and adapting existing curriculum materials to contextualize lessons and compensate for their deficiencies. Designing instruction for students is shaped by teachers' ability to apply a variety of personal resources, including their pedagogical content knowledge (PCK). This study…

Cross-cultural Adaptation of the Oral Anticoagulation Knowledge Test to the Brazilian Portuguese.

PubMed

Praxedes, Marcus Fernando da Silva; Abreu, Mauro Henrique Nogueira Guimarães; Ribeiro, Daniel Dias; Marcolino, Milena Soriano; Paiva, Saul Martins de; Martins, Maria Auxiliadora Parreiras

2017-05-01

Patients' knowledge about oral anticoagulant therapy may favor the achievement of therapeutic results and the prevention of adverse pharmacotherapy-related events. Brazil lacks validated instruments for assessing the patient's knowledge about treatment with warfarin. This study aimed to perform the cross-cultural adaptation of the Oral Anticoagulation Knowledge (OAK) Test instrument from English into Portuguese. This is a methodological study developed in an anticoagulation clinic of a public university hospital. The study included initial translation, synthesis of translations, back-translation, review by the experts committee and pre-testing with 30 individuals. We obtained semantic equivalence through the analysis of the referential and general meaning of each item. The conceptual equivalence of the items sought to demonstrate the relevance and acceptability of the instrument. The process of cross-cultural adaptation produced the final version of the OAK Test in Brazilian Portuguese entitled "Teste de Conhecimento sobre Anticoagulação Oral". There was a suitable semantic and conceptual equivalence between the adapted version and the original version, as well as an excellent acceptability of this instrument.

Secular trends on traditional ecological knowledge: An analysis of different domains of knowledge among Tsimane' men.

PubMed

Reyes-García, Victoria; Luz, Ana C; Gueze, Maximilien; Paneque-Gálvez, Jaime; Macía, Manuel J; Orta-Martínez, Martí; Pino, Joan

2013-10-01

Empirical research provides contradictory evidence of the loss of traditional ecological knowledge across societies. Researchers have argued that culture, methodological differences, and site-specific conditions are responsible for such contradictory evidences. We advance and test a third explanation: the adaptive nature of traditional ecological knowledge systems. Specifically, we test whether different domains of traditional ecological knowledge experience different secular changes and analyze trends in the context of other changes in livelihoods. We use data collected among 651 Tsimane' men (Bolivian Amazon). Our findings indicate that different domains of knowledge follow different secular trends. Among the domains of knowledge analyzed, medicinal and wild edible knowledge appear as the most vulnerable; canoe building and firewood knowledge seem to remain constant across generations; whereas house building knowledge seems to experience a slight secular increase. Our analysis reflects on the adaptive nature of traditional ecological knowledge, highlighting how changes in this knowledge system respond to the particular needs of a society in a given point of time.

Adaptive management: Chapter 1

USGS Publications Warehouse

Allen, Craig R.; Garmestani, Ahjond S.; Allen, Craig R.; Garmestani, Ahjond S.

2015-01-01

Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty, managers and policymakers must act. Unlike a traditional trial and error approach, adaptive management has explicit structure, including a careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. The process is iterative, and serves to reduce uncertainty, build knowledge and improve management over time in a goal-oriented and structured process.

Adaptive management

USGS Publications Warehouse

Allen, Craig R.; Garmestani, Ahjond S.

2015-01-01

Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty, managers and policymakers must act. Unlike a traditional trial and error approach, adaptive management has explicit structure, including a careful elucidation of goals, identification of alternative management objectives and hypotheses of causation, and procedures for the collection of data followed by evaluation and reiteration. The process is iterative, and serves to reduce uncertainty, build knowledge and improve management over time in a goal-oriented and structured process.

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

ERIC Educational Resources Information Center

Patwardhan, Kishor

2012-01-01

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

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

ERIC Educational Resources Information Center

Crawley, Edward F.; Greenwald, Suzanne B.

2006-01-01

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

New effects of Roundup on amphibians: predators reduce herbicide mortality; herbicides induce antipredator morphology.

PubMed

Relyea, Rick A

2012-03-01

The use of pesticides is important for growing crops and protecting human health by reducing the prevalence of targeted pest species. However, less attention is given to the potential unintended effects on nontarget species, including taxonomic groups that are of current conservation concern. One issue raised in recent years is the potential for pesticides to become more lethal in the presence of predatory cues, a phenomenon observed thus far only in the laboratory. A second issue is whether pesticides can induce unintended trait changes in nontarget species, particularly trait changes that might mimic adaptive responses to natural environmental stressors. Using outdoor mesocosms, I created simple wetland communities containing leaf litter, algae, zooplankton, and three species of tadpoles (wood frogs [Rana sylvatica or Lithobates sylvaticus], leopard frogs [R. pipiens or L. pipiens], and American toads [Bufo americanus or Anaxyrus americanus]). I exposed the communities to a factorial combination of environmentally relevant herbicide concentrations (0, 1, 2, or 3 mg acid equivalents [a.e.]/L of Roundup Original MAX) crossed with three predator-cue treatments (no predators, adult newts [Notophthalmus viridescens], or larval dragonflies [Anax junius]). Without predator cues, mortality rates from Roundup were consistent with past studies. Combined with cues from the most risky predator (i.e., dragonflies), Roundup became less lethal (in direct contrast to past laboratory studies). This reduction in mortality was likely caused by the herbicide stratifying in the water column and predator cues scaring the tadpoles down to the benthos where herbicide concentrations were lower. Even more striking was the discovery that Roundup induced morphological changes in the tadpoles. In wood frog and leopard frog tadpoles, Roundup induced relatively deeper tails in the same direction and of the same magnitude as the adaptive changes induced by dragonfly cues. To my knowledge, this is the first study to show that a pesticide can induce morphological changes in a vertebrate. Moreover, the data suggest that the herbicide might be activating the tadpoles' developmental pathways used for antipredator responses. Collectively, these discoveries suggest that the world's most widely applied herbicide may have much further-reaching effects on nontarget species than previous considered.

Assessing and Exploiting Functional Diversity in Germplasm Pools to Enhance Abiotic Stress Adaptation and Yield in Cereals and Food Legumes

PubMed Central

Dwivedi, Sangam L.; Scheben, Armin; Edwards, David; Spillane, Charles; Ortiz, Rodomiro

2017-01-01

There is a need to accelerate crop improvement by introducing alleles conferring host plant resistance, abiotic stress adaptation, and high yield potential. Elite cultivars, landraces and wild relatives harbor useful genetic variation that needs to be more easily utilized in plant breeding. We review genome-wide approaches for assessing and identifying alleles associated with desirable agronomic traits in diverse germplasm pools of cereals and legumes. Major quantitative trait loci and single nucleotide polymorphisms (SNPs) associated with desirable agronomic traits have been deployed to enhance crop productivity and resilience. These include alleles associated with variation conferring enhanced photoperiod and flowering traits. Genetic variants in the florigen pathway can provide both environmental flexibility and improved yields. SNPs associated with length of growing season and tolerance to abiotic stresses (precipitation, high temperature) are valuable resources for accelerating breeding for drought-prone environments. Both genomic selection and genome editing can also harness allelic diversity and increase productivity by improving multiple traits, including phenology, plant architecture, yield potential and adaptation to abiotic stresses. Discovering rare alleles and useful haplotypes also provides opportunities to enhance abiotic stress adaptation, while epigenetic variation has potential to enhance abiotic stress adaptation and productivity in crops. By reviewing current knowledge on specific traits and their genetic basis, we highlight recent developments in the understanding of crop functional diversity and identify potential candidate genes for future use. The storage and integration of genetic, genomic and phenotypic information will play an important role in ensuring broad and rapid application of novel genetic discoveries by the plant breeding community. Exploiting alleles for yield-related traits would allow improvement of selection efficiency and overall genetic gain of multigenic traits. An integrated approach involving multiple stakeholders specializing in management and utilization of genetic resources, crop breeding, molecular biology and genomics, agronomy, stress tolerance, and reproductive/seed biology will help to address the global challenge of ensuring food security in the face of growing resource demands and climate change induced stresses. PMID:28900432

Assessing and Exploiting Functional Diversity in Germplasm Pools to Enhance Abiotic Stress Adaptation and Yield in Cereals and Food Legumes.

PubMed

Dwivedi, Sangam L; Scheben, Armin; Edwards, David; Spillane, Charles; Ortiz, Rodomiro

2017-01-01

There is a need to accelerate crop improvement by introducing alleles conferring host plant resistance, abiotic stress adaptation, and high yield potential. Elite cultivars, landraces and wild relatives harbor useful genetic variation that needs to be more easily utilized in plant breeding. We review genome-wide approaches for assessing and identifying alleles associated with desirable agronomic traits in diverse germplasm pools of cereals and legumes. Major quantitative trait loci and single nucleotide polymorphisms (SNPs) associated with desirable agronomic traits have been deployed to enhance crop productivity and resilience. These include alleles associated with variation conferring enhanced photoperiod and flowering traits. Genetic variants in the florigen pathway can provide both environmental flexibility and improved yields. SNPs associated with length of growing season and tolerance to abiotic stresses (precipitation, high temperature) are valuable resources for accelerating breeding for drought-prone environments. Both genomic selection and genome editing can also harness allelic diversity and increase productivity by improving multiple traits, including phenology, plant architecture, yield potential and adaptation to abiotic stresses. Discovering rare alleles and useful haplotypes also provides opportunities to enhance abiotic stress adaptation, while epigenetic variation has potential to enhance abiotic stress adaptation and productivity in crops. By reviewing current knowledge on specific traits and their genetic basis, we highlight recent developments in the understanding of crop functional diversity and identify potential candidate genes for future use. The storage and integration of genetic, genomic and phenotypic information will play an important role in ensuring broad and rapid application of novel genetic discoveries by the plant breeding community. Exploiting alleles for yield-related traits would allow improvement of selection efficiency and overall genetic gain of multigenic traits. An integrated approach involving multiple stakeholders specializing in management and utilization of genetic resources, crop breeding, molecular biology and genomics, agronomy, stress tolerance, and reproductive/seed biology will help to address the global challenge of ensuring food security in the face of growing resource demands and climate change induced stresses.

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

PubMed

Crignon, Claire

2011-01-01

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

Antibacterial Drug Discovery: Some Assembly Required.

PubMed

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

2018-05-11

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

A new species of karst-adapted Cnemaspis Strauch, 1887 (Squamata: Gekkonidae) from a threatened karst region in Pahang, Peninsular Malaysia.

PubMed

Grismer, L Lee; Wood, Perry L; Mohamed, Maketab; Chan, Kin Onn; Heinz, Heather M; Sumarli, Alex S-I; Chan, Jacob A; Loredo, Ariel I

2013-12-12

A new species of karst-adapted gekkonid lizard of the genus Cnemaspis Strauch is described from Gua Gunting and Gua Goyang in a karst region of Merapoh, Pahang, Peninsular Malaysia whose unique limestone formations are in immediate danger of being quarried. The new species differs from all other species of Cnemaspis based on its unique suite of morphological and color pattern characters. Its discovery underscores the unique biodiversity endemic to karst regions and adds to a growing list of karst-adapted reptiles from Peninsular Malaysia. We posit that new karst-adapted species endemic to limestone forests will continue to be discovered and these regions will harbor a significant percentage of Peninsular Malaysia's biodiversity and thusly should be conserved rather than quarried.

Adaptive Management

EPA Science Inventory

Adaptive management is an approach to natural resource management that emphasizes learning through management where knowledge is incomplete, and when, despite inherent uncertainty, managers and policymakers must act. Unlike a traditional trial and error approach, adaptive managem...

An agent-based peer-to-peer architecture for semantic discovery of manufacturing services across virtual enterprises

NASA Astrophysics Data System (ADS)

Zhang, Wenyu; Zhang, Shuai; Cai, Ming; Jian, Wu

2015-04-01

With the development of virtual enterprise (VE) paradigm, the usage of serviceoriented architecture (SOA) is increasingly being considered for facilitating the integration and utilisation of distributed manufacturing resources. However, due to the heterogeneous nature among VEs, the dynamic nature of a VE and the autonomous nature of each VE member, the lack of both sophisticated coordination mechanism in the popular centralised infrastructure and semantic expressivity in the existing SOA standards make the current centralised, syntactic service discovery method undesirable. This motivates the proposed agent-based peer-to-peer (P2P) architecture for semantic discovery of manufacturing services across VEs. Multi-agent technology provides autonomous and flexible problemsolving capabilities in dynamic and adaptive VE environments. Peer-to-peer overlay provides highly scalable coupling across decentralised VEs, each of which exhibiting as a peer composed of multiple agents dealing with manufacturing services. The proposed architecture utilises a novel, efficient, two-stage search strategy - semantic peer discovery and semantic service discovery - to handle the complex searches of manufacturing services across VEs through fast peer filtering. The operation and experimental evaluation of the prototype system are presented to validate the implementation of the proposed approach.

High-throughput discovery of rare human nucleotide polymorphisms by Ecotilling

PubMed Central

Till, Bradley J.; Zerr, Troy; Bowers, Elisabeth; Greene, Elizabeth A.; Comai, Luca; Henikoff, Steven

2006-01-01

Human individuals differ from one another at only ∼0.1% of nucleotide positions, but these single nucleotide differences account for most heritable phenotypic variation. Large-scale efforts to discover and genotype human variation have been limited to common polymorphisms. However, these efforts overlook rare nucleotide changes that may contribute to phenotypic diversity and genetic disorders, including cancer. Thus, there is an increasing need for high-throughput methods to robustly detect rare nucleotide differences. Toward this end, we have adapted the mismatch discovery method known as Ecotilling for the discovery of human single nucleotide polymorphisms. To increase throughput and reduce costs, we developed a universal primer strategy and implemented algorithms for automated band detection. Ecotilling was validated by screening 90 human DNA samples for nucleotide changes in 5 gene targets and by comparing results to public resequencing data. To increase throughput for discovery of rare alleles, we pooled samples 8-fold and found Ecotilling to be efficient relative to resequencing, with a false negative rate of 5% and a false discovery rate of 4%. We identified 28 new rare alleles, including some that are predicted to damage protein function. The detection of rare damaging mutations has implications for models of human disease. PMID:16893952

Stabilization of protein-protein interactions in drug discovery.

PubMed

Andrei, Sebastian A; Sijbesma, Eline; Hann, Michael; Davis, Jeremy; O'Mahony, Gavin; Perry, Matthew W D; Karawajczyk, Anna; Eickhoff, Jan; Brunsveld, Luc; Doveston, Richard G; Milroy, Lech-Gustav; Ottmann, Christian

2017-09-01

PPIs are involved in every disease and specific modulation of these PPIs with small molecules would significantly improve our prospects of developing therapeutic agents. Both industry and academia have engaged in the identification and use of PPI inhibitors. However in comparison, the opposite strategy of employing small-molecule stabilizers of PPIs is underrepresented in drug discovery. Areas covered: PPI stabilization has not been exploited in a systematic manner. Rather, this concept validated by a number of therapeutically used natural products like rapamycin and paclitaxel has been shown retrospectively to be the basis of the activity of synthetic molecules originating from drug discovery projects among them lenalidomide and tafamidis. Here, the authors cover the growing number of synthetic small-molecule PPI stabilizers to advocate for a stronger consideration of this as a drug discovery approach. Expert opinion: Both the natural products and the growing number of synthetic molecules show that PPI stabilization is a viable strategy for drug discovery. There is certainly a significant challenge to adapt compound libraries, screening techniques and downstream methodologies to identify, characterize and optimize PPI stabilizers, but the examples of molecules reviewed here in our opinion justify these efforts.

A perfect launch viewed across Banana Creek

NASA Technical Reports Server (NTRS)

2000-01-01

Space Shuttle Discovery seems to burst forth from a pillow of smoke as it lifts off from Launch Pad 39A on mission STS-92 to the International Space Station. The brilliant light from the solid rocket booster flames is reflected in nearby water. The perfect on-time liftoff occurred at 7:17 p.m. EDT, sending a crew of seven on the 100th launch in the history of the Shuttle program. Discovery carries a payload that includes the Integrated Truss Structure Z-1, first of 10 trusses that will form the backbone of the Space Station, and the third Pressurized Mating Adapter that will provide a Shuttle docking port for solar array installation on the sixth Station flight and Lab installation on the seventh Station flight. Discovery's landing is expected Oct. 22 at 2:10 p.m. EDT.

Using diagnostic experiences in experience-based innovative design

NASA Astrophysics Data System (ADS)

Prabhakar, Sattiraju; Goel, Ashok K.

1992-03-01

Designing a novel class of devices requires innovation. Often, the design knowledge of these devices does not identify and address the constraints that are required for their performance in the real world operating environment. So any new design adapted from these devices tend to be similarly sketchy. In order to address this problem, we propose a case-based reasoning method called performance driven innovation (PDI). We model the design as a dynamic process, arrive at a design by adaptation from the known designs, generate failures for this design for some new constraints, and then use this failure knowledge to generate the required design knowledge for the new constraints. In this paper, we discuss two aspects of PDI: the representation of PDI cases and the translation of the failure knowledge into design knowledge for a constraint. Each case in PDI has two components: design and failure knowledge. Both of them are represented using a substance-behavior-function model. Failure knowledge has internal device failure behaviors and external environmental behaviors. The environmental behavior, for a constraint, interacting with the design behaviors, results in the failure internal behavior. The failure adaptation strategy generates functions, from the failure knowledge, which can be addressed using the routine design methods. These ideas are illustrated using a coffee-maker example.

Knowledge of the Disease, Perceived Social Support, and Cognitive Appraisals in Women with Urinary Incontinence

PubMed Central

Szymona-Pałkowska, Katarzyna; Janowski, Konrad; Pedrycz, Agnieszka; Ambroży, Tadeusz; Siermontowski, Piotr; Adamczuk, Jolanta; Sapalska, Marta; Mucha, Dawid; Kraczkowski, Janusz

2016-01-01

Social support and knowledge of the disease have been shown to facilitate adaptation to a chronic disease. However, the adaptation process is not fully understood. We hypothesized that these factors can contribute to better adaptation to the disease through their impact on disease-related cognitive appraisal. To analyze the links between social support and the knowledge of the disease, on one hand, and disease-related appraisals, on the other hand, one hundred fifty-eight women with stress UI, aged 32 to 79, took part in the study. Questionnaire measures of knowledge of UI, social support, and disease-related appraisals were used in the study. The level of knowledge correlated significantly negatively with the appraisal of the disease as Harm. The global level of social support correlated significantly positively with three disease-related appraisals: Profit, Challenge, and Value. Four subgroups of patients with different constellations of social support and knowledge of the disease were identified in cluster analysis and were demonstrated to differ significantly on four disease-related appraisals: Profit, Challenge, Harm, and Value. Different cognitive appraisals of UI may be specifically related to social support and knowledge of the disease, with social support affective positive disease-related appraisals, and the knowledge affecting the appraisal of Harm. PMID:28097132

Knowledge of the Disease, Perceived Social Support, and Cognitive Appraisals in Women with Urinary Incontinence.

PubMed

Szymona-Pałkowska, Katarzyna; Janowski, Konrad; Pedrycz, Agnieszka; Mucha, Dariusz; Ambroży, Tadeusz; Siermontowski, Piotr; Adamczuk, Jolanta; Sapalska, Marta; Mucha, Dawid; Kraczkowski, Janusz

2016-01-01

Social support and knowledge of the disease have been shown to facilitate adaptation to a chronic disease. However, the adaptation process is not fully understood. We hypothesized that these factors can contribute to better adaptation to the disease through their impact on disease-related cognitive appraisal. To analyze the links between social support and the knowledge of the disease, on one hand, and disease-related appraisals, on the other hand, one hundred fifty-eight women with stress UI, aged 32 to 79, took part in the study. Questionnaire measures of knowledge of UI, social support, and disease-related appraisals were used in the study. The level of knowledge correlated significantly negatively with the appraisal of the disease as Harm. The global level of social support correlated significantly positively with three disease-related appraisals: Profit, Challenge, and Value. Four subgroups of patients with different constellations of social support and knowledge of the disease were identified in cluster analysis and were demonstrated to differ significantly on four disease-related appraisals: Profit, Challenge, Harm, and Value. Different cognitive appraisals of UI may be specifically related to social support and knowledge of the disease, with social support affective positive disease-related appraisals, and the knowledge affecting the appraisal of Harm.

From vision to action: roadmapping as a strategic method and tool to implement climate change adaptation - the example of the roadmap 'water sensitive urban design 2020'.

PubMed

Hasse, J U; Weingaertner, D E

2016-01-01

As the central product of the BMBF-KLIMZUG-funded Joint Network and Research Project (JNRP) 'dynaklim - Dynamic adaptation of regional planning and development processes to the effects of climate change in the Emscher-Lippe region (North Rhine Westphalia, Germany)', the Roadmap 2020 'Regional Climate Adaptation' has been developed by the various regional stakeholders and institutions containing specific regional scenarios, strategies and adaptation measures applicable throughout the region. This paper presents the method, elements and main results of this regional roadmap process by using the example of the thematic sub-roadmap 'Water Sensitive Urban Design 2020'. With a focus on the process support tool 'KlimaFLEX', one of the main adaptation measures of the WSUD 2020 roadmap, typical challenges for integrated climate change adaptation like scattered knowledge, knowledge gaps and divided responsibilities but also potential solutions and promising chances for urban development and urban water management are discussed. With the roadmap and the related tool, the relevant stakeholders of the Emscher-Lippe region have jointly developed important prerequisites to integrate their knowledge, to clarify vulnerabilities, adaptation goals, responsibilities and interests, and to foresightedly coordinate measures, resources, priorities and schedules for an efficient joint urban planning, well-grounded decision-making in times of continued uncertainties and step-by-step implementation of adaptation measures from now on.

75 FR 71005 - American Education Week, 2010

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-22

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

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

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-26

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

Knowledge discovery based on experiential learning corporate culture management

NASA Astrophysics Data System (ADS)

Tu, Kai-Jan

2014-10-01

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