Text Mining Applications and Theory

CERN Document Server

Berry, Michael W

2010-01-01

Text Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives.  The contributors span several countries and scientific domains: universities, industrial corporations, and government laboratories, and demonstrate the use of techniques from machine learning, knowledge discovery, natural language processing and information retrieval to design computational models for automated text analysis and mining. This volume demonstrates how advancements in the fields of applied mathematics, computer science, machine learning

Working with text tools, techniques and approaches for text mining

CERN Document Server

Tourte, Gregory J L

2016-01-01

Text mining tools and technologies have long been a part of the repository world, where they have been applied to a variety of purposes, from pragmatic aims to support tools. Research areas as diverse as biology, chemistry, sociology and criminology have seen effective use made of text mining technologies. Working With Text collects a subset of the best contributions from the 'Working with text: Tools, techniques and approaches for text mining' workshop, alongside contributions from experts in the area. Text mining tools and technologies in support of academic research include supporting research on the basis of a large body of documents, facilitating access to and reuse of extant work, and bridging between the formal academic world and areas such as traditional and social media. Jisc have funded a number of projects, including NaCTem (the National Centre for Text Mining) and the ResDis programme. Contents are developed from workshop submissions and invited contributions, including: Legal considerations in te...

Contextual Text Mining

Science.gov (United States)

Mei, Qiaozhu

2009-01-01

With the dramatic growth of text information, there is an increasing need for powerful text mining systems that can automatically discover useful knowledge from text. Text is generally associated with all kinds of contextual information. Those contexts can be explicit, such as the time and the location where a blog article is written, and the…

Estimation of Cross-Lingual News Similarities Using Text-Mining Methods

Directory of Open Access Journals (Sweden)

Zhouhao Wang

2018-01-01

Full Text Available In this research, two estimation algorithms for extracting cross-lingual news pairs based on machine learning from financial news articles have been proposed. Every second, innumerable text data, including all kinds news, reports, messages, reviews, comments, and tweets are generated on the Internet, and these are written not only in English but also in other languages such as Chinese, Japanese, French, etc. By taking advantage of multi-lingual text resources provided by Thomson Reuters News, we developed two estimation algorithms for extracting cross-lingual news pairs from multilingual text resources. In our first method, we propose a novel structure that uses the word information and the machine learning method effectively in this task. Simultaneously, we developed a bidirectional Long Short-Term Memory (LSTM based method to calculate cross-lingual semantic text similarity for long text and short text, respectively. Thus, when an important news article is published, users can read similar news articles that are written in their native language using our method.

Cross-disciplinary Science and the Structure of Scientific Perspectives

DEFF Research Database (Denmark)

Alrøe, Hugo Fjelsted; Noe, Egon

2014-01-01

of science, focusing on the synchronic structure of scientific perspectives across disciplines and not on the diachronic, historical structure of shifting perspectives within single disciplines that has been widely discussed since Kuhn and Feyerabend. We show what kinds of cross-disciplinary disagreement...

Text mining for the biocuration workflow.

Science.gov (United States)

Hirschman, Lynette; Burns, Gully A P C; Krallinger, Martin; Arighi, Cecilia; Cohen, K Bretonnel; Valencia, Alfonso; Wu, Cathy H; Chatr-Aryamontri, Andrew; Dowell, Karen G; Huala, Eva; Lourenço, Anália; Nash, Robert; Veuthey, Anne-Lise; Wiegers, Thomas; Winter, Andrew G

2012-01-01

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on 'Text Mining for the BioCuration Workflow' at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community.

Text mining for the biocuration workflow

Science.gov (United States)

Hirschman, Lynette; Burns, Gully A. P. C; Krallinger, Martin; Arighi, Cecilia; Cohen, K. Bretonnel; Valencia, Alfonso; Wu, Cathy H.; Chatr-Aryamontri, Andrew; Dowell, Karen G.; Huala, Eva; Lourenço, Anália; Nash, Robert; Veuthey, Anne-Lise; Wiegers, Thomas; Winter, Andrew G.

2012-01-01

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on ‘Text Mining for the BioCuration Workflow’ at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community. PMID:22513129

A STUDY OF TEXT MINING METHODS, APPLICATIONS,AND TECHNIQUES

OpenAIRE

R. Rajamani*1 & S. Saranya2

2017-01-01

Data mining is used to extract useful information from the large amount of data. It is used to implement and solve different types of research problems. The research related areas in data mining are text mining, web mining, image mining, sequential pattern mining, spatial mining, medical mining, multimedia mining, structure mining and graph mining. Text mining also referred to text of data mining, it is also called knowledge discovery in text (KDT) or knowledge of intelligent text analysis. T...

Curriculum Development for Technology-Based Entrepreneurship Education: A Cross-Disciplinary and Cross-Cultural Approach

Science.gov (United States)

Kazakeviciute, Agne; Urbone, Renata; Petraite, Monika

2016-01-01

University-based entrepreneurship education is facing a paradigm shift between the classical "business school" and the contemporary cross-disciplinary "technology venturing" approach, mainly advocated by engineering schools and other communities outside business schools. The conflict is between structured "business…

Cross-disciplinary research programs at the Cornell TRIGA reactor

International Nuclear Information System (INIS)

Clark, D.D.

1995-01-01

This paper describes cross-disciplinary research efforts at the Cornell TRIGA reactor. A new graduate laboratory course for nonspecialists was developed which brought in graduate students from many fields, and a weekly or bimonthly nuclear methods seminars are being held to describe research methods, sample preparation, irradiation, etc

Automated detection of follow-up appointments using text mining of discharge records.

Science.gov (United States)

Ruud, Kari L; Johnson, Matthew G; Liesinger, Juliette T; Grafft, Carrie A; Naessens, James M

2010-06-01

To determine whether text mining can accurately detect specific follow-up appointment criteria in free-text hospital discharge records. Cross-sectional study. Mayo Clinic Rochester hospitals. Inpatients discharged from general medicine services in 2006 (n = 6481). Textual hospital dismissal summaries were manually reviewed to determine whether the records contained specific follow-up appointment arrangement elements: date, time and either physician or location for an appointment. The data set was evaluated for the same criteria using SAS Text Miner software. The two assessments were compared to determine the accuracy of text mining for detecting records containing follow-up appointment arrangements. Agreement of text-mined appointment findings with gold standard (manual abstraction) including sensitivity, specificity, positive predictive and negative predictive values (PPV and NPV). About 55.2% (3576) of discharge records contained all criteria for follow-up appointment arrangements according to the manual review, 3.2% (113) of which were missed through text mining. Text mining incorrectly identified 3.7% (107) follow-up appointments that were not considered valid through manual review. Therefore, the text mining analysis concurred with the manual review in 96.6% of the appointment findings. Overall sensitivity and specificity were 96.8 and 96.3%, respectively; and PPV and NPV were 97.0 and 96.1%, respectively. of individual appointment criteria resulted in accuracy rates of 93.5% for date, 97.4% for time, 97.5% for physician and 82.9% for location. Text mining of unstructured hospital dismissal summaries can accurately detect documentation of follow-up appointment arrangement elements, thus saving considerable resources for performance assessment and quality-related research.

Biomarker Identification Using Text Mining

Directory of Open Access Journals (Sweden)

Hui Li

2012-01-01

Full Text Available Identifying molecular biomarkers has become one of the important tasks for scientists to assess the different phenotypic states of cells or organisms correlated to the genotypes of diseases from large-scale biological data. In this paper, we proposed a text-mining-based method to discover biomarkers from PubMed. First, we construct a database based on a dictionary, and then we used a finite state machine to identify the biomarkers. Our method of text mining provides a highly reliable approach to discover the biomarkers in the PubMed database.

Cross-disciplinary consumer citizenship education

DEFF Research Database (Denmark)

Nielsen, Sanne Schnell; Gottschau, Jette

2005-01-01

and common everyday experience for both students and pupils: the living conditions, lifestyle choices and consumer behaviour connected to a lunchtime meal. The overall aim of the workshop is to develop transferable knowledge, attitudes and skills among the students. The students are supposed to apply......This paper examines a cross-disciplinary, problem-oriented workshop dealing with consumer issues. The workshop forms part of the four-year Danish teacher training course offered by the Copenhagen Day and Evening College of Teacher Training. The workshop covers issues related to civic, environmental...... and consumer education, along with pedagogical issues, with the aim of developing a holistic, integrated approach to consumer citizenship education. The workshop concept is based on the “IVAC” (Investigation, Visions, Actions & Changes) model (Jensen 1997). As our point of departure, we take a practical...

Chapter 16: text mining for translational bioinformatics.

Science.gov (United States)

Cohen, K Bretonnel; Hunter, Lawrence E

2013-04-01

Text mining for translational bioinformatics is a new field with tremendous research potential. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomedical research to clinical practice, and vice versa. Applications of text mining fall both into the category of T1 translational research-translating basic science results into new interventions-and T2 translational research, or translational research for public health. Potential use cases include better phenotyping of research subjects, and pharmacogenomic research. A variety of methods for evaluating text mining applications exist, including corpora, structured test suites, and post hoc judging. Two basic principles of linguistic structure are relevant for building text mining applications. One is that linguistic structure consists of multiple levels. The other is that every level of linguistic structure is characterized by ambiguity. There are two basic approaches to text mining: rule-based, also known as knowledge-based; and machine-learning-based, also known as statistical. Many systems are hybrids of the two approaches. Shared tasks have had a strong effect on the direction of the field. Like all translational bioinformatics software, text mining software for translational bioinformatics can be considered health-critical and should be subject to the strictest standards of quality assurance and software testing.

Frontiers of biomedical text mining: current progress

Science.gov (United States)

Zweigenbaum, Pierre; Demner-Fushman, Dina; Yu, Hong; Cohen, Kevin B.

2008-01-01

It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical text mining or ‘BioNLP’ in general, focusing primarily on papers published within the past year. PMID:17977867

Understanding disciplinary vocabularies using a full-text enabled domain-independent term extraction approach.

Science.gov (United States)

Yan, Erjia; Williams, Jake; Chen, Zheng

2017-01-01

Publication metadata help deliver rich analyses of scholarly communication. However, research concepts and ideas are more effectively expressed through unstructured fields such as full texts. Thus, the goals of this paper are to employ a full-text enabled method to extract terms relevant to disciplinary vocabularies, and through them, to understand the relationships between disciplines. This paper uses an efficient, domain-independent term extraction method to extract disciplinary vocabularies from a large multidisciplinary corpus of PLoS ONE publications. It finds a power-law pattern in the frequency distributions of terms present in each discipline, indicating a semantic richness potentially sufficient for further study and advanced analysis. The salient relationships amongst these vocabularies become apparent in application of a principal component analysis. For example, Mathematics and Computer and Information Sciences were found to have similar vocabulary use patterns along with Engineering and Physics; while Chemistry and the Social Sciences were found to exhibit contrasting vocabulary use patterns along with the Earth Sciences and Chemistry. These results have implications to studies of scholarly communication as scholars attempt to identify the epistemological cultures of disciplines, and as a full text-based methodology could lead to machine learning applications in the automated classification of scholarly work according to disciplinary vocabularies.

Text mining resources for the life sciences.

Science.gov (United States)

Przybyła, Piotr; Shardlow, Matthew; Aubin, Sophie; Bossy, Robert; Eckart de Castilho, Richard; Piperidis, Stelios; McNaught, John; Ananiadou, Sophia

2016-01-01

Text mining is a powerful technology for quickly distilling key information from vast quantities of biomedical literature. However, to harness this power the researcher must be well versed in the availability, suitability, adaptability, interoperability and comparative accuracy of current text mining resources. In this survey, we give an overview of the text mining resources that exist in the life sciences to help researchers, especially those employed in biocuration, to engage with text mining in their own work. We categorize the various resources under three sections: Content Discovery looks at where and how to find biomedical publications for text mining; Knowledge Encoding describes the formats used to represent the different levels of information associated with content that enable text mining, including those formats used to carry such information between processes; Tools and Services gives an overview of workflow management systems that can be used to rapidly configure and compare domain- and task-specific processes, via access to a wide range of pre-built tools. We also provide links to relevant repositories in each section to enable the reader to find resources relevant to their own area of interest. Throughout this work we give a special focus to resources that are interoperable-those that have the crucial ability to share information, enabling smooth integration and reusability. © The Author(s) 2016. Published by Oxford University Press.

Text mining resources for the life sciences

Science.gov (United States)

Shardlow, Matthew; Aubin, Sophie; Bossy, Robert; Eckart de Castilho, Richard; Piperidis, Stelios; McNaught, John; Ananiadou, Sophia

2016-01-01

Text mining is a powerful technology for quickly distilling key information from vast quantities of biomedical literature. However, to harness this power the researcher must be well versed in the availability, suitability, adaptability, interoperability and comparative accuracy of current text mining resources. In this survey, we give an overview of the text mining resources that exist in the life sciences to help researchers, especially those employed in biocuration, to engage with text mining in their own work. We categorize the various resources under three sections: Content Discovery looks at where and how to find biomedical publications for text mining; Knowledge Encoding describes the formats used to represent the different levels of information associated with content that enable text mining, including those formats used to carry such information between processes; Tools and Services gives an overview of workflow management systems that can be used to rapidly configure and compare domain- and task-specific processes, via access to a wide range of pre-built tools. We also provide links to relevant repositories in each section to enable the reader to find resources relevant to their own area of interest. Throughout this work we give a special focus to resources that are interoperable—those that have the crucial ability to share information, enabling smooth integration and reusability. PMID:27888231

Cultural text mining: using text mining to map the emergence of transnational reference cultures in public media repositories

NARCIS (Netherlands)

Pieters, Toine; Verheul, Jaap

2014-01-01

This paper discusses the research project Translantis, which uses innovative technologies for cultural text mining to analyze large repositories of digitized public media, such as newspapers and journals.1 The Translantis research team uses and develops the text mining tool Texcavator, which is

Text mining patents for biomedical knowledge.

Science.gov (United States)

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.

From Text to Political Positions: Text analysis across disciplines

NARCIS (Netherlands)

Kaal, A.R.; Maks, I.; van Elfrinkhof, A.M.E.

2014-01-01

ABSTRACT From Text to Political Positions addresses cross-disciplinary innovation in political text analysis for party positioning. Drawing on political science, computational methods and discourse analysis, it presents a diverse collection of analytical models including pure quantitative and

Text mining for biology--the way forward

DEFF Research Database (Denmark)

Altman, Russ B; Bergman, Casey M; Blake, Judith

2008-01-01

This article collects opinions from leading scientists about how text mining can provide better access to the biological literature, how the scientific community can help with this process, what the next steps are, and what role future BioCreative evaluations can play. The responses identify...... several broad themes, including the possibility of fusing literature and biological databases through text mining; the need for user interfaces tailored to different classes of users and supporting community-based annotation; the importance of scaling text mining technology and inserting it into larger...

A framing approach to cross-disciplinary research collaboration: experiences from a large-scale research project on adaptive water management

NARCIS (Netherlands)

Dewulf, A.; Francois, G.; Pahl-Wostl, C.; Taillieu, T.

2007-01-01

Although cross-disciplinary research collaboration is necessary to achieve a better understanding of how human and natural systems are dynamically linked, it often turns out to be very difficult in practice. We outline a framing approach to cross-disciplinary research that focuses on the different

Benchmarking infrastructure for mutation text mining.

Science.gov (United States)

Klein, Artjom; Riazanov, Alexandre; Hindle, Matthew M; Baker, Christopher Jo

2014-02-25

Experimental research on the automatic extraction of information about mutations from texts is greatly hindered by the lack of consensus evaluation infrastructure for the testing and benchmarking of mutation text mining systems. We propose a community-oriented annotation and benchmarking infrastructure to support development, testing, benchmarking, and comparison of mutation text mining systems. The design is based on semantic standards, where RDF is used to represent annotations, an OWL ontology provides an extensible schema for the data and SPARQL is used to compute various performance metrics, so that in many cases no programming is needed to analyze results from a text mining system. While large benchmark corpora for biological entity and relation extraction are focused mostly on genes, proteins, diseases, and species, our benchmarking infrastructure fills the gap for mutation information. The core infrastructure comprises (1) an ontology for modelling annotations, (2) SPARQL queries for computing performance metrics, and (3) a sizeable collection of manually curated documents, that can support mutation grounding and mutation impact extraction experiments. We have developed the principal infrastructure for the benchmarking of mutation text mining tasks. The use of RDF and OWL as the representation for corpora ensures extensibility. The infrastructure is suitable for out-of-the-box use in several important scenarios and is ready, in its current state, for initial community adoption.

Benchmarking infrastructure for mutation text mining

Science.gov (United States)

2014-01-01

Background Experimental research on the automatic extraction of information about mutations from texts is greatly hindered by the lack of consensus evaluation infrastructure for the testing and benchmarking of mutation text mining systems. Results We propose a community-oriented annotation and benchmarking infrastructure to support development, testing, benchmarking, and comparison of mutation text mining systems. The design is based on semantic standards, where RDF is used to represent annotations, an OWL ontology provides an extensible schema for the data and SPARQL is used to compute various performance metrics, so that in many cases no programming is needed to analyze results from a text mining system. While large benchmark corpora for biological entity and relation extraction are focused mostly on genes, proteins, diseases, and species, our benchmarking infrastructure fills the gap for mutation information. The core infrastructure comprises (1) an ontology for modelling annotations, (2) SPARQL queries for computing performance metrics, and (3) a sizeable collection of manually curated documents, that can support mutation grounding and mutation impact extraction experiments. Conclusion We have developed the principal infrastructure for the benchmarking of mutation text mining tasks. The use of RDF and OWL as the representation for corpora ensures extensibility. The infrastructure is suitable for out-of-the-box use in several important scenarios and is ready, in its current state, for initial community adoption. PMID:24568600

GPU-Accelerated Text Mining

International Nuclear Information System (INIS)

Cui, X.; Mueller, F.; Zhang, Y.; Potok, Thomas E.

2009-01-01

Accelerating hardware devices represent a novel promise for improving the performance for many problem domains but it is not clear for which domains what accelerators are suitable. While there is no room in general-purpose processor design to significantly increase the processor frequency, developers are instead resorting to multi-core chips duplicating conventional computing capabilities on a single die. Yet, accelerators offer more radical designs with a much higher level of parallelism and novel programming environments. This present work assesses the viability of text mining on CUDA. Text mining is one of the key concepts that has become prominent as an effective means to index the Internet, but its applications range beyond this scope and extend to providing document similarity metrics, the subject of this work. We have developed and optimized text search algorithms for GPUs to exploit their potential for massive data processing. We discuss the algorithmic challenges of parallelization for text search problems on GPUs and demonstrate the potential of these devices in experiments by reporting significant speedups. Our study may be one of the first to assess more complex text search problems for suitability for GPU devices, and it may also be one of the first to exploit and report on atomic instruction usage that have recently become available in NVIDIA devices

Biomedical text mining and its applications in cancer research.

Science.gov (United States)

Zhu, Fei; Patumcharoenpol, Preecha; Zhang, Cheng; Yang, Yang; Chan, Jonathan; Meechai, Asawin; Vongsangnak, Wanwipa; Shen, Bairong

2013-04-01

Cancer is a malignant disease that has caused millions of human deaths. Its study has a long history of well over 100years. There have been an enormous number of publications on cancer research. This integrated but unstructured biomedical text is of great value for cancer diagnostics, treatment, and prevention. The immense body and rapid growth of biomedical text on cancer has led to the appearance of a large number of text mining techniques aimed at extracting novel knowledge from scientific text. Biomedical text mining on cancer research is computationally automatic and high-throughput in nature. However, it is error-prone due to the complexity of natural language processing. In this review, we introduce the basic concepts underlying text mining and examine some frequently used algorithms, tools, and data sets, as well as assessing how much these algorithms have been utilized. We then discuss the current state-of-the-art text mining applications in cancer research and we also provide some resources for cancer text mining. With the development of systems biology, researchers tend to understand complex biomedical systems from a systems biology viewpoint. Thus, the full utilization of text mining to facilitate cancer systems biology research is fast becoming a major concern. To address this issue, we describe the general workflow of text mining in cancer systems biology and each phase of the workflow. We hope that this review can (i) provide a useful overview of the current work of this field; (ii) help researchers to choose text mining tools and datasets; and (iii) highlight how to apply text mining to assist cancer systems biology research. Copyright © 2012 Elsevier Inc. All rights reserved.

Text Mining in Biomedical Domain with Emphasis on Document Clustering.

Science.gov (United States)

Renganathan, Vinaitheerthan

2017-07-01

With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise.

Text mining meets workflow: linking U-Compare with Taverna

Science.gov (United States)

Kano, Yoshinobu; Dobson, Paul; Nakanishi, Mio; Tsujii, Jun'ichi; Ananiadou, Sophia

2010-01-01

Summary: Text mining from the biomedical literature is of increasing importance, yet it is not easy for the bioinformatics community to create and run text mining workflows due to the lack of accessibility and interoperability of the text mining resources. The U-Compare system provides a wide range of bio text mining resources in a highly interoperable workflow environment where workflows can very easily be created, executed, evaluated and visualized without coding. We have linked U-Compare to Taverna, a generic workflow system, to expose text mining functionality to the bioinformatics community. Availability: http://u-compare.org/taverna.html, http://u-compare.org Contact: kano@is.s.u-tokyo.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20709690

Trust Management - Building Trust for International Cross Disciplinary Collaboration on Climate Change

Science.gov (United States)

Oakley, K. V.; Gurney, R. J.

2014-12-01

Successful communication and collaboration entails mutual understanding, and transfer, of information. The risk of misunderstanding and/or miscommunication between collaborating groups is tackled in different ways around the globe; some are well documented whereas others may be unknown outside particular groups, whether defined geographically or by specialism. For example; in some countries legally binding contracts define the terms of collaboration. Some regions place greater emphasis on developing trust relationships, and sometimes an official agreement is implied, such as many electronic data transfers on the web. International collaboration on climate change increasingly involves electronic data exchange (e.g. open access publications, shared documents, data repositories etc.) and with this increased reliance on electronic data a need has arisen for scientists to collaborate both internationally and cross-disciplinarily particularly with information technology and data management specialists. Trust of data and metadata on the internet (e.g. privacy, legitimacy etc.) varies, possibly due to a lack of internationally agreed standards for data governance and management, leaving many national, regional and institutional practices tailored to the needs of that group only. It is proposed that building trust relationships between cross-disciplinary and international groups could help facilitate further communication, understanding and benefits from the relationship, while still maintaining independence as separate groups. Complex international cross-disciplinary group relationship dynamics are not easily mapped and producing a set of trust building rules that can be applied to any current and future collaboration with equal validity may be unfeasible. An alternative to such a set of rules may be found in a Trust Manager, whose role is to improve mutually beneficial knowledge exchange between groups, build trust and increase future collaborative potential. This

Mining knowledge from text repositories using information extraction ...

Indian Academy of Sciences (India)

Information extraction (IE); text mining; text repositories; knowledge discovery from .... general purpose English words. However ... of precision and recall, as extensive experimentation is required due to lack of public tagged corpora. 4. Mining ...

Identifying Understudied Nuclear Reactions by Text-mining the EXFOR Experimental Nuclear Reaction Library

Energy Technology Data Exchange (ETDEWEB)

Hirdt, J.A. [Department of Mathematics and Computer Science, St. Joseph' s College, Patchogue, NY 11772 (United States); Brown, D.A., E-mail: dbrown@bnl.gov [National Nuclear Data Center, Brookhaven National Laboratory, Upton, NY 11973-5000 (United States)

2016-01-15

The EXFOR library contains the largest collection of experimental nuclear reaction data available as well as the data's bibliographic information and experimental details. We text-mined the REACTION and MONITOR fields of the ENTRYs in the EXFOR library in order to identify understudied reactions and quantities. Using the results of the text-mining, we created an undirected graph from the EXFOR datasets with each graph node representing a single reaction and quantity and graph links representing the various types of connections between these reactions and quantities. This graph is an abstract representation of the connections in EXFOR, similar to graphs of social networks, authorship networks, etc. We use various graph theoretical tools to identify important yet understudied reactions and quantities in EXFOR. Although we identified a few cross sections relevant for shielding applications and isotope production, mostly we identified charged particle fluence monitor cross sections. As a side effect of this work, we learn that our abstract graph is typical of other real-world graphs.

Identifying Understudied Nuclear Reactions by Text-mining the EXFOR Experimental Nuclear Reaction Library

International Nuclear Information System (INIS)

Hirdt, J.A.; Brown, D.A.

2016-01-01

The EXFOR library contains the largest collection of experimental nuclear reaction data available as well as the data's bibliographic information and experimental details. We text-mined the REACTION and MONITOR fields of the ENTRYs in the EXFOR library in order to identify understudied reactions and quantities. Using the results of the text-mining, we created an undirected graph from the EXFOR datasets with each graph node representing a single reaction and quantity and graph links representing the various types of connections between these reactions and quantities. This graph is an abstract representation of the connections in EXFOR, similar to graphs of social networks, authorship networks, etc. We use various graph theoretical tools to identify important yet understudied reactions and quantities in EXFOR. Although we identified a few cross sections relevant for shielding applications and isotope production, mostly we identified charged particle fluence monitor cross sections. As a side effect of this work, we learn that our abstract graph is typical of other real-world graphs.

Text Mining of Supreme Administrative Court Jurisdictions

OpenAIRE

Feinerer, Ingo; Hornik, Kurt

2007-01-01

Within the last decade text mining, i.e., extracting sensitive information from text corpora, has become a major factor in business intelligence. The automated textual analysis of law corpora is highly valuable because of its impact on a company's legal options and the raw amount of available jurisdiction. The study of supreme court jurisdiction and international law corpora is equally important due to its effects on business sectors. In this paper we use text mining methods to investigate Au...

Text mining in cancer gene and pathway prioritization.

Science.gov (United States)

Luo, Yuan; Riedlinger, Gregory; Szolovits, Peter

2014-01-01

Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes.

Librarians as Part of Cross-Disciplinary, Multi-Institutional Team Projects: Experiences from the VIVO Collaboration

Science.gov (United States)

Garcia-Milian, Rolando; Norton, Hannah F.; Auten, Beth; Davis, Valrie I.; Holmes, Kristi L.; Johnson, Margeaux; Tennant, Michele R.

2013-01-01

Cross-disciplinary, team-based collaboration is essential for addressing today’s complex research questions, and librarians are increasingly entering into such collaborations. This study identifies skills needed as librarians integrate into cross-disciplinary teams, based on the experiences of librarians involved in the development and implementation of VIVO, a research discovery and collaboration platform. Participants discussed the challenges, skills gained, and lessons learned throughout the project. Their responses were analyzed in the light of the science of team science literature, and factors affecting collaboration on the VIVO team were identified. Skills in inclusive thinking, communication, perseverance, adaptability, and leadership were found to be essential. PMID:23833333

Cross-Disciplinary Ethics Education in MBA Programs

DEFF Research Database (Denmark)

Rasche, Andreas; Gilbert, Dirk Ulrich; Schedel, Ingo

2013-01-01

This research-based essay offers a cross-disciplinary examination of ethics education in MBA programs. Based on data underlying the Beyond Grey Pinstripes (BGP) survey we find: that business schools doubled the number of ethics-related courses in different disciplines between 2005 and 2009......: business schools increasingly risk creating a gap between their upbeat rhetoric around ethics education and their actual MBA curriculum. Such decoupling is likely to emerge because schools face a tension between increasing institutional pressures to legitimize their MBA programs and internal impediments...... to fully integrate ethics into the curriculum. We suggest that more effective ethics education requires structural changes to the curriculum, in particular more mandatory ethics courses and a stronger integration of ethics-related debates into disciplines like finance and accounting....

Cross-Disciplinary Ethics Education in MBA Programs

DEFF Research Database (Denmark)

Rasche, Andreas; Gilbert, Dirk Ulrich; Schedel, Ingo

This research-based essay offers a cross-disciplinary examination of ethics education in MBA programs. Based on data underlying the Beyond Grey Pinstripes (BGP) survey we find: that business schools doubled the number of ethics-related courses in different disciplines between 2005 and 2009......: business schools increasingly risk creating a gap between their upbeat rhetoric around ethics education and their actual MBA curriculum. Such decoupling is likely to emerge because schools face a tension between increasing institutional pressures to legitimize their MBA programs and internal impediments...... to fully integrate ethics into the curriculum. We suggest that more effective ethics education requires structural changes to the curriculum, in particular more mandatory ethics courses and a stronger integration of ethics-related debates into disciplines like finance and accounting....

Text mining and visualization case studies using open-source tools

CERN Document Server

Chisholm, Andrew

2016-01-01

Text Mining and Visualization: Case Studies Using Open-Source Tools provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python. The contributors-all highly experienced with text mining and open-source software-explain how text data are gathered and processed from a wide variety of sources, including books, server access logs, websites, social media sites, and message boards. Each chapter presents a case study that you can follow as part of a step-by-step, reproducible example. You can also easily apply and extend the techniques to other problems. All the examples are available on a supplementary website. The book shows you how to exploit your text data, offering successful application examples and blueprints for you to tackle your text mining tasks and benefit from open and freely available tools. It gets you up to date on the latest and most powerful tools, the data mining process, and specific text mining activities.

Citation Mining: Integrating Text Mining and Bibliometrics for Research User Profiling.

Science.gov (United States)

Kostoff, Ronald N.; del Rio, J. Antonio; Humenik, James A.; Garcia, Esther Ofilia; Ramirez, Ana Maria

2001-01-01

Discusses the importance of identifying the users and impact of research, and describes an approach for identifying the pathways through which research can impact other research, technology development, and applications. Describes a study that used citation mining, an integration of citation bibliometrics and text mining, on articles from the…

Financial Statement Fraud Detection using Text Mining

OpenAIRE

Rajan Gupta; Nasib Singh Gill

2013-01-01

Data mining techniques have been used enormously by the researchers’ community in detecting financial statement fraud. Most of the research in this direction has used the numbers (quantitative information) i.e. financial ratios present in the financial statements for detecting fraud. There is very little or no research on the analysis of text such as auditor’s comments or notes present in published reports. In this study we propose a text mining approach for detecting financial statement frau...

Variations in Textualization: A Cross-generic and Cross-disciplinary Study, Implications for Readability of the Academic Discourse

Directory of Open Access Journals (Sweden)

Mina Abbasi Bonabi

2018-01-01

Full Text Available According to discoursal views on language, variations in textualization strategies are always socio-contextually motivated and never happen at random. The textual forms employed in a text, along with many other discoursal and contextual factors, could certainly affect the readability of the text, making it more or less processable for the same reader. On the basis of these assumptions, the present study set out to examine how our data varied across genres and disciplines in terms of our target textual forms. These forms are as follows: the magnitude of T-unit (MOTU, the degree of embeddedness of the main verb in T-unit (DE, the physical distance between the verb and its satellite elements (PD, the magnitude of the noun phrase appearing before the verb (MOX, and the magnitude of noun phrase appearing after the verb (MOY. Our data consisted of 20 research articles randomly selected from two different disciplines of Biology and Applied Linguistics, to be analyzed in terms of the above-named textual strategies. One way ANOVA and post hoc Tukey tests were used for data analyses. The results revealed cross-generic as well as cross-disciplinary differences in the employment of the above textual forms. These findings were discussed in terms of the academic concepts and discourse on the one hand and the possible effect of the required textual forms on the readability of the text on the other hand.

Cross-disciplinary research in cancer: an opportunity to narrow the knowledge-practice gap.

Science.gov (United States)

Urquhart, R; Grunfeld, E; Jackson, L; Sargeant, J; Porter, G A

2013-12-01

Health services researchers have consistently identified a gap between what is identified as "best practice" and what actually happens in clinical care. Despite nearly two decades of a growing evidence-based practice movement, narrowing the knowledge-practice gap continues to be a slow, complex, and poorly understood process. Here, we contend that cross-disciplinary research is increasingly relevant and important to reducing that gap, particularly research that encompasses the notion of transdisciplinarity, wherein multiple academic disciplines and non-academic individuals and groups are integrated into the research process. The assimilation of diverse perspectives, research approaches, and types of knowledge is potentially effective in helping research teams tackle real-world patient care issues, create more practice-based evidence, and translate the results to clinical and community care settings. The goals of this paper are to present and discuss cross-disciplinary approaches to health research and to provide two examples of how engaging in such research may optimize the use of research in cancer care.

Application of text mining in the biomedical domain.

Science.gov (United States)

Fleuren, Wilco W M; Alkema, Wynand

2015-03-01

In recent years the amount of experimental data that is produced in biomedical research and the number of papers that are being published in this field have grown rapidly. In order to keep up to date with developments in their field of interest and to interpret the outcome of experiments in light of all available literature, researchers turn more and more to the use of automated literature mining. As a consequence, text mining tools have evolved considerably in number and quality and nowadays can be used to address a variety of research questions ranging from de novo drug target discovery to enhanced biological interpretation of the results from high throughput experiments. In this paper we introduce the most important techniques that are used for a text mining and give an overview of the text mining tools that are currently being used and the type of problems they are typically applied for. Copyright © 2015 Elsevier Inc. All rights reserved.

Text mining in livestock animal science: introducing the potential of text mining to animal sciences.

Science.gov (United States)

Sahadevan, S; Hofmann-Apitius, M; Schellander, K; Tesfaye, D; Fluck, J; Friedrich, C M

2012-10-01

In biological research, establishing the prior art by searching and collecting information already present in the domain has equal importance as the experiments done. To obtain a complete overview about the relevant knowledge, researchers mainly rely on 2 major information sources: i) various biological databases and ii) scientific publications in the field. The major difference between the 2 information sources is that information from databases is available, typically well structured and condensed. The information content in scientific literature is vastly unstructured; that is, dispersed among the many different sections of scientific text. The traditional method of information extraction from scientific literature occurs by generating a list of relevant publications in the field of interest and manually scanning these texts for relevant information, which is very time consuming. It is more than likely that in using this "classical" approach the researcher misses some relevant information mentioned in the literature or has to go through biological databases to extract further information. Text mining and named entity recognition methods have already been used in human genomics and related fields as a solution to this problem. These methods can process and extract information from large volumes of scientific text. Text mining is defined as the automatic extraction of previously unknown and potentially useful information from text. Named entity recognition (NER) is defined as the method of identifying named entities (names of real world objects; for example, gene/protein names, drugs, enzymes) in text. In animal sciences, text mining and related methods have been briefly used in murine genomics and associated fields, leaving behind other fields of animal sciences, such as livestock genomics. The aim of this work was to develop an information retrieval platform in the livestock domain focusing on livestock publications and the recognition of relevant data from

OntoGene web services for biomedical text mining.

Science.gov (United States)

Rinaldi, Fabio; Clematide, Simon; Marques, Hernani; Ellendorff, Tilia; Romacker, Martin; Rodriguez-Esteban, Raul

2014-01-01

Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry. Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest. We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange (BioC). The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges,with top ranked results in several of them.

Mining biological networks from full-text articles.

Science.gov (United States)

Czarnecki, Jan; Shepherd, Adrian J

2014-01-01

The study of biological networks is playing an increasingly important role in the life sciences. Many different kinds of biological system can be modelled as networks; perhaps the most important examples are protein-protein interaction (PPI) networks, metabolic pathways, gene regulatory networks, and signalling networks. Although much useful information is easily accessible in publicly databases, a lot of extra relevant data lies scattered in numerous published papers. Hence there is a pressing need for automated text-mining methods capable of extracting such information from full-text articles. Here we present practical guidelines for constructing a text-mining pipeline from existing code and software components capable of extracting PPI networks from full-text articles. This approach can be adapted to tackle other types of biological network.

Text mining of web-based medical content

CERN Document Server

Neustein, Amy

2014-01-01

Text Mining of Web-Based Medical Content examines web mining for extracting useful information that can be used for treating and monitoring the healthcare of patients. This work provides methodological approaches to designing mapping tools that exploit data found in social media postings. Specific linguistic features of medical postings are analyzed vis-a-vis available data extraction tools for culling useful information.

Participation across institutional and disciplinary boundaries

DEFF Research Database (Denmark)

2016-01-01

theories. But it is quite rare that spaces enabling interaction and learning about cross-institutional and cross-disciplinary participation are created. This special issue is an attempt to do just that, and thus also to stress the importance of such transdisciplinary ‘spaces’ of learning and knowledge......The concept of participation has become increasingly important in a range of institutions and disciplinary contexts. The different institutional and disciplinary fields often interact indirectly by building on the same or interconnected ideals, logics and discourses or by using the same or similar...

MeSHmap: a text mining tool for MEDLINE.

OpenAIRE

Srinivasan, P.

2001-01-01

Our research goal is to explore text mining from the metadata included in MEDLINE documents. We present MeSHmap our prototype text mining system that exploits the MeSH indexing accompanying MEDLINE records. MeSHmap supports searches via PubMed followed by user driven exploration of the MeSH terms and subheadings in the retrieved set. The potential of the system goes beyond text retrieval. It may also be used to compare entities of the same type such as pairs of drugs or pairs of procedures et...

Text mining with R a tidy approach

CERN Document Server

Silge, Julia

2017-01-01

Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you'll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You'll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media. Learn how to apply the tidy text format to NLP Use sentiment analysis to mine the emotional content of text Identify a document's most important terms with frequency measurements E...

Cross-disciplinary Participatory & Contextual Design Research: Creating a Teacher Dashboard Application

Directory of Open Access Journals (Sweden)

Troy D. Abel

2014-02-01

Full Text Available Concepts of Human Computer Interaction have crossed disciplinary boundaries allowing the discovery of underlying stakeholder affordances to emerge during the design research phase of system design. For the current scenario, middle school mathematics teachers as data-driven decision makers are inundated with diagnostic and assessment data, resulting in data deluge. The situation is unlikely to subside as digital technologies and media are broadly adopted for instruction and learning. Teachers could benefit from tools to quickly sift through this data to inform classroom instruction. Data should be visualized in a way that teachers can make real-time formative and summative assessments of student progress. The purpose of this article is to introduce a mixed-method mode of discovery to uncover affordances innate to classroom teachers during the design of an iPad data visualization application. These technology-assisted “dashboard” platforms could serve as efficient and effective interventions to deal with the copious amounts of data streams now available to teachers.

Contextualizing Corruption: A Cross-Disciplinary Approach to Studying Corruption in Organizations

Directory of Open Access Journals (Sweden)

Kanti Pertiwi

2018-04-01

Full Text Available This paper aims to establish how organization and management research, an extensive field that has contributed a great deal to research on corruption, could apply insights from other disciplines in order to advance the understanding of corruption, often considered as a form of unethical behavior in organizations. It offers an analysis of important contributions of corruption research, taking a ‘rationalist perspective’, and highlights the central tensions and debates within this vast body of literatures. It then shows how these debates can be addressed by applying insights from corruption studies, adopting anthropological lens. The paper thus proposes a cross-disciplinary approach, which focuses on studying corruption by looking at what it means to individuals implicated by the phenomenon while engaging in social relations and situated in different contexts. It also offers an alternative approach to the study of corruption amidst claims that anti-corruption efforts have failed to achieve desirable results.

Cross-disciplinary working in the sciences and humanities: historical data rescue activities in Southeast Asia and beyond

Directory of Open Access Journals (Sweden)

Fiona Williamson

2016-11-01

Full Text Available Abstract This paper argues that more work is needed to facilitate cross-disciplinary collaborations by scholars across the physical sciences and humanities to improve Data Rescue Activities (DARE. Debate over the scale and potential impact of anthropogenic global warming is one of the dominant narratives of the twenty-first century. Predicting future climates and determining how environment and society might be affected by climate change are global issues of social, economic and political importance. They require responses from different research communities and necessitate closer inter-disciplinary working relationships for an integrated approach. Improving the datasets required for long-term climate models is an important part of this process. Establishing a multi-disciplinary dialogue and approach to DARE activities is increasingly being recognised as the best way to achieve this. This paper focuses on the recovery of the long-term instrumental weather observations used for models and reconstructions of the climate over the past two-hundred years. Written from the perspective of an historian working in the field, it does not seek to explore the reconstructions themselves but the process of data gathering, advocating a closer working relationship between the arts, social sciences, and sciences to extend the geographic and temporal coverage of extant datasets. This is especially important for regions where data gaps exist currently. First, it will offer a justification for extending data recovery activities for Southeast Asia and the China Seas region. Second, it will offer a brief overview of the data recovery projects currently operating in that area and the typesof historic source material that are used. Third, it will explore the work currently being undertaken for Southeast Asia and China under the Atmospheric Circulation Reconstructions over the Earth initiative as an example of a successful cross-disciplinary program. Finally, it will

Text mining in the classification of digital documents

Directory of Open Access Journals (Sweden)

Marcial Contreras Barrera

2016-11-01

Full Text Available Objective: Develop an automated classifier for the classification of bibliographic material by means of the text mining. Methodology: The text mining is used for the development of the classifier, based on a method of type supervised, conformed by two phases; learning and recognition, in the learning phase, the classifier learns patterns across the analysis of bibliographical records, of the classification Z, belonging to library science, information sciences and information resources, recovered from the database LIBRUNAM, in this phase is obtained the classifier capable of recognizing different subclasses (LC. In the recognition phase the classifier is validated and evaluates across classification tests, for this end bibliographical records of the classification Z are taken randomly, classified by a cataloguer and processed by the automated classifier, in order to obtain the precision of the automated classifier. Results: The application of the text mining achieved the development of the automated classifier, through the method classifying documents supervised type. The precision of the classifier was calculated doing the comparison among the assigned topics manually and automated obtaining 75.70% of precision. Conclusions: The application of text mining facilitated the creation of automated classifier, allowing to obtain useful technology for the classification of bibliographical material with the aim of improving and speed up the process of organizing digital documents.

Is there such a thing as online video game addiction? A cross-disciplinary review.

NARCIS (Netherlands)

Hellman, M.; Schoenmakers, T.M.; Nordstrom, B.R.; Holst, R.J. van

2013-01-01

Drawing on explanatory pluralism this cross-disciplinary theoretical study asks whether excessive compulsive online gaming can be called an addiction on the basis of what is known about the disorder. This article discusses the concept of addiction; the social seating of the problems and it reviews,

Is there such a thing as online video game addiction? A cross-disciplinary review

NARCIS (Netherlands)

Hellman, Matilda; Schoenmakers, Tim M.; Nordstrom, Benjamin R.; van Holst, Ruth J.

2013-01-01

Drawing on explanatory pluralism this cross-disciplinary theoretical study asks whether excessive compulsive online gaming can be called an addiction on the basis of what is known about the disorder. This article discusses the concept of addiction; the social seating of the problems and it reviews,

NASA's Platform for Cross-Disciplinary Microchannel Research

Science.gov (United States)

Son, Sang Young; Spearing, Scott; Allen, Jeffrey; Monaco, Lisa A.

2003-01-01

A team from the Structural Biology group located at the NASA Marshall Space Flight Center in Huntsville, Alabama is developing a platform suitable for cross-disciplinary microchannel research. The original objective of this engineering development effort was to deliver a multi-user flight-certified facility for iterative investigations of protein crystal growth; that is, Iterative Biological Crystallization (IBC). However, the unique capabilities of this facility are not limited to the low-gravity structural biology research community. Microchannel-based research in a number of other areas may be greatly accelerated through use of this facility. In particular, the potential for gas-liquid flow investigations and cellular biological research utilizing the exceptional pressure control and simplified coupling to macroscale diagnostics inherent in the IBC facility will be discussed. In conclusion, the opportunities for research-specific modifications to the microchannel configuration, control, and diagnostics will be discussed.

Text mining for traditional Chinese medical knowledge discovery: a survey.

Science.gov (United States)

Zhou, Xuezhong; Peng, Yonghong; Liu, Baoyan

2010-08-01

Extracting meaningful information and knowledge from free text is the subject of considerable research interest in the machine learning and data mining fields. Text data mining (or text mining) has become one of the most active research sub-fields in data mining. Significant developments in the area of biomedical text mining during the past years have demonstrated its great promise for supporting scientists in developing novel hypotheses and new knowledge from the biomedical literature. Traditional Chinese medicine (TCM) provides a distinct methodology with which to view human life. It is one of the most complete and distinguished traditional medicines with a history of several thousand years of studying and practicing the diagnosis and treatment of human disease. It has been shown that the TCM knowledge obtained from clinical practice has become a significant complementary source of information for modern biomedical sciences. TCM literature obtained from the historical period and from modern clinical studies has recently been transformed into digital data in the form of relational databases or text documents, which provide an effective platform for information sharing and retrieval. This motivates and facilitates research and development into knowledge discovery approaches and to modernize TCM. In order to contribute to this still growing field, this paper presents (1) a comparative introduction to TCM and modern biomedicine, (2) a survey of the related information sources of TCM, (3) a review and discussion of the state of the art and the development of text mining techniques with applications to TCM, (4) a discussion of the research issues around TCM text mining and its future directions. Copyright 2010 Elsevier Inc. All rights reserved.

PathText: a text mining integrator for biological pathway visualizations

Science.gov (United States)

Kemper, Brian; Matsuzaki, Takuya; Matsuoka, Yukiko; Tsuruoka, Yoshimasa; Kitano, Hiroaki; Ananiadou, Sophia; Tsujii, Jun'ichi

2010-01-01

Motivation: Metabolic and signaling pathways are an increasingly important part of organizing knowledge in systems biology. They serve to integrate collective interpretations of facts scattered throughout literature. Biologists construct a pathway by reading a large number of articles and interpreting them as a consistent network, but most of the models constructed currently lack direct links to those articles. Biologists who want to check the original articles have to spend substantial amounts of time to collect relevant articles and identify the sections relevant to the pathway. Furthermore, with the scientific literature expanding by several thousand papers per week, keeping a model relevant requires a continuous curation effort. In this article, we present a system designed to integrate a pathway visualizer, text mining systems and annotation tools into a seamless environment. This will enable biologists to freely move between parts of a pathway and relevant sections of articles, as well as identify relevant papers from large text bases. The system, PathText, is developed by Systems Biology Institute, Okinawa Institute of Science and Technology, National Centre for Text Mining (University of Manchester) and the University of Tokyo, and is being used by groups of biologists from these locations. Contact: brian@monrovian.com. PMID:20529930

Aspects of Text Mining From Computational Semiotics to Systemic Functional Hypertexts

Directory of Open Access Journals (Sweden)

Alexander Mehler

2001-05-01

Full Text Available The significance of natural language texts as the prime information structure for the management and dissemination of knowledge in organisations is still increasing. Making relevant documents available depending on varying tasks in different contexts is of primary importance for any efficient task completion. Implementing this demand requires the content based processing of texts, which enables to reconstruct or, if necessary, to explore the relationship of task, context and document. Text mining is a technology that is suitable for solving problems of this kind. In the following, semiotic aspects of text mining are investigated. Based on the primary object of text mining - natural language lexis - the specific complexity of this class of signs is outlined and requirements for the implementation of text mining procedures are derived. This is done with reference to text linkage introduced as a special task in text mining. Text linkage refers to the exploration of implicit, content based relations of texts (and their annotation as typed links in corpora possibly organised as hypertexts. In this context, the term systemic functional hypertext is introduced, which distinguishes genre and register layers for the management of links in a poly-level hypertext system.

PubRunner: A light-weight framework for updating text mining results.

Science.gov (United States)

Anekalla, Kishore R; Courneya, J P; Fiorini, Nicolas; Lever, Jake; Muchow, Michael; Busby, Ben

2017-01-01

Biomedical text mining promises to assist biologists in quickly navigating the combined knowledge in their domain. This would allow improved understanding of the complex interactions within biological systems and faster hypothesis generation. New biomedical research articles are published daily and text mining tools are only as good as the corpus from which they work. Many text mining tools are underused because their results are static and do not reflect the constantly expanding knowledge in the field. In order for biomedical text mining to become an indispensable tool used by researchers, this problem must be addressed. To this end, we present PubRunner, a framework for regularly running text mining tools on the latest publications. PubRunner is lightweight, simple to use, and can be integrated with an existing text mining tool. The workflow involves downloading the latest abstracts from PubMed, executing a user-defined tool, pushing the resulting data to a public FTP or Zenodo dataset, and publicizing the location of these results on the public PubRunner website. We illustrate the use of this tool by re-running the commonly used word2vec tool on the latest PubMed abstracts to generate up-to-date word vector representations for the biomedical domain. This shows a proof of concept that we hope will encourage text mining developers to build tools that truly will aid biologists in exploring the latest publications.

Text Mining of Journal Articles for Sleep Disorder Terminologies.

Directory of Open Access Journals (Sweden)

Calvin Lam

Full Text Available Research on publication trends in journal articles on sleep disorders (SDs and the associated methodologies by using text mining has been limited. The present study involved text mining for terms to determine the publication trends in sleep-related journal articles published during 2000-2013 and to identify associations between SD and methodology terms as well as conducting statistical analyses of the text mining findings.SD and methodology terms were extracted from 3,720 sleep-related journal articles in the PubMed database by using MetaMap. The extracted data set was analyzed using hierarchical cluster analyses and adjusted logistic regression models to investigate publication trends and associations between SD and methodology terms.MetaMap had a text mining precision, recall, and false positive rate of 0.70, 0.77, and 11.51%, respectively. The most common SD term was breathing-related sleep disorder, whereas narcolepsy was the least common. Cluster analyses showed similar methodology clusters for each SD term, except narcolepsy. The logistic regression models showed an increasing prevalence of insomnia, parasomnia, and other sleep disorders but a decreasing prevalence of breathing-related sleep disorder during 2000-2013. Different SD terms were positively associated with different methodology terms regarding research design terms, measure terms, and analysis terms.Insomnia-, parasomnia-, and other sleep disorder-related articles showed an increasing publication trend, whereas those related to breathing-related sleep disorder showed a decreasing trend. Furthermore, experimental studies more commonly focused on hypersomnia and other SDs and less commonly on insomnia, breathing-related sleep disorder, narcolepsy, and parasomnia. Thus, text mining may facilitate the exploration of the publication trends in SDs and the associated methodologies.

Science and Technology Text Mining Basic Concepts

National Research Council Canada - National Science Library

Losiewicz, Paul

2003-01-01

...). It then presents some of the most widely used data and text mining techniques, including clustering and classification methods, such as nearest neighbor, relational learning models, and genetic...

A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts.

Science.gov (United States)

Westergaard, David; Stærfeldt, Hans-Henrik; Tønsberg, Christian; Jensen, Lars Juhl; Brunak, Søren

2018-02-01

Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823-2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein-protein, disease-gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only.

Concept similarity in publications precedes cross-disciplinary collaboration.

Science.gov (United States)

Post, Andrew R; Harrison, James H

2008-11-06

Innovative science frequently occurs as a result of cross-disciplinary collaboration, the importance of which is reflected by recent NIH funding initiatives that promote communication and collaboration. If shared research interests between collaborators are important for the formation of collaborations,methods for identifying these shared interests across scientific domains could potentially reveal new and useful collaboration opportunities. MEDLINE represents a comprehensive database of collaborations and research interests, as reflected by article co-authors and concept content. We analyzed six years of citations using information retrieval based methods to compute articles conceptual similarity, and found that articles by basic and clinical scientists who later collaborated had significantly higher average similarity than articles by similar scientists who did not collaborate.Refinement of these methods and characterization of found conceptual overlaps could allow automated discovery of collaboration opportunities that are currently missed.

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges.

Science.gov (United States)

Singhal, Ayush; Leaman, Robert; Catlett, Natalie; Lemberger, Thomas; McEntyre, Johanna; Polson, Shawn; Xenarios, Ioannis; Arighi, Cecilia; Lu, Zhiyong

2016-01-01

Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system 'accuracy' remains a challenge and identify several additional common difficulties and potential research directions including (i) the 'scalability' issue due to the increasing need of mining information from millions of full-text articles, (ii) the 'interoperability' issue of integrating various text-mining systems into existing curation workflows and (iii) the 'reusability' issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. Finally, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.

pubmed.mineR: An R package with text-mining algorithms to ...

Indian Academy of Sciences (India)

2015-09-29

Sep 29, 2015 ... using text-mining algorithms for biomedical research pur- poses. ... studies are described to illustrate some potential uses of ... This is the most applied task. ... other alphabets (for example, Greek alphabets) and hyphens.

A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts

Science.gov (United States)

Westergaard, David; Stærfeldt, Hans-Henrik

2018-01-01

Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823–2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein–protein, disease–gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only. PMID:29447159

EnvMine: A text-mining system for the automatic extraction of contextual information

Directory of Open Access Journals (Sweden)

de Lorenzo Victor

2010-06-01

Full Text Available Abstract Background For ecological studies, it is crucial to count on adequate descriptions of the environments and samples being studied. Such a description must be done in terms of their physicochemical characteristics, allowing a direct comparison between different environments that would be difficult to do otherwise. Also the characterization must include the precise geographical location, to make possible the study of geographical distributions and biogeographical patterns. Currently, there is no schema for annotating these environmental features, and these data have to be extracted from textual sources (published articles. So far, this had to be performed by manual inspection of the corresponding documents. To facilitate this task, we have developed EnvMine, a set of text-mining tools devoted to retrieve contextual information (physicochemical variables and geographical locations from textual sources of any kind. Results EnvMine is capable of retrieving the physicochemical variables cited in the text, by means of the accurate identification of their associated units of measurement. In this task, the system achieves a recall (percentage of items retrieved of 92% with less than 1% error. Also a Bayesian classifier was tested for distinguishing parts of the text describing environmental characteristics from others dealing with, for instance, experimental settings. Regarding the identification of geographical locations, the system takes advantage of existing databases such as GeoNames to achieve 86% recall with 92% precision. The identification of a location includes also the determination of its exact coordinates (latitude and longitude, thus allowing the calculation of distance between the individual locations. Conclusion EnvMine is a very efficient method for extracting contextual information from different text sources, like published articles or web pages. This tool can help in determining the precise location and physicochemical

Text Mining of Journal Articles for Sleep Disorder Terminologies.

Science.gov (United States)

Lam, Calvin; Lai, Fu-Chih; Wang, Chia-Hui; Lai, Mei-Hsin; Hsu, Nanly; Chung, Min-Huey

2016-01-01

Research on publication trends in journal articles on sleep disorders (SDs) and the associated methodologies by using text mining has been limited. The present study involved text mining for terms to determine the publication trends in sleep-related journal articles published during 2000-2013 and to identify associations between SD and methodology terms as well as conducting statistical analyses of the text mining findings. SD and methodology terms were extracted from 3,720 sleep-related journal articles in the PubMed database by using MetaMap. The extracted data set was analyzed using hierarchical cluster analyses and adjusted logistic regression models to investigate publication trends and associations between SD and methodology terms. MetaMap had a text mining precision, recall, and false positive rate of 0.70, 0.77, and 11.51%, respectively. The most common SD term was breathing-related sleep disorder, whereas narcolepsy was the least common. Cluster analyses showed similar methodology clusters for each SD term, except narcolepsy. The logistic regression models showed an increasing prevalence of insomnia, parasomnia, and other sleep disorders but a decreasing prevalence of breathing-related sleep disorder during 2000-2013. Different SD terms were positively associated with different methodology terms regarding research design terms, measure terms, and analysis terms. Insomnia-, parasomnia-, and other sleep disorder-related articles showed an increasing publication trend, whereas those related to breathing-related sleep disorder showed a decreasing trend. Furthermore, experimental studies more commonly focused on hypersomnia and other SDs and less commonly on insomnia, breathing-related sleep disorder, narcolepsy, and parasomnia. Thus, text mining may facilitate the exploration of the publication trends in SDs and the associated methodologies.

Text mining improves prediction of protein functional sites.

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Karin M Verspoor

Full Text Available We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites. The structure analysis was carried out using Dynamics Perturbation Analysis (DPA, which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.

A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts

DEFF Research Database (Denmark)

Westergaard, David; Stærfeldt, Hans Henrik; Tønsberg, Christian

2018-01-01

Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15...... subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full...... million English scientific full-text articles published during the period 1823-2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein-protein, disease-gene, and protein...

Imitating manual curation of text-mined facts in biomedicine.

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Raul Rodriguez-Esteban

2006-09-01

Full Text Available Text-mining algorithms make mistakes in extracting facts from natural-language texts. In biomedical applications, which rely on use of text-mined data, it is critical to assess the quality (the probability that the message is correctly extracted of individual facts--to resolve data conflicts and inconsistencies. Using a large set of almost 100,000 manually produced evaluations (most facts were independently reviewed more than once, producing independent evaluations, we implemented and tested a collection of algorithms that mimic human evaluation of facts provided by an automated information-extraction system. The performance of our best automated classifiers closely approached that of our human evaluators (ROC score close to 0.95. Our hypothesis is that, were we to use a larger number of human experts to evaluate any given sentence, we could implement an artificial-intelligence curator that would perform the classification job at least as accurately as an average individual human evaluator. We illustrated our analysis by visualizing the predicted accuracy of the text-mined relations involving the term cocaine.

A Survey of Text Mining in Social Media: Facebook and Twitter Perspectives

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Said A. Salloum

2017-01-01

Full Text Available Text mining has become one of the trendy fields that has been incorporated in several research fields such as computational linguistics, Information Retrieval (IR and data mining. Natural Language Processing (NLP techniques were used to extract knowledge from the textual text that is written by human beings. Text mining reads an unstructured form of data to provide meaningful information patterns in a shortest time period. Social networking sites are a great source of communication as most of the people in today’s world use these sites in their daily lives to keep connected to each other. It becomes a common practice to not write a sentence with correct grammar and spelling. This practice may lead to different kinds of ambiguities like lexical, syntactic, and semantic and due to this type of unclear data, it is hard to find out the actual data order. Accordingly, we are conducting an investigation with the aim of looking for different text mining methods to get various textual orders on social media websites. This survey aims to describe how studies in social media have used text analytics and text mining techniques for the purpose of identifying the key themes in the data. This survey focused on analyzing the text mining studies related to Facebook and Twitter; the two dominant social media in the world. Results of this survey can serve as the baselines for future text mining research.

Data Warehouse, Data Mining Dan Konsep Cross-Selling Pada Analisis Penjualan Produk

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Eka Miranda

2010-12-01

Full Text Available This paper is about designing and implementing data warehousing and data mining, along with their roles in supporting decision-making related to sales product analysis in cross-selling concept of PT XYZ. The database the company used is not supporting data analysis and decision-making. Therefore, it made a data warehousing design that could be used to keep data in a huge amount and could give report and answer from user’s questions in ad hoc. The method is used to design and implement data warehousing and data mining which consists of literature study, company problem analysis, and data warehousing design, and testing result. The writing results are a data warehousing design and data mining and also the implementation of cross-selling concept to analysis the sales, purchases, and customers’ cancellation data. The data could be showed and analyzed from some point of views that could help managers to analyse and acknowledge more information. 

RMS: a platform for managing cross-disciplinary and multi-institutional research project collaboration.

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Luo, Jake; Apperson-Hansen, Carolyn; Pelfrey, Clara M; Zhang, Guo-Qiang

2014-11-30

Cross-institutional cross-disciplinary collaboration has become a trend as researchers move toward building more productive and innovative teams for scientific research. Research collaboration is significantly changing the organizational structure and strategies used in the clinical and translational science domain. However, due to the obstacles of diverse administrative structures, differences in area of expertise, and communication barriers, establishing and managing a cross-institutional research project is still a challenging task. We address these challenges by creating an integrated informatics platform to reduce the barriers to biomedical research collaboration. The Request Management System (RMS) is an informatics infrastructure designed to transform a patchwork of expertise and resources into an integrated support network. The RMS facilitates investigators' initiation of new collaborative projects and supports the management of the collaboration process. In RMS, experts and their knowledge areas are categorized and managed structurally to provide consistent service. A role-based collaborative workflow is tightly integrated with domain experts and services to streamline and monitor the life-cycle of a research project. The RMS has so far tracked over 1,500 investigators with over 4,800 tasks. The research network based on the data collected in RMS illustrated that the investigators' collaborative projects increased close to 3 times from 2009 to 2012. Our experience with RMS indicates that the platform reduces barriers for cross-institutional collaboration of biomedical research projects. Building a new generation of infrastructure to enhance cross-disciplinary and multi-institutional collaboration has become an important yet challenging task. In this paper, we share the experience of developing and utilizing a collaborative project management system. The results of this study demonstrate that a web-based integrated informatics platform can facilitate and

Text Mining Improves Prediction of Protein Functional Sites

Science.gov (United States)

Cohn, Judith D.; Ravikumar, Komandur E.

2012-01-01

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. PMID:22393388

DISEASES: text mining and data integration of disease-gene associations.

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Pletscher-Frankild, Sune; Pallejà, Albert; Tsafou, Kalliopi; Binder, Janos X; Jensen, Lars Juhl

2015-03-01

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Designing a CTSA-Based Social Network Intervention to Foster Cross-Disciplinary Team Science.

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Vacca, Raffaele; McCarty, Christopher; Conlon, Michael; Nelson, David R

2015-08-01

This paper explores the application of network intervention strategies to the problem of assembling cross-disciplinary scientific teams in academic institutions. In a project supported by the University of Florida (UF) Clinical and Translational Science Institute, we used VIVO, a semantic-web research networking system, to extract the social network of scientific collaborations on publications and awarded grants across all UF colleges and departments. Drawing on the notion of network interventions, we designed an alteration program to add specific edges to the collaboration network, that is, to create specific collaborations between previously unconnected investigators. The missing collaborative links were identified by a number of network criteria to enhance desirable structural properties of individual positions or the network as a whole. We subsequently implemented an online survey (N = 103) that introduced the potential collaborators to each other through their VIVO profiles, and investigated their attitudes toward starting a project together. We discuss the design of the intervention program, the network criteria adopted, and preliminary survey results. The results provide insight into the feasibility of intervention programs on scientific collaboration networks, as well as suggestions on the implementation of such programs to assemble cross-disciplinary scientific teams in CTSA institutions. © 2015 Wiley Periodicals, Inc.

Monitoring interaction and collective text production through text mining

Directory of Open Access Journals (Sweden)

Macedo, Alexandra Lorandi

2014-04-01

Full Text Available This article presents the Concepts Network tool, developed using text mining technology. The main objective of this tool is to extract and relate terms of greatest incidence from a text and exhibit the results in the form of a graph. The Network was implemented in the Collective Text Editor (CTE which is an online tool that allows the production of texts in synchronized or non-synchronized forms. This article describes the application of the Network both in texts produced collectively and texts produced in a forum. The purpose of the tool is to offer support to the teacher in managing the high volume of data generated in the process of interaction amongst students and in the construction of the text. Specifically, the aim is to facilitate the teacher’s job by allowing him/her to process data in a shorter time than is currently demanded. The results suggest that the Concepts Network can aid the teacher, as it provides indicators of the quality of the text produced. Moreover, messages posted in forums can be analyzed without their content necessarily having to be pre-read.

Mine cross piece

Energy Technology Data Exchange (ETDEWEB)

Belik, I P; Gorbatenko, A Ye

1982-01-01

The mine cross piece includes main and supplementary parachute-formed cupolas made of air-impermeable fabric, resources for attachment made in the form of shroud lines. In order to improve its reliability with prolonged localization of an underground fire, the cavity between the cupolas connects to the source of fast-hardening material. In this case the cupolas are arranged co-directionally in relation to each other, while the shroud lines of the main are attached to the cupola of the supplemental which is made with perforations.

An Inter-Disciplinary Language for Inter-Disciplinary Communication: Academic Globalization, Ethos, Pathos, and Logos

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Marta Szabo White

2014-08-01

Full Text Available Inspired by the intersection of character, emotions, and logic, much like a Hungarian Rhapsody which is beautifully sad; this paper explores ethos, pathos, and logos in the context of Academic Globalization. As students of the world, an inter-disciplinary language is pivotal for inter-disciplinary communication. Given that the current state of the world stems primarily from miscommunications, it is imperative to launch a cognitive language tool which underscores global commonalities and mitigates cultural differences. Such a platform would foster interdisciplinary research, education, and communication. New paradigms would evolve, grounded in ethos, pathos, and logos. Like yin and yang, these states are interrelated, interacting, and interchanging learning spheres. Just as day and night blend at some point; just as the Parthenon epitomized Greek thought, celebrated the birthplace of democracy, and for the first time, depicted everyday citizens in friezes- underscoring their impactful role- ethos, pathos, and logos represent cross-disciplinary communication devices which synergistically transform and ignite academic globalization. The Literature Review links the concepts of ethos, pathos, and logos with the seminal work Lewis and his LMR framework, which has given birth to Cultureactive and subsequently to ICE [InterCultural Edge]. http://www.fuqua.duke.edu/ciber/programs/we_organize/ice/ Accessed February 14, 2014

DrugQuest - a text mining workflow for drug association discovery.

Science.gov (United States)

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Vizirianakis, Ioannis S; Iliopoulos, Ioannis

2016-06-06

Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .

Making It All Count: A Cross-Disciplinary Collaboration Model Incorporating Scholarship, Creative Activity, and Student Engagement

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Dailey, Rocky; Hauschild-Mork, Melissa

2017-01-01

This study takes a grounded theory approach as a basis for a case study examining a cross-disciplinary artistic and academic collaborative project involving faculty from the areas of English, music, dance, theatre, design, and visual journalism resulting in the creation of research, scholarly, and creative activity that fosters student engagement…

CONAN : Text Mining in the Biomedical Domain

NARCIS (Netherlands)

Malik, R.

2006-01-01

This thesis is about Text Mining. Extracting important information from literature. In the last years, the number of biomedical articles and journals is growing exponentially. Scientists might not find the information they want because of the large number of publications. Therefore a system was

Opinion Mining in Latvian Text Using Semantic Polarity Analysis and Machine Learning Approach

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Gatis Špats

2016-07-01

Full Text Available In this paper we demonstrate approaches for opinion mining in Latvian text. Authors have applied, combined and extended results of several previous studies and public resources to perform opinion mining in Latvian text using two approaches, namely, semantic polarity analysis and machine learning. One of the most significant constraints that make application of opinion mining for written content classification in Latvian text challenging is the limited publicly available text corpora for classifier training. We have joined several sources and created a publically available extended lexicon. Our results are comparable to or outperform current achievements in opinion mining in Latvian. Experiments show that lexicon-based methods provide more accurate opinion mining than the application of Naive Bayes machine learning classifier on Latvian tweets. Methods used during this study could be further extended using human annotators, unsupervised machine learning and bootstrapping to create larger corpora of classified text.

Beyond accuracy: creating interoperable and scalable text-mining web services.

Science.gov (United States)

Wei, Chih-Hsuan; Leaman, Robert; Lu, Zhiyong

2016-06-15

The biomedical literature is a knowledge-rich resource and an important foundation for future research. With over 24 million articles in PubMed and an increasing growth rate, research in automated text processing is becoming increasingly important. We report here our recently developed web-based text mining services for biomedical concept recognition and normalization. Unlike most text-mining software tools, our web services integrate several state-of-the-art entity tagging systems (DNorm, GNormPlus, SR4GN, tmChem and tmVar) and offer a batch-processing mode able to process arbitrary text input (e.g. scholarly publications, patents and medical records) in multiple formats (e.g. BioC). We support multiple standards to make our service interoperable and allow simpler integration with other text-processing pipelines. To maximize scalability, we have preprocessed all PubMed articles, and use a computer cluster for processing large requests of arbitrary text. Our text-mining web service is freely available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/#curl : Zhiyong.Lu@nih.gov. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.

Using ontology network structure in text mining.

Science.gov (United States)

Berndt, Donald J; McCart, James A; Luther, Stephen L

2010-11-13

Statistical text mining treats documents as bags of words, with a focus on term frequencies within documents and across document collections. Unlike natural language processing (NLP) techniques that rely on an engineered vocabulary or a full-featured ontology, statistical approaches do not make use of domain-specific knowledge. The freedom from biases can be an advantage, but at the cost of ignoring potentially valuable knowledge. The approach proposed here investigates a hybrid strategy based on computing graph measures of term importance over an entire ontology and injecting the measures into the statistical text mining process. As a starting point, we adapt existing search engine algorithms such as PageRank and HITS to determine term importance within an ontology graph. The graph-theoretic approach is evaluated using a smoking data set from the i2b2 National Center for Biomedical Computing, cast as a simple binary classification task for categorizing smoking-related documents, demonstrating consistent improvements in accuracy.

A tm Plug-In for Distributed Text Mining in R

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Stefan Theussl

2012-11-01

Full Text Available R has gained explicit text mining support with the tm package enabling statisticians to answer many interesting research questions via statistical analysis or modeling of (text corpora. However, we typically face two challenges when analyzing large corpora: (1 the amount of data to be processed in a single machine is usually limited by the available main memory (i.e., RAM, and (2 the more data to be analyzed the higher the need for efficient procedures for calculating valuable results. Fortunately, adequate programming models like MapReduce facilitate parallelization of text mining tasks and allow for processing data sets beyond what would fit into memory by using a distributed file system possibly spanning over several machines, e.g., in a cluster of workstations. In this paper we present a plug-in package to tm called tm.plugin.dc implementing a distributed corpus class which can take advantage of the Hadoop MapReduce library for large scale text mining tasks. We show on the basis of an application in culturomics that we can efficiently handle data sets of significant size.

A text-mining system for extracting metabolic reactions from full-text articles.

Science.gov (United States)

Czarnecki, Jan; Nobeli, Irene; Smith, Adrian M; Shepherd, Adrian J

2012-07-23

Increasingly biological text mining research is focusing on the extraction of complex relationships relevant to the construction and curation of biological networks and pathways. However, one important category of pathway - metabolic pathways - has been largely neglected.Here we present a relatively simple method for extracting metabolic reaction information from free text that scores different permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presence and location of stemmed keywords. This method extends an approach that has proved effective in the context of the extraction of protein-protein interactions. When evaluated on a set of manually-curated metabolic pathways using standard performance criteria, our method performs surprisingly well. Precision and recall rates are comparable to those previously achieved for the well-known protein-protein interaction extraction task. We conclude that automated metabolic pathway construction is more tractable than has often been assumed, and that (as in the case of protein-protein interaction extraction) relatively simple text-mining approaches can prove surprisingly effective. It is hoped that these results will provide an impetus to further research and act as a useful benchmark for judging the performance of more sophisticated methods that are yet to be developed.

Text Mining for Protein Docking.

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Varsha D Badal

2015-12-01

Full Text Available The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply proceed to the solution based on similarity to the existing, previously determined structures. A similar major paradigm shift is emerging due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction. Automated text mining has been widely used in recreating protein interaction networks, as well as in detecting small ligand binding sites on protein structures. Combining and expanding these two well-developed areas of research, we applied the text mining to structural modeling of protein-protein complexes (protein docking. Protein docking can be significantly improved when constraints on the docking mode are available. We developed a procedure that retrieves published abstracts on a specific protein-protein interaction and extracts information relevant to docking. The procedure was assessed on protein complexes from Dockground (http://dockground.compbio.ku.edu. The results show that correct information on binding residues can be extracted for about half of the complexes. The amount of irrelevant information was reduced by conceptual analysis of a subset of the retrieved abstracts, based on the bag-of-words (features approach. Support Vector Machine models were trained and validated on the subset. The remaining abstracts were filtered by the best-performing models, which decreased the irrelevant information for ~ 25% complexes in the dataset. The extracted constraints were incorporated in the docking protocol and tested on the Dockground unbound

Application of a cross-pit bridge conveyor system in mining

Energy Technology Data Exchange (ETDEWEB)

Zeindler, R.W. (Krupp Canada Inc., Calgary, Alberta); Fawcett, D.A.

1981-04-01

A summary is presented of the report completed by the Coal Mining Research Centre in 1980. This report was of a cross-pit conveyor system applied as an auxillary mining complement to the major stripping unit, a dragline. The purpose of the CMRC report was to evaluate selective mining and replacement of the upper horizons of the strata as an aid in reclamation. These strata were the topsoil, subsoil and glacial till. Past utilization of cross-pit conveyor systems and related engineering studies were assessed. The parameters of the study were based on the mining and geological conditions of the Alberta prairie coal mines. The principal excavator for stripping was a dragline. Excavation of the upper horizons was done by a bucket-wheel excavator discharging onto the cross-pit conveyor. Alternative equipment applications were economically compared. Four cases or geological sections were evaluated in detail. The economics of the alternative mining systems for each of the cases were determined. In all instances, the most economical solution was a tandem system utilizing a dragline with a bucket-wheel excavator/cross-pit conveyor system. For both the CMRC study and a similar US paper, the application of a tandem system provided the lowest annual ownership and operating costs. The tandem system consists of a dragline excavating and casting the majority of the waste or overburden and a BWE/CPCS selectively excavating and replacing the topsoil, subsoil and part of the unconsolidated overburden. The bridge spans and designs are within known technical and economic limits.

ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers.

Science.gov (United States)

Xing, Yuting; Wu, Chengkun; Yang, Xi; Wang, Wei; Zhu, En; Yin, Jianping

2018-04-27

A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing efficiency of text mining. In this paper, we address this challenge by introducing parallel processing on a supercomputer. We developed paraBTM, a runnable framework that enables parallel text mining on the Tianhe-2 supercomputer. It employs a low-cost yet effective load balancing strategy to maximize the efficiency of parallel processing. We evaluated the performance of paraBTM on several datasets, utilizing three types of named entity recognition tasks as demonstration. Results show that, in most cases, the processing efficiency can be greatly improved with parallel processing, and the proposed load balancing strategy is simple and effective. In addition, our framework can be readily applied to other tasks of biomedical text mining besides NER.

Empirical advances with text mining of electronic health records.

Science.gov (United States)

Delespierre, T; Denormandie, P; Bar-Hen, A; Josseran, L

2017-08-22

Korian is a private group specializing in medical accommodations for elderly and dependent people. A professional data warehouse (DWH) established in 2010 hosts all of the residents' data. Inside this information system (IS), clinical narratives (CNs) were used only by medical staff as a residents' care linking tool. The objective of this study was to show that, through qualitative and quantitative textual analysis of a relatively small physiotherapy and well-defined CN sample, it was possible to build a physiotherapy corpus and, through this process, generate a new body of knowledge by adding relevant information to describe the residents' care and lives. Meaningful words were extracted through Standard Query Language (SQL) with the LIKE function and wildcards to perform pattern matching, followed by text mining and a word cloud using R® packages. Another step involved principal components and multiple correspondence analyses, plus clustering on the same residents' sample as well as on other health data using a health model measuring the residents' care level needs. By combining these techniques, physiotherapy treatments could be characterized by a list of constructed keywords, and the residents' health characteristics were built. Feeding defects or health outlier groups could be detected, physiotherapy residents' data and their health data were matched, and differences in health situations showed qualitative and quantitative differences in physiotherapy narratives. This textual experiment using a textual process in two stages showed that text mining and data mining techniques provide convenient tools to improve residents' health and quality of care by adding new, simple, useable data to the electronic health record (EHR). When used with a normalized physiotherapy problem list, text mining through information extraction (IE), named entity recognition (NER) and data mining (DM) can provide a real advantage to describe health care, adding new medical material and

Inmate punishments: Disciplinary measures

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Milić Ivan D.

2016-01-01

Full Text Available After the verdict has become formal and enforceable, and the defendant a convict, sentence execution procedure follows. If the defendant is sentenced to prison, the next step to be taken is the referral institution for execution of sentence of imprisonment. Rules of conduct in the institutions for execution of imprisonment are strictly regulated by legislation governing the rights and obligations of prisoners. Conducts that are prohibited in institutions shall be prescribed as a disciplinary offense, and appropriate disciplinary measures are to be imposed. The subject of this paper are disciplinary measures stipulated by the Law on Execution of Criminal Sanctions of the Republic of Serbia. The paper gives an overview of five disciplinary measures that can be imposed for serious or minor disciplinary offenses. In particular, author focuses his attention to indicating that the imposition and execution of disciplinary measures, are not regulated by Law in the best possible way, so that, in practice, certain problems arise in the application of these measures.

Assimilating Text-Mining & Bio-Informatics Tools to Analyze Cellulase structures

Science.gov (United States)

Satyasree, K. P. N. V., Dr; Lalitha Kumari, B., Dr; Jyotsna Devi, K. S. N. V.; Choudri, S. M. Roy; Pratap Joshi, K.

2017-08-01

Text-mining is one of the best potential way of automatically extracting information from the huge biological literature. To exploit its prospective, the knowledge encrypted in the text should be converted to some semantic representation such as entities and relations, which could be analyzed by machines. But large-scale practical systems for this purpose are rare. But text mining could be helpful for generating or validating predictions. Cellulases have abundant applications in various industries. Cellulose degrading enzymes are cellulases and the same producing bacteria - Bacillus subtilis & fungus Pseudomonas putida were isolated from top soil of Guntur Dt. A.P. India. Absolute cultures were conserved on potato dextrose agar medium for molecular studies. In this paper, we presented how well the text mining concepts can be used to analyze cellulase producing bacteria and fungi, their comparative structures are also studied with the aid of well-establised, high quality standard bioinformatic tools such as Bioedit, Swissport, Protparam, EMBOSSwin with which a complete data on Cellulases like structure, constituents of the enzyme has been obtained.

Trans-Dance: Disciplinary Cross-Dressing and Integral Education in a Language and Sexuality Course

Directory of Open Access Journals (Sweden)

Matthew C. Bronson

2011-06-01

Full Text Available This article showcases an integral approach to education through the lens of a transdisciplinary graduate-level class on Sexuality and Language. The graduate-level class was co-taught by two CIIS faculty whose backgrounds span the fields of social and cultural anthropology, psychology, sociology, social policy, linguistics, education and drama-centered expressive arts therapy. The class brought together students from six separate academic programs and drew from a wide array of performative and arts-based modes of inquiry to create a deep context through which to unpack the complex relationship(s between language and sexuality. These practices were interwoven with theoretical exposition and discussion in a hermeneutic spiral leading up to students’ planned research projects. This “disciplinary cross-dressing,” where diverse students and faculty engaged each others’ points of view rigorously in a common inquiry, created powerful teachable moments and served as the foundation for a transgressive mode of scholarship and advocacy.

Tweet My Street: A Cross-Disciplinary Collaboration for the Analysis of Local Twitter Data

Directory of Open Access Journals (Sweden)

Graeme Mearns

2014-05-01

Full Text Available Tweet My Street is a cross-disciplinary project exploring the extent to which data derived from Twitter can reveal more about spatial and temporal behaviours and the meanings attached to these locally. This is done with a longer-term view to supporting the coproduction and delivery of local services, complaint mechanisms and horizontal community support networks. The project has involved the development of a web-based software application capable of retrieving, storing and visualising geo-located “tweets” (and associated digital content from Twitter’s Firehose. This has been piloted in Newcastle upon Tyne (UK and has proven a scalable tool that can aid the analysis of social media data geographically. Beyond explaining efforts to analyse pilot data via this software, this paper elucidates three methodological challenges encountered during early collaboration. These include issues relating to “proximity” with subjects, ethics and critical questions about scholars’ digital responsibilities during the neogeographic turn.

BioCreative Workshops for DOE Genome Sciences: Text Mining for Metagenomics

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Wu, Cathy H. [Univ. of Delaware, Newark, DE (United States). Center for Bioinformatics and Computational Biology; Hirschman, Lynette [The MITRE Corporation, Bedford, MA (United States)

2016-10-29

The objective of this project was to host BioCreative workshops to define and develop text mining tasks to meet the needs of the Genome Sciences community, focusing on metadata information extraction in metagenomics. Following the successful introduction of metagenomics at the BioCreative IV workshop, members of the metagenomics community and BioCreative communities continued discussion to identify candidate topics for a BioCreative metagenomics track for BioCreative V. Of particular interest was the capture of environmental and isolation source information from text. The outcome was to form a “community of interest” around work on the interactive EXTRACT system, which supported interactive tagging of environmental and species data. This experiment is included in the BioCreative V virtual issue of Database. In addition, there was broad participation by members of the metagenomics community in the panels held at BioCreative V, leading to valuable exchanges between the text mining developers and members of the metagenomics research community. These exchanges are reflected in a number of the overview and perspective pieces also being captured in the BioCreative V virtual issue. Overall, this conversation has exposed the metagenomics researchers to the possibilities of text mining, and educated the text mining developers to the specific needs of the metagenomics community.

Text mining for adverse drug events: the promise, challenges, and state of the art.

Science.gov (United States)

Harpaz, Rave; Callahan, Alison; Tamang, Suzanne; Low, Yen; Odgers, David; Finlayson, Sam; Jung, Kenneth; LePendu, Paea; Shah, Nigam H

2014-10-01

Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event (ADE) detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources-such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs-that are amenable to text mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance.

The Role of Text Mining in Export Control

Energy Technology Data Exchange (ETDEWEB)

Tae, Jae-woong; Son, Choul-woong; Shin, Dong-hoon [Korea Institute of Nuclear Nonproliferation and Control, Daejeon (Korea, Republic of)

2015-10-15

Korean government provides classification services to exporters. It is simple to copy technology such as documents and drawings. Moreover, it is also easy that new technology derived from the existing technology. The diversity of technology makes classification difficult because the boundary between strategic and nonstrategic technology is unclear and ambiguous. Reviewers should consider previous classification cases enough. However, the increase of the classification cases prevent consistent classifications. This made another innovative and effective approaches necessary. IXCRS (Intelligent Export Control Review System) is proposed to coincide with demands. IXCRS consists of and expert system, a semantic searching system, a full text retrieval system, and image retrieval system and a document retrieval system. It is the aim of the present paper to observe the document retrieval system based on text mining and to discuss how to utilize the system. This study has demonstrated how text mining technique can be applied to export control. The document retrieval system supports reviewers to treat previous classification cases effectively. Especially, it is highly probable that similarity data will contribute to specify classification criterion. However, an analysis of the system showed a number of problems that remain to be explored such as a multilanguage problem and an inclusion relationship problem. Further research should be directed to solve problems and to apply more data mining techniques so that the system should be used as one of useful tools for export control.

The Role of Text Mining in Export Control

International Nuclear Information System (INIS)

Tae, Jae-woong; Son, Choul-woong; Shin, Dong-hoon

2015-01-01

Korean government provides classification services to exporters. It is simple to copy technology such as documents and drawings. Moreover, it is also easy that new technology derived from the existing technology. The diversity of technology makes classification difficult because the boundary between strategic and nonstrategic technology is unclear and ambiguous. Reviewers should consider previous classification cases enough. However, the increase of the classification cases prevent consistent classifications. This made another innovative and effective approaches necessary. IXCRS (Intelligent Export Control Review System) is proposed to coincide with demands. IXCRS consists of and expert system, a semantic searching system, a full text retrieval system, and image retrieval system and a document retrieval system. It is the aim of the present paper to observe the document retrieval system based on text mining and to discuss how to utilize the system. This study has demonstrated how text mining technique can be applied to export control. The document retrieval system supports reviewers to treat previous classification cases effectively. Especially, it is highly probable that similarity data will contribute to specify classification criterion. However, an analysis of the system showed a number of problems that remain to be explored such as a multilanguage problem and an inclusion relationship problem. Further research should be directed to solve problems and to apply more data mining techniques so that the system should be used as one of useful tools for export control

A Text-Mining Framework for Supporting Systematic Reviews.

Science.gov (United States)

Li, Dingcheng; Wang, Zhen; Wang, Liwei; Sohn, Sunghwan; Shen, Feichen; Murad, Mohammad Hassan; Liu, Hongfang

2016-11-01

Systematic reviews (SRs) involve the identification, appraisal, and synthesis of all relevant studies for focused questions in a structured reproducible manner. High-quality SRs follow strict procedures and require significant resources and time. We investigated advanced text-mining approaches to reduce the burden associated with abstract screening in SRs and provide high-level information summary. A text-mining SR supporting framework consisting of three self-defined semantics-based ranking metrics was proposed, including keyword relevance, indexed-term relevance and topic relevance. Keyword relevance is based on the user-defined keyword list used in the search strategy. Indexed-term relevance is derived from indexed vocabulary developed by domain experts used for indexing journal articles and books. Topic relevance is defined as the semantic similarity among retrieved abstracts in terms of topics generated by latent Dirichlet allocation, a Bayesian-based model for discovering topics. We tested the proposed framework using three published SRs addressing a variety of topics (Mass Media Interventions, Rectal Cancer and Influenza Vaccine). The results showed that when 91.8%, 85.7%, and 49.3% of the abstract screening labor was saved, the recalls were as high as 100% for the three cases; respectively. Relevant studies identified manually showed strong topic similarity through topic analysis, which supported the inclusion of topic analysis as relevance metric. It was demonstrated that advanced text mining approaches can significantly reduce the abstract screening labor of SRs and provide an informative summary of relevant studies.

Nurturing Opportunity Identification for Business Sophistication in a Cross-disciplinary Study Environment

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Karine Oganisjana

2012-12-01

Full Text Available Opportunity identification is the key element of the entrepreneurial process; therefore the issue of developing this skill in students is a crucial task in contemporary European education which has recognized entrepreneurship as one of the lifelong learning key competences. The earlier opportunity identification becomes a habitual way of thinking and behavior across a broad range of contexts, the more likely that entrepreneurial disposition will steadily reside in students. In order to nurture opportunity identification in students for making them able to organize sophisticated businesses in the future, certain demands ought to be put forward as well to the teacher – the person who is to promote these qualities in their students. The paper reflects some findings of a research conducted within the frameworks of a workplace learning project for the teachers of one of Riga secondary schools (Latvia. The main goal of the project was to teach the teachers to identify hidden inner links between apparently unrelated things, phenomena and events within 10th grade study curriculum and connect them together and create new opportunities. The creation and solution of cross-disciplinary tasks were the means for achieving this goal.

pubmed.mineR: an R package with text-mining algorithms to analyse PubMed abstracts.

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Rani, Jyoti; Shah, A B Rauf; Ramachandran, Srinivasan

2015-10-01

The PubMed literature database is a valuable source of information for scientific research. It is rich in biomedical literature with more than 24 million citations. Data-mining of voluminous literature is a challenging task. Although several text-mining algorithms have been developed in recent years with focus on data visualization, they have limitations such as speed, are rigid and are not available in the open source. We have developed an R package, pubmed.mineR, wherein we have combined the advantages of existing algorithms, overcome their limitations, and offer user flexibility and link with other packages in Bioconductor and the Comprehensive R Network (CRAN) in order to expand the user capabilities for executing multifaceted approaches. Three case studies are presented, namely, 'Evolving role of diabetes educators', 'Cancer risk assessment' and 'Dynamic concepts on disease and comorbidity' to illustrate the use of pubmed.mineR. The package generally runs fast with small elapsed times in regular workstations even on large corpus sizes and with compute intensive functions. The pubmed.mineR is available at http://cran.rproject. org/web/packages/pubmed.mineR.

Text mining by Tsallis entropy

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Jamaati, Maryam; Mehri, Ali

2018-01-01

Long-range correlations between the elements of natural languages enable them to convey very complex information. Complex structure of human language, as a manifestation of natural languages, motivates us to apply nonextensive statistical mechanics in text mining. Tsallis entropy appropriately ranks the terms' relevance to document subject, taking advantage of their spatial correlation length. We apply this statistical concept as a new powerful word ranking metric in order to extract keywords of a single document. We carry out an experimental evaluation, which shows capability of the presented method in keyword extraction. We find that, Tsallis entropy has reliable word ranking performance, at the same level of the best previous ranking methods.

Text Mining to Support Gene Ontology Curation and Vice Versa.

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Ruch, Patrick

2017-01-01

In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology (GO) descriptors, the reference ontology for the characterization of genes and gene products. To illustrate the high potential of this approach, we compare the performances of an automatic text categorizer and show a large improvement of +225 % in both precision and recall on benchmarked data. We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering (QA) system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging. Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate.

Advances in Text Mining and Visualization for Precision Medicine.

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Gonzalez-Hernandez, Graciela; Sarker, Abeed; O'Connor, Karen; Greene, Casey; Liu, Hongfang

2018-01-01

According to the National Institutes of Health (NIH), precision medicine is "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." Although the text mining community has explored this realm for some years, the official endorsement and funding launched in 2015 with the Precision Medicine Initiative are beginning to bear fruit. This session sought to elicit participation of researchers with strong background in text mining and/or visualization who are actively collaborating with bench scientists and clinicians for the deployment of integrative approaches in precision medicine that could impact scientific discovery and advance the vision of precision medicine as a universal, accessible approach at the point of care.

Application of text mining for customer evaluations in commercial banking

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Tan, Jing; Du, Xiaojiang; Hao, Pengpeng; Wang, Yanbo J.

2015-07-01

Nowadays customer attrition is increasingly serious in commercial banks. To combat this problem roundly, mining customer evaluation texts is as important as mining customer structured data. In order to extract hidden information from customer evaluations, Textual Feature Selection, Classification and Association Rule Mining are necessary techniques. This paper presents all three techniques by using Chinese Word Segmentation, C5.0 and Apriori, and a set of experiments were run based on a collection of real textual data that includes 823 customer evaluations taken from a Chinese commercial bank. Results, consequent solutions, some advice for the commercial bank are given in this paper.

Using Text Mining to Characterize Online Discussion Facilitation

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Ming, Norma; Baumer, Eric

2011-01-01

Facilitating class discussions effectively is a critical yet challenging component of instruction, particularly in online environments where student and faculty interaction is limited. Our goals in this research were to identify facilitation strategies that encourage productive discussion, and to explore text mining techniques that can help…

The “Shawshank Trail”: A Cross Disciplinary Study in Film Induced Tourism and Fan Culture

Directory of Open Access Journals (Sweden)

Richard Roberson

2015-03-01

Full Text Available Tourism, as a sustainable means of economic development, has attracted a considerable amount of attention from municipalities seeking to better understand their available development options. Film induced tourism has seen appreciable growth and represents a considerable opportunity for many communities to use filming locations seen in popular movies and/or television to draw additional visitors. These opportunities create an increasing need for communities and the organizers of events to better understand their attendees, the fans, in order to better serve their unique needs and desires. Fan culture studies, as an outgrowth of media studies, examine the nature and make up of fan communities. This article posits a cross disciplinary approach using fan studies to inform tourism research. This article reports on an analysis of attendees to an event celebrating of the 20th anniversary of the filming of the popular American film “The Shawshank Redemption.” Attendee characteristics, desired outcomes, and motivating factors were examined.

The Key Events Dose-Response Framework: A cross-Disciplinary Mode-of-Action Based Approach to Examining Does-Response and Thresholds

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the ILSI Research Foundation conveded a cross-disciplinary working group to examine current approaches for assessing dose-response and identifying safe levels of intake or exposure for four categoreis of bioactive agents: food allergens, nutrients, pathogenic microorganisms, and ...

Practical text mining and statistical analysis for non-structured text data applications

CERN Document Server

Miner, Gary; Hill, Thomas; Nisbet, Robert; Delen, Dursun

2012-01-01

The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase d

Cross-cultural analysis of the verbal conflict behavior of the graduate mining engineers

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Pevneva Inna

2017-01-01

Full Text Available The article is devoted to the crucial issue of the interpersonal communication skills of engineering graduates and studies the verbal behavior of the graduates majoring in mining engineering in conflict professional communication considered in a cross-cultural aspect. The research is based on the needs that future mining engineers have for conducting successful communication, work in teams and run an effective discourse both verbally and in writing. Verbal communication involves a strategic process by which a speaker defines the language resources for its implementation. By choosing a strategy which should contribute to the goals and objectives of the interaction a speaker makes the process of communication either successful or leading to a communicative failure. The scientific importance of this work is in multidiscipline approach and cross-cultural study of ethnic and cultural influences, gender and other characteristics of the verbal behavior of Russian and American engineering graduates.

Using text-mining techniques in electronic patient records to identify ADRs from medicine use

DEFF Research Database (Denmark)

Warrer, Pernille; Hansen, Ebba Holme; Jensen, Lars Juhl

2012-01-01

This literature review included studies that use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011. We...... included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs......, medicines associated with ADRs, text-mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text-mining techniques varied over time from simple free text...

Bebop on the Hockey pitch: Cross-disciplinary creativity and skills transfer

Directory of Open Access Journals (Sweden)

Clive Maxwell Harrison

2016-02-01

Full Text Available This paper generalises task-specific (but dissimilar skills, from the jazz concert stage and from the hockey field, into the domain of creativity research. What is sought are clues to what skills or creativities are transferable across dissimilar domains. It is argued that certain domain-general skills are transferable across domains, but a domain-general or ‘c’ creative capacity, is not. Rather than transferring some over-arching capacity to be universally creative, this research highlights factors likely to facilitate successful cross-disciplinary creative expression and posits a correlation between the capacities for discriminant pattern-recognition, task-specific expertise, and sensory data-collection, and the transferability of creativity. Of particular significance is the capacity for informed, selective pattern-breaking based on the ‘depth’ or ‘insider’ perspective of the domain expert; such ‘expert variation and selective retention’ (EVSR provides creative choices and responses that are likely to be perceived by the field as creative: valuable, novel and surprising. The author is a renowned Australian studio bassist, jazz musician, and music educator who also plays field hockey for Australia at Masters level. His recently completed PhD thesis, based on a performance and composition career spanning 46 years, takes the form of an analytical autoethnography drawn from personal field notes, diaries and interviews as well as published record albums.

The Science Semester: Cross-Disciplinary Inquiry for Prospective Elementary Teachers

Science.gov (United States)

Ford, Danielle J.; Fifield, Steve; Madsen, John; Qian, Xiaoyu

2013-10-01

We describe the Science Semester, a semester-long course block that integrates three science courses and a science education methods course for elementary teacher education majors, and examine prospective elementary teachers’ developing conceptions about inquiry, science teaching efficacy, and reflections on learning through inquiry. The Science Semester was designed to provide inquiry-oriented and problem-based learning experiences, opportunities to examine socially relevant issues through cross-disciplinary perspectives, and align with content found in elementary curricula and standards. By the end of the semester, prospective elementary teachers moved from naïve to intermediate understandings of inquiry and significantly increased self-efficacy for science teaching as measured on one subscore of the STEBI-B. Reflecting on the semester, prospective teachers understood and appreciated the goals of the course and the PBL format, but struggled with the open-ended and student-directed elements of the course.

Biomedical hypothesis generation by text mining and gene prioritization.

Science.gov (United States)

Petric, Ingrid; Ligeti, Balazs; Gyorffy, Balazs; Pongor, Sandor

2014-01-01

Text mining methods can facilitate the generation of biomedical hypotheses by suggesting novel associations between diseases and genes. Previously, we developed a rare-term model called RaJoLink (Petric et al, J. Biomed. Inform. 42(2): 219-227, 2009) in which hypotheses are formulated on the basis of terms rarely associated with a target domain. Since many current medical hypotheses are formulated in terms of molecular entities and molecular mechanisms, here we extend the methodology to proteins and genes, using a standardized vocabulary as well as a gene/protein network model. The proposed enhanced RaJoLink rare-term model combines text mining and gene prioritization approaches. Its utility is illustrated by finding known as well as potential gene-disease associations in ovarian cancer using MEDLINE abstracts and the STRING database.

Cross-disciplinary working in the sciences and humanities: historical data rescue activities in Southeast Asia and beyond

Science.gov (United States)

Williamson, Fiona

2016-12-01

This paper argues that more work is needed to facilitate cross-disciplinary collaborations by scholars across the physical sciences and humanities to improve Data Rescue Activities (DARE). Debate over the scale and potential impact of anthropogenic global warming is one of the dominant narratives of the twenty-first century. Predicting future climates and determining how environment and society might be affected by climate change are global issues of social, economic and political importance. They require responses from different research communities and necessitate closer inter-disciplinary working relationships for an integrated approach. Improving the datasets required for long-term climate models is an important part of this process. Establishing a multi-disciplinary dialogue and approach to DARE activities is increasingly being recognised as the best way to achieve this. This paper focuses on the recovery of the long-term instrumental weather observations used for models and reconstructions of the climate over the past two-hundred years. Written from the perspective of an historian working in the field, it does not seek to explore the reconstructions themselves but the process of data gathering, advocating a closer working relationship between the arts, social sciences, and sciences to extend the geographic and temporal coverage of extant datasets. This is especially important for regions where data gaps exist currently. First, it will offer a justification for extending data recovery activities for Southeast Asia and the China Seas region. Second, it will offer a brief overview of the data recovery projects currently operating in that area and the typesof historic source material that are used. Third, it will explore the work currently being undertaken for Southeast Asia and China under the Atmospheric Circulation Reconstructions over the Earth initiative as an example of a successful cross-disciplinary program. Finally, it will argue the importance of

Text Mining for Information Systems Researchers: An Annotated Topic Modeling Tutorial

DEFF Research Database (Denmark)

Debortoli, Stefan; Müller, Oliver; Junglas, Iris

2016-01-01

, such as manual coding. Yet, the size of text data setsobtained from the Internet makes manual analysis virtually impossible. In this tutorial, we discuss the challengesencountered when applying automated text-mining techniques in information systems research. In particular, weshowcase the use of probabilistic...... researchers,this tutorial provides some guidance for conducting text mining studies on their own and for evaluating the quality ofothers.......t is estimated that more than 80 percent of today’s data is stored in unstructured form (e.g., text, audio, image, video);and much of it is expressed in rich and ambiguous natural language. Traditionally, the analysis of natural languagehas prompted the use of qualitative data analysis approaches...

Negation scope and spelling variation for text-mining of Danish electronic patient records

DEFF Research Database (Denmark)

Thomas, Cecilia Engel; Jensen, Peter Bjødstrup; Werge, Thomas

2014-01-01

Electronic patient records are a potentially rich data source for knowledge extraction in biomedical research. Here we present a method based on the ICD10 system for text-mining of Danish health records. We have evaluated how adding functionalities to a baseline text-mining tool affected...

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

Science.gov (United States)

Jimeno Yepes, Antonio; Berlanga, Rafael

2015-02-01

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

Mining protein function from text using term-based support vector machines

Science.gov (United States)

Rice, Simon B; Nenadic, Goran; Stapley, Benjamin J

2005-01-01

Background Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents. Results The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent. Conclusion A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2. PMID:15960835

Hot complaint intelligent classification based on text mining

Directory of Open Access Journals (Sweden)

XIA Haifeng

2013-10-01

Full Text Available The complaint recognizer system plays an important role in making sure the correct classification of the hot complaint,improving the service quantity of telecommunications industry.The customers’ complaint in telecommunications industry has its special particularity which should be done in limited time,which cause the error in classification of hot complaint.The paper presents a model of complaint hot intelligent classification based on text mining,which can classify the hot complaint in the correct level of the complaint navigation.The examples show that the model can be efficient to classify the text of the complaint.

Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies.

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Cohen, Raphael; Elhadad, Michael; Elhadad, Noémie

2013-01-16

The increasing availability of Electronic Health Record (EHR) data and specifically free-text patient notes presents opportunities for phenotype extraction. Text-mining methods in particular can help disease modeling by mapping named-entities mentions to terminologies and clustering semantically related terms. EHR corpora, however, exhibit specific statistical and linguistic characteristics when compared with corpora in the biomedical literature domain. We focus on copy-and-paste redundancy: clinicians typically copy and paste information from previous notes when documenting a current patient encounter. Thus, within a longitudinal patient record, one expects to observe heavy redundancy. In this paper, we ask three research questions: (i) How can redundancy be quantified in large-scale text corpora? (ii) Conventional wisdom is that larger corpora yield better results in text mining. But how does the observed EHR redundancy affect text mining? Does such redundancy introduce a bias that distorts learned models? Or does the redundancy introduce benefits by highlighting stable and important subsets of the corpus? (iii) How can one mitigate the impact of redundancy on text mining? We analyze a large-scale EHR corpus and quantify redundancy both in terms of word and semantic concept repetition. We observe redundancy levels of about 30% and non-standard distribution of both words and concepts. We measure the impact of redundancy on two standard text-mining applications: collocation identification and topic modeling. We compare the results of these methods on synthetic data with controlled levels of redundancy and observe significant performance variation. Finally, we compare two mitigation strategies to avoid redundancy-induced bias: (i) a baseline strategy, keeping only the last note for each patient in the corpus; (ii) removing redundant notes with an efficient fingerprinting-based algorithm. (a)For text mining, preprocessing the EHR corpus with fingerprinting yields

Text-mining analysis of mHealth research

Science.gov (United States)

Zengul, Ferhat; Oner, Nurettin; Delen, Dursun

2017-01-01

In recent years, because of the advancements in communication and networking technologies, mobile technologies have been developing at an unprecedented rate. mHealth, the use of mobile technologies in medicine, and the related research has also surged parallel to these technological advancements. Although there have been several attempts to review mHealth research through manual processes such as systematic reviews, the sheer magnitude of the number of studies published in recent years makes this task very challenging. The most recent developments in machine learning and text mining offer some potential solutions to address this challenge by allowing analyses of large volumes of texts through semi-automated processes. The objective of this study is to analyze the evolution of mHealth research by utilizing text-mining and natural language processing (NLP) analyses. The study sample included abstracts of 5,644 mHealth research articles, which were gathered from five academic search engines by using search terms such as mobile health, and mHealth. The analysis used the Text Explorer module of JMP Pro 13 and an iterative semi-automated process involving tokenizing, phrasing, and terming. After developing the document term matrix (DTM) analyses such as single value decomposition (SVD), topic, and hierarchical document clustering were performed, along with the topic-informed document clustering approach. The results were presented in the form of word-clouds and trend analyses. There were several major findings regarding research clusters and trends. First, our results confirmed time-dependent nature of terminology use in mHealth research. For example, in earlier versus recent years the use of terminology changed from “mobile phone” to “smartphone” and from “applications” to “apps”. Second, ten clusters for mHealth research were identified including (I) Clinical Research on Lifestyle Management, (II) Community Health, (III) Literature Review, (IV) Medical

Text-mining analysis of mHealth research.

Science.gov (United States)

Ozaydin, Bunyamin; Zengul, Ferhat; Oner, Nurettin; Delen, Dursun

2017-01-01

In recent years, because of the advancements in communication and networking technologies, mobile technologies have been developing at an unprecedented rate. mHealth, the use of mobile technologies in medicine, and the related research has also surged parallel to these technological advancements. Although there have been several attempts to review mHealth research through manual processes such as systematic reviews, the sheer magnitude of the number of studies published in recent years makes this task very challenging. The most recent developments in machine learning and text mining offer some potential solutions to address this challenge by allowing analyses of large volumes of texts through semi-automated processes. The objective of this study is to analyze the evolution of mHealth research by utilizing text-mining and natural language processing (NLP) analyses. The study sample included abstracts of 5,644 mHealth research articles, which were gathered from five academic search engines by using search terms such as mobile health, and mHealth. The analysis used the Text Explorer module of JMP Pro 13 and an iterative semi-automated process involving tokenizing, phrasing, and terming. After developing the document term matrix (DTM) analyses such as single value decomposition (SVD), topic, and hierarchical document clustering were performed, along with the topic-informed document clustering approach. The results were presented in the form of word-clouds and trend analyses. There were several major findings regarding research clusters and trends. First, our results confirmed time-dependent nature of terminology use in mHealth research. For example, in earlier versus recent years the use of terminology changed from "mobile phone" to "smartphone" and from "applications" to "apps". Second, ten clusters for mHealth research were identified including (I) Clinical Research on Lifestyle Management, (II) Community Health, (III) Literature Review, (IV) Medical Interventions

Using text-mining techniques in electronic patient records to identify ADRs from medicine use.

Science.gov (United States)

Warrer, Pernille; Hansen, Ebba Holme; Juhl-Jensen, Lars; Aagaard, Lise

2012-05-01

This literature review included studies that use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011. We included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs, medicines associated with ADRs, text-mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text-mining techniques varied over time from simple free text searching of outpatient visit notes and inpatient discharge summaries to more advanced techniques involving natural language processing (NLP) of inpatient discharge summaries. Performance appeared to increase with the use of NLP, although many ADRs were still missed. Due to differences in study design and populations, various types of ADRs were identified and thus we could not make comparisons across studies. The review underscores the feasibility and potential of text mining to investigate narrative documents in EPRs for ADRs. However, more empirical studies are needed to evaluate whether text mining of EPRs can be used systematically to collect new information about ADRs. © 2011 The Authors. British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society.

From university research to innovation Detecting knowledge transfer via text mining

DEFF Research Database (Denmark)

Woltmann, Sabrina; Clemmensen, Line Katrine Harder; Alkærsig, Lars

2016-01-01

and indicators such as patents, collaborative publications and license agreements, to assess the contribution to the socioeconomic surrounding of universities. In this study, we present an extension of the current empirical framework by applying new computational methods, namely text mining and pattern...... associated the former with the latter to obtain insights into possible text and semantic relatedness. The text mining methods are extrapolating the correlations, semantic patterns and content comparison of the two corpora to define the document relatedness. We expect the development of a novel tool using...... recognition. Text samples for this purpose can include files containing social media contents, company websites and annual reports. The empirical focus in the present study is on the technical sciences and in particular on the case of the Technical University of Denmark (DTU). We generated two independent...

Modals in the construction of research articles: A cross-disciplinary perspective

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Matthew Peacock

2014-03-01

Full Text Available This paper describes a corpus-based analysis of variation in the distribution and function of modals and their role in the expression of “stance” in a corpus of 600 research articles (RAs across twelve disciplines. Stance is an expression of attitudes, judgments, or assessments towards the truth of propositions (Biber et al., 1999, and part of the important function of claiming and confirming membership of discourse communities and therefore in constructing identity. Three functional categories of modals perform a valuable role in the construction of stance: Possibility/Ability, Obligation/Necessity, and Prediction (Biber et al., 1999. However, very little research seems to have investigated variation across disciplines or their use in the RA. The corpus was analysed using WordSmith Tools (Scott, 2004, followed by manual checking of the function of every occurrence. Inter- and intra-rater agreement was also checked. Many statistically significant disciplinary differences were found, along with numerous marked differences with individual modals. Further examination of the corpus revealed considerable disciplinary variation in the patterns and verbs associated with the target modals, and a number of sub-functions of the topics covered by the modals. Conclusions are that modals perform an important role in the construction of stance.

Vaccine adverse event text mining system for extracting features from vaccine safety reports.

Science.gov (United States)

Botsis, Taxiarchis; Buttolph, Thomas; Nguyen, Michael D; Winiecki, Scott; Woo, Emily Jane; Ball, Robert

2012-01-01

To develop and evaluate a text mining system for extracting key clinical features from vaccine adverse event reporting system (VAERS) narratives to aid in the automated review of adverse event reports. Based upon clinical significance to VAERS reviewing physicians, we defined the primary (diagnosis and cause of death) and secondary features (eg, symptoms) for extraction. We built a novel vaccine adverse event text mining (VaeTM) system based on a semantic text mining strategy. The performance of VaeTM was evaluated using a total of 300 VAERS reports in three sequential evaluations of 100 reports each. Moreover, we evaluated the VaeTM contribution to case classification; an information retrieval-based approach was used for the identification of anaphylaxis cases in a set of reports and was compared with two other methods: a dedicated text classifier and an online tool. The performance metrics of VaeTM were text mining metrics: recall, precision and F-measure. We also conducted a qualitative difference analysis and calculated sensitivity and specificity for classification of anaphylaxis cases based on the above three approaches. VaeTM performed best in extracting diagnosis, second level diagnosis, drug, vaccine, and lot number features (lenient F-measure in the third evaluation: 0.897, 0.817, 0.858, 0.874, and 0.914, respectively). In terms of case classification, high sensitivity was achieved (83.1%); this was equal and better compared to the text classifier (83.1%) and the online tool (40.7%), respectively. Our VaeTM implementation of a semantic text mining strategy shows promise in providing accurate and efficient extraction of key features from VAERS narratives.

Methods for Mining and Summarizing Text Conversations

CERN Document Server

Carenini, Giuseppe; Murray, Gabriel

2011-01-01

Due to the Internet Revolution, human conversational data -- in written forms -- are accumulating at a phenomenal rate. At the same time, improvements in speech technology enable many spoken conversations to be transcribed. Individuals and organizations engage in email exchanges, face-to-face meetings, blogging, texting and other social media activities. The advances in natural language processing provide ample opportunities for these "informal documents" to be analyzed and mined, thus creating numerous new and valuable applications. This book presents a set of computational methods

Biocuration workflows and text mining: overview of the BioCreative 2012 Workshop Track II.

Science.gov (United States)

Lu, Zhiyong; Hirschman, Lynette

2012-01-01

Manual curation of data from the biomedical literature is a rate-limiting factor for many expert curated databases. Despite the continuing advances in biomedical text mining and the pressing needs of biocurators for better tools, few existing text-mining tools have been successfully integrated into production literature curation systems such as those used by the expert curated databases. To close this gap and better understand all aspects of literature curation, we invited submissions of written descriptions of curation workflows from expert curated databases for the BioCreative 2012 Workshop Track II. We received seven qualified contributions, primarily from model organism databases. Based on these descriptions, we identified commonalities and differences across the workflows, the common ontologies and controlled vocabularies used and the current and desired uses of text mining for biocuration. Compared to a survey done in 2009, our 2012 results show that many more databases are now using text mining in parts of their curation workflows. In addition, the workshop participants identified text-mining aids for finding gene names and symbols (gene indexing), prioritization of documents for curation (document triage) and ontology concept assignment as those most desired by the biocurators. DATABASE URL: http://www.biocreative.org/tasks/bc-workshop-2012/workflow/.

Disciplinary Accountability in the Financial Area

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Viorel Lefter

2007-09-01

Full Text Available The disciplinary accountability of the personnel from the local public administration isdifferently regulated, depending on the personnel category. The disciplinary accountability of the civilservants is an administrative-disciplinary accountability regulated by the Law no. 188/1999 concerningthe Status of the civil servants and can take place only under the circumstances stipulated by law, whilethe disciplinary accountability of the persons hired on the basis of the individual work contract isregulated by the Work Law, Law no. 53/2003 and can take place only under the circumstances stipulatedby this law. The only basis of the disciplinary responsibility is the disciplinary infringement, that in factrepresents a deed related to work, a deed consisting in an action or inaction carried out with guilt by theemployee, through which this one broke the legal norms, the internal regulations, the individual workcontract or the applicable collective work contract, the orders and the legal dispositions of the hierarchicalsuperiors (Law no. 53/2003, Art. 263, Paragr. 2.

Mining Sequential Update Summarization with Hierarchical Text Analysis

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Chunyun Zhang

2016-01-01

Full Text Available The outbreak of unexpected news events such as large human accident or natural disaster brings about a new information access problem where traditional approaches fail. Mostly, news of these events shows characteristics that are early sparse and later redundant. Hence, it is very important to get updates and provide individuals with timely and important information of these incidents during their development, especially when being applied in wireless and mobile Internet of Things (IoT. In this paper, we define the problem of sequential update summarization extraction and present a new hierarchical update mining system which can broadcast with useful, new, and timely sentence-length updates about a developing event. The new system proposes a novel method, which incorporates techniques from topic-level and sentence-level summarization. To evaluate the performance of the proposed system, we apply it to the task of sequential update summarization of temporal summarization (TS track at Text Retrieval Conference (TREC 2013 to compute four measurements of the update mining system: the expected gain, expected latency gain, comprehensiveness, and latency comprehensiveness. Experimental results show that our proposed method has good performance.

CROSSING SUPPORT OF THE DRIFT AND CROSSCUTS IN SUBLEVEL BAUXITE MINING

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Srećko Majić

1993-12-01

Full Text Available The report discusses the excavation method in underground bauxite exploitation of the Bauxite Mine Posušjc, as well as the experiences in crossing support of drifts and crosscuts till now, where it came in about 6% cases to the breakage of the frame support and to crossing ceiling caving. On the basis of such biggest caving, the estimate and dimensioning of critical support elements (runner and bar were performed. The possibility of supporting by bolting and stell plate was also considered. For the central part of the crosscut the use of bolts was assumed, which are fixed in the up-face. and for the rest of crossing the expansion shell anchors. For the latter, the estimate for anchoring elements was elaborated. Technical, safety and economic advantage of the bolts support is proved when compared with the frame support (the paper is published in Croatian.

Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text.

Science.gov (United States)

Garten, Yael; Altman, Russ B

2009-02-05

Pharmacogenomics studies the relationship between genetic variation and the variation in drug response phenotypes. The field is rapidly gaining importance: it promises drugs targeted to particular subpopulations based on genetic background. The pharmacogenomics literature has expanded rapidly, but is dispersed in many journals. It is challenging, therefore, to identify important associations between drugs and molecular entities--particularly genes and gene variants, and thus these critical connections are often lost. Text mining techniques can allow us to convert the free-style text to a computable, searchable format in which pharmacogenomic concepts (such as genes, drugs, polymorphisms, and diseases) are identified, and important links between these concepts are recorded. Availability of full text articles as input into text mining engines is key, as literature abstracts often do not contain sufficient information to identify these pharmacogenomic associations. Thus, building on a tool called Textpresso, we have created the Pharmspresso tool to assist in identifying important pharmacogenomic facts in full text articles. Pharmspresso parses text to find references to human genes, polymorphisms, drugs and diseases and their relationships. It presents these as a series of marked-up text fragments, in which key concepts are visually highlighted. To evaluate Pharmspresso, we used a gold standard of 45 human-curated articles. Pharmspresso identified 78%, 61%, and 74% of target gene, polymorphism, and drug concepts, respectively. Pharmspresso is a text analysis tool that extracts pharmacogenomic concepts from the literature automatically and thus captures our current understanding of gene-drug interactions in a computable form. We have made Pharmspresso available at http://pharmspresso.stanford.edu.

Text Mining Untuk Analisis Sentimen Review Film Menggunakan Algoritma K-Means

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Setyo Budi

2017-02-01

Full Text Available Kemudahan manusia didalam menggunakan website mengakibatkan bertambahnya dokumen teks yang berupa pendapat dan informasi. Dalam waktu yang lama dokumen teks akan bertambah besar. Text mining merupakan salah satu teknik yang digunakan untuk menggali kumpulan dokumen text sehingga dapat diambil intisarinya. Ada beberapa algoritma yang di gunakan untuk penggalian dokumen untuk analisis sentimen, salah satunya adalah K-Means. Didalam penelitian ini algoritma yang digunakan adalah K-Means. Hasil penelitian menunjukkan bahwa akurasi K-Means dengan dataset digunakan 300 positif dan 300 negatif  akurasinya 57.83%,  700 dokumen positif dan 700  negatif akurasinya 56.71%%, 1000 dokumen positif dan 1000  negatif akurasinya 50.40%%. Dari hasil pengujian disimpulkan bahwa semakin besar dataset yang digunakan semakin rendah akurasi K-Means.   Kata Kunci : Text Mining, Analisis Sentimen, K-Means, Review Film 

GENERAL ASPECTS REGARDING THE PRIOR DISCIPLINARY RESEARCH

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ANDRA PURAN (DASCĂLU

2012-05-01

Full Text Available Disciplinary research is the first phase of the disciplinary action. According to art. 251 paragraph 1 of the Labour Code no disciplinary sanction may be ordered before performing the prior disciplinary research.These regulations provide an exception: the sanction of written warning. The current regulations in question, kept from the old regulation, provides a protection for employees against abuses made by employers, since sanctions are affecting the salary or the position held, or even the development of individual employment contract. Thus, prior research of the fact that is a misconduct, before a disciplinary sanction is applied, is an essential condition for the validity of the measure ordered. Through this study we try to highlight some general issues concerning the characteristics, processes and effects of prior disciplinary research.

The Application of Text Mining in Business Research

DEFF Research Database (Denmark)

Preuss, Bjørn

2017-01-01

The aim of this paper is to present a methodological concept in business research that has the potential to become one of the most powerful methods in the upcoming years when it comes to research qualitative phenomena in business and society. It presents a selection of algorithms as well elaborat...... on potential use cases for a text mining based approach to qualitative data analysis....

Compatibility between Text Mining and Qualitative Research in the Perspectives of Grounded Theory, Content Analysis, and Reliability

Science.gov (United States)

Yu, Chong Ho; Jannasch-Pennell, Angel; DiGangi, Samuel

2011-01-01

The objective of this article is to illustrate that text mining and qualitative research are epistemologically compatible. First, like many qualitative research approaches, such as grounded theory, text mining encourages open-mindedness and discourages preconceptions. Contrary to the popular belief that text mining is a linear and fully automated…

Identifying child abuse through text mining and machine learning

NARCIS (Netherlands)

Amrit, Chintan; Paauw, Tim; Aly, Robin; Lavric, Miha

2017-01-01

In this paper, we describe how we used text mining and analysis to identify and predict cases of child abuse in a public health institution. Such institutions in the Netherlands try to identify and prevent different kinds of abuse. A significant part of the medical data that the institutions have on

Data Processing and Text Mining Technologies on Electronic Medical Records: A Review

Directory of Open Access Journals (Sweden)

Wencheng Sun

2018-01-01

Full Text Available Currently, medical institutes generally use EMR to record patient’s condition, including diagnostic information, procedures performed, and treatment results. EMR has been recognized as a valuable resource for large-scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy, and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation, and data reduction. For semistructured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (named-entity recognition and RE (relation extraction. This paper focuses on the process of EMR processing and emphatically analyzes the key techniques. In addition, we make an in-depth study on the applications developed based on text mining together with the open challenges and research issues for future work.

Cultivating the Under-Mined: Cross-Case Analysis as Knowledge Mobilization

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Samia Khan

2008-01-01

Full Text Available Despite a plethora of case studies in the social sciences, it is the authors' opinion that case studies remain relatively under-mined sources of expertise. Cross-case analysis is a research method that can mobilize knowledge from individual case studies. The authors propose that mobilization of case knowledge occurs when researchers accumulate case knowledge, compare and contrast cases, and in doing so, produce new knowledge. In this article, the authors present theories of how people can learn from sets of cases. Second, existing techniques for cross-case analysis are discussed. Third, considerations that enable researchers to engage in cross-case analysis are suggested. Finally, the authors introduce a novel online database: the Foresee (4C database. The purpose of the database is to mobilize case knowledge by helping researchers perform cross-case analysis and by creating an online research community that facilitates dialogue and the mobilization of case knowledge. The design of the 4C database is informed by theories of how people learn from case studies and cross-case analysis techniques. We present evidence from case study research that use of the 4C database helps to mobilize previously dormant case study knowledge to foster greater expertise. URN: urn:nbn:de:0114-fqs0801348

Text-mining-assisted biocuration workflows in Argo

Science.gov (United States)

Rak, Rafal; Batista-Navarro, Riza Theresa; Rowley, Andrew; Carter, Jacob; Ananiadou, Sophia

2014-01-01

Biocuration activities have been broadly categorized into the selection of relevant documents, the annotation of biological concepts of interest and identification of interactions between the concepts. Text mining has been shown to have a potential to significantly reduce the effort of biocurators in all the three activities, and various semi-automatic methodologies have been integrated into curation pipelines to support them. We investigate the suitability of Argo, a workbench for building text-mining solutions with the use of a rich graphical user interface, for the process of biocuration. Central to Argo are customizable workflows that users compose by arranging available elementary analytics to form task-specific processing units. A built-in manual annotation editor is the single most used biocuration tool of the workbench, as it allows users to create annotations directly in text, as well as modify or delete annotations created by automatic processing components. Apart from syntactic and semantic analytics, the ever-growing library of components includes several data readers and consumers that support well-established as well as emerging data interchange formats such as XMI, RDF and BioC, which facilitate the interoperability of Argo with other platforms or resources. To validate the suitability of Argo for curation activities, we participated in the BioCreative IV challenge whose purpose was to evaluate Web-based systems addressing user-defined biocuration tasks. Argo proved to have the edge over other systems in terms of flexibility of defining biocuration tasks. As expected, the versatility of the workbench inevitably lengthened the time the curators spent on learning the system before taking on the task, which may have affected the usability of Argo. The participation in the challenge gave us an opportunity to gather valuable feedback and identify areas of improvement, some of which have already been introduced. Database URL: http://argo.nactem.ac.uk PMID

Considerations on replacing and suspending disciplinary sanctions. The issue of granting compensation for ungrounded or unlawful disciplinary sanctions

Directory of Open Access Journals (Sweden)

Barbu VLAD

2011-06-01

Full Text Available Court's ability to replace the disciplinary sanction imposed by the employer with an easier one is the power to individualize employee's disciplinary sanction imposed by the general statutory criteria – the circumstances of committing the crime, the degree of culpability of the employee consequences of a disciplinary offence, the general behaviour of the employee and any disciplinary sanctions previously incurred. Another issue under discussion and which was not brought about a unified point of view is about the possibility of temporary suspension of disciplinary decision enforcement, pending resolution of the challenge which the court was invested with. This is why it's necessary the intervention of the legislator as statuary express the legal nature of the disciplinary decision. In all cases where the court ordered the annulment of illegality punish the employee who suffered an injury will receive compensation under article 52, paragraph 2, article 78 or, where appropriate, article 269 paragraph 1 of the Labour Code.

New challenges for text mining: mapping between text and manually curated pathways

Science.gov (United States)

Oda, Kanae; Kim, Jin-Dong; Ohta, Tomoko; Okanohara, Daisuke; Matsuzaki, Takuya; Tateisi, Yuka; Tsujii, Jun'ichi

2008-01-01

Background Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge. Results To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, ‘bio-inference,’ as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of ‘bio-inference’ schemes observed in the pathway corpus. Conclusions We believe that the creation of such rich resources and their detailed analysis is the significant first step for accelerating the research of the automatic construction of pathway from text. PMID:18426550

Spectral signature verification using statistical analysis and text mining

Science.gov (United States)

DeCoster, Mallory E.; Firpi, Alexe H.; Jacobs, Samantha K.; Cone, Shelli R.; Tzeng, Nigel H.; Rodriguez, Benjamin M.

2016-05-01

In the spectral science community, numerous spectral signatures are stored in databases representative of many sample materials collected from a variety of spectrometers and spectroscopists. Due to the variety and variability of the spectra that comprise many spectral databases, it is necessary to establish a metric for validating the quality of spectral signatures. This has been an area of great discussion and debate in the spectral science community. This paper discusses a method that independently validates two different aspects of a spectral signature to arrive at a final qualitative assessment; the textual meta-data and numerical spectral data. Results associated with the spectral data stored in the Signature Database1 (SigDB) are proposed. The numerical data comprising a sample material's spectrum is validated based on statistical properties derived from an ideal population set. The quality of the test spectrum is ranked based on a spectral angle mapper (SAM) comparison to the mean spectrum derived from the population set. Additionally, the contextual data of a test spectrum is qualitatively analyzed using lexical analysis text mining. This technique analyzes to understand the syntax of the meta-data to provide local learning patterns and trends within the spectral data, indicative of the test spectrum's quality. Text mining applications have successfully been implemented for security2 (text encryption/decryption), biomedical3 , and marketing4 applications. The text mining lexical analysis algorithm is trained on the meta-data patterns of a subset of high and low quality spectra, in order to have a model to apply to the entire SigDB data set. The statistical and textual methods combine to assess the quality of a test spectrum existing in a database without the need of an expert user. This method has been compared to other validation methods accepted by the spectral science community, and has provided promising results when a baseline spectral signature is

Unsupervised text mining for assessing and augmenting GWAS results.

Science.gov (United States)

Ailem, Melissa; Role, François; Nadif, Mohamed; Demenais, Florence

2016-04-01

Text mining can assist in the analysis and interpretation of large-scale biomedical data, helping biologists to quickly and cheaply gain confirmation of hypothesized relationships between biological entities. We set this question in the context of genome-wide association studies (GWAS), an actively emerging field that contributed to identify many genes associated with multifactorial diseases. These studies allow to identify groups of genes associated with the same phenotype, but provide no information about the relationships between these genes. Therefore, our objective is to leverage unsupervised text mining techniques using text-based cosine similarity comparisons and clustering applied to candidate and random gene vectors, in order to augment the GWAS results. We propose a generic framework which we used to characterize the relationships between 10 genes reported associated with asthma by a previous GWAS. The results of this experiment showed that the similarities between these 10 genes were significantly stronger than would be expected by chance (one-sided p-value<0.01). The clustering of observed and randomly selected gene also allowed to generate hypotheses about potential functional relationships between these genes and thus contributed to the discovery of new candidate genes for asthma. Copyright © 2016 Elsevier Inc. All rights reserved.

Evolution of bayesian-related research over time: a temporal text mining task

CSIR Research Space (South Africa)

de Waal, A

2006-06-01

Full Text Available Ronald Reagan’s Radio Addresses? Bayesian Analysis 2006, Volume 1, Number 2, pp. 189-383. 2. Mei Q and Zhai C, 2005. Discovering Evolutionary Theme Patterns from Text – An Exploration of Temporal Text Mining. KDD’05, August 21-24, 2005. Chicago...

Building a glaucoma interaction network using a text mining approach.

Science.gov (United States)

Soliman, Maha; Nasraoui, Olfa; Cooper, Nigel G F

2016-01-01

The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of

DDMGD: the database of text-mined associations between genes methylated in diseases from different species

KAUST Repository

Raies, A. B.; Mansour, H.; Incitti, R.; Bajic, Vladimir B.

2014-01-01

://www.cbrc.kaust.edu.sa/ddmgd/) to provide a comprehensive repository of information related to genes methylated in diseases that can be found through text mining. DDMGD's scope is not limited to a particular group of genes, diseases or species. Using the text mining system DEMGD we

The Distribution of the Informative Intensity of the Text in Terms of its Structure (On Materials of the English Texts in the Mining Sphere

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Znikina Ludmila

2017-01-01

Full Text Available The article deals with the distribution of informative intensity of the English-language scientific text based on its structural features contributing to the process of formalization of the scientific text and the preservation of the adequacy of the text with derived semantic information in relation to the primary. Discourse analysis is built on specific compositional and meaningful examples of scientific texts taken from the mining field. It also analyzes the adequacy of the translation of foreign texts into another language, the relationships between elements of linguistic systems, the degree of a formal conformance, translation with the specific objectives and information needs of the recipient. Some key words and ideas are emphasized in the paragraphs of the English-language mining scientific texts. The article gives the characteristic features of the structure of paragraphs of technical text and examples of constructions in English scientific texts based on a mining theme with the aim to explain the possible ways of their adequate translation.

Volcanic Supersites as cross-disciplinary laboratories

Science.gov (United States)

Provenzale, Antonello; Beierkuhnlein, Carl; Giamberini, Mariasilvia; Pennisi, Maddalena; Puglisi, Giuseppe

2017-04-01

Volcanic Supersites, defined in the frame of the GEO-GSNL Initiative, are usually considered mainly for their geohazard and geological characteristics. However, volcanoes are extremely challenging areas from many other points of view, including environmental and climatic properties, ecosystems, hydrology, soil properties and biogeochemical cycling. Possibly, volcanoes are closer to early Earth conditions than most other types of environment. During FP7, EC effectively fostered the implementation of the European volcano Supersites (Mt. Etna, Campi Flegrei/Vesuvius and Iceland) through the MED-SUV and FUTUREVOLC projects. Currently, the large H2020 project ECOPOTENTIAL (2015-2019, 47 partners, http://www.ecopotential-project.eu/) contributes to GEO/GEOSS and to the GEO ECO Initiative, and it is devoted to making best use of remote sensing and in situ data to improve future ecosystem benefits, focusing on a network of Protected Areas of international relevance. In ECOPOTENTIAL, remote sensing and in situ data are collected, processed and used for a better understanding of the ecosystem dynamics, analysing and modelling the effects of global changes on ecosystem functions and services, over an array of different ecosystem types, including mountain, marine, coastal, arid and semi-arid ecosystems, and also areas of volcanic origin such as the Canary and La Reunion Islands. Here, we propose to extend the network of the ECOPOTENTIAL project to include active Volcanic Supersites, such as Mount Etna and other volcanic Protected Areas, and we discuss how they can be included in the framework of the ECOPOTENTIAL workflow. A coordinated and cross-disciplinary set of studies at these sites should include geological, biological, ecological, biogeochemical, climatic and biogeographical aspects, as well as their relationship with the antropogenic impact on the environment, and aim at the global analysis of the volcanic Earth Critical Zone - namely, the upper layer of the Earth

Space Geodesy: The Cross-Disciplinary Earth science (Vening Meinesz Medal Lecture)

Science.gov (United States)

Shum, C. K.

2012-04-01

Geodesy during the onset of the 21st Century is evolving into a transformative cross-disciplinary Earth science field. The pioneers before or after the discipline Geodesy was defined include Galileo, Descartes, Kepler, Newton, Euler, Bernoulli, Kant, Laplace, Airy, Kelvin, Jeffreys, Chandler, Meinesz, Kaula, and others. The complicated dynamic processes of the Earth system manifested by interactions between the solid Earth and its fluid layers, including ocean, atmosphere, cryosphere and hydrosphere, and their feedbacks are linked with scientific problems such as global sea-level rise resulting from natural and anthropogenic climate change. Advances in the precision and stability of geodetic and fundamental instrumentations, including clocks, satellite or quasar tracking sensors, altimetry and lidars, synthetic aperture radar interferometry (InSAR), InSAR altimetry, gravimetry and gradiometry, have enabled accentuate and transformative progress in cross-disciplinary Earth sciences. In particular, advances in the measurement of the gravity with modern free-fall methods have reached accuracies of 10-9 g (~1 μGal or 10 nm/s2) or better, allowing accurate measurements of height changes at ~3 mm relative to the Earth's center of mass, and mass transports within the Earth interior or its geophysical fluids, enabling global quantifications of climate-change signals. These contemporary space geodetic and in situ sensors include, but not limited to, satellite radar and laser altimetry/lidars, GNSS/SLR/VLBI/DORIS, InSAR, spaceborne gravimetry from GRACE (Gravity Recovery And Climate Experiment twin-satellite mission) and gradiometry from GOCE (Global Ocean Circulation Experiment), tide gauges, and hydrographic data (XBT/MBT/Argo). The 2007 Intergovernmental Panel for Climate Change (IPCC) study, the Fourth Assessment Report (AR4), substantially narrowed the discrepancy between observation and the known geophysical causes of sea-level rise, but significant uncertainties

The Distribution of the Informative Intensity of the Text in Terms of its Structure (On Materials of the English Texts in the Mining Sphere)

Science.gov (United States)

Znikina, Ludmila; Rozhneva, Elena

2017-11-01

The article deals with the distribution of informative intensity of the English-language scientific text based on its structural features contributing to the process of formalization of the scientific text and the preservation of the adequacy of the text with derived semantic information in relation to the primary. Discourse analysis is built on specific compositional and meaningful examples of scientific texts taken from the mining field. It also analyzes the adequacy of the translation of foreign texts into another language, the relationships between elements of linguistic systems, the degree of a formal conformance, translation with the specific objectives and information needs of the recipient. Some key words and ideas are emphasized in the paragraphs of the English-language mining scientific texts. The article gives the characteristic features of the structure of paragraphs of technical text and examples of constructions in English scientific texts based on a mining theme with the aim to explain the possible ways of their adequate translation.

Using Text Mining to Uncover Students' Technology-Related Problems in Live Video Streaming

Science.gov (United States)

Abdous, M'hammed; He, Wu

2011-01-01

Because of their capacity to sift through large amounts of data, text mining and data mining are enabling higher education institutions to reveal valuable patterns in students' learning behaviours without having to resort to traditional survey methods. In an effort to uncover live video streaming (LVS) students' technology related-problems and to…

Text mining approach to predict hospital admissions using early medical records from the emergency department.

Science.gov (United States)

Lucini, Filipe R; S Fogliatto, Flavio; C da Silveira, Giovani J; L Neyeloff, Jeruza; Anzanello, Michel J; de S Kuchenbecker, Ricardo; D Schaan, Beatriz

2017-04-01

Emergency department (ED) overcrowding is a serious issue for hospitals. Early information on short-term inward bed demand from patients receiving care at the ED may reduce the overcrowding problem, and optimize the use of hospital resources. In this study, we use text mining methods to process data from early ED patient records using the SOAP framework, and predict future hospitalizations and discharges. We try different approaches for pre-processing of text records and to predict hospitalization. Sets-of-words are obtained via binary representation, term frequency, and term frequency-inverse document frequency. Unigrams, bigrams and trigrams are tested for feature formation. Feature selection is based on χ 2 and F-score metrics. In the prediction module, eight text mining methods are tested: Decision Tree, Random Forest, Extremely Randomized Tree, AdaBoost, Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine (Kernel linear) and Nu-Support Vector Machine (Kernel linear). Prediction performance is evaluated by F1-scores. Precision and Recall values are also informed for all text mining methods tested. Nu-Support Vector Machine was the text mining method with the best overall performance. Its average F1-score in predicting hospitalization was 77.70%, with a standard deviation (SD) of 0.66%. The method could be used to manage daily routines in EDs such as capacity planning and resource allocation. Text mining could provide valuable information and facilitate decision-making by inward bed management teams. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

[Exploration of nursing art and aesthetic experiences: cross-disciplinary links and dialogues].

Science.gov (United States)

Sheu, Shuh-Jen

2013-08-01

Interdisciplinary understanding is crucial for readers today. This article integrates the ideas of four care-aesthetics-column writers in order to illustrate and discuss nursing art and aesthetic care experiences in a cross-disciplinary conversation. This article reflects critically on the art, culture, and nature of nursing in the five themes of: 1) the shape of nursing knowledge, "science" or "art"?; 2) the caring arts: passively regulative or consciously creative labor?; 3) busy hospital workers: a landscape of persons and objects or the creators of the scenery?; 4) nursing skills, arts, and the Tao; and 5) art liberation: is the nursing profession in need of a revolution or fundamental reform? This article utilizes diverse and occasionally contradictory points of view together with practical examples in order to encourage readers to interlink their disparate professional nursing skills and draw aesthetic knowledge from multiple sources and experiences.

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

Science.gov (United States)

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)

Associations between child disciplinary practices and bullying behavior in adolescents

OpenAIRE

Graziela A.H. Zottis; Giovanni A. Salum; Luciano R. Isolan; Gisele G. Manfro; Elizeth Heldt

2014-01-01

OBJECTIVE: to investigate associations between different types of child disciplinary practices and children and adolescents' bullying behavior in a Brazilian sample. METHODS: cross-sectional study, with a school-based sample of 10-to 15-year-old children and adolescents. Child disciplinary practices were assessed using two main subtypes: power-assertive and punitive (psychological aggression, corporal punishment, deprivation of privileges, and penalty tasks) and inductive (explaining, re...

Mining consumer health vocabulary from community-generated text.

Science.gov (United States)

Vydiswaran, V G Vinod; Mei, Qiaozhu; Hanauer, David A; Zheng, Kai

2014-01-01

Community-generated text corpora can be a valuable resource to extract consumer health vocabulary (CHV) and link them to professional terminologies and alternative variants. In this research, we propose a pattern-based text-mining approach to identify pairs of CHV and professional terms from Wikipedia, a large text corpus created and maintained by the community. A novel measure, leveraging the ratio of frequency of occurrence, was used to differentiate consumer terms from professional terms. We empirically evaluated the applicability of this approach using a large data sample consisting of MedLine abstracts and all posts from an online health forum, MedHelp. The results show that the proposed approach is able to identify synonymous pairs and label the terms as either consumer or professional term with high accuracy. We conclude that the proposed approach provides great potential to produce a high quality CHV to improve the performance of computational applications in processing consumer-generated health text.

OSCAR4: a flexible architecture for chemical text-mining

Directory of Open Access Journals (Sweden)

Jessop David M

2011-10-01

Full Text Available Abstract The Open-Source Chemistry Analysis Routines (OSCAR software, a toolkit for the recognition of named entities and data in chemistry publications, has been developed since 2002. Recent work has resulted in the separation of the core OSCAR functionality and its release as the OSCAR4 library. This library features a modular API (based on reduction of surface coupling that permits client programmers to easily incorporate it into external applications. OSCAR4 offers a domain-independent architecture upon which chemistry specific text-mining tools can be built, and its development and usage are discussed.

Academic Globalization: Universality of Cross-Cultural And Cross-Disciplinary LMR Perspectives

Directory of Open Access Journals (Sweden)

Marta Szabo White

2010-10-01

Full Text Available The contribution of this paper suggests that previous research underscoring cross-cultural differences may be misleading, when in fact it is cross-professional rather than cross-cultural differences that should be emphasized. Employing the LMR framework, this paper concludes that business or non-business predisposition has a more direct impact on one's individual cultural profile than does nationality. Regardless of culture, persons involved in business are characterized primarily by linear-active modes of communication, and persons not involved in business typically employ less linear and more multi-active/hybrid modes of communication. The linkages among individual characteristics, communication styles, work behaviors, and the extent to which the LMR constructs can facilitate and predict leadership, negotiating styles, individual behaviors, etc. are central to academic globalization and preparing global business leaders.

An overview of the BioCreative 2012 Workshop Track III: interactive text mining task.

Science.gov (United States)

Arighi, Cecilia N; Carterette, Ben; Cohen, K Bretonnel; Krallinger, Martin; Wilbur, W John; Fey, Petra; Dodson, Robert; Cooper, Laurel; Van Slyke, Ceri E; Dahdul, Wasila; Mabee, Paula; Li, Donghui; Harris, Bethany; Gillespie, Marc; Jimenez, Silvia; Roberts, Phoebe; Matthews, Lisa; Becker, Kevin; Drabkin, Harold; Bello, Susan; Licata, Luana; Chatr-aryamontri, Andrew; Schaeffer, Mary L; Park, Julie; Haendel, Melissa; Van Auken, Kimberly; Li, Yuling; Chan, Juancarlos; Muller, Hans-Michael; Cui, Hong; Balhoff, James P; Chi-Yang Wu, Johnny; Lu, Zhiyong; Wei, Chih-Hsuan; Tudor, Catalina O; Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar; Cejuela, Juan Miguel; Dubey, Pratibha; Wu, Cathy

2013-01-01

In many databases, biocuration primarily involves literature curation, which usually involves retrieving relevant articles, extracting information that will translate into annotations and identifying new incoming literature. As the volume of biological literature increases, the use of text mining to assist in biocuration becomes increasingly relevant. A number of groups have developed tools for text mining from a computer science/linguistics perspective, and there are many initiatives to curate some aspect of biology from the literature. Some biocuration efforts already make use of a text mining tool, but there have not been many broad-based systematic efforts to study which aspects of a text mining tool contribute to its usefulness for a curation task. Here, we report on an effort to bring together text mining tool developers and database biocurators to test the utility and usability of tools. Six text mining systems presenting diverse biocuration tasks participated in a formal evaluation, and appropriate biocurators were recruited for testing. The performance results from this evaluation indicate that some of the systems were able to improve efficiency of curation by speeding up the curation task significantly (∼1.7- to 2.5-fold) over manual curation. In addition, some of the systems were able to improve annotation accuracy when compared with the performance on the manually curated set. In terms of inter-annotator agreement, the factors that contributed to significant differences for some of the systems included the expertise of the biocurator on the given curation task, the inherent difficulty of the curation and attention to annotation guidelines. After the task, annotators were asked to complete a survey to help identify strengths and weaknesses of the various systems. The analysis of this survey highlights how important task completion is to the biocurators' overall experience of a system, regardless of the system's high score on design, learnability and

Text Mining for Drugs and Chemical Compounds: Methods, Tools and Applications.

Science.gov (United States)

Vazquez, Miguel; Krallinger, Martin; Leitner, Florian; Valencia, Alfonso

2011-06-01

Providing prior knowledge about biological properties of chemicals, such as kinetic values, protein targets, or toxic effects, can facilitate many aspects of drug development. Chemical information is rapidly accumulating in all sorts of free text documents like patents, industry reports, or scientific articles, which has motivated the development of specifically tailored text mining applications. Despite the potential gains, chemical text mining still faces significant challenges. One of the most salient is the recognition of chemical entities mentioned in text. To help practitioners contribute to this area, a good portion of this review is devoted to this issue, and presents the basic concepts and principles underlying the main strategies. The technical details are introduced and accompanied by relevant bibliographic references. Other tasks discussed are retrieving relevant articles, identifying relationships between chemicals and other entities, or determining the chemical structures of chemicals mentioned in text. This review also introduces a number of published applications that can be used to build pipelines in topics like drug side effects, toxicity, and protein-disease-compound network analysis. We conclude the review with an outlook on how we expect the field to evolve, discussing its possibilities and its current limitations. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Mining free-text medical records for companion animal enteric syndrome surveillance.

Science.gov (United States)

Anholt, R M; Berezowski, J; Jamal, I; Ribble, C; Stephen, C

2014-03-01

Large amounts of animal health care data are present in veterinary electronic medical records (EMR) and they present an opportunity for companion animal disease surveillance. Veterinary patient records are largely in free-text without clinical coding or fixed vocabulary. Text-mining, a computer and information technology application, is needed to identify cases of interest and to add structure to the otherwise unstructured data. In this study EMR's were extracted from veterinary management programs of 12 participating veterinary practices and stored in a data warehouse. Using commercially available text-mining software (WordStat™), we developed a categorization dictionary that could be used to automatically classify and extract enteric syndrome cases from the warehoused electronic medical records. The diagnostic accuracy of the text-miner for retrieving cases of enteric syndrome was measured against human reviewers who independently categorized a random sample of 2500 cases as enteric syndrome positive or negative. Compared to the reviewers, the text-miner retrieved cases with enteric signs with a sensitivity of 87.6% (95%CI, 80.4-92.9%) and a specificity of 99.3% (95%CI, 98.9-99.6%). Automatic and accurate detection of enteric syndrome cases provides an opportunity for community surveillance of enteric pathogens in companion animals. Copyright © 2014 Elsevier B.V. All rights reserved.

Enabling cross-disciplinary research by linking data to Open Access publications

Science.gov (United States)

Rettberg, N.

2012-04-01

OpenAIREplus focuses on the linking of research data to associated publications. The interlinking of research objects has implications for optimising the research process, allowing the sharing, enrichment and reuse of data, and ultimately serving to make open data an essential part of first class research. The growing call for more concrete data management and sharing plans, apparent at funder and national level, is complemented by the increasing support for a scientific infrastructure that supports the seamless access to a range of research materials. This paper will describe the recently launched OpenAIREplus and will detail how it plans to achieve its goals of developing an Open Access participatory infrastructure for scientific information. OpenAIREplus extends the current collaborative OpenAIRE project, which provides European researchers with a service network for the deposit of peer-reviewed FP7 grant-funded Open Access publications. This new project will focus on opening up the infrastructure to data sources from subject-specific communities to provide metadata about research data and publications, facilitating the linking between these objects. The ability to link within a publication out to a citable database, or other research data material, is fairly innovative and this project will enable users to search, browse, view, and create relationships between different information objects. In this regard, OpenAIREplus will build on prototypes of so-called "Enhanced Publications", originally conceived in the DRIVER-II project. OpenAIREplus recognizes the importance of representing the context of publications and datasets, thus linking to resources about the authors, their affiliation, location, project data and funding. The project will explore how links between text-based publications and research data are managed in different scientific fields. This complements a previous study in OpenAIRE on current disciplinary practices and future needs for infrastructural

[Text mining, a method for computer-assisted analysis of scientific texts, demonstrated by an analysis of author networks].

Science.gov (United States)

Hahn, P; Dullweber, F; Unglaub, F; Spies, C K

2014-06-01

Searching for relevant publications is becoming more difficult with the increasing number of scientific articles. Text mining as a specific form of computer-based data analysis may be helpful in this context. Highlighting relations between authors and finding relevant publications concerning a specific subject using text analysis programs are illustrated graphically by 2 performed examples. © Georg Thieme Verlag KG Stuttgart · New York.

U-Compare: share and compare text mining tools with UIMA

Science.gov (United States)

Kano, Yoshinobu; Baumgartner, William A.; McCrohon, Luke; Ananiadou, Sophia; Cohen, K. Bretonnel; Hunter, Lawrence; Tsujii, Jun'ichi

2009-01-01

Summary: Due to the increasing number of text mining resources (tools and corpora) available to biologists, interoperability issues between these resources are becoming significant obstacles to using them effectively. UIMA, the Unstructured Information Management Architecture, is an open framework designed to aid in the construction of more interoperable tools. U-Compare is built on top of the UIMA framework, and provides both a concrete framework for out-of-the-box text mining and a sophisticated evaluation platform allowing users to run specific tools on any target text, generating both detailed statistics and instance-based visualizations of outputs. U-Compare is a joint project, providing the world's largest, and still growing, collection of UIMA-compatible resources. These resources, originally developed by different groups for a variety of domains, include many famous tools and corpora. U-Compare can be launched straight from the web, without needing to be manually installed. All U-Compare components are provided ready-to-use and can be combined easily via a drag-and-drop interface without any programming. External UIMA components can also simply be mixed with U-Compare components, without distinguishing between locally and remotely deployed resources. Availability: http://u-compare.org/ Contact: kano@is.s.u-tokyo.ac.jp PMID:19414535

Communication in ecosystem management: a case study of cross-disciplinary integration in the assessment phase of the interior Columbia Basin Ecosystem Management Project.

Science.gov (United States)

Jakobsen, Christine Haugaard; McLaughlin, William J

2004-05-01

Effective communication is essential to the success of collaborative ecosystem management projects. In this paper, we investigated the dynamics of the Interior Columbia Basin Ecosystem Management Project's (ICBEMP) cross-disciplinary integration process in the assessment phase. Using a case study research design, we captured the rich trail of experience through conducting in-depth interviews and collecting information from internal and public documents, videos, and meetings related to the ICBEMP. Coding and analysis was facilitated by a qualitative analysis software, NVivo. Results include the range of internal perspectives on barriers and facilitators of cross-disciplinary integration in the Science Integration Team (SIT). These are arrayed in terms of discipline-based differences, organizational structures and activities, individual traits of scientists, and previous working relationships. The ICBEMP organization included a team of communication staffs (CT), and the data described the CT as a mixed group in terms of qualifications and educational backgrounds that played a major role in communication with actors external to the ICBEMP organization but a minor one in terms of internal communication. The data indicated that the CT-SIT communication was influenced by characteristics of actors and structures related to organizations and their cultures. We conclude that the ICBEMP members may not have had a sufficient level of shared understanding of central domains, such as the task at hand and ways and timing of information sharing. The paper concludes by suggesting that future ecosystem management assessment teams use qualified communications specialists to design and monitor the development of shared cognition among organization members in order to improve the effectiveness of communication and cross-disciplinary integration.

Text Mining of UU-ITE Implementation in Indonesia

Science.gov (United States)

Hakim, Lukmanul; Kusumasari, Tien F.; Lubis, Muharman

2018-04-01

At present, social media and networks act as one of the main platforms for sharing information, idea, thought and opinions. Many people share their knowledge and express their views on the specific topics or current hot issues that interest them. The social media texts have rich information about the complaints, comments, recommendation and suggestion as the automatic reaction or respond to government initiative or policy in order to overcome certain issues.This study examines the sentiment from netizensas part of citizen who has vocal sound about the implementation of UU ITE as the first cyberlaw in Indonesia as a means to identify the current tendency of citizen perception. To perform text mining techniques, this study used Twitter Rest API while R programming was utilized for the purpose of classification analysis based on hierarchical cluster.

The potential of text mining in data integration and network biology for plant research: a case study on Arabidopsis.

Science.gov (United States)

Van Landeghem, Sofie; De Bodt, Stefanie; Drebert, Zuzanna J; Inzé, Dirk; Van de Peer, Yves

2013-03-01

Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein-protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies.

Evaluating a Bilingual Text-Mining System with a Taxonomy of Key Words and Hierarchical Visualization for Understanding Learner-Generated Text

Science.gov (United States)

Kong, Siu Cheung; Li, Ping; Song, Yanjie

2018-01-01

This study evaluated a bilingual text-mining system, which incorporated a bilingual taxonomy of key words and provided hierarchical visualization, for understanding learner-generated text in the learning management systems through automatic identification and counting of matching key words. A class of 27 in-service teachers studied a course…

Experiences with Text Mining Large Collections of Unstructured Systems Development Artifacts at JPL

Science.gov (United States)

Port, Dan; Nikora, Allen; Hihn, Jairus; Huang, LiGuo

2011-01-01

Often repositories of systems engineering artifacts at NASA's Jet Propulsion Laboratory (JPL) are so large and poorly structured that they have outgrown our capability to effectively manually process their contents to extract useful information. Sophisticated text mining methods and tools seem a quick, low-effort approach to automating our limited manual efforts. Our experiences of exploring such methods mainly in three areas including historical risk analysis, defect identification based on requirements analysis, and over-time analysis of system anomalies at JPL, have shown that obtaining useful results requires substantial unanticipated efforts - from preprocessing the data to transforming the output for practical applications. We have not observed any quick 'wins' or realized benefit from short-term effort avoidance through automation in this area. Surprisingly we have realized a number of unexpected long-term benefits from the process of applying text mining to our repositories. This paper elaborates some of these benefits and our important lessons learned from the process of preparing and applying text mining to large unstructured system artifacts at JPL aiming to benefit future TM applications in similar problem domains and also in hope for being extended to broader areas of applications.

Text Mining the History of Medicine.

Science.gov (United States)

Thompson, Paul; Batista-Navarro, Riza Theresa; Kontonatsios, Georgios; Carter, Jacob; Toon, Elizabeth; McNaught, John; Timmermann, Carsten; Worboys, Michael; Ananiadou, Sophia

2016-01-01

Historical text archives constitute a rich and diverse source of information, which is becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it can be difficult for researchers to explore and search such large volumes of data in an efficient manner. Text mining (TM) methods can help, through their ability to recognise various types of semantic information automatically, e.g., instances of concepts (places, medical conditions, drugs, etc.), synonyms/variant forms of concepts, and relationships holding between concepts (which drugs are used to treat which medical conditions, etc.). TM analysis allows search systems to incorporate functionality such as automatic suggestions of synonyms of user-entered query terms, exploration of different concepts mentioned within search results or isolation of documents in which concepts are related in specific ways. However, applying TM methods to historical text can be challenging, according to differences and evolutions in vocabulary, terminology, language structure and style, compared to more modern text. In this article, we present our efforts to overcome the various challenges faced in the semantic analysis of published historical medical text dating back to the mid 19th century. Firstly, we used evidence from diverse historical medical documents from different periods to develop new resources that provide accounts of the multiple, evolving ways in which concepts, their variants and relationships amongst them may be expressed. These resources were employed to support the development of a modular processing pipeline of TM tools for the robust detection of semantic information in historical medical documents with varying characteristics. We applied the pipeline to two large-scale medical document archives covering wide temporal ranges as the basis for the development of a publicly accessible semantically-oriented search system. The novel resources are available for research purposes, while

Text Mining Untuk Analisis Sentimen Review Film Menggunakan Algoritma K-Means

OpenAIRE

Setyo Budi

2017-01-01

Kemudahan manusia didalam menggunakan website mengakibatkan bertambahnya dokumen teks yang berupa pendapat dan informasi. Dalam waktu yang lama dokumen teks akan bertambah besar. Text mining merupakan salah satu teknik yang digunakan untuk menggali kumpulan dokumen text sehingga dapat diambil intisarinya. Ada beberapa algoritma yang di gunakan untuk penggalian dokumen untuk analisis sentimen, salah satunya adalah K-Means. Didalam penelitian ini algoritma yang digunakan adalah K-Means. Hasil p...

Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery.

Science.gov (United States)

Gonzalez, Graciela H; Tahsin, Tasnia; Goodale, Britton C; Greene, Anna C; Greene, Casey S

2016-01-01

Precision medicine will revolutionize the way we treat and prevent disease. A major barrier to the implementation of precision medicine that clinicians and translational scientists face is understanding the underlying mechanisms of disease. We are starting to address this challenge through automatic approaches for information extraction, representation and analysis. Recent advances in text and data mining have been applied to a broad spectrum of key biomedical questions in genomics, pharmacogenomics and other fields. We present an overview of the fundamental methods for text and data mining, as well as recent advances and emerging applications toward precision medicine. © The Author 2015. Published by Oxford University Press.

Collaboration in Arctic Research: Best Practices to Build and Sustain Successful Cross- and Trans-disciplinary Efforts

Science.gov (United States)

Wiggins, H. V.; Rich, R. H.

2015-12-01

The rapid physical and social changes currently underway in the Arctic - and changes in the way in which we study and manage the region -- require coordinated research efforts to improve our understanding of the Arctic's physical, biological, and social systems and the implications of change at many scales. At the same time, policy-makers and Arctic communities need decision-support tools and synthesized information to respond and adapt to the "new Arctic". There are enormous challenges, however, in collaboration among the disparate groups of people needed for such efforts. A carefully planned strategic approach is required to bridge the scientific disciplinary and organizational boundaries, foster cooperation between local communities and science programs, and effectively communicate between scientists and policy-makers. Efforts must draw on bodies of knowledge from project management, strategic planning, organizational development, and group dynamics. This poster presentation will discuss best practices of building and sustaining networks of people to catalyze successful cross-disciplinary activities. Specific examples and case studies - both successes and failures -- will be presented that draw on several projects at the Arctic Research Consortium of the U.S. (ARCUS; www.arcus.org), a nonprofit membership organization composed of universities and institutions that have a substantial commitment to research in the Arctic.

Defining Extreme Events: A Cross-Disciplinary Review

Science.gov (United States)

McPhillips, Lauren E.; Chang, Heejun; Chester, Mikhail V.; Depietri, Yaella; Friedman, Erin; Grimm, Nancy B.; Kominoski, John S.; McPhearson, Timon; Méndez-Lázaro, Pablo; Rosi, Emma J.; Shafiei Shiva, Javad

2018-03-01

Extreme events are of interest worldwide given their potential for substantial impacts on social, ecological, and technical systems. Many climate-related extreme events are increasing in frequency and/or magnitude due to anthropogenic climate change, and there is increased potential for impacts due to the location of urbanization and the expansion of urban centers and infrastructures. Many disciplines are engaged in research and management of these events. However, a lack of coherence exists in what constitutes and defines an extreme event across these fields, which impedes our ability to holistically understand and manage these events. Here, we review 10 years of academic literature and use text analysis to elucidate how six major disciplines—climatology, earth sciences, ecology, engineering, hydrology, and social sciences—define and communicate extreme events. Our results highlight critical disciplinary differences in the language used to communicate extreme events. Additionally, we found a wide range in definitions and thresholds, with more than half of examined papers not providing an explicit definition, and disagreement over whether impacts are included in the definition. We urge distinction between extreme events and their impacts, so that we can better assess when responses to extreme events have actually enhanced resilience. Additionally, we suggest that all researchers and managers of extreme events be more explicit in their definition of such events as well as be more cognizant of how they are communicating extreme events. We believe clearer and more consistent definitions and communication can support transdisciplinary understanding and management of extreme events.

GENERAL CONSIDERATIONS ON THE DISCIPLINARY LIABILITY OF ARCHITECTS

Directory of Open Access Journals (Sweden)

Andra PURAN

2014-05-01

Full Text Available As well as other liberal professions in Romania, also the profession as an architect is regulated by special norms, the Law No 184/2001, whose provisions are amended by the Rules governing the functioning and organization of the Romanian Order of Architects and the Code of Ethics of Architects. The specificity of the disciplinary liability of the architects towards the common law is given by specific sanctions, by the authorities competent in performing the disciplinary investigation of the disciplinary offences, as well as by the specific procedural rules. The present study aims to offer a brief analysis of these aspects which differentiate the disciplinary liability of architects towards that of the employees performing their activities under an employment contract.

Supporting the annotation of chronic obstructive pulmonary disease (COPD) phenotypes with text mining workflows.

Science.gov (United States)

Fu, Xiao; Batista-Navarro, Riza; Rak, Rafal; Ananiadou, Sophia

2015-01-01

Chronic obstructive pulmonary disease (COPD) is a life-threatening lung disorder whose recent prevalence has led to an increasing burden on public healthcare. Phenotypic information in electronic clinical records is essential in providing suitable personalised treatment to patients with COPD. However, as phenotypes are often "hidden" within free text in clinical records, clinicians could benefit from text mining systems that facilitate their prompt recognition. This paper reports on a semi-automatic methodology for producing a corpus that can ultimately support the development of text mining tools that, in turn, will expedite the process of identifying groups of COPD patients. A corpus of 30 full-text papers was formed based on selection criteria informed by the expertise of COPD specialists. We developed an annotation scheme that is aimed at producing fine-grained, expressive and computable COPD annotations without burdening our curators with a highly complicated task. This was implemented in the Argo platform by means of a semi-automatic annotation workflow that integrates several text mining tools, including a graphical user interface for marking up documents. When evaluated using gold standard (i.e., manually validated) annotations, the semi-automatic workflow was shown to obtain a micro-averaged F-score of 45.70% (with relaxed matching). Utilising the gold standard data to train new concept recognisers, we demonstrated that our corpus, although still a work in progress, can foster the development of significantly better performing COPD phenotype extractors. We describe in this work the means by which we aim to eventually support the process of COPD phenotype curation, i.e., by the application of various text mining tools integrated into an annotation workflow. Although the corpus being described is still under development, our results thus far are encouraging and show great potential in stimulating the development of further automatic COPD phenotype extractors.

Using text mining for study identification in systematic reviews: a systematic review of current approaches.

Science.gov (United States)

O'Mara-Eves, Alison; Thomas, James; McNaught, John; Miwa, Makoto; Ananiadou, Sophia

2015-01-14

The large and growing number of published studies, and their increasing rate of publication, makes the task of identifying relevant studies in an unbiased way for inclusion in systematic reviews both complex and time consuming. Text mining has been offered as a potential solution: through automating some of the screening process, reviewer time can be saved. The evidence base around the use of text mining for screening has not yet been pulled together systematically; this systematic review fills that research gap. Focusing mainly on non-technical issues, the review aims to increase awareness of the potential of these technologies and promote further collaborative research between the computer science and systematic review communities. Five research questions led our review: what is the state of the evidence base; how has workload reduction been evaluated; what are the purposes of semi-automation and how effective are they; how have key contextual problems of applying text mining to the systematic review field been addressed; and what challenges to implementation have emerged? We answered these questions using standard systematic review methods: systematic and exhaustive searching, quality-assured data extraction and a narrative synthesis to synthesise findings. The evidence base is active and diverse; there is almost no replication between studies or collaboration between research teams and, whilst it is difficult to establish any overall conclusions about best approaches, it is clear that efficiencies and reductions in workload are potentially achievable. On the whole, most suggested that a saving in workload of between 30% and 70% might be possible, though sometimes the saving in workload is accompanied by the loss of 5% of relevant studies (i.e. a 95% recall). Using text mining to prioritise the order in which items are screened should be considered safe and ready for use in 'live' reviews. The use of text mining as a 'second screener' may also be used cautiously

A Framework for Text Mining in Scientometric Study: A Case Study in Biomedicine Publications

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Silalahi, V. M. M.; Hardiyati, R.; Nadhiroh, I. M.; Handayani, T.; Rahmaida, R.; Amelia, M.

2018-04-01

The data of Indonesians research publications in the domain of biomedicine has been collected to be text mined for the purpose of a scientometric study. The goal is to build a predictive model that provides a classification of research publications on the potency for downstreaming. The model is based on the drug development processes adapted from the literatures. An effort is described to build the conceptual model and the development of a corpus on the research publications in the domain of Indonesian biomedicine. Then an investigation is conducted relating to the problems associated with building a corpus and validating the model. Based on our experience, a framework is proposed to manage the scientometric study based on text mining. Our method shows the effectiveness of conducting a scientometric study based on text mining in order to get a valid classification model. This valid model is mainly supported by the iterative and close interactions with the domain experts starting from identifying the issues, building a conceptual model, to the labelling, validation and results interpretation.

tmBioC: improving interoperability of text-mining tools with BioC.

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Khare, Ritu; Wei, Chih-Hsuan; Mao, Yuqing; Leaman, Robert; Lu, Zhiyong

2014-01-01

The lack of interoperability among biomedical text-mining tools is a major bottleneck in creating more complex applications. Despite the availability of numerous methods and techniques for various text-mining tasks, combining different tools requires substantial efforts and time owing to heterogeneity and variety in data formats. In response, BioC is a recent proposal that offers a minimalistic approach to tool interoperability by stipulating minimal changes to existing tools and applications. BioC is a family of XML formats that define how to present text documents and annotations, and also provides easy-to-use functions to read/write documents in the BioC format. In this study, we introduce our text-mining toolkit, which is designed to perform several challenging and significant tasks in the biomedical domain, and repackage the toolkit into BioC to enhance its interoperability. Our toolkit consists of six state-of-the-art tools for named-entity recognition, normalization and annotation (PubTator) of genes (GenNorm), diseases (DNorm), mutations (tmVar), species (SR4GN) and chemicals (tmChem). Although developed within the same group, each tool is designed to process input articles and output annotations in a different format. We modify these tools and enable them to read/write data in the proposed BioC format. We find that, using the BioC family of formats and functions, only minimal changes were required to build the newer versions of the tools. The resulting BioC wrapped toolkit, which we have named tmBioC, consists of our tools in BioC, an annotated full-text corpus in BioC, and a format detection and conversion tool. Furthermore, through participation in the 2013 BioCreative IV Interoperability Track, we empirically demonstrate that the tools in tmBioC can be more efficiently integrated with each other as well as with external tools: Our experimental results show that using BioC reduces >60% in lines of code for text-mining tool integration. The tmBioC toolkit

Using text mining for study identification in systematic reviews: a systematic review of current approaches

OpenAIRE

O?Mara-Eves, Alison; Thomas, James; McNaught, John; Miwa, Makoto; Ananiadou, Sophia

2015-01-01

Background The large and growing number of published studies, and their increasing rate of publication, makes the task of identifying relevant studies in an unbiased way for inclusion in systematic reviews both complex and time consuming. Text mining has been offered as a potential solution: through automating some of the screening process, reviewer time can be saved. The evidence base around the use of text mining for screening has not yet been pulled together systematically; this systematic...

Complementing the Numbers: A Text Mining Analysis of College Course Withdrawals

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Michalski, Greg V.

2011-01-01

Excessive college course withdrawals are costly to the student and the institution in terms of time to degree completion, available classroom space, and other resources. Although generally well quantified, detailed analysis of the reasons given by students for course withdrawal is less common. To address this, a text mining analysis was performed…

Socially Responsible Mining: the Relationship between Mining and Poverty, Human Health and the Environment

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Maier, Raina M.; Díaz-Barriga, Fernando; Field, James A.; Hopkins, James; Klein, Bern; Poulton, Mary M.

2016-01-01

Increasing global demand for metals is straining the ability of the mining industry to physically keep up with demand (physical scarcity). On the other hand, social issues including the environmental and human health consequences of mining as well as the disparity in income distribution from mining revenues are disproportionately felt at the local community level. This has created social rifts, particularly in the developing world, between affected communities and both industry and governments. Such rifts can result in a disruption of the steady supply of metals (situational scarcity). Here we discuss the importance of mining in relationship to poverty, identify steps that have been taken to create a framework for socially responsible mining, and then discuss the need for academia to work in partnership with communities, government, and industry to develop trans-disciplinary research-based step change solutions to the intertwined problems of physical and situational scarcity. PMID:24552962

Designing Research Services: Cross-Disciplinary Administration and the Research Lifecycle

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Madden, G.

2017-12-01

The sheer number of technical and administrative offices involved in the research lifecycle, and the lack of shared governance and shared processes across those offices, creates challenges to the successful preservation of research outputs. Universities need a more integrated approach to the research lifecycle that allows us to: recognize a research project as it is being initiated; identify the data associated with the research project; document and track any compliance, security, access, and publication requirements associated with the research and its data; follow the research and its associated components across the research lifecycle; and finally recognize that the research has come to a close so we can trigger the various preservation, access, and communications processes that close the loop, inform the public, and promote the continued progress of science. Such an approach will require cooperation, communications, and shared workflow tools that tie together (often across many years) PIs, research design methodologists, grants offices, contract negotiators, central research administrators, research compliance specialists, desktop IT support units, server administrators, high performance computing facilities, data centers, specialized data transfer networks, institutional research repositories, institutional data repositories, and research communications groups, all of which play a significant role in the technical or administrative success of research. This session will focus on progress towards improving cross-disciplinary administrative and technical cooperation at Penn State University, with an emphasis on generalizable approaches that can be adopted elsewhere.

TIME SERIES ANALYSIS ON STOCK MARKET FOR TEXT MINING CORRELATION OF ECONOMY NEWS

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Sadi Evren SEKER

2014-01-01

Full Text Available This paper proposes an information retrieval methodfor the economy news. Theeffect of economy news, are researched in the wordlevel and stock market valuesare considered as the ground proof.The correlation between stock market prices and economy news is an already ad-dressed problem for most of the countries. The mostwell-known approach is ap-plying the text mining approaches to the news and some time series analysis tech-niques over stock market closing values in order toapply classification or cluster-ing algorithms over the features extracted. This study goes further and tries to askthe question what are the available time series analysis techniques for the stockmarket closing values and which one is the most suitable? In this study, the newsand their dates are collected into a database and text mining is applied over thenews, the text mining part has been kept simple with only term frequency – in-verse document frequency method. For the time series analysis part, we havestudied 10 different methods such as random walk, moving average, acceleration,Bollinger band, price rate of change, periodic average, difference, momentum orrelative strength index and their variation. In this study we have also explainedthese techniques in a comparative way and we have applied the methods overTurkish Stock Market closing values for more than a2 year period. On the otherhand, we have applied the term frequency – inversedocument frequency methodon the economy news of one of the high-circulatingnewspapers in Turkey.

Sentiment analysis of Arabic tweets using text mining techniques

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Al-Horaibi, Lamia; Khan, Muhammad Badruddin

2016-07-01

Sentiment analysis has become a flourishing field of text mining and natural language processing. Sentiment analysis aims to determine whether the text is written to express positive, negative, or neutral emotions about a certain domain. Most sentiment analysis researchers focus on English texts, with very limited resources available for other complex languages, such as Arabic. In this study, the target was to develop an initial model that performs satisfactorily and measures Arabic Twitter sentiment by using machine learning approach, Naïve Bayes and Decision Tree for classification algorithms. The datasets used contains more than 2,000 Arabic tweets collected from Twitter. We performed several experiments to check the performance of the two algorithms classifiers using different combinations of text-processing functions. We found that available facilities for Arabic text processing need to be made from scratch or improved to develop accurate classifiers. The small functionalities developed by us in a Python language environment helped improve the results and proved that sentiment analysis in the Arabic domain needs lot of work on the lexicon side.

Biomedical text mining for research rigor and integrity: tasks, challenges, directions.

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Kilicoglu, Halil

2017-06-13

An estimated quarter of a trillion US dollars is invested in the biomedical research enterprise annually. There is growing alarm that a significant portion of this investment is wasted because of problems in reproducibility of research findings and in the rigor and integrity of research conduct and reporting. Recent years have seen a flurry of activities focusing on standardization and guideline development to enhance the reproducibility and rigor of biomedical research. Research activity is primarily communicated via textual artifacts, ranging from grant applications to journal publications. These artifacts can be both the source and the manifestation of practices leading to research waste. For example, an article may describe a poorly designed experiment, or the authors may reach conclusions not supported by the evidence presented. In this article, we pose the question of whether biomedical text mining techniques can assist the stakeholders in the biomedical research enterprise in doing their part toward enhancing research integrity and rigor. In particular, we identify four key areas in which text mining techniques can make a significant contribution: plagiarism/fraud detection, ensuring adherence to reporting guidelines, managing information overload and accurate citation/enhanced bibliometrics. We review the existing methods and tools for specific tasks, if they exist, or discuss relevant research that can provide guidance for future work. With the exponential increase in biomedical research output and the ability of text mining approaches to perform automatic tasks at large scale, we propose that such approaches can support tools that promote responsible research practices, providing significant benefits for the biomedical research enterprise. Published by Oxford University Press 2017. This work is written by a US Government employee and is in the public domain in the US.

Disciplinary Literacy in History: A Toolkit for Digital Citizenship

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Wineburg, Sam; Reisman, Abby

2015-01-01

In this article, we draw clear distinctions between generic reading comprehension and disciplinary literacy in history. We argue that disciplinary reading restores agency to the reader, changing the typical relationship between text and reader, in which knowledge flows down from one to the other. Sourcing, for example, enjoins readers to engage…

Cluo: Web-Scale Text Mining System For Open Source Intelligence Purposes

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Przemyslaw Maciolek

2013-01-01

Full Text Available The amount of textual information published on the Internet is considered tobe in billions of web pages, blog posts, comments, social media updates andothers. Analyzing such quantities of data requires high level of distribution –both data and computing. This is especially true in case of complex algorithms,often used in text mining tasks.The paper presents a prototype implementation of CLUO – an Open SourceIntelligence (OSINT system, which extracts and analyzes significant quantitiesof openly available information.

Text Mining Metal-Organic Framework Papers.

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Park, Sanghoon; Kim, Baekjun; Choi, Sihoon; Boyd, Peter G; Smit, Berend; Kim, Jihan

2018-02-26

We have developed a simple text mining algorithm that allows us to identify surface area and pore volumes of metal-organic frameworks (MOFs) using manuscript html files as inputs. The algorithm searches for common units (e.g., m 2 /g, cm 3 /g) associated with these two quantities to facilitate the search. From the sample set data of over 200 MOFs, the algorithm managed to identify 90% and 88.8% of the correct surface area and pore volume values. Further application to a test set of randomly chosen MOF html files yielded 73.2% and 85.1% accuracies for the two respective quantities. Most of the errors stem from unorthodox sentence structures that made it difficult to identify the correct data as well as bolded notations of MOFs (e.g., 1a) that made it difficult identify its real name. These types of tools will become useful when it comes to discovering structure-property relationships among MOFs as well as collecting a large set of data for references.

Application of Ferulic Acid for Alzheimer's Disease: Combination of Text Mining and Experimental Validation.

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Meng, Guilin; Meng, Xiulin; Ma, Xiaoye; Zhang, Gengping; Hu, Xiaolin; Jin, Aiping; Zhao, Yanxin; Liu, Xueyuan

2018-01-01

Alzheimer's disease (AD) is an increasing concern in human health. Despite significant research, highly effective drugs to treat AD are lacking. The present study describes the text mining process to identify drug candidates from a traditional Chinese medicine (TCM) database, along with associated protein target mechanisms. We carried out text mining to identify literatures that referenced both AD and TCM and focused on identifying compounds and protein targets of interest. After targeting one potential TCM candidate, corresponding protein-protein interaction (PPI) networks were assembled in STRING to decipher the most possible mechanism of action. This was followed by validation using Western blot and co-immunoprecipitation in an AD cell model. The text mining strategy using a vast amount of AD-related literature and the TCM database identified curcumin, whose major component was ferulic acid (FA). This was used as a key candidate compound for further study. Using the top calculated interaction score in STRING, BACE1 and MMP2 were implicated in the activity of FA in AD. Exposure of SHSY5Y-APP cells to FA resulted in the decrease in expression levels of BACE-1 and APP, while the expression of MMP-2 and MMP-9 increased in a dose-dependent manner. This suggests that FA induced BACE1 and MMP2 pathways maybe novel potential mechanisms involved in AD. The text mining of literature and TCM database related to AD suggested FA as a promising TCM ingredient for the treatment of AD. Potential mechanisms interconnected and integrated with Aβ aggregation inhibition and extracellular matrix remodeling underlying the activity of FA were identified using in vitro studies.

Text Mining for Precision Medicine: Bringing structure to EHRs and biomedical literature to understand genes and health

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Simmons, Michael; Singhal, Ayush; Lu, Zhiyong

2018-01-01

The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text — found in biomedical publications and clinical notes — is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine. PMID:27807747

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health.

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Simmons, Michael; Singhal, Ayush; Lu, Zhiyong

2016-01-01

The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next-generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text-found in biomedical publications and clinical notes-is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.

Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

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Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

2013-11-01

The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

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Krasnogor Natalio

2009-10-01

Full Text Available Abstract Background Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks. Results We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining. Conclusion ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.

Coronary artery disease risk assessment from unstructured electronic health records using text mining.

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Jonnagaddala, Jitendra; Liaw, Siaw-Teng; Ray, Pradeep; Kumar, Manish; Chang, Nai-Wen; Dai, Hong-Jie

2015-12-01

Coronary artery disease (CAD) often leads to myocardial infarction, which may be fatal. Risk factors can be used to predict CAD, which may subsequently lead to prevention or early intervention. Patient data such as co-morbidities, medication history, social history and family history are required to determine the risk factors for a disease. However, risk factor data are usually embedded in unstructured clinical narratives if the data is not collected specifically for risk assessment purposes. Clinical text mining can be used to extract data related to risk factors from unstructured clinical notes. This study presents methods to extract Framingham risk factors from unstructured electronic health records using clinical text mining and to calculate 10-year coronary artery disease risk scores in a cohort of diabetic patients. We developed a rule-based system to extract risk factors: age, gender, total cholesterol, HDL-C, blood pressure, diabetes history and smoking history. The results showed that the output from the text mining system was reliable, but there was a significant amount of missing data to calculate the Framingham risk score. A systematic approach for understanding missing data was followed by implementation of imputation strategies. An analysis of the 10-year Framingham risk scores for coronary artery disease in this cohort has shown that the majority of the diabetic patients are at moderate risk of CAD. Copyright © 2015 Elsevier Inc. All rights reserved.

Text mining applied to electronic cardiovascular procedure reports to identify patients with trileaflet aortic stenosis and coronary artery disease.

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Small, Aeron M; Kiss, Daniel H; Zlatsin, Yevgeny; Birtwell, David L; Williams, Heather; Guerraty, Marie A; Han, Yuchi; Anwaruddin, Saif; Holmes, John H; Chirinos, Julio A; Wilensky, Robert L; Giri, Jay; Rader, Daniel J

2017-08-01

Interrogation of the electronic health record (EHR) using billing codes as a surrogate for diagnoses of interest has been widely used for clinical research. However, the accuracy of this methodology is variable, as it reflects billing codes rather than severity of disease, and depends on the disease and the accuracy of the coding practitioner. Systematic application of text mining to the EHR has had variable success for the detection of cardiovascular phenotypes. We hypothesize that the application of text mining algorithms to cardiovascular procedure reports may be a superior method to identify patients with cardiovascular conditions of interest. We adapted the Oracle product Endeca, which utilizes text mining to identify terms of interest from a NoSQL-like database, for purposes of searching cardiovascular procedure reports and termed the tool "PennSeek". We imported 282,569 echocardiography reports representing 81,164 individuals and 27,205 cardiac catheterization reports representing 14,567 individuals from non-searchable databases into PennSeek. We then applied clinical criteria to these reports in PennSeek to identify patients with trileaflet aortic stenosis (TAS) and coronary artery disease (CAD). Accuracy of patient identification by text mining through PennSeek was compared with ICD-9 billing codes. Text mining identified 7115 patients with TAS and 9247 patients with CAD. ICD-9 codes identified 8272 patients with TAS and 6913 patients with CAD. 4346 patients with AS and 6024 patients with CAD were identified by both approaches. A randomly selected sample of 200-250 patients uniquely identified by text mining was compared with 200-250 patients uniquely identified by billing codes for both diseases. We demonstrate that text mining was superior, with a positive predictive value (PPV) of 0.95 compared to 0.53 by ICD-9 for TAS, and a PPV of 0.97 compared to 0.86 for CAD. These results highlight the superiority of text mining algorithms applied to electronic

BRIEF CONSIDERATIONS ON THE DISCIPLINARY LIABILITY OF THE MAGISTRATES

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ELENA EMILIA ŞTEFAN

2013-05-01

Full Text Available The recent amendments in the applicable law on the disciplinary liability of the magistrates have induced many debates regarding the increase of holders that own the right to initiate the disciplinary action against a magistrate and also regarding the area of disciplinary offenses. The conferring of the status of holder of the disciplinary action to the Minister of Justice, the President of the High Court of Cassation and Justice and to the General Attorney of the Prosecutor’s Office of the High Court of Cassation and Justice, has conferred us the opportunity to present the impact of these legislative amendments on the legal environment. Therefore, the theme proposed through this study will be done by presenting the relevant legislation and the relevant constitutional jurisprudence.

Systematic analysis of molecular mechanisms for HCC metastasis via text mining approach.

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Zhen, Cheng; Zhu, Caizhong; Chen, Haoyang; Xiong, Yiru; Tan, Junyuan; Chen, Dong; Li, Jin

2017-02-21

To systematically explore the molecular mechanism for hepatocellular carcinoma (HCC) metastasis and identify regulatory genes with text mining methods. Genes with highest frequencies and significant pathways related to HCC metastasis were listed. A handful of proteins such as EGFR, MDM2, TP53 and APP, were identified as hub nodes in PPI (protein-protein interaction) network. Compared with unique genes for HBV-HCCs, genes particular to HCV-HCCs were less, but may participate in more extensive signaling processes. VEGFA, PI3KCA, MAPK1, MMP9 and other genes may play important roles in multiple phenotypes of metastasis. Genes in abstracts of HCC-metastasis literatures were identified. Word frequency analysis, KEGG pathway and PPI network analysis were performed. Then co-occurrence analysis between genes and metastasis-related phenotypes were carried out. Text mining is effective for revealing potential regulators or pathways, but the purpose of it should be specific, and the combination of various methods will be more useful.

Cross-Comparison of Leaching Strains Isolated from Two Different Regions: Chambishi and Dexing Copper Mines

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Baba Ngom

2014-01-01

Full Text Available A cross-comparison of six strains isolated from two different regions, Chambishi copper mine (Zambia, Africa and Dexing copper mine (China, Asia, was conducted to study the leaching efficiency of low grade copper ores. The strains belong to the three major species often encountered in bioleaching of copper sulfide ores under mesophilic conditions: Acidithiobacillus ferrooxidans, Acidithiobacillus thiooxidans, and Leptospirillum ferriphilum. Prior to their study in bioleaching, the different strains were characterized and compared at physiological level. The results revealed that, except for copper tolerance, strains within species presented almost similar physiological traits with slight advantages of Chambishi strains. However, in terms of leaching efficiency, native strains always achieved higher cell density and greater iron and copper extraction rates than the foreign microorganisms. In addition, microbial community analysis revealed that the different mixed cultures shared almost the same profile, and At. ferrooxidans strains always outcompeted the other strains.

The Potential of Text Mining in Data Integration and Network Biology for Plant Research: A Case Study on Arabidopsis[C][W

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Van Landeghem, Sofie; De Bodt, Stefanie; Drebert, Zuzanna J.; Inzé, Dirk; Van de Peer, Yves

2013-01-01

Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein–protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies. PMID:23532071

Can abstract screening workload be reduced using text mining? User experiences of the tool Rayyan.

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Olofsson, Hanna; Brolund, Agneta; Hellberg, Christel; Silverstein, Rebecca; Stenström, Karin; Österberg, Marie; Dagerhamn, Jessica

2017-09-01

One time-consuming aspect of conducting systematic reviews is the task of sifting through abstracts to identify relevant studies. One promising approach for reducing this burden uses text mining technology to identify those abstracts that are potentially most relevant for a project, allowing those abstracts to be screened first. To examine the effectiveness of the text mining functionality of the abstract screening tool Rayyan. User experiences were collected. Rayyan was used to screen abstracts for 6 reviews in 2015. After screening 25%, 50%, and 75% of the abstracts, the screeners logged the relevant references identified. A survey was sent to users. After screening half of the search result with Rayyan, 86% to 99% of the references deemed relevant to the study were identified. Of those studies included in the final reports, 96% to 100% were already identified in the first half of the screening process. Users rated Rayyan 4.5 out of 5. The text mining function in Rayyan successfully helped reviewers identify relevant studies early in the screening process. Copyright © 2017 John Wiley & Sons, Ltd.

The levels of disclosure relating to mine closure obligations by platinum mining companies

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Joline Sturdy

2017-06-01

Aim: The aim of this study is to establish the extent to which platinum mines listed on the Johannesburg Stock Exchange (JSE comply with a recommended disclosure framework. Setting: South Africa is the largest producer of platinum in the world. The study covers all platinum mines listed on the JSE. Methods: Using a framework, a census of the annual financial statements, integrated annual reports and sustainability reports or websites was conducted to determine the level of compliance of disclosure relating to mine closure obligations to the recommended disclosure framework. Results: The results show disclosure relating to mine closure obligations of platinum mines listed on the JSE is inconsistent and not sufficient for stakeholders to understand the scope, key assumptions, parameters or reliability of the assessment and calculation of mine closure obligations. Conclusion: The assumptions used to determine mine closure obligations are specialised and multi-disciplinary. The accuracy and reliability of mine closure obligations will improve dramatically through greater transparency and access to information. It is recommended that the JSE listings for mining companies should require a competent person’s report to provide disclosure on assumptions, key values and processes applied to determine the mine closure obligations. Furthermore, it is recommended that the Department of Mineral Resources implements a mechanism of independent assessment of mine closure obligations by experts on an ongoing basis.

The Feasibility of Using Large-Scale Text Mining to Detect Adverse Childhood Experiences in a VA-Treated Population.

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Hammond, Kenric W; Ben-Ari, Alon Y; Laundry, Ryan J; Boyko, Edward J; Samore, Matthew H

2015-12-01

Free text in electronic health records resists large-scale analysis. Text records facts of interest not found in encoded data, and text mining enables their retrieval and quantification. The U.S. Department of Veterans Affairs (VA) clinical data repository affords an opportunity to apply text-mining methodology to study clinical questions in large populations. To assess the feasibility of text mining, investigation of the relationship between exposure to adverse childhood experiences (ACEs) and recorded diagnoses was conducted among all VA-treated Gulf war veterans, utilizing all progress notes recorded from 2000-2011. Text processing extracted ACE exposures recorded among 44.7 million clinical notes belonging to 243,973 veterans. The relationship of ACE exposure to adult illnesses was analyzed using logistic regression. Bias considerations were assessed. ACE score was strongly associated with suicide attempts and serious mental disorders (ORs = 1.84 to 1.97), and less so with behaviorally mediated and somatic conditions (ORs = 1.02 to 1.36) per unit. Bias adjustments did not remove persistent associations between ACE score and most illnesses. Text mining to detect ACE exposure in a large population was feasible. Analysis of the relationship between ACE score and adult health conditions yielded patterns of association consistent with prior research. Copyright © 2015 International Society for Traumatic Stress Studies.

The Influence of Classroom Disciplinary Climate of Schools on Reading Achievement: A Cross-Country Comparative Study

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Ning, Bo; Van Damme, Jan; Van Den Noortgate, Wim; Yang, Xiangdong; Gielen, Sarah

2015-01-01

Despite considerable interest in research and practice in the effect of classroom disciplinary climate of schools on academic achievement, little is known about the generalizability of this effect over countries. Using hierarchical linear analyses, the present study reveals that a better classroom disciplinary climate in a school is significantly…

EAP course design within a context of institutional change and cross-disciplinary collaboration: Factors shaping the creating of ‘writing for commerce’

Directory of Open Access Journals (Sweden)

Jackson, Fiona

2009-12-01

Full Text Available Many reports of needs analysis and curriculum design of EAP courses focus largely on the immediate pedagogic context and ensuing decision making and materials design processes of the course designers. This paper explores the process of curriculum design from the perspectives of both debates and developments within the field of language and literacy education, and the impact of international, national and institutional shifts in higher education on one course design process within one South African university. The paper explores the realities of institutional and disciplinary histories and changes that impacted on the design of an EAP course for a linguistically, culturally and racially diverse group of first-year commerce students. The intricacies of creating such a course as an inter-disciplinary school, rather than departmental, project are explored and briefly evaluated. The key principles underpinning the course design are explained. The paper concludes with consideration of why the collaborative inter-disciplinary project has faded, although the course has continued successfully.

SUSPENSION OF THE PRIOR DISCIPLINARY INVESTIGATION ACCORDING TO LABOR LAW

Directory of Open Access Journals (Sweden)

Nicolae, GRADINARU

2014-11-01

Full Text Available In order to conduct the prior disciplinary investigation, the employee shall be convoked in writing by the person authorized by the employer to carry out the research, specifying the subject, date, time and place of the meeting. For this purpose the employer shall appoint a committee charged with conducting the prior disciplinary investigation. Prior disciplinary research cannot be done without the possibility of the accused person to defend himself. It would be an abuse of the employer to violate these provisions. Since the employee is entitled to formulate and sustain defence in proving innocence or lesser degree of guilt than imputed, it needs between the moment were disclosed to the employee and the one of performing the prior disciplinary investigation to be a reasonable term for the employee to be able to prepare a defence in this regard. The employee's failure to present at the convocation, without an objective reason entitles the employer to dispose the sanctioning without making the prior disciplinary investigation. The objective reason which makes the employee, that is subject to prior disciplinary investigation, unable to present to the preliminary disciplinary investigation, should be at the time of the investigation in question.

Towards A Model Of Knowledge Extraction Of Text Mining For Palliative Care Patients In Panama.

Directory of Open Access Journals (Sweden)

Denis Cedeno Moreno

2015-08-01

Full Text Available Solutions using information technology is an innovative way to manage the information hospice patients in hospitals in Panama. The application of techniques of text mining for the domain of medicine especially information from electronic health records of patients in palliative care is one of the most recent and promising research areas for the analysis of textual data. Text mining is based on new knowledge extraction from unstructured natural language data. We may also create ontologies to describe the terminology and knowledge in a given domain. In an ontology conceptualization of a domain that may be general or specific formalized. Knowledge can be used for decision making by health specialists or can help in research topics for improving the health system.

Text mining for literature review and knowledge discovery in cancer risk assessment and research.

Directory of Open Access Journals (Sweden)

Anna Korhonen

Full Text Available Research in biomedical text mining is starting to produce technology which can make information in biomedical literature more accessible for bio-scientists. One of the current challenges is to integrate and refine this technology to support real-life scientific tasks in biomedicine, and to evaluate its usefulness in the context of such tasks. We describe CRAB - a fully integrated text mining tool designed to support chemical health risk assessment. This task is complex and time-consuming, requiring a thorough review of existing scientific data on a particular chemical. Covering human, animal, cellular and other mechanistic data from various fields of biomedicine, this is highly varied and therefore difficult to harvest from literature databases via manual means. Our tool automates the process by extracting relevant scientific data in published literature and classifying it according to multiple qualitative dimensions. Developed in close collaboration with risk assessors, the tool allows navigating the classified dataset in various ways and sharing the data with other users. We present a direct and user-based evaluation which shows that the technology integrated in the tool is highly accurate, and report a number of case studies which demonstrate how the tool can be used to support scientific discovery in cancer risk assessment and research. Our work demonstrates the usefulness of a text mining pipeline in facilitating complex research tasks in biomedicine. We discuss further development and application of our technology to other types of chemical risk assessment in the future.

Associations between child disciplinary practices and bullying behavior in adolescents

Directory of Open Access Journals (Sweden)

Graziela A.H. Zottis

2014-07-01

Full Text Available OBJECTIVE: to investigate associations between different types of child disciplinary practices and children and adolescents' bullying behavior in a Brazilian sample. METHODS: cross-sectional study, with a school-based sample of 10-to 15-year-old children and adolescents. Child disciplinary practices were assessed using two main subtypes: power-assertive and punitive (psychological aggression, corporal punishment, deprivation of privileges, and penalty tasks and inductive (explaining, rewarding, and monitoring. A modified version of the Olweus Bully Victim Questionnaire was used to measure the frequency of bullying. RESULTS: 247 children and adolescents were evaluated and 98 (39.7% were classified as bullies. Power-assertive and punitive discipline by either mother or father was associated with bullying perpetration by their children. Mothers who mostly used this type of discipline were 4.36 (95% CI: 1.87-10.16; p < 0.001 times more likely of having a bully child. Psychological aggression and mild forms of corporal punishment presented the highest odds ratios. Overall inductive discipline was not associated with bullying. CONCLUSIONS: bullying was associated to parents' assertive and punitive discipline. Finding different ways of disciplining children and adolescents might decrease bullying behavior.

Developing Cross-Disciplinary Competencies through College Algebra

Directory of Open Access Journals (Sweden)

Reem Jaafar, PhD

2012-08-01

Full Text Available To argue for the importance of an integrative approach to learning in introductory STEM (Science, Technology, Engineering and Mathematics and other courses, we present a case study of a project incorporating cross-curricular skills in a college algebra course. We analyze student work on the project and responses to surveys, and find the assignment affects positively students’ mastery of specific quantitative skills, perceptions of learning, civic awareness, and sense of relevance of mathematical study. We use the analysis to suggest guidelines for designing other activities aiming to teach the whole student in introductory courses.

Building a Disciplinary, World‐Wide Data Infrastructure

Directory of Open Access Journals (Sweden)

Françoise Genova

2017-04-01

Full Text Available Sharing scientific data with the objective of making it discoverable, accessible, reusable, and interoperable requires work and presents challenges being faced at the disciplinary level to define in particular how the data should be formatted and described. This paper represents the Proceedings of a session held at SciDataCon 2016 (Denver, 12–13 September 2016. It explores the way a range of disciplines, namely materials science, crystallography, astronomy, earth sciences, humanities and linguistics, get organized at the international level to address those challenges. The disciplinary culture with respect to data sharing, science drivers, organization, lessons learnt and the elements of the data infrastructure which are or could be shared with others are briefly described. Commonalities and differences are assessed. Common key elements for success are identified: data sharing should be science driven; defining the disciplinary part of the interdisciplinary standards is mandatory but challenging; sharing of applications should accompany data sharing. Incentives such as journal and funding agency requirements are also similar. For all, social aspects are more challenging than technological ones. Governance is more diverse, often specific to the discipline organization. Being problem‐driven is also a key factor of success for building bridges to enable interdisciplinary research. Several international data organizations such as CODATA, RDA and WDS can facilitate the establishment of disciplinary interoperability frameworks. As a spin‐off of the session, a RDA Disciplinary Interoperability Interest Group is proposed to bring together representatives across disciplines to better organize and drive the discussion for prioritizing, harmonizing and efficiently articulating disciplinary needs.

Web services-based text-mining demonstrates broad impacts for interoperability and process simplification.

Science.gov (United States)

Wiegers, Thomas C; Davis, Allan Peter; Mattingly, Carolyn J

2014-01-01

The Critical Assessment of Information Extraction systems in Biology (BioCreAtIvE) challenge evaluation tasks collectively represent a community-wide effort to evaluate a variety of text-mining and information extraction systems applied to the biological domain. The BioCreative IV Workshop included five independent subject areas, including Track 3, which focused on named-entity recognition (NER) for the Comparative Toxicogenomics Database (CTD; http://ctdbase.org). Previously, CTD had organized document ranking and NER-related tasks for the BioCreative Workshop 2012; a key finding of that effort was that interoperability and integration complexity were major impediments to the direct application of the systems to CTD's text-mining pipeline. This underscored a prevailing problem with software integration efforts. Major interoperability-related issues included lack of process modularity, operating system incompatibility, tool configuration complexity and lack of standardization of high-level inter-process communications. One approach to potentially mitigate interoperability and general integration issues is the use of Web services to abstract implementation details; rather than integrating NER tools directly, HTTP-based calls from CTD's asynchronous, batch-oriented text-mining pipeline could be made to remote NER Web services for recognition of specific biological terms using BioC (an emerging family of XML formats) for inter-process communications. To test this concept, participating groups developed Representational State Transfer /BioC-compliant Web services tailored to CTD's NER requirements. Participants were provided with a comprehensive set of training materials. CTD evaluated results obtained from the remote Web service-based URLs against a test data set of 510 manually curated scientific articles. Twelve groups participated in the challenge. Recall, precision, balanced F-scores and response times were calculated. Top balanced F-scores for gene, chemical and

Mining for associations between text and brain activation in a functional neuroimaging database

DEFF Research Database (Denmark)

Nielsen, Finn Årup; Hansen, Lars Kai; Balslev, D.

2004-01-01

We describe a method for mining a neuroimaging database for associations between text and brain locations. The objective is to discover association rules between words indicative of cognitive function as described in abstracts of neuroscience papers and sets of reported stereotactic Talairach...

Text mining analysis of public comments regarding high-level radioactive waste disposal

International Nuclear Information System (INIS)

Kugo, Akihide; Yoshikawa, Hidekazu; Shimoda, Hiroshi; Wakabayashi, Yasunaga

2005-01-01

In order to narrow the risk perception gap as seen in social investigations between the general public and people who are involved in nuclear industry, public comments on high-level radioactive waste (HLW) disposal have been conducted to find the significant talking points with the general public for constructing an effective risk communication model of social risk information regarding HLW disposal. Text mining was introduced to examine public comments to identify the core public interest underlying the comments. The utilized test mining method is to cluster specific groups of words with negative meanings and then to analyze public understanding by employing text structural analysis to extract words from subjective expressions. Using these procedures, it was found that the public does not trust the nuclear fuel cycle promotion policy and shows signs of anxiety about the long-lasting technological reliability of waste storage. To develop effective social risk communication of HLW issues, these findings are expected to help experts in the nuclear industry to communicate with the general public more effectively to obtain their trust. (author)

4 CFR 28.132 - Disciplinary proceedings.

Science.gov (United States)

2010-01-01

... Corrective Action, Disciplinary and Stay Proceedings § 28.132 Disciplinary proceedings. (a) If the General Counsel determines after any investigation under 31 U.S.C. 752(b) that disciplinary action should be... ordering disciplinary action. (d) A final order of the Board may order disciplinary action consisting of...

In Dogs We Trust? Intersubjectivity, Response-Able Relations, and the Making of Mine Detector Dogs

Science.gov (United States)

Kirk, Robert G W

2014-01-01

The utility of the dog as a mine detector has divided the mine clearance community since dogs were first used for this purpose during the Second World War. This paper adopts a historical perspective to investigate how, why, and to what consequence, the use of minedogs remains contested despite decades of research into their abilities. It explores the changing factors that have made it possible to think that dogs could, or could not, serve as reliable detectors of landmines over time. Beginning with an analysis of the wartime context that shaped the creation of minedogs, the paper then examines two contemporaneous investigations undertaken in the 1950s. The first, a British investigation pursued by the anatomist Solly Zuckerman, concluded that dogs could never be the mine hunter's best friend. The second, an American study led by the parapsychologist J. B. Rhine, suggested dogs were potentially useful for mine clearance. Drawing on literature from science studies and the emerging subdiscipline of “animal studies,” it is argued that cross-species intersubjectivity played a significant role in determining these different positions. The conceptual landscapes of Zuckerman and Rhine's disciplinary backgrounds are shown to have produced distinct approaches to managing cross-species relations, thus explaining how diverse opinions on minedog can coexist. In conclusion, it is shown that the way one structures relationships between humans and animals has profound impact on the knowledge and labor subsequently produced, a process that cannot be separated from ethical consequence. PMID:24318987

From university research to innovation: Detecting knowledge transfer via text mining

Energy Technology Data Exchange (ETDEWEB)

Woltmann, S.; Clemmensen, L.; Alkærsig, L

2016-07-01

Knowledge transfer by universities is a top priority in innovation policy and a primary purpose for public research funding, due to being an important driver of technical change and innovation. Current empirical research on the impact of university research relies mainly on formal databases and indicators such as patents, collaborative publications and license agreements, to assess the contribution to the socioeconomic surrounding of universities. In this study, we present an extension of the current empirical framework by applying new computational methods, namely text mining and pattern recognition. Text samples for this purpose can include files containing social media contents, company websites and annual reports. The empirical focus in the present study is on the technical sciences and in particular on the case of the Technical University of Denmark (DTU). We generated two independent text collections (corpora) to identify correlations of university publications and company webpages. One corpus representing the company sites, serving as sample of the private economy and a second corpus, providing the reference to the university research, containing relevant publications. We associated the former with the latter to obtain insights into possible text and semantic relatedness. The text mining methods are extrapolating the correlations, semantic patterns and content comparison of the two corpora to define the document relatedness. We expect the development of a novel tool using contemporary techniques for the measurement of public research impact. The approach aims to be applicable across universities and thus enable a more holistic comparable assessment. This rely less on formal databases, which is certainly beneficial in terms of the data reliability. We seek to provide a supplementary perspective for the detection of the dissemination of university research and hereby enable policy makers to gain additional insights of (informal) contributions of knowledge

From Novice to Disciplinary Expert: Disciplinary Identity and Genre Mastery

Science.gov (United States)

Dressen-Hammouda, Dacia

2008-01-01

A student's emerging genre mastery is a complex process which involves learning not only relevant discoursal forms, but also a wide range of specialist knowledge frames. Recent research suggests that these knowledge frames are acquired during the development of a student's disciplinary identity. Although disciplinary identity clearly contributes…

The Bridge: Experiments in Science and Art, Experiences from the 2017 SciArt Center Cross-Disciplinary Residency Program

Science.gov (United States)

Shipman, J. S.; Chalmers, R.; Buntaine, J.

2017-12-01

Cross-disciplinary programs create the opportunity to explore new realms for scientists and artists alike. Through the collaborative process, artistic insights enable innovative approaches to emotionally connect to and visualize the world around us. Likewise, engagement across the art-science spectrum can lead to shifts in scientific thinking that create new connections in data and drive discoveries in research. The SciArt Center "The Bridge Residency Program" is a four-month long virtual residency open internationally for professionals in the arts and sciences to facilitate cross-disciplinary work and to bring together like-minded participants. The SciArt Center provides a virtual space to record and showcase the process and products of each collaboration. The work is facilitated with biweekly Skype calls and documented with weekly blog posts. Residents create either digital or physical products and share via video, images, or direct mailing with their collaborators. Past projects have produced call and response discussion, websites, skills and conference presentations, science-art studies, virtual exhibits, art shows, dance performances, and research exchange. Here we present the creative process and outcomes of one of the four collaborative teams selected for the 2017 residency. Jill Shipman, a Ph.D. Candidate in Volcanology who is also active in filmmaking and theatrical productions and Rosemary Chalmers, a UK-based lecturer, concept artist, and illustrator with a specialty in creature design. They were paired together for their shared interest in storytelling, illustration, and unique geological and environmental habitats and the life that occupies them. We will discuss the collaborative project developed by this team during their recent residency and illustrate how a virtual program can bridge the distance between geographical location to foster science and art collaboration. To follow the progress of the residency please visit: http://www.sciartcenter.org/the-bridge.html

Ion Channel ElectroPhysiology Ontology (ICEPO) - a case study of text mining assisted ontology development.

Science.gov (United States)

Elayavilli, Ravikumar Komandur; Liu, Hongfang

2016-01-01

Computational modeling of biological cascades is of great interest to quantitative biologists. Biomedical text has been a rich source for quantitative information. Gathering quantitative parameters and values from biomedical text is one significant challenge in the early steps of computational modeling as it involves huge manual effort. While automatically extracting such quantitative information from bio-medical text may offer some relief, lack of ontological representation for a subdomain serves as impedance in normalizing textual extractions to a standard representation. This may render textual extractions less meaningful to the domain experts. In this work, we propose a rule-based approach to automatically extract relations involving quantitative data from biomedical text describing ion channel electrophysiology. We further translated the quantitative assertions extracted through text mining to a formal representation that may help in constructing ontology for ion channel events using a rule based approach. We have developed Ion Channel ElectroPhysiology Ontology (ICEPO) by integrating the information represented in closely related ontologies such as, Cell Physiology Ontology (CPO), and Cardiac Electro Physiology Ontology (CPEO) and the knowledge provided by domain experts. The rule-based system achieved an overall F-measure of 68.93% in extracting the quantitative data assertions system on an independently annotated blind data set. We further made an initial attempt in formalizing the quantitative data assertions extracted from the biomedical text into a formal representation that offers potential to facilitate the integration of text mining into ontological workflow, a novel aspect of this study. This work is a case study where we created a platform that provides formal interaction between ontology development and text mining. We have achieved partial success in extracting quantitative assertions from the biomedical text and formalizing them in ontological

Weighted mining of massive collections of [Formula: see text]-values by convex optimization.

Science.gov (United States)

Dobriban, Edgar

2018-06-01

Researchers in data-rich disciplines-think of computational genomics and observational cosmology-often wish to mine large bodies of [Formula: see text]-values looking for significant effects, while controlling the false discovery rate or family-wise error rate. Increasingly, researchers also wish to prioritize certain hypotheses, for example, those thought to have larger effect sizes, by upweighting, and to impose constraints on the underlying mining, such as monotonicity along a certain sequence. We introduce Princessp , a principled method for performing weighted multiple testing by constrained convex optimization. Our method elegantly allows one to prioritize certain hypotheses through upweighting and to discount others through downweighting, while constraining the underlying weights involved in the mining process. When the [Formula: see text]-values derive from monotone likelihood ratio families such as the Gaussian means model, the new method allows exact solution of an important optimal weighting problem previously thought to be non-convex and computationally infeasible. Our method scales to massive data set sizes. We illustrate the applications of Princessp on a series of standard genomics data sets and offer comparisons with several previous 'standard' methods. Princessp offers both ease of operation and the ability to scale to extremely large problem sizes. The method is available as open-source software from github.com/dobriban/pvalue_weighting_matlab (accessed 11 October 2017).

Tracing Knowledge Transfer from Universities to Industry: A Text Mining Approach

DEFF Research Database (Denmark)

Woltmann, Sabrina; Alkærsig, Lars

2017-01-01

This paper identifies transferred knowledge between universities and the industry by proposing the use of a computational linguistic method. Current research on university-industry knowledge exchange relies often on formal databases and indicators such as patents, collaborative publications and l...... is the first step to enable the identification of common knowledge and knowledge transfer via text mining to increase its measurability....... and license agreements, to assess the contribution to the socioeconomic surrounding of universities. We, on the other hand, use the texts from university abstracts to identify university knowledge and compare them with texts from firm webpages. We use these text data to identify common key words and thereby...... identify overlapping contents among the texts. As method we use a well-established word ranking method from the field of information retrieval term frequency–inverse document frequency (TFIDF) to identify commonalities between texts from university. In examining the outcomes of the TFIDF statistic we find...

Text mining to decipher free-response consumer complaints: insights from the NHTSA vehicle owner's complaint database.

Science.gov (United States)

Ghazizadeh, Mahtab; McDonald, Anthony D; Lee, John D

2014-09-01

This study applies text mining to extract clusters of vehicle problems and associated trends from free-response data in the National Highway Traffic Safety Administration's vehicle owner's complaint database. As the automotive industry adopts new technologies, it is important to systematically assess the effect of these changes on traffic safety. Driving simulators, naturalistic driving data, and crash databases all contribute to a better understanding of how drivers respond to changing vehicle technology, but other approaches, such as automated analysis of incident reports, are needed. Free-response data from incidents representing two severity levels (fatal incidents and incidents involving injury) were analyzed using a text mining approach: latent semantic analysis (LSA). LSA and hierarchical clustering identified clusters of complaints for each severity level, which were compared and analyzed across time. Cluster analysis identified eight clusters of fatal incidents and six clusters of incidents involving injury. Comparisons showed that although the airbag clusters across the two severity levels have the same most frequent terms, the circumstances around the incidents differ. The time trends show clear increases in complaints surrounding the Ford/Firestone tire recall and the Toyota unintended acceleration recall. Increases in complaints may be partially driven by these recall announcements and the associated media attention. Text mining can reveal useful information from free-response databases that would otherwise be prohibitively time-consuming and difficult to summarize manually. Text mining can extend human analysis capabilities for large free-response databases to support earlier detection of problems and more timely safety interventions.

Goal-Based Domain Modeling as a Basis for Cross-Disciplinary Systems Engineering

Science.gov (United States)

Jarke, Matthias; Nissen, Hans W.; Rose, Thomas; Schmitz, Dominik

Small and medium-sized enterprises (SMEs) are important drivers for innovation. In particular, project-driven SMEs that closely cooperate with their customers have specific needs in regard to information engineering of their development process. They need a fast requirements capture since this is most often included in the (unpaid) offer development phase. At the same time, they need to maintain and reuse the knowledge and experiences they have gathered in previous projects extensively as it is their core asset. The situation is complicated further if the application field crosses disciplinary boundaries. To bridge the gaps and perspectives, we focus on shared goals and dependencies captured in models at a conceptual level. Such a model-based approach also offers a smarter connection to subsequent development stages, including a high share of automated code generation. In the approach presented here, the agent- and goal-oriented formalism i * is therefore extended by domain models to facilitate information organization. This extension permits a domain model-based similarity search, and a model-based transformation towards subsequent development stages. Our approach also addresses the evolution of domain models reflecting the experiences from completed projects. The approach is illustrated with a case study on software-intensive control systems in an SME of the automotive domain.

Public reactions to e-cigarette regulations on Twitter: a text mining analysis.

Science.gov (United States)

Lazard, Allison J; Wilcox, Gary B; Tuttle, Hannah M; Glowacki, Elizabeth M; Pikowski, Jessica

2017-12-01

In May 2016, the Food and Drug Administration (FDA) issued a final rule that deemed e-cigarettes to be within their regulatory authority as a tobacco product. News and opinions about the regulation were shared on social media platforms, such as Twitter, which can play an important role in shaping the public's attitudes. We analysed information shared on Twitter for insights into initial public reactions. A text mining approach was used to uncover important topics among reactions to the e-cigarette regulations on Twitter. SAS Text Miner V.12.1 software was used for descriptive text mining to uncover the primary topics from tweets collected from May 1 to May 17 2016 using NUVI software to gather the data. A total of nine topics were generated. These topics reveal initial reactions to whether the FDA's e-cigarette regulations will benefit or harm public health, how the regulations will impact the emerging e-cigarette market and efforts to share the news. The topics were dominated by negative or mixed reactions. In the days following the FDA's announcement of the new deeming regulations, the public reaction on Twitter was largely negative. Public health advocates should consider using social media outlets to better communicate the policy's intentions, reach and potential impact for public good to create a more balanced conversation. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

17 CFR 8.08 - Disciplinary committee.

Science.gov (United States)

2010-04-01

... 17 Commodity and Securities Exchanges 1 2010-04-01 2010-04-01 false Disciplinary committee. 8.08 Section 8.08 Commodity and Securities Exchanges COMMODITY FUTURES TRADING COMMISSION EXCHANGE PROCEDURES FOR DISCIPLINARY, SUMMARY, AND MEMBERSHIP DENIAL ACTIONS Disciplinary Procedure § 8.08 Disciplinary...

Classifying unstructed textual data using the Product Score Model: an alternative text mining algorithm

NARCIS (Netherlands)

He, Qiwei; Veldkamp, Bernard P.; Eggen, T.J.H.M.; Veldkamp, B.P.

2012-01-01

Unstructured textual data such as students’ essays and life narratives can provide helpful information in educational and psychological measurement, but often contain irregularities and ambiguities, which creates difficulties in analysis. Text mining techniques that seek to extract useful

The Study of Cross-layer Optimization for Wireless Rechargeable Sensor Networks Implemented in Coal Mines

Science.gov (United States)

Ding, Xu; Shi, Lei; Han, Jianghong; Lu, Jingting

2016-01-01

Wireless sensor networks deployed in coal mines could help companies provide workers working in coal mines with more qualified working conditions. With the underground information collected by sensor nodes at hand, the underground working conditions could be evaluated more precisely. However, sensor nodes may tend to malfunction due to their limited energy supply. In this paper, we study the cross-layer optimization problem for wireless rechargeable sensor networks implemented in coal mines, of which the energy could be replenished through the newly-brewed wireless energy transfer technique. The main results of this article are two-fold: firstly, we obtain the optimal relay nodes’ placement according to the minimum overall energy consumption criterion through the Lagrange dual problem and KKT conditions; secondly, the optimal strategies for recharging locomotives and wireless sensor networks are acquired by solving a cross-layer optimization problem. The cyclic nature of these strategies is also manifested through simulations in this paper. PMID:26828500

An Enhanced Text-Mining Framework for Extracting Disaster Relevant Data through Social Media and Remote Sensing Data Fusion

Science.gov (United States)

Scheele, C. J.; Huang, Q.

2016-12-01

In the past decade, the rise in social media has led to the development of a vast number of social media services and applications. Disaster management represents one of such applications leveraging massive data generated for event detection, response, and recovery. In order to find disaster relevant social media data, current approaches utilize natural language processing (NLP) methods based on keywords, or machine learning algorithms relying on text only. However, these approaches cannot be perfectly accurate due to the variability and uncertainty in language used on social media. To improve current methods, the enhanced text-mining framework is proposed to incorporate location information from social media and authoritative remote sensing datasets for detecting disaster relevant social media posts, which are determined by assessing the textual content using common text mining methods and how the post relates spatiotemporally to the disaster event. To assess the framework, geo-tagged Tweets were collected for three different spatial and temporal disaster events: hurricane, flood, and tornado. Remote sensing data and products for each event were then collected using RealEarthTM. Both Naive Bayes and Logistic Regression classifiers were used to compare the accuracy within the enhanced text-mining framework. Finally, the accuracies from the enhanced text-mining framework were compared to the current text-only methods for each of the case study disaster events. The results from this study address the need for more authoritative data when using social media in disaster management applications.

A Cross-Disciplinary Successful Aging Intervention and Evaluation: Comparison of Person-to-Person and Digital-Assisted Approaches

Directory of Open Access Journals (Sweden)

Hui-Chuan Hsu

2018-05-01

Full Text Available Background: Successful aging has been the paradigm of old-age life. The purpose of this study was to implement and evaluate a cross-disciplinary intervention program using two approaches for community-based older adults in Taichung, Taiwan. Methods: The content of the intervention included successful aging concepts and preparation, physical activity, chronic disease and health management, dietary and nutrition information, cognitive training, emotional awareness and coping skills, family relationship and resilience, legal concepts regarding financial protection, and Internet use. The traditional person-to-person (P2P intervention approach was implemented among participants at urban centers, and the personal-and-digital (P&D intervention approach was implemented among participants at rural centers; before the P&D group received the intervention, participants were assessed as the control group for comparison. Results: Healthy behavior and nutrition improved for the P2P group, although not significantly. Strategies for adapting to old age and reducing ineffective coping were significantly improved in the P2P group. The ability to search for health information improved in the P&D group, and knowledge of finance-related law increased in the P2P group. Conclusion: A continuous, well-designed and evidence-based intervention program is beneficial for improving the health of older adults, or at least delaying its decline.

Designing Online Interaction to Address Disciplinary Competencies: A Cross-Country Comparison of Faculty Perspectives

Directory of Open Access Journals (Sweden)

Elena Barberà

2014-04-01

Full Text Available This study was conducted at colleges in three countries (United States, Venezuela, and Spain and across three academic disciplines (engineering, education, and business, to examine how experienced faculty define competencies for their discipline, and design instructional interaction for online courses. A qualitative research design employing in-depth interviews was selected. Results show that disciplinary knowledge takes precedence when faculty members select competencies to be developed in online courses for their respective professions. In all three disciplines, the design of interaction to correspond with disciplinary competencies was often influenced by contextual factors that modify faculty intention. Therefore, instructional design will vary across countries in the same discipline to address the local context, such as the needs and expectations of the learners, faculty perspectives, beliefs and values, and the needs of the institution, the community, and country. The three disciplines from the three countries agreed on the importance of the following competencies: knowledge of the field, higher order cognitive processes such as critical thinking, analysis, problem solving, transfer of knowledge, oral and written communication skills, team work, decision making, leadership and management skills, indicating far more similarities in competencies than differences between the three different applied disciplines. We found a lack of correspondence between faculty’s intent to develop collaborative learning skills and the actual development of them. Contextual factors such as faculty prior experience in design, student reluctance to engage in collaborative learning, and institutional assessment systems that focus on individual performance were some of these reasons.

34 CFR 300.229 - Disciplinary information.

Science.gov (United States)

2010-07-01

... of any current or previous disciplinary action that has been taken against the child and transmit the... engaged in by the child that required disciplinary action, a description of the disciplinary action taken... statement of current or previous disciplinary action that has been taken against the child. (Authority: 20 U...

12 CFR 19.132 - Disciplinary orders.

Science.gov (United States)

2010-01-01

... PROCEDURE Disciplinary Proceedings Involving the Federal Securities Laws § 19.132 Disciplinary orders. (a... 12 Banks and Banking 1 2010-01-01 2010-01-01 false Disciplinary orders. 19.132 Section 19.132... Comptroller may serve on the bank or persons concerned a disciplinary order, as provided in the Exchange Act...

Cross-Comparison of Leaching Strains Isolated from Two Different Regions: Chambishi and Dexing Copper Mines

OpenAIRE

Ngom, Baba; Liang, Yili; Liu, Xueduan

2014-01-01

A cross-comparison of six strains isolated from two different regions, Chambishi copper mine (Zambia, Africa) and Dexing copper mine (China, Asia), was conducted to study the leaching efficiency of low grade copper ores. The strains belong to the three major species often encountered in bioleaching of copper sulfide ores under mesophilic conditions: Acidithiobacillus ferrooxidans, Acidithiobacillus thiooxidans, and Leptospirillum ferriphilum. Prior to their study in bioleaching, the different...

[Exploring the clinical characters of Shugan Jieyu capsule through text mining].

Science.gov (United States)

Pu, Zheng-Ping; Xia, Jiang-Ming; Xie, Wei; He, Jin-Cai

2017-09-01

The study was main to explore the clinical characters of Shugan Jieyu capsule through text mining. The data sets of Shugan Jieyu capsule were downloaded from CMCC database by the method of literature retrieved from May 2009 to Jan 2016. Rules of Chinese medical patterns, diseases, symptoms and combination treatment were mined out by data slicing algorithm, and they were demonstrated in frequency tables and two dimension based network. Then totally 190 literature were recruited. The outcomess suggested that SC was most frequently correlated with liver Qi stagnation. Primary depression, depression due to brain disease, concomitant depression followed by physical diseases, concomitant depression followed by schizophrenia and functional dyspepsia were main diseases treated by Shugan Jieyu capsule. Symptoms like low mood, psychic anxiety, somatic anxiety and dysfunction of automatic nerve were mainy relieved bv Shugan Jieyu capsule.For combination treatment. Shugan Jieyu capsule was most commonly used with paroxetine, sertraline and fluoxetine. The research suggested that syndrome types and mining results of Shugan Jieyu capsule were almost the same as its instructions. Syndrome of malnutrition of heart spirit was the potential Chinese medical pattern of Shugan Jieyu capsule. Primary comorbid anxiety and depression, concomitant comorbid anxiety and depression followed by physical diseases, and postpartum depression were potential diseases treated by Shugan Jieyu capsule.For combination treatment, Shugan Jieyu capsule was most commonly used with paroxetine, sertraline and fluoxetine. Copyright© by the Chinese Pharmaceutical Association.

Database citation in full text biomedical articles.

Science.gov (United States)

Kafkas, Şenay; Kim, Jee-Hyub; McEntyre, Johanna R

2013-01-01

Molecular biology and literature databases represent essential infrastructure for life science research. Effective integration of these data resources requires that there are structured cross-references at the level of individual articles and biological records. Here, we describe the current patterns of how database entries are cited in research articles, based on analysis of the full text Open Access articles available from Europe PMC. Focusing on citation of entries in the European Nucleotide Archive (ENA), UniProt and Protein Data Bank, Europe (PDBe), we demonstrate that text mining doubles the number of structured annotations of database record citations supplied in journal articles by publishers. Many thousands of new literature-database relationships are found by text mining, since these relationships are also not present in the set of articles cited by database records. We recommend that structured annotation of database records in articles is extended to other databases, such as ArrayExpress and Pfam, entries from which are also cited widely in the literature. The very high precision and high-throughput of this text-mining pipeline makes this activity possible both accurately and at low cost, which will allow the development of new integrated data services.

DiMeX: A Text Mining System for Mutation-Disease Association Extraction.

Science.gov (United States)

Mahmood, A S M Ashique; Wu, Tsung-Jung; Mazumder, Raja; Vijay-Shanker, K

2016-01-01

The number of published articles describing associations between mutations and diseases is increasing at a fast pace. There is a pressing need to gather such mutation-disease associations into public knowledge bases, but manual curation slows down the growth of such databases. We have addressed this problem by developing a text-mining system (DiMeX) to extract mutation to disease associations from publication abstracts. DiMeX consists of a series of natural language processing modules that preprocess input text and apply syntactic and semantic patterns to extract mutation-disease associations. DiMeX achieves high precision and recall with F-scores of 0.88, 0.91 and 0.89 when evaluated on three different datasets for mutation-disease associations. DiMeX includes a separate component that extracts mutation mentions in text and associates them with genes. This component has been also evaluated on different datasets and shown to achieve state-of-the-art performance. The results indicate that our system outperforms the existing mutation-disease association tools, addressing the low precision problems suffered by most approaches. DiMeX was applied on a large set of abstracts from Medline to extract mutation-disease associations, as well as other relevant information including patient/cohort size and population data. The results are stored in a database that can be queried and downloaded at http://biotm.cis.udel.edu/dimex/. We conclude that this high-throughput text-mining approach has the potential to significantly assist researchers and curators to enrich mutation databases.

Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature.

Science.gov (United States)

Ravikumar, Komandur Elayavilli; Wagholikar, Kavishwar B; Li, Dingcheng; Kocher, Jean-Pierre; Liu, Hongfang

2015-06-06

Advances in the next generation sequencing technology has accelerated the pace of individualized medicine (IM), which aims to incorporate genetic/genomic information into medicine. One immediate need in interpreting sequencing data is the assembly of information about genetic variants and their corresponding associations with other entities (e.g., diseases or medications). Even with dedicated effort to capture such information in biological databases, much of this information remains 'locked' in the unstructured text of biomedical publications. There is a substantial lag between the publication and the subsequent abstraction of such information into databases. Multiple text mining systems have been developed, but most of them focus on the sentence level association extraction with performance evaluation based on gold standard text annotations specifically prepared for text mining systems. We developed and evaluated a text mining system, MutD, which extracts protein mutation-disease associations from MEDLINE abstracts by incorporating discourse level analysis, using a benchmark data set extracted from curated database records. MutD achieves an F-measure of 64.3% for reconstructing protein mutation disease associations in curated database records. Discourse level analysis component of MutD contributed to a gain of more than 10% in F-measure when compared against the sentence level association extraction. Our error analysis indicates that 23 of the 64 precision errors are true associations that were not captured by database curators and 68 of the 113 recall errors are caused by the absence of associated disease entities in the abstract. After adjusting for the defects in the curated database, the revised F-measure of MutD in association detection reaches 81.5%. Our quantitative analysis reveals that MutD can effectively extract protein mutation disease associations when benchmarking based on curated database records. The analysis also demonstrates that incorporating

Integrated Text Mining and Chemoinformatics Analysis Associates Diet to Health Benefit at Molecular Level

DEFF Research Database (Denmark)

Jensen, Kasper; Panagiotou, Gianni; Kouskoumvekaki, Irene

2014-01-01

, lipids and nutrients. In this work, we applied text mining and Naïve Bayes classification to assemble the knowledge space of food-phytochemical and food-disease associations, where we distinguish between disease prevention/amelioration and disease progression. We subsequently searched for frequently...

The impact of texting on driver behaviour at rail level crossings.

Science.gov (United States)

Young, Kristie L; Lenné, Michael G; Salmon, Paul M; Stanton, Neville A

2018-05-21

A driver text messaging in the vicinity of a rail level crossing represents the merging of a high-risk, high-workload driving environment with a highly distracting secondary task. In this simulator study, we examined how texting impacts driver behaviour on approach to actively controlled urban rail level crossings. Twenty-eight participants drove a series of simulated urban routes containing rail level crossings, while sending text messages and while driving without performing a secondary task. At half of the crossings, drivers were required to respond to the crossing warnings as a train approached. Results revealed that texting on approach to rail level crossings had a detrimental impact on a range of driver behaviour measures. Specifically, texting more than doubled the amount of time spent with eyes off the forward roadway, resulting in drivers spending more than half of their approach time to rail level crossings looking away from the road. This lack of visual attention to the roadway was associated with a range of decrements in driving that may be indicative of a loss of situation awareness, including increased brake reaction time to the crossing warnings and a reduction in lateral position control. The findings have safety implications, not only for urban level crossings, but also for passive level crossings where no warnings are present to re-orient the distracted driver's attention toward an approaching train. Copyright © 2018 Elsevier Ltd. All rights reserved.

Catalyzing Cross-Disciplinary Research and Education Within and Beyond the Environmental and Geosciences to Address Emerging, Societally-Relevant Issues

Science.gov (United States)

Cak, A. D.; Vigdor, L. J.; Vorosmarty, C. J.; Giebel, B. M.; Santistevan, C.; Chasteau, C.

2017-12-01

Tackling emergent, societally-relevant problems in the environmental sciences is hardly confined to a single research discipline, but rather requires collaborations that bridge diverse domains and perspectives. While new technologies (e.g., Skype) can in theory unite otherwise geographically distributed participation in collaborative research, physical distance nevertheless raises the bar on intellectual dialogue. Such barriers may reveal perceptions of or real differences across disciplines, reflecting particular traditions in their histories and academic cultures. Individual disciplines are self-defined by their scientific, epistemologic, methodologic, or philosophical traditions (e.g., difficulties in understanding processes occurring at different scales, insufficient research funding for interdisciplinary work), or cultural and discursive hurdles (e.g., navigating a new field's jargon). Coupled with these challenges is a considerable deficiency in educating the next generation of scientists to help them develop a sufficient comfort level with thinking critically across multiple disciplinary domains and conceptual frameworks. To address these issues, the City University of New York (CUNY), the largest public urban university in the U.S., made a significant investment in advancing cross-disciplinary research and education, culminating in the opening of the CUNY Advanced Science Research Center (ASRC) in New York City (NYC) in late 2014. We report here on our experiences incubating new collaborative efforts to address environmental science-related research as it is interwoven with the ASRC's five research initiatives (Environmental Sciences, Neuroscience, Structural Biology, Photonics, and Nanoscience). We describe the ASRC's overall structure and function as both a stand-alone interdisciplinary center and one that collaborates more broadly with CUNY's network of twenty-four campuses distributed across NYC's five boroughs. We identify challenges we have faced so

Clinical Immersion: An Approach for Fostering Cross-disciplinary Communication and Innovation in Nursing and Engineering Students.

Science.gov (United States)

Geist, Melissa J; Sanders, Robby; Harris, Kevin; Arce-Trigatti, Andrea; Hitchcock-Cass, Cary

2018-05-24

A faculty team from nursing and chemical engineering developed a course that brought together students from each discipline for cross-disciplinary, team-based clinical immersion and collaboration. Health care processes and devices are rapidly changing, and nurses are uniquely positioned to be bedside innovators to improve patient care delivery. During each clinical immersion, the student teams rotated through various hospital units where they identified problems and worked together in the university's makerspace (iMaker Space) to design and build prototypes to improve health outcomes. Data from the Critical thinking Assessment Test provided evidence of gains in critical-thinking and problem-solving skills, while the problems identified in the clinical setting and prototypes developed demonstrated the impact of bringing nursing and engineering students together to design innovations. When challenged to identify authentic problems during their clinical immersion, the teams of nursing and engineering students proposed creative solutions and developed commercially viable prototypes.

Information Retrieval and Text Mining Technologies for Chemistry.

Science.gov (United States)

Krallinger, Martin; Rabal, Obdulia; Lourenço, Anália; Oyarzabal, Julen; Valencia, Alfonso

2017-06-28

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.

HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways.

Science.gov (United States)

Subramani, Suresh; Kalpana, Raja; Monickaraj, Pankaj Moses; Natarajan, Jeyakumar

2015-04-01

The knowledge on protein-protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer's disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks. Copyright © 2015 Elsevier Inc. All rights reserved.

pubmed. mineR: An R package with text-mining algorithms to ...

Indian Academy of Sciences (India)

2016-08-26

Aug 26, 2016 ... Three case studies are presented, namely, `Evolving role of diabetes educators', `Cancer risk assessment' and `Dynamic concepts on disease and comorbidity' to illustrate the use of pubmed.mineR. The package generally runs fast with small elapsed times in regular workstations even on large corpus ...

In dogs we trust? Intersubjectivity, response-able relations, and the making of mine detector dogs.

Science.gov (United States)

Kirk, Robert G W

2014-01-01

The utility of the dog as a mine detector has divided the mine clearance community since dogs were first used for this purpose during the Second World War. This paper adopts a historical perspective to investigate how, why, and to what consequence, the use of minedogs remains contested despite decades of research into their abilities. It explores the changing factors that have made it possible to think that dogs could, or could not, serve as reliable detectors of landmines over time. Beginning with an analysis of the wartime context that shaped the creation of minedogs, the paper then examines two contemporaneous investigations undertaken in the 1950s. The first, a British investigation pursued by the anatomist Solly Zuckerman, concluded that dogs could never be the mine hunter's best friend. The second, an American study led by the parapsychologist J. B. Rhine, suggested dogs were potentially useful for mine clearance. Drawing on literature from science studies and the emerging subdiscipline of "animal studies," it is argued that cross-species intersubjectivity played a significant role in determining these different positions. The conceptual landscapes of Zuckerman and Rhine's disciplinary backgrounds are shown to have produced distinct approaches to managing cross-species relations, thus explaining how diverse opinions on minedog can coexist. In conclusion, it is shown that the way one structures relationships between humans and animals has profound impact on the knowledge and labor subsequently produced, a process that cannot be separated from ethical consequence. © 2013 The Authors. Journal of the History of the Behavioral Sciences published by Wiley Periodicals, Inc.

The WONP-NURT corpus as nuclear knowledge base for text mining in the INIS database

International Nuclear Information System (INIS)

Guerra Valdes, R.

2011-01-01

In the present work the WONP-NURT corpus is taken as knowledge base for text mining in the INIS database. Main components of the information processing system, as well as computational methods for content analysis of INIS database record files are described. Results of the content analysis of the WONP-NURT corpus are reported. Furthermore, results of two comparative text mining studies in the INIS database are also shown. The first one explores 10 research areas in the more familiar nearest range of WONP-NURT corpus, while the second one surveys 15 regions in the more exotic far range. The results provide new elements to asses the significance of the WONP-NURT corpus in the context of the current state of nuclear science and technology research areas. (Author)

Agile text mining for the 2014 i2b2/UTHealth Cardiac risk factors challenge.

Science.gov (United States)

Cormack, James; Nath, Chinmoy; Milward, David; Raja, Kalpana; Jonnalagadda, Siddhartha R

2015-12-01

This paper describes the use of an agile text mining platform (Linguamatics' Interactive Information Extraction Platform, I2E) to extract document-level cardiac risk factors in patient records as defined in the i2b2/UTHealth 2014 challenge. The approach uses a data-driven rule-based methodology with the addition of a simple supervised classifier. We demonstrate that agile text mining allows for rapid optimization of extraction strategies, while post-processing can leverage annotation guidelines, corpus statistics and logic inferred from the gold standard data. We also show how data imbalance in a training set affects performance. Evaluation of this approach on the test data gave an F-Score of 91.7%, one percent behind the top performing system. Copyright © 2015 Elsevier Inc. All rights reserved.

The Effects of Cell Phone and Text Message Conversations on Simulated Street Crossing.

Science.gov (United States)

Banducci, Sarah E; Ward, Nathan; Gaspar, John G; Schab, Kurt R; Crowell, James A; Kaczmarski, Henry; Kramer, Arthur F

2016-02-01

A fully immersive, high-fidelity street-crossing simulator was used to examine the effects of texting on pedestrian street-crossing performance. Research suggests that street-crossing performance is impaired when pedestrians engage in cell phone conversations. Less is known about the impact of texting on street-crossing performance. Thirty-two young adults completed three distraction conditions in a simulated street-crossing task: no distraction, phone conversation, and texting. A hands-free headset and a mounted tablet were used to conduct the phone and texting conversations, respectively. Participants moved through the virtual environment via a manual treadmill, allowing them to select crossing gaps and change their gait. During the phone conversation and texting conditions, participants had fewer successful crossings and took longer to initiate crossing. Furthermore, in the texting condition, smaller percentage of time with head orientation toward the tablet, fewer number of head orientations toward the tablet, and greater percentage of total characters typed before initiating crossing predicted greater crossing success. Our results suggest that (a) texting is as unsafe as phone conversations for street-crossing performance and (b) when subjects completed most of the texting task before initiating crossing, they were more likely to make it safely across the street. Sending and receiving text messages negatively impact a range of real-world behaviors. These results may inform personal and policy decisions. © 2015, Human Factors and Ergonomics Society.

Unpacking physics representations: Towards an appreciation of disciplinary affordance

Directory of Open Access Journals (Sweden)

Tobias Fredlund

2014-12-01

Full Text Available This theoretical article problematizes the access to disciplinary knowledge that different physics representations have the possibility to provide; that is, their disciplinary affordances. It is argued that historically such access has become increasingly constrained for students as physics representations have been rationalized over time. Thus, the case is made that such rationalized representations, while powerful for communication from a disciplinary point of view, manifest as learning challenges for students. The proposal is illustrated using a vignette from a student discussion in the physics laboratory about circuit connections for an experimental investigation of the charging and discharging of a capacitor. It is concluded that in order for students to come to appreciate the disciplinary affordances of representations, more attention needs to be paid to their “unpacking.” Building on this conclusion, two questions are proposed that teachers can ask themselves in order to begin to unpack the representations that they use in their teaching. The paper ends by proposing directions for future research in this area.

Insight into the Disciplinary Structure of Nanoscience & Nanotechnology

Directory of Open Access Journals (Sweden)

Chunjuan Luan

2017-01-01

Full Text Available Purpose: This paper aims to gain an insight into the disciplinary structure of nanoscience & nanotechnology (N&N: What is the disciplinary network of N&N like? Which disciplines are being integrated into N&N over time? For a specific discipline, how many other disciplines have direct or indirect connections with it? What are the distinct subgroups of N&N at different evolutionary stages? Such critical issues are to be addressed in this paper. Design/methodology/approach: We map the disciplinary network structure of N&N by employing the social network analysis tool, Netdraw, identifying which Web of Science Categories (WCs mediate nbetweenness centrality in different stages of nano development. Cliques analysis embedded in the Ucinet program is applied to do the disciplinary cluster analysis in the study according to the path of “Network-Subgroup-Cliques,” and a tree diagram is selected as the visualizing type. Findings: The disciplinary network structure reveals the relationships among different disciplines in the N&N developing process clearly, and it is easy for us to identify which disciplines are connected with the core “N&N” directly or indirectly. The tree diagram showing N&N related disciplines provides an interesting perspective on nano research and development (R&D structure. Research limitations: The matrices used to draw the N&N disciplinary network are the original ones, and normalized matrix could be tried in future similar studies. Practical implications: Results in this paper can help us better understand the disciplinary structure of N&N, and the dynamic evolution of N&N related disciplines over time. The findings could benefit R&D decision making. It can support policy makers from government agencies engaging in science and technology (S&T management or S&T strategy planners to formulate efficient decisions according to a perspective of converging sciences and technologies. Originality/value: The novelty of this study

Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends.

Science.gov (United States)

Jurca, Gabriela; Addam, Omar; Aksac, Alper; Gao, Shang; Özyer, Tansel; Demetrick, Douglas; Alhajj, Reda

2016-04-26

Breast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer. We utilized PubMed for the testing. We investigated gene-gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries. Interesting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene-gene relations and gene functions.

Mine drivage in hydraulic mines

Energy Technology Data Exchange (ETDEWEB)

Ehkber, B Ya

1983-09-01

From 20 to 25% of labor cost in hydraulic coal mines falls on mine drivage. Range of mine drivage is high due to the large number of shortwalls mined by hydraulic monitors. Reducing mining cost in hydraulic mines depends on lowering drivage cost by use of new drivage systems or by increasing efficiency of drivage systems used at present. The following drivage methods used in hydraulic mines are compared: heading machines with hydraulic haulage of cut rocks and coal, hydraulic monitors with hydraulic haulage, drilling and blasting with hydraulic haulage of blasted rocks. Mining and geologic conditions which influence selection of the optimum mine drivage system are analyzed. Standardized cross sections of mine roadways driven by the 3 methods are shown in schemes. Support systems used in mine roadways are compared: timber supports, roof bolts, roof bolts with steel elements, and roadways driven in rocks without a support system. Heading machines (K-56MG, GPKG, 4PU, PK-3M) and hydraulic monitors (GMDTs-3M, 12GD-2) used for mine drivage are described. Data on mine drivage in hydraulic coal mines in the Kuzbass are discussed. From 40 to 46% of roadways are driven by heading machines with hydraulic haulage and from 12 to 15% by hydraulic monitors with hydraulic haulage.

Reading Deeply for Disciplinary Awareness and Political Judgment

Directory of Open Access Journals (Sweden)

Alison Kathryn Staudinger

2017-03-01

Full Text Available What happens when students become better readers? Cultivating deep reading habits in students to help them navigate disciplinary cultures respects student autonomy. Scholarly literature predicts that three linked practices improve student reading: practice with feedback, explicit in-class work on reading strategies, and disciplinary norm discussions. To see what happens when students engage in these practices, I studied two years of students in an American Political Thought (APT course, comparing essays written at the start and end of the courses. In this article, I analyze evidence of student learning by reading their work closely, and in the context of political theory as a humanistic sub-discipline, speaking both to “what is?” student reading and exploring its implications for citizenship through political theorist Hannah Arendt’s reflective political judgment. As students deepen their reading practices, they are cultivating habits of citizenship, even if they still struggle with disciplinary awareness.

Towards Technological Approaches for Concept Maps Mining from Text

Directory of Open Access Journals (Sweden)

Camila Zacche Aguiar

2018-04-01

Full Text Available Concept maps are resources for the representation and construction of knowledge. They allow showing, through concepts and relationships, how knowledge about a subject is organized. Technological advances have boosted the development of approaches for the automatic construction of a concept map, to facilitate and provide the benefits of that resource more broadly. Due to the need to better identify and analyze the functionalities and characteristics of those approaches, we conducted a detailed study on technological approaches for automatic construction of concept maps published between 1994 and 2016 in the IEEE Xplore, ACM and Elsevier Science Direct data bases. From this study, we elaborate a categorization defined on two perspectives, Data Source and Graphic Representation, and fourteen categories. That study collected 30 relevant articles, which were applied to the proposed categorization to identify the main features and limitations of each approach. A detailed view on these approaches, their characteristics and techniques are presented enabling a quantitative analysis. In addition, the categorization has given us objective conditions to establish new specification requirements for a new technological approach aiming at concept maps mining from texts.

29 CFR 1400.735-60 - Disciplinary actions.

Science.gov (United States)

2010-07-01

... 29 Labor 4 2010-07-01 2010-07-01 false Disciplinary actions. 1400.735-60 Section 1400.735-60 Labor..., RESPONSIBILITIES, AND DISCIPLINE Disciplinary Actions and Penalties § 1400.735-60 Disciplinary actions. The Service shall take prompt disciplinary action against an employee committing prohibited activity, or whose...

Numerical Modeling Tools for the Prediction of Solution Migration Applicable to Mining Site

International Nuclear Information System (INIS)

Martell, M.; Vaughn, P.

1999-01-01

Mining has always had an important influence on cultures and traditions of communities around the globe and throughout history. Today, because mining legislation places heavy emphasis on environmental protection, there is great interest in having a comprehensive understanding of ancient mining and mining sites. Multi-disciplinary approaches (i.e., Pb isotopes as tracers) are being used to explore the distribution of metals in natural environments. Another successful approach is to model solution migration numerically. A proven method to simulate solution migration in natural rock salt has been applied to project through time for 10,000 years the system performance and solution concentrations surrounding a proposed nuclear waste repository. This capability is readily adaptable to simulate solution migration around mining

Stopping Antidepressants and Anxiolytics as Major Concerns Reported in Online Health Communities: A Text Mining Approach.

Science.gov (United States)

Abbe, Adeline; Falissard, Bruno

2017-10-23

Internet is a particularly dynamic way to quickly capture the perceptions of a population in real time. Complementary to traditional face-to-face communication, online social networks help patients to improve self-esteem and self-help. The aim of this study was to use text mining on material from an online forum exploring patients' concerns about treatment (antidepressants and anxiolytics). Concerns about treatment were collected from discussion titles in patients' online community related to antidepressants and anxiolytics. To examine the content of these titles automatically, we used text mining methods, such as word frequency in a document-term matrix and co-occurrence of words using a network analysis. It was thus possible to identify topics discussed on the forum. The forum included 2415 discussions on antidepressants and anxiolytics over a period of 3 years. After a preprocessing step, the text mining algorithm identified the 99 most frequently occurring words in titles, among which were escitalopram, withdrawal, antidepressant, venlafaxine, paroxetine, and effect. Patients' concerns were related to antidepressant withdrawal, the need to share experience about symptoms, effects, and questions on weight gain with some drugs. Patients' expression on the Internet is a potential additional resource in addressing patients' concerns about treatment. Patient profiles are close to that of patients treated in psychiatry. ©Adeline Abbe, Bruno Falissard. Originally published in JMIR Mental Health (http://mental.jmir.org), 23.10.2017.

Analysis of Nature of Science Included in Recent Popular Writing Using Text Mining Techniques

Science.gov (United States)

Jiang, Feng; McComas, William F.

2014-01-01

This study examined the inclusion of nature of science (NOS) in popular science writing to determine whether it could serve supplementary resource for teaching NOS and to evaluate the accuracy of text mining and classification as a viable research tool in science education research. Four groups of documents published from 2001 to 2010 were…

28 CFR 544.75 - Disciplinary action.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Disciplinary action. 544.75 Section 544... EDUCATION Literacy Program § 544.75 Disciplinary action. As with other mandatory programs, such as work assignments, staff may take disciplinary action against an inmate lacking a GED credential or high school...

28 CFR 544.44 - Disciplinary action.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Disciplinary action. 544.44 Section 544... EDUCATION Mandatory English-as-a-Second Language Program (ESL) § 544.44 Disciplinary action. As with any other mandatory programs, such as work assignments, staff may take disciplinary action against an inmate...

Classroom disciplinary climate of schools and gender

DEFF Research Database (Denmark)

Sortkær, Bent; Reimer, David

2018-01-01

Classroom disciplinary climate has emerged as a crucial factor with regard to student achievement. However, most previous studies have not explored potential gender differences in both students’ perceptions of the classroom disciplinary climate and the association between classroom disciplinary...... and students’ mathematics performance across countries. On the basis of an analysis of a pooled sample consisting of all 5 Nordic countries, we found that the correlation between classroom disciplinary climate of schools and maths achievement is significantly stronger for boys than for girls. Further analyses...... showed that this finding may partly be attributable to gender differences in the perception of the disciplinary climate of schools, whereby boys seemed to perceive the classroom disciplinary climate of schools more positively than girls....

Disciplinary climate and student achievement

DEFF Research Database (Denmark)

Sortkær, Bent; Reimer, David

Disciplinary climate has emerged as one of the single most important factors related to student achievement. Using data from the OECD Programme for International Student Assessment (PISA) 2003 for Canada, Denmark, Finland, Iceland, Latvia and Norway we find a significant and nontrivial association...... between the perceived disciplinary climate in the classroom and students’ mathematics performance in Canada, Denmark and Norway. Furthermore we exploit country specific class-size rules in order to single out a subsample with classroom-level data (PISA is sampled by age and not by classes) and find...... that the estimates based on school-level data might underestimate the relationship between disciplinary climate and student achievement. Finally we find evidence for gender differences in the association between disciplinary climate and student achievement that can partly be explained by gender-specific perceptions...

Data mining of text as a tool in authorship attribution

Science.gov (United States)

Visa, Ari J. E.; Toivonen, Jarmo; Autio, Sami; Maekinen, Jarno; Back, Barbro; Vanharanta, Hannu

2001-03-01

It is common that text documents are characterized and classified by keywords that the authors use to give them. Visa et al. have developed a new methodology based on prototype matching. The prototype is an interesting document or a part of an extracted, interesting text. This prototype is matched with the document database of the monitored document flow. The new methodology is capable of extracting the meaning of the document in a certain degree. Our claim is that the new methodology is also capable of authenticating the authorship. To verify this claim two tests were designed. The test hypothesis was that the words and the word order in the sentences could authenticate the author. In the first test three authors were selected. The selected authors were William Shakespeare, Edgar Allan Poe, and George Bernard Shaw. Three texts from each author were examined. Every text was one by one used as a prototype. The two nearest matches with the prototype were noted. The second test uses the Reuters-21578 financial news database. A group of 25 short financial news reports from five different authors are examined. Our new methodology and the interesting results from the two tests are reported in this paper. In the first test, for Shakespeare and for Poe all cases were successful. For Shaw one text was confused with Poe. In the second test the Reuters-21578 financial news were identified by the author relatively well. The resolution is that our text mining methodology seems to be capable of authorship attribution.

Comparison between BIDE, PrefixSpan, and TRuleGrowth for Mining of Indonesian Text

Science.gov (United States)

Sa'adillah Maylawati, Dian; Irfan, Mohamad; Budiawan Zulfikar, Wildan

2017-01-01

Mining proscess for Indonesian language still be an interesting research. Multiple of words representation was claimed can keep the meaning of text better than bag of words. In this paper, we compare several sequential pattern algortihm, among others BIDE (BIDirectional Extention), PrefixSpan, and TRuleGrowth. All of those algorithm produce frequent word sequence to keep the meaning of text. However, the experiment result, with 14.006 of Indonesian tweet from Twitter, shows that BIDE can produce more efficient frequent word sequence than PrefixSpan and TRuleGrowth without missing the meaning of text. Then, the average of time process of PrefixSpan is faster than BIDE and TRuleGrowth. In the other hand, PrefixSpan and TRuleGrowth is more efficient in using memory than BIDE.

tagtog: interactive and text-mining-assisted annotation of gene mentions in PLOS full-text articles.

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Cejuela, Juan Miguel; McQuilton, Peter; Ponting, Laura; Marygold, Steven J; Stefancsik, Raymund; Millburn, Gillian H; Rost, Burkhard

2014-01-01

The breadth and depth of biomedical literature are increasing year upon year. To keep abreast of these increases, FlyBase, a database for Drosophila genomic and genetic information, is constantly exploring new ways to mine the published literature to increase the efficiency and accuracy of manual curation and to automate some aspects, such as triaging and entity extraction. Toward this end, we present the 'tagtog' system, a web-based annotation framework that can be used to mark up biological entities (such as genes) and concepts (such as Gene Ontology terms) in full-text articles. tagtog leverages manual user annotation in combination with automatic machine-learned annotation to provide accurate identification of gene symbols and gene names. As part of the BioCreative IV Interactive Annotation Task, FlyBase has used tagtog to identify and extract mentions of Drosophila melanogaster gene symbols and names in full-text biomedical articles from the PLOS stable of journals. We show here the results of three experiments with different sized corpora and assess gene recognition performance and curation speed. We conclude that tagtog-named entity recognition improves with a larger corpus and that tagtog-assisted curation is quicker than manual curation. DATABASE URL: www.tagtog.net, www.flybase.org.

Disciplinary maintenance of process of overcoming of deformations of professional-pedagogical authentication of future teachers

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Zhanna P. Pavlova

2011-04-01

Full Text Available In the article examined disciplinary maintenance of process of overcoming of deformations of professional-pedagogical authentication of future teachers and maintenance of process of overcoming of deformations, which is built on module principle on the basis of disciplinary connections.

Appraising the Corporate Sustainability Reports - Text Mining and Multi-Discriminatory Analysis

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Modapothala, J. R.; Issac, B.; Jayamani, E.

The voluntary disclosure of the sustainability reports by the companies attracts wider stakeholder groups. Diversity in these reports poses challenge to the users of information and regulators. This study appraises the corporate sustainability reports as per GRI (Global Reporting Initiative) guidelines (the most widely accepted and used) across all industrial sectors. Text mining is adopted to carry out the initial analysis with a large sample size of 2650 reports. Statistical analyses were performed for further investigation. The results indicate that the disclosures made by the companies differ across the industrial sectors. Multivariate Discriminant Analysis (MDA) shows that the environmental variable is a greater significant contributing factor towards explanation of sustainability report.

Text mining applications in psychiatry: a systematic literature review.

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Abbe, Adeline; Grouin, Cyril; Zweigenbaum, Pierre; Falissard, Bruno

2016-06-01

The expansion of biomedical literature is creating the need for efficient tools to keep pace with increasing volumes of information. Text mining (TM) approaches are becoming essential to facilitate the automated extraction of useful biomedical information from unstructured text. We reviewed the applications of TM in psychiatry, and explored its advantages and limitations. A systematic review of the literature was carried out using the CINAHL, Medline, EMBASE, PsycINFO and Cochrane databases. In this review, 1103 papers were screened, and 38 were included as applications of TM in psychiatric research. Using TM and content analysis, we identified four major areas of application: (1) Psychopathology (i.e. observational studies focusing on mental illnesses) (2) the Patient perspective (i.e. patients' thoughts and opinions), (3) Medical records (i.e. safety issues, quality of care and description of treatments), and (4) Medical literature (i.e. identification of new scientific information in the literature). The information sources were qualitative studies, Internet postings, medical records and biomedical literature. Our work demonstrates that TM can contribute to complex research tasks in psychiatry. We discuss the benefits, limits, and further applications of this tool in the future. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Mining concepts of health responsibility using text mining and exploratory graph analysis.

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Kjellström, Sofia; Golino, Hudson

2018-05-24

Occupational therapists need to know about people's beliefs about personal responsibility for health to help them pursue everyday activities. The study aims to employ state-of-the-art quantitative approaches to understand people's views of health and responsibility at different ages. A mixed method approach was adopted, using text mining to extract information from 233 interviews with participants aged 5 to 96 years, and then exploratory graph analysis to estimate the number of latent variables. The fit of the structure estimated via the exploratory graph analysis was verified using confirmatory factor analysis. Exploratory graph analysis estimated three dimensions of health responsibility: (1) creating good health habits and feeling good; (2) thinking about one's own health and wanting to improve it; and 3) adopting explicitly normative attitudes to take care of one's health. The comparison between the three dimensions among age groups showed, in general, that children and adolescents, as well as the old elderly (>73 years old) expressed ideas about personal responsibility for health less than young adults, adults and young elderly. Occupational therapists' knowledge of the concepts of health responsibility is of value when working with a patient's health, but an identified challenge is how to engage children and older persons.

Astronomical References in Chaucer: What Can Modern Students Learn from Studying Ancient Texts?

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Victor Kennedy

2005-06-01

Full Text Available One of the problems in the field of English literature studies is that, with compartmentalization and specialization, it becomes introspective to the point where it devolves into the study of metafiction and metacriticism. At its heart, however, literature has to be about something: Thackeray claimed its subject is human nature, but human nature is based in the interface between human and nature. This paper explores some of the problems in the interface between human knowledge, institutions, and nature, and will offer an example of cross-disciplinary, historical study to illustrate a well-known but, to most modern readers, impenetrable medieval text, Chaucer’s Treatise on the Astrolabe. It ends with three recommendations: look to history, cross boundaries between academic fields, and use practical, as well as theoretical, teaching methods.

Examining Mobile Learning Trends 2003-2008: A Categorical Meta-Trend Analysis Using Text Mining Techniques

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Hung, Jui-Long; Zhang, Ke

2012-01-01

This study investigated the longitudinal trends of academic articles in Mobile Learning (ML) using text mining techniques. One hundred and nineteen (119) refereed journal articles and proceedings papers from the SCI/SSCI database were retrieved and analyzed. The taxonomies of ML publications were grouped into twelve clusters (topics) and four…

Text mining effectively scores and ranks the literature for improving chemical-gene-disease curation at the comparative toxicogenomics database.

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Allan Peter Davis

Full Text Available The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/ is a public resource that curates interactions between environmental chemicals and gene products, and their relationships to diseases, as a means of understanding the effects of environmental chemicals on human health. CTD provides a triad of core information in the form of chemical-gene, chemical-disease, and gene-disease interactions that are manually curated from scientific articles. To increase the efficiency, productivity, and data coverage of manual curation, we have leveraged text mining to help rank and prioritize the triaged literature. Here, we describe our text-mining process that computes and assigns each article a document relevancy score (DRS, wherein a high DRS suggests that an article is more likely to be relevant for curation at CTD. We evaluated our process by first text mining a corpus of 14,904 articles triaged for seven heavy metals (cadmium, cobalt, copper, lead, manganese, mercury, and nickel. Based upon initial analysis, a representative subset corpus of 3,583 articles was then selected from the 14,094 articles and sent to five CTD biocurators for review. The resulting curation of these 3,583 articles was analyzed for a variety of parameters, including article relevancy, novel data content, interaction yield rate, mean average precision, and biological and toxicological interpretability. We show that for all measured parameters, the DRS is an effective indicator for scoring and improving the ranking of literature for the curation of chemical-gene-disease information at CTD. Here, we demonstrate how fully incorporating text mining-based DRS scoring into our curation pipeline enhances manual curation by prioritizing more relevant articles, thereby increasing data content, productivity, and efficiency.

Gender Differences in Decisions on Student Disciplinary Behaviours ...

African Journals Online (AJOL)

The study investigated gender differences in decisions on student disciplinary behaviours by selected Kenyan secondary school disciplinary panels which may be due to composition of disciplinary panels, perceptions of students presenting with disciplinary behaviours and behaviour expectations of students on the basis of ...

Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER)system.

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Pandey, Abhishek; Kreimeyer, Kory; Foster, Matthew; Botsis, Taxiarchis; Dang, Oanh; Ly, Thomas; Wang, Wei; Forshee, Richard

2018-01-01

Structured Product Labels follow an XML-based document markup standard approved by the Health Level Seven organization and adopted by the US Food and Drug Administration as a mechanism for exchanging medical products information. Their current organization makes their secondary use rather challenging. We used the Side Effect Resource database and DailyMed to generate a comparison dataset of 1159 Structured Product Labels. We processed the Adverse Reaction section of these Structured Product Labels with the Event-based Text-mining of Health Electronic Records system and evaluated its ability to extract and encode Adverse Event terms to Medical Dictionary for Regulatory Activities Preferred Terms. A small sample of 100 labels was then selected for further analysis. Of the 100 labels, Event-based Text-mining of Health Electronic Records achieved a precision and recall of 81 percent and 92 percent, respectively. This study demonstrated Event-based Text-mining of Health Electronic Record's ability to extract and encode Adverse Event terms from Structured Product Labels which may potentially support multiple pharmacoepidemiological tasks.

EarthServer: Cross-Disciplinary Earth Science Through Data Cube Analytics

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Baumann, P.; Rossi, A. P.

2016-12-01

The unprecedented increase of imagery, in-situ measurements, and simulation data produced by Earth (and Planetary) Science observations missions bears a rich, yet not leveraged potential for getting insights from integrating such diverse datasets and transform scientific questions into actual queries to data, formulated in a standardized way.The intercontinental EarthServer [1] initiative is demonstrating new directions for flexible, scalable Earth Science services based on innovative NoSQL technology. Researchers from Europe, the US and Australia have teamed up to rigorously implement the concept of the datacube. Such a datacube may have spatial and temporal dimensions (such as a satellite image time series) and may unite an unlimited number of scenes. Independently from whatever efficient data structuring a server network may perform internally, users (scientist, planners, decision makers) will always see just a few datacubes they can slice and dice.EarthServer has established client [2] and server technology for such spatio-temporal datacubes. The underlying scalable array engine, rasdaman [3,4], enables direct interaction, including 3-D visualization, common EO data processing, and general analytics. Services exclusively rely on the open OGC "Big Geo Data" standards suite, the Web Coverage Service (WCS). Conversely, EarthServer has shaped and advanced WCS based on the experience gained. The first phase of EarthServer has advanced scalable array database technology into 150+ TB services. Currently, Petabyte datacubes are being built for ad-hoc and cross-disciplinary querying, e.g. using climate, Earth observation and ocean data.We will present the EarthServer approach, its impact on OGC / ISO / INSPIRE standardization, and its platform technology, rasdaman.References: [1] Baumann, et al. (2015) DOI: 10.1080/17538947.2014.1003106 [2] Hogan, P., (2011) NASA World Wind, Proceedings of the 2nd International Conference on Computing for Geospatial Research

Trends of E-Learning Research from 2000 to 2008: Use of Text Mining and Bibliometrics

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Hung, Jui-long

2012-01-01

This study investigated the longitudinal trends of e-learning research using text mining techniques. Six hundred and eighty-nine (689) refereed journal articles and proceedings were retrieved from the Science Citation Index/Social Science Citation Index database in the period from 2000 to 2008. All e-learning publications were grouped into two…

Seqenv: linking sequences to environments through text mining

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Lucas Sinclair

2016-12-01

Full Text Available Understanding the distribution of taxa and associated traits across different environments is one of the central questions in microbial ecology. High-throughput sequencing (HTS studies are presently generating huge volumes of data to address this biogeographical topic. However, these studies are often focused on specific environment types or processes leading to the production of individual, unconnected datasets. The large amounts of legacy sequence data with associated metadata that exist can be harnessed to better place the genetic information found in these surveys into a wider environmental context. Here we introduce a software program, seqenv, to carry out precisely such a task. It automatically performs similarity searches of short sequences against the “nt” nucleotide database provided by NCBI and, out of every hit, extracts–if it is available–the textual metadata field. After collecting all the isolation sources from all the search results, we run a text mining algorithm to identify and parse words that are associated with the Environmental Ontology (EnvO controlled vocabulary. This, in turn, enables us to determine both in which environments individual sequences or taxa have previously been observed and, by weighted summation of those results, to summarize complete samples. We present two demonstrative applications of seqenv to a survey of ammonia oxidizing archaea as well as to a plankton paleome dataset from the Black Sea. These demonstrate the ability of the tool to reveal novel patterns in HTS and its utility in the fields of environmental source tracking, paleontology, and studies of microbial biogeography. To install seqenv, go to: https://github.com/xapple/seqenv.

Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine.

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Singhal, Ayush; Simmons, Michael; Lu, Zhiyong

2016-11-01

The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient's genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature. In this paper we propose a high-performance machine learning approach to automate the extraction of disease-gene-variant triplets from biomedical literature. Our approach is unique because we identify the genes and protein products associated with each mutation from not just the local text content, but from a global context as well (from the Internet and from all literature in PubMed). Our approach also incorporates protein sequence validation and disease association using a novel text-mining-based machine learning approach. We extract disease-gene-variant triplets from all abstracts in PubMed related to a set of ten important diseases (breast cancer, prostate cancer, pancreatic cancer, lung cancer, acute myeloid leukemia, Alzheimer's disease, hemochromatosis, age-related macular degeneration (AMD), diabetes mellitus, and cystic fibrosis). We then evaluate our approach in two ways: (1) a direct comparison with the state of the art using benchmark datasets; (2) a validation study comparing the results of our approach with entries in a popular human-curated database (UniProt) for each of the previously mentioned diseases. In the benchmark comparison, our full approach achieves a 28% improvement in F1-measure (from 0.62 to 0.79) over the state-of-the-art results. For the validation study with UniProt Knowledgebase (KB), we present a thorough analysis of the results and errors. Across all diseases, our approach returned 272 triplets (disease

Text mining of rheumatoid arthritis and diabetes mellitus to understand the mechanisms of Chinese medicine in different diseases with same treatment.

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Zhao, Ning; Zheng, Guang; Li, Jian; Zhao, Hong-Yan; Lu, Cheng; Jiang, Miao; Zhang, Chi; Guo, Hong-Tao; Lu, Ai-Ping

2018-01-09

To identify the commonalities between rheumatoid arthritis (RA) and diabetes mellitus (DM) to understand the mechanisms of Chinese medicine (CM) in different diseases with the same treatment. A text mining approach was adopted to analyze the commonalities between RA and DM according to CM and biological elements. The major commonalities were subsequently verifified in RA and DM rat models, in which herbal formula for the treatment of both RA and DM identifified via text mining was used as the intervention. Similarities were identifified between RA and DM regarding the CM approach used for diagnosis and treatment, as well as the networks of biological activities affected by each disease, including the involvement of adhesion molecules, oxidative stress, cytokines, T-lymphocytes, apoptosis, and inflfl ammation. The Ramulus Cinnamomi-Radix Paeoniae Alba-Rhizoma Anemarrhenae is an herbal combination used to treat RA and DM. This formula demonstrated similar effects on oxidative stress and inflfl ammation in rats with collagen-induced arthritis, which supports the text mining results regarding the commonalities between RA and DM. Commonalities between the biological activities involved in RA and DM were identifified through text mining, and both RA and DM might be responsive to the same intervention at a specifific stage.

Data Mining of Acupoint Characteristics from the Classical Medical Text: DongUiBoGam of Korean Medicine

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Taehyung Lee

2014-01-01

Full Text Available Throughout the history of East Asian medicine, different kinds of acupuncture treatment experiences have been accumulated in classical medical texts. Reexamining knowledge from classical medical texts is expected to provide meaningful information that could be utilized in current medical practices. In this study, we used data mining methods to analyze the association between acupoints and patterns of disorder with the classical medical book DongUiBoGam of Korean medicine. Using the term frequency-inverse document frequency (tf-idf method, we quantified the significance of acupoints to its targeting patterns and, conversely, the significance of patterns to acupoints. Through these processes, we extracted characteristics of each acupoint based on its treating patterns. We also drew practical information for selecting acupoints on certain patterns according to their association. Data analysis on DongUiBoGam’s acupuncture treatment gave us an insight into the main idea of DongUiBoGam. We strongly believe that our approach can provide a novel understanding of unknown characteristics of acupoint and pattern identification from the classical medical text using data mining methods.

A Study on Environmental Research Trends Using Text-Mining Method - Focus on Spatial information and ICT -

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Lee, M. J.; Oh, K. Y.; Joung-ho, L.

2016-12-01

Recently there are many research about analysing the interaction between entities by text-mining analysis in various fields. In this paper, we aimed to quantitatively analyse research-trends in the area of environmental research relating either spatial information or ICT (Information and Communications Technology) by Text-mining analysis. To do this, we applied low-dimensional embedding method, clustering analysis, and association rule to find meaningful associative patterns of key words frequently appeared in the articles. As the authors suppose that KCI (Korea Citation Index) articles reflect academic demands, total 1228 KCI articles that have been published from 1996 to 2015 were reviewed and analysed by Text-mining method. First, we derived KCI articles from NDSL(National Discovery for Science Leaders) site. And then we pre-processed their key-words elected from abstract and then classified those in separable sectors. We investigated the appearance rates and association rule of key-words for articles in the two fields: spatial-information and ICT. In order to detect historic trends, analysis was conducted separately for the four periods: 1996-2000, 2001-2005, 2006-2010, 2011-2015. These analysis were conducted with the usage of R-software. As a result, we conformed that environmental research relating spatial information mainly focused upon such fields as `GIS(35%)', `Remote-Sensing(25%)', `environmental theme map(15.7%)'. Next, `ICT technology(23.6%)', `ICT service(5.4%)', `mobile(24%)', `big data(10%)', `AI(7%)' are primarily emerging from environmental research relating ICT. Thus, from the analysis results, this paper asserts that research trends and academic progresses are well-structured to review recent spatial information and ICT technology and the outcomes of the analysis can be an adequate guidelines to establish environment policies and strategies. KEY WORDS: Big data, Test-mining, Environmental research, Spatial-information, ICT Acknowledgements: The

Alkemio: association of chemicals with biomedical topics by text and data mining.

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Gijón-Correas, José A; Andrade-Navarro, Miguel A; Fontaine, Jean F

2014-07-01

The PubMed® database of biomedical citations allows the retrieval of scientific articles studying the function of chemicals in biology and medicine. Mining millions of available citations to search reported associations between chemicals and topics of interest would require substantial human time. We have implemented the Alkemio text mining web tool and SOAP web service to help in this task. The tool uses biomedical articles discussing chemicals (including drugs), predicts their relatedness to the query topic with a naïve Bayesian classifier and ranks all chemicals by P-values computed from random simulations. Benchmarks on seven human pathways showed good retrieval performance (areas under the receiver operating characteristic curves ranged from 73.6 to 94.5%). Comparison with existing tools to retrieve chemicals associated to eight diseases showed the higher precision and recall of Alkemio when considering the top 10 candidate chemicals. Alkemio is a high performing web tool ranking chemicals for any biomedical topics and it is free to non-commercial users. http://cbdm.mdc-berlin.de/∼medlineranker/cms/alkemio. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

A method for extracting design rationale knowledge based on Text Mining

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Liu Jihong

2017-01-01

Full Text Available Capture design rationale (DR knowledge and presenting it to designers by good form, which have great significance for design reuse and design innovation. Since the 1970s design rationality began to develop, many teams have developed their own design rational system. However, the DR acquisition system is not intelligent enough, and it still requires designers to do a lot of operations. In addition, the existing design documents contain a large number of DR knowledge, but it has not been well excavated. Therefore, a method and system are needed to better extract DR knowledge in design documents. We have proposed a DRKH (design rationale knowledge hierarchy model for DR representation. The DRKH model has three layers, respectively as design intent layer, design decision layer and design basis layer. In this paper, we use text mining method to extract DR from design documents and construct DR model. Finally, the welding robot design specification is taken as an example to demonstrate the system interface.

Internet of Things in Health Trends Through Bibliometrics and Text Mining.

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Konstantinidis, Stathis Th; Billis, Antonis; Wharrad, Heather; Bamidis, Panagiotis D

2017-01-01

Recently a new buzzword has slowly but surely emerged, namely the Internet of Things (IoT). The importance of IoT is identified worldwide both by organisations and governments and the scientific community with an incremental number of publications during the last few years. IoT in Health is one of the main pillars of this evolution, but limited research has been performed on future visions and trends. Thus, in this study we investigate the longitudinal trends of Internet of Things in Health through bibliometrics and use of text mining. Seven hundred seventy eight (778) articles were retrieved form The Web of Science database from 1998 to 2016. The publications are grouped into thirty (30) clusters based on abstract text analysis resulting into some eight (8) trends of IoT in Health. Research in this field is obviously obtaining a worldwide character with specific trends, which are worth delineating to be in favour of some areas.

Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy.

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Kirk, Jon; Shah, Nirav; Noll, Braxton; Stevens, Craig B; Lawler, Marshall; Mougeot, Farah B; Mougeot, Jean-Luc C

2018-08-01

Oral mucositis (OM) is a major dose-limiting side effect of chemotherapy and radiation used in cancer treatment. Due to the complex nature of OM, currently available drug-based treatments are of limited efficacy. Our objectives were (i) to determine genes and molecular pathways associated with OM and wound healing using computational tools and publicly available data and (ii) to identify drugs formulated for topical use targeting the relevant OM molecular pathways. OM and wound healing-associated genes were determined by text mining, and the intersection of the two gene sets was selected for gene ontology analysis using the GeneCodis program. Protein interaction network analysis was performed using STRING-db. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in OM. Our analysis identified 447 genes common to both the "OM" and "wound healing" text mining concepts. Gene enrichment analysis yielded 20 genes representing six pathways and targetable by a total of 32 drugs which could possibly be formulated for topical application. A manual search on ClinicalTrials.gov confirmed no relevant pathway/drug candidate had been overlooked. Twenty-five of the 32 drugs can directly affect the PTGS2 (COX-2) pathway, the pathway that has been targeted in previous clinical trials with limited success. Drug discovery using in silico text mining and pathway analysis tools can facilitate the identification of existing drugs that have the potential of topical administration to improve OM treatment.

Text Mining Genotype-Phenotype Relationships from Biomedical Literature for Database Curation and Precision Medicine.

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Ayush Singhal

2016-11-01

Full Text Available The practice of precision medicine will ultimately require databases of genes and mutations for healthcare providers to reference in order to understand the clinical implications of each patient's genetic makeup. Although the highest quality databases require manual curation, text mining tools can facilitate the curation process, increasing accuracy, coverage, and productivity. However, to date there are no available text mining tools that offer high-accuracy performance for extracting such triplets from biomedical literature. In this paper we propose a high-performance machine learning approach to automate the extraction of disease-gene-variant triplets from biomedical literature. Our approach is unique because we identify the genes and protein products associated with each mutation from not just the local text content, but from a global context as well (from the Internet and from all literature in PubMed. Our approach also incorporates protein sequence validation and disease association using a novel text-mining-based machine learning approach. We extract disease-gene-variant triplets from all abstracts in PubMed related to a set of ten important diseases (breast cancer, prostate cancer, pancreatic cancer, lung cancer, acute myeloid leukemia, Alzheimer's disease, hemochromatosis, age-related macular degeneration (AMD, diabetes mellitus, and cystic fibrosis. We then evaluate our approach in two ways: (1 a direct comparison with the state of the art using benchmark datasets; (2 a validation study comparing the results of our approach with entries in a popular human-curated database (UniProt for each of the previously mentioned diseases. In the benchmark comparison, our full approach achieves a 28% improvement in F1-measure (from 0.62 to 0.79 over the state-of-the-art results. For the validation study with UniProt Knowledgebase (KB, we present a thorough analysis of the results and errors. Across all diseases, our approach returned 272 triplets

Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining.

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Kreula, Sanna M; Kaewphan, Suwisa; Ginter, Filip; Jones, Patrik R

2018-01-01

The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm (filter) was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and filter to ( i ) discover novel candidate associations between different genes or proteins in the network, and ( ii ) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open-source resource.

VisualUrText: A Text Analytics Tool for Unstructured Textual Data

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Zainol, Zuraini; Jaymes, Mohd T. H.; Nohuddin, Puteri N. E.

2018-05-01

The growing amount of unstructured text over Internet is tremendous. Text repositories come from Web 2.0, business intelligence and social networking applications. It is also believed that 80-90% of future growth data is available in the form of unstructured text databases that may potentially contain interesting patterns and trends. Text Mining is well known technique for discovering interesting patterns and trends which are non-trivial knowledge from massive unstructured text data. Text Mining covers multidisciplinary fields involving information retrieval (IR), text analysis, natural language processing (NLP), data mining, machine learning statistics and computational linguistics. This paper discusses the development of text analytics tool that is proficient in extracting, processing, analyzing the unstructured text data and visualizing cleaned text data into multiple forms such as Document Term Matrix (DTM), Frequency Graph, Network Analysis Graph, Word Cloud and Dendogram. This tool, VisualUrText, is developed to assist students and researchers for extracting interesting patterns and trends in document analyses.

Unsupervised text mining methods for literature analysis: a case study for Thomas Pynchon's V.

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Christos Iraklis Tsatsoulis

2013-08-01

Full Text Available We investigate the use of unsupervised text mining methods for the analysis of prose literature works, using Thomas Pynchon's novel 'V'. as a case study. Our results suggest that such methods may be employed to reveal meaningful information regarding the novel’s structure. We report results using a wide variety of clustering algorithms, several distinct distance functions, and different visualization techniques. The application of a simple topic model is also demonstrated. We discuss the meaningfulness of our results along with the limitations of our approach, and we suggest some possible paths for further study.

Text Mining of the Classical Medical Literature for Medicines That Show Potential in Diabetic Nephropathy

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Lei Zhang

2014-01-01

Full Text Available Objectives. To apply modern text-mining methods to identify candidate herbs and formulae for the treatment of diabetic nephropathy. Methods. The method we developed includes three steps: (1 identification of candidate ancient terms; (2 systemic search and assessment of medical records written in classical Chinese; (3 preliminary evaluation of the effect and safety of candidates. Results. Ancient terms Xia Xiao, Shen Xiao, and Xiao Shen were determined as the most likely to correspond with diabetic nephropathy and used in text mining. A total of 80 Chinese formulae for treating conditions congruent with diabetic nephropathy recorded in medical books from Tang Dynasty to Qing Dynasty were collected. Sao si tang (also called Reeling Silk Decoction was chosen to show the process of preliminary evaluation of the candidates. It had promising potential for development as new agent for the treatment of diabetic nephropathy. However, further investigations about the safety to patients with renal insufficiency are still needed. Conclusions. The methods developed in this study offer a targeted approach to identifying traditional herbs and/or formulae as candidates for further investigation in the search for new drugs for modern disease. However, more effort is still required to improve our techniques, especially with regard to compound formulae.

A cross disciplinary study of link decay and the effectiveness of mitigation techniques.

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Hennessey, Jason; Ge, Steven

2013-01-01

The dynamic, decentralized world-wide-web has become an essential part of scientific research and communication. Researchers create thousands of web sites every year to share software, data and services. These valuable resources tend to disappear over time. The problem has been documented in many subject areas. Our goal is to conduct a cross-disciplinary investigation of the problem and test the effectiveness of existing remedies. We accessed 14,489 unique web pages found in the abstracts within Thomson Reuters' Web of Science citation index that were published between 1996 and 2010 and found that the median lifespan of these web pages was 9.3 years with 62% of them being archived. Survival analysis and logistic regression were used to find significant predictors of URL lifespan. The availability of a web page is most dependent on the time it is published and the top-level domain names. Similar statistical analysis revealed biases in current solutions: the Internet Archive favors web pages with fewer layers in the Universal Resource Locator (URL) while WebCite is significantly influenced by the source of publication. We also created a prototype for a process to submit web pages to the archives and increased coverage of our list of scientific webpages in the Internet Archive and WebCite by 22% and 255%, respectively. Our results show that link decay continues to be a problem across different disciplines and that current solutions for static web pages are helping and can be improved.

Textpresso Central: a customizable platform for searching, text mining, viewing, and curating biomedical literature.

Science.gov (United States)

Müller, H-M; Van Auken, K M; Li, Y; Sternberg, P W

2018-03-09

The biomedical literature continues to grow at a rapid pace, making the challenge of knowledge retrieval and extraction ever greater. Tools that provide a means to search and mine the full text of literature thus represent an important way by which the efficiency of these processes can be improved. We describe the next generation of the Textpresso information retrieval system, Textpresso Central (TPC). TPC builds on the strengths of the original system by expanding the full text corpus to include the PubMed Central Open Access Subset (PMC OA), as well as the WormBase C. elegans bibliography. In addition, TPC allows users to create a customized corpus by uploading and processing documents of their choosing. TPC is UIMA compliant, to facilitate compatibility with external processing modules, and takes advantage of Lucene indexing and search technology for efficient handling of millions of full text documents. Like Textpresso, TPC searches can be performed using keywords and/or categories (semantically related groups of terms), but to provide better context for interpreting and validating queries, search results may now be viewed as highlighted passages in the context of full text. To facilitate biocuration efforts, TPC also allows users to select text spans from the full text and annotate them, create customized curation forms for any data type, and send resulting annotations to external curation databases. As an example of such a curation form, we describe integration of TPC with the Noctua curation tool developed by the Gene Ontology (GO) Consortium. Textpresso Central is an online literature search and curation platform that enables biocurators and biomedical researchers to search and mine the full text of literature by integrating keyword and category searches with viewing search results in the context of the full text. It also allows users to create customized curation interfaces, use those interfaces to make annotations linked to supporting evidence statements

22 CFR 1203.735-105 - Disciplinary action.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 2 2010-04-01 2010-04-01 true Disciplinary action. 1203.735-105 Section 1203... RESPONSIBILITIES AND CONDUCT General Provisions § 1203.735-105 Disciplinary action. A violation of the regulations in this part by an employee or special Government employee may be cause for appropriate disciplinary...

Multi- Inter- and Trans-disciplinary research promoted by the European Cooperation in Science and Technology (COST): Lessons and experiments

OpenAIRE

Stavridou , Ioanna; Ferreira , Afonso

2010-01-01

Multi-, inter-, trans- disciplinary research has gained a lot of interest and investment during the past two decades as a result of the realization that many of today's challenges are resistant to traditional research approaches and require cross-fertilization between different disciplines and integrated knowledge from heterogeneous sources. Despite these needs, evaluation of multi- / inter- / trans- disciplinary research remains one of the least defined aspects. For the purpose of this paper...

Text Mining Effectively Scores and Ranks the Literature for Improving Chemical-Gene-Disease Curation at the Comparative Toxicogenomics Database

Science.gov (United States)

Johnson, Robin J.; Lay, Jean M.; Lennon-Hopkins, Kelley; Saraceni-Richards, Cynthia; Sciaky, Daniela; Murphy, Cynthia Grondin; Mattingly, Carolyn J.

2013-01-01

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a public resource that curates interactions between environmental chemicals and gene products, and their relationships to diseases, as a means of understanding the effects of environmental chemicals on human health. CTD provides a triad of core information in the form of chemical-gene, chemical-disease, and gene-disease interactions that are manually curated from scientific articles. To increase the efficiency, productivity, and data coverage of manual curation, we have leveraged text mining to help rank and prioritize the triaged literature. Here, we describe our text-mining process that computes and assigns each article a document relevancy score (DRS), wherein a high DRS suggests that an article is more likely to be relevant for curation at CTD. We evaluated our process by first text mining a corpus of 14,904 articles triaged for seven heavy metals (cadmium, cobalt, copper, lead, manganese, mercury, and nickel). Based upon initial analysis, a representative subset corpus of 3,583 articles was then selected from the 14,094 articles and sent to five CTD biocurators for review. The resulting curation of these 3,583 articles was analyzed for a variety of parameters, including article relevancy, novel data content, interaction yield rate, mean average precision, and biological and toxicological interpretability. We show that for all measured parameters, the DRS is an effective indicator for scoring and improving the ranking of literature for the curation of chemical-gene-disease information at CTD. Here, we demonstrate how fully incorporating text mining-based DRS scoring into our curation pipeline enhances manual curation by prioritizing more relevant articles, thereby increasing data content, productivity, and efficiency. PMID:23613709

Dune Mining and the Nhlabane Estuary, South Africa: the Effect of a Dredger Crossing on the Zoobenthic Community

International Nuclear Information System (INIS)

Vivier, L.; Cyrus, D.P.

1999-01-01

The Nhlabane Estuary, located on the north-east coast of South Africa, is situated in a titanium dune mining lease area. During 1993, a mining dredger and concentrator crossed the middle reaches of the estuary. For this purpose, two berm walls were constructed across the estuary. Two impacts stemmed from the crossing. A series of fine sediment intrusions into the estuary from the berm wall area occurred during late 1993 and early 1994 and caused a rapid decline in benthic densities and number of taxa. Recovery of the affected area was slow and characterized by initial proliferation of opportunistic colonizers. The berm walls, which divided the estuary in half, were kept in place for nearly three years and caused changes in water quality and the benthic community of the upper and lower halves of the estuary. Artificial breaching of the estuary in August 1995 and removal of the berm walls in May 1996 initiated recovery of the estuary. The success of a second dredger crossing, scheduled for January 1999, depends on addressing the mistakes made during the first crossing and on the speed with which the carefully planned crossing operation, berm wall removal and estuary rehabilitation proceed

Mining Tasks from the Web Anchor Text Graph: MSR Notebook Paper for the TREC 2015 Tasks Track

Science.gov (United States)

2015-11-20

Mining Tasks from the Web Anchor Text Graph: MSR Notebook Paper for the TREC 2015 Tasks Track Paul N. Bennett Microsoft Research Redmond, USA pauben...anchor text graph has proven useful in the general realm of query reformulation [2], we sought to quantify the value of extracting key phrases from...anchor text in the broader setting of the task understanding track. Given a query, our approach considers a simple method for identifying a relevant

Argo: an integrative, interactive, text mining-based workbench supporting curation

Science.gov (United States)

Rak, Rafal; Rowley, Andrew; Black, William; Ananiadou, Sophia

2012-01-01

Curation of biomedical literature is often supported by the automatic analysis of textual content that generally involves a sequence of individual processing components. Text mining (TM) has been used to enhance the process of manual biocuration, but has been focused on specific databases and tasks rather than an environment integrating TM tools into the curation pipeline, catering for a variety of tasks, types of information and applications. Processing components usually come from different sources and often lack interoperability. The well established Unstructured Information Management Architecture is a framework that addresses interoperability by defining common data structures and interfaces. However, most of the efforts are targeted towards software developers and are not suitable for curators, or are otherwise inconvenient to use on a higher level of abstraction. To overcome these issues we introduce Argo, an interoperable, integrative, interactive and collaborative system for text analysis with a convenient graphic user interface to ease the development of processing workflows and boost productivity in labour-intensive manual curation. Robust, scalable text analytics follow a modular approach, adopting component modules for distinct levels of text analysis. The user interface is available entirely through a web browser that saves the user from going through often complicated and platform-dependent installation procedures. Argo comes with a predefined set of processing components commonly used in text analysis, while giving the users the ability to deposit their own components. The system accommodates various areas and levels of user expertise, from TM and computational linguistics to ontology-based curation. One of the key functionalities of Argo is its ability to seamlessly incorporate user-interactive components, such as manual annotation editors, into otherwise completely automatic pipelines. As a use case, we demonstrate the functionality of an in

Monitoring Students' Academic & Disciplinary Progression.

Science.gov (United States)

McDonald, Fred; Kellogg, Larry J.

This document outlines the objectives and procedures of a program at a New Mexico school district whose purpose is to enable school personnel to systematically monitor students' academic and disciplinary progression. The objectives of the program are to diagnose academic or disciplinary problems and prescribe remedies, to establish an oncampus…

PubstractHelper: A Web-based Text-Mining Tool for Marking Sentences in Abstracts from PubMed Using Multiple User-Defined Keywords.

Science.gov (United States)

Chen, Chou-Cheng; Ho, Chung-Liang

2014-01-01

While a huge amount of information about biological literature can be obtained by searching the PubMed database, reading through all the titles and abstracts resulting from such a search for useful information is inefficient. Text mining makes it possible to increase this efficiency. Some websites use text mining to gather information from the PubMed database; however, they are database-oriented, using pre-defined search keywords while lacking a query interface for user-defined search inputs. We present the PubMed Abstract Reading Helper (PubstractHelper) website which combines text mining and reading assistance for an efficient PubMed search. PubstractHelper can accept a maximum of ten groups of keywords, within each group containing up to ten keywords. The principle behind the text-mining function of PubstractHelper is that keywords contained in the same sentence are likely to be related. PubstractHelper highlights sentences with co-occurring keywords in different colors. The user can download the PMID and the abstracts with color markings to be reviewed later. The PubstractHelper website can help users to identify relevant publications based on the presence of related keywords, which should be a handy tool for their research. http://bio.yungyun.com.tw/ATM/PubstractHelper.aspx and http://holab.med.ncku.edu.tw/ATM/PubstractHelper.aspx.

Social networking mining, visualization, and security

CERN Document Server

Dehuri, Satchidananda; Wang, Gi-Nam

2014-01-01

With the proliferation of social media and on-line communities in networked world a large gamut of data has been collected and stored in databases. The rate at which such data is stored is growing at a phenomenal rate and pushing the classical methods of data analysis to their limits. This book presents an integrated framework of recent empirical and theoretical research on social network analysis based on a wide range of techniques from various disciplines like data mining, social sciences, mathematics, statistics, physics, network science, machine learning with visualization techniques, and security. The book illustrates the potential of multi-disciplinary techniques in various real life problems and intends to motivate researchers in social network analysis to design more effective tools by integrating swarm intelligence and data mining.  

Risk-based analysis and decision making in multi-disciplinary environments

Science.gov (United States)

Feather, Martin S.; Cornford, Steven L.; Moran, Kelly

2003-01-01

A risk-based decision-making process conceived of and developed at JPL and NASA, has been used to help plan and guide novel technology applications for use on spacecraft. These applications exemplify key challenges inherent in multi-disciplinary design of novel technologies deployed in mission-critical settings. 1) Cross-disciplinary concerns are numerous (e.g., spacecraft involve navigation, propulsion, telecommunications). These concems are cross-coupled and interact in multiple ways (e.g., electromagnetic interference, heat transfer). 2) Time and budget pressures constrain development, operational resources constrain the resulting system (e.g., mass, volume, power). 3) Spacecraft are critical systems that must operate correctly the first time in only partially understood environments, with no chance for repair. 4) Past experience provides only a partial guide: New mission concepts are enhanced and enabled by new technologies, for which past experience is lacking. The decision-making process rests on quantitative assessments of the relationships between three classes of information - objectives (the things the system is to accomplish and constraints on its operation and development), risks (whose occurrence detracts from objectives), and mitigations (options for reducing the likelihood and or severity of risks). The process successfully guides experts to pool their knowledge, using custom-built software to support information gathering and decision-making.

E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter

OpenAIRE

Lazard, Allison J; Saffer, Adam J; Wilcox, Gary B; Chung, Arnold DongWoo; Mackert, Michael S; Bernhardt, Jay M

2016-01-01

Background As the use of electronic cigarettes (e-cigarettes) rises, social media likely influences public awareness and perception of this emerging tobacco product. Objective This study examined the public conversation on Twitter to determine overarching themes and insights for trending topics from commercial and consumer users. Methods Text mining uncovered key patterns and important topics for e-cigarettes on Twitter. SAS Text Miner 12.1 software (SAS Institute Inc) was used for descriptiv...

Multi-disciplinary decision making in general practice.

Science.gov (United States)

Kirby, Ann; Murphy, Aileen; Bradley, Colin

2018-04-09

Purpose Internationally, healthcare systems are moving towards delivering care in an integrated manner which advocates a multi-disciplinary approach to decision making. Such an approach is formally encouraged in the management of Atrial Fibrillation patients through the European Society of Cardiology guidelines. Since the emergence of new oral anticoagulants switching between oral anticoagulants (OACs) has become prevalent. This case study considers the role of multi-disciplinary decision making, given the complex nature of the agents. The purpose of this paper is to explore Irish General Practitioners' (GPs) experience of switching between all OACs for Arial Fibrillation (AF) patients; prevalence of multi-disciplinary decision making in OAC switching decisions and seeks to determine the GP characteristics that appear to influence the likelihood of multi-disciplinary decision making. Design/methodology/approach A probit model is used to determine the factors influencing multi-disciplinary decision making and a multinomial logit is used to examine the factors influencing who is involved in the multi-disciplinary decisions. Findings Results reveal that while some multi-disciplinary decision-making is occurring (64 per cent), it is not standard practice despite international guidelines on integrated care. Moreover, there is a lack of patient participation in the decision-making process. Female GPs and GPs who have initiated prescriptions for OACs are more likely to engage in multi-disciplinary decision-making surrounding switching OACs amongst AF patients. GPs with training practices were less likely to engage with cardiac consultants and those in urban areas were more likely to engage with other (non-cardiac) consultants. Originality/value For optimal decision making under uncertainty multi-disciplinary decision-making is needed to make a more informed judgement and to improve treatment decisions and reduce the opportunity cost of making the wrong decision.

Supporting the education evidence portal via text mining

Science.gov (United States)

Ananiadou, Sophia; Thompson, Paul; Thomas, James; Mu, Tingting; Oliver, Sandy; Rickinson, Mark; Sasaki, Yutaka; Weissenbacher, Davy; McNaught, John

2010-01-01

The UK Education Evidence Portal (eep) provides a single, searchable, point of access to the contents of the websites of 33 organizations relating to education, with the aim of revolutionizing work practices for the education community. Use of the portal alleviates the need to spend time searching multiple resources to find relevant information. However, the combined content of the websites of interest is still very large (over 500 000 documents and growing). This means that searches using the portal can produce very large numbers of hits. As users often have limited time, they would benefit from enhanced methods of performing searches and viewing results, allowing them to drill down to information of interest more efficiently, without having to sift through potentially long lists of irrelevant documents. The Joint Information Systems Committee (JISC)-funded ASSIST project has produced a prototype web interface to demonstrate the applicability of integrating a number of text-mining tools and methods into the eep, to facilitate an enhanced searching, browsing and document-viewing experience. New features include automatic classification of documents according to a taxonomy, automatic clustering of search results according to similar document content, and automatic identification and highlighting of key terms within documents. PMID:20643679

Text mining for search term development in systematic reviewing: A discussion of some methods and challenges.

Science.gov (United States)

Stansfield, Claire; O'Mara-Eves, Alison; Thomas, James

2017-09-01

Using text mining to aid the development of database search strings for topics described by diverse terminology has potential benefits for systematic reviews; however, methods and tools for accomplishing this are poorly covered in the research methods literature. We briefly review the literature on applications of text mining for search term development for systematic reviewing. We found that the tools can be used in 5 overarching ways: improving the precision of searches; identifying search terms to improve search sensitivity; aiding the translation of search strategies across databases; searching and screening within an integrated system; and developing objectively derived search strategies. Using a case study and selected examples, we then reflect on the utility of certain technologies (term frequency-inverse document frequency and Termine, term frequency, and clustering) in improving the precision and sensitivity of searches. Challenges in using these tools are discussed. The utility of these tools is influenced by the different capabilities of the tools, the way the tools are used, and the text that is analysed. Increased awareness of how the tools perform facilitates the further development of methods for their use in systematic reviews. Copyright © 2017 John Wiley & Sons, Ltd.

Whole field tendencies in transcranial magnetic stimulation: A systematic review with data and text mining.

Science.gov (United States)

Dias, Alvaro Machado; Mansur, Carlos Gustavo; Myczkowski, Martin; Marcolin, Marco

2011-06-01

Transcranial magnetic stimulation (TMS) has played an important role in the fields of psychiatry, neurology and neuroscience, since its emergence in the mid-1980s; and several high quality reviews have been produced since then. Most high quality reviews serve as powerful tools in the evaluation of predefined tendencies, but they cannot actually uncover new trends within the literature. However, special statistical procedures to 'mine' the literature have been developed which aid in achieving such a goal. This paper aims to uncover patterns within the literature on TMS as a whole, as well as specific trends in the recent literature on TMS for the treatment of depression. Data mining and text mining. Currently there are 7299 publications, which can be clustered in four essential themes. Considering the frequency of the core psychiatric concepts within the indexed literature, the main results are: depression is present in 13.5% of the publications; Parkinson's disease in 2.94%; schizophrenia in 2.76%; bipolar disorder in 0.158%; and anxiety disorder in 0.142% of all the publications indexed in PubMed. Several other perspectives are discussed in the article. Copyright © 2011 Elsevier B.V. All rights reserved.

A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

Science.gov (United States)

Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

2017-08-01

The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

Educators' disciplinary capabilities after the banning of corporal punishment in South African schools

Directory of Open Access Journals (Sweden)

Cosmas Maphosa

2010-01-01

Full Text Available The escalation of learner indiscipline cases in schools suggests failure by teachers to institute adequate alternative disciplinary measures after corporal punishment was outlawed in South African schools. We sought to address the following two research questions: (a How do educators view their disciplinary capabilities in the post-corporal punishment period? and (b How do educators view the usefulness of alternative disciplinary measures? The study adopted a qualitative approach. A case study of three purposively selected practising junior secondary school educators was used. Data were collected through interviews. We found that educators generally feel disempowered in their ability to institute discipline in schools in the absence of corporal punishment. Educators revealed that learners do not fear or respect educators because they know that nothing will happen to them. Although educators are aware of alternative disciplinary measures, they view them as ineffective and time consuming.

ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials

Science.gov (United States)

2012-01-01

Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be time-consuming, especially for new researchers. In this paper we present ASCOT, an efficient search application customised for clinical trials. ASCOT uses text mining and data mining methods to enrich clinical trials with metadata, that in turn serve as effective tools to narrow down search. In addition, ASCOT integrates a component for recommending eligibility criteria based on a set of selected protocols. PMID:22595088

Individualism-Collectivism and Power Distance Cultural Dimensions: How Each Influences Parental Disciplinary Methods

Science.gov (United States)

Schwab, Karen Walker

2013-01-01

This paper is a literature review using the Douglas-Widavasky Grid/Group theory as a framework to examine, from a cross cultural perspective, preferred parental disciplinary methods. The four rival cultures defined in the Grid/Group theory mirror the cultural dimensions of individualism-collectivism and power distance described by Geert Hofstede.…

ATTITUDES OF MEDICAL STUDENTS TO VIOLENT DISCIPLINARY METHODS, SOCIAL GENDER ROLES AND CHILDREN’S RIGHTS: A CROSS-SECTIONAL RESEARCH

OpenAIRE

AKGÜL KALKAN, Esin

2018-01-01

Theuse of all types of violent disciplinary methods degrading the child includingphysical punishment is a common violation of children’s rights. As a result,the aim of this study is to investigate the attitudes of medical studentsrelated to “violent disciplinary methods, social gender roles and children’srights” and to examine the correlation between these attitudes. Based on theUnited Nations Convention of the Rights of the Child and the child abuseliterature, a survey developed by the resea...

Analysing Customer Opinions with Text Mining Algorithms

Science.gov (United States)

Consoli, Domenico

2009-08-01

Knowing what the customer thinks of a particular product/service helps top management to introduce improvements in processes and products, thus differentiating the company from their competitors and gain competitive advantages. The customers, with their preferences, determine the success or failure of a company. In order to know opinions of the customers we can use technologies available from the web 2.0 (blog, wiki, forums, chat, social networking, social commerce). From these web sites, useful information must be extracted, for strategic purposes, using techniques of sentiment analysis or opinion mining.

The differential production cross section of the [Formula: see text](1020) meson in [Formula: see text] = 7 TeV [Formula: see text] collisions measured with the ATLAS detector.

Science.gov (United States)

Aad, G; Abajyan, T; Abbott, B; Abdallah, J; Abdel Khalek, S; Abdelalim, A A; Abdinov, O; Aben, R; Abi, B; Abolins, M; AbouZeid, O S; Abramowicz, H; Abreu, H; Acharya, B S; Adamczyk, L; Adams, D L; Addy, T N; Adelman, J; Adomeit, S; Adragna, P; Adye, T; Aefsky, S; Aguilar-Saavedra, J A; Agustoni, M; Aharrouche, M; Ahlen, S P; Ahles, F; Ahmad, A; Ahsan, M; Aielli, G; Åkesson, T P A; Akimoto, G; Akimov, A V; Alam, M S; Alam, M A; Albert, J; Albrand, S; Aleksa, M; Aleksandrov, I N; Alessandria, F; Alexa, C; Alexander, G; Alexandre, G; Alexopoulos, T; Alhroob, M; Aliev, M; Alimonti, G; Alison, J; Allbrooke, B M M; Allport, P P; Allwood-Spiers, S E; Almond, J; Aloisio, A; Alon, R; Alonso, A; Alonso, F; Altheimer, A; Alvarez Gonzalez, B; Alviggi, M G; Amako, K; Amelung, C; Ammosov, V V; Amor Dos Santos, S P; Amorim, A; Amram, N; Anastopoulos, C; Ancu, L S; Andari, N; Andeen, T; Anders, C F; Anders, G; Anderson, K J; Andreazza, A; Andrei, V; Andrieux, M-L; Anduaga, X S; Angelidakis, S; Anger, P; Angerami, A; Anghinolfi, F; Anisenkov, A; Anjos, N; Annovi, A; Antonaki, A; Antonelli, M; Antonov, A; Antos, J; Anulli, F; Aoki, M; Aoun, S; Aperio Bella, L; Apolle, R; Arabidze, G; Aracena, I; Arai, Y; Arce, A T H; Arfaoui, S; Arguin, J-F; Argyropoulos, S; Arik, E; Arik, M; Armbruster, A J; Arnaez, O; Arnal, V; Arnault, C; Artamonov, A; Artoni, G; Arutinov, D; Asai, S; Ask, S; Åsman, B; Asquith, L; Assamagan, K; Astbury, A; Atkinson, M; Aubert, B; Auge, E; Augsten, K; Aurousseau, M; Avolio, G; Avramidou, R; Axen, D; Azuelos, G; Azuma, Y; Baak, M A; Baccaglioni, G; Bacci, C; Bach, A M; Bachacou, H; Bachas, K; Backes, M; Backhaus, M; Backus Mayes, J; Badescu, E; Bagnaia, P; Bahinipati, S; Bai, Y; Bailey, D C; Bain, T; Baines, J T; Baker, O K; Baker, M D; Baker, S; Balek, P; Banas, E; Banerjee, P; Banerjee, Sw; Banfi, D; Bangert, A; Bansal, V; Bansil, H S; Barak, L; Baranov, S P; Barbaro Galtieri, A; Barber, T; Barberio, E L; Barberis, D; Barbero, M; Bardin, D Y; Barillari, T; Barisonzi, M; Barklow, T; Barlow, N; Barnett, B M; Barnett, R M; Baroncelli, A; Barone, G; Barr, A J; Barreiro, F; Barreiro Guimarães da Costa, J; Barrillon, P; Bartoldus, R; Barton, A E; Bartsch, V; Basye, A; Bates, R L; Batkova, L; Batley, J R; Battaglia, A; Battistin, M; Bauer, F; Bawa, H S; Beale, S; Beau, T; Beauchemin, P H; Beccherle, R; Bechtle, P; Beck, H P; Becker, A K; Becker, S; Beckingham, M; Becks, K H; Beddall, A J; Beddall, A; Bedikian, S; Bednyakov, V A; Bee, C P; Beemster, L J; Begel, M; Behar Harpaz, S; Behera, P K; Beimforde, M; Belanger-Champagne, C; Bell, P J; Bell, W H; Bella, G; Bellagamba, L; Bellomo, M; Belloni, A; Beloborodova, O; Belotskiy, K; Beltramello, O; Benary, O; Benchekroun, D; Bendtz, K; Benekos, N; Benhammou, Y; Benhar Noccioli, E; Benitez Garcia, J A; Benjamin, D P; Benoit, M; Bensinger, J R; Benslama, K; Bentvelsen, S; Berge, D; Bergeaas Kuutmann, E; Berger, N; Berghaus, F; Berglund, E; Beringer, J; Bernat, P; Bernhard, R; Bernius, C; Berry, T; Bertella, C; Bertin, A; 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A measurement is presented of the [Formula: see text] production cross section at [Formula: see text] = 7 TeV using [Formula: see text] collision data corresponding to an integrated luminosity of 383 [Formula: see text], collected with the ATLAS experiment at the LHC. Selection of [Formula: see text](1020) mesons is based on the identification of charged kaons by their energy loss in the pixel detector. The differential cross section is measured as a function of the transverse momentum, [Formula: see text], and rapidity, [Formula: see text], of the [Formula: see text](1020) meson in the fiducial region 500 [Formula: see text] 1200 MeV, [Formula: see text] 0.8, kaon [Formula: see text] 230 MeV and kaon momentum [Formula: see text] 800 MeV. The integrated [Formula: see text]-meson production cross section in this fiducial range is measured to be [Formula: see text] = 570 [Formula: see text] 8 (stat) [Formula: see text] 66 (syst) [Formula: see text] 20 (lumi) [Formula: see text].

Working with Data: Discovering Knowledge through Mining and Analysis; Systematic Knowledge Management and Knowledge Discovery; Text Mining; Methodological Approach in Discovering User Search Patterns through Web Log Analysis; Knowledge Discovery in Databases Using Formal Concept Analysis; Knowledge Discovery with a Little Perspective.

Science.gov (United States)

Qin, Jian; Jurisica, Igor; Liddy, Elizabeth D.; Jansen, Bernard J; Spink, Amanda; Priss, Uta; Norton, Melanie J.

2000-01-01

These six articles discuss knowledge discovery in databases (KDD). Topics include data mining; knowledge management systems; applications of knowledge discovery; text and Web mining; text mining and information retrieval; user search patterns through Web log analysis; concept analysis; data collection; and data structure inconsistency. (LRW)

38 CFR 17.106 - Authority for disciplinary action.

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2010-07-01

... disciplinary action. 17.106 Section 17.106 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF VETERANS AFFAIRS MEDICAL Disciplinary Control of Beneficiaries Receiving Hospital, Domiciliary Or Nursing Home Care § 17.106 Authority for disciplinary action. The good conduct of beneficiaries receiving hospitalization...

Mine Water Treatment in Hongai Coal Mines

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Dang Phuong Thao

2018-01-01

Full Text Available Acid mine drainage (AMD is recognized as one of the most serious environmental problem associated with mining industry. Acid water, also known as acid mine drainage forms when iron sulfide minerals found in the rock of coal seams are exposed to oxidizing conditions in coal mining. Until 2009, mine drainage in Hongai coal mines was not treated, leading to harmful effects on humans, animals and aquatic ecosystem. This report has examined acid mine drainage problem and techniques for acid mine drainage treatment in Hongai coal mines. In addition, selection and criteria for the design of the treatment systems have been presented.

The Determination of Children's Knowledge of Global Lunar Patterns from Online Essays Using Text Mining Analysis

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Cheon, Jongpil; Lee, Sangno; Smith, Walter; Song, Jaeki; Kim, Yongjin

2013-01-01

The purpose of this study was to use text mining analysis of early adolescents' online essays to determine their knowledge of global lunar patterns. Australian and American students in grades five to seven wrote about global lunar patterns they had discovered by sharing observations with each other via the Internet. These essays were analyzed for…

5 CFR 2635.106 - Disciplinary and corrective action.

Science.gov (United States)

2010-01-01

... 5 Administrative Personnel 3 2010-01-01 2010-01-01 false Disciplinary and corrective action. 2635... supplemental agency regulations may be cause for appropriate corrective or disciplinary action to be taken... appropriate disciplinary or corrective action in individual cases. However, corrective action may be ordered...

Practice-based evidence: profiling the safety of cilostazol by text-mining of clinical notes.

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Nicholas J Leeper

Full Text Available Peripheral arterial disease (PAD is a growing problem with few available therapies. Cilostazol is the only FDA-approved medication with a class I indication for intermittent claudication, but carries a black box warning due to concerns for increased cardiovascular mortality. To assess the validity of this black box warning, we employed a novel text-analytics pipeline to quantify the adverse events associated with Cilostazol use in a clinical setting, including patients with congestive heart failure (CHF.We analyzed the electronic medical records of 1.8 million subjects from the Stanford clinical data warehouse spanning 18 years using a novel text-mining/statistical analytics pipeline. We identified 232 PAD patients taking Cilostazol and created a control group of 1,160 PAD patients not taking this drug using 1:5 propensity-score matching. Over a mean follow up of 4.2 years, we observed no association between Cilostazol use and any major adverse cardiovascular event including stroke (OR = 1.13, CI [0.82, 1.55], myocardial infarction (OR = 1.00, CI [0.71, 1.39], or death (OR = 0.86, CI [0.63, 1.18]. Cilostazol was not associated with an increase in any arrhythmic complication. We also identified a subset of CHF patients who were prescribed Cilostazol despite its black box warning, and found that it did not increase mortality in this high-risk group of patients.This proof of principle study shows the potential of text-analytics to mine clinical data warehouses to uncover 'natural experiments' such as the use of Cilostazol in CHF patients. We envision this method will have broad applications for examining difficult to test clinical hypotheses and to aid in post-marketing drug safety surveillance. Moreover, our observations argue for a prospective study to examine the validity of a drug safety warning that may be unnecessarily limiting the use of an efficacious therapy.

School Disciplinary Style and Adolescent Health.

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Lau, Claudia; Wong, Mitchell; Dudovitz, Rebecca

2018-02-01

Parenting style is strongly associated with adolescent health. However, little is known about how school disciplinary style relates to health. We categorized adolescents' perceptions of their schools as authoritative, authoritarian, permissive, or neglectful, and test whether perceived school disciplinary style is associated with health. We analyze data from the RISE Up study (Reducing Health Inequities Through Social and Educational Change Follow-up), comprised of baseline (eighth grade) and 2-year follow-up surveys (10th grade) from 1,159 low-income minority adolescents in Los Angeles attending 157 schools. At 10th grade, students' ratings of school support and structure were used to categorize perceived school disciplinary style as authoritative (highest tertile for support and structure), authoritarian (low support, high structure), permissive (high support, low structure), neglectful (low on both dimensions), and average (middle tertile on either dimension). Mixed effects logistic regressions controlling for sociodemographic factors, parenting style, grades, and baseline health tested whether school disciplinary style was associated with substance use, violence, bullying, and depression symptoms. Risky behaviors varied by school disciplinary style. After adjusting for covariates, compared with an average school disciplinary style, a neglectful school was associated with higher odds of substance use (adjusted odds ratio [AOR] 2.3, p authoritative school was associated with lower odds of substance use (AOR .6, p = .049), violence (AOR .6, p = .03), and bullying (AOR .5, p = .001). Structured and supportive school environments may impact the health of vulnerable adolescents. Copyright © 2017 The Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Some considerations on disciplinary liability overlapping criminal liability

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Ştefania DUMITRACHE

2011-12-01

Full Text Available Among the various forms of legal liability there are many points of contact reflected in their common goal - the encouragement of active members of society. Starting from the statement - the independent nature of the various forms of legal liability does not mean they are excluded - in what follows, given the legal autonomy of spheres of social relations protected by various laws, we will consider disciplinary overlapping with other forms of legal liability - criminal liability. Of course, this is possible only if the act committed by the employee is both disciplinary and criminal. This form of accumulation are possible without violating the principle of non bis in idem that since each of the envisaged legal rules protect different social relations. In addition of this applying the same principle prohibits two or more same kind sanctions for an unlawful action

Assertions of Japanese Websites for and Against Cancer Screening: a Text Mining Analysis

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Okuhara, Tsuyoshi; Ishikawa, Hirono; Okada, Masahumi; Kato, Mio; Kiuchi, Takahiro

2017-04-01

Background: Cancer screening rates are lower in Japan than in Western countries such as the United States and the United Kingdom. While health professionals publish pro-cancer-screening messages online to encourage proactive seeking for screening, anti-screening activists use the same medium to warn readers against following guidelines. Contents of pro- and anti-cancer-screening sites may contribute to readers’ acceptance of one or the other position. We aimed to use a text-mining method to examine frequently appearing contents on sites for and against cancer screening. Methods: We conducted online searches in December 2016 using two major search engines in Japan (Google Japan and Yahoo! Japan). Targeted websites were classified as “pro”, “anti”, or “neutral” depending on their claims, with the author(s) classified as “health professional”, “mass media”, or “layperson”. Text-mining analyses were conducted, and statistical analysis was performed using the chi-square test. Results: Of the 169 websites analyzed, the top-three most frequently appearing content topics in pro sites were reducing mortality via cancer screening, benefits of early detection, and recommendations for obtaining detailed examination. The top three most frequent in anti-sites were harm from radiation exposure, non-efficacy of cancer screening, and lack of necessity of early detection. Anti-sites also frequently referred to a well-known Japanese radiologist, Makoto Kondo, who rejects the standard forms of cancer care. Conclusion: Our findings should enable authors of pro-cancer-screening sites to write to counter misleading anti-cancer-screening messages and facilitate dissemination of accurate information. Creative Commons Attribution License

22 CFR 18.22 - Notice of disciplinary action.

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2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Notice of disciplinary action. 18.22 Section 18... INTEREST Administrative Enforcement Proceedings § 18.22 Notice of disciplinary action. Upon the issuance of... Department during the period of suspension. The Director General shall take other appropriate disciplinary...

20 CFR 638.538 - Disciplinary procedures and appeals.

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2010-04-01

... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Disciplinary procedures and appeals. 638.538... disciplinary proceedings, in accordance with procedures developed by the Job Corps Director. Such center... PROGRAM UNDER TITLE IV-B OF THE JOB TRAINING PARTNERSHIP ACT Center Operations § 638.538 Disciplinary...

BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs

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Tsafnat Guy

2011-04-01

Full Text Available Abstract Background The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed a statistical text-mining approach (BInary Characteristics Extractor and biomedical Properties Predictor: BICEPP to help experts screen drugs that may have important clinical characteristics of interest. Results BICEPP first retrieves MEDLINE abstracts containing drug names, then selects tokens that best predict the list of drugs which represents the characteristic of interest. Machine learning is then used to classify drugs using a document frequency-based measure. Evaluation experiments were performed to validate BICEPP's performance on 484 characteristics of 857 drugs, identified from the Australian Medicines Handbook (AMH and the PharmacoKinetic Interaction Screening (PKIS database. Stratified cross-validations revealed that BICEPP was able to classify drugs into all 20 major therapeutic classes (100% and 157 (of 197 minor drug classes (80% with areas under the receiver operating characteristic curve (AUC > 0.80. Similarly, AUC > 0.80 could be obtained in the classification of 173 (of 238 adverse events (73%, up to 12 (of 15 groups of clinically significant cytochrome P450 enzyme (CYP inducers or inhibitors (80%, and up to 11 (of 14 groups of narrow therapeutic index drugs (79%. Interestingly, it was observed that the keywords used to describe a drug characteristic were not necessarily the most predictive ones for the classification task. Conclusions BICEPP has sufficient classification power to automatically distinguish a wide range of clinical properties of drugs. This may be used in pharmacovigilance applications to assist with rapid screening of large drug databases to identify important characteristics for further evaluation.

Automated Assessment of Patients' Self-Narratives for Posttraumatic Stress Disorder Screening Using Natural Language Processing and Text Mining.

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He, Qiwei; Veldkamp, Bernard P; Glas, Cees A W; de Vries, Theo

2017-03-01

Patients' narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four machine-learning algorithms-including decision tree, naive Bayes, support vector machine, and an alternative classification approach called the product score model-were used in combination with n-gram representation models to identify patterns between verbal features in self-narratives and psychiatric diagnoses. With our sample, the product score model with unigrams attained the highest prediction accuracy when compared with practitioners' diagnoses. The addition of multigrams contributed most to balancing the metrics of sensitivity and specificity. This article also demonstrates that text mining is a promising approach for analyzing patients' self-expression behavior, thus helping clinicians identify potential patients from an early stage.

Prevention and protection against propagation of explosionsin underground coal mines

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Л. М. Пейч

2017-06-01

Full Text Available Over the past century, the coal mining industry experienced a large number of explosions leading to a considerable loss of life. The objective of this study is preventing the propagation of methane and/or coal dust explosions through the use of passive water barriers and its implementation to the Spanish coal mining industry. Physical and chemical properties, flammability and explosibility parameters of typical Spanish coals are presented. In this paper,   a flexible approach to meet the requirements of the EN-14591-2:2007 standard is presented for the very specific local conditions, characterized by small cross-sections galleries, vertical seem, use of explosives, etc. Authors have proven the viability of standard requirements to the typical roadway from Spanish underground mines, considering realistic roadway lengths as well as available cross-sections taking into account ubiquitous obstacles such as: locomotives, conveyor belt, ventilation ducts, etc.

Automated assessment of patients' self-narratives for posttraumatic stress disorder screening using natural language processing and text mining

NARCIS (Netherlands)

He, Qiwei; Veldkamp, Bernard P.; Glas, Cornelis A.W.; de Vries, Theo

2017-01-01

Patients’ narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four

DDMGD: the database of text-mined associations between genes methylated in diseases from different species

KAUST Repository

Raies, A. B.

2014-11-14

Gathering information about associations between methylated genes and diseases is important for diseases diagnosis and treatment decisions. Recent advancements in epigenetics research allow for large-scale discoveries of associations of genes methylated in diseases in different species. Searching manually for such information is not easy, as it is scattered across a large number of electronic publications and repositories. Therefore, we developed DDMGD database (http://www.cbrc.kaust.edu.sa/ddmgd/) to provide a comprehensive repository of information related to genes methylated in diseases that can be found through text mining. DDMGD\\'s scope is not limited to a particular group of genes, diseases or species. Using the text mining system DEMGD we developed earlier and additional post-processing, we extracted associations of genes methylated in different diseases from PubMed Central articles and PubMed abstracts. The accuracy of extracted associations is 82% as estimated on 2500 hand-curated entries. DDMGD provides a user-friendly interface facilitating retrieval of these associations ranked according to confidence scores. Submission of new associations to DDMGD is provided. A comparison analysis of DDMGD with several other databases focused on genes methylated in diseases shows that DDMGD is comprehensive and includes most of the recent information on genes methylated in diseases.

Applied Linguistics in Its Disciplinary Context

Science.gov (United States)

Liddicoat, Anthony J.

2010-01-01

Australia's current attempt to develop a process to evaluate the quality of research (Excellence in Research for Australia--ERA) places a central emphasis on the disciplinary organisation of academic work. This disciplinary focus poses particular problems for Applied Linguistics in Australia. This paper will examine Applied Linguistics in relation…

Measurement of the [Formula: see text] and [Formula: see text] production cross sections in multilepton final states using 3.2 fb[Formula: see text] of [Formula: see text] collisions at [Formula: see text] = 13 TeV with the ATLAS detector.

Science.gov (United States)

Aaboud, M; Aad, G; Abbott, B; Abdallah, J; Abdinov, O; Abeloos, B; Aben, R; AbouZeid, O S; Abraham, N L; Abramowicz, H; Abreu, H; Abreu, R; Abulaiti, Y; Acharya, B S; Adamczyk, L; Adams, D L; Adelman, J; Adomeit, S; Adye, T; Affolder, A A; Agatonovic-Jovin, T; Agricola, J; Aguilar-Saavedra, J A; Ahlen, S P; Ahmadov, F; Aielli, G; Akerstedt, H; Åkesson, T P A; Akimov, A V; Alberghi, G L; Albert, J; Albrand, S; Alconada Verzini, M J; Aleksa, M; Aleksandrov, I N; Alexa, C; Alexander, G; Alexopoulos, T; Alhroob, M; Ali, B; Aliev, M; Alimonti, G; Alison, J; Alkire, S P; Allbrooke, B M M; Allen, B W; Allport, P P; Aloisio, A; Alonso, A; Alonso, F; Alpigiani, C; Alstaty, M; Alvarez Gonzalez, B; Álvarez Piqueras, D; Alviggi, M G; Amadio, B T; Amako, K; Amaral Coutinho, Y; Amelung, C; Amidei, D; Amor Dos Santos, S P; Amorim, A; Amoroso, S; Amundsen, G; Anastopoulos, C; Ancu, L S; Andari, N; Andeen, T; Anders, C F; Anders, G; Anders, J K; Anderson, K J; Andreazza, A; Andrei, V; Angelidakis, S; Angelozzi, I; Anger, P; Angerami, A; Anghinolfi, F; Anisenkov, A V; Anjos, N; Annovi, A; Antel, C; Antonelli, M; Antonov, A; Anulli, F; Aoki, M; Aperio Bella, L; Arabidze, G; Arai, Y; Araque, J P; Arce, A T H; Arduh, F A; Arguin, J-F; Argyropoulos, S; Arik, M; Armbruster, A J; Armitage, L J; Arnaez, O; Arnold, H; Arratia, M; Arslan, O; Artamonov, A; Artoni, G; Artz, S; Asai, S; Asbah, N; Ashkenazi, A; Åsman, B; Asquith, L; Assamagan, K; Astalos, R; Atkinson, M; Atlay, N B; Augsten, K; Avolio, G; Axen, B; Ayoub, M K; Azuelos, G; Baak, M A; Baas, A E; Baca, M J; Bachacou, H; Bachas, K; Backes, M; Backhaus, M; Bagiacchi, P; Bagnaia, P; Bai, Y; Baines, J T; Baker, O K; Baldin, E M; Balek, P; Balestri, T; Balli, F; Balunas, W K; Banas, E; Banerjee, Sw; Bannoura, A A E; Barak, L; Barberio, E L; Barberis, D; Barbero, M; Barillari, T; Barklow, T; Barlow, N; Barnes, S L; Barnett, B M; Barnett, R M; Barnovska-Blenessy, Z; Baroncelli, A; Barone, G; Barr, A J; Barranco Navarro, L; Barreiro, F; Barreiro Guimarães da Costa, J; 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Zhou, M; Zhou, N; Zhu, C G; Zhu, H; Zhu, J; Zhu, Y; Zhuang, X; Zhukov, K; Zibell, A; Zieminska, D; Zimine, N I; Zimmermann, C; Zimmermann, S; Zinonos, Z; Zinser, M; Ziolkowski, M; Živković, L; Zobernig, G; Zoccoli, A; Zur Nedden, M; Zwalinski, L

2017-01-01

A measurement of the [Formula: see text] and [Formula: see text] production cross sections in final states with either two same-charge muons, or three or four leptons (electrons or muons) is presented. The analysis uses a data sample of proton-proton collisions at [Formula: see text] TeV recorded with the ATLAS detector at the Large Hadron Collider in 2015, corresponding to a total integrated luminosity of 3.2 fb[Formula: see text]. The inclusive cross sections are extracted using likelihood fits to signal and control regions, resulting in [Formula: see text] pb and [Formula: see text] pb, in agreement with the Standard Model predictions.

[Disciplinary action and its degree of implementation].

Science.gov (United States)

Gordon, M; Betzalel, S

2004-04-01

The aim of disciplinary action against dental practitioners is to uphold professional standards, to protect the safety of the patients and to maintain public confidence in the profession. Disciplinary action against dentists in Israel is based on the Dentists' Ordinance of 1979. The main principle behind disciplinary action is trial by peers, which in effect means that the profession upholds the required standards. Seven examples are mentioned in the law for which the Ministry of Health can reprimand or suspend the license of a dentist permanently or for a limited period of time. The panel for disciplinary action consists of three judges: one lawyer and two dentists--one (the chairman) represents the General Director of the Ministry of Health, the other represents the Israel Dental Association. This article deals with the form of legal discussion, types of punishment and their limitations as interpreted by the authors. All 26 complaint files presented to the disciplinary committees between 1997-2002 that were concluded are discussed. The accusations, as well as the verdicts, are listed.

A Text Matching Method to Facilitate the Validation of Frequent Order Sets Obtained Through Data Mining

OpenAIRE

Che, Chengjian; Rocha, Roberto A.

2006-01-01

In order to compare order sets discovered using a data mining algorithm with existing order sets, we developed an order matching tool based on Oracle Text. The tool includes both automated searching and manual review processes. The comparison between the automated process and the manual review process indicates that the sensitivity of the automated matching is 81% and the specificity is 84%.

Text mining with emergent self organizing maps and multi-dimensional scaling: a comparative study on domestic violence

NARCIS (Netherlands)

Poelmans, J.; van Hulle, M.M.; Viaene, S.; Elzinga, P.; Dedene, G.

2011-01-01

In this paper we compare the usability of ESOM and MDS as text exploration instruments in police investigations. We combine them with traditional classification instruments such as the SVM and Naïve Bayes. We perform a case of real-life data mining using a dataset consisting of police reports

Measurement of the [Formula: see text] production cross-section in proton-proton collisions via the decay [Formula: see text].

Science.gov (United States)

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Tourneur, S; Tran, M T; Tresch, M; Tsaregorodtsev, A; Tsopelas, P; Tuning, N; Ubeda Garcia, M; Ukleja, A; Ustyuzhanin, A; Uwer, U; Vacca, C; Vagnoni, V; Valenti, G; Vallier, A; Vazquez Gomez, R; Vazquez Regueiro, P; Vázquez Sierra, C; Vecchi, S; Velthuis, J J; Veltri, M; Veneziano, G; Vesterinen, M; Viaud, B; Vieira, D; Vieites Diaz, M; Vilasis-Cardona, X; Vollhardt, A; Volyanskyy, D; Voong, D; Vorobyev, A; Vorobyev, V; Voß, C; Voss, H; de Vries, J A; Waldi, R; Wallace, C; Wallace, R; Walsh, J; Wandernoth, S; Wang, J; Ward, D R; Watson, N K; Websdale, D; Whitehead, M; Wicht, J; Wiedner, D; Wilkinson, G; Williams, M P; Williams, M; Wilschut, H W; Wilson, F F; Wimberley, J; Wishahi, J; Wislicki, W; Witek, M; Wormser, G; Wotton, S A; Wright, S; Wyllie, K; Xie, Y; Xing, Z; Xu, Z; Yang, Z; Yuan, X; Yushchenko, O; Zangoli, M; Zavertyaev, M; Zhang, L; Zhang, W C; Zhang, Y; Zhelezov, A; Zhokhov, A; Zhong, L; Zvyagin, A

The production of the [Formula: see text] state in proton-proton collisions is probed via its decay to the [Formula: see text] final state with the LHCb detector, in the rapidity range [Formula: see text] and in the meson transverse-momentum range [Formula: see text]. The cross-section for prompt production of [Formula: see text] mesons relative to the prompt [Formula: see text] cross-section is measured, for the first time, to be [Formula: see text] at a centre-of-mass energy [Formula: see text] using data corresponding to an integrated luminosity of 0.7 fb[Formula: see text], and [Formula: see text] at [Formula: see text] using 2.0 fb[Formula: see text]. The uncertainties quoted are, in order, statistical, systematic, and that on the ratio of branching fractions of the [Formula: see text] and [Formula: see text] decays to the [Formula: see text] final state. In addition, the inclusive branching fraction of [Formula: see text]-hadron decays into [Formula: see text] mesons is measured, for the first time, to be [Formula: see text], where the third uncertainty includes also the uncertainty on the [Formula: see text] inclusive branching fraction from [Formula: see text]-hadron decays. The difference between the [Formula: see text] and [Formula: see text] meson masses is determined to be [Formula: see text].

PubMed-EX: a web browser extension to enhance PubMed search with text mining features.

Science.gov (United States)

Tsai, Richard Tzong-Han; Dai, Hong-Jie; Lai, Po-Ting; Huang, Chi-Hsin

2009-11-15

PubMed-EX is a browser extension that marks up PubMed search results with additional text-mining information. PubMed-EX's page mark-up, which includes section categorization and gene/disease and relation mark-up, can help researchers to quickly focus on key terms and provide additional information on them. All text processing is performed server-side, freeing up user resources. PubMed-EX is freely available at http://bws.iis.sinica.edu.tw/PubMed-EX and http://iisr.cse.yzu.edu.tw:8000/PubMed-EX/.

Pluri-Disciplinary; Against the Common Perception of Collaboration Among Disciplines

Directory of Open Access Journals (Sweden)

B. Shabani Varaki

2015-02-01

Full Text Available There are numerous kinds of definitions and discourses of conceptualization for the collaboration among disciplines. Examining a wide range of the related texts represents various, divergent and also contradictory discourses back to this up. Carefully and critically examining the common perception of collaboration among disciplines, in this paper, authors introduce an alternative so-called pluridisciplinary.rn rnAnd, it is argued that pluri-disciplinary could be considered as an umbrella term for all other modes of collaboration among disciplines including multidisciplinary, interdisciplinary, and transdiciplinary. It is also contended that unlike the conventional perception of collaborations between disciplines, epistemological and instrument rationales need to be seen as a continuous integration, so such a holistic approach will lead to a new so-called discipline; transdiciplinary. It is also articulated that there is a hierarchical relationship between disciplines in the alternative. In this paper, simple knowledge in pluridisciplinary studies will be replaced by super-complex knowledge, so called; trans-disciplinary, as a new-fashioned discipline, emerges.

Texts and data mining and their possibilities applied to the process of news production

Directory of Open Access Journals (Sweden)

Walter Teixeira Lima Jr

2008-06-01

Full Text Available The proposal of this essay is to discuss the challenges of representing in a formalist computational process the knowledge which the journalist uses to articulate news values for the purpose of selecting and imposing hierarchy on news. It discusses how to make bridges to emulate this knowledge obtained in an empirical form with the bases of computational science, in the area of storage, recovery and linked to data in a database, which must show the way human brains treat information obtained through their sensorial system. Systemizing and automating part of the journalistic process in a database contributes to eliminating distortions, faults and to applying, in an efficient manner, techniques for Data Mining and/or Texts which, by definition, permit the discovery of nontrivial relations.

Texts and data mining and their possibilities applied to the process of news production

Directory of Open Access Journals (Sweden)

Walter Teixeira Lima Jr

2011-02-01

Full Text Available The proposal of this essay is to discuss the challenges of representing in a formalist computational process the knowledge which the journalist uses to articulate news values for the purpose of selecting and imposing hierarchy on news. It discusses how to make bridges to emulate this knowledge obtained in an empirical form with the bases of computational science, in the area of storage, recovery and linked to data in a database, which must show the way human brains treat information obtained through their sensorial system. Systemizing and automating part of the journalistic process in a database contributes to eliminating distortions, faults and to applying, in an efficient manner, techniques for Data Mining and/or Texts which, by definition, permit the discovery of nontrivial relations.

Interactive text mining with Pipeline Pilot: a bibliographic web-based tool for PubMed.

Science.gov (United States)

Vellay, S G P; Latimer, N E Miller; Paillard, G

2009-06-01

Text mining has become an integral part of all research in the medical field. Many text analysis software platforms support particular use cases and only those. We show an example of a bibliographic tool that can be used to support virtually any use case in an agile manner. Here we focus on a Pipeline Pilot web-based application that interactively analyzes and reports on PubMed search results. This will be of interest to any scientist to help identify the most relevant papers in a topical area more quickly and to evaluate the results of query refinement. Links with Entrez databases help both the biologist and the chemist alike. We illustrate this application with Leishmaniasis, a neglected tropical disease, as a case study.

The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews.

Science.gov (United States)

Hao, Haijing; Zhang, Kunpeng

2016-05-10

Many Web-based health care platforms allow patients to evaluate physicians by posting open-end textual reviews based on their experiences. These reviews are helpful resources for other patients to choose high-quality doctors, especially in countries like China where no doctor referral systems exist. Analyzing such a large amount of user-generated content to understand the voice of health consumers has attracted much attention from health care providers and health care researchers. The aim of this paper is to automatically extract hidden topics from Web-based physician reviews using text-mining techniques to examine what Chinese patients have said about their doctors and whether these topics differ across various specialties. This knowledge will help health care consumers, providers, and researchers better understand this information. We conducted two-fold analyses on the data collected from the "Good Doctor Online" platform, the largest online health community in China. First, we explored all reviews from 2006-2014 using descriptive statistics. Second, we applied the well-known topic extraction algorithm Latent Dirichlet Allocation to more than 500,000 textual reviews from over 75,000 Chinese doctors across four major specialty areas to understand what Chinese health consumers said online about their doctor visits. On the "Good Doctor Online" platform, 112,873 out of 314,624 doctors had been reviewed at least once by April 11, 2014. Among the 772,979 textual reviews, we chose to focus on four major specialty areas that received the most reviews: Internal Medicine, Surgery, Obstetrics/Gynecology and Pediatrics, and Chinese Traditional Medicine. Among the doctors who received reviews from those four medical specialties, two-thirds of them received more than two reviews and in a few extreme cases, some doctors received more than 500 reviews. Across the four major areas, the most popular topics reviewers found were the experience of finding doctors, doctors' technical

Professional licensure: investigation and disciplinary action.

Science.gov (United States)

Brous, Edie

2012-11-01

This is the second article in a three-part series on nursing boards' disciplinary actions and what nurses need to know to maintain their license in good standing. This article discusses common reasons boards of nursing conduct investigations and take disciplinary action. The third and final article will discuss strategies for protecting your license.

Text mining factor analysis (TFA) in green tea patent data

Science.gov (United States)

Rahmawati, Sela; Suprijadi, Jadi; Zulhanif

2017-03-01

Factor analysis has become one of the most widely used multivariate statistical procedures in applied research endeavors across a multitude of domains. There are two main types of analyses based on factor analysis: Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Both EFA and CFA aim to observed relationships among a group of indicators with a latent variable, but they differ fundamentally, a priori and restrictions made to the factor model. This method will be applied to patent data technology sector green tea to determine the development technology of green tea in the world. Patent analysis is useful in identifying the future technological trends in a specific field of technology. Database patent are obtained from agency European Patent Organization (EPO). In this paper, CFA model will be applied to the nominal data, which obtain from the presence absence matrix. While doing processing, analysis CFA for nominal data analysis was based on Tetrachoric matrix. Meanwhile, EFA model will be applied on a title from sector technology dominant. Title will be pre-processing first using text mining analysis.

Event-based text mining for biology and functional genomics

Science.gov (United States)

Thompson, Paul; Nawaz, Raheel; McNaught, John; Kell, Douglas B.

2015-01-01

The assessment of genome function requires a mapping between genome-derived entities and biochemical reactions, and the biomedical literature represents a rich source of information about reactions between biological components. However, the increasingly rapid growth in the volume of literature provides both a challenge and an opportunity for researchers to isolate information about reactions of interest in a timely and efficient manner. In response, recent text mining research in the biology domain has been largely focused on the identification and extraction of ‘events’, i.e. categorised, structured representations of relationships between biochemical entities, from the literature. Functional genomics analyses necessarily encompass events as so defined. Automatic event extraction systems facilitate the development of sophisticated semantic search applications, allowing researchers to formulate structured queries over extracted events, so as to specify the exact types of reactions to be retrieved. This article provides an overview of recent research into event extraction. We cover annotated corpora on which systems are trained, systems that achieve state-of-the-art performance and details of the community shared tasks that have been instrumental in increasing the quality, coverage and scalability of recent systems. Finally, several concrete applications of event extraction are covered, together with emerging directions of research. PMID:24907365

Multilevel Association Rule Mining for Bridge Resource Management Based on Immune Genetic Algorithm

Directory of Open Access Journals (Sweden)

Yang Ou

2014-01-01

Full Text Available This paper is concerned with the problem of multilevel association rule mining for bridge resource management (BRM which is announced by IMO in 2010. The goal of this paper is to mine the association rules among the items of BRM and the vessel accidents. However, due to the indirect data that can be collected, which seems useless for the analysis of the relationship between items of BIM and the accidents, the cross level association rules need to be studied, which builds the relation between the indirect data and items of BRM. In this paper, firstly, a cross level coding scheme for mining the multilevel association rules is proposed. Secondly, we execute the immune genetic algorithm with the coding scheme for analyzing BRM. Thirdly, based on the basic maritime investigation reports, some important association rules of the items of BRM are mined and studied. Finally, according to the results of the analysis, we provide the suggestions for the work of seafarer training, assessment, and management.

Practice-based evidence: profiling the safety of cilostazol by text-mining of clinical notes.

Science.gov (United States)

Leeper, Nicholas J; Bauer-Mehren, Anna; Iyer, Srinivasan V; Lependu, Paea; Olson, Cliff; Shah, Nigam H

2013-01-01

Peripheral arterial disease (PAD) is a growing problem with few available therapies. Cilostazol is the only FDA-approved medication with a class I indication for intermittent claudication, but carries a black box warning due to concerns for increased cardiovascular mortality. To assess the validity of this black box warning, we employed a novel text-analytics pipeline to quantify the adverse events associated with Cilostazol use in a clinical setting, including patients with congestive heart failure (CHF). We analyzed the electronic medical records of 1.8 million subjects from the Stanford clinical data warehouse spanning 18 years using a novel text-mining/statistical analytics pipeline. We identified 232 PAD patients taking Cilostazol and created a control group of 1,160 PAD patients not taking this drug using 1:5 propensity-score matching. Over a mean follow up of 4.2 years, we observed no association between Cilostazol use and any major adverse cardiovascular event including stroke (OR = 1.13, CI [0.82, 1.55]), myocardial infarction (OR = 1.00, CI [0.71, 1.39]), or death (OR = 0.86, CI [0.63, 1.18]). Cilostazol was not associated with an increase in any arrhythmic complication. We also identified a subset of CHF patients who were prescribed Cilostazol despite its black box warning, and found that it did not increase mortality in this high-risk group of patients. This proof of principle study shows the potential of text-analytics to mine clinical data warehouses to uncover 'natural experiments' such as the use of Cilostazol in CHF patients. We envision this method will have broad applications for examining difficult to test clinical hypotheses and to aid in post-marketing drug safety surveillance. Moreover, our observations argue for a prospective study to examine the validity of a drug safety warning that may be unnecessarily limiting the use of an efficacious therapy.

Associations between child disciplinary practices and bullying behavior in adolescents.

Science.gov (United States)

Zottis, Graziela A H; Salum, Giovanni A; Isolan, Luciano R; Manfro, Gisele G; Heldt, Elizeth

2014-01-01

to investigate associations between different types of child disciplinary practices and children and adolescents' bullying behavior in a Brazilian sample. cross-sectional study, with a school-based sample of 10- to 15-year-old children and adolescents. Child disciplinary practices were assessed using two main subtypes: power-assertive and punitive (psychological aggression, corporal punishment, deprivation of privileges, and penalty tasks) and inductive (explaining, rewarding, and monitoring). A modified version of the Olweus Bully Victim Questionnaire was used to measure the frequency of bullying. 247 children and adolescents were evaluated and 98 (39.7%) were classified as bullies. Power-assertive and punitive discipline by either mother or father was associated with bullying perpetration by their children. Mothers who mostly used this type of discipline were 4.36 (95% CI: 1.87-10.16; pbullying. bullying was associated to parents' assertive and punitive discipline. Finding different ways of disciplining children and adolescents might decrease bullying behavior. Copyright © 2014 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

NARCIS (Netherlands)

K.M. Hettne (Kristina); J. Boorsma (Jeffrey); D.A.M. van Dartel (Dorien A M); J.J. Goeman (Jelle); E.C. de Jong (Esther); A.H. Piersma (Aldert); R.H. Stierum (Rob); J. Kleinjans (Jos); J.A. Kors (Jan)

2013-01-01

textabstractBackground: Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with

Development and testing of a text-mining approach to analyse patients' comments on their experiences of colorectal cancer care.

Science.gov (United States)

Wagland, Richard; Recio-Saucedo, Alejandra; Simon, Michael; Bracher, Michael; Hunt, Katherine; Foster, Claire; Downing, Amy; Glaser, Adam; Corner, Jessica

2016-08-01

Quality of cancer care may greatly impact on patients' health-related quality of life (HRQoL). Free-text responses to patient-reported outcome measures (PROMs) provide rich data but analysis is time and resource-intensive. This study developed and tested a learning-based text-mining approach to facilitate analysis of patients' experiences of care and develop an explanatory model illustrating impact on HRQoL. Respondents to a population-based survey of colorectal cancer survivors provided free-text comments regarding their experience of living with and beyond cancer. An existing coding framework was tested and adapted, which informed learning-based text mining of the data. Machine-learning algorithms were trained to identify comments relating to patients' specific experiences of service quality, which were verified by manual qualitative analysis. Comparisons between coded retrieved comments and a HRQoL measure (EQ5D) were explored. The survey response rate was 63.3% (21 802/34 467), of which 25.8% (n=5634) participants provided free-text comments. Of retrieved comments on experiences of care (n=1688), over half (n=1045, 62%) described positive care experiences. Most negative experiences concerned a lack of post-treatment care (n=191, 11% of retrieved comments) and insufficient information concerning self-management strategies (n=135, 8%) or treatment side effects (n=160, 9%). Associations existed between HRQoL scores and coded algorithm-retrieved comments. Analysis indicated that the mechanism by which service quality impacted on HRQoL was the extent to which services prevented or alleviated challenges associated with disease and treatment burdens. Learning-based text mining techniques were found useful and practical tools to identify specific free-text comments within a large dataset, facilitating resource-efficient qualitative analysis. This method should be considered for future PROM analysis to inform policy and practice. Study findings indicated that

49 CFR 805.735-27 - Disciplinary or remedial action.

Science.gov (United States)

2010-10-01

... 49 Transportation 7 2010-10-01 2010-10-01 false Disciplinary or remedial action. 805.735-27... TRANSPORTATION SAFETY BOARD EMPLOYEE RESPONSIBILITIES AND CONDUCT § 805.735-27 Disciplinary or remedial action... cause for disciplinary action in addition to any penalty prescribed by Federal statute or regulation...

5 CFR 2636.104 - Civil, disciplinary and other action.

Science.gov (United States)

2010-01-01

... 5 Administrative Personnel 3 2010-01-01 2010-01-01 false Civil, disciplinary and other action... Provisions § 2636.104 Civil, disciplinary and other action. (a) Civil action. Except when the employee... prohibited conduct, whichever is greater. (b) Disciplinary and corrective action. An agency may initiate...

DDMGD: the database of text-mined associations between genes methylated in diseases from different species.

Science.gov (United States)

Bin Raies, Arwa; Mansour, Hicham; Incitti, Roberto; Bajic, Vladimir B

2015-01-01

Gathering information about associations between methylated genes and diseases is important for diseases diagnosis and treatment decisions. Recent advancements in epigenetics research allow for large-scale discoveries of associations of genes methylated in diseases in different species. Searching manually for such information is not easy, as it is scattered across a large number of electronic publications and repositories. Therefore, we developed DDMGD database (http://www.cbrc.kaust.edu.sa/ddmgd/) to provide a comprehensive repository of information related to genes methylated in diseases that can be found through text mining. DDMGD's scope is not limited to a particular group of genes, diseases or species. Using the text mining system DEMGD we developed earlier and additional post-processing, we extracted associations of genes methylated in different diseases from PubMed Central articles and PubMed abstracts. The accuracy of extracted associations is 82% as estimated on 2500 hand-curated entries. DDMGD provides a user-friendly interface facilitating retrieval of these associations ranked according to confidence scores. Submission of new associations to DDMGD is provided. A comparison analysis of DDMGD with several other databases focused on genes methylated in diseases shows that DDMGD is comprehensive and includes most of the recent information on genes methylated in diseases. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Annotated chemical patent corpus: a gold standard for text mining.

Directory of Open Access Journals (Sweden)

Saber A Akhondi

Full Text Available Exploring the chemical and biological space covered by patent applications is crucial in early-stage medicinal chemistry activities. Patent analysis can provide understanding of compound prior art, novelty checking, validation of biological assays, and identification of new starting points for chemical exploration. Extracting chemical and biological entities from patents through manual extraction by expert curators can take substantial amount of time and resources. Text mining methods can help to ease this process. To validate the performance of such methods, a manually annotated patent corpus is essential. In this study we have produced a large gold standard chemical patent corpus. We developed annotation guidelines and selected 200 full patents from the World Intellectual Property Organization, United States Patent and Trademark Office, and European Patent Office. The patents were pre-annotated automatically and made available to four independent annotator groups each consisting of two to ten annotators. The annotators marked chemicals in different subclasses, diseases, targets, and modes of action. Spelling mistakes and spurious line break due to optical character recognition errors were also annotated. A subset of 47 patents was annotated by at least three annotator groups, from which harmonized annotations and inter-annotator agreement scores were derived. One group annotated the full set. The patent corpus includes 400,125 annotations for the full set and 36,537 annotations for the harmonized set. All patents and annotated entities are publicly available at www.biosemantics.org.

Construction accident narrative classification: An evaluation of text mining techniques.

Science.gov (United States)

Goh, Yang Miang; Ubeynarayana, C U

2017-11-01

Learning from past accidents is fundamental to accident prevention. Thus, accident and near miss reporting are encouraged by organizations and regulators. However, for organizations managing large safety databases, the time taken to accurately classify accident and near miss narratives will be very significant. This study aims to evaluate the utility of various text mining classification techniques in classifying 1000 publicly available construction accident narratives obtained from the US OSHA website. The study evaluated six machine learning algorithms, including support vector machine (SVM), linear regression (LR), random forest (RF), k-nearest neighbor (KNN), decision tree (DT) and Naive Bayes (NB), and found that SVM produced the best performance in classifying the test set of 251 cases. Further experimentation with tokenization of the processed text and non-linear SVM were also conducted. In addition, a grid search was conducted on the hyperparameters of the SVM models. It was found that the best performing classifiers were linear SVM with unigram tokenization and radial basis function (RBF) SVM with uni-gram tokenization. In view of its relative simplicity, the linear SVM is recommended. Across the 11 labels of accident causes or types, the precision of the linear SVM ranged from 0.5 to 1, recall ranged from 0.36 to 0.9 and F1 score was between 0.45 and 0.92. The reasons for misclassification were discussed and suggestions on ways to improve the performance were provided. Copyright © 2017 Elsevier Ltd. All rights reserved.

32 CFR 776.66 - Bar admission and disciplinary matters.

Science.gov (United States)

2010-07-01

... 32 National Defense 5 2010-07-01 2010-07-01 false Bar admission and disciplinary matters. 776.66... ADVOCATE GENERAL Rules of Professional Conduct § 776.66 Bar admission and disciplinary matters. (a) Bar admission and disciplinary matters. A covered attorney, in connection with any application for bar admission...

Disciplinary Literacy : What You Want to Know about It

Science.gov (United States)

Fang, Zhihui; Coatoam, Suzanne

2013-01-01

The recent call for a disciplinary perspective on literacy instruction in the content areas has generated considerable interest among literacy educators. This column addresses some of the questions that have been raised about disciplinary literacy. These questions concern the definition and assessment of disciplinary literacy, as well as the…

22 CFR 905.1 - Grievances other than disciplinary actions.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 2 2010-04-01 2010-04-01 true Grievances other than disciplinary actions. 905... other than disciplinary actions. (a) In all grievances other than those concerning disciplinary actions... may have been a substantial factor in an agency action, and the question is presented whether the...

49 CFR 1019.6 - Disciplinary and other remedial action.

Science.gov (United States)

2010-10-01

... 49 Transportation 8 2010-10-01 2010-10-01 false Disciplinary and other remedial action. 1019.6... SURFACE TRANSPORTATION BOARD EMPLOYEES § 1019.6 Disciplinary and other remedial action. Any violation of the regulations in this part by an employee shall be cause for appropriate disciplinary or other...

36 CFR 905.735-108 - Remedial and disciplinary action.

Science.gov (United States)

2010-07-01

... DEVELOPMENT CORPORATION STANDARDS OF CONDUCT General Provisions § 905.735-108 Remedial and disciplinary action... assignment; (3) Changes in the assigned duties of the individual; or (4) Disciplinary action. (b) Where the situation warrants some form of disciplinary action, the Chairman may choose from a wide range including a...

31 CFR 15.737-28 - Notice of disciplinary action.

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2010-07-01

... 31 Money and Finance: Treasury 1 2010-07-01 2010-07-01 false Notice of disciplinary action. 15.737... period of suspension. (b) The Director shall take other appropriate disciplinary action as may be... EMPLOYMENT CONFLICT OF INTEREST Administrative Enforcement Proceedings § 15.737-28 Notice of disciplinary...

Trans-Disciplinary Education for Sustainable Marine and Coastal Management: A Case Study in Taiwan

Directory of Open Access Journals (Sweden)

Hsiao-Chien Lee

2016-10-01

Full Text Available The present study aims to investigate the effect of a trans-disciplinary design of curricula, deemed a powerful tool for teaching and research on complex environmental problems, with a goal to help solve the real problems that climate change has brought to the coastal environment in Taiwan. Three major real-life problems in southern Taiwan—declining mullet fisheries, flooding, and coral bleaching—were integrated into four courses. Adopting a qualitative case study method, the researchers investigated the student perceptions of the trans-disciplinary learning experiences, their attitudes toward marine and coastal environmental protection, and their capability of solving the problems related to marine and coastal environments. The researchers employed various methods to analyze the student reflection reports, student self-evaluation forms, and the tape-recorded class meetings. The findings suggest the following: the trans-disciplinary curriculum stands to be an innovative yet indispensable design for coastal management education; such a curriculum benefits students by equipping them with essential knowledge and skills to succeed in future marine conservation; action learning for marine and coastal sustainability serves as the final goal of trans-disciplinary learning project; a trans-disciplinary case study on the design of curricula provides effective knowledge integration of marine and coastal sustainability.

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

NARCIS (Netherlands)

Hettne, K.M.; Boorsma, A.; Dartel, D.A. van; Goeman, J.J.; Jong, E. de; Piersma, A.H.; Stierum, R.H.; Kleinjans, J.C.; Kors, J.A.

2013-01-01

BACKGROUND: Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

NARCIS (Netherlands)

Hettne, K.M.; Boorsma, A.; Dartel, van D.A.M.; Goeman, J.J.; Jong, de E.; Piersma, A.H.; Stierum, R.H.; Kleinjans, J.C.; Kors, J.A.

2013-01-01

Background: Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set

Harnessing the Power of Text Mining for the Detection of Abusive Content in Social Media

OpenAIRE

Chen, Hao; McKeever, Susan; Delany, Sarah Jane

2016-01-01

Abstract The issues of cyberbullying and online harassment have gained considerable coverage in the last number of years. Social media providers need to be able to detect abusive content both accurately and efficiently in order to protect their users. Our aim is to investigate the application of core text mining techniques for the automatic detection of abusive content across a range of social media sources include blogs, forums, media-sharing, Q&A and chat - using datasets from Twitter, YouT...

Mining

Directory of Open Access Journals (Sweden)

Khairullah Khan

2014-09-01

Full Text Available Opinion mining is an interesting area of research because of its applications in various fields. Collecting opinions of people about products and about social and political events and problems through the Web is becoming increasingly popular every day. The opinions of users are helpful for the public and for stakeholders when making certain decisions. Opinion mining is a way to retrieve information through search engines, Web blogs and social networks. Because of the huge number of reviews in the form of unstructured text, it is impossible to summarize the information manually. Accordingly, efficient computational methods are needed for mining and summarizing the reviews from corpuses and Web documents. This study presents a systematic literature survey regarding the computational techniques, models and algorithms for mining opinion components from unstructured reviews.

Orthopedic board certification and physician performance: an analysis of medical malpractice, hospital disciplinary action, and state medical board disciplinary action rates.

Science.gov (United States)

Kocher, Mininder S; Dichtel, Laura; Kasser, James R; Gebhardt, Mark C; Katz, Jeffery N

2008-02-01

Specialty board certification status has become the de facto standard of competency by which the profession and the public recognize physician specialists. However, the relationship between orthopedic board certification and physician performance has not been established. Rates of medical malpractice claims, hospital disciplinary actions, and state medical board disciplinary actions were compared between 1309 board-certified (BC) and 154 non-board-certified (NBC) orthopedic surgeons in 3 states. There was no significant difference between BC and NBC surgeons in medical malpractice claim proportions (BC, 19.1% NBC, 16.9% P = .586) or in hospital disciplinary action proportions (BC, 0.9% NBC, 0.8% P = 1.000). There was a significantly higher proportion of state medical board disciplinary action for NBC surgeons (BC, 7.6% NBC, 13.0% P = .028). An association between board certification status and physician performance is necessary to validate its status as the de facto standard of competency. In this study, BC surgeons had lower rates of state medical board disciplinary action.

11 CFR 7.6 - Disciplinary and other remedial action.

Science.gov (United States)

2010-01-01

... 11 Federal Elections 1 2010-01-01 2010-01-01 false Disciplinary and other remedial action. 7.6... Disciplinary and other remedial action. (a) A violation of this part by an employee or special Commission employee may be cause for appropriate disciplinary action which may be in addition to any penalty...

LimTox: a web tool for applied text mining of adverse event and toxicity associations of compounds, drugs and genes.

Science.gov (United States)

Cañada, Andres; Capella-Gutierrez, Salvador; Rabal, Obdulia; Oyarzabal, Julen; Valencia, Alfonso; Krallinger, Martin

2017-07-03

A considerable effort has been devoted to retrieve systematically information for genes and proteins as well as relationships between them. Despite the importance of chemical compounds and drugs as a central bio-entity in pharmacological and biological research, only a limited number of freely available chemical text-mining/search engine technologies are currently accessible. Here we present LimTox (Literature Mining for Toxicology), a web-based online biomedical search tool with special focus on adverse hepatobiliary reactions. It integrates a range of text mining, named entity recognition and information extraction components. LimTox relies on machine-learning, rule-based, pattern-based and term lookup strategies. This system processes scientific abstracts, a set of full text articles and medical agency assessment reports. Although the main focus of LimTox is on adverse liver events, it enables also basic searches for other organ level toxicity associations (nephrotoxicity, cardiotoxicity, thyrotoxicity and phospholipidosis). This tool supports specialized search queries for: chemical compounds/drugs, genes (with additional emphasis on key enzymes in drug metabolism, namely P450 cytochromes-CYPs) and biochemical liver markers. The LimTox website is free and open to all users and there is no login requirement. LimTox can be accessed at: http://limtox.bioinfo.cnio.es. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Sources of Law: Approach in the Light of Disciplinary Process Right

Directory of Open Access Journals (Sweden)

Alexandre dos Santos Lopes

2016-10-01

Full Text Available This article aims to analyze the sources of law that has an correlation with the disciplinary procedural law, especially when you realize the reverberation of principles inflows and axiological values arising from the constitution that procedural species. Calls that outline the sources  of  law  that  are  related  to  this  kind  of  administrative  process,  translates  into significant challenge, insofar as its structure, especially in the new constitutional order (post- positivist allows, starting from the look and constitutional filter, define more precisely the height, feature and densification in the context of the Brazilian legal system, enabling better framing of disciplinary procedural legal relationship.

Seqenv: linking sequences to environments through text mining.

Science.gov (United States)

Sinclair, Lucas; Ijaz, Umer Z; Jensen, Lars Juhl; Coolen, Marco J L; Gubry-Rangin, Cecile; Chroňáková, Alica; Oulas, Anastasis; Pavloudi, Christina; Schnetzer, Julia; Weimann, Aaron; Ijaz, Ali; Eiler, Alexander; Quince, Christopher; Pafilis, Evangelos

2016-01-01

Understanding the distribution of taxa and associated traits across different environments is one of the central questions in microbial ecology. High-throughput sequencing (HTS) studies are presently generating huge volumes of data to address this biogeographical topic. However, these studies are often focused on specific environment types or processes leading to the production of individual, unconnected datasets. The large amounts of legacy sequence data with associated metadata that exist can be harnessed to better place the genetic information found in these surveys into a wider environmental context. Here we introduce a software program, seqenv, to carry out precisely such a task. It automatically performs similarity searches of short sequences against the "nt" nucleotide database provided by NCBI and, out of every hit, extracts-if it is available-the textual metadata field. After collecting all the isolation sources from all the search results, we run a text mining algorithm to identify and parse words that are associated with the Environmental Ontology (EnvO) controlled vocabulary. This, in turn, enables us to determine both in which environments individual sequences or taxa have previously been observed and, by weighted summation of those results, to summarize complete samples. We present two demonstrative applications of seqenv to a survey of ammonia oxidizing archaea as well as to a plankton paleome dataset from the Black Sea. These demonstrate the ability of the tool to reveal novel patterns in HTS and its utility in the fields of environmental source tracking, paleontology, and studies of microbial biogeography. To install seqenv, go to: https://github.com/xapple/seqenv.

Teacher Fear of Litigation for Disciplinary Actions

Science.gov (United States)

Holben, Diane M.; Zirkel, Perry A.; Caskie, Grace I. L.

2009-01-01

The present study determined the extent to which teachers' fear of litigation limits their disciplinary actions, including any significant differences by period, demographic factors, and item type. Teachers' perceptions of limitations placed on their disciplinary actions do not substantiate the "paralyzing fear" of litigation that…

Configurable User Interface Framework for Data Discovery in Cross-Disciplinary and Citizen Science

Science.gov (United States)

Rozell, E.; Wang, H.; West, P.; Zednik, S.; Fox, P.

2012-04-01

Use cases for data discovery and analysis vary widely when looking across disciplines and levels of expertise. Domain experts across disciplines may have a thorough understanding of self-describing data formats, such as netCDF, and the software packages that are compatible. However, they may be unfamiliar with specific vocabulary terms used to describe the data parameters or instrument packages in someone else's collection, which are often useful in data discovery. Citizen scientists may struggle with both expert vocabularies and knowledge of existing tools for analyzing and visualizing data. There are some solutions for each problem individually. For expert vocabularies, semantic technologies like the Resource Description Framework (RDF) have been used to map terms from an expert vocabulary to layperson terminology. For data analysis and visualization, tools can be mapped to data products using semantic technologies as well. This presentation discusses a solution to both problems based on the S2S Framework, a configurable user interface (UI) framework for Web services. S2S unifies the two solutions previously described using a data service abstraction ("search services") and a UI abstraction ("widgets"). Using the OWL Web Ontology Language, S2S defines a vocabulary for describing search services and their outputs, and the compatibility of those outputs with UI widgets. By linking search service outputs to widgets, S2S can automatically compose UIs for search and analysis of data, making it easier for citizen scientists to manipulate data. We have also created Linked Data widgets for S2S, which can leverage distributed RDF resources to present alternative views of expert vocabularies. This presentation covers some examples where we have applied these solutions to improve data discovery for both cross-disciplinary and non-expert users.

Studies on medicinal herbs for cognitive enhancement based on the text mining of Dongeuibogam and preliminary evaluation of its effects.

Science.gov (United States)

Pak, Malk Eun; Kim, Yu Ri; Kim, Ha Neui; Ahn, Sung Min; Shin, Hwa Kyoung; Baek, Jin Ung; Choi, Byung Tae

2016-02-17

In literature on Korean medicine, Dongeuibogam (Treasured Mirror of Eastern Medicine), published in 1613, represents the overall results of the traditional medicines of North-East Asia based on prior medicinal literature of this region. We utilized this medicinal literature by text mining to establish a list of candidate herbs for cognitive enhancement in the elderly and then performed an evaluation of their effects. Text mining was performed for selection of candidate herbs. Cell viability was determined in HT22 hippocampal cells and immunohistochemistry and behavioral analysis was performed in a kainic acid (KA) mice model in order to observe alterations of hippocampal cells and cognition. Twenty four herbs for cognitive enhancement in the elderly were selected by text mining of Dongeuibogam. In HT22 cells, pretreatment with 3 candidate herbs resulted in significantly reduced glutamate-induced cell death. Panax ginseng was the most neuroprotective herb against glutamate-induced cell death. In the hippocampus of a KA mice model, pretreatment with 11 candidate herbs resulted in suppression of caspase-3 expression. Treatment with 7 candidate herbs resulted in significantly enhanced expression levels of phosphorylated cAMP response element binding protein. Number of proliferated cells indicated by BrdU labeling was increased by treatment with 10 candidate herbs. Schisandra chinensis was the most effective herb against cell death and proliferation of progenitor cells and Rehmannia glutinosa in neuroprotection in the hippocampus of a KA mice model. In a KA mice model, we confirmed improved spatial and short memory by treatment with the 3 most effective candidate herbs and these recovered functions were involved in a higher number of newly formed neurons from progenitor cells in the hippocampus. These established herbs and their combinations identified by text-mining technique and evaluation for effectiveness may have value in further experimental and clinical

Proximity to mining industry and respiratory diseases in children in a community in Northern Chile: A cross-sectional study.

Science.gov (United States)

Herrera, Ronald; Radon, Katja; von Ehrenstein, Ondine S; Cifuentes, Stella; Muñoz, Daniel Moraga; Berger, Ursula

2016-06-07

In a community in northern Chile, explosive procedures are used by two local industrial mines (gold, copper). We hypothesized that the prevalence of asthma and rhinoconjunctivitis in the community may be associated with air pollution emissions generated by the mines. A cross-sectional study of 288 children (aged 6-15 years) was conducted in a community in northern Chile using a validated questionnaire in 2009. The proximity between each child's place of residence and the mines was assessed as indicator of exposure to mining related air pollutants. Logistic regression, semiparametric models and spatial Bayesian models with a parametric form for distance were used to calculate odds ratios and 95 % confidence intervals. The prevalence of asthma and rhinoconjunctivitis was 24 and 34 %, respectively. For rhinoconjunctivitis, the odds ratio for average distance between both mines and child's residence was 1.72 (95 % confidence interval 1.00, 3.04). The spatial Bayesian models suggested a considerable increase in the risk for respiratory diseases closer to the mines, and only beyond a minimum distance of more than 1800 m the health impact was considered to be negligible. The findings indicate that air pollution emissions related to industrial gold or copper mines mainly occurring in rural Chilean communities might increase the risk of respiratory diseases in children.

A preliminary approach to creating an overview of lactoferrin multi-functionality utilizing a text mining method.

Science.gov (United States)

Shimazaki, Kei-ichi; Kushida, Tatsuya

2010-06-01

Lactoferrin is a multi-functional metal-binding glycoprotein that exhibits many biological functions of interest to many researchers from the fields of clinical medicine, dentistry, pharmacology, veterinary medicine, nutrition and milk science. To date, a number of academic reports concerning the biological activities of lactoferrin have been published and are easily accessible through public data repositories. However, as the literature is expanding daily, this presents challenges in understanding the larger picture of lactoferrin function and mechanisms. In order to overcome the "analysis paralysis" associated with lactoferrin information, we attempted to apply a text mining method to the accumulated lactoferrin literature. To this end, we used the information extraction system GENPAC (provided by Nalapro Technologies Inc., Tokyo). This information extraction system uses natural language processing and text mining technology. This system analyzes the sentences and titles from abstracts stored in the PubMed database, and can automatically extract binary relations that consist of interactions between genes/proteins, chemicals and diseases/functions. We expect that such information visualization analysis will be useful in determining novel relationships among a multitude of lactoferrin functions and mechanisms. We have demonstrated the utilization of this method to find pathways of lactoferrin participation in neovascularization, Helicobacter pylori attack on gastric mucosa, atopic dermatitis and lipid metabolism.

MET network in PubMed: a text-mined network visualization and curation system.

Science.gov (United States)

Dai, Hong-Jie; Su, Chu-Hsien; Lai, Po-Ting; Huang, Ming-Siang; Jonnagaddala, Jitendra; Rose Jue, Toni; Rao, Shruti; Chou, Hui-Jou; Milacic, Marija; Singh, Onkar; Syed-Abdul, Shabbir; Hsu, Wen-Lian

2016-01-01

Metastasis is the dissemination of a cancer/tumor from one organ to another, and it is the most dangerous stage during cancer progression, causing more than 90% of cancer deaths. Improving the understanding of the complicated cellular mechanisms underlying metastasis requires investigations of the signaling pathways. To this end, we developed a METastasis (MET) network visualization and curation tool to assist metastasis researchers retrieve network information of interest while browsing through the large volume of studies in PubMed. MET can recognize relations among genes, cancers, tissues and organs of metastasis mentioned in the literature through text-mining techniques, and then produce a visualization of all mined relations in a metastasis network. To facilitate the curation process, MET is developed as a browser extension that allows curators to review and edit concepts and relations related to metastasis directly in PubMed. PubMed users can also view the metastatic networks integrated from the large collection of research papers directly through MET. For the BioCreative 2015 interactive track (IAT), a curation task was proposed to curate metastatic networks among PubMed abstracts. Six curators participated in the proposed task and a post-IAT task, curating 963 unique metastatic relations from 174 PubMed abstracts using MET.Database URL: http://btm.tmu.edu.tw/metastasisway. © The Author(s) 2016. Published by Oxford University Press.

E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter

Science.gov (United States)

2016-01-01

Background As the use of electronic cigarettes (e-cigarettes) rises, social media likely influences public awareness and perception of this emerging tobacco product. Objective This study examined the public conversation on Twitter to determine overarching themes and insights for trending topics from commercial and consumer users. Methods Text mining uncovered key patterns and important topics for e-cigarettes on Twitter. SAS Text Miner 12.1 software (SAS Institute Inc) was used for descriptive text mining to reveal the primary topics from tweets collected from March 24, 2015, to July 3, 2015, using a Python script in conjunction with Twitter’s streaming application programming interface. A total of 18 keywords related to e-cigarettes were used and resulted in a total of 872,544 tweets that were sorted into overarching themes through a text topic node for tweets (126,127) and retweets (114,451) that represented more than 1% of the conversation. Results While some of the final themes were marketing-focused, many topics represented diverse proponent and user conversations that included discussion of policies, personal experiences, and the differentiation of e-cigarettes from traditional tobacco, often by pointing to the lack of evidence for the harm or risks of e-cigarettes or taking the position that e-cigarettes should be promoted as smoking cessation devices. Conclusions These findings reveal that unique, large-scale public conversations are occurring on Twitter alongside e-cigarette advertising and promotion. Proponents and users are turning to social media to share knowledge, experience, and questions about e-cigarette use. Future research should focus on these unique conversations to understand how they influence attitudes towards and use of e-cigarettes. PMID:27956376

An Integrated Assessment Approach to Address Artisanal and Small-Scale Gold Mining in Ghana

Directory of Open Access Journals (Sweden)

Niladri Basu

2015-09-01

Full Text Available Artisanal and small-scale gold mining (ASGM is growing in many regions of the world including Ghana. The problems in these communities are complex and multi-faceted. To help increase understanding of such problems, and to enable consensus-building and effective translation of scientific findings to stakeholders, help inform policies, and ultimately improve decision making, we utilized an Integrated Assessment approach to study artisanal and small-scale gold mining activities in Ghana. Though Integrated Assessments have been used in the fields of environmental science and sustainable development, their use in addressing specific matter in public health, and in particular, environmental and occupational health is quite limited despite their many benefits. The aim of the current paper was to describe specific activities undertaken and how they were organized, and the outputs and outcomes of our activity. In brief, three disciplinary workgroups (Natural Sciences, Human Health, Social Sciences and Economics were formed, with 26 researchers from a range of Ghanaian institutions plus international experts. The workgroups conducted activities in order to address the following question: What are the causes, consequences and correctives of small-scale gold mining in Ghana? More specifically: What alternatives are available in resource-limited settings in Ghana that allow for gold-mining to occur in a manner that maintains ecological health and human health without hindering near- and long-term economic prosperity? Several response options were identified and evaluated, and are currently being disseminated to various stakeholders within Ghana and internationally.

76 FR 77327 - Disciplinary Appeals Board Panel

Science.gov (United States)

2011-12-12

... DEPARTMENT OF VETERANS AFFAIRS Disciplinary Appeals Board Panel AGENCY: Department of Veterans... Affairs Health Care Personnel Act of 1991 (Pub. L. 102-40), dated May 7, 1991, revised the disciplinary grievance and appeal procedures for employees appointed under 38 U.S.C. 7401(1). It also required the...

76 FR 8848 - Disciplinary Appeals Board Panel

Science.gov (United States)

2011-02-15

... DEPARTMENT OF VETERANS AFFAIRS Disciplinary Appeals Board Panel AGENCY: Department of Veterans... Affairs Health Care Personnel Act of 1991 (Pub. L. 102-40), dated May 7, 1991, revised the disciplinary grievance and appeal procedures for employees appointed under 38 U.S.C. 7401(1). It also required the...

Reviewing the College Disciplinary Procedure. Mendip Papers.

Science.gov (United States)

Kedney, R. J.; Saunders, R.

This paper provides practical advice on reviewing and designing disciplinary procedures and is set in the context of incorporation of further education and sixth form colleges in England. Reasons are provided for having disciplinary rules, based on the Advisory Conciliation and Arbitration Service's (ACAS) Code of Practice. Relevant English…

[Disciplinary verdicts in cases of child abuse; lessons for paediatricians].

Science.gov (United States)

Berkers, Gitte; Biesaart, Monique C I H; Leeuwenburgh-Pronk, Wendela G

2015-01-01

To give an overview of disciplinary cases regarding action taken by paediatricians and paediatric residents in cases of (suspected) child abuse and to discuss the considerations of the disciplinary board in these cases. Retrospective, descriptive study. We considered all disciplinary cases instigated from 2001 to 2013 against paediatricians or paediatric residents and selected complaints regarding action taken in cases of (suspected) child abuse. We divided these complaints into six categories and studied the considerations of the disciplinary board in these cases. From 33 disciplinary cases instigated from 2001 to 2013, we selected 76 complaints regarding action taken by paediatricians or paediatric residents in cases of (suspected) child abuse. The majority of these complaints concerned the reporting or requesting of information in the context of (suspected) child abuse. All of the complaints in the category 'unwarranted reporting of child abuse' were declared unfounded by the disciplinary judge. The disciplinary board declared all complaints unfounded in cases where the paediatrician or paediatric resident had followed the Dutch national protocol regarding reporting of child abuse and domestic violence. The disciplinary board examines whether action was taken in accordance with reasonable standards of professional competence and considers that paediatricians have an important role in identifying child abuse.

Self-Reported Disciplinary Practices among Women in the Child Welfare System: Association with Domestic Violence Victimization

Science.gov (United States)

Kelleher, Kelly J.; Hazen, Andrea L.; Coben, Jeffrey H.; Wang, Yun; McGeehan, Jennifer; Kohl, Patricia L.; Gardner, William P.

2008-01-01

Objective: To examine the association between physical domestic violence victimization (both recent and more than a year in past measured by self-report) and self-reported disciplinary practices among female parents/caregivers in a national sample of families referred to child welfare. Methods: Cross-sectional survey of more than 3,000 female…

Roles for text mining in protein function prediction.

Science.gov (United States)

Verspoor, Karin M

2014-01-01

The Human Genome Project has provided science with a hugely valuable resource: the blueprints for life; the specification of all of the genes that make up a human. While the genes have all been identified and deciphered, it is proteins that are the workhorses of the human body: they are essential to virtually all cell functions and are the primary mechanism through which biological function is carried out. Hence in order to fully understand what happens at a molecular level in biological organisms, and eventually to enable development of treatments for diseases where some aspect of a biological system goes awry, we must understand the functions of proteins. However, experimental characterization of protein function cannot scale to the vast amount of DNA sequence data now available. Computational protein function prediction has therefore emerged as a problem at the forefront of modern biology (Radivojac et al., Nat Methods 10(13):221-227, 2013).Within the varied approaches to computational protein function prediction that have been explored, there are several that make use of biomedical literature mining. These methods take advantage of information in the published literature to associate specific proteins with specific protein functions. In this chapter, we introduce two main strategies for doing this: association of function terms, represented as Gene Ontology terms (Ashburner et al., Nat Genet 25(1):25-29, 2000), to proteins based on information in published articles, and a paradigm called LEAP-FS (Literature-Enhanced Automated Prediction of Functional Sites) in which literature mining is used to validate the predictions of an orthogonal computational protein function prediction method.

Decision support methods for the environmental assessment of contamination at mining sites.

Science.gov (United States)

Jordan, Gyozo; Abdaal, Ahmed

2013-09-01

Polluting mine accidents and widespread environmental contamination associated with historic mining in Europe and elsewhere has triggered the improvement of related environmental legislation and of the environmental assessment and management methods for the mining industry. Mining has some unique features such as natural background pollution associated with natural mineral deposits, industrial activities and contamination located in the three-dimensional sub-surface space, the problem of long-term remediation after mine closure, problem of secondary contaminated areas around mine sites and abandoned mines in historic regions like Europe. These mining-specific problems require special tools to address the complexity of the environmental problems of mining-related contamination. The objective of this paper is to review and evaluate some of the decision support methods that have been developed and applied to mining contamination. In this paper, only those methods that are both efficient decision support tools and provide a 'holistic' approach to the complex problem as well are considered. These tools are (1) landscape ecology, (2) industrial ecology, (3) landscape geochemistry, (4) geo-environmental models, (5) environmental impact assessment, (6) environmental risk assessment, (7) material flow analysis and (8) life cycle assessment. This unique inter-disciplinary study should enable both the researcher and the practitioner to obtain broad view on the state-of-the-art of decision support methods for the environmental assessment of contamination at mine sites. Documented examples and abundant references are also provided.

17 CFR 200.735-13 - Disciplinary and other remedial action.

Science.gov (United States)

2010-04-01

... conflicting interest; (3) disciplinary action; or (4) disqualification for a particular assignment. Remedial action, whether disciplinary or otherwise, shall be effected in accordance with any applicable laws... 17 Commodity and Securities Exchanges 2 2010-04-01 2010-04-01 false Disciplinary and other...

Quality assurance in clinical trials : a multi-disciplinary approach

International Nuclear Information System (INIS)

Cornes, D.

2001-01-01

Full text: Multi-disciplinary groups, such as medical physicists and radiation therapists, which work effectively together, can ensure continued improvements in radiation therapy quality. The same is also true for clinical trials, which have the added complication of requiring multi-institutional participation to collate sufficient data to effectively assess treatment benefits. It can be difficult to manage quality across all aspects of a multi-disciplinary and multi-institutional trial. A planned system of quality assurance is necessary to provide support for participating centres and facilitate a collaborative approach. To ensure protocol compliance a good relationship between the clinical trial group and treatment centre is idea with definition of mutual goals and objectives before and during the trial, and ongoing consultation and feedback throughout the trial process. To ensure good quality data and maximise the validity of results the study protocol must be strictly adhered to. Because of the need for meticulous attention to detail, both in treatment delivery and standards of documentation, clinical trials are often seen to further complicate the process of delivery of radiation therapy treatment. The Declaration of Helsinki and Good Clinical Practise Guidelines (adopted in May 1996, ICH) provide 'international ethical and scientific standards for designing, conducting, recording and reporting clinical research' and multi-disciplinary groups in each participating centre should also adhere to these guidelines. Copyright (2001) Australasian College of Physical Scientists and Engineers in Medicine

The Disciplinary Society and the Birth of Sociology: A Foucauldian Perspective

Directory of Open Access Journals (Sweden)

Dušan Ristić

2016-12-01

Full Text Available This paper is genealogical research that aims to present one of the historical ways that led to the emergence of sociology as a modern science. We discuss how and why this kind of genealogical research is important for explaining the emergence, transformation and regionalisation of power/knowledge. By following the arguments developed by Michel Foucault, we argue that the disciplinary practices emerging in European societies during the 18th and 19th centuries strongly influenced the upsurge of power/knowledge that would be transformed in sociology. We conclude that the appearance of the institutions – elements of what Foucault called the disciplinary society – led to the rise of new discourses of their legitimisation and to the birth of sociology.

An unsupervised text mining method for relation extraction from biomedical literature.

Directory of Open Access Journals (Sweden)

Changqin Quan

Full Text Available The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies interaction words from unlabeled data; these interaction words are then used in relation extraction between entity pairs. Dependency parsing and phrase structure parsing are combined for relation extraction. Based on the semi-supervised KNN algorithm, we extend the proposed unsupervised approach to a semi-supervised approach by combining pattern clustering, dependency parsing and phrase structure parsing rules. We evaluated the approaches on two different tasks: (1 Protein-protein interactions extraction, and (2 Gene-suicide association extraction. The evaluation of task (1 on the benchmark dataset (AImed corpus showed that our proposed unsupervised approach outperformed three supervised methods. The three supervised methods are rule based, SVM based, and Kernel based separately. The proposed semi-supervised approach is superior to the existing semi-supervised methods. The evaluation on gene-suicide association extraction on a smaller dataset from Genetic Association Database and a larger dataset from publicly available PubMed showed that the proposed unsupervised and semi-supervised methods achieved much higher F-scores than co-occurrence based method.

Students as Producers: An "X" Disciplinary Client-Based Approach to Collaborative Art, Design and Media Pedagogy

Science.gov (United States)

Cocchiarella, Fabrizio; Booth, Paul

2015-01-01

This article presents the findings of a cross-disciplinary project between BA (Hons) Interior Design, Creative Multimedia and Film and Media Studies at a large Metropolitan University in the North of England. The collaboration was part of Unit X, a faculty-wide credit-bearing initiative to enable better collaboration across art and design courses.…

Sustainable Mining Environment: Technical Review of Post-mining Plans

Directory of Open Access Journals (Sweden)

Restu Juniah

2017-12-01

Full Text Available The mining industry exists because humans need mining commodities to meet their daily needs such as motor vehicles, mobile phones, electronic equipment and others. Mining commodities as mentioned in Government Regulation No. 23 of 2010 on Implementation of Mineral and Coal Mining Business Activities are radioactive minerals, metal minerals, nonmetallic minerals, rocks and coal. Mineral and coal mining is conducted to obtain the mining commodities through production operations. Mining and coal mining companies have an obligation to ensure that the mining environment in particular after the post production operation or post mining continues. The survey research aims to examine technically the post-mining plan in coal mining of PT Samantaka Batubara in Indragiri Hulu Regency of Riau Province towards the sustainability of the mining environment. The results indicate that the post-mining plan of PT Samantaka Batubara has met the technical aspects required in post mining planning for a sustainable mining environment. Postponement of post-mining land of PT Samantaka Batubara for garden and forest zone. The results of this study are expected to be useful and can be used by stakeholders, academics, researchers, practitioners and associations of mining, and the environment.

LocText

DEFF Research Database (Denmark)

Cejuela, Juan Miguel; Vinchurkar, Shrikant; Goldberg, Tatyana

2018-01-01

trees and was trained and evaluated on a newly improved LocTextCorpus. Combined with an automatic named-entity recognizer, LocText achieved high precision (P = 86%±4). After completing development, we mined the latest research publications for three organisms: human (Homo sapiens), budding yeast...

Disciplinary and Legal Actions Against Dermatologists in Canada.

Science.gov (United States)

Nasseri, Eiman

2016-01-01

Dermatologists face a litany of professional and legal risks in practice. To review cases of disciplinary and legal action against dermatologists in Canada. The Canadian Medical Protective Association, all 10 provincial medical colleges, and the Canadian Legal Information Institute were contacted to obtain data on legal or disciplinary action taken against dermatologists in their records. A literature review was performed regarding litigation against dermatologists in other countries. Six dermatologists in Canada faced disciplinary action in the last 5 to 30 years. Seven dermatologists and 5 other specialists in Canada faced lawsuits relating to dermatology in the last 1 to 144 years. Procedures and therapy are the most frequently sources of lawsuits against dermatologists both at home and abroad. Dermatologists need to remain vigilant to avoid disciplinary action and lawsuits from their increasing and varied interactions with patients. © The Author(s) 2015.

Impact of Text-Mining and Imitating Strategies on Lexical Richness, Lexical Diversity and General Success in Second Language Writing

Science.gov (United States)

Çepni, Sevcan Bayraktar; Demirel, Elif Tokdemir

2016-01-01

This study aimed to find out the impact of "text mining and imitating" strategies on lexical richness, lexical diversity and general success of students in their compositions in second language writing. The participants were 98 students studying their first year in Karadeniz Technical University in English Language and Literature…

Development of Workshops on Biodiversity and Evaluation of the Educational Effect by Text Mining Analysis

Science.gov (United States)

Baba, R.; Iijima, A.

2014-12-01

Conservation of biodiversity is one of the key issues in the environmental studies. As means to solve this issue, education is becoming increasingly important. In the previous work, we have developed a course of workshops on the conservation of biodiversity. To disseminate the course as a tool for environmental education, determination of the educational effect is essential. A text mining enables analyses of frequency and co-occurrence of words in the freely described texts. This study is intended to evaluate the effect of workshop by using text mining technique. We hosted the originally developed workshop on the conservation of biodiversity for 22 college students. The aim of the workshop was to inform the definition of biodiversity. Generally, biodiversity refers to the diversity of ecosystem, diversity between species, and diversity within species. To facilitate discussion, supplementary materials were used. For instance, field guides of wildlife species were used to discuss about the diversity of ecosystem. Moreover, a hierarchical framework in an ecological pyramid was shown for understanding the role of diversity between species. Besides, we offered a document material on the historical affair of Potato Famine in Ireland to discuss about the diversity within species from the genetic viewpoint. Before and after the workshop, we asked students for free description on the definition of biodiversity, and analyzed by using Tiny Text Miner. This technique enables Japanese language morphological analysis. Frequently-used words were sorted into some categories. Moreover, a principle component analysis was carried out. After the workshop, frequency of the words tagged to diversity between species and diversity within species has significantly increased. From a principle component analysis, the 1st component consists of the words such as producer, consumer, decomposer, and food chain. This indicates that the students have comprehended the close relationship between

E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter.

Science.gov (United States)

Lazard, Allison J; Saffer, Adam J; Wilcox, Gary B; Chung, Arnold DongWoo; Mackert, Michael S; Bernhardt, Jay M

2016-12-12

As the use of electronic cigarettes (e-cigarettes) rises, social media likely influences public awareness and perception of this emerging tobacco product. This study examined the public conversation on Twitter to determine overarching themes and insights for trending topics from commercial and consumer users. Text mining uncovered key patterns and important topics for e-cigarettes on Twitter. SAS Text Miner 12.1 software (SAS Institute Inc) was used for descriptive text mining to reveal the primary topics from tweets collected from March 24, 2015, to July 3, 2015, using a Python script in conjunction with Twitter's streaming application programming interface. A total of 18 keywords related to e-cigarettes were used and resulted in a total of 872,544 tweets that were sorted into overarching themes through a text topic node for tweets (126,127) and retweets (114,451) that represented more than 1% of the conversation. While some of the final themes were marketing-focused, many topics represented diverse proponent and user conversations that included discussion of policies, personal experiences, and the differentiation of e-cigarettes from traditional tobacco, often by pointing to the lack of evidence for the harm or risks of e-cigarettes or taking the position that e-cigarettes should be promoted as smoking cessation devices. These findings reveal that unique, large-scale public conversations are occurring on Twitter alongside e-cigarette advertising and promotion. Proponents and users are turning to social media to share knowledge, experience, and questions about e-cigarette use. Future research should focus on these unique conversations to understand how they influence attitudes towards and use of e-cigarettes. ©Allison J Lazard, Adam J Saffer, Gary B Wilcox, Arnold DongWoo Chung, Michael S Mackert, Jay M Bernhardt. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 12.12.2016.

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.

Science.gov (United States)

Papamokos, George; Silins, Ilona

2016-01-01

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.

Life priorities in the HIV-positive Asians: a text-mining analysis in young vs. old generation.

Science.gov (United States)

Chen, Wei-Ti; Barbour, Russell

2017-04-01

HIV/AIDS is one of the most urgent and challenging public health issues, especially since it is now considered a chronic disease. In this project, we used text mining techniques to extract meaningful words and word patterns from 45 transcribed in-depth interviews of people living with HIV/AIDS (PLWHA) conducted in Taipei, Beijing, Shanghai, and San Francisco from 2006 to 2013. Text mining analysis can predict whether an emerging field will become a long-lasting source of academic interest or whether it is simply a passing source of interest that will soon disappear. The data were analyzed by age group (45 and older vs. 44 and younger). The highest ranking fragments in the order of frequency were: "care", "daughter", "disease", "family", "HIV", "hospital", "husband", "medicines", "money", "people", "son", "tell/disclosure", "thought", "want", and "years". Participants in the 44-year-old and younger group were focused mainly on disease disclosure, their families, and their financial condition. In older PLWHA, social supports were one of the main concerns. In this study, we learned that different age groups perceive the disease differently. Therefore, when designing intervention, researchers should consider to tailor an intervention to a specific population and to help PLWHA achieve a better quality of life. Promoting self-management can be an effective strategy for every encounter with HIV-positive individuals.

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

Science.gov (United States)

Papamokos, George; Silins, Ilona

2016-01-01

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens. PMID:27625608

Experiences and Challenges of Evidence Leaders ("Prosecutors" in Learner Disciplinary Hearings in Public Schools

Directory of Open Access Journals (Sweden)

Anthony Smith

2015-12-01

Full Text Available After the abolition of corporal punishment at schools, teachers have been faced with an increase in unacceptable learner behaviour and threatening situations in their classrooms. An urgent need arose to address learner discipline in innovative ways. Disciplinary hearings that deal with cases of serious misconduct represent a shift away from authoritarian control towards a corrective and restorative approach. This article presents views of educators that had acted as evidence leaders (“ELs” at disciplinary hearings. Qualitative data was collected through semi-structured interviews in a district of the Gauteng Education Department. AtlasTi software was utilised to analyse the verbatim interview transcriptions. Educators that usually served as evidence leaders (“prosecutors”, but had not been trained in law, experienced problems in conducting quasi-judicial functions without proper support and training. ELs regularly experience animosity from parents and learners; are frustrated by the unwillingness and failure of the provincial education departments to act in accordance with an SGB recommendation. Disciplinary hearings are time-consuming and lawyers representing learners complicate rather than facilitate the process. These weaknesses jeopardise the efficacy and fairness of the process and may ultimately defeat the purpose of a disciplinary hearing.

Design thinking - crossing disciplinary borders

CSIR Research Space (South Africa)

Viljoen, NM

2009-01-01

Full Text Available as to call this loss with reality a ‘mathematical masturbation’. (This is particularly evident in the history of American OR society and to date American OR is deemed more ‘mathematical’ while European and British OR are more ‘practical’.) This perceived...

The Cultural: Trans-disciplinary Looks in Plastic and Visual Arts Environment

Directory of Open Access Journals (Sweden)

Liliana Cortés Garzón

2011-05-01

Full Text Available The article carries out an approach to some theoretical positions that draw near cultural studies, cultural history and its relationship with plastic and visual arts, in the historiographical analysis of contemporary thinkers that undertake trans-disciplinary looks, to elaborate new theories that sustain index research in plastic and visual arts.

Disciplinary Interflow of Library and Information Science in Taiwan

Directory of Open Access Journals (Sweden)

Chiung-fang Liang

2004-09-01

Full Text Available This study investigates the indexed papers dated from 1996 to 2002, included in the Taiwan Humanities Citation Index (THCI. The goal is to explore disciplinary interflow of Library & Information Science (LIS studies in Taiwan. The results show that the researchers of LIS mostly cooperate with researchers and scholars in the fields of social science and engineering & technology. In addition, LIS researchers focusing on “Library & Information Technology” and “Reader Services” frequently cooperate with researchers from other disciplines. With regard to their citation behaviors, LIS researchers frequently cite literatures of the Social Science, Engineering & Technology, and History. Especially, the major of cited literatures are written in Chinese and published 5 to 10 years earlier than the citing papers.The LIS research topic, “Administration and Management”, has the largest COC (citation outside category index and WCOC (weighted citation outside category index. As an LIS research topic, “Administration and Management” might have relatively higher degree of disciplinary interflow. [Article content in Chinese

19 CFR 200.735-104 - Disciplinary and other remedial action.

Science.gov (United States)

2010-04-01

... 19 Customs Duties 3 2010-04-01 2010-04-01 false Disciplinary and other remedial action. 200.735... RESPONSIBILITIES AND CONDUCT General Provisions § 200.735-104 Disciplinary and other remedial action. (a) An employee who violates any of the regulations in this part may be disciplined. The disciplinary action may...

47 CFR 19.735-107 - Disciplinary and other remedial action.

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2010-10-01

... 47 Telecommunication 1 2010-10-01 2010-10-01 false Disciplinary and other remedial action. 19.735... RESPONSIBILITIES AND CONDUCT General Provisions § 19.735-107 Disciplinary and other remedial action. (a) A violation of the regulations in this part by an employee may be cause for appropriate disciplinary action...

15 CFR 0.735-40 - Disciplinary and other remedial action.

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2010-01-01

... 15 Commerce and Foreign Trade 1 2010-01-01 2010-01-01 false Disciplinary and other remedial action... RESPONSIBILITIES AND CONDUCT Administration § 0.735-40 Disciplinary and other remedial action. (a) Violation of a requirement established in or pursuant to this part shall be cause for appropriate disciplinary action, which...

The Key Events Dose-Response Framework: a cross-disciplinary mode-of-action based approach to examining dose-response and thresholds.

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Julien, Elizabeth; Boobis, Alan R; Olin, Stephen S

2009-09-01

The ILSI Research Foundation convened a cross-disciplinary working group to examine current approaches for assessing dose-response and identifying safe levels of intake or exposure for four categories of bioactive agents-food allergens, nutrients, pathogenic microorganisms, and environmental chemicals. This effort generated a common analytical framework-the Key Events Dose-Response Framework (KEDRF)-for systematically examining key events that occur between the initial dose of a bioactive agent and the effect of concern. Individual key events are considered with regard to factors that influence the dose-response relationship and factors that underlie variability in that relationship. This approach illuminates the connection between the processes occurring at the level of fundamental biology and the outcomes observed at the individual and population levels. Thus, it promotes an evidence-based approach for using mechanistic data to reduce reliance on default assumptions, to quantify variability, and to better characterize biological thresholds. This paper provides an overview of the KEDRF and introduces a series of four companion papers that illustrate initial application of the approach to a range of bioactive agents.

Use of Multi-Disciplinary Projects To Develop Competence.

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Trotman-Dickenson, Danusia

1992-01-01

Undergraduate technology and business students at the Polytechnic of Wales (United Kingdom) participated in multi-disciplinary team projects to experience real life business challenges and develop competences that employers expect in professionals. Lists characteristics of successful multi-disciplinary projects, discusses cost and industry…

EOQ estimation for imperfect quality items using association rule mining with clustering

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Mandeep Mittal

2015-09-01

Full Text Available Timely identification of newly emerging trends is needed in business process. Data mining techniques like clustering, association rule mining, classification, etc. are very important for business support and decision making. This paper presents a method for redesigning the ordering policy by including cross-selling effect. Initially, association rules are mined on the transactional database and EOQ is estimated with revenue earned. Then, transactions are clustered to obtain homogeneous clusters and association rules are mined in each cluster to estimate EOQ with revenue earned for each cluster. Further, this paper compares ordering policy for imperfect quality items which is developed by applying rules derived from apriori algorithm viz. a without clustering the transactions, and b after clustering the transactions. A numerical example is illustrated to validate the results.

Chemical Topic Modeling: Exploring Molecular Data Sets Using a Common Text-Mining Approach.

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Schneider, Nadine; Fechner, Nikolas; Landrum, Gregory A; Stiefl, Nikolaus

2017-08-28

Big data is one of the key transformative factors which increasingly influences all aspects of modern life. Although this transformation brings vast opportunities it also generates novel challenges, not the least of which is organizing and searching this data deluge. The field of medicinal chemistry is not different: more and more data are being generated, for instance, by technologies such as DNA encoded libraries, peptide libraries, text mining of large literature corpora, and new in silico enumeration methods. Handling those huge sets of molecules effectively is quite challenging and requires compromises that often come at the expense of the interpretability of the results. In order to find an intuitive and meaningful approach to organizing large molecular data sets, we adopted a probabilistic framework called "topic modeling" from the text-mining field. Here we present the first chemistry-related implementation of this method, which allows large molecule sets to be assigned to "chemical topics" and investigating the relationships between those. In this first study, we thoroughly evaluate this novel method in different experiments and discuss both its disadvantages and advantages. We show very promising results in reproducing human-assigned concepts using the approach to identify and retrieve chemical series from sets of molecules. We have also created an intuitive visualization of the chemical topics output by the algorithm. This is a huge benefit compared to other unsupervised machine-learning methods, like clustering, which are commonly used to group sets of molecules. Finally, we applied the new method to the 1.6 million molecules of the ChEMBL22 data set to test its robustness and efficiency. In about 1 h we built a 100-topic model of this large data set in which we could identify interesting topics like "proteins", "DNA", or "steroids". Along with this publication we provide our data sets and an open-source implementation of the new method (CheTo) which

Pharmacists subjected to disciplinary action: characteristics and risk factors.

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Phipps, Denham L; Noyce, Peter R; Walshe, Kieran; Parker, Dianne; Ashcroft, Darren M

2011-10-01

OBJECTIVE To establish whether there are any characteristics of pharmacists that predict their likelihood of being subjected to disciplinary action. METHODS  The setting was the Royal Pharmaceutical Society of Great Britain's Disciplinary Committee. One hundred and seventeen pharmacists, all of whom had been referred to the Disciplinary Committee, were matched with a quota sample of 580 pharmacists who had not been subjected to disciplinary action but that matched the disciplined pharmacists on a set of demographic factors (gender, country of residence, year of registration). Frequency analysis and regression analysis were used to compare the two groups of pharmacists in terms of sector of work, ethnicity, age and country of training. Descriptive statistics were also obtained from the disciplined pharmacists to further explore characteristics of disciplinary cases and those pharmacists who undergo them. KEY FINDINGS  While a number of characteristics appeared to increase the likelihood of a pharmacist being referred to the disciplinary committee, only one of these - working in a community pharmacy - was statistically significant. Professional misconduct accounted for a greater proportion of referrals than did clinical malpractice, and approximately one-fifth of pharmacists who went before the Disciplinary Committee had previously been disciplined by the Society. CONCLUSIONS  This study provides initial evidence of pharmacist characteristics that are associated with an increased risk of being disciplined, based upon the data currently available. It is recommended that follow-up work is carried out using a more extensive dataset in order to confirm the statistical trends identified here. © 2011 The Authors. IJPP © 2011 Royal Pharmaceutical Society.

Reading Deeply for Disciplinary Awareness and Political Judgment

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Staudinger, Alison

2017-01-01

What happens when students become better readers? Cultivating deep reading habits in students to help them navigate disciplinary cultures respects student autonomy. Scholarly literature predicts that three linked practices improve student reading: practice with feedback, explicit in-class work on reading strategies, and disciplinary norm…

A cross-sectional survey on knowledge and perceptions of health risks associated with arsenic and mercury contamination from artisanal gold mining in Tanzania

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Charles Elias

2013-01-01

Full Text Available Abstract Background An estimated 0.5 to 1.5 million informal miners, of whom 30-50% are women, rely on artisanal mining for their livelihood in Tanzania. Mercury, used in the processing gold ore, and arsenic, which is a constituent of some ores, are common occupational exposures that frequently result in widespread environmental contamination. Frequently, the mining activities are conducted haphazardly without regard for environmental, occupational, or community exposure. The primary objective of this study was to assess community risk knowledge and perception of potential mercury and arsenic toxicity and/or exposure from artisanal gold mining in Rwamagasa in northwestern Tanzania. Methods A cross-sectional survey of respondents in five sub-villages in the Rwamagasa Village located in Geita District in northwestern Tanzania near Lake Victoria was conducted. This area has a history of artisanal gold mining and many of the population continue to work as miners. Using a clustered random selection approach for recruitment, a total of 160 individuals over 18 years of age completed a structured interview. Results The interviews revealed wide variations in knowledge and risk perceptions concerning mercury and arsenic exposure, with 40.6% (n=65 and 89.4% (n=143 not aware of the health effects of mercury and arsenic exposure respectively. Males were significantly more knowledgeable (n=59, 36.9% than females (n=36, 22.5% with regard to mercury (x2=3.99, px2=22.82, p= Conclusions The knowledge of individuals living in Rwamagasa, Tanzania, an area with a history of artisanal gold mining, varied widely with regard to the health hazards of mercury and arsenic. In these communities there was limited awareness of the threats to health associated with exposure to mercury and arsenic. This lack of knowledge, combined with minimal environmental monitoring and controlled waste management practices, highlights the need for health education, surveillance, and policy

RHETORICAL PATTERNS, VERB TENSE, AND VOICE IN CROSS DISCIPLINARY RESEARCH ARTICLE ABSTRACT

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Sharifah Hanidar

2016-05-01

Full Text Available This article investigates research article abstracts in terms of their rhetorical patterns and the use of verb tenses and voice. A total of 40 abstracts were selected from four international journals in the fields of Biology, Mechanical Engineering, Linguistics, and Medicine. A four move model was adopted from Hardjanto (1997 to analyze the structure of the abstracts. The results show that all the abstracts have Move 1, creating a research space; 70% have Move 2, describing research procedure; 85% have Move 3, summarizing principal results; and 85% have Move 4, evaluating results. All the abstracts in medicine have Moves 1, 2, 3 and 4, whereas the most common pattern in Biology is Moves 1, 3 and 4, in Mechanical Engineering Moves 1, 2 and 3, and in Linguistics Moves 1, 2 and 4. This seems to suggest that there is a disciplinary variation in the structuring of RA abstracts in the four disciplines under investigation. With regard to the use of verb tense and voice in each move, the present tense and past tense in the active voice and the past tense in the passive voice were the most frequently used tenses. The present tense in the active voice was frequently used in Moves 1 and 4, while the past tense in the active voice was commonly used in Move 3 and the past tense in the passive voice was frequently found in Move 2. Furthermore, it was found that the present tense in the active voice was frequently used in Biology, Mechanical Engineering and Linguistics, whereas the past tense in the active voice occurred more frequently in Medicine, and the past tense in the passive voice was more frequently found in Mechanical Engineering than in other disciplines.

Disciplinary Literacy from a Speech-Language Pathologist's Perspective