Acquiring 3-D information about thick objects from differential interference contrast images using texture extraction

NASA Astrophysics Data System (ADS)

Sierra, Heidy; Brooks, Dana; Dimarzio, Charles

2010-07-01

The extraction of 3-D morphological information about thick objects is explored in this work. We extract this information from 3-D differential interference contrast (DIC) images by applying a texture detection method. Texture extraction methods have been successfully used in different applications to study biological samples. A 3-D texture image is obtained by applying a local entropy-based texture extraction method. The use of this method to detect regions of blastocyst mouse embryos that are used in assisted reproduction techniques such as in vitro fertilization is presented as an example. Results demonstrate the potential of using texture detection methods to improve morphological analysis of thick samples, which is relevant to many biomedical and biological studies. Fluorescence and optical quadrature microscope phase images are used for validation.

Enhancing biomedical text summarization using semantic relation extraction.

PubMed

Shang, Yue; Li, Yanpeng; Lin, Hongfei; Yang, Zhihao

2011-01-01

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization.

Generating disease-pertinent treatment vocabularies from MEDLINE citations.

PubMed

Wang, Liqin; Del Fiol, Guilherme; Bray, Bruce E; Haug, Peter J

2017-01-01

Healthcare communities have identified a significant need for disease-specific information. Disease-specific ontologies are useful in assisting the retrieval of disease-relevant information from various sources. However, building these ontologies is labor intensive. Our goal is to develop a system for an automated generation of disease-pertinent concepts from a popular knowledge resource for the building of disease-specific ontologies. A pipeline system was developed with an initial focus of generating disease-specific treatment vocabularies. It was comprised of the components of disease-specific citation retrieval, predication extraction, treatment predication extraction, treatment concept extraction, and relevance ranking. A semantic schema was developed to support the extraction of treatment predications and concepts. Four ranking approaches (i.e., occurrence, interest, degree centrality, and weighted degree centrality) were proposed to measure the relevance of treatment concepts to the disease of interest. We measured the performance of four ranks in terms of the mean precision at the top 100 concepts with five diseases, as well as the precision-recall curves against two reference vocabularies. The performance of the system was also compared to two baseline approaches. The pipeline system achieved a mean precision of 0.80 for the top 100 concepts with the ranking by interest. There were no significant different among the four ranks (p=0.53). However, the pipeline-based system had significantly better performance than the two baselines. The pipeline system can be useful for an automated generation of disease-relevant treatment concepts from the biomedical literature. Copyright © 2016 Elsevier Inc. All rights reserved.

Introduction to the JASIST Special Topic Issue on Web Retrieval and Mining: A Machine Learning Perspective.

ERIC Educational Resources Information Center

Chen, Hsinchun

2003-01-01

Discusses information retrieval techniques used on the World Wide Web. Topics include machine learning in information extraction; relevance feedback; information filtering and recommendation; text classification and text clustering; Web mining, based on data mining techniques; hyperlink structure; and Web size. (LRW)

Automation of DNA and miRNA co-extraction for miRNA-based identification of human body fluids and tissues.

PubMed

Kulstein, Galina; Marienfeld, Ralf; Miltner, Erich; Wiegand, Peter

2016-10-01

In the last years, microRNA (miRNA) analysis came into focus in the field of forensic genetics. Yet, no standardized and recommendable protocols for co-isolation of miRNA and DNA from forensic relevant samples have been developed so far. Hence, this study evaluated the performance of an automated Maxwell® 16 System-based strategy (Promega) for co-extraction of DNA and miRNA from forensically relevant (blood and saliva) samples compared to (semi-)manual extraction methods. Three procedures were compared on the basis of recovered quantity of DNA and miRNA (as determined by real-time PCR and Bioanalyzer), miRNA profiling (shown by Cq values and extraction efficiency), STR profiles, duration, contamination risk and handling. All in all, the results highlight that the automated co-extraction procedure yielded the highest miRNA and DNA amounts from saliva and blood samples compared to both (semi-)manual protocols. Also, for aged and genuine samples of forensically relevant traces the miRNA and DNA yields were sufficient for subsequent downstream analysis. Furthermore, the strategy allows miRNA extraction only in cases where it is relevant to obtain additional information about the sample type. Besides, this system enables flexible sample throughput and labor-saving sample processing with reduced risk of cross-contamination. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Enhancing Biomedical Text Summarization Using Semantic Relation Extraction

PubMed Central

Shang, Yue; Li, Yanpeng; Lin, Hongfei; Yang, Zhihao

2011-01-01

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization. PMID:21887336

Representation control increases task efficiency in complex graphical representations.

PubMed

Moritz, Julia; Meyerhoff, Hauke S; Meyer-Dernbecher, Claudia; Schwan, Stephan

2018-01-01

In complex graphical representations, the relevant information for a specific task is often distributed across multiple spatial locations. In such situations, understanding the representation requires internal transformation processes in order to extract the relevant information. However, digital technology enables observers to alter the spatial arrangement of depicted information and therefore to offload the transformation processes. The objective of this study was to investigate the use of such a representation control (i.e. the users' option to decide how information should be displayed) in order to accomplish an information extraction task in terms of solution time and accuracy. In the representation control condition, the participants were allowed to reorganize the graphical representation and reduce information density. In the control condition, no interactive features were offered. We observed that participants in the representation control condition solved tasks that required reorganization of the maps faster and more accurate than participants without representation control. The present findings demonstrate how processes of cognitive offloading, spatial contiguity, and information coherence interact in knowledge media intended for broad and diverse groups of recipients.

Representation control increases task efficiency in complex graphical representations

PubMed Central

Meyerhoff, Hauke S.; Meyer-Dernbecher, Claudia; Schwan, Stephan

2018-01-01

In complex graphical representations, the relevant information for a specific task is often distributed across multiple spatial locations. In such situations, understanding the representation requires internal transformation processes in order to extract the relevant information. However, digital technology enables observers to alter the spatial arrangement of depicted information and therefore to offload the transformation processes. The objective of this study was to investigate the use of such a representation control (i.e. the users' option to decide how information should be displayed) in order to accomplish an information extraction task in terms of solution time and accuracy. In the representation control condition, the participants were allowed to reorganize the graphical representation and reduce information density. In the control condition, no interactive features were offered. We observed that participants in the representation control condition solved tasks that required reorganization of the maps faster and more accurate than participants without representation control. The present findings demonstrate how processes of cognitive offloading, spatial contiguity, and information coherence interact in knowledge media intended for broad and diverse groups of recipients. PMID:29698443

Electronic processing of informed consents in a global pharmaceutical company environment.

PubMed

Vishnyakova, Dina; Gobeill, Julien; Oezdemir-Zaech, Fatma; Kreim, Olivier; Vachon, Therese; Clade, Thierry; Haenning, Xavier; Mikhailov, Dmitri; Ruch, Patrick

2014-01-01

We present an electronic capture tool to process informed consents, which are mandatory recorded when running a clinical trial. This tool aims at the extraction of information expressing the duration of the consent given by the patient to authorize the exploitation of biomarker-related information collected during clinical trials. The system integrates a language detection module (LDM) to route a document into the appropriate information extraction module (IEM). The IEM is based on language-specific sets of linguistic rules for the identification of relevant textual facts. The achieved accuracy of both the LDM and IEM is 99%. The architecture of the system is described in detail.

Document Exploration and Automatic Knowledge Extraction for Unstructured Biomedical Text

NASA Astrophysics Data System (ADS)

Chu, S.; Totaro, G.; Doshi, N.; Thapar, S.; Mattmann, C. A.; Ramirez, P.

2015-12-01

We describe our work on building a web-browser based document reader with built-in exploration tool and automatic concept extraction of medical entities for biomedical text. Vast amounts of biomedical information are offered in unstructured text form through scientific publications and R&D reports. Utilizing text mining can help us to mine information and extract relevant knowledge from a plethora of biomedical text. The ability to employ such technologies to aid researchers in coping with information overload is greatly desirable. In recent years, there has been an increased interest in automatic biomedical concept extraction [1, 2] and intelligent PDF reader tools with the ability to search on content and find related articles [3]. Such reader tools are typically desktop applications and are limited to specific platforms. Our goal is to provide researchers with a simple tool to aid them in finding, reading, and exploring documents. Thus, we propose a web-based document explorer, which we called Shangri-Docs, which combines a document reader with automatic concept extraction and highlighting of relevant terms. Shangri-Docsalso provides the ability to evaluate a wide variety of document formats (e.g. PDF, Words, PPT, text, etc.) and to exploit the linked nature of the Web and personal content by performing searches on content from public sites (e.g. Wikipedia, PubMed) and private cataloged databases simultaneously. Shangri-Docsutilizes Apache cTAKES (clinical Text Analysis and Knowledge Extraction System) [4] and Unified Medical Language System (UMLS) to automatically identify and highlight terms and concepts, such as specific symptoms, diseases, drugs, and anatomical sites, mentioned in the text. cTAKES was originally designed specially to extract information from clinical medical records. Our investigation leads us to extend the automatic knowledge extraction process of cTAKES for biomedical research domain by improving the ontology guided information extraction process. We will describe our experience and implementation of our system and share lessons learned from our development. We will also discuss ways in which this could be adapted to other science fields. [1] Funk et al., 2014. [2] Kang et al., 2014. [3] Utopia Documents, http://utopiadocs.com [4] Apache cTAKES, http://ctakes.apache.org

Profiling Lung Cancer Patients Using Electronic Health Records.

PubMed

Menasalvas Ruiz, Ernestina; Tuñas, Juan Manuel; Bermejo, Guzmán; Gonzalo Martín, Consuelo; Rodríguez-González, Alejandro; Zanin, Massimiliano; González de Pedro, Cristina; Méndez, Marta; Zaretskaia, Olga; Rey, Jesús; Parejo, Consuelo; Cruz Bermudez, Juan Luis; Provencio, Mariano

2018-05-31

If Electronic Health Records contain a large amount of information about the patient's condition and response to treatment, which can potentially revolutionize the clinical practice, such information is seldom considered due to the complexity of its extraction and analysis. We here report on a first integration of an NLP framework for the analysis of clinical records of lung cancer patients making use of a telephone assistance service of a major Spanish hospital. We specifically show how some relevant data, about patient demographics and health condition, can be extracted; and how some relevant analyses can be performed, aimed at improving the usefulness of the service. We thus demonstrate that the use of EHR texts, and their integration inside a data analysis framework, is technically feasible and worth of further study.

Statistical interpretation of machine learning-based feature importance scores for biomarker discovery.

PubMed

Huynh-Thu, Vân Anh; Saeys, Yvan; Wehenkel, Louis; Geurts, Pierre

2012-07-01

Univariate statistical tests are widely used for biomarker discovery in bioinformatics. These procedures are simple, fast and their output is easily interpretable by biologists but they can only identify variables that provide a significant amount of information in isolation from the other variables. As biological processes are expected to involve complex interactions between variables, univariate methods thus potentially miss some informative biomarkers. Variable relevance scores provided by machine learning techniques, however, are potentially able to highlight multivariate interacting effects, but unlike the p-values returned by univariate tests, these relevance scores are usually not statistically interpretable. This lack of interpretability hampers the determination of a relevance threshold for extracting a feature subset from the rankings and also prevents the wide adoption of these methods by practicians. We evaluated several, existing and novel, procedures that extract relevant features from rankings derived from machine learning approaches. These procedures replace the relevance scores with measures that can be interpreted in a statistical way, such as p-values, false discovery rates, or family wise error rates, for which it is easier to determine a significance level. Experiments were performed on several artificial problems as well as on real microarray datasets. Although the methods differ in terms of computing times and the tradeoff, they achieve in terms of false positives and false negatives, some of them greatly help in the extraction of truly relevant biomarkers and should thus be of great practical interest for biologists and physicians. As a side conclusion, our experiments also clearly highlight that using model performance as a criterion for feature selection is often counter-productive. Python source codes of all tested methods, as well as the MATLAB scripts used for data simulation, can be found in the Supplementary Material.

The LifeWatch approach to the exploration of distributed species information

PubMed Central

Fuentes, Daniel; Fiore, Nicola

2014-01-01

Abstract This paper introduces a new method of automatically extracting, integrating and presenting information regarding species from the most relevant online taxonomic resources. First, the information is extracted and joined using data wrappers and integration solutions. Then, an analytical tool is used to provide a visual representation of the data. The information is then integrated into a user friendly content management system. The proposal has been implemented using data from the Global Biodiversity Information Facility (GBIF), the Catalogue of Life (CoL), the World Register of Marine Species (WoRMS), the Integrated Taxonomic Information System (ITIS) and the Global Names Index (GNI). The approach improves data quality, avoiding taxonomic and nomenclature errors whilst increasing the availability and accessibility of the information. PMID:25589865

Research on Crowdsourcing Emergency Information Extraction of Based on Events' Frame

NASA Astrophysics Data System (ADS)

Yang, Bo; Wang, Jizhou; Ma, Weijun; Mao, Xi

2018-01-01

At present, the common information extraction method cannot extract the structured emergency event information accurately; the general information retrieval tool cannot completely identify the emergency geographic information; these ways also do not have an accurate assessment of these results of distilling. So, this paper proposes an emergency information collection technology based on event framework. This technique is to solve the problem of emergency information picking. It mainly includes emergency information extraction model (EIEM), complete address recognition method (CARM) and the accuracy evaluation model of emergency information (AEMEI). EIEM can be structured to extract emergency information and complements the lack of network data acquisition in emergency mapping. CARM uses a hierarchical model and the shortest path algorithm and allows the toponomy pieces to be joined as a full address. AEMEI analyzes the results of the emergency event and summarizes the advantages and disadvantages of the event framework. Experiments show that event frame technology can solve the problem of emergency information drawing and provides reference cases for other applications. When the emergency disaster is about to occur, the relevant departments query emergency's data that has occurred in the past. They can make arrangements ahead of schedule which defense and reducing disaster. The technology decreases the number of casualties and property damage in the country and world. This is of great significance to the state and society.

Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns

PubMed Central

Alvarez-Meza, Andres M.; Orozco-Gutierrez, Alvaro; Castellanos-Dominguez, German

2017-01-01

We introduce Enhanced Kernel-based Relevance Analysis (EKRA) that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i) feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii) enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand. PMID:29056897

Using UMLS to construct a generalized hierarchical concept-based dictionary of brain functions for information extraction from the fMRI literature.

PubMed

Hsiao, Mei-Yu; Chen, Chien-Chung; Chen, Jyh-Horng

2009-10-01

With a rapid progress in the field, a great many fMRI studies are published every year, to the extent that it is now becoming difficult for researchers to keep up with the literature, since reading papers is extremely time-consuming and labor-intensive. Thus, automatic information extraction has become an important issue. In this study, we used the Unified Medical Language System (UMLS) to construct a hierarchical concept-based dictionary of brain functions. To the best of our knowledge, this is the first generalized dictionary of this kind. We also developed an information extraction system for recognizing, mapping and classifying terms relevant to human brain study. The precision and recall of our system was on a par with that of human experts in term recognition, term mapping and term classification. Our approach presented in this paper presents an alternative to the more laborious, manual entry approach to information extraction.

A Low-Storage-Consumption XML Labeling Method for Efficient Structural Information Extraction

NASA Astrophysics Data System (ADS)

Liang, Wenxin; Takahashi, Akihiro; Yokota, Haruo

Recently, labeling methods to extract and reconstruct the structural information of XML data, which are important for many applications such as XPath query and keyword search, are becoming more attractive. To achieve efficient structural information extraction, in this paper we propose C-DO-VLEI code, a novel update-friendly bit-vector encoding scheme, based on register-length bit operations combining with the properties of Dewey Order numbers, which cannot be implemented in other relevant existing schemes such as ORDPATH. Meanwhile, the proposed method also achieves lower storage consumption because it does not require either prefix schema or any reserved codes for node insertion. We performed experiments to evaluate and compare the performance and storage consumption of the proposed method with those of the ORDPATH method. Experimental results show that the execution times for extracting depth information and parent node labels using the C-DO-VLEI code are about 25% and 15% less, respectively, and the average label size using the C-DO-VLEI code is about 24% smaller, comparing with ORDPATH.

40 CFR 125.134 - As an owner or operator of a new offshore oil and gas extraction facility, what must I do to...

Code of Federal Regulations, 2012 CFR

2012-07-01

... intake structure; or (ii) Based on information submitted by any fishery management agency(ies) or other...) Based on information submitted by any fishery management agency(ies) or other relevant information, that...) and (5) of this section, would still contribute unacceptable stress to the protected species, critical...

40 CFR 125.134 - As an owner or operator of a new offshore oil and gas extraction facility, what must I do to...

Code of Federal Regulations, 2013 CFR

2013-07-01

... intake structure; or (ii) Based on information submitted by any fishery management agency(ies) or other...) Based on information submitted by any fishery management agency(ies) or other relevant information, that...) and (5) of this section, would still contribute unacceptable stress to the protected species, critical...

40 CFR 125.134 - As an owner or operator of a new offshore oil and gas extraction facility, what must I do to...

Code of Federal Regulations, 2014 CFR

2014-07-01

... intake structure; or (ii) Based on information submitted by any fishery management agency(ies) or other...) Based on information submitted by any fishery management agency(ies) or other relevant information, that...) and (5) of this section, would still contribute unacceptable stress to the protected species, critical...

The potential of satellite data to study individual wildfire events

NASA Astrophysics Data System (ADS)

Benali, Akli; López-Saldana, Gerardo; Russo, Ana; Sá, Ana C. L.; Pinto, Renata M. S.; Nikos, Koutsias; Owen, Price; Pereira, Jose M. C.

2014-05-01

Large wildfires have important social, economic and environmental impacts. In order to minimize their impacts, understand their main drivers and study their dynamics, different approaches have been used. The reconstruction of individual wildfire events is usually done by collection of field data, interviews and by implementing fire spread simulations. All these methods have clear limitations in terms of spatial and temporal coverage, accuracy, subjectivity of the collected information and lack of objective independent validation information. In this sense, remote sensing is a promising tool with the potential to provide relevant information for stakeholders and the research community, by complementing or filling gaps in existing information and providing independent accurate quantitative information. In this work we show the potential of satellite data to provide relevant information regarding the dynamics of individual large wildfire events, filling an important gap in wildfire research. We show how MODIS active-fire data, acquired up to four times per day, and satellite-derived burnt perimeters can be combined to extract relevant information wildfire events by describing the methods involved and presenting results for four regions of the world: Portugal, Greece, SE Australia and California. The information that can be retrieved encompasses the start and end date of a wildfire event and its ignition area. We perform an evaluation of the information retrieved by comparing the satellite-derived parameters with national databases, highlighting the strengths and weaknesses of both and showing how the former can complement the latter leading to more complete and accurate datasets. We also show how the spatio-temporal distribution of wildfire spread dynamics can be reconstructed using satellite-derived active-fires and how relevant descriptors can be extracted. Applying graph theory to satellite active-fire data, we define the major fire spread paths that yield information about the major spatial corridors through which fires spread, and their relative importance in the full fire event. These major fire paths are then used to extract relevant descriptors, such as the distribution of fire spread direction, rate of spread and fire intensity (i.e. energy emitted). The reconstruction of the fire spread is shown for some case studies for Portugal and is also compared with fire progressions obtained by air-borne sensors for SE Australia. The approach shows solid results, providing a valuable tool for the reconstruction of individual fire events, understand their complex spread patterns and their main drivers of fire propagation. The major fire pathsand the spatio-temporal distribution of active fires are being currently combined with fire spread simulations within the scope oftheFIRE-MODSATproject, to provideuseful information to support and improve fire suppression strategies.

Chapter 4. Arceuthobium in North America

Treesearch

F. G. Hawksworth; D. Wiens; B. W. Geils

2002-01-01

The biology, pathology, and systematics of dwarf mistletoes are recently and well reviewed in Hawksworth and Wiens (1996). That monograph forms the basis for the text in this and chapter 5 and should be consulted for more information (for example, references, photographs, and distribution maps). In addition to extracting the information that would be most relevant to...

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

PubMed

Ng; Wong

1999-01-01

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

Supporting Better Treatments for Meeting Health Consumers' Needs: Extracting Semantics in Social Data for Representing a Consumer Health Ontology

ERIC Educational Resources Information Center

Choi, Yunseon

2016-01-01

Introduction: The purpose of this paper is to provide a framework for building a consumer health ontology using social tags. This would assist health users when they are accessing health information and increase the number of documents relevant to their needs. Methods: In order to extract concepts from social tags, this study conducted an…

Extracting laboratory test information from biomedical text

PubMed Central

Kang, Yanna Shen; Kayaalp, Mehmet

2013-01-01

Background: No previous study reported the efficacy of current natural language processing (NLP) methods for extracting laboratory test information from narrative documents. This study investigates the pathology informatics question of how accurately such information can be extracted from text with the current tools and techniques, especially machine learning and symbolic NLP methods. The study data came from a text corpus maintained by the U.S. Food and Drug Administration, containing a rich set of information on laboratory tests and test devices. Methods: The authors developed a symbolic information extraction (SIE) system to extract device and test specific information about four types of laboratory test entities: Specimens, analytes, units of measures and detection limits. They compared the performance of SIE and three prominent machine learning based NLP systems, LingPipe, GATE and BANNER, each implementing a distinct supervised machine learning method, hidden Markov models, support vector machines and conditional random fields, respectively. Results: Machine learning systems recognized laboratory test entities with moderately high recall, but low precision rates. Their recall rates were relatively higher when the number of distinct entity values (e.g., the spectrum of specimens) was very limited or when lexical morphology of the entity was distinctive (as in units of measures), yet SIE outperformed them with statistically significant margins on extracting specimen, analyte and detection limit information in both precision and F-measure. Its high recall performance was statistically significant on analyte information extraction. Conclusions: Despite its shortcomings against machine learning methods, a well-tailored symbolic system may better discern relevancy among a pile of information of the same type and may outperform a machine learning system by tapping into lexically non-local contextual information such as the document structure. PMID:24083058

An Ontology-Based Approach to Incorporate User-Generated Geo-Content Into Sdi

NASA Astrophysics Data System (ADS)

Deng, D.-P.; Lemmens, R.

2011-08-01

The Web is changing the way people share and communicate information because of emergence of various Web technologies, which enable people to contribute information on the Web. User-Generated Geo-Content (UGGC) is a potential resource of geographic information. Due to the different production methods, UGGC often cannot fit in geographic information model. There is a semantic gap between UGGC and formal geographic information. To integrate UGGC into geographic information, this study conducts an ontology-based process to bridge this semantic gap. This ontology-based process includes five steps: Collection, Extraction, Formalization, Mapping, and Deployment. In addition, this study implements this process on Twitter messages, which is relevant to Japan Earthquake disaster. By using this process, we extract disaster relief information from Twitter messages, and develop a knowledge base for GeoSPARQL queries in disaster relief information.

Extracting Information from Narratives: An Application to Aviation Safety Reports

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

Posse, Christian; Matzke, Brett D.; Anderson, Catherine M.

2005-05-12

Aviation safety reports are the best available source of information about why a flight incident happened. However, stream of consciousness permeates the narratives making difficult the automation of the information extraction task. We propose an approach and infrastructure based on a common pattern specification language to capture relevant information via normalized template expression matching in context. Template expression matching handles variants of multi-word expressions. Normalization improves the likelihood of correct hits by standardizing and cleaning the vocabulary used in narratives. Checking for the presence of negative modifiers in the proximity of a potential hit reduces the chance of false hits.more » We present the above approach in the context of a specific application, which is the extraction of human performance factors from NASA ASRS reports. While knowledge infusion from experts plays a critical role during the learning phase, early results show that in a production mode, the automated process provides information that is consistent with analyses by human subjects.« less

Information based universal feature extraction

NASA Astrophysics Data System (ADS)

Amiri, Mohammad; Brause, Rüdiger

2015-02-01

In many real world image based pattern recognition tasks, the extraction and usage of task-relevant features are the most crucial part of the diagnosis. In the standard approach, they mostly remain task-specific, although humans who perform such a task always use the same image features, trained in early childhood. It seems that universal feature sets exist, but they are not yet systematically found. In our contribution, we tried to find those universal image feature sets that are valuable for most image related tasks. In our approach, we trained a neural network by natural and non-natural images of objects and background, using a Shannon information-based algorithm and learning constraints. The goal was to extract those features that give the most valuable information for classification of visual objects hand-written digits. This will give a good start and performance increase for all other image learning tasks, implementing a transfer learning approach. As result, in our case we found that we could indeed extract features which are valid in all three kinds of tasks.

Extracting the Behaviorally Relevant Stimulus: Unique Neural Representation of Farnesol, a Component of the Recruitment Pheromone of Bombus terrestris

PubMed Central

Strube-Bloss, Martin F.; Brown, Austin; Spaethe, Johannes; Schmitt, Thomas; Rössler, Wolfgang

2015-01-01

To trigger innate behavior, sensory neural networks are pre-tuned to extract biologically relevant stimuli. Many male-female or insect-plant interactions depend on this phenomenon. Especially communication among individuals within social groups depends on innate behaviors. One example is the efficient recruitment of nest mates by successful bumblebee foragers. Returning foragers release a recruitment pheromone in the nest while they perform a ‘dance’ behavior to activate unemployed nest mates. A major component of this pheromone is the sesquiterpenoid farnesol. How farnesol is processed and perceived by the olfactory system, has not yet been identified. It is much likely that processing farnesol involves an innate mechanism for the extraction of relevant information to trigger a fast and reliable behavioral response. To test this hypothesis, we used population response analyses of 100 antennal lobe (AL) neurons recorded in alive bumblebee workers under repeated stimulation with four behaviorally different, but chemically related odorants (geraniol, citronellol, citronellal and farnesol). The analysis identified a unique neural representation of the recruitment pheromone component compared to the other odorants that are predominantly emitted by flowers. The farnesol induced population activity in the AL allowed a reliable separation of farnesol from all other chemically related odor stimuli we tested. We conclude that the farnesol induced population activity may reflect a predetermined representation within the AL-neural network allowing efficient and fast extraction of a behaviorally relevant stimulus. Furthermore, the results show that population response analyses of multiple single AL-units may provide a powerful tool to identify distinct representations of behaviorally relevant odors. PMID:26340263

Decoding 2D-PAGE complex maps: relevance to proteomics.

PubMed

Pietrogrande, Maria Chiara; Marchetti, Nicola; Dondi, Francesco; Righetti, Pier Giorgio

2006-03-20

This review describes two mathematical approaches useful for decoding the complex signal of 2D-PAGE maps of protein mixtures. These methods are helpful for interpreting the large amount of data of each 2D-PAGE map by extracting all the analytical information hidden therein by spot overlapping. Here the basic theory and application to 2D-PAGE maps are reviewed: the means for extracting information from the experimental data and their relevance to proteomics are discussed. One method is based on the quantitative theory of statistical model of peak overlapping (SMO) using the spot experimental data (intensity and spatial coordinates). The second method is based on the study of the 2D-autocovariance function (2D-ACVF) computed on the experimental digitised map. They are two independent methods that are able to extract equal and complementary information from the 2D-PAGE map. Both methods permit to obtain fundamental information on the sample complexity and the separation performance and to single out ordered patterns present in spot positions: the availability of two independent procedures to compute the same separation parameters is a powerful tool to estimate the reliability of the obtained results. The SMO procedure is an unique tool to quantitatively estimate the degree of spot overlapping present in the map, while the 2D-ACVF method is particularly powerful in simply singling out the presence of order in the spot position from the complexity of the whole 2D map, i.e., spot trains. The procedures were validated by extensive numerical computation on computer-generated maps describing experimental 2D-PAGE gels of protein mixtures. Their applicability to real samples was tested on reference maps obtained from literature sources. The review describes the most relevant information for proteomics: sample complexity, separation performance, overlapping extent, identification of spot trains related to post-translational modifications (PTMs).

Bacillus subtilis Lipid Extract, A Branched-Chain Fatty Acid Model Membrane

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

Nickels, Jonathan D.; Chatterjee, Sneha; Mostofian, Barmak

Lipid extracts are an excellent choice of model biomembrane; however at present, there are no commercially available lipid extracts or computational models that mimic microbial membranes containing the branched-chain fatty acids found in many pathogenic and industrially relevant bacteria. Here, we advance the extract of Bacillus subtilis as a standard model for these diverse systems, providing a detailed experimental description and equilibrated atomistic bilayer model included as Supporting Information to this Letter and at (http://cmb.ornl.gov/members/cheng). The development and validation of this model represents an advance that enables more realistic simulations and experiments on bacterial membranes and reconstituted bacterial membrane proteins.

Compositional and textural information from the dual inversion of visible, near and thermal infrared remotely sensed data

NASA Technical Reports Server (NTRS)

Brackett, Robert A.; Arvidson, Raymond E.

1993-01-01

A technique is presented that allows extraction of compositional and textural information from visible, near and thermal infrared remotely sensed data. Using a library of both emissivity and reflectance spectra, endmember abundances and endmember thermal inertias are extracted from AVIRIS (Airborne Visible and Infrared Imaging Spectrometer) and TIMS (Thermal Infrared Mapping Spectrometer) data over Lunar Crater Volcanic Field, Nevada, using a dual inversion. The inversion technique is motivated by upcoming Mars Observer data and the need for separation of composition and texture parameters from sub pixel mixtures of bedrock and dust. The model employed offers the opportunity to extract compositional and textural information for a variety of endmembers within a given pixel. Geologic inferences concerning grain size, abundance, and source of endmembers can be made directly from the inverted data. These parameters are of direct relevance to Mars exploration, both for Mars Observer and for follow-on missions.

Applying Analogical Reasoning Techniques for Teaching XML Document Querying Skills in Database Classes

ERIC Educational Resources Information Center

Mitri, Michel

2012-01-01

XML has become the most ubiquitous format for exchange of data between applications running on the Internet. Most Web Services provide their information to clients in the form of XML. The ability to process complex XML documents in order to extract relevant information is becoming as important a skill for IS students to master as querying…

Uncovering the essential links in online commercial networks

NASA Astrophysics Data System (ADS)

Zeng, Wei; Fang, Meiling; Shao, Junming; Shang, Mingsheng

2016-09-01

Recommender systems are designed to effectively support individuals' decision-making process on various web sites. It can be naturally represented by a user-object bipartite network, where a link indicates that a user has collected an object. Recently, research on the information backbone has attracted researchers' interests, which is a sub-network with fewer nodes and links but carrying most of the relevant information. With the backbone, a system can generate satisfactory recommenda- tions while saving much computing resource. In this paper, we propose an enhanced topology-aware method to extract the information backbone in the bipartite network mainly based on the information of neighboring users and objects. Our backbone extraction method enables the recommender systems achieve more than 90% of the accuracy of the top-L recommendation, however, consuming only 20% links. The experimental results show that our method outperforms the alternative backbone extraction methods. Moreover, the structure of the information backbone is studied in detail. Finally, we highlight that the information backbone is one of the most important properties of the bipartite network, with which one can significantly improve the efficiency of the recommender system.

Stimulus encoding and feature extraction by multiple sensory neurons.

PubMed

Krahe, Rüdiger; Kreiman, Gabriel; Gabbiani, Fabrizio; Koch, Christof; Metzner, Walter

2002-03-15

Neighboring cells in topographical sensory maps may transmit similar information to the next higher level of processing. How information transmission by groups of nearby neurons compares with the performance of single cells is a very important question for understanding the functioning of the nervous system. To tackle this problem, we quantified stimulus-encoding and feature extraction performance by pairs of simultaneously recorded electrosensory pyramidal cells in the hindbrain of weakly electric fish. These cells constitute the output neurons of the first central nervous stage of electrosensory processing. Using random amplitude modulations (RAMs) of a mimic of the fish's own electric field within behaviorally relevant frequency bands, we found that pyramidal cells with overlapping receptive fields exhibit strong stimulus-induced correlations. To quantify the encoding of the RAM time course, we estimated the stimuli from simultaneously recorded spike trains and found significant improvements over single spike trains. The quality of stimulus reconstruction, however, was still inferior to the one measured for single primary sensory afferents. In an analysis of feature extraction, we found that spikes of pyramidal cell pairs coinciding within a time window of a few milliseconds performed significantly better at detecting upstrokes and downstrokes of the stimulus compared with isolated spikes and even spike bursts of single cells. Coincident spikes can thus be considered "distributed bursts." Our results suggest that stimulus encoding by primary sensory afferents is transformed into feature extraction at the next processing stage. There, stimulus-induced coincident activity can improve the extraction of behaviorally relevant features from the stimulus.

Eyes Matched to the Prize: The State of Matched Filters in Insect Visual Circuits.

PubMed

Kohn, Jessica R; Heath, Sarah L; Behnia, Rudy

2018-01-01

Confronted with an ever-changing visual landscape, animals must be able to detect relevant stimuli and translate this information into behavioral output. A visual scene contains an abundance of information: to interpret the entirety of it would be uneconomical. To optimally perform this task, neural mechanisms exist to enhance the detection of important features of the sensory environment while simultaneously filtering out irrelevant information. This can be accomplished by using a circuit design that implements specific "matched filters" that are tuned to relevant stimuli. Following this rule, the well-characterized visual systems of insects have evolved to streamline feature extraction on both a structural and functional level. Here, we review examples of specialized visual microcircuits for vital behaviors across insect species, including feature detection, escape, and estimation of self-motion. Additionally, we discuss how these microcircuits are modulated to weigh relevant input with respect to different internal and behavioral states.

PLAN2L: a web tool for integrated text mining and literature-based bioentity relation extraction.

PubMed

Krallinger, Martin; Rodriguez-Penagos, Carlos; Tendulkar, Ashish; Valencia, Alfonso

2009-07-01

There is an increasing interest in using literature mining techniques to complement information extracted from annotation databases or generated by bioinformatics applications. Here we present PLAN2L, a web-based online search system that integrates text mining and information extraction techniques to access systematically information useful for analyzing genetic, cellular and molecular aspects of the plant model organism Arabidopsis thaliana. Our system facilitates a more efficient retrieval of information relevant to heterogeneous biological topics, from implications in biological relationships at the level of protein interactions and gene regulation, to sub-cellular locations of gene products and associations to cellular and developmental processes, i.e. cell cycle, flowering, root, leaf and seed development. Beyond single entities, also predefined pairs of entities can be provided as queries for which literature-derived relations together with textual evidences are returned. PLAN2L does not require registration and is freely accessible at http://zope.bioinfo.cnio.es/plan2l.

Taming Big Data: An Information Extraction Strategy for Large Clinical Text Corpora.

PubMed

Gundlapalli, Adi V; Divita, Guy; Carter, Marjorie E; Redd, Andrew; Samore, Matthew H; Gupta, Kalpana; Trautner, Barbara

2015-01-01

Concepts of interest for clinical and research purposes are not uniformly distributed in clinical text available in electronic medical records. The purpose of our study was to identify filtering techniques to select 'high yield' documents for increased efficacy and throughput. Using two large corpora of clinical text, we demonstrate the identification of 'high yield' document sets in two unrelated domains: homelessness and indwelling urinary catheters. For homelessness, the high yield set includes homeless program and social work notes. For urinary catheters, concepts were more prevalent in notes from hospitalized patients; nursing notes accounted for a majority of the high yield set. This filtering will enable customization and refining of information extraction pipelines to facilitate extraction of relevant concepts for clinical decision support and other uses.

SPECTRa-T: machine-based data extraction and semantic searching of chemistry e-theses.

PubMed

Downing, Jim; Harvey, Matt J; Morgan, Peter B; Murray-Rust, Peter; Rzepa, Henry S; Stewart, Diana C; Tonge, Alan P; Townsend, Joe A

2010-02-22

The SPECTRa-T project has developed text-mining tools to extract named chemical entities (NCEs), such as chemical names and terms, and chemical objects (COs), e.g., experimental spectral assignments and physical chemistry properties, from electronic theses (e-theses). Although NCEs were readily identified within the two major document formats studied, only the use of structured documents enabled identification of chemical objects and their association with the relevant chemical entity (e.g., systematic chemical name). A corpus of theses was analyzed and it is shown that a high degree of semantic information can be extracted from structured documents. This integrated information has been deposited in a persistent Resource Description Framework (RDF) triple-store that allows users to conduct semantic searches. The strength and weaknesses of several document formats are reviewed.

A Review of Feature Extraction Software for Microarray Gene Expression Data

PubMed Central

Tan, Ching Siang; Ting, Wai Soon; Mohamad, Mohd Saberi; Chan, Weng Howe; Deris, Safaai; Ali Shah, Zuraini

2014-01-01

When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method. PMID:25250315

ExaCT: automatic extraction of clinical trial characteristics from journal publications

PubMed Central

2010-01-01

Background Clinical trials are one of the most important sources of evidence for guiding evidence-based practice and the design of new trials. However, most of this information is available only in free text - e.g., in journal publications - which is labour intensive to process for systematic reviews, meta-analyses, and other evidence synthesis studies. This paper presents an automatic information extraction system, called ExaCT, that assists users with locating and extracting key trial characteristics (e.g., eligibility criteria, sample size, drug dosage, primary outcomes) from full-text journal articles reporting on randomized controlled trials (RCTs). Methods ExaCT consists of two parts: an information extraction (IE) engine that searches the article for text fragments that best describe the trial characteristics, and a web browser-based user interface that allows human reviewers to assess and modify the suggested selections. The IE engine uses a statistical text classifier to locate those sentences that have the highest probability of describing a trial characteristic. Then, the IE engine's second stage applies simple rules to these sentences to extract text fragments containing the target answer. The same approach is used for all 21 trial characteristics selected for this study. Results We evaluated ExaCT using 50 previously unseen articles describing RCTs. The text classifier (first stage) was able to recover 88% of relevant sentences among its top five candidates (top5 recall) with the topmost candidate being relevant in 80% of cases (top1 precision). Precision and recall of the extraction rules (second stage) were 93% and 91%, respectively. Together, the two stages of the extraction engine were able to provide (partially) correct solutions in 992 out of 1050 test tasks (94%), with a majority of these (696) representing fully correct and complete answers. Conclusions Our experiments confirmed the applicability and efficacy of ExaCT. Furthermore, they demonstrated that combining a statistical method with 'weak' extraction rules can identify a variety of study characteristics. The system is flexible and can be extended to handle other characteristics and document types (e.g., study protocols). PMID:20920176

Extracting business vocabularies from business process models: SBVR and BPMN standards-based approach

NASA Astrophysics Data System (ADS)

Skersys, Tomas; Butleris, Rimantas; Kapocius, Kestutis

2013-10-01

Approaches for the analysis and specification of business vocabularies and rules are very relevant topics in both Business Process Management and Information Systems Development disciplines. However, in common practice of Information Systems Development, the Business modeling activities still are of mostly empiric nature. In this paper, basic aspects of the approach for business vocabularies' semi-automated extraction from business process models are presented. The approach is based on novel business modeling-level OMG standards "Business Process Model and Notation" (BPMN) and "Semantics for Business Vocabularies and Business Rules" (SBVR), thus contributing to OMG's vision about Model-Driven Architecture (MDA) and to model-driven development in general.

Comparative sensitivity of centroptilum triangulifer, ceriodaphnia dubia and daphnia magna to standard salt and copper reference toxicants

EPA Science Inventory

Development of methods for assessing exposure and effects of produced waters from energy and mineral resource extraction operations on stream invertebrate species is important in order to elucidate environmentally relevant information. Centroptilum triangulifer is a parthenogene...

Constructing and Modifying Sequence Statistics for relevent Using informR in 𝖱

PubMed Central

Marcum, Christopher Steven; Butts, Carter T.

2015-01-01

The informR package greatly simplifies the analysis of complex event histories in 𝖱 by providing user friendly tools to build sufficient statistics for the relevent package. Historically, building sufficient statistics to model event sequences (of the form a→b) using the egocentric generalization of Butts’ (2008) relational event framework for modeling social action has been cumbersome. The informR package simplifies the construction of the complex list of arrays needed by the rem() model fitting for a variety of cases involving egocentric event data, multiple event types, and/or support constraints. This paper introduces these tools using examples from real data extracted from the American Time Use Survey. PMID:26185488

Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives.

PubMed

Gehrmann, Sebastian; Dernoncourt, Franck; Li, Yeran; Carlson, Eric T; Wu, Joy T; Welt, Jonathan; Foote, John; Moseley, Edward T; Grant, David W; Tyler, Patrick D; Celi, Leo A

2018-01-01

In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. In this work, we compare concept extraction based methods with CNNs and other commonly used models in NLP in ten phenotyping tasks using 1,610 discharge summaries from the MIMIC-III database. We show that CNNs outperform concept extraction based methods in almost all of the tasks, with an improvement in F1-score of up to 26 and up to 7 percentage points in area under the ROC curve (AUC). We additionally assess the interpretability of both approaches by presenting and evaluating methods that calculate and extract the most salient phrases for a prediction. The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated. Moreover, the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions.

Point Cloud Classification of Tesserae from Terrestrial Laser Data Combined with Dense Image Matching for Archaeological Information Extraction

NASA Astrophysics Data System (ADS)

Poux, F.; Neuville, R.; Billen, R.

2017-08-01

Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. This paper presents an automatic knowledge-based method for pre-processing multi-sensory data and classifying a hybrid point cloud from both terrestrial laser scanning and dense image matching. Using 18 features including sensor's biased data, each tessera in the high-density point cloud from the 3D captured complex mosaics of Germigny-des-prés (France) is segmented via a colour multi-scale abstraction-based featuring extracting connectivity. A 2D surface and outline polygon of each tessera is generated by a RANSAC plane extraction and convex hull fitting. Knowledge is then used to classify every tesserae based on their size, surface, shape, material properties and their neighbour's class. The detection and semantic enrichment method shows promising results of 94% correct semantization, a first step toward the creation of an archaeological smart point cloud.

Capturing patient information at nursing shift changes: methodological evaluation of speech recognition and information extraction

PubMed Central

Suominen, Hanna; Johnson, Maree; Zhou, Liyuan; Sanchez, Paula; Sirel, Raul; Basilakis, Jim; Hanlen, Leif; Estival, Dominique; Dawson, Linda; Kelly, Barbara

2015-01-01

Objective We study the use of speech recognition and information extraction to generate drafts of Australian nursing-handover documents. Methods Speech recognition correctness and clinicians’ preferences were evaluated using 15 recorder–microphone combinations, six documents, three speakers, Dragon Medical 11, and five survey/interview participants. Information extraction correctness evaluation used 260 documents, six-class classification for each word, two annotators, and the CRF++ conditional random field toolkit. Results A noise-cancelling lapel-microphone with a digital voice recorder gave the best correctness (79%). This microphone was also the most preferred option by all but one participant. Although the participants liked the small size of this recorder, their preference was for tablets that can also be used for document proofing and sign-off, among other tasks. Accented speech was harder to recognize than native language and a male speaker was detected better than a female speaker. Information extraction was excellent in filtering out irrelevant text (85% F1) and identifying text relevant to two classes (87% and 70% F1). Similarly to the annotators’ disagreements, there was confusion between the remaining three classes, which explains the modest 62% macro-averaged F1. Discussion We present evidence for the feasibility of speech recognition and information extraction to support clinicians’ in entering text and unlock its content for computerized decision-making and surveillance in healthcare. Conclusions The benefits of this automation include storing all information; making the drafts available and accessible almost instantly to everyone with authorized access; and avoiding information loss, delays, and misinterpretations inherent to using a ward clerk or transcription services. PMID:25336589

Visual Representations of DNA Replication: Middle Grades Students' Perceptions and Interpretations

ERIC Educational Resources Information Center

Patrick, Michelle D.; Carter, Glenda; Wiebe, Eric N.

2005-01-01

Visual representations play a critical role in the communication of science concepts for scientists and students alike. However, recent research suggests that novice students experience difficulty extracting relevant information from representations. This study examined students' interpretations of visual representations of DNA replication. Each…

Reporting Differences Between Spacecraft Sequence Files

NASA Technical Reports Server (NTRS)

Khanampompan, Teerapat; Gladden, Roy E.; Fisher, Forest W.

2010-01-01

A suite of computer programs, called seq diff suite, reports differences between the products of other computer programs involved in the generation of sequences of commands for spacecraft. These products consist of files of several types: replacement sequence of events (RSOE), DSN keyword file [DKF (wherein DSN signifies Deep Space Network)], spacecraft activities sequence file (SASF), spacecraft sequence file (SSF), and station allocation file (SAF). These products can include line numbers, request identifications, and other pieces of information that are not relevant when generating command sequence products, though these fields can result in the appearance of many changes to the files, particularly when using the UNIX diff command to inspect file differences. The outputs of prior software tools for reporting differences between such products include differences in these non-relevant pieces of information. In contrast, seq diff suite removes the fields containing the irrelevant pieces of information before processing to extract differences, so that only relevant differences are reported. Thus, seq diff suite is especially useful for reporting changes between successive versions of the various products and in particular flagging difference in fields relevant to the sequence command generation and review process.

Observer properties for understanding dynamical displays: Capacities, limitations, and defaults

NASA Technical Reports Server (NTRS)

Proffitt, Dennis R.; Kaiser, Mary K.

1991-01-01

People's ability to extract relevant information while viewing ongoing events is discussed in terms of human capabilities, limitations, and defaults. A taxonomy of event complexity is developed which predicts which dynamical events people can and cannot construe. This taxonomy is related to the distinction drawn in classical mechanics between particle and extended body motions. People's commonsense understandings of simple mechanical systems are impacted little by formal training, but rather reflect heuristical simplifications that focus on a single dimension of perceived dynamical relevance.

[Radiological dose and metadata management].

PubMed

Walz, M; Kolodziej, M; Madsack, B

2016-12-01

This article describes the features of management systems currently available in Germany for extraction, registration and evaluation of metadata from radiological examinations, particularly in the digital imaging and communications in medicine (DICOM) environment. In addition, the probable relevant developments in this area concerning radiation protection legislation, terminology, standardization and information technology are presented.

Extracting Behaviorally Relevant Traits from Natural Stimuli: Benefits of Combinatorial Representations at the Accessory Olfactory Bulb

PubMed Central

Kahan, Anat; Ben-Shaul, Yoram

2016-01-01

For many animals, chemosensation is essential for guiding social behavior. However, because multiple factors can modulate levels of individual chemical cues, deriving information about other individuals via natural chemical stimuli involves considerable challenges. How social information is extracted despite these sources of variability is poorly understood. The vomeronasal system provides an excellent opportunity to study this topic due to its role in detecting socially relevant traits. Here, we focus on two such traits: a female mouse’s strain and reproductive state. In particular, we measure stimulus-induced neuronal activity in the accessory olfactory bulb (AOB) in response to various dilutions of urine, vaginal secretions, and saliva, from estrus and non-estrus female mice from two different strains. We first show that all tested secretions provide information about a female’s receptivity and genotype. Next, we investigate how these traits can be decoded from neuronal activity despite multiple sources of variability. We show that individual neurons are limited in their capacity to allow trait classification across multiple sources of variability. However, simple linear classifiers sampling neuronal activity from small neuronal ensembles can provide a substantial improvement over that attained with individual units. Furthermore, we show that some traits are more efficiently detected than others, and that particular secretions may be optimized for conveying information about specific traits. Across all tested stimulus sources, discrimination between strains is more accurate than discrimination of receptivity, and detection of receptivity is more accurate with vaginal secretions than with urine. Our findings highlight the challenges of chemosensory processing of natural stimuli, and suggest that downstream readout stages decode multiple behaviorally relevant traits by sampling information from distinct but overlapping populations of AOB neurons. PMID:26938460

Extracting Behaviorally Relevant Traits from Natural Stimuli: Benefits of Combinatorial Representations at the Accessory Olfactory Bulb.

PubMed

Kahan, Anat; Ben-Shaul, Yoram

2016-03-01

For many animals, chemosensation is essential for guiding social behavior. However, because multiple factors can modulate levels of individual chemical cues, deriving information about other individuals via natural chemical stimuli involves considerable challenges. How social information is extracted despite these sources of variability is poorly understood. The vomeronasal system provides an excellent opportunity to study this topic due to its role in detecting socially relevant traits. Here, we focus on two such traits: a female mouse's strain and reproductive state. In particular, we measure stimulus-induced neuronal activity in the accessory olfactory bulb (AOB) in response to various dilutions of urine, vaginal secretions, and saliva, from estrus and non-estrus female mice from two different strains. We first show that all tested secretions provide information about a female's receptivity and genotype. Next, we investigate how these traits can be decoded from neuronal activity despite multiple sources of variability. We show that individual neurons are limited in their capacity to allow trait classification across multiple sources of variability. However, simple linear classifiers sampling neuronal activity from small neuronal ensembles can provide a substantial improvement over that attained with individual units. Furthermore, we show that some traits are more efficiently detected than others, and that particular secretions may be optimized for conveying information about specific traits. Across all tested stimulus sources, discrimination between strains is more accurate than discrimination of receptivity, and detection of receptivity is more accurate with vaginal secretions than with urine. Our findings highlight the challenges of chemosensory processing of natural stimuli, and suggest that downstream readout stages decode multiple behaviorally relevant traits by sampling information from distinct but overlapping populations of AOB neurons.

Joint Feature Extraction and Classifier Design for ECG-Based Biometric Recognition.

PubMed

Gutta, Sandeep; Cheng, Qi

2016-03-01

Traditional biometric recognition systems often utilize physiological traits such as fingerprint, face, iris, etc. Recent years have seen a growing interest in electrocardiogram (ECG)-based biometric recognition techniques, especially in the field of clinical medicine. In existing ECG-based biometric recognition methods, feature extraction and classifier design are usually performed separately. In this paper, a multitask learning approach is proposed, in which feature extraction and classifier design are carried out simultaneously. Weights are assigned to the features within the kernel of each task. We decompose the matrix consisting of all the feature weights into sparse and low-rank components. The sparse component determines the features that are relevant to identify each individual, and the low-rank component determines the common feature subspace that is relevant to identify all the subjects. A fast optimization algorithm is developed, which requires only the first-order information. The performance of the proposed approach is demonstrated through experiments using the MIT-BIH Normal Sinus Rhythm database.

Simulating Expert Clinical Comprehension: Adapting Latent Semantic Analysis to Accurately Extract Clinical Concepts from Psychiatric Narrative

PubMed Central

Cohen, Trevor; Blatter, Brett; Patel, Vimla

2008-01-01

Cognitive studies reveal that less-than-expert clinicians are less able to recognize meaningful patterns of data in clinical narratives. Accordingly, psychiatric residents early in training fail to attend to information that is relevant to diagnosis and the assessment of dangerousness. This manuscript presents cognitively motivated methodology for the simulation of expert ability to organize relevant findings supporting intermediate diagnostic hypotheses. Latent Semantic Analysis is used to generate a semantic space from which meaningful associations between psychiatric terms are derived. Diagnostically meaningful clusters are modeled as geometric structures within this space and compared to elements of psychiatric narrative text using semantic distance measures. A learning algorithm is defined that alters components of these geometric structures in response to labeled training data. Extraction and classification of relevant text segments is evaluated against expert annotation, with system-rater agreement approximating rater-rater agreement. A range of biomedical informatics applications for these methods are suggested. PMID:18455483

CellBase, a comprehensive collection of RESTful web services for retrieving relevant biological information from heterogeneous sources.

PubMed

Bleda, Marta; Tarraga, Joaquin; de Maria, Alejandro; Salavert, Francisco; Garcia-Alonso, Luz; Celma, Matilde; Martin, Ainoha; Dopazo, Joaquin; Medina, Ignacio

2012-07-01

During the past years, the advances in high-throughput technologies have produced an unprecedented growth in the number and size of repositories and databases storing relevant biological data. Today, there is more biological information than ever but, unfortunately, the current status of many of these repositories is far from being optimal. Some of the most common problems are that the information is spread out in many small databases; frequently there are different standards among repositories and some databases are no longer supported or they contain too specific and unconnected information. In addition, data size is increasingly becoming an obstacle when accessing or storing biological data. All these issues make very difficult to extract and integrate information from different sources, to analyze experiments or to access and query this information in a programmatic way. CellBase provides a solution to the growing necessity of integration by easing the access to biological data. CellBase implements a set of RESTful web services that query a centralized database containing the most relevant biological data sources. The database is hosted in our servers and is regularly updated. CellBase documentation can be found at http://docs.bioinfo.cipf.es/projects/cellbase.

Evaluating Health Information Systems Using Ontologies

PubMed Central

Anderberg, Peter; Larsson, Tobias C; Fricker, Samuel A; Berglund, Johan

2016-01-01

Background There are several frameworks that attempt to address the challenges of evaluation of health information systems by offering models, methods, and guidelines about what to evaluate, how to evaluate, and how to report the evaluation results. Model-based evaluation frameworks usually suggest universally applicable evaluation aspects but do not consider case-specific aspects. On the other hand, evaluation frameworks that are case specific, by eliciting user requirements, limit their output to the evaluation aspects suggested by the users in the early phases of system development. In addition, these case-specific approaches extract different sets of evaluation aspects from each case, making it challenging to collectively compare, unify, or aggregate the evaluation of a set of heterogeneous health information systems. Objectives The aim of this paper is to find a method capable of suggesting evaluation aspects for a set of one or more health information systems—whether similar or heterogeneous—by organizing, unifying, and aggregating the quality attributes extracted from those systems and from an external evaluation framework. Methods On the basis of the available literature in semantic networks and ontologies, a method (called Unified eValuation using Ontology; UVON) was developed that can organize, unify, and aggregate the quality attributes of several health information systems into a tree-style ontology structure. The method was extended to integrate its generated ontology with the evaluation aspects suggested by model-based evaluation frameworks. An approach was developed to extract evaluation aspects from the ontology that also considers evaluation case practicalities such as the maximum number of evaluation aspects to be measured or their required degree of specificity. The method was applied and tested in Future Internet Social and Technological Alignment Research (FI-STAR), a project of 7 cloud-based eHealth applications that were developed and deployed across European Union countries. Results The relevance of the evaluation aspects created by the UVON method for the FI-STAR project was validated by the corresponding stakeholders of each case. These evaluation aspects were extracted from a UVON-generated ontology structure that reflects both the internally declared required quality attributes in the 7 eHealth applications of the FI-STAR project and the evaluation aspects recommended by the Model for ASsessment of Telemedicine applications (MAST) evaluation framework. The extracted evaluation aspects were used to create questionnaires (for the corresponding patients and health professionals) to evaluate each individual case and the whole of the FI-STAR project. Conclusions The UVON method can provide a relevant set of evaluation aspects for a heterogeneous set of health information systems by organizing, unifying, and aggregating the quality attributes through ontological structures. Those quality attributes can be either suggested by evaluation models or elicited from the stakeholders of those systems in the form of system requirements. The method continues to be systematic, context sensitive, and relevant across a heterogeneous set of health information systems. PMID:27311735

Evaluating Health Information Systems Using Ontologies.

PubMed

Eivazzadeh, Shahryar; Anderberg, Peter; Larsson, Tobias C; Fricker, Samuel A; Berglund, Johan

2016-06-16

There are several frameworks that attempt to address the challenges of evaluation of health information systems by offering models, methods, and guidelines about what to evaluate, how to evaluate, and how to report the evaluation results. Model-based evaluation frameworks usually suggest universally applicable evaluation aspects but do not consider case-specific aspects. On the other hand, evaluation frameworks that are case specific, by eliciting user requirements, limit their output to the evaluation aspects suggested by the users in the early phases of system development. In addition, these case-specific approaches extract different sets of evaluation aspects from each case, making it challenging to collectively compare, unify, or aggregate the evaluation of a set of heterogeneous health information systems. The aim of this paper is to find a method capable of suggesting evaluation aspects for a set of one or more health information systems-whether similar or heterogeneous-by organizing, unifying, and aggregating the quality attributes extracted from those systems and from an external evaluation framework. On the basis of the available literature in semantic networks and ontologies, a method (called Unified eValuation using Ontology; UVON) was developed that can organize, unify, and aggregate the quality attributes of several health information systems into a tree-style ontology structure. The method was extended to integrate its generated ontology with the evaluation aspects suggested by model-based evaluation frameworks. An approach was developed to extract evaluation aspects from the ontology that also considers evaluation case practicalities such as the maximum number of evaluation aspects to be measured or their required degree of specificity. The method was applied and tested in Future Internet Social and Technological Alignment Research (FI-STAR), a project of 7 cloud-based eHealth applications that were developed and deployed across European Union countries. The relevance of the evaluation aspects created by the UVON method for the FI-STAR project was validated by the corresponding stakeholders of each case. These evaluation aspects were extracted from a UVON-generated ontology structure that reflects both the internally declared required quality attributes in the 7 eHealth applications of the FI-STAR project and the evaluation aspects recommended by the Model for ASsessment of Telemedicine applications (MAST) evaluation framework. The extracted evaluation aspects were used to create questionnaires (for the corresponding patients and health professionals) to evaluate each individual case and the whole of the FI-STAR project. The UVON method can provide a relevant set of evaluation aspects for a heterogeneous set of health information systems by organizing, unifying, and aggregating the quality attributes through ontological structures. Those quality attributes can be either suggested by evaluation models or elicited from the stakeholders of those systems in the form of system requirements. The method continues to be systematic, context sensitive, and relevant across a heterogeneous set of health information systems.

An extractables/leachables strategy facilitated by collaboration between drug product vendors and plastic material/system suppliers.

PubMed

Jenke, Dennis

2007-01-01

Leaching of plastic materials, packaging, or containment systems by finished drug products and/or their related solutions can happen when contact occurs between such materials, systems, and products. While the drug product vendor has the regulatory/legal responsibility to demonstrate that such leaching does not affect the safety, efficacy, and/or compliance of the finished drug product, the plastic's supplier can facilitate that demonstration by providing the drug product vendor with appropriate and relevant information. Although it is a reasonable expectation that suppliers would possess and share such facilitating information, it is not reasonable for vendors to expect suppliers to (1) reveal confidential information without appropriate safeguards and (2) possess information specific to the vendor's finished drug product. Any potential conflict between the vendor's desire for information and the supplier's willingness to either generate or supply such information can be mitigated if the roles and responsibilities of these two stakeholders are established up front. The vendor of the finished drug product is responsible for supplying regulators with a full and complete leachables assessment for its finished drug product. To facilitate (but not take the place of) the vendor's leachables assessment, suppliers of the materials, components, or systems can provide the vendor with a full and complete extractables assessment for their material/system. The vendor and supplier share the responsibility for reconciling or correlating the extractables and leachables data. While this document establishes the components of a full and complete extractables assessment, specifying the detailed process by which a full and complete extractables assessment is performed is beyond its scope.

Semantic Location Extraction from Crowdsourced Data

NASA Astrophysics Data System (ADS)

Koswatte, S.; Mcdougall, K.; Liu, X.

2016-06-01

Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction.

PANTHER. Pattern ANalytics To support High-performance Exploitation and Reasoning.

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

Czuchlewski, Kristina Rodriguez; Hart, William E.

Sandia has approached the analysis of big datasets with an integrated methodology that uses computer science, image processing, and human factors to exploit critical patterns and relationships in large datasets despite the variety and rapidity of information. The work is part of a three-year LDRD Grand Challenge called PANTHER (Pattern ANalytics To support High-performance Exploitation and Reasoning). To maximize data analysis capability, Sandia pursued scientific advances across three key technical domains: (1) geospatial-temporal feature extraction via image segmentation and classification; (2) geospatial-temporal analysis capabilities tailored to identify and process new signatures more efficiently; and (3) domain- relevant models of humanmore » perception and cognition informing the design of analytic systems. Our integrated results include advances in geographical information systems (GIS) in which we discover activity patterns in noisy, spatial-temporal datasets using geospatial-temporal semantic graphs. We employed computational geometry and machine learning to allow us to extract and predict spatial-temporal patterns and outliers from large aircraft and maritime trajectory datasets. We automatically extracted static and ephemeral features from real, noisy synthetic aperture radar imagery for ingestion into a geospatial-temporal semantic graph. We worked with analysts and investigated analytic workflows to (1) determine how experiential knowledge evolves and is deployed in high-demand, high-throughput visual search workflows, and (2) better understand visual search performance and attention. Through PANTHER, Sandia's fundamental rethinking of key aspects of geospatial data analysis permits the extraction of much richer information from large amounts of data. The project results enable analysts to examine mountains of historical and current data that would otherwise go untouched, while also gaining meaningful, measurable, and defensible insights into overlooked relationships and patterns. The capability is directly relevant to the nation's nonproliferation remote-sensing activities and has broad national security applications for military and intelligence- gathering organizations.« less

Component-resolved evaluation of the content of major allergens in therapeutic extracts for specific immunotherapy of honeybee venom allergy

PubMed Central

Blank, Simon; Etzold, Stefanie; Darsow, Ulf; Schiener, Maximilian; Eberlein, Bernadette; Russkamp, Dennis; Wolf, Sara; Graessel, Anke; Biedermann, Tilo; Ollert, Markus; Schmidt-Weber, Carsten B.

2017-01-01

ABSTRACT Allergen-specific immunotherapy is the only curative treatment of honeybee venom (HBV) allergy, which is able to protect against further anaphylactic sting reactions. Recent analyses on a molecular level have demonstrated that HBV represents a complex allergen source that contains more relevant major allergens than formerly anticipated. Moreover, allergic patients show very diverse sensitization profiles with the different allergens. HBV-specific immunotherapy is conducted with HBV extracts which are derived from pure venom. The allergen content of these therapeutic extracts might differ due to natural variations of the source material or different down-stream processing strategies of the manufacturers. Since variations of the allergen content of therapeutic HBV extracts might be associated with therapeutic failure, we adressed the component-resolved allergen composition of different therapeutic grade HBV extracts which are approved for immunotherapy in numerous countries. The extracts were analyzed for their content of the major allergens Api m 1, Api m 2, Api m 3, Api m 5 and Api m 10. Using allergen-specific antibodies we were able to demonstrate the underrepresentation of relevant major allergens such as Api m 3, Api m 5 and Api m 10 in particular therapeutic extracts. Taken together, standardization of therapeutic extracts by determination of the total allergenic potency might imply the intrinsic pitfall of losing information about particular major allergens. Moreover, the variable allergen composition of different therapeutic HBV extracts might have an impact on therapy outcome and the clinical management of HBV-allergic patients with specific IgE to particular allergens. PMID:28494206

PlantDB – a versatile database for managing plant research

PubMed Central

Exner, Vivien; Hirsch-Hoffmann, Matthias; Gruissem, Wilhelm; Hennig, Lars

2008-01-01

Background Research in plant science laboratories often involves usage of many different species, cultivars, ecotypes, mutants, alleles or transgenic lines. This creates a great challenge to keep track of the identity of experimental plants and stored samples or seeds. Results Here, we describe PlantDB – a Microsoft® Office Access database – with a user-friendly front-end for managing information relevant for experimental plants. PlantDB can hold information about plants of different species, cultivars or genetic composition. Introduction of a concise identifier system allows easy generation of pedigree trees. In addition, all information about any experimental plant – from growth conditions and dates over extracted samples such as RNA to files containing images of the plants – can be linked unequivocally. Conclusion We have been using PlantDB for several years in our laboratory and found that it greatly facilitates access to relevant information. PMID:18182106

An Electronic Engineering Curriculum Design Based on Concept-Mapping Techniques

ERIC Educational Resources Information Center

Toral, S. L.; Martinez-Torres, M. R.; Barrero, F.; Gallardo, S.; Duran, M. J.

2007-01-01

Curriculum design is a concern in European Universities as they face the forthcoming European Higher Education Area (EHEA). This process can be eased by the use of scientific tools such as Concept-Mapping Techniques (CMT) that extract and organize the most relevant information from experts' experience using statistics techniques, and helps a…

Application of the remote-sensing communication model to a time-sensitive wildfire remote-sensing system

Treesearch

Christopher D. Lippitt; Douglas A. Stow; Philip J. Riggan

2016-01-01

Remote sensing for hazard response requires a priori identification of sensor, transmission, processing, and distribution methods to permit the extraction of relevant information in timescales sufficient to allow managers to make a given time-sensitive decision. This study applies and demonstrates the utility of the Remote Sensing Communication...

Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials

PubMed Central

Federer, Callie; Yoo, Minjae

2016-01-01

Abstract Drug adverse events (AEs) are a major health threat to patients seeking medical treatment and a significant barrier in drug discovery and development. AEs are now required to be submitted during clinical trials and can be extracted from ClinicalTrials.gov (https://clinicaltrials.gov/), a database of clinical studies around the world. By extracting drug and AE information from ClinicalTrials.gov and structuring it into a database, drug-AEs could be established for future drug development and repositioning. To our knowledge, current AE databases contain mainly U.S. Food and Drug Administration (FDA)-approved drugs. However, our database contains both FDA-approved and experimental compounds extracted from ClinicalTrials.gov. Our database contains 8,161 clinical trials of 3,102,675 patients and 713,103 reported AEs. We extracted the information from ClinicalTrials.gov using a set of python scripts, and then used regular expressions and a drug dictionary to process and structure relevant information into a relational database. We performed data mining and pattern analysis of drug-AEs in our database. Our database can serve as a tool to assist researchers to discover drug-AE relationships for developing, repositioning, and repurposing drugs. PMID:27631620

Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials.

PubMed

Federer, Callie; Yoo, Minjae; Tan, Aik Choon

2016-12-01

Drug adverse events (AEs) are a major health threat to patients seeking medical treatment and a significant barrier in drug discovery and development. AEs are now required to be submitted during clinical trials and can be extracted from ClinicalTrials.gov ( https://clinicaltrials.gov/ ), a database of clinical studies around the world. By extracting drug and AE information from ClinicalTrials.gov and structuring it into a database, drug-AEs could be established for future drug development and repositioning. To our knowledge, current AE databases contain mainly U.S. Food and Drug Administration (FDA)-approved drugs. However, our database contains both FDA-approved and experimental compounds extracted from ClinicalTrials.gov . Our database contains 8,161 clinical trials of 3,102,675 patients and 713,103 reported AEs. We extracted the information from ClinicalTrials.gov using a set of python scripts, and then used regular expressions and a drug dictionary to process and structure relevant information into a relational database. We performed data mining and pattern analysis of drug-AEs in our database. Our database can serve as a tool to assist researchers to discover drug-AE relationships for developing, repositioning, and repurposing drugs.

Correlation filtering in financial time series (Invited Paper)

NASA Astrophysics Data System (ADS)

Aste, T.; Di Matteo, Tiziana; Tumminello, M.; Mantegna, R. N.

2005-05-01

We apply a method to filter relevant information from the correlation coefficient matrix by extracting a network of relevant interactions. This method succeeds to generate networks with the same hierarchical structure of the Minimum Spanning Tree but containing a larger amount of links resulting in a richer network topology allowing loops and cliques. In Tumminello et al.,1 we have shown that this method, applied to a financial portfolio of 100 stocks in the USA equity markets, is pretty efficient in filtering relevant information about the clustering of the system and its hierarchical structure both on the whole system and within each cluster. In particular, we have found that triangular loops and 4 element cliques have important and significant relations with the market structure and properties. Here we apply this filtering procedure to the analysis of correlation in two different kind of interest rate time series (16 Eurodollars and 34 US interest rates).

SKIMMR: facilitating knowledge discovery in life sciences by machine-aided skim reading

PubMed Central

Burns, Gully A.P.C.

2014-01-01

Background. Unlike full reading, ‘skim-reading’ involves the process of looking quickly over information in an attempt to cover more material whilst still being able to retain a superficial view of the underlying content. Within this work, we specifically emulate this natural human activity by providing a dynamic graph-based view of entities automatically extracted from text. For the extraction, we use shallow parsing, co-occurrence analysis and semantic similarity computation techniques. Our main motivation is to assist biomedical researchers and clinicians in coping with increasingly large amounts of potentially relevant articles that are being published ongoingly in life sciences. Methods. To construct the high-level network overview of articles, we extract weighted binary statements from the text. We consider two types of these statements, co-occurrence and similarity, both organised in the same distributional representation (i.e., in a vector-space model). For the co-occurrence weights, we use point-wise mutual information that indicates the degree of non-random association between two co-occurring entities. For computing the similarity statement weights, we use cosine distance based on the relevant co-occurrence vectors. These statements are used to build fuzzy indices of terms, statements and provenance article identifiers, which support fuzzy querying and subsequent result ranking. These indexing and querying processes are then used to construct a graph-based interface for searching and browsing entity networks extracted from articles, as well as articles relevant to the networks being browsed. Last but not least, we describe a methodology for automated experimental evaluation of the presented approach. The method uses formal comparison of the graphs generated by our tool to relevant gold standards based on manually curated PubMed, TREC challenge and MeSH data. Results. We provide a web-based prototype (called ‘SKIMMR’) that generates a network of inter-related entities from a set of documents which a user may explore through our interface. When a particular area of the entity network looks interesting to a user, the tool displays the documents that are the most relevant to those entities of interest currently shown in the network. We present this as a methodology for browsing a collection of research articles. To illustrate the practical applicability of SKIMMR, we present examples of its use in the domains of Spinal Muscular Atrophy and Parkinson’s Disease. Finally, we report on the results of experimental evaluation using the two domains and one additional dataset based on the TREC challenge. The results show how the presented method for machine-aided skim reading outperforms tools like PubMed regarding focused browsing and informativeness of the browsing context. PMID:25097821

GeoDeepDive: Towards a Machine Reading-Ready Digital Library and Information Integration Resource

NASA Astrophysics Data System (ADS)

Husson, J. M.; Peters, S. E.; Livny, M.; Ross, I.

2015-12-01

Recent developments in machine reading and learning approaches to text and data mining hold considerable promise for accelerating the pace and quality of literature-based data synthesis, but these advances have outpaced even basic levels of access to the published literature. For many geoscience domains, particularly those based on physical samples and field-based descriptions, this limitation is significant. Here we describe a general infrastructure to support published literature-based machine reading and learning approaches to information integration and knowledge base creation. This infrastructure supports rate-controlled automated fetching of original documents, along with full bibliographic citation metadata, from remote servers, the secure storage of original documents, and the utilization of considerable high-throughput computing resources for the pre-processing of these documents by optical character recognition, natural language parsing, and other document annotation and parsing software tools. New tools and versions of existing tools can be automatically deployed against original documents when they are made available. The products of these tools (text/XML files) are managed by MongoDB and are available for use in data extraction applications. Basic search and discovery functionality is provided by ElasticSearch, which is used to identify documents of potential relevance to a given data extraction task. Relevant files derived from the original documents are then combined into basic starting points for application building; these starting points are kept up-to-date as new relevant documents are incorporated into the digital library. Currently, our digital library stores contains more than 360K documents supplied by Elsevier and the USGS and we are actively seeking additional content providers. By focusing on building a dependable infrastructure to support the retrieval, storage, and pre-processing of published content, we are establishing a foundation for complex, and continually improving, information integration and data extraction applications. We have developed one such application, which we present as an example, and invite new collaborations to develop other such applications.

Quantitative evaluation of translational medicine based on scientometric analysis and information extraction.

PubMed

Zhang, Yin; Diao, Tianxi; Wang, Lei

2014-12-01

Designed to advance the two-way translational process between basic research and clinical practice, translational medicine has become one of the most important areas in biomedicine. The quantitative evaluation of translational medicine is valuable for the decision making of global translational medical research and funding. Using the scientometric analysis and information extraction techniques, this study quantitatively analyzed the scientific articles on translational medicine. The results showed that translational medicine had significant scientific output and impact, specific core field and institute, and outstanding academic status and benefit. While it is not considered in this study, the patent data are another important indicators that should be integrated in the relevant research in the future. © 2014 Wiley Periodicals, Inc.

Smelling time: a neural basis for olfactory scene analysis

PubMed Central

Ache, Barry W.; Hein, Andrew M.; Bobkov, Yuriy V.; Principe, Jose C.

2016-01-01

Behavioral evidence from phylogenetically diverse animals and humans suggests that olfaction could be much more involved in interpreting space and time than heretofore imagined by extracting temporal information inherent in the olfactory signal. If this is the case, the olfactory system must have neural mechanisms capable of encoding time at intervals relevant to the turbulent odor world in which many animals live. We review evidence that animals can use populations of rhythmically active or ‘bursting’ olfactory receptor neurons (bORNs) to extract and encode temporal information inherent in natural olfactory signals. We postulate that bORNs represent an unsuspected neural mechanism through which time can be accurately measured, and that ‘smelling time’ completes the requirements for true olfactory scene analysis. PMID:27594700

Requirement of scientific documentation for the development of Naturopathy.

PubMed

Rastogi, Rajiv

2006-01-01

Past few decades have witnessed explosion of knowledge in almost every field. This has resulted not only in the advancement of the subjects in particular but also have influenced the growth of various allied subjects. The present paper explains about the advancement of science through efforts made in specific areas and also through discoveries in different allied fields having an indirect influence upon the subject in proper. In Naturopathy this seems that though nothing particular is added to the basic thoughts or fundamental principles of the subject yet the entire treatment understanding is revolutionised under the influence of scientific discoveries of past few decades. Advent of information technology has further added to the boom of knowledge and many times this seems impossible to utilize these informations for the good of human being because these are not logically arranged in our minds. In the above background, the author tries to define documentation stating that we have today ocean of information and knowledge about various things- living or dead, plants, animals or human beings; the geographical conditions or changing weather and environment. What required to be done is to extract the relevant knowledge and information required to enrich the subject. The author compares documentation with churning of milk to extract butter. Documentation, in fact, is churning of ocean of information to extract the specific, most appropriate, relevant and defined information and knowledge related to the particular subject . The paper besides discussing the definition of documentation, highlights the areas of Naturopathy requiring an urgent necessity to make proper documentations. Paper also discusses the present status of Naturopathy in India, proposes short-term and long-term goals to be achieved and plans the strategies for achieving them. The most important aspect of the paper is due understanding of the limitations of Naturopathy but a constant effort to improve the same with the growth made in various discipline of science so far.

A high-precision rule-based extraction system for expanding geospatial metadata in GenBank records

PubMed Central

Weissenbacher, Davy; Rivera, Robert; Beard, Rachel; Firago, Mari; Wallstrom, Garrick; Scotch, Matthew; Gonzalez, Graciela

2016-01-01

Objective The metadata reflecting the location of the infected host (LOIH) of virus sequences in GenBank often lacks specificity. This work seeks to enhance this metadata by extracting more specific geographic information from related full-text articles and mapping them to their latitude/longitudes using knowledge derived from external geographical databases. Materials and Methods We developed a rule-based information extraction framework for linking GenBank records to the latitude/longitudes of the LOIH. Our system first extracts existing geospatial metadata from GenBank records and attempts to improve it by seeking additional, relevant geographic information from text and tables in related full-text PubMed Central articles. The final extracted locations of the records, based on data assimilated from these sources, are then disambiguated and mapped to their respective geo-coordinates. We evaluated our approach on a manually annotated dataset comprising of 5728 GenBank records for the influenza A virus. Results We found the precision, recall, and f-measure of our system for linking GenBank records to the latitude/longitudes of their LOIH to be 0.832, 0.967, and 0.894, respectively. Discussion Our system had a high level of accuracy for linking GenBank records to the geo-coordinates of the LOIH. However, it can be further improved by expanding our database of geospatial data, incorporating spell correction, and enhancing the rules used for extraction. Conclusion Our system performs reasonably well for linking GenBank records for the influenza A virus to the geo-coordinates of their LOIH based on record metadata and information extracted from related full-text articles. PMID:26911818

A high-precision rule-based extraction system for expanding geospatial metadata in GenBank records.

PubMed

Tahsin, Tasnia; Weissenbacher, Davy; Rivera, Robert; Beard, Rachel; Firago, Mari; Wallstrom, Garrick; Scotch, Matthew; Gonzalez, Graciela

2016-09-01

The metadata reflecting the location of the infected host (LOIH) of virus sequences in GenBank often lacks specificity. This work seeks to enhance this metadata by extracting more specific geographic information from related full-text articles and mapping them to their latitude/longitudes using knowledge derived from external geographical databases. We developed a rule-based information extraction framework for linking GenBank records to the latitude/longitudes of the LOIH. Our system first extracts existing geospatial metadata from GenBank records and attempts to improve it by seeking additional, relevant geographic information from text and tables in related full-text PubMed Central articles. The final extracted locations of the records, based on data assimilated from these sources, are then disambiguated and mapped to their respective geo-coordinates. We evaluated our approach on a manually annotated dataset comprising of 5728 GenBank records for the influenza A virus. We found the precision, recall, and f-measure of our system for linking GenBank records to the latitude/longitudes of their LOIH to be 0.832, 0.967, and 0.894, respectively. Our system had a high level of accuracy for linking GenBank records to the geo-coordinates of the LOIH. However, it can be further improved by expanding our database of geospatial data, incorporating spell correction, and enhancing the rules used for extraction. Our system performs reasonably well for linking GenBank records for the influenza A virus to the geo-coordinates of their LOIH based on record metadata and information extracted from related full-text articles. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A compilation of safety impact information for extractables associated with materials used in pharmaceutical packaging, delivery, administration, and manufacturing systems.

PubMed

Jenke, Dennis; Carlson, Tage

2014-01-01

Demonstrating suitability for intended use is necessary to register packaging, delivery/administration, or manufacturing systems for pharmaceutical products. During their use, such systems may interact with the pharmaceutical product, potentially adding extraneous entities to those products. These extraneous entities, termed leachables, have the potential to affect the product's performance and/or safety. To establish the potential safety impact, drug products and their packaging, delivery, or manufacturing systems are tested for leachables or extractables, respectively. This generally involves testing a sample (either the extract or the drug product) by a means that produces a test method response and then correlating the test method response with the identity and concentration of the entity causing the response. Oftentimes, analytical tests produce responses that cannot readily establish the associated entity's identity. Entities associated with un-interpretable responses are termed unknowns. Scientifically justifiable thresholds are used to establish those individual unknowns that represent an acceptable patient safety risk and thus which do not require further identification and, conversely, those unknowns whose potential safety impact require that they be identified. Such thresholds are typically based on the statistical analysis of datasets containing toxicological information for more or less relevant compounds. This article documents toxicological information for over 540 extractables identified in laboratory testing of polymeric materials used in pharmaceutical applications. Relevant toxicological endpoints, such as NOELs (no observed effects), NOAELs (no adverse effects), TDLOs (lowest published toxic dose), and others were collated for these extractables or their structurally similar surrogates and were systematically assessed to produce a risk index, which represents a daily intake value for life-long intravenous administration. This systematic approach uses four uncertainty factors, each assigned a factor of 10, which consider the quality and relevance of the data, differences in route of administration, non-human species to human extrapolations, and inter-individual variation among humans. In addition to the risk index values, all extractables and most of their surrogates were classified for structural safety alerts using Cramer rules and for mutagenicity alerts using an in silico approach (Benigni/Bossa rule base for mutagenicity via Toxtree). Lastly, in vitro mutagenicity data (Ames Salmonella typimurium and Mouse Lymphoma tests) were collected from available databases (Chemical Carcinogenesis Research Information and Carcinogenic Potency Database). The frequency distributions of the resulting data were established; in general risk index values were normally distributed around a band ranging from 5 to 20 mg/day. The risk index associated with 95% level of the cumulative distribution plot was approximately 0.1 mg/day. Thirteen extractables in the dataset had individual risk index values less than 0.1 mg/day, although four of these had additional risk indices, based on multiple different toxicological endpoints, above 0.1 mg/day. Additionally, approximately 50% of the extractables were classified in Cramer Class 1 (low risk of toxicity) and approximately 35% were in Cramer Class 3 (no basis to assume safety). Lastly, roughly 20% of the extractables triggered either an in vitro or in silico alert for mutagenicity. When Cramer classifications and the mutagenicity alerts were compared to the risk indices, extractables with safety alerts generally had lower risk index values, although the differences in the risk index data distributions, extractables with or without alerts, were small and subtle. Leachables from packaging systems, manufacturing systems, or delivery devices can accumulate in drug products and potentially affect the drug product. Although drug products can be analyzed for leachables (and material extracts can be analyzed for extractables), not all leachables or extractables can be fully identified. Safety thresholds can be used to establish whether the unidentified substances can be deemed to be safe or whether additional analytical efforts need to be made to secure the identities. These thresholds are typically based on the statistical analysis of datasets containing toxicological information for more or less relevant compounds. This article contains safety data for over 500 extractables that were identified in laboratory characterizations of polymers used in pharmaceutical applications. The safety data consists of structural toxicity classifications of the extractables as well as calculated risk indices, where the risk indices were obtained by subjecting toxicological safety data, such as NOELs (no observed effects), NOAELs (no adverse effects), TDLOs (lowest published toxic dose), and others to a systematic evaluation process using appropriate uncertainty factors. Thus the risk index values represent daily exposures for the lifetime intravenous administration of drugs. The frequency distributions of the risk indices and Cramer classifications were examined. The risk index values were normally distributed around a range of 5 to 20 mg/day, and the risk index associated with the 95% level of the cumulative frequency plot was 0.1 mg/day. Approximately 50% of the extractables were in Cramer Class 1 (low risk of toxicity) and approximately 35% were in Cramer Class 3 (high risk of toxicity). Approximately 20% of the extractables produced an in vitro or in silico mutagenicity alert. In general, the distribution of risk index values was not strongly correlated with the either extractables' Cramer classification or by mutagenicity alerts. However, extractables with either in vitro or in silico alerts were somewhat more likely to have low risk index values. © PDA, Inc. 2014.

Survey of the Methods and Reporting Practices in Published Meta-analyses of Test Performance: 1987 to 2009

ERIC Educational Resources Information Center

Dahabreh, Issa J.; Chung, Mei; Kitsios, Georgios D.; Terasawa, Teruhiko; Raman, Gowri; Tatsioni, Athina; Tobar, Annette; Lau, Joseph; Trikalinos, Thomas A.; Schmid, Christopher H.

2013-01-01

We performed a survey of meta-analyses of test performance to describe the evolution in their methods and reporting. Studies were identified through MEDLINE (1966-2009), reference lists, and relevant reviews. We extracted information on clinical topics, literature review methods, quality assessment, and statistical analyses. We reviewed 760…

Saffron, passionflower, valerian and sage for mental health.

PubMed

Modabbernia, Amirhossein; Akhondzadeh, Shahin

2013-03-01

Many cultures have developed folk herbal remedies to treat symptoms of mental illness. An evidence-based view is now being developed for some of these so-called alternative herbal treatments. This article discusses clinically relevant scientific information on medicinal extracts of 4 herbs: saffron, passionflower, valerian, and sage. Copyright © 2013 Elsevier Inc. All rights reserved.

Finding Relevant Data in a Sea of Languages

DTIC Science & Technology

2016-04-26

full machine-translated text , unbiased word clouds , query-biased word clouds , and query-biased sentence...and information retrieval to automate language processing tasks so that the limited number of linguists available for analyzing text and spoken...the crime (stock market). The Cross-LAnguage Search Engine (CLASE) has already preprocessed the documents, extracting text to identify the language

Microbial genotype-phenotype mapping by class association rule mining.

PubMed

Tamura, Makio; D'haeseleer, Patrik

2008-07-01

Microbial phenotypes are typically due to the concerted action of multiple gene functions, yet the presence of each gene may have only a weak correlation with the observed phenotype. Hence, it may be more appropriate to examine co-occurrence between sets of genes and a phenotype (multiple-to-one) instead of pairwise relations between a single gene and the phenotype. Here, we propose an efficient class association rule mining algorithm, netCAR, in order to extract sets of COGs (clusters of orthologous groups of proteins) associated with a phenotype from COG phylogenetic profiles and a phenotype profile. netCAR takes into account the phylogenetic co-occurrence graph between COGs to restrict hypothesis space, and uses mutual information to evaluate the biconditional relation. We examined the mining capability of pairwise and multiple-to-one association by using netCAR to extract COGs relevant to six microbial phenotypes (aerobic, anaerobic, facultative, endospore, motility and Gram negative) from 11,969 unique COG profiles across 155 prokaryotic organisms. With the same level of false discovery rate, multiple-to-one association can extract about 10 times more relevant COGs than one-to-one association. We also reveal various topologies of association networks among COGs (modules) from extracted multiple-to-one correlation rules relevant with the six phenotypes; including a well-connected network for motility, a star-shaped network for aerobic and intermediate topologies for the other phenotypes. netCAR outperforms a standard CAR mining algorithm, CARapriori, while requiring several orders of magnitude less computational time for extracting 3-COG sets. Source code of the Java implementation is available as Supplementary Material at the Bioinformatics online website, or upon request to the author. Supplementary data are available at Bioinformatics online.

Natural Language Processing in Radiology: A Systematic Review.

PubMed

Pons, Ewoud; Braun, Loes M M; Hunink, M G Myriam; Kors, Jan A

2016-05-01

Radiological reporting has generated large quantities of digital content within the electronic health record, which is potentially a valuable source of information for improving clinical care and supporting research. Although radiology reports are stored for communication and documentation of diagnostic imaging, harnessing their potential requires efficient and automated information extraction: they exist mainly as free-text clinical narrative, from which it is a major challenge to obtain structured data. Natural language processing (NLP) provides techniques that aid the conversion of text into a structured representation, and thus enables computers to derive meaning from human (ie, natural language) input. Used on radiology reports, NLP techniques enable automatic identification and extraction of information. By exploring the various purposes for their use, this review examines how radiology benefits from NLP. A systematic literature search identified 67 relevant publications describing NLP methods that support practical applications in radiology. This review takes a close look at the individual studies in terms of tasks (ie, the extracted information), the NLP methodology and tools used, and their application purpose and performance results. Additionally, limitations, future challenges, and requirements for advancing NLP in radiology will be discussed. (©) RSNA, 2016 Online supplemental material is available for this article.

Feature Vector Construction Method for IRIS Recognition

NASA Astrophysics Data System (ADS)

Odinokikh, G.; Fartukov, A.; Korobkin, M.; Yoo, J.

2017-05-01

One of the basic stages of iris recognition pipeline is iris feature vector construction procedure. The procedure represents the extraction of iris texture information relevant to its subsequent comparison. Thorough investigation of feature vectors obtained from iris showed that not all the vector elements are equally relevant. There are two characteristics which determine the vector element utility: fragility and discriminability. Conventional iris feature extraction methods consider the concept of fragility as the feature vector instability without respect to the nature of such instability appearance. This work separates sources of the instability into natural and encodinginduced which helps deeply investigate each source of instability independently. According to the separation concept, a novel approach of iris feature vector construction is proposed. The approach consists of two steps: iris feature extraction using Gabor filtering with optimal parameters and quantization with separated preliminary optimized fragility thresholds. The proposed method has been tested on two different datasets of iris images captured under changing environmental conditions. The testing results show that the proposed method surpasses all the methods considered as a prior art by recognition accuracy on both datasets.

Adapting heart failure guidelines for nursing care in home health settings: challenges and solutions.

PubMed

Radhakrishnan, Kavita; Topaz, Maxim; Masterson Creber, Ruth

2014-07-01

Nurses provide most of home health services for patients with heart failure, and yet there are no evidence-based practice guidelines developed for home health nurses. The purpose of this article was to review the challenges and solutions for adapting generally available HF clinical practice guidelines to home health nursing. Appropriate HF guidelines were identified and home health nursing-relevant guidelines were extracted by the research team. In addition, a team of nursing academic and practice experts evaluated the extracted guidelines and reached consensus through Delphi rounds. We identified 172 recommendations relevant to home health nursing from the American Heart Association and Heart Failure Society of America guidelines. The recommendations were divided into 5 groups (generic, minority populations, normal ejection fraction, reduced ejection fraction, and comorbidities) and further subgroups. Experts agreed that 87% of the recommendations selected by the research team were relevant to home health nursing and rejected 6% of the selected recommendations. Experts' opinions were split on 7% of guideline recommendations. Experts mostly disagreed on recommendations related to HF medication and laboratory prescription as well as HF patient assessment. These disagreements were due to lack of patient information available to home health nurses as well as unclear understanding of scope of practice regulations for home health nursing. After 2 Delphi rounds over 8 months, we achieved 100% agreement on the recommendations. The finalized guideline included 153 recommendations. Guideline adaptation projects should include a broad scope of nursing practice recommendations from which home health agencies can customize relevant recommendations in accordance with available information and state and agency regulations.

The use of multimedia consent programs for surgical procedures: a systematic review.

PubMed

Nehme, Jean; El-Khani, Ussamah; Chow, Andre; Hakky, Sherif; Ahmed, Ahmed R; Purkayastha, Sanjay

2013-02-01

To compare multimedia and standard consent, in respect to patient comprehension, anxiety, and satisfaction, for various surgical/interventional procedures. Electronic searches of PubMed, MEDLINE, Ovid, Embase, and Google Scholar were performed. Relevant articles were assessed by 2 independent reviewers. Comparative (randomized and nonrandomized control trials) studies of multimedia and standard consent for a variety of surgical/interventional procedures were included. Studies had to report on at least one of the outcome measures. Studies were reviewed by 2 independent investigators. The first investigator extracted all relevant data, and consensus of each extraction was performed by a second investigator to verify the data. Overall, this review suggests that the use of multimedia as an adjunct to conventional consent appears to improve patient comprehension. Multimedia leads to high patient satisfaction in terms of feasibility, ease of use, and availability of information. There is no conclusive evidence demonstrating a significant reduction in preoperative anxiety.

Bayesian learning of visual chunks by human observers

PubMed Central

Orbán, Gergő; Fiser, József; Aslin, Richard N.; Lengyel, Máté

2008-01-01

Efficient and versatile processing of any hierarchically structured information requires a learning mechanism that combines lower-level features into higher-level chunks. We investigated this chunking mechanism in humans with a visual pattern-learning paradigm. We developed an ideal learner based on Bayesian model comparison that extracts and stores only those chunks of information that are minimally sufficient to encode a set of visual scenes. Our ideal Bayesian chunk learner not only reproduced the results of a large set of previous empirical findings in the domain of human pattern learning but also made a key prediction that we confirmed experimentally. In accordance with Bayesian learning but contrary to associative learning, human performance was well above chance when pair-wise statistics in the exemplars contained no relevant information. Thus, humans extract chunks from complex visual patterns by generating accurate yet economical representations and not by encoding the full correlational structure of the input. PMID:18268353

Learning to research in first grade: Bridging the transition from narrative to expository texts and tasks

NASA Astrophysics Data System (ADS)

Weise, Richard

Decades of research indicate that students at all academic grade and performance levels perform poorly with informational texts and tasks and particularly with locating assignment-relevant information in expository texts. Students have little understanding of the individual tasks required, the arc of the activity, the hierarchical structure of the information they seek, or how to reconstitute and interpret the information they extract. Poor performance begins with the introduction of textbooks and research assignments in fourth grade and continues into adulthood. However, to date, neither educators nor researchers have substantially addressed this problem. In this quasi-experimental study, we ask if first-grade children can perform essential tasks in identifying, extracting, and integrating assignment-relevant information and if instruction improves their performance. To answer this question, we conducted a 15-week, teacher-led, intervention in two first-grade classrooms in an inner-city Nashville elementary school. We created a computer learning environment (NoteTaker) to facilitate children's creation of a mental model of the research process and a narrative/expository bridge curriculum to support the children's transition from all narrative to all expository texts and tasks. We also created a new scaffolding taxonomy and a reading-to-research model to focus our research. Teachers participated in weekly professional development workshops. The results of this quasi-experimental study indicate that at-risk, first-grade children are able to (a) identify relevant information in an expository text, (b) categorize the information they identify, and (c) justify their choice of category. Children's performance in the first and last tasks significantly improved with instruction, and low-performing readers showed the greatest benefits from instruction. We find that the children's performance in categorizing information depended upon content-specific knowledge that was not taught, as well as on the process knowledge that was taught. We also find that children's narrative reading performance predicted their initial-performance for each assessment measure. We argue that first-grade children are developmentally ready to read expository texts and to learn reading-to-research tasks and that primary-school literacy instruction should not be limited to reading and writing stories.

Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations.

PubMed

Ziatdinov, Maxim; Dyck, Ondrej; Maksov, Artem; Li, Xufan; Sang, Xiahan; Xiao, Kai; Unocic, Raymond R; Vasudevan, Rama; Jesse, Stephen; Kalinin, Sergei V

2017-12-26

Recent advances in scanning transmission electron and scanning probe microscopies have opened exciting opportunities in probing the materials structural parameters and various functional properties in real space with angstrom-level precision. This progress has been accompanied by an exponential increase in the size and quality of data sets produced by microscopic and spectroscopic experimental techniques. These developments necessitate adequate methods for extracting relevant physical and chemical information from the large data sets, for which a priori information on the structures of various atomic configurations and lattice defects is limited or absent. Here we demonstrate an application of deep neural networks to extract information from atomically resolved images including location of the atomic species and type of defects. We develop a "weakly supervised" approach that uses information on the coordinates of all atomic species in the image, extracted via a deep neural network, to identify a rich variety of defects that are not part of an initial training set. We further apply our approach to interpret complex atomic and defect transformation, including switching between different coordination of silicon dopants in graphene as a function of time, formation of peculiar silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of molecular "rotor". This deep learning-based approach resembles logic of a human operator, but can be scaled leading to significant shift in the way of extracting and analyzing information from raw experimental data.

Automated Text Markup for Information Retrieval from an Electronic Textbook of Infectious Disease

PubMed Central

Berrios, Daniel C.; Kehler, Andrew; Kim, David K.; Yu, Victor L.; Fagan, Lawrence M.

1998-01-01

The information needs of practicing clinicians frequently require textbook or journal searches. Making these sources available in electronic form improves the speed of these searches, but precision (i.e., the fraction of relevant to total documents retrieved) remains low. Improving the traditional keyword search by transforming search terms into canonical concepts does not improve search precision greatly. Kim et al. have designed and built a prototype system (MYCIN II) for computer-based information retrieval from a forthcoming electronic textbook of infectious disease. The system requires manual indexing by experts in the form of complex text markup. However, this mark-up process is time consuming (about 3 person-hours to generate, review, and transcribe the index for each of 218 chapters). We have designed and implemented a system to semiautomate the markup process. The system, information extraction for semiautomated indexing of documents (ISAID), uses query models and existing information-extraction tools to provide support for any user, including the author of the source material, to mark up tertiary information sources quickly and accurately.

Visual content highlighting via automatic extraction of embedded captions on MPEG compressed video

NASA Astrophysics Data System (ADS)

Yeo, Boon-Lock; Liu, Bede

1996-03-01

Embedded captions in TV programs such as news broadcasts, documentaries and coverage of sports events provide important information on the underlying events. In digital video libraries, such captions represent a highly condensed form of key information on the contents of the video. In this paper we propose a scheme to automatically detect the presence of captions embedded in video frames. The proposed method operates on reduced image sequences which are efficiently reconstructed from compressed MPEG video and thus does not require full frame decompression. The detection, extraction and analysis of embedded captions help to capture the highlights of visual contents in video documents for better organization of video, to present succinctly the important messages embedded in the images, and to facilitate browsing, searching and retrieval of relevant clips.

Robust Tomography using Randomized Benchmarking

NASA Astrophysics Data System (ADS)

Silva, Marcus; Kimmel, Shelby; Johnson, Blake; Ryan, Colm; Ohki, Thomas

2013-03-01

Conventional randomized benchmarking (RB) can be used to estimate the fidelity of Clifford operations in a manner that is robust against preparation and measurement errors -- thus allowing for a more accurate and relevant characterization of the average error in Clifford gates compared to standard tomography protocols. Interleaved RB (IRB) extends this result to the extraction of error rates for individual Clifford gates. In this talk we will show how to combine multiple IRB experiments to extract all information about the unital part of any trace preserving quantum process. Consequently, one can compute the average fidelity to any unitary, not just the Clifford group, with tighter bounds than IRB. Moreover, the additional information can be used to design improvements in control. MS, BJ, CR and TO acknowledge support from IARPA under contract W911NF-10-1-0324.

Evaluation of Nuclear Facility Decommissioning Projects program: a reference research reactor. Project summary report

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

Baumann, B.L.; Miller, R.L.

1983-10-01

This document presents, in summary form, generic conceptual information relevant to the decommissioning of a reference research reactor (RRR). All of the data presented were extracted from NUREG/CR-1756 and arranged in a form that will provide a basis for future comparison studies for the Evaluation of Nuclear Facility Decommissioning Projects (ENFDP) program.

No Two Cues Are Alike: Depth of Learning during Infancy Is Dependent on What Orients Attention

ERIC Educational Resources Information Center

Wu, Rachel; Kirkham, Natasha Z.

2010-01-01

Human infants develop a variety of attentional mechanisms that allow them to extract relevant information from a cluttered multimodal world. We know that both social and nonsocial cues shift infants' attention, but not how these cues differentially affect learning of multimodal events. Experiment 1 used social cues to direct 8- and 4-month-olds'…

Analyzing research trends on drug safety using topic modeling.

PubMed

Zou, Chen

2018-06-01

Published drug safety data has evolved in the past decade due to scientific and technological advances in the relevant research fields. Considering that a vast amount of scientific literature has been published in this area, it is not easy to identify the key information. Topic modeling has emerged as a powerful tool to extract meaningful information from a large volume of unstructured texts. Areas covered: We analyzed the titles and abstracts of 4347 articles in four journals dedicated to drug safety from 2007 to 2016. We applied Latent Dirichlet allocation (LDA) model to extract 50 main topics, and conducted trend analysis to explore the temporal popularity of these topics over years. Expert Opinion/Commentary: We found that 'benefit-risk assessment and communication', 'diabetes' and 'biologic therapy for autoimmune diseases' are the top 3 most published topics. The topics relevant to the use of electronic health records/observational data for safety surveillance are becoming increasingly popular over time. Meanwhile, there is a slight decrease in research on signal detection based on spontaneous reporting, although spontaneous reporting still plays an important role in benefit-risk assessment. The topics related to medical conditions and treatment showed highly dynamic patterns over time.

Fuzzy geometry, entropy, and image information

NASA Technical Reports Server (NTRS)

Pal, Sankar K.

1991-01-01

Presented here are various uncertainty measures arising from grayness ambiguity and spatial ambiguity in an image, and their possible applications as image information measures. Definitions are given of an image in the light of fuzzy set theory, and of information measures and tools relevant for processing/analysis e.g., fuzzy geometrical properties, correlation, bound functions and entropy measures. Also given is a formulation of algorithms along with management of uncertainties for segmentation and object extraction, and edge detection. The output obtained here is both fuzzy and nonfuzzy. Ambiguity in evaluation and assessment of membership function are also described.

Enhancing Comparative Effectiveness Research With Automated Pediatric Pneumonia Detection in a Multi-Institutional Clinical Repository: A PHIS+ Pilot Study.

PubMed

Meystre, Stephane; Gouripeddi, Ramkiran; Tieder, Joel; Simmons, Jeffrey; Srivastava, Rajendu; Shah, Samir

2017-05-15

Community-acquired pneumonia is a leading cause of pediatric morbidity. Administrative data are often used to conduct comparative effectiveness research (CER) with sufficient sample sizes to enhance detection of important outcomes. However, such studies are prone to misclassification errors because of the variable accuracy of discharge diagnosis codes. The aim of this study was to develop an automated, scalable, and accurate method to determine the presence or absence of pneumonia in children using chest imaging reports. The multi-institutional PHIS+ clinical repository was developed to support pediatric CER by expanding an administrative database of children's hospitals with detailed clinical data. To develop a scalable approach to find patients with bacterial pneumonia more accurately, we developed a Natural Language Processing (NLP) application to extract relevant information from chest diagnostic imaging reports. Domain experts established a reference standard by manually annotating 282 reports to train and then test the NLP application. Findings of pleural effusion, pulmonary infiltrate, and pneumonia were automatically extracted from the reports and then used to automatically classify whether a report was consistent with bacterial pneumonia. Compared with the annotated diagnostic imaging reports reference standard, the most accurate implementation of machine learning algorithms in our NLP application allowed extracting relevant findings with a sensitivity of .939 and a positive predictive value of .925. It allowed classifying reports with a sensitivity of .71, a positive predictive value of .86, and a specificity of .962. When compared with each of the domain experts manually annotating these reports, the NLP application allowed for significantly higher sensitivity (.71 vs .527) and similar positive predictive value and specificity . NLP-based pneumonia information extraction of pediatric diagnostic imaging reports performed better than domain experts in this pilot study. NLP is an efficient method to extract information from a large collection of imaging reports to facilitate CER. ©Stephane Meystre, Ramkiran Gouripeddi, Joel Tieder, Jeffrey Simmons, Rajendu Srivastava, Samir Shah. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 15.05.2017.

Quality tools and resources to support organisational improvement integral to high-quality primary care: a systematic review of published and grey literature.

PubMed

Janamian, Tina; Upham, Susan J; Crossland, Lisa; Jackson, Claire L

2016-04-18

To conduct a systematic review of the literature to identify existing online primary care quality improvement tools and resources to support organisational improvement related to the seven elements in the Primary Care Practice Improvement Tool (PC-PIT), with the identified tools and resources to progress to a Delphi study for further assessment of relevance and utility. Systematic review of the international published and grey literature. CINAHL, Embase and PubMed databases were searched in March 2014 for articles published between January 2004 and December 2013. GreyNet International and other relevant websites and repositories were also searched in March-April 2014 for documents dated between 1992 and 2012. All citations were imported into a bibliographic database. Published and unpublished tools and resources were included in the review if they were in English, related to primary care quality improvement and addressed any of the seven PC-PIT elements of a high-performing practice. Tools and resources that met the eligibility criteria were then evaluated for their accessibility, relevance, utility and comprehensiveness using a four-criteria appraisal framework. We used a data extraction template to systematically extract information from eligible tools and resources. A content analysis approach was used to explore the tools and resources and collate relevant information: name of the tool or resource, year and country of development, author, name of the organisation that provided access and its URL, accessibility information or problems, overview of each tool or resource and the quality improvement element(s) it addresses. If available, a copy of the tool or resource was downloaded into the bibliographic database, along with supporting evidence (published or unpublished) on its use in primary care. This systematic review identified 53 tools and resources that can potentially be provided as part of a suite of tools and resources to support primary care practices in improving the quality of their practice, to achieve improved health outcomes.

The utility of an automated electronic system to monitor and audit transfusion practice.

PubMed

Grey, D E; Smith, V; Villanueva, G; Richards, B; Augustson, B; Erber, W N

2006-05-01

Transfusion laboratories with transfusion committees have a responsibility to monitor transfusion practice and generate improvements in clinical decision-making and red cell usage. However, this can be problematic and expensive because data cannot be readily extracted from most laboratory information systems. To overcome this problem, we developed and introduced a system to electronically extract and collate extensive amounts of data from two laboratory information systems and to link it with ICD10 clinical codes in a new database using standard information technology. Three data files were generated from two laboratory information systems, ULTRA (version 3.2) and TM, using standard information technology scripts. These were patient pre- and post-transfusion haemoglobin, blood group and antibody screen, and cross match and transfusion data. These data together with ICD10 codes for surgical cases were imported into an MS ACCESS database and linked by means of a unique laboratory number. Queries were then run to extract the relevant information and processed in Microsoft Excel for graphical presentation. We assessed the utility of this data extraction system to audit transfusion practice in a 600-bed adult tertiary hospital over an 18-month period. A total of 52 MB of data were extracted from the two laboratory information systems for the 18-month period and together with 2.0 MB theatre ICD10 data enabled case-specific transfusion information to be generated. The audit evaluated 15,992 blood group and antibody screens, 25,344 cross-matched red cell units and 15,455 transfused red cell units. Data evaluated included cross-matched to transfusion ratios and pre- and post-transfusion haemoglobin levels for a range of clinical diagnoses. Data showed significant differences between clinical units and by ICD10 code. This method to electronically extract large amounts of data and linkage with clinical databases has provided a powerful and sustainable tool for monitoring transfusion practice. It has been successfully used to identify areas requiring education, training and clinical guidance and allows for comparison with national haemoglobin-based transfusion guidelines.

Definition of variables required for comprehensive description of drug dosage and clinical pharmacokinetics.

PubMed

Medem, Anna V; Seidling, Hanna M; Eichler, Hans-Georg; Kaltschmidt, Jens; Metzner, Michael; Hubert, Carina M; Czock, David; Haefeli, Walter E

2017-05-01

Electronic clinical decision support systems (CDSS) require drug information that can be processed by computers. The goal of this project was to determine and evaluate a compilation of variables that comprehensively capture the information contained in the summary of product characteristic (SmPC) and unequivocally describe the drug, its dosage options, and clinical pharmacokinetics. An expert panel defined and structured a set of variables and drafted a guideline to extract and enter information on dosage and clinical pharmacokinetics from textual SmPCs as published by the European Medicines Agency (EMA). The set of variables was iteratively revised and evaluated by data extraction and variable allocation of roughly 7% of all centrally approved drugs. The information contained in the SmPC was allocated to three information clusters consisting of 260 variables. The cluster "drug characterization" specifies the nature of the drug. The cluster "dosage" provides information on approved drug dosages and defines corresponding specific conditions. The cluster "clinical pharmacokinetics" includes pharmacokinetic parameters of relevance for dosing in clinical practice. A first evaluation demonstrated that, despite the complexity of the current free text SmPCs, dosage and pharmacokinetic information can be reliably extracted from the SmPCs and comprehensively described by a limited set of variables. By proposing a compilation of variables well describing drug dosage and clinical pharmacokinetics, the project represents a step forward towards the development of a comprehensive database system serving as information source for sophisticated CDSS.

Use of Social Media to Target Information-Driven Arms Control and Nonproliferation Verification

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

Kreyling, Sean J.; Williams, Laura S.; Gastelum, Zoe N.

There has been considerable discussion within the national security community, including a recent workshop sponsored by the U.S. State Department, about the use of social media for extracting patterns of collective behavior and influencing public perception in areas relevant to arms control and nonproliferation. This paper seeks to explore if, and how, social media can be used to supplement nonproliferation and arms control inspection and monitoring activities on states and sites of greatest proliferation relevance. In this paper, we set the stage for how social media can be applied in this problem space and describe some of the foreseen challenges,more » including data validation, sources and attributes, verification, and security. Using information analytics and data visualization capabilities available at Pacific Northwest National Laboratory (PNNL), we provide graphical examples of some social media "signatures" of potential relevance for nonproliferation and arms control purposes. We conclude by describing a proposed case study and offering recommendations both for further research and next steps by the policy community.« less

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment.

PubMed

Alahmadi, Hanin H; Shen, Yuan; Fouad, Shereen; Luft, Caroline Di B; Bentham, Peter; Kourtzi, Zoe; Tino, Peter

2016-01-01

Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a "Learning with privileged information" approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants. MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on a probabilistic sequence learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI.

Validity of self-reported family history of cancer: A systematic literature review on selected cancers.

PubMed

Fiederling, Jonas; Shams, Ahmad Zia; Haug, Ulrike

2016-10-01

Evidence regarding validity of self-reported family history of cancer (FHC) has been reviewed only for breast, colorectal, prostate, ovarian, endometrial and uterine cancer. We aimed to systematically review studies assessing validity of self-reported family history for the remaining cancer sites. We searched the Medline database for relevant studies published by January 2016. We extracted information on the study design and the positive predictive value (PPV) of self-reported FHC, defined as the proportion of reported cancer diagnoses among relatives that was confirmed by a reference standard (as a measure of over-reporting). We also extracted information on sensitivity of self-reported FHC (as a measure of underreporting). Overall, 21 studies were included that provided information on the PPV of self-reported FHC for relevant cancers and four studies also provided information on sensitivity. The PPV was highest (mostly >70%) for pancreatic, lung, thyroid and urinary system cancers and for leukemia and lymphoma, while it was lowest for stomach and liver cancer. Sensitivity was highest (>70%) for pancreatic cancer, lung cancer, brain cancer, melanoma, leukemia and lymphoma. For several cancers, sample sizes were low and the number of studies limited, particularly regarding sensitivity of self-reported FHC. In conclusion, for some cancers (e.g., pancreatic cancer, lung cancer, leukemia, lymphoma) self-reported FHC can be considered sufficiently valid to be useful, for example, in preventive counseling. For several cancers, it is not sufficiently studied or the pattern is inconsistent. This needs to be taken into account when using self-reported information about FHC in clinical practice or epidemiological research. © 2016 UICC.

Mapping model behaviour using Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Herbst, M.; Gupta, H. V.; Casper, M. C.

2009-03-01

Hydrological model evaluation and identification essentially involves extracting and processing information from model time series. However, the type of information extracted by statistical measures has only very limited meaning because it does not relate to the hydrological context of the data. To overcome this inadequacy we exploit the diagnostic evaluation concept of Signature Indices, in which model performance is measured using theoretically relevant characteristics of system behaviour. In our study, a Self-Organizing Map (SOM) is used to process the Signatures extracted from Monte-Carlo simulations generated by the distributed conceptual watershed model NASIM. The SOM creates a hydrologically interpretable mapping of overall model behaviour, which immediately reveals deficits and trade-offs in the ability of the model to represent the different functional behaviours of the watershed. Further, it facilitates interpretation of the hydrological functions of the model parameters and provides preliminary information regarding their sensitivities. Most notably, we use this mapping to identify the set of model realizations (among the Monte-Carlo data) that most closely approximate the observed discharge time series in terms of the hydrologically relevant characteristics, and to confine the parameter space accordingly. Our results suggest that Signature Index based SOMs could potentially serve as tools for decision makers inasmuch as model realizations with specific Signature properties can be selected according to the purpose of the model application. Moreover, given that the approach helps to represent and analyze multi-dimensional distributions, it could be used to form the basis of an optimization framework that uses SOMs to characterize the model performance response surface. As such it provides a powerful and useful way to conduct model identification and model uncertainty analyses.

Mapping model behaviour using Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Herbst, M.; Gupta, H. V.; Casper, M. C.

2008-12-01

Hydrological model evaluation and identification essentially depends on the extraction of information from model time series and its processing. However, the type of information extracted by statistical measures has only very limited meaning because it does not relate to the hydrological context of the data. To overcome this inadequacy we exploit the diagnostic evaluation concept of Signature Indices, in which model performance is measured using theoretically relevant characteristics of system behaviour. In our study, a Self-Organizing Map (SOM) is used to process the Signatures extracted from Monte-Carlo simulations generated by a distributed conceptual watershed model. The SOM creates a hydrologically interpretable mapping of overall model behaviour, which immediately reveals deficits and trade-offs in the ability of the model to represent the different functional behaviours of the watershed. Further, it facilitates interpretation of the hydrological functions of the model parameters and provides preliminary information regarding their sensitivities. Most notably, we use this mapping to identify the set of model realizations (among the Monte-Carlo data) that most closely approximate the observed discharge time series in terms of the hydrologically relevant characteristics, and to confine the parameter space accordingly. Our results suggest that Signature Index based SOMs could potentially serve as tools for decision makers inasmuch as model realizations with specific Signature properties can be selected according to the purpose of the model application. Moreover, given that the approach helps to represent and analyze multi-dimensional distributions, it could be used to form the basis of an optimization framework that uses SOMs to characterize the model performance response surface. As such it provides a powerful and useful way to conduct model identification and model uncertainty analyses.

Information Retrieval and Text Mining Technologies for Chemistry.

PubMed

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.

Radiative corrections to double-Dalitz decays revisited

NASA Astrophysics Data System (ADS)

Kampf, Karol; Novotný, Jiři; Sanchez-Puertas, Pablo

2018-03-01

In this study, we revisit and complete the full next-to-leading order corrections to pseudoscalar double-Dalitz decays within the soft-photon approximation. Comparing to the previous study, we find small differences, which are nevertheless relevant for extracting information about the pseudoscalar transition form factors. Concerning the latter, these processes could offer the opportunity to test them—for the first time—in their double-virtual regime.

Computer-based synthetic data to assess the tree delineation algorithm from airborne LiDAR survey

Treesearch

Lei Wang; Andrew G. Birt; Charles W. Lafon; David M. Cairns; Robert N. Coulson; Maria D. Tchakerian; Weimin Xi; Sorin C. Popescu; James M. Guldin

2013-01-01

Small Footprint LiDAR (Light Detection And Ranging) has been proposed as an effective tool for measuring detailed biophysical characteristics of forests over broad spatial scales. However, by itself LiDAR yields only a sample of the true 3D structure of a forest. In order to extract useful forestry relevant information, this data must be interpreted using mathematical...

Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations

DOE PAGES

Ziatdinov, Maxim; Dyck, Ondrej; Maksov, Artem; ...

2017-12-07

Recent advances in scanning transmission electron and scanning probe microscopies have opened unprecedented opportunities in probing the materials structural parameters and various functional properties in real space with an angstrom-level precision. This progress has been accompanied by exponential increase in the size and quality of datasets produced by microscopic and spectroscopic experimental techniques. These developments necessitate adequate methods for extracting relevant physical and chemical information from the large datasets, for which a priori information on the structures of various atomic configurations and lattice defects is limited or absent. Here we demonstrate an application of deep neural networks to extracting informationmore » from atomically resolved images including location of the atomic species and type of defects. We develop a “weakly-supervised” approach that uses information on the coordinates of all atomic species in the image, extracted via a deep neural network, to identify a rich variety of defects that are not part of an initial training set. We further apply our approach to interpret complex atomic and defect transformation, including switching between different coordination of silicon dopants in graphene as a function of time, formation of peculiar silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of molecular “rotor”. In conclusion, this deep learning based approach resembles logic of a human operator, but can be scaled leading to significant shift in the way of extracting and analyzing information from raw experimental data.« less

Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations

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

Ziatdinov, Maxim; Dyck, Ondrej; Maksov, Artem

Recent advances in scanning transmission electron and scanning probe microscopies have opened unprecedented opportunities in probing the materials structural parameters and various functional properties in real space with an angstrom-level precision. This progress has been accompanied by exponential increase in the size and quality of datasets produced by microscopic and spectroscopic experimental techniques. These developments necessitate adequate methods for extracting relevant physical and chemical information from the large datasets, for which a priori information on the structures of various atomic configurations and lattice defects is limited or absent. Here we demonstrate an application of deep neural networks to extracting informationmore » from atomically resolved images including location of the atomic species and type of defects. We develop a “weakly-supervised” approach that uses information on the coordinates of all atomic species in the image, extracted via a deep neural network, to identify a rich variety of defects that are not part of an initial training set. We further apply our approach to interpret complex atomic and defect transformation, including switching between different coordination of silicon dopants in graphene as a function of time, formation of peculiar silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of molecular “rotor”. In conclusion, this deep learning based approach resembles logic of a human operator, but can be scaled leading to significant shift in the way of extracting and analyzing information from raw experimental data.« less

Biological network extraction from scientific literature: state of the art and challenges.

PubMed

Li, Chen; Liakata, Maria; Rebholz-Schuhmann, Dietrich

2014-09-01

Networks of molecular interactions explain complex biological processes, and all known information on molecular events is contained in a number of public repositories including the scientific literature. Metabolic and signalling pathways are often viewed separately, even though both types are composed of interactions involving proteins and other chemical entities. It is necessary to be able to combine data from all available resources to judge the functionality, complexity and completeness of any given network overall, but especially the full integration of relevant information from the scientific literature is still an ongoing and complex task. Currently, the text-mining research community is steadily moving towards processing the full body of the scientific literature by making use of rich linguistic features such as full text parsing, to extract biological interactions. The next step will be to combine these with information from scientific databases to support hypothesis generation for the discovery of new knowledge and the extension of biological networks. The generation of comprehensive networks requires technologies such as entity grounding, coordination resolution and co-reference resolution, which are not fully solved and are required to further improve the quality of results. Here, we analyse the state of the art for the extraction of network information from the scientific literature and the evaluation of extraction methods against reference corpora, discuss challenges involved and identify directions for future research. © The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Decoding rule search domain in the left inferior frontal gyrus

PubMed Central

Babcock, Laura; Vallesi, Antonino

2018-01-01

Traditionally, the left hemisphere has been thought to extract mainly verbal patterns of information, but recent evidence has shown that the left Inferior Frontal Gyrus (IFG) is active during inductive reasoning in both the verbal and spatial domains. We aimed to understand whether the left IFG supports inductive reasoning in a domain-specific or domain-general fashion. To do this we used Multi-Voxel Pattern Analysis to decode the representation of domain during a rule search task. Thirteen participants were asked to extract the rule underlying streams of letters presented in different spatial locations. Each rule was either verbal (letters forming words) or spatial (positions forming geometric figures). Our results show that domain was decodable in the left prefrontal cortex, suggesting that this region represents domain-specific information, rather than processes common to the two domains. A replication study with the same participants tested two years later confirmed these findings, though the individual representations changed, providing evidence for the flexible nature of representations. This study extends our knowledge on the neural basis of goal-directed behaviors and on how information relevant for rule extraction is flexibly mapped in the prefrontal cortex. PMID:29547623

Video Skimming and Characterization through the Combination of Image and Language Understanding Techniques

NASA Technical Reports Server (NTRS)

Smith, Michael A.; Kanade, Takeo

1997-01-01

Digital video is rapidly becoming important for education, entertainment, and a host of multimedia applications. With the size of the video collections growing to thousands of hours, technology is needed to effectively browse segments in a short time without losing the content of the video. We propose a method to extract the significant audio and video information and create a "skim" video which represents a very short synopsis of the original. The goal of this work is to show the utility of integrating language and image understanding techniques for video skimming by extraction of significant information, such as specific objects, audio keywords and relevant video structure. The resulting skim video is much shorter, where compaction is as high as 20:1, and yet retains the essential content of the original segment.

DARHT Multi-intelligence Seismic and Acoustic Data Analysis

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

Stevens, Garrison Nicole; Van Buren, Kendra Lu; Hemez, Francois M.

The purpose of this report is to document the analysis of seismic and acoustic data collected at the Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility at Los Alamos National Laboratory for robust, multi-intelligence decision making. The data utilized herein is obtained from two tri-axial seismic sensors and three acoustic sensors, resulting in a total of nine data channels. The goal of this analysis is to develop a generalized, automated framework to determine internal operations at DARHT using informative features extracted from measurements collected external of the facility. Our framework involves four components: (1) feature extraction, (2) data fusion, (3) classification, andmore » finally (4) robustness analysis. Two approaches are taken for extracting features from the data. The first of these, generic feature extraction, involves extraction of statistical features from the nine data channels. The second approach, event detection, identifies specific events relevant to traffic entering and leaving the facility as well as explosive activities at DARHT and nearby explosive testing sites. Event detection is completed using a two stage method, first utilizing signatures in the frequency domain to identify outliers and second extracting short duration events of interest among these outliers by evaluating residuals of an autoregressive exogenous time series model. Features extracted from each data set are then fused to perform analysis with a multi-intelligence paradigm, where information from multiple data sets are combined to generate more information than available through analysis of each independently. The fused feature set is used to train a statistical classifier and predict the state of operations to inform a decision maker. We demonstrate this classification using both generic statistical features and event detection and provide a comparison of the two methods. Finally, the concept of decision robustness is presented through a preliminary analysis where uncertainty is added to the system through noise in the measurements.« less

Cancer Imaging Phenomics Software Suite: Application to Brain and Breast Cancer | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

The transition of oncologic imaging from its “industrial era” to it is “information era” demands analytical methods that 1) extract information from this data that is clinically and biologically relevant; 2) integrate imaging, clinical, and genomic data via rigorous statistical and computational methodologies in order to derive models valuable for understanding cancer mechanisms, diagnosis, prognostic assessment, response evaluation, and personalized treatment management; 3) are available to the biomedical community for easy use and application, with the aim of understanding, diagnosing, an

Improving life sciences information retrieval using semantic web technology.

PubMed

Quan, Dennis

2007-05-01

The ability to retrieve relevant information is at the heart of every aspect of research and development in the life sciences industry. Information is often distributed across multiple systems and recorded in a way that makes it difficult to piece together the complete picture. Differences in data formats, naming schemes and network protocols amongst information sources, both public and private, must be overcome, and user interfaces not only need to be able to tap into these diverse information sources but must also assist users in filtering out extraneous information and highlighting the key relationships hidden within an aggregated set of information. The Semantic Web community has made great strides in proposing solutions to these problems, and many efforts are underway to apply Semantic Web techniques to the problem of information retrieval in the life sciences space. This article gives an overview of the principles underlying a Semantic Web-enabled information retrieval system: creating a unified abstraction for knowledge using the RDF semantic network model; designing semantic lenses that extract contextually relevant subsets of information; and assembling semantic lenses into powerful information displays. Furthermore, concrete examples of how these principles can be applied to life science problems including a scenario involving a drug discovery dashboard prototype called BioDash are provided.

A systematic approach to infer biological relevance and biases of gene network structures.

PubMed

Antonov, Alexey V; Tetko, Igor V; Mewes, Hans W

2006-01-10

The development of high-throughput technologies has generated the need for bioinformatics approaches to assess the biological relevance of gene networks. Although several tools have been proposed for analysing the enrichment of functional categories in a set of genes, none of them is suitable for evaluating the biological relevance of the gene network. We propose a procedure and develop a web-based resource (BIOREL) to estimate the functional bias (biological relevance) of any given genetic network by integrating different sources of biological information. The weights of the edges in the network may be either binary or continuous. These essential features make our web tool unique among many similar services. BIOREL provides standardized estimations of the network biases extracted from independent data. By the analyses of real data we demonstrate that the potential application of BIOREL ranges from various benchmarking purposes to systematic analysis of the network biology.

Text mining by Tsallis entropy

NASA Astrophysics Data System (ADS)

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.

Sexual affordances, perceptual-motor invariance extraction and intentional nonlinear dynamics: sexually deviant and non-deviant patterns in male subjects.

PubMed

Renaud, Patrice; Goyette, Mathieu; Chartier, Sylvain; Zhornitski, Simon; Trottier, Dominique; Rouleau, Joanne-L; Proulx, Jean; Fedoroff, Paul; Bradford, John-P; Dassylva, Benoit; Bouchard, Stephane

2010-10-01

Sexual arousal and gaze behavior dynamics are used to characterize deviant sexual interests in male subjects. Pedophile patients and non-deviant subjects are immersed with virtual characters depicting relevant sexual features. Gaze behavior dynamics as indexed from correlation dimensions (D2) appears to be fractal in nature and significantly different from colored noise (surrogate data tests and recurrence plot analyses were performed). This perceptual-motor fractal dynamics parallels sexual arousal and differs from pedophiles to non-deviant subjects when critical sexual information is processed. Results are interpreted in terms of sexual affordance, perceptual invariance extraction and intentional nonlinear dynamics.

How to prepare a systematic review of economic evaluations for clinical practice guidelines: database selection and search strategy development (part 2/3).

PubMed

Thielen, F W; Van Mastrigt, Gapg; Burgers, L T; Bramer, W M; Majoie, Hjm; Evers, Smaa; Kleijnen, J

2016-12-01

This article is part of the series "How to prepare a systematic review of economic evaluations (EES) for informing evidence-based healthcare decisions", in which a five-step approach is proposed. Areas covered: This paper focuses on the selection of relevant databases and developing a search strategy for detecting EEs, as well as on how to perform the search and how to extract relevant data from retrieved records. Expert commentary: Thus far, little has been published on how to conduct systematic review EEs. Moreover, reliable sources of information, such as the Health Economic Evaluation Database, have ceased to publish updates. Researchers are thus left without authoritative guidance on how to conduct SR-EEs. Together with van Mastrigt et al. we seek to fill this gap.

Geomatic Methods for the Analysis of Data in the Earth Sciences: Lecture Notes in Earth Sciences, Vol. 95

NASA Astrophysics Data System (ADS)

Pavlis, Nikolaos K.

Geomatics is a trendy term that has been used in recent years to describe academic departments that teach and research theories, methods, algorithms, and practices used in processing and analyzing data related to the Earth and other planets. Naming trends aside, geomatics could be considered as the mathematical and statistical “toolbox” that allows Earth scientists to extract information about physically relevant parameters from the available data and accompany such information with some measure of its reliability. This book is an attempt to present the mathematical-statistical methods used in data analysis within various disciplines—geodesy, geophysics, photogrammetry and remote sensing—from a unifying perspective that inverse problem formalism permits. At the same time, it allows us to stretch the relevance of statistical methods in achieving an optimal solution.

Materials in Manufacturing and Packaging Systems as Sources of Elemental Impurities in Packaged Drug Products: A Literature Review.

PubMed

Jenke, Dennis R; Stults, Cheryl L M; Paskiet, Diane M; Ball, Douglas J; Nagao, Lee M

Elemental impurities in drug products can arise from a number of different sources and via a number of different means, including the active pharmaceutical ingredient, excipients, the vehicle, and leaching of elemental entities that are present in the drug product's manufacturing or packaging systems. Thus, knowledge about the presence, level, and likelihood of leaching of elemental entities in manufacturing and packaging systems is relevant to understanding how these systems contribute to a drug product's total elemental impurity burden. To that end, a joint team from the Extractables and Leachables Safety Information Exchange (ELSIE) Consortium and the International Pharmaceutical Aerosol Consortium on Regulation and Science (IPAC-RS) has conducted a review of the available literature on elemental entities in pharmaceutically relevant polymers and the presence of these elemental entities in material extracts and/or drug products. This review article contains the information compiled from the available body of literature and considers two questions: (1) What elemental entities are present in the relevant polymers and materials and at what levels are they present? (2) To what extent are these elemental entities leached from these materials under conditions relevant to the manufacturing and storage/distribution of solution drug products? Conclusions drawn from the compiled data are as follows: (1) Elemental entities are present in the materials used to construct packaging and manufacturing systems as these materials either contain these elemental entities as additives or are exposed to elemental entities during their production. (2) Unless the elemental entities are parts of the materials themselves (for example, SiO 2 in glass) or intentionally added to the materials (for example, metal stearates in polymers), their incidental amounts in the materials are generally low. (3) When elemental entities are present in materials and systems, generally only a very small fraction of the total available amount of the entity can be leached under conditions that are relevant to packaged drug products. Thus, while sources of certain elemental impurities may be ubiquitous in the natural environment, they are not ubiquitous in materials used in pharmaceutical packaging and manufacturing systems and when they are present, they are not extensively leached under relevant conditions. The information summarized here can be utilized to aid the elemental impurity risk assessment process by providing the identities of commonly reported elements and data to support probability estimates of those becoming elemental impurities in the drug product. Furthermore, recommendations are made related to establishing elements of potential product impact for individual materials. Extraneous impurities in drug products provide no therapeutic benefit and thus should be known and controlled. Elemental impurities can arise from a number of sources and by a number of means, including the leaching of elemental entities from drug product packaging and manufacturing systems. To understand the extent to which materials used in packaging systems contain elemental entities and the extent to which those entities leach into drug products to become elemental impurities, the Extractables and Leachables Safety Information Exchange (ELSIE) and International Pharmaceutical Aerosol Consortium on Regulation and Science (IPAC-RS) Consortia have jointly performed a literature review on this subject. Using the compiled information, it was concluded that while packaging materials may contain elemental entities, unless those entities are intentional parts of the materials, the amounts of those elemental entities are generally low. Furthermore, generally only a very small fraction of the total available amount of the entity can be leached under conditions that are relevant to packaged drug products. Thus, risk assessment of sources of elemental impurities in drug products that may be related to materials used in pharmaceutical packaging and manufacturing systems can utilize the information and recommendations presented here. © PDA, Inc. 2015.

A New Bibliographical Feature for SIMBAD: Highlighting the Most Relevant Papers for One Astronomical Object

NASA Astrophysics Data System (ADS)

Oberto, A.; Lesteven, S.; Derriere, S.; Bonnin, C.; Buga, M.; Brouty, M.; Bruneau, C.; Brunet, C.; Eisele, A.; Genova, F.; Guéhenneux, S.; Neuville, M.; Ochsenbein, F.; Perret, E.; Son, E.; Vannier, P.; Vonflie, P.; Wenger, M.; Woelfel, F.

2015-04-01

The number of bibliographical references attached to an astronomical object in SIMBAD is has been growing continuously over the years. It is important for astronomers to retrieve the most relevant papers, those that give important information about the object of study. This is not easy since there can be many references attached to one object. For instance, in 2014, more than 15,000 objects had been attached to more than 50 references. The location of the object's citations inside the paper and its number of occurrences are important criteria to extract the most relevant papers. Since 2008, because of the DJIN application (a semi-automatic tool to search for object names in full text) this information has been collected. For each article associated with an astronomical object, we know where it is cited and how many times and with which name it appears. Since September 2013, the users of SIMBAD web site can choose to retrieve the most relevant references for an astronomical object depending on its location in the publication. A new formula to sort references by combining all locations, number of occurrences, total number of objects studied, citation count, and year is presented in this paper.

Asymptotic Cramer-Rao bounds for Morlet wavelet filter bank transforms of FM signals

NASA Astrophysics Data System (ADS)

Scheper, Richard

2002-03-01

Wavelet filter banks are potentially useful tools for analyzing and extracting information from frequency modulated (FM) signals in noise. Chief among the advantages of such filter banks is the tendency of wavelet transforms to concentrate signal energy while simultaneously dispersing noise energy over the time-frequency plane, thus raising the effective signal to noise ratio of filtered signals. Over the past decade, much effort has gone into devising new algorithms to extract the relevant information from transformed signals while identifying and discarding the transformed noise. Therefore, estimates of the ultimate performance bounds on such algorithms would serve as valuable benchmarks in the process of choosing optimal algorithms for given signal classes. Discussed here is the specific case of FM signals analyzed by Morlet wavelet filter banks. By making use of the stationary phase approximation of the Morlet transform, and assuming that the measured signals are well resolved digitally, the asymptotic form of the Fisher Information Matrix is derived. From this, Cramer-Rao bounds are analytically derived for simple cases.

A Reward-Maximizing Spiking Neuron as a Bounded Rational Decision Maker.

PubMed

Leibfried, Felix; Braun, Daniel A

2015-08-01

Rate distortion theory describes how to communicate relevant information most efficiently over a channel with limited capacity. One of the many applications of rate distortion theory is bounded rational decision making, where decision makers are modeled as information channels that transform sensory input into motor output under the constraint that their channel capacity is limited. Such a bounded rational decision maker can be thought to optimize an objective function that trades off the decision maker's utility or cumulative reward against the information processing cost measured by the mutual information between sensory input and motor output. In this study, we interpret a spiking neuron as a bounded rational decision maker that aims to maximize its expected reward under the computational constraint that the mutual information between the neuron's input and output is upper bounded. This abstract computational constraint translates into a penalization of the deviation between the neuron's instantaneous and average firing behavior. We derive a synaptic weight update rule for such a rate distortion optimizing neuron and show in simulations that the neuron efficiently extracts reward-relevant information from the input by trading off its synaptic strengths against the collected reward.

KneeTex: an ontology-driven system for information extraction from MRI reports.

PubMed

Spasić, Irena; Zhao, Bo; Jones, Christopher B; Button, Kate

2015-01-01

In the realm of knee pathology, magnetic resonance imaging (MRI) has the advantage of visualising all structures within the knee joint, which makes it a valuable tool for increasing diagnostic accuracy and planning surgical treatments. Therefore, clinical narratives found in MRI reports convey valuable diagnostic information. A range of studies have proven the feasibility of natural language processing for information extraction from clinical narratives. However, no study focused specifically on MRI reports in relation to knee pathology, possibly due to the complexity of knee anatomy and a wide range of conditions that may be associated with different anatomical entities. In this paper we describe KneeTex, an information extraction system that operates in this domain. As an ontology-driven information extraction system, KneeTex makes active use of an ontology to strongly guide and constrain text analysis. We used automatic term recognition to facilitate the development of a domain-specific ontology with sufficient detail and coverage for text mining applications. In combination with the ontology, high regularity of the sublanguage used in knee MRI reports allowed us to model its processing by a set of sophisticated lexico-semantic rules with minimal syntactic analysis. The main processing steps involve named entity recognition combined with coordination, enumeration, ambiguity and co-reference resolution, followed by text segmentation. Ontology-based semantic typing is then used to drive the template filling process. We adopted an existing ontology, TRAK (Taxonomy for RehAbilitation of Knee conditions), for use within KneeTex. The original TRAK ontology expanded from 1,292 concepts, 1,720 synonyms and 518 relationship instances to 1,621 concepts, 2,550 synonyms and 560 relationship instances. This provided KneeTex with a very fine-grained lexico-semantic knowledge base, which is highly attuned to the given sublanguage. Information extraction results were evaluated on a test set of 100 MRI reports. A gold standard consisted of 1,259 filled template records with the following slots: finding, finding qualifier, negation, certainty, anatomy and anatomy qualifier. KneeTex extracted information with precision of 98.00 %, recall of 97.63 % and F-measure of 97.81 %, the values of which are in line with human-like performance. KneeTex is an open-source, stand-alone application for information extraction from narrative reports that describe an MRI scan of the knee. Given an MRI report as input, the system outputs the corresponding clinical findings in the form of JavaScript Object Notation objects. The extracted information is mapped onto TRAK, an ontology that formally models knowledge relevant for the rehabilitation of knee conditions. As a result, formally structured and coded information allows for complex searches to be conducted efficiently over the original MRI reports, thereby effectively supporting epidemiologic studies of knee conditions.

Looking, Feeling, and Doing: Are There Age Differences in Attention, Mood and Behavioral Responses to Skin Cancer Information?

PubMed Central

Isaacowitz, Derek M.; Choi, YoonSun

2012-01-01

Overview Previous studies on aging and attention to emotional information found that older adults may look away from negative stimuli to regulate their moods. However, it is an open question whether older adults’ tendency to look less at negative material comes at the expense of learning when negative information is also health-relevant. This study investigated how age-related changes in attention to negative but relevant information about skin cancer risk reduction influenced both subsequent health behavior and mood regulation. Methods Younger (18-25, n = 78) and older (60-92, n = 77) adults’ fixations toward videos containing negatively-valenced content and risk-reduction information about skin cancer were recorded with eye-tracking. Self-reported mood ratings were measured throughout. Behavioral outcome measures (e.g., answering knowledge questions about skin cancer, choosing a sunscreen, completing a skin self-exam) assessed participants’ learning of key health-relevant information, their interest in seeking additional information, and their engagement in protective behaviors. Results Older adults generally looked less at the negative video content, more rapidly regulated their moods, and learned fewer facts about skin cancer; yet, they engaged in a greater number of protective behaviors than did younger adults. Conclusions Older adults may demonstrate an efficient looking strategy that extracts important information without disrupting their moods, and they may compensate for less learning by engaging in a greater number of protective behaviors. Younger adults may be distracted by disruptions to their mood, constraining their engagement in protective behaviors. PMID:22149125

Looking, feeling, and doing: are there age differences in attention, mood, and behavioral responses to skin cancer information?

PubMed

Isaacowitz, Derek M; Choi, Yoonsun

2012-09-01

Previous studies on aging and attention to emotional information found that older adults may look away from negative stimuli to regulate their moods. However, it is an open question whether older adults' tendency to look less at negative material comes at the expense of learning when negative information is also health-relevant. This study investigated how age-related changes in attention to negative but relevant information about skin cancer risk reduction influenced both subsequent health behavior and mood regulation. Younger (18-25 years of age, n = 78) and older (60-92 years of age, n = 77) adults' fixations toward videos containing negatively valenced content and risk-reduction information about skin cancer were recorded with eye-tracking. Self-reported mood ratings were measured throughout. Behavioral outcome measures (e.g., answering knowledge questions about skin cancer, choosing a sunscreen, completing a skin self-exam) assessed participants' learning of key health-relevant information, their interest in seeking additional information, and their engagement in protective behaviors. Older adults generally looked less at the negative video content, more rapidly regulated their moods, and learned fewer facts about skin cancer; yet, they engaged in a greater number of protective behaviors than did younger adults. Older adults may demonstrate an efficient looking strategy that extracts important information without disrupting their moods, and they may compensate for less learning by engaging in a greater number of protective behaviors. Younger adults may be distracted by disruptions to their mood, constraining their engagement in protective behaviors. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Quantity and unit extraction for scientific and technical intelligence analysis

NASA Astrophysics Data System (ADS)

David, Peter; Hawes, Timothy

2017-05-01

Scientific and Technical (S and T) intelligence analysts consume huge amounts of data to understand how scientific progress and engineering efforts affect current and future military capabilities. One of the most important types of information S and T analysts exploit is the quantities discussed in their source material. Frequencies, ranges, size, weight, power, and numerous other properties and measurements describing the performance characteristics of systems and the engineering constraints that define them must be culled from source documents before quantified analysis can begin. Automating the process of finding and extracting the relevant quantities from a wide range of S and T documents is difficult because information about quantities and their units is often contained in unstructured text with ad hoc conventions used to convey their meaning. Currently, even simple tasks, such as searching for documents discussing RF frequencies in a band of interest, is a labor intensive and error prone process. This research addresses the challenges facing development of a document processing capability that extracts quantities and units from S and T data, and how Natural Language Processing algorithms can be used to overcome these challenges.

How to Assess Gaming-Induced Benefits on Attention and Working Memory.

PubMed

Mishra, Jyoti; Bavelier, Daphne; Gazzaley, Adam

2012-06-01

Our daily actions are driven by our goals in the moment, constantly forcing us to choose among various options. Attention and working memory are key enablers of that process. Attention allows for selective processing of goal-relevant information and rejecting task-irrelevant information. Working memory functions to maintain goal-relevant information in memory for brief periods of time for subsequent recall and/or manipulation. Efficient attention and working memory thus support the best extraction and retention of environmental information for optimal task performance. Recent studies have evidenced that attention and working memory abilities can be enhanced by cognitive training games as well as entertainment videogames. Here we review key cognitive paradigms that have been used to evaluate the impact of game-based training on various aspects of attention and working memory. Common use of such methodology within the scientific community will enable direct comparison of the efficacy of different games across age groups and clinical populations. The availability of common assessment tools will ultimately facilitate development of the most effective forms of game-based training for cognitive rehabilitation and education.

How to Assess Gaming-Induced Benefits on Attention and Working Memory

PubMed Central

Mishra, Jyoti; Bavelier, Daphne

2012-01-01

Abstract Our daily actions are driven by our goals in the moment, constantly forcing us to choose among various options. Attention and working memory are key enablers of that process. Attention allows for selective processing of goal-relevant information and rejecting task-irrelevant information. Working memory functions to maintain goal-relevant information in memory for brief periods of time for subsequent recall and/or manipulation. Efficient attention and working memory thus support the best extraction and retention of environmental information for optimal task performance. Recent studies have evidenced that attention and working memory abilities can be enhanced by cognitive training games as well as entertainment videogames. Here we review key cognitive paradigms that have been used to evaluate the impact of game-based training on various aspects of attention and working memory. Common use of such methodology within the scientific community will enable direct comparison of the efficacy of different games across age groups and clinical populations. The availability of common assessment tools will ultimately facilitate development of the most effective forms of game-based training for cognitive rehabilitation and education. PMID:24761314

The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process

PubMed Central

Dahamna, Badisse; Guillemin-Lanne, Sylvie; Darmoni, Stefan J; Faviez, Carole; Huot, Charles; Katsahian, Sandrine; Leroux, Vincent; Pereira, Suzanne; Richard, Christophe; Schück, Stéphane; Souvignet, Julien; Lillo-Le Louët, Agnès; Texier, Nathalie

2017-01-01

Background Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Classical Pharmacovigilance process is limited by underreporting which justifies the current interest in new knowledge sources such as social media. The Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project aims to extract ADRs reported by patients in these media. We identified 5 major challenges to overcome to operationalize the analysis of patient posts: (1) variable quality of information on social media, (2) guarantee of data privacy, (3) response to pharmacovigilance expert expectations, (4) identification of relevant information within Web pages, and (5) robust and evolutive architecture. Objective This article aims to describe the current state of advancement of the ADR-PRISM project by focusing on the solutions we have chosen to address these 5 major challenges. Methods In this article, we propose methods and describe the advancement of this project on several aspects: (1) a quality driven approach for selecting relevant social media for the extraction of knowledge on potential ADRs, (2) an assessment of ethical issues and French regulation for the analysis of data on social media, (3) an analysis of pharmacovigilance expert requirements when reviewing patient posts on the Internet, (4) an extraction method based on natural language processing, pattern based matching, and selection of relevant medical concepts in reference terminologies, and (5) specifications of a component-based architecture for the monitoring system. Results Considering the 5 major challenges, we (1) selected a set of 21 validated criteria for selecting social media to support the extraction of potential ADRs, (2) proposed solutions to guarantee data privacy of patients posting on Internet, (3) took into account pharmacovigilance expert requirements with use case diagrams and scenarios, (4) built domain-specific knowledge resources embeding a lexicon, morphological rules, context rules, semantic rules, syntactic rules, and post-analysis processing, and (5) proposed a component-based architecture that allows storage of big data and accessibility to third-party applications through Web services. Conclusions We demonstrated the feasibility of implementing a component-based architecture that allows collection of patient posts on the Internet, near real-time processing of those posts including annotation, and storage in big data structures. In the next steps, we will evaluate the posts identified by the system in social media to clarify the interest and relevance of such approach to improve conventional pharmacovigilance processes based on spontaneous reporting. PMID:28935617

Criteria for assessing the quality of mHealth apps: a systematic review.

PubMed

Nouri, Rasool; R Niakan Kalhori, Sharareh; Ghazisaeedi, Marjan; Marchand, Guillaume; Yasini, Mobin

2018-05-16

Review the existing studies including an assessment tool/method to assess the quality of mHealth apps; extract their criteria; and provide a classification of the collected criteria. In accordance with the PRISMA statement, a literature search was conducted in MEDLINE, EMBase, ISI and Scopus for English language citations published from January 1, 2008 to December 22, 2016 for studies including tools or methods for quality assessment of mHealth apps. Two researchers screened the titles and abstracts of all retrieved citations against the inclusion and exclusion criteria. The full text of relevant papers was then individually examined by the same researchers. A senior researcher resolved eventual disagreements and confirmed the relevance of all included papers. The authors, date of publication, subject fields of target mHealth apps, development method, and assessment criteria were extracted from each paper. The extracted assessment criteria were then reviewed, compared, and classified by an expert panel of two medical informatics specialists and two health information management specialists. Twenty-three papers were included in the review. Thirty-eight main classes of assessment criteria were identified. These were reorganized by expert panel into 7 main classes (Design, Information/Content, Usability, Functionality, Ethical Issues, Security and Privacy, and User-perceived value) with 37 sub-classes of criteria. There is a wide heterogeneity in assessment criteria for mHealth apps. It is necessary to define the exact meanings and degree of distinctness of each criterion. This will help to improve the existing tools and may lead to achieve a better comprehensive mHealth app assessment tool.

Temporal tuning in the bat auditory cortex is sharper when studied with natural echolocation sequences.

PubMed

Beetz, M Jerome; Hechavarría, Julio C; Kössl, Manfred

2016-06-30

Precise temporal coding is necessary for proper acoustic analysis. However, at cortical level, forward suppression appears to limit the ability of neurons to extract temporal information from natural sound sequences. Here we studied how temporal processing can be maintained in the bats' cortex in the presence of suppression evoked by natural echolocation streams that are relevant to the bats' behavior. We show that cortical neurons tuned to target-distance actually profit from forward suppression induced by natural echolocation sequences. These neurons can more precisely extract target distance information when they are stimulated with natural echolocation sequences than during stimulation with isolated call-echo pairs. We conclude that forward suppression does for time domain tuning what lateral inhibition does for selectivity forms such as auditory frequency tuning and visual orientation tuning. When talking about cortical processing, suppression should be seen as a mechanistic tool rather than a limiting element.

Extracting information from multiplex networks

NASA Astrophysics Data System (ADS)

Iacovacci, Jacopo; Bianconi, Ginestra

2016-06-01

Multiplex networks are generalized network structures that are able to describe networks in which the same set of nodes are connected by links that have different connotations. Multiplex networks are ubiquitous since they describe social, financial, engineering, and biological networks as well. Extending our ability to analyze complex networks to multiplex network structures increases greatly the level of information that is possible to extract from big data. For these reasons, characterizing the centrality of nodes in multiplex networks and finding new ways to solve challenging inference problems defined on multiplex networks are fundamental questions of network science. In this paper, we discuss the relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks and we characterize the utility of the recently introduced indicator function Θ ˜ S for describing their mesoscale organization and community structure. As working examples for studying these measures, we consider three multiplex network datasets coming for social science.

3D electron tomography of pretreated biomass informs atomic modeling of cellulose microfibrils.

PubMed

Ciesielski, Peter N; Matthews, James F; Tucker, Melvin P; Beckham, Gregg T; Crowley, Michael F; Himmel, Michael E; Donohoe, Bryon S

2013-09-24

Fundamental insights into the macromolecular architecture of plant cell walls will elucidate new structure-property relationships and facilitate optimization of catalytic processes that produce fuels and chemicals from biomass. Here we introduce computational methodology to extract nanoscale geometry of cellulose microfibrils within thermochemically treated biomass directly from electron tomographic data sets. We quantitatively compare the cell wall nanostructure in corn stover following two leading pretreatment strategies: dilute acid with iron sulfate co-catalyst and ammonia fiber expansion (AFEX). Computational analysis of the tomographic data is used to extract mathematical descriptions for longitudinal axes of cellulose microfibrils from which we calculate their nanoscale curvature. These nanostructural measurements are used to inform the construction of atomistic models that exhibit features of cellulose within real, process-relevant biomass. By computational evaluation of these atomic models, we propose relationships between the crystal structure of cellulose Iβ and the nanoscale geometry of cellulose microfibrils.

Mutual information, neural networks and the renormalization group

NASA Astrophysics Data System (ADS)

Koch-Janusz, Maciej; Ringel, Zohar

2018-06-01

Physical systems differing in their microscopic details often display strikingly similar behaviour when probed at macroscopic scales. Those universal properties, largely determining their physical characteristics, are revealed by the powerful renormalization group (RG) procedure, which systematically retains `slow' degrees of freedom and integrates out the rest. However, the important degrees of freedom may be difficult to identify. Here we demonstrate a machine-learning algorithm capable of identifying the relevant degrees of freedom and executing RG steps iteratively without any prior knowledge about the system. We introduce an artificial neural network based on a model-independent, information-theoretic characterization of a real-space RG procedure, which performs this task. We apply the algorithm to classical statistical physics problems in one and two dimensions. We demonstrate RG flow and extract the Ising critical exponent. Our results demonstrate that machine-learning techniques can extract abstract physical concepts and consequently become an integral part of theory- and model-building.

Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants.

PubMed

Charlton, Peter H; Bonnici, Timothy; Tarassenko, Lionel; Alastruey, Jordi; Clifton, David A; Beale, Richard; Watkinson, Peter J

2017-05-01

Breathing rate (BR) can be estimated by extracting respiratory signals from the electrocardiogram (ECG) or photoplethysmogram (PPG). The extracted respiratory signals may be influenced by several technical and physiological factors. In this study, our aim was to determine how technical and physiological factors influence the quality of respiratory signals. Using a variety of techniques 15 respiratory signals were extracted from the ECG, and 11 from PPG signals collected from 57 healthy subjects. The quality of each respiratory signal was assessed by calculating its correlation with a reference oral-nasal pressure respiratory signal using Pearson's correlation coefficient. Relevant results informing device design and clinical application were obtained. The results informing device design were: (i) seven out of 11 respiratory signals were of higher quality when extracted from finger PPG compared to ear PPG; (ii) laboratory equipment did not provide higher quality of respiratory signals than a clinical monitor; (iii) the ECG provided higher quality respiratory signals than the PPG; (iv) during downsampling of the ECG and PPG significant reductions in quality were first observed at sampling frequencies of  <250 Hz and  <16 Hz respectively. The results informing clinical application were: (i) frequency modulation-based respiratory signals were generally of lower quality in elderly subjects compared to young subjects; (ii) the qualities of 23 out of 26 respiratory signals were reduced at elevated BRs; (iii) there were no differences associated with gender. Recommendations based on the results are provided regarding device designs for BR estimation, and clinical applications. The dataset and code used in this study are publicly available.

Immunophenotyping of acute leukaemias by flow cytometry: a review.

PubMed

Pamnani, R

2009-12-01

To provide an overview of the utility of flow cytometry for phenotyping of acute leukaemias and selection-of monoclonal antibodies. The literature review was obtained through internet, journals and chapters in the relevant books. Relevant articles and chapters on immunophenotyping of acute leukaemias were selected from respected international journals and books in the field of haematology and were reviewed. Complete articles relevant to the topic were selected and reviewed and the necessary information extracted for this review. Flow cytometry has been used extensively in recent years to characterise haemopoeitic malignancies and done routinely in the developed world. This technique has greatly improved the diagnosis and classification of haemopoeitic malignancies and has been recommended by World Health Organisation classification (WHO) of tumours of haemopoeitic and lymphoid tissue. Application of flow cytometry for the diagnosis of leukaemias has been recently introduced in Kenya and is currently being undertaken in research using limited but appropriate panels of monoclonal antibodies. It is hoped that findings of this research will inform the use of flow cytometry as an ancillary diagnostic technique in our resource-constrained set up.

Towards Assisted Moderation in Online Healthcare Social Networks: Improving Trust in YouTube Searches.

PubMed

Cañon, Daniel E; Lopez, Diego M; Blobel, Bernd

2014-01-01

Moderation of content in online Health Social Networks (HSN) is critical because information is not only published and produced by experts or health professionals, but also by users of that information. The objective of this paper is to propose a semi-automatic moderation Web Service for assessing the quality (trustworthiness) of health-related videos published on the YouTube social network. The service is relevant for moderators or community managers, who get enabled to control the quality of videos published on their online HSN sites. The HealthTrust metric was selected as the metric to be implemented in the service in order to support the assessment of trustworthiness of videos in Online HSN. The service is a RESTful service which can be integrated into open source Virtual Social Network Platforms, therefore improving trust in the process of searching and publishing content extracted from YouTube. A preliminary pilot evaluation in a simple use case demonstrated that the relevance of videos retrieved using the moderation service was higher compared to the relevance of the videos retrieved using the YouTube search engine.

A Semantic Web-based System for Mining Genetic Mutations in Cancer Clinical Trials.

PubMed

Priya, Sambhawa; Jiang, Guoqian; Dasari, Surendra; Zimmermann, Michael T; Wang, Chen; Heflin, Jeff; Chute, Christopher G

2015-01-01

Textual eligibility criteria in clinical trial protocols contain important information about potential clinically relevant pharmacogenomic events. Manual curation for harvesting this evidence is intractable as it is error prone and time consuming. In this paper, we develop and evaluate a Semantic Web-based system that captures and manages mutation evidences and related contextual information from cancer clinical trials. The system has 2 main components: an NLP-based annotator and a Semantic Web ontology-based annotation manager. We evaluated the performance of the annotator in terms of precision and recall. We demonstrated the usefulness of the system by conducting case studies in retrieving relevant clinical trials using a collection of mutations identified from TCGA Leukemia patients and Atlas of Genetics and Cytogenetics in Oncology and Haematology. In conclusion, our system using Semantic Web technologies provides an effective framework for extraction, annotation, standardization and management of genetic mutations in cancer clinical trials.

Semantic Analysis of Email Using Domain Ontologies and WordNet

NASA Technical Reports Server (NTRS)

Berrios, Daniel C.; Keller, Richard M.

2005-01-01

The problem of capturing and accessing knowledge in paper form has been supplanted by a problem of providing structure to vast amounts of electronic information. Systems that can construct semantic links for natural language documents like email messages automatically will be a crucial element of semantic email tools. We have designed an information extraction process that can leverage the knowledge already contained in an existing semantic web, recognizing references in email to existing nodes in a network of ontology instances by using linguistic knowledge and knowledge of the structure of the semantic web. We developed a heuristic score that uses several forms of evidence to detect references in email to existing nodes in the Semanticorganizer repository's network. While these scores cannot directly support automated probabilistic inference, they can be used to rank nodes by relevance and link those deemed most relevant to email messages.

Aviation obstacle auto-extraction using remote sensing information

NASA Astrophysics Data System (ADS)

Zimmer, N.; Lugsch, W.; Ravenscroft, D.; Schiefele, J.

2008-10-01

An Obstacle, in the aviation context, may be any natural, man-made, fixed or movable object, permanent or temporary. Currently, the most common way to detect relevant aviation obstacles from an aircraft or helicopter for navigation purposes and collision avoidance is the use of merged infrared and synthetic information of obstacle data. Several algorithms have been established to utilize synthetic and infrared images to generate obstacle information. There might be a situation however where the system is error-prone and may not be able to consistently determine the current environment. This situation can be avoided when the system knows the true position of the obstacle. The quality characteristics of the obstacle data strongly depends on the quality of the source data such as maps and official publications. In some countries such as newly industrializing and developing countries, quality and quantity of obstacle information is not available. The aviation world has two specifications - RTCA DO-276A and ICAO ANNEX 15 Ch. 10 - which describe the requirements for aviation obstacles. It is essential to meet these requirements to be compliant with the specifications and to support systems based on these specifications, e.g. 3D obstacle warning systems where accurate coordinates based on WGS-84 is a necessity. Existing aerial and satellite or soon to exist high quality remote sensing data makes it feasible to think about automated aviation obstacle data origination. This paper will describe the feasibility to auto-extract aviation obstacles from remote sensing data considering limitations of image and extraction technologies. Quality parameters and possible resolution of auto-extracted obstacle data will be discussed and presented.

Evaluation of Nuclear Facility Decommissioning Projects program: a reference test reactor. Project summary report

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

Boing, L.E.; Miller, R.L.

1983-10-01

This document presents, in summary form, generic conceptual information relevant to the decommissioning of a reference test reactor (RTR). All of the data presented were extracted from NUREG/CR-1756 and arranged in a form that will provide a basis for future comparison studies for the Evaluation of Nuclear Facility Decommissioning Projects (ENFDP) program. During the data extraction process no attempt was made to challenge any of the assumptions used in the original studies nor was any attempt made to update assumed methods or processes to state-of-the-art decommissioning techniques. In a few instances obvious errors were corrected after consultation with the studymore » author.« less

Quantification of urban structure on building block level utilizing multisensoral remote sensing data

NASA Astrophysics Data System (ADS)

Wurm, Michael; Taubenböck, Hannes; Dech, Stefan

2010-10-01

Dynamics of urban environments are a challenge to a sustainable development. Urban areas promise wealth, realization of individual dreams and power. Hence, many cities are characterized by a population growth as well as physical development. Traditional, visual mapping and updating of urban structure information of cities is a very laborious and cost-intensive task, especially for large urban areas. For this purpose, we developed a workflow for the extraction of the relevant information by means of object-based image classification. In this manner, multisensoral remote sensing data has been analyzed in terms of very high resolution optical satellite imagery together with height information by a digital surface model to retrieve a detailed 3D city model with the relevant land-use / land-cover information. This information has been aggregated on the level of the building block to describe the urban structure by physical indicators. A comparison between the indicators derived by the classification and a reference classification has been accomplished to show the correlation between the individual indicators and a reference classification of urban structure types. The indicators have been used to apply a cluster analysis to group the individual blocks into similar clusters.

Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach.

PubMed

Rinaldi, Fabio; Schneider, Gerold; Kaljurand, Kaarel; Hess, Michael; Andronis, Christos; Konstandi, Ourania; Persidis, Andreas

2007-02-01

The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. This paper presents and evaluates an approach aimed at automating the process of extracting functional relations (e.g. interactions between genes and proteins) from scientific literature in the biomedical domain. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus. We have implemented a state-of-the-art text mining system for biomedical literature, based on a deep-linguistic, full-parsing approach. The results are validated on two different corpora: the manually annotated genomics information access (GENIA) corpus and the automatically annotated arabidopsis thaliana circadian rhythms (ATCR) corpus. We show how a deep-linguistic approach (contrary to common belief) can be used in a real world text mining application, offering high-precision relation extraction, while at the same time retaining a sufficient recall.

Methodological update in Medicina Intensiva.

PubMed

García Garmendia, J L

2018-04-01

Research in the critically ill is complex by the heterogeneity of patients, the difficulties to achieve representative sample sizes and the number of variables simultaneously involved. However, the quantity and quality of records is high as well as the relevance of the variables used, such as survival. The methodological tools have evolved to offering new perspectives and analysis models that allow extracting relevant information from the data that accompanies the critically ill patient. The need for training in methodology and interpretation of results is an important challenge for the intensivists who wish to be updated on the research developments and clinical advances in Intensive Medicine. Copyright © 2017 Elsevier España, S.L.U. y SEMNIM. All rights reserved.

Usability-driven pruning of large ontologies: the case of SNOMED CT.

PubMed

López-García, Pablo; Boeker, Martin; Illarramendi, Arantza; Schulz, Stefan

2012-06-01

To study ontology modularization techniques when applied to SNOMED CT in a scenario in which no previous corpus of information exists and to examine if frequency-based filtering using MEDLINE can reduce subset size without discarding relevant concepts. Subsets were first extracted using four graph-traversal heuristics and one logic-based technique, and were subsequently filtered with frequency information from MEDLINE. Twenty manually coded discharge summaries from cardiology patients were used as signatures and test sets. The coverage, size, and precision of extracted subsets were measured. Graph-traversal heuristics provided high coverage (71-96% of terms in the test sets of discharge summaries) at the expense of subset size (17-51% of the size of SNOMED CT). Pre-computed subsets and logic-based techniques extracted small subsets (1%), but coverage was limited (24-55%). Filtering reduced the size of large subsets to 10% while still providing 80% coverage. Extracting subsets to annotate discharge summaries is challenging when no previous corpus exists. Ontology modularization provides valuable techniques, but the resulting modules grow as signatures spread across subhierarchies, yielding a very low precision. Graph-traversal strategies and frequency data from an authoritative source can prune large biomedical ontologies and produce useful subsets that still exhibit acceptable coverage. However, a clinical corpus closer to the specific use case is preferred when available.

Facilitating access to pre-processed research evidence in public health

PubMed Central

2010-01-01

Background Evidence-informed decision making is accepted in Canada and worldwide as necessary for the provision of effective health services. This process involves: 1) clearly articulating a practice-based issue; 2) searching for and accessing relevant evidence; 3) appraising methodological rigor and choosing the most synthesized evidence of the highest quality and relevance to the practice issue and setting that is available; and 4) extracting, interpreting, and translating knowledge, in light of the local context and resources, into practice, program and policy decisions. While the public health sector in Canada is working toward evidence-informed decision making, considerable barriers, including efficient access to synthesized resources, exist. Methods In this paper we map to a previously developed 6 level pyramid of pre-processed research evidence, relevant resources that include public health-related effectiveness evidence. The resources were identified through extensive searches of both the published and unpublished domains. Results Many resources with public health-related evidence were identified. While there were very few resources dedicated solely to public health evidence, many clinically focused resources include public health-related evidence, making tools such as the pyramid, that identify these resources, particularly helpful for public health decisions makers. A practical example illustrates the application of this model and highlights its potential to reduce the time and effort that would be required by public health decision makers to address their practice-based issues. Conclusions This paper describes an existing hierarchy of pre-processed evidence and its adaptation to the public health setting. A number of resources with public health-relevant content that are either freely accessible or requiring a subscription are identified. This will facilitate easier and faster access to pre-processed, public health-relevant evidence, with the intent of promoting evidence-informed decision making. Access to such resources addresses several barriers identified by public health decision makers to evidence-informed decision making, most importantly time, as well as lack of knowledge of resources that house public health-relevant evidence. PMID:20181270

Advance in ERG Analysis: From Peak Time and Amplitude to Frequency, Power, and Energy

PubMed Central

Lina, Jean-Marc; Lachapelle, Pierre

2014-01-01

Purpose. To compare time domain (TD: peak time and amplitude) analysis of the human photopic electroretinogram (ERG) with measures obtained in the frequency domain (Fourier analysis: FA) and in the time-frequency domain (continuous (CWT) and discrete (DWT) wavelet transforms). Methods. Normal ERGs (n = 40) were analyzed using traditional peak time and amplitude measurements of the a- and b-waves in the TD and descriptors extracted from FA, CWT, and DWT. Selected descriptors were also compared in their ability to monitor the long-term consequences of disease process. Results. Each method extracted relevant information but had distinct limitations (i.e., temporal and frequency resolutions). The DWT offered the best compromise by allowing us to extract more relevant descriptors of the ERG signal at the cost of lesser temporal and frequency resolutions. Follow-ups of disease progression were more prolonged with the DWT (max 29 years compared to 13 with TD). Conclusions. Standardized time domain analysis of retinal function should be complemented with advanced DWT descriptors of the ERG. This method should allow more sensitive/specific quantifications of ERG responses, facilitate follow-up of disease progression, and identify diagnostically significant changes of ERG waveforms that are not resolved when the analysis is only limited to time domain measurements. PMID:25061605

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

Crain, Steven P.; Yang, Shuang-Hong; Zha, Hongyuan

Access to health information by consumers is ham- pered by a fundamental language gap. Current attempts to close the gap leverage consumer oriented health information, which does not, however, have good coverage of slang medical terminology. In this paper, we present a Bayesian model to automatically align documents with different dialects (slang, com- mon and technical) while extracting their semantic topics. The proposed diaTM model enables effective information retrieval, even when the query contains slang words, by explicitly modeling the mixtures of dialects in documents and the joint influence of dialects and topics on word selection. Simulations us- ing consumermore » questions to retrieve medical information from a corpus of medical documents show that diaTM achieves a 25% improvement in information retrieval relevance by nDCG@5 over an LDA baseline.« less

Antimicrobial potential of Australian macrofungi extracts against foodborne and other pathogens.

PubMed

Bala, Neeraj; Aitken, Elizabeth A B; Cusack, Andrew; Steadman, Kathryn J

2012-03-01

Basidiomycetous macrofungi have therapeutic potential due to antimicrobial activity but little information is available for Australian macrofungi. Therefore, the present study investigated 12 Australian basidiomycetous macrofungi, previously shown to have promising activity against Staphylococcus aureus and Escherichia coli, for their antimicrobial potential against a range of other clinically relevant micro-organisms. Fruiting bodies were collected from across Queensland, Australia, freeze-dried and sequentially extracted with water and ethanol. The crude extracts were tested at 10 mg/mL and 1 mg/mL against six pathogens including two Gram-positive and two Gram-negative bacteria along with two fungi using a high throughput 96-well microplate bioassay. A degree of specificity in activity was exhibited by the water extract of Ramaria sp. (Gomphaceae) and the ethanol extracts of Psathyrella sp. (Psathyrellaceae) and Hohenbuehelia sp., which inhibited the growth of the two fungal pathogens used in the assay. Similarly, the ethanol extract of Fomitopsis lilacinogilva (Fomitopsidaceae) was active against the Gram-positive bacteria B. cereus only. Activity against a wider range of the microorganisms used in the assay was exhibited by the ethanol extract of Ramaria sp. and the water extract of Hohenbuehelia sp. (Pleurotaceae). These macrofungi can serve as new sources for the discovery and development of much needed new antimicrobials. Copyright © 2011 John Wiley & Sons, Ltd.

Psychopharmacological and Other Treatments in Preschool Children with Attention-Deficit/Hyperactivity Disorder: Current Evidence and Practice

PubMed Central

Arnold, L. Eugene; Anthony, Bruno J.

2008-01-01

Abstract Objective This article reviews rational approaches to treating attention-deficit/hyperactivity disorder (ADHD) in preschool children, including pharmacological and nonpharmacological treatments. Implications for clinical practice are discussed. Data Sources We searched MEDLINE, PsychINFO, Cumulative Index to Nursing & Allied Health, Educational Resources Information Center, Cochrane Database of Systematic Reviews and Database of Abstracts of Reviews of Effects for relevant literature published in English from 1967 to 2007 on preschool ADHD. We also reviewed the references cited in identified reports. Study Selection Studies were reviewed if the sample included at least some children younger than 6 years of age or attending kindergarten, the study participants had a diagnosis of ADHD or equivalent symptoms, received intervention aimed at ADHD symptoms, and included a relevant outcome measure. Data Extraction Studies were reviewed for type of intervention and outcome relevant to ADHD and were rated for the level of evidence for adequacy of the data to inform clinical practice. Conclusions The current level of evidence for adequacy of empirical data to inform clinical practice for short-term treatment of ADHD in preschool children is Level A for methylphenidate and Level B for parent behavior training, child training, and additive-free elimination diet. PMID:18844482

Are figure legends sufficient? Evaluating the contribution of associated text to biomedical figure comprehension.

PubMed

Yu, Hong; Agarwal, Shashank; Johnston, Mark; Cohen, Aaron

2009-01-06

Biomedical scientists need to access figures to validate research facts and to formulate or to test novel research hypotheses. However, figures are difficult to comprehend without associated text (e.g., figure legend and other reference text). We are developing automated systems to extract the relevant explanatory information along with figures extracted from full text articles. Such systems could be very useful in improving figure retrieval and in reducing the workload of biomedical scientists, who otherwise have to retrieve and read the entire full-text journal article to determine which figures are relevant to their research. As a crucial step, we studied the importance of associated text in biomedical figure comprehension. Twenty subjects evaluated three figure-text combinations: figure+legend, figure+legend+title+abstract, and figure+full-text. Using a Likert scale, each subject scored each figure+text according to the extent to which the subject thought he/she understood the meaning of the figure and the confidence in providing the assigned score. Additionally, each subject entered a free text summary for each figure-text. We identified missing information using indicator words present within the text summaries. Both the Likert scores and the missing information were statistically analyzed for differences among the figure-text types. We also evaluated the quality of text summaries with the text-summarization evaluation method the ROUGE score. Our results showed statistically significant differences in figure comprehension when varying levels of text were provided. When the full-text article is not available, presenting just the figure+legend left biomedical researchers lacking 39-68% of the information about a figure as compared to having complete figure comprehension; adding the title and abstract improved the situation, but still left biomedical researchers missing 30% of the information. When the full-text article is available, figure comprehension increased to 86-97%; this indicates that researchers felt that only 3-14% of the necessary information for full figure comprehension was missing when full text was available to them. Clearly there is information in the abstract and in the full text that biomedical scientists deem important for understanding the figures that appear in full-text biomedical articles. We conclude that the texts that appear in full-text biomedical articles are useful for understanding the meaning of a figure, and an effective figure-mining system needs to unlock the information beyond figure legend. Our work provides important guidance to the figure mining systems that extract information only from figure and figure legend.

Are figure legends sufficient? Evaluating the contribution of associated text to biomedical figure comprehension

PubMed Central

2009-01-01

Background Biomedical scientists need to access figures to validate research facts and to formulate or to test novel research hypotheses. However, figures are difficult to comprehend without associated text (e.g., figure legend and other reference text). We are developing automated systems to extract the relevant explanatory information along with figures extracted from full text articles. Such systems could be very useful in improving figure retrieval and in reducing the workload of biomedical scientists, who otherwise have to retrieve and read the entire full-text journal article to determine which figures are relevant to their research. As a crucial step, we studied the importance of associated text in biomedical figure comprehension. Methods Twenty subjects evaluated three figure-text combinations: figure+legend, figure+legend+title+abstract, and figure+full-text. Using a Likert scale, each subject scored each figure+text according to the extent to which the subject thought he/she understood the meaning of the figure and the confidence in providing the assigned score. Additionally, each subject entered a free text summary for each figure-text. We identified missing information using indicator words present within the text summaries. Both the Likert scores and the missing information were statistically analyzed for differences among the figure-text types. We also evaluated the quality of text summaries with the text-summarization evaluation method the ROUGE score. Results Our results showed statistically significant differences in figure comprehension when varying levels of text were provided. When the full-text article is not available, presenting just the figure+legend left biomedical researchers lacking 39–68% of the information about a figure as compared to having complete figure comprehension; adding the title and abstract improved the situation, but still left biomedical researchers missing 30% of the information. When the full-text article is available, figure comprehension increased to 86–97%; this indicates that researchers felt that only 3–14% of the necessary information for full figure comprehension was missing when full text was available to them. Clearly there is information in the abstract and in the full text that biomedical scientists deem important for understanding the figures that appear in full-text biomedical articles. Conclusion We conclude that the texts that appear in full-text biomedical articles are useful for understanding the meaning of a figure, and an effective figure-mining system needs to unlock the information beyond figure legend. Our work provides important guidance to the figure mining systems that extract information only from figure and figure legend. PMID:19126221

Ethical experiential learning in medical, nursing and allied health education: A narrative review.

PubMed

Grace, Sandra; Innes, Ev; Patton, Narelle; Stockhausen, Lynette

2017-04-01

Students enrolled in medical, nursing and health science programs often participate in experiential learning in their practical classes. Experiential learning includes peer physical examination and peer-assisted learning where students practise clinical skills on each other. To identify effective strategies that enable ethical experiential learning for health students during practical classes. A narrative review of the literature. Pubmed, Cinahl and Scopus databases were searched because they include most of the health education journals where relevant articles would be published. A data extraction framework was developed to extract information from the included papers. Data were entered into a fillable form in Google Docs. Findings from identified studies were extracted to a series of tables (e.g. strategies for fostering ethical conduct; facilitators and barriers to peer-assisted learning). Themes were identified from these findings through a process of line by line coding and organisation of codes into descriptive themes using a constant comparative method. Finally understandings and hypotheses of relevance to our research question were generated from the descriptive themes. A total of 35 articles were retrieved that met the inclusion criteria. A total of 13 strategies for ethical experiential learning were identified and one evaluation was reported. The most frequently reported strategies were gaining written informed consent from students, providing information about the benefits of experiential learning and what to expect in practical classes, and facilitating discussions in class about potential issues. Contexts that facilitated participation in experiential learning included allowing students to choose their own groups, making participation voluntary, and providing adequate supervision, feedback and encouragement. A total of 13 strategies for ethical experiential learning were identified in the literature. A formal process for written consent was evaluated as effective; the effectiveness of other strategies remains to be determined. A comprehensive framework that integrates all recommendations from the literature is needed to guide future research and practise of ethical experiential learning in health courses. Copyright © 2017 Elsevier Ltd. All rights reserved.

Structuring and extracting knowledge for the support of hypothesis generation in molecular biology

PubMed Central

Roos, Marco; Marshall, M Scott; Gibson, Andrew P; Schuemie, Martijn; Meij, Edgar; Katrenko, Sophia; van Hage, Willem Robert; Krommydas, Konstantinos; Adriaans, Pieter W

2009-01-01

Background Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes. Results We describe progress towards automated support for the generation of biomolecular hypotheses. Semantic Web technologies are used to structure and store knowledge, while a workflow extracts knowledge from text. We designed minimal proto-ontologies in OWL for capturing different aspects of a text mining experiment: the biological hypothesis, text and documents, text mining, and workflow provenance. The models fit a methodology that allows focus on the requirements of a single experiment while supporting reuse and posterior analysis of extracted knowledge from multiple experiments. Our workflow is composed of services from the 'Adaptive Information Disclosure Application' (AIDA) toolkit as well as a few others. The output is a semantic model with putative biological relations, with each relation linked to the corresponding evidence. Conclusion We demonstrated a 'do-it-yourself' approach for structuring and extracting knowledge in the context of experimental research on biomolecular mechanisms. The methodology can be used to bootstrap the construction of semantically rich biological models using the results of knowledge extraction processes. Models specific to particular experiments can be constructed that, in turn, link with other semantic models, creating a web of knowledge that spans experiments. Mapping mechanisms can link to other knowledge resources such as OBO ontologies or SKOS vocabularies. AIDA Web Services can be used to design personalized knowledge extraction procedures. In our example experiment, we found three proteins (NF-Kappa B, p21, and Bax) potentially playing a role in the interplay between nutrients and epigenetic gene regulation. PMID:19796406

Development of an information retrieval tool for biomedical patents.

PubMed

Alves, Tiago; Rodrigues, Rúben; Costa, Hugo; Rocha, Miguel

2018-06-01

The volume of biomedical literature has been increasing in the last years. Patent documents have also followed this trend, being important sources of biomedical knowledge, technical details and curated data, which are put together along the granting process. The field of Biomedical text mining (BioTM) has been creating solutions for the problems posed by the unstructured nature of natural language, which makes the search of information a challenging task. Several BioTM techniques can be applied to patents. From those, Information Retrieval (IR) includes processes where relevant data are obtained from collections of documents. In this work, the main goal was to build a patent pipeline addressing IR tasks over patent repositories to make these documents amenable to BioTM tasks. The pipeline was developed within @Note2, an open-source computational framework for BioTM, adding a number of modules to the core libraries, including patent metadata and full text retrieval, PDF to text conversion and optical character recognition. Also, user interfaces were developed for the main operations materialized in a new @Note2 plug-in. The integration of these tools in @Note2 opens opportunities to run BioTM tools over patent texts, including tasks from Information Extraction, such as Named Entity Recognition or Relation Extraction. We demonstrated the pipeline's main functions with a case study, using an available benchmark dataset from BioCreative challenges. Also, we show the use of the plug-in with a user query related to the production of vanillin. This work makes available all the relevant content from patents to the scientific community, decreasing drastically the time required for this task, and provides graphical interfaces to ease the use of these tools. Copyright © 2018 Elsevier B.V. All rights reserved.

Detecting diffusion-diffraction patterns in size distribution phantoms using double-pulsed field gradient NMR: Theory and experiments.

PubMed

Shemesh, Noam; Ozarslan, Evren; Basser, Peter J; Cohen, Yoram

2010-01-21

NMR observable nuclei undergoing restricted diffusion within confining pores are important reporters for microstructural features of porous media including, inter-alia, biological tissues, emulsions and rocks. Diffusion NMR, and especially the single-pulsed field gradient (s-PFG) methodology, is one of the most important noninvasive tools for studying such opaque samples, enabling extraction of important microstructural information from diffusion-diffraction phenomena. However, when the pores are not monodisperse and are characterized by a size distribution, the diffusion-diffraction patterns disappear from the signal decay, and the relevant microstructural information is mostly lost. A recent theoretical study predicted that the diffusion-diffraction patterns in double-PFG (d-PFG) experiments have unique characteristics, such as zero-crossings, that make them more robust with respect to size distributions. In this study, we theoretically compared the signal decay arising from diffusion in isolated cylindrical pores characterized by lognormal size distributions in both s-PFG and d-PFG methodologies using a recently presented general framework for treating diffusion in NMR experiments. We showed the gradual loss of diffusion-diffraction patterns in broadening size distributions in s-PFG and the robustness of the zero-crossings in d-PFG even for very large standard deviations of the size distribution. We then performed s-PFG and d-PFG experiments on well-controlled size distribution phantoms in which the ground-truth is well-known a priori. We showed that the microstructural information, as manifested in the diffusion-diffraction patterns, is lost in the s-PFG experiments, whereas in d-PFG experiments the zero-crossings of the signal persist from which relevant microstructural information can be extracted. This study provides a proof of concept that d-PFG may be useful in obtaining important microstructural features in samples characterized by size distributions.

Uncovering the spatial structure of mobility networks

NASA Astrophysics Data System (ADS)

Louail, Thomas; Lenormand, Maxime; Picornell, Miguel; García Cantú, Oliva; Herranz, Ricardo; Frias-Martinez, Enrique; Ramasco, José J.; Barthelemy, Marc

2015-01-01

The extraction of a clear and simple footprint of the structure of large, weighted and directed networks is a general problem that has relevance for many applications. An important example is seen in origin-destination matrices, which contain the complete information on commuting flows, but are difficult to analyze and compare. We propose here a versatile method, which extracts a coarse-grained signature of mobility networks, under the form of a 2 × 2 matrix that separates the flows into four categories. We apply this method to origin-destination matrices extracted from mobile phone data recorded in 31 Spanish cities. We show that these cities essentially differ by their proportion of two types of flows: integrated (between residential and employment hotspots) and random flows, whose importance increases with city size. Finally, the method allows the determination of categories of networks, and in the mobility case, the classification of cities according to their commuting structure.

Laboratory Spectroscopy of Ices of Astrophysical Interest

NASA Technical Reports Server (NTRS)

Hudson, Reggie; Moore, M. H.

2011-01-01

Ongoing and future NASA and ESA astronomy missions need detailed information on the spectra of a variety of molecular ices to help establish the identity and abundances of molecules observed in astronomical data. Examples of condensed-phase molecules already detected on cold surfaces include H2O, CO, CO2, N2, NH3, CH4, SO2, O2, and O3. In addition, strong evidence exists for the solid-phase nitriles HCN, HC3N, and C2N2 in Titan's atmosphere. The wavelength region over which these identifications have been made is roughly 0.5 to 100 micron. Searches for additional features of complex carbon-containing species are in progress. Existing and future observations often impose special requirements on the information that comes from the laboratory. For example, the measurement of spectra, determination of integrated band strengths, and extraction of complex refractive indices of ices (and icy mixtures) in both amorphous and crystalline phases at relevant temperatures are all important tasks. In addition, the determination of the index of refraction of amorphous and crystalline ices in the visible region is essential for the extraction of infrared optical constants. Similarly, the measurement of spectra of ions and molecules embedded in relevant ices is important. This laboratory review will examine some of the existing experimental work and capabilities in these areas along with what more may be needed to meet current and future NASA and ESA planetary needs.

Robustness of cortical and subcortical processing in the presence of natural masking sounds.

PubMed

Beetz, M Jerome; García-Rosales, Francisco; Kössl, Manfred; Hechavarría, Julio C

2018-05-01

Processing of ethologically relevant stimuli could be interfered by non-relevant stimuli. Animals have behavioral adaptations to reduce signal interference. It is largely unexplored whether the behavioral adaptations facilitate neuronal processing of relevant stimuli. Here, we characterize behavioral adaptations in the presence of biotic noise in the echolocating bat Carollia perspicillata and we show that the behavioral adaptations could facilitate neuronal processing of biosonar information. According to the echolocation behavior, bats need to extract their own signals in the presence of vocalizations from conspecifics. With playback experiments, we demonstrate that C. perspicillata increases the sensory acquisition rate by emitting groups of echolocation calls when flying in noisy environments. Our neurophysiological results from the auditory midbrain and cortex show that the high sensory acquisition rate does not vastly increase neuronal suppression and that the response to an echolocation sequence is partially preserved in the presence of biosonar signals from conspecifics.

Advances in Spectral-Spatial Classification of Hyperspectral Images

NASA Technical Reports Server (NTRS)

Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2012-01-01

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation and contrast of the spatial structures present in the image. Then the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines using the available spectral information and the extracted spatial information. Spatial post-processing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple classifier system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

Political and institutional influences on the use of evidence in public health policy. A systematic review.

PubMed

Liverani, Marco; Hawkins, Benjamin; Parkhurst, Justin O

2013-01-01

There is increasing recognition that the development of evidence-informed health policy is not only a technical problem of knowledge exchange or translation, but also a political challenge. Yet, while political scientists have long considered the nature of political systems, the role of institutional structures, and the political contestation of policy issues as central to understanding policy decisions, these issues remain largely unexplored by scholars of evidence-informed policy making. We conducted a systematic review of empirical studies that examined the influence of key features of political systems and institutional mechanisms on evidence use, and contextual factors that may contribute to the politicisation of health evidence. Eligible studies were identified through searches of seven health and social sciences databases, websites of relevant organisations, the British Library database, and manual searches of academic journals. Relevant findings were extracted using a uniform data extraction tool and synthesised by narrative review. 56 studies were selected for inclusion. Relevant political and institutional aspects affecting the use of health evidence included the level of state centralisation and democratisation, the influence of external donors and organisations, the organisation and function of bureaucracies, and the framing of evidence in relation to social norms and values. However, our understanding of such influences remains piecemeal given the limited number of empirical analyses on this subject, the paucity of comparative works, and the limited consideration of political and institutional theory in these studies. This review highlights the need for a more explicit engagement with the political and institutional factors affecting the use of health evidence in decision-making. A more nuanced understanding of evidence use in health policy making requires both additional empirical studies of evidence use, and an engagement with theories and approaches beyond the current remit of public health or knowledge utilisation studies.

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

NASA Technical Reports Server (NTRS)

Gettig, Gary A.

1988-01-01

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

A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation

PubMed Central

2017-01-01

Finding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging “Big Data” era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more complex queries. This is especially the case with routing and navigation services where the ability to retrieve points of interest and landmarks make the routing service personalized, precise, and relevant. In this paper, we propose a new geospatial information approach that enables the retrieval of implicit information, i.e., geospatial entities that do not exist explicitly in the available source. We present an information broker that uses a rule-based spatial reasoning algorithm to detect topological relations. The information broker is embedded into a framework where annotations and mappings between OpenStreetMap data attributes and external resources, such as taxonomies, support the enrichment of queries to improve the ability of the system to retrieve information. Our method is tested with two case studies that leads to enriching the completeness of OpenStreetMap data with footway crossing points-of-interests as well as building entrances for routing and navigation purposes. It is concluded that the proposed approach can uncover implicit entities and contribute to extract required information from the existing datasets. PMID:29088125

Evaluation of petroleum heterogeneities in the Eldfish Chalk reservoir using thermal extraction and pyrolysis GC and thermal extraction GC-MS

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

Hall, P.B.; Bjory, M.; Stoddart, D.

1993-09-01

Petroleum geochemical research is being applied more and more to production-oriented problems such as improving recovery of oil from existing fields and identifying satellite fields. Recent studies by ourselves and others have suggested that compositional heterogeneities within the petroleum column of reservoirs can provide information relevant to these objectives. Some of the best data for distinguishing petroleum [open quotes]populations[close quotes] in reservoirs comes from conventional geochemical techniques, some of which are very time consuming (e.g., solvent extraction, EOM fractionation, and GC and GC-MS of saturated and aromatic hydrocarbon fractions). Furthermore, a large number of samples need to be analyzed onmore » order to detect the often slight heterogeneities present in a reservoir. The techniques employed in this study (particularly thermal extraction and pyrolysis GC and thermal extraction GC-MS) can be used to generate large amounts of relevant data quickly. In this study, data were acquired on core samples ([approximately]750) from the Paleocene/Cretaceous Eldfish Chalk reservoir (2/7 block Norwegian North Sea). The acquired data reveal distinct maturity gradients and also small, but still significant, variations in source of the oil within the different Eldfisk oil pools (Alpha, Bravo, and East). This indicates incomplete mixing of the hydrocarbons. These variations have been used to delineate lateral and vertical barriers to petroleum mixing. These observations, when integrated with geological data, can lead to an improved understanding of the possible field filling directions and the intrareservoir compartmentalization. The conclusions from this may also aid in exploration for satellite fields as well as influencing the production plan for the Eldfisk field.« less

Crowdsourcing the Measurement of Interstate Conflict

PubMed Central

2016-01-01

Much of the data used to measure conflict is extracted from news reports. This is typically accomplished using either expert coders to quantify the relevant information or machine coders to automatically extract data from documents. Although expert coding is costly, it produces quality data. Machine coding is fast and inexpensive, but the data are noisy. To diminish the severity of this tradeoff, we introduce a method for analyzing news documents that uses crowdsourcing, supplemented with computational approaches. The new method is tested on documents about Militarized Interstate Disputes, and its accuracy ranges between about 68 and 76 percent. This is shown to be a considerable improvement over automated coding, and to cost less and be much faster than expert coding. PMID:27310427

Kudi: A free open-source python library for the analysis of properties along reaction paths.

PubMed

Vogt-Geisse, Stefan

2016-05-01

With increasing computational capabilities, an ever growing amount of data is generated in computational chemistry that contains a vast amount of chemically relevant information. It is therefore imperative to create new computational tools in order to process and extract this data in a sensible way. Kudi is an open source library that aids in the extraction of chemical properties from reaction paths. The straightforward structure of Kudi makes it easy to use for users and allows for effortless implementation of new capabilities, and extension to any quantum chemistry package. A use case for Kudi is shown for the tautomerization reaction of formic acid. Kudi is available free of charge at www.github.com/stvogt/kudi.

Making a protein extract from plant pathogenic fungi for gel- and LC-based proteomics.

PubMed

Fernández, Raquel González; Redondo, Inmaculada; Jorrin-Novo, Jesus V

2014-01-01

Proteomic technologies have become a successful tool to provide relevant information on fungal biology. In the case of plant pathogenic fungi, this approach would allow a deeper knowledge of the interaction and the biological cycle of the pathogen, as well as the identification of pathogenicity and virulence factors. These two elements open up new possibilities for crop disease diagnosis and environment-friendly crop protection. Phytopathogenic fungi, due to its particular cellular characteristics, can be considered as a recalcitrant biological material, which makes it difficult to obtain quality protein samples for proteomic analysis. This chapter focuses on protein extraction for gel- and LC-based proteomics with specific protocols of our current research with Botrytis cinerea.

Text-in-context: a method for extracting findings in mixed-methods mixed research synthesis studies.

PubMed

Sandelowski, Margarete; Leeman, Jennifer; Knafl, Kathleen; Crandell, Jamie L

2013-06-01

Our purpose in this paper is to propose a new method for extracting findings from research reports included in mixed-methods mixed research synthesis studies. International initiatives in the domains of systematic review and evidence synthesis have been focused on broadening the conceptualization of evidence, increased methodological inclusiveness and the production of evidence syntheses that will be accessible to and usable by a wider range of consumers. Initiatives in the general mixed-methods research field have been focused on developing truly integrative approaches to data analysis and interpretation. The data extraction challenges described here were encountered, and the method proposed for addressing these challenges was developed, in the first year of the ongoing (2011-2016) study: Mixed-Methods Synthesis of Research on Childhood Chronic Conditions and Family. To preserve the text-in-context of findings in research reports, we describe a method whereby findings are transformed into portable statements that anchor results to relevant information about sample, source of information, time, comparative reference point, magnitude and significance and study-specific conceptions of phenomena. The data extraction method featured here was developed specifically to accommodate mixed-methods mixed research synthesis studies conducted in nursing and other health sciences, but reviewers might find it useful in other kinds of research synthesis studies. This data extraction method itself constitutes a type of integration to preserve the methodological context of findings when statements are read individually and in comparison to each other. © 2012 Blackwell Publishing Ltd.

Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information.

PubMed

Zhang, Chi; Tong, Li; Zeng, Ying; Jiang, Jingfang; Bu, Haibing; Yan, Bin; Li, Jianxin

2015-01-01

Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition.

Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information

PubMed Central

Zhang, Chi; Tong, Li; Zeng, Ying; Jiang, Jingfang; Bu, Haibing; Li, Jianxin

2015-01-01

Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition. PMID:26380294

Definition of information technology architectures for continuous data management and medical device integration in diabetes.

PubMed

Hernando, M Elena; Pascual, Mario; Salvador, Carlos H; García-Sáez, Gema; Rodríguez-Herrero, Agustín; Martínez-Sarriegui, Iñaki; Gómez, Enrique J

2008-09-01

The growing availability of continuous data from medical devices in diabetes management makes it crucial to define novel information technology architectures for efficient data storage, data transmission, and data visualization. The new paradigm of care demands the sharing of information in interoperable systems as the only way to support patient care in a continuum of care scenario. The technological platforms should support all the services required by the actors involved in the care process, located in different scenarios and managing diverse information for different purposes. This article presents basic criteria for defining flexible and adaptive architectures that are capable of interoperating with external systems, and integrating medical devices and decision support tools to extract all the relevant knowledge to support diabetes care.

Analytic processing of distance.

PubMed

Dopkins, Stephen; Galyer, Darin

2018-01-01

How does a human observer extract from the distance between two frontal points the component corresponding to an axis of a rectangular reference frame? To find out we had participants classify pairs of small circles, varying on the horizontal and vertical axes of a computer screen, in terms of the horizontal distance between them. A response signal controlled response time. The error rate depended on the irrelevant vertical as well as the relevant horizontal distance between the test circles with the relevant distance effect being larger than the irrelevant distance effect. The results implied that the horizontal distance between the test circles was imperfectly extracted from the overall distance between them. The results supported an account, derived from the Exemplar Based Random Walk model (Nosofsky & Palmieri, 1997), under which distance classification is based on the overall distance between the test circles, with relevant distance being extracted from overall distance to the extent that the relevant and irrelevant axes are differentially weighted so as to reduce the contribution of irrelevant distance to overall distance. The results did not support an account, derived from the General Recognition Theory (Ashby & Maddox, 1994), under which distance classification is based on the relevant distance between the test circles, with the irrelevant distance effect arising because a test circle's perceived location on the relevant axis depends on its location on the irrelevant axis, and with relevant distance being extracted from overall distance to the extent that this dependency is absent. Copyright © 2017 Elsevier B.V. All rights reserved.

A unified coding strategy for processing faces and voices

PubMed Central

Yovel, Galit; Belin, Pascal

2013-01-01

Both faces and voices are rich in socially-relevant information, which humans are remarkably adept at extracting, including a person's identity, age, gender, affective state, personality, etc. Here, we review accumulating evidence from behavioral, neuropsychological, electrophysiological, and neuroimaging studies which suggest that the cognitive and neural processing mechanisms engaged by perceiving faces or voices are highly similar, despite the very different nature of their sensory input. The similarity between the two mechanisms likely facilitates the multi-modal integration of facial and vocal information during everyday social interactions. These findings emphasize a parsimonious principle of cerebral organization, where similar computational problems in different modalities are solved using similar solutions. PMID:23664703

Relevance feedback-based building recognition

NASA Astrophysics Data System (ADS)

Li, Jing; Allinson, Nigel M.

2010-07-01

Building recognition is a nontrivial task in computer vision research which can be utilized in robot localization, mobile navigation, etc. However, existing building recognition systems usually encounter the following two problems: 1) extracted low level features cannot reveal the true semantic concepts; and 2) they usually involve high dimensional data which require heavy computational costs and memory. Relevance feedback (RF), widely applied in multimedia information retrieval, is able to bridge the gap between the low level visual features and high level concepts; while dimensionality reduction methods can mitigate the high-dimensional problem. In this paper, we propose a building recognition scheme which integrates the RF and subspace learning algorithms. Experimental results undertaken on our own building database show that the newly proposed scheme appreciably enhances the recognition accuracy.

A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text

PubMed Central

Miwa, Makoto; Ohta, Tomoko; Rak, Rafal; Rowley, Andrew; Kell, Douglas B.; Pyysalo, Sampo; Ananiadou, Sophia

2013-01-01

Motivation: To create, verify and maintain pathway models, curators must discover and assess knowledge distributed over the vast body of biological literature. Methods supporting these tasks must understand both the pathway model representations and the natural language in the literature. These methods should identify and order documents by relevance to any given pathway reaction. No existing system has addressed all aspects of this challenge. Method: We present novel methods for associating pathway model reactions with relevant publications. Our approach extracts the reactions directly from the models and then turns them into queries for three text mining-based MEDLINE literature search systems. These queries are executed, and the resulting documents are combined and ranked according to their relevance to the reactions of interest. We manually annotate document-reaction pairs with the relevance of the document to the reaction and use this annotation to study several ranking methods, using various heuristic and machine-learning approaches. Results: Our evaluation shows that the annotated document-reaction pairs can be used to create a rule-based document ranking system, and that machine learning can be used to rank documents by their relevance to pathway reactions. We find that a Support Vector Machine-based system outperforms several baselines and matches the performance of the rule-based system. The success of the query extraction and ranking methods are used to update our existing pathway search system, PathText. Availability: An online demonstration of PathText 2 and the annotated corpus are available for research purposes at http://www.nactem.ac.uk/pathtext2/. Contact: makoto.miwa@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23813008

The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process.

PubMed

Bousquet, Cedric; Dahamna, Badisse; Guillemin-Lanne, Sylvie; Darmoni, Stefan J; Faviez, Carole; Huot, Charles; Katsahian, Sandrine; Leroux, Vincent; Pereira, Suzanne; Richard, Christophe; Schück, Stéphane; Souvignet, Julien; Lillo-Le Louët, Agnès; Texier, Nathalie

2017-09-21

Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Classical Pharmacovigilance process is limited by underreporting which justifies the current interest in new knowledge sources such as social media. The Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project aims to extract ADRs reported by patients in these media. We identified 5 major challenges to overcome to operationalize the analysis of patient posts: (1) variable quality of information on social media, (2) guarantee of data privacy, (3) response to pharmacovigilance expert expectations, (4) identification of relevant information within Web pages, and (5) robust and evolutive architecture. This article aims to describe the current state of advancement of the ADR-PRISM project by focusing on the solutions we have chosen to address these 5 major challenges. In this article, we propose methods and describe the advancement of this project on several aspects: (1) a quality driven approach for selecting relevant social media for the extraction of knowledge on potential ADRs, (2) an assessment of ethical issues and French regulation for the analysis of data on social media, (3) an analysis of pharmacovigilance expert requirements when reviewing patient posts on the Internet, (4) an extraction method based on natural language processing, pattern based matching, and selection of relevant medical concepts in reference terminologies, and (5) specifications of a component-based architecture for the monitoring system. Considering the 5 major challenges, we (1) selected a set of 21 validated criteria for selecting social media to support the extraction of potential ADRs, (2) proposed solutions to guarantee data privacy of patients posting on Internet, (3) took into account pharmacovigilance expert requirements with use case diagrams and scenarios, (4) built domain-specific knowledge resources embeding a lexicon, morphological rules, context rules, semantic rules, syntactic rules, and post-analysis processing, and (5) proposed a component-based architecture that allows storage of big data and accessibility to third-party applications through Web services. We demonstrated the feasibility of implementing a component-based architecture that allows collection of patient posts on the Internet, near real-time processing of those posts including annotation, and storage in big data structures. In the next steps, we will evaluate the posts identified by the system in social media to clarify the interest and relevance of such approach to improve conventional pharmacovigilance processes based on spontaneous reporting. ©Cedric Bousquet, Badisse Dahamna, Sylvie Guillemin-Lanne, Stefan J Darmoni, Carole Faviez, Charles Huot, Sandrine Katsahian, Vincent Leroux, Suzanne Pereira, Christophe Richard, Stéphane Schück, Julien Souvignet, Agnès Lillo-Le Louët, Nathalie Texier. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 21.09.2017.

Visual Representations of DNA Replication: Middle Grades Students' Perceptions and Interpretations

NASA Astrophysics Data System (ADS)

Patrick, Michelle D.; Carter, Glenda; Wiebe, Eric N.

2005-09-01

Visual representations play a critical role in the communication of science concepts for scientists and students alike. However, recent research suggests that novice students experience difficulty extracting relevant information from representations. This study examined students' interpretations of visual representations of DNA replication. Each of the four steps of DNA replication included in the instructional presentation was represented as a text slide, a simple 2D graphic, and a rich 3D graphic. Participants were middle grade girls ( n = 21) attending a summer math and science program. Students' eye movements were measured as they viewed the representations. Participants were interviewed following instruction to assess their perceived salient features. Eye tracking fixation counts indicated that the same features (look zones) in the corresponding 2D and 3D graphics had different salience. The interviews revealed that students used different characteristics such as color, shape, and complexity to make sense of the graphics. The results of this study have implications for the design of instructional representations. Since many students have difficulty distinguishing between relevant and irrelevant information, cueing and directing student attention through the instructional representation could allow cognitive resources to be directed to the most relevant material.

Automated classification of optical coherence tomography images of human atrial tissue

NASA Astrophysics Data System (ADS)

Gan, Yu; Tsay, David; Amir, Syed B.; Marboe, Charles C.; Hendon, Christine P.

2016-10-01

Tissue composition of the atria plays a critical role in the pathology of cardiovascular disease, tissue remodeling, and arrhythmogenic substrates. Optical coherence tomography (OCT) has the ability to capture the tissue composition information of the human atria. In this study, we developed a region-based automated method to classify tissue compositions within human atria samples within OCT images. We segmented regional information without prior information about the tissue architecture and subsequently extracted features within each segmented region. A relevance vector machine model was used to perform automated classification. Segmentation of human atrial ex vivo datasets was correlated with trichrome histology and our classification algorithm had an average accuracy of 80.41% for identifying adipose, myocardium, fibrotic myocardium, and collagen tissue compositions.

Managing interoperability and complexity in health systems.

PubMed

Bouamrane, M-M; Tao, C; Sarkar, I N

2015-01-01

In recent years, we have witnessed substantial progress in the use of clinical informatics systems to support clinicians during episodes of care, manage specialised domain knowledge, perform complex clinical data analysis and improve the management of health organisations' resources. However, the vision of fully integrated health information eco-systems, which provide relevant information and useful knowledge at the point-of-care, remains elusive. This journal Focus Theme reviews some of the enduring challenges of interoperability and complexity in clinical informatics systems. Furthermore, a range of approaches are proposed in order to address, harness and resolve some of the many remaining issues towards a greater integration of health information systems and extraction of useful or new knowledge from heterogeneous electronic data repositories.

DEFINING THE RELEVANT OUTCOME MEASURES IN MEDICAL DEVICE ASSESSMENTS: AN ANALYSIS OF THE DEFINITION PROCESS IN HEALTH TECHNOLOGY ASSESSMENT.

PubMed

Jacobs, Esther; Antoine, Sunya-Lee; Prediger, Barbara; Neugebauer, Edmund; Eikermann, Michaela

2017-01-01

Defining relevant outcome measures for clinical trials on medical devices (MD) is complex, as there is a large variety of potentially relevant outcomes. The chosen outcomes vary widely across clinical trials making the assessment in evidence syntheses very challenging. The objective is to provide an overview on the current common procedures of health technology assessment (HTA) institutions in defining outcome measures in MD trials. In 2012-14, the Web pages of 126 institutions involved in HTA were searched for methodological manuals written in English or German that describe methods for the predefinition process of outcome measures. Additionally, the institutions were contacted by email. Relevant information was extracted. All process steps were performed independently by two reviewers. Twenty-four manuals and ten responses from the email request were included in the analysis. Overall, 88.5 percent of the institutions describe the type of outcomes that should be considered in detail and 84.6 percent agree that the main focus should be on patient relevant outcomes. Specifically related to MD, information could be obtained in 26 percent of the included manuals and email responses. Eleven percent of the institutions report a particular consideration of MD related outcomes. This detailed analysis on common procedures of HTA institutions in the context of defining relevant outcome measures for the assessment of MD shows that standardized procedures for MD from the perspective of HTA institutions are not widespread. This leads to the question if a homogenous approach should be implemented in the field of HTA on MD.

Significance of wood extractives for wood bonding.

PubMed

Roffael, Edmone

2016-02-01

Wood contains primary extractives, which are present in all woods, and secondary extractives, which are confined in certain wood species. Extractives in wood play a major role in wood-bonding processes, as they can contribute to or determine the bonding relevant properties of wood such as acidity and wettability. Therefore, extractives play an immanent role in bonding of wood chips and wood fibres with common synthetic adhesives such as urea-formaldehyde-resins (UF-resins) and phenol-formaldehyde-resins (PF-resins). Extractives of high acidity accelerate the curing of acid curing UF-resins and decelerate bonding with alkaline hardening PF-resins. Water-soluble extractives like free sugars are detrimental for bonding of wood with cement. Polyphenolic extractives (tannins) can be used as a binder in the wood-based industry. Additionally, extractives in wood can react with formaldehyde and reduce the formaldehyde emission of wood-based panels. Moreover, some wood extractives are volatile organic compounds (VOC) and insofar also relevant to the emission of VOC from wood and wood-based panels.

Identifying and applying psychological theory to setting and achieving rehabilitation goals.

PubMed

Scobbie, Lesley; Wyke, Sally; Dixon, Diane

2009-04-01

Goal setting is considered to be a fundamental part of rehabilitation; however, theories of behaviour change relevant to goal-setting practice have not been comprehensively reviewed. (i) To identify and discuss specific theories of behaviour change relevant to goal-setting practice in the rehabilitation setting. (ii) To identify 'candidate' theories that that offer most potential to inform clinical practice. The rehabilitation and self-management literature was systematically searched to identify review papers or empirical studies that proposed a specific theory of behaviour change relevant to setting and/or achieving goals in a clinical context. Data from included papers were extracted under the headings of: key constructs, clinical application and empirical support. Twenty-four papers were included in the review which proposed a total of five theories: (i) social cognitive theory, (ii) goal setting theory, (iii) health action process approach, (iv) proactive coping theory, and (v) the self-regulatory model of illness behaviour. The first three of these theories demonstrated most potential to inform clinical practice, on the basis of their capacity to inform interventions that resulted in improved patient outcomes. Social cognitive theory, goal setting theory and the health action process approach are theories of behaviour change that can inform clinicians in the process of setting and achieving goals in the rehabilitation setting. Overlapping constructs within these theories have been identified, and can be applied in clinical practice through the development and evaluation of a goal-setting practice framework.

Ontologies in medicinal chemistry: current status and future challenges.

PubMed

Gómez-Pérez, Asunción; Martínez-Romero, Marcos; Rodríguez-González, Alejandro; Vázquez, Guillermo; Vázquez-Naya, José M

2013-01-01

Recent years have seen a dramatic increase in the amount and availability of data in the diverse areas of medicinal chemistry, making it possible to achieve significant advances in fields such as the design, synthesis and biological evaluation of compounds. However, with this data explosion, the storage, management and analysis of available data to extract relevant information has become even a more complex task that offers challenging research issues to Artificial Intelligence (AI) scientists. Ontologies have emerged in AI as a key tool to formally represent and semantically organize aspects of the real world. Beyond glossaries or thesauri, ontologies facilitate communication between experts and allow the application of computational techniques to extract useful information from available data. In medicinal chemistry, multiple ontologies have been developed during the last years which contain knowledge about chemical compounds and processes of synthesis of pharmaceutical products. This article reviews the principal standards and ontologies in medicinal chemistry, analyzes their main applications and suggests future directions.

Classification of hepatocellular carcinoma stages from free-text clinical and radiology reports

PubMed Central

Yim, Wen-wai; Kwan, Sharon W; Johnson, Guy; Yetisgen, Meliha

2017-01-01

Cancer stage information is important for clinical research. However, they are not always explicitly noted in electronic medical records. In this paper, we present our work on automatic classification of hepatocellular carcinoma (HCC) stages from free-text clinical and radiology notes. To accomplish this, we defined 11 stage parameters used in the three HCC staging systems, American Joint Committee on Cancer (AJCC), Barcelona Clinic Liver Cancer (BCLC), and Cancer of the Liver Italian Program (CLIP). After aggregating stage parameters to the patient-level, the final stage classifications were achieved using an expert-created decision logic. Each stage parameter relevant for staging was extracted using several classification methods, e.g. sentence classification and automatic information structuring, to identify and normalize text as cancer stage parameter values. Stage parameter extraction for the test set performed at 0.81 F1. Cancer stage prediction for AJCC, BCLC, and CLIP stage classifications were 0.55, 0.50, and 0.43 F1.

Text Mining for Protein Docking

PubMed Central

Badal, Varsha D.; Kundrotas, Petras J.; Vakser, Ilya A.

2015-01-01

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 benchmark set, significantly increasing the docking success rate. PMID:26650466

Usability-driven pruning of large ontologies: the case of SNOMED CT

PubMed Central

Boeker, Martin; Illarramendi, Arantza; Schulz, Stefan

2012-01-01

Objectives To study ontology modularization techniques when applied to SNOMED CT in a scenario in which no previous corpus of information exists and to examine if frequency-based filtering using MEDLINE can reduce subset size without discarding relevant concepts. Materials and Methods Subsets were first extracted using four graph-traversal heuristics and one logic-based technique, and were subsequently filtered with frequency information from MEDLINE. Twenty manually coded discharge summaries from cardiology patients were used as signatures and test sets. The coverage, size, and precision of extracted subsets were measured. Results Graph-traversal heuristics provided high coverage (71–96% of terms in the test sets of discharge summaries) at the expense of subset size (17–51% of the size of SNOMED CT). Pre-computed subsets and logic-based techniques extracted small subsets (1%), but coverage was limited (24–55%). Filtering reduced the size of large subsets to 10% while still providing 80% coverage. Discussion Extracting subsets to annotate discharge summaries is challenging when no previous corpus exists. Ontology modularization provides valuable techniques, but the resulting modules grow as signatures spread across subhierarchies, yielding a very low precision. Conclusion Graph-traversal strategies and frequency data from an authoritative source can prune large biomedical ontologies and produce useful subsets that still exhibit acceptable coverage. However, a clinical corpus closer to the specific use case is preferred when available. PMID:22268217

Methods for the guideline-based development of quality indicators--a systematic review

PubMed Central

2012-01-01

Background Quality indicators (QIs) are used in many healthcare settings to measure, compare, and improve quality of care. For the efficient development of high-quality QIs, rigorous, approved, and evidence-based development methods are needed. Clinical practice guidelines are a suitable source to derive QIs from, but no gold standard for guideline-based QI development exists. This review aims to identify, describe, and compare methodological approaches to guideline-based QI development. Methods We systematically searched medical literature databases (Medline, EMBASE, and CINAHL) and grey literature. Two researchers selected publications reporting methodological approaches to guideline-based QI development. In order to describe and compare methodological approaches used in these publications, we extracted detailed information on common steps of guideline-based QI development (topic selection, guideline selection, extraction of recommendations, QI selection, practice test, and implementation) to predesigned extraction tables. Results From 8,697 hits in the database search and several grey literature documents, we selected 48 relevant references. The studies were of heterogeneous type and quality. We found no randomized controlled trial or other studies comparing the ability of different methodological approaches to guideline-based development to generate high-quality QIs. The relevant publications featured a wide variety of methodological approaches to guideline-based QI development, especially regarding guideline selection and extraction of recommendations. Only a few studies reported patient involvement. Conclusions Further research is needed to determine which elements of the methodological approaches identified, described, and compared in this review are best suited to constitute a gold standard for guideline-based QI development. For this research, we provide a comprehensive groundwork. PMID:22436067

"What is relevant in a text document?": An interpretable machine learning approach

PubMed Central

Arras, Leila; Horn, Franziska; Montavon, Grégoire; Müller, Klaus-Robert

2017-01-01

Text documents can be described by a number of abstract concepts such as semantic category, writing style, or sentiment. Machine learning (ML) models have been trained to automatically map documents to these abstract concepts, allowing to annotate very large text collections, more than could be processed by a human in a lifetime. Besides predicting the text’s category very accurately, it is also highly desirable to understand how and why the categorization process takes place. In this paper, we demonstrate that such understanding can be achieved by tracing the classification decision back to individual words using layer-wise relevance propagation (LRP), a recently developed technique for explaining predictions of complex non-linear classifiers. We train two word-based ML models, a convolutional neural network (CNN) and a bag-of-words SVM classifier, on a topic categorization task and adapt the LRP method to decompose the predictions of these models onto words. Resulting scores indicate how much individual words contribute to the overall classification decision. This enables one to distill relevant information from text documents without an explicit semantic information extraction step. We further use the word-wise relevance scores for generating novel vector-based document representations which capture semantic information. Based on these document vectors, we introduce a measure of model explanatory power and show that, although the SVM and CNN models perform similarly in terms of classification accuracy, the latter exhibits a higher level of explainability which makes it more comprehensible for humans and potentially more useful for other applications. PMID:28800619

An automated procedure to identify biomedical articles that contain cancer-associated gene variants.

PubMed

McDonald, Ryan; Scott Winters, R; Ankuda, Claire K; Murphy, Joan A; Rogers, Amy E; Pereira, Fernando; Greenblatt, Marc S; White, Peter S

2006-09-01

The proliferation of biomedical literature makes it increasingly difficult for researchers to find and manage relevant information. However, identifying research articles containing mutation data, a requisite first step in integrating large and complex mutation data sets, is currently tedious, time-consuming and imprecise. More effective mechanisms for identifying articles containing mutation information would be beneficial both for the curation of mutation databases and for individual researchers. We developed an automated method that uses information extraction, classifier, and relevance ranking techniques to determine the likelihood of MEDLINE abstracts containing information regarding genomic variation data suitable for inclusion in mutation databases. We targeted the CDKN2A (p16) gene and the procedure for document identification currently used by CDKN2A Database curators as a measure of feasibility. A set of abstracts was manually identified from a MEDLINE search as potentially containing specific CDKN2A mutation events. A subset of these abstracts was used as a training set for a maximum entropy classifier to identify text features distinguishing "relevant" from "not relevant" abstracts. Each document was represented as a set of indicative word, word pair, and entity tagger-derived genomic variation features. When applied to a test set of 200 candidate abstracts, the classifier predicted 88 articles as being relevant; of these, 29 of 32 manuscripts in which manual curation found CDKN2A sequence variants were positively predicted. Thus, the set of potentially useful articles that a manual curator would have to review was reduced by 56%, maintaining 91% recall (sensitivity) and more than doubling precision (positive predictive value). Subsequent expansion of the training set to 494 articles yielded similar precision and recall rates, and comparison of the original and expanded trials demonstrated that the average precision improved with the larger data set. Our results show that automated systems can effectively identify article subsets relevant to a given task and may prove to be powerful tools for the broader research community. This procedure can be readily adapted to any or all genes, organisms, or sets of documents. Published 2006 Wiley-Liss, Inc.

Automated extraction and semantic analysis of mutation impacts from the biomedical literature

PubMed Central

2012-01-01

Background Mutations as sources of evolution have long been the focus of attention in the biomedical literature. Accessing the mutational information and their impacts on protein properties facilitates research in various domains, such as enzymology and pharmacology. However, manually curating the rich and fast growing repository of biomedical literature is expensive and time-consuming. As a solution, text mining approaches have increasingly been deployed in the biomedical domain. While the detection of single-point mutations is well covered by existing systems, challenges still exist in grounding impacts to their respective mutations and recognizing the affected protein properties, in particular kinetic and stability properties together with physical quantities. Results We present an ontology model for mutation impacts, together with a comprehensive text mining system for extracting and analysing mutation impact information from full-text articles. Organisms, as sources of proteins, are extracted to help disambiguation of genes and proteins. Our system then detects mutation series to correctly ground detected impacts using novel heuristics. It also extracts the affected protein properties, in particular kinetic and stability properties, as well as the magnitude of the effects and validates these relations against the domain ontology. The output of our system can be provided in various formats, in particular by populating an OWL-DL ontology, which can then be queried to provide structured information. The performance of the system is evaluated on our manually annotated corpora. In the impact detection task, our system achieves a precision of 70.4%-71.1%, a recall of 71.3%-71.5%, and grounds the detected impacts with an accuracy of 76.5%-77%. The developed system, including resources, evaluation data and end-user and developer documentation is freely available under an open source license at http://www.semanticsoftware.info/open-mutation-miner. Conclusion We present Open Mutation Miner (OMM), the first comprehensive, fully open-source approach to automatically extract impacts and related relevant information from the biomedical literature. We assessed the performance of our work on manually annotated corpora and the results show the reliability of our approach. The representation of the extracted information into a structured format facilitates knowledge management and aids in database curation and correction. Furthermore, access to the analysis results is provided through multiple interfaces, including web services for automated data integration and desktop-based solutions for end user interactions. PMID:22759648

Whisking mechanics and active sensing

PubMed Central

Bush, Nicholas E; Solla, Sara A

2017-01-01

We describe recent advances in quantifying the three-dimensional (3D) geometry and mechanics of whisking. Careful delineation of relevant 3D reference frames reveals important geometric and mechanical distinctions between the localization problem (‘where’ is an object) and the feature extraction problem (‘what’ is an object). Head-centered and resting-whisker reference frames lend themselves to quantifying temporal and kinematic cues used for object localization. The whisking-centered reference frame lends itself to quantifying the contact mechanics likely associated with feature extraction. We offer the ‘windowed sampling’ hypothesis for active sensing: that rats can estimate an object’s spatial features by integrating mechanical information across whiskers during brief (25–60 ms) windows of ‘haptic enclosure’ with the whiskers, a motion that resembles a hand grasp. PMID:27632212

Whisking mechanics and active sensing.

PubMed

Bush, Nicholas E; Solla, Sara A; Hartmann, Mitra Jz

2016-10-01

We describe recent advances in quantifying the three-dimensional (3D) geometry and mechanics of whisking. Careful delineation of relevant 3D reference frames reveals important geometric and mechanical distinctions between the localization problem ('where' is an object) and the feature extraction problem ('what' is an object). Head-centered and resting-whisker reference frames lend themselves to quantifying temporal and kinematic cues used for object localization. The whisking-centered reference frame lends itself to quantifying the contact mechanics likely associated with feature extraction. We offer the 'windowed sampling' hypothesis for active sensing: that rats can estimate an object's spatial features by integrating mechanical information across whiskers during brief (25-60ms) windows of 'haptic enclosure' with the whiskers, a motion that resembles a hand grasp. Copyright © 2016. Published by Elsevier Ltd.

Translating Knowledge: The role of Shared Learning in Bridging the Science-Application Divide

NASA Astrophysics Data System (ADS)

Moench, M.

2014-12-01

As the organizers of this session state: "Understanding and managing our future relation with the Earth requires research and knowledge spanning diverse fields, and integrated, societally-relevant science that is geared toward solutions." In most cases, however, integration is weak and scientific outputs do not match decision maker requirements. As a result, while scientific results may be highly relevant to society that relevance is operationally far from clear. This paper explores the use of shared learning processes to bridge the gap between the evolving body of scientific information on climate change and its relevance for resilience planning in cities across Asia. Examples related to understanding uncertainty, the evolution of scientific knowledge from different sources, and data extraction and presentation are given using experiences generated over five years of work as part of the Rockefeller Foundation supported Asian Cities Climate Change Resilience Network and other programs. Results suggest that processes supporting effective translation of knowledge between different sources and different applications are essential for the identification of solutions that respond to the dynamics and uncertainties inherent in global change processes.

Automated ancillary cancer history classification for mesothelioma patients from free-text clinical reports

PubMed Central

Wilson, Richard A.; Chapman, Wendy W.; DeFries, Shawn J.; Becich, Michael J.; Chapman, Brian E.

2010-01-01

Background: Clinical records are often unstructured, free-text documents that create information extraction challenges and costs. Healthcare delivery and research organizations, such as the National Mesothelioma Virtual Bank, require the aggregation of both structured and unstructured data types. Natural language processing offers techniques for automatically extracting information from unstructured, free-text documents. Methods: Five hundred and eight history and physical reports from mesothelioma patients were split into development (208) and test sets (300). A reference standard was developed and each report was annotated by experts with regard to the patient’s personal history of ancillary cancer and family history of any cancer. The Hx application was developed to process reports, extract relevant features, perform reference resolution and classify them with regard to cancer history. Two methods, Dynamic-Window and ConText, for extracting information were evaluated. Hx’s classification responses using each of the two methods were measured against the reference standard. The average Cohen’s weighted kappa served as the human benchmark in evaluating the system. Results: Hx had a high overall accuracy, with each method, scoring 96.2%. F-measures using the Dynamic-Window and ConText methods were 91.8% and 91.6%, which were comparable to the human benchmark of 92.8%. For the personal history classification, Dynamic-Window scored highest with 89.2% and for the family history classification, ConText scored highest with 97.6%, in which both methods were comparable to the human benchmark of 88.3% and 97.2%, respectively. Conclusion: We evaluated an automated application’s performance in classifying a mesothelioma patient’s personal and family history of cancer from clinical reports. To do so, the Hx application must process reports, identify cancer concepts, distinguish the known mesothelioma from ancillary cancers, recognize negation, perform reference resolution and determine the experiencer. Results indicated that both information extraction methods tested were dependant on the domain-specific lexicon and negation extraction. We showed that the more general method, ConText, performed as well as our task-specific method. Although Dynamic- Window could be modified to retrieve other concepts, ConText is more robust and performs better on inconclusive concepts. Hx could greatly improve and expedite the process of extracting data from free-text, clinical records for a variety of research or healthcare delivery organizations. PMID:21031012

Text-in-Context: A Method for Extracting Findings in Mixed-Methods Mixed Research Synthesis Studies

PubMed Central

Leeman, Jennifer; Knafl, Kathleen; Crandell, Jamie L.

2012-01-01

Aim Our purpose in this paper is to propose a new method for extracting findings from research reports included in mixed-methods mixed research synthesis studies. Background International initiatives in the domains of systematic review and evidence synthesis have been focused on broadening the conceptualization of evidence, increased methodological inclusiveness and the production of evidence syntheses that will be accessible to and usable by a wider range of consumers. Initiatives in the general mixed-methods research field have been focused on developing truly integrative approaches to data analysis and interpretation. Data source The data extraction challenges described here were encountered and the method proposed for addressing these challenges was developed, in the first year of the ongoing (2011–2016) study: Mixed-Methods Synthesis of Research on Childhood Chronic Conditions and Family. Discussion To preserve the text-in-context of findings in research reports, we describe a method whereby findings are transformed into portable statements that anchor results to relevant information about sample, source of information, time, comparative reference point, magnitude and significance and study-specific conceptions of phenomena. Implications for nursing The data extraction method featured here was developed specifically to accommodate mixed-methods mixed research synthesis studies conducted in nursing and other health sciences, but reviewers might find it useful in other kinds of research synthesis studies. Conclusion This data extraction method itself constitutes a type of integration to preserve the methodological context of findings when statements are read individually and in comparison to each other. PMID:22924808

Waveform shape analysis: extraction of physiologically relevant information from Doppler recordings.

PubMed

Ramsay, M M; Broughton Pipkin, F; Rubin, P C; Skidmore, R

1994-05-01

1. Doppler recordings were made from the brachial artery of healthy female subjects during a series of manoeuvres which altered the pressure-flow characteristics of the vessel. 2. Changes were induced in the peripheral circulation of the forearm by the application of heat or ice-packs. A sphygmomanometer cuff was used to create graded occlusion of the vessel above and below the point of measurement. Recordings were also made whilst the subjects performed a standardized Valsalva manoeuvre. 3. The Doppler recordings were analysed both with the standard waveform indices (systolic/diastolic ratio, pulsatility index and resistance index) and by the method of Laplace transform analysis. 4. The waveform parameters obtained by Laplace transform analysis distinguished the different changes in flow conditions; they thus had direct physiological relevance, unlike the standard waveform indices.

Kinetic modeling in PET imaging of hypoxia

PubMed Central

Li, Fan; Joergensen, Jesper T; Hansen, Anders E; Kjaer, Andreas

2014-01-01

Tumor hypoxia is associated with increased therapeutic resistance leading to poor treatment outcome. Therefore the ability to detect and quantify intratumoral oxygenation could play an important role in future individual personalized treatment strategies. Positron Emission Tomography (PET) can be used for non-invasive mapping of tissue oxygenation in vivo and several hypoxia specific PET tracers have been developed. Evaluation of PET data in the clinic is commonly based on visual assessment together with semiquantitative measurements e.g. standard uptake value (SUV). However, dynamic PET contains additional valuable information on the temporal changes in tracer distribution. Kinetic modeling can be used to extract relevant pharmacokinetic parameters of tracer behavior in vivo that reflects relevant physiological processes. In this paper, we review the potential contribution of kinetic analysis for PET imaging of hypoxia. PMID:25250200

Keyword extraction by entropy difference between the intrinsic and extrinsic mode

NASA Astrophysics Data System (ADS)

Yang, Zhen; Lei, Jianjun; Fan, Kefeng; Lai, Yingxu

2013-10-01

This paper proposes a new metric to evaluate and rank the relevance of words in a text. The method uses the Shannon’s entropy difference between the intrinsic and extrinsic mode, which refers to the fact that relevant words significantly reflect the author’s writing intention, i.e., their occurrences are modulated by the author’s purpose, while the irrelevant words are distributed randomly in the text. By using The Origin of Species by Charles Darwin as a representative text sample, the performance of our detector is demonstrated and compared to previous proposals. Since a reference text “corpus” is all of an author’s writings, books, papers, etc. his collected works is not needed. Our approach is especially suitable for single documents of which there is no a priori information available.

Verification of nonlinear dynamic structural test results by combined image processing and acoustic analysis

NASA Astrophysics Data System (ADS)

Tene, Yair; Tene, Noam; Tene, G.

1993-08-01

An interactive data fusion methodology of video, audio, and nonlinear structural dynamic analysis for potential application in forensic engineering is presented. The methodology was developed and successfully demonstrated in the analysis of heavy transportable bridge collapse during preparation for testing. Multiple bridge elements failures were identified after the collapse, including fracture, cracks and rupture of high performance structural materials. Videotape recording by hand held camcorder was the only source of information about the collapse sequence. The interactive data fusion methodology resulted in extracting relevant information form the videotape and from dynamic nonlinear structural analysis, leading to full account of the sequence of events during the bridge collapse.

Information Theoretic Extraction of EEG Features for Monitoring Subject Attention

NASA Technical Reports Server (NTRS)

Principe, Jose C.

2000-01-01

The goal of this project was to test the applicability of information theoretic learning (feasibility study) to develop new brain computer interfaces (BCI). The difficulty to BCI comes from several aspects: (1) the effective data collection of signals related to cognition; (2) the preprocessing of these signals to extract the relevant information; (3) the pattern recognition methodology to detect reliably the signals related to cognitive states. We only addressed the two last aspects in this research. We started by evaluating an information theoretic measure of distance (Bhattacharyya distance) for BCI performance with good predictive results. We also compared several features to detect the presence of event related desynchronization (ERD) and synchronization (ERS), and concluded that at least for now the bandpass filtering is the best compromise between simplicity and performance. Finally, we implemented several classifiers for temporal - pattern recognition. We found out that the performance of temporal classifiers is superior to static classifiers but not by much. We conclude by stating that the future of BCI should be found in alternate approaches to sense, collect and process the signals created by populations of neurons. Towards this goal, cross-disciplinary teams of neuroscientists and engineers should be funded to approach BCIs from a much more principled view point.

Advances in Spectral-Spatial Classification of Hyperspectral Images

NASA Technical Reports Server (NTRS)

Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

2012-01-01

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral–spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

Integrating GPCR-specific information with full text articles

PubMed Central

2011-01-01

Background With the continued growth in the volume both of experimental G protein-coupled receptor (GPCR) data and of the related peer-reviewed literature, the ability of GPCR researchers to keep up-to-date is becoming increasingly curtailed. Results We present work that integrates the biological data and annotations in the GPCR information system (GPCRDB) with next-generation methods for intelligently exploring, visualising and interacting with the scientific articles used to disseminate them. This solution automatically retrieves relevant information from GPCRDB and displays it both within and as an adjunct to an article. Conclusions This approach allows researchers to extract more knowledge more swiftly from literature. Importantly, it allows reinterpretation of data in articles published before GPCR structure data became widely available, thereby rescuing these valuable data from long-dormant sources. PMID:21910883

Visual search in scenes involves selective and non-selective pathways

PubMed Central

Wolfe, Jeremy M; Vo, Melissa L-H; Evans, Karla K; Greene, Michelle R

2010-01-01

How do we find objects in scenes? For decades, visual search models have been built on experiments in which observers search for targets, presented among distractor items, isolated and randomly arranged on blank backgrounds. Are these models relevant to search in continuous scenes? This paper argues that the mechanisms that govern artificial, laboratory search tasks do play a role in visual search in scenes. However, scene-based information is used to guide search in ways that had no place in earlier models. Search in scenes may be best explained by a dual-path model: A “selective” path in which candidate objects must be individually selected for recognition and a “non-selective” path in which information can be extracted from global / statistical information. PMID:21227734

Using texts in science education: cognitive processes and knowledge representation.

PubMed

van den Broek, Paul

2010-04-23

Texts form a powerful tool in teaching concepts and principles in science. How do readers extract information from a text, and what are the limitations in this process? Central to comprehension of and learning from a text is the construction of a coherent mental representation that integrates the textual information and relevant background knowledge. This representation engenders learning if it expands the reader's existing knowledge base or if it corrects misconceptions in this knowledge base. The Landscape Model captures the reading process and the influences of reader characteristics (such as working-memory capacity, reading goal, prior knowledge, and inferential skills) and text characteristics (such as content/structure of presented information, processing demands, and textual cues). The model suggests factors that can optimize--or jeopardize--learning science from text.

The Pursuit of Word Meanings

PubMed Central

Stevens, Jon Scott; Gleitman, Lila R.; Trueswell, John C.; Yang, Charles

2016-01-01

We evaluate here the performance of four models of cross-situational word learning; two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed Pursuit, uses an associative learning mechanism to estimate word-referent probability but pursues and tests the best referent-meaning at any given time. Pursuit is found to perform as well as global models under many conditions extracted from naturalistic corpora of parent child-interactions, even though the model maintains far less information than global models. Moreover, Pursuit is found to best capture human experimental findings from several relevant cross-situational word-learning experiments, including those of Yu and Smith (2007), the paradigm example of a finding believed to support fully global cross-situational models. Implications and limitations of these results are discussed, most notably that the model characterizes only the earliest stages of word learning, when reliance on the co-occurring referent world is at its greatest. PMID:27666335

Tool for Constructing Data Albums for Significant Weather Events

NASA Astrophysics Data System (ADS)

Kulkarni, A.; Ramachandran, R.; Conover, H.; McEniry, M.; Goodman, H.; Zavodsky, B. T.; Braun, S. A.; Wilson, B. D.

2012-12-01

Case study analysis and climatology studies are common approaches used in Atmospheric Science research. Research based on case studies involves a detailed description of specific weather events using data from different sources, to characterize physical processes in play for a given event. Climatology-based research tends to focus on the representativeness of a given event, by studying the characteristics and distribution of a large number of events. To gather relevant data and information for case studies and climatology analysis is tedious and time consuming; current Earth Science data systems are not suited to assemble multi-instrument, multi mission datasets around specific events. For example, in hurricane science, finding airborne or satellite data relevant to a given storm requires searching through web pages and data archives. Background information related to damages, deaths, and injuries requires extensive online searches for news reports and official storm summaries. We will present a knowledge synthesis engine to create curated "Data Albums" to support case study analysis and climatology studies. The technological challenges in building such a reusable and scalable knowledge synthesis engine are several. First, how to encode domain knowledge in a machine usable form? This knowledge must capture what information and data resources are relevant and the semantic relationships between the various fragments of information and data. Second, how to extract semantic information from various heterogeneous sources including unstructured texts using the encoded knowledge? Finally, how to design a structured database from the encoded knowledge to store all information and to support querying? The structured database must allow both knowledge overviews of an event as well as drill down capability needed for detailed analysis. An application ontology driven framework is being used to design the knowledge synthesis engine. The knowledge synthesis engine is being applied to build a portal for hurricane case studies at the Global Hydrology and Resource Center (GHRC), a NASA Data Center. This portal will auto-generate Data Albums for specific hurricane events, compiling information from distributed resources such as NASA field campaign collections, relevant data sets, storm reports, pictures, videos and other useful sources.

Compositional Determination of Shale with Simultaneous Neutron and X-ray Tomography

NASA Astrophysics Data System (ADS)

LaManna, J.; Hussey, D. S.; Baltic, E.; Jacobson, D. L.

2017-12-01

Understanding the distribution of organic material, mineral inclusions, and porosity are critical to properly model the flow of fluids through rock formations in applications ranging from hydraulic fracturing and gas extraction, CO2 sequestration, geothermal power, and aquifer management. Typically, this information is obtained on the pore scale using destructive techniques such as focused ion beam scanning electron microscopy. Neutrons and X-rays provide non-destructive, complementary probes to gain three-dimensional distributions of porosity, minerals, and organic content along with fluid interactions in fractures and pore networks on the core scale. By capturing both neutron and X-ray tomography simultaneously it is possible to capture slowly dynamic or stochastic processes with both imaging modes. To facilitate this, NIST offers a system for simultaneous neutron and X-ray tomography at the Center for Neutron Research. This instrument provides neutron and X-ray beams capable of penetrating through pressure vessels to image the specimen inside at relevant geological conditions at resolutions ranging from 15 micrometers to 100 micrometers. This talk will discuss current efforts at identifying mineral and organic content and fracture and wettability in shales relevant to gas extraction.

Robot Evolutionary Localization Based on Attentive Visual Short-Term Memory

PubMed Central

Vega, Julio; Perdices, Eduardo; Cañas, José M.

2013-01-01

Cameras are one of the most relevant sensors in autonomous robots. However, two of their challenges are to extract useful information from captured images, and to manage the small field of view of regular cameras. This paper proposes implementing a dynamic visual memory to store the information gathered from a moving camera on board a robot, followed by an attention system to choose where to look with this mobile camera, and a visual localization algorithm that incorporates this visual memory. The visual memory is a collection of relevant task-oriented objects and 3D segments, and its scope is wider than the current camera field of view. The attention module takes into account the need to reobserve objects in the visual memory and the need to explore new areas. The visual memory is useful also in localization tasks, as it provides more information about robot surroundings than the current instantaneous image. This visual system is intended as underlying technology for service robot applications in real people's homes. Several experiments have been carried out, both with simulated and real Pioneer and Nao robots, to validate the system and each of its components in office scenarios. PMID:23337333

LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC.

PubMed

Allot, Alexis; Peng, Yifan; Wei, Chih-Hsuan; Lee, Kyubum; Phan, Lon; Lu, Zhiyong

2018-05-14

The identification and interpretation of genomic variants play a key role in the diagnosis of genetic diseases and related research. These tasks increasingly rely on accessing relevant manually curated information from domain databases (e.g. SwissProt or ClinVar). However, due to the sheer volume of medical literature and high cost of expert curation, curated variant information in existing databases are often incomplete and out-of-date. In addition, the same genetic variant can be mentioned in publications with various names (e.g. 'A146T' versus 'c.436G>A' versus 'rs121913527'). A search in PubMed using only one name usually cannot retrieve all relevant articles for the variant of interest. Hence, to help scientists, healthcare professionals, and database curators find the most up-to-date published variant research, we have developed LitVar for the search and retrieval of standardized variant information. In addition, LitVar uses advanced text mining techniques to compute and extract relationships between variants and other associated entities such as diseases and chemicals/drugs. LitVar is publicly available at https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/LitVar.

Seaweed allelopathy to corals: are active compounds on, or in, seaweeds?

NASA Astrophysics Data System (ADS)

Longo, G. O.; Hay, M. E.

2017-03-01

Numerous seaweeds produce secondary metabolites that are allelopathic to corals. To date, most of the compounds identified in this interaction are lipid-soluble instead of water-soluble. Thus, understanding whether these compounds are stored internally where they would not contact corals, or occur on external surfaces where they could be transferred to corals, is critical to understanding seaweed-coral interactions and to informing realistic experiments on chemically mediated interactions. We conducted field experiments assessing the effects of lipid-soluble extracts from macroalgal surfaces alone versus total lipid-soluble extracts from both internal and external tissues on the coral Pocillopora verrucosa. Extracts of the red algae Amansia rhodantha and Asparagopsis taxiformis, the green alga Chlorodesmis fastigiata, and the brown alga Dictyota bartayresiana suppressed coral photochemical efficiency; in these bioactive species, the total lipid-soluble extracts were not more potent than surface-only extracts despite the concentration of total extracts being many times greater than surface-only extracts. This suggests that previous assays with total extracts may be ecologically meaningful, but also that future assays should be conducted with the simpler, less concentrated, and more ecologically relevant surface extracts. Allelopathic effects of As. taxiformis and C. fastigiata were significantly greater than the effect of D. bartayresiana, with effects of Am. rhodantha intermediate between these groups. Neither surface-only nor total lipid-soluble extracts of the seaweed Turbinaria ornata were allelopathic, and its lack of potency differed significantly from all other species. Our results suggest that lipid-soluble, allelopathic compounds are usually deployed on seaweed surfaces where they can be effective in surface-mediated interactions against other species.

Clinical relevance of smartphone apps for diabetes management: A global overview.

PubMed

Huang, Zhilian; Soljak, Michael; Boehm, Bernhard Otto; Car, Josip

2018-05-01

We assessed the number, proportion, and clinical relevance of diabetes self-management apps in major languages spoken by 10 countries with the highest number of people with diabetes. China, India, USA, Brazil, Russian Federation, Mexico, Indonesia, Egypt, Japan, and Pakistan were identified as the 10 countries with the largest number of people with diabetes based on the latest NCD-RisC survey. Android and iOS apps in the 10 national languages were extracted with a search strategy. App titles and descriptions were systematically screened by trained reviewers, including apps specific for diabetes self-management and excluding apps for health care providers, general well-being, health and product promotion, and traditional cure. Eighteen apps in the above languages were then downloaded based on availability and popularity and assessed for clinical relevance to diabetes self-management with reference to current clinical guidelines. The diabetes-related search terms identified 3374 Android and 4477 iOS apps, where 1019 Android and 1303 iOS apps were screened as being relevant for diabetes self-management. Chinese and English language apps constitute above 80% of the diabetes apps, have more downloads, and more comprehensive clinically relevant functions compared with other languages. None of the apps assessed met all criteria for information provision and app functionalities nor provided information cited from accredited sources. Our study showed that apps could play an important role in complementing multifaceted diabetes care, but should preferably be regulated, context specific, and more tailored to users' needs with clear guidance for patients and clinicians about the choices. Copyright © 2018 John Wiley & Sons, Ltd.

Simulated Leaching (Migration) Study for a Model Container-Closure System Applicable to Parenteral and Ophthalmic Drug Products.

PubMed

Jenke, Dennis; Egert, Thomas; Hendricker, Alan; Castner, James; Feinberg, Tom; Houston, Christopher; Hunt, Desmond G; Lynch, Michael; Nicholas, Kumudini; Norwood, Daniel L; Paskiet, Diane; Ruberto, Michael; Smith, Edward J; Holcomb, Frank; Markovic, Ingrid

2017-01-01

A simulating leaching (migration) study was performed on a model container-closure system relevant to parenteral and ophthalmic drug products. This container-closure system consisted of a linear low-density polyethylene bottle (primary container), a polypropylene cap and an elastomeric cap liner (closure), an adhesive label (labeling), and a foil overpouch (secondary container). The bottles were filled with simulating solvents (aqueous salt/acid mixture at pH 2.5, aqueous buffer at pH 9.5, and 1/1 v/v isopropanol/water), a label was affixed to the filled and capped bottles, the filled bottles were placed into the foil overpouch, and the filled and pouched units were stored either upright or inverted for up to 6 months at 40 °C. After storage, the leaching solutions were tested for leached substances using multiple complementary analytical techniques to address volatile, semi-volatile, and non-volatile organic and inorganic extractables as potential leachables.The leaching data generated supported several conclusions, including that (1) the extractables (leachables) profile revealed by a simulating leaching study can qualitatively be correlated with compositional information for materials of construction, (2) the chemical nature of both the extracting medium and the individual extractables (leachables) can markedly affect the resulting profile, and (3) while direct contact between a drug product and a system's material of construction may exacerbate the leaching of substances from that material by the drug product, direct contact is not a prerequisite for migration and leaching to occur. LAY ABSTRACT: The migration of container-related extractables from a model pharmaceutical container-closure system and into simulated drug product solutions was studied, focusing on circumstances relevant to parenteral and ophthalmic drug products. The model system was constructed specifically to address the migration of extractables from labels applied to the outside of the primary container. The study demonstrated that (1) the extractables that do migrate can be correlated to the composition of the materials used to construct the container-closure systems, (2) the extent of migration is affected by the chemical nature of the simulating solutions and the extractables themselves, and (3) even though labels may not be in direct contact with a contained solution, label-related extractables can accumulate as leachables in those solutions. © PDA, Inc. 2017.

Health Information Infrastructure for People with Intellectual and Developmental Disabilities (I/DD) Living in Supported Accommodation: Communication, Co-Ordination and Integration of Health Information.

PubMed

Dahm, Maria R; Georgiou, Andrew; Balandin, Susan; Hill, Sophie; Hemsley, Bronwyn

2017-10-25

People with intellectual and/or developmental disability (I/DD) commonly have complex health care needs, but little is known about how their health information is managed in supported accommodation, and across health services providers. This study aimed to describe the current health information infrastructure (i.e., how data and information are collected, stored, communicated, and used) for people with I/DD living in supported accommodation in Australia. It involved a scoping review and synthesis of research, policies, and health documents relevant in this setting. Iterative database and hand searches were conducted across peer-reviewed articles internationally in English and grey literature in Australia (New South Wales) up to September 2015. Data were extracted from the selected relevant literature and analyzed for content themes. Expert stakeholders were consulted to verify the authors' interpretations of the information and content categories. The included 286 sources (peer-reviewed n = 27; grey literature n = 259) reflect that the health information for people with I/DD in supported accommodation is poorly communicated, coordinated and integrated across isolated systems. 'Work-as-imagined' as outlined in policies, does not align with 'work-as-done' in reality. This gap threatens the quality of care and safety of people with I/DD in these settings. The effectiveness of the health information infrastructure and services for people with I/DD can be improved by integrating the information sources and placing people with I/DD and their supporters at the centre of the information exchange process.

Economics of Lunar Mineral Exploration

NASA Astrophysics Data System (ADS)

Blair, Brad R.

1999-01-01

Exploration of space is increasingly being rationalized by the potential for long-term commercial payoffs. The commercial use of lunar resources is gaining relevance as technology and infrastructure increase, and will depend on an adequate foundation of geological information. While past lunar exploration has provided detailed knowledge about the composition, geologic history and structural characteristics of the lunar surface at six locations, the rest of the Moon remains largely unexplored. The purpose of this paper is to describe traditional methods and decision criteria used in the mineral exploration business. Rationale for terrestrial mineral exploration is firmly entrenched within the context of economic gain, with asset valuation forming the primary feedback to decision making. The paper presents a summary of relevant knowledge from the field of exploration economics, applying it to the case of space mineral development. It includes a description of the current paradigm of both space exploration and terrestrial mineral exploration, as each pertains to setting priorities and decision making. It briefly examines issues related to space resource demand, extraction and transportation to establish its relevance.

3D numerical simulations of negative hydrogen ion extraction using realistic plasma parameters, geometry of the extraction aperture and full 3D magnetic field map

NASA Astrophysics Data System (ADS)

Mochalskyy, S.; Wünderlich, D.; Ruf, B.; Franzen, P.; Fantz, U.; Minea, T.

2014-02-01

Decreasing the co-extracted electron current while simultaneously keeping negative ion (NI) current sufficiently high is a crucial issue on the development plasma source system for ITER Neutral Beam Injector. To support finding the best extraction conditions the 3D Particle-in-Cell Monte Carlo Collision electrostatic code ONIX (Orsay Negative Ion eXtraction) has been developed. Close collaboration with experiments and other numerical models allows performing realistic simulations with relevant input parameters: plasma properties, geometry of the extraction aperture, full 3D magnetic field map, etc. For the first time ONIX has been benchmarked with commercial positive ions tracing code KOBRA3D. A very good agreement in terms of the meniscus position and depth has been found. Simulation of NI extraction with different e/NI ratio in bulk plasma shows high relevance of the direct negative ion extraction from the surface produced NI in order to obtain extracted NI current as in the experimental results from BATMAN testbed.

Parental perceptions of facilitators and barriers to physical activity for children with intellectual disabilities: A mixed methods systematic review.

PubMed

McGarty, Arlene M; Melville, Craig A

2018-02-01

There is a need increase our understanding of what factors affect physical activity participation in children with intellectual disabilities (ID) and develop effective methods to overcome barriers and increase activity levels. This study aimed to systematically review parental perceptions of facilitators and barriers to physical activity for children with ID. A systematic search of Embase, Medline, ERIC, Web of Science, and PsycINFO was conducted (up to and including August, 2017) to identify relevant papers. A meta-ethnography approach was used to synthesise qualitative and quantitative results through the generation of third-order themes and a theoretical model. Ten studies were included, which ranged from weak to strong quality. Seventy-one second-order themes and 12 quantitative results were extracted. Five third-order themes were developed: family, child factors, inclusive programmes and facilities, social motivation, and child's experiences of physical activity. It is theorised that these factors can be facilitators or barriers to physical activity, depending on the information and education of relevant others, e.g. parents and coaches. Parents have an important role in supporting activity in children with ID. Increasing the information and education given to relevant others could be an important method of turning barriers into facilitators. Copyright © 2017 Elsevier Ltd. All rights reserved.

Systematic review to inform prevention and management of chronic disease for Indigenous Australians: overview and priorities.

PubMed

Gomersall, Judith Streak; Canuto, Karla; Aromataris, Edoardo; Braunack-Mayer, Annette; Brown, Alex

2016-02-01

To describe the main characteristics of systematic reviews addressing questions of chronic disease and related risk factors for Indigenous Australians. We searched databases for systematic reviews meeting inclusion criteria. Two reviewers assessed quality and extracted characteristics using pre-defined tools. We identified 14 systematic reviews. Seven synthesised evidence about health intervention effectiveness; four addressed chronic disease or risk factor prevalence; and six conducted critical appraisal as per current best practice. Only three reported steps to align the review with standards for ethical research with Indigenous Australians and/or capture Indigenous-specific knowledge. Most called for more high-quality research. Systematic review is an under-utilised method for gathering evidence to inform chronic disease prevention and management for Indigenous Australians. Relevance of future systematic reviews could be improved by: 1) aligning questions with community priorities as well as decision maker needs; 2) involvement of, and leadership by, Indigenous researchers with relevant cultural and contextual knowledge; iii) use of critical appraisal tools that include traditional risk of bias assessment criteria and criteria that reflect Indigenous standards of appropriate research. Systematic review method guidance, tools and reporting standards are required to ensure alignment with ethical obligations and promote rigor and relevance. © 2015 Public Health Association of Australia.

Associating Human-Centered Concepts with Social Networks Using Fuzzy Sets

NASA Astrophysics Data System (ADS)

Yager, Ronald R.

The rapidly growing global interconnectivity, brought about to a large extent by the Internet, has dramatically increased the importance and diversity of social networks. Modern social networks cut across a spectrum from benign recreational focused websites such as Facebook to occupationally oriented websites such as LinkedIn to criminally focused groups such as drug cartels to devastation and terror focused groups such as Al-Qaeda. Many organizations are interested in analyzing and extracting information related to these social networks. Among these are governmental police and security agencies as well marketing and sales organizations. To aid these organizations there is a need for technologies to model social networks and intelligently extract information from these models. While established technologies exist for the modeling of relational networks [1-7] few technologies exist to extract information from these, compatible with human perception and understanding. Data bases is an example of a technology in which we have tools for representing our information as well as tools for querying and extracting the information contained. Our goal is in some sense analogous. We want to use the relational network model to represent information, in this case about relationships and interconnections, and then be able to query the social network using intelligent human-centered concepts. To extend our capabilities to interact with social relational networks we need to associate with these network human concepts and ideas. Since human beings predominantly use linguistic terms in which to reason and understand we need to build bridges between human conceptualization and the formal mathematical representation of the social network. Consider for example a concept such as "leader". An analyst may be able to express, in linguistic terms, using a network relevant vocabulary, properties of a leader. Our task is to translate this linguistic description into a mathematical formalism that allows us to determine how true it is that a particular node is a leader. In this work we look at the use of fuzzy set methodologies [8-10] to provide a bridge between the human analyst and the formal model of the network.

Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation.

PubMed

Pereira, Sérgio; Meier, Raphael; McKinley, Richard; Wiest, Roland; Alves, Victor; Silva, Carlos A; Reyes, Mauricio

2018-02-01

Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes". In this paper, we propose a methodology to enhance the interpretability of automatically extracted machine learning features. The proposed system is composed of a Restricted Boltzmann Machine for unsupervised feature learning, and a Random Forest classifier, which are combined to jointly consider existing correlations between imaging data, features, and target variables. We define two levels of interpretation: global and local. The former is devoted to understanding if the system learned the relevant relations in the data correctly, while the later is focused on predictions performed on a voxel- and patient-level. In addition, we propose a novel feature importance strategy that considers both imaging data and target variables, and we demonstrate the ability of the approach to leverage the interpretability of the obtained representation for the task at hand. We evaluated the proposed methodology in brain tumor segmentation and penumbra estimation in ischemic stroke lesions. We show the ability of the proposed methodology to unveil information regarding relationships between imaging modalities and extracted features and their usefulness for the task at hand. In both clinical scenarios, we demonstrate that the proposed methodology enhances the interpretability of automatically learned features, highlighting specific learning patterns that resemble how an expert extracts relevant data from medical images. Copyright © 2017 Elsevier B.V. All rights reserved.

Impact of stride-coupled gaze shifts of walking blowflies on the neuronal representation of visual targets

PubMed Central

Kress, Daniel; Egelhaaf, Martin

2014-01-01

During locomotion animals rely heavily on visual cues gained from the environment to guide their behavior. Examples are basic behaviors like collision avoidance or the approach to a goal. The saccadic gaze strategy of flying flies, which separates translational from rotational phases of locomotion, has been suggested to facilitate the extraction of environmental information, because only image flow evoked by translational self-motion contains relevant distance information about the surrounding world. In contrast to the translational phases of flight during which gaze direction is kept largely constant, walking flies experience continuous rotational image flow that is coupled to their stride-cycle. The consequences of these self-produced image shifts for the extraction of environmental information are still unclear. To assess the impact of stride-coupled image shifts on visual information processing, we performed electrophysiological recordings from the HSE cell, a motion sensitive wide-field neuron in the blowfly visual system. This cell has been concluded to play a key role in mediating optomotor behavior, self-motion estimation and spatial information processing. We used visual stimuli that were based on the visual input experienced by walking blowflies while approaching a black vertical bar. The response of HSE to these stimuli was dominated by periodic membrane potential fluctuations evoked by stride-coupled image shifts. Nevertheless, during the approach the cell’s response contained information about the bar and its background. The response components evoked by the bar were larger than the responses to its background, especially during the last phase of the approach. However, as revealed by targeted modifications of the visual input during walking, the extraction of distance information on the basis of HSE responses is much impaired by stride-coupled retinal image shifts. Possible mechanisms that may cope with these stride-coupled responses are discussed. PMID:25309362

Reconfigurable quadruple quantum dots in a silicon nanowire transistor

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

Betz, A. C., E-mail: ab2106@cam.ac.uk; Broström, M.; Gonzalez-Zalba, M. F.

2016-05-16

We present a reconfigurable metal-oxide-semiconductor multi-gate transistor that can host a quadruple quantum dot in silicon. The device consists of an industrial quadruple-gate silicon nanowire field-effect transistor. Exploiting the corner effect, we study the versatility of the structure in the single quantum dot and the serial double quantum dot regimes and extract the relevant capacitance parameters. We address the fabrication variability of the quadruple-gate approach which, paired with improved silicon fabrication techniques, makes the corner state quantum dot approach a promising candidate for a scalable quantum information architecture.

Context-based electronic health record: toward patient specific healthcare.

PubMed

Hsu, William; Taira, Ricky K; El-Saden, Suzie; Kangarloo, Hooshang; Bui, Alex A T

2012-03-01

Due to the increasingly data-intensive clinical environment, physicians now have unprecedented access to detailed clinical information from a multitude of sources. However, applying this information to guide medical decisions for a specific patient case remains challenging. One issue is related to presenting information to the practitioner: displaying a large (irrelevant) amount of information often leads to information overload. Next-generation interfaces for the electronic health record (EHR) should not only make patient data easily searchable and accessible, but also synthesize fragments of evidence documented in the entire record to understand the etiology of a disease and its clinical manifestation in individual patients. In this paper, we describe our efforts toward creating a context-based EHR, which employs biomedical ontologies and (graphical) disease models as sources of domain knowledge to identify relevant parts of the record to display. We hypothesize that knowledge (e.g., variables, relationships) from these sources can be used to standardize, annotate, and contextualize information from the patient record, improving access to relevant parts of the record and informing medical decision making. To achieve this goal, we describe a framework that aggregates and extracts findings and attributes from free-text clinical reports, maps findings to concepts in available knowledge sources, and generates a tailored presentation of the record based on the information needs of the user. We have implemented this framework in a system called Adaptive EHR, demonstrating its capabilities to present and synthesize information from neurooncology patients. This paper highlights the challenges and potential applications of leveraging disease models to improve the access, integration, and interpretation of clinical patient data. © 2012 IEEE

Information and decision-making needs among people with mental disorders: a systematic review of the literature.

PubMed

Tlach, Lisa; Wüsten, Caroline; Daubmann, Anne; Liebherz, Sarah; Härter, Martin; Dirmaier, Jörg

2015-12-01

Assessment of users' information and decision-making needs is one key step in the development of decision-support interventions. To identify patients' information and decision-making needs as a pre-requisite for the development of high-quality web-based patient decision aids (PtDAs) for common mental disorders. A systematic MEDLINE search for papers published until December 2012 was conducted, and reference lists of included articles and relevant reviews were searched. Original studies containing data on information or decision-making needs of adults with depression, anxiety disorders, somatoform disorders, alcohol-related disorders and schizophrenia were included. Data extraction was performed using a standardized form, and data synthesis was conducted using a theory-based deductive approach by two independent reviewers. Studies were quality assessed using the Mixed Methods Appraisal Tool. Twelve studies were included focusing on information needs or the identification of decisions patients with depression and schizophrenia were facing. No studies were found for the other mental disorders. Overall, seven information needs categories were identified with the topics 'basic facts', 'treatment' and 'coping' being of major relevance. Six decision categories were identified of which decisions on 'medication' and 'treatment setting' were most often classified. This review reveals that patients with schizophrenia and depression show extensive information and decision-making needs. The identified needs can initially inform the design of PtDAs for schizophrenia and depression. However, there is an urgent need to investigate information and decision-making needs among patients with other mental disorders. © 2014 John Wiley & Sons Ltd.

Designing basin-customized combined drought indices via feature extraction

NASA Astrophysics Data System (ADS)

Zaniolo, Marta; Giuliani, Matteo; Castelletti, Andrea

2017-04-01

The socio-economic costs of drought are progressively increasing worldwide due to the undergoing alteration of hydro-meteorological regimes induced by climate change. Although drought management is largely studied in the literature, most of the traditional drought indexes fail in detecting critical events in highly regulated systems, which generally rely on ad-hoc formulations and cannot be generalized to different context. In this study, we contribute a novel framework for the design of a basin-customized drought index. This index represents a surrogate of the state of the basin and is computed by combining the available information about the water available in the system to reproduce a representative target variable for the drought condition of the basin (e.g., water deficit). To select the relevant variables and how to combine them, we use an advanced feature extraction algorithm called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS). The W-QEISS algorithm relies on a multi-objective evolutionary algorithm to find Pareto-efficient subsets of variables by maximizing the wrapper accuracy, minimizing the number of selected variables (cardinality) and optimizing relevance and redundancy of the subset. The accuracy objective is evaluated trough the calibration of a pre-defined model (i.e., an extreme learning machine) of the water deficit for each candidate subset of variables, with the index selected from the resulting solutions identifying a suitable compromise between accuracy, cardinality, relevance, and redundancy. The proposed methodology is tested in the case study of Lake Como in northern Italy, a regulated lake mainly operated for irrigation supply to four downstream agricultural districts. In the absence of an institutional drought monitoring system, we constructed the combined index using all the hydrological variables from the existing monitoring system as well as the most common drought indicators at multiple time aggregations. The soil moisture deficit in the root zone computed by a distributed-parameter water balance model of the agricultural districts is used as target variable. Numerical results show that our framework succeeds in constructing a combined drought index that reproduces the soil moisture deficit. Moreover, this index represents a valuable information for supporting appropriate drought management strategies, including the possibility of directly informing the lake operations about the drought conditions and improve the overall reliability of the irrigation supply system.

Balancing the presentation of information and options in patient decision aids: an updated review.

PubMed

Abhyankar, Purva; Volk, Robert J; Blumenthal-Barby, Jennifer; Bravo, Paulina; Buchholz, Angela; Ozanne, Elissa; Vidal, Dale Colins; Col, Nananda; Stalmeier, Peep

2013-01-01

Standards for patient decision aids require that information and options be presented in a balanced manner; this requirement is based on the argument that balanced presentation is essential to foster informed decision making. If information is presented in an incomplete/non-neutral manner, it can stimulate cognitive biases that can unduly affect individuals' knowledge, perceptions of risks and benefits, and, ultimately, preferences. However, there is little clarity about what constitutes balance, and how it can be determined and enhanced. We conducted a literature review to examine the theoretical and empirical evidence related to balancing the presentation of information and options. A literature search related to patient decision aids and balance was conducted on Medline, using MeSH terms and PubMed; this search supplemented the 2011 Cochrane Collaboration's review of patient decision aids trials. Only English language articles relevant to patient decision making and addressing the balance of information and options were included. All members of the team independently screened clusters of articles; uncertainties were resolved by seeking review by another member. The team then worked in sub-groups to extract and synthesise data on theory, definitions, and evidence reported in these studies. A total of 40 articles met the inclusion criteria. Of these, six explained the rationale for balancing the presentation of information and options. Twelve defined "balance"; the definition of "balance" that emerged is as follows: "The complete and unbiased presentation of the relevant options and the information about those options-in content and in format-in a way that enables individuals to process this information without bias". Ten of the 40 articles reported assessing the balance of the relevant decision aid. All 10 did so exclusively from the users' or patients' perspective, using a five-point Likert-type scale. Presenting information in a side-by-side display form was associated with more respondents (ranging from 70% to 96%) judging the information as "balanced". There is a need for comparative studies investigating different ways to improve and measure balance in the presentation of information and options in patient decision aids.

Balancing the presentation of information and options in patient decision aids: an updated review

PubMed Central

2013-01-01

Background Standards for patient decision aids require that information and options be presented in a balanced manner; this requirement is based on the argument that balanced presentation is essential to foster informed decision making. If information is presented in an incomplete/non-neutral manner, it can stimulate cognitive biases that can unduly affect individuals’ knowledge, perceptions of risks and benefits, and, ultimately, preferences. However, there is little clarity about what constitutes balance, and how it can be determined and enhanced. We conducted a literature review to examine the theoretical and empirical evidence related to balancing the presentation of information and options. Methods A literature search related to patient decision aids and balance was conducted on Medline, using MeSH terms and PubMed; this search supplemented the 2011 Cochrane Collaboration’s review of patient decision aids trials. Only English language articles relevant to patient decision making and addressing the balance of information and options were included. All members of the team independently screened clusters of articles; uncertainties were resolved by seeking review by another member. The team then worked in sub-groups to extract and synthesise data on theory, definitions, and evidence reported in these studies. Results A total of 40 articles met the inclusion criteria. Of these, six explained the rationale for balancing the presentation of information and options. Twelve defined “balance”; the definition of “balance” that emerged is as follows: “The complete and unbiased presentation of the relevant options and the information about those options—in content and in format—in a way that enables individuals to process this information without bias”. Ten of the 40 articles reported assessing the balance of the relevant decision aid. All 10 did so exclusively from the users’ or patients’ perspective, using a five-point Likert-type scale. Presenting information in a side-by-side display form was associated with more respondents (ranging from 70% to 96%) judging the information as “balanced”. Conclusion There is a need for comparative studies investigating different ways to improve and measure balance in the presentation of information and options in patient decision aids. PMID:24625214

Inducing task-relevant responses to speech in the sleeping brain.

PubMed

Kouider, Sid; Andrillon, Thomas; Barbosa, Leonardo S; Goupil, Louise; Bekinschtein, Tristan A

2014-09-22

Falling asleep leads to a loss of sensory awareness and to the inability to interact with the environment [1]. While this was traditionally thought as a consequence of the brain shutting down to external inputs, it is now acknowledged that incoming stimuli can still be processed, at least to some extent, during sleep [2]. For instance, sleeping participants can create novel sensory associations between tones and odors [3] or reactivate existing semantic associations, as evidenced by event-related potentials [4-7]. Yet, the extent to which the brain continues to process external stimuli remains largely unknown. In particular, it remains unclear whether sensory information can be processed in a flexible and task-dependent manner by the sleeping brain, all the way up to the preparation of relevant actions. Here, using semantic categorization and lexical decision tasks, we studied task-relevant responses triggered by spoken stimuli in the sleeping brain. Awake participants classified words as either animals or objects (experiment 1) or as either words or pseudowords (experiment 2) by pressing a button with their right or left hand, while transitioning toward sleep. The lateralized readiness potential (LRP), an electrophysiological index of response preparation, revealed that task-specific preparatory responses are preserved during sleep. These findings demonstrate that despite the absence of awareness and behavioral responsiveness, sleepers can still extract task-relevant information from external stimuli and covertly prepare for appropriate motor responses. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Road marking features extraction using the VIAPIX® system

NASA Astrophysics Data System (ADS)

Kaddah, W.; Ouerhani, Y.; Alfalou, A.; Desthieux, M.; Brosseau, C.; Gutierrez, C.

2016-07-01

Precise extraction of road marking features is a critical task for autonomous urban driving, augmented driver assistance, and robotics technologies. In this study, we consider an autonomous system allowing us lane detection for marked urban roads and analysis of their features. The task is to relate the georeferencing of road markings from images obtained using the VIAPIX® system. Based on inverse perspective mapping and color segmentation to detect all white objects existing on this road, the present algorithm enables us to examine these images automatically and rapidly and also to get information on road marks, their surface conditions, and their georeferencing. This algorithm allows detecting all road markings and identifying some of them by making use of a phase-only correlation filter (POF). We illustrate this algorithm and its robustness by applying it to a variety of relevant scenarios.

Software for hyperspectral, joint photographic experts group (.JPG), portable network graphics (.PNG) and tagged image file format (.TIFF) segmentation

NASA Astrophysics Data System (ADS)

Bruno, L. S.; Rodrigo, B. P.; Lucio, A. de C. Jorge

2016-10-01

This paper presents a system developed by an application of a neural network Multilayer Perceptron for drone acquired agricultural image segmentation. This application allows a supervised user training the classes that will posteriorly be interpreted by neural network. These classes will be generated manually with pre-selected attributes in the application. After the attribute selection a segmentation process is made to allow the relevant information extraction for different types of images, RGB or Hyperspectral. The application allows extracting the geographical coordinates from the image metadata, geo referencing all pixels on the image. In spite of excessive memory consume on hyperspectral images regions of interest, is possible to perform segmentation, using bands chosen by user that can be combined in different ways to obtain different results.

Extractables characterization for five materials of construction representative of packaging systems used for parenteral and ophthalmic drug products.

PubMed

Jenke, Dennis; Castner, James; Egert, Thomas; Feinberg, Tom; Hendricker, Alan; Houston, Christopher; Hunt, Desmond G; Lynch, Michael; Shaw, Arthur; Nicholas, Kumudini; Norwood, Daniel L; Paskiet, Diane; Ruberto, Michael; Smith, Edward J; Holcomb, Frank

2013-01-01

Polymeric and elastomeric materials are commonly encountered in medical devices and packaging systems used to manufacture, store, deliver, and/or administer drug products. Characterizing extractables from such materials is a necessary step in establishing their suitability for use in these applications. In this study, five individual materials representative of polymers and elastomers commonly used in packaging systems and devices were extracted under conditions and with solvents that are relevant to parenteral and ophthalmic drug products (PODPs). Extraction methods included elevated temperature sealed vessel extraction, sonication, refluxing, and Soxhlet extraction. Extraction solvents included a low-pH (pH = 2.5) salt mixture, a high-pH (pH = 9.5) phosphate buffer, a 1/1 isopropanol/water mixture, isopropanol, and hexane. The resulting extracts were chemically characterized via spectroscopic and chromatographic means to establish the metal/trace element and organic extractables profiles. Additionally, the test articles themselves were tested for volatile organic substances. The results of this testing established the extractables profiles of the test articles, which are reported herein. Trends in the extractables, and their estimated concentrations, as a function of the extraction and testing methodologies are considered in the context of the use of the test article in medical applications and with respect to establishing best demonstrated practices for extractables profiling of materials used in PODP-related packaging systems and devices. Plastic and rubber materials are commonly encountered in medical devices and packaging/delivery systems for drug products. Characterizing the extractables from these materials is an important part of determining that they are suitable for use. In this study, five materials representative of plastics and rubbers used in packaging and medical devices were extracted by several means, and the extracts were analytically characterized to establish each material's profile of extracted organic compounds and trace element/metals. This information was utilized to make generalizations about the appropriateness of the test methods and the appropriate use of the test materials.

Evolving the capacity to understand actions, intentions, and goals.

PubMed

Hauser, Marc; Wood, Justin

2010-01-01

We synthesize the contrasting predictions of motor simulation and teleological theories of action comprehension and present evidence from a series of studies showing that monkeys and apes-like humans-extract the meaning of an event by (a) going beyond the surface appearance of actions, attributing goals and intentions to the agent; (b) using details about the environment to infer when an action is rational or irrational; (c) making predictions about an agent's goal and the most probable action to obtain the goal, within the constraints of the situation; (d) predicting the most probable outcome of actions even when they are physiologically incapable of producing the actions; and (e) combining information about means and outcomes to make decisions about social interactions, some with moral relevance. These studies reveal the limitations of motor simulation theories, especially those that rely on the notion of direct matching and mirror neuron activation. They provide support, however, for a teleological theory, rooted in an inferential process that extracts information about action means, potential goals, and the environmental constraints that limit rational action.

Extracting Semantic Building Models from Aerial Stereo Images and Conversion to Citygml

NASA Astrophysics Data System (ADS)

Sengul, A.

2012-07-01

The collection of geographic data is of primary importance for the creation and maintenance of a GIS. Traditionally the acquisition of 3D information has been the task of photogrammetry using aerial stereo images. Digital photogrammetric systems employ sophisticated software to extract digital terrain models or to plot 3D objects. The demand for 3D city models leads to new applications and new standards. City Geography Mark-up Language (CityGML), a concept for modelling and exchange of 3D city and landscape models, defines the classes and relations for the most relevant topographic objects in cities and regional models with respect to their geometrical, topological, semantically and topological properties. It now is increasingly accepted, since it fulfils the prerequisites required e.g. for risk analysis, urban planning, and simulations. There is a need to include existing 3D information derived from photogrammetric processes in CityGML databases. In order to filling the gap, this paper reports on a framework transferring data plotted by Erdas LPS and Stereo Analyst for ArcGIS software to CityGML using Safe Software's Feature Manupulate Engine (FME)

Processing of Crawled Urban Imagery for Building Use Classification

NASA Astrophysics Data System (ADS)

Tutzauer, P.; Haala, N.

2017-05-01

Recent years have shown a shift from pure geometric 3D city models to data with semantics. This is induced by new applications (e.g. Virtual/Augmented Reality) and also a requirement for concepts like Smart Cities. However, essential urban semantic data like building use categories is often not available. We present a first step in bridging this gap by proposing a pipeline to use crawled urban imagery and link it with ground truth cadastral data as an input for automatic building use classification. We aim to extract this city-relevant semantic information automatically from Street View (SV) imagery. Convolutional Neural Networks (CNNs) proved to be extremely successful for image interpretation, however, require a huge amount of training data. Main contribution of the paper is the automatic provision of such training datasets by linking semantic information as already available from databases provided from national mapping agencies or city administrations to the corresponding façade images extracted from SV. Finally, we present first investigations with a CNN and an alternative classifier as a proof of concept.

Wire bonding quality monitoring via refining process of electrical signal from ultrasonic generator

NASA Astrophysics Data System (ADS)

Feng, Wuwei; Meng, Qingfeng; Xie, Youbo; Fan, Hong

2011-04-01

In this paper, a technique for on-line quality detection of ultrasonic wire bonding is developed. The electrical signals from the ultrasonic generator supply, namely, voltage and current, are picked up by a measuring circuit and transformed into digital signals by a data acquisition system. A new feature extraction method is presented to characterize the transient property of the electrical signals and further evaluate the bond quality. The method includes three steps. First, the captured voltage and current are filtered by digital bandpass filter banks to obtain the corresponding subband signals such as fundamental signal, second harmonic, and third harmonic. Second, each subband envelope is obtained using the Hilbert transform for further feature extraction. Third, the subband envelopes are, respectively, separated into three phases, namely, envelope rising, stable, and damping phases, to extract the tiny waveform changes. The different waveform features are extracted from each phase of these subband envelopes. The principal components analysis (PCA) method is used for the feature selection in order to remove the relevant information and reduce the dimension of original feature variables. Using the selected features as inputs, an artificial neural network (ANN) is constructed to identify the complex bond fault pattern. By analyzing experimental data with the proposed feature extraction method and neural network, the results demonstrate the advantages of the proposed feature extraction method and the constructed artificial neural network in detecting and identifying bond quality.

Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval.

PubMed

Ferreira, José Raniery; de Azevedo-Marques, Paulo Mazzoncini; Oliveira, Marcelo Costa

2017-03-01

Lung cancer is the leading cause of cancer-related deaths in the world. Its diagnosis is a challenge task to specialists due to several aspects on the classification of lung nodules. Therefore, it is important to integrate content-based image retrieval methods on the lung nodule classification process, since they are capable of retrieving similar cases from databases that were previously diagnosed. However, this mechanism depends on extracting relevant image features in order to obtain high efficiency. The goal of this paper is to perform the selection of 3D image features of margin sharpness and texture that can be relevant on the retrieval of similar cancerous and benign lung nodules. A total of 48 3D image attributes were extracted from the nodule volume. Border sharpness features were extracted from perpendicular lines drawn over the lesion boundary. Second-order texture features were extracted from a cooccurrence matrix. Relevant features were selected by a correlation-based method and a statistical significance analysis. Retrieval performance was assessed according to the nodule's potential malignancy on the 10 most similar cases and by the parameters of precision and recall. Statistical significant features reduced retrieval performance. Correlation-based method selected 2 margin sharpness attributes and 6 texture attributes and obtained higher precision compared to all 48 extracted features on similar nodule retrieval. Feature space dimensionality reduction of 83 % obtained higher retrieval performance and presented to be a computationaly low cost method of retrieving similar nodules for the diagnosis of lung cancer.

Visualizing unstructured patient data for assessing diagnostic and therapeutic history.

PubMed

Deng, Yihan; Denecke, Kerstin

2014-01-01

Having access to relevant patient data is crucial for clinical decision making. The data is often documented in unstructured texts and collected in the electronic health record. In this paper, we evaluate an approach to visualize information extracted from clinical documents by means of tag cloud. Tag clouds will be generated using a bag of word approach and by exploiting part of speech tags. For a real word data set comprising radiological reports, pathological reports and surgical operation reports, tag clouds are generated and a questionnaire-based study is conducted as evaluation. Feedback from the physicians shows that the tag cloud visualization is an effective and rapid approach to represent relevant parts of unstructured patient data. To handle the different medical narratives, we have summarized several possible improvements according to the user feedback and evaluation results.

Computational Tools for Metabolic Engineering

PubMed Central

Copeland, Wilbert B.; Bartley, Bryan A.; Chandran, Deepak; Galdzicki, Michal; Kim, Kyung H.; Sleight, Sean C.; Maranas, Costas D.; Sauro, Herbert M.

2012-01-01

A great variety of software applications are now employed in the metabolic engineering field. These applications have been created to support a wide range of experimental and analysis techniques. Computational tools are utilized throughout the metabolic engineering workflow to extract and interpret relevant information from large data sets, to present complex models in a more manageable form, and to propose efficient network design strategies. In this review, we present a number of tools that can assist in modifying and understanding cellular metabolic networks. The review covers seven areas of relevance to metabolic engineers. These include metabolic reconstruction efforts, network visualization, nucleic acid and protein engineering, metabolic flux analysis, pathway prospecting, post-structural network analysis and culture optimization. The list of available tools is extensive and we can only highlight a small, representative portion of the tools from each area. PMID:22629572

Political and Institutional Influences on the Use of Evidence in Public Health Policy. A Systematic Review

PubMed Central

Liverani, Marco; Hawkins, Benjamin; Parkhurst, Justin O.

2013-01-01

Background There is increasing recognition that the development of evidence-informed health policy is not only a technical problem of knowledge exchange or translation, but also a political challenge. Yet, while political scientists have long considered the nature of political systems, the role of institutional structures, and the political contestation of policy issues as central to understanding policy decisions, these issues remain largely unexplored by scholars of evidence-informed policy making. Methods We conducted a systematic review of empirical studies that examined the influence of key features of political systems and institutional mechanisms on evidence use, and contextual factors that may contribute to the politicisation of health evidence. Eligible studies were identified through searches of seven health and social sciences databases, websites of relevant organisations, the British Library database, and manual searches of academic journals. Relevant findings were extracted using a uniform data extraction tool and synthesised by narrative review. Findings 56 studies were selected for inclusion. Relevant political and institutional aspects affecting the use of health evidence included the level of state centralisation and democratisation, the influence of external donors and organisations, the organisation and function of bureaucracies, and the framing of evidence in relation to social norms and values. However, our understanding of such influences remains piecemeal given the limited number of empirical analyses on this subject, the paucity of comparative works, and the limited consideration of political and institutional theory in these studies. Conclusions This review highlights the need for a more explicit engagement with the political and institutional factors affecting the use of health evidence in decision-making. A more nuanced understanding of evidence use in health policy making requires both additional empirical studies of evidence use, and an engagement with theories and approaches beyond the current remit of public health or knowledge utilisation studies. PMID:24204823

HEALTH GeoJunction: place-time-concept browsing of health publications.

PubMed

MacEachren, Alan M; Stryker, Michael S; Turton, Ian J; Pezanowski, Scott

2010-05-18

The volume of health science publications is escalating rapidly. Thus, keeping up with developments is becoming harder as is the task of finding important cross-domain connections. When geographic location is a relevant component of research reported in publications, these tasks are more difficult because standard search and indexing facilities have limited or no ability to identify geographic foci in documents. This paper introduces HEALTH GeoJunction, a web application that supports researchers in the task of quickly finding scientific publications that are relevant geographically and temporally as well as thematically. HEALTH GeoJunction is a geovisual analytics-enabled web application providing: (a) web services using computational reasoning methods to extract place-time-concept information from bibliographic data for documents and (b) visually-enabled place-time-concept query, filtering, and contextualizing tools that apply to both the documents and their extracted content. This paper focuses specifically on strategies for visually-enabled, iterative, facet-like, place-time-concept filtering that allows analysts to quickly drill down to scientific findings of interest in PubMed abstracts and to explore relations among abstracts and extracted concepts in place and time. The approach enables analysts to: find publications without knowing all relevant query parameters, recognize unanticipated geographic relations within and among documents in multiple health domains, identify the thematic emphasis of research targeting particular places, notice changes in concepts over time, and notice changes in places where concepts are emphasized. PubMed is a database of over 19 million biomedical abstracts and citations maintained by the National Center for Biotechnology Information; achieving quick filtering is an important contribution due to the database size. Including geography in filters is important due to rapidly escalating attention to geographic factors in public health. The implementation of mechanisms for iterative place-time-concept filtering makes it possible to narrow searches efficiently and quickly from thousands of documents to a small subset that meet place-time-concept constraints. Support for a more-like-this query creates the potential to identify unexpected connections across diverse areas of research. Multi-view visualization methods support understanding of the place, time, and concept components of document collections and enable comparison of filtered query results to the full set of publications.

Enhanced visualization of the retinal vasculature using depth information in OCT.

PubMed

de Moura, Joaquim; Novo, Jorge; Charlón, Pablo; Barreira, Noelia; Ortega, Marcos

2017-12-01

Retinal vessel tree extraction is a crucial step for analyzing the microcirculation, a frequently needed process in the study of relevant diseases. To date, this has normally been done by using 2D image capture paradigms, offering a restricted visualization of the real layout of the retinal vasculature. In this work, we propose a new approach that automatically segments and reconstructs the 3D retinal vessel tree by combining near-infrared reflectance retinography information with Optical Coherence Tomography (OCT) sections. Our proposal identifies the vessels, estimates their calibers, and obtains the depth at all the positions of the entire vessel tree, thereby enabling the reconstruction of the 3D layout of the complete arteriovenous tree for subsequent analysis. The method was tested using 991 OCT images combined with their corresponding near-infrared reflectance retinography. The different stages of the methodology were validated using the opinion of an expert as a reference. The tests offered accurate results, showing coherent reconstructions of the 3D vasculature that can be analyzed in the diagnosis of relevant diseases affecting the retinal microcirculation, such as hypertension or diabetes, among others.

Accounting for unintended binding events in the analysis of quartz crystal microbalance kinetic data.

PubMed

Heller, Gabriella T; Zwang, Theodore J; Sarapata, Elizabeth A; Haber, Michael A; Sazinsky, Matthew H; Radunskaya, Ami E; Johal, Malkiat S

2014-05-01

Previous methods for analyzing protein-ligand binding events using the quartz crystal microbalance with dissipation monitoring (QCM-D) fail to account for unintended binding that inevitably occurs during surface measurements and obscure kinetic information. In this article, we present a system of differential equations that accounts for both reversible and irreversible unintended interactions. This model is tested on three protein-ligand systems, each of which has different features, to establish the feasibility of using the QCM-D for protein binding analysis. Based on this analysis, we were able to obtain kinetic information for the intended interaction that is consistent with those obtained in literature via bulk-phase methods. In the appendix, we include a method for decoupling these from the intended binding events and extracting relevant affinity information. Copyright © 2014 Elsevier B.V. All rights reserved.

Harnessing Biomedical Natural Language Processing Tools to Identify Medicinal Plant Knowledge from Historical Texts.

PubMed

Sharma, Vivekanand; Law, Wayne; Balick, Michael J; Sarkar, Indra Neil

2017-01-01

The growing amount of data describing historical medicinal uses of plants from digitization efforts provides the opportunity to develop systematic approaches for identifying potential plant-based therapies. However, the task of cataloguing plant use information from natural language text is a challenging task for ethnobotanists. To date, there have been only limited adoption of informatics approaches used for supporting the identification of ethnobotanical information associated with medicinal uses. This study explored the feasibility of using biomedical terminologies and natural language processing approaches for extracting relevant plant-associated therapeutic use information from historical biodiversity literature collection available from the Biodiversity Heritage Library. The results from this preliminary study suggest that there is potential utility of informatics methods to identify medicinal plant knowledge from digitized resources as well as highlight opportunities for improvement.

Harnessing Biomedical Natural Language Processing Tools to Identify Medicinal Plant Knowledge from Historical Texts

PubMed Central

Sharma, Vivekanand; Law, Wayne; Balick, Michael J.; Sarkar, Indra Neil

2017-01-01

The growing amount of data describing historical medicinal uses of plants from digitization efforts provides the opportunity to develop systematic approaches for identifying potential plant-based therapies. However, the task of cataloguing plant use information from natural language text is a challenging task for ethnobotanists. To date, there have been only limited adoption of informatics approaches used for supporting the identification of ethnobotanical information associated with medicinal uses. This study explored the feasibility of using biomedical terminologies and natural language processing approaches for extracting relevant plant-associated therapeutic use information from historical biodiversity literature collection available from the Biodiversity Heritage Library. The results from this preliminary study suggest that there is potential utility of informatics methods to identify medicinal plant knowledge from digitized resources as well as highlight opportunities for improvement. PMID:29854223

Use of keyword hierarchies to interpret gene expression patterns.

PubMed

Masys, D R; Welsh, J B; Lynn Fink, J; Gribskov, M; Klacansky, I; Corbeil, J

2001-04-01

High-density microarray technology permits the quantitative and simultaneous monitoring of thousands of genes. The interpretation challenge is to extract relevant information from this large amount of data. A growing variety of statistical analysis approaches are available to identify clusters of genes that share common expression characteristics, but provide no information regarding the biological similarities of genes within clusters. The published literature provides a potential source of information to assist in interpretation of clustering results. We describe a data mining method that uses indexing terms ('keywords') from the published literature linked to specific genes to present a view of the conceptual similarity of genes within a cluster or group of interest. The method takes advantage of the hierarchical nature of Medical Subject Headings used to index citations in the MEDLINE database, and the registry numbers applied to enzymes.

The singular nature of auditory and visual scene analysis in autism

PubMed Central

Lin, I.-Fan; Shirama, Aya; Kato, Nobumasa

2017-01-01

Individuals with autism spectrum disorder often have difficulty acquiring relevant auditory and visual information in daily environments, despite not being diagnosed as hearing impaired or having low vision. Resent psychophysical and neurophysiological studies have shown that autistic individuals have highly specific individual differences at various levels of information processing, including feature extraction, automatic grouping and top-down modulation in auditory and visual scene analysis. Comparison of the characteristics of scene analysis between auditory and visual modalities reveals some essential commonalities, which could provide clues about the underlying neural mechanisms. Further progress in this line of research may suggest effective methods for diagnosing and supporting autistic individuals. This article is part of the themed issue ‘Auditory and visual scene analysis'. PMID:28044025

Feasibility of extracting data from electronic medical records for research: an international comparative study.

PubMed

van Velthoven, Michelle Helena; Mastellos, Nikolaos; Majeed, Azeem; O'Donoghue, John; Car, Josip

2016-07-13

Electronic medical records (EMR) offer a major potential for secondary use of data for research which can improve the safety, quality and efficiency of healthcare. They also enable the measurement of disease burden at the population level. However, the extent to which this is feasible in different countries is not well known. This study aimed to: 1) assess information governance procedures for extracting data from EMR in 16 countries; and 2) explore the extent of EMR adoption and the quality and consistency of EMR data in 7 countries, using management of diabetes type 2 patients as an exemplar. We included 16 countries from Australia, Asia, the Middle East, and Europe to the Americas. We undertook a multi-method approach including both an online literature review and structured interviews with 59 stakeholders, including 25 physicians, 23 academics, 7 EMR providers, and 4 information commissioners. Data were analysed and synthesised thematically considering the most relevant issues. We found that procedures for information governance, levels of adoption and data quality varied across the countries studied. The required time and ease of obtaining approval also varies widely. While some countries seem ready for secondary uses of data from EMR, in other countries several barriers were found, including limited experience with using EMR data for research, lack of standard policies and procedures, bureaucracy, confidentiality, data security concerns, technical issues and costs. This is the first international comparative study to shed light on the feasibility of extracting EMR data across a number of countries. The study will inform future discussions and development of policies that aim to accelerate the adoption of EMR systems in high and middle income countries and seize the rich potential for secondary use of data arising from the use of EMR solutions.

MememxGATE: Unearthing Latent Content Features for Improved Search and Relevancy Ranking Across Scientific Literature

NASA Astrophysics Data System (ADS)

Wilson, B. D.; McGibbney, L. J.; Mattmann, C. A.; Ramirez, P.; Joyce, M.; Whitehall, K. D.

2015-12-01

Quantifying scientific relevancy is of increasing importance to NASA and the research community. Scientific relevancy may be defined by mapping the impacts of a particular NASA mission, instrument, and/or retrieved variables to disciplines such as climate predictions, natural hazards detection and mitigation processes, education, and scientific discoveries. Related to relevancy, is the ability to expose data with similar attributes. This in turn depends upon the ability for us to extract latent, implicit document features from scientific data and resources and make them explicit, accessible and useable for search activities amongst others. This paper presents MemexGATE; a server side application, command line interface and computing environment for running large scale metadata extraction, general architecture text engineering, document classification and indexing tasks over document resources such as social media streams, scientific literature archives, legal documentation, etc. This work builds on existing experiences using MemexGATE (funded, developed and validated through the DARPA Memex Progrjam PI Mattmann) for extracting and leveraging latent content features from document resources within the Materials Research domain. We extend the software functionality capability to the domain of scientific literature with emphasis on the expansion of gazetteer lists, named entity rules, natural language construct labeling (e.g. synonym, antonym, hyponym, etc.) efforts to enable extraction of latent content features from data hosted by wide variety of scientific literature vendors (AGU Meeting Abstract Database, Springer, Wiley Online, Elsevier, etc.) hosting earth science literature. Such literature makes both implicit and explicit references to NASA datasets and relationships between such concepts stored across EOSDIS DAAC's hence we envisage that a significant part of this effort will also include development and understanding of relevancy signals which can ultimately be utilized for improved search and relevancy ranking across scientific literature.

Gender differences in the impact of population-level alcohol policy interventions: evidence synthesis of systematic reviews.

PubMed

Fitzgerald, Niamh; Angus, Kathryn; Emslie, Carol; Shipton, Deborah; Bauld, Linda

2016-10-01

Consistent review-level evidence supports the effectiveness of population-level alcohol policies in reducing alcohol-related harms. Such policies interact with well-established social, cultural and biological differences in how men and women perceive, relate to and use alcohol, and with wider inequalities, in ways which may give rise to gender differences in policy effectiveness. This paper aimed to examine the extent to which gender-specific data and analyses were considered in, and are available from, systematic reviews of population-level alcohol policy interventions, and where possible, to conduct a narrative synthesis of relevant data. A prior systematic 'review of reviews' of population level alcohol interventions 2002-2012 was updated to May 2014, all gender-relevant data extracted, and the level and quality of gender reporting assessed. A narrative synthesis of extracted findings was conducted. Sixty-three systematic reviews, covering ten policy areas, were included. Five reviews (8%) consistently provided information on baseline participation by gender for each individual study in the review and twenty-nine (46%) reported some gender-specific information on the impact of the policies under consideration. Specific findings include evidence of possible gender differences in the impact of and exposure to alcohol marketing, and a failure to consider potential unintended consequences and harm to others in most reviews. Gender is poorly reported in systematic reviews of population-level interventions to reduce alcohol-related harm, hindering assessment of the intended and unintended effects of such policies on women and men. © 2016 Society for the Study of Addiction.

Coroners' recommendations for prevention of resident deaths in aged care: The role of primary care providers

PubMed

Aitken, Georgia; Demosthenous, Athena; Bugeja, Lyndal; Willoughby, Melissa; Young, Carmel; E Ibrahim, Joseph

2018-05-01

Currently, very little is known about how coroners consider a role for general practitioners (GPs) and registered nurses (RNs) in recommendations for the prevention of premature death. Involving these professions in recommendations generally directed towards government organisations or residential aged care providers and management may contribute to more successful broader policy changes. The aim of this article was to examine whether coroners' recommendations describe a specific role for GPs and RNs in the prevention of premature death in residential aged care settings and, if so, what domains of practice were considered. This study was part of a larger retrospective cohort study. The National Coronial Information System (NCIS) was used to extract coroners' reports that included recommendations directed towards GPs and RNs. The following information was extracted: mechanism of death, incident location, text of coroners' recommendations. Of 162 unique recommendations, 14 (8.6%) were relevant to GPs and 10 (6.2%) were relevant to RNs. Most recommendations were made in the domains of 'applied professional knowledge and skills', 'organisations and legal dimensions' and 'provision and coordination of care'. Recommendations were primarily made in response to natural cause deaths and complications of clinical care. Coroners' recommendations have a limited focus directed towards GPs and RNs, and recommendations focus on their roles in application of skills and knowledge, legal domains, and provision and coordination of care. Recommendations were mainly made in response to deaths due to suboptimal care or from 'complications of clinical care'. Formulating recommendations for these health professions may increase accountability and the likelihood of a recommendation being effectively implemented.

Adverse Drug Reaction Identification and Extraction in Social Media: A Scoping Review

PubMed Central

Bellet, Florelle; Asfari, Hadyl; Souvignet, Julien; Texier, Nathalie; Jaulent, Marie-Christine; Beyens, Marie-Noëlle; Burgun, Anita; Bousquet, Cédric

2015-01-01

Background The underreporting of adverse drug reactions (ADRs) through traditional reporting channels is a limitation in the efficiency of the current pharmacovigilance system. Patients’ experiences with drugs that they report on social media represent a new source of data that may have some value in postmarketing safety surveillance. Objective A scoping review was undertaken to explore the breadth of evidence about the use of social media as a new source of knowledge for pharmacovigilance. Methods Daubt et al’s recommendations for scoping reviews were followed. The research questions were as follows: How can social media be used as a data source for postmarketing drug surveillance? What are the available methods for extracting data? What are the different ways to use these data? We queried PubMed, Embase, and Google Scholar to extract relevant articles that were published before June 2014 and with no lower date limit. Two pairs of reviewers independently screened the selected studies and proposed two themes of review: manual ADR identification (theme 1) and automated ADR extraction from social media (theme 2). Descriptive characteristics were collected from the publications to create a database for themes 1 and 2. Results Of the 1032 citations from PubMed and Embase, 11 were relevant to the research question. An additional 13 citations were added after further research on the Internet and in reference lists. Themes 1 and 2 explored 11 and 13 articles, respectively. Ways of approaching the use of social media as a pharmacovigilance data source were identified. Conclusions This scoping review noted multiple methods for identifying target data, extracting them, and evaluating the quality of medical information from social media. It also showed some remaining gaps in the field. Studies related to the identification theme usually failed to accurately assess the completeness, quality, and reliability of the data that were analyzed from social media. Regarding extraction, no study proposed a generic approach to easily adding a new site or data source. Additional studies are required to precisely determine the role of social media in the pharmacovigilance system. PMID:26163365

Adverse Drug Reaction Identification and Extraction in Social Media: A Scoping Review.

PubMed

Lardon, Jérémy; Abdellaoui, Redhouane; Bellet, Florelle; Asfari, Hadyl; Souvignet, Julien; Texier, Nathalie; Jaulent, Marie-Christine; Beyens, Marie-Noëlle; Burgun, Anita; Bousquet, Cédric

2015-07-10

The underreporting of adverse drug reactions (ADRs) through traditional reporting channels is a limitation in the efficiency of the current pharmacovigilance system. Patients' experiences with drugs that they report on social media represent a new source of data that may have some value in postmarketing safety surveillance. A scoping review was undertaken to explore the breadth of evidence about the use of social media as a new source of knowledge for pharmacovigilance. Daubt et al's recommendations for scoping reviews were followed. The research questions were as follows: How can social media be used as a data source for postmarketing drug surveillance? What are the available methods for extracting data? What are the different ways to use these data? We queried PubMed, Embase, and Google Scholar to extract relevant articles that were published before June 2014 and with no lower date limit. Two pairs of reviewers independently screened the selected studies and proposed two themes of review: manual ADR identification (theme 1) and automated ADR extraction from social media (theme 2). Descriptive characteristics were collected from the publications to create a database for themes 1 and 2. Of the 1032 citations from PubMed and Embase, 11 were relevant to the research question. An additional 13 citations were added after further research on the Internet and in reference lists. Themes 1 and 2 explored 11 and 13 articles, respectively. Ways of approaching the use of social media as a pharmacovigilance data source were identified. This scoping review noted multiple methods for identifying target data, extracting them, and evaluating the quality of medical information from social media. It also showed some remaining gaps in the field. Studies related to the identification theme usually failed to accurately assess the completeness, quality, and reliability of the data that were analyzed from social media. Regarding extraction, no study proposed a generic approach to easily adding a new site or data source. Additional studies are required to precisely determine the role of social media in the pharmacovigilance system.

Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System.

PubMed

Roth, Zvi N

2016-01-01

Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream.

Functional MRI Representational Similarity Analysis Reveals a Dissociation between Discriminative and Relative Location Information in the Human Visual System

PubMed Central

Roth, Zvi N.

2016-01-01

Neural responses in visual cortex are governed by a topographic mapping from retinal locations to cortical responses. Moreover, at the voxel population level early visual cortex (EVC) activity enables accurate decoding of stimuli locations. However, in many cases information enabling one to discriminate between locations (i.e., discriminative information) may be less relevant than information regarding the relative location of two objects (i.e., relative information). For example, when planning to grab a cup, determining whether the cup is located at the same retinal location as the hand is hardly relevant, whereas the location of the cup relative to the hand is crucial for performing the action. We have previously used multivariate pattern analysis techniques to measure discriminative location information, and found the highest levels in EVC, in line with other studies. Here we show, using representational similarity analysis, that availability of discriminative information in fMRI activation patterns does not entail availability of relative information. Specifically, we find that relative location information can be reliably extracted from activity patterns in posterior intraparietal sulcus (pIPS), but not from EVC, where we find the spatial representation to be warped. We further show that this variability in relative information levels between regions can be explained by a computational model based on an array of receptive fields. Moreover, when the model's receptive fields are extended to include inhibitory surround regions, the model can account for the spatial warping in EVC. These results demonstrate how size and shape properties of receptive fields in human visual cortex contribute to the transformation of discriminative spatial representations into relative spatial representations along the visual stream. PMID:27242455

The reporting of theoretical health risks by the media: Canadian newspaper reporting of potential blood transmission of Creutzfeldt-Jakob disease

PubMed Central

Wilson, Kumanan; Code, Catherine; Dornan, Christopher; Ahmad, Nadya; Hébert, Paul; Graham, Ian

2004-01-01

Background The media play an important role at the interface of science and policy by communicating scientific information to the public and policy makers. In issues of theoretical risk, in which there is scientific uncertainty, the media's role as disseminators of information is particularly important due to the potential to influence public perception of the severity of the risk. In this article we describe how the Canadian print media reported the theoretical risk of blood transmission of Creutzfeldt-Jakob disease (CJD). Methods We searched 3 newspaper databases for articles published by 6 major Canadian daily newspapers between January 1990 and December 1999. We identified all articles relating to blood transmission of CJD. In duplicate we extracted information from the articles and entered the information into a qualitative software program. We compared the observations obtained from this content analysis with information obtained from a previous policy analysis examining the Canadian blood system's decision-making concerning the potential transfusion transmission of CJD. Results Our search identified 245 relevant articles. We observed that newspapers in one instance accelerated a policy decision, which had important resource and health implication, by communicating information on risk to the public. We also observed that newspapers primarily relied upon expert opinion (47 articles) as opposed to published medical evidence (28 articles) when communicating risk information. Journalists we interviewed described the challenges of balancing their responsibility to raise awareness of potential health threats with not unnecessarily arousing fear amongst the public. Conclusions Based on our findings we recommend that journalists report information from both expert opinion sources and from published studies when communicating information on risk. We also recommend researchers work more closely with journalists to assist them in identifying and appraising relevant scientific information on risk. PMID:14706119

Overview of the Cancer Genetics and Pathway Curation tasks of BioNLP Shared Task 2013

PubMed Central

2015-01-01

Background Since their introduction in 2009, the BioNLP Shared Task events have been instrumental in advancing the development of methods and resources for the automatic extraction of information from the biomedical literature. In this paper, we present the Cancer Genetics (CG) and Pathway Curation (PC) tasks, two event extraction tasks introduced in the BioNLP Shared Task 2013. The CG task focuses on cancer, emphasizing the extraction of physiological and pathological processes at various levels of biological organization, and the PC task targets reactions relevant to the development of biomolecular pathway models, defining its extraction targets on the basis of established pathway representations and ontologies. Results Six groups participated in the CG task and two groups in the PC task, together applying a wide range of extraction approaches including both established state-of-the-art systems and newly introduced extraction methods. The best-performing systems achieved F-scores of 55% on the CG task and 53% on the PC task, demonstrating a level of performance comparable to the best results achieved in similar previously proposed tasks. Conclusions The results indicate that existing event extraction technology can generalize to meet the novel challenges represented by the CG and PC task settings, suggesting that extraction methods are capable of supporting the construction of knowledge bases on the molecular mechanisms of cancer and the curation of biomolecular pathway models. The CG and PC tasks continue as open challenges for all interested parties, with data, tools and resources available from the shared task homepage. PMID:26202570

Readability, relevance and quality of the information in Spanish on the Web for patients with rheumatoid arthritis.

PubMed

Castillo-Ortiz, Jose Dionisio; Valdivia-Nuno, Jose de Jesus; Ramirez-Gomez, Andrea; Garagarza-Mariscal, Heber; Gallegos-Rios, Carlos; Flores-Hernandez, Gabriel; Hernandez-Sanchez, Luis; Brambila-Barba, Victor; Castaneda-Sanchez, Jose Juan; Barajas-Ochoa, Zalathiel; Suarez-Rico, Angel; Sanchez-Gonzalez, Jorge Manuel; Ramos-Remus, Cesar

Education is a major health determinant and one of the main independent outcome predictors in rheumatoid arthritis (RA). The use of the Internet by patients has grown exponentially in the last decade. To assess the characteristics, legibility and quality of the information available in Spanish in the Internet regarding to rheumatoid arthritis. The search was performed in Google using the phrase rheumatoid arthritis. Information from the first 30 pages was evaluated according to a pre-established format (relevance, scope, authorship, type of publication and financial objective). The quality and legibility of the pages were assessed using two validated tools, DISCERN and INFLESZ respectively. Data extraction was performed by senior medical students and evaluation was achieved by consensus. The Google search returned 323 hits but only 63% were considered relevant; 80% of them were information sites (71% discussed exclusively RA, 44% conventional treatment and 12% alternative therapies) and 12.5% had a primary financial interest. 60% of the sites were created by nonprofit organizations and 15% by medical associations. Web sites posted by medical institutions from the United States of America were better positioned in Spanish (Arthritis Foundation 4th position and American College of Rheumatology 10th position) than web sites posted by Spanish speaking countries. There is a risk of disinformation for patients with RA that use the Internet. We identified a window of opportunity for rheumatology medical institutions from Spanish-speaking countries to have a more prominent societal involvement in the education of their patients with RA. Copyright © 2016 Elsevier España, S.L.U. and Sociedad Española de Reumatología y Colegio Mexicano de Reumatología. All rights reserved.

Loose-Lipped Mobile Device Intelligent Personal Assistants: A Discussion of Information Gleaned from Siri on Locked iOS Devices.

PubMed

Horsman, Graeme

2018-04-23

The forensic analysis of mobile handsets is becoming a more prominent factor in many criminal investigations. Despite such devices frequently storing relevant evidential content to support an investigation, accessing this information is becoming an increasingly difficult task due to enhanced effective security features. Where access to a device's resident data is not possible via traditional mobile forensic methods, in some cases it may still be possible to extract user information via queries made to an installed intelligent personal assistant. This article presents an evaluation of the information which is retrievable from Apple's Siri when interacted with on a locked iOS device running iOS 11.2.5 (the latest at the time of testing). The testing of verbal commands designed to elicit a response from Siri demonstrate the ability to recover call log, SMS, Contacts, Apple Maps, Calendar, and device information which may support any further investigation. © 2018 American Academy of Forensic Sciences.

From Principal Component to Direct Coupling Analysis of Coevolution in Proteins: Low-Eigenvalue Modes are Needed for Structure Prediction

PubMed Central

Cocco, Simona; Monasson, Remi; Weigt, Martin

2013-01-01

Various approaches have explored the covariation of residues in multiple-sequence alignments of homologous proteins to extract functional and structural information. Among those are principal component analysis (PCA), which identifies the most correlated groups of residues, and direct coupling analysis (DCA), a global inference method based on the maximum entropy principle, which aims at predicting residue-residue contacts. In this paper, inspired by the statistical physics of disordered systems, we introduce the Hopfield-Potts model to naturally interpolate between these two approaches. The Hopfield-Potts model allows us to identify relevant ‘patterns’ of residues from the knowledge of the eigenmodes and eigenvalues of the residue-residue correlation matrix. We show how the computation of such statistical patterns makes it possible to accurately predict residue-residue contacts with a much smaller number of parameters than DCA. This dimensional reduction allows us to avoid overfitting and to extract contact information from multiple-sequence alignments of reduced size. In addition, we show that low-eigenvalue correlation modes, discarded by PCA, are important to recover structural information: the corresponding patterns are highly localized, that is, they are concentrated in few sites, which we find to be in close contact in the three-dimensional protein fold. PMID:23990764

Machine learning-based diagnosis of melanoma using macro images.

PubMed

Gautam, Diwakar; Ahmed, Mushtaq; Meena, Yogesh Kumar; Ul Haq, Ahtesham

2018-05-01

Cancer bears a poisoning threat to human society. Melanoma, the skin cancer, originates from skin layers and penetrates deep into subcutaneous layers. There exists an extensive research in melanoma diagnosis using dermatoscopic images captured through a dermatoscope. While designing a diagnostic model for general handheld imaging systems is an emerging trend, this article proposes a computer-aided decision support system for macro images captured by a general-purpose camera. General imaging conditions are adversely affected by nonuniform illumination, which further affects the extraction of relevant information. To mitigate it, we process an image to define a smooth illumination surface using the multistage illumination compensation approach, and the infected region is extracted using the proposed multimode segmentation method. The lesion information is numerated as a feature set comprising geometry, photometry, border series, and texture measures. The redundancy in feature set is reduced using information theory methods, and a classification boundary is modeled to distinguish benign and malignant samples using support vector machine, random forest, neural network, and fast discriminative mixed-membership-based naive Bayesian classifiers. Moreover, the experimental outcome is supported by hypothesis testing and boxplot representation for classification losses. The simulation results prove the significance of the proposed model that shows an improved performance as compared with competing arts. Copyright © 2017 John Wiley & Sons, Ltd.

Impaired visual recognition of biological motion in schizophrenia.

PubMed

Kim, Jejoong; Doop, Mikisha L; Blake, Randolph; Park, Sohee

2005-09-15

Motion perception deficits have been suggested to be an important feature of schizophrenia but the behavioral consequences of such deficits are unknown. Biological motion refers to the movements generated by living beings. The human visual system rapidly and effortlessly detects and extracts socially relevant information from biological motion. A deficit in biological motion perception may have significant consequences for detecting and interpreting social information. Schizophrenia patients and matched healthy controls were tested on two visual tasks: recognition of human activity portrayed in point-light animations (biological motion task) and a perceptual control task involving detection of a grouped figure against the background noise (global-form task). Both tasks required detection of a global form against background noise but only the biological motion task required the extraction of motion-related information. Schizophrenia patients performed as well as the controls in the global-form task, but were significantly impaired on the biological motion task. In addition, deficits in biological motion perception correlated with impaired social functioning as measured by the Zigler social competence scale [Zigler, E., Levine, J. (1981). Premorbid competence in schizophrenia: what is being measured? Journal of Consulting and Clinical Psychology, 49, 96-105.]. The deficit in biological motion processing, which may be related to the previously documented deficit in global motion processing, could contribute to abnormal social functioning in schizophrenia.

Detection of gene communities in multi-networks reveals cancer drivers

NASA Astrophysics Data System (ADS)

Cantini, Laura; Medico, Enzo; Fortunato, Santo; Caselle, Michele

2015-12-01

We propose a new multi-network-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multi-networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multi-network we studied its partition into communities. To this end we applied a consensus clustering algorithm based on state of art community detection methods. Even if our procedure is valid in principle for any pathology in this work we concentrate on gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new. The combination of the different layers of information allowed us to extract from the multi-network indications on the regulatory pattern and functional role of both the already known and the new candidate driver genes.

Cat swarm optimization based evolutionary framework for multi document summarization

NASA Astrophysics Data System (ADS)

Rautray, Rasmita; Balabantaray, Rakesh Chandra

2017-07-01

Today, World Wide Web has brought us enormous quantity of on-line information. As a result, extracting relevant information from massive data has become a challenging issue. In recent past text summarization is recognized as one of the solution to extract useful information from vast amount documents. Based on number of documents considered for summarization, it is categorized as single document or multi document summarization. Rather than single document, multi document summarization is more challenging for the researchers to find accurate summary from multiple documents. Hence in this study, a novel Cat Swarm Optimization (CSO) based multi document summarizer is proposed to address the problem of multi document summarization. The proposed CSO based model is also compared with two other nature inspired based summarizer such as Harmony Search (HS) based summarizer and Particle Swarm Optimization (PSO) based summarizer. With respect to the benchmark Document Understanding Conference (DUC) datasets, the performance of all algorithms are compared in terms of different evaluation metrics such as ROUGE score, F score, sensitivity, positive predicate value, summary accuracy, inter sentence similarity and readability metric to validate non-redundancy, cohesiveness and readability of the summary respectively. The experimental analysis clearly reveals that the proposed approach outperforms the other summarizers included in the study.

Screening of plant extracts for antimicrobial activity against bacteria and yeasts with dermatological relevance.

PubMed

Weckesser, S; Engel, K; Simon-Haarhaus, B; Wittmer, A; Pelz, K; Schempp, C M

2007-08-01

There is cumulative resistance against antibiotics of many bacteria. Therefore, the development of new antiseptics and antimicrobial agents for the treatment of skin infections is of increasing interest. We have screened six plant extracts and isolated compounds for antimicrobial effects on bacteria and yeasts with dermatological relevance. The following plant extracts have been tested: Gentiana lutea, Harpagophytum procumbens, Boswellia serrata (dry extracts), Usnea barbata, Rosmarinus officinalis and Salvia officinalis (supercritical carbon dioxide [CO2] extracts). Additionally, the following characteristic plant substances were tested: usnic acid, carnosol, carnosic acid, ursolic acid, oleanolic acid, harpagoside, boswellic acid and gentiopicroside. The extracts and compounds were tested against 29 aerobic and anaerobic bacteria and yeasts in the agar dilution test. U. barbata-extract and usnic acid were the most active compounds, especially in anaerobic bacteria. Usnea CO2-extract effectively inhibited the growth of several Gram-positive bacteria like Staphylococcus aureus (including methicillin-resistant strains - MRSA), Propionibacterium acnes and Corynebacterium species. Growth of the dimorphic yeast Malassezia furfur was also inhibited by Usnea-extract. Besides the Usnea-extract, Rosmarinus-, Salvia-, Boswellia- and Harpagophytum-extracts proved to be effective against a panel of bacteria. It is concluded that due to their antimicrobial effects some of the plant extracts may be used for the topical treatment of skin disorders like acne vulgaris and seborrhoic eczema.

An integrated method for cancer classification and rule extraction from microarray data

PubMed Central

Huang, Liang-Tsung

2009-01-01

Different microarray techniques recently have been successfully used to investigate useful information for cancer diagnosis at the gene expression level due to their ability to measure thousands of gene expression levels in a massively parallel way. One important issue is to improve classification performance of microarray data. However, it would be ideal that influential genes and even interpretable rules can be explored at the same time to offer biological insight. Introducing the concepts of system design in software engineering, this paper has presented an integrated and effective method (named X-AI) for accurate cancer classification and the acquisition of knowledge from DNA microarray data. This method included a feature selector to systematically extract the relative important genes so as to reduce the dimension and retain as much as possible of the class discriminatory information. Next, diagonal quadratic discriminant analysis (DQDA) was combined to classify tumors, and generalized rule induction (GRI) was integrated to establish association rules which can give an understanding of the relationships between cancer classes and related genes. Two non-redundant datasets of acute leukemia were used to validate the proposed X-AI, showing significantly high accuracy for discriminating different classes. On the other hand, I have presented the abilities of X-AI to extract relevant genes, as well as to develop interpretable rules. Further, a web server has been established for cancer classification and it is freely available at . PMID:19272192

Structural health monitoring feature design by genetic programming

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Todd, Michael D.

2014-09-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems.

Wireless AE Event and Environmental Monitoring for Wind Turbine Blades at Low Sampling Rates

NASA Astrophysics Data System (ADS)

Bouzid, Omar M.; Tian, Gui Y.; Cumanan, K.; Neasham, J.

Integration of acoustic wireless technology in structural health monitoring (SHM) applications introduces new challenges due to requirements of high sampling rates, additional communication bandwidth, memory space, and power resources. In order to circumvent these challenges, this chapter proposes a novel solution through building a wireless SHM technique in conjunction with acoustic emission (AE) with field deployment on the structure of a wind turbine. This solution requires a low sampling rate which is lower than the Nyquist rate. In addition, features extracted from aliased AE signals instead of reconstructing the original signals on-board the wireless nodes are exploited to monitor AE events, such as wind, rain, strong hail, and bird strike in different environmental conditions in conjunction with artificial AE sources. Time feature extraction algorithm, in addition to the principal component analysis (PCA) method, is used to extract and classify the relevant information, which in turn is used to classify or recognise a testing condition that is represented by the response signals. This proposed novel technique yields a significant data reduction during the monitoring process of wind turbine blades.

A Robust Concurrent Approach for Road Extraction and Urbanization Monitoring Based on Superpixels Acquired from Spectral Remote Sensing Images

NASA Astrophysics Data System (ADS)

Seppke, Benjamin; Dreschler-Fischer, Leonie; Wilms, Christian

2016-08-01

The extraction of road signatures from remote sensing images as a promising indicator for urbanization is a classical segmentation problem. However, some segmentation algorithms often lead to non-sufficient results. One way to overcome this problem is the usage of superpixels, that represent a locally coherent cluster of connected pixels. Superpixels allow flexible, highly adaptive segmentation approaches due to the possibility of merging as well as splitting and form new basic image entities. On the other hand, superpixels require an appropriate representation containing all relevant information about topology and geometry to maximize their advantages.In this work, we present a combined geometric and topological representation based on a special graph representation, the so-called RS-graph. Moreover, we present the use of the RS-graph by means of a case study: the extraction of partially occluded road networks in rural areas from open source (spectral) remote sensing images by tracking. In addition, multiprocessing and GPU-based parallelization is used to speed up the construction of the representation and the application.

A novel method for extraction of neural response from single channel cochlear implant auditory evoked potentials.

PubMed

Sinkiewicz, Daniel; Friesen, Lendra; Ghoraani, Behnaz

2017-02-01

Cortical auditory evoked potentials (CAEP) are used to evaluate cochlear implant (CI) patient auditory pathways, but the CI device produces an electrical artifact, which obscures the relevant information in the neural response. Currently there are multiple methods, which attempt to recover the neural response from the contaminated CAEP, but there is no gold standard, which can quantitatively confirm the effectiveness of these methods. To address this crucial shortcoming, we develop a wavelet-based method to quantify the amount of artifact energy in the neural response. In addition, a novel technique for extracting the neural response from single channel CAEPs is proposed. The new method uses matching pursuit (MP) based feature extraction to represent the contaminated CAEP in a feature space, and support vector machines (SVM) to classify the components as normal hearing (NH) or artifact. The NH components are combined to recover the neural response without artifact energy, as verified using the evaluation tool. Although it needs some further evaluation, this approach is a promising method of electrical artifact removal from CAEPs. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

Classifying patents based on their semantic content.

PubMed

Bergeaud, Antonin; Potiron, Yoann; Raimbault, Juste

2017-01-01

In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information.

Knowledge Discovery in Spectral Data by Means of Complex Networks

PubMed Central

Zanin, Massimiliano; Papo, David; Solís, José Luis González; Espinosa, Juan Carlos Martínez; Frausto-Reyes, Claudio; Anda, Pascual Palomares; Sevilla-Escoboza, Ricardo; Boccaletti, Stefano; Menasalvas, Ernestina; Sousa, Pedro

2013-01-01

In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled) subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease. PMID:24957895

Knowledge discovery in spectral data by means of complex networks.

PubMed

Zanin, Massimiliano; Papo, David; Solís, José Luis González; Espinosa, Juan Carlos Martínez; Frausto-Reyes, Claudio; Anda, Pascual Palomares; Sevilla-Escoboza, Ricardo; Jaimes-Reategui, Rider; Boccaletti, Stefano; Menasalvas, Ernestina; Sousa, Pedro

2013-03-11

In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled) subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease.

Extracting Sexual Trauma Mentions from Electronic Medical Notes Using Natural Language Processing.

PubMed

Divita, Guy; Brignone, Emily; Carter, Marjorie E; Suo, Ying; Blais, Rebecca K; Samore, Matthew H; Fargo, Jamison D; Gundlapalli, Adi V

2017-01-01

Patient history of sexual trauma is of clinical relevance to healthcare providers as survivors face adverse health-related outcomes. This paper describes a method for identifying mentions of sexual trauma within the free text of electronic medical notes. A natural language processing pipeline for information extraction was developed and scaled to handle a large corpus of electronic medical notes used for this study from US Veterans Health Administration medical facilities. The tool was used to identify sexual trauma mentions and create snippets around every asserted mention based on a domain-specific lexicon developed for this purpose. All snippets were evaluated by trained human reviewers. An overall positive predictive value (PPV) of 0.90 for identifying sexual trauma mentions from the free text and a PPV of 0.71 at the patient level are reported. The metrics are superior for records from female patients.

TriageTools: tools for partitioning and prioritizing analysis of high-throughput sequencing data.

PubMed

Fimereli, Danai; Detours, Vincent; Konopka, Tomasz

2013-04-01

High-throughput sequencing is becoming a popular research tool but carries with it considerable costs in terms of computation time, data storage and bandwidth. Meanwhile, some research applications focusing on individual genes or pathways do not necessitate processing of a full sequencing dataset. Thus, it is desirable to partition a large dataset into smaller, manageable, but relevant pieces. We present a toolkit for partitioning raw sequencing data that includes a method for extracting reads that are likely to map onto pre-defined regions of interest. We show the method can be used to extract information about genes of interest from DNA or RNA sequencing samples in a fraction of the time and disk space required to process and store a full dataset. We report speedup factors between 2.6 and 96, depending on settings and samples used. The software is available at http://www.sourceforge.net/projects/triagetools/.

First measurement of the muon neutrino charged current quasielastic double differential cross section

NASA Astrophysics Data System (ADS)

Aguilar-Arevalo, A. A.; Anderson, C. E.; Bazarko, A. O.; Brice, S. J.; Brown, B. C.; Bugel, L.; Cao, J.; Coney, L.; Conrad, J. M.; Cox, D. C.; Curioni, A.; Djurcic, Z.; Finley, D. A.; Fleming, B. T.; Ford, R.; Garcia, F. G.; Garvey, G. T.; Grange, J.; Green, C.; Green, J. A.; Hart, T. L.; Hawker, E.; Imlay, R.; Johnson, R. A.; Karagiorgi, G.; Kasper, P.; Katori, T.; Kobilarcik, T.; Kourbanis, I.; Koutsoliotas, S.; Laird, E. M.; Linden, S. K.; Link, J. M.; Liu, Y.; Liu, Y.; Louis, W. C.; Mahn, K. B. M.; Marsh, W.; Mauger, C.; McGary, V. T.; McGregor, G.; Metcalf, W.; Meyers, P. D.; Mills, F.; Mills, G. B.; Monroe, J.; Moore, C. D.; Mousseau, J.; Nelson, R. H.; Nienaber, P.; Nowak, J. A.; Osmanov, B.; Ouedraogo, S.; Patterson, R. B.; Pavlovic, Z.; Perevalov, D.; Polly, C. C.; Prebys, E.; Raaf, J. L.; Ray, H.; Roe, B. P.; Russell, A. D.; Sandberg, V.; Schirato, R.; Schmitz, D.; Shaevitz, M. H.; Shoemaker, F. C.; Smith, D.; Soderberg, M.; Sorel, M.; Spentzouris, P.; Spitz, J.; Stancu, I.; Stefanski, R. J.; Sung, M.; Tanaka, H. A.; Tayloe, R.; Tzanov, M.; van de Water, R. G.; Wascko, M. O.; White, D. H.; Wilking, M. J.; Yang, H. J.; Zeller, G. P.; Zimmerman, E. D.; MiniBooNE Collaboration

2010-05-01

A high-statistics sample of charged-current muon neutrino scattering events collected with the MiniBooNE experiment is analyzed to extract the first measurement of the double differential cross section ((d2σ)/(dTμdcos⁡θμ)) for charged-current quasielastic (CCQE) scattering on carbon. This result features minimal model dependence and provides the most complete information on this process to date. With the assumption of CCQE scattering, the absolute cross section as a function of neutrino energy (σ[Eν]) and the single differential cross section ((dσ)/(dQ2)) are extracted to facilitate comparison with previous measurements. These quantities may be used to characterize an effective axial-vector form factor of the nucleon and to improve the modeling of low-energy neutrino interactions on nuclear targets. The results are relevant for experiments searching for neutrino oscillations.

Classifying patents based on their semantic content

PubMed Central

2017-01-01

In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information. PMID:28445550

Biomedical question answering using semantic relations.

PubMed

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

2015-01-16

The proliferation of the scientific literature in the field of biomedicine makes it difficult to keep abreast of current knowledge, even for domain experts. While general Web search engines and specialized information retrieval (IR) systems have made important strides in recent decades, the problem of accurate knowledge extraction from the biomedical literature is far from solved. Classical IR systems usually return a list of documents that have to be read by the user to extract relevant information. This tedious and time-consuming work can be lessened with automatic Question Answering (QA) systems, which aim to provide users with direct and precise answers to their questions. In this work we propose a novel methodology for QA based on semantic relations extracted from the biomedical literature. We extracted semantic relations with the SemRep natural language processing system from 122,421,765 sentences, which came from 21,014,382 MEDLINE citations (i.e., the complete MEDLINE distribution up to the end of 2012). A total of 58,879,300 semantic relation instances were extracted and organized in a relational database. The QA process is implemented as a search in this database, which is accessed through a Web-based application, called SemBT (available at http://sembt.mf.uni-lj.si ). We conducted an extensive evaluation of the proposed methodology in order to estimate the accuracy of extracting a particular semantic relation from a particular sentence. Evaluation was performed by 80 domain experts. In total 7,510 semantic relation instances belonging to 2,675 distinct relations were evaluated 12,083 times. The instances were evaluated as correct 8,228 times (68%). In this work we propose an innovative methodology for biomedical QA. The system is implemented as a Web-based application that is able to provide precise answers to a wide range of questions. A typical question is answered within a few seconds. The tool has some extensions that make it especially useful for interpretation of DNA microarray results.

On the meniscus formation and the negative hydrogen ion extraction from ITER neutral beam injection relevant ion source

NASA Astrophysics Data System (ADS)

Mochalskyy, S.; Wünderlich, D.; Ruf, B.; Fantz, U.; Franzen, P.; Minea, T.

2014-10-01

The development of a large area (Asource,ITER = 0.9 × 2 m2) hydrogen negative ion (NI) source constitutes a crucial step in construction of the neutral beam injectors of the international fusion reactor ITER. To understand the plasma behaviour in the boundary layer close to the extraction system the 3D PIC MCC code ONIX is exploited. Direct cross checked analysis of the simulation and experimental results from the ITER-relevant BATMAN source testbed with a smaller area (Asource,BATMAN ≈ 0.32 × 0.59 m2) has been conducted for a low perveance beam, but for a full set of plasma parameters available. ONIX has been partially benchmarked by comparison to the results obtained using the commercial particle tracing code for positive ion extraction KOBRA3D. Very good agreement has been found in terms of meniscus position and its shape for simulations of different plasma densities. The influence of the initial plasma composition on the final meniscus structure was then investigated for NIs. As expected from the Child-Langmuir law, the results show that not only does the extraction potential play a crucial role on the meniscus formation, but also the initial plasma density and its electronegativity. For the given parameters, the calculated meniscus locates a few mm downstream of the plasma grid aperture provoking a direct NI extraction. Most of the surface produced NIs do not reach the plasma bulk, but move directly towards the extraction grid guided by the extraction field. Even for artificially increased electronegativity of the bulk plasma the extracted NI current from this region is low. This observation indicates a high relevance of the direct NI extraction. These calculations show that the extracted NI current from the bulk region is low even if a complete ion-ion plasma is assumed, meaning that direct extraction from surface produced ions should be present in order to obtain sufficiently high extracted NI current density. The calculated extracted currents, both ions and electrons, agree rather well with the experiment.

Towards more ecological relevance in sediment toxicity testing with fish: Evaluation of multiple bioassays with embryos of the benthic weatherfish (Misgurnus fossilis).

PubMed

Schreiber, Benjamin; Fischer, Jonas; Schiwy, Sabrina; Hollert, Henner; Schulz, Ralf

2018-04-01

The effects of sediment contamination on fish are of high significance for the protection of ecosystems, human health and economy. However, standardized sediment bioassays with benthic fish species, that mimic bioavailability of potentially toxic compounds and comply with the requirements of alternative test methods, are still scarce. In order to address this issue, embryos of the benthic European weatherfish (Misgurnus fossilis) were exposed to freeze-dried sediment (via sediment contact assays (SCA)) and sediment extracts (via acute fish embryo toxicity tests) varying in contamination level. The extracts were gained by accelerated solvent extraction with (i) acetone and (ii) pressurized hot water (PHWE) and subsequently analyzed for polycyclic aromatic hydrocarbons, polychlorinated biphenyls and polychlorinated dibenzodioxins and dibenzofurans. Furthermore, embryos of the predominately used zebrafish (Danio rerio) were exposed to extracts from the two most contaminated sediments. Results indicated sufficient robustness of weatherfish embryos towards varying test conditions and sensitivity towards relevant sediment-bound compounds. Furthermore, a compliance of effect concentrations derived from weatherfish embryos exposed to sediment extracts (96h-LC 50 ) with both measured gradient of sediment contamination and previously published results was observed. In comparison to zebrafish, weatherfish embryos showed higher sensitivity to the bioavailability-mimicking extracts from PHWE but lower sensitivity to extracts gained with acetone. SCAs conducted with weatherfish embryos revealed practical difficulties that prevented an implementation with three of four sediments tested. In summary, an application of weatherfish embryos, using bioassays with sediment extracts from PHWE might increase the ecological relevance of sediment toxicity testing: it allows investigations using benthic and temperate fish species considering both bioavailable contaminants and animal welfare concerns. Copyright © 2017 Elsevier B.V. All rights reserved.

Identification of Diet-Derived Constituents as Potent Inhibitors of Intestinal Glucuronidation

PubMed Central

Gufford, Brandon T.; Chen, Gang; Lazarus, Philip; Graf, Tyler N.; Oberlies, Nicholas H.

2014-01-01

Drug-metabolizing enzymes within enterocytes constitute a key barrier to xenobiotic entry into the systemic circulation. Furanocoumarins in grapefruit juice are cornerstone examples of diet-derived xenobiotics that perpetrate interactions with drugs via mechanism-based inhibition of intestinal CYP3A4. Relative to intestinal CYP3A4-mediated inhibition, alternate mechanisms underlying dietary substance–drug interactions remain understudied. A working systematic framework was applied to a panel of structurally diverse diet-derived constituents/extracts (n = 15) as inhibitors of intestinal UDP-glucuronosyl transferases (UGTs) to identify and characterize additional perpetrators of dietary substance–drug interactions. Using a screening assay involving the nonspecific UGT probe substrate 4-methylumbelliferone, human intestinal microsomes, and human embryonic kidney cell lysates overexpressing gut-relevant UGT1A isoforms, 14 diet-derived constituents/extracts inhibited UGT activity by >50% in at least one enzyme source, prompting IC50 determination. The IC50 values of 13 constituents/extracts (≤10 μM with at least one enzyme source) were well below intestinal tissue concentrations or concentrations in relevant juices, suggesting that these diet-derived substances can inhibit intestinal UGTs at clinically achievable concentrations. Evaluation of the effect of inhibitor depletion on IC50 determination demonstrated substantial impact (up to 2.8-fold shift) using silybin A and silybin B, two key flavonolignans from milk thistle (Silybum marianum) as exemplar inhibitors, highlighting an important consideration for interpretation of UGT inhibition in vitro. Results from this work will help refine a working systematic framework to identify dietary substance–drug interactions that warrant advanced modeling and simulation to inform clinical assessment. PMID:25008344

Neural mechanisms of selective attention in the somatosensory system.

PubMed

Gomez-Ramirez, Manuel; Hysaj, Kristjana; Niebur, Ernst

2016-09-01

Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates. Copyright © 2016 the American Physiological Society.

Neural mechanisms of selective attention in the somatosensory system

PubMed Central

Hysaj, Kristjana; Niebur, Ernst

2016-01-01

Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates. PMID:27334956

Visualising associations between paired ‘omics’ data sets

PubMed Central

2012-01-01

Background Each omics platform is now able to generate a large amount of data. Genomics, proteomics, metabolomics, interactomics are compiled at an ever increasing pace and now form a core part of the fundamental systems biology framework. Recently, several integrative approaches have been proposed to extract meaningful information. However, these approaches lack of visualisation outputs to fully unravel the complex associations between different biological entities. Results The multivariate statistical approaches ‘regularized Canonical Correlation Analysis’ and ‘sparse Partial Least Squares regression’ were recently developed to integrate two types of highly dimensional ‘omics’ data and to select relevant information. Using the results of these methods, we propose to revisit few graphical outputs to better understand the relationships between two ‘omics’ data and to better visualise the correlation structure between the different biological entities. These graphical outputs include Correlation Circle plots, Relevance Networks and Clustered Image Maps. We demonstrate the usefulness of such graphical outputs on several biological data sets and further assess their biological relevance using gene ontology analysis. Conclusions Such graphical outputs are undoubtedly useful to aid the interpretation of these promising integrative analysis tools and will certainly help in addressing fundamental biological questions and understanding systems as a whole. Availability The graphical tools described in this paper are implemented in the freely available R package mixOmics and in its associated web application. PMID:23148523

Development of Sample Handling and Analytical Expertise For the Stardust Comet Sample Return

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

Bradley, J; Bajt, S; Brennan, S

NASA's Stardust mission returned to Earth in January 2006 with ''fresh'' cometary particles from a young Jupiter family comet. The cometary particles were sampled during the spacecraft flyby of comet 81P/Wild-2 in January 2004, when they impacted low-density silica aerogel tiles and aluminum foils on the sample tray assembly at approximately 6.1 km/s. This LDRD project has developed extraction and sample recovery methodologies to maximize the scientific information that can be obtained from the analysis of natural and man-made nano-materials of relevance to the LLNL programs.

Joint principal trend analysis for longitudinal high-dimensional data.

PubMed

Zhang, Yuping; Ouyang, Zhengqing

2018-06-01

We consider a research scenario motivated by integrating multiple sources of information for better knowledge discovery in diverse dynamic biological processes. Given two longitudinal high-dimensional datasets for a group of subjects, we want to extract shared latent trends and identify relevant features. To solve this problem, we present a new statistical method named as joint principal trend analysis (JPTA). We demonstrate the utility of JPTA through simulations and applications to gene expression data of the mammalian cell cycle and longitudinal transcriptional profiling data in response to influenza viral infections. © 2017, The International Biometric Society.

Sudden early-morning awakening impairs immediate tactical planning in a changing 'emergency' scenario.

PubMed

Horne, Jim; Moseley, Robert

2011-06-01

This was a realistic military-type exercise assessing unexpected, abrupt early-morning awakening effects on immediate 'executive function' and the ability to comprehend and deal with a sudden emergency under a changing situation. Twenty (average age 21years) healthy, highly motivated junior officer reservists were assigned randomly to two equal, independent groups, unforewarned as to what would happen. The experimental group was woken abruptly at 03:00h (<3h sleep) and confronted immediately with a 'paper exercise' of an enemy attack, requiring a feasible plan of engagement with minimal loss of resources, to be completed within 15min. A control group slept until 07:30h; they were then presented with the identical emergency 1h later. Participants worked individually, under time pressure, receiving written information, map and other details, all containing relevant, irrelevant and misleading information. Halfway through, they were given (unexpectedly) a critical update necessitating a change of tactics. Performance was scored blind by instructors, under five categories. Eight of the experimental group versus three controls failed overall, with significant group differences on three specific categories relying on flexible decision-making: 'identification of available cover', 'use of available assets' and 'extraction of relevant from irrelevant information'. Other, logical and highly trained skills were unimpaired. Ours was a 'worst case scenario', combining short sleep, circadian 'trough' and sleep inertia, all of which differentiated the two groups, unlike typical laboratory studies. Nevertheless, it was relevant to real-life situations involving highly motivated, trained individuals making critical innovative decisions in the early morning versus the normal waking day. © 2010 European Sleep Research Society.

The proactive brain: memory for predictions

PubMed Central

Bar, Moshe

2009-01-01

It is proposed that the human brain is proactive in that it continuously generates predictions that anticipate the relevant future. In this proposal, analogies are derived from elementary information that is extracted rapidly from the input, to link that input with the representations that exist in memory. Finding an analogical link results in the generation of focused predictions via associative activation of representations that are relevant to this analogy, in the given context. Predictions in complex circumstances, such as social interactions, combine multiple analogies. Such predictions need not be created afresh in new situations, but rather rely on existing scripts in memory, which are the result of real as well as of previously imagined experiences. This cognitive neuroscience framework provides a new hypothesis with which to consider the purpose of memory, and can help explain a variety of phenomena, ranging from recognition to first impressions, and from the brain's ‘default mode’ to a host of mental disorders. PMID:19528004

THERMODYNAMICS OF FE-CU ALLOYS AS DESCRIBED BY A CLASSIC POTENTIALS

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

Caro, A; Caro, M; Lopasso, E M

2005-04-14

The Fe-Cu system is of relevance to the nuclear industry because of the deleterious consequences of Cu precipitates in the mechanical properties of Fe. Several sets of classical potentials are used in molecular dynamics simulations studies of this system, in particular that proposed by Ludwig et al. (Modelling Simul. Mater. Sci. Eng. 6, 19 (1998)). In this work we extract thermodynamic information from this interatomic potentials. We obtain equilibrium phase diagram and find a reasonable agreement with the experimental phases in the regions of relevance to radiation damage studies. We compare the results with the predicted phase diagram based onmore » other potential, as calculated in previous work. We discuss the disagreements found between the phase diagram calculated here and experimental results, focusing on the pure components and discuss the applicability of these potentials; finally we suggest an approach to improve existing potentials for this system.« less

A meta-analysis of observational studies of the association between chronic occupational exposure to lead and amyotrophic lateral sclerosis.

PubMed

Wang, Ming-Dong; Gomes, James; Cashman, Neil R; Little, Julian; Krewski, Daniel

2014-12-01

The association between occupational exposure to lead and amyotrophic lateral sclerosis (ALS) was examined through systematic review and meta-analyses of relevant epidemiological studies and reported according to PRISMA guidelines. Relevant studies were searched in multiple bibliographic databases through September 2013; additional articles were tracked through PubMed until submission. All records were screened in DistillerSR, and the data extracted from included articles were synthesized with meta-analysis. The risk of developing ALS among individuals with a history of exposure to lead was almost doubled (odds ratio, 1.81; 95% confidence interval, 1.39 to 2.36) on the basis of nine included case-control studies with specific lead exposure information, with no apparent heterogeneity across included studies (I = 14%). The attributable risk of ALS because of exposure to lead was estimated to be 5%. Previous exposure to lead may be a risk factor for ALS.

A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning.

PubMed

Arena, Paolo; Calí, Marco; Patané, Luca; Portera, Agnese; Strauss, Roland

2016-09-01

Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shapes the behavioral response. On the basis of previously developed neural models, inspired by Drosophila melanogaster, a new architecture for classification and sequence learning is here presented under the perspective of the Neural Reuse theory. Classification of relevant input stimuli is performed through resonant neurons, activated by the complex dynamics generated in a lattice of recurrent spiking neurons modeling the insect Mushroom Bodies neuropile. The network devoted to context formation is able to reconstruct the learned sequence and also to trace the subsequences present in the provided input. A sensitivity analysis to parameter variation and noise is reported. Experiments on a roving robot are reported to show the capabilities of the architecture used as a neural controller.

Automatic Decision Support for Clinical Diagnostic Literature Using Link Analysis in a Weighted Keyword Network.

PubMed

Li, Shuqing; Sun, Ying; Soergel, Dagobert

2017-12-23

We present a novel approach to recommending articles from the medical literature that support clinical diagnostic decision-making, giving detailed descriptions of the associated ideas and principles. The specific goal is to retrieve biomedical articles that help answer questions of a specified type about a particular case. Based on the filtered keywords, MeSH(Medical Subject Headings) lexicon and the automatically extracted acronyms, the relationship between keywords and articles was built. The paper gives a detailed description of the process of by which keywords were measured and relevant articles identified based on link analysis in a weighted keywords network. Some important challenges identified in this study include the extraction of diagnosis-related keywords and a collection of valid sentences based on the keyword co-occurrence analysis and existing descriptions of symptoms. All data were taken from medical articles provided in the TREC (Text Retrieval Conference) clinical decision support track 2015. Ten standard topics and one demonstration topic were tested. In each case, a maximum of five articles with the highest relevance were returned. The total user satisfaction of 3.98 was 33% higher than average. The results also suggested that the smaller the number of results, the higher the average satisfaction. However, a few shortcomings were also revealed since medical literature recommendation for clinical diagnostic decision support is so complex a topic that it cannot be fully addressed through the semantic information carried solely by keywords in existing descriptions of symptoms. Nevertheless, the fact that these articles are actually relevant will no doubt inspire future research.

Characterizing brain patterns in conversion from mild cognitive impairment (MCI) to Alzheimer's disease

NASA Astrophysics Data System (ADS)

Silva R., Santiago S.; Giraldo, Diana L.; Romero, Eduardo

2017-11-01

Structural Magnetic Resonance (MR) brain images should provide quantitative information about the stage and progression of Alzheimer's disease. However, the use of MRI is limited and practically reduced to corroborate a diagnosis already performed with neuropsychological tools. This paper presents an automated strategy for extraction of relevant anatomic patterns related with the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) using T1-weighted MR images. The process starts by representing each of the possible classes with models generated from a linear combination of volumes. The difference between models allows us to establish which are the regions where relevant patterns might be located. The approach searches patterns in a space of brain sulci, herein approximated by the most representative gradients found in regions of interest defined by the difference between the linear models. This hypothesis is assessed by training a conventional SVM model with the found relevant patterns under a leave-one-out scheme. The resultant AUC was 0.86 for the group of women and 0.61 for the group of men.

Electroencephalogram Signal Classification for Automated Epileptic Seizure Detection Using Genetic Algorithm

PubMed Central

Nanthini, B. Suguna; Santhi, B.

2017-01-01

Background: Epilepsy causes when the repeated seizure occurs in the brain. Electroencephalogram (EEG) test provides valuable information about the brain functions and can be useful to detect brain disorder, especially for epilepsy. In this study, application for an automated seizure detection model has been introduced successfully. Materials and Methods: The EEG signals are decomposed into sub-bands by discrete wavelet transform using db2 (daubechies) wavelet. The eight statistical features, the four gray level co-occurrence matrix and Renyi entropy estimation with four different degrees of order, are extracted from the raw EEG and its sub-bands. Genetic algorithm (GA) is used to select eight relevant features from the 16 dimension features. The model has been trained and tested using support vector machine (SVM) classifier successfully for EEG signals. The performance of the SVM classifier is evaluated for two different databases. Results: The study has been experimented through two different analyses and achieved satisfactory performance for automated seizure detection using relevant features as the input to the SVM classifier. Conclusion: Relevant features using GA give better accuracy performance for seizure detection. PMID:28781480

Quantitative magnetic resonance micro-imaging methods for pharmaceutical research.

PubMed

Mantle, M D

2011-09-30

The use of magnetic resonance imaging (MRI) as a tool in pharmaceutical research is now well established and the current literature covers a multitude of different pharmaceutically relevant research areas. This review focuses on the use of quantitative magnetic resonance micro-imaging techniques and how they have been exploited to extract information that is of direct relevance to the pharmaceutical industry. The article is divided into two main areas. The first half outlines the theoretical aspects of magnetic resonance and deals with basic magnetic resonance theory, the effects of nuclear spin-lattice (T(1)), spin-spin (T(2)) relaxation and molecular diffusion upon image quantitation, and discusses the applications of rapid magnetic resonance imaging techniques. In addition to the theory, the review aims to provide some practical guidelines for the pharmaceutical researcher with an interest in MRI as to which MRI pulse sequences/protocols should be used and when. The second half of the article reviews the recent advances and developments that have appeared in the literature concerning the use of quantitative micro-imaging methods to pharmaceutically relevant research. Copyright © 2010 Elsevier B.V. All rights reserved.

Eye health programs within remote Aboriginal communities in Australia: a review of the literature.

PubMed

Durkin, Shane R

2008-11-01

To review the literature regarding the most sustainable and culturally appropriate ways in which to implement eye health care programs within remote Aboriginal communities in Australia from a primary health care perspective. The search included letters, editorials and papers (published and unpublished) from January 1955 to April 2006. The search revealed 1,106,758 papers, books and other related material. The relevancy of this material was determined by abstract and 378 relevant articles were reviewed in their entirety. After reading the relevant articles and the interview transcripts the themes that emerged from each source were extracted. The ten areas to consider include: clinical practice and access, sustainability, regional-based programs, information technology systems, health worker training, self-determination, cultural and language barriers, funding body responsibilities, embedding specialist programs in primary care services, and other considerations. Further research needs to be undertaken within Aboriginal communities in the area of primary eye health care and barriers to the acceptance of treatment. This may be undertaken using more interactive research methods such as cooperative and narrative inquiry.

An Ensemble Method for Spelling Correction in Consumer Health Questions

PubMed Central

Kilicoglu, Halil; Fiszman, Marcelo; Roberts, Kirk; Demner-Fushman, Dina

2015-01-01

Orthographic and grammatical errors are a common feature of informal texts written by lay people. Health-related questions asked by consumers are a case in point. Automatic interpretation of consumer health questions is hampered by such errors. In this paper, we propose a method that combines techniques based on edit distance and frequency counts with a contextual similarity-based method for detecting and correcting orthographic errors, including misspellings, word breaks, and punctuation errors. We evaluate our method on a set of spell-corrected questions extracted from the NLM collection of consumer health questions. Our method achieves a F1 score of 0.61, compared to an informed baseline of 0.29, achieved using ESpell, a spelling correction system developed for biomedical queries. Our results show that orthographic similarity is most relevant in spelling error correction in consumer health questions and that frequency and contextual information are complementary to orthographic features. PMID:26958208

Metabolic fate of cardiac glycosides and flavonoids upon fermentation of aqueous sea squill (Drimia maritima L.) extracts.

PubMed

Knittel, Diana N; Stintzing, Florian C; Kammerer, Dietmar R

2015-06-10

Sea squill (Drimia maritima L.) extracts have been used for centuries for the medical treatment of heart diseases. A procedure for the preparation of Drimia extracts applied for such purposes comprising a fermentation step is described in the German Homoeopathic Pharmacopoeia (GHP). However, little is known about the secondary metabolite profile of such extracts and the fate of these components upon processing and storage. Thus, in the present study sea squill extracts were monitored during fermentation and storage by HPLC-DAD-MS(n) and GC-MS to characterise and quantitate individual cardiac glycosides and phenolic compounds. For this purpose, a previously established HPLC method for the separation and quantitation of pharmacologically relevant cardiac glycosides (bufadienolides) was validated. Within 12 months of storage, total bufadienolide contents decreased by about 50%, which was attributed to microbial and plant enzyme activities. The metabolisation and degradation rates of individual bufadienolide glycosides significantly differed, which was attributed to differing structures of the aglycones. Further degradation of bufadienolide aglycones was also observed. Besides reactions well known from human metabolism studies, dehydration of individual compounds was monitored. Quantitatively predominating flavonoids were also metabolised throughout the fermentation process. The present study provides valuable information about the profile and stability of individual cardiac glycosides and phenolic compounds in fermented Drimia extracts prepared for medical applications, and expands the knowledge of cardiac glycoside conversion upon microbial fermentation. Copyright © 2015 Elsevier B.V. All rights reserved.

Effect of botanical immunomodulators on human CYP3A4 inhibition: implications for concurrent use as adjuvants in cancer therapy.

PubMed

Patil, Dada; Gautam, Manish; Gairola, Sunil; Jadhav, Suresh; Patwardhan, Bhushan

2014-03-01

Many botanical immunomodulators are used as adjuvants along with cancer chemotherapy. However, information on the impact of concurrent administration of such botanicals on pharmacokinetics of chemotherapy agents is inadequate. This study investigates inhibitory activities of 3 popular botanical adjuvants: ASPARAGUS RACEMOSU: (root aqueous extract; ARE), WITHANIA SOMNIFER: (root aqueous extract; WSE), and TINOSPORA CORDIFOLI: (stem aqueous extract, TCE) on human CYP3A4 isoenzyme, responsible for metabolism of several chemotherapy agents. . Testosterone 6-β hydroxylation was monitored using high-performance liquid chromatography as an indicator of CYP3A4 catalytic activities. Ketoconazole (positive control) and extracts were studied at their in vivo-relevant concentrations. TCE showed mild inhibition while no significant inhibitory activities were observed in WSE and ARE. TCE was further fractionated to obtain polar and nonpolar fractions. The nonpolar fraction showed significant CYP3A4 inhibition with IC50 13.06 ± 1.38 µg/mL. Major constituents of nonpolar fraction were identified using HPLC-DAD-MS profiling as berberine, jatrorrhizine, and palmatine, which showed IC50 values as 6.25 ± 0.30, 15.18 ± 1.59, and 15.53 ± 1.89 µg/mL, respectively. Our findings suggest that constituents of TCE extract especially protoberberine alkaloids have the potential to interact with cancer chemotherapy agents that are metabolized by CYP3A4 in vivo.

Advanced eddy current test signal analysis for steam generator tube defect classification and characterization

NASA Astrophysics Data System (ADS)

McClanahan, James Patrick

Eddy Current Testing (ECT) is a Non-Destructive Examination (NDE) technique that is widely used in power generating plants (both nuclear and fossil) to test the integrity of heat exchanger (HX) and steam generator (SG) tubing. Specifically for this research, laboratory-generated, flawed tubing data were examined. The purpose of this dissertation is to develop and implement an automated method for the classification and an advanced characterization of defects in HX and SG tubing. These two improvements enhanced the robustness of characterization as compared to traditional bobbin-coil ECT data analysis methods. A more robust classification and characterization of the tube flaw in-situ (while the SG is on-line but not when the plant is operating), should provide valuable information to the power industry. The following are the conclusions reached from this research. A feature extraction program acquiring relevant information from both the mixed, absolute and differential data was successfully implemented. The CWT was utilized to extract more information from the mixed, complex differential data. Image Processing techniques used to extract the information contained in the generated CWT, classified the data with a high success rate. The data were accurately classified, utilizing the compressed feature vector and using a Bayes classification system. An estimation of the upper bound for the probability of error, using the Bhattacharyya distance, was successfully applied to the Bayesian classification. The classified data were separated according to flaw-type (classification) to enhance characterization. The characterization routine used dedicated, flaw-type specific ANNs that made the characterization of the tube flaw more robust. The inclusion of outliers may help complete the feature space so that classification accuracy is increased. Given that the eddy current test signals appear very similar, there may not be sufficient information to make an extremely accurate (>95%) classification or an advanced characterization using this system. It is necessary to have a larger database fore more accurate system learning.

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

PubMed Central

Ortega-Martorell, Sandra; Ruiz, Héctor; Vellido, Alfredo; Olier, Iván; Romero, Enrique; Julià-Sapé, Margarida; Martín, José D.; Jarman, Ian H.; Arús, Carles; Lisboa, Paulo J. G.

2013-01-01

Background The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal. Methodology/Principal Findings Non-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification. Conclusions/Significance We show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing. PMID:24376744

Characterization of human kidney stones using micro-PIXE and RBS: a comparative study between two different populations.

PubMed

Pineda-Vargas, C A; Eisa, M E M; Rodgers, A L

2009-03-01

The micro-PIXE and RBS techniques are used to investigate the matrix as well as the trace elemental composition of calcium-rich human tissues on a microscopic scale. This paper deals with the spatial distribution of trace metals in hard human tissues such as kidney stone concretions, undertaken at the nuclear microprobe (NMP) facility. Relevant information about ion beam techniques used for material characterization will be discussed. Mapping correlation between different trace metals to extract information related to micro-regions composition will be illustrated with an application using proton energies of 1.5 and 3.0 MeV and applied to a comparative study for human kidney stone concretions nucleation region analysis from two different population groups (Sudan and South Africa).

NIAS-Server: Neighbors Influence of Amino acids and Secondary Structures in Proteins.

PubMed

Borguesan, Bruno; Inostroza-Ponta, Mario; Dorn, Márcio

2017-03-01

The exponential growth in the number of experimentally determined three-dimensional protein structures provide a new and relevant knowledge about the conformation of amino acids in proteins. Only a few of probability densities of amino acids are publicly available for use in structure validation and prediction methods. NIAS (Neighbors Influence of Amino acids and Secondary structures) is a web-based tool used to extract information about conformational preferences of amino acid residues and secondary structures in experimental-determined protein templates. This information is useful, for example, to characterize folds and local motifs in proteins, molecular folding, and can help the solution of complex problems such as protein structure prediction, protein design, among others. The NIAS-Server and supplementary data are available at http://sbcb.inf.ufrgs.br/nias .

Techniques for Automatically Generating Biographical Summaries from News Articles

DTIC Science & Technology

2007-09-01

non-trivial because of the many NLP areas that must be used to efficiently extract the relevant facts. Yet, no study has been done to determine how...also non-trivial because of the many NLP areas that must be used to efficiently extract the relevant facts. Yet, no study has been done to determine...AI) research is called Natural Language Processing ( NLP ). NLP seeks to find ways for computers to read and write documents in as human a way as

The Burn Wound Exudate – an under-utilized resource

PubMed Central

Widgerow, Alan D; King, Kassandra; Tussardi, Ilaria Tocco; Banyard, Derek A.; Chiang, Ryan; Awad, Antony; Afzel, Hassan; Bhatnager, Shweta; Melkumyan, Satenik; Wirth, Garrett; Evans, Gregory R.D

2014-01-01

Introduction The burn wound exudate represents the burn tissue microenvironment. Extracting information from the exudate relating to cellular components, signaling mediators and protein content can provide much needed data relating to the local tissue damage, depth of the wound and probable systemic complications. This review examines the scientific data extracted from burn wound exudates over the years and proposes new investigations that will provide useful information from this underutilized resource. Method A literature review was conducted using the electronic database PubMed to search for literature pertaining to burn wound or blister fluid analysis. Key words included burn exudate, blister fluid, wound exudate, cytokine burn fluid, subeschar fluid, cytokine burns, serum cytokines. 32 relevant article were examined and 29 selected as relevant to the review. 3 papers were discarded due to questionable methodology or conclusions. The reports were assessed for their affect on management decisions and diagnostics. Furthermore, traditional blood level analysis of these mediators was made to compare the accuracy of blood versus exudate in burn wound management. Extrapolations are made for new possibilities of burn wound exudate analysis. Results Studies pertaining to burn wound exudate, subeschar fluid and blister fluid analyses may have contributed to burn wound management decisions particularly related to escharectomies and early burn wound excision. In addition, information from these studies have the potential to impact on areas such as healing, scarring, burn wound conversion and burn wound depth analysis. Conclusion Burn wound exudate analysis has proven useful in burn wound management decisions. It appears to offer a far more accurate reflection of the burn wound pathophysiology than the traditional blood/serum investigations undertaken in the past. New approaches to diagnostics and treatment efficacy assessment are possible utilizing data from this fluid. Burn wound exudate is a useful, currently under-utilized resource that is likely to take a more prominent role in burn wound management. PMID:24986597

Real-time Social Media Data Analytics for Situational Awareness of the Electric Grid

NASA Astrophysics Data System (ADS)

Mao, H.; Chinthavali, S.; Lee, S.; Shankar, M.; Thiagarajan, S.

2016-12-01

With the increasing frequency of extreme events due to climate change, wide area situational awareness (SA) of the electric grid has become a primary need for federal agencies like DOE,FEMA etc. for emergency preparedness and recovery purposes. While several sensor feeds from Genscape, GridEye, PMUs provide a comprehensive view of the transmission grid, national-scale situational awareness tools are still relying on utility websites for outage information at a distribution level. The inconsistency and the variety in outage website's data formats makes this approach unreliable and also incurs huge software maintenance costs. Social media has emerged as a great medium for the utilities to share outage information with their customers. Despite their potential usefulness, extracting relevant data from these social media data-streams is challenging due to the inherent noise and irrelevant information such as tips to customers during storms, marketing, etc. In this study, we implement a practical and novel machine learning based data-analytics pipeline (Fig 1) for SA, which extracts real-time tweets from around 300 utility companies, processes these tweets using keyword filtering and Naïve-Bayes text classifier trained using supervised learning techniques to detect only relevant tweets. We validated the results by comparing it with the results identified by a human analyst for a period of 48 hours, and it showed around 98.3% accuracy. In addition to the tweets posted by utility companies, millions of twitter users, who are considered as human "social sensors", report power outages online. Therefore, we use Twitter Streaming API to extract real-time tweets containing keywords such as "power outage", "blackout", and "power cuts". An advanced natural language processing technique is proposed to identify the geo-locations associated with this power outage data. The detected tweets are visualized as a color-coded state and a county US map based on the number of outage tweets posted. Therefore, by analyzing a large amount of tweets posted by utilities and the general public, our approach can detect real-time power outages at a national-scale. This framework has been integrated into existing SA tools such as VERDE, EARSS and EAGLE-I, which is deployed by the Oak Ridge National Laboratory for the DOE.

[Application of regular expression in extracting key information from Chinese medicine literatures about re-evaluation of post-marketing surveillance].

PubMed

Wang, Zhifei; Xie, Yanming; Wang, Yongyan

2011-10-01

Computerizing extracting information from Chinese medicine literature seems more convenient than hand searching, which could simplify searching process and improve the accuracy. However, many computerized auto-extracting methods are increasingly used, regular expression is so special that could be efficient for extracting useful information in research. This article focused on regular expression applying in extracting information from Chinese medicine literature. Two practical examples were reported in this article about regular expression to extract "case number (non-terminology)" and "efficacy rate (subgroups for related information identification)", which explored how to extract information in Chinese medicine literature by means of some special research method.

Clinical extracts of biomedical literature for patient-centered problem solving.

PubMed Central

Florance, V

1996-01-01

This paper reports on a four-part qualitative research project aimed at designing an online document surrogate tailored to the needs of physicians seeking biomedical literature for use in clinical problem solving. The clinical extract, designed in collaboration with three practicing physicians, combines traditional elements of the MEDLINE record (e.g., title, author, source, abstract) with new elements (e.g., table captions, text headings, case profiles) suggested by the physicians. Specifications for the prototype clinical extract were developed through a series of relevance-scoring exercises and semi-structured interviews. For six clinical questions, three physicians assessed the applicability of selected articles and their document surrogates, articulating relevance criteria and reasons for their judgments. A prototype clinical extract based on their suggestions was developed, tested, evaluated, and revised. The final version includes content and format aids to make the extract easy to use. The goals, methods, and outcomes of the research study are summarized, and a template of the final design is provided. PMID:8883986

Biological Activities and Chemical Characterization of Cordia verbenacea DC. as Tool to Validate the Ethnobiological Usage.

PubMed

Matias, Edinardo Fagner Ferreira; Alves, Erivânia Ferreira; Santos, Beatriz Sousa; Sobral de Souza, Celestina Elba; de Alencar Ferreira, João Victor; Santos de Lavor, Anne Karyzia Lima; Figueredo, Fernando Gomes; Ferreira de Lima, Luciene; Vieira Dos Santos, Francisco Antônio; Neves Peixoto, Flórido Sampaio; Viana Colares, Aracélio; Augusti Boligon, Aline; Saraiva, Rogério de Aquino; Athayde, Margareth Linde; da Rocha, João Batista Teixeira; Alencar Menezes, Irwin Rose; Douglas Melo Coutinho, Henrique; da Costa, José Galberto Martins

2013-01-01

Knowledge of medicinal plants is often the only therapeutic resource of many communities and ethnic groups. "Erva-baleeira", Cordia verbenacea DC., is one of the species of plants currently exploited for the purpose of producing a phytotherapeutic product extracted from its leaves. In Brazil, its major distribution is in the region of the Atlantic Forest and similar vegetation. The crude extract is utilized in popular cultures in the form of hydroalcoholic, decoctions and infusions, mainly as antimicrobial, anti-inflammatory and analgesic agents. The aim of the present study was to establish a chemical and comparative profile of the experimental antibacterial activity and resistance modifying activity with ethnopharmacological reports. Phytochemical prospecting and HPLC analysis of the extract and fractions were in agreement with the literature with regard to the presence of secondary metabolites (tannins and flavonoids). The extract and fraction tested did not show clinically relevant antibacterial activity, but a synergistic effect was observed when combined with antibiotic, potentiating the antibacterial effect of aminoglycosides. We conclude that tests of antibacterial activity and modulating the resistance presented in this work results confirm the ethnobotanical and ethnopharmacological information, serving as a parameter in the search for new alternatives for the treatment of diseases.

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

PubMed

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.

Identification of the traditional Tibetan medicine "Shaji" and their different extracts through tri-step infrared spectroscopy

NASA Astrophysics Data System (ADS)

Liu, Yue; Li, Jingyi; Fan, Gang; Sun, Suqin; Zhang, Yuxin; Zhang, Yi; Tu, Ya

2016-11-01

Hippophae rhamnoides subsp. sinensis Rousi, Hippophae gyantsensis (Rousi) Y. S. Lian, Hippophae neurocarpa S. W. Liu & T. N. He and Hippophae tibetana Schlechtendal are typically used under one name "Shaji", to treat cardiovascular diseases and lung disorders in Tibetan medicine (TM). A complete set of infrared (IR) macro-fingerprints of these four Hippophae species should be characterized and compared simply, accurately, and in detail for identification. In the present study, tri-step IR spectroscopy, which included Fourier transform IR (FT-IR) spectroscopy, second derivative IR (SD-IR) spectroscopy and two-dimensional correlation IR (2D-IR) spectroscopy, was employed to discriminate the four Hippophae species and their corresponding extracts using different solvents. The relevant spectra exhibited the holistic chemical compositions and variations. Flavonoids, fatty acids and sugars were found to be the main chemical components. Characteristic peak positions, intensities and shapes derived from FT-IR, SD-IR and 2D-IR spectra provided valuable information for sample discrimination. Principal component analysis (PCA) of spectral differences was performed to illustrate the objective identification. Results showed that the species and their extracts can be clearly distinguished. Thus, a quick, precise and effective tri-step IR spectroscopy combined with PCA can be applied to identify and discriminate medicinal materials and their extracts in TM research.

Biological Activities and Chemical Characterization of Cordia verbenacea DC. as Tool to Validate the Ethnobiological Usage

PubMed Central

Matias, Edinardo Fagner Ferreira; Alves, Erivânia Ferreira; Santos, Beatriz Sousa; Sobral de Souza, Celestina Elba; de Alencar Ferreira, João Victor; Santos de Lavor, Anne Karyzia Lima; Figueredo, Fernando Gomes; Ferreira de Lima, Luciene; Vieira dos Santos, Francisco Antônio; Neves Peixoto, Flórido Sampaio; Viana Colares, Aracélio; Augusti Boligon, Aline; Saraiva, Rogério de Aquino; Athayde, Margareth Linde; da Rocha, João Batista Teixeira; Alencar Menezes, Irwin Rose; Douglas Melo Coutinho, Henrique; da Costa, José Galberto Martins

2013-01-01

Knowledge of medicinal plants is often the only therapeutic resource of many communities and ethnic groups. “Erva-baleeira”, Cordia verbenacea DC., is one of the species of plants currently exploited for the purpose of producing a phytotherapeutic product extracted from its leaves. In Brazil, its major distribution is in the region of the Atlantic Forest and similar vegetation. The crude extract is utilized in popular cultures in the form of hydroalcoholic, decoctions and infusions, mainly as antimicrobial, anti-inflammatory and analgesic agents. The aim of the present study was to establish a chemical and comparative profile of the experimental antibacterial activity and resistance modifying activity with ethnopharmacological reports. Phytochemical prospecting and HPLC analysis of the extract and fractions were in agreement with the literature with regard to the presence of secondary metabolites (tannins and flavonoids). The extract and fraction tested did not show clinically relevant antibacterial activity, but a synergistic effect was observed when combined with antibiotic, potentiating the antibacterial effect of aminoglycosides. We conclude that tests of antibacterial activity and modulating the resistance presented in this work results confirm the ethnobotanical and ethnopharmacological information, serving as a parameter in the search for new alternatives for the treatment of diseases. PMID:23818919

A systematic review of the safety of kava extract in the treatment of anxiety.

PubMed

Stevinson, Clare; Huntley, Alyson; Ernst, Edzard

2002-01-01

This paper systematically reviews the clinical evidence relating to the safety of extracts of the herbal anxiolytic kava (Piper methysticum). Literature searches were conducted in four electronic databases and the reference lists of all papers located were checked for further relevant publications. Information was also sought from the spontaneous reporting schemes of the WHO and national drug safety bodies and ten manufacturers of kava preparations were contacted. Data from short-term post-marketing surveillance studies and clinical trials suggest that adverse events are, in general, rare, mild and reversible. However, published case reports indicate that serious adverse events are possible including dermatological reactions, neurological complications and, of greatest concern, liver damage. Spontaneous reporting schemes also suggest that the most common adverse events are mild, but that serious ones occur. Controlled trials suggest that kava extracts do not impair cognitive performance and vigilance or potentiate the effects of central nervous system depressants. However, a possible interaction with benzodiazepines has been reported. It is concluded that when taken as a short-term monotherapy at recommended doses, kava extracts appear to be well tolerated by most users. Serious adverse events have been reported and further research is required to determine the nature and frequency of such events.

Inexpensive metagenomic DNA extraction protocol with high quality from marine sediments contaminated by petroleum hydrocarbons.

PubMed

García-Bautista, I; Toledano-Thompson, T; Dantán-González, E; González-Montilla, J; Valdez-Ojeda, R

2017-09-21

Marine environments are a reservoir of relevant information on dangerous contaminants such as hydrocarbons, as well as microbial communities with probable degradation skills. However, to access microbial diversity, it is necessary to obtain high-quality DNA. An inexpensive, reliable, and effective metagenomic DNA (mgDNA) extraction protocol from marine sediments contaminated with petroleum hydrocarbons was established in this study from modifications to Zhou's protocol. The optimization included pretreatment of sediment with saline solutions for the removal of contaminants, a second precipitation and enzymatic degradation of RNA, followed by purification of mgDNA extracted by electroelution. The results obtained indicated that the modifications applied to 12 sediments with total petroleum hydrocarbon (TPH) concentrations from 22.6-174.3 (µg/g dry sediment) yielded 20.3-321.3 ng/µL mgDNA with A 260 /A 280 and A 260 /A 230 ratios of 1.75 ± 0.08 and 1.19 ± 0.22, respectively. The 16S rRNA amplification confirmed the purity of the mgDNA. The suitability of this mgDNA extraction protocol lies in the fact that all chemical solutions utilized are common in all molecular biology laboratories, and the use of dialysis membrane does not require any sophisticated or expensive equipment, only an electrophoretic chamber.

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

PubMed Central

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. PMID:29186141

Comparing Goldstone Solar System Radar Earth-based Observations of Mars with Orbital Datasets

NASA Technical Reports Server (NTRS)

Haldemann, A. F. C.; Larsen, K. W.; Jurgens, R. F.; Slade, M. A.

2005-01-01

The Goldstone Solar System Radar (GSSR) has collected a self-consistent set of delay-Doppler near-nadir radar echo data from Mars since 1988. Prior to the Mars Global Surveyor (MGS) Mars Orbiter Laser Altimeter (MOLA) global topography for Mars, these radar data provided local elevation information, along with radar scattering information with global coverage. Two kinds of GSSR Mars delay-Doppler data exist: low 5 km x 150 km resolution and, more recently, high (5 to 10 km) spatial resolution. Radar data, and non-imaging delay-Doppler data in particular, requires significant data processing to extract elevation, reflectivity and roughness of the reflecting surface. Interpretation of these parameters, while limited by the complexities of electromagnetic scattering, provide information directly relevant to geophysical and geomorphic analyses of Mars. In this presentation we want to demonstrate how to compare GSSR delay-Doppler data to other Mars datasets, including some idiosyncracies of the radar data. Additional information is included in the original extended abstract.

Self-relevant beauty evaluation: Evidence from an event-related potentials study.

PubMed

Kong, Fanchang; Zhang, Yan; Tian, Yuan; Fan, Cuiying; Zhou, Zongkui

2015-03-01

This study examines the electrophysiological correlates of beauty evaluation when participants performed the self-reference task. About 13 (7 men, 6 women) undergraduates participated in the experiment using event-related potentials. Results showed that the response to self-relevant information was faster compared to other-relevant information and no significant differences for self-relevant relative to mother-relevant information were observed. Both physical and interior beauty words for self-relevant information showed an enhanced late positive component as compared to other-relevant information. Physical beauty for self-relevant information yielded a larger late positive component in contrast to mother-relevant information but not for interior beauty. This study indicates that beauty is specific to the person who judges it though an individual and one's mother may hold similar views of interior beauty.

International or national publication of case reports.

PubMed

Lundh, Andreas; Christensen, Mikkel; Jørgensen, Anders W

2011-02-01

Case reports are often regarded as second-class research, but are an important part of medical science as they often present first evidence of new discoveries. We here describe the type of case reports published in a Danish general medical journal. We included all case reports published in Ugeskrift for Laeger in 2009. For each report, two authors extracted information on study characteristics and classified the relevance and the role of the report. We included 139 case reports written in Danish. Thirty-nine (28%) were of general relevance and 100 (72%) of speciality relevance. The median number of authors was three (range: 1-7). The first author was a non-specialist physician in 119 (86%) of the reports and the last author a specialist in 103 (78%). A total of 124 (89%) reports had an educational role, six (4%) dealt with new diseases, two (1%) with new side effects, three (2%) with new mechanisms and four (3%) were curiosities. A total of 59 (42%) reports were surgical, 64 (46%) non-surgical and 16 (12%) paraclinical. We found that most case reports published in Ugeskrift for Laeger were of speciality relevance and had an educational perspective. The journal may consider focusing on cases of more general educational relevance and should also consider whether the current form and language suit the aim and role of the various types of case reports.

Scalable Data Mining and Archiving for the Square Kilometre Array

NASA Astrophysics Data System (ADS)

Jones, D. L.; Mattmann, C. A.; Hart, A. F.; Lazio, J.; Bennett, T.; Wagstaff, K. L.; Thompson, D. R.; Preston, R.

2011-12-01

As the technologies for remote observation improve, the rapid increase in the frequency and fidelity of those observations translates into an avalanche of data that is already beginning to eclipse the resources, both human and technical, of the institutions and facilities charged with managing the information. Common data management tasks like cataloging both data itself and contextual meta-data, creating and maintaining scalable permanent archive, and making data available on-demand for research present significant software engineering challenges when considered at the scales of modern multi-national scientific enterprises such as the upcoming Square Kilometre Array project. The NASA Jet Propulsion Laboratory (JPL), leveraging internal research and technology development funding, has begun to explore ways to address the data archiving and distribution challenges with a number of parallel activities involving collaborations with the EVLA and ALMA teams at the National Radio Astronomy Observatory (NRAO), and members of the Square Kilometre Array South Africa team. To date, we have leveraged the Apache OODT Process Control System framework and its catalog and archive service components that provide file management, workflow management, resource management as core web services. A client crawler framework ingests upstream data (e.g., EVLA raw directory output), identifies its MIME type and automatically extracts relevant metadata including temporal bounds, and job-relevant/processing information. A remote content acquisition (pushpull) service is responsible for staging remote content and handing it off to the crawler framework. A science algorithm wrapper (called CAS-PGE) wraps underlying code including CASApy programs for the EVLA, such as Continuum Imaging and Spectral Line Cube generation, executes the algorithm, and ingests its output (along with relevant extracted metadata). In addition to processing, the Process Control System has been leveraged to provide data curation and automatic ingestion for the MeerKAT/KAT-7 precursor instrument in South Africa, helping to catalog and archive correlator and sensor output from KAT-7, and to make the information available for downstream science analysis. These efforts, supported by the increasing availability of high-quality open source software, represent a concerted effort to seek a cost-conscious methodology for maintaining the integrity of observational data from the upstream instrument to the archive, and at the same time ensuring that the data, with its richly annotated catalog of meta-data, remains a viable resource for research into the future.

A new feature extraction method for signal classification applied to cord dorsum potentials detection

PubMed Central

Vidaurre, D.; Rodríguez, E. E.; Bielza, C.; Larrañaga, P.; Rudomin, P.

2012-01-01

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods. PMID:22929924

A new feature extraction method for signal classification applied to cord dorsum potential detection.

PubMed

Vidaurre, D; Rodríguez, E E; Bielza, C; Larrañaga, P; Rudomin, P

2012-10-01

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

UK surveillance: provision of quality assured information from combined datasets.

PubMed

Paiba, G A; Roberts, S R; Houston, C W; Williams, E C; Smith, L H; Gibbens, J C; Holdship, S; Lysons, R

2007-09-14

Surveillance information is most useful when provided within a risk framework, which is achieved by presenting results against an appropriate denominator. Often the datasets are captured separately and for different purposes, and will have inherent errors and biases that can be further confounded by the act of merging. The United Kingdom Rapid Analysis and Detection of Animal-related Risks (RADAR) system contains data from several sources and provides both data extracts for research purposes and reports for wider stakeholders. Considerable efforts are made to optimise the data in RADAR during the Extraction, Transformation and Loading (ETL) process. Despite efforts to ensure data quality, the final dataset inevitably contains some data errors and biases, most of which cannot be rectified during subsequent analysis. So, in order for users to establish the 'fitness for purpose' of data merged from more than one data source, Quality Statements are produced as defined within the overarching surveillance Quality Framework. These documents detail identified data errors and biases following ETL and report construction as well as relevant aspects of the datasets from which the data originated. This paper illustrates these issues using RADAR datasets, and describes how they can be minimised.

Effective use of latent semantic indexing and computational linguistics in biological and biomedical applications.

PubMed

Chen, Hongyu; Martin, Bronwen; Daimon, Caitlin M; Maudsley, Stuart

2013-01-01

Text mining is rapidly becoming an essential technique for the annotation and analysis of large biological data sets. Biomedical literature currently increases at a rate of several thousand papers per week, making automated information retrieval methods the only feasible method of managing this expanding corpus. With the increasing prevalence of open-access journals and constant growth of publicly-available repositories of biomedical literature, literature mining has become much more effective with respect to the extraction of biomedically-relevant data. In recent years, text mining of popular databases such as MEDLINE has evolved from basic term-searches to more sophisticated natural language processing techniques, indexing and retrieval methods, structural analysis and integration of literature with associated metadata. In this review, we will focus on Latent Semantic Indexing (LSI), a computational linguistics technique increasingly used for a variety of biological purposes. It is noted for its ability to consistently outperform benchmark Boolean text searches and co-occurrence models at information retrieval and its power to extract indirect relationships within a data set. LSI has been used successfully to formulate new hypotheses, generate novel connections from existing data, and validate empirical data.

Automatic video summarization driven by a spatio-temporal attention model

NASA Astrophysics Data System (ADS)

Barland, R.; Saadane, A.

2008-02-01

According to the literature, automatic video summarization techniques can be classified in two parts, following the output nature: "video skims", which are generated using portions of the original video and "key-frame sets", which correspond to the images, selected from the original video, having a significant semantic content. The difference between these two categories is reduced when we consider automatic procedures. Most of the published approaches are based on the image signal and use either pixel characterization or histogram techniques or image decomposition by blocks. However, few of them integrate properties of the Human Visual System (HVS). In this paper, we propose to extract keyframes for video summarization by studying the variations of salient information between two consecutive frames. For each frame, a saliency map is produced simulating the human visual attention by a bottom-up (signal-dependent) approach. This approach includes three parallel channels for processing three early visual features: intensity, color and temporal contrasts. For each channel, the variations of the salient information between two consecutive frames are computed. These outputs are then combined to produce the global saliency variation which determines the key-frames. Psychophysical experiments have been defined and conducted to analyze the relevance of the proposed key-frame extraction algorithm.

Synthesising quantitative and qualitative research in evidence-based patient information.

PubMed

Goldsmith, Megan R; Bankhead, Clare R; Austoker, Joan

2007-03-01

Systematic reviews have, in the past, focused on quantitative studies and clinical effectiveness, while excluding qualitative evidence. Qualitative research can inform evidence-based practice independently of other research methodologies but methods for the synthesis of such data are currently evolving. Synthesising quantitative and qualitative research in a single review is an important methodological challenge. This paper describes the review methods developed and the difficulties encountered during the process of updating a systematic review of evidence to inform guidelines for the content of patient information related to cervical screening. Systematic searches of 12 electronic databases (January 1996 to July 2004) were conducted. Studies that evaluated the content of information provided to women about cervical screening or that addressed women's information needs were assessed for inclusion. A data extraction form and quality assessment criteria were developed from published resources. A non-quantitative synthesis was conducted and a tabular evidence profile for each important outcome (eg "explain what the test involves") was prepared. The overall quality of evidence for each outcome was then assessed using an approach published by the GRADE working group, which was adapted to suit the review questions and modified to include qualitative research evidence. Quantitative and qualitative studies were considered separately for every outcome. 32 papers were included in the systematic review following data extraction and assessment of methodological quality. The review questions were best answered by evidence from a range of data sources. The inclusion of qualitative research, which was often highly relevant and specific to many components of the screening information materials, enabled the production of a set of recommendations that will directly affect policy within the NHS Cervical Screening Programme. A practical example is provided of how quantitative and qualitative data sources might successfully be brought together and considered in one review.

Active learning-based information structure analysis of full scientific articles and two applications for biomedical literature review.

PubMed

Guo, Yufan; Silins, Ilona; Stenius, Ulla; Korhonen, Anna

2013-06-01

Techniques that are capable of automatically analyzing the information structure of scientific articles could be highly useful for improving information access to biomedical literature. However, most existing approaches rely on supervised machine learning (ML) and substantial labeled data that are expensive to develop and apply to different sub-fields of biomedicine. Recent research shows that minimal supervision is sufficient for fairly accurate information structure analysis of biomedical abstracts. However, is it realistic for full articles given their high linguistic and informational complexity? We introduce and release a novel corpus of 50 biomedical articles annotated according to the Argumentative Zoning (AZ) scheme, and investigate active learning with one of the most widely used ML models-Support Vector Machines (SVM)-on this corpus. Additionally, we introduce two novel applications that use AZ to support real-life literature review in biomedicine via question answering and summarization. We show that active learning with SVM trained on 500 labeled sentences (6% of the corpus) performs surprisingly well with the accuracy of 82%, just 2% lower than fully supervised learning. In our question answering task, biomedical researchers find relevant information significantly faster from AZ-annotated than unannotated articles. In the summarization task, sentences extracted from particular zones are significantly more similar to gold standard summaries than those extracted from particular sections of full articles. These results demonstrate that active learning of full articles' information structure is indeed realistic and the accuracy is high enough to support real-life literature review in biomedicine. The annotated corpus, our AZ classifier and the two novel applications are available at http://www.cl.cam.ac.uk/yg244/12bioinfo.html

Features: Real-Time Adaptive Feature and Document Learning for Web Search.

ERIC Educational Resources Information Center

Chen, Zhixiang; Meng, Xiannong; Fowler, Richard H.; Zhu, Binhai

2001-01-01

Describes Features, an intelligent Web search engine that is able to perform real-time adaptive feature (i.e., keyword) and document learning. Explains how Features learns from users' document relevance feedback and automatically extracts and suggests indexing keywords relevant to a search query, and learns from users' keyword relevance feedback…

Comparing the Behavior of Polarimetric SAR Imagery (TerraSAR-X and Radarsat-2) for Automated Sea Ice Classification

NASA Astrophysics Data System (ADS)

Ressel, Rudolf; Singha, Suman; Lehner, Susanne

2016-08-01

Arctic Sea ice monitoring has attracted increasing attention over the last few decades. Besides the scientific interest in sea ice, the operational aspect of ice charting is becoming more important due to growing navigational possibilities in an increasingly ice free Arctic. For this purpose, satellite borne SAR imagery has become an invaluable tool. In past, mostly single polarimetric datasets were investigated with supervised or unsupervised classification schemes for sea ice investigation. Despite proven sea ice classification achievements on single polarimetric data, a fully automatic, general purpose classifier for single-pol data has not been established due to large variation of sea ice manifestations and incidence angle impact. Recently, through the advent of polarimetric SAR sensors, polarimetric features have moved into the focus of ice classification research. The higher information content four polarimetric channels promises to offer greater insight into sea ice scattering mechanism and overcome some of the shortcomings of single- polarimetric classifiers. Two spatially and temporally coincident pairs of fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X and RADARSAT-2 satellites are investigated. Proposed supervised classification algorithm consists of two steps: The first step comprises a feature extraction, the results of which are ingested into a neural network classifier in the second step. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction).

An efficient and scalable extraction and quantification method for algal derived biofuel.

PubMed

Lohman, Egan J; Gardner, Robert D; Halverson, Luke; Macur, Richard E; Peyton, Brent M; Gerlach, Robin

2013-09-01

Microalgae are capable of synthesizing a multitude of compounds including biofuel precursors and other high value products such as omega-3-fatty acids. However, accurate analysis of the specific compounds produced by microalgae is important since slight variations in saturation and carbon chain length can affect the quality, and thus the value, of the end product. We present a method that allows for fast and reliable extraction of lipids and similar compounds from a range of algae, followed by their characterization using gas chromatographic analysis with a focus on biodiesel-relevant compounds. This method determines which range of biologically synthesized compounds is likely responsible for each fatty acid methyl ester (FAME) produced; information that is fundamental for identifying preferred microalgae candidates as a biodiesel source. Traditional methods of analyzing these precursor molecules are time intensive and prone to high degrees of variation between species and experimental conditions. Here we detail a new method which uses microwave energy as a reliable, single-step cell disruption technique to extract lipids from live cultures of microalgae. After extractable lipid characterization (including lipid type (free fatty acids, mono-, di- or tri-acylglycerides) and carbon chain length determination) by GC-FID, the same lipid extracts are transesterified into FAMEs and directly compared to total biodiesel potential by GC-MS. This approach provides insight into the fraction of total FAMEs derived from extractable lipids compared to FAMEs derived from the residual fraction (i.e. membrane bound phospholipids, sterols, etc.). This approach can also indicate which extractable lipid compound, based on chain length and relative abundance, is responsible for each FAME. This method was tested on three species of microalgae; the marine diatom Phaeodactylum tricornutum, the model Chlorophyte Chlamydomonas reinhardtii, and the freshwater green alga Chlorella vulgaris. The method is shown to be robust, highly reproducible, and fast, allowing for multiple samples to be analyzed throughout the time course of culturing, thus providing time-resolved information regarding lipid quantity and quality. Total time from harvesting to obtaining analytical results is less than 2h. © 2013.

Challenges in Managing Information Extraction

ERIC Educational Resources Information Center

Shen, Warren H.

2009-01-01

This dissertation studies information extraction (IE), the problem of extracting structured information from unstructured data. Example IE tasks include extracting person names from news articles, product information from e-commerce Web pages, street addresses from emails, and names of emerging music bands from blogs. IE is all increasingly…

A First Standardized Swiss Electronic Maternity Record.

PubMed

Murbach, Michel; Martin, Sabine; Denecke, Kerstin; Nüssli, Stephan

2017-01-01

During the nine months of pregnancy, women have to regularly visit several physicians for continuous monitoring of the health and development of the fetus and mother. Comprehensive examination results of different types are generated in this process; documentation and data transmission standards are still unavailable or not in use. Relevant information is collected in a paper-based maternity record carried by the pregnant women. To improve availability and transmission of data, we aim at developing a first prototype for an electronic maternity record for Switzerland. By analyzing the documentation workflow during pregnancy, we determined a maternity record data set. Further, we collected requirements towards a digital maternity record. As data exchange format, the Swiss specific exchange format SMEEX (swiss medical data exchange) was exploited. Feedback from 27 potential users was collected to identify further improvements. The relevant data is extracted from the primary care information system as SMEEX file, stored in a database and made available in a web and a mobile application, developed as prototypes of an electronic maternity record. The user confirmed the usefulness of the system and provided multiple suggestions for an extension. An electronical maternity record as developed in this work could be in future linked to the electronic patient record.

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

PubMed

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

2017-09-25

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

Food allergen extracts to diagnose food-induced allergic diseases: How they are made.

PubMed

David, Natalie A; Penumarti, Anusha; Burks, A Wesley; Slater, Jay E

2017-08-01

To review the manufacturing procedures of food allergen extracts and applicable regulatory requirements from government agencies, potential approaches to standardization, and clinical application of these products. The effects of thermal processing on allergenicity of common food allergens are also considered. A broad literature review was conducted on the natural history of food allergy, the manufacture of allergen extracts, and the allergenicity of heated food. Regulations, guidance documents, and pharmacopoeias related to food allergen extracts from the United States and Europe were also reviewed. Authoritative and peer-reviewed research articles relevant to the topic were chosen for review. Selected regulations and guidance documents are current and relevant to food allergen extracts. Preparation of a food allergen extract may require careful selection and identification of source materials, grinding, defatting, extraction, clarification, sterilization, and product testing. Although extractions for all products licensed in the United States are performed using raw source materials, many foods are not consumed in their raw form. Heating foods may change their allergenicity, and doing so before extraction may change their allergenicity and the composition of the final product. The manufacture of food allergen extracts requires many considerations to achieve the maximal quality of the final product. Allergen extracts for a select number of foods may be inconsistent between manufacturers or unreliable in a clinical setting, indicating a potential area for future improvement. Copyright © 2016 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Descriptions of Adverse Drug Reactions Are Less Informative in Forums Than in the French Pharmacovigilance Database but Provide More Unexpected Reactions

PubMed Central

Karapetiantz, Pierre; Bellet, Florelle; Audeh, Bissan; Lardon, Jérémy; Leprovost, Damien; Aboukhamis, Rim; Morlane-Hondère, François; Grouin, Cyril; Burgun, Anita; Katsahian, Sandrine; Jaulent, Marie-Christine; Beyens, Marie-Noëlle; Lillo-Le Louët, Agnès; Bousquet, Cédric

2018-01-01

Background: Social media have drawn attention for their potential use in Pharmacovigilance. Recent work showed that it is possible to extract information concerning adverse drug reactions (ADRs) from posts in social media. The main objective of the Vigi4MED project was to evaluate the relevance and quality of the information shared by patients on web forums about drug safety and its potential utility for pharmacovigilance. Methods: After selecting websites of interest, we manually evaluated the relevance of the content of posts for pharmacovigilance related to six drugs (agomelatine, baclofen, duloxetine, exenatide, strontium ranelate, and tetrazepam). We compared forums to the French Pharmacovigilance Database (FPVD) to (1) evaluate whether they contained relevant information to characterize a pharmacovigilance case report (patient’s age and sex; treatment indication, dose and duration; time-to-onset (TTO) and outcome of the ADR, and drug dechallenge and rechallenge) and (2) perform impact analysis (nature, seriousness, unexpectedness, and outcome of the ADR). Results: The cases in the FPVD were significantly more informative than posts in forums for patient description (age, sex), treatment description (dose, duration, TTO), and outcome of the ADR, but the indication for the treatment was more often found in forums. Cases were more often serious in the FPVD than in forums (46% vs. 4%), but forums more often contained an unexpected ADR than the FPVD (24% vs. 17%). Moreover, 197 unexpected ADRs identified in forums were absent from the FPVD and the distribution of the MedDRA System Organ Classes (SOCs) was different between the two data sources. Discussion: This study is the first to evaluate if patients’ posts may qualify as potential and informative case reports that should be stored in a pharmacovigilance database in the same way as case reports submitted by health professionals. The posts were less informative (except for the indication) and focused on less serious ADRs than the FPVD cases, but more unexpected ADRs were presented in forums than in the FPVD and their SOCs were different. Thus, web forums should be considered as a secondary, but complementary source for pharmacovigilance. PMID:29765326

Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks.

PubMed

Blatti, Charles; Sinha, Saurabh

2016-07-15

Analysis of co-expressed gene sets typically involves testing for enrichment of different annotations or 'properties' such as biological processes, pathways, transcription factor binding sites, etc., one property at a time. This common approach ignores any known relationships among the properties or the genes themselves. It is believed that known biological relationships among genes and their many properties may be exploited to more accurately reveal commonalities of a gene set. Previous work has sought to achieve this by building biological networks that combine multiple types of gene-gene or gene-property relationships, and performing network analysis to identify other genes and properties most relevant to a given gene set. Most existing network-based approaches for recognizing genes or annotations relevant to a given gene set collapse information about different properties to simplify (homogenize) the networks. We present a network-based method for ranking genes or properties related to a given gene set. Such related genes or properties are identified from among the nodes of a large, heterogeneous network of biological information. Our method involves a random walk with restarts, performed on an initial network with multiple node and edge types that preserve more of the original, specific property information than current methods that operate on homogeneous networks. In this first stage of our algorithm, we find the properties that are the most relevant to the given gene set and extract a subnetwork of the original network, comprising only these relevant properties. We then re-rank genes by their similarity to the given gene set, based on a second random walk with restarts, performed on the above subnetwork. We demonstrate the effectiveness of this algorithm for ranking genes related to Drosophila embryonic development and aggressive responses in the brains of social animals. DRaWR was implemented as an R package available at veda.cs.illinois.edu/DRaWR. blatti@illinois.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Content-based audio authentication using a hierarchical patchwork watermark embedding

NASA Astrophysics Data System (ADS)

Gulbis, Michael; Müller, Erika

2010-05-01

Content-based audio authentication watermarking techniques extract perceptual relevant audio features, which are robustly embedded into the audio file to protect. Manipulations of the audio file are detected on the basis of changes between the original embedded feature information and the anew extracted features during verification. The main challenges of content-based watermarking are on the one hand the identification of a suitable audio feature to distinguish between content preserving and malicious manipulations. On the other hand the development of a watermark, which is robust against content preserving modifications and able to carry the whole authentication information. The payload requirements are significantly higher compared to transaction watermarking or copyright protection. Finally, the watermark embedding should not influence the feature extraction to avoid false alarms. Current systems still lack a sufficient alignment of watermarking algorithm and feature extraction. In previous work we developed a content-based audio authentication watermarking approach. The feature is based on changes in DCT domain over time. A patchwork algorithm based watermark was used to embed multiple one bit watermarks. The embedding process uses the feature domain without inflicting distortions to the feature. The watermark payload is limited by the feature extraction, more precisely the critical bands. The payload is inverse proportional to segment duration of the audio file segmentation. Transparency behavior was analyzed in dependence of segment size and thus the watermark payload. At a segment duration of about 20 ms the transparency shows an optimum (measured in units of Objective Difference Grade). Transparency and/or robustness are fast decreased for working points beyond this area. Therefore, these working points are unsuitable to gain further payload, needed for the embedding of the whole authentication information. In this paper we present a hierarchical extension of the watermark method to overcome the limitations given by the feature extraction. The approach is a recursive application of the patchwork algorithm onto its own patches, with a modified patch selection to ensure a better signal to noise ratio for the watermark embedding. The robustness evaluation was done by compression (mp3, ogg, aac), normalization, and several attacks of the stirmark benchmark for audio suite. Compared on the base of same payload and transparency the hierarchical approach shows improved robustness.

A novel approach to automatic threat detection in MMW imagery of people scanned in portals

NASA Astrophysics Data System (ADS)

Vaidya, Nitin M.; Williams, Thomas

2008-04-01

We have developed a novel approach to performing automatic detection of concealed threat objects in passive MMW imagery of people scanned in a portal setting. It is applicable to the significant class of imaging scanners that use the protocol of having the subject rotate in front of the camera in order to image them from several closely spaced directions. Customary methods of dealing with MMW sequences rely on the analysis of the spatial images in a frame-by-frame manner, with information extracted from separate frames combined by some subsequent technique of data association and tracking over time. We contend that the pooling of information over time in traditional methods is not as direct as can be and potentially less efficient in distinguishing threats from clutter. We have formulated a more direct approach to extracting information about the scene as it evolves over time. We propose an atypical spatio-temporal arrangement of the MMW image data - to which we give the descriptive name Row Evolution Image (REI) sequence. This representation exploits the singular aspect of having the subject rotate in front of the camera. We point out which features in REIs are most relevant to detecting threats, and describe the algorithms we have developed to extract them. We demonstrate results of successful automatic detection of threats, including ones whose faint image contrast renders their disambiguation from clutter very challenging. We highlight the ease afforded by the REI approach in permitting specialization of the detection algorithms to different parts of the subject body. Finally, we describe the execution efficiency advantages of our approach, given its natural fit to parallel processing. mage

Deep Question Answering for protein annotation

PubMed Central

Gobeill, Julien; Gaudinat, Arnaud; Pasche, Emilie; Vishnyakova, Dina; Gaudet, Pascale; Bairoch, Amos; Ruch, Patrick

2015-01-01

Biomedical professionals have access to a huge amount of literature, but when they use a search engine, they often have to deal with too many documents to efficiently find the appropriate information in a reasonable time. In this perspective, question-answering (QA) engines are designed to display answers, which were automatically extracted from the retrieved documents. Standard QA engines in literature process a user question, then retrieve relevant documents and finally extract some possible answers out of these documents using various named-entity recognition processes. In our study, we try to answer complex genomics questions, which can be adequately answered only using Gene Ontology (GO) concepts. Such complex answers cannot be found using state-of-the-art dictionary- and redundancy-based QA engines. We compare the effectiveness of two dictionary-based classifiers for extracting correct GO answers from a large set of 100 retrieved abstracts per question. In the same way, we also investigate the power of GOCat, a GO supervised classifier. GOCat exploits the GOA database to propose GO concepts that were annotated by curators for similar abstracts. This approach is called deep QA, as it adds an original classification step, and exploits curated biological data to infer answers, which are not explicitly mentioned in the retrieved documents. We show that for complex answers such as protein functional descriptions, the redundancy phenomenon has a limited effect. Similarly usual dictionary-based approaches are relatively ineffective. In contrast, we demonstrate how existing curated data, beyond information extraction, can be exploited by a supervised classifier, such as GOCat, to massively improve both the quantity and the quality of the answers with a +100% improvement for both recall and precision. Database URL: http://eagl.unige.ch/DeepQA4PA/ PMID:26384372

Medical device procurement in low- and middle-income settings: protocol for a systematic review.

PubMed

Diaconu, Karin; Chen, Yen-Fu; Manaseki-Holland, Semira; Cummins, Carole; Lilford, Richard

2014-10-21

Medical device procurement processes for low- and middle-income countries (LMICs) are a poorly understood and researched topic. To support LMIC policy formulation in this area, international public health organizations and research institutions issue a large body of predominantly grey literature including guidelines, manuals and recommendations. We propose to undertake a systematic review to identify and explore the medical device procurement methodologies suggested within this and further literature. Procurement facilitators and barriers will be identified, and methodologies for medical device prioritization under resource constraints will be discussed. Searches of both bibliographic and grey literature will be conducted to identify documents relating to the procurement of medical devices in LMICs. Data will be extracted according to protocol on a number of pre-specified issues and variables. First, data relating to the specific settings described within the literature will be noted. Second, information relating to medical device procurement methodologies will be extracted, including prioritization of procurement under resource constraints, the use of evidence (e.g. cost-effectiveness evaluations, burden of disease data) as well as stakeholders participating in procurement processes. Information relating to prioritization methodologies will be extracted in the form of quotes or keywords, and analysis will include qualitative meta-summary. Narrative synthesis will be employed to analyse data otherwise extracted. The PRISMA guidelines for reporting will be followed. The current review will identify recommended medical device procurement methodologies for LMICs. Prioritization methods for medical device acquisition will be explored. Relevant stakeholders, facilitators and barriers will be discussed. The review is aimed at both LMIC decision makers and the international research community and hopes to offer a first holistic conceptualization of this topic.

Database integration in a multimedia-modeling environment

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

Dorow, Kevin E.

2002-09-02

Integration of data from disparate remote sources has direct applicability to modeling, which can support Brownfield assessments. To accomplish this task, a data integration framework needs to be established. A key element in this framework is the metadata that creates the relationship between the pieces of information that are important in the multimedia modeling environment and the information that is stored in the remote data source. The design philosophy is to allow modelers and database owners to collaborate by defining this metadata in such a way that allows interaction between their components. The main parts of this framework include toolsmore » to facilitate metadata definition, database extraction plan creation, automated extraction plan execution / data retrieval, and a central clearing house for metadata and modeling / database resources. Cross-platform compatibility (using Java) and standard communications protocols (http / https) allow these parts to run in a wide variety of computing environments (Local Area Networks, Internet, etc.), and, therefore, this framework provides many benefits. Because of the specific data relationships described in the metadata, the amount of data that have to be transferred is kept to a minimum (only the data that fulfill a specific request are provided as opposed to transferring the complete contents of a data source). This allows for real-time data extraction from the actual source. Also, the framework sets up collaborative responsibilities such that the different types of participants have control over the areas in which they have domain knowledge-the modelers are responsible for defining the data relevant to their models, while the database owners are responsible for mapping the contents of the database using the metadata definitions. Finally, the data extraction mechanism allows for the ability to control access to the data and what data are made available.« less

Medical device procurement in low- and middle-income settings: protocol for a systematic review

PubMed Central

2014-01-01

Background Medical device procurement processes for low- and middle-income countries (LMICs) are a poorly understood and researched topic. To support LMIC policy formulation in this area, international public health organizations and research institutions issue a large body of predominantly grey literature including guidelines, manuals and recommendations. We propose to undertake a systematic review to identify and explore the medical device procurement methodologies suggested within this and further literature. Procurement facilitators and barriers will be identified, and methodologies for medical device prioritization under resource constraints will be discussed. Methods/design Searches of both bibliographic and grey literature will be conducted to identify documents relating to the procurement of medical devices in LMICs. Data will be extracted according to protocol on a number of pre-specified issues and variables. First, data relating to the specific settings described within the literature will be noted. Second, information relating to medical device procurement methodologies will be extracted, including prioritization of procurement under resource constraints, the use of evidence (e.g. cost-effectiveness evaluations, burden of disease data) as well as stakeholders participating in procurement processes. Information relating to prioritization methodologies will be extracted in the form of quotes or keywords, and analysis will include qualitative meta-summary. Narrative synthesis will be employed to analyse data otherwise extracted. The PRISMA guidelines for reporting will be followed. Discussion The current review will identify recommended medical device procurement methodologies for LMICs. Prioritization methods for medical device acquisition will be explored. Relevant stakeholders, facilitators and barriers will be discussed. The review is aimed at both LMIC decision makers and the international research community and hopes to offer a first holistic conceptualization of this topic. PMID:25336161

Deep Question Answering for protein annotation.

PubMed

Gobeill, Julien; Gaudinat, Arnaud; Pasche, Emilie; Vishnyakova, Dina; Gaudet, Pascale; Bairoch, Amos; Ruch, Patrick

2015-01-01

Biomedical professionals have access to a huge amount of literature, but when they use a search engine, they often have to deal with too many documents to efficiently find the appropriate information in a reasonable time. In this perspective, question-answering (QA) engines are designed to display answers, which were automatically extracted from the retrieved documents. Standard QA engines in literature process a user question, then retrieve relevant documents and finally extract some possible answers out of these documents using various named-entity recognition processes. In our study, we try to answer complex genomics questions, which can be adequately answered only using Gene Ontology (GO) concepts. Such complex answers cannot be found using state-of-the-art dictionary- and redundancy-based QA engines. We compare the effectiveness of two dictionary-based classifiers for extracting correct GO answers from a large set of 100 retrieved abstracts per question. In the same way, we also investigate the power of GOCat, a GO supervised classifier. GOCat exploits the GOA database to propose GO concepts that were annotated by curators for similar abstracts. This approach is called deep QA, as it adds an original classification step, and exploits curated biological data to infer answers, which are not explicitly mentioned in the retrieved documents. We show that for complex answers such as protein functional descriptions, the redundancy phenomenon has a limited effect. Similarly usual dictionary-based approaches are relatively ineffective. In contrast, we demonstrate how existing curated data, beyond information extraction, can be exploited by a supervised classifier, such as GOCat, to massively improve both the quantity and the quality of the answers with a +100% improvement for both recall and precision. Database URL: http://eagl.unige.ch/DeepQA4PA/. © The Author(s) 2015. Published by Oxford University Press.

High quality topic extraction from business news explains abnormal financial market volatility.

PubMed

Hisano, Ryohei; Sornette, Didier; Mizuno, Takayuki; Ohnishi, Takaaki; Watanabe, Tsutomu

2013-01-01

Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc.) affects trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affects stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their "thematic" features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized facts in financial economies, namely that at certain times trading volumes appear to be "abnormally large," can be partially explained by the flow of news. In this sense, our results prove that there is no "excess trading," when restricting to times when news is genuinely novel and provides relevant financial information.

a Probability-Based Statistical Method to Extract Water Body of TM Images with Missing Information

NASA Astrophysics Data System (ADS)

Lian, Shizhong; Chen, Jiangping; Luo, Minghai

2016-06-01

Water information cannot be accurately extracted using TM images because true information is lost in some images because of blocking clouds and missing data stripes, thereby water information cannot be accurately extracted. Water is continuously distributed in natural conditions; thus, this paper proposed a new method of water body extraction based on probability statistics to improve the accuracy of water information extraction of TM images with missing information. Different disturbing information of clouds and missing data stripes are simulated. Water information is extracted using global histogram matching, local histogram matching, and the probability-based statistical method in the simulated images. Experiments show that smaller Areal Error and higher Boundary Recall can be obtained using this method compared with the conventional methods.

Anticipation by basketball defenders: an explanation based on the three-dimensional inverted pendulum model.

PubMed

Fujii, Keisuke; Shinya, Masahiro; Yamashita, Daichi; Kouzaki, Motoki; Oda, Shingo

2014-01-01

We previously estimated the timing when ball game defenders detect relevant information through visual input for reacting to an attacker's running direction after a cutting manoeuvre, called cue timing. The purpose of this study was to investigate what specific information is relevant for defenders, and how defenders process this information to decide on their opponents' running direction. In this study, we hypothesised that defenders extract information regarding the position and velocity of the attackers' centre of mass (CoM) and the contact foot. We used a model which simulates the future trajectory of the opponent's CoM based upon an inverted pendulum movement. The hypothesis was tested by comparing observed defender's cue timing, model-estimated cue timing using the inverted pendulum model (IPM cue timing) and cue timing using only the current CoM position (CoM cue timing). The IPM cue timing was defined as the time when the simulated pendulum falls leftward or rightward given the initial values for position and velocity of the CoM and the contact foot at the time. The model-estimated IPM cue timing and the empirically observed defender's cue timing were comparable in median value and were significantly correlated, whereas the CoM cue timing was significantly more delayed than the IPM and the defender's cue timings. Based on these results, we discuss the possibility that defenders may be able to anticipate the future direction of an attacker by forwardly simulating inverted pendulum movement.

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species.

PubMed

Ludeña-Choez, Jimmy; Quispe-Soncco, Raisa; Gallardo-Antolín, Ascensión

2017-01-01

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC.

Cancer Chemoprevention with Korean Angelica: Active Compounds, Pharmacokinetics, and Human Translational Considerations

PubMed Central

Lü, Junxuan; Zhang, Jinhui; Li, Li; Jiang, Cheng; Xing, Chengguo

2015-01-01

Angelica gigas Nakai (AGN) is a major medicinal herb used in Korea and several other Asian countries. Traditionally, its dried root has been used to treat anemia, pain, infection and articular rheumatism, most often through boiling in water to prepare the dosage forms. AGN extract or AGN-containing herbal mixtures are sold in the US and globally as dietary supplements for pain killing, memory enhancement and post-menopausal symptom relief. Decursin (D) and its isomer decursinol angelate (DA) are the major chemicals in the alcoholic extracts of the root of AGN. The anti-cancer activity of AGN alcoholic extract has been established in a number of animal cancer models, including a transgenic model of prostate carcinogenesis. Cell culture structure-activity studies have uncovered distinct cellular and molecular effects of D and DA vs. their pyranocoumarin core decursinol (DOH) with respect to cancer cells and those associated with their microenvironment. Pharmacokinetic (PK) study by us and others in rodent models indicated that DOH is the major and rapid in vivo first-pass liver metabolite of D and DA. Cognizant of metabolic differences among rodents and humans, we carried out a first-in-human PK study of D/DA to inform the translational relevance of efficacy and mechanism studies with rodent models. The combined use of vigorous animal tests and human PK studies can provide stronger scientific rationale to inform design and execution of translational studies to move AGN toward evidence-based herbal medicine. PMID:26623248

Cancer Chemoprevention with Korean Angelica: Active Compounds, Pharmacokinetics, and Human Translational Considerations.

PubMed

Lü, Junxuan; Zhang, Jinhui; Li, Li; Jiang, Cheng; Xing, Chengguo

2015-12-01

Angelica gigas Nakai (AGN) is a major medicinal herb used in Korea and several other Asian countries. Traditionally, its dried root has been used to treat anemia, pain, infection and articular rheumatism, most often through boiling in water to prepare the dosage forms. AGN extract or AGN-containing herbal mixtures are sold in the US and globally as dietary supplements for pain killing, memory enhancement and post-menopausal symptom relief. Decursin (D) and its isomer decursinol angelate (DA) are the major chemicals in the alcoholic extracts of the root of AGN. The anti-cancer activity of AGN alcoholic extract has been established in a number of animal cancer models, including a transgenic model of prostate carcinogenesis. Cell culture structure-activity studies have uncovered distinct cellular and molecular effects of D and DA vs. their pyranocoumarin core decursinol (DOH) with respect to cancer cells and those associated with their microenvironment. Pharmacokinetic (PK) study by us and others in rodent models indicated that DOH is the major and rapid in vivo first-pass liver metabolite of D and DA. Cognizant of metabolic differences among rodents and humans, we carried out a first-in-human PK study of D/DA to inform the translational relevance of efficacy and mechanism studies with rodent models. The combined use of vigorous animal tests and human PK studies can provide stronger scientific rationale to inform design and execution of translational studies to move AGN toward evidence-based herbal medicine.

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species

PubMed Central

Quispe-Soncco, Raisa

2017-01-01

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC. PMID:28628630

Fast 3D 1H MRSI of the Corticospinal Tract in Pediatric Brain

PubMed Central

Kim, Dong-Hyun; Gu, Meng; Cunningham, Charles; Chen, Albert; Baumer, Fiona; Glenn, Orit A.; Vigneron, Daniel B.; Spielman, Daniel Mark; Barkovich, Anthony James

2010-01-01

Purpose: To develop a 1H magnetic resonance spectroscopic imaging (MRSI) sequence that can be used to image infants/children at 3T and by combining it with diffusion tensor imaging (DTI) tractography, extract relevant metabolic information corresponding to the corticospinal tract (CST). Materials and Methods: A fast 3D MRSI sequence was developed for pediatric neuroimaging at 3T using spiral k-space readout and dual band RF pulses (32 × 32 × 8 cm field of view [FOV], 1 cc iso-resolution, TR/TE = 1500/130, 6:24 minute scan). Using DTI tractography to identify the motor tracts, spectra were extracted from the CSTs and quantified. Initial data from infants/children with suspected motor delay (n = 5) and age-matched controls (n = 3) were collected and N-acetylaspartate (NAA) ratios were quantified. Results: The average signal-to-noise ratio of the NAA peak from the studies was ≈22. Metabolite profiles were successfully acquired from the CST by using DTI tractography. Decreased NAA ratios in those with motor delay compared to controls of ≈10% at the CST were observed. Conclusion: A fast and robust 3D MRSI technique targeted for pediatric neuroimaging has been developed. By combining with DTI tractography, metabolic information from the CSTs can be retrieved and estimated. By combining DTI and 3D MRSI, spectral information from various tracts can be obtained and processed. PMID:19097091

Information extraction system

DOEpatents

Lemmond, Tracy D; Hanley, William G; Guensche, Joseph Wendell; Perry, Nathan C; Nitao, John J; Kidwell, Paul Brandon; Boakye, Kofi Agyeman; Glaser, Ron E; Prenger, Ryan James

2014-05-13

An information extraction system and methods of operating the system are provided. In particular, an information extraction system for performing meta-extraction of named entities of people, organizations, and locations as well as relationships and events from text documents are described herein.

Assessment of YouTube videos as a source of information on medication use in pregnancy.

PubMed

Hansen, Craig; Interrante, Julia D; Ailes, Elizabeth C; Frey, Meghan T; Broussard, Cheryl S; Godoshian, Valerie J; Lewis, Courtney; Polen, Kara N D; Garcia, Amanda P; Gilboa, Suzanne M

2016-01-01

When making decisions about medication use in pregnancy, women consult many information sources, including the Internet. The aim of this study was to assess the content of publicly accessible YouTube videos that discuss medication use in pregnancy. Using 2023 distinct combinations of search terms related to medications and pregnancy, we extracted metadata from YouTube videos using a YouTube video Application Programming Interface. Relevant videos were defined as those with a medication search term and a pregnancy-related search term in either the video title or description. We viewed relevant videos and abstracted content from each video into a database. We documented whether videos implied each medication to be "safe" or "unsafe" in pregnancy and compared that assessment with the medication's Teratogen Information System (TERIS) rating. After viewing 651 videos, 314 videos with information about medication use in pregnancy were available for the final analyses. The majority of videos were from law firms (67%), television segments (10%), or physicians (8%). Selective serotonin reuptake inhibitors (SSRIs) were the most common medication class named (225 videos, 72%), and 88% of videos about SSRIs indicated that they were unsafe for use in pregnancy. However, the TERIS ratings for medication products in this class range from "unlikely" to "minimal" teratogenic risk. For the majority of medications, current YouTube video content does not adequately reflect what is known about the safety of their use in pregnancy and should be interpreted cautiously. However, YouTube could serve as a platform for communicating evidence-based medication safety information. Copyright © 2015 John Wiley & Sons, Ltd.

A 17-month time course study of human RNA and DNA degradation in body fluids under dry and humid environmental conditions.

PubMed

Sirker, Miriam; Schneider, Peter M; Gomes, Iva

2016-11-01

Blood, saliva, and semen are some of the forensically most relevant biological stains commonly found at crime scenes, which can often be of small size or challenging due to advanced decay. In this context, it is of great importance to possess reliable knowledge about the effects of degradation under different environmental conditions and to use appropriate methods for retrieving maximal information from limited sample amount. In the last decade, RNA analysis has been demonstrated to be a reliable approach identifying the cell or tissue type of an evidentiary body fluid trace. Hence, messenger RNA (mRNA) profiling is going to be implemented into forensic casework to supplement the routinely performed short tandem repeat (STR) analysis, and therefore, the ability to co-isolate RNA and DNA from the same sample is a prerequisite. The objective of this work was to monitor and compare the degradation process of both nucleic acids for human blood, saliva, and semen stains at three different concentrations, exposed to dry and humid conditions during a 17-month time period. This study also addressed the question whether there are relevant differences in the efficiency of automated, magnetic bead-based single DNA or RNA extraction methods compared to a manually performed co-extraction method using silica columns. Our data show that mRNA, especially from blood and semen, can be recovered over the entire time period surveyed without compromising the success of DNA profiling; mRNA analysis indicates to be a robust and reliable technique to identify the biological source of aged stain material. The co-extraction method appears to provide mRNA and DNA of sufficient quantity and quality for all different forensic investigation procedures. Humidity and accompanied mold formation are detrimental to both nucleic acids.

Endocrine, teratogenic and neurotoxic effects of cyanobacteria detected by cellular in vitro and zebrafish embryos assays.

PubMed

Jonas, Adam; Scholz, Stefan; Fetter, Eva; Sychrova, Eliska; Novakova, Katerina; Ortmann, Julia; Benisek, Martin; Adamovsky, Ondrej; Giesy, John P; Hilscherova, Klara

2015-02-01

Cyanobacteria contain various types of bioactive compounds, which could cause adverse effects on organisms. They are released into surface waters during cyanobacterial blooms, but there is little information on their potential relevance for effects in vivo. In this study presence of bioactive compounds was characterized in cyanobacteria Microcystis aeruginosa (Chroococcales), Planktothrix agardhii (Oscillatoriales) and Aphanizomenon gracile (Nostocales) with selected in vitro assays. The in vivo relevance of detected bioactivities was analysed using transgenic zebrafish embryos tg(cyp19a1b-GFP). Teratogenic potency was assessed by analysis of developmental disorders and effects on functions of the neuromuscular system by video tracking of locomotion. Estrogenicity in vitro corresponded to 0.95-54.6 ng estradiol equivalent(g dry weight (dw))(-1). In zebrafish embryos, estrogenic effects could not be detected potentially because they were masked by high toxicity. There was no detectable (anti)androgenic/glucocorticoid activity in any sample. Retinoid-like activity was determined at 1-1.3 μg all-trans-retinoic acid equivalent(g dw)(-1). Corresponding to the retinoid-like activity A. gracile extract also caused teratogenic effects in zebrafish embryos. Furthermore, exposure to biomass extracts at 0.3 gd wL(-1) caused increase of body length in embryos. There were minor effects on locomotion caused by 0.3 gd wL(-1)M. aeruginosa and P. agardhii extracts. The traditionally measured cyanotoxins microcystins did not seem to play significant role in observed effects. This indicates importance of other cyanobacterial compounds at least towards some species or their developmental phases. More attention should be paid to activity of retinoids, estrogens and other bioactive substances in phytoplankton using in vitro and in vivo bioassays. Copyright © 2014 Elsevier Ltd. All rights reserved.

Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application to Biomarker Discovery

NASA Astrophysics Data System (ADS)

Picard de Muller, Gaël; Ait-Belkacem, Rima; Bonnel, David; Longuespée, Rémi; Stauber, Jonathan

2017-12-01

Mass spectrometry imaging datasets are mostly analyzed in terms of average intensity in regions of interest. However, biological tissues have different morphologies with several sizes, shapes, and structures. The important biological information, contained in this highly heterogeneous cellular organization, could be hidden by analyzing the average intensities. Finding an analytical process of morphology would help to find such information, describe tissue model, and support identification of biomarkers. This study describes an informatics approach for the extraction and identification of mass spectrometry image features and its application to sample analysis and modeling. For the proof of concept, two different tissue types (healthy kidney and CT-26 xenograft tumor tissues) were imaged and analyzed. A mouse kidney model and tumor model were generated using morphometric - number of objects and total surface - information. The morphometric information was used to identify m/z that have a heterogeneous distribution. It seems to be a worthwhile pursuit as clonal heterogeneity in a tumor is of clinical relevance. This study provides a new approach to find biomarker or support tissue classification with more information. [Figure not available: see fulltext.

The dynamics of information-driven coordination phenomena: A transfer entropy analysis

PubMed Central

Borge-Holthoefer, Javier; Perra, Nicola; Gonçalves, Bruno; González-Bailón, Sandra; Arenas, Alex; Moreno, Yamir; Vespignani, Alessandro

2016-01-01

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data. PMID:27051875

The dynamics of information-driven coordination phenomena: A transfer entropy analysis.

PubMed

Borge-Holthoefer, Javier; Perra, Nicola; Gonçalves, Bruno; González-Bailón, Sandra; Arenas, Alex; Moreno, Yamir; Vespignani, Alessandro

2016-04-01

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data.

Characterizing Urban Volumetry Using LIDAR Data

NASA Astrophysics Data System (ADS)

Santos, T.; Rodrigues, A. M.; Tenedório, J. A.

2013-05-01

Urban indicators are efficient tools designed to simplify, quantify and communicate relevant information for land planners. Since urban data has a strong spatial representation, one can use geographical data as the basis for constructing information regarding urban environments. One important source of information about the land status is imagery collected through remote sensing. Afterwards, using digital image processing techniques, thematic detail can be extracted from those images and used to build urban indicators. Most common metrics are based on area (2D) measurements. These include indicators like impervious area per capita or surface occupied by green areas, having usually as primary source a spectral image obtained through a satellite or airborne camera. More recently, laser scanning data has become available for large-scale applications. Such sensors acquire altimetric information and are used to produce Digital Surface Models (DSM). In this context, LiDAR data available for the city is explored along with demographic information, and a framework to produce volumetric (3D) urban indexes is proposed, and measures like Built Volume per capita, Volumetric Density and Volumetric Homogeneity are computed.

Detection of signals in mRNAs that influence translation.

PubMed

Brown, Chris M; Jacobs, Grant; Stockwell, Peter; Schreiber, Mark

2003-01-01

Genome sequencing efforts mean that we now have extensive data from a wide range of organisms to study. Understanding the differing natures of the biology of these organisms is an important aim of genome analysis. We are interested in signals that affect translation of mRNAs. Some signals in the mRNA influence how efficiently it is translated into protein. Previous studies have indicated that many important signals are located around the initiation and termination codons. We have developed tools described here to extract the relevant sequence regions from GenBank. To create databases organised by species, or higher taxonomic groupings (eg planta), a program was developed to dynamically view and edit the taxonomy database. Data from relevant species were then extracted using our Genbank feature table parser. We analysed all available sequences, particularly those from complete genomes. Patterns were then identified using information theory. The software is available from http://transterm.otago.ac.nz. Patterns around the initiation codons for most of the organisms fall into two groups, containing the previously known Shine-Dalgarno and Kozaks efficiency signals. However, we have identified several organisms that appear to utilise novel systems. Our analysis indicates that some organisms with extremely high GC% genomes do not have a strong dependence on base pairing ribosome binding sites, as the complementary sequence is absent from many genes.

Adopting Quality Criteria for Websites Providing Medical Information About Rare Diseases

PubMed Central

Göbel, Jens; Storf, Holger; Litzkendorf, Svenja; Babac, Ana; Frank, Martin; Lührs, Verena; Schauer, Franziska; Schmidtke, Jörg; Biehl, Lisa; Wagner, Thomas OF; Ückert, Frank; Graf von der Schulenburg, Johann-Matthias; Hartz, Tobias

2016-01-01

Background The European Union considers diseases to be rare when they affect less than 5 in 10,000 people. It is estimated that there are between 5000 and 8000 different rare diseases. Consistent with this diversity, the quality of information available on the Web varies considerably. Thus, quality criteria for websites about rare diseases are needed. Objective The objective of this study was to generate a catalog of quality criteria suitable for rare diseases. Methods First, relevant certificates and quality recommendations for health information websites were identified through a comprehensive Web search. Second, all considered quality criteria of each certification program and catalog were examined, extracted into an overview table, and analyzed by thematic content. Finally, an interdisciplinary expert group verified the relevant quality criteria. Results We identified 9 quality certificates and criteria catalogs for health information websites with 304 single criteria items. Through this, we aggregated 163 various quality criteria, each assigned to one of the following categories: thematic, technical, service, content, and legal. Finally, a consensus about 13 quality criteria for websites offering medical information on rare diseases was determined. Of these categories, 4 (data protection concept, imprint, creation and updating date, and possibility to contact the website provider) were identified as being the most important for publishing medical information about rare diseases. Conclusions The large number of different quality criteria appearing within a relatively small number of criteria catalogs shows that the opinion of what is important in the quality of health information differs. In addition, to define useful quality criteria for websites about rare diseases, which are an essential source of information for many patients, a trade-off is necessary between the high standard of quality criteria for health information websites in general and the limited provision of information about some rare diseases. Finally, transparently presented quality assessments can help people to find reliable information and to assess its quality. PMID:27562540

Adopting Quality Criteria for Websites Providing Medical Information About Rare Diseases.

PubMed

Pauer, Frédéric; Göbel, Jens; Storf, Holger; Litzkendorf, Svenja; Babac, Ana; Frank, Martin; Lührs, Verena; Schauer, Franziska; Schmidtke, Jörg; Biehl, Lisa; Wagner, Thomas Of; Ückert, Frank; Graf von der Schulenburg, Johann-Matthias; Hartz, Tobias

2016-08-25

The European Union considers diseases to be rare when they affect less than 5 in 10,000 people. It is estimated that there are between 5000 and 8000 different rare diseases. Consistent with this diversity, the quality of information available on the Web varies considerably. Thus, quality criteria for websites about rare diseases are needed. The objective of this study was to generate a catalog of quality criteria suitable for rare diseases. First, relevant certificates and quality recommendations for health information websites were identified through a comprehensive Web search. Second, all considered quality criteria of each certification program and catalog were examined, extracted into an overview table, and analyzed by thematic content. Finally, an interdisciplinary expert group verified the relevant quality criteria. We identified 9 quality certificates and criteria catalogs for health information websites with 304 single criteria items. Through this, we aggregated 163 various quality criteria, each assigned to one of the following categories: thematic, technical, service, content, and legal. Finally, a consensus about 13 quality criteria for websites offering medical information on rare diseases was determined. Of these categories, 4 (data protection concept, imprint, creation and updating date, and possibility to contact the website provider) were identified as being the most important for publishing medical information about rare diseases. The large number of different quality criteria appearing within a relatively small number of criteria catalogs shows that the opinion of what is important in the quality of health information differs. In addition, to define useful quality criteria for websites about rare diseases, which are an essential source of information for many patients, a trade-off is necessary between the high standard of quality criteria for health information websites in general and the limited provision of information about some rare diseases. Finally, transparently presented quality assessments can help people to find reliable information and to assess its quality.

Single molecule optical measurements of orientation and rotations of biological macromolecules.

PubMed

Shroder, Deborah Y; Lippert, Lisa G; Goldman, Yale E

2016-11-22

Subdomains of macromolecules often undergo large orientation changes during their catalytic cycles that are essential for their activity. Tracking these rearrangements in real time opens a powerful window into the link between protein structure and functional output. Site-specific labeling of individual molecules with polarized optical probes and measurement of their spatial orientation can give insight into the crucial conformational changes, dynamics, and fluctuations of macromolecules. Here we describe the range of single molecule optical technologies that can extract orientation information from these probes, review the relevant types of probes and labeling techniques, and highlight the advantages and disadvantages of these technologies for addressing specific inquiries.

Habitability issues in long duration undersea and space missions

NASA Technical Reports Server (NTRS)

Parker, J. F., Jr.; Every, M. G.

1972-01-01

The report reviews a number of studies in the area of habitability. Emphasis was placed on extracting from these studies that information most relevant to any long-term mission in confinement. It is concluded that, whereas the basic laws of habitability are known, there is much yet to be learned concerning development of social structures in small groups in relative isolation, planning for necessary hygiene needs, development of proper work spaces, and construction of internal and external communications systems. With respect to testing for habitability and the documentation of habitability principles, the space program was found to be considerably more advanced than was the program for undersea missions.

49 CFR 556.9 - Public inspection of relevant information.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 6 2010-10-01 2010-10-01 false Public inspection of relevant information. 556.9... NONCOMPLIANCE § 556.9 Public inspection of relevant information. Information relevant to a petition under this... Administration, 400 Seventh Street, SW., Washington, DC 20590. Copies of available information may be obtained in...

The role of emotion in the learning and transfer of clinical skills and knowledge.

PubMed

McConnell, Meghan M; Eva, Kevin W

2012-10-01

Medical school and residency are emotional experiences for trainees. Most research examining emotion in medicine has focused on negative moods associated with physician burnout and poor quality of life. However, positive emotional states also may have important influences on student learning and performance. The authors present a review of the literature on the influence of emotion on cognition, specifically how individuals learn complex skills and knowledge and how they transfer that information to new scenarios. From September 2011 to February 2012, the authors searched Medline, PsycInfo, GoogleScholar, ERIC, and Web of Science, as well as the reference lists of relevant articles, for research on the interaction between emotion, learning, and knowledge transfer. They extracted representative themes and noted particularly relevant empirical findings. The authors found articles that show that emotion influences various cognitive processes that are involved in the acquisition and transfer of knowledge and skills. More specifically, emotion influences how individuals identify and perceive information, how they interpret it, and how they act on the information available in learning and practice situations. There are many ways in which emotions may influence medical education. Researchers must further explore the implications of these findings to ensure that learning is not treated simply as a rational, mechanistic process but that trainees are effectively prepared to perform under a wide range of emotional conditions.

Describing knowledge encounters in healthcare: a mixed studies systematic review and development of a classification.

PubMed

Hurst, Dominic; Mickan, Sharon

2017-03-14

Implementation science seeks to promote the uptake of research and other evidence-based findings into practice, but for healthcare professionals, this is complex as practice draws on, in addition to scientific principles, rules of thumb and a store of practical wisdom acquired from a range of informational and experiential sources. The aims of this review were to identify sources of information and professional experiences encountered by healthcare workers and from this to build a classification system, for use in future observational studies, that describes influences on how healthcare professionals acquire and use information in their clinical practice. This was a mixed studies systematic review of observational studies. OVID MEDLINE and Embase and Google Scholar were searched using terms around information, knowledge or evidence and sharing, searching and utilisation combined with terms relating to healthcare groups. Studies were eligible if one of the intentions was to identify information or experiential encounters by healthcare workers. Data was extracted by one author after piloting with another. Studies were assessed using the Mixed Methods Appraisal Tool (MMAT). The primary outcome extracted was the information source or professional experience encounter. Similar encounters were grouped together as single constructs. Our synthesis involved a mixed approach using the top-down logic of the Bliss Bibliographic Classification System (BC2) to generate classification categories and a bottom-up approach to develop descriptive codes (or "facets") for each category, from the data. The generic terms of BC2 were customised by an iterative process of thematic content analysis. Facets were developed by using available theory and keeping in mind the pragmatic end use of the classification. Eighty studies were included from which 178 discreet knowledge encounters were extracted. Six classification categories were developed: what information or experience was encountered; how was the information or experience encountered; what was the mode of encounter; from whom did the information originate or with whom was the experience; how many participants were there; and where did the encounter take place. For each of these categories, relevant descriptive facets were identified. We have sought to identify and classify all knowledge encounters, and we have developed a faceted description of key categories which will support richer descriptions and interrogations of knowledge encounters in healthcare research.

RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials.

PubMed

Marshall, Iain J; Kuiper, Joël; Wallace, Byron C

2016-01-01

To develop and evaluate RobotReviewer, a machine learning (ML) system that automatically assesses bias in clinical trials. From a (PDF-formatted) trial report, the system should determine risks of bias for the domains defined by the Cochrane Risk of Bias (RoB) tool, and extract supporting text for these judgments. We algorithmically annotated 12,808 trial PDFs using data from the Cochrane Database of Systematic Reviews (CDSR). Trials were labeled as being at low or high/unclear risk of bias for each domain, and sentences were labeled as being informative or not. This dataset was used to train a multi-task ML model. We estimated the accuracy of ML judgments versus humans by comparing trials with two or more independent RoB assessments in the CDSR. Twenty blinded experienced reviewers rated the relevance of supporting text, comparing ML output with equivalent (human-extracted) text from the CDSR. By retrieving the top 3 candidate sentences per document (top3 recall), the best ML text was rated more relevant than text from the CDSR, but not significantly (60.4% ML text rated 'highly relevant' v 56.5% of text from reviews; difference +3.9%, [-3.2% to +10.9%]). Model RoB judgments were less accurate than those from published reviews, though the difference was <10% (overall accuracy 71.0% with ML v 78.3% with CDSR). Risk of bias assessment may be automated with reasonable accuracy. Automatically identified text supporting bias assessment is of equal quality to the manually identified text in the CDSR. This technology could substantially reduce reviewer workload and expedite evidence syntheses. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Computer-based interventions to improve self-management in adults with type 2 diabetes: a systematic review and meta-analysis.

PubMed

Pal, Kingshuk; Eastwood, Sophie V; Michie, Susan; Farmer, Andrew; Barnard, Maria L; Peacock, Richard; Wood, Bindie; Edwards, Phil; Murray, Elizabeth

2014-06-01

Structured patient education programs can reduce the risk of diabetes-related complications. However, people appear to have difficulties attending face-to-face education and alternatives are needed. This review looked at the impact of computer-based diabetes self-management interventions on health status, cardiovascular risk factors, and quality of life of adults with type 2 diabetes. We searched The Cochrane Library, Medline, Embase, PsycINFO, Web of Science, and CINAHL for relevant trials from inception to November 2011. Reference lists from relevant published studies were screened and authors contacted for further information when required. Two authors independently extracted relevant data using standard data extraction templates. Sixteen randomized controlled trials with 3,578 participants met the inclusion criteria. Interventions were delivered via clinics, the Internet, and mobile phones. Computer-based diabetes self-management interventions appear to have small benefits on glycemic control: the pooled effect on HbA1c was -0.2% (-2.3 mmol/mol [95% CI -0.4 to -0.1%]). A subgroup analysis on mobile phone-based interventions showed a larger effect: the pooled effect on HbA1c from three studies was -0.50% (-5.46 mmol/mol [95% CI -0.7 to -0.3%]). There was no evidence of improvement in depression, quality of life, blood pressure, serum lipids, or weight. There was no evidence of significant adverse effects. Computer-based diabetes self-management interventions to manage type 2 diabetes appear to have a small beneficial effect on blood glucose control, and this effect was larger in the mobile phone subgroup. There was no evidence of benefit for other biological, cognitive, behavioral, or emotional outcomes. © 2014 by the American Diabetes Association.

Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research.

PubMed

Bravo, Àlex; Piñero, Janet; Queralt-Rosinach, Núria; Rautschka, Michael; Furlong, Laura I

2015-02-21

Current biomedical research needs to leverage and exploit the large amount of information reported in scientific publications. Automated text mining approaches, in particular those aimed at finding relationships between entities, are key for identification of actionable knowledge from free text repositories. We present the BeFree system aimed at identifying relationships between biomedical entities with a special focus on genes and their associated diseases. By exploiting morpho-syntactic information of the text, BeFree is able to identify gene-disease, drug-disease and drug-target associations with state-of-the-art performance. The application of BeFree to real-case scenarios shows its effectiveness in extracting information relevant for translational research. We show the value of the gene-disease associations extracted by BeFree through a number of analyses and integration with other data sources. BeFree succeeds in identifying genes associated to a major cause of morbidity worldwide, depression, which are not present in other public resources. Moreover, large-scale extraction and analysis of gene-disease associations, and integration with current biomedical knowledge, provided interesting insights on the kind of information that can be found in the literature, and raised challenges regarding data prioritization and curation. We found that only a small proportion of the gene-disease associations discovered by using BeFree is collected in expert-curated databases. Thus, there is a pressing need to find alternative strategies to manual curation, in order to review, prioritize and curate text-mining data and incorporate it into domain-specific databases. We present our strategy for data prioritization and discuss its implications for supporting biomedical research and applications. BeFree is a novel text mining system that performs competitively for the identification of gene-disease, drug-disease and drug-target associations. Our analyses show that mining only a small fraction of MEDLINE results in a large dataset of gene-disease associations, and only a small proportion of this dataset is actually recorded in curated resources (2%), raising several issues on data prioritization and curation. We propose that joint analysis of text mined data with data curated by experts appears as a suitable approach to both assess data quality and highlight novel and interesting information.

Functional source separation and hand cortical representation for a brain–computer interface feature extraction

PubMed Central

Tecchio, Franca; Porcaro, Camillo; Barbati, Giulia; Zappasodi, Filippo

2007-01-01

A brain–computer interface (BCI) can be defined as any system that can track the person's intent which is embedded in his/her brain activity and, from it alone, translate the intention into commands of a computer. Among the brain signal monitoring systems best suited for this challenging task, electroencephalography (EEG) and magnetoencephalography (MEG) are the most realistic, since both are non-invasive, EEG is portable and MEG could provide more specific information that could be later exploited also through EEG signals. The first two BCI steps require set up of the appropriate experimental protocol while recording the brain signal and then to extract interesting features from the recorded cerebral activity. To provide information useful in these BCI stages, our aim is to provide an overview of a new procedure we recently developed, named functional source separation (FSS). As it comes from the blind source separation algorithms, it exploits the most valuable information provided by the electrophysiological techniques, i.e. the waveform signal properties, remaining blind to the biophysical nature of the signal sources. FSS returns the single trial source activity, estimates the time course of a neuronal pool along different experimental states on the basis of a specific functional requirement in a specific time period, and uses the simulated annealing as the optimization procedure allowing the exploit of functional constraints non-differentiable. Moreover, a minor section is included, devoted to information acquired by MEG in stroke patients, to guide BCI applications aiming at sustaining motor behaviour in these patients. Relevant BCI features – spatial and time-frequency properties – are in fact altered by a stroke in the regions devoted to hand control. Moreover, a method to investigate the relationship between sensory and motor hand cortical network activities is described, providing information useful to develop BCI feedback control systems. This review provides a description of the FSS technique, a promising tool for the BCI community for online electrophysiological feature extraction, and offers interesting information to develop BCI applications to sustain hand control in stroke patients. PMID:17331989

Conjunctive programming: An interactive approach to software system synthesis

NASA Technical Reports Server (NTRS)

Tausworthe, Robert C.

1992-01-01

This report introduces a technique of software documentation called conjunctive programming and discusses its role in the development and maintenance of software systems. The report also describes the conjoin tool, an adjunct to assist practitioners. Aimed at supporting software reuse while conforming with conventional development practices, conjunctive programming is defined as the extraction, integration, and embellishment of pertinent information obtained directly from an existing database of software artifacts, such as specifications, source code, configuration data, link-edit scripts, utility files, and other relevant information, into a product that achieves desired levels of detail, content, and production quality. Conjunctive programs typically include automatically generated tables of contents, indexes, cross references, bibliographic citations, tables, and figures (including graphics and illustrations). This report presents an example of conjunctive programming by documenting the use and implementation of the conjoin program.

Automatically identifying health- and clinical-related content in wikipedia.

PubMed

Liu, Feifan; Moosavinasab, Soheil; Agarwal, Shashank; Bennett, Andrew S; Yu, Hong

2013-01-01

Physicians are increasingly using the Internet for finding medical information related to patient care. Wikipedia is a valuable online medical resource to be integrated into existing clinical question answering (QA) systems. On the other hand, Wikipedia contains a full spectrum of world's knowledge and therefore comprises a large partition of non-health-related content, which makes disambiguation more challenging and consequently leads to large overhead for existing systems to effectively filter irrelevant information. To overcome this, we have developed both unsupervised and supervised approaches to identify health-related articles as well as clinically relevant articles. Furthermore, we explored novel features by extracting health related hierarchy from the Wikipedia category network, from which a variety of features were derived and evaluated. Our experiments show promising results and also demonstrate that employing the category hierarchy can effectively improve the system performance.

Supercritical-Multiple-Solvent Extraction From Coal

NASA Technical Reports Server (NTRS)

Corcoran, W.; Fong, W.; Pichaichanarong, P.; Chan, P.; Lawson, D.

1983-01-01

Large and small molecules dissolve different constituents. Experimental apparatus used to test supercritical extraction of hydrogen rich compounds from coal in various organic solvents. In decreasing order of importance, relevant process parameters were found to be temperature, solvent type, pressure, and residence time.

Comparison of different methodologies for detailed screening of Taraxacum officinale honey volatiles.

PubMed

Jerković, Igor; Marijanović, Zvonimir; Kranjac, Marina; Radonić, Ani

2015-02-01

Headspace solid-phase microextraction (HS-SPME), ultrasonic solvent extraction (USE) and solid phase extraction (SPE), followed by GC-FID/MS were used for screening of dandelion (Taraxacum officinale Weber) honey headspace, volatiles and semi-volatiles. The obtained results constitute a breakthrough towards screening of dandelion honey since dominant compounds identified in the extracts were not previously reported for this honey type. Nitriles dominated in the headspace, particularly 3-methylpentanenitrile (up to 29.9%) and phenylacetonitrile (up to 20.9%). Lower methyl branched aliphatic acids and norisoprenoids were relevant minor constituents of the headspace. The extracts contained phenylacetic acid (up to 24.0%) and dehydrovomifoliol (up to 19.3%) as predominant compounds, while 3-methylpentanenitrile and phenylacetonitrile were detected in the extracts in minor abundance. Dehydrovomifoliol can be considered more characteristic for dandelion honey in distinction from phenylacetic acid. Low molecular aliphatic acids, benzene derivatives and an array of higher aliphatic compounds were also found in the extracts. The results of SPE/GC-FID/MS were very similar to USE/GC-FID/MS with the solvent dichloromethane. The use of all applied methodologies was relevant for the comprehensive chemical fingerprinting of dandelion honey volatiles.

Plant extracts and natural compounds used against UVB-induced photoaging.

PubMed

Cavinato, Maria; Waltenberger, Birgit; Baraldo, Giorgia; Grade, Carla V C; Stuppner, Hermann; Jansen-Dürr, Pidder

2017-08-01

Skin is continuously exposed to a variety of environmental stresses, including ultraviolet (UV) radiation. UVB is an inherent component of sunlight that crosses the epidermis and reaches the upper dermis, leading to increased oxidative stress, activation of inflammatory response and accumulation of DNA damage among other effects. The increase in UVB radiation on earth due to the destruction of stratospheric ozone poses a major environmental threat to the skin, increasing the risk of damage with long-term consequences, such as photoaging and photocarcinogenesis. Extracts from plants and natural compounds have been historically used in traditional medicine in the form of teas and ointments but the effect of most of these compounds has yet to be verified. Regarding the increasing concern of the population with issues related to quality of life and appearance, the cosmetic market for anti-aging and photoprotective products based on natural compounds is continuously growing, and there is increasing requirement of expansion on research in this field. In this review we summarized the most current and relevant information concerning plant extracts and natural compounds that are able to protect or mitigate the deleterious effects caused by photoaging in different experimental models.

Extraction and analysis methods for the determination of pyrethroid insecticides in surface water, sediments and biological tissues at environmentally relevant concentrations.

PubMed

Mekebri, A; Crane, D B; Blondina, G J; Oros, D R; Rocca, J L

2008-05-01

The aim of this study was to develop and validate chemical methods for measuring pyrethroid insecticides at environmentally relevant concentrations in different matrices. The analytes included six synthetic pyrethroids with the highest agricultural and commercial structural uses in California: bifenthrin, cyfluthrin, cypermethrin, esfenvalerate/fenvalerate, lambda-cyhalothrin, permethrin, and their corresponding stereoisomers, which includes enantiomers, diastereomers and racemic mixtures. Fortified water samples were extracted for analysis of synthetic pyrethroids using liquid-liquid extraction, while fortified sediment and fish tissue samples were extracted using pressurized fluid extraction followed by gel permeation chromatography (GPC) to remove matrix interferences. A florisil column was used for additional cleanup and fractionation of sediment and tissue extracts. Extracts were analyzed using dual column high resolution gas chromatography with electron capture detection (GC/ECD) and confirmation was obtained with gas chromatography mass spectrometry using a quadrupole ion trap detector in MS-MS mode. Method detection limits (MDLs) have been established for water (1-3 ng/L), sediment (0.5-4 ng/g dry weight) and tissue (1-3 ng/g fresh weight). Mean percent recoveries of fortified blanks and samples ranged from 75 to 115% with relative standard deviation (RSD) values less than 20% for all target compounds.

Kinect as a Tool for Gait Analysis: Validation of a Real-Time Joint Extraction Algorithm Working in Side View

PubMed Central

Cippitelli, Enea; Gasparrini, Samuele; Spinsante, Susanna; Gambi, Ennio

2015-01-01

The Microsoft Kinect sensor has gained attention as a tool for gait analysis for several years. Despite the many advantages the sensor provides, however, the lack of a native capability to extract joints from the side view of a human body still limits the adoption of the device to a number of relevant applications. This paper presents an algorithm to locate and estimate the trajectories of up to six joints extracted from the side depth view of a human body captured by the Kinect device. The algorithm is then applied to extract data that can be exploited to provide an objective score for the “Get Up and Go Test”, which is typically adopted for gait analysis in rehabilitation fields. Starting from the depth-data stream provided by the Microsoft Kinect sensor, the proposed algorithm relies on anthropometric models only, to locate and identify the positions of the joints. Differently from machine learning approaches, this solution avoids complex computations, which usually require significant resources. The reliability of the information about the joint position output by the algorithm is evaluated by comparison to a marker-based system. Tests show that the trajectories extracted by the proposed algorithm adhere to the reference curves better than the ones obtained from the skeleton generated by the native applications provided within the Microsoft Kinect (Microsoft Corporation, Redmond, WA, USA, 2013) and OpenNI (OpenNI organization, Tel Aviv, Israel, 2013) Software Development Kits. PMID:25594588

Interventions to assist health consumers to find reliable online health information: a comprehensive review.

PubMed

Lee, Kenneth; Hoti, Kreshnik; Hughes, Jeffery D; Emmerton, Lynne M

2014-01-01

Health information on the Internet is ubiquitous, and its use by health consumers prevalent. Finding and understanding relevant online health information, and determining content reliability, pose real challenges for many health consumers. To identify the types of interventions that have been implemented to assist health consumers to find reliable online health information, and where possible, describe and compare the types of outcomes studied. PubMed, PsycINFO, CINAHL Plus and Cochrane Library databases; WorldCat and Scirus 'gray literature' search engines; and manual review of reference lists of selected publications. Publications were selected by firstly screening title, abstract, and then full text. Seven publications met the inclusion criteria, and were summarized in a data extraction form. The form incorporated the PICOS (Population Intervention Comparators Outcomes and Study Design) Model. Two eligible gray literature papers were also reported. Relevant data from included studies were tabulated to enable descriptive comparison. A brief critique of each study was included in the tables. This review was unable to follow systematic review methods due to the paucity of research and humanistic interventions reported. While extensive, the gray literature search may have had limited reach in some countries. The paucity of research on this topic limits conclusions that may be drawn. The few eligible studies predominantly adopted a didactic approach to assisting health consumers, whereby consumers were either taught how to find credible websites, or how to use the Internet. Common types of outcomes studied include knowledge and skills pertaining to Internet use and searching for reliable health information. These outcomes were predominantly self-assessed by participants. There is potential for further research to explore other avenues for assisting health consumers to find reliable online health information, and to assess outcomes via objective measures.

The virtual library: Coming of age

NASA Technical Reports Server (NTRS)

Hunter, Judy F.; Cotter, Gladys A.

1994-01-01

With the high speed networking capabilities, multiple media options, and massive amounts of information that exist in electronic format today, the concept of a 'virtual' library or 'library without walls' is becoming viable. In virtual library environment, the information processed goes beyond the traditional definition of documents to include the results of scientific and technical research and development (reports, software, data) recorded in any format or media: electronic, audio, video, or scanned images. Network access to information must include tools to help locate information sources and navigate the networks to connect to the sources, as well as methods to extract the relevant information. Graphical User Interfaces (GUI's) that are intuitive and navigational tools such as Intelligent Gateway Processors (IGP) will provide users with seamless and transparent use of high speed networks to access, organize, and manage information. Traditional libraries will become points of electronic access to information on multiple medias. The emphasis will be towards unique collections of information at each library rather than entire collections at every library. It is no longer a question of whether there is enough information available; it is more a question of how to manage the vast volumes of information. The future equation will involve being able to organize knowledge, manage information, and provide access at the point of origin.

Information extraction from multi-institutional radiology reports.

PubMed

Hassanpour, Saeed; Langlotz, Curtis P

2016-01-01

The radiology report is the most important source of clinical imaging information. It documents critical information about the patient's health and the radiologist's interpretation of medical findings. It also communicates information to the referring physicians and records that information for future clinical and research use. Although efforts to structure some radiology report information through predefined templates are beginning to bear fruit, a large portion of radiology report information is entered in free text. The free text format is a major obstacle for rapid extraction and subsequent use of information by clinicians, researchers, and healthcare information systems. This difficulty is due to the ambiguity and subtlety of natural language, complexity of described images, and variations among different radiologists and healthcare organizations. As a result, radiology reports are used only once by the clinician who ordered the study and rarely are used again for research and data mining. In this work, machine learning techniques and a large multi-institutional radiology report repository are used to extract the semantics of the radiology report and overcome the barriers to the re-use of radiology report information in clinical research and other healthcare applications. We describe a machine learning system to annotate radiology reports and extract report contents according to an information model. This information model covers the majority of clinically significant contents in radiology reports and is applicable to a wide variety of radiology study types. Our automated approach uses discriminative sequence classifiers for named-entity recognition to extract and organize clinically significant terms and phrases consistent with the information model. We evaluated our information extraction system on 150 radiology reports from three major healthcare organizations and compared its results to a commonly used non-machine learning information extraction method. We also evaluated the generalizability of our approach across different organizations by training and testing our system on data from different organizations. Our results show the efficacy of our machine learning approach in extracting the information model's elements (10-fold cross-validation average performance: precision: 87%, recall: 84%, F1 score: 85%) and its superiority and generalizability compared to the common non-machine learning approach (p-value<0.05). Our machine learning information extraction approach provides an effective automatic method to annotate and extract clinically significant information from a large collection of free text radiology reports. This information extraction system can help clinicians better understand the radiology reports and prioritize their review process. In addition, the extracted information can be used by researchers to link radiology reports to information from other data sources such as electronic health records and the patient's genome. Extracted information also can facilitate disease surveillance, real-time clinical decision support for the radiologist, and content-based image retrieval. Copyright © 2015 Elsevier B.V. All rights reserved.

Review of Extracting Information From the Social Web for Health Personalization

PubMed Central

Karlsen, Randi; Bonander, Jason

2011-01-01

In recent years the Web has come into its own as a social platform where health consumers are actively creating and consuming Web content. Moreover, as the Web matures, consumers are gaining access to personalized applications adapted to their health needs and interests. The creation of personalized Web applications relies on extracted information about the users and the content to personalize. The Social Web itself provides many sources of information that can be used to extract information for personalization apart from traditional Web forms and questionnaires. This paper provides a review of different approaches for extracting information from the Social Web for health personalization. We reviewed research literature across different fields addressing the disclosure of health information in the Social Web, techniques to extract that information, and examples of personalized health applications. In addition, the paper includes a discussion of technical and socioethical challenges related to the extraction of information for health personalization. PMID:21278049

Big Data and Machine Learning in Plastic Surgery: A New Frontier in Surgical Innovation.

PubMed

Kanevsky, Jonathan; Corban, Jason; Gaster, Richard; Kanevsky, Ari; Lin, Samuel; Gilardino, Mirko

2016-05-01

Medical decision-making is increasingly based on quantifiable data. From the moment patients come into contact with the health care system, their entire medical history is recorded electronically. Whether a patient is in the operating room or on the hospital ward, technological advancement has facilitated the expedient and reliable measurement of clinically relevant health metrics, all in an effort to guide care and ensure the best possible clinical outcomes. However, as the volume and complexity of biomedical data grow, it becomes challenging to effectively process "big data" using conventional techniques. Physicians and scientists must be prepared to look beyond classic methods of data processing to extract clinically relevant information. The purpose of this article is to introduce the modern plastic surgeon to machine learning and computational interpretation of large data sets. What is machine learning? Machine learning, a subfield of artificial intelligence, can address clinically relevant problems in several domains of plastic surgery, including burn surgery; microsurgery; and craniofacial, peripheral nerve, and aesthetic surgery. This article provides a brief introduction to current research and suggests future projects that will allow plastic surgeons to explore this new frontier of surgical science.

Learning to rank-based gene summary extraction.

PubMed

Shang, Yue; Hao, Huihui; Wu, Jiajin; Lin, Hongfei

2014-01-01

In recent years, the biomedical literature has been growing rapidly. These articles provide a large amount of information about proteins, genes and their interactions. Reading such a huge amount of literature is a tedious task for researchers to gain knowledge about a gene. As a result, it is significant for biomedical researchers to have a quick understanding of the query concept by integrating its relevant resources. In the task of gene summary generation, we regard automatic summary as a ranking problem and apply the method of learning to rank to automatically solve this problem. This paper uses three features as a basis for sentence selection: gene ontology relevance, topic relevance and TextRank. From there, we obtain the feature weight vector using the learning to rank algorithm and predict the scores of candidate summary sentences and obtain top sentences to generate the summary. ROUGE (a toolkit for summarization of automatic evaluation) was used to evaluate the summarization result and the experimental results showed that our method outperforms the baseline techniques. According to the experimental result, the combination of three features can improve the performance of summary. The application of learning to rank can facilitate the further expansion of features for measuring the significance of sentences.

Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals

PubMed Central

Eugster, Manuel J. A.; Ruotsalo, Tuukka; Spapé, Michiel M.; Barral, Oswald; Ravaja, Niklas; Jacucci, Giulio; Kaski, Samuel

2016-01-01

Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user’s interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recommending information by relevance inferred directly from brain signals. In experiments, participants were asked to read Wikipedia documents about a selection of topics while their EEG was recorded. Based on the prediction of word relevance, the individual’s search intent was modeled and successfully used for retrieving new relevant documents from the whole English Wikipedia corpus. The results show that the users’ interests toward digital content can be modeled from the brain signals evoked by reading. The introduced brain-relevance paradigm enables the recommendation of information without any explicit user interaction and may be applied across diverse information-intensive applications. PMID:27929077

Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals

NASA Astrophysics Data System (ADS)

Eugster, Manuel J. A.; Ruotsalo, Tuukka; Spapé, Michiel M.; Barral, Oswald; Ravaja, Niklas; Jacucci, Giulio; Kaski, Samuel

2016-12-01

Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user’s interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recommending information by relevance inferred directly from brain signals. In experiments, participants were asked to read Wikipedia documents about a selection of topics while their EEG was recorded. Based on the prediction of word relevance, the individual’s search intent was modeled and successfully used for retrieving new relevant documents from the whole English Wikipedia corpus. The results show that the users’ interests toward digital content can be modeled from the brain signals evoked by reading. The introduced brain-relevance paradigm enables the recommendation of information without any explicit user interaction and may be applied across diverse information-intensive applications.

Research of information classification and strategy intelligence extract algorithm based on military strategy hall

NASA Astrophysics Data System (ADS)

Chen, Lei; Li, Dehua; Yang, Jie

2007-12-01

Constructing virtual international strategy environment needs many kinds of information, such as economy, politic, military, diploma, culture, science, etc. So it is very important to build an information auto-extract, classification, recombination and analysis management system with high efficiency as the foundation and component of military strategy hall. This paper firstly use improved Boost algorithm to classify obtained initial information, then use a strategy intelligence extract algorithm to extract strategy intelligence from initial information to help strategist to analysis information.

External cephalic version for singleton breech presentation: proposal of a practical check-list for obstetricians.

PubMed

Indraccolo, U; Graziani, C; Di Iorio, R; Corona, G; Bonito, M; Indraccolo, S R

2015-07-01

External cephalic version (ECV) for breech presentation is not routinely performed by obstetricians in many clinical settings. The aim of this work is to assess to what extent the factors involved in performing ECV are relevant for the success and safety of ECV, in order to propose a practical check-list for assessing the feasibility of ECV. Review of 214 references. Factors involved in the success and risks of ECV (feasibility of ECV) were extracted and were scored in a semi-quantitative way according to textual information, type of publication, year of publication, number of cases. Simple conjoint analysis was used to describe the relevance found for each factor. Parity has the pivotal role in ECV feasibility (relevance 16.6%), followed by tocolysis (10.8%), gestational age (10.6%), amniotic fluid volume (4.7%), breech variety (1.9%), and placenta location (1.7%). Other factors with estimated relevance around 0 (regional anesthesia, station, estimated fetal weight, fetal position, obesity/BMI, fetal birth weight, duration of manoeuvre/number of attempts) have some role in the feasibility of ECV. Yet other factors, with negative values of estimated relevance, have even less importance. From a logical interpretation of the relevance of each factor assessed, ECV should be proposed with utmost prudence if a stringent check-list is followed. Such a check-list should take into account: parity, tocolytic therapy, gestational age, amniotic fluid volume, breech variety, placenta location, regional anesthesia, breech engagement, fetal well-being, uterine relaxation, fetal size, fetal position, fetal head grasping capability and fetal turning capability.

Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study.

PubMed

Mercan, Ezgi; Aksoy, Selim; Shapiro, Linda G; Weaver, Donald L; Brunyé, Tad T; Elmore, Joann G

2016-08-01

Whole slide digital imaging technology enables researchers to study pathologists' interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists' actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors.

Antibiofilm Activity of the Brown Alga Halidrys siliquosa against Clinically Relevant Human Pathogens

PubMed Central

Busetti, Alessandro; Thompson, Thomas P.; Tegazzini, Diana; Megaw, Julianne; Maggs, Christine A.; Gilmore, Brendan F.

2015-01-01

The marine brown alga Halidrys siliquosa is known to produce compounds with antifouling activity against several marine bacteria. The aim of this study was to evaluate the antimicrobial and antibiofilm activity of organic extracts obtained from the marine brown alga H. siliquosa against a focused panel of clinically relevant human pathogens commonly associated with biofilm-related infections. The partially fractionated methanolic extract obtained from H. siliquosa collected along the shores of Co. Donegal; Ireland; displayed antimicrobial activity against bacteria of the genus Staphylococcus; Streptococcus; Enterococcus; Pseudomonas; Stenotrophomonas; and Chromobacterium with MIC and MBC values ranging from 0.0391 to 5 mg/mL. Biofilms of S. aureus MRSA were found to be susceptible to the algal methanolic extract with MBEC values ranging from 1.25 mg/mL to 5 mg/mL respectively. Confocal laser scanning microscopy using LIVE/DEAD staining confirmed the antimicrobial nature of the antibiofilm activity observed using the MBEC assay. A bioassay-guided fractionation method was developed yielding 10 active fractions from which to perform purification and structural elucidation of clinically-relevant antibiofilm compounds. PMID:26058011

Pharmacogenetic testing through the direct-to-consumer genetic testing company 23andMe.

PubMed

Lu, Mengfei; Lewis, Cathryn M; Traylor, Matthew

2017-06-19

Rapid advances in scientific research have led to an increase in public awareness of genetic testing and pharmacogenetics. Direct-to-consumer (DTC) genetic testing companies, such as 23andMe, allow consumers to access their genetic information directly through an online service without the involvement of healthcare professionals. Here, we evaluate the clinical relevance of pharmacogenetic tests reported by 23andMe in their UK tests. The research papers listed under each 23andMe report were evaluated, extracting information on effect size, sample size and ethnicity. A wider literature search was performed to provide a fuller assessment of the pharmacogenetic test and variants were matched to FDA recommendations. Additional evidence from CPIC guidelines, PharmGKB, and Dutch Pharmacogenetics Working Group was reviewed to determine current clinical practice. The value of the tests across ethnic groups was determined, including information on linkage disequilibrium between the tested SNP and causal pharmacogenetic variant, where relevant. 23andMe offers 12 pharmacogenetic tests to their UK customers, some of which are in standard clinical practice, and others which are less widely applied. The clinical validity and clinical utility varies extensively between tests. The variants tested are likely to have different degrees of sensitivity due to different risk allele frequencies and linkage disequilibrium patterns across populations. The clinical relevance depends on the ethnicity of the individual and variability of pharmacogenetic markers. Further research is required to determine causal variants and provide more complete assessment of drug response and side effects. 23andMe reports provide some useful pharmacogenetics information, mirroring clinical tests that are in standard use. Other tests are unspecific, providing limited guidance and may not be useful for patients without professional interpretation. Nevertheless, DTC companies like 23andMe act as a powerful intermediate step to integrate pharmacogenetic testing into clinical practice.

Hierarchical ensemble of global and local classifiers for face recognition.

PubMed

Su, Yu; Shan, Shiguang; Chen, Xilin; Gao, Wen

2009-08-01

In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher's linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol.

Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD.

PubMed

Bullinaria, John A; Levy, Joseph P

2012-09-01

In a previous article, we presented a systematic computational study of the extraction of semantic representations from the word-word co-occurrence statistics of large text corpora. The conclusion was that semantic vectors of pointwise mutual information values from very small co-occurrence windows, together with a cosine distance measure, consistently resulted in the best representations across a range of psychologically relevant semantic tasks. This article extends that study by investigating the use of three further factors--namely, the application of stop-lists, word stemming, and dimensionality reduction using singular value decomposition (SVD)--that have been used to provide improved performance elsewhere. It also introduces an additional semantic task and explores the advantages of using a much larger corpus. This leads to the discovery and analysis of improved SVD-based methods for generating semantic representations (that provide new state-of-the-art performance on a standard TOEFL task) and the identification and discussion of problems and misleading results that can arise without a full systematic study.

Genomic Definition of Hypervirulent and Multidrug-Resistant Klebsiella pneumoniae Clonal Groups

PubMed Central

Bialek-Davenet, Suzanne; Criscuolo, Alexis; Ailloud, Florent; Passet, Virginie; Jones, Louis; Delannoy-Vieillard, Anne-Sophie; Garin, Benoit; Le Hello, Simon; Arlet, Guillaume; Nicolas-Chanoine, Marie-Hélène; Decré, Dominique

2014-01-01

Multidrug-resistant and highly virulent Klebsiella pneumoniae isolates are emerging, but the clonal groups (CGs) corresponding to these high-risk strains have remained imprecisely defined. We aimed to identify K. pneumoniae CGs on the basis of genome-wide sequence variation and to provide a simple bioinformatics tool to extract virulence and resistance gene data from genomic data. We sequenced 48 K. pneumoniae isolates, mostly of serotypes K1 and K2, and compared the genomes with 119 publicly available genomes. A total of 694 highly conserved genes were included in a core-genome multilocus sequence typing scheme, and cluster analysis of the data enabled precise definition of globally distributed hypervirulent and multidrug-resistant CGs. In addition, we created a freely accessible database, BIGSdb-Kp, to enable rapid extraction of medically and epidemiologically relevant information from genomic sequences of K. pneumoniae. Although drug-resistant and virulent K. pneumoniae populations were largely nonoverlapping, isolates with combined virulence and resistance features were detected. PMID:25341126

Fractal-like Distributions over the Rational Numbers in High-throughput Biological and Clinical Data

NASA Astrophysics Data System (ADS)

Trifonov, Vladimir; Pasqualucci, Laura; Dalla-Favera, Riccardo; Rabadan, Raul

2011-12-01

Recent developments in extracting and processing biological and clinical data are allowing quantitative approaches to studying living systems. High-throughput sequencing (HTS), expression profiles, proteomics, and electronic health records (EHR) are some examples of such technologies. Extracting meaningful information from those technologies requires careful analysis of the large volumes of data they produce. In this note, we present a set of fractal-like distributions that commonly appear in the analysis of such data. The first set of examples are drawn from a HTS experiment. Here, the distributions appear as part of the evaluation of the error rate of the sequencing and the identification of tumorogenic genomic alterations. The other examples are obtained from risk factor evaluation and analysis of relative disease prevalence and co-mordbidity as these appear in EHR. The distributions are also relevant to identification of subclonal populations in tumors and the study of quasi-species and intrahost diversity of viral populations.

Machine learning based sample extraction for automatic speech recognition using dialectal Assamese speech.

PubMed

Agarwalla, Swapna; Sarma, Kandarpa Kumar

2016-06-01

Automatic Speaker Recognition (ASR) and related issues are continuously evolving as inseparable elements of Human Computer Interaction (HCI). With assimilation of emerging concepts like big data and Internet of Things (IoT) as extended elements of HCI, ASR techniques are found to be passing through a paradigm shift. Oflate, learning based techniques have started to receive greater attention from research communities related to ASR owing to the fact that former possess natural ability to mimic biological behavior and that way aids ASR modeling and processing. The current learning based ASR techniques are found to be evolving further with incorporation of big data, IoT like concepts. Here, in this paper, we report certain approaches based on machine learning (ML) used for extraction of relevant samples from big data space and apply them for ASR using certain soft computing techniques for Assamese speech with dialectal variations. A class of ML techniques comprising of the basic Artificial Neural Network (ANN) in feedforward (FF) and Deep Neural Network (DNN) forms using raw speech, extracted features and frequency domain forms are considered. The Multi Layer Perceptron (MLP) is configured with inputs in several forms to learn class information obtained using clustering and manual labeling. DNNs are also used to extract specific sentence types. Initially, from a large storage, relevant samples are selected and assimilated. Next, a few conventional methods are used for feature extraction of a few selected types. The features comprise of both spectral and prosodic types. These are applied to Recurrent Neural Network (RNN) and Fully Focused Time Delay Neural Network (FFTDNN) structures to evaluate their performance in recognizing mood, dialect, speaker and gender variations in dialectal Assamese speech. The system is tested under several background noise conditions by considering the recognition rates (obtained using confusion matrices and manually) and computation time. It is found that the proposed ML based sentence extraction techniques and the composite feature set used with RNN as classifier outperform all other approaches. By using ANN in FF form as feature extractor, the performance of the system is evaluated and a comparison is made. Experimental results show that the application of big data samples has enhanced the learning of the ASR system. Further, the ANN based sample and feature extraction techniques are found to be efficient enough to enable application of ML techniques in big data aspects as part of ASR systems. Copyright © 2015 Elsevier Ltd. All rights reserved.

High-Resolution Remote Sensing Image Building Extraction Based on Markov Model

NASA Astrophysics Data System (ADS)

Zhao, W.; Yan, L.; Chang, Y.; Gong, L.

2018-04-01

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize "pseudo-buildings" in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

Overview of image processing tools to extract physical information from JET videos

NASA Astrophysics Data System (ADS)

Craciunescu, T.; Murari, A.; Gelfusa, M.; Tiseanu, I.; Zoita, V.; EFDA Contributors, JET

2014-11-01

In magnetic confinement nuclear fusion devices such as JET, the last few years have witnessed a significant increase in the use of digital imagery, not only for the surveying and control of experiments, but also for the physical interpretation of results. More than 25 cameras are routinely used for imaging on JET in the infrared (IR) and visible spectral regions. These cameras can produce up to tens of Gbytes per shot and their information content can be very different, depending on the experimental conditions. However, the relevant information about the underlying physical processes is generally of much reduced dimensionality compared to the recorded data. The extraction of this information, which allows full exploitation of these diagnostics, is a challenging task. The image analysis consists, in most cases, of inverse problems which are typically ill-posed mathematically. The typology of objects to be analysed is very wide, and usually the images are affected by noise, low levels of contrast, low grey-level in-depth resolution, reshaping of moving objects, etc. Moreover, the plasma events have time constants of ms or tens of ms, which imposes tough conditions for real-time applications. On JET, in the last few years new tools and methods have been developed for physical information retrieval. The methodology of optical flow has allowed, under certain assumptions, the derivation of information about the dynamics of video objects associated with different physical phenomena, such as instabilities, pellets and filaments. The approach has been extended in order to approximate the optical flow within the MPEG compressed domain, allowing the manipulation of the large JET video databases and, in specific cases, even real-time data processing. The fast visible camera may provide new information that is potentially useful for disruption prediction. A set of methods, based on the extraction of structural information from the visual scene, have been developed for the automatic detection of MARFE (multifaceted asymmetric radiation from the edge) occurrences, which precede disruptions in density limit discharges. An original spot detection method has been developed for large surveys of videos in JET, and for the assessment of the long term trends in their evolution. The analysis of JET IR videos, recorded during JET operation with the ITER-like wall, allows the retrieval of data and hence correlation of the evolution of spots properties with macroscopic events, in particular series of intentional disruptions.

Systematic screening of plant extracts from the Brazilian Pantanal with antimicrobial activity against bacteria with cariogenic relevance.

PubMed

Brighenti, F L; Salvador, M J; Delbem, Alberto Carlos Botazzo; Delbem, Ádina Cleia Bottazzo; Oliveira, M A C; Soares, C P; Freitas, L S F; Koga-Ito, C Y

2014-01-01

This study proposes a bioprospection methodology regarding the antimicrobial potential of plant extracts against bacteria with cariogenic relevance. Sixty extracts were obtained from ten plants--(1) Jatropha weddelliana, (2) Attalea phalerata, (3) Buchenavia tomentosa, (4) Croton doctoris, (5) Mouriri elliptica, (6) Mascagnia benthamiana, (7) Senna aculeata, (8) Unonopsis guatterioides, (9) Allagoptera leucocalyx and (10) Bactris glaucescens--using different extraction methods - (A) 70° ethanol 72 h/25°C, (B) water 5 min/100°C, (C) water 1 h/55°C, (D) water 72 h/25°C, (E) hexane 72 h/25°C and (F) 90° ethanol 72 h/25°C. The plants were screened for antibacterial activity at 50 mg/ml using the agar well diffusion test against Actinomyces naeslundii ATCC 19039, Lactobacillus acidophilus ATCC 4356, Streptococcus gordonii ATCC 10558, Streptococcus mutans ATCC 35688, Streptococcus sanguinis ATCC 10556, Streptococcus sobrinus ATCC 33478 and Streptococcus mitis ATCC 9811. The active extracts were tested to determine their minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), cytotoxicity and chemical characterization. Forty-seven extracts (78%) were active against at least one microorganism. Extract 4A demonstrated the lowest MIC and MBC for all microorganisms except S. gordonii and the extract at MIC concentration was non-cytotoxic. The concentrated extracts were slightly cytotoxic. Electrospray ionization with tandem mass spectrometry analyses demonstrated that the extract constituents coincided with the mass of the terpenoids and phenolics. Overall, the best results were obtained for extraction methods A, B and C. The present work proved the antimicrobial activity of several plants. Particularly, extracts from C. doctoris were the most active against bacteria involved in dental caries disease. © 2014 S. Karger AG, Basel.

Epidermal photonic devices for quantitative imaging of temperature and thermal transport characteristics of the skin

NASA Astrophysics Data System (ADS)

Gao, Li; Zhang, Yihui; Malyarchuk, Viktor; Jia, Lin; Jang, Kyung-In; Chad Webb, R.; Fu, Haoran; Shi, Yan; Zhou, Guoyan; Shi, Luke; Shah, Deesha; Huang, Xian; Xu, Baoxing; Yu, Cunjiang; Huang, Yonggang; Rogers, John A.

2014-09-01

Characterization of temperature and thermal transport properties of the skin can yield important information of relevance to both clinical medicine and basic research in skin physiology. Here we introduce an ultrathin, compliant skin-like, or ‘epidermal’, photonic device that combines colorimetric temperature indicators with wireless stretchable electronics for thermal measurements when softly laminated on the skin surface. The sensors exploit thermochromic liquid crystals patterned into large-scale, pixelated arrays on thin elastomeric substrates; the electronics provide means for controlled, local heating by radio frequency signals. Algorithms for extracting patterns of colour recorded from these devices with a digital camera and computational tools for relating the results to underlying thermal processes near the skin surface lend quantitative value to the resulting data. Application examples include non-invasive spatial mapping of skin temperature with milli-Kelvin precision (±50 mK) and sub-millimetre spatial resolution. Demonstrations in reactive hyperaemia assessments of blood flow and hydration analysis establish relevance to cardiovascular health and skin care, respectively.

Epidermal photonic devices for quantitative imaging of temperature and thermal transport characteristics of the skin.

PubMed

Gao, Li; Zhang, Yihui; Malyarchuk, Viktor; Jia, Lin; Jang, Kyung-In; Webb, R Chad; Fu, Haoran; Shi, Yan; Zhou, Guoyan; Shi, Luke; Shah, Deesha; Huang, Xian; Xu, Baoxing; Yu, Cunjiang; Huang, Yonggang; Rogers, John A

2014-09-19

Characterization of temperature and thermal transport properties of the skin can yield important information of relevance to both clinical medicine and basic research in skin physiology. Here we introduce an ultrathin, compliant skin-like, or 'epidermal', photonic device that combines colorimetric temperature indicators with wireless stretchable electronics for thermal measurements when softly laminated on the skin surface. The sensors exploit thermochromic liquid crystals patterned into large-scale, pixelated arrays on thin elastomeric substrates; the electronics provide means for controlled, local heating by radio frequency signals. Algorithms for extracting patterns of colour recorded from these devices with a digital camera and computational tools for relating the results to underlying thermal processes near the skin surface lend quantitative value to the resulting data. Application examples include non-invasive spatial mapping of skin temperature with milli-Kelvin precision (±50 mK) and sub-millimetre spatial resolution. Demonstrations in reactive hyperaemia assessments of blood flow and hydration analysis establish relevance to cardiovascular health and skin care, respectively.

Antibody responses to Sarcoptes scabiei apolipoprotein in a porcine model: relevance to immunodiagnosis of recent infection.

PubMed

Rampton, Melanie; Walton, Shelley F; Holt, Deborah C; Pasay, Cielo; Kelly, Andrew; Currie, Bart J; McCarthy, James S; Mounsey, Kate E

2013-01-01

No commercial immunodiagnostic tests for human scabies are currently available, and existing animal tests are not sufficiently sensitive. The recombinant Sarcoptes scabiei apolipoprotein antigen Sar s 14.3 is a promising immunodiagnostic, eliciting high levels of IgE and IgG in infected people. Limited data are available regarding the temporal development of antibodies to Sar s 14.3, an issue of relevance in terms of immunodiagnosis. We utilised a porcine model to prospectively compare specific antibody responses to a primary infestation by ELISA, to Sar s 14.3 and to S. scabiei whole mite antigen extract (WMA). Differences in the antibody profile between antigens were apparent, with Sar s 14.3 responses detected earlier, and declining significantly after peak infestation compared to WMA. Both antigens resulted in >90% diagnostic sensitivity from weeks 8-16 post infestation. These data provide important information on the temporal development of humoral immune responses in scabies and further supports the development of recombinant antigen based immunodiagnostic tests for recent scabies infestations.

A Meta-Analysis of Observational Studies of the Association Between Chronic Occupational Exposure to Lead and Amyotrophic Lateral Sclerosis

PubMed Central

Gomes, James; Cashman, Neil R.; Little, Julian; Krewski, Daniel

2014-01-01

Objective: The association between occupational exposure to lead and amyotrophic lateral sclerosis (ALS) was examined through systematic review and meta-analyses of relevant epidemiological studies and reported according to PRISMA guidelines. Methods: Relevant studies were searched in multiple bibliographic databases through September 2013; additional articles were tracked through PubMed until submission. All records were screened in DistillerSR, and the data extracted from included articles were synthesized with meta-analysis. Results: The risk of developing ALS among individuals with a history of exposure to lead was almost doubled (odds ratio, 1.81; 95% confidence interval, 1.39 to 2.36) on the basis of nine included case-control studies with specific lead exposure information, with no apparent heterogeneity across included studies (I2 = 14%). The attributable risk of ALS because of exposure to lead was estimated to be 5%. Conclusions: Previous exposure to lead may be a risk factor for ALS. PMID:25479292

A-MADMAN: Annotation-based microarray data meta-analysis tool

PubMed Central

Bisognin, Andrea; Coppe, Alessandro; Ferrari, Francesco; Risso, Davide; Romualdi, Chiara; Bicciato, Silvio; Bortoluzzi, Stefania

2009-01-01

Background Publicly available datasets of microarray gene expression signals represent an unprecedented opportunity for extracting genomic relevant information and validating biological hypotheses. However, the exploitation of this exceptionally rich mine of information is still hampered by the lack of appropriate computational tools, able to overcome the critical issues raised by meta-analysis. Results This work presents A-MADMAN, an open source web application which allows the retrieval, annotation, organization and meta-analysis of gene expression datasets obtained from Gene Expression Omnibus. A-MADMAN addresses and resolves several open issues in the meta-analysis of gene expression data. Conclusion A-MADMAN allows i) the batch retrieval from Gene Expression Omnibus and the local organization of raw data files and of any related meta-information, ii) the re-annotation of samples to fix incomplete, or otherwise inadequate, metadata and to create user-defined batches of data, iii) the integrative analysis of data obtained from different Affymetrix platforms through custom chip definition files and meta-normalization. Software and documentation are available on-line at . PMID:19563634

Application of Laser Scanning Confocal Microscopy to Heat and Mass Transport Modeling in Porous Microstructures

NASA Technical Reports Server (NTRS)

Marshall, Jochen; Milos, Frank; Fredrich, Joanne; Rasky, Daniel J. (Technical Monitor)

1997-01-01

Laser Scanning Confocal Microscopy (LSCM) has been used to obtain digital images of the complicated 3-D (three-dimensional) microstructures of rigid, fibrous thermal protection system (TPS) materials. These orthotropic materials are comprised of refractory ceramic fibers with diameters in the range of 1 to 10 microns and have open porosities of 0.8 or more. Algorithms are being constructed to extract quantitative microstructural information from the digital data so that it may be applied to specific heat and mass transport modeling efforts; such information includes, for example, the solid and pore volume fractions, the internal surface area per volume, fiber diameter distributions, and fiber orientation distributions. This type of information is difficult to obtain in general, yet it is directly relevant to many computational efforts which seek to model macroscopic thermophysical phenomena in terms of microscopic mechanisms or interactions. Two such computational efforts for fibrous TPS materials are: i) the calculation of radiative transport properties; ii) the modeling of gas permeabilities.

Heatwave early warning systems and adaptation advice to reduce human health consequences of heatwaves.

PubMed

Lowe, Dianne; Ebi, Kristie L; Forsberg, Bertil

2011-12-01

With climate change, there has been an increase in the frequency, intensity and duration of heatwave events. In response to the devastating mortality and morbidity of recent heatwave events, many countries have introduced heatwave early warning systems (HEWS). HEWS are designed to reduce the avoidable human health consequences of heatwaves through timely notification of prevention measures to vulnerable populations. To identify the key characteristics of HEWS in European countries to help inform modification of current, and development of, new systems and plans. We searched the internet to identify HEWS policy or government documents for 33 European countries and requested information from relevant organizations. We translated the HEWS documents and extracted details on the trigger indicators, thresholds for action, notification strategies, message intermediaries, communication and dissemination strategies, prevention strategies recommended and specified target audiences. Twelve European countries have HEWS. Although there are many similarities among the HEWS, there also are differences in key characteristics that could inform improvements in heatwave early warning plans.

The Evolution of Holistic Processing of Faces

PubMed Central

Burke, Darren; Sulikowski, Danielle

2013-01-01

In this paper we examine the holistic processing of faces from an evolutionary perspective, clarifying what such an approach entails, and evaluating the extent to which the evidence currently available permits any strong conclusions. While it seems clear that the holistic processing of faces depends on mechanisms evolved to perform that task, our review of the comparative literature reveals that there is currently insufficient evidence (or sometimes insufficiently compelling evidence) to decide when in our evolutionary past such processing may have arisen. It is also difficult to assess what kinds of selection pressures may have led to evolution of such a mechanism, or even what kinds of information holistic processing may have originally evolved to extract, given that many sources of socially relevant face-based information other than identity depend on integrating information across different regions of the face – judgments of expression, behavioral intent, attractiveness, sex, age, etc. We suggest some directions for future research that would help to answer these important questions. PMID:23382721

Self-relevance and wishful thinking: facilitation and distortion in source monitoring.

PubMed

Barber, Sarah J; Gordon, Ruthanna; Franklin, Nancy

2009-06-01

When making source attributions, people tend to attribute desirable statements to reliable sources and undesirable statements to unreliable sources, a phenomenon known as the wishful thinking effect (Gordon, Franklin, & Beck, 2005). In the present study, we examined the influence of wishful thinking on source monitoring for self-relevant information. On one hand, wishful thinking is expected, because self-relevant desires are presumably strong. However, self-relevance is known to confer a memory advantage and may thus provide protection from desire-based biases. In Experiment 1, source memory for self-relevant information was contrasted against source memory for information relevant to others and for neutral information. Results indicated that self-relevant information was affected by wishful thinking and was remembered more accurately than was other information. Experiment 2 showed that the magnitude of the self-relevant wishful thinking effect did not increase with a delay.

Cataloging the biomedical world of pain through semi-automated curation of molecular interactions

PubMed Central

Jamieson, Daniel G.; Roberts, Phoebe M.; Robertson, David L.; Sidders, Ben; Nenadic, Goran

2013-01-01

The vast collection of biomedical literature and its continued expansion has presented a number of challenges to researchers who require structured findings to stay abreast of and analyze molecular mechanisms relevant to their domain of interest. By structuring literature content into topic-specific machine-readable databases, the aggregate data from multiple articles can be used to infer trends that can be compared and contrasted with similar findings from topic-independent resources. Our study presents a generalized procedure for semi-automatically creating a custom topic-specific molecular interaction database through the use of text mining to assist manual curation. We apply the procedure to capture molecular events that underlie ‘pain’, a complex phenomenon with a large societal burden and unmet medical need. We describe how existing text mining solutions are used to build a pain-specific corpus, extract molecular events from it, add context to the extracted events and assess their relevance. The pain-specific corpus contains 765 692 documents from Medline and PubMed Central, from which we extracted 356 499 unique normalized molecular events, with 261 438 single protein events and 93 271 molecular interactions supplied by BioContext. Event chains are annotated with negation, speculation, anatomy, Gene Ontology terms, mutations, pain and disease relevance, which collectively provide detailed insight into how that event chain is associated with pain. The extracted relations are visualized in a wiki platform (wiki-pain.org) that enables efficient manual curation and exploration of the molecular mechanisms that underlie pain. Curation of 1500 grouped event chains ranked by pain relevance revealed 613 accurately extracted unique molecular interactions that in the future can be used to study the underlying mechanisms involved in pain. Our approach demonstrates that combining existing text mining tools with domain-specific terms and wiki-based visualization can facilitate rapid curation of molecular interactions to create a custom database. Database URL: ••• PMID:23707966

Cataloging the biomedical world of pain through semi-automated curation of molecular interactions.

PubMed

Jamieson, Daniel G; Roberts, Phoebe M; Robertson, David L; Sidders, Ben; Nenadic, Goran

2013-01-01

The vast collection of biomedical literature and its continued expansion has presented a number of challenges to researchers who require structured findings to stay abreast of and analyze molecular mechanisms relevant to their domain of interest. By structuring literature content into topic-specific machine-readable databases, the aggregate data from multiple articles can be used to infer trends that can be compared and contrasted with similar findings from topic-independent resources. Our study presents a generalized procedure for semi-automatically creating a custom topic-specific molecular interaction database through the use of text mining to assist manual curation. We apply the procedure to capture molecular events that underlie 'pain', a complex phenomenon with a large societal burden and unmet medical need. We describe how existing text mining solutions are used to build a pain-specific corpus, extract molecular events from it, add context to the extracted events and assess their relevance. The pain-specific corpus contains 765 692 documents from Medline and PubMed Central, from which we extracted 356 499 unique normalized molecular events, with 261 438 single protein events and 93 271 molecular interactions supplied by BioContext. Event chains are annotated with negation, speculation, anatomy, Gene Ontology terms, mutations, pain and disease relevance, which collectively provide detailed insight into how that event chain is associated with pain. The extracted relations are visualized in a wiki platform (wiki-pain.org) that enables efficient manual curation and exploration of the molecular mechanisms that underlie pain. Curation of 1500 grouped event chains ranked by pain relevance revealed 613 accurately extracted unique molecular interactions that in the future can be used to study the underlying mechanisms involved in pain. Our approach demonstrates that combining existing text mining tools with domain-specific terms and wiki-based visualization can facilitate rapid curation of molecular interactions to create a custom database. Database URL: •••

Hybrid ontology for semantic information retrieval model using keyword matching indexing system.

PubMed

Uthayan, K R; Mala, G S Anandha

2015-01-01

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology.

Hybrid Ontology for Semantic Information Retrieval Model Using Keyword Matching Indexing System

PubMed Central

Uthayan, K. R.; Anandha Mala, G. S.

2015-01-01

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology. PMID:25922851

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

NASA Technical Reports Server (NTRS)

Mathe, Nathalie; Chen, James

1994-01-01

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

A rapid extraction of landslide disaster information research based on GF-1 image

NASA Astrophysics Data System (ADS)

Wang, Sai; Xu, Suning; Peng, Ling; Wang, Zhiyi; Wang, Na

2015-08-01

In recent years, the landslide disasters occurred frequently because of the seismic activity. It brings great harm to people's life. It has caused high attention of the state and the extensive concern of society. In the field of geological disaster, landslide information extraction based on remote sensing has been controversial, but high resolution remote sensing image can improve the accuracy of information extraction effectively with its rich texture and geometry information. Therefore, it is feasible to extract the information of earthquake- triggered landslides with serious surface damage and large scale. Taking the Wenchuan county as the study area, this paper uses multi-scale segmentation method to extract the landslide image object through domestic GF-1 images and DEM data, which uses the estimation of scale parameter tool to determine the optimal segmentation scale; After analyzing the characteristics of landslide high-resolution image comprehensively and selecting spectrum feature, texture feature, geometric features and landform characteristics of the image, we can establish the extracting rules to extract landslide disaster information. The extraction results show that there are 20 landslide whose total area is 521279.31 .Compared with visual interpretation results, the extraction accuracy is 72.22%. This study indicates its efficient and feasible to extract earthquake landslide disaster information based on high resolution remote sensing and it provides important technical support for post-disaster emergency investigation and disaster assessment.

46 CFR 560.5 - Receipt of relevant information.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 9 2010-10-01 2010-10-01 false Receipt of relevant information. 560.5 Section 560.5... FOREIGN PORTS § 560.5 Receipt of relevant information. (a) In making its decision on matters arising under... submissions should be supported by affidavits of fact and memorandum of law. Relevant information may include...

Assistance to neurosurgical planning: using a fuzzy spatial graph model of the brain for locating anatomical targets in MRI

NASA Astrophysics Data System (ADS)

Villéger, Alice; Ouchchane, Lemlih; Lemaire, Jean-Jacques; Boire, Jean-Yves

2007-03-01

Symptoms of neurodegenerative pathologies such as Parkinson's disease can be relieved through Deep Brain Stimulation. This neurosurgical technique relies on high precision positioning of electrodes in specific areas of the basal ganglia and the thalamus. These subcortical anatomical targets must be located at pre-operative stage, from a set of MRI acquired under stereotactic conditions. In order to assist surgical planning, we designed a semi-automated image analysis process for extracting anatomical areas of interest. Complementary information, provided by both patient's data and expert knowledge, is represented as fuzzy membership maps, which are then fused by means of suitable possibilistic operators in order to achieve the segmentation of targets. More specifically, theoretical prior knowledge on brain anatomy is modelled within a 'virtual atlas' organised as a spatial graph: a list of vertices linked by edges, where each vertex represents an anatomical structure of interest and contains relevant information such as tissue composition, whereas each edge represents a spatial relationship between two structures, such as their relative directions. The model is built using heterogeneous sources of information such as qualitative descriptions from the expert, or quantitative information from prelabelled images. For each patient, tissue membership maps are extracted from MR data through a classification step. Prior model and patient's data are then matched by using a research algorithm (or 'strategy') which simultaneously computes an estimation of the location of every structures. The method was tested on 10 clinical images, with promising results. Location and segmentation results were statistically assessed, opening perspectives for enhancements.

A Compositional Relevance Model for Adaptive Information Retrieval

NASA Technical Reports Server (NTRS)

Mathe, Nathalie; Chen, James; Lu, Henry, Jr. (Technical Monitor)

1994-01-01

There is a growing need for rapid and effective access to information in large electronic documentation systems. Access can be facilitated if information relevant in the current problem solving context can be automatically supplied to the user. This includes information relevant to particular user profiles, tasks being performed, and problems being solved. However most of this knowledge on contextual relevance is not found within the contents of documents, and current hypermedia tools do not provide any easy mechanism to let users add this knowledge to their documents. We propose a compositional relevance network to automatically acquire the context in which previous information was found relevant. The model records information on the relevance of references based on user feedback for specific queries and contexts. It also generalizes such information to derive relevant references for similar queries and contexts. This model lets users filter information by context of relevance, build personalized views of documents over time, and share their views with other users. It also applies to any type of multimedia information. Compared to other approaches, it is less costly and doesn't require any a priori statistical computation, nor an extended training period. It is currently being implemented into the Computer Integrated Documentation system which enables integration of various technical documents in a hypertext framework.

Representational analysis of extended disorder in atomistic ensembles derived from total scattering data

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

Neilson, James R.; McQueen, Tyrel M.

With the increased availability of high-intensity time-of-flight neutron and synchrotron X-ray scattering sources that can access wide ranges of momentum transfer, the pair distribution function method has become a standard analysis technique for studying disorder of local coordination spheres and at intermediate atomic separations. In some cases, rational modeling of the total scattering data (Bragg and diffuse) becomes intractable with least-squares approaches, necessitating reverse Monte Carlo simulations using large atomistic ensembles. However, the extraction of meaningful information from the resulting atomistic ensembles is challenging, especially at intermediate length scales. Representational analysis is used here to describe the displacements of atomsmore » in reverse Monte Carlo ensembles from an ideal crystallographic structure in an approach analogous to tight-binding methods. Rewriting the displacements in terms of a local basis that is descriptive of the ideal crystallographic symmetry provides a robust approach to characterizing medium-range order (and disorder) and symmetry breaking in complex and disordered crystalline materials. Lastly, this method enables the extraction of statistically relevant displacement modes (orientation, amplitude and distribution) of the crystalline disorder and provides directly meaningful information in a locally symmetry-adapted basis set that is most descriptive of the crystal chemistry and physics.« less

Representational analysis of extended disorder in atomistic ensembles derived from total scattering data

DOE PAGES

Neilson, James R.; McQueen, Tyrel M.

2015-09-20

With the increased availability of high-intensity time-of-flight neutron and synchrotron X-ray scattering sources that can access wide ranges of momentum transfer, the pair distribution function method has become a standard analysis technique for studying disorder of local coordination spheres and at intermediate atomic separations. In some cases, rational modeling of the total scattering data (Bragg and diffuse) becomes intractable with least-squares approaches, necessitating reverse Monte Carlo simulations using large atomistic ensembles. However, the extraction of meaningful information from the resulting atomistic ensembles is challenging, especially at intermediate length scales. Representational analysis is used here to describe the displacements of atomsmore » in reverse Monte Carlo ensembles from an ideal crystallographic structure in an approach analogous to tight-binding methods. Rewriting the displacements in terms of a local basis that is descriptive of the ideal crystallographic symmetry provides a robust approach to characterizing medium-range order (and disorder) and symmetry breaking in complex and disordered crystalline materials. Lastly, this method enables the extraction of statistically relevant displacement modes (orientation, amplitude and distribution) of the crystalline disorder and provides directly meaningful information in a locally symmetry-adapted basis set that is most descriptive of the crystal chemistry and physics.« less

Sensing in a noisy world: lessons from auditory specialists, echolocating bats.

PubMed

Corcoran, Aaron J; Moss, Cynthia F

2017-12-15

All animals face the essential task of extracting biologically meaningful sensory information from the 'noisy' backdrop of their environments. Here, we examine mechanisms used by echolocating bats to localize objects, track small prey and communicate in complex and noisy acoustic environments. Bats actively control and coordinate both the emission and reception of sound stimuli through integrated sensory and motor mechanisms that have evolved together over tens of millions of years. We discuss how bats behave in different ecological scenarios, including detecting and discriminating target echoes from background objects, minimizing acoustic interference from competing conspecifics and overcoming insect noise. Bats tackle these problems by deploying a remarkable array of auditory behaviors, sometimes in combination with the use of other senses. Behavioral strategies such as ceasing sonar call production and active jamming of the signals of competitors provide further insight into the capabilities and limitations of echolocation. We relate these findings to the broader topic of how animals extract relevant sensory information in noisy environments. While bats have highly refined abilities for operating under noisy conditions, they face the same challenges encountered by many other species. We propose that the specialized sensory mechanisms identified in bats are likely to occur in analogous systems across the animal kingdom. © 2017. Published by The Company of Biologists Ltd.

RIEMS: a software pipeline for sensitive and comprehensive taxonomic classification of reads from metagenomics datasets.

PubMed

Scheuch, Matthias; Höper, Dirk; Beer, Martin

2015-03-03

Fuelled by the advent and subsequent development of next generation sequencing technologies, metagenomics became a powerful tool for the analysis of microbial communities both scientifically and diagnostically. The biggest challenge is the extraction of relevant information from the huge sequence datasets generated for metagenomics studies. Although a plethora of tools are available, data analysis is still a bottleneck. To overcome the bottleneck of data analysis, we developed an automated computational workflow called RIEMS - Reliable Information Extraction from Metagenomic Sequence datasets. RIEMS assigns every individual read sequence within a dataset taxonomically by cascading different sequence analyses with decreasing stringency of the assignments using various software applications. After completion of the analyses, the results are summarised in a clearly structured result protocol organised taxonomically. The high accuracy and performance of RIEMS analyses were proven in comparison with other tools for metagenomics data analysis using simulated sequencing read datasets. RIEMS has the potential to fill the gap that still exists with regard to data analysis for metagenomics studies. The usefulness and power of RIEMS for the analysis of genuine sequencing datasets was demonstrated with an early version of RIEMS in 2011 when it was used to detect the orthobunyavirus sequences leading to the discovery of Schmallenberg virus.

MIPS: curated databases and comprehensive secondary data resources in 2010.

PubMed

Mewes, H Werner; Ruepp, Andreas; Theis, Fabian; Rattei, Thomas; Walter, Mathias; Frishman, Dmitrij; Suhre, Karsten; Spannagl, Manuel; Mayer, Klaus F X; Stümpflen, Volker; Antonov, Alexey

2011-01-01

The Munich Information Center for Protein Sequences (MIPS at the Helmholtz Center for Environmental Health, Neuherberg, Germany) has many years of experience in providing annotated collections of biological data. Selected data sets of high relevance, such as model genomes, are subjected to careful manual curation, while the bulk of high-throughput data is annotated by automatic means. High-quality reference resources developed in the past and still actively maintained include Saccharomyces cerevisiae, Neurospora crassa and Arabidopsis thaliana genome databases as well as several protein interaction data sets (MPACT, MPPI and CORUM). More recent projects are PhenomiR, the database on microRNA-related phenotypes, and MIPS PlantsDB for integrative and comparative plant genome research. The interlinked resources SIMAP and PEDANT provide homology relationships as well as up-to-date and consistent annotation for 38,000,000 protein sequences. PPLIPS and CCancer are versatile tools for proteomics and functional genomics interfacing to a database of compilations from gene lists extracted from literature. A novel literature-mining tool, EXCERBT, gives access to structured information on classified relations between genes, proteins, phenotypes and diseases extracted from Medline abstracts by semantic analysis. All databases described here, as well as the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.helmholtz-muenchen.de).

MIPS: curated databases and comprehensive secondary data resources in 2010

PubMed Central

Mewes, H. Werner; Ruepp, Andreas; Theis, Fabian; Rattei, Thomas; Walter, Mathias; Frishman, Dmitrij; Suhre, Karsten; Spannagl, Manuel; Mayer, Klaus F.X.; Stümpflen, Volker; Antonov, Alexey

2011-01-01

The Munich Information Center for Protein Sequences (MIPS at the Helmholtz Center for Environmental Health, Neuherberg, Germany) has many years of experience in providing annotated collections of biological data. Selected data sets of high relevance, such as model genomes, are subjected to careful manual curation, while the bulk of high-throughput data is annotated by automatic means. High-quality reference resources developed in the past and still actively maintained include Saccharomyces cerevisiae, Neurospora crassa and Arabidopsis thaliana genome databases as well as several protein interaction data sets (MPACT, MPPI and CORUM). More recent projects are PhenomiR, the database on microRNA-related phenotypes, and MIPS PlantsDB for integrative and comparative plant genome research. The interlinked resources SIMAP and PEDANT provide homology relationships as well as up-to-date and consistent annotation for 38 000 000 protein sequences. PPLIPS and CCancer are versatile tools for proteomics and functional genomics interfacing to a database of compilations from gene lists extracted from literature. A novel literature-mining tool, EXCERBT, gives access to structured information on classified relations between genes, proteins, phenotypes and diseases extracted from Medline abstracts by semantic analysis. All databases described here, as well as the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.helmholtz-muenchen.de). PMID:21109531

Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis

PubMed Central

Zarinabad, Niloufar; Meeus, Emma M; Manias, Karen; Foster, Katharine

2018-01-01

Background Advances in magnetic resonance imaging and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyze relevant information from magnetic resonance imaging data to aid decision making, prevent errors, and enhance health care. Objective The aim of this study was to design and develop a modular medical image region of interest analysis tool and repository (MIROR) for automatic processing, classification, evaluation, and representation of advanced magnetic resonance imaging data. Methods The clinical decision support system was developed and evaluated for diffusion-weighted imaging of body tumors in children (cohort of 48 children, with 37 malignant and 11 benign tumors). Mevislab software and Python have been used for the development of MIROR. Regions of interests were drawn around benign and malignant body tumors on different diffusion parametric maps, and extracted information was used to discriminate the malignant tumors from benign tumors. Results Using MIROR, the various histogram parameters derived for each tumor case when compared with the information in the repository provided additional information for tumor characterization and facilitated the discrimination between benign and malignant tumors. Clinical decision support system cross-validation showed high sensitivity and specificity in discriminating between these tumor groups using histogram parameters. Conclusions MIROR, as a diagnostic tool and repository, allowed the interpretation and analysis of magnetic resonance imaging images to be more accessible and comprehensive for clinicians. It aims to increase clinicians’ skillset by introducing newer techniques and up-to-date findings to their repertoire and make information from previous cases available to aid decision making. The modular-based format of the tool allows integration of analyses that are not readily available clinically and streamlines the future developments. PMID:29720361

High Quality Topic Extraction from Business News Explains Abnormal Financial Market Volatility

PubMed Central

Hisano, Ryohei; Sornette, Didier; Mizuno, Takayuki; Ohnishi, Takaaki; Watanabe, Tsutomu

2013-01-01

Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc.) affects trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affects stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their “thematic” features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized facts in financial economies, namely that at certain times trading volumes appear to be “abnormally large,” can be partially explained by the flow of news. In this sense, our results prove that there is no “excess trading,” when restricting to times when news is genuinely novel and provides relevant financial information. PMID:23762258

Extracting Useful Semantic Information from Large Scale Corpora of Text

ERIC Educational Resources Information Center

Mendoza, Ray Padilla, Jr.

2012-01-01

Extracting and representing semantic information from large scale corpora is at the crux of computer-assisted knowledge generation. Semantic information depends on collocation extraction methods, mathematical models used to represent distributional information, and weighting functions which transform the space. This dissertation provides a…

Drama advertisements: moderating effects of self-relevance on the relations among empathy, information processing, and attitudes.

PubMed

Chebat, Jean-Charles; Vercollier, Sarah Drissi; Gélinas-Chebat, Claire

2003-06-01

The effects of drama versus lecture format in public service advertisements are studied in a 2 (format) x 2 (malaria vs AIDS) factorial design. Two structural equation models are built (one for each level of self-relevance), showing two distinct patterns. In both low and high self-relevant situations, empathy plays a key role. Under low self-relevance conditions, drama enhances information processing through empathy. Under high self-relevant conditions, the advertisement format has neither significant cognitive or empathetic effects. The information processing generated by the highly relevant topic affects viewers' empathy, which in turn affects the attitude the advertisement and the behavioral intent. As predicted by the Elaboration Likelihood Model, the advertisement format enhances the attitudes and information processing mostly under low self-relevant conditions. Under low self-relevant conditions, empathy enhances information processing while under high self-relevance, the converse relation holds.

Measuring the Interestingness of Articles in a Limited User Environment

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

Pon, R; Cardenas, A; Buttler, David

Search engines, such as Google, assign scores to news articles based on their relevance to a query. However, not all relevant articles for the query may be interesting to a user. For example, if the article is old or yields little new information, the article would be uninteresting. Relevance scores do not take into account what makes an article interesting, which would vary from user to user. Although methods such as collaborative filtering have been shown to be effective in recommendation systems, in a limited user environment, there are not enough users that would make collaborative filtering effective. A generalmore » framework, called iScore, is presented for defining and measuring the ‘‘interestingness of articles, incorporating user-feedback. iScore addresses the various aspects of what makes an article interesting, such as topic relevance, uniqueness, freshness, source reputation, and writing style. It employs various methods, such as multiple topic tracking, online parameter selection, language models, clustering, sentiment analysis, and phrase extraction to measure these features. Due to varying reasons that users hold about why an article is interesting, an online feature selection method in naι¨ve Bayes is also used to improve recommendation results. iScore can outperform traditional IR techniques by as much as 50.7%. iScore and its components are evaluated in the news recommendation task using three datasets from Yahoo! News, actual users, and Digg.« less

An analysis of methods used to synthesize evidence and grade recommendations in food-based dietary guidelines

PubMed Central

Blake, Phillipa; Durão, Solange; Naude, Celeste E; Bero, Lisa

2018-01-01

Abstract Evidence-informed guideline development methods underpinned by systematic reviews ensure that guidelines are transparently developed, free from overt bias, and based on the best available evidence. Only recently has the nutrition field begun using these methods to develop public health nutrition guidelines. Given the importance of following an evidence-informed approach and recent advances in related methods, this study sought to describe the methods used to synthesize evidence, rate evidence quality, grade recommendations, and manage conflicts of interest (COIs) in national food-based dietary guidelines (FBDGs). The Food and Agriculture Organization’s FBDGs database was searched to identify the latest versions of FBDGs published from 2010 onward. Relevant data from 32 FBDGs were extracted, and the findings are presented narratively. This study shows that despite advances in evidence-informed methods for developing dietary guidelines, there are variations and deficiencies in methods used to review evidence, rate evidence quality, and grade recommendations. Dietary guidelines should follow systematic and transparent methods and be informed by the best available evidence, while considering important contextual factors and managing conflicts of interest. PMID:29425371

Supporting health professionals through information and communication technologies: a systematic review of the effects of information and communication technologies on recruitment and retention.

PubMed

Gagnon, Marie-Pierre; Pollender, Hugo; Trépanier, Amélie; Duplàa, Emmanuel; Ly, Birama Apho

2011-05-01

Healthcare personnel shortage is a growing concern in many countries, especially in remote areas, where it has major consequences on the accessibility of health services. Information and communication technologies (ICTs) have often been proposed as having positive effects on certain dimensions of the recruitment and retention of professionals working in the healthcare sector. This study aims to explore the impact of interventions using ICTs on recruitment and retention of healthcare professionals. A systematic review of the literature was conducted, including the following steps: exploring scientific and gray literature through established criteria and data extraction of relevant information by two independent reviewers. Of the 2,225 screened studies, 13 were included. Nine studies showed a positive, often indirect, influence that ICTs may have on recruitment and retention. Despite the conclusions of 9 of 13 studies reporting a possible positive influence of ICTs on the recruitment and retention of healthcare professionals, these results highlight the need of a deeper reflection on that topic. Therefore, more research is needed.

Two sources of meaning in infant communication: preceding action contexts and act-accompanying characteristics

PubMed Central

Liszkowski, Ulf

2014-01-01

How do infants communicate before they have acquired a language? This paper supports the hypothesis that infants possess social–cognitive skills that run deeper than language alone, enabling them to understand others and make themselves understood. I suggested that infants, like adults, use two sources of extralinguistic information to communicate meaningfully and react to and express communicative intentions appropriately. In support, a review of relevant experiments demonstrates, first, that infants use information from preceding shared activities to tailor their comprehension and production of communication. Second, a series of novel findings from our laboratory shows that in the absence of distinguishing information from preceding routines or activities, infants use accompanying characteristics (such as prosody and posture) that mark communicative intentions to extract and transmit meaning. Findings reveal that before infants begin to speak they communicate in meaningful ways by binding preceding and simultaneous multisensory information to a communicative act. These skills are not only a precursor to language, but also an outcome of social–cognitive development and social experience in the first year of life. PMID:25092662

Stream mesocosm response sensitivities to simulated ion stress in produced waters from resource extraction activities

EPA Science Inventory

To increase the ecological relevance of laboratory exposures intent on determining species sensitivity to ion stress from resource extraction activities we have conducted several stream mesocosm dosing studies that pair single-species and community-level responses in-situ and all...

Spectral monitoring of toluene and ethanol in gasoline blends using Fourier-Transform Raman spectroscopy

NASA Astrophysics Data System (ADS)

Ortega Clavero, Valentin; Weber, Andreas; Schröder, Werner; Curticapean, Dan; Meyrueis, Patrick; Javahiraly, Nicolas

2013-04-01

The combination of fossil-derived fuels with ethanol and methanol has acquired relevance and attention in several countries in recent years. This trend is strongly affected by market prices, constant geopolitical events, new sustainability policies, new laws and regulations, etc. Besides bio-fuels these materials also include different additives as anti-shock agents and as octane enhancer. Some of the chemical compounds in these additives may have harmful properties for both environment and public health (besides the inherent properties, like volatility). We present detailed Raman spectral information from toluene (C7H8) and ethanol (C2H6O) contained in samples of ElO gasoline-ethanol blends. The spectral information has been extracted by using a robust, high resolution Fourier-Transform Raman spectrometer (FT-Raman) prototype. This spectral information has been also compared with Raman spectra from pure additives and with standard Raman lines in order to validate its accuracy in frequency. The spectral information is presented in the range of 0 cm-1 to 3500 cm-1 with a resolution of 1.66cm-1. This allows resolving tight adjacent Raman lines like the ones observed around 1003cm-1 and 1030cm-1 (characteristic lines of toluene). The Raman spectra obtained show a reduced frequency deviation when compared to standard Raman spectra from different calibration materials. The FT-Raman spectrometer prototype used for the analysis consist basically of a Michelson interferometer and a self-designed photon counter cooled down on a Peltier element arrangement. The light coupling is achieved with conventional62.5/125μm multi-mode fibers. This FT-Raman setup is able to extract high resolution and frequency precise Raman spectra from the additives in the fuels analyzed. The proposed prototype has no additional complex hardware components or costly software modules. The mechanical and thermal disturbances affecting the FT-Raman system are mathematically compensated by accurately extracting the optical path information of the Michelson interferometer. This is accomplished by generating an additional interference pattern with a λ = 632.8 nm Helium-Neon laser (HeNe laser). It enables the FT-Raman system to perform reliable and clean spectral measurements from the materials under observation.

Performance of multi-aperture grid extraction systems for an ITER-relevant RF-driven negative hydrogen ion source

NASA Astrophysics Data System (ADS)

Franzen, P.; Gutser, R.; Fantz, U.; Kraus, W.; Falter, H.; Fröschle, M.; Heinemann, B.; McNeely, P.; Nocentini, R.; Riedl, R.; Stäbler, A.; Wünderlich, D.

2011-07-01

The ITER neutral beam system requires a negative hydrogen ion beam of 48 A with an energy of 0.87 MeV, and a negative deuterium beam of 40 A with an energy of 1 MeV. The beam is extracted from a large ion source of dimension 1.9 × 0.9 m2 by an acceleration system consisting of seven grids with 1280 apertures each. Currently, apertures with a diameter of 14 mm in the first grid are foreseen. In 2007, the IPP RF source was chosen as the ITER reference source due to its reduced maintenance compared with arc-driven sources and the successful development at the BATMAN test facility of being equipped with the small IPP prototype RF source ( {\\sim}\\frac{1}{8} of the area of the ITER NBI source). These results, however, were obtained with an extraction system with 8 mm diameter apertures. This paper reports on the comparison of the source performance at BATMAN of an ITER-relevant extraction system equipped with chamfered apertures with a 14 mm diameter and 8 mm diameter aperture extraction system. The most important result is that there is almost no difference in the achieved current density—being consistent with ion trajectory calculations—and the amount of co-extracted electrons. Furthermore, some aspects of the beam optics of both extraction systems are discussed.

Assessment of surveys for the management of hospital clinical pharmacy services.

PubMed

Čufar, Andreja; Mrhar, Aleš; Robnik-Šikonja, Marko

2015-06-01

Survey data sets are important sources of data, and their successful exploitation is of key importance for informed policy decision-making. We present how a survey analysis approach initially developed for customer satisfaction research in marketing can be adapted for an introduction of clinical pharmacy services into a hospital. We use a data mining analytical approach to extract relevant managerial consequences. We evaluate the importance of competences for users of a clinical pharmacy with the OrdEval algorithm and determine their nature according to the users' expectations. For this, we need substantially fewer questions than are required by the Kano approach. From 52 clinical pharmacy activities we were able to identify seven activities with a substantial negative impact (i.e., negative reinforcement) on the overall satisfaction of clinical pharmacy services, and two activities with a strong positive impact (upward reinforcement). Using analysis of individual feature values, we identified six performance, 10 excitement, and one basic clinical pharmacists' activity. We show how the OrdEval algorithm can exploit the information hidden in the ordering of class and attribute values, and their inherent correlation using a small sample of highly relevant respondents. The visualization of the outputs turns out highly useful in our clinical pharmacy research case study. Copyright © 2015 Elsevier B.V. All rights reserved.

Detection of cylinder unbalance from Bayesian inference combining cylinder pressure and vibration block measurement in a Diesel engine

NASA Astrophysics Data System (ADS)

Nguyen, Emmanuel; Antoni, Jerome; Grondin, Olivier

2009-12-01

In the automotive industry, the necessary reduction of pollutant emission for new Diesel engines requires the control of combustion events. This control is efficient provided combustion parameters such as combustion occurrence and combustion energy are relevant. Combustion parameters are traditionally measured from cylinder pressure sensors. However this kind of sensor is expensive and has a limited lifetime. Thus this paper proposes to use only one cylinder pressure on a multi-cylinder engine and to extract combustion parameters from the other cylinders with low cost knock sensors. Knock sensors measure the vibration circulating on the engine block, hence they do not all contain the information on the combustion processes, but they are also contaminated by other mechanical noises that disorder the signal. The question is how to combine the information coming from one cylinder pressure and knock sensors to obtain the most relevant combustion parameters in all engine cylinders. In this paper, the issue is addressed trough the Bayesian inference formalism. In that cylinder where a cylinder pressure sensor is mounted, combustion parameters will be measured directly. In the other cylinders, they will be measured indirectly from Bayesian inference. Experimental results obtained on a four cylinder Diesel engine demonstrate the effectiveness of the proposed algorithm toward that purpose.

UManSysProp: an online facility for molecular property prediction and atmospheric aerosol calculations

NASA Astrophysics Data System (ADS)

Topping, D.; Barley, M. H.; Bane, M.; Higham, N.; Aumont, B.; McFiggans, G.

2015-11-01

In this paper we describe the development and application of a new web based facility, UManSysProp (http://umansysprop.seaes.manchester.ac.uk), for automating predictions of molecular and atmospheric aerosol properties. Current facilities include: pure component vapour pressures, critical properties and sub-cooled densities of organic molecules; activity coefficient predictions for mixed inorganic-organic liquid systems; hygroscopic growth factors and CCN activation potential of mixed inorganic/organic aerosol particles; absorptive partitioning calculations with/without a treatment of non-ideality. The aim of this new facility is to provide a single point of reference for all properties relevant to atmospheric aerosol that have been checked for applicability to atmospheric compounds where possible. The group contribution approach allows users to upload molecular information in the form of SMILES strings and UManSysProp will automatically extract the relevant information for calculations. Built using open source chemical informatics, and hosted at the University of Manchester, the facilities are provided via a browser and device-friendly web-interface, or can be accessed using the user's own code via a JSON API. In this paper we demonstrate its use with specific examples that can be simulated using the web-browser interface.

msBiodat analysis tool, big data analysis for high-throughput experiments.

PubMed

Muñoz-Torres, Pau M; Rokć, Filip; Belužic, Robert; Grbeša, Ivana; Vugrek, Oliver

2016-01-01

Mass spectrometry (MS) are a group of a high-throughput techniques used to increase knowledge about biomolecules. They produce a large amount of data which is presented as a list of hundreds or thousands of proteins. Filtering those data efficiently is the first step for extracting biologically relevant information. The filtering may increase interest by merging previous data with the data obtained from public databases, resulting in an accurate list of proteins which meet the predetermined conditions. In this article we present msBiodat Analysis Tool, a web-based application thought to approach proteomics to the big data analysis. With this tool, researchers can easily select the most relevant information from their MS experiments using an easy-to-use web interface. An interesting feature of msBiodat analysis tool is the possibility of selecting proteins by its annotation on Gene Ontology using its Gene Id, ensembl or UniProt codes. The msBiodat analysis tool is a web-based application that allows researchers with any programming experience to deal with efficient database querying advantages. Its versatility and user-friendly interface makes easy to perform fast and accurate data screening by using complex queries. Once the analysis is finished, the result is delivered by e-mail. msBiodat analysis tool is freely available at http://msbiodata.irb.hr.

Automating document classification for the Immune Epitope Database

PubMed Central

Wang, Peng; Morgan, Alexander A; Zhang, Qing; Sette, Alessandro; Peters, Bjoern

2007-01-01

Background The Immune Epitope Database contains information on immune epitopes curated manually from the scientific literature. Like similar projects in other knowledge domains, significant effort is spent on identifying which articles are relevant for this purpose. Results We here report our experience in automating this process using Naïve Bayes classifiers trained on 20,910 abstracts classified by domain experts. Improvements on the basic classifier performance were made by a) utilizing information stored in PubMed beyond the abstract itself b) applying standard feature selection criteria and c) extracting domain specific feature patterns that e.g. identify peptides sequences. We have implemented the classifier into the curation process determining if abstracts are clearly relevant, clearly irrelevant, or if no certain classification can be made, in which case the abstracts are manually classified. Testing this classification scheme on an independent dataset, we achieve 95% sensitivity and specificity in the 51.1% of abstracts that were automatically classified. Conclusion By implementing text classification, we have sped up the reference selection process without sacrificing sensitivity or specificity of the human expert classification. This study provides both practical recommendations for users of text classification tools, as well as a large dataset which can serve as a benchmark for tool developers. PMID:17655769

Cyclodextrin-enhanced extraction and energy transfer of carcinogens in complex oil environments.

PubMed

Serio, Nicole; Chanthalyma, Chitapom; Prignano, Lindsey; Levine, Mindy

2013-11-27

Reported herein is the use of γ-cyclodextrin for two tandem functions: (a) the extraction of carcinogenic polycyclic aromatic hydrocarbons (PAHs) from oil samples into aqueous solution and (b) the promotion of highly efficient energy transfer from the newly extracted PAHs to a high-quantum-yield fluorophore. The extraction proceeded in moderate to good efficiencies, and the resulting cyclodextrin-promoted energy transfer led to a new, brightly fluorescent signal in aqueous solution. The resulting dual-function system (extraction followed by energy transfer) has significant relevance in the environmental detection and cleanup of oil-spill-related carcinogens.

ASSESSMENT OF YOUTUBE VIDEOS AS A SOURCE OF INFORMATION ON MEDICATION USE IN PREGNANCY

PubMed Central

Hansen, Craig; Interrante, Julia D; Ailes, Elizabeth C; Frey, Meghan T; Broussard, Cheryl S; Godoshian, Valerie J; Lewis, Courtney; Polen, Kara ND; Garcia, Amanda P; Gilboa, Suzanne M

2015-01-01

Background When making decisions about medication use in pregnancy, women consult many information sources, including the Internet. The aim of this study was to assess the content of publicly-accessible YouTube videos that discuss medication use in pregnancy. Methods Using 2,023 distinct combinations of search terms related to medications and pregnancy, we extracted metadata from YouTube videos using a YouTube video Application Programming Interface. Relevant videos were defined as those with a medication search term and a pregnancy-related search term in either the video title or description. We viewed relevant videos and abstracted content from each video into a database. We documented whether videos implied each medication to be ‘safe’ or ‘unsafe’ in pregnancy and compared that assessment with the medication’s Teratogen Information System (TERIS) rating. Results After viewing 651 videos, 314 videos with information about medication use in pregnancy were available for the final analyses. The majority of videos were from law firms (67%), television segments (10%), or physicians (8%). Selective serotonin reuptake inhibitors (SSRIs) were the most common medication class named (225 videos, 72%), and 88% percent of videos about SSRIs indicated they were ‘unsafe’ for use in pregnancy. However, the TERIS ratings for medication products in this class range from ‘unlikely’ to ‘minimal’ teratogenic risk. Conclusion For the majority of medications, current YouTube video content does not adequately reflect what is known about the safety of their use in pregnancy and should be interpreted cautiously. However, YouTube could serve as a valuable platform for communicating evidence-based medication safety information. PMID:26541372

Current state of ethics literature synthesis: a systematic review of reviews.

PubMed

Mertz, Marcel; Kahrass, Hannes; Strech, Daniel

2016-10-03

Modern standards for evidence-based decision making in clinical care and public health still rely solely on eminence-based input when it comes to normative ethical considerations. Manuals for clinical guideline development or health technology assessment (HTA) do not explain how to search, analyze, and synthesize relevant normative information in a systematic and transparent manner. In the scientific literature, however, systematic or semi-systematic reviews of ethics literature already exist, and scholarly debate on their opportunities and limitations has recently bloomed. A systematic review was performed of all existing systematic or semi-systematic reviews for normative ethics literature on medical topics. The study further assessed how these reviews report on their methods for search, selection, analysis, and synthesis of ethics literature. We identified 84 reviews published between 1997 and 2015 in 65 different journals and demonstrated an increasing publication rate for this type of review. While most reviews reported on different aspects of search and selection methods, reporting was much less explicit for aspects of analysis and synthesis methods: 31 % did not fulfill any criteria related to the reporting of analysis methods; for example, only 25 % of the reviews reported the ethical approach needed to analyze and synthesize normative information. While reviews of ethics literature are increasingly published, their reporting quality for analysis and synthesis of normative information should be improved. Guiding questions are: What was the applied ethical approach and technical procedure for identifying and extracting the relevant normative information units? What method and procedure was employed for synthesizing normative information? Experts and stakeholders from bioethics, HTA, guideline development, health care professionals, and patient organizations should work together to further develop this area of evidence-based health care.

Spectral identification of minerals using imaging spectrometry data: Evaluating the effects of signal to noise and spectral resolution using the tricorder algorithm

NASA Technical Reports Server (NTRS)

Swayze, Gregg A.; Clark, Roger N.

1995-01-01

The rapid development of sophisticated imaging spectrometers and resulting flood of imaging spectrometry data has prompted a rapid parallel development of spectral-information extraction technology. Even though these extraction techniques have evolved along different lines (band-shape fitting, endmember unmixing, near-infrared analysis, neural-network fitting, and expert systems to name a few), all are limited by the spectrometer's signal to noise (S/N) and spectral resolution in producing useful information. This study grew from a need to quantitatively determine what effects these parameters have on our ability to differentiate between mineral absorption features using a band-shape fitting algorithm. We chose to evaluate the AVIRIS, HYDICE, MIVIS, GERIS, VIMS, NIMS, and ASTER instruments because they collect data over wide S/N and spectral-resolution ranges. The study evaluates the performance of the Tricorder algorithm, in differentiating between mineral spectra in the 0.4-2.5 micrometer spectral region. The strength of the Tricorder algorithm is in its ability to produce an easily understood comparison of band shape that can concentrate on small relevant portions of the spectra, giving it an advantage over most unmixing schemes, and in that it need not spend large amounts of time reoptimizing each time a new mineral component is added to its reference library, as is the case with neural-network schemes. We believe the flexibility of the Tricorder algorithm is unparalleled among spectral-extraction techniques and that the results from this study, although dealing with minerals, will have direct applications to spectral identification in other disciplines.

Automated encoding of clinical documents based on natural language processing.

PubMed

Friedman, Carol; Shagina, Lyudmila; Lussier, Yves; Hripcsak, George

2004-01-01

The aim of this study was to develop a method based on natural language processing (NLP) that automatically maps an entire clinical document to codes with modifiers and to quantitatively evaluate the method. An existing NLP system, MedLEE, was adapted to automatically generate codes. The method involves matching of structured output generated by MedLEE consisting of findings and modifiers to obtain the most specific code. Recall and precision applied to Unified Medical Language System (UMLS) coding were evaluated in two separate studies. Recall was measured using a test set of 150 randomly selected sentences, which were processed using MedLEE. Results were compared with a reference standard determined manually by seven experts. Precision was measured using a second test set of 150 randomly selected sentences from which UMLS codes were automatically generated by the method and then validated by experts. Recall of the system for UMLS coding of all terms was .77 (95% CI.72-.81), and for coding terms that had corresponding UMLS codes recall was .83 (.79-.87). Recall of the system for extracting all terms was .84 (.81-.88). Recall of the experts ranged from .69 to .91 for extracting terms. The precision of the system was .89 (.87-.91), and precision of the experts ranged from .61 to .91. Extraction of relevant clinical information and UMLS coding were accomplished using a method based on NLP. The method appeared to be comparable to or better than six experts. The advantage of the method is that it maps text to codes along with other related information, rendering the coded output suitable for effective retrieval.

Relevance of ERTS-1 to the State of Ohio. [agriculture, forestry, land use, mining, and environmental quality management

NASA Technical Reports Server (NTRS)

Sweet, D. C.; Pincura, P. G.; Wukelic, G. E. (Principal Investigator)

1974-01-01

The author has identified the following significant results. During the first year of project effort the ability of ERTS-1 imagery to be used for mapping and inventorying strip-mined areas in south eastern Ohio, the potential of using ERTS-1 imagery in water quality and coastal zone management in the Lake Erie region, and the extent that ERTS-1 imagery could contribute to localized (metropolitan/urban), multicounty, and overall state land use needs were experimentally demonstrated and reported as significant project results. Significant research accomplishments were achieved in the technological development of manual and computerized methods to extract multi-feature information as well as singular feature information from ERTS-1 data as is exemplified by the forestry transparency overlay. Fabrication of an image transfer device to superimpose ERTS-1 data onto existing maps and other data sources was also a significant analytical accomplishment.

The cognitive neuroscience of prehension: recent developments.

PubMed

Grafton, Scott T

2010-08-01

Prehension, the capacity to reach and grasp, is the key behavior that allows humans to change their environment. It continues to serve as a remarkable experimental test case for probing the cognitive architecture of goal-oriented action. This review focuses on recent experimental evidence that enhances or modifies how we might conceptualize the neural substrates of prehension. Emphasis is placed on studies that consider how precision grasps are selected and transformed into motor commands. Then, the mechanisms that extract action relevant information from vision and touch are considered. These include consideration of how parallel perceptual networks within parietal cortex, along with the ventral stream, are connected and share information to achieve common motor goals. On-line control of grasping action is discussed within a state estimation framework. The review ends with a consideration about how prehension fits within larger action repertoires that solve more complex goals and the possible cortical architectures needed to organize these actions.

A new version of the ERICA tool to facilitate impact assessments of radioactivity on wild plants and animals.

PubMed

Brown, J E; Alfonso, B; Avila, R; Beresford, N A; Copplestone, D; Hosseini, A

2016-03-01

A new version of the ERICA Tool (version 1.2) was released in November 2014; this constitutes the first major update of the Tool since release in 2007. The key features of the update are presented in this article. Of particular note are new transfer databases extracted from an international compilation of concentration ratios (CRwo-media) and the modification of 'extrapolation' approaches used to select transfer data in cases where information is not available. Bayesian updating approaches have been used in some cases to draw on relevant information that would otherwise have been excluded in the process of deriving CRwo-media statistics. All of these efforts have in turn led to the requirement to update Environmental Media Concentration Limits (EMCLs) used in Tier 1 assessments. Some of the significant changes with regard to EMCLs are highlighted. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Pattern recognition by wavelet transforms using macro fibre composites transducers

NASA Astrophysics Data System (ADS)

Ruiz de la Hermosa González-Carrato, Raúl; García Márquez, Fausto Pedro; Dimlaye, Vichaar; Ruiz-Hernández, Diego

2014-10-01

This paper presents a novel pattern recognition approach for a non-destructive test based on macro fibre composite transducers applied in pipes. A fault detection and diagnosis (FDD) method is employed to extract relevant information from ultrasound signals by wavelet decomposition technique. The wavelet transform is a powerful tool that reveals particular characteristics as trends or breakdown points. The FDD developed for the case study provides information about the temperatures on the surfaces of the pipe, leading to monitor faults associated with cracks, leaks or corrosion. This issue may not be noticeable when temperatures are not subject to sudden changes, but it can cause structural problems in the medium and long-term. Furthermore, the case study is completed by a statistical method based on the coefficient of determination. The main purpose will be to predict future behaviours in order to set alarm levels as a part of a structural health monitoring system.

Forensic trace DNA: a review

PubMed Central

2010-01-01

DNA analysis is frequently used to acquire information from biological material to aid enquiries associated with criminal offences, disaster victim identification and missing persons investigations. As the relevance and value of DNA profiling to forensic investigations has increased, so too has the desire to generate this information from smaller amounts of DNA. Trace DNA samples may be defined as any sample which falls below recommended thresholds at any stage of the analysis, from sample detection through to profile interpretation, and can not be defined by a precise picogram amount. Here we review aspects associated with the collection, DNA extraction, amplification, profiling and interpretation of trace DNA samples. Contamination and transfer issues are also briefly discussed within the context of trace DNA analysis. Whilst several methodological changes have facilitated profiling from trace samples in recent years it is also clear that many opportunities exist for further improvements. PMID:21122102

Advanced Video Analysis Needs for Human Performance Evaluation

NASA Technical Reports Server (NTRS)

Campbell, Paul D.

1994-01-01

Evaluators of human task performance in space missions make use of video as a primary source of data. Extraction of relevant human performance information from video is often a labor-intensive process requiring a large amount of time on the part of the evaluator. Based on the experiences of several human performance evaluators, needs were defined for advanced tools which could aid in the analysis of video data from space missions. Such tools should increase the efficiency with which useful information is retrieved from large quantities of raw video. They should also provide the evaluator with new analytical functions which are not present in currently used methods. Video analysis tools based on the needs defined by this study would also have uses in U.S. industry and education. Evaluation of human performance from video data can be a valuable technique in many industrial and institutional settings where humans are involved in operational systems and processes.

Word Spotting for Indic Documents to Facilitate Retrieval

NASA Astrophysics Data System (ADS)

Bhardwaj, Anurag; Setlur, Srirangaraj; Govindaraju, Venu

With advances in the field of digitization of printed documents and several mass digitization projects underway, information retrieval and document search have emerged as key research areas. However, most of the current work in these areas is limited to English and a few oriental languages. The lack of efficient solutions for Indic scripts has hampered information extraction from a large body of documents of cultural and historical importance. This chapter presents two relevant topics in this area. First, we describe the use of a script-specific keyword spotting for Devanagari documents that makes use of domain knowledge of the script. Second, we address the needs of a digital library to provide access to a collection of documents from multiple scripts. This requires intelligent solutions which scale across different scripts. We present a script-independent keyword spotting approach for this purpose. Experimental results illustrate the efficacy of our methods.

Assessing healthcare professionals' experiences of integrated care: do surveys tell the full story?

PubMed

Stephenson, Matthew D; Campbell, Jared M; Lisy, Karolina; Aromataris, Edoardo C

2017-09-01

Integrated care is the combination of different healthcare services with the goal to provide comprehensive, seamless, effective and efficient patient care. Assessing the experiences of healthcare professionals (HCPs) is an important aspect when evaluating integrated care strategies. The aim of this rapid review was to investigate if quantitative surveys used to assess HCPs' experiences with integrated care capture all the aspects highlighted as being important in qualitative research, with a view to informing future survey development. The review considered all types of health professionals in primary care, and hospital and specialist services, with a specific focus on the provision of integrated care aimed at improving the patient journey. PubMed, CINAHL and grey literature sources were searched for relevant surveys/program evaluations and qualitative research studies. Full text articles deemed to be of relevance to the review were appraised for methodological quality using abridged critical appraisal instruments from the Joanna Briggs Institute. Data were extracted from included studies using standardized data extraction templates. Findings from included studies were grouped into domains based on similarity of meaning. Similarities and differences in the domains covered in quantitative surveys and those identified as being important in qualitative research were explored. A total of 37 studies (19 quantitative surveys, 14 qualitative studies and four mixed-method studies) were included in the review. A range of healthcare professions participated in the included studies, the majority being primary care providers. Common domains identified from quantitative surveys and qualitative studies included Communication, Agreement on Clear Roles and Responsibilities, Facilities, Information Systems, and Coordination of Care and Access. Qualitative research highlighted domains identified by HCPs as being relevant to their experiences with integrated care that have not routinely being surveyed, including Workload, Clear Leadership/Decision-Making, Management, Flexibility of Integrated Care Model, Engagement, Usefulness of Integrated Care and Collaboration, and Positive Impact/Clinical Benefits/Practice Level Benefits. There were several domains identified from qualitative research that are not routinely included in quantitative surveys to assess health professionals' experiences of integrated care. In addition, the qualitative findings suggest that the experiences of HCPs are often impacted by deeper aspects than those measured by existing surveys. Incorporation of targeted items within these domains in the design of surveys should enhance the capture of data that are relevant to the experiences of HCPs with integrated care, which may assist in more comprehensive evaluation and subsequent improvement of integrated care programs.

Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction.

PubMed

Mainsah, B O; Reeves, G; Collins, L M; Throckmorton, C S

2017-08-01

The role of a brain-computer interface (BCI) is to discern a user's intended message or action by extracting and decoding relevant information from brain signals. Stimulus-driven BCIs, such as the P300 speller, rely on detecting event-related potentials (ERPs) in response to a user attending to relevant or target stimulus events. However, this process is error-prone because the ERPs are embedded in noisy electroencephalography (EEG) data, representing a fundamental problem in communication of the uncertainty in the information that is received during noisy transmission. A BCI can be modeled as a noisy communication system and an information-theoretic approach can be exploited to design a stimulus presentation paradigm to maximize the information content that is presented to the user. However, previous methods that focused on designing error-correcting codes failed to provide significant performance improvements due to underestimating the effects of psycho-physiological factors on the P300 ERP elicitation process and a limited ability to predict online performance with their proposed methods. Maximizing the information rate favors the selection of stimulus presentation patterns with increased target presentation frequency, which exacerbates refractory effects and negatively impacts performance within the context of an oddball paradigm. An information-theoretic approach that seeks to understand the fundamental trade-off between information rate and reliability is desirable. We developed a performance-based paradigm (PBP) by tuning specific parameters of the stimulus presentation paradigm to maximize performance while minimizing refractory effects. We used a probabilistic-based performance prediction method as an evaluation criterion to select a final configuration of the PBP. With our PBP, we demonstrate statistically significant improvements in online performance, both in accuracy and spelling rate, compared to the conventional row-column paradigm. By accounting for refractory effects, an information-theoretic approach can be exploited to significantly improve BCI performance across a wide range of performance levels.

Optimizing the stimulus presentation paradigm design for the P300-based brain-computer interface using performance prediction

NASA Astrophysics Data System (ADS)

Mainsah, B. O.; Reeves, G.; Collins, L. M.; Throckmorton, C. S.

2017-08-01

Objective. The role of a brain-computer interface (BCI) is to discern a user’s intended message or action by extracting and decoding relevant information from brain signals. Stimulus-driven BCIs, such as the P300 speller, rely on detecting event-related potentials (ERPs) in response to a user attending to relevant or target stimulus events. However, this process is error-prone because the ERPs are embedded in noisy electroencephalography (EEG) data, representing a fundamental problem in communication of the uncertainty in the information that is received during noisy transmission. A BCI can be modeled as a noisy communication system and an information-theoretic approach can be exploited to design a stimulus presentation paradigm to maximize the information content that is presented to the user. However, previous methods that focused on designing error-correcting codes failed to provide significant performance improvements due to underestimating the effects of psycho-physiological factors on the P300 ERP elicitation process and a limited ability to predict online performance with their proposed methods. Maximizing the information rate favors the selection of stimulus presentation patterns with increased target presentation frequency, which exacerbates refractory effects and negatively impacts performance within the context of an oddball paradigm. An information-theoretic approach that seeks to understand the fundamental trade-off between information rate and reliability is desirable. Approach. We developed a performance-based paradigm (PBP) by tuning specific parameters of the stimulus presentation paradigm to maximize performance while minimizing refractory effects. We used a probabilistic-based performance prediction method as an evaluation criterion to select a final configuration of the PBP. Main results. With our PBP, we demonstrate statistically significant improvements in online performance, both in accuracy and spelling rate, compared to the conventional row-column paradigm. Significance. By accounting for refractory effects, an information-theoretic approach can be exploited to significantly improve BCI performance across a wide range of performance levels.

Initiatives promoting seamless care in medication management: an international review of the grey literature.

PubMed

Claeys, Coraline; Foulon, Veerle; de Winter, Sabrina; Spinewine, Anne

2013-12-01

Patients' transition between hospital and community is a high-risk period for the occurrence of medication-related problems. The objective was to review initiatives, implemented at national and regional levels in seven selected countries, aiming at improving continuity in medication management upon admission and hospital discharge. We performed a structured search of grey literature, mainly through relevant websites (scientific, professional and governmental organizations). Regional or national initiatives were selected. For each initiative data on the characteristics, impact, success factors and barriers were extracted. National experts were asked to validate the initiatives identified and the data extracted. Most initiatives have been implemented since the early 2000 and are still ongoing. The principal actions include: development and implementation of guidelines for healthcare professionals, national information campaigns, education of healthcare professionals and development of information technologies to share data across settings of care. Positive results have been partially reported in terms of intake into practice or process measures. Critical success factors identified included: leadership and commitment to convey national and local forces, tailoring to local settings, development of a regulatory framework and information technology support. Barriers identified included: lack of human and financial resources, questions relative to responsibility and accountability, lack of training and lack of agreement on privacy issues. Although not all initiatives are applicable as such to a particular healthcare setting, most of them convey very interesting data that should be used when drawing recommendations and implementing approaches to optimize continuity of care.

Imaging and Analytics: The changing face of Medical Imaging

NASA Astrophysics Data System (ADS)

Foo, Thomas

There have been significant technological advances in imaging capability over the past 40 years. Medical imaging capabilities have developed rapidly, along with technology development in computational processing speed and miniaturization. Moving to all-digital, the number of images that are acquired in a routine clinical examination has increased dramatically from under 50 images in the early days of CT and MRI to more than 500-1000 images today. The staggering number of images that are routinely acquired poses significant challenges for clinicians to interpret the data and to correctly identify the clinical problem. Although the time provided to render a clinical finding has not substantially changed, the amount of data available for interpretation has grown exponentially. In addition, the image quality (spatial resolution) and information content (physiologically-dependent image contrast) has also increased significantly with advances in medical imaging technology. On its current trajectory, medical imaging in the traditional sense is unsustainable. To assist in filtering and extracting the most relevant data elements from medical imaging, image analytics will have a much larger role. Automated image segmentation, generation of parametric image maps, and clinical decision support tools will be needed and developed apace to allow the clinician to manage, extract and utilize only the information that will help improve diagnostic accuracy and sensitivity. As medical imaging devices continue to improve in spatial resolution, functional and anatomical information content, image/data analytics will be more ubiquitous and integral to medical imaging capability.

An Adaptive Neural Mechanism for Acoustic Motion Perception with Varying Sparsity

PubMed Central

Shaikh, Danish; Manoonpong, Poramate

2017-01-01

Biological motion-sensitive neural circuits are quite adept in perceiving the relative motion of a relevant stimulus. Motion perception is a fundamental ability in neural sensory processing and crucial in target tracking tasks. Tracking a stimulus entails the ability to perceive its motion, i.e., extracting information about its direction and velocity. Here we focus on auditory motion perception of sound stimuli, which is poorly understood as compared to its visual counterpart. In earlier work we have developed a bio-inspired neural learning mechanism for acoustic motion perception. The mechanism extracts directional information via a model of the peripheral auditory system of lizards. The mechanism uses only this directional information obtained via specific motor behaviour to learn the angular velocity of unoccluded sound stimuli in motion. In nature however the stimulus being tracked may be occluded by artefacts in the environment, such as an escaping prey momentarily disappearing behind a cover of trees. This article extends the earlier work by presenting a comparative investigation of auditory motion perception for unoccluded and occluded tonal sound stimuli with a frequency of 2.2 kHz in both simulation and practice. Three instances of each stimulus are employed, differing in their movement velocities–0.5°/time step, 1.0°/time step and 1.5°/time step. To validate the approach in practice, we implement the proposed neural mechanism on a wheeled mobile robot and evaluate its performance in auditory tracking. PMID:28337137

Multiplexed, quantitative, and targeted metabolite profiling by LC-MS/MRM.

PubMed

Wei, Ru; Li, Guodong; Seymour, Albert B

2014-01-01

Targeted metabolomics, which focuses on a subset of known metabolites representative of biologically relevant metabolic pathways, is a valuable tool to discover biomarkers and link disease phenotypes to underlying mechanisms or therapeutic modes of action. A key advantage of targeted metabolomics, compared to discovery metabolomics, is its immediate readiness for extracting biological information derived from known metabolites and quantitative measurements. However, simultaneously analyzing hundreds of endogenous metabolites presents a challenge due to their diverse chemical structures and properties. Here we report a method which combines different chromatographic separation conditions, optimal ionization polarities, and the most sensitive triple-quadrupole MS-based data acquisition mode, multiple reaction monitoring (MRM), to quantitatively profile 205 endogenous metabolites in 10 min.

Single molecule optical measurements of orientation and rotations of biological macromolecules

PubMed Central

Shroder, Deborah Y; Lippert, Lisa G; Goldman, Yale E

2016-01-01

The subdomains of macromolecules often undergo large orientation changes during their catalytic cycles that are essential for their activity. Tracking these rearrangements in real time opens a powerful window into the link between protein structure and functional output. Site-specific labeling of individual molecules with polarized optical probes and measuring their spatial orientation can give insight into the crucial conformational changes, dynamics, and fluctuations of macromolecules. Here we describe the range of single molecule optical technologies that can extract orientation information from these probes, we review the relevant types of probes and labeling techniques, and we highlight the advantages and disadvantages of these technologies for addressing specific inquiries. PMID:28192292

Particle Pollution Estimation Based on Image Analysis

PubMed Central

Liu, Chenbin; Tsow, Francis; Zou, Yi; Tao, Nongjian

2016-01-01

Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM2.5 index. The results demonstrate that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction. PMID:26828757

Automated detection and classification of dice

NASA Astrophysics Data System (ADS)

Correia, Bento A. B.; Silva, Jeronimo A.; Carvalho, Fernando D.; Guilherme, Rui; Rodrigues, Fernando C.; de Silva Ferreira, Antonio M.

1995-03-01

This paper describes a typical machine vision system in an unusual application, the automated visual inspection of a Casino's playing tables. The SORTE computer vision system was developed at INETI under a contract with the Portuguese Gaming Inspection Authorities IGJ. It aims to automate the tasks of detection and classification of the dice's scores on the playing tables of the game `Banca Francesa' (which means French Banking) in Casinos. The system is based on the on-line analysis of the images captured by a monochrome CCD camera placed over the playing tables, in order to extract relevant information concerning the score indicated by the dice. Image processing algorithms for real time automatic throwing detection and dice classification were developed and implemented.

A software package for interactive motor unit potential classification using fuzzy k-NN classifier.

PubMed

Rasheed, Sarbast; Stashuk, Daniel; Kamel, Mohamed

2008-01-01

We present an interactive software package for implementing the supervised classification task during electromyographic (EMG) signal decomposition process using a fuzzy k-NN classifier and utilizing the MATLAB high-level programming language and its interactive environment. The method employs an assertion-based classification that takes into account a combination of motor unit potential (MUP) shapes and two modes of use of motor unit firing pattern information: the passive and the active modes. The developed package consists of several graphical user interfaces used to detect individual MUP waveforms from a raw EMG signal, extract relevant features, and classify the MUPs into motor unit potential trains (MUPTs) using assertion-based classifiers.

Data-driven system to predict academic grades and dropout.

PubMed

Rovira, Sergi; Puertas, Eloi; Igual, Laura

2017-01-01

Nowadays, the role of a tutor is more important than ever to prevent students dropout and improve their academic performance. This work proposes a data-driven system to extract relevant information hidden in the student academic data and, thus, help tutors to offer their pupils a more proactive personal guidance. In particular, our system, based on machine learning techniques, makes predictions of dropout intention and courses grades of students, as well as personalized course recommendations. Moreover, we present different visualizations which help in the interpretation of the results. In the experimental validation, we show that the system obtains promising results with data from the degree studies in Law, Computer Science and Mathematics of the Universitat de Barcelona.

Particle Pollution Estimation Based on Image Analysis.

PubMed

Liu, Chenbin; Tsow, Francis; Zou, Yi; Tao, Nongjian

2016-01-01

Exposure to fine particles can cause various diseases, and an easily accessible method to monitor the particles can help raise public awareness and reduce harmful exposures. Here we report a method to estimate PM air pollution based on analysis of a large number of outdoor images available for Beijing, Shanghai (China) and Phoenix (US). Six image features were extracted from the images, which were used, together with other relevant data, such as the position of the sun, date, time, geographic information and weather conditions, to predict PM2.5 index. The results demonstrate that the image analysis method provides good prediction of PM2.5 indexes, and different features have different significance levels in the prediction.

The evolution of phase holographic imaging from a research idea to publicly traded company

NASA Astrophysics Data System (ADS)

Egelberg, Peter

2018-02-01

Recognizing the value and unmet need for label-free kinetic cell analysis, Phase Holograhic Imaging defines its market segment as automated, easy to use and affordable time-lapse cytometry. The process of developing new technology, meeting customer expectations, sources of corporate funding and R&D adjustments prompted by field experience will be reviewed. Additionally, it is discussed how relevant biological information can be extracted from a sequence of quantitative phase images, with negligible user assistance and parameter tweaking, to simultaneously provide cell culture characteristics such as cell growth rate, viability, division rate, mitosis duration, phagocytosis rate, migration, motility and cell-cell adherence without requiring any artificial cell manipulation.

A extract method of mountainous area settlement place information from GF-1 high resolution optical remote sensing image under semantic constraints

NASA Astrophysics Data System (ADS)

Guo, H., II

2016-12-01

Spatial distribution information of mountainous area settlement place is of great significance to the earthquake emergency work because most of the key earthquake hazardous areas of china are located in the mountainous area. Remote sensing has the advantages of large coverage and low cost, it is an important way to obtain the spatial distribution information of mountainous area settlement place. At present, fully considering the geometric information, spectral information and texture information, most studies have applied object-oriented methods to extract settlement place information, In this article, semantic constraints is to be added on the basis of object-oriented methods. The experimental data is one scene remote sensing image of domestic high resolution satellite (simply as GF-1), with a resolution of 2 meters. The main processing consists of 3 steps, the first is pretreatment, including ortho rectification and image fusion, the second is Object oriented information extraction, including Image segmentation and information extraction, the last step is removing the error elements under semantic constraints, in order to formulate these semantic constraints, the distribution characteristics of mountainous area settlement place must be analyzed and the spatial logic relation between settlement place and other objects must be considered. The extraction accuracy calculation result shows that the extraction accuracy of object oriented method is 49% and rise up to 86% after the use of semantic constraints. As can be seen from the extraction accuracy, the extract method under semantic constraints can effectively improve the accuracy of mountainous area settlement place information extraction. The result shows that it is feasible to extract mountainous area settlement place information form GF-1 image, so the article proves that it has a certain practicality to use domestic high resolution optical remote sensing image in earthquake emergency preparedness.

Analysis of ecologically relevant pharmaceuticals in wastewater and surface water using selective solid phase extraction and UPLC/MS/MS

EPA Science Inventory

A rapid and sensitive method has been developed for the analysis of 48 human prescription active pharmaceutical ingredients (APIs) and 6 metabolites of interest, utilizing selective solid-phase extraction (SPE) and ultra performance liquid chromatography in combination with tripl...

The Extraction of Metals from Their Oxides and Sulphides.

ERIC Educational Resources Information Center

Price, Alun H.

1980-01-01

Briefly describes the application of thermodynamics (system at equilibrium) to the study of the extraction of metals from their oxides (dynamic situation). It is more relevant to study the temperature variation of the equilibrium constants of the reaction than to study the free energy approach. (Author/SK)

Extraction and derivatization of chemical weapons convention relevant aminoalcohols on magnetic cation-exchange resins.

PubMed

Singh, Varoon; Garg, Prabhat; Chinthakindi, Sridhar; Tak, Vijay; Dubey, Devendra Kumar

2014-02-14

Analysis and identification of nitrogen containing aminoalcohols is an integral part of the verification analysis of chemical weapons convention (CWC). This study was aimed to develop extraction and derivatization of aminoalcohols of CWC relevance by using magnetic dispersive solid-phase extraction (MDSPE) in combination with on-resin derivatization (ORD). For this purpose, sulfonated magnetic cation-exchange resins (SMRs) were prepared using magnetite nanoparticles as core, styrene and divinylbenzene as polymer coat and sulfonic acid as acidic cation exchanger. SMRs were successfully employed as extractant for targeted basic analytes. Adsorbed analytes were derivatized with hexamethyldisilazane (HMDS) on the surface of extractant. Derivatized (silylated) compounds were analyzed by GC-MS in SIM and full scan mode. The linearity of the method ranged from 5 to 200ngmL(-1). The LOD and LOQ ranged from 2 to 6ngmL(-1) and 5 to 19ngmL(-1) respectively. The relative standard deviation for intra-day repeatability and inter-day intermediate precision ranged from 5.1% to 6.6% and 0.2% to 7.6% respectively. Recoveries of analytes from spiked water samples from different sources varied from 28.4% to 89.3%. Copyright © 2014 Elsevier B.V. All rights reserved.

Optimization and Scale-up of Inulin Extraction from Taraxacum kok-saghyz roots.

PubMed

Hahn, Thomas; Klemm, Andrea; Ziesse, Patrick; Harms, Karsten; Wach, Wolfgang; Rupp, Steffen; Hirth, Thomas; Zibek, Susanne

2016-05-01

The optimization and scale-up of inulin extraction from Taraxacum kok-saghyz Rodin was successfully performed. Evaluating solubility investigations, the extraction temperature was fixed at 85 degrees C. The inulin stability regarding degradation or hydrolysis could be confirmed by extraction in the presence of model inulin. Confirming stability at the given conditions the isolation procedure was transferred from a 1 L- to a 1 m3-reactor. The Reynolds number was selected as the relevant dimensionless number that has to remain constant in both scales. The stirrer speed in the large scale was adjusted to 3.25 rpm regarding a 300 rpm stirrer speed in the 1 L-scale and relevant physical and process engineering parameters. Assumptions were confirmed by approximately homologous extraction kinetics in both scales. Since T. kok-saghyz is in the focus of research due to its rubber content side-product isolation from residual biomass it is of great economic interest. Inulin is one of these additional side-products that can be isolated in high quantity (- 35% of dry mass) and with a high average degree of polymerization (15.5) in large scale with a purity of 77%.

A new approach to the concept of "relevance" in information retrieval (IR).

PubMed

Kagolovsky, Y; Möhr, J R

2001-01-01

The concept of "relevance" is the fundamental concept of information science in general and information retrieval, in particular. Although "relevance" is extensively used in evaluation of information retrieval, there are considerable problems associated with reaching an agreement on its definition, meaning, evaluation, and application in information retrieval. There are a number of different views on "relevance" and its use for evaluation. Based on a review of the literature the main problems associated with the concept of "relevance" in information retrieval are identified. The authors argue that the proposal for the solution of the problems can be based on the conceptual IR framework built using a systems analytic approach to IR. Using this framework different kinds of "relevance" relationships in the IR process are identified, and a methodology for evaluation of "relevance" based on methods of semantics capturing and comparison is proposed.

75 FR 20843 - Notice of Workshop To Discuss Policy-Relevant Science to Inform EPA's Integrated Plan for the...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-21

... Policy-Relevant Science to Inform EPA's Integrated Plan for the Review of the Lead National Ambient Air...-Relevant Science to Inform EPA's Integrated Plan for Review of the National Ambient Air Quality Standards... review focuses on the key policy-relevant issues and considers the most meaningful new science to inform...

Connecting Projects to Complete the In Situ Resource Utilization Paradigm

NASA Technical Reports Server (NTRS)

Linne, Diane L.; Sanders, Gerald B.

2017-01-01

Terrain Identify specifics such as slope, rockiness, traction parameters Identify what part of ISRU needs each Physical Geotechnical Hardness, density, cohesion, etc. Identify what part of ISRU needs each (e.g., excavation needs to know hardness, density; soil processing needs to know density, cohesion; etc.)Mineral Identify specifics Identify what part of ISRU needs each Volatile Identify specifics Identify what part of ISRU needs each Atmosphere Identify specifics Identify what part of ISRU needs each Environment Identify specifics Identify what part of ISRU needs each Resource Characterization What: Develop an instrument suite to locate and evaluate the physical, mineral, and volatile resources at the lunar poles Neutron Spectrometer Near Infrared (IR) to locate subsurface hydrogen surface water Near IR for mineral identification Auger drill for sample removal down to 1 m Oven with Gas Chromatograph Mass Spectrometer to quantify volatiles present ISRU relevance: Water volatile resource characterization and subsurface material access removal Site Evaluation Resource Mapping What: Develop and utilize new data products and tools for evaluating potential exploration sites for selection and overlay mission data to map terrain, environment, and resource information e.g., New techniques applied to generate Digital Elevation Map (DEMs) at native scale of images (1mpxl)ISRU relevance: Resource mapping and estimation with terrain and environment information is needed for extraction planning Mission Planning and Operations What: Develop and utilize tools and procedures for planning mission operations and real time changes Planning tools include detailed engineering models (e.g., power and data) of surface segment systems allows evaluation of designs ISRU relevance: Allows for iterative engineering as a function of environment and hardware performance.

Automated analysis of long-term bridge behavior and health using a cyber-enabled wireless monitoring system

NASA Astrophysics Data System (ADS)

O'Connor, Sean M.; Zhang, Yilan; Lynch, Jerome; Ettouney, Mohammed; van der Linden, Gwen

2014-04-01

A worthy goal for the structural health monitoring field is the creation of a scalable monitoring system architecture that abstracts many of the system details (e.g., sensors, data) from the structure owner with the aim of providing "actionable" information that aids in their decision making process. While a broad array of sensor technologies have emerged, the ability for sensing systems to generate large amounts of data have far outpaced advances in data management and processing. To reverse this trend, this study explores the creation of a cyber-enabled wireless SHM system for highway bridges. The system is designed from the top down by considering the damage mechanisms of concern to bridge owners and then tailoring the sensing and decision support system around those concerns. The enabling element of the proposed system is a powerful data repository system termed SenStore. SenStore is designed to combine sensor data with bridge meta-data (e.g., geometric configuration, material properties, maintenance history, sensor locations, sensor types, inspection history). A wireless sensor network deployed to a bridge autonomously streams its measurement data to SenStore via a 3G cellular connection for storage. SenStore securely exposes the bridge meta- and sensor data to software clients that can process the data to extract information relevant to the decision making process of the bridge owner. To validate the proposed cyber-enable SHM system, the system is implemented on the Telegraph Road Bridge (Monroe, MI). The Telegraph Road Bridge is a traditional steel girder-concrete deck composite bridge located along a heavily travelled corridor in the Detroit metropolitan area. A permanent wireless sensor network has been installed to measure bridge accelerations, strains and temperatures. System identification and damage detection algorithms are created to automatically mine bridge response data stored in SenStore over an 18-month period. Tools like Gaussian Process (GP) regression are used to predict changes in the bridge behavior as a function of environmental parameters. Based on these analyses, pertinent behavioral information relevant to bridge management is autonomously extracted.

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

PubMed

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.

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

PubMed Central

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. PMID:27073839

In vitro activity of bergamot natural essence and furocoumarin-free and distilled extracts, and their associations with boric acid, against clinical yeast isolates.

PubMed

Romano, L; Battaglia, F; Masucci, L; Sanguinetti, M; Posteraro, B; Plotti, G; Zanetti, S; Fadda, G

2005-01-01

There is very little information, to date, on the antifungal activity of bergamot oil. In this study, we investigated the in vitro activity of three bergamot oils (natural essence, furocoumarin-free extract and distilled extract) against clinically relevant Candida species. We studied the two derivatives, components of Italian pharmaceutical products, that are supposed to be less toxic than the essential oil. In vitro susceptibility of 40 clinical isolates of Candida spp. (Candida albicans, n=20; Candida glabrata, n=13; Candida krusei, n=4; Candida tropicalis, n=2; Candida parapsilosis, n=1), associated with symptomatic and asymptomatic vulvovaginal candidiasis, was determined using a modification of the NCCLS M27-A2 broth microdilution method. MICs were evaluated for each of the oils alone and combined with sub-inhibitory concentrations of the well-known antiseptic, boric acid. To boric acid, all isolates had MIC values ranging from 0.094% to 0.187% (w/v). At 24 h readings, the MIC(90 )s (for all isolates) were (v/v): 5% for natural essence of bergamot, 2.5% for the furocoumarin-free extract, and 1.25% for the distilled extract. At the 48 h reading, these values increased to >10%, 5% and 2.5%, respectively. At both readings, MIC(90 )s for all oil+boric acid combinations were significantly lower than corresponding values for the oils alone (P <0.05). These data indicate that bergamot oils are active in vitro against Candida spp., suggesting their potential role for the topical treatment of Candida infections.

Real-time movement detection and analysis for video surveillance applications

NASA Astrophysics Data System (ADS)

Hueber, Nicolas; Hennequin, Christophe; Raymond, Pierre; Moeglin, Jean-Pierre

2014-06-01

Pedestrian movement along critical infrastructures like pipes, railways or highways, is of major interest in surveillance applications as well as its behavior in urban environment. The goal is to anticipate illicit or dangerous human activities. For this purpose, we propose an all-in-one small autonomous system which delivers high level statistics and reports alerts in specific cases. This situational awareness project leads us to manage efficiently the scene by performing movement analysis. A dynamic background extraction algorithm is developed to reach the degree of robustness against natural and urban environment perturbations and also to match the embedded implementation constraints. When changes are detected in the scene, specific patterns are applied to detect and highlight relevant movements. Depending on the applications, specific descriptors can be extracted and fused in order to reach a high level of interpretation. In this paper, our approach is applied to two operational use cases: pedestrian urban statistics and railway surveillance. In the first case, a grid of prototypes is deployed over a city centre to collect pedestrian movement statistics up to a macroscopic level of analysis. The results demonstrate the relevance of the delivered information; in particular, the flow density map highlights pedestrian preferential paths along the streets. In the second case, one prototype is set next to high speed train tracks to secure the area. The results exhibit a low false alarm rate and assess our approach of a large sensor network for delivering a precise operational picture without overwhelming a supervisor.

Change Detection in Uav Video Mosaics Combining a Feature Based Approach and Extended Image Differencing

NASA Astrophysics Data System (ADS)

Saur, Günter; Krüger, Wolfgang

2016-06-01

Change detection is an important task when using unmanned aerial vehicles (UAV) for video surveillance. We address changes of short time scale using observations in time distances of a few hours. Each observation (previous and current) is a short video sequence acquired by UAV in near-Nadir view. Relevant changes are, e.g., recently parked or moved vehicles. Examples for non-relevant changes are parallaxes caused by 3D structures of the scene, shadow and illumination changes, and compression or transmission artifacts. In this paper we present (1) a new feature based approach to change detection, (2) a combination with extended image differencing (Saur et al., 2014), and (3) the application to video sequences using temporal filtering. In the feature based approach, information about local image features, e.g., corners, is extracted in both images. The label "new object" is generated at image points, where features occur in the current image and no or weaker features are present in the previous image. The label "vanished object" corresponds to missing or weaker features in the current image and present features in the previous image. This leads to two "directed" change masks and differs from image differencing where only one "undirected" change mask is extracted which combines both label types to the single label "changed object". The combination of both algorithms is performed by merging the change masks of both approaches. A color mask showing the different contributions is used for visual inspection by a human image interpreter.

Prioritising lexical patterns to increase axiomatisation in biomedical ontologies. The role of localisation and modularity.

PubMed

Quesada-Martínez, M; Fernández-Breis, J T; Stevens, R; Mikroyannidi, E

2015-01-01

This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". In previous work, we have defined methods for the extraction of lexical patterns from labels as an initial step towards semi-automatic ontology enrichment methods. Our previous findings revealed that many biomedical ontologies could benefit from enrichment methods using lexical patterns as a starting point.Here, we aim to identify which lexical patterns are appropriate for ontology enrichment, driving its analysis by metrics to prioritised the patterns. We propose metrics for suggesting which lexical regularities should be the starting point to enrich complex ontologies. Our method determines the relevance of a lexical pattern by measuring its locality in the ontology, that is, the distance between the classes associated with the pattern, and the distribution of the pattern in a certain module of the ontology. The methods have been applied to four significant biomedical ontologies including the Gene Ontology and SNOMED CT. The metrics provide information about the engineering of the ontologies and the relevance of the patterns. Our method enables the suggestion of links between classes that are not made explicit in the ontology. We propose a prioritisation of the lexical patterns found in the analysed ontologies. The locality and distribution of lexical patterns offer insights into the further engineering of the ontology. Developers can use this information to improve the axiomatisation of their ontologies.

Autism, Context/Noncontext Information Processing, and Atypical Development

PubMed Central

Skoyles, John R.

2011-01-01

Autism has been attributed to a deficit in contextual information processing. Attempts to understand autism in terms of such a defect, however, do not include more recent computational work upon context. This work has identified that context information processing depends upon the extraction and use of the information hidden in higher-order (or indirect) associations. Higher-order associations underlie the cognition of context rather than that of situations. This paper starts by examining the differences between higher-order and first-order (or direct) associations. Higher-order associations link entities not directly (as with first-order ones) but indirectly through all the connections they have via other entities. Extracting this information requires the processing of past episodes as a totality. As a result, this extraction depends upon specialised extraction processes separate from cognition. This information is then consolidated. Due to this difference, the extraction/consolidation of higher-order information can be impaired whilst cognition remains intact. Although not directly impaired, cognition will be indirectly impaired by knock on effects such as cognition compensating for absent higher-order information with information extracted from first-order associations. This paper discusses the implications of this for the inflexible, literal/immediate, and inappropriate information processing of autistic individuals. PMID:22937255

An Etic-Emic Analysis of Individualism and Collectivism.

ERIC Educational Resources Information Center

Triandis, Harry C.; And Others

1993-01-01

An analysis of the responses of 1,614 adult subjects from 10 cultures show that the Leung-Bond procedure provides ways of extracting both strong and weak etics relevant to individualism and weak etics relevant to collectivism. The most complete picture is obtained when both etics and emics are examined. (SLD)