Learning from biomedical linked data to suggest valid pharmacogenes.

J Biomed Semantics. 2017 Apr 20;8(1):16

Authors: Dalleau K, Marzougui Y, Da Silva S, Ringot P, Ndiaye NC, Coulet A

Abstract
BACKGROUND: A standard task in pharmacogenomics research is identifying genes that may be involved in drug response variability, i.e., pharmacogenes. Because genomic experiments tended to generate many false positives, computational approaches based on the use of background knowledge have been proposed. Until now, only molecular networks or the biomedical literature were used, whereas many other resources are available.
METHOD: We propose here to consume a diverse and larger set of resources using linked data related either to genes, drugs or diseases. One of the advantages of linked data is that they are built on a standard framework that facilitates the joint use of various sources, and thus facilitates considering features of various origins. We propose a selection and linkage of data sources relevant to pharmacogenomics, including for example DisGeNET and Clinvar. We use machine learning to identify and prioritize pharmacogenes that are the most probably valid, considering the selected linked data. This identification relies on the classification of gene-drug pairs as either pharmacogenomically associated or not and was experimented with two machine learning methods -random forest and graph kernel-, which results are compared in this article.
RESULTS: We assembled a set of linked data relative to pharmacogenomics, of 2,610,793 triples, coming from six distinct resources. Learning from these data, random forest enables identifying valid pharmacogenes with a F-measure of 0.73, on a 10 folds cross-validation, whereas graph kernel achieves a F-measure of 0.81. A list of top candidates proposed by both approaches is provided and their obtention is discussed.

PMID: 28427468 [PubMed - indexed for MEDLINE]

LAILAPS-QSM: A RESTful API and JAVA library for semantic query suggestions.

PLoS Comput Biol. 2018 Mar 12;14(3):e1006058

Authors: Chen J, Scholz U, Zhou R, Lange M

Abstract
In order to access and filter content of life-science databases, full text search is a widely applied query interface. But its high flexibility and intuitiveness is paid for with potentially imprecise and incomplete query results. To reduce this drawback, query assistance systems suggest those combinations of keywords with the highest potential to match most of the relevant data records. Widespread approaches are syntactic query corrections that avoid misspelling and support expansion of words by suffixes and prefixes. Synonym expansion approaches apply thesauri, ontologies, and query logs. All need laborious curation and maintenance. Furthermore, access to query logs is in general restricted. Approaches that infer related queries by their query profile like research field, geographic location, co-authorship, affiliation etc. require user's registration and its public accessibility that contradict privacy concerns. To overcome these drawbacks, we implemented LAILAPS-QSM, a machine learning approach that reconstruct possible linguistic contexts of a given keyword query. The context is referred from the text records that are stored in the databases that are going to be queried or extracted for a general purpose query suggestion from PubMed abstracts and UniProt data. The supplied tool suite enables the pre-processing of these text records and the further computation of customized distributed word vectors. The latter are used to suggest alternative keyword queries. An evaluated of the query suggestion quality was done for plant science use cases. Locally present experts enable a cost-efficient quality assessment in the categories trait, biological entity, taxonomy, affiliation, and metabolic function which has been performed using ontology term similarities. LAILAPS-QSM mean information content similarity for 15 representative queries is 0.70, whereas 34% have a score above 0.80. In comparison, the information content similarity for human expert made query suggestions is 0.90. The software is either available as tool set to build and train dedicated query suggestion services or as already trained general purpose RESTful web service. The service uses open interfaces to be seamless embeddable into database frontends. The JAVA implementation uses highly optimized data structures and streamlined code to provide fast and scalable response for web service calls. The source code of LAILAPS-QSM is available under GNU General Public License version 2 in Bitbucket GIT repository: https://bitbucket.org/ipk_bit_team/bioescorte-suggestion.

PMID: 29529024 [PubMed - as supplied by publisher]

Ontology-Based Method for Fault Diagnosis of Loaders.

Sensors (Basel). 2018 Feb 28;18(3):

Authors: Xu F, Liu X, Chen W, Zhou C, Cao B

Abstract
This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model is proposed to achieve the integrating, sharing and reusing of fault diagnosis knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate fault diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the fault causes, fault locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study.

PMID: 29495646 [PubMed - in process]

Understanding Nomophobia: Structural Equation Modeling and Semantic Network Analysis of Smartphone Separation Anxiety.

Cyberpsychol Behav Soc Netw. 2017 Jul;20(7):419-427

Authors: Han S, Kim KJ, Kim JH

Abstract
This study explicates nomophobia by developing a research model that identifies several determinants of smartphone separation anxiety and by conducting semantic network analyses on smartphone users' verbal descriptions of the meaning of their smartphones. Structural equation modeling of the proposed model indicates that personal memories evoked by smartphones encourage users to extend their identity onto their devices. When users perceive smartphones as their extended selves, they are more likely to get attached to the devices, which, in turn, leads to nomophobia by heightening the phone proximity-seeking tendency. This finding is also supplemented by the results of the semantic network analyses revealing that the words related to memory, self, and proximity-seeking are indeed more frequently used in the high, compared with low, nomophobia group.

PMID: 28650222 [PubMed - indexed for MEDLINE]

An Interoperable System toward Cardiac Risk Stratification from ECG Monitoring.

Int J Environ Res Public Health. 2018 Mar 01;15(3):

Authors: Soguero-Ruiz C, Mora-Jiménez I, Ramos-López J, Quintanilla Fernández T, García-García A, Díez-Mazuela D, García-Alberola A, Rojo-Álvarez JL

Abstract
Many indices have been proposed for cardiovascular risk stratification from electrocardiogram signal processing, still with limited use in clinical practice. We created a system integrating the clinical definition of cardiac risk subdomains from ECGs and the use of diverse signal processing techniques. Three subdomains were defined from the joint analysis of the technical and clinical viewpoints. One subdomain was devoted to demographic and clinical data. The other two subdomains were intended to obtain widely defined risk indices from ECG monitoring: a simple-domain (heart rate turbulence (HRT)), and a complex-domain (heart rate variability (HRV)). Data provided by the three subdomains allowed for the generation of alerts with different intensity and nature, as well as for the grouping and scrutinization of patients according to the established processing and risk-thresholding criteria. The implemented system was tested by connecting data from real-world in-hospital electronic health records and ECG monitoring by considering standards for syntactic (HL7 messages) and semantic interoperability (archetypes based on CEN/ISO EN13606 and SNOMED-CT). The system was able to provide risk indices and to generate alerts in the health records to support decision-making. Overall, the system allows for the agile interaction of research and clinical practice in the Holter-ECG-based cardiac risk domain.

PMID: 29494497 [PubMed - in process]

PhLeGrA: Graph Analytics in Pharmacology over the Web of Life Sciences Linked Open Data.

Proc Int World Wide Web Conf. 2017 Apr;2017:321-329

Authors: Kamdar MR, Musen MA

Abstract
Integrated approaches for pharmacology are required for the mechanism-based predictions of adverse drug reactions that manifest due to concomitant intake of multiple drugs. These approaches require the integration and analysis of biomedical data and knowledge from multiple, heterogeneous sources with varying schemas, entity notations, and formats. To tackle these integrative challenges, the Semantic Web community has published and linked several datasets in the Life Sciences Linked Open Data (LSLOD) cloud using established W3C standards. We present the PhLeGrA platform for Linked Graph Analytics in Pharmacology in this paper. Through query federation, we integrate four sources from the LSLOD cloud and extract a drug-reaction network, composed of distinct entities. We represent this graph as a hidden conditional random field (HCRF), a discriminative latent variable model that is used for structured output predictions. We calculate the underlying probability distributions in the drug-reaction HCRF using the datasets from the U.S. Food and Drug Administration's Adverse Event Reporting System. We predict the occurrence of 146 adverse reactions due to multiple drug intake with an AUROC statistic greater than 0.75. The PhLeGrA platform can be extended to incorporate other sources published using Semantic Web technologies, as well as to discover other types of pharmacological associations.

PMID: 29479581 [PubMed]

Tutorial on Protein Ontology Resources.

Methods Mol Biol. 2017;1558:57-78

Authors: Arighi CN, Drabkin H, Christie KR, Ross KE, Natale DA

Abstract
The Protein Ontology (PRO) is the reference ontology for proteins in the Open Biomedical Ontologies (OBO) foundry and consists of three sub-ontologies representing protein classes of homologous genes, proteoforms (e.g., splice isoforms, sequence variants, and post-translationally modified forms), and protein complexes. PRO defines classes of proteins and protein complexes, both species-specific and species nonspecific, and indicates their relationships in a hierarchical framework, supporting accurate protein annotation at the appropriate level of granularity, analyses of protein conservation across species, and semantic reasoning. In the first section of this chapter, we describe the PRO framework including categories of PRO terms and the relationship of PRO to other ontologies and protein resources. Next, we provide a tutorial about the PRO website ( proconsortium.org ) where users can browse and search the PRO hierarchy, view reports on individual PRO terms, and visualize relationships among PRO terms in a hierarchical table view, a multiple sequence alignment view, and a Cytoscape network view. Finally, we describe several examples illustrating the unique and rich information available in PRO.

PMID: 28150233 [PubMed - indexed for MEDLINE]

Representation of Time-Relevant Common Data Elements in the Cancer Data Standards Repository: Statistical Evaluation of an Ontological Approach.

JMIR Med Inform. 2018 Feb 22;6(1):e7

Authors: Chen HW, Du J, Song HY, Liu X, Jiang G, Tao C

Abstract
BACKGROUND: Today, there is an increasing need to centralize and standardize electronic health data within clinical research as the volume of data continues to balloon. Domain-specific common data elements (CDEs) are emerging as a standard approach to clinical research data capturing and reporting. Recent efforts to standardize clinical study CDEs have been of great benefit in facilitating data integration and data sharing. The importance of the temporal dimension of clinical research studies has been well recognized; however, very few studies have focused on the formal representation of temporal constraints and temporal relationships within clinical research data in the biomedical research community. In particular, temporal information can be extremely powerful to enable high-quality cancer research.
OBJECTIVE: The objective of the study was to develop and evaluate an ontological approach to represent the temporal aspects of cancer study CDEs.
METHODS: We used CDEs recorded in the National Cancer Institute (NCI) Cancer Data Standards Repository (caDSR) and created a CDE parser to extract time-relevant CDEs from the caDSR. Using the Web Ontology Language (OWL)-based Time Event Ontology (TEO), we manually derived representative patterns to semantically model the temporal components of the CDEs using an observing set of randomly selected time-related CDEs (n=600) to create a set of TEO ontological representation patterns. In evaluating TEO's ability to represent the temporal components of the CDEs, this set of representation patterns was tested against two test sets of randomly selected time-related CDEs (n=425).
RESULTS: It was found that 94.2% (801/850) of the CDEs in the test sets could be represented by the TEO representation patterns.
CONCLUSIONS: In conclusion, TEO is a good ontological model for representing the temporal components of the CDEs recorded in caDSR. Our representative model can harness the Semantic Web reasoning and inferencing functionalities and present a means for temporal CDEs to be machine-readable, streamlining meaningful searches.

PMID: 29472179 [PubMed]

An Advanced IoT-based System for Intelligent Energy Management in Buildings.

Sensors (Basel). 2018 Feb 16;18(2):

Authors: Marinakis V, Doukas H

Abstract
The energy sector is closely interconnected with the building sector and integrated Information and Communication Technologies (ICT) solutions for effective energy management supporting decision-making at building, district and city level are key fundamental elements for making a city Smart. The available systems are designed and intended exclusively for a predefined number of cases and systems without allowing for expansion and interoperability with other applications that is partially due to the lack of semantics. This paper presents an advanced Internet of Things (IoT) based system for intelligent energy management in buildings. A semantic framework is introduced aiming at the unified and standardised modelling of the entities that constitute the building environment. Suitable rules are formed, aiming at the intelligent energy management and the general modus operandi of Smart Building. In this context, an IoT-based system was implemented, which enhances the interactivity of the buildings' energy management systems. The results from its pilot application are presented and discussed. The proposed system extends existing approaches and integrates cross-domain data, such as the building's data (e.g., energy management systems), energy production, energy prices, weather data and end-users' behaviour, in order to produce daily and weekly action plans for the energy end-users with actionable personalised information.

PMID: 29462957 [PubMed - in process]

Design and Implementation of e-Health System Based on Semantic Sensor Network Using IETF YANG.

Sensors (Basel). 2018 Feb 20;18(2):

Authors: Jin W, Kim DH

Abstract
Recently, healthcare services can be delivered effectively to patients anytime and anywhere using e-Health systems. e-Health systems are developed through Information and Communication Technologies (ICT) that involve sensors, mobiles, and web-based applications for the delivery of healthcare services and information. Remote healthcare is an important purpose of the e-Health system. Usually, the eHealth system includes heterogeneous sensors from diverse manufacturers producing data in different formats. Device interoperability and data normalization is a challenging task that needs research attention. Several solutions are proposed in the literature based on manual interpretation through explicit programming. However, programmatically implementing the interpretation of the data sender and data receiver in the e-Health system for the data transmission is counterproductive as modification will be required for each new device added into the system. In this paper, an e-Health system with the Semantic Sensor Network (SSN) is proposed to address the device interoperability issue. In the proposed system, we have used IETF YANG for modeling the semantic e-Health data to represent the information of e-Health sensors. This modeling scheme helps in provisioning semantic interoperability between devices and expressing the sensing data in a user-friendly manner. For this purpose, we have developed an ontology for e-Health data that supports different styles of data formats. The ontology is defined in YANG for provisioning semantic interpretation of sensing data in the system by constructing meta-models of e-Health sensors. The proposed approach assists in the auto-configuration of eHealth sensors and querying the sensor network with semantic interoperability support for the e-Health system.

PMID: 29461493 [PubMed - in process]

Semantic network analysis of vaccine sentiment in online social media.

Vaccine. 2017 Jun 22;35(29):3621-3638

Authors: Kang GJ, Ewing-Nelson SR, Mackey L, Schlitt JT, Marathe A, Abbas KM, Swarup S

Abstract
OBJECTIVE: To examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information from highly shared websites of Twitter users in the United States; and to assist public health communication of vaccines.
BACKGROUND: Vaccine hesitancy continues to contribute to suboptimal vaccination coverage in the United States, posing significant risk of disease outbreaks, yet remains poorly understood.
METHODS: We constructed semantic networks of vaccine information from internet articles shared by Twitter users in the United States. We analyzed resulting network topology, compared semantic differences, and identified the most salient concepts within networks expressing positive, negative, and neutral vaccine sentiment.
RESULTS: The semantic network of positive vaccine sentiment demonstrated greater cohesiveness in discourse compared to the larger, less-connected network of negative vaccine sentiment. The positive sentiment network centered around parents and focused on communicating health risks and benefits, highlighting medical concepts such as measles, autism, HPV vaccine, vaccine-autism link, meningococcal disease, and MMR vaccine. In contrast, the negative network centered around children and focused on organizational bodies such as CDC, vaccine industry, doctors, mainstream media, pharmaceutical companies, and United States. The prevalence of negative vaccine sentiment was demonstrated through diverse messaging, framed around skepticism and distrust of government organizations that communicate scientific evidence supporting positive vaccine benefits.
CONCLUSION: Semantic network analysis of vaccine sentiment in online social media can enhance understanding of the scope and variability of current attitudes and beliefs toward vaccines. Our study synthesizes quantitative and qualitative evidence from an interdisciplinary approach to better understand complex drivers of vaccine hesitancy for public health communication, to improve vaccine confidence and vaccination coverage in the United States.

PMID: 28554500 [PubMed - indexed for MEDLINE]

The canonical semantic network supports residual language function in chronic post-stroke aphasia.

Hum Brain Mapp. 2017 Mar;38(3):1636-1658

Authors: Griffis JC, Nenert R, Allendorfer JB, Vannest J, Holland S, Dietz A, Szaflarski JP

Abstract
Current theories of language recovery after stroke are limited by a reliance on small studies. Here, we aimed to test predictions of current theory and resolve inconsistencies regarding right hemispheric contributions to long-term recovery. We first defined the canonical semantic network in 43 healthy controls. Then, in a group of 43 patients with chronic post-stroke aphasia, we tested whether activity in this network predicted performance on measures of semantic comprehension, naming, and fluency while controlling for lesion volume effects. Canonical network activation accounted for 22%-33% of the variance in language test scores. Whole-brain analyses corroborated these findings, and revealed a core set of regions showing positive relationships to all language measures. We next evaluated the relationship between activation magnitudes in left and right hemispheric portions of the network, and characterized how right hemispheric activation related to the extent of left hemispheric damage. Activation magnitudes in each hemispheric network were strongly correlated, but four right frontal regions showed heightened activity in patients with large lesions. Activity in two of these regions (inferior frontal gyrus pars opercularis and supplementary motor area) was associated with better language abilities in patients with larger lesions, but poorer language abilities in patients with smaller lesions. Our results indicate that bilateral language networks support language processing after stroke, and that right hemispheric activations related to extensive left hemispheric damage occur outside of the canonical semantic network and differentially relate to behavior depending on the extent of left hemispheric damage. Hum Brain Mapp 38:1636-1658, 2017. © 2016 Wiley Periodicals, Inc.

PMID: 27981674 [PubMed - indexed for MEDLINE]

DMTO: a realistic ontology for standard diabetes mellitus treatment.

J Biomed Semantics. 2018 Feb 06;9(1):8

Authors: El-Sappagh S, Kwak D, Ali F, Kwak KS

Abstract
BACKGROUND: Treatment of type 2 diabetes mellitus (T2DM) is a complex problem. A clinical decision support system (CDSS) based on massive and distributed electronic health record data can facilitate the automation of this process and enhance its accuracy. The most important component of any CDSS is its knowledge base. This knowledge base can be formulated using ontologies. The formal description logic of ontology supports the inference of hidden knowledge. Building a complete, coherent, consistent, interoperable, and sharable ontology is a challenge.
RESULTS: This paper introduces the first version of the newly constructed Diabetes Mellitus Treatment Ontology (DMTO) as a basis for shared-semantics, domain-specific, standard, machine-readable, and interoperable knowledge relevant to T2DM treatment. It is a comprehensive ontology and provides the highest coverage and the most complete picture of coded knowledge about T2DM patients' current conditions, previous profiles, and T2DM-related aspects, including complications, symptoms, lab tests, interactions, treatment plan (TP) frameworks, and glucose-related diseases and medications. It adheres to the design principles recommended by the Open Biomedical Ontologies Foundry and is based on ontological realism that follows the principles of the Basic Formal Ontology and the Ontology for General Medical Science. DMTO is implemented under Protégé 5.0 in Web Ontology Language (OWL) 2 format and is publicly available through the National Center for Biomedical Ontology's BioPortal at http://bioportal.bioontology.org/ontologies/DMTO . The current version of DMTO includes more than 10,700 classes, 277 relations, 39,425 annotations, 214 semantic rules, and 62,974 axioms. We provide proof of concept for this approach to modeling TPs.
CONCLUSION: The ontology is able to collect and analyze most features of T2DM as well as customize chronic TPs with the most appropriate drugs, foods, and physical exercises. DMTO is ready to be used as a knowledge base for semantically intelligent and distributed CDSS systems.

PMID: 29409535 [PubMed - in process]

Semantic annotation of consumer health questions.

BMC Bioinformatics. 2018 Feb 06;19(1):34

Authors: Kilicoglu H, Ben Abacha A, Mrabet Y, Shooshan SE, Rodriguez L, Masterton K, Demner-Fushman D

Abstract
BACKGROUND: Consumers increasingly use online resources for their health information needs. While current search engines can address these needs to some extent, they generally do not take into account that most health information needs are complex and can only fully be expressed in natural language. Consumer health question answering (QA) systems aim to fill this gap. A major challenge in developing consumer health QA systems is extracting relevant semantic content from the natural language questions (question understanding). To develop effective question understanding tools, question corpora semantically annotated for relevant question elements are needed. In this paper, we present a two-part consumer health question corpus annotated with several semantic categories: named entities, question triggers/types, question frames, and question topic. The first part (CHQA-email) consists of relatively long email requests received by the U.S. National Library of Medicine (NLM) customer service, while the second part (CHQA-web) consists of shorter questions posed to MedlinePlus search engine as queries. Each question has been annotated by two annotators. The annotation methodology is largely the same between the two parts of the corpus; however, we also explain and justify the differences between them. Additionally, we provide information about corpus characteristics, inter-annotator agreement, and our attempts to measure annotation confidence in the absence of adjudication of annotations.
RESULTS: The resulting corpus consists of 2614 questions (CHQA-email: 1740, CHQA-web: 874). Problems are the most frequent named entities, while treatment and general information questions are the most common question types. Inter-annotator agreement was generally modest: question types and topics yielded highest agreement, while the agreement for more complex frame annotations was lower. Agreement in CHQA-web was consistently higher than that in CHQA-email. Pairwise inter-annotator agreement proved most useful in estimating annotation confidence.
CONCLUSIONS: To our knowledge, our corpus is the first focusing on annotation of uncurated consumer health questions. It is currently used to develop machine learning-based methods for question understanding. We make the corpus publicly available to stimulate further research on consumer health QA.

PMID: 29409442 [PubMed - in process]

Rich semantic networks applied to schizophrenia: A new framework.

Schizophr Res. 2016 10;176(2-3):454-455

Authors: De Deyne S, Elvevåg B, Hui CLM, Poon VWY, Chen EYH

PMID: 27245710 [PubMed - indexed for MEDLINE]

A systematic approach to analyze the social determinants of cardiovascular disease.

PLoS One. 2018;13(1):e0190960

Authors: Martínez-García M, Salinas-Ortega M, Estrada-Arriaga I, Hernández-Lemus E, García-Herrera R, Vallejo M

Abstract
Cardiovascular diseases are the leading cause of human mortality worldwide. Among the many factors associated with the etiology, incidence, and evolution of such diseases; social and environmental issues constitute an important and often overlooked component. Understanding to a greater extent the scope to which such social determinants of cardiovascular diseases (SDCVD) occur as well as the connections among them would be useful for public health policy making. Here, we will explore the historical trends and associations among the main SDCVD in the published literature. Our aim will be finding meaningful relations among those that will help us to have an integrated view on this complex phenomenon by providing historical context and a relational framework. To uncover such relations, we used a data mining approach to the current literature, followed by network analysis of the interrelationships discovered. To this end, we systematically mined the PubMed/MEDLINE database for references of published studies on the subject, as outlined by the World Health Organization's framework on social determinants of health. The analyzed structured corpus consisted in circa 1190 articles categorized by means of the Medical Subheadings (MeSH) content-descriptor. The use of data analytics techniques allowed us to find a number of non-trivial connections among SDCVDs. Such relations may be relevant to get a deeper understanding of the social and environmental issues associated with cardiovascular disease and are often overlooked by traditional literature survey approaches, such as systematic reviews and meta-analyses.

PMID: 29370200 [PubMed - indexed for MEDLINE]

Cross-linking BioThings APIs through JSON-LD to facilitate knowledge exploration.

BMC Bioinformatics. 2018 Feb 01;19(1):30

Authors: Xin J, Afrasiabi C, Lelong S, Adesara J, Tsueng G, Su AI, Wu C

Abstract
BACKGROUND: Application Programming Interfaces (APIs) are now widely used to distribute biological data. And many popular biological APIs developed by many different research teams have adopted Javascript Object Notation (JSON) as their primary data format. While usage of a common data format offers significant advantages, that alone is not sufficient for rich integrative queries across APIs.
RESULTS: Here, we have implemented JSON for Linking Data (JSON-LD) technology on the BioThings APIs that we have developed, MyGene.info , MyVariant.info and MyChem.info . JSON-LD provides a standard way to add semantic context to the existing JSON data structure, for the purpose of enhancing the interoperability between APIs. We demonstrated several use cases that were facilitated by semantic annotations using JSON-LD, including simpler and more precise query capabilities as well as API cross-linking.
CONCLUSIONS: We believe that this pattern offers a generalizable solution for interoperability of APIs in the life sciences.

PMID: 29390967 [PubMed - in process]

Semantic and syntactic interoperability in online processing of big Earth observation data.

Int J Digit Earth. 2018;11(1):95-112

Authors: Sudmanns M, Tiede D, Lang S, Baraldi A

Abstract
The challenge of enabling syntactic and semantic interoperability for comprehensive and reproducible online processing of big Earth observation (EO) data is still unsolved. Supporting both types of interoperability is one of the requirements to efficiently extract valuable information from the large amount of available multi-temporal gridded data sets. The proposed system wraps world models, (semantic interoperability) into OGC Web Processing Services (syntactic interoperability) for semantic online analyses. World models describe spatio-temporal entities and their relationships in a formal way. The proposed system serves as enabler for (1) technical interoperability using a standardised interface to be used by all types of clients and (2) allowing experts from different domains to develop complex analyses together as collaborative effort. Users are connecting the world models online to the data, which are maintained in a centralised storage as 3D spatio-temporal data cubes. It allows also non-experts to extract valuable information from EO data because data management, low-level interactions or specific software issues can be ignored. We discuss the concept of the proposed system, provide a technical implementation example and describe three use cases for extracting changes from EO images and demonstrate the usability also for non-EO, gridded, multi-temporal data sets (CORINE land cover).

PMID: 29387171 [PubMed]

Listeners and Readers Generalize Their Experience With Word Meanings Across Modalities.

J Exp Psychol Learn Mem Cogn. 2018 Feb 01;:

Authors: Gilbert RA, Davis MH, Gaskell MG, Rodd JM

Abstract
Research has shown that adults' lexical-semantic representations are surprisingly malleable. For instance, the interpretation of ambiguous words (e.g., bark) is influenced by experience such that recently encountered meanings become more readily available (Rodd et al., 2016, 2013). However, the mechanism underlying this word-meaning priming effect remains unclear, and competing accounts make different predictions about the extent to which information about word meanings that is gained within one modality (e.g., speech) is transferred to the other modality (e.g., reading) to aid comprehension. In two Web-based experiments, ambiguous target words were primed with either written or spoken sentences that biased their interpretation toward a subordinate meaning, or were unprimed. About 20 min after the prime exposure, interpretation of these target words was tested by presenting them in either written or spoken form, using word association (Experiment 1, N = 78) and speeded semantic relatedness decisions (Experiment 2, N = 181). Both experiments replicated the auditory unimodal priming effect shown previously (Rodd et al., 2016, 2013) and revealed significant cross-modal priming: primed meanings were retrieved more frequently and swiftly across all primed conditions compared with the unprimed baseline. Furthermore, there were no reliable differences in priming levels between unimodal and cross-modal prime-test conditions. These results indicate that recent experience with ambiguous word meanings can bias the reader's or listener's later interpretation of these words in a modality-general way. We identify possible loci of this effect within the context of models of long-term priming and ambiguity resolution. (PsycINFO Database Record

PMID: 29389181 [PubMed - as supplied by publisher]

The effects of thematic relations on picture naming abilities across the lifespan.

Neuropsychol Dev Cogn B Aging Neuropsychol Cogn. 2016 Jul;23(4):499-512

Authors: Hashimoto N, Johnson B, Peterson A

Abstract
A picture-word interference paradigm tracked patterns of activation during picture naming in 87 individuals (age range 17-80 years old). Distractor words were presented at stimulus onset asynchronies (SOAs) of -200, -100, and 0 ms bearing a has a-, location, or no relationship to the picture. Analyses of group naming reaction times revealed significant facilitation effects for both semantic relation types for all age groups. Analyses of temporal patterns of activation revealed significant effects primarily at SOAs of -200 and -100 ms. These findings provide evidence that both thematic relations are particularly salient in how semantic knowledge is organized, and that the patterns of effects from these semantic relations remain the same as one ages.

PMID: 26667786 [PubMed - indexed for MEDLINE]