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Semantic maps

Andrew James Anderson, Edmund C Lalor, Feng Lin, Jeffrey R Binder, Leonardo Fernandino, Colin J Humphries, Lisa L Conant, Rajeev D S Raizada, Scott Grimm, Xixi Wang
Deciphering how sentence meaning is represented in the brain remains a major challenge to science. Semantically related neural activity has recently been shown to arise concurrently in distributed brain regions as successive words in a sentence are read. However, what semantic content is represented by different regions, what is common across them, and how this relates to words in different grammatical positions of sentences is weakly understood. To address these questions, we apply a semantic model of word meaning to interpret brain activation patterns elicited in sentence reading...
May 16, 2018: Cerebral Cortex
Johannes Lohmann, Philipp A Schroeder, Hans-Christoph Nuerk, Christian Plewnia, Martin V Butz
Spatial, physical, and semantic magnitude dimensions can influence action decisions in human cognitive processing and interact with each other. For example, in the spatial-numerical associations of response code (SNARC) effect, semantic numerical magnitude facilitates left-hand or right-hand responding dependent on the small or large magnitude of number symbols. SNARC-like interactions of numerical magnitudes with the radial spatial dimension (depth) were postulated from early on. Usually, the SNARC effect in any direction is investigated using fronto-parallel computer monitors for presentation of stimuli...
2018: Frontiers in Psychology
Anil Pacaci, Suat Gonul, A Anil Sinaci, Mustafa Yuksel, Gokce B Laleci Erturkmen
Background: Utilization of the available observational healthcare datasets is key to complement and strengthen the postmarketing safety studies. Use of common data models (CDM) is the predominant approach in order to enable large scale systematic analyses on disparate data models and vocabularies. Current CDM transformation practices depend on proprietarily developed Extract-Transform-Load (ETL) procedures, which require knowledge both on the semantics and technical characteristics of the source datasets and target CDM...
2018: Frontiers in Pharmacology
Duc-Hau Le, Lan T M Dao
Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease-lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs...
May 11, 2018: Journal of Molecular Biology
Jeffrey S Bowers, Peter N Bowers
Taylor, Davis, and Rastle employed an artificial language learning paradigm to compare phonics and meaning-based approaches to reading instruction. Adults were taught consonant, vowel, and consonant (CVC) words composed of novel letters when the mappings between letters and sounds were completely systematic and the mappings between letters and meaning were completely arbitrary. At test, performance on naming tasks was better following training that emphasised the phonological rather than the semantic mappings, whereas performance on semantic tasks was similar in the two conditions...
May 1, 2018: Quarterly Journal of Experimental Psychology: QJEP
Hildegard Janouschek, Claudia R Eickhoff, Thomas W Mühleisen, Simon B Eickhoff, Thomas Nickl-Jockschat
Imaging genetics has become a highly popular approach in the field of schizophrenia research. A frequently reported finding is that effects from common genetic variation are associated with a schizophrenia-related structural endophenotype. Genetic contributions to a structural endophenotype may be easier to delineate, when referring to biological rather than diagnostic criteria. We used coordinate-based meta-analyses, namely the anatomical likelihood estimation (ALE) algorithm on 30 schizophrenia-related imaging genetics studies, representing 44 single-nucleotide polymorphisms at 26 gene loci investigated in 4682 subjects...
May 5, 2018: Brain Structure & Function
Ling Dai, Ruogu Fang, Huating Li, Xuhong Hou, Bin Sheng, Qiang Wu, Weiping Jia
Timely detection and treatment of microaneurysms is a critical step to prevent the development of vision-threatening eye diseases such as diabetic retinopathy. However, detecting microaneurysms in fundus images is a highly challenging task due to the low image contrast, misleading cues of other red lesions, and the large variation of imaging conditions. Existing methods tend to fail in face of the large intra-class variation and small inter-class variations for microaneurysm detection in fundus images. Recently, hybrid text/image mining computer-aided diagnosis systems have emerged to offer a promise of bridging the semantic gap between images and diagnostic information...
May 2018: IEEE Transactions on Medical Imaging
Zhenyu Zhang, Chunyan Xu, Jian Yang, Junbin Gao, Zhen Cui
Depth estimation from the monocular RGB image is a challenging task for computer vision due to no reliable cues as the prior knowledge. Most existing monocular depth estimation works including various geometric or network learning methods lack of an effective mechanism to preserve the cross-border details of depth maps, which yet is very important for the performance promotion. In this paper, we propose a novel end-to-end progressive hard-mining network (PHN) framework to address this problem. Specifically, we construct the hard-mining objective function, the intra-scale and inter-scale refinement subnetworks to accurately localize and refine those hard-mining regions...
August 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Seong Kyu Han, Donghyo Kim, Heetak Lee, Inhae Kim, Sanguk Kim
Mice have been widely used as a model organism to investigate human gene-phenotype relationships based on a conjecture that orthologous genes generally perform similar functions and are associated with similar phenotypes. However, phenotypes associated with orthologous genes often turn out to be quite different between human and mouse. Herein, we devised a method to quantitatively compare phenotypes annotations associated with mouse models and human. Using semantic similarity comparisons, we identified orthologous genes with different phenotype annotations, of which the similarity score is on a par with that of random gene pairs...
April 24, 2018: Molecular Biology and Evolution
Yiqing Zhao, Nooshin J Fesharaki, Xiaohui Li, Timothy B Patrick, Jake Luo
Most current image retrieval methods require constructing semantic metadata for representing image content. To manually create semantic metadata for medical images is time-consuming, yet it is a crucial component for query expansion. We proposed a new method for searching medical image notes that uses semantic metadata to improve query expansion and leverages a knowledge model developed specifically for the medical image domain to create relevant metadata. We used a syntactic parser and the Unified Medical Language System to analyze the corpus and store text information as semantic metadata in a knowledge model...
April 25, 2018: Journal of Medical Systems
Grégoire Python, Raphaël Fargier, Marina Laganaro
Background : Producing a word in referential naming requires to select the right word in our mental lexicon among co-activated semantically related words. The mechanisms underlying semantic context effects during speech planning are still controversial, particularly for semantic facilitation which investigation remains under-represented in contrast to the plethora of studies dealing with interference. Our aim is to study the time-course of semantic facilitation in picture naming, using a picture-word "interference" paradigm and event-related potentials (ERPs)...
2018: Frontiers in Human Neuroscience
Maria Kondratova, Nicolas Sompairac, Emmanuel Barillot, Andrei Zinovyev, Inna Kuperstein
Generation and usage of high-quality molecular signalling network maps can be augmented by standardizing notations, establishing curation workflows and application of computational biology methods to exploit the knowledge contained in the maps. In this manuscript, we summarize the major aims and challenges of assembling information in the form of comprehensive maps of molecular interactions. Mainly, we share our experience gained while creating the Atlas of Cancer Signalling Network. In the step-by-step procedure, we describe the map construction process and suggest solutions for map complexity management by introducing a hierarchical modular map structure...
January 1, 2018: Database: the Journal of Biological Databases and Curation
Jean-Baptiste Lamy, Rosy Tsopra
VCM (Visualization of Concept in Medicine) is an iconic language that represents medical concepts, such as disorders, by icons. VCM has a formal semantics described by an ontology. The icons can be used in medical software for providing a visual summary or enriching texts. However, the use of VCM icons in user interfaces requires to map standard medical terminologies to VCM. Here, we present a method combining semantic and lexical approaches for mapping MedDRA to VCM. The method takes advantage of the hierarchical relations in MedDRA...
2018: Studies in Health Technology and Informatics
Julian Sass, Kim Becker, Dominik Ludmann, Elisabeth Pantazoglou, Heike Dewenter, Sylvia Thun
A nationally uniform medication plan has recently been part of German legislation. The specification for the German medication plan was developed in cooperation between various stakeholders of the healthcare system. Its' goal is to enhance usability and interoperability while also providing patients and physicians with the necessary information they require for a safe and high-quality therapy. Within the research and development project named Medication Plan PLUS, the specification of the medication plan was tested and reviewed for semantic interoperability in particular...
2018: Studies in Health Technology and Informatics
Kirstine Rosenbeck Gøeg, Mark Hummeluhr
Information exchange at the level of semantic interoperability requires that information models and clinical terminologies work well together. In HL7 FHIR resources, terminology binding to standard terminologies such as SNOMED CT are suggested, and even though most are suggestions rather than rules, they still must reflect the clinical domain accurately. In this study, we suggest a method for empirically evaluating whether a terminology binding represents the value sets used in practice. We evaluated the terminology binding associated with the MedicationRequest...
2018: Studies in Health Technology and Informatics
Stephen M Wilson, Melodie Yen, Dana K Eriksson
Research on neuroplasticity in recovery from aphasia depends on the ability to identify language areas of the brain in individuals with aphasia. However, tasks commonly used to engage language processing in people with aphasia, such as narrative comprehension and picture naming, are limited in terms of reliability (test-retest reproducibility) and validity (identification of language regions, and not other regions). On the other hand, paradigms such as semantic decision that are effective in identifying language regions in people without aphasia can be prohibitively challenging for people with aphasia...
April 17, 2018: Human Brain Mapping
Vasileios Karyotis, Konstantinos Tsitseklis, Konstantinos Sotiropoulos, Symeon Papavassiliou
In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding...
April 15, 2018: Sensors
Pasquale Anthony Della Rosa, Eleonora Catricalà, Matteo Canini, Gabriella Vigliocco, Stefano F Cappa
Evidence from both neuropsychology and neuroimaging suggests that different types of information are necessary for representing and processing concrete and abstract word meanings. Both abstract and concrete concepts, however, conjointly rely on perceptual, verbal and contextual knowledge, with abstract concepts characterized by low values of imageability (IMG) (low sensory-motor grounding) and low context availability (CA) (more difficult to contextualize). Imaging studies supporting differences between abstract and concrete concepts show a greater recruitment of the left inferior frontal gyrus (LIFG) for abstract concepts, which has been attributed either to the representation of abstract-specific semantic knowledge or to the request for more executive control than in the case of concrete concepts...
April 12, 2018: NeuroImage
John Cuzzola, Ebrahim Bagheri, Jelena Jovanovic
Objective: The goal of this work is to map Unified Medical Language System (UMLS) concepts to DBpedia resources using widely accepted ontology relations from the Simple Knowledge Organization System (skos:exactMatch, skos:closeMatch) and from the Resource Description Framework Schema (rdfs:seeAlso), as a result of which a complete mapping from UMLS (UMLS 2016AA) to DBpedia (DBpedia 2015-10) is made publicly available that includes 221 690 skos:exactMatch, 26 276 skos:closeMatch, and 6 784 322 rdfs:seeAlso mappings...
April 10, 2018: Journal of the American Medical Informatics Association: JAMIA
Huanying Gu, Zhe He, Duo Wei, Gai Elhanan, Yan Chen
BACKGROUND: The UMLS assigns semantic types to all its integrated concepts. The semantic types are widely used in various natural language processing tasks in the biomedical domain, such as named entity recognition, semantic disambiguation, and semantic annotation. Due to the size of the UMLS, erroneous semantic type assignments are hard to detect. It is imperative to devise automated techniques to identify errors and inconsistencies in semantic type assignments. OBJECTIVES: Designing a methodology to perform programmatic checks to detect semantic type assignment errors for UMLS concepts with one or more SNOMED CT terms and evaluating concepts in a selected set of SNOMED CT hierarchies to verify our hypothesis that UMLS semantic type assignment errors may exist in concepts residing in semantically inconsistent groups...
February 2018: Methods of Information in Medicine
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