ライフサイエンスコーパス: semantic

1 two functionally distinct tasks (visual and semantic).

2 xplicitly distinguishes between diverse data semantics.

3 related to language and to general-knowledge semantics.

4 les more accurately when combining different semantics.

5 ather support substantial overlap of lexical-semantic activation and word selection mechanisms in the

6 amplitude revealed effects linked to lexical-semantic activation and word selection observed in wides

7 ual disruption predicted the upregulation of semantic activity in phonological regions.

8 Predictive processing of semantic alternatives in negated sentences is further su

9 was associated with severe episodic but not semantic amnesia for postmorbid autobiographical events

10 Subsequently, Latent Semantic Analysis (LSA) was applied to remove the redund

11 tex, such as the PPA, has been linked to the semantic and categorical properties of the images.

12 es during perception, can robustly represent semantic and emotional properties during imagery, but th

13 ipitotemporal cortex, which encoded sensory, semantic and emotional properties during perception, can

14 ses that traditional differentiation between semantic and environmental context should be replaced wi

15 , the Digit Symbol Substitution Task (DSST), semantic and phonemic fluency tests, and the Stroop Colo

16 ning-matching task, indicating that both the semantic and phonological processes can be involved in t

17 plausibility of a counterfactual through the semantic and pragmatic modulation of the mental represen

18 We investigated the associations between semantic and radiomic features in CT images of 258 non-s

19 There are associations between semantic and radiomic features, however the associations

20 ces, largely because (i) its straightforward semantics and clean syntax make it a readily accessible

21 nd quantitatively using radiologist-defined "semantic" and computer-derived "radiomic" features, resp

22 ive matrix factorization to derive different semantics, and it formulates the growing of the modules

23 ated with both action performance and action semantics, and their activation is consistent with a num

24 rd List Learning, World List Delayed Recall, Semantic Animal Fluency) and Six-Item Screener (SIS) ass

25 la and pregenual anterior cingulate, and the semantic appraisal network (SAN), anchored in the anteri

26 Our results demonstrate that a heavily semantic approach to sortal anaphora resolution is large

27 cognized uniquely and only the target word's semantics are active.

28 ame participants used a finger press to make semantic association decisions on the same stimuli.

29 novel generative model to create a detailed semantic atlas.

30 rdinary human language results in human-like semantic biases.

31 ction and the left temporal lobe to language semantics; both these regions clustered together on the

32 Therapy-related reduction in the number of semantic but not phonemic errors was associated with str

33 High CR was associated with performance on semantic but not visuospatial tasks.

34 congruent with readily available contextual semantics can trigger an accelerated onset of the neural

35 and words produced by parents in 3 different semantic categories (content-specific words) per minute

36 e active integration of words with congruent semantic categories enhances memory for words and increa

37 anguage (BSL) signs for the objects, (2) the semantic category of the objects, and (3) the physical f

38 aticalized constructions that have undergone semantic change also have undergone syntactic reanalysis

39 orpus, for an increase of 76% percent in the semantic classes of the eight ontologies that have been

40 ensive annotation of several domain-relevant semantic classes, and connection to complete syntactic a

41 cially on existing knowledge, technology for semantic classification of scientific literature accordi

42 nd Wernicke's area predicts performance on a semantic classification task with words but not other ca

43 Understanding the functional connectivity of semantic cognition allows greater understanding of how t

44 Previous studies have shown that semantic cognition depends on subregions of the anterior

45 This supports the necessity for semantic cognition in internal processes occurring durin

46 unctional network responsible for multimodal semantic cognition regardless of state.

47 connect to areas responsible for multimodal semantic cognition, including bilateral ATL, inferior fr

48 e neurobiological contribution of the ATL to semantic cognition.

49 ntegrate conceptual information into complex semantic combinations.

50 and left inferior frontal gyrus (IFG) and of semantic competition in MTG, left angular gyrus, and IFG

51 Memory Test (LMT) delayed recall) executive/semantic components.

52 the latter being consistent with predictive semantic computation of alternatives to the negated expe

53 in the MTG forms distinct representations of semantic concepts and that these representations are rei

54 mild emotional interference trials (without semantic conflict) versus intense emotional interference

55 intense emotional interference trials (with semantic conflict), revealed that while concurrent activ

56 ous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in

57 Neural activity related to the semantic content of stimuli was conserved in light non-r

58 n and integration through the biological and semantics content of cell lines.

59 Semantic context led to rapid reduction of listening eff

60 l differences identify items in a well-known semantic context.

61 Structural semantics could enable us to identify more general embed

62 for the formation and use of episodes, with semantic (cue identity) information relayed to the struc

63 ciated with individual lexical concepts in a semantic decision task.

64 errors in our patients were not related to a semantic deficit.

65 Semantic deficits on tests of single-word comprehension

66 peutic applications in patients with lexical-semantic deficits.

67 opying) or higher (naming, semantic fluency) semantic demands.

68 psychological studies of surface dyslexia in semantic dementia and the connectionist triangle model o

69 hat differed between patients with bvFTD and semantic dementia but included the cingulate cortices, t

70 [(18)F]AV-1451, the pathological regions in semantic dementia do not normally contain significant le

71 [(18)F]AV-1451 binding potential, separated semantic dementia from controls with 86% sensitivity and

72 tients (five with svPPA and two with 'right' semantic dementia) and 12 healthy controls underwent pos

73 ant primary progressive aphasia (also called semantic dementia) are two clinical variants of frontote

74 tients with dementia (19 with bvFTD, 15 with semantic dementia, and 15 with Alzheimer disease) were r

75 INTRODUCTION: Semantic dementia, including the semantic variant of pri

76 tices and nucleus accumbens in patients with semantic dementia.

77 ith those areas known to be most affected in semantic dementia.

78 We find that the utility of different semantics depends on disease categories and that, overal

79 e derived from several resources including a semantic-derived knowledge-base.

80 also successfully predicted 8 among 19 rated semantic descriptors ("garlic," "fish," "sweet," "fruit,

81 imal categories that spanned the dissociable semantic dimensions of threat and taxonomic class.

82 5 healthy controls, showed that patients had semantic disorders predominating with living categories

83 system represent information about specific semantic domains, or groups of related concepts, and our

84 no competitive effects, only target-specific semantic effects in angular gyrus and MTG.

85 the transformation from spectral features to semantic elements occurs early in the cortical speech-pr

86 Whether the effects of CR depend on the semantic/executive components of the task remains unknow

87 erse set of regions show some involvement in semantic false memory, none have revealed the nature of

88 mic features were associated with the binary semantic features (AUC = 0.56-0.76).

89 The semantic features approach to tumor phenotyping, accompl

90 that fail to be perceived by naked eye that semantic features do not describe and vice versa.

91 We examine both graph-theoretic and semantic features for the classification task.

92 eromodal cortical hubs integrate distributed semantic features into coherent representations.

93 en all radiomic features and the categorical semantic features ranged from weak to moderate (|Spearme

94 henotypes were described using 9 qualitative semantic features that were scored by radiologists, and

95 Of the 9 semantic features, 3 were rated on a binary scale (cavit

96 sodic memory score 4.4 versus 4.3, p = 0.79; semantic fluency score 15.7 versus 14.0, p = 0.21; calcu

97 nd lower (figure copying) or higher (naming, semantic fluency) semantic demands.

98 cognitive function, verbal episodic memory, semantic fluency, and calculation as well as a measure o

99 ic memory, attention, working memory, verbal semantic fluency, or calculation.

100 syntactic structure are instead captured by semantics; however, the syntactic level includes some sp

101 ral pole, a region that has been called the "semantic hub" of the brain.

102 f NSCLC showed multiple associations between semantic image features and metagenes that represented c

103 atistically significant correlations between semantic image features and metagenes.

104 A thoracic radiologist annotated 89 semantic image features of each patient's tumor.

105 t, a radiogenomics map was built that linked semantic image features to metagenes by using the t stat

106 ch tumor, a thoracic radiologist recorded 87 semantic image features, selected to reflect radiologic

107 ntrast to patients with surface dyslexia and semantic impairment from anterior temporal lobe degenera

108 res of articulatory and semantic in STS, and semantic in STS and beyond.

109 ulatory in STG, mixtures of articulatory and semantic in STS, and semantic in STS and beyond.

110 automated text mining approach using Latent Semantic Indexing (LSI) for prioritization, clustering a

111 rich feature approach for NER and supervised semantic indexing for normalization.

112 nt converted from Reactome and the wealth of semantic information about interactions.

113 We propose DeepMeSH that incorporates deep semantic information for large-scale MeSH indexing.

114 Comparison results show that semantic information is beneficial to biomedical entity

115 then ranks the candidates according to their semantic information modeled by CNN as well as their mor

116 ation as a ranking problem and benefits from semantic information of biomedical entities.

117 at actively integrating words with congruent semantic information provided by a category cue enhances

118 al mechanisms, supporting the integration of semantic information with the event input.

119 ing mechanisms supporting the integration of semantic information with the event input.

120 at "syntactic representations do not contain semantic information", while supported by structural-pri

121 a causal role in the integration of lexical-semantic information, and that high-definition tDCS to a

122 (AnG) in the retrieval of both episodic and semantic information, but the region's precise function

123 OSE-SSL incorporates semantic information, partial class label, into a ML sch

124 till lack a rich annotation framework to add semantic information, such as machine-readable descripti

125 regions during the retrieval of episodic and semantic information.

126 the acquisition and retrieval of lexical and semantic information.

127 f entity mentions, but rarely consider their semantic information.

128 biological questions and challenges needing semantic infrastructure for information modeling.

129 al role of the left angular gyrus in lexical-semantic integration and provide motivation for novel th

130 isms and the causal role of these regions in semantic integration.

131 cond timing and cortical region, HFB indexed semantic interference (i.e., more activity in HOM than H

132 In contrast, word selection is indexed by semantic interference and is hampered in semantically ho

133 A semantic interference effect was observed with naming la

134 incorporated into SemRep, extending its core semantic interpretation capability from sentence level t

135 ncreased its inhibitory influence on another semantic key node.

136 lying structure of neural representations of semantic knowledge, and how this semantic structure can

137 CR may relate to executive functioning and semantic knowledge, leading to preserved cognitive perfo

138 We investigate the impact of sleep on new semantic learning using a property inference task in whi

139 ingle shallow level of syntax connected to a semantic level containing information about quantificati

140 al system because it is not represented on a semantic level.

141 existing semantic link prediction algorithm, Semantic Link Association Prediction (SLAP), to predict

142 rithm significantly outperformed an existing semantic link prediction algorithm, Semantic Link Associ

143 The additional semantic links significantly improved the predictive per

144 o the frontal cortex and supported by visual semantic loops within the left fusiform gyrus and that t

145 hat the hippocampal representations follow a semantic map.

146 It assigns a semantic meaning (such as genus name, species epithet, r

147 omprehension requires that the brain extract semantic meaning from the spectral features represented

148 Medusa is flexible in choosing or combining semantic meanings and provides theoretical guarantees ab

149 The data provide novel evidence for a post-semantic mechanism mediating the production of surface e

150 contribute to the retrieval of episodic and semantic memories.

151 ns (episodic memory -0.12 [0.04], p=0.00090; semantic memory -0.10 [0.04], p=0.013; working memory -0

152 Cluster 2 had a lower intercept on a test of semantic memory and both Cluster 2 and Cluster 3 had ste

153 pproach to studying integrative processes in semantic memory by applying focal brain stimulation to a

154 Semantic memory encompasses knowledge about both the pro

155 entral for understanding the organization of semantic memory in the human brain.

156 eneralized knowledge, suggesting that we use semantic memory in the service of episodic memory [2, 3]

157 This focus on semantic memory leaves out many aspects of memory, such

158 "plaid jacket." Many neuroanatomic models of semantic memory propose that heteromodal cortical hubs i

159 scores: beta estimate, -0.18; P < .001; and semantic memory scores: beta estimate, -0.06; P = .04, n

160 Human participants completed episodic and semantic memory tasks involving unimodal (auditory or vi

161 longitudinal assessments of episodic memory, semantic memory, executive function, and global cognitiv

162 sitron emission tomography, and episodic and semantic memory, language, executive and visuospatial fu

163 s of RL have largely involved procedural and semantic memory, the way in which knowledge about action

164 electively modulate integrative processes in semantic memory.

165 ory: medial temporal lobe and angular gyrus; semantic memory: left anterior temporal regions; languag

166 cs of clinical terms and that distributional semantic methods are useful for clinical and biomedical

167 the same semantic regions suggesting a core semantic network active during rest and task states.

168 n induced suppression of activity in a large semantic network and upregulation of neighbouring phonol

169 e to SLAP in order to predict DTIs using the semantic network that integrates chemical, pharmacologic

170 othesis that cortical areas in this "general semantic network" (GSN) encode multimodal information de

171 o take into account the heterogeneity of the semantic network, meta-path-based topological patterns w

172 In a semantic network, the types of the nodes and links are d

173 eta-path topological features of an enriched semantic network, which was derived from Chem2Bio2RDF, a

174 nd stronger BOLD-related fluctuations in the semantic network.

175 uctions and then generated future events and semantic object comparisons during fMRI scanning.

176 MRI data acquired while subjects performed a semantic oddball detection task.

177 in lieu of clinical reports to represent the semantics of clinical terms and that distributional sema

178 substitute for clinical domain to represent semantics of clinical terms remains to be demonstrated.

179 We view this correlation as structural semantics of sequence data that allows for a different i

180 uring rapid reading of connected text, where semantics of words may be activated only partially, the

181 region was engaged in phonological, but not semantic or orthographic, processing.

182 al processing, but not in tasks that require semantic or visuospatial processing.

183 GABA concentration in the ATL showed better semantic performance and stronger BOLD-related fluctuati

184 addition, three further fMRI tasks captured semantic, phonological, and orthographic processing to s

185 creased activation in right Crus I/II during semantic prediction and enhanced resting-state functiona

186 brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) proc

187 t the cerebellum is specifically involved in semantic prediction during sentence processing.

188 In language, semantic prediction speeds speech production and compreh

189 or the cerebellum in semantic processing and semantic prediction.

190 nternal models of linguistic stimuli support semantic prediction.SIGNIFICANCE STATEMENT Cerebellar in

191 entation of a critical word, context-induced semantic predictions are reflected by a neurophysiologic

192 The granularity of the semantic predictions was so fine grained that the cortic

193 Specific semantic predictions were indexed by SRP sources within

194 ion within right Crus I/II was enhanced when semantic predictions were made, and we show that modulat

195 e., more activity in HOM than HET blocks) or semantic priming effects (i.e., more activity in HET tha

196 cal and conceptual activation is measured by semantic priming.

197 study investigated how the phonological and semantic processes in Chinese disyllabic spoken word rec

198 temporal lobe, known to be an amodal hub for semantic processing and related to cross-situational lea

199 consistent with a role for the cerebellum in semantic processing and semantic prediction.

200 gated the neurochemical mechanism underlying semantic processing in the ATL.

201 in the late stage of recognition, even when semantic processing is not required.

202 s with higher GABA may have a more efficient semantic processing leading to better task performance a

203 se compensation is most prominent at lexical/semantic processing levels.

204 ons, whereas additional and deeper levels of semantic processing likely require more anterior tempora

205 ns of the brain that include the distributed semantic processing network [4-6], but it is unknown whe

206 , the neurochemical nature of the ATL in the semantic processing remains unclear.

207 terality in speech processing and associated semantic processing to higher levels of cortex than repo

208 ment effects in areas related to lexical and semantic processing, an effect that continued to increas

209 in the human ATL and task performance during semantic processing.

210 einstate the neural activity responsible for semantic processing.

211 to a heteromodal cortical hub implicated in semantic processing.

212 at represent the spectral, articulatory, and semantic properties of speech.

213 ous process of activation of the lexical and semantic properties of the word candidates matching the

214 al responses to the scenes, but not by their semantic properties.

215 Recent advances in formal semantics provide a framework for incorporating both ima

216 iological index, which we therefore call the semantic readiness potential (SRP).

217 We show that perturbation of a semantic region in the healthy brain induced suppression

218 The PPI analysis highlighted the same semantic regions suggesting a core semantic network acti

219 and poor living conditions; Combined search semantics, related to obesity, how to quit smoking and i

220 A key obstacle to development of semantic relatedness measures is the limited availabilit

221 esent novel semantic rules that identify the semantic relationship between each co-occurrence of a pr

222 e of a protein-molecule pair that represents semantic relationship between the pair.

223 This is because they do not consider the semantic relationships between terms in a sentence (i.e.

224 is because these system do not consider the semantic relationships between terms in a sentence (i.e.

225 words more for the individuals with stronger semantic reliance.

226 ncrease in external detail production (i.e., semantic, repetitive, or off-topic information), reflect

227 The neural substrates of semantic representation have been the subject of much co

228 ortlessly combine these concepts to form the semantic representation of "plaid jacket." Many neuroana

229 This finding suggests that the semantic representation of the spoken words can be activ

230 yrus (MTG), a cortical region supporting the semantic representation of words [9-11], as participants

231 ous types of evidence generated from the new semantic representation.

232 the human anterior temporal lobe (ATL) is a semantic representational hub.

233 sing routines that map strings of words onto semantic representations (and vice versa) without the me

234 monstrate category-selective organization of semantic representations in LATL into spatially distinct

235 The location and specificity of semantic representations in the brain are still widely d

236 To decode semantic representations of these TPWs, we used multivar

237 memory, none have revealed the nature of the semantic representations underpinning the phenomenon.

238 stimulus-specific activation of sensory and semantic representations, even for objects that we do no

239 n cortex participate in the reinstatement of semantic representations.

240 y tools for searching and extending existing semantic resources, c) education and guidance about stan

241 er that function differs across episodic and semantic retrieval have yet to be determined.

242 responding activity during both episodic and semantic retrieval, which mirrored the functional specia

243 Towards this, we present novel semantic rules that identify the semantic relationship b

244 idation of a series of objects embedded in a semantic schema was associated with a buildup of activit

245 e of a left temporal-pole network for verbal semantics selectively modulated through both left-excita

246 involved in the reaction and for non-enzymes semantic similarities based on the GO.

247 ised language model (Word2Vec), which learns semantic similarities between terms and phrases, allowin

248 interesting genes are found, which have high semantic similarity among them, but are not significantl

249 By assessing network replicability, semantic similarity and overall functional connectivity,

250 nce standards used in this study to evaluate semantic similarity and relatedness measures are publicl

251 Both semantic similarity and word-initial phonological simila

252 nality for straightforward visualization and semantic similarity calculations, including statistical

253 sis (PCAN); a method to assess the consensus semantic similarity of phenotypes in a candidate gene's

254 otional experiences are represented within a semantic space best captured by categorical labels, the

255 s-that is, the geometric organization of the semantic space of emotion-have sparked intense debate.

256 mmunity and mechanisms needed to develop EHS semantic standards that will advance understanding about

257 development, and implementation of relevant semantic standards, such as ontologies or hierarchical v

258 ntations of semantic knowledge, and how this semantic structure can both enhance and distort our memo

259 patients with sporadic PPA, divided into 29 semantic (svPPA), 25 nonfluent (nfvPPA), 11 logopenic (l

260 We show that the semantic system is organized into intricate patterns tha

261 r results suggest that most areas within the semantic system represent information about specific sem

262 ese aims using both resting-state and active semantic task data in humans in combination with a dual-

263 of functional connectivity during an active semantic task were performed using a psychophysiological

264 he stronger ATL BOLD response induced by the semantic task, the lower GABA concentration in the same

265 High CR was associated with semantic tasks in patients with both MCI and AD, but was

266 he ability to perform phonological more than semantic tasks.

267 Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genot

268 me to successfully automate the method using semantic technology.

269 in part, with hundreds, if not thousands, of semantic terms.

270 hierarchy as in STS are more correlated with semantic than spectral representations.

271 ping psychologically plausible syntactic and semantic theories.

272 Beyond semantics, this has important implications as a starting

273 e role of stem cells, providing material and semantics to construct differentiated tissues and organi

274 udy used a predictive encoding model of word semantics to decode conceptual information from neural a

275 designs for the deterministic and stochastic semantics using Microsoft's GEC tool and the probabilist

276 Finally, we describe recent improvement in semantic utility, with PRO now represented in OWL and as

277 c variant (n = 12, age = 71 +/- 8 years) and semantic variant (n = 13, age = 65 +/- 7 years) using ma

278 The semantic variant of primary progressive aphasia (svPPA)

279 variant frontotemporal dementia (rtFTD), (3) semantic variant of primary progressive aphasia (svPPA),

280 TRODUCTION: Semantic dementia, including the semantic variant of primary progressive aphasia (svPPA),

281 bvFTD), 7 non-fluent variant PPA (nfvPPA), 6 semantic variant PPA (svPPA) and 25 patients with subjec

282 sentation of word meaning in a discussion of semantic variant PPA, grammatical comprehension and expr

283 avioural variant frontotemporal dementia and semantic variant primary progressive aphasia (also calle

284 showed that the seed region derived from the semantic variant primary progressive aphasia analysis wa

285 ol subjects (P < 0.001), while patients with semantic variant primary progressive aphasia discounted

286 se findings suggest that cortical atrophy in semantic variant primary progressive aphasia may follow

287 rophy in 100% of individual patients in both semantic variant primary progressive aphasia samples.

288 rominent and consistent region of atrophy in semantic variant primary progressive aphasia using corti

289 alth of neuroimaging research has associated semantic variant primary progressive aphasia with distri

290 resembled the distributed atrophy pattern in semantic variant primary progressive aphasia.

291 mporo-parietal junction in patients with the semantic variant.

292 luding logopenic, non-fluent/agrammatic, and semantic variants.

293 n regions was positively associated with the semantic verbal fluency.

294 Having biological pathways in the semantic web allows rapid integration with data from oth

295 ill thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in

296 We combined various semantic web resources with the newly converted WikiPath

297 ata and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graph

298 to several other biological resources in the semantic web.

299 mpute an intermediate representation linking semantics with phonology.

300 Semantic work on sign language iconicity suggests, as do