ライフサイエンスコーパス: receiver

1 tor' can allocate money to the partner (the 'receiver').

2 se activity of the donor but not that of the receiver.

3 value calculation in both communicators and receivers.

4 measured from the remaining of the coils as receivers.

5 quantified the minimum bandwidths of optical receivers and signal processing devices to ensure the op

6 w little about how the perceptual systems of receivers are evolutionarily adapted to avoid the costs

7 iological coupling between communicators and receivers as a mechanism through which perceptions of va

8 ated aggression, benefiting both senders and receivers by facilitating social cohesion.

9 aints, efficiency was optimal via a circular receiver coil wrapped into a half-cylinder with a meande

10 ('donor') and demonstrate phosphorylation of receiver DDR1 by donor DDR1 in response to collagen.

11 s not affect receptor function, we show that receiver dimers are phosphorylated in trans by the donor

12 f CKI1RD, the corresponding loop of the ETR1 receiver domain (ETR1RD) exhibited little conformational

13 The receiver domain of ETR1 is involved in this function in

14 ukaryotic system, the phosphorylation of the receiver domain of the histidine kinase CYTOKININ-INDEPE

15 rial chemotaxis protein CheY, the N-terminal receiver domain of the nitrogen regulation protein NT-Nt

16 1, a hybrid of a CheW and a phosphorylatable receiver domain.

17 identical and similar to the active state of receiver domains of bacterial response regulators.

18 The receiver ectodomain is not required, but phosphorylation

19 rustal and upper mantle structures along two receiver function profiles across Qilian, an orogen expe

20 lgorithm we compute TEC variations at 56 GPS receivers in Hawaii as induced by the 2012 Haida Gwaii t

21 ffects of social influence and persuasion on receivers, in turn, arise from changes in the receiver's

22 st Global Navigation Satellite System (GNSS) receivers located in the footwall and hangingwall of the

23 oving activator motor to a nearby activated (receiver) motor by release of Ag(+) ions from a Janus po

24 Stream ecosystems are the primary receivers of nutrient and organic carbon exported from t

25 The area under the receiver operating characteristic (AUC) was also high fo

26 te-of-the-art methods in terms of area under receiver operating characteristic (auROC) curve.

27 Receiver operating characteristic (ROC) analysis demonst

28 Analysis of variance (ANOVA) and receiver operating characteristic (ROC) analysis reveale

29 Receiver operating characteristic (ROC) analysis was use

30 c generalized linear mixed-effect models and receiver operating characteristic (ROC) analysis.

31 ients and healthy controls using RT-qPCR and receiver operating characteristic (ROC) analysis.

32 ark datasets and three legacy datasets using Receiver Operating Characteristic (ROC) and Precision Re

33 kers and EMR data achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0

34 imulated datasets in terms of area under the receiver operating characteristic (ROC) curve (AUC), num

35 Receiver operating characteristic (ROC) curve analysis w

36 ami distribution) and entropy imaging by the receiver operating characteristic (ROC) curve analysis.

37 e used the area under the curve (AUC) of the receiver operating characteristic (ROC) curve for perfor

38 The mean of the area under the receiver operating characteristic (ROC) curve for the cl

39 +/- 0.7% and 85 +/- 0.9% and areas under the receiver operating characteristic (ROC) curve of 0.75 +/

40 ROCker employs the receiver operating characteristic (ROC) curve to minimiz

41 Receiver operating characteristic (ROC) curve was drawn

42 For each test, we generated a receiver operating characteristic (ROC) curve, and calcu

43 rformance, as measured by the area under the receiver operating characteristic (ROC) curve, relative

44 -to-lymphocyte ratio (MLR) were optimized by receiver operating characteristic (ROC) curve.

45 Comparison of receiver operating characteristic (ROC) curves demonstra

46 re mapped against the consensus equation and receiver operating characteristic (ROC) curves produced.

47 Receiver operating characteristic (ROC) curves were cons

48 Receiver operating characteristic (ROC) curves were cons

49 Receiver Operating Characteristic (ROC) curves were obta

50 CT HU and BV/TV for identification of TMJOA, receiver operating characteristic (ROC) curves were plot

51 Receiver operating characteristic (ROC) curves, category

52 Using receiver operating characteristic (ROC) curves, we deter

53 controls were calculated and compared using receiver operating characteristic (ROC) curves.

54 tive approach for software optimization uses receiver operating characteristic (ROC) curves.

55 n mortality and survival (the area under the receiver operating characteristic [ROC] curve [AUROC] fo

56 s used to analyze changes from baseline, and receiver operating characteristic analyses were used to

57 euromelanin volume was investigated by using receiver operating characteristic analyses, and correlat

58 Mann-Whitney U tests, receiver operating characteristic analyses, Spearman cor

59 Following receiver operating characteristic analysis (Youden's ind

60 and area under the curve with time-dependent receiver operating characteristic analysis for 1-year su

61 Receiver operating characteristic analysis for detecting

62 Receiver operating characteristic analysis for the 3 tau

63 Receiver operating characteristic analysis revealed an a

64 Receiver operating characteristic analysis revealed an a

65 Receiver operating characteristic analysis revealed that

66 patient subsets and control subjects, and a receiver operating characteristic analysis was performed

67 reviews of PET/CT images was calculated and receiver operating characteristic analysis was performed

68 Receiver operating characteristic analysis was used to d

69 Receiver operating characteristic analysis was used to d

70 Multivariate regression analysis and receiver operating characteristic analysis were performe

71 objective was the test performance, based on receiver operating characteristic analysis, and cut-off

72 In receiver operating characteristic analysis, the area und

73 ultivariate prediction model was tested with receiver operating characteristic analysis.

74 city of CLS in determining symptoms based on receiver operating characteristic analysis.

75 ) test, Pearson correlation coefficient, and receiver operating characteristic analysis.

76 ssessed by binary logistic regression and by receiver operating characteristic analysis.

77 MFQ clinical cutoff following the results of receiver operating characteristic analysis.

78 Receiver operating characteristic and area under receive

79 95.2% sensitivity and 97.6% specificity; the receiver operating characteristic area under curve value

80 rols (no rejection histologically), P<0.001 (receiver operating characteristic area under the curve [

81 t association with sputum eosinophil counts (receiver operating characteristic area under the curve o

82 Receiver operating characteristic area under the curve w

83 radiography, as given by the area under the receiver operating characteristic curve (1.23-fold, P <

84 c accuracy as measured by the area under the receiver operating characteristic curve (AROC).

85 sign of effective sgRNAs with area under the receiver operating characteristic curve (AUC) >0.8, and

86 ents with BE or EAC using the area under the receiver operating characteristic curve (AUC) analysis.

87 We used area under the receiver operating characteristic curve (AUC) as a metri

88 Area under the receiver operating characteristic curve (AUC) is used to

89 tality prediction achieved an area under the receiver operating characteristic curve (AUC) of 0.53 (9

90 ng set with a cross-validated area under the receiver operating characteristic curve (AUC) of 0.807,

91 city (95% CI: 76%, 92%), with area under the receiver operating characteristic curve (AUC) of 0.91 wi

92 ty in the test cohort with an area under the receiver operating characteristic curve (AUC) of 0.92.

93 nd menopausal status, and the area under the receiver operating characteristic curve (AUC) was comput

94 Sensitivity, specificity, and area under receiver operating characteristic curve (AUC) were calcu

95 ayer and sector with the best area under the receiver operating characteristic curve (AUC) were ident

96 mination was evaluated by the area under the receiver operating characteristic curve (AUC), and clini

97 the gold standard to compute area under the receiver operating characteristic curve (AUC).

98 The area under the receiver operating characteristic curve (AUC, in g/mL. m

99 imaging achieved a validated area under the receiver operating characteristic curve (AUROC) of 0.98,

100 ens), specificity (Spec), and area under the receiver operating characteristic curve (AUROC) values w

101 nation was assessed using the area under the receiver operating characteristic curve (AUROC).

102 ic risk differed by MS group by applying the receiver operating characteristic curve (ROC) cut point.

103 d Early Warning Score (median area under the receiver operating characteristic curve 0.67), and highe

104 l Early Warning Score (median area under the receiver operating characteristic curve 0.71) and electr

105 ac Arrest Risk Triage (median area under the receiver operating characteristic curve 0.73).

106 .74) than a model using MELD (area under the receiver operating characteristic curve = 0.62) or MELD

107 urve = 0.62) or MELD and age (area under the receiver operating characteristic curve = 0.67) to predi

108 re had better discrimination (area under the receiver operating characteristic curve = 0.74) than a m

109 s with a similar reliability (area under the receiver operating characteristic curve = 0.973 [0.838-1

110 erentiating ACR from non-ACR (area under the receiver operating characteristic curve = 90%, 95% confi

111 Results LAS (area under the receiver operating characteristic curve [AUC] = 0.93, P

112 ed risks, (2) discrimination (area under the receiver operating characteristic curve [AUC]) between i

113 [18F]flutemetamol PET status (area under the receiver operating characteristic curve [AUC], 0.92) com

114 n low- and high-grade glioma (area under the receiver operating characteristic curve [AUC], 1) for th

115 gher discriminatory accuracy (area under the receiver operating characteristic curve [AUC]: 0.96 and

116 ood accuracy (cross-validated area under the receiver operating characteristic curve [principal + sec

117 Receiver operating characteristic curve analyses and pre

118 isons were performed in template space, with receiver operating characteristic curve analyses to asse

119 Receiver operating characteristic curve analyses were us

120 The summary receiver operating characteristic curve analysis demonst

121 Receiver operating characteristic curve analysis evidenc

122 Conventional, frequentist receiver operating characteristic curve analysis was con

123 Receiver operating characteristic curve analysis was use

124 lly overt severe sepsis syndrome patients by receiver operating characteristic curve analysis, with a

125 8.7% and specificity was 77.3% at case-based receiver operating characteristic curve analysis.

126 DIT, and DIS plus DIT with a time-dependent receiver operating characteristic curve analysis.

127 ict malignant disease was determined using a receiver operating characteristic curve analysis.

128 Model discrimination was assessed using receiver operating characteristic curve analysis.

129 es for patient outcome were determined using receiver operating characteristic curve analysis.

130 Receiver operating characteristic curve and the area und

131 Receiver operating characteristic curve areas trended lo

132 iagnostic sensitivity and specificity in the receiver operating characteristic curve did not differ b

133 were compared on the basis of nonparametric receiver operating characteristic curve estimations by u

134 Mean validation areas under the receiver operating characteristic curve for discriminati

135 Areas under the receiver operating characteristic curve for early and la

136 The area under the receiver operating characteristic curve for internal and

137 leep quality for concurrent validity and the receiver operating characteristic curve for predictive v

138 The area under the receiver operating characteristic curve for screening be

139 According to the area under the receiver operating characteristic curve for SUVpeak, DIB

140 The area under the receiver operating characteristic curve for the diagnosi

141 The area under the receiver operating characteristic curve for the differen

142 Similarly, for non-AC data, areas under the receiver operating characteristic curve for the experts

143 The receiver operating characteristic curve for the full reg

144 ariate random-effects and hierarchic summary receiver operating characteristic curve models.

145 ur prediction method shows an area under the Receiver Operating Characteristic curve of 0.85 for all

146 iagnostic values with maximum area under the receiver operating characteristic curve of 0.878 for CCA

147 NLST database demonstrated an area under the receiver operating characteristic curve of 0.963 (95% co

148 -validation Area Under Curve of 0.85 for the Receiver Operating Characteristic curve of our model.

149 The area under the receiver operating characteristic curve of SVC was signi

150 The area under the receiver operating characteristic curve of the King's Co

151 developed de novo HCC with an area under the receiver operating characteristic curve value higher tha

152 ning diet vs controls with an area under the receiver operating characteristic curve value of 0.95 (9

153 liac disease on a GFD with an area under the receiver operating characteristic curve value of 0.96 (9

154 more data becomes available, area under the receiver operating characteristic curve values increase

155 Area under the receiver operating characteristic curve was 0.85 (95% co

156 Area under the receiver operating characteristic curve was 0.88 (95% co

157 Area under the multivariate model receiver operating characteristic curve was 0.881.

158 on-specific enolase (NSE; the area under the receiver operating characteristic curve was 0.91 for tau

159 Area under the receiver operating characteristic curve was 93.2% (95% C

160 7 to June 2013, out-of-sample area under the receiver operating characteristic curve was approximatel

161 (0.5%) and 91.6% (0.1%), the area under the receiver operating characteristic curve was between 0.94

162 s limited, with an AUC score (area under the receiver operating characteristic curve) that reaches 0.

163 d to accurately predict (>80% area under the receiver operating characteristic curve) the clinical en

164 nce in some datasets was low (area under the receiver operating characteristic curve, < 0.7) for the

165 ory response syndrome (median area under the receiver operating characteristic curve, 0.60) and Sepsi

166 lure Assessment score (median area under the receiver operating characteristic curve, 0.62), intermed

167 an Failure Assessment (median area under the receiver operating characteristic curve, 0.65) and Modif

168 PD diameter cutoff of 7.2 mm (area under the receiver operating characteristic curve, 0.70; 95% CI, 0

169 with colon-only CD (P = .001; area under the receiver operating characteristic curve, 0.72).

170 improved survival prediction (area under the receiver operating characteristic curve, 0.73 vs 0.60, r

171 complications (overall model area under the receiver operating characteristic curve, 0.77).

172 riple-negative breast cancer (area under the receiver operating characteristic curve, 0.834).

173 AD with dementia vs controls (area under the receiver operating characteristic curve, 0.87, which is

174 e, likelihood ratio negative, area under the receiver operating characteristic curve, and by cross-va

175 With a receiver operating characteristic curve, IDO activity ha

176 Receiver operating characteristic curve, Kaplan-Meier me

177 d sensitivity and specificity metrics on the receiver operating characteristic curve.

178 automatically, and so was the area under the receiver operating characteristic curve.

179 stently frequent" trajectory (area under the receiver operating characteristic curve: 0.84, sensitivi

180 Area under receiver operating characteristic curves (AUC) and sensi

181 Descriptive statistics and areas under the receiver operating characteristic curves (AUCs) were cal

182 gm, using logistic model and areas under the receiver operating characteristic curves (AUCs).

183 However, receiver operating characteristic curves (ROC) curve ana

184 ed fluid responsiveness with areas under the receiver operating characteristic curves (with 95% CIs)

185 gingivalis (0.23%) and T. forsythia (0.35%), receiver operating characteristic curves analysis demons

186 Receiver operating characteristic curves constructed usi

187 Receiver operating characteristic curves determined the

188 Analysis of receiver operating characteristic curves implicates aber

189 dentified in models that had areas under the receiver operating characteristic curves of 0.57 (95% CI

190 iver operating characteristic and area under receiver operating characteristic curves revealed CCI to

191 Receiver operating characteristic curves revealed that l

192 Receiver operating characteristic curves summarizing dia

193 Lastly, we used receiver operating characteristic curves to show that se

194 Receiver operating characteristic curves were used to de

195 nstrate that our method has uniformly better receiver operating characteristic curves, and identifies

196 yzed with mixed effects logistic regression, receiver operating characteristic curves, and the Fisher

197 Using receiver operating characteristic curves, diffusion char

198 sion-weighted imaging scores (area under the receiver operating characteristic curves, respectively,

199 libration, and area under the curve (AUC) of receiver operating characteristic curves.

200 Density cutoffs were determined using receiver operating characteristic curves.

201 itivity to be predicted were calculated with receiver operating characteristic curves.

202 Discriminatory value was assessed by using receiver operating characteristic curves.A total of 2882

203 acy were measured by using the free-response receiver operating characteristic method and the receive

204 iver operating characteristic method and the receiver operating characteristic method for nodule dete

205 Random-effects and hierarchical summary receiver operating characteristic model meta-analyses we

206 The area under the receiver operating characteristic of Systemic Inflammato

207 to SEA with ECFP on ChEMBL20 and equivalent receiver operating characteristic performance.

208 eters were calculated for each question, and receiver operating characteristic statistics were genera

209 Using 7 metabolites improved the receiver operating characteristic with area under the cu

210 n-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials

211 ortality or severe morbidity (area under the receiver operating characteristic, 0.77 [0.70-0.83] vs 0

212 o predict AF: increase in the area under the receiver operating characteristic, 0.88 (95% confidence

213 Optimal cutoff from receiver operating characteristics (2.39 mmol/L) showed

214 Ten-fold cross-validation was used for the receiver operating characteristics (ROC) analysis.

215 Receiver operating characteristics analysis was used to

216 Accuracy, receiver operating characteristics and area under the cu

217 -risk coronary computed tomography features (receiver operating characteristics area under the curve

218 ng had a statistically higher area under the receiver operating characteristics curve (AUC) than non-

219 tion method was used to describe the summary receiver operating characteristics curve and bivariate m

220 M65 and M30 both had an area under the receiver operating characteristics curve of 0.84 to esti

221 Receiver operating characteristics curve showed aspartat

222 nd Scotland by assessing the areas under the receiver operating characteristics curves (ROC-AUCs).

223 Area under the receiver operating characteristics curves to detect ball

224 fy intraretinal and SRF using area under the receiver operating characteristics curves, precision, an

225 te gadolinium enhancement was compared using receiver operating characteristics curves.

226 In receiver operating characteristics, the calculated area

227 The highest receiver operating characteristics-area under the curve

228 Receiver operating characteristics-area under the curve

229 n status was evaluated by the area under the receiver operating curve (AUC) and its significance was

230 n-hospital mortality, with an area under the receiver operating curve (AUROC) of 0.80 (95% CI, 0.74-0

231 The areas under the receiver operating curve (AUROC) value, sensitivity and

232 ely risk-stratified patients (area under the receiver operating curve = 0.84; 95% CI = 0.79-0.89).

233 Brier was 31%, 26%, and 21%; area under the receiver operating curve was 0.84, 0.87, and 0.89; and s

234 The area under the receiver operating curve was used to indicate the most a

235 rmance was analyzed using the area under the receiver operating curve, scaled Brier's score, and stan

236 In receiver operating curves, Ana o 3 discriminates between

237 rrelations, area under the curve values from receiver-operating characteristic analyses, and diagnost

238 -way ANOVA, logistic regression analysis and receiver-operating characteristic analysis (ROC).

239 Receiver-operating characteristic analysis for SPT and B

240 Receiver-operating characteristic analysis showed that v

241 [CI]: 1.24 to 2.00; p < 0.0005) with a model receiver-operating characteristic area of 0.69.

242 for AMI, as quantified by the area under the receiver-operating characteristic curve (AUC), was compa

243 sensitivity, specificity, and area under the receiver-operating characteristic curve (AUROC) of HBeAg

244 tion cohort, the score had an area under the receiver-operating characteristic curve of 0.87 (p < 0.0

245 pendent predictor of outcome (area under the receiver-operating characteristic curve of the model = 0

246 Areas under the receiver-operating characteristic curves of the 2 differ

247 thod at predicting ischemia (areas under the receiver-operating characteristic curves, 0.87 versus 0.

248 n the epicardial myocardium (areas under the receiver-operating characteristic curves, 0.87 versus 0.

249 t statistically significant (areas under the receiver-operating characteristic curves, 0.90 versus 0.

250 Receiver-operating characteristics analysis identified a

251 urrence in the next 48 hours (area under the receiver-operating characteristics curve = 0.88 +/- 0.07

252 S and non-NRS plaques, whereas we calculated receiver-operating characteristics' area under the curve

253 ogs from those without were calculated using receiver-operating characteristics.

254 duced similar results with an area under the receiver-operating curve of 0.84 (+AC) to 0.87 (-AC).

255 ts on outcome prediction were compared using receiver-operating-characteristic (ROC) curve analysis a

256 n signed rank, and McNemar tests, as well as receiver-operating-characteristic analyses, were perform

257 d after 9-11 wk of erlotinib treatment using receiver-operating-characteristic analysis, linear regre

258 In receiver-operating-characteristic analysis, PET/CT asses

259 At receiver-operating-characteristic analysis, sensitivity

260 On the basis of receiver-operating-characteristic analysis, the optimal

261 alue of these parameters was defined using a receiver-operating-characteristic analysis.

262 lesions for both AR and ER was determined by receiver-operating-characteristic analysis.

263 tures were compared using the area under the receiver-operating-characteristic curve (AUC).

264 c regression analysis showed areas under the receiver-operating-characteristic curve (AUCs) of 0.78 c

265 Receiver-operating-characteristic curve analysis of the

266 Receiver operator characteristic (ROC) curve analysis sh

267 Summary receiver operator characteristic (ROC) curve demonstrate

268 and non-cases by calculating area under the receiver operator characteristic (ROCAUC) for the predic

269 ess-of-fit and optimal cutoff values through receiver operator characteristic analysis and Youden ind

270 of negative correlates) was most predictive (receiver operator characteristic area under the curve of

271 t centiles was compared using area under the receiver operator characteristic curve (AUROC) and net r

272 ort that predicted mortality (area under the receiver operator characteristic curve [AUC], 0.79; 95%

273 The time-dependent area under the receiver operator characteristic curve for the score was

274 The area under the receiver operator characteristic curve was 0.79 (95% con

275 , 64%, and 92%, respectively (area under the receiver operator characteristic curve, 0.792), and 57%,

276 , 78%, and 74%, respectively (area under the receiver operator characteristic curve, 0.848).

277 m forest machine learning was used to obtain receiver operator characteristic curves and to determine

278 Receiver operator characteristics and C statistics for e

279 vice (as characterized by the area under the receiver operator characteristics curve) is 89% and 83%

280 e EPIC urine samples on the basis of partial receiver operator curve analyses with permutation testin

281 -3.55) predicted VTE, with an area under the receiver operator curve of 0.730.

282 -6.08) predicted VTE, with an area under the receiver operator curve of 0.760.

283 Receiver operator curves (ROC) were constructed at an in

284 Receiver operator curves generated from the classificati

285 miR-27b-3p, compared to healthy controls and receiver operator curves of the miRNAs had AUCs of 91 to

286 ssification improvement (NRI), comparison of receiver-operator characteristic (ROC) curves and Decisi

287 ll LVAD patients (n=111) using Cox modeling, receiver-operator characteristic (ROC) curves, and calib

288 The area under the receiver-operator characteristic curve (AUC) of UMT (0.8

289 ntify independent predictors; area under the receiver-operator characteristic curves (AUC) were used

290 Organ Dysfunction Scores gave area under the receiver-operator characteristic curves of 0.67, 0.68, 0

291 ia in both cohorts, and their area under the receiver-operator characteristic curves was calculated t

292 Participants were allocated to the role of receiver or of an uninvolved observer; and evaluated to

293 ial signal transduction systems commonly use receiver (REC) domains, which regulate adaptive response

294 the dictator in the observer role as in the receiver role.

295 ver without requiring any corrections on the receiver's side.

296 eceivers, in turn, arise from changes in the receiver's subjective valuation of objects, ideas, and b

297 r the relative orientation of the source and receiver units is varied.

298 nk between a ground transmitter and a ground receiver via a moving unmanned-aerial-vehicle (UAV).

299 pes of signalling-incompetent DDR1 mutants ('receiver') with functional DDR1 ('donor') and demonstrat

300 on protocol which transmits the state to the receiver without requiring any corrections on the receiv