ライフサイエンスコーパス: receiver

1 irect manipulation of the signal stimulus or receivers.

2 of sexual communication in both senders and receivers.

3 a millimeter-sized piezoelectric ultrasonic receiver, a rectifier circuit, and a pair of platinum el

4 and relative orientation between emitter and receiver and evaluate it in a sample of 401 realistic hu

5 signals is shaped by the sensory systems of receivers and the habitat conditions under which signals

6 l DoA estimators where multiple antennas and receivers are classically required.

7 e-of-the-art cooled and room-temperature THz receivers based on low-noise amplifiers and mixers, prov

8 how that upconversion-based room-temperature receivers can outperform state-of-the-art cooled and roo

9 ding feedback loops or morphogen cascades to receiver cell response circuits.

10 nal, teasing apart the effects of signal and receiver characteristics.

11 ns of these results as they relate to signal-receiver coevolution, mate choice, and reproductive isol

12 om the transmit coil to the resonant-coupled receiver coil with an efficiency of 73% for a 5 cm dista

13 consisting of ultrasound emitter, ultrasound receiver, data acquisition and wireless transmitter, has

14 sphorylation on a conserved aspartate in the receiver domain of the type-A ARRs.

15 The analysis shows that the upconversion receiver is quantum limited like conventional amplifiers

16 f KLK8 in CSF and blood was determined using receiver operating characteristic (ROC) analyses and com

17 Receiver operating characteristic (ROC) analysis along w

18 Receiver operating characteristic (ROC) analysis was per

19 Receiver operating characteristic (ROC) analysis, odds r

20 er diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis, with t

21 nd the threshold ADC values were computed by receiver operating characteristic (ROC) analysis.

22 Receiver operating characteristic (ROC) curve analysis d

23 parameter, we calculated the area under the receiver operating characteristic (ROC) curve and the se

24 The area under the receiver operating characteristic (ROC) curve was 0.97 i

25 The area under the receiver operating characteristic (ROC) curves (AUCs) on

26 Receiver operating characteristic (ROC) curves analysis

27 redictive performance was assessed using the receiver operating characteristic (ROC) curves and area

28 erformance over existing methods in terms of receiver operating characteristic (ROC) curves in high-d

29 cant slopes of SD OCT change was assessed by receiver operating characteristic (ROC) curves.

30 Diagnostic accuracy was evaluated using Receiver Operating Characteristic (ROC) curves.

31 es and optimal thresholds were calculated by receiver operating characteristic (ROC) curves.

32 GRS in SLE risk prediction was evaluated by receiver operating characteristic (ROC) curves.

33 Models were evaluated using receiver operating characteristic (ROC) curves.

34 rimination markers between CAP and AECOPD in receiver operating characteristic analyses, with an area

35 correct top diagnosis (TDx), as well as with receiver operating characteristic analyses.

36 Receiver operating characteristic analysis confirmed tha

37 Receiver operating characteristic analysis identified op

38 s in patients of all ages with cystinosis; a receiver operating characteristic analysis ranked chitot

39 Receiver operating characteristic analysis revealed comb

40 Receiver operating characteristic analysis showed an are

41 Receiver operating characteristic analysis showed that t

42 Further, receiver operating characteristic analysis shows that ou

43 We performed receiver operating characteristic analysis to define opt

44 Receiver operating characteristic analysis was performed

45 A Mann-Whitney U test and receiver operating characteristic analysis was performed

46 Receiver operating characteristic analysis was used to d

47 Receiver operating characteristic analysis was used to e

48 Receiver operating characteristic analysis with logistic

49 ht independent observers were measured using receiver operating characteristic analysis, linearly wei

50 Independent predictors were assessed through receiver operating characteristic analysis, time-series

51 formance was analyzed by using free-response receiver operating characteristic analysis.

52 in risk starting at 3 days as determined by receiver operating characteristic analysis.

53 model was described using the area under the receiver operating characteristic and average precision

54 Receiver operating characteristic and net reclassificati

55 aving higher area under the curve values for receiver operating characteristic and precision-recall c

56 clinical variables yielded a cross-validated receiver operating characteristic area under the curve (

57 Model performance was evaluated using receiver operating characteristic area under the curve (

58 The receiver operating characteristic area under the curve b

59 rentiated AD from both clinically diagnosed (receiver operating characteristic area under the curve o

60 ated good discrimination (all area under the receiver operating characteristic curve >= 0.84) and cal

61 xcellent discrimination (both area under the receiver operating characteristic curve >= 0.85), but po

62 orizons up to 8 years of age (area under the receiver operating characteristic curve >= 0.9), doubles

63 emia had significantly higher area under the receiver operating characteristic curve (0.78 [95% CI 0.

64 An receiver operating characteristic curve (area under the

65 nce of CT was estimated using area under the receiver operating characteristic curve (AUC) analysis a

66 Performance was measured by area under receiver operating characteristic curve (AUC) analysis.

67 Area under the receiver operating characteristic curve (AUC) and area u

68 rformance was assessed by the area under the receiver operating characteristic curve (AUC) and differ

69 rmance was evaluated based on area under the receiver operating characteristic curve (AUC) and label

70 nesses were calculated as the area under the receiver operating characteristic curve (AUC) and Pearso

71 the SJLIFE cohort) using the areas under the receiver operating characteristic curve (AUC) and the pr

72 Area under the receiver operating characteristic curve (AUC) difference

73 Area under the receiver operating characteristic curve (AUC) for ADC, D

74 sensitivity, specificity and area under the receiver operating characteristic curve (AUC) for the di

75 The area under the receiver operating characteristic curve (AUC) for the im

76 acetylneuraminate achieved an area under the receiver operating characteristic curve (AUC) of 0.66 at

77 initial study visits with an area under the receiver operating characteristic curve (AUC) of 0.71 an

78 on, the algorithm achieved an area under the receiver operating characteristic curve (AUC) of 0.84 (8

79 d approach achieved values of area under the receiver operating characteristic curve (AUC) of 0.89 (9

80 nt prediction of HAD with the area under the receiver operating characteristic curve (AUC) of 89.4% i

81 .1 v > 13.1), as indicated by area under the receiver operating characteristic curve (AUC) values of

82 .64), respectively, and with areas under the receiver operating characteristic curve (AUC) values of

83 OCT parameters with the best area under the receiver operating characteristic curve (AUC) were deter

84 mparisons were made using the area under the receiver operating characteristic curve (AUC), a measure

85 ance was summarized using the area under the receiver operating characteristic curve (AUC), calculate

86 Area under the receiver operating characteristic curve (AUC), sensitivi

87 Metrics included area under the receiver operating characteristic curve (AUC), sensitivi

88 s smaller than 50 mm by using area under the receiver operating characteristic curve (AUC).

89 y and the DeLong test for the area under the receiver operating characteristic curve (AUC).

90 essed independently using the area under the receiver operating characteristic curve (AUC).

91 all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for th

92 Areas under the receiver operating characteristic curve (AUCs) for a com

93 ients; 10 782 foci), the CNN areas under the receiver operating characteristic curve (AUCs) for deter

94 PDAC stage I-II samples, the areas under the receiver operating characteristic curve (AUCs) increased

95 , and 3) performed well with areas under the receiver operating characteristic curve (AUCs) of 0.91,

96 Areas under the receiver operating characteristic curve (AUCs) were 0.84

97 The odds ratios and areas under the receiver operating characteristic curve (AUCs) were high

98 ctor and their corresponding areas under the receiver operating characteristic curve (AUCs) were obta

99 ted failed revascularization: area under the receiver operating characteristic curve (AUROC) 0.95, ca

100 ted NASH with cross-validated area under the receiver operating characteristic curve (AUROC) = 0.73,

101 s collected from DrugCentral [area under the receiver operating characteristic curve (AUROC) = 0.868]

102 y score <3) was assessed by using area under receiver operating characteristic curve (AUROC) analysis

103 Models were evaluated on area under the receiver operating characteristic curve (AUROC) and area

104 4% accuracy and 0.95 +/- 0.02 area under the receiver operating characteristic curve (AUROC).

105 covered mutual targets for drugs [area under Receiver Operating Characteristic curve (AUROC)=0.75] an

106 0.94 [0.88-0.99]) and SBFBT (area under the receiver operating characteristic curve 0.83 [0.73-0.93]

107 emia had high discrimination (area under the receiver operating characteristic curve 0.88 [95% CI 0.8

108 DeltaSBF/DeltaT (area under the receiver operating characteristic curve 0.94 [0.88-0.99]

109 tablets' front-facing camera (area under the receiver operating characteristic curve = 0.78).

110 dent predictors of phenotype (area under the receiver operating characteristic curve = 0.95).

111 ion according to maximum SUV (area under the receiver operating characteristic curve = 100%; 95% conf

112 : 99%, 100%) and minimum ADC (area under the receiver operating characteristic curve = 98%; 95% CI: 9

113 pproach was predictive of IA (area under the receiver operating characteristic curve [AUC] 0.91) and

114 ed reasonable discrimination (area under the receiver operating characteristic curve [AUC] = 0.71) an

115 cy to define disease by mPAP (area under the receiver operating characteristic curve [AUC], 0.78) and

116 th the discovery cohort (mean area under the receiver operating characteristic curve [AUC], 0.89; 95%

117 pes in the validation cohort (area under the receiver operating characteristic curve [AUC], 0.95; 95%

118 high diagnostic performance (area under the receiver operating characteristic curve [AUC], 0.97; 95%

119 tion of the objective tools (SORT Area Under Receiver Operating Characteristic curve [AUROC] = 0.90,

120 inadequate for clinical use (area under the receiver operating characteristic curve [AUROC], < 0.8),

121 Receiver operating characteristic curve analyses for the

122 ic and non-amnestic Alzheimer's disease, and receiver operating characteristic curve analyses indicat

123 Hazard ratios (HRs) and receiver operating characteristic curve analyses were pe

124 Receiver operating characteristic curve analysis for cal

125 Receiver operating characteristic curve analysis of the

126 Receiver operating characteristic curve analysis of thes

127 A receiver operating characteristic curve analysis showed

128 Performance was evaluated by area under the receiver operating characteristic curve analysis, sensit

129 ual to 100 Gy (best cut-off according to the receiver operating characteristic curve and median tumor

130 nce without and with PRSs via area under the receiver operating characteristic curve and net reclassi

131 predictive performances with areas under the receiver operating characteristic curve and precision re

132 classification had a greater area under the receiver operating characteristic curve and reclassified

133 Specifically, receiver operating characteristic curve areas (AUC) for

134 Receiver operating characteristic curve demonstrated tha

135 Area under the receiver operating characteristic curve for Ct vs positi

136 sIn DS1, population-adjusted areas under the receiver operating characteristic curve for pneumothorax

137 The area under the receiver operating characteristic curve for renal resist

138 The median (SD) of the area under the receiver operating characteristic curve for the natural

139 The MSKCC model receiver operating characteristic curve had a predictive

140 Receiver operating characteristic curve identified >15.5

141 characteristics by achieving area under the receiver operating characteristic curve improvements of

142 ements and Main Results: The areas under the receiver operating characteristic curve in the external

143 ing-based genetic algorithm, with an overall receiver operating characteristic curve in the internal

144 rformance was assessed by the area under the receiver operating characteristic curve in the validatio

145 and procedures yielded a mean area under the receiver operating characteristic curve of 0.76 (ranging

146 enous thromboembolism with an area under the receiver operating characteristic curve of 0.760 (95% CI

147 %, specificity 71.1%, with an area under the receiver operating characteristic curve of 0.80.

148 as COVID-19 pneumonia with an area under the receiver operating characteristic curve of 0.81.

149 (<=1 year or >1 year) with an area under the receiver operating characteristic curve of 0.86 (sensiti

150 2 years corrected age with an area under the receiver operating characteristic curve of 0.86, 0.66 an

151 f the clinical trial, with an area under the receiver operating characteristic curve of 0.86.Conclusi

152 a median discriminating power area under the receiver operating characteristic curve of 0.883 (95% CI

153 rithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI,

154 ier yielded a cross-validated area under the receiver operating characteristic curve of 0.89 (95% con

155 88) on the test data, with an area under the receiver operating characteristic curve of 0.91 (95% CI:

156 %, specificity 88.7%, with an area under the receiver operating characteristic curve of 0.91.

157 l validation datasets with an area under the receiver operating characteristic curve of 0.912 (95% CI

158 8%, specificity 93.9%, and an area under the receiver operating characteristic curve of 0.93 in the t

159 , classified tumor type with areas under the receiver operating characteristic curve of 0.94 (95% con

160 h in advance, resulting in an area under the receiver operating characteristic curve of 0.94 and an a

161 those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI:

162 The area under the curve of the receiver operating characteristic curve of the highest e

163 The area under the receiver operating characteristic curve of the LUCK clas

164 The area under the receiver operating characteristic curve of the model was

165 The area under the receiver operating characteristic curve was 0.88 (0.86-0

166 The area under the receiver operating characteristic curve was assessed to

167 With ChestX-ray14, areas under the receiver operating characteristic curve were 0.94 (95% C

168 sensitivity, specificity, and area under the receiver operating characteristic curve were 83% (95% co

169 is, Kaplan-Meier curves, and cross-validated receiver operating characteristic curve with area under

170 g lipid-poor angiomyolipomas (area under the receiver operating characteristic curve, >0.9), indicati

171 ity = 41%, specificity = 88%, area under the receiver operating characteristic curve, 0.64).

172 the hysteresis ratio was 28% (area under the receiver operating characteristic curve, 0.80; 95% CI, 0

173 ity = 77%, specificity = 97%, area under the receiver operating characteristic curve, 0.87) than the

174 n ejection fraction <50%, the area under the receiver operating characteristic curve, accuracy, sensi

175 erformance is presented as an area under the receiver operating characteristic curve.

176 y: 92.9%; sensitivity: 67.1%; area under the receiver operating characteristic curve: 0.83; p < 0.000

177 e days of hospital admission (area under the receiver operating characteristic curve=0.80 (95%CI 0.75

178 The area under the receiver operating characteristic curves (AUC) and parti

179 s was determined by comparing area under the receiver operating characteristic curves (AUC).

180 s assessed by calculating the area under the receiver operating characteristic curves (AUC).

181 mepoints were evaluated using area under the receiver operating characteristic curves (AUROCs).

182 pic asthma with reporting of areas under the receiver operating characteristic curves as a measure of

183 The area under the receiver operating characteristic curves for coprevalent

184 Comparisons were made by using receiver operating characteristic curves for diagnostic

185 age groups for serological monitoring using receiver operating characteristic curves for different e

186 was poor with respectively an area under the receiver operating characteristic curves of 0.57 (95% CI

187 nonatopic participants with areas under the receiver operating characteristic curves of at least 0.8

188 and external validation, the area under the receiver operating characteristic curves of the DLA with

189 nt Method for ICU resulted in area under the receiver operating characteristic curves that were not s

190 Receiver operating characteristic curves were used to de

191 Using receiver operating characteristic curves, we obtained op

192 ography as gold standard, were defined using receiver operating characteristic curves.

193 by using jackknife alternative free-response receiver operating characteristic figure of merit (FOM)

194 isting of 17 variables had an area under the receiver operating characteristic of 0.80 (95% CI, 0.78-

195 ing of eight variables had an area under the receiver operating characteristic of 0.96 (95% CI, 0.91-

196 the survival scatter plot, the hazard ratio receiver operating characteristic, the area between curv

197 th a highly significant relationship to UOC (Receiver operating characteristic-area under the curve:

198 e (PPV) (63.5%), and area under the curve of receiver operating characteristics (AUC ROC) (0.978).

199 The area under the receiver operating characteristics (AUROC) curve for sep

200 nd Mehralivand EPE score were compared using receiver operating characteristics (ROC) and decision cu

201 Moreover, the receiver operating characteristics (ROC) curve analyses

202 In the validation dataset, the receiver operating characteristics (ROC) were compared b

203 Furthermore, receiver operating characteristics analyses revealed tha

204 Receiver operating characteristics analysis correctly as

205 We assessed discriminatory performance by receiver operating characteristics and tumour extent pre

206 The areas under the receiver operating characteristics curve (95% confidence

207 on the entire dataset provided an area under receiver operating characteristics curve (AUC) with 95%

208 Moreover, in the receiver operating characteristics curve analyses tear o

209 RC as three input variables, the area under receiver operating characteristics curve for predicting

210 The AI achieved an area under the receiver operating characteristics curve of 0.997 (95% C

211 Analysis of area under the receiver operating characteristics curve revealed that P

212 The area under receiver operating characteristics curve was 0.77 (95% c

213 The respective areas under the receiver operating characteristics curves for the cluste

214 of the patients with sCD14 levels above the receiver operating characteristics cutoff were deceased

215 Receiver operating characteristics set pressure threshol

216 e in all sepsis patients (all area under the receiver operating curve >= 0.80).

217 Area under the receiver operating curve (AUC) was calculated in symptom

218 compared with the ICH Score (area under the receiver operating curve [AUROC], German cohort: 0.81 [0

219 distinguish responders from non-responders (receiver operating curve area under the curve for region

220 e of 30-day mortality with an area under the receiver operating curve of 0.7933 (95% CI 0.745-0.841).

221 aseline using a model with an area under the receiver operating curve of 0.81 (95% confidence interva

222 The area under the receiver operating curve was 0.78 (95% CI, 0.70-0.86).

223 The area under the receiver operating curve was 0.83 (0.71-0.90).

224 c accuracy as measured by the area under the receiver operating curve was 0.965 for both LPA and FPL,

225 The area under the curve of the receiver operating curve was 82.5% (95% CI 73.9% to 91%)

226 The test area under the receiver operating curves (AUCs) of the two models were

227 The receiver operating curves (ROCs) were plotted for every

228 We constructed receiver operating curves and reported performance in re

229 Receiver operating curves quantified the discrimination

230 Using receiver operating curves, the combination of preoperati

231 n, multiple regression models and area under receiver-operating characteristic (AUROC) curves were us

232 Receiver-operating characteristic (ROC) curves were then

233 Receiver-operating characteristic analysis demonstrated

234 el had better discrimination (area under the receiver-operating characteristic curve [AUC]) for incid

235 Receiver-operating characteristic curve analysis demonst

236 al intelligence algorithm were quantified by receiver-operating characteristic curve analysis.

237 equirement by calculating the area under the receiver-operating characteristic curve and by classific

238 The area under the receiver-operating characteristic curve for CT-FFR was 0

239 8.6%), with a partial area under the summary receiver-operating characteristic curve of 0.420 (I(2) =

240 8.3%), with a partial area under the summary receiver-operating characteristic curve of 0.686 (I(2) =

241 2-0.817) versus radiologist's area under the receiver-operating characteristic curve of 0.698 (0.646-

242 ithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.737 (0.659-

243 ithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.740 (0.662-

244 9-0.815) versus radiologist's area under the receiver-operating characteristic curve of 0.779 (0.723-

245 or 30-day oxygen requirement (area under the receiver-operating characteristic curve, 0.84; 95% CI, 0

246 30-day intubation/mortality (area under the receiver-operating characteristic curve, 0.86; 95% CI, 0

247 infected persons, assessed by area under the receiver-operating characteristic curve, exceeded 85%.

248 l, and markedly increased the area under the receiver-operating characteristic curves of obstructive

249 f 0.779 (0.723-0.836), diagnostic metrics of receiver-operating characteristic operating points did n

250 accuracy) were calculated based on different receiver-operating characteristic operating points.

251 6-0.749) with similar diagnostic metrics for receiver-operating characteristic operating points.

252 dex were analyzed using Pearson coefficient, receiver-operating characteristics analysis and by univa

253 ansferase showing the highest area under the receiver-operating characteristics curve (0.84).

254 Receiver-operating characteristics curve analyses were u

255 t algorithms and improves the area under the receiver-operating curve (AUROC) and the area under the

256 l, with a minimum increase of area under the receiver-operating curve of 5.4 percentage points (P <=

257 rison with young-control SUV ratios (SUVRs), receiver-operating-characteristic (ROC) curves based on

258 eters to predict outcome were established by receiver-operating-characteristic analyses using a media

259 curacies of PET parameters were evaluated by receiver-operating-characteristic analyses using the cli

260 of (18)F-FET PET parameters was evaluated by receiver-operating-characteristic analysis and chi(2) te

261 their ability to predict amyloid status in a receiver-operating-characteristic analysis and validated

262 sis of volumes of interest and examined with receiver-operating-characteristic analysis to determine

263 Receiver-operating-characteristic analysis was performed

264 Receiver-operating-characteristic analysis was used to d

265 At receiver-operating-characteristic analysis, an (18)F-FDG

266 sed to estimate the area under the localized receiver-operating-characteristic curve (ALROC).

267 Receiver-operating-characteristic curve analysis was use

268 were highly accurate, with an area under the receiver-operating-characteristic curve of more than 0.9

269 Areas under receiver-operating-characteristic curves were calculated

270 sensitivity, specificity, and area under the receiver-operating-curve (AUC).

271 of the studied parameters were derived using receiver operative characteristic (ROC) curves.

272 idization (FISH) analyses when analyzed with Receiver Operative Characteristics analysis (ROC) respec

273 onably high internal validation performance (receiver operator characteristic > 0.7).

274 Performance parameters were determined, and receiver operator characteristic (ROC) analysis was used

275 Receiver operator characteristic (ROC) curve analysis wa

276 and MCC respectively, with an area under the receiver operator characteristic (ROC) curve of 0.8.

277 value is automatically optimized by using a receiver operator characteristic (ROC) curve.

278 The area under the receiver operator characteristic (ROC) curves (Az value)

279 e standard grading system by analysing their Receiver Operator Characteristic (ROC) curves; Area Unde

280 The area under the receiver operator characteristic curve for HU lumen was

281 d of their execution, with an area under the receiver operator characteristic curve of 0.80 +/- 0.04

282 t-risk cirrhotic patients with an area under receiver operator characteristic curve of 0.93 (95% CI,

283 d in predictive modeling, and area under the receiver operator characteristic curves (AUC) and accura

284 tilized as gold standards to generate serial receiver operator characteristics (ROC) curves to assess

285 The Area Under Receiver Operator Characteristics Curves (AUROC curves)

286 accuracy was determined using area under the receiver operator curve and model validated confirmed wi

287 The area under the receiver operator curve was 0.78 (95% CI: 0.77-0.80).

288 We generated receiver-operator characteristic (ROC) curves for the fu

289 that reflects the intrinsic perspective of a receiver or sender of a single symbol, who has no access

290 supplemented with 12-OPDA or KODA increased receiver plant resistance in a dose-dependent manner, wi

291 the numbers of disseminator's followers and receiver's followees.

292 non-attended signals arrive unaligned to the receiver's oscillation, reducing signal transfer.

293 However, this type of receiver shows advantages as a THz photon counter, where

294 a sender stimulation site communicates with receiver sites.

295 altering relative lengths of the sender and receiver slugs.

296 the transfer front of dynamic contact among receiver substrate, liquid, and film, and can be well co

297 ntrolled by a selectable motion direction of receiver substrates at a high speed.

298 ls multiple senders transmitting to a single receiver, such as the uplink from many mobile phones to

299 In a network of mobile receivers using narrow directional beams, how do the nod

300 models of UFP and BC monitors as well as GPS receivers were sampled within 140 m of each other for 2