ライフサイエンスコーパス: curve

1 lusively from this water potential curve (WP curve).

2 the area under the fluorescence versus time curve.

3 26% decrease in dolutegravir area under the curve.

4 ero curve, and rise-and-fall to steady-state curve.

5 ing branches of the hysteretic magnetization curve.

6 used to identify the periods of the learning curve.

7 valuated by examination of a viral load-time curve.

8 ing a receiver operator characteristic (ROC) curve.

9 detect a 3% difference in the area under the curve.

10 dless of whether a cell is straight, bent or curved.

11 sing receiver operative characteristic (ROC) curves.

12 sing receiver operating characteristic (ROC) curves.

13 parameters from rapid fluorescence response curves.

14 le size provoke stretching of the relaxation curves.

15 mpirical gradients of access period response curves.

16 [Formula: see text] data such as the Keeling curve?

17 e to predict R1 resection in OLS (area under curve 0.712; 95% confidence interval 0.665-0.739) and in

18 interval 0.665-0.739) and in LLS (area under curve 0.724; 95% confidence interval 0.671-0.745).

19 under the receiver operating characteristic curve 0.83 [0.73-0.93]) had the best predictive value fo

20 compared to control samples (Area under the curve 0.851, ROC-analysis).

21 under the receiver operating characteristic curve 0.94 [0.88-0.99]) and SBFBT (area under the receiv

22 under the receiver operating characteristic curve (0.78 [95% CI 0.77-0.78]) than the logistic regres

23 vity and 61% specificity with area under the curve = 0.63 (95% confidence interval, 0.51-0.76) and P

24 criminative ability (adjusted area under the curve = 0.69) and calibration characteristics as baselin

25 s of preeclampsia in this cohort (area under curve = 0.975 +/- 0.020).

26 for predicting inducible VT (area under the curve, 0.81; P<0.001).

27 significant predictive value (area under the curve, 0.96; 95% CI, 0.95-0.97), best combination of sub

28 iver operating characteristic-area under the curve: 0.77; P = 0.001).

29 dicting symptomatic patients (area under the curve: 0.78 vs. 0.70; p = 0.01).

30 redict the presence of AS-CA (area under the curve: 0.86; 95% confidence interval: 0.78 to 0.94; p <

31 als in the validation cohort (area under the curve: 0.91 [95% CI 0.87-0.96] at baseline and 0.98 [95%

32 under the receiver operating characteristic curve=0.80 (95%CI 0.75-0.85)).

33 were mixtures of straight and "intermediate-curved" (100-300 nm diameter) in pH 7.5 solution and for

34 rategy applied to weekly data of the Keeling curve (1974-2020) gives an estimated rate of change whic

35 ET uptake and the slope of the time-activity curves (20-50 min after injection) were determined.

36 Here, atomically curved 2H-WS(2) nanosheets with precisely tunable strain

37 the Model 2 showed higher area under the ROC curve (82.2%, 95% CI 79.6%-84.7%) and good calibration.

38 uished PAH with 87% accuracy (area under the curve 95% confidence interval: 0.791-0.945) in model val

39 under the receiver operating characteristics curve (95% confidence interval) for the combined PIRO mo

40 rocessing, including relative area under the curve accumulation, time of maximum drug concentration,

41 The qualified calibration curve (accuracy within 8% and 2% for measuring degradati

42 c adaptation, forward suppression and tuning-curve adaptation, as well as the influence of PVs on fee

43 y and enable quantification without standard curves, after initial characterization of the parameters

44 Tradeoff curves allow for a visualized understanding on the effic

45 These buildup curves allow the confirmation of binding through a chang

46 sease, and receiver operating characteristic curve analyses indicated that amyloid-beta1-42 was equal

47 (HRs) and receiver operating characteristic curve analyses were performed.

48 ng, referred to as amplification and melting curve analysis (AMCA), which leverages the kinetic infor

49 Receiver operating characteristic (ROC) curve analysis demonstrated a CT-score cut-off of 14.5 t

50 By survival curve analysis median healing time for cure was 102 for

51 o evaluate the ssDNA pool size and remelting curve analysis of qPCR amplicons to monitor changes in p

52 Receiver operating characteristic curve analysis of these four miRNAs supported the discri

53 Specification curve analysis reveals that one finding is robust, one i

54 A ROC curve analysis was performed in the derivation cohort ac

55 Receiver-operating-characteristic curve analysis was used to determine the diagnostic accu

56 On decision curve analysis, CXR-LC had higher net benefit than CMS e

57 f noise and bias, we introduce specification curve analysis, which consists of three steps: (1) ident

58 bration) and clinical utility using decision curve analysis.

59 and NCC in discerning disease severity (ROC curve analysis: AUC = 0.746, P = 0.027) was superior to

60 and area under the receiving characteristics curves analysis.

61 ent survival was assessed using Kaplan-Meier curves analysis.

62 le features of the surface plasmon resonance curve and allows for a more precise analysis of the plas

63 Theta(2)) separating the three phases of the curve and corresponding slope values.

64 ading processes by "flattening" the epidemic curve and delaying the spread to geographically distant

65 li by small gull flocks followed a lognormal curve and gulls shed one strain >10(1) log10 CFU/g in th

66 mined using area under the receiver operator curve and model validated confirmed with and fivefold cr

67 the receiver operating characteristic (ROC) curve and the sensitivity with fixed specificities of 80

68 tructed using the freezing and vitrification curve and values characterizing the conditions of maximu

69 Kaplan-Meier curves and Cox proportional hazards models were used to

70 m cohort participants to estimate protection curves and decay trajectories.

71 ork, we modeled the fingerprints with Bezier curves and proposed a novel algorithm to detect and rest

72 We constructed receiver operating curves and reported performance in reference to the manu

73 aussian mixture models, Euler characteristic curves and texture analysis.

74 We calculated Kaplan-Meier curves and used adjusted Cox proportional-hazards models

75 ion exponential curve, rise-and-fall to zero curve, and rise-and-fall to steady-state curve.

76 e shape of the instantaneous current-voltage curve, and substituting one or two residues in the selec

77 ganizing map, extracting flow characteristic curves, and predicting flood hydrographs.

78 rominent label-free parameters: the full SPR curve angular and intensity shifts, we present how this

79 an inverse probability-weighted Kaplan-Meier curve applied after treating bacteremia as censoring eve

80 der the curve of a conventional Kaplan-Meier curve applied to the observed data was compared with tha

81 their Receiver Operator Characteristic (ROC) curves; Area Under Curve (AUC) and accuracy were calcula

82 ntage difference between the areas under the curves as well as the absolute percentage difference in

83 derived from the time-to-event Kaplan-Meier curve at 10 years was 0.64 (95% CI 0.58-0.69).

84 Finally, we constructed the torque-speed curve at various [ATP]s and discuss rotary models in whi

85 nnection between response and DOR makes this curve attractive for assessing the treatment effect.

86 delayed cerebral ischemia (DCI) (area under curve (AUC) > 0.750), and had better results than clinic

87 ssion tomography (PET) scans (area under the curve (AUC) = 0.87-0.91 for different brain regions).

88 under the receiver operating characteristic curve (AUC) analysis and diagnostic odds ratios against

89 ator Characteristic (ROC) curves; Area Under Curve (AUC) and accuracy were calculated and compared us

90 under the receiver operating characteristic curve (AUC) and differences between models were assessed

91 under the receiver operating characteristic curve (AUC) and the precision-recall curve (average prec

92 (ROC) analysis, with the area under the ROC curve (AUC) as a figure of merit in the task of distingu

93 under the receiver operating characteristic curve (AUC) differences and optimal thresholds were dete

94 under the receiver operating characteristic curve (AUC) for ADC, D(app), and K(app) to discriminate

95 OC analysis, the highest value of area under curve (AUC) for the GM/WM density ratio was found at the

96 under the receiver operating characteristic curve (AUC) for the image-based model was 0.73 (95% conf

97 We ascertained the area under the curve (AUC) in an analysis of waitlist mortality to find

98 pocket probing depth) had an area under the curve (AUC) of 0.694 (95% Confidence Interval: 0.612-0.7

99 creased significantly from an area under the curve (AUC) of 0.75 in larger LVs to 0.67 in smaller LVs

100 work reaches the average best area under the curve (AUC) of 0.883 across the 37 circRNA datasets when

101 rtion of a chest tube with an area under the curve (AUC) of 0.93 (95% confidence interval, 0.89-0.98)

102 ss validation with an average area under the curve (AUC) of 0.95, for the 8-class problem.

103 3.12%, sensitivity of 94.94%, area under the curve (AUC) of 0.9641, and specificity of 90% when teste

104 U while area-under plasma concentration-time curve (AUC) of 5FU-SLN(4) was 3.6 fold high compared wit

105 ance was scored using the Area Under the ROC Curve (AUC) statistic.

106 ons and for comparison of the area under the curve (AUC) versus that from other combined MRI predicto

107 Area under the receiver operating curve (AUC) was calculated in symptomatic versus asympto

108 h a reduced risk for hydrops [area under the curve (AUC), 0.93; 95% confidence interval (CI), 0.87-1.

109 ss for identifying GVFD using area under the curve (AUC), sensitivity, and specificity.

110 under the receiver operating characteristic curve (AUC), sensitivity, and specificity.

111 under the receiver operating characteristic curves (AUC) and partial AUC for the region of clinicall

112 under the receiver operating characteristic curves (AUC).

113 under the receiver operating characteristic curve [AUC] 0.91) and progression to diabetes (AUC 0.92)

114 us carboplatin (area under the concentration curve [AUC] 4, day 1) plus gemcitabine (1000 mg/m(2), da

115 dividuals with periodontitis (area under the curve [AUC] = 0.85 95% CI 0.78-0.92) and its moderate/se

116 nd neurofilament light chain (area under the curve [AUC] = 0.95).

117 under the receiver-operating characteristic curve [AUC]) for incident lung cancer than CMS eligibili

118 cally defined AD from non-AD (area under the curve [AUC], 0.89 [95% CI, 0.81-0.97]) with significantl

119 under the receiver operating characteristic curve [AUC], 0.97; 95% confidence interval [CI]: 0.94, 1

120 eive intravenous carboplatin (area under the curve [AUC]5 or AUC6) and intravenous paclitaxel (175 mg

121 The median area under the curve (AUC0-24 h) after day 7 and 14 were compared as we

122 tic regression analyses with areas under the curve (AUCs) as outputs were performed.

123 under the receiver operating characteristic curve (AUCs) increased from 0.900 (95% CI 0.843-0.957) a

124 under the receiver operating characteristic curve (AUCs) were 0.84 and 0.86 for predicting N1 or hig

125 ROC) and the area under the precision-recall curve (AUPRC) by 17% and 44% at the gene-level, respecti

126 proves the area under the receiver-operating curve (AUROC) and the area under the precision-recall cu

127 Area Under Receiver Operating Characteristic curve [AUROC] = 0.90, 95% confidence interval [CI]: 0.88

128 eristic curve (AUC) and the precision-recall curve (average precision [AP; average positive predictiv

129 iver operating characteristic area under the curve based on regional betweenness centrality was 0.88,

130 ating shape of photosynthetic light response curves but also by plant acclimation to dimming that gra

131 .004) and attenuated the 30 s area under the curve by 56 +/- 14% (n = 9; P < 0.0001).

132 where drug interactions and pharmacokinetic curves can be estimated from user-provided data or model

133 n individual, and the "common area under the curve (cAUC) kernel" to model the multi-feature CNV effe

134 tream of collection medium, is formed in the curved channel.

135 Analysis of the area under the curve confirmed the PIUKALL groups were significantly be

136 Cumulative response curves confirmed greater (P = 0.003) efficacy for G:G ho

137 nsity and a negative shift in the activation curve, consistent with S4-S5(L) stabilizing the open sta

138 ROC curves constructed to determine optimal size and attenua

139 measurement of the input-output recruitment curve, cortical silent period, and amplitude of the moto

140 Receiver operating characteristic curve demonstrated that STEV signal threshold cut-off of

141 Inactivation curves dependency on heating technology suggests specifi

142 of 1-500 muM DA, with two linear calibration curve, detection limit of 0.22 muM DA, and sensitivity o

143 elerated around age 70; average levels of BA curves differed by sex across the age span (50-90 years)

144 cipants underwent a modified diurnal tension curve (DTC) 1 week before the TSST, with 3 IOP measureme

145 We sought to estimate an exposure-response curve (ERC) and quantify between-study heterogeneity usi

146 From the prediction of curve estimation, in survivor group total CT score incre

147 y method performed over 140-fold faster than curve fitting, obtaining whole volume perfusion maps in

148 g affinity capture, we generated calibration curves (five-parameter logistic fit p < 0.05) by plottin

149 The CA hexamers are intrinsically curved, flexible and asymmetric, revealing the capsomere

150 s well as DeltaR(2)* peak and area under the curve for 30 and 60 seconds from DSC MRI were associated

151 Median daily steady-state areas under the curve for apixaban 5 mg twice daily were 5512 ng/(mL.h)

152 ing to the area under the concentration-time curve for C-peptide level in response to a 4-hour mixed-

153 The area under the curve for each image and patient were 0.82+/-0.01 and 0.

154 eatures and (2) difference in area under the curve for headache intensity scores.

155 The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary coh

156 e receiver operating characteristics (AUROC) curve for separation of patients with UC from healthy co

157 The area under the curve for the Functional Status Score for the ICU at ICU

158 Z-curve for trial sequential analyses of mortality associa

159 s examined by determining the area under the curve for viral RNA shedding using logistic regression a

160 e by using receiver operating characteristic curves for diagnostic performance and paired t tests for

161 measured real-time full-angular SPR response curves for G(s), G(q), and G(i) signaling pathways in li

162 have been developed to record stress-strain curves for thin proton-irradiated surface layers of SA-5

163 OX regression analyses and produces survival curves for variation-ceRNA events.

164 high-resolution R-T (10-70 degrees C range) curves for: (a) 62 evergreen species measured in two con

165 The values of AUC (area under curve) for all five hazards using the best models are gr

166 sed the 5-year time-dependent area under the curve from 0.68 to 0.81 (P=0.006).

167 ares mean [LSM]) of oral/IV 5-day area under curve from time 0 to last measurable concentration (AUCl

168 of common arthropods is complicated by their curved geometry.

169 all predictive of mortality (area under the curve >0.70), as were low albumin level, lymphocyte coun

170 ients (all area under the receiver operating curve >= 0.80).

171 under the receiver operating characteristic curve >= 0.84) and calibration.

172 under the receiver operating characteristic curve >= 0.85), but poor calibration.

173 under the receiver operating characteristic curve >= 0.9), doubles the estimated efficiency of popul

174 the need for using standards or calibration curves, herein we present a coulometric mass spectrometr

175 ments produce, called access period response curves (i.e., transmission success vs access period).

176 under the receiver operating characteristic curve in the external validation cohorts were 83.5% (95%

177 an overall receiver operating characteristic curve in the internal validation cohort of 0.76 [95% con

178 shion with the dependency significantly more curved in females compared to males.

179 r 1 h captured blood and tumor time-activity curves indicative of largely reversible uptake of (18)F-

180 summary of treatment efficacy based on this curve is also proposed.

181 pe of model-predicted incidence and fatality curves is consistent with observations from many jurisdi

182 unocapture followed by in-sample calibration curve (ISCC) strategy with multiple isotopologue reactio

183 nts developing CPC vs no-CPC (area under the curve), median (interquartile range): 5196 (1823-9061) v

184 up on all postoperative days [area under the curve: median (interquartile range) pg/mL: 3285 (1697-61

185 ntly been reported to be regulated by highly curved membranes.

186 her skeletal modifications, e.g. a ventrally curved mid to anterior presacral spine to hinder the dor

187 tive BP was calculated as the area under the curve (mm Hgxyears) from baseline up to year 30 examinat

188 The curved neuromorphic image sensor array is based on a het

189 he summary receiver-operating characteristic curve of 0.420 (I(2) = 8.5%).

190 athletes from controls with areas under the curve of 0.68 to 0.84.

191 sted Brier score of 0.114 and area under the curve of 0.735.

192 under the receiver operating characteristic curve of 0.76 (ranging from 0.73 to 0.80).

193 under the receiver operating characteristic curve of 0.760 (95% CI, 0.661-0.858; p < 0.0001), sensit

194 r the receiver operator characteristic (ROC) curve of 0.8.

195 a under the receiver operator characteristic curve of 0.80 +/- 0.04 before imperative cues when the d

196 under the receiver operating characteristic curve of 0.80.

197 el with an area under the receiver operating curve of 0.81 (95% confidence interval = 0.69-0.93).

198 under the receiver operating characteristic curve of 0.81.

199 With an area under the curve of 0.84 and 0.70, both (68)Ga-PSMA uptake (SUV(max

200 88.1% in our dataset with an AUC at the ROC curve of 0.85, whereas the accuracy values in the datase

201 under the receiver operating characteristic curve of 0.86 (sensitivity, 67%; specificity, 89%) (P <

202 9) and F2-F4 fibrosis with an area under the curve of 0.88 (95% confidence interval, 0.80-0.95).

203 under the receiver operating characteristic curve of 0.89 (95% confidence interval: 0.82, 0.96) and

204 area under receiver operator characteristic curve of 0.93 (95% CI, 0.86 to 1.00; sensitivity = 94.4%

205 ents with NAS 5-8/LCI with an area under the curve of 0.95 (95% confidence interval, 0.91-0.99) and F

206 under the received operating characteristic curve of 0.95 (95% confidence interval, 0.92-0.98).Concl

207 0.96) and an area under the precision-recall curve of 0.96 (95% confidence interval: 0.93, 0.99).

208 le), plus either carboplatin (area under the curve of 4.5 mg/mL per min administered intravenously) o

209 ncrease of area under the receiver-operating curve of 5.4 percentage points (P <= 1.0 x 10(-9)).

210 The area under the curve of a conventional Kaplan-Meier curve applied to th

211 T3 C18 column, with an external calibration curve of excellent linearity, and a low limit of detecti

212 The learning curve of experienced pancreatic surgeons for PAR was 15

213 gent interventions are needed to flatten the curve of HF in South Asia.

214 The area under the curve of highest measured optic disc elevation to detect

215 er the optimized conditions, the calibration curve of ketamine at buffer solution (pH 12) exhibits a

216 here is currently no data about the learning curve of L-LLS.

217 The area under the curve of the models exceeded 0.85 in all age groups in t

218 under the receiver operating characteristic curves of 0.57 (95% CI, 0.45-0.68), 0.58 (95% CI, 0.47-0

219 under the receiver operating characteristic curves of at least 0.84 and 0.72 in the training and val

220 The adjusted AUROC curves of cpCD for discriminating between healthy and gl

221 that determine the antibiotic dose-response curves of Escherichia coli strains, and previous observa

222 Dose response curves of ML385, an NRF2 inhibitor, showed that ischemia

223 From the dose-response curves of mortality data created as a function of variou

224 under the receiver-operating characteristic curves of obstructive CAD: for the PTP model, 72 (95% co

225 ecessary for reproducing experimental growth curves of the baker's yeast Saccharomyces cerevisiae gro

226 and was highly correlated with the learning curves of the mice.

227 The I-V curves of this single-molecule system also exhibit memri

228 rrogate marker for the target area under the curve over 24 hours to minimum inhibitory concentration

229 and fatty acids generally followed U-shaped curve patterns across gestation.

230 for the phenotype measured in Area Under the Curve: random forest (0.782), XGBoost (0.781), support v

231 The GRS results in an area under the ROC curve ranging between 0.64 and 0.72, within European and

232 et reveal high accuracy with areas under ROC curve ranging from 0.842 to 0.894.

233 We find that this curve reconstruction problem is analogous to solving the

234 Doubling time estimates, dose response curve regression, and comparison analyses were performed

235 some outstanding questions of these CumB-TL curves related to their sensitivity to various drivers b

236 Multivariate curve resolution (MCR) analysis of Raman spectra can be

237 Multivariate curve resolution analyses of the one and two-dimensional

238 processing method, based on the multivariate curve resolution approach (MCR), to denoise 2D solid-sta

239 Multivariate curve resolution-alternating least-squares (MCR-ALS) is

240 under the receiver operating characteristics curve revealed that PGSs predicted patient survival more

241 Time-activity curves revealed a steady accumulation of tumor radioacti

242 : the straight line, association exponential curve, rise-and-fall to zero curve, and rise-and-fall to

243 The receiver operating curves (ROCs) were plotted for every measurement and con

244 thogen Vibrio cholerae typically exists as a curved rod, but straight rods have been observed under c

245 Two diagonally opposed paralimbal, curved self-sealing scleral pockets were made 3 mm away

246 , accuracy, precision, recovery, calibration curve, sensitivity, reproducibility, and stability param

247 for the same d and bias, with very different curve shape and much earlier onset with bias.

248 e course data usually conform to one of four curve shapes: the straight line, association exponential

249 Child growth Victora curves show improvements in height-for-age z-scores (HAZ

250 The ROC curve showed the optimum cut-off point at 0.628 x 10(-3)

251 for training, the validation area under the curve significantly decreased (0.47).

252 d, more generally, two-dimensional fluids on curved substrates.

253 lly valuable in situations, like growth on a curved surface, where the resulting long-ranged, elastic

254 a difference in metal-carbon bonding at the curved surfaces as confirmed by density functional theor

255 topy, that is, glomeruli with similar tuning curves tend to be located in spatial proximity in the dO

256 perating characteristic and precision-recall curves than PPI networks for both zebrafish and mouse.

257 circulation time (17-fold higher area under curve) than the free agent, allowing increased opportuni

258 cokinetic profiles and multidrug interaction curves that are estimated from data.

259 dings, receptive field maps, and sensitivity curves that show a hair bundle is desensitized by effere

260 oblem by deriving formulae for the empirical curves that these experiments produce, called access per

261 According to the ROC curve, the learning curve was completed after 25 procedu

262 ithm tracks whiskers, by fitting a 3D Bezier curve to the basal section of each target whisker.

263 By normalizing existing time-activity curves to a single measurement time, it is possible to c

264 sed on inverse probability-weighted survival curves to address this limitation.

265 Fittings of NMR and ITC binding curves to the Hill model yielded n(Hill) ~2.9, near maxi

266 he proposed concepts of "copy number profile curves" to describe the CNV profile of an individual, an

267 liver resections necessitate a long learning curve trough a stepwise fulfillment of difficulties.

268 Peak quality assessment and calibration curves using GC-qMS and GCxGC-qMS/FID document the trans

269 n coefficient of 0.803 and an area under the curve value of 0.963, and outperforming previous state-o

270 erventions (surface under cumulative ranking curve values [SUCRCV]: 95.6%, standardized mean differen

271 performances by having higher area under the curve values for receiver operating characteristic and p

272 istent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the AD

273 red by the area under the receiver operating curve was 0.965 for both LPA and FPL, with a sensitivity

274 the receiver operating characteristic (ROC) curve was 0.97 in the de novo cross-validation when eval

275 under the receiver operating characteristic curve was assessed to compare prognostic accuracy betwee

276 According to the ROC curve, the learning curve was completed after 25 procedures.

277 different scavenging capacity, a calibration curve was established between the color decay rate and t

278 A calibration curve was linear from 0.1 to 15 g L(-1) for glucose and

279 The calibration curve was linear over the range of 5-1000 ng g(-1) with

280 00 CFU/ml and theoretically using a standard curve, was 2 CFU/ml.

281 andle long plateaus in the tails of survival curves, we also exploited "cure models" to estimate the

282 under the receiver operating characteristic curve were 0.94 (95% CI: 0.93, 0.96), 0.91 (95% CI: 0.89

283 proportional-hazards model and Kaplan-Meier curve were used to evaluate the association of covariate

284 The areas under the ROC curves were 0.89 (95% CI: 0.84-0.95) for global RNFLT an

285 The negative predictive value curves were also superimposable but remained flat betwee

286 -matched calibration was used and analytical curves were estimated by weighted least squares regressi

287 Regional time-activity curves were generated for 14 regions of interest.

288 Survival curves were generated using Kaplan-Meier method, and com

289 rossflow protein concentration, flux decline curves were studied according to Hermia's fouling mechan

290 Receiver-operating characteristic (ROC) curves were then calculated to determine the optimum cut

291 Age-specific 10-year cumulative risk curves were used to draw conclusions about how many year

292 Or35a) showed a shift in their dose-response curves when Orco was co-integrated, reflecting an increa

293 Our analysis shows that the dose-response curve with the FSS data clearly differs from that with t

294 D) were analyzed using Kaplan-Meier survival curves with log-rank test and Cox regression.

295 e (about 2.5), we reproduce actual infection curves with remarkable precision, without fitting or fin

296 k and are in some cases able to resolve melt curves with single-nucleotide resolution.

297 exhibits an "inverted U-shaped dose-response curve" with increasing cAMP production at low doses of T

298 is a new feature of the IntCal20 calibration curve, with far-reaching impacts for scientific communit

299 r loss exclusively from this water potential curve (WP curve).

300 Together these master curves yield a trajectory universal to particles with a