ライフサイエンスコーパス: parametric

1 estimation on hazard and survival time, the parametric Accelerated Failure Time model was chosen to

2 of action observation and motor imagery to a parametric action space containing 25 stick-figure blend

3 We identified 117 datasets and fit parametric age distributions to each country dataset and

4 experimentally demonstrate high-gain optical parametric amplification using USRN, which is compositio

5 synthesiser based on a mid-infrared optical parametric amplifier and its application to high-harmoni

6 ng Quantum Interference Filters (SQIFs), and parametric amplifiers for quantum information systems.

7 ly based on inefficient, multi-stage optical parametric amplifiers or optical parametric chirped puls

8 wer than the frequently used travelling-wave parametric amplifiers(11).

9 ct of noise on accuracy and precision of the parametric analyses of dynamic (18)F-FLT PET/CT to asses

10 d serve as predictors in machine learning or parametric analyses of the following scenarios: (A) Homo

11 Here, we employ a non-parametric analysis framework to analyze seasonal hydroc

12 Moreover, a parametric analysis is presented in order to fit the exp

13 Parametric analysis of release characteristics of CRFs i

14 Based on a parametric analysis of synthesis conditions, we postulat

15 d ensuing behavior, we performed a multifold parametric analysis.

16 A parametric analytical model is developed to estimate the

17 For regional correlation of parametric and functional measures, the left ventricle w

18 However, as the dataset was found to be non-parametric and heteroscedastic, paired with unequal samp

19 Working within a causal framework and using parametric and nonparametric estimation techniques, the

20 Using parametric and nonparametric tests, we determined the to

21 paper, we propose computationally efficient parametric and semiparametric tests based on a set of sp

22 Here we present a non-parametric approach based on Fisher Information which ob

23 ideas on a toy example, and the power of the parametric approach to TDA modeling in an analysis of co

24 d to detect unmeasured confounding using non-parametric approach.

25 resented here can be readily applied to dual-parametric assays for various targets.

26 distance and contact probability without any parametric assumption.

27 Furthermore, our test does not rely on parametric assumptions and can be used to validly assess

28 ispectral optical signatures provide a multi-parametric bacteria identification at an exceptionally h

29 are performances of conventional statistical parametric (based on Nakagami distribution) and entropy

30 lak analysis, the R(2) between NLR-based and parametric-based (Patlak) tumor Ki was 0.95 (slope, 0.71

31 There was no bias between NLR and parametric-based Ki values.

32 This novel non-parametric Bayesian approach is demonstrated on a variet

33 ent method for pseudotime inference with non-parametric Bayesian clustering methods, efficient Markov

34 me Atlas (TCGA) and developed iDriver, a non-parametric Bayesian framework based on multivariate stat

35 zation, a viewpoint compatible both with non-parametric Bayesian modelling and with sub-symbolic meth

36 Parametric binding images of (18)F-MK-9470 were correcte

37 tatistical analyses were performed using non-parametric bivariate or multivariable logistic regressio

38 Here we use a parametric bootstrap model to estimate rho for the ocula

39 We then utilize parametric bootstrap to conduct differential expression

40 Inference was calculated using the non-parametric bootstrap.

41 the performance of reconstruction tools, for parametric bootstrapping and for detecting data outliers

42 We used non-parametric bootstrapping and multilevel random effects m

43 cremental costs per life-year saved, and non-parametric bootstrapping was done.

44 Conclusion: The use of parametric BP(ND) images for visual assessment of (18)F-

45 We used multi-parametric cardiovascular magnetic resonance (CMR) mappi

46 Moreover, parametric changes to an object's skeleton led to propor

47 s mass-produced, low-cost, disposable, multi-parametric chemical sensing diagnostic platforms.

48 chirped pulse amplifier (CPA) or an optical parametric chirped pulse amplifier (OPCPA) for achieving

49 age optical parametric amplifiers or optical parametric chirped pulse amplifiers pumped by femtosecon

50 expression test with a permutation-based non-parametric combination methodology, we identified 149 di

51 nthetic flexible scaled armor analogue using parametric computational modeling and multi-material 3D

52 lti-modal distributions of movements; during parametric computations, time pressure elicits a shiftin

53 s-response contingencies, and time-consuming parametric computations.

54 given survival to S2P) was calculated using parametric conditional survival analysis.

55 ts are critical in understanding and setting parametric constraints indispensable to develop and enha

56 on uncertainty, which is an advantage of the parametric counterparts.

57 Quantitative analysis was based on parametric CVRC maps generated by voxelwise image subtra

58 roups were analysed using Student t-test for parametric data and Mann-Whitney U test for non-parametr

59 The structure of the multi-parametric data was shaped primarily by transactivation.

60 udent t test were used for nonparametric and parametric data, respectively.

61 ametric data and Mann-Whitney U test for non-parametric data.

62 ters, such as blood flow, were calculated by parametric deconvolution for each myocardial voxel.

63 for detection of specific analytes to multi-parametric devices for real time monitoring and assessme

64 oiety, with the help of a recently developed parametric/DFT hybrid computational method DU8+, has rev

65 examined with the help of a relatively fast parametric/DFT hybrid computational method, DU8+.

66 We propose a new semi-parametric differential abundance analysis (SDA) method

67 large number of candidate models, including parametric differential equations or their corresponding

68 on-entangled photons produced by Spontaneous Parametric Down Conversions using an intensified high-sp

69 the observation of strong nonlinearities in parametric down-conversion (PDC) of X-rays to long wavel

70 n pairs have been generated from spontaneous parametric down-conversion (SPDC), a process that is int

71 pectrum of the entangled photons produced by parametric down-conversion and report a broad spectrum w

72 ssimilar quantum nodes, as elements based on parametric downconversion sources, quantum dots, colour

73 l device based on the process of spontaneous parametric downconversion to confirm it behaves as a gen

74 elated intensity measurements of spontaneous parametric downconversion using a commercially available

75 a orthogonal quasi-phase-matched spontaneous parametric downconversion.

76 fects related to univariate and multivariate parametric effects in the MTL, mPFC, and Parahippocampal

77 Using parametric empirical Bayes for optimal model inversion a

78 Dynamical parametric encircling of the EP can lead to non-adiabati

79 derived white matter-based reference region (parametric estimate of reference signal intensity [PERSI

80 Here, we apply an ensemble of parametric estimators and a novel technique that include

81 uffing oscillator with a slowly periodically parametric excitation.

82 In this work, we investigate a parametric family of Gaussian DPPs with a clearly interp

83 Parametric feature selection methods for machine learnin

84 The aim of this study is to develop a parametric finite element (FE) model of the LC skulls th

85 Multi-parametric flow cytometry analyses were used to monitor

86 l blood cells using rigorously applied multi-parametric flow cytometry panels and miniaturized functi

87 e vibrating frequency that results from this parametric forcing is usually shaped by the boundary con

88 e existing schedule (every 3 months) using a parametric frailty model.

89 mitations, here we develop a data-driven non-parametric framework to estimate the tolerance of non-eq

90 nities to environmental changes within a non-parametric framework.

91 anced with signal shape constraints based on parametric functions.

92 We used parametric g-computation to estimate the effects of HCV

93 Parametric g-computation was used to estimate adjusted m

94 We used parametric G-computation, semiparametric inverse-probabi

95 more stillbirths per 1,000 pregnancies using parametric G-computation.

96 We used the parametric g-formula to estimate 10-year all-cause morta

97 ime-varying risk factors for death using the parametric g-formula.

98 es, heart disease, and lung cancer using the parametric g-formula.

99 while controlling for confounding using the parametric g-formula.

100 Optical parametric gain of 42.5 dB, as well as cascaded four-wav

101 dge, representing one of the largest optical parametric gains to date on a CMOS platform.

102 In the current analysis, parametric generalized gamma models were fitted and extr

103 Here, Zeng and Zhou develop a non-parametric genetic prediction method based on latent Dir

104 irected connectivity was estimated using non-parametric Granger causality between visual areas V1 and

105 viewed a variety of naturalistic images and parametric gratings.

106 Traditional parametric growth curve models capture the population gr

107 rwood to S2P and (2) S2P to 3 years by using parametric hazard analysis.

108 A multiphase parametric hazard model identified 2 different periods b

109 ing Oscope and provide a well-calibrated non-parametric hypothesis test to select oscillatory genes a

110 Conclusion Parametric images are not superior to static images for

111 Constructing ultrasound statistical parametric images by using a sliding window is a widely

112 various methods for generating quantitative parametric images of dynamic (11)C-phenytoin PET studies

113 , and accordingly there is a need to compute parametric images showing Ki at the voxel level.

114 Parametric images were generated from SUV ratio (SUVr) a

115 Parametric images were generated using Logan plot analys

116 Parametric images were generated using plasma input Loga

117 fectiveness of quantitative ultrasound (QUS) parametric imaging to characterize intra-tumour heteroge

118 higher than 0.79 obtained using statistical parametric imaging.

119 g a small window for implementing ultrasound parametric imaging.

120 ing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for m

121 and general because it includes both of the parametric imputation models used by PrediXcan and FUSIO

122 Dirichlet process regression is non-parametric in nature, relies on the Dirichlet process to

123 Relief-based estimators are non-parametric in the statistical sense that they do not hav

124 Enumeration and multi-parametric information were successfully measured across

125 demonstration of the control of a nonlinear parametric interaction via coherent oscillation phenomen

126 -retest (TRT) variability was determined for parametric K1 and VT values.

127 andom Forest and Gradient Boosting) and semi-parametric kernel models (Reproducing Kernel Hilbert spa

128 de novo strategy, termed SArKS, applies non-parametric kernel smoothing to uncover promoter motif si

129 aluate parametric methods for computation of parametric Ki images by comparison to volume of interest

130 tumor-to-liver contrast was superior in the parametric Ki images compared with whole-body images for

131 tatistical analyses were performed using non-parametric Kruskal-Wallis tests adjusted for multiple co

132 traits, we developed a rare variant (RV) non-parametric linkage (NPL) analysis method, which has adva

133 We used non-parametric linkage analysis and genome sequencing to ide

134 egression for the zero proportion and a semi-parametric log-linear model for the possibly non-normall

135 Multi-parametric magnetic resonance imaging (MP-MRI) used as a

136 Using multi-parametric magnetic resonance imaging, glaucoma patients

137 A parametric map was generated from each LGE image.

138 We used statistical parametric mapping (SPM V.12) software to compare groups

139 Statistical parametric mapping (SPM) is a technique with which one c

140 We used Statistical Parametric Mapping (SPM) to analyze time-series contribu

141 atched for age and sex, by using statistical parametric mapping (SPM).

142 sex-matched control group using statistical parametric mapping (SPM).

143 We used statistical parametric mapping 12-based, voxel-wise, multiple-regres

144 nt of cerebellar hypometabolism (statistical parametric mapping analyses, false discovery rate correc

145 ted voxel-based PCA and standard Statistical Parametric Mapping analysis (as a reference) to disclose

146 w data points to existing embeddings using a parametric mapping function, and scales linearly to hund

147 Statistical parametric mapping software (SPM5) was used to identify

148 cognitive domain, and we used the Biological Parametric Mapping toolbox to further control for local

149 s a function of stride cycle (1d statistical parametric mapping) revealed significant differences bet

150 ical changes were assessed using statistical parametric mapping, thresholded at P < 0.05 after correc

151 statistical shape modelling, and statistical parametric mapping, we show an 18% improvement in predic

152 el- and region-level analyses in statistical parametric mapping.

153 a study-specific brain anatomical template, parametric maps of the probability of a voxel belonging

154 avoiding large vessels, on imager-generated parametric maps to measure hepatic PDFF.

155 rtmental analysis and providing high-quality parametric maps when applied in voxelwise fashion.

156 chi-square statistics to develop full-brain parametric maps, implementing Gaussian random field theo

157 patial similarity of whole-brain statistical parametric maps, indicating tDCS- and l-DOPA-induced act

158 particles (MNPs) pass by thus enabling multi-parametric measurement like optical flow cytometers (FCM

159 g automated conforming to complex shapes and parametric meta-topology control.

160 We therefore recommend BFM as the preferred parametric method for analysis of dynamic (18)F-FLT PET/

161 atural for scRNA-seq data and provides a non-parametric method for analyzing count data.

162 loped a rapid and sensitive single-well dual-parametric method introduced in linked RAS nucleotide ex

163 We used a non-parametric method to estimate recurrence-free survival a

164 the help of a recently developed hybrid DFT/parametric method, DU8+, and revised the structures of b

165 We found that parametric methods can provide comparable, and often imp

166 The aim of this study was to evaluate parametric methods for computation of parametric Ki imag

167 While non-parametric methods make less model assumptions and are f

168 Among the various parametric methods tested, the basis function method pro

169 We introduce non-parametric methods to evaluate cell identities by testin

170 researchers have predominantly relied on non-parametric methods when studying the relations between b

171 ree statistics offer a robust alternative to parametric methods, their practical utility can be limit

172 and often improved inference compared to non-parametric methods; the latter, however, require no kine

173 To this end, quantum parametric mode sorting (QPMS) can achieve signal to noi

174 The method we suggest works for any parametric model and also for some nonparametric models,

175 In this article, we propose a parametric model for dose-time responses that follows Go

176 many motoneurons, there is no agreement on a parametric model for single motoneuron stimulation of in

177 ion, for forecasting, we estimated a dynamic parametric model of nursing home use and spending.

178 Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies th

179 A parametric model of the explosion, previously introduced

180 mmetric sinusoidal models, FMM is a flexible parametric model that allows deformations to sinusoidal

181 We employ a non-parametric model that allows for clustering of the genes

182 A-seq differential expression analysis fit a parametric model to the counts for each gene or transcri

183 By introducing the two-part semi-parametric model, SDA is able to handle both non-normall

184 l data are helpful in identifying a specific parametric model-the first of its kind, to our knowledge

185 a types without requiring reformulation of a parametric model.

186 While multiple parametric modeling approaches have been proposed, unfor

187 Parametric modeling revealed that choices in each sequen

188 ncidence rates were estimated using flexible parametric modeling, and positive predictive values (PPV

189 d with Cox proportional hazards and flexible parametric models adjusted for stratification factors.

190 estimated in ProMort using weighted flexible parametric models and compared with the corresponding es

191 her, our previous work has demonstrated that parametric models are insufficient to explain and predic

192 Flexible parametric models based on relative survival were used t

193 We fit parametric models of likelihood distributions for five d

194 We therefore employ parametric models previously established in model organi

195 esearchers have relied almost exclusively on parametric models, which require correct specification o

196 s of much higher tree diversity derived from parametric models.

197 DPPs with a clearly interpretable effect of parametric modulation on the observed points.

198 mical detections, can be coupled for a multi-parametric monitoring of mitochondrial activities, with

199 arametric inverse-probability weighting, and parametric/nonparametric targeted minimum loss-based est

200 study, the novel integrated system for multi-parametric optical phenotyping and characterization of b

201 FOS production after parametric optimization (sucrose - 50% w/v, m-FTase dose

202 work has shown that by harnessing structural parametric optimization of DOEs, one can design MDLs to

203 sing a network of coupled degenerate optical parametric oscillators (DOPOs) to effectively find the g

204 hine-learning models to capture subtle multi-parametric perfusion properties, including heterogeneity

205 more recently using the quantum Hall effect, parametric permittivity modulation or Josephson nonlinea

206 Probabilistic stimulation maps and non-parametric permutation statistics were applied to identi

207 Whole-tumor-averaged parametric pharmacokinetic parameters were compared with

208 pology, bi-anisotropy and nonlinearity makes parametric photon generation tunable and non-reciprocal.

209 We implement both parametric PrediXcan and nonparametric Bayesian methods

210 ors, without the need of the assumption of a parametric probability distribution of gene measurements

211 ions, owing to the ability to collect highly parametric proteomic data at a single cell level.

212 It is shown that a parametric pumping scheme can be implemented through cap

213 The dual-parametric QTR-FRET technique enables the linking of gua

214 Proportionate percentiles parametric quantile regression assuming lognormal distri

215 las project were combined to develop a multi-parametric Random Forest classifier.

216 In this study, we developed GuanRank, a non-parametric ranking-based technique to transform patients

217 tions on enzyme mechanisms have obscured its parametric rarity.

218 tinuous, the X-learner can still achieve the parametric rate under regularity conditions.

219 t through parameters, so sizes and shapes of parametric regions offer an integrated global estimate o

220 rporation of machine learning may outperform parametric regression in observational data settings.

221 Our simulation study compares methods under parametric regression misspecification; our results high

222 recursive partitioning providing a fully non-parametric regression model.

223 To estimate PM2.5 concentrations, many parametric regression models have been developed, while

224 c modelling with the powerful methods of non-parametric regression with Gaussian processes.

225 forage "wait times" were assessed using non-parametric regression.

226 hodology of Breiman (2001) applied in a (non-parametric) regression setting.

227 These estimates were included as parametric regressors for analyzing the BOLD time series

228 o assumptions concerning its shape, form, or parametric representation.

229 rential equations or their corresponding non-parametric representations, we evaluate the network infe

230 y characterize the climbing behavior at high parametric resolution in 3 contexts.

231 The photoinduced electron parametric resonance measurements indicate that the inte

232 Parametric response mapping (PRM) of paired CT lung imag

233 Parametric response mapping (PRM) was used to calculate

234 To determine if parametric response mapping (PRM), a novel computed tomo

235 We used parametric response mapping analysis of paired inspirato

236 assessment in DCM with the hope of creating parametric risk models to predict sudden cardiac death a

237 Monte Carlo simulation and parametric sensitivity analyses were performed to evalua

238 imiting the ability to conduct comprehensive parametric sensitivity analyses.

239 an accelerate model spin-up, permit thorough parametric sensitivity tests, enable pool-based data ass

240 th simulated and real datasets, this new non-parametric shrinkage (NPS) method can reliably allow for

241 In a parametric simulation using Genetic Investigation of ANt

242 Power is calculated using a semi-parametric simulation-based approach in which DNAm data

243 cal analysis, digital image correlation, and parametric simulations, here we reveal that the characte

244 and dynamic quantities in a multidimensional parametric space defines the ISGs' properties for differ

245 cal applications, exploration of the massive parametric space of a mechanism-based model can impose a

246 Our work can be a platform for fast parametric space screening of biological models with use

247 rk can then be used to explore a much larger parametric space.

248 econds, and is robust, occurring over a wide parametric space.

249 r show that despite our loop entropy model's parametric sparsity, it performs better than or on par w

250 d processed fin trimmings (n = 2000) and non-parametric species estimators to investigate the species

251 The SM has deep connections with parametric statistical models and the theory of phase tr

252 Parametric statistical models such as ones assuming expo

253 udies rarely consider such patterns from non-parametric statistical standpoint.

254 Results from non-parametric statistical tests indicate that the separatio

255 Non-parametric statistical tests were used for between-group

256 understanding of an analogue of a classical parametric statistical theory is rather limited for this

257 ization experiment reveals that discrete and parametric strategies produce, respectively, more locali

258 Parametric studies were conducted to provide insight int

259 In this article, we present a parametric study of nine hydrolytically stable MOFs with

260 Performing a parametric study reveals that mechanical properties of b

261 The parametric study shows that when different boundary cond

262 configuration were identified by systematic, parametric study.

263 and the photoluminescent properties provide parametric support of challenge-response pairs.

264 d are comparable to those derived from multi-parametric surface plasmon resonance measurements and mo

265 We used non-parametric survival analysis methods to estimate gains i

266 nstructed Kaplan-Meier estimates and applied parametric survival analysis to examine proportions of p

267 tes, and adjusted analyses employed flexible parametric survival analysis.

268 Survival probabilities were estimated using parametric survival distributions based on CELESTIAL tri

269 as estimated for niraparib and RS by fitting parametric survival distributions to Kaplan-Meier data f

270 erved in ATTR-ACT; future projections used a parametric survival model in the control arm, with const

271 n probabilities from randomized trials using parametric survival modeling.

272 Optimal assumptions were estimated using parametric survival models in individual participant dat

273 Data were analyzed with flexible parametric survival models that adjusted for potential c

274 nvestigate effect modification, and flexible parametric survival models to estimate absolute excess r

275 We used flexible parametric survival models to estimate the 2-year probab

276 ortality records was analyzed using flexible parametric survival models.

277 Parametric SUVR images were used to identify regions of

278 Data were analyzed by parametric test (ANOVA) with Tukey post-hoc test (P < 0.

279 Non-parametric tests and logistic regression models were use

280 Statistical analysis included non-parametric tests for intra- and intergroup comparisons.

281 Non-parametric tests were used for subgroup comparisons base

282 r's Exact Test, Student's t-test, ANOVA, non-parametric tests, linear regression, logistic regression

283 were assessed by infection status, using non-parametric tests.

284 hological parameters were analyzed using non-parametric tests.

285 Six-month survival was described with parametric time-to-event models.

286 obability weighting, nonparametric TMLE, and parametric TMLE represented 6.9 (95% CI: 3.7, 10.0), 0.4

287 ection via the Wilcoxon rank sum test (a non-parametric two sample test method) based on the raw data

288 bial respiration by 5-19%, and reduces model parametric uncertainty by 55-71%.

289 was used to assess the relationship between parametric variables and Spearman in the case of non-par

290 ic variables and Spearman in the case of non-parametric variables.

291 Using the responses to parametric variation in these stimulus variables, we ext

292 uggest that maintenance of excitability amid parametric variation is a low-dimensional, physiological

293 diversity of shell forms and highlights how parametric variations in the growth process result in mo

294 he Hodgkin-Huxley model is more sensitive to parametric variations of protein densities and kinetics

295 assessment of false positive differences in parametric versus permutation testing, was also performe

296 s tested, the basis function method provided parametric VT and K1 values with the least bias compared

297 We used parametric Weibull regression models to estimate the tim

298 A non-parametric weighted k-nearest neighbor classifier evalua

299 e adapt novel tools inspired by Bayesian non-parametrics, which starts from the direct analysis of th

300 Fisher's exact and non-parametric Wilcoxon rank-sum tests were used to identify