ライフサイエンスコーパス: equation

1 quivalently, the attractive Gross-Pitaevskii equation).

2 , and epsilon = 1: no partial agonism (Clark equation).

3 andard methods (e.g., generalized estimating equations).

4 ral dynamics follow a fractional order state equation.

5 ayers was calculated using the van Genuchten equation.

6 correct derivation of the modified Arrhenius equation.

7 teractions by solving the Lippmann-Schwinger equation.

8 modelling efforts uses a modified Arrhenius equation.

9 ed with the discretized version of the Dirac equation.

10 95 min(-1), respectively using the Arrhenius equation.

11 oretical model based on the Gross-Pitaevskii equation.

12 potential changes with respect to the Nernst equation.

13 be connected by the same universal geodesic equation.

14 tion dynamics with a specific catchment area equation.

15 cm(2)) were calculated using the calibration equation.

16 predicted by solutions to the master kinetic equation.

17 as a linear, parabolic partial-differential equation.

18 y a theoretical model based on the diffusion equation.

19 an Heart Association 2013 pooled cohort risk equation.

20 ted from the conventional Clausius-Clapeyron Equation.

21 understood as coupled nonlinear differential equations.

22 n adults) compared with the CKiD and CKD-EPI equations.

23 Poisson's equation and semiconductor charge equations.

24 eded to externally validate these predictive equations.

25 concentrations using generalized estimating equations.

26 the Deltag-based excitonic model using rate equations.

27 handled with multiple imputation by chained equations.

28 for each metric using generalized estimating equations.

29 g of OMAM to experimental data using derived equations.

30 l reaction networks, stochastic differential equations.

31 nt traces were fitted to parametrized master equations.

32 marker, the model uses ordinary differential equations.

33 this difficulty using impulsive differential equations.

34 another risk category using the re-estimated equations.

35 ly modeled by means of ordinary differential equations.

36 e based estimated glomerular filtration rate equations.

37 were determined using generalized estimating equations.

38 dividual parameter values in pharmacokinetic equations.

39 cting reactions and 26 ordinary differential equations.

40 tion of newts using a system of differential equations.

41 s were assessed using generalized estimating equations.

42 ally more accurate than previously published equations.

43 iate and multivariate generalized estimating equations.

44 thms based on discretization of differential equations.

45 ic kidney disease epidemiology collaboration equations.

46 s was estimated using generalized estimating equations.

47 he theoretical value predicted by the Nernst Equation (-59.2 mV pH(-1)).

48 isson regression with generalized estimating equations adjusted for design covariates were conducted

49 The constitutive equation also reveals previously unknown ballistic and d

50 Absolute risk estimation by PCE and RECODe equations also improved with VA-specific coefficients; t

51 text])-in a system of ordinary differential equations analogous to the Susceptible-Exposed-Infected-

52 ese using a statistical thermodynamics-based equation and electrophysiological experiments to show th

53 p, age, and RTI using generalized estimating equation and generalized linear models (non-ART group pV

54 the value predicted according to the Nernst equation and inversely proportional to the charge in the

55 ependent Ginzburg Landau equation, Poisson's equation and semiconductor charge equations.

56 FM and LM were predicted using validated equations and compared with dual-energy x-ray absorptiom

57 cal calculations based on the heat diffusion equations and experimental measurements based on the ene

58 ed by a critical discussion of the different equations and models used to quantify selectivity.

59 assessed by means of generalized estimation equations and multilevel logistic regression models.

60 energy expenditure calculated by predictive equations and resting energy expenditure measured by ind

61 ted the Framingham general CVD 1991 and 2008 equations and the Pooled Cohort equations for atheroscle

62 endence of chemical reactions, the Arrhenius equation, and related Q(10) temperature coefficient, has

63 y of the polygenic risk score, pooled cohort equations, and both combined for incident CAD.

64 tion for polygenic risk score, pooled cohort equations, and both combined resulted in C statistics of

65 rs is described through partial differential equations, and immune cells (neutrophils and macrophages

66 OM was analyzed using generalized estimation equations, and their relative contributions using popula

67 f Diet in Renal Disease, and Cockcroft-Gault equations, and we evaluated baseline factors associated

68 w parameters in the nearest-neighbor hopping equation are introduced to account for percolation, cros

69 s for the three-dimensional Faddeev integral equation are the off-shell boost two-body t-matrices, wh

70 Then, four equilibrium equations are derived under the limit equilibrium condit

71 t, when its non-linear ordinary differential equations are integrated numerically, shows evidence for

72 orage capacity is examined and two empirical equations are rationalized to predict the hydrogen stora

73 The equations are useful in bioinformatic tools for analyzin

74 The predictions by these empirical equations are validated by several MOFs with an average

75 on of matter are super-positioned in the CDR equation as sink and source functions, respectively, the

76 We use mean-field equations as well as stochastic simulations to derive th

77 ine or cystatin C-based standard and kinetic equations as well as urinary creatinine clearance.

78 o support patients is a critical part of the equation, as is creating connections between clinical pr

79 ng a relatively inexpensive system of linear equations at most time steps.

80 statistically assess the solutions to these equations at ~109 parameter points in total.

81 ly solving the resulting Kolmogorov backward equation backward in time while reweighting the solution

82 s developed for healthy adults or predictive equations based on "static" variables.

83 Equations based on interfacial tension measurements show

84 using a system of two ordinary differential equations based on the Lotka-Volterra model.

85 fat-free mass (FFM) and fat mass (FM) using equations based on the Reference Child and Adolescent mo

86 functions, respectively, then the governing equation becomes an unsteady convection-diffusion-reacti

87 However, as these equations cannot be solved directly, nuclear interaction

88 models such as the well-known Clark or Hill equations cannot be used.

89 west performance related to creatinine-based equations compared with cystatin C.

90 produced by a system of partial differential equations coupling landscape evolution dynamics with a s

91 The derived equations create an alternative method for estimating la

92 Mismatches between generating and analytical equations created several intractable problems for estim

93 or activated carbon materials, the empirical equations demonstrate superior accuracy especially for M

94 iption inhibitor (F07#13) into the governing equations demonstrates how the model can be used to asse

95 Linear models with generalised estimating equations described trends in HIV viral load through 1 y

96 Here we develop a master equation describing the stochastic dynamics of the proba

97 y common cosmological models, along with the equations describing energy conditions for the reconstru

98 We derived equations describing mutual relationships among paramete

99 Advection-diffusion equation determined the mixing dynamics.

100 To date, there has been no equation developed for predicting outcomes among Asian K

101 ndirect calorimetry compared with predictive equations developed for healthy adults or predictive equ

102 he polygenic risk score to the pooled cohort equations did not provide significant improvement in rec

103 xpenditure values calculated from predictive equations differing by +/- 10% from resting energy expen

104 l known estimated glomerular filtration rate equations displayed high biases and unacceptable errors

105 of diet in renal disease and Cockcroft-Gault equations displayed the lowest performance.

106 armacology, bioassay data are fit to general equations (e.g. the dose response equation) to determine

107 The derived equations enable the precise and fast determination of i

108 cribed by the focusing nonlinear Schrodinger equation (equivalently, the attractive Gross-Pitaevskii

109 We asked how robustly different analysis equations estimate key parameters of interest and under

110 of the variation, whereas the Pooled Cohort equations explained 5.81%.

111 Predictors in the re-estimated Framingham equations explained 7.37% of the variation, whereas the

112 The equation, expressed as Euler-Lagrange equations on the R

113 in principle, described by the Navier-Stokes equations, extracting the velocity and pressure fields d

114 Compared with a previous equation for activated carbon materials, the empirical e

115 Disease Epidemiology Collaboration (CKD-EPI) equation for adults are recommended serum creatinine (SC

116 onic Kidney Disease in Children Study (CKiD) equation for children and the Chronic Kidney Disease Epi

117 ages with an analysis based on the Langevin equation for diffusive dynamics, which allows us to deco

118 orimetric process produced linear regression equation for H(2)O(2) as A = 0.00105C + 0.0630 (C:muM, R

119 solutions of the time-dependent Schrodinger equation for ionization of helium atom and neon atom.

120 atively by the one-dimensional Nernst-Planck equation for mass transport over the full range of exper

121 xact solutions of the electronic Schrodinger equation for molecules with up to 30 electrons.

122 er (CT)-IT system have considered the Nernst equation for the CT, while there is no empirical evidenc

123 Here we formulate a consistent, constitutive equation for the magnetophoretic flux of magnetic nanopa

124 the analog of quadrupole order in Maxwell's equations for a gyromagnetic photonic crystal (PhC) thro

125 e reference values with previously developed equations for AEX (by gender and race), and found that t

126 ls outperformed the de novo regression-based equations for AEX(predicted) and AEX z scores using race

127 991 and 2008 equations and the Pooled Cohort equations for atherosclerotic CVD within five years in a

128 We aimed to evaluate risk equations for initial ASCVD events in US veterans with d

129 heat and mass transfer model with Maxwell's equations for microwave heating and the chemical reactio

130 The cohort was used to estimate risk equations for outcomes and develop a chronic kidney dise

131 Peat specific allometric equations for palm (R(2) = 0.92) and frond biomass are d

132 Generalized estimating equations for Poisson regression were used to investigat

133 We developed equations for predicting 5- and 10-year patient survival

134 We developed equations for predicting MAKE30 and MAKE365 and divided

135 The field equations for the resulting [Formula: see text] theory a

136 Additionally, we find that an equation-free approach that does not presume a specific

137 aches using generalized Lotka-Volterra (gLV) equations from community ecology and compositional data

138 We used generalized estimating equation (GEE) models to analyze changes among sites imp

139 sis was done by using generalized estimating equations (GEE).

140 the dynamic causal modelling neuronal state equation generalises to a Fokker-Planck formalism if one

141 nergies as computed by the GW-Bethe-Salpeter equation (GW-BSE) method.

142 nergy expenditure calculated from predictive equations had very good agreement, Swinamer (1990) appea

143 solving complex single-molecule differential equations has the potential to address some of the criti

144 A body of theory, based on the logistic equation, has extended predictions of carrying capacity

145 Recent guidelines endorse ACR use, and equations have been developed incorporating ACR to predi

146 Three commonly used equations have only fair agreement, with potential impli

147 hunt for a connection among the fundamental equations in physics.

148 Our SMAI-specific equations include variables available in clinical practi

149 At the baseline level, the regression equation including age and DeltaMRT of negative word-col

150 r demonstrate that more than ten fundamental equations, including that of classical mechanics, fluid

151 imation with Stochastic Partial Differential Equation, INLA-SPDE) is used to predict the occurrence o

152 mework capable of encoding the Navier-Stokes equations into the neural networks while being agnostic

153 rporates the system of ordinary differential equations into the neural networks.

154 w European Kidney Function Consortium (EKFC) equation is a FAS equation with low bias (-1.2 mL/min/1.

155 , a general 1D analytic solution of the CDRS equation is obtained by using a one-sided Laplace transf

156 urpose, the relativistic form of the Faddeev equations is solved in momentum space as a function of t

157 al hazards regression and general estimating equation logistic regression.

158 Their modeling largely rests on the master equation (ME) approach introduced in 1975 by Hermann A.

159 d better eGFR than projected by a prediction equation (mean difference +/-SD for observed versus pred

160 ed model, mediation analysis, and structural equation methods in a longitudinal analysis.

161 his paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression

162 e estimated in a specific type of structural equation model known as the Actor-Partner Interdependenc

163 We constructed an ordinary differential equation model of SHR and SCR in the QC and CEI which in

164 Additionally, the structural equation model showed that water input and RR(WP) with t

165 on of individuals and a partial differential equation model that describes locust density.

166 cy, here we develop an ordinary differential equation model that predicts bacterial growth as a funct

167 per-protocol) used a generalised estimating equation model to account for clustering of patients tre

168 A generalized estimating equation model was used to perform the primary analysis

169 re effectively than an ordinary differential equation model with generalized mass action rate laws wh

170 Here, we used a differential equations model of the signalling network to assess whic

171 or weathering and environmental factors, two equations model the influence of seawater temperature an

172 Here we used multilevel structural equation modeling and N fertilizer rate trials to show t

173 We develop a Bayesian structural equation modeling coupled with linear regressions and lo

174 Structural equation modeling identified CO(2) as the dominant limit

175 Structural equation modeling is utilized to identify relationships

176 In particular, structural equation modeling revealed that the responsiveness of th

177 Structural equation modeling revealed that, consistent with an Alle

178 om the goodness-of-fit indices of structural equation modeling show as following: (1) The effect of s

179 Structural equation modeling showed that changes in plant biomass a

180 istical analysis technique called structural equation modeling to assess the effects of these latent

181 ated Conditions (NESARC), we used structural equation modeling to examine the shared and specific eff

182 and environmental components, and structural equation modeling to test mediation models between the P

183 Structural equation modeling was used to evaluate the importance of

184 ivariate analysis of variance and structural equation modeling was used to test the effectiveness and

185 Confirmatory factor analysis and structural equation modeling were used to model the relationships b

186 Using meta-analysis and structural equation modeling, we show that declines in decomposer d

187 size those findings, we performed structural equation modeling, which showed that plants but not micr

188 GEP with commonality analysis and structural equation modeling.

189 Mediation was assessed using structural equation modeling.

190 and internalizing symptoms, using structural equation modeling.

191 based on a voxel-wise whole-brain structural equation modelling framework.

192 Structural equation modelling revealed that cerebrovascular risk is

193 We used piecewise structural equation modelling to determine direct and indirect rela

194 Using structural equation modelling, we tested the pre-registered hypothe

195 Structural equation models (SEMs) demonstrated a psychometric isomo

196 We used structural equation models (which is not designed for CSD) as contr

197 Generalized estimating equation models assessed the association of DPN and CAN

198 Structural equation models estimated that PM(2.5) and mitochondrial

199 Nonetheless, structural equation models revealed more nuanced relationships, sho

200 inear regression with generalized estimating equation models to account for correlation between both

201 We used generalized equation models to estimate the prevalence of growth def

202 ultivariate partial least-squares structural equation models, to generate and test hypotheses concern

203 inear regression, and generalized estimating equation models.

204 ce ratio-which was examined using structural equation models.

205 ducted in addition to generalized structural equations models, while considering sampling design comp

206 d using repeated measures general estimating equations models.

207 al view (and to the prediction of the Wenzel equation), namely, a rough hydrophilic surface should ha

208 n the form of nonlinear partial differential equations (NPDEs) against the conservation laws of mass,

209 ventilation (adjusted generalized estimating equation odds ratio, 0.36; 95% CI, 0.32-0.40) and in-hos

210 The governing equation of motion is derived from Hamilton's principle.

211 tidal signature depends predominantly on the equation of state (EoS)-related tidal deformability para

212 namic properties are vital to build accurate equation of state and transport models.

213 We observed that the equation of state degenerates, and there is a universal

214 Our results illuminate the equation of state of the white dwarf envelope (the regio

215 derived from a Cubic Plus Association (CPA) equation of state.

216 ith the Hannay angle obtained from classical equations of motion.

217 ane coverage by ENTH, validating theoretical equations of state.

218 Hydraulic models use governing equations of the flow in motion (conservation of mass an

219 volume method is used to solve the governing equations of the LFTFs and the nanofluid.

220 convection-diffusion-reaction-source (CDRS) equation, of which general solutions are unknown.

221 The measurements test these equation-of-state relations that are used in the modelli

222 f the rederivation of the modified Arrhenius equation on modelled daily carbon gain causes a meaningf

223 lly solving the monodomain electrophysiology equations on anatomically-detailed models of normal, HF

224 The equation, expressed as Euler-Lagrange equations on the Riemannian manifold, was obtained from

225 comes, presented through simple mathematical equations, outline the best CHD prevention strategy usin

226 Existing ASCVD risk equations overestimate risk in veterans with diabetes me

227 The two original Framingham equations overestimated the CVD risk, whereas the origin

228 ning reaction-diffusion partial differential equation (PDE).

229 The partial differential equations (PDEs) are derived using the extended Hamilton

230 To solve the corresponding system of equations, penalization approaches are often the methods

231 ently solving time-dependent Ginzburg Landau equation, Poisson's equation and semiconductor charge eq

232 te (CER) to CER estimated using a 4-variable equation previously developed and validated using robust

233 Generalized estimating equations, propensity-matched models, and marginal struc

234 Therefore, fitted ordinary differential equations provide a basis for single-trial single-cell s

235 ient (HC) of the in vitro dose-response Hill equation provided a better prediction of in vivo efficac

236 CI:1.01-1.22) using a generalized estimating equation regression model.

237 We used Poisson generalized estimating equation regression models for longitudinal binary outco

238 We used linear generalized estimating equation regression models, adjusting for race/ethnicity

239 of the polygenic risk score to pooled cohort equations resulted in a net reclassification improvement

240 no; 1 = yes) were used to develop predictive equations, separately in spontaneously breathing and mec

241 The population-specific equations showed a lower interindividual variability of

242 nergy expenditure calculated from predictive equations showed very good agreement or accuracy.

243 escribed by a simple bimolecular equilibrium equation, similar mathematical tools are currently not a

244 ded within a system of ordinary differential equations, similar to the well-known susceptible-infecte

245 Predictive equations tend to either over- or underestimate resting

246 idual variability of the bias than the other equation tested, however, they showed a high root mean s

247 underlying physics of the relativistic Dirac equation that describes the low energy excitations of su

248 ritical piece of the diversity and inclusion equation that is, however, overlooked: institution.

249 We then derived an exponential equation that predicts the overpotentials of known and h

250 ptations on both sides of the energy balance equation that try to bring body weight back to its origi

251 a wild population-a proportionally balanced equation that uses high-precision diet estimates from ne

252 s-state space models based upon differential equations-that can be used to distinguish scale symmetry

253 3 m(2), we then used the kidney failure risk equation to compare kidney failure risk using measured A

254 It provides a single equation to fit complex cases within a full two-state fr

255 We used the potential impact fraction (PIF) equation to model the projected impact of the tax-trigge

256 nd analytical modeling with a master kinetic equation to show that cluster sizes and correlation leng

257 odel was fitted using generalized estimating equations to account for household-level clustering.

258 mial regression using generalized estimating equations to account for repeat pregnancies and adjustin

259 levance of nonlinear reaction-diffusion type equations to capture essential features of the disease.

260 exity, which implies that exact mathematical equations to describe biological processes cannot genera

261 egression and applied generalized estimating equations to estimate change in the degree of photoaging

262 We developed equations to estimate the median ACR from a PCR, optiona

263 age-group estimates, and used the trend-line equations to estimate the number of United States person

264 In this work, equations to estimate the sensitivity in MLP-based calib

265 We used generalized estimating equations to examine associations between concentrations

266 CAC may be superior to the pooled cohort equations to inform the allocation of aspirin in primary

267 tal measurements based on the energy balance equations to measure the [Formula: see text] and the tem

268 We applied the Pooled Cohort Equations to Prevent HF tool to calculate sex and race-s

269 the predictive accuracy of the Pooled Cohort Equations to Prevent HF within a primary prevention coho

270 ry banding data, and used allometric scaling equations to quantify community-level connectivity based

271 We used generalized estimating equations to test associations of baseline factors with

272 to general equations (e.g. the dose response equation) to determine empirical drug parameters (e.g. E

273 CVD risk, whereas the original Pooled Cohort equations underestimated it.

274 ically by directly solving the Fokker-Planck equation using efficient numerical methods, yielding a 1

275 s scale, we find that the macroscopic Kelvin equation using the characteristics of bulk water describ

276 e-DOCTORS, which solves the linear Boltzmann equation using the discrete ordinates method.

277 that enables us to compare the differential equation version with an agent-based version of the mode

278 HMBP with Al(3+) from the Benesi-Hildebrand equation was determined to be 1.21 x 10(8) M(-1).

279 ression analysis with generalized estimating equations was employed to examine the longitudinal assoc

280 Discrimination by the RECODe equations was improved by substituting VA-specific coeff

281 Using the Jacobian of this generalised state equation, we show that an initially unstable system can

282 merical turbulent solutions to Navier-Stokes equations, we find that when vorticity becomes very larg

283 thermodynamics and kinetics phenomenological equations, we rigorously derive the theoretical relation

284 o improve the predictive performance of such equations, we updated the Framingham general CVD 1991 an

285 iptive statistics and generalized estimating equations were performed.

286 These equations were then used to predict WCI in 5594 NHANES p

287 (three-point scale.) Generalized estimating equations were used for sensitivity comparisons between

288 gression analyses and generalized estimating equations were used to determine the relationship betwee

289 time using the ligand acidity constant (LAC) equation where contributions to the pK(a)(MeCN) from eac

290 unsteady convection-diffusion-reaction (CDR) equation, which is classified in mathematics as a linear

291 n is implemented to discretize the nonlinear equations, which are solved via a computational method c

292 owed overestimation of risk by pooled cohort equations, which was corrected after recalibration.

293 Function Consortium (EKFC) equation is a FAS equation with low bias (-1.2 mL/min/1.73 m(2) [95% CI, -

294 lization by using the generalized estimating equation with robust variance estimator for the top thre

295 Predictive equations with "dynamic" variables and respiratory data

296 gression analysis and generalized estimating equations with Bonferroni adjustments were conducted for

297 Generalized estimating equations with Gaussian family, identity link, and an ex

298 level lead to a set of ordinary differential equations with many unknown parameters that need to be i

299 ined over 21 days was described by quadratic equations, with coefficients varying with tannin botanic

300 ed using multivariate generalized estimating equations, with results expressed as odds ratios (OR) an