ライフサイエンスコーパス: model

1 = 0.063; CI 95% 0.99 to 1.45 in the adjusted model).

2 the AD clinical spectrum and amyloid Tg rat model.

3 n using a stage-structured Lefkovitch matrix model.

4 n be adopted by fitting all bins to a single model.

5 s, as well as in the Dahl salt-sensitive rat model.

6 stablished splinted excisional skin wounding model.

7 field are in agreement with our theoretical model.

8 onset, assessed with a linear mixed-effects model.

9 cells from a previously validated prediction model.

10 sion splines in a generalized additive mixed model.

11 fication via defining parameter values for a model.

12 l known as the Actor-Partner Interdependence Model.

13 timating the quality of a protein structural model.

14 ivo in the DDC-mediated mouse biliary injury model.

15 assessments into a levelized cost of driving model.

16 provement were assessed in this multivariate model.

17 and it is equal to unity in a well-mixed ODE model.

18 results in a murine Leishmania ear infection model.

19 ce of diabetes, both in humans and in animal models.

20 n stress intensity was highly variable among models.

21 ctional recurrent neural networks into their models.

22 te compositions in experimental and clinical models.

23 hyma-absorbed dose was assessed using linear models.

24 infarction (MI)-induced heart failure mouse models.

25 as estimated by means of land-use regression models.

26 , as in susceptible-infected-recovered (SIR) models.

27 hesis inhibitors in standard mouse infection models.

28 using bacterial and human HtrA proteases as models.

29 hem difficult to study in traditional animal models.

30 ected variables to multiple machine learning models.

31 prevented RVH in antenatal and postnatal BPD models.

32 o extract the patterns learned by the ResNet models.

33 eed for computational reconstruction of gene models.

34 cesses and for building realistic predictive models.

35 for the RRM2,3 isolated domains and homology modeling.

36 luded a range of caloric compensation in the model (0%, 39%, and 100%).

37 cs in TLF and on dry SS followed the Weibull model (0.96 <= R(2) <= 0.99), but the model overestimate

38 For model A, a Pi jump induced a tension fall at a rate simi

39 fferent databases to generate four different models: (a) 375 neuroradiologist-labeled clinical DW pos

40 Model accuracy, precision and recall for endoleak diagno

41 S from Streptomyces coelicolor (VanS(SC)), a model Actinomycete.

42 Our model addresses the disconnect in biotin levels between

43 We fitted mixed-effects Cox regression models adjusting for multiple pregnancies per individual

44 eration levels, as predicted by an empirical model, agree with target values within a given level of

45 This study used models allowing the convenient cell isolation from Ccl17

46 mprehensively transitioned its care delivery model and administrative organization to conform to a ne

47 We examined both an optimal observer model and an approximate Bayesian model in which partici

48 ue consideration is given to the fit between model and data.

49 Using an HD mouse striatal cell model and HD mouse organotypic brain slices we found tha

50 ng study, based on a receptor-driven docking model and including a systematic free energy perturbatio

51 plications such as drug repurposing, disease modeling and gene function prediction.

52 We use hydrological modeling and new 1200-year tree-ring reconstructions of

53 nstrating their future potential for disease modeling and therapeutic screening applications.

54 SONATA is used in multiple modeling and visualization tools, and we also provide re

55 Here we use behavioural modelling and functional magnetic resonance imaging to d

56 ations in schizophrenia and relevant disease models and discuss their putative origin.

57 expectedly, in two different mouse xenograft models and four human and mouse cell lines we examined i

58 Studies in experimental models and humans have identified 9 highly interconnecte

59 ding and evaluating multivariate statistical models and machine learning methods for the classificati

60 ilated-Net, original U-Net, and Faster R-CNN models and the conventional region growing (RG) method.

61 e modeling of kinematic loops (e.g., cycling models) and complex anatomy (e.g., patellar motion).

62 L cholesterol, and triglycerides in the same model, apoB retained a robust effect (p < 0.05), whereas

63 k-based variation in benefit, and an "effect-modeling" approach, wherein a model is developed on RCT

64 gories of predictive HTE approaches: a "risk-modeling" approach, wherein a multivariable model predic

65 ment is mainly based on studies of the dicot model Arabidopsis.

66 Zebrafish models are a powerful system for discovery, live imaging

67 Though the developed models are not a definitive descriptor of O2 carrier int

68 de-MHC complex of interest with a structural model as input, representing an important step toward co

69 cells and that has shown potential in animal models as a treatment for eosinophilic gastritis and duo

70 tal structure of the DNA-binding domain of a model ASO-binding protein PC4, in complex with a full PS

71 My model assumes that an Hsp90-bound client can transition

72 t quantities of products were formed in food models at pH 6.4, which is close to the pH optimum of LO

73 We created multivariable regression models at the year, day, and visit level after adjustmen

74 Here, we use a general model based on biochemical kinetics to quantify the comb

75 We evaluated pre-HD stratification models based on single visit resting-state functional MR

76 l-based iterative reconstruction (SBIR), and model-based iterative reconstruction (MBIR) in a retrosp

77 cement of rewarded actions, and the other is model-based learning, which considers the structure of t

78 linear regression was applied to select the model best predicting the global test-retest variability

79 We introduce an alternative photosynthesis model (beta (PSII) model) incorporating parameters from

80 ructural equation modeling to test mediation models between the PRSBMI, eating behavior patterns, and

81 achieve 6 degrees C (7 degrees C on a scale-model building) below the ambient temperature under a so

82 g a new dimension to the nuclear theranostic model by showing a requirement for imaging to quantify,

83 To address this problem, we generalized such models by adopting a nonparametric approach in which gro

84 We describe a Bayesian hierarchical model, called Bayesian Inference of Regulatory Differenc

85 Thus, this model can be used to determine the contribution of host

86 It is under debate whether PRS models can be applied-without loss of precision-to popul

87 We modelled CCF50 as a time-series random walk function of

88 We model chemotactic transport of bacteria within a leaf ti

89 Such a model could aid in the development of prebiotic, probiot

90 nal logistic mixed-effects model was used to model daily abstinence from alcohol over the 21 days aft

91 Experimental animal models demonstrate that maternal immune activation (MIA)

92 Without adaptation, the model demonstrated substantial agreement with the origin

93 The CFP model demonstrated substantially superior performance to

94 We developed a minimal mathematical model demonstrating growth factor signaling is sufficien

95 incompressible transversely isotropic (NITI) model depicting corneal biomechanics.

96 Model discrimination and calibration were both evaluated

97 iction of relative permeability using legacy models (e.g. Brooks-Corey (B-C), van Genuchten, etc.) th

98 Moreover, in cells, MANF bound to a model ER protein exhibiting improper disulfide bond form

99 orest structure and dynamics in Earth system models (ESMs).

100 rence Application Programming Interfaces and model examples to catalyze further adoption.

101 Models excluding neurologic deaths, for intubated subjec

102 The model explains the trade-off among chemical design param

103 ated in cellular assays and a murine colitis model expressing hPXR by a significant reduction in infl

104 In the adjusted model, factors associated with eGFR <90 mL/min/1.73 m2 i

105 especially in subjects who obtained a better model fit.

106 MSlambdaD) provides a realistic pH-dependent model for membrane proteins.

107 We investigate the three-state majority-vote model for opinion dynamics on scale-free and regular net

108 We established an in vitro model for the induction of these structures in mouse mac

109 roves fitness by 70% and 77% over the random models for a discoidal or an ellipsoidal stem cell confi

110 Multivariable generalized linear mixed models for binary outcomes were used to examine the rela

111 hat excessive placental DNA damage in murine models for Cornelia de Lange syndrome results in an inef

112 However, genetic evidence in mouse models for prostate cancer to support the crucial role o

113 se NUP98-fusion proteins, we developed mouse models for regulatable expression of NUP98/NSD1, NUP98/J

114 Cox models for RSClin were compared with RS alone and clinic

115 A lack of cost-effective, reproducible models for the study of M. haemolytica pathogenesis has

116 One is model-free learning, i.e., simple reinforcement of rewar

117 ing interactions change across developmental models, genetic perturbations, drug treatments, and dise

118 Our results show the value of model-guided design as an approach for generating useful

119 h committee scores used as ground truth, the model had an average F1 score of 0.70 and an accuracy of

120 This gene-centric model has shaped the field of cancer biology and advance

121 Metabolic modelling has the potential to provide insights into the

122 Several metabolic disease models have shown that dysregulation of sarcoplasmic ret

123 bjective assessment with a parsimonious risk model improved perioperative risk estimation.

124 Adding CAC to a traditional risk factor model improved risk discrimination and reclassification

125 Denoising with this model improves micrograph interpretability and allows us

126 The best of our models improves fitness by 70% and 77% over the random m

127 lculation protocols using a machine learning model in conjunction with standard DFT methods.

128 growth in vivo using an orthotopic xenograft model in immunocompromised mice.

129 roduction of a high-grade spinal cord glioma model in pigs using lentiviral gene transfer.

130 l observer model and an approximate Bayesian model in which participants were assumed to attend (and

131 the need to refine PSB and crack-initiation models in metals to account for gradual and heterogeneou

132 capsaicin and partial sciatic nerve ligation models in mice.

133 ring are expanding the use of human neuronal models in vitro.

134 The data are best fit by models in which people incorporate their trial-to-trial

135 To overcome this limitation, we devised cell models in which the AML1-ETO protein could be quickly de

136 A multivariable model including the ALL subtype (P = 1.1 x 10-14), the S

137 model with those of the state-of-the-art DL models including the fully convolutional network (FCN),

138 t change was described by using linear mixed models, including biomarker [log10(P/B ratio) and/or AMY

139 we used an ensemble of state-of-the-art fire models, including effects of land use and the ensemble m

140 training performance of all machine learning models, including six other algorithms, was evaluated by

141 lternative photosynthesis model (beta (PSII) model) incorporating parameters from rapid fluorescence

142 Results from the GEE model indicated the odds of hyperuricemia increased by 4

143 Consistent with the model, integration in S. calendulacea did not affect bio

144 Modelling involves identifying typhoon track vectors, cl

145 A simple model involving the biocatalytic reaction network couple

146 and an "effect-modeling" approach, wherein a model is developed on RCT data by incorporating a term f

147 Inference in our model is performed in a Bayesian framework, allowing us

148 The perivascular flow model is solved numerically, discovering that the perist

149 A major problem with such mouse models is that bnAb expression often hinders B cell deve

150 ed in a specific type of structural equation model known as the Actor-Partner Interdependence Model.

151 Overall, the FDA found that the model label was adequate for use in the development of a

152 Seminal studies using squid as a model led to breakthroughs in neurobiology.

153 tion suggested a reasonably good fit for the model (likelihood-ratio statistic: 2.81, P = 0.094), pro

154 In this study, we present an updated land model (LM3PPA-TV) to improve the representation of tropi

155 tive we explain how executable computational models meet this need.

156 the human immune receptor CD59 in a nanodisc model membrane.

157 ations have investigated phase separation in model membranes at the coarse-grained level, but atomist

158 In our model, mice clustered in two groups displaying high or l

159 In line with predictions of this Sync model, midfrontal theta power was stronger when rule swi

160 The data standards and models needed to achieve this integration do not current

161 This model obtains significant predictive power (AUC = 0.841)

162 antitative traits was Fisher's infinitesimal model of a large number of genetic variants, each with v

163 can rescue synaptic plasticity in this mouse model of AD (P = 0.007 to untreated APP/PS1).

164 with CLASSED we developed a context-specific model of beta-adrenergic cardiac hypertrophy.

165 vivo with an angiotensin II-mediated murine model of cardiac fibrosis in both preventive and therape

166 This paper proposes a novel nonlinear model of cascade failure in weighted complex networks co

167 present an infection and transmission animal model of COVID-19 that may facilitate development of SAR

168 An emerging model of COVID-related cardiometabolic syndrome encompas

169 ergone chronic social defeat stress, a mouse model of depression, at both the level of synaptic funct

170 ronic unpredictable mild stress (CUMS) mouse model of depression.

171 In conclusion, this robust cell culture model of HEV infection provides a powerful tool for stud

172 ated the role of PAG1 in a preclinical mouse model of house dust mite (HDM)-induced allergic sensitiz

173 ective at lowering bacterial load in a mouse model of infection.

174 we used the well-characterized iSLK.219 cell model of KSHV infection and established a new infection

175 major downstream target of RAC1, in a mouse model of melanoma driven by BRAF(V600E);PTEN loss.

176 We validated its effects in an in vitro model of MI/IRI in mammalian cardiac cells.

177 HV infection and established a new infection model of primary lymphatic endothelial cells (LECs) infe

178 Also, we found that during a murine model of sepsis, P2X7 receptor activity is important for

179 The 3-factor model of sexual behavior stigma cut across social contex

180 Here, we build a simple model of sexual reproduction and create a theoretical fr

181 hese effects, we implemented a computational model of the hippocampus, performing the same task as th

182 o handle kinematic constraints, which enable modeling of kinematic loops (e.g., cycling models) and c

183 nting an important step toward comprehensive modeling of the MHC class I pathway.

184 dexamethasone and apomorphine were active in models of AD and PD.

185 Although animal and theoretical models of addiction emphasize the importance of differen

186 Furthermore, using PDX models of colon cancer and resected tumors from colon ca

187 icular value of this construct for informing models of developmental psychopathology and individual d

188 readouts can be obtained in both preclinical models of diabetes mellitus and patients with diabetes m

189 This limitation has led to models of early life in which the first cells used simpl

190 mergence of persistence, we consider several models of environmental volatility described by continuo

191 from studies of patients with MS and animal models of how specific cytokines produced by autoreactiv

192 Tumor models of human PCa epithelia with CAF expanded similarl

193 In vivo, m-RCT was evaluated in mouse models of hypercholesterolemia that were naturally defic

194 Stimulation of the terminals in simulated models of inflammatory or neuropathic hyperexcitability

195 review, we place risk taking within existing models of information processing in pediatric anxiety di

196 Here, we discuss genetic models of mouse DC development and function that have ai

197 uctural-biology approaches, to obtain atomic models of multiple protein complexes implicated in intra

198 spectrometry to build integrative structural models of protein complexes.

199 Predictive processing models of psychopathologies are not explanatorily consis

200 Most previous models of quark nuggets have assumed no intrinsic magnet

201 functional and structural outcomes in animal models of retinal injury and retinal degenerative diseas

202 ite hydrogels can be therefore envisioned as models of secondary plant cell walls prior to lignificat

203 matory and neuroprotective actions in rodent models of status epilepticus.

204 In the MMTV-Delta16HER2 transgenic mouse model, oncogene transformation resulted in a timely abro

205 Current models only partially recapitulate key disease features,

206 eibull model (0.96 <= R(2) <= 0.99), but the model overestimated inactivation by small-dose DUV on we

207 = 6.0 x 10(-8)) and in the transgenic sheep model (p = 2.4 x 10(-88)).

208 re replicated in the Q175 Htt knock-in mouse model (p = 6.0 x 10(-8)) and in the transgenic sheep mod

209 al data, and show that we can robustly infer model parameters using a relatively small number of meas

210 Cox proportional-hazards model (PHM) and propensity score matching were used to i

211 uccessfully discovered and published for the model plant Arabidopsis thaliana.

212 Our model predicts that increasing the impacts of infanticid

213 The model predicts that, without immune escape, tumor neoant

214 -modeling" approach, wherein a multivariable model predicts the risk for an outcome and is applied to

215 The machine learning model pretrained on Fourier spectrum features allows eff

216 Our model projects that 2,700,000 (95% credible interval [Cr

217 of the principle of detailed balancing in a model proposed for the UV/chloramine process.

218 Overall, the created benchtop model provides an initial platform for better characteri

219 Additionally, a model reflecting perceived frequency of social interacti

220 We model residual errors with a heavy-tailed Student's t-di

221 Computational modeling revealed that dominant learning mechanisms unde

222 Theoretical modeling revealed that these regulatory strategies (burs

223 estigation using a hippocampal computational model, revealing increased representational dissimilarit

224 most probable stoichiometry, we introduce a model-selection method that is applicable for any multim

225 Finally, CA1 network modeling showed that desynchronized inputs can impair th

226 This model shows the importance of initial asymmetry and its

227 ler than the direct effects, during the 14-y model simulation period.

228 otential distribution, estimated with MaxEnt modelling software, is mainly centered in subtropical re

229 olutionary divergence of diatoms, additional model species are emerging, such as Fragilariopsis cylin

230 We illustrate how to extend the platform to model specific virus types (e.g., hepatitis C) and add a

231 ization, which is an essential procedure for model specification via defining parameter values for a

232 The integrative disease-modelling strategy may reveal new insights into mechanis

233 Modeling studies unveil the specific binding sites for a

234 identified were substantiated by a molecular modeling study, based on a receptor-driven docking model

235 Our proof of principle model successfully predicted the daily evolution of AD s

236 ta provide an important proof-of-concept and model system for the potential use of allele-specific sm

237 This model tested whether each person's depression and stress

238 quantum states of the transverse-field Ising model (TFIM) by preparing thermofield double states at a

239 process and use a mechanistic computational model that combines polarity protein biochemical interac

240 here we derive a general and broadly useful model that matches stimulus history to odor sensation an

241 Performance of the model that used the multimodal inputs was consistent acr

242 We further present advances in animal models that are important for understanding the pathogen

243 We present 2 new animal models that will serve to elucidate the underlying mecha

244 The major issue is modeling the complex crosstalk among transcription facto

245 research efforts have been directed towards modeling the structure and dynamics of the underlying ne

246 Modeling the thermodynamics of a transition metal (TM) i

247 Using osteoblast models, the identified mutations are demonstrated to exe

248 CCL17-driven inflammatory pain and arthritis models, the latter permitting a radiation chimera approa

249 nd a discrepancy with the liquid drop fusion model: the fusion was faster for spheroids from epitheli

250 We have trained a machine learning model to analyze the correlation between SARS-CoV-2 test

251 to fill this gap by fitting a computational model to data (n = 1754) from a modified serial dictator

252 We developed a hierarchical Bayesian model to estimate population numbers in small areas base

253 udy indicated that Dp16 mice can be a useful model to examine the pathophysiology of increased upper

254 Then, we use a coevolutionary model to illustrate how shifts in the magnitude of anore

255 ated and validated a multiscale mathematical model to investigate the impact of cross-talk between tu

256 adipose tissue-specific MMP14 overexpression model to study its regulatory function.

257 We used random coefficient modeling to account for the nesting effect of multiple o

258 s to identify eating behavior patterns, twin modeling to decompose correlations into genetic and envi

259 onmental components, and structural equation modeling to test mediation models between the PRSBMI, ea

260 e compounds using transient spectroscopy and modelling to unravel the singlet and triplet dynamics.

261 We used mixed-effects linear models to analyze associations of changes in standardize

262 facilitating the expansion of computational models to incorporate these newly-discovered components.

263 Here, we use multiple mouse models to investigate in vivo consequences.

264 letely understood, with limited experimental models to investigate the mechanisms driving influenza v

265 ing approach, we built gene expression-based models to predict drug sensitivity for 265 common antica

266 We fit hierarchical Bayesian models to these data to describe both the mean trip numb

267 elf-administration and a mouse noncontingent model, to investigate whether changes in the cerebrovasc

268 The CEACOV model tracks infections accrued by students and faculty,

269 omycin aminonucleoside (PAN) nephropathy rat model treated with amiloride, an inhibitor of plasminoge

270 More specifically, the Sync model uses bursts at theta frequency to flexibly bind ap

271 We created a computational model using sparse maximum likelihood to estimate the re

272 Explore/exploit decisions were modeled using reinforcement learning algorithms.

273 tion, feature extraction, classification and model validation.

274 s ratios (ICERs) and report the mean and 90% model variability of 250 runs, using a cost-effectivenes

275 Development of the prediction model was based on clinical information available during

276 The model was constructed at two levels of granularity, usin

277 A predictive model was developed using density functional theory (DFT

278 Finally, the model was extended to reflect mixture toxicity via conce

279 An additional model was trained to distinguish between non-TEs and TEs

280 A Broken line model was used to identify the periods of the learning c

281 A longitudinal logistic mixed-effects model was used to model daily abstinence from alcohol ov

282 The model was validated against surgical data.

283 Regression modelling was used for a statistical analysis.

284 To further support this model, we altered the structure of osteoblast HS genetic

285 o observational datasets than any individual model, we consider the here presented results to be the

286 Using a reaction-diffusion model, we demonstrate that growth rates are inextricably

287 melanoma lineage survival oncogene MITF as a model, we show that low-affinity binding sites act as a

288 the rich information contained in multistate models, we investigate cell-to-cell variability of chrom

289 Using these predictive models, we provide a global-scale quantitative and gridd

290 While energy management models were first discussed in the 1990s, application of

291 Regression models were fitted to assess association between residin

292 Deep learning models were trained to use SD OCT retinal nerve fiber la

293 Multilevel models were used to characterize county-level variation

294 Random regression models were used to jointly analyse live body weight mea

295 oices, estimated by a reinforcement learning model, were regressed against BOLD signal.

296 We propose a model where PICALM modulates glutamatergic transmission,

297 es of ATI, we developed a final multivariate model with a highly significant relationship to UOC (Rec

298 itfalls, of the approach using a birth-death model with both synthetic and experimental data, and sho

299 We compared the performance of the proposed model with those of the state-of-the-art DL models inclu

300 y important water-related ecosystem services modeled with the web-based tool AguAAndes.

301 gs of NMR and ITC binding curves to the Hill model yielded n(Hill) ~2.9, near maximal cooperativity (

302 More generally, the model yields physiologically realistic estimates of the