ライフサイエンスコーパス: random

1 ry 1, 2008, through December 31, 2014, for a random 5% sample of 1 618 059 Medicare beneficiaries old

2 (with hypothetical on-chip digital resistive random access memory).

3 ied, for example, in modern magnetoresistive random access memory.

4 and addressed optically through a two-pulse random access scheme.

5 um processor, with an ideal processor having random access-the ability of arbitrary qubit pairs to in

6 fficiency, both in terms of speed and use of random-access memory (RAM).

7 we show for the first time that a portable, random-access platform may be implemented in practice us

8 PREMISE: Although trauma may be considered a random act, geographical patterns of trauma potentially

9 l processes and is based on contractility of random actin arrays.

10 Our hypothesis is that increased random afferent synaptic activity (i.e. synaptic noise)

11 ents were enrolled and 676 were eligible for random allocation, 598 (88%) of whom were randomly assig

12 estation of increased fitness resulting from random amplification mechanisms, or if a genomic locus-s

13 nally, a novel multiplex PCR assay, based on random amplified polymorphic DNA (RAPD) analysis was dev

14 Surprisingly, complete overlapping of random and aligned RBS spectra from the sample with T g

15 l clearly distinguished true annotation from random annotation with Bayesian annotation probability >

16 At post-natal day 200, random appearance of testicular atrophy was noted in exp

17 In addition, we investigate a more random application of forces with a Monte Carlo method a

18 Communities within flies were not random assemblages drawn from a common pool; instead, ma

19 Random assignment 2:1 to CGM (n = 105) or usual care (co

20 The primary end point was OS 18 months post-random assignment based on an intent-to-treat analysis.

21 ian follow-up of 11 years (IQR 10.09-11.53), random assignment to 1 year of trastuzumab significantly

22 Random assignment to aflibercept, 2.0 mg; bevacizumab, 1

23 Random assignment to an intensive or standard SBP goal (

24 rs or their congregations were not masked to random assignment.

25 Using statistical mechanics, a random-binder model without fitting parameters, with gen

26 e the relationship between glycemic control (random blood glucose [RBG], fasting blood glucose [FBG],

27 aged 18-80 years with type 2 diabetes and a random blood glucose concentration of 7.8-22.2 mmol/L wh

28 perperfused regions, as these are likely not random, but represent an electrically connected epilepti

29 xhibit cooling and warming than predicted by random chance and that spatial variations in these chang

30 we report a new computational method, named random circuit perturbation (RACIPE), for interrogating

31 [Formula: see text], a uniformity similar to random close packing and early universe fluctuations, bu

32 Random cluster sampling with probability proportionate t

33 Multistage random-cluster sampling was used to select 3098 non-Indi

34 e cytosol and acquires a more beta-sheet and random coil character in the nucleus.

35 pts unfolded structure dominated by turn and random coil conformations.

36 ecifically recognize the epitope region in a random coil state.

37 erception is stunted in samples containing a random coil, ionic, mucoadhesive thickener, the retentio

38 melanogaster, which we conclude is a compact random coil.

39 The functionally relevant random-coil-alpha-helix transition associated with Ca(2+

40 We find that most Kenyon cells integrate random combinations of inputs but that a subset receives

41 s suggest the possibility that specific, non-random components of the developmental programs underlyi

42 mined properties of mushroom body circuitry (random connectivity [7], sparse coding [8], and synaptic

43 namics of an in silico chemical network with random connectivity in an environment that makes strong

44 gic projection neurons (PNs) with apparently random connectivity.

45 r each identifiable song sequence during two random days of each month with recordings.

46 of receptive field properties exhibited non-random dendritic clustering.

47 he Golgi apparatus that is reached either by random diffusion or, in the case of large unilamellar ve

48 Random digit dialing was used to recruit adult participa

49 However, its non-random distribution along the chromosomes constrains the

50 the formation of TMD alloys and identifies a random distribution of the alloyed elements.

51 eiosis II when the sister chromatids exhibit random distribution.

52 e phase of intense treatment, leading to non-random distributions of the resistant strain among the i

53 study has surprisingly concluded that 25% of random DNA sequences yield beneficial products when expr

54 ntral vision loss on motion perception using random dot kinematograms to test the capacity for form f

55 Dispersers are often not a random draw from a population, dispersal propensity bein

56 deleterious amino acid changes were fixed by random drift; predicted effects include energy deficit,

57 iance we used mixed-effects modelling with a random effect for trials incorporated in all models.

58 ndard errors in a variance-correction model, random effect in a frailty model).

59 n model with the individual physician as the random effect in the model and used intraclass correlati

60 A random effect meta-analysis was conducted on the CIR and

61 1,300 [15.7%]) (n = 2,507; p = 0.08; I, 53%; random effect model: odds ratio, 0.63; 95% CI, 0.37-1.06

62 Meta-analyses were conducted with random effect models, and heterogeneity was addressed wi

63 n, spirometer, and including study center as random effect.

64 as fixed effects and research assistant as a random effect.

65 We calculated pooled random-effect estimates of the association between preva

66 CI and CABG were assessed using longitudinal random-effect growth curve models.

67 f all polymorphisms were estimated using the random-effect model.

68 ean differences (SMDs) were calculated using random-effect models.

69 We propose a linear mixed model, Random Effects for the Identification of Differential Sp

70 We did random effects meta-analyses to examine individual, hous

71 We calculated pooled estimates using random effects meta-analyses where appropriate.

72 Finally, we report a random effects meta-analysis for each result.

73 individual layer segmentation performance by random effects meta-analysis for MSON eyes versus contro

74 were available, pooled for each country via random effects meta-analysis.

75 A random effects model demonstrated that ICD use was assoc

76 and 95% CIs were estimated with the use of a random effects model for high-intake compared with low-i

77 a 2-level meta-meta-analytic approach with a random effects model to allow for intra- and inter-meta-

78 timate, when available, were reported, and a random effects model was run to account for clustering o

79 Summary estimates were calculated using a random effects model.

80 ummary relative risks were estimated using a random effects model.

81 nge was related to group attendance, we used random effects models to assess associations between out

82 In the second stage of the meta-analysis, random effects models were applied using summary-level e

83 Meta-analyses used random effects models.

84 fects into a GP model that also included the random effects of the whole genome background.

85 First, the random effects output of the hierarchical model was used

86 Random effects were tested to quantify country-level var

87 conventional meta-analyses (fixed effect or random effects) on the basis of clinical and methodologi

88 s model the stochastic nature of the spatial random effects, where the mean surface and the covarianc

89 e over time using linear mixed modeling with random effects.

90 predictor, while including subject-specific random effects.

91 Findings were pooled by using bivariate random-effects and hierarchic summary receiver operating

92 Random-effects meta-analyses for outcomes with >/=4 RCTs

93 tect association for MR-MEGA over fixed- and random-effects meta-analysis across a range of scenarios

94 We conducted random-effects meta-analysis and Bayesian network meta-a

95 Random-effects meta-analysis methods were used to estima

96 Random-effects meta-analysis of the highest quality rand

97 We used random-effects meta-analysis to produce pooled estimates

98 from the individual studies and pooled with random-effects meta-analysis.

99 Data and estimates were pooled using random-effects meta-analysis.

100 l activity and both early and late AMD using random-effects meta-analysis.

101 ith results from the 2 settings pooled using random-effects meta-analysis.

102 eneity was explored using stratification and random-effects meta-regression.

103 Heterogeneity was explored by random-effects meta-regression.

104 Pooled analysis using a random-effects model demonstrated a significant improvem

105 We used a random-effects model to derive overall excess risk.

106 We used a random-effects model to pool odds ratios.

107 A random-effects model was used for the analyses.

108 Statistical analysis comprised a random-effects model with associated heterogeneity analy

109 Pooled analysis was done using a random-effects model, and quality of the studies was ass

110 calculated pooled odds ratios (ORs) using a random-effects model.

111 of 0.95 (95% CI, 0.96-0.99) using a 2-sample random-effects model.

112 Outcomes were pooled using a random-effects model.

113 sion in patients with MCI was pooled using a random-effects model.

114 utcomes were derived using a binomial-normal random-effects model.

115 ly combined into a pooled odds ratio using a random-effects model.

116 c relative risks (RRs) were aggregated using random-effects models and were grouped by study-level ch

117 We generated random-effects models for analysis and evaluated for pub

118 Random-effects models were used to estimate pooled effec

119 Data were pooled using fixed- and random-effects models.

120 s for OS of LCC vs RCC according to fixed or random-effects models.

121 es and moderator variables were tested using random-effects models.

122 andomisation was done by minimisation with a random element of 20%, minimisation by hospital site, si

123 implemented a minimisation algorithm with a random element to establish the allocation for the patie

124 location method used was minimisation with a random element, stratified by institution, planned numbe

125 premature responses, regressive errors, and random errors in males and perseverative errors in femal

126 asurements, but is limited by systematic and random errors.

127 The spatially random eruption hypothesis was found to be highly improb

128 at initiation and termination are inherently random events.

129 y inhibit directed exploration while leaving random exploration intact.

130 loration, driven by information seeking, and random exploration, driven by decision noise.

131 Loss of Bcl11b results in random expression of these factors and, thereby, lineage

132 y-ecosystem function relations following non-random extinctions.

133 induced transcriptional readthrough is not a random failure process, but is rather differentially ind

134 most of the VAN structures tend to grow in a random fashion, hindering the future integration in nano

135 Even though models with random feedforward connectivity are capable of creating

136 replicated by a model wherein cells receive random feedforward inputs.

137 Incomplete lineage sorting leads to random fixation of genetic markers and hence, random sig

138 BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated fr

139 , a tree-augmented naive Bayesian network, a random forest algorithm, and a gradient-boosted model an

140 ions without interactions, thresholdout with random forest and private Evaporative Cooling give compa

141 developed following superior performance of random forest based methods in NCI-DREAM drug sensitivit

142 g, which uses Relief-F feature selection and random forest classification.

143 The result showed that random forest classifier with composition of K-spaced am

144 The multivariate random forest implementation included in the package inc

145 In this paper, we develop a random forest model incorporating aerosol optical depth

146 g data and global databases, we calibrated a Random Forest model to correlatively link tree cover wit

147 A random forest model was built using a set of cell type-i

148 A machine learning random forest model was developed with 671 HRLs and test

149 g the antigenicity of influenza viruses by a random forest model.

150 variate analysis of variance (PERMANOVA) and random forest models.

151 ing open with proprietary data, (2) choosing Random Forest over alternative tested Machine Learning m

152 od for correcting MeDIP-Seq results based on Random Forest regression.

153 at least 45 infectious viruses, we show that random forest together with the relative word frequency

154 on and two machine learning methods (SVM and Random Forest) were used to evaluate GS accuracy of the

155 the reusable holdout, reusable holdout with random forest, and private Evaporative Cooling, which us

156 ctures of CNN, Recurrent Neural Network, and Random Forest, the simple CNN architecture with only one

157 and clinical variables were constructed via random forests and imbalance-adjustment strategies using

158 We used Random Forests modelling to demonstrate that the site an

159 terizations using univariate or multivariate random forests that includes various options for error e

160 dbSNP, to train an accurate classifier using Random Forests.

161 Patients were recruited at random from allergy outpatient clinics in 101 health cen

162 recommend N > 10(3) to reliably distinguish random from ordered arrays.

163 ogenes and are ten-fold higher than those of random genes.

164 We model the cytoskeleton as a random geometric graph, with nodes corresponding to junc

165 role for ETAA1 in this process by surveying random germ line mutations in mice using exome sequencin

166 Results in both synthetic random graphs and real networks show that the proposed m

167 st modelling, which identifies HTEs, using a random half of the trial data (the training set).

168 The orientation of the clusters is not random in the crystal structure, such that the side-by-s

169 cular sequence data of a locus obtained from random individuals in a population are often related by

170 the strategy of selecting latest contacts of random individuals provide the most amount of lead time.

171 number of secondary infections produced by a random infectious individual.

172 d a rapid increase with age in the number of random insertions and a dramatic increase in diversity.

173 w dose of irradiation, frequently results in random integration of the transgene in the genome and it

174 ted, which included repeated measures with a random intercept and an unstructured covariance matrix.S

175 using multiple linear regression models and random intercept and random slope hierarchical models.

176 uded descriptive statistics and multivariate random intercept logistic regression.

177 We used random intercepts to account for clustering of patients

178 A composite model from 200 random iterations with 25% of the molecules in each case

179 RACIPE generates an ensemble of random kinetic models corresponding to a fixed circuit t

180 Random lasers are low-coherence sources of stimulated em

181 ructed three general forms of planar network-random loops, mazes and trees-on the surface of self-ass

182 Here, we show that during a pseudo-random manual tracking task in the monkey (Macaca mulatt

183 yses assessing sensitivity to the missing-at-random (MAR) assumption.

184 anizations and reproductive modes, from near-random mating in protandry, to aggregate- and harem-spaw

185 te missing data under general missing-not-at-random mechanisms.

186 In 3-dimensional polarized random media the polarization orientation around singula

187 imulations, the size of potential effects of random mixtures of aromatase inhibitors on the dynamics

188 he corresponding springy billiards and their random models show equilibration with similar positive r

189 The variation was not random: models that overestimated at one experiment simu

190 ssion pattern that does not follow classical random monoallelic expression or imprinting.

191 bility was enriched at distal enhancers, but random monoallelically accessible (RAMA) elements were e

192 (or large) grain-filled side or an unbiased random motion.

193 ess incoherent phonon transport in the above random multilayer (RML) structure to further reduce kapp

194 Using a random mutagenesis and growth selection approach, we ide

195 tic analysis of SSV1 using both specific and random mutagenesis and thereby generate mutations in all

196 Previously, we performed random mutagenesis in a DeltaveA strain and identified r

197 In polyploid species, altering a trait by random mutagenesis is highly inefficient due to gene red

198 Here we performed random mutagenesis of the RNA-guided Cas9 nuclease to lo

199 rotein as a model system and subjected it to random mutagenesis, followed by screening for variants w

200 ntify mutations from a total of 7,300 TRPV1 random mutant clones.

201 of HIV-1 continuously evolve in the host by random mutations and recombination events.

202 CNVs are considered random mutations but often arise through replication def

203 on of the UCE, in a reporter minigene or via random mutations in the genomic context using CRISPR/Cas

204 ated with concurrent design to produce quasi-random nanostructures in amorphous silicon at wafer scal

205 ocal self-uniformity (LSU) as a measure of a random network's internal structural similarity, ranking

206 ow region of the parameter space compared to random networks.

207 Meanwhile, transcranial random noise stimulation (tRNS), a painless and more dir

208 Random number generation is crucial in many aspects of e

209 signed (1:1) via block randomisation using a random number generator to receive either clemastine fum

210 re randomly allocated (1:1), via a web-based random number generator with block sizes of four and six

211 -13 group by permuting blocks of four with a random number generator.

212 it telephone randomisation service by use of random number generators.

213 privacy depend ultimately on the quality of random numbers.

214 ccurs on both sides of the midbody ring with random order and that completion of the scission process

215 In random order at intervals of 14 d and after a 12-h fast,

216 e, and gadoterate meglumine, administered in random order in a single session.

217 daily) and placebo (orally, twice daily) in random order separated by a 2 week washout period.

218 ferent amounts of the variable nutrient in a random order.

219 , anonymized, and presented to the raters in random order.

220 expiratory pressure levels were applied in a random order: hyperinflation, 6 cm H2O above; open lung

221 Reproducible radial and longitudinal non-random organization was observed for all investigated lo

222 r the fields that elicited significantly non-random orientation, swimming in the experimentally obser

223 BNNSs partially overlapping one another with random orientations.

224 Random oxidative modification of cryptic side chains exp

225 rease folding requires no sheet bending, and random patterns with high-energy folding, in which the s

226 , the whole proteome can be represented by a random peptide phage display library.

227 We compare experimental (and random phase approximation) reference values to those of

228 Spatially random phase modulation was implemented for the lower sp

229 onsists of a microlens array and an array of random phase/amplitude masks.

230 effler (TS), many-body dispersion (MBD), and random-phase approximation (RPA) approaches).

231 r cosyntropin (250 mug) administration and a random plasma cortisol of < 10 mug/dL may be used by cli

232 otic processes were able to synthesize short random polymers.

233 NA sequencing (mRNAseq) is generally done by random priming, creating multiple sequencing fragments a

234 In this study, we proposed a random regression model to estimate genome-wide imprinti

235 However, random regression models found variation between individ

236 switch of the T reg cell TCR repertoire to a random repertoire also preserved, albeit to a limited de

237 diac tissue model is used to investigate how random RyR gating gives rise to probabilistic triggered

238 A systematic random sample of 1,300 in-camp households were interview

239 al study, What About Youth, which enrolled a random sample of 298 080 school pupils drawn from 564 88

240 A stratified random sample of 863 patients with newly diagnosed SCC o

241 d all pregnancies ending in stillbirth and a random sample of livebirths between Jan 1, 2006, and Dec

242 ing all MCRPEC from infection isolates and a random sample of mcr-1-negative E coli infections from t

243 A cross-sectional, population-based random sample of participants 65 years and older was cho

244 d marginally better endothelial integrity in random samples (85% versus 88% versus 93% for closed tun

245 We used stratified random sampling (according to age, residence [urban vs r

246 Participants were selected using stratified random sampling from general surgery residency programs

247 ifurcation sets, numerical continuation, and random sampling of parameters.

248 ased, observational study, we used two-phase random sampling to recruit adults with disabilities and

249 Listeners were asked to reproduce random "seed" rhythms; their reproductions were fed back

250 nd ALC loci were not linked, as indicated by random segregation in the T2 generation.

251 NDTISE is much better than random selection and slightly outperforms NCPIS.

252 includes a non-linear remnant resulting from random sensorimotor noise from multiple sources, and non

253 ls for re-examination of the extent to which random sensorimotor noise is required to explain the non

254 We test six coalescent priors and six random sequence samples of H3N2 influenza during the 201

255 chips on which 130,000 peptides chosen from random sequence space have been synthesized.

256 f testing was chosen by a computer-generated random sequence, assigned by independent members of the

257 he selection of bicyclic peptides from large random-sequence libraries.

258 all sizes stacking-disordered, consisting of random sequences of cubic and hexagonal ice layers.

259 We study the folding and binding of random sequences of hydrophobic ([Formula: see text]) an

260 d in the classification of enhancers against random sequences, exhibiting advantages of deep learning

261 By allowing fast evaluations, whether of random sets or real functional ones, provides the user w

262 rectly linking the SV with the PM, varies by random shortening and lengthening of the macromolecules

263 tion of the alpha-cyclodextrin rings between random shuttling and stationary states through solvent e

264 andom fixation of genetic markers and hence, random signals of relationships in phylogenetic reconstr

265 r regression models and random intercept and random slope hierarchical models.

266 A random slopes interrupted time series analysis then exam

267 Most cancers develop following random somatic alterations of key oncogenic genes, which

268 elaxes density fluctuations toward a uniform random state whose variance in regions of volume [Formul

269 ue representation of non-deterministic quasi-random structures in the Fourier space with greatly redu

270 ) trial in 983 participants [230 cases and a random subcohort of 790 participants (37 overlapping cas

271 colonising these locations tend to be a non-random subset from source communities, which is thought

272 We draw a random subset of [Formula: see text] rows from a frame w

273 lity is an especially challenging task, with random substitution yielding stabilizing mutations in on

274 A random survival forest was trained to predict individual

275 Random survival forests are a simple and straightforward

276 To test the ability of random survival forests, a machine learning technique, t

277 s using a machine-learning approach based on random survival forests.

278 The random-survival-forest analysis identified baseline chol

279 ld be called "p-bits", where the output is a random telegraphic signal continuously fluctuating betwe

280 hese data depends upon how many independent, random times each location is visited (Nvis) and the num

281 s of transposon-mediated transgenesis, where random transgene integration into the host genome result

282 ons of genes through construction of complex random transposon insertion libraries and quantification

283 ignificantly predicted PTSD after subsequent random traumas (odds ratio (OR)=1.3-2.5).

284 tive behavior and substance disorders before random traumas.

285 Random treatment group assignment was stratified by plas

286 e first illustrate how the occurrence of non-random unknown parents in population pedigrees can subst

287 conditional expectation of a network-valued random variable across the values of a continuous predic

288 ociated variants than from frequency matched random variants (P < 0.05), demonstrating height-related

289 at calculated from frequency matched sets of random variants.

290 It is insensitive to random variation in shape or propulsion (biological nois

291 test cost, prevalence of HSV infection, and random variation to study assumptions.

292 HIT'nDRIVE aims to solve the "random walk facility location" (RWFL) problem in a gene

293 g time," the expected length of the shortest random walk from any one of the set of sequence-altered

294 presented state in the Markov chain, take a random walk from the presented state for any number of s

295 ional methods, we developed a global network random walk model for predicting lncRNA-disease associat

296 perimental data are explained by a family of random-walk-based models and probabilistic analytical ap

297 s in the collision probabilities of multiple random walks.

298 deviate lateral diffusion from the expected random walks.

299 lies on calculating the coalescence times of random walks.

300 f highly weighted voxels to be approximately random, with a modest bias toward more foveal voxels.