ライフサイエンスコーパス: ensemble

1 orests, gradient boosted trees and a stacked ensemble.

2 into secondary structure features of the IDP ensemble.

3 or "width" of the underlying conformational ensemble.

4 d on the least trimmed square (enetLTS), and Ensemble.

5 l information processing at the level of the ensemble.

6 ytosim, to implement several models of NMMII ensembles.

7 rmation exchange across task-relevant neural ensembles.

8 ects of mutations on the observed structural ensembles.

9 eeking suggest a finely tuned specificity of ensembles.

10 interactions in artificial multichromophoric ensembles.

11 ncoded by the electrically-stimulated neural ensembles.

12 may reflect another key feature of approach ensembles.

13 object, or alternatively group objects into ensembles.

14 perature to populate relevant conformational ensembles.

15 tural biology to characterize conformational ensembles.

16 ichiometric aggregates, but as combinatorial ensembles.

17 Calpha-Calpha distance distributions of two ensembles.

18 s, resulting in multiple semigrand canonical ensembles.

19 etween excitatory and inhibitory presynaptic ensembles.

20 sembles and comparing these matrices between ensembles.

21 tion of interneurons into functional circuit ensembles.

22 ated biophysically unique PFC pyramidal cell ensembles.

23 ndent of the frequency tuning of A1 neuronal ensembles.

24 ars, such that for heterogeneous nanocrystal ensembles, a single, atomically precise representative s

25 Our results suggest that PRC1 ensembles act similarly to a mechanical dashpot, produci

26 Changes in ensemble activity differ significantly between susceptib

27 rial-to-trial shared variability of neuronal ensemble activity during a two-choice interval-discrimin

28 evalence, the degree to which it disrupts OR ensemble activity, and its conservation across phylogene

29 The stacked ensemble algorithm in the ML model and variable combinat

30 Detection methods that use ensemble algorithms and emerging sequencing technologies

31 27 exchanges between distinct conformational ensembles: an extended regime exhibiting worm-like chain

32 Ensemble analyses indicated limited change through KSI's

33 Ensemble analyses of surrounding groups for WT and mutan

34 classic transfer learning framework through ensemble and demonstrate its general utility with three

35 E)PbBr(4) arises from a time-averaged single ensemble and not from a set of excited states emitting w

36 trate remarkable homogeneity of the nanotube ensemble and reveal that ultrafast (~50 fs) modulation o

37 We used in vitro ensemble and single-molecule assays to assess the impact

38 Using ensemble and single-molecule biochemistry, we show that

39 city can be dramatically altered at both the ensemble and single-molecule levels.

40 Calpha-Calpha distance distributions within ensembles and comparing these matrices between ensembles

41 form highly dynamic, polydisperse oligomeric ensembles and contain long intrinsically disordered regi

42 monstrate how Medusa can be used to generate ensembles and perform ensemble simulations, and how mach

43 mer formation on the composition of the sHsp ensembles and their chaperone activities depends strongl

44 The multivalence of NMMII ensembles and their multiple roles have confounded the r

45 With a tailored architecture and an ensemble approach to CNN parameter interpretation, we sh

46 We propose a weighted ensemble approach using a surrogate variable.

47 nopus egg extracts using single-molecule and ensemble approaches.

48 effects of NMR or smFRET labels on the Sic1 ensemble are minimal.

49 tterns that favor an expanded conformational ensemble are under selection for efficient autotransport

50 genetic model, we demonstrated that altered ensembles are associated with pathological sensory corti

51 The ETV/PEA3*Med25 ensembles are directed by specific contacts between the

52 ns that lead to more expanded conformational ensembles are enriched across water-soluble proteins in

53 ensembles are nonoverlapping while departure ensembles are more overlapping than chance, which may re

54 eurons comprising partner and novel-approach ensembles are nonoverlapping while departure ensembles a

55 f great interest, since their conformational ensembles are the link between their sequences and funct

56 ngle-molecule precision with the accuracy of ensemble assays to determine concentration sensitivities

57 situ TEM observations of single particle and ensemble assembly dynamics with simulations of interpart

58 n low-resolution Hi-C heat maps representing ensemble average interaction frequencies from millions o

59 on are often experimentally characterized by ensemble averages.

60 s, we show that committee machines employing ensemble averaging can successfully increase inference a

61 QM/MM simulation with ensemble averaging has an important role to play in assi

62 full-length BTK, shifting the conformational ensemble away from the autoinhibited form.

63 ion of photosynthesis in cyanobacteria using ensemble-based measurements remains a challenge due to p

64 ding experimental evidence for the predicted ensemble behavior of myosin head domains.

65 redictable switching because the statistical ensemble behavior of oxygen vacancy defects is determini

66 Apart from a few studies on their ensemble behaviour (as monolayers or thin films), this p

67 es the structural accuracy of the reweighted ensemble but still allows for useful insights into the d

68 musicians freely improvising within a large ensemble can achieve significant levels of coordination,

69 When the proportion of outliers is > 5%, Ensemble can be used for variable selection on a subset

70 When the proportion of outliers is <=5%, Ensemble can be used for variable selection.

71 data suggests that details of the generated ensembles can now be examined with a high degree of conf

72 ermine how SCN cells interact to generate an ensemble circadian signal.

73 Ensemble is an ensembled classification based on distinct feature selec

74 gely distinct from neurons recruited into an ensemble coding for sucrose seeking suggest a finely tun

75 larly defined neuron populations for grouped-ensemble coding of survival behaviors.

76 ral networks, gradient boosted trees, and an ensemble combining all three algorithms, to predict anti

77 al investigations have been limited to large ensembles comprised of equal numbers of monopoles and an

78 ips: two-state and multistate conformational ensembles, conformational hierarchies, osmolytes, coupli

79 see text] decibels over the SQL by using an ensemble consisting of a few hundred ytterbium-171 atoms

80 With learning, orbitofrontal cortex ensembles converged onto a low-dimensional neural code a

81 These neural ensemble correlation statistics capture high-order sound

82 ions reflect temporally coordinated neuronal ensembles-coupling distant brain regions, gating process

83 The average ensemble cross-correlation coefficient of the thyroid ul

84 motion was detected by estimating normalized ensemble cross-correlation coefficient.

85 nd what affects the relative contribution of ensembles' cross-linking versus motoring activities.

86 In contrast, neurons comprising departure ensembles do not change over time and are not correlated

87 Several noise components of the ensemble dynamics grew proportionally to the ensemble si

88 l cluster model, previously used to describe ensemble dynamics via statistical mechanics, allowed bet

89 strong chiral recognition and a significant ensemble effect in the PdGa system, hence highlighting t

90 By applying an ensemble empirical mode decomposition method to satellit

91 We demonstrate that hippocampal ensembles encode space at a finer scale following fear m

92 for identification of neuronal assemblies ("ensembles") encoding recent experience.

93 tification, and prediction of three methods (Ensemble, enetLTS, Rlogreg) were compared for simulated

94 erns that support an expanded conformational ensemble enhance pertactin secretion to the bacterial ce

95 independent satellite datasets reduced model ensemble error by better constraining unobservable proce

96 tacked deep ResNets to perform residue-level ensemble error classifications at multiple predefined er

97 Moment-to-moment tracking of neural ensemble firing patterns revealed that the prelimbic net

98 lities of nanomaterials, and compare it with ensemble fluorescence spectroscopy.

99 We applied an ensemble forecasting niche modelling approach to project

100 ve, multidimensional imaging of mammary cell ensembles from GCaMP6 transgenic mice, we reveal how sti

101 ent to recover correctly weighted structural ensembles from simulations, even when the relevant confo

102 ships, providing the basis for a new era of "ensemble-function" interrogation of enzymes.

103 Model ensembles gave greater accuracy than any model taken at

104 gyrus contain functionally distinct neuronal ensembles, genetically defined by the Fos- or Npas4-depe

105 ocal object individuation versus distributed ensemble grouping, because they are thought to regulate

106 to A/B compartments, which are detected with ensemble Hi-C.

107 22qDS model mice exhibited CA1 excitatory ensemble hyperexcitability and concomitant behavioral de

108 We analyze this path ensemble in detail, constructing descriptors that can fa

109 tics activate an endogenous analgesia neural ensemble in the central nucleus of the amygdala.

110 nervation of a particular region or neuronal ensemble in the NAcSh.

111 Ensemble in vitro transcription assays indicate that PQS

112 s the correct number of syllable patterns as ensembles in an interconnected NIf network.

113 be encoded by sparsely distributed neuronal ensembles in memory-related regions.

114 Neural ensembles in prefrontal cortex, hippocampus, and simulat

115 relations between frequency organized neuron ensembles in the awake rabbit inferior colliculus (IC).

116 to disjoint, cell-type specific pathological ensembles in which abGCs play an outsized role.

117 otion-based ensemble versus optic-flow-based ensemble, in RSC, optic flow appears to override locomot

118 xt, to assess whether the same mPFC neuronal ensemble is activated during cue-food learning and later

119 Ensemble is an ensembled classification based on distinc

120 io at both the single-molecule level and the ensemble level in the SWIR relative to visible wavelengt

121 demonstrates that exciton generation at the ensemble level is consistent with the charge-confinement

122 likelihood estimation (TMLE) paired with the ensemble machine learning algorithm Super Learner, and c

123 An ensemble machine learning model forecasted ED visits and

124 : see text]) that, when incorporated into an Ensemble machine learning prediction model, distinguishe

125 ex vivo and tracked the drop and rebound in ensemble mean FR induced by prolonged monocular deprivat

126 s rather than individual species or when the ensemble mean instead of individual simulations are used

127 Ensemble mean projected warming of 3.5 degrees C shifted

128 sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation.

129 odels, including effects of land use and the ensemble median or mean compares better to observational

130 n order to study both the performance of the ensemble members and the relation between the ensemble s

131 th truncation values was used to select five ensemble members for the SWME method.

132 We then select and use only the ensemble members with a North Atlantic Oscillation suffi

133 iable to determine the weight values for the ensemble members.

134 Despite the high turnover in active ensemble membership across learning, the population dist

135 Here we propose an ensemble method for simultaneous dimensionality reductio

136 Results show that our SAME-clustering ensemble method yields enhanced clustering, in terms of

137 e results suggest that by employing advanced ensemble methods and using past information, it is possi

138 Further, we find that ensemble methods outperform whole series classifiers for

139 ariant filtering tool based on decision tree ensemble methods that overcomes the main drawbacks of VQ

140 primary somatic variant callers and multiple ensemble methods using both real and synthetic whole-gen

141 e at least an order of magnitude faster than ensemble methods using parallel tempering and appeared t

142 acy, but with higher proportions of outliers Ensemble missed many mislabeled samples.

143 Consequently, the final ensemble model achieved a superior performance with an a

144 The GAM ensemble model outperformed the four separate models, de

145 The ensemble model outperformed the separate models and prod

146 A statistical ensemble model shows how Hsp33 function results from the

147 r Light Gradient Boosting Machine (LightGBM) ensemble model that integrates several single feature-ba

148 M models were then integrated into a unified ensemble model to further improve the predictive perform

149 ly weighted generalized additive model (GAM) ensemble model was used to fuse the estimations from the

150 Based on our proposed optimal ensemble model, we further developed an accessible onlin

151 s to the design methodology that incorporate ensemble modeling of crystallographic data.

152 tiguous United States by training tree-based ensemble models on 6 million land sales.

153 Benchmarking experiments indicate that the ensemble neural network reaches the average best area un

154 and against a Ni catalyst with multi-atomic ensemble Ni sites.

155 In this work we demonstrate ensemble NV detection of spinwaves generated via a nonli

156 y compressing a set of models into a compact ensemble object, providing functions for the generation

157 Hierarchical cluster analysis of an ensemble of 21(st)-century simulations reveals that a mi

158 na and hot semi-arid Mali and Benin using an ensemble of 25 maize models.

159 We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simul

160 Here, we use an ensemble of about a million strontium-88 atoms in an opt

161 This flexibility translates into an ensemble of ACE2 homodimer conformations that could ster

162 o-qubit quantum register using the nanoscale ensemble of arsenic quadrupolar nuclear spins as its har

163 is limited by interaction with the nanoscale ensemble of atomic nuclear spins, which is particularly

164 Often, the active site is formed by an ensemble of atoms, thus introducing further complication

165 le X-ray scattering (SAXS), we elucidate the ensemble of Bvht RNA conformations in solution, revealin

166 ed that different GA drugs activate a shared ensemble of CeA(GA) neurons.

167 This ensemble of chemically and functionally diverse biomolec

168 Using an ensemble of CMIP5 long-term climate projections that was

169 e show that our approach can characterize an ensemble of conformations adopted by dynamic DNA nanostr

170 changes in their local environment across an ensemble of conformations are more likely to be conserve

171 Computational protein design of an ensemble of conformations for one protein-i.e., multi-st

172 1 bp) loxP sites to investigate the in-aqueo ensemble of conformations for the longest-lived looped D

173 re satisfied with enumerating the low-energy ensemble of conformations.

174 Training an ensemble of convolutional neural networks jointly on the

175 l-type-specific gene-expression program; the ensemble of core TFs and their regulatory loops constitu

176 The proposed method is an ensemble of deep convolutional neural networks (CNNs) th

177 driving factors of SCA slowing-down, with an ensemble of dynamic global vegetation models (DGVMs) cou

178 An ensemble of generative adversarial networks (GANs) was t

179 rnative splicing event, VALERIE generates an ensemble of informative plots to visualise cell-to-cell

180 Here we use an ensemble of land-use and biodiversity models to assess w

181 tandard image-sensor array is coated with an ensemble of metallic plasmonic nanostructures that only

182 to many previous studies, OPUS-TASS uses an ensemble of models and achieves better results.

183 R sequence, TCRBuilder returns a model or an ensemble of models covering the potential conformations

184 Establishing that cocaine recruits an ensemble of NAcore neurons largely distinct from neurons

185 of neural synchronization patterns within an ensemble of network activity that relates to the physica

186 we comprehensively characterize the specific ensemble of neurons built through experience that are li

187 Moreover, the heterogeneous ensemble of oligomeric species interconverts on timescal

188 and polyfluoroalkyl substances (PFAS) are an ensemble of persistent organic pollutants of global inte

189 In this approach, an ensemble of protein conformations is first generated, ty

190 nkoi provides a platform for elucidating the ensemble of proteins that link photosynthetic metabolism

191 efined as the transmission performance of an ensemble of QDCs of length N (N-QDCs) to assess the robu

192 sults suggest that ssDNA strands exist as an ensemble of relatively compact conformations at high ion

193 bility of Raman spectroscopy to estimate the ensemble of secondary structures present in proteins.

194 any particular structure but rather, with an ensemble of secondary/tertiary structures that interact

195 membrane pore formation was analyzed with an ensemble of single vesicles providing detailed statistic

196 evision 4 (CB6r4) mechanism and to create an ensemble of six chemical mechanisms representing higher

197 As we used an ensemble of state-of-the-art fire models, including effe

198 t static, but rather is best described as an ensemble of states.

199 article analysis we describe the equilibrium ensemble of structures of neuronal GIRK2 as a function o

200 with a transition to an ordered structure or ensemble of structures.

201 ignificantly redistribute the conformational ensemble of the complex while not affecting overall affi

202 (XSI), to directly probe the conformational ensemble of the Holliday junction across a wide range of

203 spectroscopy to characterise the structural ensemble of the pre-fibrillar state of CsgA within the b

204 onic conditions, the complete conformational ensemble of this motif, especially at low ionic conditio

205 An ensemble of three-dimensional CNNs detected lymph nodes

206 An ensemble of three-dimensional patch-based, multiresoluti

207 tical methods are necessary to determine the ensemble of three-dimensional structures associated to t

208 ansforms an isotropic group of cells into an ensemble of tissues that is ordered with reference to th

209 We evaluate a large ensemble of variations of this hierarchical model, where

210 f a reversible supramolecular gate, i.e., an ensemble of various components linked by chemical commun

211 Such ensembles of atoms in cavities have been proposed for st

212 metamaterial engineering based on structured ensembles of atoms(4,8,9) and paves the way towards cont

213 e uses ENS, as a rule of life, to create the ensembles of biomacromolecules for emergent properties/f

214 We find that the ensembles of bound peptide conformations generated by th

215 ring the effects of bond type, we found that ensembles of catch-slip and catch motors were the best f

216 we propose a fully Bayesian method to infer ensembles of chromatin structures and to determine the o

217 are powerful tools for analyzing structural ensembles of chromatin.

218 Until recently, ensembles of coupled climate simulations producing tempo

219 sts work with more complex and heterogeneous ensembles of electrocatalysts.

220 conformational biases within the disordered ensembles of foldable proteins affect function in vivo.

221 n package for the generation and analysis of ensembles of genome-scale metabolic network reconstructi

222 ation of large, low-entropy, highly coherent ensembles of identical quantum systems is fundamental fo

223 ging, characterization of the conformational ensembles of IDPs is of great interest, since their conf

224 Similar ensembles of interconverting conformers may be common in

225 ure-function relationships in conformational ensembles of intrinsically disordered proteins derived u

226 s being paid to conformational biases in the ensembles of intrinsically disordered proteins.

227 on not obtainable by typical measurements of ensembles of ions.

228 de novo fitting produced occupancy-weighted ensembles of many conformers that were generally better-

229 Biomolecules form dynamic ensembles of many inter-converting conformations which a

230 id spontaneous transitions between selective ensembles of neurons emerge in such networks, consistent

231 emories are believed to be encoded by sparse ensembles of neurons in the brain.

232 Instead, we identified distinct ensembles of neurons in this region that are recruited d

233 ational model, CHROMATIX, which reconstructs ensembles of single-cell chromatin structures by deconvo

234 t transcriptional activation marker in large ensembles of spiny projection neurons (SPNs) expressing

235 nerative adversarial networks (GAN) to spawn ensembles of synthetic heterogeneous energetic material

236 We ran ensembles of the Ecosystem Demography model v2.2 with di

237 ice to assess the temporal dynamics by which ensembles of vCA1 neurons mediate encoding and retrieval

238 ated through dynamic recruitment of neuronal ensembles on the basis of excitability and functional co

239 ing its output responses to form the overall ensemble output.

240 The resulting ensemble performance including an average sensitivity of

241 Most generally, this ensemble perspective extends traditional structure-funct

242 This result demonstrates the power of an ensemble perspective for interrogating sequence-function

243 activation of these socially active neuronal ensembles phenocopied the effects of cagemate presence i

244 rrelated with bond strength, indicating that ensemble plasticity is specific to partner approach.

245 used the 3D genome information along with an ensemble pMHC-I coding strategy, and developed a group f

246 nal model of the peptide secondary structure ensemble produced by all-atom molecular dynamics simulat

247 The Npas4-dependent ensemble promotes memory discrimination and receives enh

248 The KSI ensembles provided evidence against catalytic proposals

249 ng dimension reduction, we present SHARP, an ensemble random projection-based algorithm that is scala

250 ionality reduction technique on single-trial ensemble recordings from the middle temporal (MT) area d

251 ement of such distances for a conformational ensemble remains a significant challenge.

252 e evaluate this approach, referred to as HDX ensemble reweighting (HDXer), for artificial target data

253 e the determination of atomic-resolution RNA ensembles routine.

254 theoretical work proposes that how a neural ensemble's sensory tuning properties relate statisticall

255 , and extending constraint-based analyses to ensemble scale.

256 With 1% or 2% outliers, Ensemble showed high outlier detection accuracy, but wit

257 Using a generalized-ensemble simulation approach, we determine the thermodyn

258 an be used to generate ensembles and perform ensemble simulations, and how machine learning can be us

259 ensemble dynamics grew proportionally to the ensemble size and the encoded visual signals, revealing

260 ation in PFC about saccades as a function of ensemble size.

261 imally diverse subset to produce an improved ensemble solution.

262 (PMIP) Phase 3 Last Millennium climate model ensemble spanning the same time period, to examine the i

263 nsemble members and the relation between the ensemble spread and the uncertainties associated with th

264 the long-term effects of shock on place cell ensemble stability, demonstrating that shock delivery in

265 ochastic neighbor embedding (t-SNE)-enhanced ensemble stacking, clearly categorized hiPSCs in differe

266 used to obtain information complementary to ensemble structural studies.

267 as is then applied post hoc to the resulting ensemble such that averaged peptide-deuteration levels,

268 or by chemogenetic activation of the neural ensembles tagged by learning matures memory functional c

269 revious framework by utilizing more advanced ensemble techniques, and providing prediction and visual

270 MMII), a molecular motor that assembles into ensembles that bind, slide, and cross-link actin filamen

271 earing to be stable, tightly self-associated ensembles that coexist in dynamic equilibrium with mM100

272 esulted in occupancy-weighted conformational ensembles that exhibited low strain energy.

273 veloped a methodology to generate structural ensembles that faithfully reproduce HDX-MS measurements.

274 This work reveals an elusive geometric ensemble, thus clearly identifying the active site in al

275 synaptic and circuit mechanisms used by each ensemble to regulate the memory discrimination-generaliz

276 to enzyme design by shifting conformational ensembles to favor catalytically-productive sub-states,

277 enzymes by triggering a shift in population ensembles toward functionally distinct conformational or

278 An Artificial Neural Network ensemble trained on features extracted from those sets r

279 ectivity was still markedly reduced for this ensemble type with coarse graining.

280 approach measures Abeta assembly similar to ensemble-type measurements but with per-fibril resolutio

281 he inward-facing state in the conformational ensemble under ATP turnover conditions.

282 t, providing functions for the generation of ensembles using experimental data, and extending constra

283 resentation into equivalent locomotion-based ensemble versus optic-flow-based ensemble, in RSC, optic

284 us experiments-and that the transition state ensemble was characterized by formation of just a few ke

285 The clutter-filtered Doppler ensemble was motion corrected prior to slow-time power D

286 The fitted model ensemble was robust to variation in proxies for "cell si

287 ers was <=5%, the positive selection rate of Ensemble was similar to that of enetLTS.

288 1-associated kinase 1 (BAK1) conformational ensemble, we performed Gaussian accelerated molecular dy

289 optogenetic manipulations of natively active ensembles, we further show that these specific represent

290 degree of helical content in the structural ensembles when adding different ratios of protic solvent

291 es are fluid and can only be described by an ensemble, which is consistent with the observation that

292 es a marked transition in the conformational ensemble, which is coupled to a preference for CsgE bind

293 ting worm-like chain behavior, and a compact ensemble, which may protect p27 against nonspecific inte

294 ligomers are best described as heterogeneous ensembles with (essentially) random combinations of mono

295 realizing strongly interacting atomic defect ensembles with applications to quantum information proce

296 sisting of 30-member large initial-condition ensembles with five Earth System Models (ESM-LE).

297 n a switching between compositions of folded ensembles with varying helical content.

298 dendritic spans of neurons as well as neural ensembles within multiple cortical regions over two hemi

299 To compare different reward-specific ensembles within the same mouse, we used a dual cocaine

300 y evaluating cocaine- and sucrose-associated ensembles within the same mouse.