ライフサイエンスコーパス: stochastic

1 e passing of objects is periodic rather than stochastic.

2 of accumulation is a relevant parameter for stochastic accumulation models of saccade initiation.SIG

3 neural activity have been well described by stochastic accumulation-to-threshold models.

4 Stochastic accumulator models provide a comprehensive fr

5 targeting of H3S10 or H3S28 results from the stochastic acetylation of H3 by CBP/p300 or PCAF, a proc

6 ise can arise from at least two sources: (i) stochastic activities of adaptation enzymes, and (ii) re

7 y being applied to animal networks, of which stochastic actor-oriented models (SAOMs) are a principal

8 light adaptation happens dynamically through stochastic adaptive quantal information sampling.

9 relate neural activity in frontal cortex to stochastic and deterministic components of waiting behav

10 Their local effects are extremely stochastic and difficult to measure.

11 ply voltages for CMOS architectures, and the stochastic and energy-costly switching of memristors com

12 However, such positional shifts are stochastic and not heritable, consistent with prior find

13 al changes induced by many perturbations are stochastic, and therefore lead to transitions from stabl

14 ot form the components show unrestricted and stochastic association.

15 Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect

16 Here, we demonstrate that stochastic bacterial community assembly in the Caenorhab

17 Using a stochastic, bead-spring representation of chromatin in b

18 availability and taxation interacts with the stochastic behavior of the population within irrigation

19 inverse noise-mean relationship to maintain stochastic bimodality despite increasing mean expression

20 that knowing the precise timing sequence of stochastic binding and unbinding events allows one recep

21 on the precise control of the nonlinear and stochastic bistable dynamics of a levitated nanoparticle

22 evitated nanoparticles as a model system for stochastic bistable dynamics, with applications to a wid

23 However, the stochastic blinking of single fluorophores can introduce

24 he landing probabilities of two classes in a stochastic block model and find, surprisingly, that unde

25 y studying seed set expansion applied to the stochastic block model.

26 tify the link reliability (an index based on stochastic block models and Bayesian inference) of each

27 del is distinct from other deterministic and stochastic Boolean network models in removing the requir

28 Stochastic bottlenecks during bacterial colonization of

29 simulate time-homogeneous and inhomogeneous stochastic branching processes under a very flexible set

30 and settling or rising effects, rather than stochastic Brownian motion.

31 ibroblasts, dynamin GTP hydrolysis occurs as stochastic bursts, which are randomly distributed relati

32 ll (ISC) of Drosophila, the fate of which is stochastic but dependent on the Notch/Delta pathway.

33 ifically, we consider burrowing events to be stochastic but memoryless, leading to exponential inter-

34 uces realistic Ca2+ waves and DADs driven by stochastic Ca2+ release channel (RyR) gating and is used

35 The nonlinear, cooperative, and stochastic character of the interactions between compone

36 ion approach is sample heterogeneity and the stochastic character of the labeling procedure.

37 rast, mOFC/vmPFC-lesioned patients made more stochastic choices than Controls when the decision was f

38 A simple model incorporating stochastic colonization suggests that heterogeneity betw

39 METHODS AND We developed a stochastic compartmental model representing the natural

40 rmer with even longer DAD, establishing that stochastic conformational sampling is required to achiev

41 t can be parsed into a combination of global stochastic conformational thermal fluctuations and local

42 ll as continuous-wave sources, supernovae, a stochastic confusion background of compact-object merger

43 increase in phenotypic variability is due to stochastic conversion of normally symmetric cell divisio

44 oseconds writing speed, originating from the stochastic crystal nucleation during the crystallization

45 n structure, which led to identification of "stochastic death" as the most appropriate death mechanis

46 Subtype specification is controlled by a stochastic decision in R7 and instructed to the underlyi

47 l true random number generator utilizing the stochastic delay time of threshold switching in a Ag:SiO

48 We analyze the stochastic demography and evolution of a density-depende

49 trees by incorporating uncertainty through a stochastic differential equation.

50 equencing approach presented here detected a stochastic distribution of human pathogens, such as Heli

51 bioanalytical applications of this SNA-based stochastic DNA walker by exploiting movement-triggered c

52 a type of exonuclease III (Exo III)-powered stochastic DNA walker that can autonomously move on a sp

53 ls science-based solution to the problems of stochastic domain wall pinning in soft ferromagnetic nan

54 We reject stochastic drift in favour of selection in some cases bu

55 antify the strength of selection relative to stochastic drift in language evolution.

56 Stochastic drift must also occur in language as a result

57 ommon approach to statistical inference with stochastic dynamic models relies on producing large numb

58 nt an efficient numerical method to fit such stochastic dynamic models to in vivo experimental IT dat

59 Using stochastic dynamic programming, we find that protecting

60 In order to analyse large complex stochastic dynamical models such as those studied in sys

61 he outcomes of evolution are determined by a stochastic dynamical process that governs how mutations

62 The stochastic dynamics and regulatory mechanisms that gover

63 vestigate experimentally and numerically the stochastic dynamics and the time-dependent response of c

64 ften met by vertebrate populations, that the stochastic dynamics of population size can be accurately

65 ata-driven PP-GLMs and shed new light on the stochastic dynamics of state-of-the-art statistical mode

66 We study the stochastic dynamics of strongly-coupled excitable elemen

67 longitudinal studies, i.e., SPM relates the stochastic dynamics of variables (e.g., physiological or

68 These stochastic dynamics were independent of the nucleosomal

69 Reducing the stochastic effects of the differentiation process by cor

70 n ways that would otherwise be attributed to stochastic effects, such as dispersal limitation or demo

71 ons to the deterministic FBA paradigm as the stochastic elements dissipate, and C) RAMP can identify

72 variation around these estimates due to the stochastic elements of measles importation and sensitivi

73 fication of cellular expression by nonlinear stochastic embedding (ACCENSE); and cluster identificati

74 We use a data-driven global stochastic epidemic model to analyze the spread of the Z

75 A stochastic epidemic model with stochastic simulations is

76 ular genes, suggesting that integration is a stochastic event and that site of integration may be lar

77 ility of extreme (i.e. rare, high-amplitude) stochastic events from a limited set of spontaneous Ca2+

78 Stochastic events in a single ion channel system can be

79 Whereas many stochastic events will be inconsequential "passengers,"

80 Here, we study stochastic evolutionary dynamics along these equilibrium

81 Using a stochastic evolutionary model allowing for mixed populat

82 Here we develop a stochastic evolutionary model and show how genetic trans

83 Cancer growth is a multistage, stochastic evolutionary process.

84 genetic mosaic ovaries and blastocysts with stochastic expression of wild-type or mutant H3.3 allele

85 n factor levels are finely tuned to regulate stochastic expression, setting the ratio of alternative

86 In this model, stochastic extinction of oncogenic Kras signalling and e

87 invasion dynamics of a zoonotic virus where stochastic fadeout have played a major role and may indu

88 In the absence of stochastic fadeout, viral prevalence is predicted to con

89 V before 2010 can be interpreted as repeated stochastic fadeouts after multiple introductions of infe

90 cratchpads were altered in a progressive and stochastic fashion as the cells proliferated.

91 that MuSCs undergo symmetric expansions with stochastic fate acquisition during tissue repair.

92 y related to event-level factors, apparently stochastic features of the introduction (time since firs

93 vestment decisions and the central role that stochastic financial modeling should play in support of

94 This allowed direct observation of stochastic firing of DNA replication origins, which diff

95 gulation, we reveal the relationship between stochastic fluctuations and feedback topology at the sin

96 tanding the relationship between spontaneous stochastic fluctuations and the topology of the underlyi

97 ister cell fates highlights the potential of stochastic fluctuations during clonal growth to rapidly

98 migrating cells, feedback loops can amplify stochastic fluctuations in actin dynamics, often resulti

99 Strong stochastic fluctuations witnessed as very broad resistan

100 llular networks are intrinsically subject to stochastic fluctuations, but analysis of the resulting n

101 Stochastic fluctuations, termed "noise," in the level of

102 sed on the Langevin equation with non-Wiener stochastic forcing that originates in animal's response

103 The stochastic frameshift of the unstable repeat DNA in a su

104 We developed a discrete-state stochastic framework that allowed us to investigate the

105 fact, preliminary research has revealed that stochastic functionalities also underlie the spiking beh

106 xley model neurons with channel noise (i.e., stochastic gating variable dynamics), and the network co

107 ghts into how chromatin regulation modulates stochastic gene expression and transcriptional bursting,

108 ions, suggesting an alternative mechanism to stochastic gene expression in bistable gene regulatory c

109 in copy number in a general kinetic model of stochastic gene expression with nonlinear feedback regul

110 ntal importance for the study of single-cell stochastic gene expression.

111 sgnesR (Stochastic Gene Network Expression Simulator in R) is an

112 boration with bioinformaticians who research stochastic gene networks.

113 alizing the complex probability landscape of stochastic gene regulatory networks can further biologis

114 Dispersal is highly stochastic, generating variability in species arrival hi

115 A case can be made for stochastic germination and interactions among germinatin

116 This work suggests that stochastic germination is commonly affected by the commu

117 Curvilinear Components Analysis (CCA) and a stochastic gradient decent (SGD) optimization method giv

118 for estimating model parameters such as (i) stochastic gradient descent (SGD) and its multiple varia

119 Using these data sets, the Stochastic Human Exposure and Dose Simulation (SHEDS) mo

120 Using these data sets, the Stochastic Human Exposure and Dose Simulation (SHEDS) mo

121 pport a model of genomic degradation that is stochastic in outcome and nonadaptive for the host.

122 Conventional Monte Carlo simulations are stochastic in the sense that the acceptance of a trial m

123 VTx of ZIKV was stochastic, in that not all fetuses/pups within the same

124 We use a mechanistic, spatially explicit, stochastic, individual-based mathematical model to simul

125 without double-stranded DNA cleavage, excess stochastic insertions and deletions, or dependence on ho

126 h arbitrary noise intensities through A-type stochastic integration, which preserves the dynamical st

127 system, where SPR response is formed by the stochastic interactions within the whole variety of proc

128 A stochastic investigation of lithium deinsertion from ind

129 at every point along the equilibrium paths: stochastic jumps off the paths return with, on average,

130 simple one-dimensional diffusion process and stochastic Langevin dynamics.

131 to multistable stochastic system, we use the stochastic least-action principle to derive the entropy

132 simple null models of diversification under stochastic lineage birth and death and random genetic dr

133 ock factor 1 (Hsf1) in a highly variable and stochastic manner.

134 Then a three-state stochastic Markov jump process is used to drive the wind

135 The most promising approach relies on stochastic Markovian models of bacterial population dyna

136 To this end, we consider a minimal stochastic mass balance model and identify a parsimoniou

137 A stochastic mathematical model was adapted for infectious

138 y defective, noninfectious particles and the stochastic minefield blocking access to host DNA.

139 rocess and found that it can be modeled by a stochastic minimization process, which causes the scaled

140 A simple stochastic model accounting for the essential steps of c

141 Using a stochastic model fit to seasonal flu surveillance data f

142 Here we use a simple stochastic model of translation to characterize the effe

143 We developed an individual-based, stochastic model of tuberculosis disease in a hypothetic

144 ccounted for by a newly-developed Lagrangian stochastic model of weakly-flying insect movements in th

145 It is based on a discrete-state stochastic model that takes into account the most releva

146 albopictus; the former was assessed using a stochastic model to calculate R0 and the latter was asse

147 In this work we extend the stochastic model to more realistic BKCa-CaV complexes wi

148 n, we calibrated a dynamic, individual-based stochastic model, the HIV Synthesis Model, to multiple d

149 ate during use) nanoRelease is designed as a stochastic model.

150 Using measurements and stochastic modeling of mycobacterial cell size and cell-

151 Using deterministic and stochastic modeling, we reproduced in silico the differe

152 a proof of concept, this approach shows that stochastic modelling of a specific immune networks rende

153 ection of images is estimated by first using stochastic modelling where the locations of clusters in

154 intensive time-series datasets and improved stochastic modelling will help to explore their importan

155 ta analysis method that combines mechanistic stochastic modelling with the powerful methods of non-pa

156 Both deterministic and stochastic models are constructed to describe the transm

157 roaches for calibration and prediction using stochastic models of epidemics.

158 d for the first-passage time distribution in stochastic models of gene expression.

159 estimation and prediction for these types of stochastic models remain limited.

160 issect this data requires the development of stochastic models that can both deconvolve the stages of

161 We illustrate the use of spatially explicit stochastic models to optimize targeting of surveillance

162 To test this, first we used stochastic models to predict that variability in the num

163 an accurate coarse-grained model for DNA and stochastic molecular dynamics simulations to study the p

164 To achieve this end, we used a stochastic Moran process model of tumor cell kinetics co

165 We consider this problem using a theory of stochastic motion based on the Langevin equation with no

166 quarters of the step occurs by bidirectional stochastic motion of the TH.

167 d generate general regulatory principles for stochastic, mutually exclusive gene expression programs.

168 The basic constituents are stochastic nanomagnets which switch randomly between the

169 proof-of-concept, spike-like transients of a stochastic nature are reported in the current-time respo

170 signaling domains, and for understanding the stochastic nature of calcium signaling.

171 Existing research has focused on the stochastic nature of endocytosis.

172 Because of the stochastic nature of genetic alterations, this intratumo

173 Moreover, capsid size constraints and/or the stochastic nature of status quo approaches (viral and no

174 information from radioactive decays, but the stochastic nature of the process precludes high-throughp

175 The approach aims to take advantage of the stochastic nature of the single-molecule regime to achie

176 Gaussian processes model the stochastic nature of the spatial random effects, where t

177 of all major cellular processes despite the stochastic nature of underlying chemical processes.

178 ity-normalized events (SPADE); t-distributed stochastic neighbor embedding (t-SNE)-based visualizatio

179 ME-SIMS analysis followed by t-distributed stochastic neighbor embedding of peaks in the lipid mole

180 This paper proposes a stochastic network disease game model that captures the

181 is Boltzmann sample is known to be robust to stochastic noise, the conditioning and robustness under

182 Gene expression is intrinsically a stochastic (noisy) process with important implications f

183 A stochastic normalization model containing populations of

184 The real-time visualization of stochastic nucleation events at electrode surfaces is on

185 To the best of our knowledge, stochastic nucleation events of nanoscale copper deposit

186 number of ordinary differential equation and stochastic numerical solvers for single-compartment simu

187 The unpredictable and stochastic occurrence of transgene silencing and epigene

188 g R7 photoreceptor subtypes is determined by stochastic on/off expression of the transcription factor

189 st, permissive or "plastic" states may allow stochastic oncogene activation or nonphysiologic cell fa

190 fluctuations, which are quantifiable through stochastic optical fluctuation imaging.

191 FP collection, mass spectrometry, and direct stochastic optical reconstitution microscopy of cross-li

192 Here, we use 3D direct stochastic optical reconstruction microscopy (dSTORM) to

193 Using direct stochastic optical reconstruction microscopy (dSTORM), w

194 ith virtually molecular resolution by direct stochastic optical reconstruction microscopy (dSTORM).

195 Techniques such as Stochastic Optical Reconstruction Microscopy (STORM) and

196 Stochastic optical reconstruction microscopy (STORM) sho

197 We used stochastic optical reconstruction microscopy (STORM) wit

198 ., photoactivated localization microscopy or stochastic optical reconstruction microscopy), which ena

199 In this study, using stochastic optical reconstruction microscopy, correlated

200 Here, using dual-color direct stochastic optical reconstruction microscopy, we report

201 procedure based on information derived from stochastic optical reconstruction microscopy.

202 The drawbacks of global stochastic optimization methods are: (i) no guarantee of

203 cutively applies a pre-defined set of global stochastic optimization methods in case of stagnation in

204 oding of fermionic Hamiltonians and a robust stochastic optimization routine.

205 coarse-grained, macroscopic information from stochastic or atomistic simulations, such as molecular d

206 evelopmental nuclear differentiation, either stochastic or maternally inherited.

207 e majority of observed variation was tied to stochastic or preexisting differences in the epigenome o

208 ave also been observed, which are related to stochastic or uncoordinated molecular processes.

209 inverse relationship between the variance of stochastic pacing and the occurrence of spatially discor

210 ellular dynamics, electrotonic coupling, and stochastic pacing on the nodal dynamics of spatially dis

211 First, a stochastic parameterization of the wind bursts including

212 Optimization and realization of stochastic patterns have typically relied on serial, dir

213 ant-sibling studies) were predicted by worse stochastic patterns in their spontaneous head movements

214 tem, we develop the Optometrist Algorithm, a stochastic perturbation method combined with human choic

215 bining modeling with experiments, we related stochastic phasing to the dynamical structure of the cya

216 y our model include the total suppression of stochastic pinning at single notches in thick nanowires

217 mplex DW-defect interactions at the heart of stochastic pinning behaviours.

218 re simulations to probe how this affects the stochastic pinning of domain walls at notch-shaped artif

219 tisation structure, propagation dynamics and stochastic pinning of domain walls in rare earth-doped N

220 ations, thus suppressing dynamically-induced stochastic pinning/depinning phenomena.

221 attered radiation from "noise", arising from stochastic plasma fluctuations that competes with extern

222 time of a mutant is an important measure of stochastic population dynamics, widely studied in ecolog

223 nonbreeders' influence on deterministic and stochastic population dynamics.

224 454 plant and animal populations to simulate stochastic population growth rates (log lambdas ) under

225 We assess this tradeoff using a stochastic population-genetic model.

226 We introduce private Evaporative Cooling, a stochastic privacy-preserving machine learning algorithm

227 Our mathematical study of the stochastic problem shows that FBA is a limiting case of

228 epresentation of reinforcement learning as a stochastic process in finite 'populations of ideas'.

229 The Stochastic Process Model (SPM) represents a general fram

230 e show that the magnetisation switching is a stochastic process.

231 jority of RAMA elements is consistent with a stochastic process; however, up to 30% of RAMA elements

232 s, provoking new questions about the role of stochastic processes in DNA repair and mutagenesis.

233 This class of stochastic processes includes both an incremental and a

234 up to 10 days, after which the importance of stochastic processes increased.

235 Quantifying stochastic processes is essential to understand many nat

236 results indicate that both deterministic and stochastic processes structure soil fungal communities f

237 o environmental cues or exposures as well as stochastic processes that lead to heterogeneity and pote

238 le Space Reducing (SSR) processes are simple stochastic processes that offer a new route to understan

239 icate porous scaffolds, most of them rely on stochastic processes that typically result in scaffolds

240 le gut diversity may be driven by inherently stochastic processes, which has important implications f

241 nge for species whose dynamics are driven by stochastic processes.Adelie penguins are a key Antarctic

242 Furthermore, by monitoring how these stochastic properties are affected by spore density and

243 he results are relevant to understanding the stochastic properties of dendritic Ca(2+) spikes in neoc

244 In this article, using a stochastic puff model and a single-channel data-based IP

245 l senses a chemical gradient and chemotaxes, stochastic receptor-ligand binding can be a fundamental

246 ing dilutions, the d-AQuA reactions follow a stochastic regime indicative of the detection of single

247 t of Pcdh isoforms, which assemble to form a stochastic repertoire of cis-dimers.

248 A mass-transfer model is coupled with a stochastic residential water demand generator to investi

249 Inverse Stochastic Resonance (ISR) is a phenomenon in which the

250 passage through a bifurcation combined with stochastic resonance - a mechanism by which irregular fl

251 Stochastic resonance is a phenomenon in which noise enha

252 wever, is possible by using the principle of stochastic resonance, where noise is added to the input

253 it is a good candidate to take advantage of stochastic resonance.

254 This stochastic response accounts for previously unexplained

255 ation-induced diseases like cataract and the stochastic risk of left-sided brain tumors.

256 general in nature and can be applied to the stochastic sampling of any set of models, not just struc

257 st diverse partition functions for Boltzmann stochastic sampling.

258 s have been improved by utilizing a Bayesian stochastic search variable selection (BSSVS) framework.

259 lidate the designs for the deterministic and stochastic semantics using Microsoft's GEC tool and the

260 d sensing analysis has wide implications for stochastic sensing platforms that operate using multiple

261 strate how assessments of Ne can incorporate stochastic sex- and age-specific demography and elucidat

262 ion data from a given gene network using the stochastic simulation algorithm (SSA).

263 treatment eligibility were calculated using stochastic simulation and population data for the 37 cou

264 An agent-based stochastic simulation model of P. falciparum transmissio

265 A stochastic simulation shows the essential role of Kif15'

266 pH and particle size effects, we developed a stochastic simulation that exactly mimics these experime

267 xibility of the method are demonstrated with stochastic simulation, a sensitivity analysis, and unbia

268 l RNA sequencing data together with discrete stochastic simulation, to explore the role of chromatin

269 Performing stochastic simulations at the level of individual hexame

270 A stochastic epidemic model with stochastic simulations is also presented to supply a com

271 It is validated by comparison with stochastic simulations of force generation by actin poly

272 The stochastic simulations of such multiscale BRNs are prohi

273 Using three-dimensional stochastic simulations with parameters constrained by th

274 rks and find an excellent agreement with the stochastic simulations.

275 Stochastic single particle trajectories are used to expl

276 cles is observed at Au microelectrodes using stochastic single-nanoparticle collision amperometry.

277 on barbed-end-anchored actin filaments in a stochastic sliding-filament mechanism.

278 To enable rule-based deterministic and stochastic spatial simulations and network-free agent-ba

279 Stochastic stability is essential when sampling from the

280 ges of individual colicinogenic cells showed stochastic switching between expressers and non-expresse

281 To generalize to multistable stochastic system, we use the stochastic least-action pr

282 The proposed method, Multiple Shooting for Stochastic systems (MSS), applies a linear noise approxi

283 d preference theory in which preferences are stochastic; the monkeys behaved "as if" they had well-st

284 Stochastic thermodynamics extends classical thermodynami

285 is possible to change tin nucleation from a stochastic to a deterministic process, and to generate s

286 Stochastic transmission dynamic models are especially us

287 signaling activity of the entire cell into a stochastic two-state switching regime.

288 ifferent paradigm is based on three-terminal stochastic units which could be called "p-bits", where t

289 merization of HsRAD51 leads to stepwise, but stochastic unwrapping of the DNA from the HO in the pres

290 ase at inhibitory and excitatory synapses by stochastic VACC activity that extend beyond the cortex t

291 This stochastic variability is evident between clones, by RNA

292 tions for the two lower-order moments of the stochastic variables (mean, variance and covariance).

293 e the transition temperature, are treated as stochastic variables.

294 experimentally that mean expansion speed and stochastic variation in speed are both increased by rapi

295 ious noise sources such as batch effects and stochastic variation.

296 Stochastic versions of published models of the circadian

297 In stochastic versions of such models, the system can go by

298 t account for variant calling thresholds and stochastic viral replication dynamics within recipient h

299 (Xa) and inactive (Xi) X chromosomes because stochastic X-chromosome inactivation (XCI) confounds all

300 Interestingly, this cadre of genes displays stochastic Xi expression in human fibroblasts ahead of r