ライフサイエンスコーパス: stochastic

1 within and between cells that are inherently stochastic.

2 terministic even when individual defects are stochastic.

3 nges, whereas about 40% is attributed to the stochastic accumulation of mutations whose pleiotropic e

4 ata-dependent acquisition methods effectuate stochastic acquisition of data in complex mixtures, whic

5 Using stochastic actor-oriented models, we investigate the lon

6 Here, we present results of a stochastic agent-based microsimulation model of the COVI

7 progenitors that transition from a phase of stochastic amplification by cell division into a phase o

8 ervised learning systems using nanoscale and stochastic analog memory synapses.

9 hemical setups, the background processes are stochastic and appear at distinct frequencies that do no

10 se tracking to study model-based learning in stochastic and deterministic (pattern-based) environment

11 for a system to maintain balance between its stochastic and deterministic assembly processes (as in t

12 s life, with emphasis on the balance between stochastic and deterministic processes.

13 We found that navigation was stochastic and did not rely on the continuous modulation

14 zation by the nuclear membrane provides both stochastic and functional buffering of transcript activi

15 together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream effec

16 ecially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelih

17 dynamics between states are best captured by stochastic and reversible models.

18 racking of individual receptors are based on stochastic and transient binding between aptamers and th

19 les where dispersal is high, but may be more stochastic and unpredictable at larger scales.

20 nd and carries information on deterministic, stochastic, and control influences.

21 s, undergoing autoinhibition (TBAT) or quasi-stochastic autoacceleration (NaI) and cogenerating perfl

22 ilizing the giant spin Hall effect, enabling stochastic behavior.

23 ans strains UA159 and GS-5 were examined for stochastic behaviors in transcription of the lac operon.

24 re maintained by population asymmetry, where stochastic behaviors of multiple individual cells collec

25 e framework of the classical network theory (stochastic block model, SBM) does not take into account

26 Our model combines the class of Stochastic Block Models for community formation with Gau

27 itative datasets, (iii) the integration of a stochastic Boolean simulator, (iv) a tool to identify mi

28 We combine this stochastic breast tumor induction model with inverted li

29 We find that infrequent, stochastic bursts of transcription result in the co-expr

30 again and activated transcription occurs in stochastic bursts.

31 in the level of a TF that is synthesized in stochastic bursts.

32 ution or on the membrane, and we predict how stochastic but localized dephosphorylation of membrane l

33 The anchor cell is initially specified in a stochastic cell fate decision mediated by Notch signalin

34 best-fit rates and statistical intervals for stochastic cell-state transitions.

35 Stochastic changes in DNA methylation (i.e., spontaneous

36 Gradual and stochastic changes in genomic and epigenomic regulation

37 The rate constants for the stochastic clock network are consistent with those deter

38 Here, we use stochastic closure analysis to determine if a genomic tr

39 urce competition, metabolic crossfeeding and stochastic colonization - can qualitatively reproduce pa

40 ministic constrains imposed by diet and age, stochastic colonization in early life has long-lasting i

41 A stochastic compartmental network model of SARS-CoV-2 spr

42 A stochastic computational framework was successfully deve

43 ted binding surface in the unbound form, and stochastic conformational changes in D1 facilitate initi

44 apture nested RNA base-pairing correlations (stochastic context-free grammars).

45 nified threshold models of survival assuming stochastic death), indicating that sensitivity toward cy

46 associated to Ag oscillations evolving into stochastic decaying fluctuations.

47 Statistical analysis disclosed a stochastic deterministic relationship between methylatio

48 ationship between organism body size and the stochastic-deterministic balance operating on community

49 to foster mutagenesis, thereby enhancing the stochastic development of acquired resistance mutations.

50 on models, e.g., chemical reaction networks, stochastic differential equations.

51 Following studies of a model system (urea), stochastic differential scanning calorimetry (SDSC) was

52 ing to show that stochastic forces (drift or stochastic dispersal) act on fungal community assembly i

53 positions, and analyze the influence of the stochastic drift on the search success.

54 methods for the analysis of large, nonlinear stochastic dynamic models, and develop computational alg

55 about one event per month, which suggests a stochastic dynamics behind beach stabilization.

56 In summary, our analysis reveals stochastic dynamics emerging from nonspecific binding of

57 we develop a master equation describing the stochastic dynamics of the probability density function

58 locities, and uncertainty quantification for stochastic effects in spatial matrix games.

59 -host relationship between selection and the stochastic effects of genetic drift, estimating an effec

60 sults address functional consequences of the stochastic effects of microbiota perturbations, whereby

61 handful of these channels, it is likely that stochastic effects play an important role in the pattern

62 y to bone marrow-both direct suppression and stochastic effects, leading to neoplasia.

63 netic, and environmental factors, as well as stochastic elements, in diverse phenotypic variance is n

64 ingly, depending on the initiation kinetics, stochastic elongation can either enhance or suppress cel

65 Collectively, these data suggest that stochastic encounters with Ag occur frequently enough to

66 ctions as a general RNA chaperone by using a stochastic, energy-intensive mechanism to promote RNA un

67 Since microstructures are stochastic, ensembles of meso-scale simulations are requ

68 racts with a quantum annealer that plays the stochastic environment role of learning automata.

69 Fluctuating population density in stochastic environments can contribute to maintain life-

70 amplifications also tune gene expression in stochastic environments in which transcription-factor-ba

71 source availability, where depleted and more stochastic environments promote investment in each repro

72 ion of social strategies needed to thrive in stochastic environments, strategies that in our case stu

73 totic tissues such as the brain are prone to stochastic (epi-)genetic alterations that may cause incr

74 were rendered observable by extracting rare stochastic events out of an overwhelming background usin

75 thod for estimating the precise rate of such stochastic events using pedigree-based DNA methylation d

76 native protein conformations, sampled during stochastic excursions by polymorphic/metamorphic protein

77 Finally, a stochastic extension of the model explains non-trivial c

78 xtinctions play an equally important role to stochastic extinction, even when the disturbance itself

79 eneity, thereby increasing the likelihood of stochastic extinctions.

80 ocal success is driven largely by biotic and stochastic factors and raise the possibility that purple

81 lso found that herds with smaller K had less stochastic fluctuation in abundances around K, but highe

82 ce switches after fundamental changes versus stochastic fluctuations in reward contingencies.

83 gnaling systematically shapes the intrinsic, stochastic fluctuations of actin in the growth cone to p

84 tical framework that explicitly accounts for stochastic fluctuations of an individual consumer's ener

85 babilistic mechanism by which Abl biases the stochastic fluctuations of growth cone actin to direct a

86 m system with abundant sampling to show that stochastic forces (drift or stochastic dispersal) act on

87 l membrane show that concentration-dependent stochastic forces inside cells, compatible in magnitude

88 stable selective pressures, during which the stochastic forces of drift and mutation conspire to gene

89 ranscription-coupled activity is a source of stochastic forces that are substantially larger than the

90 is heterogeneity is impacted by climatic and stochastic forcing, but additional high-resolution paleo

91 c rate, which is the product of an intrinsic stochastic fractal rate and a global modulatory gain.

92 d structure) manner by exploiting underlying stochastic fracturing process.

93 The simulations are carried out in a stochastic framework that naturally captures rare events

94 Our results demonstrate stochastic gene expression dependent on positioning rela

95 We investigated impact of stochastic gene expression on pattern formation, focusin

96 We investigate a multihousehold dynamic stochastic general equilibrium (DSGE) model in which pas

97 he human microbiome, and investigate whether stochastic generalized Lotka-Volterra models could repro

98 olleagues, that show how programmed but also stochastic generation of variation in cellular circadian

99 further examination in random matrix theory, stochastic geometry, and related topics.

100 gh-dimensional loss function, typically by a stochastic gradient descent (SGD) strategy.

101 and why simple optimization schemes, such as stochastic gradient descent, do not end up trapped in lo

102 and optimized through spectral embedding and stochastic gradient descent.

103 We consider a simplified model based on a stochastic growth process driven by a continuous time ra

104 Across all coral taxa, projected stochastic growth rates (lambda(s) ) were found to be lo

105 on control treatment [CCT] scenarios), EPA's Stochastic Human Exposure and Dose Simulation (SHEDS)-Mu

106 analysis, fractional Brownian motion with a stochastic Hurst exponent was used to interpret, for the

107 tions of wetlandscape connectivity driven by stochastic hydroclimatic forcing, conceptualizing wetlan

108 nsically limited because of volume-dependent stochastic ice formation at subzero temperatures.

109 then quantified how explicit spatial and/or stochastic implementations alter outcomes, even when all

110 suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinc

111 n bacteria to humans has been observed to be stochastic in nature: toggling between active and inacti

112 Although neural responses were stochastic in time, cue identity could be read out from

113 We simulate stochastic infection curves incorporating core elements

114 l lines have relied on frameshifts caused by stochastic insertion/deletion in all alleles.

115 bitrary probability functions and controlled stochastic interaction or correlation between probabilis

116 Stochastic Resonance at one common level of stochastic intracellular noise across days from 6 to 36

117 resilience, initial resilience and intrinsic stochastic invariability) using first-order multivariate

118 ICC-SS fired stochastic localized Ca(2+) transients.

119 el based on environmental stochasticity, the stochastic logistic model, quantitatively predicts the t

120 Antibiotics caused stochastic loss of members of the microbiota, but the mi

121 tion between the two sites in a repeated and stochastic manner, and average dwell times in the respec

122 These hinge angles fluctuate in a stochastic manner, and perfectly cylindrical microtubule

123 generate spontaneous Ca(2+) transients in a stochastic manner.

124 d carbonate delta(238)U data) with a coupled stochastic mass balance model.

125 ce onset were characterized and a multiscale stochastic mathematical model was proposed to link the i

126 Stochastic mathematical modeling provides insight to the

127 analyse patterns in root trajectories and a stochastic-mechanical theory to establish how root defle

128 h/field observations with spatially explicit stochastic metapopulation models to study the near-term

129 nsferase alleles revealed that intracellular stochastic methylation generates a mosaic of methylomes

130 methylation patterns in C. neoformans, rare stochastic methylation loss and gain events, and the act

131 A stochastic model confirms the presence of an autocatalyt

132 Here, by developing a system-level stochastic model constrained by a large set of single-ce

133 A stochastic model demonstrated the separation of TF input

134 larization model based on coupling between a stochastic model for the segregation of signaling molecu

135 nalysis of disease progression is based on a stochastic model of a population of infectious agents in

136 Using a parameterized stochastic model of expansion, we find that this slowdow

137 erty line can be derived from an agent-based stochastic model of market exchange, combining all expen

138 Here we develop a stochastic model of severe acute respiratory syndrome co

139 We then apply our framework to a stochastic model of the rocky intertidal food web, parti

140 nd replicated experimental system and in our stochastic model simulations points to potential fundame

141 To fit the resulting stochastic model to data from FRAP measurements and to e

142 By fitting a stochastic model to the observed mRNA distributions, we

143 gical implement for control is a large scale stochastic model with countless parameters lacking robus

144 We also developed a stochastic model with physically meaningful parameters t

145 yses of these dependencies by establishing a stochastic model.

146 One such case is the stochastic modeling approach, which can be important whe

147 lts using Monte Carlo simulation in a tiered stochastic modeling approach: exposures were the highest

148 A stochastic modeling framework was developed to depict th

149 Stochastic modeling using reasonable biophysical paramet

150 Stochastic modelling and experimental data demonstrated

151 boundary layer, but we found that Lagrangian stochastic modelling is effective at predicting flight m

152 Spatially structured stochastic models can capture these important features o

153 Alignment-free, stochastic models derived from k-mer distributions repre

154 r experimental results with predictions from stochastic models of transcription, which indicated that

155 nce and model selection for deterministic or stochastic models using (i) standard rejection ABC or se

156 and this difference across aluminosilicates, stochastic molecular models of the various aluminosilica

157 ks for understanding the dynamics of complex stochastic molecular systems.

158 imate change need to account for large-scale stochastic mortality events to preserve critical habitat

159 We show that stochastic motor force not only enhances diffusion but a

160 lations of DNA to simulate deterministic and stochastic motor models.

161 ansmission, and operations-the Johns Hopkins Stochastic Multistage Integrated Network Expansion Model

162 accuracy of their energy functions and their stochastic nature imposes generation of a large number o

163 The stochastic nature of bond-based interactions facilitates

164 ypes, which is typically associated with the stochastic nature of gene expression processes.

165 ncy only in neuronal cells and emphasize the stochastic nature of lytic versus latency decision of HS

166 m both clinically and scientifically, as the stochastic nature of metastatic lesion formation introdu

167 Despite the stochastic nature of our small-scale, short-term, and ra

168 ocial behaviors, we need to measure both the stochastic nature of the behavior of isolated individual

169 landslide dynamics is challenging due to the stochastic nature of the environment.

170 We define the duration, stochastic nature, and molecular mechanisms of IFNgamma-

171 imensionality reduction method t-Distributed Stochastic Neighbor Embedding (t-SNE) to plot the distri

172 lassification model, utilizing t-distributed stochastic neighbor embedding (t-SNE)-enhanced ensemble

173 s and methadone users, we demonstrate, via t-Stochastic Neighbor Embedding and k-means cluster analys

174 negative matrix factorization, t-distributed stochastic neighbor embedding, and uniform manifold appr

175 of mass spectrometry imaging: t-distributed stochastic neighborhood embedding (t-SNE) and uniform ma

176 Like t-distributed Stochastic Neighborhood Embedding, the model can produce

177 Bayesian networks can be implemented by our stochastic network.

178 Stochastic networks for the clock were identified by ens

179 ain processes information accurately despite stochastic neural activity is a longstanding question(1)

180 hich model parameters agree with complex and stochastic neural data presents a significant challenge.

181 and bi-stable seizure end-points arise when stochastic noise is included.

182 ations are challenging as DS transitions are stochastic, non-repeatable and often strongly coupled ac

183 are well captured by a simple model for the stochastic nonlinear dynamics of the cells, which reveal

184 in early neural development is the origin of stochastic nuclear movement between apical and basal sur

185 As a result, neutrally stochastic null models such as the SAR and rarefaction a

186 A data movie of stochastic optical localization nanoscopy contains spati

187 We use direct stochastic optical reconstruction microscopy (dSTORM) an

188 microscopy (AFM) in conjunction with direct stochastic optical reconstruction microscopy (dSTORM), m

189 otoactivated localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM) and

190 chnique to remove autofluorescent noise from stochastic optical reconstruction microscopy (STORM) dat

191 Direct stochastic optical reconstruction microscopy resolved a

192 Using stochastic optical reconstruction microscopy we identifi

193 h a statistical model of detection and use a stochastic optimisation routine to identify which arrang

194 We use stochastic optimization to derive triggers that ensure h

195 whether promiscuous gene expression (PGE) is stochastic or coordinated, we sequenced transcriptomes o

196 ness aspect of the otherwise inefficient and stochastic OSKM reprogramming.

197 nations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showi

198 learning about the expectation, or mean, of stochastic outcomes.

199 Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation, INLA-SPDE) is

200 Inspired by restraining the stochastic paths of particles in the vicinity of the ele

201 mented ecology, are then vulnerable to small stochastic perturbations that further reduce the populat

202 Stochastic phase transformations within individual cryst

203 on-level changes in tissues can be driven by stochastic plasticity, meaning rare stochastic transitio

204 distinguishing error-enriched regions among stochastic polymerase errors.

205 As such, a stochastic process can produce cellular behaviours that

206 -like receptors (KIR) in human NK cells is a stochastic process exclusive to subsets of mature NK cel

207 The time of a stochastic process first passing through a boundary is i

208 Mathematical modeling of the stochastic process of cancer evolution can be used to de

209 Gene expression is an inherently stochastic process(1,2); however, organismal development

210 As opposed to an indiscriminate stochastic process, we find pronounced demethylation of

211 ukaryotic protein synthesis is an inherently stochastic process.

212 the population to the detrimental effects of stochastic processes (i.e. low densities where random ba

213 sed upon null modeling results, we show that stochastic processes drove molecular properties while bi

214 How stochastic processes give rise to the robust outcomes th

215 king the relative importance of adaptive and stochastic processes in insular evolution difficult to a

216 erlooked, but potentially important, role of stochastic processes in stabilising population dynamics

217 Ultimately, these inherently stochastic processes manifest as noise in gene expressio

218 g and particle-associated communities, where stochastic processes play a larger role.

219 By contrast, stochastic processes tended to dominate soil fungal comm

220 rs how SOC cycling rates are governed by the stochastic processes that influence the proximity betwee

221 ata allowed us to reject the hypothesis that stochastic processes were responsible for community asse

222 ad using a hybrid model of deterministic and stochastic processes, fitted to previously published HTL

223 elatively more influenced by dispersal-based stochastic processes, while larger ones (fungi, protists

224 d because motion at the atomic scale follows stochastic processes.

225 pulations more susceptible to the effects of stochastic processes.

226 ntal volatility described by continuous-time stochastic processes.

227 ction exerted by the plant host, rather than stochastic processes.

228 Stochastic pulsatile dynamics have been observed in an i

229 showed that flagellar genes are activated in stochastic pulses without the means of feedback.

230 Stochastic pulsing of gene expression can generate pheno

231 Our results demonstrate how stochastic pulsing of gene expression can play a key rol

232 Here, we show how stochastic pulsing of gene expression enables spatial pa

233 ants maximize the expected carbon gain under stochastic rainfall in a competitive environment.

234 in Hittorf's and fibrous phosphorus to seed stochastic ("random") structure searches over hundreds o

235 y reversed mechanical hypersensitivity, with stochastic rate modulation achieving the highest efficac

236 ors in the neocortex of mice may result from stochastic rather than deterministic processes.

237 w that kinetic proofreading results from the stochastic removal and reformation of promoter nucleosom

238 Stochastic resonance (SR) is an ingenious phenomenon obs

239 Stochastic resonance (SR) is one of those astounding phe

240 microfluidics provided evidence for only one Stochastic Resonance at one common level of stochastic i

241 On the other hand, in processes such as stochastic resonance, noise can improve the detection of

242 describe our observations in the context of stochastic resonance, which we propose as a mechanism by

243 n the single cell oscillations away from the stochastic resonance.

244 rceptible transcriptional changes induced by stochastic responses to the cessation of biological func

245 s related to updating current beliefs during stochastic reversal learning.

246 nd responding to the immediate experience of stochastic rewards.

247 ssfully retrieved is an emergent property of stochastic sampling, requiring no explicit mechanism to

248 ees and optimal pipelines discovered using a stochastic search method called genetic programing.

249 Consequently, the efficiency of the stochastic search process of CTLs in the ECM should stro

250 Our recent advances in stochastic separability of highdimensional data have pro

251 ng process, the result of each cell making a stochastic, signal-based decision.

252 We then present the stochastic simulation of an example case of a four node

253 We used in silico stochastic simulation of future hybrid performance in a

254 Using stochastic simulations and a synthetic biology approach

255 show that the tQSSA can be used for accurate stochastic simulations at a lower computational cost tha

256 ally-resolved human cell model and performed stochastic simulations for up to 15 minutes of biologica

257 Stochastic simulations of transmitter release from vesic

258 We use mean-field equations as well as stochastic simulations to derive the epidemic threshold,

259 Further, we performed stochastic simulations to evaluate if identified differe

260 tional experiments: each averaged over 1,000 stochastic simulations.

261 We then explore its use on the stochastic SIR model to predict the final size distribut

262 coarse-grained model of SUHI coupled with a stochastic soil water balance is developed to demonstrat

263 ndividual-level variation into a two-species stochastic spatial Ricker model, we provide evidence tha

264 omplex flow of electrical activity driven by stochastic spatio-temporal synaptic input streams in the

265 k building block implemented with inherently stochastic spintronic devices based on the natural physi

266 ithin the workflow included repeated runs of stochastic steps and cell subsampling.

267 hieved by a combination of two intrinsically stochastic steps: random receptor generation by VDJ reco

268 Stochastic strategies are more robust in some cases due

269 s at very high levels of missing data, where stochastic strategies, like acquaintance immunization, b

270 tupalong-Balukhali Expansion Site, we used a stochastic Susceptible Exposed Infectious Recovered (SEI

271 nificantly different than those predicted by stochastic switching alone.

272 ive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of

273 , we perform experiments on thermally driven stochastic switching in the gamma ray environment.

274 models are non-Markovian, recent results on stochastic systems with random delays allow us to rigoro

275 ations with the number of variables in large stochastic systems.

276 estimates suggested no such learning in the stochastic task.

277 Overall, the results indicate that stochastic tegument packaging provides a mechanism enabl

278 l, which constituted no heatwave but natural stochastic temperature variability (0HW), two treatments

279 velocity, pausing and slipping are primarily stochastic temporal events.

280 Using stochastic thermodynamics, we compute the total energy c

281 earns a probabilistic model of 1-dimensional stochastic trajectories generated from higher-dimensiona

282 ELs from post-transcriptional modifications, stochastic transcription errors, and technical noise, im

283 The relationship between stochastic transcriptional bursts and dynamic 3D chromat

284 underlying design principles for generating stochastic transcriptional pulses without feedback.

285 ptor phosphorylation events as a sequence of stochastic transitions between discrete biochemical stat

286 riven by stochastic plasticity, meaning rare stochastic transitions of single-cell phenotype.

287 Quantifying the rates of these stochastic transitions requires time-intensive experimen

288 tween multiple species and subspecies with a stochastic translocation pattern into the bloodstream.

289 We developed a stochastic transmission model, parameterised to the COVI

290 n, single-stranded regions formed because of stochastic uncoupling of the leading-strand DNA polymera

291 s that are not efficient in representing the stochastic variables in a Bayesian network that encode t

292 ertain schizophrenia risk loci can influence stochastic variation in gene expression through epigenet

293 F12 functions heterochronically to fine-tune stochastic variation in wild type floral number and simi

294 Our data indicate that, rather than stochastic variation, read-level CpG methylation pattern

295 lay more heterogeneity because of the higher stochastic variations in n.

296 splay a more uniform uptake, reflecting less-stochastic variations in n.

297 However, debate remains on how stochastic versus deterministic assembly processes influ

298 y, and flooded) that represent a gradient of stochastic versus deterministic assembly processes.

299 e expression (either in a deterministic or a stochastic way), the interaction between different cell

300 The occurrence of this stochastic yield catastrophe does not depend on model de