ライフサイエンスコーパス: mathematical

1 The mathematical aberration of the Gregorian chronology's mi

2 und to be associated with impairment in both mathematical abilities and estimation of continuous quan

3 nonatopic healthy controls (NAC, n = 13) by mathematical algorithms developed to manage high-dimensi

4 Moreover, mathematical analyses showed that the model can be simpl

5 Then, mathematical analysis and detailed numerical investigati

6 The model is amenable to detailed mathematical analysis and gives insight on the mechanism

7 The quantitative mathematical analysis of the dynamic bright-field imagin

8 The mathematical analysis of this model, simulations of a si

9 Our numerical simulations and mathematical analysis revealed that, contrary to the pri

10 We compare our mathematical analysis with the results of agent-based co

11 Mathematical and computational (in silico) models can pr

12 Consistent with these results, our mathematical and computational approaches predict that g

13 However, new mathematical and computational methods are required to r

14 rimental techniques and the incipient use of mathematical and computational models to characterize th

15 ce-base underpinning policy decision making, mathematical and health economic modelling can be a valu

16 Zebrafish her6::Venus reporter combined with mathematical and in vivo experimentation, we explore how

17 We will describe the mathematical and physiologic background underlying chang

18 ud computing and machine learning, with deep mathematical and statistical questions, and it does this

19 erged across mechanical, physical, chemical, mathematical, and biological disciplines to address the

20 Computer simulations using a mathematical antibiotic treatment model indicated that a

21 Here, we present a mathematical approach based on the mutual Shannon inform

22 e a contagion model as a simple and powerful mathematical approach for predicting the spatial spread

23 ions on the basis of 'additive area' but not mathematical area predicts number discrimination ability

24 ents reveal that (1) perceived area, but not mathematical area, strongly influences numerosity judgme

25 etrics for patient similarity using unbiased mathematical, biological, and clinical measures of inter

26 ironments by explicitly unifying theory from mathematical biology and finance.

27 We have created a mathematical bottom-up model for a synergistic mixture e

28 usceptible-exposed-infectious-removed (SEIR) mathematical compartmental model system that characteriz

29 We present a mathematical/computational model of GJ channel voltage g

30 We argue that PSO security is a mathematical concept with legal consequences.

31 Topology, a mathematical concept, has recently become a popular and

32 We suggest mathematical conjectures and questions arising from thes

33 three theoretical approaches that have deep mathematical connections: dynamical systems, Bayesian in

34 The findings suggest that the proposed mathematical contagion model offers great potential to s

35 dentification of the physical meaning of the mathematical definition of CT opens up new possibilities

36 icle, I provide a full, exact, and universal mathematical description of the TC system at equilibrium

37 nty and complexity, which implies that exact mathematical equations to describe biological processes

38 research outcomes, presented through simple mathematical equations, outline the best CHD prevention

39 ent, we developed and analyzed an integrated mathematical-experimental model.

40 m dynamics allows us to derive an ab initio, mathematical expression for the Leidenfrost point of a f

41 The second phase of the study asks: can a mathematical feedback model account for the rotation tra

42 integrative approach of experimentation and mathematical flux balance analyses in BRAF-mutated melan

43 , we present a computational methodology and mathematical formalism to encode and curate the morpholo

44 From the mathematical formulation of biocultural complexity, form

45 athematical models, but they ensure that any mathematical formulation of the biological system is led

46 e a large improvement in the theoretical and mathematical foundations of movement ecology, to include

47 Here, we review the mathematical foundations of these algorithms as well as

48 We first introduce its underlying mathematical framework and present the types of question

49 e philosophy of science emerge from a common mathematical framework and provide insight into why peop

50 In this work, we propose a mathematical framework based on counting processes that

51 Then, with a focus on bringing this mathematical framework closer to neurobiology, we detail

52 ls from their environmental context, using a mathematical framework combined with multiscale data giv

53 material properties within a single unified mathematical framework derived from an energy perspectiv

54 el parameters provide a physically intuitive mathematical framework for describing the transition fro

55 Here we present a trait-based mathematical framework for investigating how tri-trophic

56 n and Huxley explicit formulation provides a mathematical framework for understanding excitability, a

57 To enable such studies, we introduce a mathematical framework that contrasts MR analysis of pro

58 improved immunotherapy, we have developed a mathematical framework that incorporates complex interac

59 Here we present a mathematical framework which reveals that, for estimatin

60 tiretroviral treatment (ART) using a popular mathematical framework.

61 We describe a mathematical generalization that builds on prior work in

62 Worm morphologies are encoded with mathematical graphs based on anatomical ontology terms t

63 These models are founded on robust mathematical hypotheses that describe how A responds to

64 is workflow that combines deep learning with mathematical image analysis to accurately segment and cl

65 This paper presents a mathematical implementation of the new concept and descr

66 We used a mathematical model and Monte Carlo algorithm to estimate

67 he local hydrodynamics and, as revealed by a mathematical model benchmarked on the observations, on c

68 Here, we create a simple mathematical model coupling T-cell recognition with an e

69 Herein, a mathematical model demonstrates that the large amount of

70 We developed a minimal mathematical model demonstrating growth factor signaling

71 The mathematical model derived in this work can be easily ad

72 A mathematical model describing the viral infection dynami

73 We develop a mathematical model for fitness landscapes generated by s

74 A mathematical model for RNAPI elongation confirmed the im

75 We propose a mathematical model for the dynamics of c-di-GMP and (p)p

76 ormation processing capabilities, we built a mathematical model for the eS6 phosphorylation (eS6-P) c

77 Using a mathematical model for transmission and control of VL, w

78 The mathematical model for tumour spheroid growth is paramet

79 rticle, we describe the assembly of a hybrid mathematical model in which the spatial spread of inflam

80 To test this, we present an individual-based mathematical model incorporating all the important cell-

81 Our mathematical model indicates that local nutrient depleti

82 A mathematical model is used to rapidly evaluate dosing st

83 Our initial mathematical model obtained a correlation coefficient of

84 Although these properties fit a recent mathematical model of automatic gradient scaling, that m

85 ased on these data, we constructed a dynamic mathematical model of bacterial interactions in the lake

86 Here we develop a mathematical model of colorectal cancer initiation throu

87 f colorectal cancer can be recovered using a mathematical model of colorectal cancer initiation toget

88 OR activity fitted a mathematical model of competitive receptor binding and s

89 of Europe's largest countries, and develop a mathematical model of COVID-19 dynamics, using latest da

90 We developed and calibrated a mathematical model of gonorrhea transmission among men w

91 ariation in infection outcomes, we devised a mathematical model of malaria infection that allowed hos

92 aging of MCTS systems to drive a biophysical mathematical model of MCTS growth and mechanical interac

93 tigens in a growing cancer by constructing a mathematical model of neoantigen evolution.

94 to urinary protein excretion, we developed a mathematical model of protein reabsorption in the human

95 erences are based only on a well-established mathematical model of recombination and make no assumpti

96 and empirical understanding by developing a mathematical model of sexual conflict that incorporates

97 We developed a mathematical model of TB transmission to project the imp

98 Here, we develop the first mechanistic mathematical model of the 2-input BLADE platform based o

99 Focusing on mutational events, we provide a mathematical model of the full process of tumor evolutio

100 We use a mathematical model of the HIV epidemic in South Africa t

101 The principle is based on a mathematical model of the sporozoite rate (the proportio

102 A mathematical model of the wild boar ASF system is develo

103 The mathematical model predicted preferential CPP into cells

104 tors) are parameterized and used to inform a mathematical model that can predict circuit performance,

105 ic outcomes, we created a hybrid agent-based mathematical model that captures both the overall tumor

106 nisms driving these dynamics by developing a mathematical model that captures consortia response as s

107 We developed a mathematical model that combines biochemical signaling w

108 egulation of flagella quantity, we propose a mathematical model that connects the flagellar gene regu

109 A phenomenological mathematical model that forms an essential basis for opt

110 disease and normal individuals, we devised a mathematical model that incorporates two PT transport pr

111 tand this relationship, we used a multiscale mathematical model that integrates data from biology and

112 We developed a serocatalytic mathematical model to capture the change in seroprevalen

113 Further, we developed and validated a mathematical model to determine that fast, small-volume

114 e possibilities by fitting an age-structured mathematical model to epidemic data from China, Italy, J

115 Here we established a descriptive mathematical model to estimate the probability of self-r

116 including machine learning and a mechanistic mathematical model to find the optimal protocol for admi

117 We applied a previously published mathematical model to investigate the effect of interven

118 study we created and validated a multiscale mathematical model to investigate the impact of cross-ta

119 his article, we study a general, but simple, mathematical model to investigate whether the presence o

120 We used a mathematical model to predict the efficacy and effective

121 f relevant pathogen-specific RDTs, we used a mathematical model to predict the probability of correct

122 esent the results of utilizing a biophysical mathematical model to predict tumor response for two HER

123 Here, we fitted a mathematical model to seroprevalence livestock and human

124 Here we develop a mathematical model to show, that in highly variable envi

125 Finally, we used a mathematical model to simulate HRV under decreased "coup

126 rmed an allocative efficiency study, using a mathematical model to simulate the progression of HCV in

127 A recently-developed population-level T2DM mathematical model was adapted and applied to Jordan.

128 re characterized and a multiscale stochastic mathematical model was proposed to link the in vitro and

129 A mathematical model was set up and validated to predict t

130 An individual-based mathematical model was used to simulate annual HIV incid

131 As predicted by a simple mathematical model, increasing sigma(B) expression shift

132 We demonstrate, using a mathematical model, that dissolution of calcium that has

133 Using a mathematical model, we estimate that a large fraction of

134 Guided by a resource-aware mathematical model, we identify and engineer natural and

135 Using a mathematical model, we show that the temporal asynchrony

136 analysis of ESCRT-III-depleted cells with a mathematical model, we show that upstream ESCRT-induced

137 ons from the literature to formulate a novel mathematical model.

138 lementing the strategy in a US state using a mathematical model.

139 nderstand this heterogeneity, we developed a mathematical model.

140 eriments, including treatments unseen by the mathematical model.

141 oint of data saturation was estimated with a mathematical model.

142 Experimental and mathematical modeling analyses suggest that active cargo

143 In this study, mathematical modeling and 3D printing were used to analy

144 types of hybrid error correction methods by mathematical modeling and analysis on both simulated and

145 Mathematical modeling and corresponding in vivo experime

146 Here, using mathematical modeling and statistical analyses of T cell

147 This mathematical modeling approach provides strong evidence

148 Using a mathematical modeling approach, we found that simply int

149 ristics of EMT dynamics, with a focus on the mathematical modeling approaches that have been instrume

150 tion to key experimental findings, we review mathematical modeling approaches that help researchers i

151 Mathematical modeling based on our data provides estimat

152 o, and in vitro studies, in combination with mathematical modeling can help optimize and guide the de

153 Mathematical modeling demonstrates that the NAC-induced

154 llow the optimal schedules generated through mathematical modeling entirely, but travelers who better

155 In this study, we developed a mathematical modeling framework to quantify the contribu

156 To that end, we developed a mathematical modeling framework to simulate the AMP tria

157 Conversely, recent work in mathematical modeling has demonstrated the relevance of

158 ization (smFISH), time-lapse microscopy, and mathematical modeling in single fission yeast cells to u

159 High-complexity DNA barcoding and mathematical modeling indicate a high rate of de novo ac

160 Mathematical modeling indicates that the Plk4 oscillatio

161 for researchers interested in incorporating mathematical modeling into their scientific process.

162 Rationale: Mathematical modeling is used to understand disease dyna

163 Current mathematical modeling methods for assessing cytopenia ri

164 This study demonstrates how mathematical modeling of cancer evolution can be used to

165 Mathematical modeling of healthcare-associated infection

166 Mathematical modeling of plant metabolism enables the pl

167 Lastly, mathematical modeling of tGD spread within populations r

168 Mathematical modeling of the stochastic process of cance

169 By combining our sequencing data and mathematical modeling of transcription, we found that Xr

170 Mathematical modeling provides a means to investigate th

171 Stochastic mathematical modeling provides insight to the biological

172 Mathematical modeling revealed that SOC reduces mechanic

173 There is hence an urgent need for mathematical modeling that can quantitatively describe t

174 Here, we combine experiments and mathematical modeling to elaborate the minimal autonomou

175 We used mathematical modeling to estimate HDV-HBsAg-host paramet

176 Here, we use mathematical modeling to examine the epidemiological imp

177 ere, we combine an experimental approach and mathematical modeling to explore how phages and their mo

178 Here, we utilize mathematical modeling to investigate how small outward c

179 ents in computer-controlled bioreactors with mathematical modeling to investigate whether containment

180 analysis of the RAD51-ssDNA interaction with mathematical modeling to show that the flexibility of DN

181 In this study we use mathematical modeling to understand better how these con

182 ostate cancer and CRPC.Significance: Merging mathematical modeling with experimental data, this study

183 We further combined mathematical modeling with quantitative trait and expres

184 We tested plausibility for CPP by combined mathematical modeling, comparison of predictions from th

185 of experiments, large-scale omics data, and mathematical modeling, complemented by the use of synthe

186 This approach, in conjunction with mathematical modeling, enabled us to quantify the nuclea

187 Using genetics and mathematical modeling, we develop an alternative model o

188 Using mathematical modeling, we found that ratio-sensing is a

189 ysiology, optogenetics, behavioral tasks and mathematical modeling, we found that subthalamic stimula

190 Based on mathematical modeling, we here demonstrate that clusters

191 fixed cell analysis, live cell imaging, and mathematical modeling, we show that populations of newly

192 Together with mathematical modeling, we unambiguously demonstrate no c

193 ual patient level, and (3) computational and mathematical modeling, which blends in silico simulation

194 ociated with sexual receptivity length using mathematical modeling.

195 nto finite-sized target structures employing mathematical modeling.

196 rcial and institutional IR databases without mathematical modeling.

197 perturbation, bioinformatic predictions, and mathematical modeling.

198 Here, we combine mathematical modelling and data from eleven bipartite pl

199 icroscopy, transmission electron microscopy, mathematical modelling and genetic manipulation to visua

200 Taking a mathematical modelling approach, we investigate how tumo

201 In plants, mathematical modelling has been comprehensively integrat

202 Mathematical modelling has successfully been used to pro

203 Whilst mathematical modelling is well established in informing

204 Mathematical modelling of real complex networks aims to

205 destructive monitoring of cancer clones, and mathematical modelling of tumour evolution.

206 Mathematical modelling shows that amplifications also tu

207 In this mathematical modelling study of perinatal hepatitis B tr

208 Mathematical modelling suggests that the probability of

209 Supported by mathematical modelling, our results establish that multi

210 Here we show, through mathematical modelling, that the patterns of dominant Sp

211 ia and combining single-cell RNA sequencing, mathematical modelling, transplantation assays and intra

212 Using mathematical modelling, we propose that the BotC network

213 ct under a range of supply constraints using mathematical modelling.

214 While many mathematical models (MMs) have described the course of i

215 ed environment, it is necessary to integrate mathematical models across biological scales.

216 In addition, assumptions to make the mathematical models analytically tractable limit the sea

217 Mathematical models and computer simulations demonstrate

218 paid to the comprehensive description of the mathematical models and parameters of the active and pas

219 burden-is sufficiently high, an effect that mathematical models attribute to increased competition.

220 Mathematical models demonstrate that a serological testi

221 We further evaluated how well bio-mathematical models designed to predict performance chan

222 In this work we study mathematical models for a basic chemical signal, the arr

223 s increasing importance placed on the use of mathematical models for the effective design and managem

224 Yet, few mathematical models have aimed to identify optimal treat

225 While several mathematical models have been explored in the context of

226 Mathematical models imply that a prolonged time (decades

227 Mathematical models incorporating variability successful

228 However, developing useful mathematical models is challenging because of the often

229 Unfortunately, the construction of complete mathematical models is often hindered by a pervasive pro

230 Mathematical models of biological reactions at the syste

231 vel therapeutic approaches and build further mathematical models of combination therapies to treat pr

232 n 2015, the EPA used these data to construct mathematical models of ER agonist and antagonist pathway

233 Mathematical models of gene flow in populations, which i

234 e clinical trials guided by patient-specific mathematical models of intratumoral evolutionary dynamic

235 Mathematical models of mosquito-barrier interactions ide

236 d yield to intervention techniques guided by mathematical models of neuronal ensemble dynamics.

237 Here, we developed novel mathematical models of Process S based on cortical activ

238 We compared experimental data to mathematical models of spreading mechanisms to determine

239 However, mathematical models require in vitro data prior to predi

240 ther MSCs exert a pro- or anti-tumor action, mathematical models such as this one help to quantitativ

241 In mathematical models that are frequently used to guide su

242 mplex processes into simpler subsystems; and mathematical models that can accelerate the design-build

243 iour can make it difficult to create concise mathematical models that can be easily extended or modif

244 This can be achieved with mathematical models that properly account for risk heter

245 Behavioral strategies and abstract mathematical models thereof have been described in detai

246 We applied existing and developed new mathematical models to calculate the health and climate

247 hat moved across its sonar field and applied mathematical models to differentiate between nonpredicti

248 t synthetic Lactococcus lactis consortia and mathematical models to elucidate the role of interaction

249 We use mathematical models to explore how the efficacy of LAIV

250 We use mathematical models to illustrate their possible modes o

251 experiments, extensive field data and novel mathematical models to indirectly estimate the magnitude

252 dying prion aggregation to show the power of mathematical models to synergistically interact with exp

253 ciation constants were determined by fitting mathematical models to the experimental data.

254 We found that bio-mathematical models were able to predict average changes

255 C and five different semi-theoretical drying mathematical models were examined to characterize the dr

256 computational fluid dynamics and analytical mathematical models).

257 tions for behavioral modifications, based on mathematical models, are most likely to be followed in t

258 rve as the basis for developing a variety of mathematical models, but they ensure that any mathematic

259 Using 3 well-established mathematical models, we show that low-level prevalence c

260 to estimate key HIV epidemic indicators from mathematical models.

261 is based on the combination of two existing mathematical models: one representing the dynamics of ph

262 eview selected experimental and theoretical (mathematical) models pertaining to the hypothesis that l

263 e risks of considering networks as idealized mathematical objects: overestimating the accuracy of net

264 (HAL) is a Java Library developed for use in mathematical oncology modeling.

265 Equally rapid progress in mathematical oncology over this time period has often co

266 de between the variables of interest and the mathematical operations necessitated.

267 Using mathematical optimization, and subsequently the Liver Si

268 Therefore, it is difficult to develop a mathematical or artificial intelligence model that descr

269 lize theories of suicide, expressing them as mathematical or computational models.

270 bes the experimental methods of defining the mathematical parameters for characterization of branched

271 applied to other polymers, and the relevant mathematical parameters for their characterization are a

272 the Kingman coalescent, the cornerstones of mathematical population genetics, in further discussions

273 idemic process to permit the construction of mathematical predictive models.

274 ns and combine these operations to solve the mathematical problem Learning Parity with Noise (LPN).

275 Thus, physical machines can solve the mathematical problem of optimization, including constrai

276 We propose new mathematical programming models for optimal partitioning

277 Exploiting the mathematical properties of the phasor method, we transfo

278 ation processes based on simple yet powerful mathematical properties that can be used to describe man

279 Mathematical properties, such as minimal ring or circuit

280 al interaction we recover the aforementioned mathematical relation with the Hannay angle obtained fro

281 t with cortical recordings, and has a direct mathematical relationship to artificial neural networks.

282 Significant mathematical relationships between the individual abunda

283 Recent mathematical results concerning ranked genealogies witho

284 Using a mathematical simulation, we examined 3 screening strateg

285 Social inequality in mathematical skill is apparent at kindergarten entry and

286 n these two basic phenomena and more complex mathematical skills is mixed.

287 number sense, has been recurrently linked to mathematical skills, although the nature of the mechanis

288 studied in participants with normal or poor mathematical skills, not in mathematicians.

289 A novel mathematical-statistical framework that included EAs wit

290 nalstructure of the biological system by the mathematical structure of the DNN.

291 Our mathematical study establishes a consumption based pover

292 Here, we use ideas from the mathematical theory of self-assembly to enumerate the en

293 Here, we develop mathematical theory to show that the rate of such intera

294 tudy supports the potential application of a mathematical tool to predict therapeutic feasibility, wh

295 le bimolecular equilibrium equation, similar mathematical tools are currently not available to descri

296 strategies that have been praised for their mathematical tractability and performance against single

297 tterns were compared with those generated by mathematical transmission models to test different hypot

298 ntroduce RANKCORR, a fast method with strong mathematical underpinnings that performs multi-class mar

299 plex multi-component technology as well as a mathematical understanding of pairs, sets, and numbers.

300 Recent theoretical and mathematical work supports this idea and provide a bluep