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Letteratura scientifica selezionata sul tema "Extrinsic noise"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 14 marzo 2023

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Articoli di riviste sul tema "Extrinsic noise"

1

Sun, Mengyi, and Jianzhi Zhang. "Allele-specific single-cell RNA sequencing reveals different architectures of intrinsic and extrinsic gene expression noises." Nucleic Acids Research 48, no. 2 (December 4, 2019): 533–47. http://dx.doi.org/10.1093/nar/gkz1134.

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Abstract Gene expression noise refers to the variation of the expression level of a gene among isogenic cells in the same environment, and has two sources: extrinsic noise arising from the disparity of the cell state and intrinsic noise arising from the stochastic process of gene expression in the same cell state. Due to the low throughput of the existing method for measuring the two noise components, the architectures of intrinsic and extrinsic expression noises remain elusive. Using allele-specific single-cell RNA sequencing, we here estimate the two noise components of 3975 genes in mouse fibroblast cells. Our analyses verify predicted influences of several factors such as the TATA-box and microRNA targeting on intrinsic or extrinsic noises and reveal gene function-associated noise trends implicating the action of natural selection. These findings unravel differential regulations, optimizations, and biological consequences of intrinsic and extrinsic noises and can aid the construction of desired synthetic circuits.

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2

VÁZQUEZ-JIMÉNEZ, AARÓN, MOISÉS SANTILLÁN, and JESÚS RODRÍGUEZ-GONZÁLEZ. "CHARACTERIZATION OF INTRINSIC AND EXTRINSIC NOISE EFFECTS IN POSITIVELY REGULATED GENES." Journal of Biological Systems 27, no. 03 (September 2019): 383–98. http://dx.doi.org/10.1142/s0218339019500165.

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Gene regulation is fundamental for cell survival. This regulation must be both robust to noise and sensitive enough to external stimuli to elicit the proper responses. In this work, we study, through stochastic numerical simulations, how a gene regulatory network with a positive feedback loop responds to environmental changes in the presence of intrinsic and extrinsic noises. Noise effects were characterized by measuring the statistical differences between two protein time series resulting from identical systems subject to the same source of extrinsic noise. A robust analysis was implemented by modifying the kinetic system parameters. We found that the common source of time-varying extrinsic fluctuations leads to a correlation in the systems it affects. The correlation and the extrinsic and intrinsic noise components are modulated by the update period and noise intensity parameters. Our results suggest that noise perception is controlled through the parameters associated with the response time: degradation rates and promoter dissociation constant.

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3

XIE, ZHI, and DON KULASIRI. "ON EXPLORING EFFECTS OF MOLECULAR NOISE IN A SIMPLE VIRAL INFECTION MODEL." International Journal of Biomathematics 03, no. 01 (March 2010): 1–19. http://dx.doi.org/10.1142/s1793524510000891.

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Intrinsic and extrinsic noises are all believed to be important in the development and function of many living organisms. In this study, we investigate the sources of the intrinsic noise and the influence of the extrinsic noise on an intracellular viral infection system. The contribution of the intrinsic noise from each reaction is measured by means of a special form of stochastic differential equations (SDEs), chemical Langevin equation. The intrinsic noise of the system is a linear sum of the noise in each of the reactions. The intrinsic noise mainly arises from the degradation of mRNA and the transcription processes. We then study the effects of extrinsic noise by the means of a general form of SDE. It is found that the noise of the viral components grows logarithmically with the increasing noise intensities. The system is most susceptible to the noise in the virus assembly process. A high level of noise in this process can even inhibit the growth of the viruses. This study also demonstrates the utility of SDEs in analyzing genetic regulatory networks perturbed by either inherent or parametric stochasticity.

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4

Liu, Shengjun, Qi Wang, and Hai Feng. "The correlation between intrinsic noise and extrinsic noise." Physica A: Statistical Mechanics and its Applications 392, no. 20 (October 2013): 5138–42. http://dx.doi.org/10.1016/j.physa.2013.06.032.

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5

Justman, Quincey A. "An Explicit Source for Extrinsic Noise." Cell Systems 1, no. 5 (November 2015): 308–9. http://dx.doi.org/10.1016/j.cels.2015.11.003.

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6

Zeng, Chunhua, Tao Yang, Qinglin Han, Chun Zhang, Dong Tian, and Hua Wang. "Noises-induced toggle switch and stability in a gene regulation network." International Journal of Modern Physics B 28, no. 31 (December 8, 2014): 1450223. http://dx.doi.org/10.1142/s0217979214502233.

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It is well-known that noises are inevitable in gene regulatory networks due to the low-copy numbers of molecules and environmental fluctuations. In this paper, we investigate the stationary probability distribution (SPD) between both low (OFF state) and high (ON state) protein levels and mean first passage time (MFPT) in an abstract model of the Myc/E2F/miR-17-92 network presented by Aguda et al., PNAS 105, 19678 (2008), where the gene expression is assumed to be disturbed simultaneously by intrinsic and extrinsic noises that were correlated. Our results show that (i) the OFF state is enhanced by the extrinsic noise (D), while the ON state is enhanced by the intrinsic noise (Q) or cross-correlation between two noises (λ); (ii) for the cases of negative or no cross-correlation (λ⩽0.0), the increase of the noise intensity (D or Q) leads to a decline of the MFPT and enhances the probability of toggle switch to the OFF state; (iii) but for the case of positive cross-correlation (λ>0.0), the MFPT as a function of the noise intensity (D or Q) exhibits a maximum, this maximum for MFPT identifies the characteristic of noise enhanced stability of the ON state and (iv) the cross-correlation between two noises can enhance stability of the ON state.

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Hughes, B. "A temperature noise model for extrinsic FETs." IEEE Transactions on Microwave Theory and Techniques 40, no. 9 (1992): 1821–32. http://dx.doi.org/10.1109/22.156610.

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8

Gupta, M. S., and P. T. Greiling. "Microwave noise characterization of GaAs MESFET's: determination of extrinsic noise parameters." IEEE Transactions on Microwave Theory and Techniques 36, no. 4 (April 1988): 745–51. http://dx.doi.org/10.1109/22.3580.

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9

Thomas, Philipp, and Vahid Shahrezaei. "Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations." Journal of The Royal Society Interface 18, no. 178 (May 2021): 20210274. http://dx.doi.org/10.1098/rsif.2021.0274.

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The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution of the chemical master equation—including static extrinsic noise—exactly agrees with the agent-based formulation when the network under study exhibits stochastic concentration homeostasis , a novel condition that generalizes concentration homeostasis in deterministic systems to higher order moments and distributions. We illustrate stochastic concentration homeostasis for a range of common gene expression networks. When this condition is not met, we demonstrate by extending the linear noise approximation to agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the chemical master equation. Surprisingly, the total noise of the agent-based approach can still be well approximated by extrinsic noise models.

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GRÜNEIS, FERDINAND. "1/f NOISE IN EXTRINSIC SEMICONDUCTOR MATERIALS INTERPRETED AS MODULATED GENERATION-RECOMBINATION NOISE." Fluctuation and Noise Letters 09, no. 02 (June 2010): 229–43. http://dx.doi.org/10.1142/s0219477510000137.

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We investigate fixed dopants in the presence of mobile point defects. A defect entering the first Bohr radius RB of a dopant will modulate the generation-recombination (g-r) process. The times a defect walks inside and outside of RB are found to be power-law distributed; correspondingly, the modulated g-r process exhibits 1/fb noise. The predicted Hooge coefficient depends on RB, on the normalized fluctuations of charge carriers, the number of lattice sites and on the modulation depth of the g-r process.

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Più fonti

Tesi sul tema "Extrinsic noise"

1

Neves, Ricardo Neves Pires das. "The origin of global extrinsic noise in gene expression." Thesis, University of Oxford, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.531994.

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2

Hofmann, Ariane Leoni. "A Stochastic Framework to Model Extrinsic Noise in Gene Regulatory Networks." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/76841.

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Stochastic modeling to represent intrinsic and extrinsic noise is an important challenge in molecular systems biology. There are numerous ways to model intrinsic noise. One framework for intrinsic noise in gene regulatory networks was recently proposed within the discrete setting. In contrast, extrinsic perturbations were rarely modeled due to the complex mechanisms that contribute to its emergence. Here a discrete framework to model extrinsic noise is proposed. The interacting species of the model are represented by discrete variables and are perturbed to represent extrinsic noise. In particular, they are subject to a discretized lognormal distribution. Additionally, a delay is imposed on the update with a certain probability. These two perturbations represent global extrinsic noise and pathway-specic extrinsic noise. It leads to large variations in the concentration of proteins, which is consistent with an existing continuous way of modeling extrinsic fluctuations. The framework is applied to three different published discrete models: the cell fate of lambda phage infection of bacteria, the lactose utilization system in E. coli, and a signaling network in melanoma cells. The framework captures factors that signicantly contribute to the random decision between lysis and lysogeny as well as explains the bistable switch in the model of the lac operon. Finally, a feed-forward loop analysis is conducted by measuring and comparing the noise level in the target protein of feed-forward loops. This analysis reveals the ability of certain feed-forward loops to attenuate or amplify fluctuations, dependent upon various levels of noise. In conclusion, this thesis aims to resolve the question of how the extrinsic noise can be modeled and how biological systems are able to maintain functionality in the wake of such large variations.<br>Master of Science

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Lenive, Oleg. "The role of extrinsic noise in biomolecular information processing systems : an in silico analysis." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/31575.

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The intrinsic stochasticity of biomolecular systems is a well studied phe- nomenon. Less attention has been paied to other sources of variability, so called extrinsic noise. While the precise definition of extrinsic noise de- pends on the system in question, it affects all cells and its significance has been demonstrated experimentally. Information theory provides a rigorous mathematical framework for quan- tifying both the amount of information available to a signalling system and its ability to transmit this information. Intracellular signal transduction re- mains a relatively unexplored frontier for the application of information theory. In this thesis, we rely on a metric called mutual information to quantify in- formation flow in models of biochemical signalling systems. After briefly discussing the theoretical background and some of the practical difficulties of estimating mutual information in Chapter 2, we apply it in the context of simplified models of intracellular signalling, referred to as motifs. Using a comprehensive set of two-node motifs we explore the effects of extrinsic noise, model parameters and various combinations of interaction, on the system's ability to transmit information about an input signal, repre- sented by a telegraph process. Our results illustrate the importance of the system's response time and demonstrate a trade-off in transmitting infor- mation about the current state of the input or its average intensity over a period of time. In Chapter 4, we address the problem of determining the magnitude of ex- trinsic noise in the presence of intrinsic stochasticity. Using the Approximate Bayesian Computation - sequential Monte Carlo algorithm, together with published experimental data, we infer parameters describing extrinsic noise in a model of E. coli gene expression. Lastly, in Chapter 5, we construct and analyse models of bacterial two- component signalling, bringing together insights gleaned from earlier work. The results show how the abundances of different molecular species in the system may transmit information about the input signal despite its stochas-tic nature and considerable variation in the numbers of protein molecules present.

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4

Xie, Zhi. "Modelling genetic regulatory networks: a new model for circadian rhythms in Drosophila and investigation of genetic noise in a viral infection process." Phd thesis, Lincoln University. Agriculture and Life Sciences Division, 2007. http://theses.lincoln.ac.nz/public/adt-NZLIU20070712.144258/.

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In spite of remarkable progress in molecular biology, our understanding of the dynamics and functions of intra- and inter-cellular biological networks has been hampered by their complexity. Kinetics modelling, an important type of mathematical modelling, provides a rigorous and reliable way to reveal the complexity of biological networks. In this thesis, two genetic regulatory networks have been investigated via kinetic models. In the first part of the study, a model is developed to represent the transcriptional regulatory network essential for the circadian rhythms in Drosophila. The model incorporates the transcriptional feedback loops revealed so far in the network of the circadian clock (PER/TIM and VRI/PDP1 loops). Conventional Hill functions are not used to describe the regulation of genes, instead the explicit reactions of binding and unbinding processes of transcription factors to promoters are modelled. The model is described by a set of ordinary differential equations and the parameters are estimated from the in vitro experimental data of the clocks components. The simulation results show that the model reproduces sustained circadian oscillations in mRNA and protein concentrations that are in agreement with experimental observations. It also simulates the entrainment by light-dark cycles, the disappearance of the rhythmicity in constant light and the shape of phase response curves resembling that of experimental results. The model is robust over a wide range of parameter variations. In addition, the simulated E-box mutation, perS and perL mutants are similar to that observed in the experiments. The deficiency between the simulated mRNA levels and experimental observations in per01, tim01 and clkJrk mutants suggests some differences in the model from reality. Finally, a possible function of VRI/PDP1 loops is proposed to increase the robustness of the clock. In the second part of the study, the sources of intrinsic noise and the influence of extrinsic noise are investigated on an intracellular viral infection system. The contribution of the intrinsic noise from each reaction is measured by means of a special form of stochastic differential equation, the chemical Langevin equation. The intrinsic noise of the system is the linear sum of the noise in each of the reactions. The intrinsic noise arises mainly from the degradation of mRNA and the transcription processes. Then, the effects of extrinsic noise are studied by means of a general form of stochastic differential equation. It is found that the noise of the viral components grows logarithmically with increasing noise intensities. The system is most susceptible to noise in the virus assembly process. A high level of noise in this process can even inhibit the replication of the viruses. In summary, the success of this thesis demonstrates the usefulness of models for interpreting experimental data, developing hypotheses, as well as for understanding the design principles of genetic regulatory networks.

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Deloupy, Alexandre. "Expression stochastique des gènes chez Bacillus subtilis." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS443.

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Une population d'individus génétiquement identiques partageant le même environnement présente une certaine variabilité phénotypique résiduelle. Cette hétérogénéité découle de la nature stochastique, ou aléatoire, de l'expression des gènes, également appelée bruit. Cette stochasticité résulte d'une part de la rencontre aléatoire d'espèces chimiques pendant la transcription et la traduction (bruit intrinsèque), et d'autre part des fluctuations dans la concentration de ces substances chimiques (bruit extrinsèque). Un modèle stochastique ne faisant intervenir que le bruit intrinsèque prédit que la force du bruit phénotypique varie linéairement avec l'efficacité de la traduction, mais qu'il ne dépend pas du taux de transcription. Cette prédiction s'est révélée compatible avec des données portant sur un nombre limité de souches et de conditions, mais n'a jamais été entièrement testée sur un grand nombre de souches ayant différentes efficacités de transcription et de traduction. Notre objectif est d'aller plus loin dans le test de cette prédiction en utilisant une collection d'une quarantaine de souches de la bactérie Bacillus subtilis où la protéine GFP est exprimée sous le contrôle de différents promoteurs, TSS et RBS. Pour chaque souche, l'hétérogénéité entre cellules est étudiée en quantifiant le signal de fluorescence au niveau de la cellule unique, à l'aide de techniques de cytométrie en flux et de microscopie en épifluorescence. Nos résultats montrent que, contrairement aux attentes, la force du bruit phénotypique montre une forte corrélation positive avec l'efficacité transcriptionnelle. Nous avons démontré que sur une large gamme d'expression couvrant la majeure partie du protéome de B. subtilis, le bruit d'expression est dominé par les sources de bruit extrinsèques. Par conséquent, les modèles stochastiques d'expression génique ne conviennent pas pour quantifier les effets de la traduction et de la transcription sur le bruit d'expression génétique<br>A population of genetically identical individuals sharing the same environment exhibits some residual phenotypic variability. Such heterogeneity arises from the stochastic, or random, nature of gene expression also referred as noise. This stochasticity results on the one hand from the random encounter of chemical species during both transcription and translation (intrinsic noise), and on the other hand from the fluctuations in the concentration of these chemicals (extrinsic noise). A stochastic model involving only intrinsic noise predicts that phenotypic noise strength varies linearly with translational efficiency but does not depend on transcriptional one. This prediction was shown to be compatible with data on a limited number of strains and conditions but has never been fully tested on a large collection of strains with different transcription and translation efficiencies. We aim to go further in the test of this prediction by using a collection of ~40 strains of the bacterium Bacillus subtilis where GFP is expressed under the control of different Promoters, TSS and RBS. For each strain, cell-to-cell heterogeneity is investigated by quantifying fluorescence signal at the single cell level, based on flow cytometry techniques and epifluorescence microscopy. Our results show that, contrary to expectations, phenotypic noise strength shows a strong positive correlation with transcriptional efficiency. We demonstrated that over a wide range of expression covering most of the proteome of B. subtilis, the expression noise is dominated by external noise sources. Therefore, stochastic models of gene expression are not suitable for quantifying the effects of translation and transcription on gene expression noise

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Magierowski, Sebastian C. "Nonlinear noise analysis of LC-tuned CMOS VCOs and extrinsic noise effects." 2004. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=80277&T=F.

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Song, Cheng-Yan, and 宋程硯. "Physical decomposition of intrinsic and extrinsic noises in biochemical networks." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/v2snfs.

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碩士<br>國立交通大學<br>物理研究所<br>104<br>It is rather often that intrinsic and extrinsic noises coexist in a biological system. A typical example is the transcription and translation of DNA. This raises numerous works devoting to decomposing these two effects. Apart from the previous definition of intrinsic and extrinsic noises from biological aspect, this work adopts the definition of van Kampen from physical aspect. Based on that definition, we perturb the chemical master equation and derive a physical version of decomposition formula for intrinsic and extrinsic noises. We apply this theory to the network model of Yi-Der Chen. The derived exact solutions and numerical studies on that model reveal the possibility of “suppressing intrinsic noise induced stochasticity by extrinsic noises”. The condition for this possibility is derived in a low dimensional network. This suppression indicates that the fluctuations could decline when intrinsic and extrinsic noises are added together, which is a bit counter-intuition. We compare this physical version of decomposition formula with the biological version of formula derived by Swain and analyze the common and distinct features between these two formulas.

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Libri sul tema "Extrinsic noise"

1

Magierowski, Sebastian C. Nonlinear noise analysis of LC-tuned CMOS VCOs and extrinsic noise effects. 2004.

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Capitoli di libri sul tema "Extrinsic noise"

1

Weik, Martin H. "extrinsic noise." In Computer Science and Communications Dictionary, 561. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_6699.

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Wang, Ruiqi. "Noise, Intrinsic and Extrinsic." In Encyclopedia of Systems Biology, 1527. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_353.

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Mackey, Michael C., Moisés Santillán, Marta Tyran-Kamińska, and Eduardo S. Zeron. "Noise Effects in Gene Regulation: Intrinsic Versus Extrinsic." In Lecture Notes on Mathematical Modelling in the Life Sciences, 49–69. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45318-7_4.

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Oliveira, Samuel M. D., Mohamed N. M. Bahrudeen, Sofia Startceva, and Andre S. Ribeiro. "Estimating Effects of Extrinsic Noise on Model Genes and Circuits with Empirically Validated Kinetics." In Communications in Computer and Information Science, 181–93. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78658-2_14.

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Caravagna, Giulio, Giancarlo Mauri, and Alberto d’Onofrio. "Bounded Extrinsic Noises Affecting Biochemical Networks with Low Molecule Numbers." In Bounded Noises in Physics, Biology, and Engineering, 201–21. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7385-5_13.

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TEITSWORTH, S. W., and R. M. WESTERVELT. "DETERMINISTIC NOISE IN EXTRINSIC PHOTOCONDUCTORS." In Noise in Physical Systems and 1/f Noise 1985, 341–44. Elsevier, 1986. http://dx.doi.org/10.1016/b978-0-444-86992-0.50075-8.

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Barba, Antonio, and Juan M. Martinez-Orozco. "Approaches for Noise Barrier Effectiveness Evaluation based on In Situ “Insertion Loss” Determination." In Noise Control [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.104397.

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In situ evaluation of the effectiveness of noise barriers may be based on the assessment of their intrinsic or extrinsic characteristics. The evaluation of intrinsic characteristics is based on acoustic properties, such as noise barrier absorption or insulation. The evaluation of the extrinsic characteristics is based on the calculation of the barrier Insertion Loss, which is defined as the difference in the noise level before and after the installation of the barrier. Insertion Loss is calculated using two different approaches: the direct and indirect methods. The direct method is used when the barrier has not been installed yet or can be removed, while the indirect method is used when the barrier is already installed and cannot be easily removed. This chapter describes the different approaches used in the scientific literature for in situ evaluation of the effectiveness of noise barriers and discusses the noise attenuation levels obtained with each approach.

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Barba, Antonio, and Juan M. Martinez-Orozco. "Approaches for Noise Barrier Effectiveness Evaluation based on In Situ “Insertion Loss” Determination." In Noise Control [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.104397.

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Abstract (sommario):

In situ evaluation of the effectiveness of noise barriers may be based on the assessment of their intrinsic or extrinsic characteristics. The evaluation of intrinsic characteristics is based on acoustic properties, such as noise barrier absorption or insulation. The evaluation of the extrinsic characteristics is based on the calculation of the barrier Insertion Loss, which is defined as the difference in the noise level before and after the installation of the barrier. Insertion Loss is calculated using two different approaches: the direct and indirect methods. The direct method is used when the barrier has not been installed yet or can be removed, while the indirect method is used when the barrier is already installed and cannot be easily removed. This chapter describes the different approaches used in the scientific literature for in situ evaluation of the effectiveness of noise barriers and discusses the noise attenuation levels obtained with each approach.

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Drake, John M., Suzanne M. O’Regan, Vasilis Dakos, Sonia Kéfi, and Pejman Rohani. "Alternative stable states, tipping points, and early warning signals of ecological transitions." In Theoretical Ecology, 263–84. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198824282.003.0015.

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Ecological systems are prone to dramatic shifts between alternative stable states. In reality, these shifts are often caused by slow forces external to the system that eventually push it over a tipping point. Theory predicts that when ecological systems are brought close to a tipping point, the dynamical feedback intrinsic to the system interact with intrinsic noise and extrinsic perturbations in characteristic ways. The resulting phenomena thus serve as “early warning signals” for shifts such as population collapse. In this chapter, we review the basic (qualitative) theory of such systems. We then illustrate the main ideas with a series of models that both represent fundamental ecological ideas (e.g. density-dependence) and are amenable to mathematical analysis. These analyses provide theoretical predictions about the nature of measurable fluctuations in the vicinity of a tipping point. We conclude with a review of empirical evidence from laboratory microcosms, field manipulations, and observational studies.

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Tadiparth, Sujatha, and Kayvan Shokrollahi. "Frostbite." In Burns (OSH Surgery), 353–66. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780199699537.003.0042.

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Frostbite is the damage sustained by tissues when exposed to temperatures below their freezing point (<0°C). Extremities such as fingers, toes, nose and ears are most commonly affected. A number of extrinsic and intrinsic factors predispose to frostbite. The severity of frostbite can be classified into four degrees. Tissue injury occurs firstly, by direct cell damage and death from the formation of intracellular ice crystals and secondly, by vascular insufficiency and tissue ischaemia which result from vasoconstriction, endothelial injury and thromboembolism. Treatment is focused on gentle rewarming in a warm circulating bath (40–41°C). Tissues should be allowed to demarcate with rewarming and then surgical debridement or amputation performed if necrotic tissue is present. Tissue plasminogen activator can lead to rapid clearance of vascular thromboses, restore arterial perfusion and improve tissue salvage. Long-term sequelae can result including, cold hypersensitivity, sensory deficits, chronic pain, heterotropic calcification, and pigmentary changes.

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Atti di convegni sul tema "Extrinsic noise"

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Ren, L., and F. N. Hooge. "Intrinsic and extrinsic 1/f noise sources in irradiated n-GaAs." In Noise in physical systems and 1/. AIP, 1993. http://dx.doi.org/10.1063/1.44629.

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Bahrudeen, Mohamed N. M., Sofia Startceva, and Andre S. Ribeiro. "Effects of Extrinsic Noise are Promoter Kinetics Dependent." In the 9th International Conference. New York, New York, USA: ACM Press, 2017. http://dx.doi.org/10.1145/3093293.3093295.

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Shorin, Alexander, Georgy Gimel'farb, Patricia Riddle, and Patrice Delmas. "Robust rigid image registration with arbitrary extrinsic photometric noise." In 2009 24th International Conference Image and Vision Computing New Zealand (IVCNZ). IEEE, 2009. http://dx.doi.org/10.1109/ivcnz.2009.5378374.

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Bahrudeen, Mohamed N. M., and Andre S. Ribeiro. "Tuning extrinsic noise effects on a small genetic circuit." In Proceedings of the 14th European Conference on Artificial Life ECAL 2017. Cambridge, MA: MIT Press, 2017. http://dx.doi.org/10.7551/ecal_a_075.

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Belquin, J. M., F. Danneville, A. Cappy, and G. Dambrine. "HEMTs extrinsic noise model for millimeter waves integrated circuits design." In 26th European Microwave Conference, 1996. IEEE, 1996. http://dx.doi.org/10.1109/euma.1996.337721.

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Carignano, Alberto, Sumit Mukherjee, Abhyudai Singh, and Georg Seelig. "Extrinsic Noise Suppression in Micro RNA Mediated Incoherent Feedforward Loops." In 2018 IEEE Conference on Decision and Control (CDC). IEEE, 2018. http://dx.doi.org/10.1109/cdc.2018.8619371.

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Shirazi, Masoud Jahromi, and Nicole Abaid. "Comparing the Effects of Intrinsic and Extrinsic Noise on the Vicsek Model in Three Dimensions." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5303.

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Abstract (sommario):

A group of simple individuals may show ordered, complex behavior through local interactions. This phenomenon is called collective behavior, which has been observed in a vast variety of natural systems such as fish schools or bird flocks. The Vicsek model is a well-established mathematical model to study collective behavior through interaction of individuals with their neighbors in the presence of noise. How noise is modeled can impact the collective behavior of the group. Extrinsic noise captures uncertainty imposed on individuals, such as noise in measurements, while intrinsic noise models uncertainty inherent to individuals, akin to free will. In this paper, the effects of intrinsic and extrinsic noise on characteristics of the transition between order and disorder in the Vicsek model in three dimensions are studied through numerical simulation.

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8

Li, You, Yassine Ruichek, and Cindy Cappelle. "Extrinsic calibration between a stereoscopic system and a LIDAR with sensor noise models." In 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2012). IEEE, 2012. http://dx.doi.org/10.1109/mfi.2012.6343010.

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9

Carr, Dustin W., and Yong Wang. "Integration of ultralow-noise single-frequency lasers with extrinsic seismic sensors using optical transducers." In OFS2012 22nd International Conference on Optical Fiber Sensor, edited by Yanbiao Liao, Wei Jin, David D. Sampson, Ryozo Yamauchi, Youngjoo Chung, Kentaro Nakamura, and Yunjiang Rao. SPIE, 2012. http://dx.doi.org/10.1117/12.975310.

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Cox, C., J. McCollum, R. Dar, D. Austin, M. Allen, N. Samatova, G. Sayler, and M. Simpson. "Calibration of a stochastic model of gene expression including feedback and extrinsic noise sources." In 2006 Bio Micro and Nanosystems Conference. IEEE, 2006. http://dx.doi.org/10.1109/bmn.2006.330904.

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