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Journal articles on the topic 'Extrinsic noise'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 March 2023

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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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7

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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10

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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11

Johnston, Iain G., Bernadett Gaal, Ricardo Pires das Neves, Tariq Enver, Francisco J. Iborra, and Nick S. Jones. "Mitochondrial Variability as a Source of Extrinsic Cellular Noise." PLoS Computational Biology 8, no. 3 (March 8, 2012): e1002416. http://dx.doi.org/10.1371/journal.pcbi.1002416.

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12

Reppas, Andreas I., Georgios Lolas, Andreas Deutsch, and Haralampos Hatzikirou. "The Extrinsic Noise Effect on Lateral Inhibition Differentiation Waves." ACM Transactions on Modeling and Computer Simulation 26, no. 3 (January 29, 2016): 1–18. http://dx.doi.org/10.1145/2832908.

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13

LONG, S., L. ESCOTTE, J. GRAFFEUIL, P. FELLON, D. GEIGER, and D. PONS. "HIGH-FREQUENCY NOISE IN PSEUDOMORPHIC DOUBLE-HETEROJUNCTION HIGH ELECTRON MOBILITY TRANSISTORS." Fluctuation and Noise Letters 02, no. 01 (March 2002): L13—L19. http://dx.doi.org/10.1142/s0219477502000531.

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The noise behavior of pseudomorphic double-heterojunction high electron mobility transistors dedicated to power applications is investigated in this paper and compared to conventional low noise field effect transistors. The noise is analyzed from an extrinsic and an intrinsic point of view. It appears that the minimum noise figure is similar for the two devices even if the intrinsic noise sources are different. We explain this phenomenon using the double-heterojunction mode of operation.

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14

Wang, Haohua, Zhanjiang Yuan, Peijiang Liu, and Tianshou Zhou. "Division time-based amplifiers for stochastic gene expression." Molecular BioSystems 11, no. 9 (2015): 2417–28. http://dx.doi.org/10.1039/c5mb00391a.

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While cell-to-cell variability is a phenotypic consequence of gene expression noise, sources of this noise may be complex – apart from intrinsic sources such as the random birth/death of mRNA and stochastic switching between promoter states, there are also extrinsic sources of noise such as cell division where division times are either constant or random.

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15

Stamatakis, Michail, Rhys M. Adams, and Gábor Balázsi. "A common repressor pool results in indeterminacy of extrinsic noise." Chaos: An Interdisciplinary Journal of Nonlinear Science 21, no. 4 (December 2011): 047523. http://dx.doi.org/10.1063/1.3658618.

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16

Bressloff, Paul C., and Yi Lai. "Stochastic synchronization of neuronal populations with intrinsic and extrinsic noise." Journal of Mathematical Neuroscience 1, no. 1 (2011): 2. http://dx.doi.org/10.1186/2190-8567-1-2.

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17

Roberts, Elijah, and Michael Assaf. "Extrinsic Noise Enhances Switching in Models of Cellular Decision Making." Biophysical Journal 104, no. 2 (January 2013): 160a. http://dx.doi.org/10.1016/j.bpj.2012.11.900.

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18

Vázquez-Jiménez, Aarón, Moisés Santillán, and Jesús Rodríguez-González. "How the extrinsic noise in gene expression can be controlled?" IFAC-PapersOnLine 50, no. 1 (July 2017): 15092–96. http://dx.doi.org/10.1016/j.ifacol.2017.08.2236.

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19

Assaf, Michael, Elijah Roberts, Zaida Luthey-Schulten, and Nigel Goldenfeld. "Extrinsic Noise Driven Phenotype Switching in a Self-Regulating Gene." Biophysical Journal 106, no. 2 (January 2014): 374a—375a. http://dx.doi.org/10.1016/j.bpj.2013.11.2119.

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20

Marshall, Wallace. "Intrinsic and Extrinsic Noise in the Flagellar Length Control System." Biophysical Journal 106, no. 2 (January 2014): 637a. http://dx.doi.org/10.1016/j.bpj.2013.11.3527.

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21

Hasegawa, Yoshihiko, and Masanori Arita. "Fluctuating noise drives Brownian transport." Journal of The Royal Society Interface 9, no. 77 (September 12, 2012): 3554–63. http://dx.doi.org/10.1098/rsif.2012.0603.

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The transport properties of Brownian ratchet were studied in the presence of stochastic intensity noise in both overdamped and underdamped regimes. In the overdamped case, an analytical solution using the matrix-continued fraction method revealed the existence of a maximum current when the noise intensity fluctuates on intermediate timescale regions. Similar effects were observed for the underdamped case by Monte Carlo simulations. The optimal time-correlation for Brownian transport coincided with the experimentally observed time-correlation of the extrinsic noise in Escherichia coli gene expression and implied the importance of environmental noise for molecular mechanisms.

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22

Deloupy, A., V. Sauveplane, J. Robert, S. Aymerich, M. Jules, and L. Robert. "Extrinsic noise prevents the independent tuning of gene expression noise and protein mean abundance in bacteria." Science Advances 6, no. 41 (October 2020): eabc3478. http://dx.doi.org/10.1126/sciadv.abc3478.

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It is generally accepted that prokaryotes can tune gene expression noise independently of protein mean abundance by varying the relative levels of transcription and translation. Here, we address this question quantitatively, using a custom-made library of 40 Bacillus subtilis strains expressing a fluorescent protein under the control of different transcription and translation control elements. We quantify noise and mean protein abundance by fluorescence microscopy and show that for most of the natural transcription range of B. subtilis, expression noise is equally sensitive to variations in the transcription or translation rate because of the prevalence of extrinsic noise. In agreement, analysis of whole-genome transcriptomic and proteomic datasets suggests that noise optimization through transcription and translation tuning during evolution may only occur in a regime of weak transcription. Therefore, independent control of mean abundance and noise can rarely be achieved, which has strong implications for both genome evolution and biological engineering.

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23

Persson, Sebastian, Niek Welkenhuysen, Sviatlana Shashkova, Samuel Wiqvist, Patrick Reith, Gregor W. Schmidt, Umberto Picchini, and Marija Cvijovic. "Scalable and flexible inference framework for stochastic dynamic single-cell models." PLOS Computational Biology 18, no. 5 (May 19, 2022): e1010082. http://dx.doi.org/10.1371/journal.pcbi.1010082.

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Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability.

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24

CHENG, YUHUA. "MOSFET MODELING FOR RF IC DESIGN." International Journal of High Speed Electronics and Systems 11, no. 04 (December 2001): 1007–84. http://dx.doi.org/10.1142/s0129156401001052.

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High frequency (HF) AC and noise modeling of MOSFETs for radio frequency (RF) integrated circuit (IC) design is discussed. Equivalent circuits representing both intrinsic and extrinsic components in a MOSFET are analyzed to obtain a physics-based RF model. Modeling of the intrinsic device and the extrinsic components is disussed by accounting for the important physical effects at both DC and HF. Based on the Y-parameter analysis of the equivalent circuit model, procedures of the HF model parameter extraction are also developed. With the discussed approaches, a sub-circuit RF model incorporating the modeling of parasitics is presented. This model is compared with the measured data for both y parameter and fr characteristics. Good model accuracy is achieved against the measurements for a 0.25μm RF CMOS technology. The non-quasi-static (NQS) modeling issue has also been discussed by using the BSIM3v3 based RF model to predict the HF characteristics of devices with serious NQS effects. Further, noise modeling issues are discussed by analyzing the theoretical and experimental results in both the flicker noise and thermal noise modeling. Modeling efforts to in corporate new physical effects are needed to predict better the flicker noise characteristics in today's MOSFETs. A detailed analysis of the HF noise parameters has been conducted to establish the relationship between the noise parameters preferred by circuit designers and obtained by HF noise measurement. Analytical calcultion of the noise parameters has also been discussed to understand the noise characteristics with/without some parasitic components such as gate and substrate resistances as well as the influence of the induced gate noise. The HF noise predictivities of several HF noise models are also examined with the measured data. The results show that the BSIM3v3 based RF model can predict the channel thermal noise better than the other models.

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25

MacLaurin, James N., and Pedro A. Vilanova. "Synchronization in Stochastic Biochemical Oscillators Subject to Common Multiplicative Extrinsic Noise." SIAM Journal on Applied Dynamical Systems 20, no. 3 (January 2021): 1253–76. http://dx.doi.org/10.1137/20m1332402.

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26

Samel, Alexander, and Mathias Basner. "Extrinsische Schlafstorungen und Larmwirkungen. Extrinsic Sleep Disorders and Effects of Noise." Somnologie 9, no. 2 (May 2005): 58–67. http://dx.doi.org/10.1111/j.1439-054x.2005.00049.x.

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27

Pischel, Dennis, Kai Sundmacher, and Robert J. Flassig. "Efficient simulation of intrinsic, extrinsic and external noise in biochemical systems." Bioinformatics 34, no. 16 (April 3, 2018): 2880. http://dx.doi.org/10.1093/bioinformatics/bty171.

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28

Dixit, Purushottam D. "Quantifying Extrinsic Noise in Gene Expression Using the Maximum Entropy Framework." Biophysical Journal 104, no. 12 (June 2013): 2743–50. http://dx.doi.org/10.1016/j.bpj.2013.05.010.

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29

Pischel, Dennis, Kai Sundmacher, and Robert J. Flassig. "Efficient simulation of intrinsic, extrinsic and external noise in biochemical systems." Bioinformatics 33, no. 14 (July 12, 2017): i319—i324. http://dx.doi.org/10.1093/bioinformatics/btx253.

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30

Rudge, Tim, and Kevin Burrage. "Effects of Intrinsic and Extrinsic Noise Can Accelerate Juxtacrine Pattern Formation." Bulletin of Mathematical Biology 70, no. 4 (March 13, 2008): 971–91. http://dx.doi.org/10.1007/s11538-007-9286-x.

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31

Lin, Hung-Yu. "The Effects of White Noise on Attentional Performance and On-Task Behaviors in Preschoolers with ADHD." International Journal of Environmental Research and Public Health 19, no. 22 (November 21, 2022): 15391. http://dx.doi.org/10.3390/ijerph192215391.

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Several models have tentatively associated improving attention-deficit/hyperactivity disorder (ADHD) symptoms with arousal and external environmental stimulation. In order to further clarify the relationships between ADHD symptoms, arousal, and external stimulation, this study focused on exploring the “simultaneous” effects of white noise on intrinsic attentional performance and extrinsic on-task behaviors in preschoolers with and without ADHD. By using the computerized task (K-CPT 2), 104 preschoolers, including 52 ADHD children and 52 typically developing (TD) children, were tested and analyzed for their intrinsic attention (such as detectability, omission errors, commission errors, and reaction time). Simultaneously, these preschoolers’ external on-task behaviors were recorded for analysis through systematic observation. This study showed that white noise could effectively improve attention performance, including enhancing the ability to differentiate non-targets from targets and decreasing omission errors. It could also reduce the extrinsic hyperactive behaviors of preschoolers with ADHD. The findings of this study highlighted that white noise stimulation is a beneficial non-pharmacological treatment for preschoolers with ADHD. In contrast, for TD preschoolers, the results of this study showed that the external white noise stimuli were not only unhelpful but also a burden.

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32

Babochkina, Tatyana Ivanovna, Ludmila Alekseevna Gerlinskaya, Margarita Vladimirovna Anisimova, Galina Vladimirovna Kontsevaya, Natalia Aleksandrovna Feofanova, Aliya Konstantinovna Stanova, Mikhail Pavlovich Moshkin, and Yuri Mikhailovich Moshkin. "Mother–Fetus Immune Cross-Talk Coordinates “Extrinsic”/“Intrinsic” Embryo Gene Expression Noise and Growth Stability." International Journal of Molecular Sciences 23, no. 20 (October 18, 2022): 12467. http://dx.doi.org/10.3390/ijms232012467.

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Developmental instability (DI) is thought to be inversely related to a capacity of an organism to buffer its development against random genetic and environmental perturbations. DI is represented by a trait’s inter- and intra-individual variabilities. The inter-individual variability (inversely referred to as canalization) indicates the capability of organisms to reproduce a trait from individual to individual. The intra-individual variability reflects an organism’s capability to stabilize a trait internally under the same conditions, and, for symmetric traits, it is expressed as fluctuating asymmetry (FA). When representing a trait as a random variable conditioned on environmental fluctuations, it is clear that, in statistical terms, the DI partitions into “extrinsic” (canalization) and “intrinsic” (FA) components of a trait’s variance/noise. We established a simple statistical framework to dissect both parts of a symmetric trait variance/noise using a PCA (principal component analysis) projection of the left/right measurements on eigenvectors followed by GAMLSS (generalized additive models for location scale and shape) modeling of eigenvalues. The first eigenvalue represents “extrinsic” and the second—“intrinsic” DI components. We applied this framework to investigate the impact of mother–fetus major histocompatibility complex (MHC)-mediated immune cross-talk on gene expression noise and developmental stability. We showed that “intrinsic” gene noise for the entire transcriptional landscape could be estimated from a small subset of randomly selected genes. Using a diagnostic set of genes, we found that allogeneic MHC combinations tended to decrease “extrinsic” and “intrinsic” gene noise in C57BL/6J embryos developing in the surrogate NOD-SCID and BALB/c mothers. The “intrinsic” gene noise was negatively correlated with growth (embryonic mass) and the levels of placental growth factor (PLGF), but not vascular endothelial growth factor (VEGF). However, it was positively associated with phenotypic growth instability and noise in PLGF. In mammals, the mother–fetus MHC interaction plays a significant role in development, contributing to the fitness of the offspring. Our results demonstrate that a positive impact of distant MHC combinations on embryonic growth could be mediated by the reduction of “intrinsic” gene noise followed by the developmental stabilization of growth.

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33

Giesel, Eileen Sophie, Basundhara Ghosh, and Björn Malte Schäfer. "Intrinsic and extrinsic gravitational flexions." Monthly Notices of the Royal Astronomical Society 510, no. 2 (December 17, 2021): 2773–89. http://dx.doi.org/10.1093/mnras/stab3680.

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ABSTRACT The topic of this paper is a generalization of the linear model for intrinsic alignments of galaxies to intrinsic flexions: In this model, third moments of the brightness distribution reflect distortions of elliptical galaxies caused by third derivatives of the gravitational potential, or, equivalently, gradients of the tidal gravitational fields. With this extension of the linear model mediating between the brightness distribution and the tidal gravitational fields and with a quantification of the shape of the galaxy at third order provided by the HOLICs-formalism, we are able to compute angular spectra of intrinsic flexions and the cross-spectra with weak lensing flexions. Spectra for intrinsic flexions are typically an order of magnitude smaller than lensing flexions, exactly as in the case of intrinsic ellipticity in comparison to weak shear. We find a negative cross-correlation between intrinsic and extrinsic gravitational flexions, too, complementing the analogous correlation between intrinsic and extrinsic ellipticity. After discussing the physical details of the alignment model to provide intrinsic flexions and their scaling properties, we quantify the observability of the intrinsic and extrinsic flexions and estimate with the Fisher-formalism how well the alignment parameter can be determined from a Euclid-like weak lensing survey. Intrinsic flexions are very difficult to measure and yield appreciable signals only with highly optimistic parameter choices and noise levels, while being basically undetectable for more realistic flexion measurement errors.

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34

Ahmad, Norhawati, S. S. Jamuar, M. Mohammad Isa, Siti Salwa Mat Isa, Muhammad Mahyiddin Ramli, N. Khalid, N. I. M. Nor, Shahrir Rizal Kasjoo, Sohiful Anuar Zainol Murad, and M. Missous. "Extrinsic and Intrinsic Modeling of InGaAs/InAlAs pHEMT for Wireless Applications." Applied Mechanics and Materials 815 (November 2015): 369–73. http://dx.doi.org/10.4028/www.scientific.net/amm.815.369.

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This paper presents the linear modelling of high breakdown InP pseudomorphic High Electron Mobility Transistors (pHEMT) that have been developed and fabricated at the University of Manchester (UoM) for low noise applications mainly for the Square Kilometre Array (SKA) project. The ultra-low leakage properties of a novel InGaAs/InAlAs/InP pHEMTs structure were used to fabricate a series of transistor with total gate width ranging from 0.2 mm to 1.2 mm. The measured DC and S-Parameters data from the fabricated devices were then used for the transistors’ modelling. The transistors demonstrated to operate up to frequencies of 25 GHz. These transistors models are used in the design of Low Noise Amplifiers (LNAs) using fully Monolithic Microwave Integrated Circuit (MMIC) technology.

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35

Haffane, Ahmed, Abdelhafid Hasni, Mustapha Khelifi, and Boufeldja Kadri. "Convergence analysis of the unpunctured turbo trellis-coded modulation (UTTCM)." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 2 (February 1, 2019): 447. http://dx.doi.org/10.11591/ijeecs.v13.i2.pp447-452.

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In this paper, the performance of the Unpunctured Turbo Trellis-Coded Modulation (UTTCM) over Additive White Gaussian Noise (AWGN) channel is analyzed using the non-binary extrinsic information transfer (EXIT) chart. The exchange of the extrinsic information between the decoder components is tracked, allowing the generation of an EXIT chart, which is a powerful tool for analyzing the convergence behavior of iterative decoding and prediction of convergence position. The Simulation results are compared with the turbo cliff positions on the BER curves.

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36

Gardès, Cyrille, Sonia Bagumako, Ludovic Desplanque, Nicolas Wichmann, Sylvain Bollaert, François Danneville, Xavier Wallart, and Yannick Roelens. "100 nm AlSb/InAs HEMT for Ultra-Low-Power Consumption, Low-Noise Applications." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/136340.

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We report on high frequency (HF) and noise performances of AlSb/InAs high electron mobility transistor (HEMT) with 100 nm gate length at room temperature in low-power regime. Extrinsic cut-off frequenciesfT/fmaxof 100/125 GHz together with minimum noise figureNFmin=0.5 dB and associated gainGass=12 dB at 12 GHz have been obtained at drain bias of only 80 mV, corresponding to 4 mW/mm DC power dissipation. This demonstrates the great ability of AlSb/InAs HEMT for high-frequency operation combined with low-noise performances in ultra-low-power regime.

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37

Engl, Christoph. "Noise in bacterial gene expression." Biochemical Society Transactions 47, no. 1 (December 21, 2018): 209–17. http://dx.doi.org/10.1042/bst20180500.

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AbstractThe expression level of a gene can fluctuate significantly between individuals within a population of genetically identical cells. The resultant phenotypic heterogeneity could be exploited by bacteria to adapt to changing environmental conditions. Noise is hence a genome-wide phenomenon that arises from the stochastic nature of the biochemical reactions that take place during gene expression and the relatively low abundance of the molecules involved. The production of mRNA and proteins therefore occurs in bursts, with alternating episodes of high and low activity during transcription and translation. Single-cell and single-molecule studies demonstrated that noise within gene expression is influenced by a combination of both intrinsic and extrinsic factors. However, our mechanistic understanding of this process at the molecular level is still rather limited. Further investigation is necessary that takes into account the detailed knowledge of gene regulation gained from biochemical studies.

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Berthelot, A., C. Voisin, C. Delalande, Ph Roussignol, R. Ferreira, and G. Cassabois. "From Random Telegraph to Gaussian Stochastic Noises: Decoherence and Spectral Diffusion in a Semiconductor Quantum Dot." Advances in Mathematical Physics 2010 (2010): 1–13. http://dx.doi.org/10.1155/2010/494738.

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We present a general theoretical description of the extrinsic dephasing mechanism of spectral diffusion that dominates the decoherence dynamics in semiconductor quantum dots at low temperature. We discuss the limits of random telegraph and Gaussian stochastic noises and show that the combination of both approaches in the framework of the pre-Gaussian noise theory allows a quantitative interpretation of high-resolution experiments in single semiconductor quantum dots. We emphasize the generality and the versatility of our model where the inclusion of asymmetric jump processes appears as an essential extension for the understanding of semiconductor quantum dot physics.

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Aston, Stacey, Cat Pattie, Ulrik Beierholm, and Marko Nardini. "Failure to account for extrinsic visual noise leads to suboptimal multisensory integration." Journal of Vision 20, no. 11 (October 20, 2020): 880. http://dx.doi.org/10.1167/jov.20.11.880.

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40

Del Giudice, Marco, Stefano Bo, Silvia Grigolon, and Carla Bosia. "On the role of extrinsic noise in microRNA-mediated bimodal gene expression." PLOS Computational Biology 14, no. 4 (April 17, 2018): e1006063. http://dx.doi.org/10.1371/journal.pcbi.1006063.

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Scott, Matthew, Brian Ingalls, and Mads Kærn. "Estimations of intrinsic and extrinsic noise in models of nonlinear genetic networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 16, no. 2 (June 2006): 026107. http://dx.doi.org/10.1063/1.2211787.

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Bravi, Barbara, Katy J. Rubin, and Peter Sollich. "Systematic model reduction captures the dynamics of extrinsic noise in biochemical subnetworks." Journal of Chemical Physics 153, no. 2 (July 14, 2020): 025101. http://dx.doi.org/10.1063/5.0008304.

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Rubin, Katy J., Katherine Lawler, Peter Sollich, and Tony Ng. "Memory effects in biochemical networks as the natural counterpart of extrinsic noise." Journal of Theoretical Biology 357 (September 2014): 245–67. http://dx.doi.org/10.1016/j.jtbi.2014.06.002.

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44

Baudrimont, Antoine, Vincent Jaquet, Sandrine Wallerich, Sylvia Voegeli, and Attila Becskei. "Contribution of RNA Degradation to Intrinsic and Extrinsic Noise in Gene Expression." Cell Reports 26, no. 13 (March 2019): 3752–61. http://dx.doi.org/10.1016/j.celrep.2019.03.001.

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45

Mc Mahon, Siobhan S., Oleg Lenive, Sarah Filippi, and Michael P. H. Stumpf. "Information processing by simple molecular motifs and susceptibility to noise." Journal of The Royal Society Interface 12, no. 110 (September 2015): 20150597. http://dx.doi.org/10.1098/rsif.2015.0597.

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Biological organisms rely on their ability to sense and respond appropriately to their environment. The molecular mechanisms that facilitate these essential processes are however subject to a range of random effects and stochastic processes, which jointly affect the reliability of information transmission between receptors and, for example, the physiological downstream response. Information is mathematically defined in terms of the entropy; and the extent of information flowing across an information channel or signalling system is typically measured by the ‘mutual information’, or the reduction in the uncertainty about the output once the input signal is known. Here, we quantify how extrinsic and intrinsic noise affects the transmission of simple signals along simple motifs of molecular interaction networks. Even for very simple systems, the effects of the different sources of variability alone and in combination can give rise to bewildering complexity. In particular, extrinsic variability is apt to generate ‘apparent’ information that can, in extreme cases, mask the actual information that for a single system would flow between the different molecular components making up cellular signalling pathways. We show how this artificial inflation in apparent information arises and how the effects of different types of noise alone and in combination can be understood.

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Zhang, Jing Xi. "Optimization of LDPC Codes by Extrinsic Information Transfer Chart." Applied Mechanics and Materials 427-429 (September 2013): 1518–23. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1518.

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The optimization of degree profiles of low-density parity-check (LDPC) code in additive white Gaussian noise (AWGN) multiple access channel (MAC) by fitting the transfer characteristics of variable nodes detector (VND) and that of check nodes detector (CND) is discussed. Extrinsic information transfer (EXIT) characteristics are used for determining the degree profiles based on curve fitting. The convergence the optimized LDPC code is ensured by the EXIT characteristics of VND and CND. Degree profiles are obtained and check matrix is constructed. Simulation results show that the method is variable in designing LDPC code degree profiles in MAC with reduced complexity compared with density evolution based on Gaussian approximation.

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GRÜNEIS, FERDINAND. "1/f NOISE DUE TO ATOMIC DIFFUSION OF IMPURITY CENTERS IN SEMICONDUCTORS." Fluctuation and Noise Letters 01, no. 04 (December 2001): L197—L220. http://dx.doi.org/10.1142/s0219477501000433.

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Atomic diffusion of impurity centers is investigated as a possible origin of 1/f noise in semiconductors. Following the trace of an individual impurity center, the noise produced at a certain site is calculated; due to diffusion of centers this is an intermittent process. Besides generation-recombination (= g-r) noise, an excess noise is obtained which is attributed to diffusion of impurity centers. This excess noise exhibits 1/f noise and g-r burst noise. 1/f noise is attributed to the return time of a center to the origin; g-r burst noise is the noise produced by centers residing at a certain site. For a n-type strongly extrinsic semiconductor, the Hooge coefficient α of the present model is derived and impact of compensating acceptors or additional doping by shallow centers is investigated. Increasing the concentration of additional shallow centers α is decreased; an increase of concentration of compensating acceptors results in an increase of α. The temperature dependence of the Hooge coefficient α(T) is calculated and is compared with empirical findings.

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Lu, Lance, Thomas Kurfess, and Christopher Saldana. "Effects of Extrinsic Noise Factors on Machine Learning–Based Chatter Detection in Machining." Smart and Sustainable Manufacturing Systems 5, no. 1 (August 13, 2021): 20210007. http://dx.doi.org/10.1520/ssms20210007.

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Aston, Stacey, Reeve Molly, Yip Esther, Nardini Marko, and Beierholm Ulrik. "Failure to account for extrinsic noise when integrating visual cues and prior information." Journal of Vision 21, no. 9 (September 27, 2021): 2278. http://dx.doi.org/10.1167/jov.21.9.2278.

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Fisher, N. E., and D. A. Jackson. "A common-mode optical noise-rejection scheme for an extrinsic Faraday current sensor." Measurement Science and Technology 7, no. 5 (May 1, 1996): 796–800. http://dx.doi.org/10.1088/0957-0233/7/5/012.

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