scholarly journals Spatio-temporal correlations between catastrophe events in a microtubule bundle: a computational study

2020 ◽  
Vol 49 (3-4) ◽  
pp. 215-222
Author(s):  
Makarand Diwe ◽  
Manoj Gopalakrishnan
2019 ◽  
Author(s):  
Makarand Diwe ◽  
Manoj Gopalakrishnan

AbstractWe explore correlations between dynamics of different microtubules in a bundle, via numerical simulations, using a one-dimensional stochastic model of a microtubule. The GTP-bound tubulins undergo diffusion-limited binding to the tip. Random hydrolysis events take place along the filament, and converts GTP-tubulin to GDP-tubulin. The filament starts depolymerising when the monomer at the tip becomes GDP-bound; in this case, detachment of GDP-tubulin ensues and continues until either GTP-bound tubulin is exposed or complete depolymerisation is achieved. In the latter case, the filament is defined to have undergone a “catastrophe”. Our results show that, in general, the dynamics of growth and catastrophe in different filaments are coupled to each other; closer the filaments are, the stronger the coupling. In particular, all filaments grow slower, on average, when brought closer together. The reduction in growth velocity also leads to more frequent catastrophes. More dramatically, catastrophe events in the different filaments forming a bundle are found to be correlated; a catastrophe event in one filament is more likely to be followed by a similar event in the same filament. This propensity of bunching disappears when the filaments move farther apart.


2015 ◽  
Vol 778 ◽  
pp. 216-252 ◽  
Author(s):  
C. D. Pokora ◽  
J. J. McGuirk

Stereoscopic three-component particle image velocimetry (3C-PIV) measurements have been made in a turbulent round jet to investigate the spatio-temporal correlations that are the origin of aerodynamic noise. Restricting attention to subsonic, isothermal jets, measurements were taken in a water flow experiment where, for the same Reynolds number and nozzle size, the shortest time scale of the dynamically important turbulent structures is more than an order of magnitude greater that in equivalent airflow experiments, greatly facilitating time-resolved PIV measurements. Results obtained (for a jet nozzle diameter and velocity of 40 mm and $1~\text{m}~\text{s}^{-1}$, giving $\mathit{Re}=4\times 10^{4}$) show that, on the basis of both single-point statistics and two-point quantities (correlation functions, integral length scales) the present incompressible flow data are in excellent agreement with published compressible, subsonic airflow measurements. The 3C-PIV data are first compared to higher-spatial-resolution 2C-PIV data and observed to be in good agreement, although some deterioration in quality for higher-order correlations caused by high-frequency noise in the 3C-PIV data is noted. A filter method to correct for this is proposed, based on proper orthogonal decomposition (POD) of the 3C-PIV data. The corrected data are then used to construct correlation maps at the second- and fourth-order level for all velocity components. The present data are in accordance with existing hot-wire measurements, but provide significantly more detailed information on correlation components than has previously been available. The measured relative magnitudes of various components of the two-point fourth-order turbulence correlation coefficient ($R_{ij,kl}$) – the fundamental building block for free shear flow aerodynamic noise sources – are presented and represent a valuable source of validation data for acoustic source modelling. The relationship between fourth-order and second-order velocity correlations is also examined, based on an assumption of a quasi-Gaussian nearly normal p.d.f. for the velocity fluctuations. The present results indicate that this approximation shows reasonable agreement for the measured relative magnitudes of several correlation components; however, areas of discrepancy are identified, indicating the need for work on alternative models such as the shell turbulence concept of Afsar (Eur. J. Mech. (B/Fluids), vol. 31, 2012, pp. 129–139).


2010 ◽  
Vol 09 (04) ◽  
pp. 381-406 ◽  
Author(s):  
J. BOSCH-BAYARD ◽  
J. RIERA-DIAZ ◽  
R. BISCAY-LIRIO ◽  
K. F. K. WONG ◽  
A. GALKA ◽  
...  

2019 ◽  
Vol 9 (4) ◽  
pp. 615 ◽  
Author(s):  
Panbiao Liu ◽  
Yong Zhang ◽  
Dehui Kong ◽  
Baocai Yin

Buses, as the most commonly used public transport, play a significant role in cities. Predicting bus traffic flow cannot only build an efficient and safe transportation network but also improve the current situation of road traffic congestion, which is very important for urban development. However, bus traffic flow has complex spatial and temporal correlations, as well as specific scenario patterns compared with other modes of transportation, which is one of the biggest challenges when building models to predict bus traffic flow. In this study, we explore bus traffic flow and its specific scenario patterns, then we build improved spatio-temporal residual networks to predict bus traffic flow, which uses fully connected neural networks to capture the bus scenario patterns and improved residual networks to capture the bus traffic flow spatio-temporal correlation. Experiments on Beijing transportation smart card data demonstrate that our method achieves better results than the four baseline methods.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Tomasz J. Kolanowski ◽  
Natalia Rozwadowska ◽  
Agnieszka Zimna ◽  
Magdalena Nowaczyk ◽  
Marcin Siatkowski ◽  
...  

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