scholarly journals Investigating Optimal Time Step Intervals of Imaging for Data Quality through a Novel Fully-Automated Cell Tracking Approach

2020 ◽  
Vol 6 (7) ◽  
pp. 66
Author(s):  
Feng Wei Yang ◽  
Lea Tomášová ◽  
Zeno v. Guttenberg ◽  
Ke Chen ◽  
Anotida Madzvamuse
Keyword(s):  
Data Acquisition ◽  
Cell Biology ◽  
Cell Tracking ◽  
Cell Movement ◽  
Optimal Time ◽  
Tracking Accuracy ◽  
Quality Of Data ◽  
Time Step ◽  
Tracking Algorithms ◽  
Step Interval

Computer-based fully-automated cell tracking is becoming increasingly important in cell biology, since it provides unrivalled capacity and efficiency for the analysis of large datasets. However, automatic cell tracking’s lack of superior pattern recognition and error-handling capability compared to its human manual tracking counterpart inspired decades-long research. Enormous efforts have been made in developing advanced cell tracking packages and software algorithms. Typical research in this field focuses on dealing with existing data and finding a best solution. Here, we investigate a novel approach where the quality of data acquisition could help improve the accuracy of cell tracking algorithms and vice-versa. Generally speaking, when tracking cell movement, the more frequent the images are taken, the more accurate cells are tracked and, yet, issues such as damage to cells due to light intensity, overheating in equipment, as well as the size of the data prevent a constant data streaming. Hence, a trade-off between the frequency at which data images are collected and the accuracy of the cell tracking algorithms needs to be studied. In this paper, we look at the effects of different choices of the time step interval (i.e., the frequency of data acquisition) within the microscope to our existing cell tracking algorithms. We generate several experimental data sets where the true outcomes are known (i.e., the direction of cell migration) by either using an effective chemoattractant or employing no-chemoattractant. We specify a relatively short time step interval (i.e., 30 s) between pictures that are taken at the data generational stage, so that, later on, we may choose some portion of the images to produce datasets with different time step intervals, such as 1 min, 2 min, and so on. We evaluate the accuracy of our cell tracking algorithms to illustrate the effects of these different time step intervals. We establish that there exist certain relationships between the tracking accuracy and the time step interval associated with experimental microscope data acquisition. We perform fully-automatic adaptive cell tracking on multiple datasets, to identify optimal time step intervals for data acquisition, while at the same time demonstrating the performance of the computer cell tracking algorithms.

Quality Monitoring System for Seismic While Drilling

2013 ◽  
Vol 318 ◽  
pp. 572-575
Author(s):  
Li Li Yu ◽  
Yu Hong Li ◽  
Ai Feng Wang
Keyword(s):  
Data Acquisition ◽  
Monitoring System ◽  
Signal Analysis ◽  
Quality Evaluation ◽  
Quality Monitoring ◽  
Quality Of Data ◽  
Whole Process ◽  
Engineering Parameters ◽  
Network Status

In this paper a quality monitoring system for seismic while drilling (SWD) that integrates the whole process of data acquisition was developed. The acquisition equipment, network status and signals of accelerometer and geophone were monitored real-time. With fast signal analysis and quality evaluation, the acquisition parameters and drilling engineering parameters can be adjusted timely. The application of the system can improve the quality of data acquisition and provide subsequent processing and interpretation with high qualified reliable data.

scholarly journals Data-driven modeling reveals cell behaviors controlling self-organization during Myxococcus xanthus development

2017 ◽  
Vol 114 (23) ◽  
pp. E4592-E4601 ◽  
Author(s):  
Christopher R. Cotter ◽  
Heinz-Bernd Schüttler ◽  
Oleg A. Igoshin ◽  
Lawrence J. Shimkets
Keyword(s):  
Cell Tracking ◽  
Myxococcus Xanthus ◽  
Cell Movement ◽  
Modeling Method ◽  
Data Driven ◽  
Self Organization ◽  
Emergent Properties ◽  
Developmental Cycle ◽  
Fluorescent Cell ◽  
Cell Behaviors

Collective cell movement is critical to the emergent properties of many multicellular systems, including microbial self-organization in biofilms, embryogenesis, wound healing, and cancer metastasis. However, even the best-studied systems lack a complete picture of how diverse physical and chemical cues act upon individual cells to ensure coordinated multicellular behavior. Known for its social developmental cycle, the bacterium Myxococcus xanthus uses coordinated movement to generate three-dimensional aggregates called fruiting bodies. Despite extensive progress in identifying genes controlling fruiting body development, cell behaviors and cell–cell communication mechanisms that mediate aggregation are largely unknown. We developed an approach to examine emergent behaviors that couples fluorescent cell tracking with data-driven models. A unique feature of this approach is the ability to identify cell behaviors affecting the observed aggregation dynamics without full knowledge of the underlying biological mechanisms. The fluorescent cell tracking revealed large deviations in the behavior of individual cells. Our modeling method indicated that decreased cell motility inside the aggregates, a biased walk toward aggregate centroids, and alignment among neighboring cells in a radial direction to the nearest aggregate are behaviors that enhance aggregation dynamics. Our modeling method also revealed that aggregation is generally robust to perturbations in these behaviors and identified possible compensatory mechanisms. The resulting approach of directly combining behavior quantification with data-driven simulations can be applied to more complex systems of collective cell movement without prior knowledge of the cellular machinery and behavioral cues.

scholarly journals Estimation of Optimal Time Step and Compare with of ODE Solver of MATLAB Package of RLC Circuit using Numerical Methods

1970 ◽  
Vol 7 (1) ◽  
pp. 67-73 ◽  
Author(s):  
Sheikh Md Rabiul Islam
Keyword(s):  
Mesh Size ◽  
Paper Analysis ◽  
Optimal Time ◽  
Time Step ◽  
Minimum Error ◽  
Optimal Output ◽  
Runge Kutta Method ◽  
Rlc Circuit ◽  
Matlab Package ◽  
Theoretical Results

In this paper analysis of a RLC circuit model that has been described optimal time step and minimize of error using numerical method. The goal is to reach the optimal time response due to the input for which optimal output response reaches a minimum error and also compared with ODE solver of MATLAB packages for the different cell (mesh) size of the RLC model. Table is constructed of the model to evaluate optimal time step and also CPU time into the simulation using MATLAB 7.6.0(R2008a).The values of register, capacitor and inductor as well as electromagnetic force are obtained through the mathematical relations of the model. The general analysis of the RLC circuit due to the optimal time step and minimum error is developed after several analysis and operations. The theoretical results show effectiveness of optimized of the model. Keywords: Optimal time step; MATLAB; Trapezoidal; Implicit Euler; Runge-Kutta method; RLC circuit. DOI: http://dx.doi.org/10.3329/diujst.v7i1.9650   Daffodil International University Journal of Science and Technology Vol.7(1) 2012 67-73

scholarly journals Comparing lifeact and phalloidin for super-resolution imaging of actin in fixed cells

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0246138
Author(s):  
Hanieh Mazloom-Farsibaf ◽  
Farzin Farzam ◽  
Mohamadreza Fazel ◽  
Michael J. Wester ◽  
Marjolein B. M. Meddens ◽  
...  
Keyword(s):  
Single Molecule ◽  
Cell Biology ◽  
Cell Movement ◽  
Super Resolution ◽  
Actin Binding ◽  
Thin Filaments ◽  
Reversible Binding ◽  
Multiple Regions ◽  
Resolution Imaging ◽  
Division Cell

Visualizing actin filaments in fixed cells is of great interest for a variety of topics in cell biology such as cell division, cell movement, and cell signaling. We investigated the possibility of replacing phalloidin, the standard reagent for super-resolution imaging of F-actin in fixed cells, with the actin binding peptide ‘lifeact’. We compared the labels for use in single molecule based super-resolution microscopy, where AlexaFluor 647 labeled phalloidin was used in a dSTORM modality and Atto 655 labeled lifeact was used in a single molecule imaging, reversible binding modality. We found that imaging with lifeact had a comparable resolution in reconstructed images and provided several advantages over phalloidin including lower costs, the ability to image multiple regions of interest on a coverslip without degradation, simplified sequential super-resolution imaging, and more continuous labeling of thin filaments.

Intelligent Time-Stepping for Practical Numerical Simulation

2021 ◽  
Author(s):  
Soham Sheth ◽  
Francois McKee ◽  
Kieran Neylon ◽  
Ghazala Fazil
Keyword(s):  
Heuristic Method ◽  
Simulation Models ◽  
Model Complexity ◽  
Large Field ◽  
Optimal Time ◽  
Time Step ◽  
Non Linear ◽  
Set Up ◽  
Reservoir Simulator ◽  
Step Selection

Abstract We present a novel reservoir simulator time-step selection approach which uses machine-learning (ML) techniques to analyze the mathematical and physical state of the system and predict time-step sizes which are large while still being efficient to solve, thus making the simulation faster. An optimal time-step choice avoids wasted non-linear and linear equation set-up work when the time-step is too small and avoids highly non-linear systems that take many iterations to solve. Typical time-step selectors use a limited set of features to heuristically predict the size of the next time-step. While they have been effective for simple simulation models, as model complexity increases, there is an increasing need for robust data-driven time-step selection algorithms. We propose two workflows – static and dynamic – that use a diverse set of physical (e.g., well data) and mathematical (e.g., CFL) features to build a predictive ML model. This can be pre-trained or dynamically trained to generate an inference model. The trained model can also be reinforced as new data becomes available and efficiently used for transfer learning. We present the application of these workflows in a commercial reservoir simulator using distinct types of simulation model including black oil, compositional and thermal steam-assisted gravity drainage (SAGD). We have found that history-match and uncertainty/optimization studies benefit most from the static approach while the dynamic approach produces optimum step-sizes for prediction studies. We use a confidence monitor to manage the ML time-step selector at runtime. If the confidence level falls below a threshold, we switch to traditional heuristic method for that time-step. This avoids any degradation in the performance when the model features are outside the training space. Application to several complex cases, including a large field study, shows a significant speedup for single simulations and even better results for multiple simulations. We demonstrate that any simulation can take advantage of the stored state of the trained model and even augment it when new situations are encountered, so the system becomes more effective as it is exposed to more data.

Information Cleansing and Processing

2010 ◽  
pp. 84-96
Author(s):  
Manjunath Ramachandra
Keyword(s):  
Supply Chain ◽  
Data Acquisition ◽  
Poor Quality ◽  
Quality Of Data ◽  
Data Cleansing

The data gathered from the sources are often noisy Poor quality of data results in business losses that keep increasing down the supply chain. The end customer finds it absolutely useless and misguiding. So, cleansing of data is to be performed immediately and automatically after the data acquisition. This chapter provides the different techniques for data cleansing and processing to achieve the same.

scholarly journals Joining forces: crosstalk between biochemical signalling and physical forces orchestrates cellular polarity and dynamics

2018 ◽  
Vol 373 (1747) ◽  
pp. 20170145 ◽  
Author(s):  
Suvrajit Saha ◽  
Tamas L. Nagy ◽  
Orion D. Weiner
Keyword(s):  
Cell Biology ◽  
Cell Movement ◽  
Special Focus ◽  
Cellular Motility ◽  
Cellular Polarity ◽  
Self Organized ◽  
Cytoskeletal Dynamics ◽  
Physical Forces ◽  
Cytoskeletal Rearrangements

Dynamic processes like cell migration and morphogenesis emerge from the self-organized interaction between signalling and cytoskeletal rearrangements. How are these molecular to sub-cellular scale processes integrated to enable cell-wide responses? A growing body of recent studies suggest that forces generated by cytoskeletal dynamics and motor activity at the cellular or tissue scale can organize processes ranging from cell movement, polarity and division to the coordination of responses across fields of cells. To do so, forces not only act mechanically but also engage with biochemical signalling. Here, we review recent advances in our understanding of this dynamic crosstalk between biochemical signalling, self-organized cortical actomyosin dynamics and physical forces with a special focus on the role of membrane tension in integrating cellular motility. This article is part of the theme issue ‘Self-organization in cell biology’.

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