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How long should a system be observed to obtain reliable concentration estimates from the measurement of fluctuations?

ABSTRACT: The interior of cells is a highly fluctuating environment. Fluctuations set limits to the accuracy with which endogenous processes can occur. The physical principles that rule these limits also affect the experimental quantification of biophysical parameters in situ. The characterization of fluctuations, on the other hand, provides a way to quantify biophysical parameters. But as with any random process, enough data has to be collected to achieve a reliable quantitative description. In this article we study the accuracy with which intracellular concentrations can be estimated using fluorescence correlation spectroscopy. We show that, when the observed molecules interact with immobile species or experience other restrictions to their movement, the hypotheses commonly used to estimate concentrations are no longer valid. The interactions with immobile sites reduce the fluorescence variance by a finite amount. The time that is necessary to obtain an accurate concentration estimate, on the other hand, is hundreds of times larger than the slowest correlation time and is much larger when the sites move slowly than when they are immobile. Our analysis is applicable to other related techniques and it also sheds light on the way in which effector concentrations are read by target molecules in cells.

SUBMITTER: Perez Ipina E

PROVIDER: S-EPMC4255965 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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How long should a system be observed to obtain reliable concentration estimates from the measurement of fluctuations?

Pérez Ipiña Emiliano E Ponce Dawson Silvina S

Biophysical journal 20141202 11

The interior of cells is a highly fluctuating environment. Fluctuations set limits to the accuracy with which endogenous processes can occur. The physical principles that rule these limits also affect the experimental quantification of biophysical parameters in situ. The characterization of fluctuations, on the other hand, provides a way to quantify biophysical parameters. But as with any random process, enough data has to be collected to achieve a reliable quantitative description. In this arti ...[more]

PMID: 25468346

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Project description:BackgroundThe critical shoulder angle (CSA) has been reported to be highly associated with rotator cuff tears (RCTs) and an increased risk of RCT re-tears. However, the measurement of the CSA is greatly affected by the malpositioning of the shoulder. To address this issue, a two-step neural network-based guiding system was developed to obtain reliable CSA radiographs, and its feasibility and accuracy was evaluated.MethodsA total of 1,754 shoulder anteroposterior (AP) radiographs were retrospectively acquired to train and validate a two-step neural network-based guiding system to obtain reliable CSA radiographs. The study included patients aged 18 years or older who underwent X-rays and/or computed tomography (CT) scans of the shoulder. Patients who had undergone shoulder surgery, had a confirmed fracture, or were diagnosed with a musculoskeletal tumor or glenoid defect were excluded from the study. The system consisted of a two-step neural network that in the first step, localized the region of interest of the shoulder, and in the second step, classified the radiography according to type [i.e., 'forward' when the non-overlapping coracoid process is above the glenoid rim, 'backward' when the non-overlapping coracoid process is below or aligned with the glenoid rim, a ratio of the transverse to longitudinal diameter of the glenoid projection (RTL) ≤0.25, or a RTL >0.25]. The performance of the model was assessed in an offline, prospective manner, focusing on the sensitivity and specificity for the forward, backward, RTL ≤0.25, or RTL >0.25 types (denoted as SensF, B, -, + and SpecF, B, -, +, respectively), and Cohen's kappa was also reported.ResultsOf 273 cases in the offline prospective test, the SensF, SensB, Sens-, and Sens+ were 88.88% [95% confidence interval (CI): 50.67-99.41%], 94.11% (95% CI: 82.77-98.47%), 96.96% (95% CI: 91.94-99.02%), and 95.06% (95% CI: 87.15-98.40%), respectively. The SpecF, SpecB, Spec-, and Spec+ were 98.48% (95% CI: 95.90-99.51%), 99.55% (95% CI: 97.12-99.97%), 95.04% (95% CI: 89.65-97.81%), and 97.39% (93.69-99.03%), respectively. A high classification rate (93.41%; 95% CI: 89.14-96.24%) and almost perfect agreement (Cohen's kappa: 0.903, 95% CI: 0.86-0.95) were achieved.ConclusionsThe guiding system can rapidly and accurately classify the types of AP shoulder radiography, thereby guiding the adjustment of patient positioning. This will facilitate the rapid obtainment of reliable CSA radiography to measure the CSA on proper AP radiographs.

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How long should a system be observed to obtain reliable concentration estimates from the measurement of fluctuations?

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How long should a system be observed to obtain reliable concentration estimates from the measurement of fluctuations?

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