scholarly journals Correcting Artifacts in Ratiometric Biosensor Imaging; an Improved Approach for Dividing Noisy Signals

2021 ◽  
Vol 9 ◽  
Author(s):  
Daniel J. Marston ◽  
Scott D. Slattery ◽  
Klaus M. Hahn ◽  
Denis Tsygankov
Keyword(s):  
Background Subtraction ◽  
Ground Truth ◽  
Single Chain ◽  
Signal To Noise ◽  
New Approach ◽  
Cell Edge ◽  
Noisy Signals ◽  
Ratio Imaging ◽  
Noise Correction ◽  
Uniform Ratio

The accuracy of biosensor ratio imaging is limited by signal/noise. Signals can be weak when biosensor concentrations must be limited to avoid cell perturbation. This can be especially problematic in imaging of low volume regions, e.g., along the cell edge. The cell edge is an important imaging target in studies of cell motility. We show how the division of fluorescence intensities with low signal-to-noise at the cell edge creates specific artifacts due to background subtraction and division by small numbers, and that simply improving the accuracy of background subtraction cannot address these issues. We propose a new approach where, rather than simply subtracting background from the numerator and denominator, we subtract a noise correction factor (NCF) from the numerator only. This NCF can be derived from the analysis of noise distribution in the background near the cell edge or from ratio measurements in the cell regions where signal-to-noise is high. We test the performance of the method first by examining two noninteracting fluorophores distributed evenly in cells. This generated a uniform ratio that could provide a ground truth. We then analyzed actual protein activities reported by a single chain biosensor for the guanine exchange factor (GEF) Asef, and a dual chain biosensor for the GTPase Cdc42. The reduction of edge artifacts revealed persistent Asef activity in a narrow band (∼640 nm wide) immediately adjacent to the cell edge. For Cdc42, the NCF method revealed an artifact that would have been obscured by traditional background subtraction approaches.

scholarly journals Correcting artifacts in ratiometric biosensor imaging; an improved approach for dividing noisy signals

2021 ◽  
Author(s):  
Daniel J. Marston ◽  
Scott Slattery ◽  
Klaus M. Hahn ◽  
Denis Tsygankov
Keyword(s):  
Background Subtraction ◽  
Ground Truth ◽  
Single Chain ◽  
Signal To Noise ◽  
New Approach ◽  
Cell Edge ◽  
Noisy Signals ◽  
Ratio Imaging ◽  
Noise Correction ◽  
Uniform Ratio

AbstractThe accuracy of biosensor ratio imaging is limited by signal/noise. Signals can be weak when biosensor concentrations must be limited to avoid cell perturbation. This can be especially problematic in imaging of low volume regions, e.g., along the cell edge. The cell edge is an important imaging target in studies of cell motility. We show how the division of fluorescence intensities with low signal-to-noise at the cell edge creates specific artifacts due to background subtraction and division by small numbers, and that simply improving the accuracy of background subtraction cannot address these issues. We propose a new approach where, rather than simply subtracting background from the numerator and denominator, we subtract a noise correction factor (NCF) from the numerator only. This NCF can be derived from the analysis of noise distribution in the background near the cell edge or from ratio measurements in the cell regions where signal-to-noise is high. We test the performance of the method first by examining two noninteracting fluorophores distributed evenly in cells. This generated a uniform ratio that could provide a ground truth. We then analyzed actual protein activities reported by a single chain biosensor for the guanine exchange factor Asef, and a dual chain biosensor for the GTPase Cdc42. The reduction of edge artifacts revealed persistent Asef activity in a narrow band (∼640 nm wide) immediately adjacent to the cell edge. For Cdc42, the NCF method revealed an artefact that would have been obscured by traditional background subtraction approaches.

scholarly journals A New Model to Predict Weak Lensing Peak Counts

2014 ◽  
Vol 10 (S306) ◽  
pp. 107-109 ◽  
Author(s):  
Chieh-An Lin ◽  
Martin Kilbinger
Keyword(s):  
Gravitational Lensing ◽  
Mass Function ◽  
Weak Lensing ◽  
High Signal ◽  
Dark Matter Halos ◽  
Signal To Noise ◽  
New Approach ◽  
Promising Tool ◽  
Peak Abundance ◽  
Good Agreement

AbstractPeak statistics from weak gravitational lensing have been shown to be a promising tool for cosmology. Here we propose a new approach to predict weak lensing peak counts. For an arbitrary cosmology, we draw dark matter halos from the halo mass function, and calculate the number of peaks from the projected halo mass distribution. This procedure is much faster than time-consuming N-body simulations. By comparing these “fast simulations” to N-body runs, we find that the peak abundance is in very good agreement. Furthermore, our model is able to discriminate cosmologies with different sets of parameters, using high signal-to-noise peaks (≳ 4). This encourages us to examine the optimal combinations of parameters to this approach in the future.

A Direct, Plasmin Independent Assay For Plasminogen Activation

1981 ◽  
Author(s):  
Luciana Mussoni ◽  
Dan Lawrence ◽  
David Loskutoff
Keyword(s):  
Polyacrylamide Gel Electrophoresis ◽  
Caproic Acid ◽  
Plasminogen Activation ◽  
Single Chain ◽  
New Approach ◽  
Aortic Endothelial Cells ◽  
Cleavage Assay ◽  
Km Value ◽  
Triton X ◽  
Dose Dependent

We have modified the direct, 125I-plasminogen cleavage assay for plasminogen activator (PA) and employed it to compare urokinase (UK), tissue activator (TA), and PAs produced by cultured bovine aortic endothelial cells. The assay is based on conversion of single chain plasminogen to two chain plasmin as revealed by polyacrylamide gel electrophoresis in the presence of SDS andβ-mercapto- ethanol. Inclusion of Triton X-100, albumin and trasylol in the reaction mixture reduced the adsorptive and hydrolytic loss of reactants, and increased the linearity and sensitivity of the assay. Under these conditions, plasmin formation was linear for at least 6 hrs, dose- dependent over a 20-fold range of UK concentrations, and at least 100-fold more sensitive (0.01 units/ml) than previously reported direct assays for UK. In preliminary experiments, we determined a Km value of 10yM for UK and plasminogen. Activation of plasminogen by TA was minimal in the absence of fibrin, and independent of the concentration of activator. However, in the presence of fibrin, (1) the initial rate of activation increased dramatically, (2) 100-1000 fold less TA was required, and (3) activation was proportional to the concentration of both TA and fibrin. Surprisingly, activation by UK and cellular PAs was partially inhibited by fibrin. Epsilon amino caproic acid (EACA; 0.5-100mM) stimulated activation by UK both in the presence and absence of fibrin by 30-40%. In contrast, EACA (0.1-100mM) inhibited TA activity in the presence of fibrin by over 90%. However, in the absence of fibrin, TA was inhibited by only 50%, even at high EACA concentrations (lOOmM). These results indicate that cleavage of 1251- plasminogen can be employed as a direct, sensitive and quantitative assay for various PAs, and offers a new approach for studying plasminogen activation and agents that stimulate or inhibit it.

New Approach for Designing Single-Chain Magnets: Organization of Chains via Hydrogen Bonding between Nucleobases

2012 ◽  
Vol 134 (16) ◽  
pp. 6908-6911 ◽  
Author(s):  
Wei-Xiong Zhang ◽  
Takuya Shiga ◽  
Hitoshi Miyasaka ◽  
Masahiro Yamashita
Keyword(s):  
Hydrogen Bonding ◽  
Single Chain ◽  
New Approach ◽  
Single Chain Magnets

scholarly journals Vehicle Classification using IPCP and EsKNN Algorithm for Surveillance Camera

2019 ◽  
Vol 18 (01) ◽  
pp. 15-30
Author(s):  
Atyanta Nika Rumaksari
Keyword(s):  
Background Subtraction ◽  
Nearest Neighbor ◽  
Direct Detection ◽  
Vehicle Classification ◽  
Surveillance Video ◽  
New Approach ◽  
Surveillance Camera ◽  
Classification Technique ◽  
Outstanding Result

This paper presents a new approach vehicles detection and classification. In these works, we are creating a system to detect the vehicles and automatically classifying them into three desired classes as Car, Suv, and Truck. Datasets from changedetect.net were used for testing the method. The proposed approach combine Incremental Principle Component Pursuit (IPCP) background subtraction for vehicle detection and it complies with Ensemble subspace  -Nearest Neighbor (EsKNN) classifier.  A combination of background subtraction and classification of filtering background is used in order to focus on vehicle’s features extraction using HOG corner detections. Then, this feature extraction is classified using an ensemble-based classifiers technique. The choice of classifier depends on features data characteristics, which they were formed in certain unique distance shape stationary relative between classes. The proposed method is evaluated using three datasets of common highway surveillance video. Comparing with other direct detection and classification technique our method has achieved outstanding result. The proposed approach delivers the accuracy 96.5%, the highest among the tested methods. Experimental results show the outstanding performance of the proposed method.

scholarly journals On the Reconstruction of Magnetic Resonance Current Density Images of the Human Brain: Pitfalls and Perspectives

2021 ◽  
Author(s):  
Hasan H. Eroğlu ◽  
Oula Puonti ◽  
Cihan Göksu ◽  
Fróði Gregersen ◽  
Hartwig R. Siebner ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Current Density ◽  
Signal To Noise Ratio ◽  
Ground Truth ◽  
Statistical Testing ◽  
Signal To Noise ◽  
Testing Approach ◽  
Resonance Current

ABSTRACTMagnetic resonance current density imaging (MRCDI) of the human brain aims to reconstruct the current density distribution caused by transcranial electric stimulation from MR-based measurements of the current-induced magnetic fields. The reconstruction problem is challenging due to a low signal-to-noise ratio and a limited volume coverage of the MR-based measurements, the lack of data from the scalp and skull regions and because MRCDI is only sensitive to the component of the current-induced magnetic field parallel to the scanner field. Most existing reconstruction approaches have been validated using simulation studies and measurements in phantoms with simplified geometries. Only one reconstruction method, the projected current density algorithm, has been applied to human in-vivo data so far, however resulting in blurred current density estimates even when applied to noise-free simulated data.We analyze the underlying causes for the limited performance of the projected current density algorithm when applied to human brain data. In addition, we compare it with an approach that relies on the optimization of the conductivities of a small number of tissue compartments of anatomically detailed head models reconstructed from structural MR data. Both for simulated ground truth data and human in-vivo MRCDI data, our results indicate that the estimation of current densities benefits more from using a personalized volume conductor model than from applying the projected current density algorithm. In particular, we introduce a hierarchical statistical testing approach as a principled way to test and compare the quality of reconstructed current density images that accounts for the limited signal-to-noise ratio of the human in-vivo MRCDI data and the fact that the ground truth of the current density is unknown for measured data. Our results indicate that the statistical testing approach constitutes a valuable framework for the further development of accurate volume conductor models of the head. Our findings also highlight the importance of tailoring the reconstruction approaches to the quality and specific properties of the available data.

scholarly journals A new approach to nonparametric spectral density estimation

2021 ◽  
Author(s):  
Sudeshna Pal
Keyword(s):  
Spectral Density ◽  
Density Estimation ◽  
Frequency Resolution ◽  
Spectrum Estimation ◽  
New Approach ◽  
Spectral Density Estimation ◽  
Noise Interference ◽  
Novel Approach ◽  
Power Spectral ◽  
Noisy Signals

A novel approach to nonparametric spectral density estimation has been proposed. The approach is based on a new evaluation criterion called autocorrelation mean square error (AMSE) for power spectral density (PSD) estimates of available finite length data. Minimization of this criterion not only provides the optimum segmentation for existing PSDE approaches , but also provides a new optimum windowing within the segments that can be combined additionally to the existing methods of nonparametric PSDE. Furthermore, the problem of frequency resolution in existing PSDE methods for noisy signals has been analyzed. In the existing approaches, the additive noise and the finiteness of data which are the causes of the original loss of the frequency resolution are not treated separately. The suggested new approach to spectrum estimation takes advantage of these two different causes of the problem and tackles the problem of resolution in two steps. First, the method optimally reduces noise interference with the signal via minimum noiseless description length (MNDL). The new power spectrum estimation MNDL-Periodogram of the denoised signal is then computed via conventional indirect periodogram to improve frequency resolution.

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