MRT letter: A unified accelerated maximum likelihood technique for widefield, confocal, and super-resolution 4Pi microscopy

2015 ◽  
Vol 78 (5) ◽  
pp. 331-335 ◽  
Author(s):  
Rasmi Chelur K ◽  
Rajan Kanhirodan ◽  
Partha Pratim Mondal
Keyword(s):  
Maximum Likelihood ◽  
Super Resolution ◽  
4Pi Microscopy ◽  
Maximum Likelihood Technique

Statistically optimal event detection using small array data

1990 ◽  
Vol 80 (6B) ◽  
pp. 1934-1950 ◽  
Author(s):  
A. F. Kushnir ◽  
V. M. Lapshin ◽  
V. I. Pinsky ◽  
J. Fyen
Keyword(s):  
Maximum Likelihood ◽  
Power Spectrum ◽  
Event Detection ◽  
Underground Explosion ◽  
Seismic Signals ◽  
Array Data ◽  
On Line ◽  
Small Array ◽  
Maximum Likelihood Technique ◽  
Channel Filter

Abstract A generalization of Capon's maximum-likelihood technique for detection and estimation of seismic signals is introduced. By using a multi-dimensional autoregressive approximation of seismic array noise, we have developed a technique to use Capon's multi-channel filter for on-line processing. Such autoregressive adaptation to the curent noise matrix power spectrum is shown to yield good suppression of mutually correlated array noise processes. As an example, this technique is applied to detection of a small Semipalatinsk underground explosion recorded at the ARCESS array.

SAMPLING SYSTEMS FOR THE BIRCH CASEBEARER (LEPIDOPTERA: COLEOPHORIDAE)

1979 ◽  
Vol 111 (8) ◽  
pp. 875-882
Author(s):  
A. G. Raske ◽  
M. Alvo
Keyword(s):  
Maximum Likelihood ◽  
Sampling Plan ◽  
New Method ◽  
Betula Papyrifera ◽  
White Birch ◽  
Destructive Sampling ◽  
Damage Level ◽  
Sampling Systems ◽  
Non Destructive ◽  
Maximum Likelihood Technique

AbstractSample sizes needed to measure population levels of the birch casebearer, Coleophora fuscedinella Zeller, and its damage to white birch, Betula papyrifera Marsh, were calculated for various degrees of confidence and assurance. Both a non-destructive and a destructive sampling plan are presented and a new method to classify the damage level of a stand. This method uses a maximum likelihood technique to estimate the proportion of trees of various damage classes.

Method of super-resolution based on array detection and maximum-likelihood estimation

2016 ◽  
Vol 55 (35) ◽  
pp. 9925 ◽  
Author(s):  
Haoyang Li ◽  
Yujia Huang ◽  
Cuifang Kuang ◽  
Xu Liu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Super Resolution ◽  
Likelihood Estimation ◽  
Array Detection

scholarly journals Maximum-likelihood model fitting for quantitative analysis of SMLM data

2021 ◽  
Author(s):  
Yu-Le Wu ◽  
Philipp Hoess ◽  
Aline Tschanz ◽  
Ulf Matti ◽  
Markus Mund ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Maximum Likelihood ◽  
Single Molecule ◽  
Model Fitting ◽  
Super Resolution ◽  
Likelihood Estimation ◽  
Nuclear Pore ◽  
Analysis Workflow ◽  
Likelihood Model ◽  
Protein Density

Quantitative analysis is an important part of any single-molecule localization microscopy (SMLM) data analysis workflow to extract biological insights from the coordinates of the single fluorophores, but current approaches are restricted to simple geometries or do not work on heterogenous structures. Here, we present LocMoFit (Localization Model Fit), an open-source framework to fit an arbitrary model directly to the localization coordinates in SMLM data. Using maximum likelihood estimation, this tool extracts the most likely parameters for a given model that best describe the data, and can select the most likely model from alternative models. We demonstrate the versatility of LocMoFit by measuring precise dimensions of the nuclear pore complex and microtubules. We also use LocMoFit to assemble static and dynamic multi-color protein density maps from thousands of snapshots. In case an underlying geometry cannot be postulated, LocMoFit can perform single-particle averaging of super-resolution structures without any assumption about geometry or symmetry. We provide extensive simulation and visualization routines to validate the robustness of LocMoFit and tutorials based on example data to enable any user to increase the information content they can extract from their SMLM data.

Sign in / Sign up

Export Citation Format

Share Document