25 papers · ranked by Valyu relevance
Zachary H. Hendrix, Peter Brown, Tim Flanagan, Douglas P. Shepherd + 2 more
Motivated Bayesian Deconvolution Authors: ['Zachary H. Hendrix' 'Peter Brown' 'Tim Flanagan' 'Douglas P. Shepherd' 'Ayush Saurabh' 'Steve Pressé'] Richardson-Lucy deconvolution is widely used to restore images from degradation caused by the broadening effects of a point spread function and corruption by photon shot…
Yuebo Zha, Yulin Huang, Zhichao Sun, Yue Wang + 2 more
'James Churnside'] Scanning radar is of notable importance for ground surveillance, terrain mapping and disaster rescue. However, the angular resolution of a scanning radar image is poor compared to the achievable range resolution. This paper presents a deconvolution algorithm for angular super-resolution in scanning…
Gabriel Torregrosa-Cortés, David Oriola, Vikas Trivedi, Jordi Garcia-Ojalvo
'Jordi Garcia-Ojalvo'] Title: Summary Individual cells exhibit substantial heterogeneity in protein abundance and activity, which is frequently reflected in broad distributions of fluorescently labeled reporters. Since all cellular components are intrinsically fluorescent to some extent, the observed distributions…
Zhe Chen
Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing…
Gabriel Torregrosa, David Oriola, Vikas Trivedi, Jordi Garcia-Ojalvo
Flow cytometry enables monitoring protein abundance and activity at the single-cell level in a high-throughput manner, through the use of fluorescent labeling. Given the significant levels of autofluorescence emitted by cells at the spectral ranges used by this technique, removing the corresponding background signal is…
Alexander Wong, Xiao Yu Wang, Maud Gorbet
Fluorescence microscopy is widely used for the study of biological specimens. Deconvolution can significantly improve the resolution and contrast of images produced using fluorescence microscopy; in particular, Bayesian-based methods have become very popular in deconvolution fluorescence microscopy. An ongoing…
Alain Durmus, Éric Moulines, Marcelo Pereyra
> Abstract. Modern imaging methods rely strongly on Bayesian inference techniques to solve challenging imaging problems. Currently, the predominant Bayesian computation approach is convex optimisation, which scales very efficiently to high dimensional image models and delivers accurate point estimation results.…
An-Shun Tai, George C. Tseng, Wen-Ping Hsieh
Gene expression deconvolution is a powerful tool for exploring the microenvironment of complex tissues comprised of multiple cell groups using transcriptomic data. Characterizing cell activities for a particular condition has been regarded as a primary mission against diseases. For example, cancer immunology aims to…
Siavash Arjomand Bigdeli, Meiguang Jin, Paolo Favaro, Matthias Zwicker
'Matthias Zwicker'] In this paper we introduce a natural image prior that directly represents a Gaussiansmoothed version of the natural image distribution. We include our prior in a formulation of image restoration as a Bayes estimator that also allows us to solve noise-blind image restoration problems. We show that…
Parker S. Ruth, Tommy DeBenedetti, Lily O’Brien, James A. Landay + 2 more
Vascular waveforms, which measure bulk flow in blood vessels, are widely used to measure vital signs, diagnose conditions, and predict long-term health outcomes. Analyzing vascular waveforms depends on three fundamentally interdependent tasks: signal filtering, pulse timing detection, and pulse shape extraction. We…
Alexander Wong, Xiaoyu Wang, Maud Gorbet
Fluorescence microscopy is widely used for the study of biological specimens. Deconvolution can significantly improve the resolution and contrast of images produced using fluorescence microscopy; in particular, Bayesian-based methods have become very popular in deconvolution fluorescence microscopy. An ongoing…
Vimalathithan Devaraj, Biplab Bose
The expression of a gene is commonly estimated by quantitative PCR (qPCR) using RNA isolated from a large number of pooled cells. Such pooled samples often have subpopulations of cells with different levels of expression of the target gene. Estimation of gene expression from an ensemble of cells obscures the pattern of…
Ahmed Karam Eldaly, Yoann Altmann, Antonios Perperidis, Nikola Krstajić + 3 more
'Nikola Krstajić' 'Tushar R. Choudhary' 'Kevin Dhaliwal' 'Stephen McLaughlin'] Optical endomicroscopy (OEM) is an emerging technology platform with preclinical and clinical imaging applications. Pulmonary OEM via fibre bundles has the potential to provide in vivo, in situ molecular signatures of disease such as…
Oscar Hernán Madrid Padilla, Nicholas G. Polson, James G. Scott
We propose a class of estimators for deconvolution in mixture models based on a simple two-step "bin-and-smooth" procedure applied to histogram counts. The method is both statistically and computationally efficient: by exploiting recent advances in convex optimization, we are able to provide a full deconvolution path…
Authors not listed
Electrochemical impedance spectroscopy (EIS) coupled with distribution of relaxation times (DRT) analysis is a robust framework for characterizing electrochemical systems. However, DRT deconvolution is often plagued by spurious peaks, hindering accurate process identification and quantitative parameter estimation. To…
Adeleke Maradesa, Baptiste Py, Ting Hei Wan, Mohammed B. Effat + 1 more
Electrochemical impedance spectroscopy (EIS) is a characterization technique used widely in electrochemistry. Obtaining EIS data is simple when modern electrochemical workstations are used; however, analyzing EIS spectra is still a considerable quandary. The distribution of relaxation times (DRT) has emerged as a…
Konstantinos Bakas, John Kornak, Hernando Ombao
Bayesian methods are commonly applied to solve image analysis problems such as noise-reduction, feature enhancement and object detection. A primary limitation of these approaches is the computational complexity due to the interdependence of neighboring pixels which limits the ability to perform full posterior sampling…
César Caballero-Gaudes, Stefano Moia, Puja Panwar, Peter A. Bandettini + 1 more
This work introduces a novel algorithm for deconvolution of the BOLD signal in multiecho fMRI data: Multiecho Sparse Paradigm Free Mapping (ME-SPFM). Assuming a linear dependence of the BOLD percent signal change on the echo time (TE) and using sparsity-promoting regularized least squares estimation, ME-SPFM yields…
Karl J. Friston, Vladimir Litvak, Ashwini Oswal, Adeel Razi + 4 more
'Klaas E. Stephan' 'Bernadette C.M. van Wijk' 'Gabriel Ziegler' 'Peter Zeidman'] This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level - e.g., dynamic causal models - and linear models at subsequent (between-subject)…
Martin Robinson, Alan Bond, Alexandr Simonov, Jie Zhang + 1 more
Recently, we have introduced the use of techniques drawn from Bayesian statistics to recover kinetic and thermodynamic parameters from voltammetric data, and were able to show that the technique of large amplitude ac voltammetry yielded significantly more accurate parameter values than the equivalent dc approach. In…
Authors not listed
The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
Denice van Herwerden, Jake O'Brien, Sascha Lege, Bob Pirok + 2 more
Fragment deconvolution is a crucial step during componentization of non-targeted analysis (NTA) high-resolution mass spectrometry (HRMS) data, aiming to filter out false positive (FP) signals that do not belong to the component. Moreover, inclusion of FP fragments could lead to, for example, wrong identification…
Brandon S. Coventry, Edward L. Bartlett
Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…