The fidelity of stochastic single-molecule super-resolution reconstructions critically depends upon robust background estimation.
Ontology highlight
ABSTRACT:
SUBMITTER: Hoogendoorn E
PROVIDER: S-EPMC3900998 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Mediation analysis is critical to understanding the mechanisms underlying exposure-outcome relationships. In this paper, we identify the instrumental variable-direct effect of the exposure on the outcome not through the mediator, using randomization of the instrument. We call this estimand the complier stochastic direct effect (CSDE). To our knowledge, such an estimand has not previously been considered or estimated. We propose and evaluate several estimators for the CSDE: a ratio of inverse-probability of treatment-weighted estimators (IPTW), a ratio of estimating equation estimators (EE), a ratio of targeted minimum loss-based estimators (TMLE), and a TMLE that targets the CSDE directly. These estimators are applicable for a variety of study designs, including randomized encouragement trials, like the Moving to Opportunity housing voucher experiment we consider as an illustrative example, treatment discontinuities, and Mendelian randomization. We found the IPTW estimator to be the most sensitive to finite sample bias, resulting in bias of over 40% even when all models were correctly specified in a sample size of N=100. In contrast, the EE estimator and TMLE that targets the CSDE directly were far less sensitive. The EE and TML estimators also have advantages in terms of efficiency and reduced reliance on correct parametric model specification.
| S-EPMC8439556 | biostudies-literature
Project description:BackgroundSCN5A is a susceptibility gene for type 3 long QT syndrome, Brugada syndrome, and sudden infant death syndrome. INa dysfunction from mutated SCN5A can depend upon the splice variant background in which it is expressed and also upon environmental factors such as acidosis. S1787N was reported previously as a LQT3-associated mutation and has also been observed in 1 of 295 healthy white controls. Here, we determined the in vitro biophysical phenotype of SCN5A-S1787N in an effort to further assess its possible pathogenicity.Methods and resultsWe engineered S1787N in the two most common alternatively spliced SCN5A isoforms, the major isoform lacking a glutamine at position 1077 (Q1077del) and the minor isoform containing Q1077, and expressed these two engineered constructs in HEK293 cells for electrophysiological study. Macroscopic voltage-gated INa was measured 24 hours after transfection with standard whole-cell patch clamp techniques. We applied intracellular solutions with pH7.4 or pH6.7. S1787N in the Q1077 background had WT-like INa including peak INa density, activation and inactivation parameters, and late INa amplitude in both pH 7.4 and pH 6.7. However, with S1787N in the Q1077del background, the percentages of INa late/peak were increased by 2.1 fold in pH 7.4 and by 2.9 fold in pH 6.7 when compared to WT.ConclusionThe LQT3-like biophysical phenotype for S1787N depends on both the SCN5A splice variant and on the intracellular pH. These findings provide further evidence that the splice variant and environmental factors affect the molecular phenotype of cardiac SCN5A-encoded sodium channel (Nav1.5), has implications for the clinical phenotype, and may provide insight into acidosis-induced arrhythmia mechanisms.
| S-EPMC4414567 | biostudies-literature
Project description:In tomosynthesis, super-resolution has been demonstrated using reconstruction planes parallel to the detector. Super-resolution allows for subpixel resolution relative to the detector. The purpose of this work is to develop an analytical model that generalizes super-resolution to oblique reconstruction planes.In a digital tomosynthesis system, a sinusoidal test object is modeled along oblique angles (i.e., "pitches") relative to the plane of the detector in a 3D divergent-beam acquisition geometry. To investigate the potential for super-resolution, the input frequency is specified to be greater than the alias frequency of the detector. Reconstructions are evaluated in an oblique plane along the extent of the object using simple backprojection (SBP) and filtered backprojection (FBP). By comparing the amplitude of the reconstruction against the attenuation coefficient of the object at various frequencies, the modulation transfer function (MTF) is calculated to determine whether modulation is within detectable limits for super-resolution. For experimental validation of super-resolution, a goniometry stand was used to orient a bar pattern phantom along various pitches relative to the breast support in a commercial digital breast tomosynthesis system.Using theoretical modeling, it is shown that a single projection image cannot resolve a sine input whose frequency exceeds the detector alias frequency. The high frequency input is correctly visualized in SBP or FBP reconstruction using a slice along the pitch of the object. The Fourier transform of this reconstructed slice is maximized at the input frequency as proof that the object is resolved. Consistent with the theoretical results, experimental images of a bar pattern phantom showed super-resolution in oblique reconstructions. At various pitches, the highest frequency with detectable modulation was determined by visual inspection of the bar patterns. The dependency of the highest detectable frequency on pitch followed the same trend as the analytical model. It was demonstrated that super-resolution is not achievable if the pitch of the object approaches 90°, corresponding to the case in which the test frequency is perpendicular to the breast support. Only low frequency objects are detectable at pitches close to 90°.This work provides a platform for investigating super-resolution in oblique reconstructions for tomosynthesis. In breast imaging, this study should have applications in visualizing microcalcifications and other subtle signs of cancer.
| S-EPMC3808432 | biostudies-other
Project description:BackgroundCausal mediation analysis can improve understanding of the mechanism s underlying epidemiologic associations. However, the utility of natural direct and indirect effect estimation has been limited by the assumption of no confounder of the mediator-outcome relationship that is affected by prior exposure (which we call an intermediate confounder)--an assumption frequently violated in practice.MethodsWe build on recent work that identified alternative estimands that do not require this assumption and propose a flexible and double robust targeted minimum loss-based estimator for stochastic direct and indirect effects. The proposed method intervenes stochastically on the mediator using a distribution which conditions on baseline covariates and marginalizes over the intermediate confounder.ResultsWe demonstrate the estimator's finite sample and robustness properties in a simple simulation study. We apply the method to an example from the Moving to Opportunity experiment. In this application, randomization to receive a housing voucher is the treatment/instrument that influenced moving with the voucher out of public housing, which is the intermediate confounder. We estimate the stochastic direct effect of randomization to the voucher group on adolescent marijuana use not mediated by change in school district and the stochastic indirect effect mediated by change in school district. We find no evidence of mediation.ConclusionsOur estimator is easy to implement in standard statistical software, and we provide annotated R code to further lower implementation barriers.
| S-EPMC8136358 | biostudies-literature
Project description:In this paper, we review current practices for establishing the resolution in single-particle reconstructions. The classical Raleigh criterion for the resolution is not applicable in this case, and the resolution is commonly defined by a consistency test, whereby the data set is randomly split in half and the two resulting reconstructions are then compared. Such a procedure, however, may introduce statistical dependence between the two half-sets, which leads to a too optimistic resolution estimate. On the other hand, this overestimation is counteracted by the diminished statistical properties of a mere half of the data set. The "true" resolution of the whole data set can be estimated when the functional relationship between the data size and the resolution is known. We are able to estimate this functional by taking into account the B-factor and the geometry of data collection. Finally, the drawbacks of resolution estimation are entirely avoided by computing the correlation of neighboring voxels in the Fourier domain.
| S-EPMC2923553 | biostudies-literature
Project description:DksA is an auxiliary transcription factor that interacts with RNA polymerase and influences gene expression. Depending on the promoter, DksA can be a positive or negative regulator of transcription initiation. Moreover, DksA has a substantial effect on transcription elongation where it prevents the collision of transcription and replication machineries, plays a key role in maintaining transcription elongation when translation and transcription are uncoupled and has been shown to be involved in transcription fidelity. Here, we assessed the role of DksA in transcription fidelity by monitoring stochastic epigenetic switching in the lac operon (with and without an error-prone transcription slippage sequence), partial phenotypic suppression of a lacZ nonsense allele, as well as monitoring the number of lacI mRNA transcripts produced in the presence and absence of DksA via an operon fusion and single molecule fluorescent in situ hybridization studies. We present data showing that DksA acts to maintain transcription fidelity in vivo and the role of DksA seems to be distinct from that of the GreA and GreB transcription fidelity factors.
| S-EPMC4666387 | biostudies-literature
Project description:Relative error estimation has been recently used in regression analysis. A crucial issue of the existing relative error estimation procedures is that they are sensitive to outliers. To address this issue, we employ the ? -likelihood function, which is constructed through ? -cross entropy with keeping the original statistical model in use. The estimating equation has a redescending property, a desirable property in robust statistics, for a broad class of noise distributions. To find a minimizer of the negative ? -likelihood function, a majorize-minimization (MM) algorithm is constructed. The proposed algorithm is guaranteed to decrease the negative ? -likelihood function at each iteration. We also derive asymptotic normality of the corresponding estimator together with a simple consistent estimator of the asymptotic covariance matrix, so that we can readily construct approximate confidence sets. Monte Carlo simulation is conducted to investigate the effectiveness of the proposed procedure. Real data analysis illustrates the usefulness of our proposed procedure.
| S-EPMC7513150 | biostudies-literature
Project description:Structure from Motion (SfM) can produce highly detailed 3D reconstructions, but distinguishing real surface roughness from reconstruction noise and geometric inaccuracies has always been a difficult problem to solve. Existing SfM commercial solutions achieve noise removal by a combination of aggressive global smoothing and the reconstructed texture for smaller details, which is a subpar solution when the results are used for surface inspection. Other noise estimation and removal algorithms do not take advantage of all the additional data connected with SfM. We propose a number of geometrical and statistical metrics for noise assessment, based on both the reconstructed object and the capturing camera setup. We test the correlation of each of the metrics to the presence of noise on reconstructed surfaces and demonstrate that classical supervised learning methods, trained with these metrics can be used to distinguish between noise and roughness with an accuracy above 85%, with an additional 5-6% performance coming from the capturing setup metrics. Our proposed solution can easily be integrated into existing SfM workflows as it does not require more image data or additional sensors. Finally, as part of the testing we create an image dataset for SfM from a number of objects with varying shapes and sizes, which are available online together with ground truth annotations.
| S-EPMC7582591 | biostudies-literature
Project description:Although cell polarity is an essential feature of living cells, it is far from being well-understood. Using a combination of computational modeling and biological experiments we closely examine an important prototype of cell polarity: the pheromone-induced formation of the yeast polarisome. Focusing on the role of noise and spatial heterogeneity, we develop and investigate two mechanistic spatial models of polarisome formation, one deterministic and the other stochastic, and compare the contrasting predictions of these two models against experimental phenotypes of wild-type and mutant cells. We find that the stochastic model can more robustly reproduce two fundamental characteristics observed in wild-type cells: a highly polarized phenotype via a mechanism that we refer to as spatial stochastic amplification, and the ability of the polarisome to track a moving pheromone input. Moreover, we find that only the stochastic model can simultaneously reproduce these characteristics of the wild-type phenotype and the multi-polarisome phenotype of a deletion mutant of the scaffolding protein Spa2. Significantly, our analysis also demonstrates that higher levels of stochastic noise results in increased robustness of polarization to parameter variation. Furthermore, our work suggests a novel role for a polarisome protein in the stabilization of actin cables. These findings elucidate the intricate role of spatial stochastic effects in cell polarity, giving support to a cellular model where noise and spatial heterogeneity combine to achieve robust biological function.
| S-EPMC3723497 | biostudies-literature
Project description:In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.
| S-EPMC4796016 | biostudies-literature