Resolution Analysis
Resolution Analysis - Related Videos
07:38
Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
0 Views •
Cited by 9 •
2017
This manuscript uses the Fiji-based open-source software package VirusMapper to apply single-particle analysis to super-resolution microscopy images in order to generate precise models of nanoscale structure.
07:14
Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx
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Cited by 21 •
2016
Previous work suggests that the nitrogen isotopic composition of atmospheric nitrogen oxides might distinguish the influence of different sources in the environment. We report on an automated, mobile, field-based method for the high collection efficiency of atmospheric NOx for N isotopic analysis at an hourly time resolution.
13:28
High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE
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Cited by 218 •
2017
This paper presents a protocol for processing cryo-EM images using the software suite SPHIRE. The present protocol can be applied for nearly all single particle EM projects that target near-atomic resolution.
09:33
High-Resolution Complexome Profiling by Cryoslicing BN-MS Analysis
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Cited by 13 •
2019
A versatile cryoslicing BN-MS protocol using a microtome is presented for high-resolution complexome profiling.
10:23
High-resolution Imaging and Analysis of Individual Astral Microtubule Dynamics in Budding Yeast
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Cited by 33 •
2017
Budding yeast is an advantageous model for studying microtubule dynamics in vivo due to its powerful genetics and the simplicity of its microtubule cytoskeleton. The following protocol describes how to transform and culture yeast cells, acquire confocal microscopy images, and quantitatively analyze microtubule dynamics in living yeast cells.
FAQs
Sampling rate and measurement uncertainty set practical limits on what a system can distinguish. Resolution analysis relates the smallest detectable difference to how frequently a signal is sampled and how uncertain each measurement is. Examining both factors helps engineers determine whether an apparent fine change is adequately represented or whether the measurement conditions limit confidence in that detail.
Instrument response matters because it influences how faithfully a system represents closely spaced features or small signal changes. Resolution analysis considers that response alongside sampling rate, uncertainty, and spatial or temporal scale. This prevents engineers from attributing missing or distorted detail to the system under study when it may instead arise from the instrument’s measurement behavior.
The relevant scale determines what counts as meaningful detail. In an imaging or inspection context, engineers may examine spatial separation, whereas signal measurements or simulations may emphasize change over time. Resolution analysis places the smallest detectable difference within that spatial or temporal context, making results more useful for judging whether captured detail matches the engineering question.
Start by identifying the smallest feature or signal change that the application must detect. Then examine sampling rate, measurement uncertainty, spatial or temporal scale, and instrument response in relation to that requirement. Finally, interpret whether the system can capture the needed detail and document limitations imposed by measurement conditions. This sequence supports equipment selection and experimental design.
Applications include sensor assessment, imaging systems, manufacturing inspections, and numerical simulations. In each case, the analysis helps determine whether the available detail is sufficient for reliable decisions. It can guide equipment selection, reveal limitations in inspection or simulation results, and clarify whether observed features reflect the system being studied or the measurement setup.
Resolution results connect measurement capability with the detail required by a task. Engineers can use them to optimize systems, plan experiments, choose suitable equipment, and interpret data with greater care. The analysis also helps separate genuine features from artifacts or limitations caused by measurement conditions, reducing the risk of treating unavailable detail as evidence.