{"doi":"10.17615/hx1a-wr08","title":"Erratum to: Model Convolution: A Computational Approach to Digital Image Interpretation","abstract":"Digital fluorescence microscopy is commonly used to track individual proteins and their dynamics in living cells. However, extracting molecule-specific information from fluorescence images is often limited by the noise and blur intrinsic to the cell and the imaging system. Here we discuss a method called “model-convolution,” which uses experimentally measured noise and blur to simulate the process of imaging fluorescent proteins whose spatial distribution cannot be resolved. We then compare model-convolution to the more standard approach of experimental deconvolution. In some circumstances, standard experimental deconvolution approaches fail to yield the correct underlying fluorophore distribution. In these situations, model-convolution removes the uncertainty associated with deconvolution and therefore allows direct statistical comparison of experimental and theoretical data. Thus, if there are structural constraints on molecular organization, the model-convolution method better utilizes information gathered via fluorescence microscopy, and naturally integrates experiment and theory.","journal":"UNC Libraries","year":2020,"id":142680,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9485,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":447397,"name":"Chad G. Pearson","orcid":"0000-0003-1915-6593","position":1,"is_corresponding":false},{"id":610247,"name":"Andrew D. Bicek","orcid":null,"position":2,"is_corresponding":false},{"id":230535,"name":"Benjamin D. Cosgrove","orcid":"0000-0003-2164-350X","position":3,"is_corresponding":false},{"id":523860,"name":"Melissa K. Gardner","orcid":"0000-0001-5906-7363","position":4,"is_corresponding":false},{"id":341564,"name":"David J. Odde","orcid":"0000-0001-7731-2799","position":5,"is_corresponding":false},{"id":370981,"name":"Kerry Bloom","orcid":"0000-0002-3457-004X","position":6,"is_corresponding":false},{"id":438838,"name":"E. D. Salmon","orcid":null,"position":7,"is_corresponding":false},{"id":335035,"name":"Brian L. Sprague","orcid":"0000-0001-7948-8317","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:17:34.768426Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}