{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:USP63A5RGF4G5ZW7XAB7UKMYP5","short_pith_number":"pith:USP63A5R","schema_version":"1.0","canonical_sha256":"a49fed83b131786ee6dfb803fa29987f64590ef20dcf4386bcea38eec9a1143f","source":{"kind":"arxiv","id":"1904.12960","version":1},"attestation_state":"computed","paper":{"title":"Modelling Diffuse Subcellular Protein Structures as Dynamic Social Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.SC","authors_text":"Andrew Durden","submitted_at":"2019-04-17T01:32:31Z","abstract_excerpt":"Fluorescence microscopy has led to impressive quantitative models and new insights gained from richer sets of biomedical imagery. However, there is a dearth of rigorous and established bioimaging strategies for modeling spatiotemporal behavior of diffuse, subcellular components such as mitochondria or actin. In many cases, these structures are assessed by hand or with other semi-quantitative measures. We propose to build descriptive and dynamic models of diffuse subcellular morphologies, using the mitochondrial protein patterns of cervical epithelial (HeLa) cells. We develop a parametric repre"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1904.12960","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.SC","submitted_at":"2019-04-17T01:32:31Z","cross_cats_sorted":[],"title_canon_sha256":"226351fdb766680788e0375748ec99a54112534cee1206fc24b2033a173e537e","abstract_canon_sha256":"8969afa480b5c6e3033dae64f15328e22cf60c9cb6c0ff6f5ec99d76bb634b4e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:24.365219Z","signature_b64":"+OMXlht+kF9WzYgqkQJZzpt3rVBE4pLRP6KtNuLAfvkGgeHs9ZJgSFnnVtKrAUFOquUqrw6kQ//dkwy5bz25AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a49fed83b131786ee6dfb803fa29987f64590ef20dcf4386bcea38eec9a1143f","last_reissued_at":"2026-05-17T23:47:24.364549Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:24.364549Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Modelling Diffuse Subcellular Protein Structures as Dynamic Social Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.SC","authors_text":"Andrew Durden","submitted_at":"2019-04-17T01:32:31Z","abstract_excerpt":"Fluorescence microscopy has led to impressive quantitative models and new insights gained from richer sets of biomedical imagery. However, there is a dearth of rigorous and established bioimaging strategies for modeling spatiotemporal behavior of diffuse, subcellular components such as mitochondria or actin. In many cases, these structures are assessed by hand or with other semi-quantitative measures. We propose to build descriptive and dynamic models of diffuse subcellular morphologies, using the mitochondrial protein patterns of cervical epithelial (HeLa) cells. We develop a parametric repre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.12960","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1904.12960","created_at":"2026-05-17T23:47:24.364639+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.12960v1","created_at":"2026-05-17T23:47:24.364639+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.12960","created_at":"2026-05-17T23:47:24.364639+00:00"},{"alias_kind":"pith_short_12","alias_value":"USP63A5RGF4G","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"USP63A5RGF4G5ZW7","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"USP63A5R","created_at":"2026-05-18T12:33:30.264802+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.01119","citing_title":"Object Tracking in a $360^o$ View: A Novel Perspective on Bridging the Gap to Biomedical Advancements","ref_index":86,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/USP63A5RGF4G5ZW7XAB7UKMYP5","json":"https://pith.science/pith/USP63A5RGF4G5ZW7XAB7UKMYP5.json","graph_json":"https://pith.science/api/pith-number/USP63A5RGF4G5ZW7XAB7UKMYP5/graph.json","events_json":"https://pith.science/api/pith-number/USP63A5RGF4G5ZW7XAB7UKMYP5/events.json","paper":"https://pith.science/paper/USP63A5R"},"agent_actions":{"view_html":"https://pith.science/pith/USP63A5RGF4G5ZW7XAB7UKMYP5","download_json":"https://pith.science/pith/USP63A5RGF4G5ZW7XAB7UKMYP5.json","view_paper":"https://pith.science/paper/USP63A5R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.12960&json=true","fetch_graph":"https://pith.science/api/pith-number/USP63A5RGF4G5ZW7XAB7UKMYP5/graph.json","fetch_events":"https://pith.science/api/pith-number/USP63A5RGF4G5ZW7XAB7UKMYP5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/USP63A5RGF4G5ZW7XAB7UKMYP5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/USP63A5RGF4G5ZW7XAB7UKMYP5/action/storage_attestation","attest_author":"https://pith.science/pith/USP63A5RGF4G5ZW7XAB7UKMYP5/action/author_attestation","sign_citation":"https://pith.science/pith/USP63A5RGF4G5ZW7XAB7UKMYP5/action/citation_signature","submit_replication":"https://pith.science/pith/USP63A5RGF4G5ZW7XAB7UKMYP5/action/replication_record"}},"created_at":"2026-05-17T23:47:24.364639+00:00","updated_at":"2026-05-17T23:47:24.364639+00:00"}