{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:TM6L642O3AUXPHJ63ZXLFW5UMR","short_pith_number":"pith:TM6L642O","canonical_record":{"source":{"id":"2502.17761","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-25T01:31:54Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"f79042afacf1b48de3cfb50081cc8bdd610f1a8c06d7a5fab065d0fddae8149f","abstract_canon_sha256":"a71cf178da10f63c853d46c16d8b81c27eb93791bcc604c02afc96a30fcfcb25"},"schema_version":"1.0"},"canonical_sha256":"9b3cbf734ed829779d3ede6eb2dbb464629bbccdfaad8353afa8440c34b555f9","source":{"kind":"arxiv","id":"2502.17761","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.17761","created_at":"2026-07-05T10:19:43Z"},{"alias_kind":"arxiv_version","alias_value":"2502.17761v1","created_at":"2026-07-05T10:19:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.17761","created_at":"2026-07-05T10:19:43Z"},{"alias_kind":"pith_short_12","alias_value":"TM6L642O3AUX","created_at":"2026-07-05T10:19:43Z"},{"alias_kind":"pith_short_16","alias_value":"TM6L642O3AUXPHJ6","created_at":"2026-07-05T10:19:43Z"},{"alias_kind":"pith_short_8","alias_value":"TM6L642O","created_at":"2026-07-05T10:19:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:TM6L642O3AUXPHJ63ZXLFW5UMR","target":"record","payload":{"canonical_record":{"source":{"id":"2502.17761","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-25T01:31:54Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"f79042afacf1b48de3cfb50081cc8bdd610f1a8c06d7a5fab065d0fddae8149f","abstract_canon_sha256":"a71cf178da10f63c853d46c16d8b81c27eb93791bcc604c02afc96a30fcfcb25"},"schema_version":"1.0"},"canonical_sha256":"9b3cbf734ed829779d3ede6eb2dbb464629bbccdfaad8353afa8440c34b555f9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:19:43.630881Z","signature_b64":"CjEX1effF1JcvQPxbpAfEY6//5OeLm0DvE/j2KCknYQnXLsZ698YBVIhzZwTFZQJQWfXijxTVrex4YmrFS0HBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b3cbf734ed829779d3ede6eb2dbb464629bbccdfaad8353afa8440c34b555f9","last_reissued_at":"2026-07-05T10:19:43.630388Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:19:43.630388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.17761","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:19:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WeviHw4HZNM1HSxT+RTlvQobBaa2fFU24TZEc/MMVvs7UGTieouAAx/V9QUllc4nEbJLBEfUW4BA/A/ftiCrAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T18:24:18.482408Z"},"content_sha256":"6b45b9ef579242233418fb9455982e786c9afdc1eab84d2d321072a5a236d2ef","schema_version":"1.0","event_id":"sha256:6b45b9ef579242233418fb9455982e786c9afdc1eab84d2d321072a5a236d2ef"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:TM6L642O3AUXPHJ63ZXLFW5UMR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AI-driven 3D Spatial Transcriptomics","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"cs.CV","authors_text":"Ahrong Kim, Alexander S. Baras, Ali Bashashati, Andrew H. Song, Bowen Chen, Cristina Almagro-P\\'erez, Drew F.K. Williamson, Faisal Mahmood, Guillaume Jaume, Jonathan T.C. Liu, Konstantin Hemker, Kritika Singh, Long Phi Le, Luca Weishaupt, Ming Y. Lu, Sizun Jiang","submitted_at":"2025-02-25T01:31:54Z","abstract_excerpt":"A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications. However, most spatial transcriptomics (ST) approaches remain limited to two-dimensional (2D) sections of tissue. Although current 3D ST methods hold promise, they typically require extensive tissue sectioning, are complex, are not compatible with non-destructive 3D tissue imaging technologies, and often lack scalability. Here, we present VOlumetrically Resolved Transcriptomics EXpression (VORTEX), a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.17761","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.17761/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:19:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4/2N0BfUj68rVWSaXFBLA9Ic+OxIVUryX7C4xjYdkPugAKboHXhVesRok/hWyBDlp73viszXQrdfDHAD6wVUCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T18:24:18.482975Z"},"content_sha256":"bd642702548678e002ecffb473e9991d597342f1c514e33c7ff2a310da2d122a","schema_version":"1.0","event_id":"sha256:bd642702548678e002ecffb473e9991d597342f1c514e33c7ff2a310da2d122a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TM6L642O3AUXPHJ63ZXLFW5UMR/bundle.json","state_url":"https://pith.science/pith/TM6L642O3AUXPHJ63ZXLFW5UMR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TM6L642O3AUXPHJ63ZXLFW5UMR/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-15T18:24:18Z","links":{"resolver":"https://pith.science/pith/TM6L642O3AUXPHJ63ZXLFW5UMR","bundle":"https://pith.science/pith/TM6L642O3AUXPHJ63ZXLFW5UMR/bundle.json","state":"https://pith.science/pith/TM6L642O3AUXPHJ63ZXLFW5UMR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TM6L642O3AUXPHJ63ZXLFW5UMR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TM6L642O3AUXPHJ63ZXLFW5UMR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a71cf178da10f63c853d46c16d8b81c27eb93791bcc604c02afc96a30fcfcb25","cross_cats_sorted":["stat.AP"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-25T01:31:54Z","title_canon_sha256":"f79042afacf1b48de3cfb50081cc8bdd610f1a8c06d7a5fab065d0fddae8149f"},"schema_version":"1.0","source":{"id":"2502.17761","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.17761","created_at":"2026-07-05T10:19:43Z"},{"alias_kind":"arxiv_version","alias_value":"2502.17761v1","created_at":"2026-07-05T10:19:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.17761","created_at":"2026-07-05T10:19:43Z"},{"alias_kind":"pith_short_12","alias_value":"TM6L642O3AUX","created_at":"2026-07-05T10:19:43Z"},{"alias_kind":"pith_short_16","alias_value":"TM6L642O3AUXPHJ6","created_at":"2026-07-05T10:19:43Z"},{"alias_kind":"pith_short_8","alias_value":"TM6L642O","created_at":"2026-07-05T10:19:43Z"}],"graph_snapshots":[{"event_id":"sha256:bd642702548678e002ecffb473e9991d597342f1c514e33c7ff2a310da2d122a","target":"graph","created_at":"2026-07-05T10:19:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2502.17761/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications. However, most spatial transcriptomics (ST) approaches remain limited to two-dimensional (2D) sections of tissue. Although current 3D ST methods hold promise, they typically require extensive tissue sectioning, are complex, are not compatible with non-destructive 3D tissue imaging technologies, and often lack scalability. Here, we present VOlumetrically Resolved Transcriptomics EXpression (VORTEX), a","authors_text":"Ahrong Kim, Alexander S. Baras, Ali Bashashati, Andrew H. Song, Bowen Chen, Cristina Almagro-P\\'erez, Drew F.K. Williamson, Faisal Mahmood, Guillaume Jaume, Jonathan T.C. Liu, Konstantin Hemker, Kritika Singh, Long Phi Le, Luca Weishaupt, Ming Y. Lu, Sizun Jiang","cross_cats":["stat.AP"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-25T01:31:54Z","title":"AI-driven 3D Spatial Transcriptomics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.17761","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6b45b9ef579242233418fb9455982e786c9afdc1eab84d2d321072a5a236d2ef","target":"record","created_at":"2026-07-05T10:19:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a71cf178da10f63c853d46c16d8b81c27eb93791bcc604c02afc96a30fcfcb25","cross_cats_sorted":["stat.AP"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-25T01:31:54Z","title_canon_sha256":"f79042afacf1b48de3cfb50081cc8bdd610f1a8c06d7a5fab065d0fddae8149f"},"schema_version":"1.0","source":{"id":"2502.17761","kind":"arxiv","version":1}},"canonical_sha256":"9b3cbf734ed829779d3ede6eb2dbb464629bbccdfaad8353afa8440c34b555f9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9b3cbf734ed829779d3ede6eb2dbb464629bbccdfaad8353afa8440c34b555f9","first_computed_at":"2026-07-05T10:19:43.630388Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:19:43.630388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CjEX1effF1JcvQPxbpAfEY6//5OeLm0DvE/j2KCknYQnXLsZ698YBVIhzZwTFZQJQWfXijxTVrex4YmrFS0HBw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:19:43.630881Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.17761","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6b45b9ef579242233418fb9455982e786c9afdc1eab84d2d321072a5a236d2ef","sha256:bd642702548678e002ecffb473e9991d597342f1c514e33c7ff2a310da2d122a"],"state_sha256":"febc3b0032ab1f28631b21cae01c20f02785ed089d4f5f5f9de02fe278ed94ad"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xggKU95M7LQQuPA40frponI6qfWLhHU7VA4+JO7MQ5aitw4OcOY7JDdUNYtTsDE8xMBT+yFsirUvYCQrW+D7AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T18:24:18.490665Z","bundle_sha256":"b7023ad5a6eb6aac949734ab82d9dde5d3a69d3bcbc7753a343a68f0d6f734bf"}}