{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:VNOIYJJLFF5DLNF32C7SIS5TXQ","short_pith_number":"pith:VNOIYJJL","canonical_record":{"source":{"id":"2605.14565","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-14T08:36:45Z","cross_cats_sorted":["math.ST","stat.AP","stat.TH"],"title_canon_sha256":"6c4bcf76abdfaeeb104713747fdc2d0314adaae2d7c510d62cbf81aa7d41cd13","abstract_canon_sha256":"f3b091e9aff2fb667f2b647883b213b9fc21f54a768f1ff8cece31830a3179e1"},"schema_version":"1.0"},"canonical_sha256":"ab5c8c252b297a35b4bbd0bf244bb3bc1969388bb9522ecab7853113c08992a4","source":{"kind":"arxiv","id":"2605.14565","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14565","created_at":"2026-05-17T23:39:05Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14565v1","created_at":"2026-05-17T23:39:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14565","created_at":"2026-05-17T23:39:05Z"},{"alias_kind":"pith_short_12","alias_value":"VNOIYJJLFF5D","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"VNOIYJJLFF5DLNF3","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"VNOIYJJL","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:VNOIYJJLFF5DLNF32C7SIS5TXQ","target":"record","payload":{"canonical_record":{"source":{"id":"2605.14565","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-14T08:36:45Z","cross_cats_sorted":["math.ST","stat.AP","stat.TH"],"title_canon_sha256":"6c4bcf76abdfaeeb104713747fdc2d0314adaae2d7c510d62cbf81aa7d41cd13","abstract_canon_sha256":"f3b091e9aff2fb667f2b647883b213b9fc21f54a768f1ff8cece31830a3179e1"},"schema_version":"1.0"},"canonical_sha256":"ab5c8c252b297a35b4bbd0bf244bb3bc1969388bb9522ecab7853113c08992a4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:05.542603Z","signature_b64":"OqP2FmNqIiGb0Mq7oCnFbFaNf9ICms98js6Yz/sincX7lFRW+Kuo6SdnQ3ncThUJVc6+ebMh/LjfzY1aRUpJDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab5c8c252b297a35b4bbd0bf244bb3bc1969388bb9522ecab7853113c08992a4","last_reissued_at":"2026-05-17T23:39:05.541963Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:05.541963Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.14565","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":"2026-05-17T23:39:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rshLzp5ulVO5oUAot908YJvHSQSpyALLRUtH4sqn9LNI/i2kZ2wEhD48WdF/Gg7WSz2TTn21PV4L64tduEpICg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T16:56:21.724141Z"},"content_sha256":"c0f9700c976985a0528bf97afe64fbc917d2123e054786b89983bfe581f1d0d9","schema_version":"1.0","event_id":"sha256:c0f9700c976985a0528bf97afe64fbc917d2123e054786b89983bfe581f1d0d9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:VNOIYJJLFF5DLNF32C7SIS5TXQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Bayesian Longitudinal Spatial Normative Model for Individualized Brain Deviation Mapping","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Bayesian longitudinal spatial model reduces brain deviation map reconstruction error by jointly capturing temporal and spatial dependencies.","cross_cats":["math.ST","stat.AP","stat.TH"],"primary_cat":"stat.ME","authors_text":"J. T. Korley","submitted_at":"2026-05-14T08:36:45Z","abstract_excerpt":"Normative modeling enables individualized characterization of structural brain deviations by evaluating subjects against a reference population rather than a group average. Most existing implementations treat brain regions independently and remain cross-sectional, despite the availability of repeated neuroimaging measurements and the well-documented spatial organization of neuroanatomical variation. We propose a Bayesian longitudinal spatial normative model that jointly captures within-subject temporal dependence and spatially structured subject-specific deviations within a unified hierarchica"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Across six simulation scenarios and OASIS-3 structural MRI data, the proposed Bayesian longitudinal spatial normative model reduced deviation-map reconstruction error relative to independent cross-sectional and longitudinal non-spatial benchmarks, with RMSE reductions of 54% and 45% respectively in the real data application.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The model assumes that subject-specific deviations can be adequately represented as a latent spatial process whose posterior can be computed under the chosen hierarchical Bayesian specification, with the spatial dependence structure correctly specified for the neuroanatomical data.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A new Bayesian model jointly models longitudinal and spatial dependencies in brain MRI to produce individualized deviation maps with substantially lower error than independent or non-spatial alternatives.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Bayesian longitudinal spatial model reduces brain deviation map reconstruction error by jointly capturing temporal and spatial dependencies.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"4b17194122d0c5fbd242e99c308dd95442a0aa3150ebc47d2206b76f2c66b2b4"},"source":{"id":"2605.14565","kind":"arxiv","version":1},"verdict":{"id":"ec3c4b9f-3c29-40fe-b41e-9c92c334dfb8","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T01:39:58.229404Z","strongest_claim":"Across six simulation scenarios and OASIS-3 structural MRI data, the proposed Bayesian longitudinal spatial normative model reduced deviation-map reconstruction error relative to independent cross-sectional and longitudinal non-spatial benchmarks, with RMSE reductions of 54% and 45% respectively in the real data application.","one_line_summary":"A new Bayesian model jointly models longitudinal and spatial dependencies in brain MRI to produce individualized deviation maps with substantially lower error than independent or non-spatial alternatives.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The model assumes that subject-specific deviations can be adequately represented as a latent spatial process whose posterior can be computed under the chosen hierarchical Bayesian specification, with the spatial dependence structure correctly specified for the neuroanatomical data.","pith_extraction_headline":"Bayesian longitudinal spatial model reduces brain deviation map reconstruction error by jointly capturing temporal and spatial dependencies."},"references":{"count":13,"sample":[{"doi":"","year":null,"title":"Ifτ 2 u = 0, the model reduces to a longitudinal non-spatial normative model","work_id":"6b7fa367-73ed-447c-9038-a5a8aaca1b26","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Ifσ 2 b = 0, dependence across regions is induced only through the spatial deviation process","work_id":"fca3f70f-2d54-4f8a-a64c-2759a5da4317","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Ifτ 2 u = 0,σ 2 b = 0, andT i = 1for all subjects, the model reduces to an independent cross-sectional regional model. S1","work_id":"eff7538a-254d-472a-be55-5d2753ee8675","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Proof.For part (1), settingτ 2 u = 0implies uir = 0 almost surely for all regions","work_id":"88e6fe33-c673-4105-a4cc-dc228e78780f","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"baseline linear longitudinal structure,","work_id":"527f6150-90fc-48fb-ad13-8430d3867182","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":13,"snapshot_sha256":"b29dc9cd16e6e2d1c5d23f4272327a5e99d619582336b341be4cf3908459cc5d","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"7248aba057eed0f7853bf2eee4f89ea72309b46ee40f15550f87ecf9ed107b76"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"ec3c4b9f-3c29-40fe-b41e-9c92c334dfb8"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+X9T0K4Ah0Q+hiPOQuxyitnKd5rX+luYXz95Qa41Z0bu/j1RElI8n4GPsHmNKDzdm7RCvsJ0UfbBUfLRPpeYCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T16:56:21.725268Z"},"content_sha256":"8fa80b84592a3060f8d3cd53a31b48b51a7e1c9b75ef0caf715ed08899d9550c","schema_version":"1.0","event_id":"sha256:8fa80b84592a3060f8d3cd53a31b48b51a7e1c9b75ef0caf715ed08899d9550c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VNOIYJJLFF5DLNF32C7SIS5TXQ/bundle.json","state_url":"https://pith.science/pith/VNOIYJJLFF5DLNF32C7SIS5TXQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VNOIYJJLFF5DLNF32C7SIS5TXQ/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":"2026-05-27T16:56:21Z","links":{"resolver":"https://pith.science/pith/VNOIYJJLFF5DLNF32C7SIS5TXQ","bundle":"https://pith.science/pith/VNOIYJJLFF5DLNF32C7SIS5TXQ/bundle.json","state":"https://pith.science/pith/VNOIYJJLFF5DLNF32C7SIS5TXQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VNOIYJJLFF5DLNF32C7SIS5TXQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:VNOIYJJLFF5DLNF32C7SIS5TXQ","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":"f3b091e9aff2fb667f2b647883b213b9fc21f54a768f1ff8cece31830a3179e1","cross_cats_sorted":["math.ST","stat.AP","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-14T08:36:45Z","title_canon_sha256":"6c4bcf76abdfaeeb104713747fdc2d0314adaae2d7c510d62cbf81aa7d41cd13"},"schema_version":"1.0","source":{"id":"2605.14565","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14565","created_at":"2026-05-17T23:39:05Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14565v1","created_at":"2026-05-17T23:39:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14565","created_at":"2026-05-17T23:39:05Z"},{"alias_kind":"pith_short_12","alias_value":"VNOIYJJLFF5D","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"VNOIYJJLFF5DLNF3","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"VNOIYJJL","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:8fa80b84592a3060f8d3cd53a31b48b51a7e1c9b75ef0caf715ed08899d9550c","target":"graph","created_at":"2026-05-17T23:39:05Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Across six simulation scenarios and OASIS-3 structural MRI data, the proposed Bayesian longitudinal spatial normative model reduced deviation-map reconstruction error relative to independent cross-sectional and longitudinal non-spatial benchmarks, with RMSE reductions of 54% and 45% respectively in the real data application."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The model assumes that subject-specific deviations can be adequately represented as a latent spatial process whose posterior can be computed under the chosen hierarchical Bayesian specification, with the spatial dependence structure correctly specified for the neuroanatomical data."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A new Bayesian model jointly models longitudinal and spatial dependencies in brain MRI to produce individualized deviation maps with substantially lower error than independent or non-spatial alternatives."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Bayesian longitudinal spatial model reduces brain deviation map reconstruction error by jointly capturing temporal and spatial dependencies."}],"snapshot_sha256":"4b17194122d0c5fbd242e99c308dd95442a0aa3150ebc47d2206b76f2c66b2b4"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"7248aba057eed0f7853bf2eee4f89ea72309b46ee40f15550f87ecf9ed107b76"},"paper":{"abstract_excerpt":"Normative modeling enables individualized characterization of structural brain deviations by evaluating subjects against a reference population rather than a group average. Most existing implementations treat brain regions independently and remain cross-sectional, despite the availability of repeated neuroimaging measurements and the well-documented spatial organization of neuroanatomical variation. We propose a Bayesian longitudinal spatial normative model that jointly captures within-subject temporal dependence and spatially structured subject-specific deviations within a unified hierarchica","authors_text":"J. T. Korley","cross_cats":["math.ST","stat.AP","stat.TH"],"headline":"Bayesian longitudinal spatial model reduces brain deviation map reconstruction error by jointly capturing temporal and spatial dependencies.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-14T08:36:45Z","title":"A Bayesian Longitudinal Spatial Normative Model for Individualized Brain Deviation Mapping"},"references":{"count":13,"internal_anchors":0,"resolved_work":13,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Ifτ 2 u = 0, the model reduces to a longitudinal non-spatial normative model","work_id":"6b7fa367-73ed-447c-9038-a5a8aaca1b26","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Ifσ 2 b = 0, dependence across regions is induced only through the spatial deviation process","work_id":"fca3f70f-2d54-4f8a-a64c-2759a5da4317","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Ifτ 2 u = 0,σ 2 b = 0, andT i = 1for all subjects, the model reduces to an independent cross-sectional regional model. S1","work_id":"eff7538a-254d-472a-be55-5d2753ee8675","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Proof.For part (1), settingτ 2 u = 0implies uir = 0 almost surely for all regions","work_id":"88e6fe33-c673-4105-a4cc-dc228e78780f","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"baseline linear longitudinal structure,","work_id":"527f6150-90fc-48fb-ad13-8430d3867182","year":null}],"snapshot_sha256":"b29dc9cd16e6e2d1c5d23f4272327a5e99d619582336b341be4cf3908459cc5d"},"source":{"id":"2605.14565","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T01:39:58.229404Z","id":"ec3c4b9f-3c29-40fe-b41e-9c92c334dfb8","model_set":{"reader":"grok-4.3"},"one_line_summary":"A new Bayesian model jointly models longitudinal and spatial dependencies in brain MRI to produce individualized deviation maps with substantially lower error than independent or non-spatial alternatives.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Bayesian longitudinal spatial model reduces brain deviation map reconstruction error by jointly capturing temporal and spatial dependencies.","strongest_claim":"Across six simulation scenarios and OASIS-3 structural MRI data, the proposed Bayesian longitudinal spatial normative model reduced deviation-map reconstruction error relative to independent cross-sectional and longitudinal non-spatial benchmarks, with RMSE reductions of 54% and 45% respectively in the real data application.","weakest_assumption":"The model assumes that subject-specific deviations can be adequately represented as a latent spatial process whose posterior can be computed under the chosen hierarchical Bayesian specification, with the spatial dependence structure correctly specified for the neuroanatomical data."}},"verdict_id":"ec3c4b9f-3c29-40fe-b41e-9c92c334dfb8"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c0f9700c976985a0528bf97afe64fbc917d2123e054786b89983bfe581f1d0d9","target":"record","created_at":"2026-05-17T23:39:05Z","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":"f3b091e9aff2fb667f2b647883b213b9fc21f54a768f1ff8cece31830a3179e1","cross_cats_sorted":["math.ST","stat.AP","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-14T08:36:45Z","title_canon_sha256":"6c4bcf76abdfaeeb104713747fdc2d0314adaae2d7c510d62cbf81aa7d41cd13"},"schema_version":"1.0","source":{"id":"2605.14565","kind":"arxiv","version":1}},"canonical_sha256":"ab5c8c252b297a35b4bbd0bf244bb3bc1969388bb9522ecab7853113c08992a4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ab5c8c252b297a35b4bbd0bf244bb3bc1969388bb9522ecab7853113c08992a4","first_computed_at":"2026-05-17T23:39:05.541963Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:05.541963Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OqP2FmNqIiGb0Mq7oCnFbFaNf9ICms98js6Yz/sincX7lFRW+Kuo6SdnQ3ncThUJVc6+ebMh/LjfzY1aRUpJDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:05.542603Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.14565","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c0f9700c976985a0528bf97afe64fbc917d2123e054786b89983bfe581f1d0d9","sha256:8fa80b84592a3060f8d3cd53a31b48b51a7e1c9b75ef0caf715ed08899d9550c"],"state_sha256":"58936b299717f5e88893b21c0184d7ee881a27fd9f9254e6c490ead5d19cfca2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"57HttwDBa/DQdDg2mHprBbxTfEvbsC75jYyhu70xK6qJX2cMa/liFOA3jN2HU8sAhzw4/1lxXV1QI7uvQu+bCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T16:56:21.729327Z","bundle_sha256":"98cad1cea4418d5ab0760fabb32467339c7058e9abee93510c20f409e6392d91"}}