{"id":"W3000736671","doi":"10.1101/2020.01.16.909291","title":"A bright and high-performance genetically encoded Ca <sup>2+</sup> indicator based on mNeonGreen fluorescent protein","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Japan Society for the Promotion of Science; Canadian Institutes of Health Research; University of Alberta","keywords":"Green fluorescent protein; Zebrafish; Aequorea victoria; Fluorescence; Chemistry; Biophysics; In vivo; Fluorescent protein; Mantle (geology); Cell biology; Biology; Biochemistry; Physics; Genetics; Gene; Optics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029409,0.0003263455,0.0003065071,0.0001884009,0.0002263774,0.0005584288,0.0005416697,0.0005079444,0.0009112161],"category_scores_gemma":[0.0002323346,0.0001687341,0.0001497114,0.0002128938,0.000480408,0.0004459416,0.0004478921,0.0005891254,0.0008335977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000793359,"about_ca_system_score_gemma":0.000436208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002351803,"about_ca_topic_score_gemma":0.002869663,"domain_scores_codex":[0.9998426,0.00001123805,0.000007247678,0.00005067453,0.00006115167,0.00002706012],"domain_scores_gemma":[0.9998921,0.00001751639,0.00002191349,0.00001289598,0.00002350015,0.00003208716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002015471,0.000003635181,0.0001072247,0.00001708097,0.000001615604,0.00003604751,0.000007190455,0.00009633411,0.9978339,0.0004271854,0.0001425322,0.001307104],"study_design_scores_gemma":[0.000003990775,0.0000137986,0.0004836514,0.000003101616,0.000002874282,0.00008898993,0.000005662997,0.001406137,0.9933267,0.00007699712,0.004583086,0.000005101685],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8277268,0.00223834,0.155009,0.001301899,0.0001915767,0.0001685566,0.001558976,0.001945059,0.009859775],"genre_scores_gemma":[0.8293919,0.001183391,0.1534864,0.0001691927,0.0000177132,0.00009541007,0.001649527,0.0004152292,0.01359124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002351803,"threshold_uncertainty_score":0.005756199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00663121039783361,"score_gpt":0.2142436492631666,"score_spread":0.207612438865333,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}