{"id":"W2908861291","doi":"10.1021/jacs.8b11997","title":"Spying on Neuronal Membrane Potential with Genetically Targetable Voltage Indicators","year":2019,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Biochemical and Structural Characterization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Biological Infrastructure; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Agency for Science, Technology and Research; Esther A. and Joseph Klingenstein Fund; Simons Foundation","keywords":"Chemistry; Biophysics; Covalent bond; Fluorescence; Förster resonance energy transfer; Linker; Polyethylene glycol; Conjugated system; Nanotechnology; Combinatorial chemistry; Biochemistry; Polymer; Materials science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008165347,0.0001416977,0.0002117105,0.00001131548,0.00004589588,0.00002114757,0.0003440403,0.00007444902,0.00005412669],"category_scores_gemma":[0.000029073,0.00008085229,0.0002894864,0.0001754679,0.0002224716,0.000005641531,0.0001018898,0.0003023051,0.000002980474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002402531,"about_ca_system_score_gemma":0.0000606109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002099166,"about_ca_topic_score_gemma":4.033594e-8,"domain_scores_codex":[0.9990379,0.00002482039,0.0002269281,0.0001874882,0.0003195458,0.0002033201],"domain_scores_gemma":[0.9992306,0.00001206818,0.0003952447,0.0001928596,0.00006953518,0.00009970821],"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":[0.0002597973,0.00003440108,0.003394227,0.000008295293,0.00007591889,7.410886e-7,0.00001252231,0.00007382785,0.9948192,0.000003316748,0.000470644,0.0008471421],"study_design_scores_gemma":[0.0004162161,0.0003321366,0.01219146,0.00001498408,0.00003105408,0.00004927259,0.0000263469,0.00005923796,0.983972,0.00001201913,0.002767076,0.0001282153],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988889,0.00002847332,0.000259312,0.0005312902,0.0001137942,0.00007132995,0.000006965878,0.000003693094,0.00009625043],"genre_scores_gemma":[0.9965301,0.00004676398,0.001005888,0.001828283,0.0003699974,8.661755e-7,0.00001119987,0.00001681493,0.0001901199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01084719,"threshold_uncertainty_score":0.329706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002572009878338699,"score_gpt":0.1913635412824626,"score_spread":0.1887915314041239,"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."}}