{"id":"W2781665323","doi":"10.1021/acs.analchem.7b04852","title":"Engineering Biosensors with Dual Programmable Dynamic Ranges","year":2018,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Key Research and Development Program of China; Ministry of Science and Technology of the People's Republic of China; Natural Sciences and Engineering Research Council of Canada; Government of Jiangxi Province; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Aptamer; Biosensor; Dynamic range; Chemistry; Molecular recognition; DNA; Wide dynamic range; SIGNAL (programming language); Molecular machine; Adenosine triphosphate; Nanotechnology; Combinatorial chemistry; Computer science; Molecule; Biochemistry; Materials science","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":[0.0007900406,0.0008965681,0.0005596544,0.0005096377,0.0001542661,0.000729833,0.001213499,0.001209099,0.0006163421],"category_scores_gemma":[0.001059413,0.0006367477,0.0003914502,0.0003876063,0.000687474,0.001152253,0.001114177,0.001403065,0.0007921386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006503006,"about_ca_system_score_gemma":0.0003125652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001800395,"about_ca_topic_score_gemma":0.0002714045,"domain_scores_codex":[0.9991143,0.0001668641,0.00008607663,0.0002914241,0.0002418102,0.0000995031],"domain_scores_gemma":[0.9994585,0.0001773251,0.0001487725,0.00005726951,0.0000908822,0.00006714052],"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":[0.00001839336,0.00002266334,0.00009174655,0.00009275317,0.000007079223,0.00003500223,0.00002932852,0.0004569003,0.9881759,0.002453856,0.0001155647,0.008500891],"study_design_scores_gemma":[0.00001051449,0.00007174212,0.0001280777,0.00000790099,0.00001003,0.0001679709,0.00001240801,0.00518299,0.9871923,0.0006870525,0.006509862,0.00001921909],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3077414,0.007039104,0.6728076,0.001289419,0.0004083859,0.0003184434,0.0003884907,0.002515089,0.007491965],"genre_scores_gemma":[0.5569178,0.003228704,0.4322897,0.0008601153,0.00008881223,0.0004965785,0.0003214375,0.0001681914,0.005628504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001213499,"threshold_uncertainty_score":0.004718304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004636113876069074,"score_gpt":0.2449811273782113,"score_spread":0.2403450135021422,"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."}}