{"id":"W2406540431","doi":"10.1007/s00253-016-7615-4","title":"Optimal secretion of alkali-tolerant xylanase in Bacillus subtilis by signal peptide screening","year":2016,"lang":"en","type":"article","venue":"Applied Microbiology and Biotechnology","topic":"Bacterial Genetics and Biotechnology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Key Science and Technology Program of Shaanxi Province; National Health and Medical Research Council; Australian Research Council; Queensland Cyber Infrastructure Foundation; Griffith University; National Medical Research Council; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Cutinase; Bacillus subtilis; Signal peptide; Xylanase; Bacillus pumilus; Secretion; Biochemistry; Peptide; Biology; Enzyme; Peptide sequence; Bacteria; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0001959337,0.0002641075,0.0003760763,0.0001898528,0.00005569852,0.000005019168,0.0002882967,0.001587384,0.00004556396],"category_scores_gemma":[0.00002213303,0.0002013004,0.00005869948,0.000158597,0.0009769406,0.000003215415,0.0003141875,0.0002016282,0.000006418566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000128692,"about_ca_system_score_gemma":0.0000295936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003294228,"about_ca_topic_score_gemma":0.00008664179,"domain_scores_codex":[0.9983937,0.00005027862,0.0004056393,0.0006527887,0.00003199844,0.0004656216],"domain_scores_gemma":[0.9993838,0.00002438487,0.0001656588,0.000345463,0.00003172042,0.00004898652],"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":[0.0003580671,0.00005912004,0.0004772522,0.00001144716,0.00004882042,0.000003868949,0.00001087537,0.000002883663,0.9778887,0.0005984789,0.0004183971,0.02012208],"study_design_scores_gemma":[0.001458865,0.0005553795,0.0002825644,0.00001956163,0.00001465667,0.00009866065,0.00006516084,0.000007398578,0.9603631,0.00009925219,0.0367672,0.0002681559],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922827,0.0008132886,0.004950651,0.001324391,0.00006593604,0.0002457103,0.0001933583,0.00003539802,0.00008852704],"genre_scores_gemma":[0.9963097,0.0009743173,0.002151065,0.0001660233,0.00004153828,0.0000242219,0.0002122557,0.00002086986,0.00009999791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0363488,"threshold_uncertainty_score":0.9997088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004634486902723934,"score_gpt":0.1879821135105661,"score_spread":0.1833476266078422,"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."}}