{"id":"W2258606127","doi":"10.1186/s12934-016-0420-z","title":"Adaptive evolution and metabolic engineering of a cellobiose- and xylose- negative Corynebacterium glutamicum that co-utilizes cellobiose and xylose","year":2016,"lang":"en","type":"article","venue":"Microbial Cell Factories","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Korea Carbon Capture and Sequestration R and D Center; National Research Council of Science and Technology; National Research Foundation of Korea; Korea Institute of Science and Technology; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Cellobiose; Xylose; Corynebacterium glutamicum; Biochemistry; Metabolic engineering; Fermentation; Lignocellulosic biomass; Pentose; Chemistry; Ruminococcus; Cellulose; Biology; Enzyme; Gene; Cellulase","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003031551,0.0006551887,0.000321664,0.000350117,0.0002136379,0.0005244601,0.0005062222,0.00037174,0.0002433762],"category_scores_gemma":[0.0003564118,0.0001386645,0.0003972061,0.0004597306,0.0002074376,0.0001411119,0.000659817,0.0006132334,0.0001199753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003636436,"about_ca_system_score_gemma":0.0003784847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001487442,"about_ca_topic_score_gemma":0.001821193,"domain_scores_codex":[0.9997455,0.00003657647,0.00003121709,0.0000575179,0.00006847802,0.00006063889],"domain_scores_gemma":[0.9998538,0.00001816159,0.00004094119,0.00002706164,0.00002615989,0.00003386836],"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":[0.0000548243,0.00005466267,0.0006740483,0.00004143501,0.00001505199,0.000155782,0.00004581808,0.0004880918,0.9949067,0.0001359588,0.00003428586,0.003393215],"study_design_scores_gemma":[0.00004770241,0.0008738289,0.0145205,0.00004037293,0.0001384238,0.001295921,0.0002813888,0.01384989,0.961803,0.000223763,0.006893323,0.00003203749],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932044,0.0003117696,0.005028964,0.0001017378,0.00002258542,0.00007194359,0.00027377,0.00007661285,0.000908205],"genre_scores_gemma":[0.9841596,0.0003775897,0.01301132,0.00005460064,0.000005191893,0.00007058871,0.0007537688,0.00007746799,0.001489822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001487442,"threshold_uncertainty_score":0.002957523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006170699312228372,"score_gpt":0.1856940407685517,"score_spread":0.1795233414563233,"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."}}