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In addition, since, under various simulation conditions, ML is always more accurate than MP in face of across-lineage rate variation, investigators continued to prefer ML for analysing real data.
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The effluent samples were collected using a fraction collector (2 3 ml) for Ca2+ analyses and pH measurements.
If Enterococcus (MPN/100 ml) concentrations were < 1 mlN/100 ml, we used a surrogate value of 0.1 mlN/100 ml for statistical analyses.
ML: maximum likelihood LR-ELW; MLTN: ML LR-ELW for analyses where 2nd codon positions were analyzed under the TN+I+Γ model; MLB: ML bootstrap; MB: BPP for analyses performed with MrBayes; PB BPPP for analyses performed with PhyloBayes (CAT-GTR for nucleotides); PBC: BPP for PB analyses using CAT-Poisson model; PBM BPPP for PB analyses using the mtREV+I+Γ model.
Water samples (500 mL) for water quality analyses were collected at approximately 0.5 m above the lake bottom before sampling sediments.
Quantification of extracellular metabolites, Triplicate samples (5 ml) for extracellular metabolite analyses were centrifuged at 6,000 rpm for 3 min in a micro centrifuge (Minispin, Eppendorf) to remove the cells, and subsequently filtered through 0.45 mm-filters (Millipore type HAWP).
At the predetermined time, wine samples of 1 mL were taken for analyses.
ML analyses for alternative topologies were conducted in PAUP* v4.0b10 (for rRNA data [75]) and PAML v4.4b (for mtDNA data [76]).
We conducted maximum likelihood (ML) analyses for each plant family on the complete concatenated data sets and, for comparison, on data sets including only one or two genes.
Each single-gene dataset was thoroughly tested by bootstrapped maximum likelihood (ML) analyses for deep paralogy or suspicious relationships possibly indicative of lateral gene transfer (LGT) or contamination.
Studies including 29 nuclear loci found strong concatenated Bayesian support for many relationships but weak support from ML bootstrap analyses for many of the same relationships [ 34].
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