Table10.DOCX by Brittney N. Keel (4886881)

“…Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. …”

Image7.PDF by Brittney N. Keel (4886881)

“…Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. …”

Table7.DOCX by Brittney N. Keel (4886881)

“…Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. …”

Table8.DOCX by Brittney N. Keel (4886881)

“…Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. …”

Image8.PDF by Brittney N. Keel (4886881)

“…Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. …”

Image2.PDF by Brittney N. Keel (4886881)

“…Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. …”

Table9.DOCX by Brittney N. Keel (4886881)

“…Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. …”

Table2.XLSX by Brittney N. Keel (4886881)

“…Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. …”

Table14.DOCX by Brittney N. Keel (4886881)

“…Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. …”

Table12.DOCX by Brittney N. Keel (4886881)

“…Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. …”

Table11.DOCX by Brittney N. Keel (4886881)

“…Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. …”

Table13.DOCX by Brittney N. Keel (4886881)

“…Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. …”

Table3.XLSX by Brittney N. Keel (4886881)

“…Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. …”

Image6.PDF by Brittney N. Keel (4886881)

“…Mapping sequence reads onto a reference genome is a fundamental step in the analysis of NGS data. …”

Effect of <i>LipB</i> Disruption on Lipoic Acid Levels and PDH-E2 Lipoylation by Svenja Günther (37469)

“…<div><p>(A) WT 3D7 and <i>LipB</i>KO mutants were analysed for their LA content. …”

DataSheet2_PK-PD integration of enrofloxacin and cefquinome alone and in combination against Klebsiella pneumoniae using an in vitro dynamic model.ZIP by Yanzhe Wei (8980829)

“…The dynamic time-kill curves indicated that enrofloxacin and cefquinome had bactericidal and bacteriostatic activities, respectively at ≥1.5 mg/L (single-dose) and 4 mg/L (8 h split-dose) causing a decrease in bacterial counts of ≥4.45 and >2 log₁₀ CFU/mL. …”

DataSheet1_PK-PD integration of enrofloxacin and cefquinome alone and in combination against Klebsiella pneumoniae using an in vitro dynamic model.ZIP by Yanzhe Wei (8980829)

“…The dynamic time-kill curves indicated that enrofloxacin and cefquinome had bactericidal and bacteriostatic activities, respectively at ≥1.5 mg/L (single-dose) and 4 mg/L (8 h split-dose) causing a decrease in bacterial counts of ≥4.45 and >2 log₁₀ CFU/mL. …”

DataSheet3_PK-PD integration of enrofloxacin and cefquinome alone and in combination against Klebsiella pneumoniae using an in vitro dynamic model.PDF by Yanzhe Wei (8980829)

“…The dynamic time-kill curves indicated that enrofloxacin and cefquinome had bactericidal and bacteriostatic activities, respectively at ≥1.5 mg/L (single-dose) and 4 mg/L (8 h split-dose) causing a decrease in bacterial counts of ≥4.45 and >2 log₁₀ CFU/mL. …”

Table1_PK-PD integration of enrofloxacin and cefquinome alone and in combination against Klebsiella pneumoniae using an in vitro dynamic model.xlsx by Yanzhe Wei (8980829)

“…The dynamic time-kill curves indicated that enrofloxacin and cefquinome had bactericidal and bacteriostatic activities, respectively at ≥1.5 mg/L (single-dose) and 4 mg/L (8 h split-dose) causing a decrease in bacterial counts of ≥4.45 and >2 log₁₀ CFU/mL. …”

Land Management Practices Associated with House Loss in Wildfires by Philip Gibbons (120631)

“…Significant fuel variables in a logistic regression model we selected to predict house loss were (in order of decreasing effect): (1) the cover of trees and shrubs within 40 m of houses, (2) whether trees and shrubs within 40 m of houses was predominantly remnant or planted, (3) the upwind distance from houses to groups of trees or shrubs, (4) the upwind distance from houses to public forested land (irrespective of whether it was managed for nature conservation or logging), (5) the upwind distance from houses to prescribed burning within 5 years, and (6) the number of buildings or structures within 40 m of houses. …”