Journal: Environmental research

Article Title: Differences in urine cadmium associations with kidney outcomes based on serum creatinine and cystatin C

doi: 10.1016/j.envres.2011.07.012

Figure Lengend Snippet: Scatterplot between MDRD eGFR and multi-variable cystatin C eGFR in 712 Korean lead workers. The line of equivalency (solid) and a smoothed line (dotted), which was estimated using the scatterplot smoothing method (SAS/GRAPH software), are shown.

Article Snippet: The line of equivalency (solid) and a smoothed line (dotted), which was estimated using the scatterplot smoothing method (SAS/GRAPH software), are shown. table ft1 table-wrap mode="anchored" t5 caption a7 Urine creatinine Urine cadmium μg/g Serum cystatin C Serum creatinine Single variable cystatin C eGFR Multi-variable cystatin C eGFR Dual eGFR MDRD eGFR Unadjusted urine cadmium μg/L 0.69 c 0.51 c 0.13 c −0.12 c −0.13 c −0.18 c −0.06 0.03 Urine creatinine, mg/dL −0.22 c 0.08 a 0.13 c −0.08 a 0.004 0.02 0.03 Urine cadmium μg/g 0.07 −0.34 c −0.07 −0.24 c −0.10 b 0.01 Serum cystatin C, mg/L 0.37 c −1.0 c −0.93 c −0.76 c −0.47 c Serum creatinine, mg/dL −0.37 c −0.19 c −0.59 c −0.73 c Single variable cystatin C eGFR, mL/min/1.73 m 2 0.93 c 0.76 c 0.47 c Multi-variable cystatin C eGFR, mL/min/1.73 m 2 0.80 c 0.50 c Dual biomarker eGFR, mL/min/1.73 m 2 0.90 c Open in a separate window a p-value less than 0.05; b p-value less than 0.01; c p-value less than 0.001 Spearman correlation coefficients for urine cadmium, urine creatinine and kidney function measures in 712 lead workers

Techniques: Software

Journal: Biotechnology and Bioengineering

Article Title: Segmented linear modeling of CHO fed‐batch culture and its application to large scale production

doi: 10.1002/bit.26214

Figure Lengend Snippet: Developed methodology to identify and characterize metabolic phases. Experimental data are first cleaned using the methodology presented in Figure 2 and additionally by removing data with a viability below 50% or a depletion of metabolites during a measurement interval. The number of metabolic phases during the cell culture process are determined by differentiating the smoothed (LOWESS) reaction rates of all metabolites with respect to the growth rate (dR/dµ). Recursive partitioning is then applied on those derivatives to get a vector of possible metabolic phase breakpoints. Hierarchical clustering is then applied on this vector of possible breakpoints to define the number of final metabolic phases (clusters). Knowing the number of metabolic phases, the segmented regression can then be calibrated on the calibration dataset for each metabolite and validated on the cross validation dataset of the 2 L bioreactor and also of the 2000 L bioreactor.

Article Snippet: As the derivative can amplify possible biological and analytical errors, the specific production rates were, preliminarily to deriving, smoothed as a function of the specific growth rate with the linear Locally Weighted Scatterplot Smoother (LOWESS) method (Cleveland, ) by using SAS software JMP 11 ©.

Techniques: Cell Culture, Plasmid Preparation, Biomarker Discovery