Coagulation-adsorption of reactive orange from aqueous solution by freshly formed magnesium hydroxide: mixing time and mechanistic study.

The utilization of magnesium hydroxide was successfully carried out to remove reactive orange by coagulation-adsorption from aqueous solution. The coagulation-adsorption mechanisms and magnesium hydroxide-reactive orange floc property were analyzed through zeta potential, scanning electron microscope (SEM), X-ray diffraction and Fourier transform infrared spectroscopy (FT-IR). Flocculation Index was then discussed with controlled experiments using intelligent Particle Dispersion Analyzer (iPDA) and optimum rapid mixing time of 90 s was obtained for pH 12. The results of this study indicate that charge neutralization and adsorption are proposed to be the main coagulation mechanisms. The FT-IR spectra and SEM showed that reactive orange was adsorbed on the magnesium hydroxide surface during coagulation and adsorption. Freshly generated magnesium hydroxide can effectively remove reactive orange and the removal efficiency can reach 96.7% and 46.3% for coagulation and adsorption, respectively. Adsorption process accounts for 48% of the whole coagulation experiment. The removal efficiency decreased significantly with increasing magnesium hydroxide formation time.

Magnesium hydroxide coagulation performance and floc properties in treating high pH reactive orange wastewater.

Application of magnesium hydroxide as a coagulant for treating high pH reactive orange wastewater was studied. The coagulation performance and magnesium hydroxide-reactive orange floc properties were investigated under different dosages, feeding modes and pH values. Flocculation index (FI) was then discussed with controlled experiments using an intelligent particle dispersion analyzer and optimum coagulant dose of 150 mg/L (magnesium ion) was obtained for pH value 12. The results showed that the optimum magnesium ion dose tended to decrease with the increase of initial pH value. One time addition feeding mode led to relatively large FI value and higher removal efficiency compared with other addition modes. All of the flocs under investigation showed a limited capacity for re-growth when they had been previously broken. Based on the changes of zeta potential and floc properties, charge neutralization and precipitate enmeshment were proposed to be the main coagulation mechanisms.

Gene expression profiling for mechanistic understanding of cellular aggregation in mammalian cell perfusion cultures.

Aggregation of baby hamster kidney (BHK) cells cultivated in perfusion mode for manufacturing recombinant proteins was characterized. The potential impact of cultivation time on cell aggregation for an aggregating culture (cell line A) was studied by comparing expression profiles of 84 genes in the extracellular adhesion molecules (ECM) pathway by qRT-PCR from 9 and 25 day shake flask samples and 80 and 94 day bioreactor samples. Significant up-regulation of THBS2 (4.4- to 6.9-fold) was seen in both the 25 day shake flask and 80 and 94 day bioreactor samples compared to the 9 day shake flask while NCAM1 was down-regulated 5.1- to 8.9-fold in the 80 and 94 day bioreactor samples. Subsequent comparisons were made between cell line A and a non-aggregating culture (cell line B). A 65 day perfusion bioreactor sample from cell line B served as the control for 80 and 94 day samples from four different perfusion bioreactors for cell line A. Of the 84 genes in the ECM pathway, four (COL1A1, COL4A1, THBS2, and VCAN) were consistently up-regulated in cell line A while two (NCAM1 and THBS1) were consistently down-regulated. The magnitudes of differential gene expression were much higher when cell lines were compared (4.1- to 44.6-fold) than when early and late cell line B samples were compared (4.4- to 6.9-fold) indicating greater variability between aggregating and non-aggregating cell lines. Based on the differential gene expression results, two mechanistic models were proposed for aggregation of BHK cells in perfusion cultures.

Progress curve analysis of qRT-PCR reactions using the logistic growth equation.

We present an alternate approach for analyzing data from real-time reverse transcription polymerase chain reaction (qRT-PCR) experiments by fitting individual fluorescence vs. cycle number (F vs. C) curves to the logistic growth equation. The best fit parameters determined by nonlinear least squares were used to compute the second derivative of the logistic equation and the cycle threshold, C(t), was determined from the maximum value of the second derivative. This C(t) value was subsequently used to determine ΔΔC(t) and the amplification efficiency, E(n), thereby completing the analysis on a qRT-PCR data set. The robustness of the logistic approach was verified by testing ~600 F vs. C curves using both new and previously published data sets. In most cases, comparisons were made between the logistic estimates and those from the standard curve and comparative C(t) methods. Deviations between the logistic and standard curve method ranged between 3-10% for C(t) estimates, 2-10% for ΔΔC(t) estimates, and 1-11% for E(n) estimates. The correlations between C(t) estimates from the logistic and standard curve methods were very high, often >0.95. When compared with five other established methods of qRT-PCR data analysis to predict initial concentrations of two genes encompassing a total of 500 F vs. C curves, the logistic estimates were of comparable accuracy. This reliable performance of the logistic approach comes without the need to construct standard curves which can be a laborious undertaking. Also, no a priori assumptions for E(n) are necessary while some other methods assume equal E(n) values for the reference and target genes, an assumption that is not universally valid. In addition, by accurately describing the data in the plateau region of the F vs. C curve, the logistic method overcomes the limitations of the sigmoidal curve fitting method. The streamlined nature of the logistic approach makes it ideal for complete automation on a variety of computing environments thereby completely eliminating user bias. The simplicity, robustness, and ease of computer implementation of the logistic approach should make it an attractive alternative for rapidly analyzing qRT-PCR data.