Effects of ultrahigh pressure extraction conditions on yields and antioxidant activity of ginsenoside from ginseng

Ultrahigh pressure technique was employed to extract ginsenosides from roots of ginseng (Panax ginseng C.A. Meyer). The optimal conditions for ultrahigh pressure extraction (UPE) of total ginsenosides were quantified by UV-vis spectrophotometry with the ginsenoside Re as standard, the signal ginsenosides were quantified by HPLC and ELSD with ginsenosides Re, Rg(1), Rb-1, Rc and Rb-2 as standards. Orthogonal design was applied to evaluate the effects of four independent factors (extraction pressure, extraction temperature, extraction time and ethanol concentration) on the yield and 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity of ginsenoside, which are based on microwave extraction (ME), ultrasound extraction (UE), soxhlet extraction (SE) and heat reflux extraction (HRE) method. The results showed that UPE method can produce ginsenoside with the highest yield and the best radical scavenging activity compared to other used ones. Scanning electron microscopic (SEM) images of the plant cells after ultrahigh pressure treatment was obtained to provide visual evidence of the disruption effect.

Optimization and parameter study for chiral separation by capillary electrophoresis

Orthogonal design and uniform design were used for the optimization of separation of enantiomers using 2,6-di-O-methyl-beta-cyclodextrin (DM-beta-CD) as a chiral selector by capillary zone electrophoresis, The concentration of DM-beta-CD, buffer pH, running voltage, and capillary temperature were selected as variable parameters, their different effects on peak resolution were studied by the design methods. It was concluded that orthogonal design offers a rapid and efficient means for testing the importance of individual parameters and for determining the optimum operating conditions. However, for a large number of both factors and levels, uniform design is more efficient, The effect of addition of methanol and citric acid buffer on the separation of enantiomers was also examined.

A non-orthogonal distributed space-time coded protocol - Part 1:Signal model and design criteria

In this two-part series of papers, a generalized non-orthogonal amplify and forward (GNAF) protocol which generalizes several known cooperative diversity protocols is proposed. Transmission in the GNAF protocol comprises of two phases - the broadcast phase and the cooperation phase. In the broadcast phase, the source broadcasts its information to the relays as well as the destination. In the cooperation phase, the source and the relays together transmit a space-time code in a distributed fashion. The GNAF protocol relaxes the constraints imposed by the protocol of Jing and Hassibi on the code structure. In Part-I of this paper, a code design criteria is obtained and it is shown that the GNAF protocol is delay efficient and coding gain efficient as well. Moreover GNAF protocol enables the use of sphere decoders at the destination with a non-exponential Maximum likelihood (ML) decoding complexity. In Part-II, several low decoding complexity code constructions are studied and a lower bound on the Diversity-Multiplexing Gain tradeoff of the GNAF protocol is obtained.

Orthogonal biofunctionalization of magnetic nanoparticles via ``clickable'' poly(ethylene glycol) silanes: a ``universal ligand'' strategy to design stealth and target-specific nanocarriers

The present work demonstrates a novel strategy to synthesize orthogonally bio-engineered magnetonanohybrids (MNPs) through the design of versatile, biocompatible linkers whose structure includes: (i) a robust anchor to bind with metal-oxide surfaces; (ii) tailored surface groups to act as spacers and (iii) a general method to implement orthogonal functionalizations of the substrate via ``click chemistry''. Ligands that possess the synthetic generality of features (i)-(iii) are categorized as ``universal ligands''. Herein, we report the synthesis of a novel, azido-terminated poly(ethylene glycol) (PEG) silane that can easily self-assemble on MNPs through hetero-condensation between surface hydroxyl groups and the silane end of the ligand, and simultaneously provide multiple clickable sites for high density, chemoselective bio-conjugation. To establish the universal-ligand-strategy, we clicked alkyl-functionalized folate onto the surface of PEGylated MNPs. By further integrating a near-infrared fluorescent (NIRF) marker (Alexa-Fluor 647) with MNPs, we demonstrated their folate-receptor mediated internalization inside cancer cells and subsequent translocation into lysosomes and mitochondria. Ex vivo NIRF imaging established that the azido-PEG-silane developed in course of the study can effectively reduce the sequestration of MNPs by macrophage organs (viz. liver and spleen). These folate-PEG-MNPs were not only stealth and noncytotoxic but their dual optical and magnetic properties aided in tracking their whereabouts through combined magnetic resonance and optical imaging. Together, these results provided a strong motivation for the future use of the ``universal ligand'' strategy towards development of ``smart'' nanohybrids for theragnostic applications.

Optimization by orthogonal array design and humoral immunity of the bivalent vaccine against Aeromonas hydrophila and Vibrio fluvialis infection in crucian carp (Carassius auratus L.)

Aeromonas hydrophila and Vibrio fluvialis are the causative agents of a serious haemorrhagic septicaemia that affects a wide range of freshwater fish in China. In order to develop a bivalent anti-A. hydrophila and anti-V. fluvialis formalin-killed vaccine to prevent this disease, an orthogonal array design (OAD) method was used to optimize the production conditions, using three factors, each having three levels. The effects of these factors and levels on the relative per cent survival for crucian carp were quantitatively evaluated by analysis of variance. The final optimized formulation was established. The data showed that inactivation temperature had a significant effect on the potency of vaccine, but formalin concentration did not. The bivalent vaccine could elicit a strong humoral response in crucian carp (Carassius auratus L.) against both A. hydrophila and V. fluvialis simultaneously, which peaked at 3 or 5 weeks respectively. Antibody titres remained high until week 12, the end of the experiment, after a single intraperitoneal injection. The verification experiment confirmed that an optimized preparation could provide protection for fish at least against A. hydrophila infection, and did perform better than the non-optimized vaccine judged by the antibody levels and protection rate, suggesting that OAD is of value in the development of improved vaccine formulations.

Determination of ochratoxin A in bread: evaluation of microwave-assisted extraction using an orthogonal composite design coupled with response surface methodology

An analytical method using microwave-assisted extraction (MAE) and liquid chromatography (LC) with fluorescence detection (FD) for the determination of ochratoxin A (OTA) in bread samples is described. A 24 orthogonal composite design coupled with response surface methodology was used to study the influence of MAE parameters (extraction time, temperature, solvent volume, and stirring speed) in order to maximize OTA recovery. The optimized MAE conditions were the following: 25 mL of acetonitrile, 10 min of extraction, at 80 °C, and maximum stirring speed. Validation of the overall methodology was performed by spiking assays at five levels (0.1–3.00 ng/g). The quantification limit was 0.005 ng/g. The established method was then applied to 64 bread samples (wheat, maize, and wheat/maize bread) collected in Oporto region (Northern Portugal). OTAwas detected in 84 % of the samples with a maximum value of 2.87 ng/g below the European maximum limit established for OTA in cereal products of 3 ng/g.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design

A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Nonlinear model structure design and construction using orthogonal least squares and D-optimality design

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model robustness and adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the model subset selection cost function includes a D-optimality design criterion that maximizes the determinant of the design matrix of the subset to ensure the model robustness, adequacy, and parsimony of the final model. The proposed approach is based on the forward orthogonal least square (OLS) algorithm, such that new D-optimality-based cost function is constructed based on the orthogonalization process to gain computational advantages and hence to maintain the inherent advantage of computational efficiency associated with the conventional forward OLS approach. Illustrative examples are included to demonstrate the effectiveness of the new approach.

Nonlinear model structure detection using optimum experimental design and orthogonal least squares

A very efficient learning algorithm for model subset selection is introduced based on a new composite cost function that simultaneously optimizes the model approximation ability and model adequacy. The derived model parameters are estimated via forward orthogonal least squares, but the subset selection cost function includes an A-optimality design criterion to minimize the variance of the parameter estimates that ensures the adequacy and parsimony of the final model. An illustrative example is included to demonstrate the effectiveness of the new approach.