The preparation and bioactivity research of agaro-oligosaccharides

Agaro-oligosaccharides were hydrolytically obtained from agar using hydrochloric acid, citric acid, and cationic exchange resin (solid acid). The FT-IR and NMR data showed that the hydrolysate has the structure of agaro-oligomers. Orthogonal matrix method was applied to optimize the preparation conditions based on alpha-naphthylamine end-labeled HPLC analysis method. The optimal way for oligosaccharides with different degree of polymerization (DP) was achieved by using solid acid degradation, which could give high yield and avoid solution neutralization process. Agaro-oligosaccharides with high purity were consequently obtained by activated carbon column isolation. Furthermore, the antioxidant and alpha-glucosidase inhibitory activity of three fractions were also investigated. The result indicated that 8% ethanol-eluted fraction showed highest activity against alpha-glucosidase with IC50 of 8.84 mg/mL, while 25% ethanol-eluted fraction possessed excellent antioxidant ability.

Product monitoring and quantitation of oligosaccharides composition in agar hydrolysates by precolumn labeling HPLC

Since the discovery of multiple bioactivities for agarobiose oligomers, a quantitative method has been in great need to monitor the agarobiose oligomers. This report demonstrates that agarobiose oligomers can be separated with high resolution in HPLC after introducing a-naphthylamine into compounds. Agarobiose oligomers ranged from biose to decaose were isolated by Sephadex column. HPLC analysis indicated that each oliomer could be quantified with good linearity and a low detection limit of 0.1-4 mug/ml. The chromatographic profiles of agaro-oligosaccharides with different hydrolysis modes (hydrochloride, citric acid, solid acid, and hydroxyl radical degradation) showed that agarobiose could be obtained more than 57.8% using solid acid mediated hydrolysis, while hydrochloride acid could degrade agar into a series of agaro-oligosaccharides from biose to decaose. The yield of oligosaccharides was low if hydrolyzed by citric acid. The Fenton degradation can increase the speed of hydrolysis, but the product was complex. (C) 2004 Elsevier B.V. All rights reserved.

A study of a molecular mechanics field used in simulating enantiorecognition

A molecular mechanics field, Alchemy II, was utilized to model the chiral recognition between S-N-acetyl-alpha-methyl-alpha-naphthylamine and (R, S)-N-(3, 5-dinitrophrnyl)-alpha-methyl-benzeneacetamide and between beta-cyclodextrin and (R, S)-fenoprofen. Some preliminary results have: been obtained to sustain the three-point action models and the induce-fit action in enantiorecognition.

Toxicity of polychlorinated biphenyls and N-phenyl-1-naphthylamine in the juvenile turtle, Chelydra serpentina

Polychlorinated biphenyls (PCBs) and substituted phenylamine antioxidants (SPAs) are two chemical groups that have been used in multiple Canadian industrial processes. Despite the production ban of PCBs in North America in 1977, they are still ubiquitous in the environment and in wildlife tissues. Previous studies of fish, amphibians, birds, and mammals have shown that PCBs are toxic and act as endocrine disruptors. In contrast, SPAs, specifically N-phenyl-1-naphthylamine (PANA), have received very little attention despite their current use in Canada and their expected environmental releases. The effects of PCB and PANA exposures in reptiles remain unknown thus, juvenile Chelydra serpentina were used in this thesis as a model vertebrate to fill in missing toxicity research gaps due to their importance as an environmental indicator. First, food pellets were spiked at an environmentally relevant concentration of the PCB mixture Aroclor 1254 (A1254) to model hepatic bioaccumulation (0.45 μg/g A1254 for 31 days) and depuration (clean food for 50 days) of PCBs in turtles. No significant differences in PCB concentrations were observed between the control and treated animals, suggesting that juvenile turtles exposed to environmentally relevant concentrations of PCBs can likely detoxify low concentrations of PCBs. Additionally, two dose-response experiments were performed using A1254 or PANA spiked food (0-12.7 μg/g and 0-3,446 μg/g, respectively) to determine hepatic toxicity and bioaccumulation in juvenile C. serpentina. An increase in hepatic cyp1a was observed when exposed to the highest dose of both chemicals: 1) for A1254, induction correlated to the significant increase in hepatic PCB congeners that are known to be metabolized by CYP1A; and 2) for PANA, induction suggested that CYP1A has a potential role in its detoxification. PCBs are known endocrine disruptors, but no significant changes were observed for both thyroid receptors (alpha and beta) or by estrogen and androgen receptors. This lack of response, also noted in the PANA exposure, suggests that C. serpentina is less sensitive to endocrine disruption than other vertebrates. Furthermore, the expression of genes involved in cellular stress was not altered in PCB and PANA exposed animals, supporting the resilience of turtles to oxidative stress. This is the first study to demonstrate the toxicity of PCBs and PANA in C. serpentina, demonstrating the turtle’s high tolerance to contamination.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.