Optimization of ultrasonic circulating extraction of polysaccharides from Asparagus officinalis using response surface methodology

Polysaccharides were extracted from Asparagus officinalis. A novel ultrasonic circulating extraction (UCE) technology was applied for the polysaccharide extraction. Three-factor-three-level Box-Behnken design was employed to optimize ultrasonic power, extraction time and the liquid-solid ratio to obtain a high polysaccharide yield. The optimal extraction conditions were as follows: ultrasonic power was 600 W, extraction time was 46 min, the liquid-solid ratio was 35 mL/g. Under these conditions, the experimental yield of polysaccharides was 3.134%, which was agreed closely to the predicted value. The average molecular weight of A. officinalis polysaccharide was about 6.18 × 10⁴ Da. The polysaccharides were composed of glucose, fucose, arabinose, galactose and rhamnose in a ratio of 2.18:1.86:1.50:0.98:1.53. Compared with hot water extraction (HWE), UCE showed time-saving, higher yield and no influence on the structure of asparagus polysaccharides. The results indicated that ultrasonic circulating extraction technology could be an effective and advisable technique for the large scale production of plant polysaccharides.     

Methyl jasmonate treatments reduce chilling injury and activate the defense response of guava fruits

Tropical fruits cannot be stored at low temperatures due to the chilling injury phenomena. With the goal of reducing the chilling injury, we tested 10(-4) and 10(-5) M of methyl jasmonate (MJ) treatment before the storage of red and white cultivars of guava fruits at 5 degrees C for up to 15 days plus two days at 20 degrees C. Every five days, we evaluated chilling injury index, ion leakage percentage, vitamin C, sugars, total phenols, and the activity of the enzymes lipoxygenase (LOX) and phenylalanine-ammonia lyase (PAL). We found that methyl jasmonate treatments reduce the chilling injury index and the ion leakage percentage. Furthermore, MJ did not affect vitamin C, chlorophyll, and total phenols. MJ increased sugar content, PAL, and LOX activities. We concluded that MJ reduces chilling injury and activates the fruit defense response as indicated by the behavior of total phenols and the increase in sugar content, PAL, and LOX activities.     

Partial purification and characterization of pectinmethylesterase from ripening guava (<Emphasis Type="Italic">Psidium guajava</Emphasis> L.) fruits

Employing the techniques of (NH₄)₂SO₄ fractionation, ion exchange chromatography on DEAE cellulose and gel filtration through Sephadex G-100, pectinmethylesterase (EC 3.1.1.11) was purified from guava (Psidium guajava L.) fruits var. Hisar Safeda harvested at turning stage of maturity to 129-fold with 28% recovery. Molecular weight as determined by gel filtration was found to be 51 kDa and the enzyme preparation exhibited the same molecular weight under native (Native-PAGE) and denaturating conditions (SDS-PAGE) indicating that the enzyme was a monomer. With pectin as the substrate, it exhibited the Michaelis Menten kinetics with K _m value of 3.1 g l⁻¹. The enzyme was found to be stimulated by Ca⁺⁺ and Na⁺ and inhibited competitively by d-galacturonic acid with K _i value of 1.97 mM. The enzyme was completely inactivated by iodine while with diethyl pyrocarbonate and N-acetylimidazole, the enzyme was inhibited up to the extent of 56 and 45%, respectively. However, DTNB had no inhibitory effect whatsoever precluding the participation of any –SH group in the active centre. It is tentatively proposed that the enzyme has tyrosine and histidine residues at its active centre.     

Anti-fatigue activity of polysaccharides extract from Radix Rehmanniae Preparata

The anti-fatigue effects of the Radix Rehmanniae Preparata polysaccharides (RRPP) were studied in mice. The RRPP were orally administered at doses of 50, 100 and 200 mg/kg for 4 weeks and the anti-fatigue activity was evaluated using a weight-loaded swimming test, along with the determination of serum urea nitrogen (SUN), hepatic glycogen and blood lactic acid (BLA) contents. The results showed that there was no significant difference in the body weight of mice in the three RRPP groups compared with the negative control group during initial, intermediate and terminal stages in the experiment (p > 0.05). The ratio of exhausting swimming time was obviously increased 31.48% (p < 0.05) and 61.51% (p < 0.01) in the middle-dose group and the high-dose RRPP group, respectively. The BLA and SUN levels were decreased in middle-dose and high-dose RRPP groups (p < 0.01). Hepatic glycogen level was increased in three RRPP treated groups (p < 0.01). Therefore, RRPP may be responsible for the pharmacological effect of anti-fatigue of Radix Rehmanniae Preparata. The mechanism was related to the increase of the storage of hepatic glycogen and the decrease of the accumulation of SUN and BLA.     

Extraction of polysaccharides and the antioxidant activity from the seeds of Plantago asiatica L

The extraction conditions of polysaccharides from Plantago asiatica L. seeds were investigated. Four parameters affecting the polysaccharides extraction, extraction times, water to sample, extraction temperature and single extraction time, were determined by orthogonal experiments. Under the optimized conditions, the polysaccharides yield of P. asiatica L. seeds was 2.467%. The antioxidant activities of the polysaccharides were investigated. The reducing power of the polysaccharides was dose dependent, and the reducing capacity of the polysaccharides was inferior to butylated hydroxytoluene, which is known to be a strong reducing agent. The scavenging rates of the polysaccharides on superoxide and 1,1-diphenyl-2-picrylhydrazyl radicals were79.7% and 81.4%, at polysaccharides concentration of 0.75 mg/mL, respectively, a scavenging rates approximately similar to that of 0.75 mg/mL ascorbic acid (83.5% and 85.1%, respectively). Furthermore, it exhibited a moderate concentration-dependent ABTS radical scavenging activity, ferrous ion chelating potency and H₂O₂ scavenging activity. The data obtained in the in vitro models clearly establish the antioxidant potency of the polysaccharides extracted from Semen Plantaginis.     

Pyrolysis patterns of 5 close Corynebacterium species analyzed by artificial neural networks

In the present study, an artificial neural network was trained with the Stuttgart Neural Networks Simulator, in order to identify Corynebacterium species by analyzing their pyrolysis patterns. An earlier study described the combination of pyrolysis, gas chromatography and atomic emission detection we used on whole cell bacteria. Carbon, sulfur and nitrogen were detected in the pyrolysis compounds. Pyrolysis patterns were obtained from 52 Corynebacterium strains belonging to 5 close species. These data were previously analyzed by Euclidean distances calculation followed by Unweighted Pair Group Method of Averages, a clustering method. With this early method, strains from 3 of the 5 species (C. xerosis, C. freneyi and C. amycolatum) were correctly characterized even if the 29 strains of C. amycolatum were grouped into 2 subgroups. Strains from the 2 remaining species (C. minutissimum and C. striatum) cannot be separated. To build an artificial neural network, able to discriminate the 5 previous species, the pyrolysis data of 42 selected strains were used as learning set and the 10 remaining strains as testing set. The chosen learning algorithm was Back-Propagation with Momentum. Parameters used to train a correct network are described here, and the results analyzed. The obtained artificial neural network has the following cone-shaped structure: 144 nodes in input, 25 and 9 nodes in 2 successive hidden layers, and then 5 outputs. It could classify all the strains in their species group. This network completes a chemotaxonomic method for Corynebacterium identification.     

Prediction of N-linked glycan branching patterns using artificial neural networks

A model was developed for novel prediction of N-linked glycan branching pattern classification for CHO-derived N-linked glycoproteins. The model consists of 30 independent recurrent neural networks and uses predicted quantities of secondary structure elements and residue solvent accessibility as an input vector. The model was designed to predict the major component of a heterogeneous mixture of CHO-derived glycoforms of a recombinant protein under normal growth conditions. Resulting glycosylation prediction is classified as either complex-type or high mannose. The incorporation of predicted quantities in the input vector allowed for theoretical mutant N-linked glycan branching predictions without initial experimental analysis of protein structures. Primary amino acid sequence data were effectively eliminated from the input vector space based on neural network prediction analyses. This provided further evidence that localized protein secondary structure elements and conformational structure may play more important roles in determining glycan branching patterns than does the primary sequence of a polypeptide. A confidence interval parameter was incorporated into the model to enable identification of false predictions. The model was further tested using published experimental results for mutants of the tissue-type plasminogen activator protein [J. Wilhelm, S.G. Lee, N.K. Kalyan, S.M. Cheng, F. Wiener, W. Pierzchala, P.P. Hung, Alterations in the domain structure of tissue-type plasminogen activator change the nature of asparagine glycosylation. Biotechnology (N.Y.) 8 (1990) 321-325].     

Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis

A method for simultaneous, nondestructive analysis of aminopyrine and phenacetin in compound aminopyrine phenacetin tablets with different concentrations has been developed by principal component artificial neural networks (PC-ANNs) on near-infrared (NIR) spectroscopy. In PC-ANN models, the spectral data were initially analyzed by principal component analysis. Then the scores of the principal components were chosen as input nodes for the input layer instead of the spectral data. The artificial neural network models using the spectral data as input nodes were also established and compared with the PC-ANN models. Four different preprocessing methods (first-derivative, second-derivative, standard normal variate (SNV), and multiplicative scatter correction) were applied to three sets of NIR spectra of compound aminopyrine phenacetin tablets. The PC-ANNs approach with SNV preprocessing spectra was found to provide the best results. The degree of approximation was performed as the selective criterion of the optimum network parameters.     

Host–parasitoid population density prediction using artificial neural networks: diamondback moth and its natural enemies

• 1 An integrated pest management (IPM) system incorporating the introduction and field release of Diadegma semiclausum (Hellén), a parasitoid of diamondback moth (DBM) Plutella xylostella (L.), comprising the worst insect pest of the cabbage family, has been developed in Kenya to replace the pesticides‐only approach.
• 2 Mathematical modelling using differential equations has been used in theoretical studies of host–parasitoid systems. Although, this method helps in gaining an understanding of the system's dynamics, it is generally less accurate when used for prediction. The artificial neural network (ANN) approach was therefore chosen to aid prediction.
• 3 The ANN methodology was applied to predict the population density of the DBM and D. semiclausum, its larval parasitoid. Two data sets, each from different release areas in the Kenya highlands, and both collected during a 3‐year period after the release of the parasitoid, were used in the present study. Two ANN models were developed using these data.
• 4 The ANN approach gave satisfactory results for DBM and for D. semiclausum. Sensitivity analysis suggested that pest populations may be naturally controlled by rainfall.
• 5 The ANN provides a powerful tool for predicting host–parasitoid population densities and made few assumptions on the field data. The approach allowed the use of data collected at any appropriate scale of the system, bypassing the assumptions and uncertainties that could have occurred when parameters are imported from other systems. The methodology can be explored with respect to the development of tools for monitoring and forecasting the population densities of a pest and its natural enemies. In addition, the model can be used to evaluate the relative effectiveness of the natural enemies and to investigate augmentative biological control strategies.

     

A simulation study comparing the impact of experimental error on the performance of experimental designs and artificial neural networks used for process screening

Many variables and their interactions can affect a biotechnological process. Testing a large number of variables and all their possible interactions is a cumbersome task and its cost can be prohibitive. Several screening strategies, with a relatively low number of experiments, can be used to find which variables have the largest impact on the process and estimate the magnitude of their effect. One approach for process screening is the use of experimental designs, among which fractional factorial and Plackett–Burman designs are frequent choices. Other screening strategies involve the use of artificial neural networks (ANNs). The advantage of ANNs is that they have fewer assumptions than experimental designs, but they render black-box models (i.e., little information can be extracted about the process mechanics). In this paper, we simulate a biotechnological process (fed-batch growth of bakers yeast) to analyze and compare the effect of random experimental errors of different magnitudes and statistical distributions on experimental designs and ANNs. Except for the situation in which the error has a normal distribution and the standard deviation is constant, it was not possible to determine a clear-cut rule for favoring one screening strategy over the other. Instead, we found that the data can be better analyzed using both strategies simultaneously.     

Antioxidant and immunological activities of water-soluble polysaccharides from Aconitum kusnezoffii Reichb

Aconitum kusnezoffii Reichb., one of the earliest recorded toxic species of genus Aconitum, has been used as traditional Chinese medicine and medicinal diet over the last 2500 years to treat heart failure congestion, neuralgia, rheumatism and gout, etc. In the present paper, four water-soluble polysaccharide fractions isolated from the tubers of A. kusnezoffii Reichb. were studied the antioxidant and immunological activities for the first time. In vitro antioxidant assays indicated that fraction WKCP-A had noticeable scavenging activities on DPPH radical, hydroxyl radical, superoxide anion, H₂O₂ and self-oxidation of 1,2,3-phentriol, ferrous ion-chelating ability and reducing power. Moreover, the in vivo immunological assay exhibited that fractions WKCP-A and WKHP could more significantly enhance splenic lymphocyte proliferation and macrophage phagocytosis than other fractions. Therefore, the water-soluble polysaccharides from A. kusnezoffii Reichb., especially WKCP-A, have the potential to be explored as novel natural antioxidants and immunostimulating agents for using in functional foods or medicine.     

Studies of macrophage immuno-modulating activity of polysaccharides isolated from Paecilomyces tenuipes

The objective of this study was to evaluate the immunomodulatory effects of the Paecilomyces sinensis polysaccharides (PtP) on the activity of macrophages and human monocytes. A water-soluble polysaccharide, with estimated molecular weight of 2.04 × 10⁴ Da, was isolated from P. sinensis. The results indicate that PtP can increase the activity of LDH and ACP in AMφ and PMφ of rats and human mononuclear cells, and enhance the pinocytic activity of macrophages and TNF-α production by human peripheral blood mononuclear cells (PBMC), suggesting that PtP had potent immunomodulatory properties and could be explored as a novel potential immunostimulants for the food and pharmaceutical purpose.     

A kinetic spectrophotometric method for simultaneous determination of glycine and lysine by artificial neural networks

A spectrophotometric method for simultaneous analysis of glycine and lysine is proposed by application of neural networks on the spectral kinetic data. The method is based on the reaction of glycine and lysine with 1,2-naphthoquinone-4-sulfonate (NQS) in slightly basic medium. On the basis of the difference in the rate between the two reactions, these two amino acids can be determined simultaneously in binary mixtures. Feed-forward neural networks have been trained to quantify considered amino acids in mixtures under optimum conditions. In this way, a one-layer network was trained. Sigmoidal and linear transfer functions were used in the hidden and output layers, respectively. Linear calibration graphs were obtained in the concentration range of 1 to 25microgml(-1) for glycine and 1 to 19microgml(-1) for lysine. The analytical performance of this method was characterized by the relative standard error. The proposed method was applied to the determination of considered amino acids in synthetic samples.     

Rules extraction from neural networks applied to the prediction and recognition of prokaryotic promoters

Promoters are DNA sequences located upstream of the gene region and play a central role in gene expression. Computational techniques show good accuracy in gene prediction but are less successful in predicting promoters, primarily because of the high number of false positives that reflect characteristics of the promoter sequences. Many machine learning methods have been used to address this issue. Neural Networks (NN) have been successfully used in this field because of their ability to recognize imprecise and incomplete patterns characteristic of promoter sequences. In this paper, NN was used to predict and recognize promoter sequences in two data sets: (i) one based on nucleotide sequence information and (ii) another based on stability sequence information. The accuracy was approximately 80% for simulation (i) and 68% for simulation (ii). In the rules extracted, biological consensus motifs were important parts of the NN learning process in both simulations.