Neural network modeling for nutrient dynamics in a recycling piggery slurry treatment system

A recycling reactor system operated under sequential anoxic and oxic conditions was evaluated, in which the nutrients of piggery slurry were anaerobically and aerobically treated and then a portion of the effluent was recycled to the pigsty. The most dominant aerobic heterotrophs from the reactor were Alcaligenes faecalis (TSA-3), Brevundimonas diminuta (TSA-1) and Abiotrophia defectiva (TSA-2) in decreasing order, whereas lactic acid bacteria, LAB (MRS-1, etc.) were most dominantly observed in the anoxic tank. Here we have tried to model the nutrient removal process for each tank in the system based on population densities of heterotrophic and LAB. Principal component analysis (PCA) was first applied to delineate a relationship between input (microbial densities and treatment parameters such as population densities of heterotrophic and LAB, suspended solids (SS), COD, NH₄ ⁺–N, ortho-phosphorus, and total phosphorus) and output. Multi-layer neural networks using an error back-propagation learning algorithm were then employed to model the nutrient removal process for each tank. PCA filtration of microbial densities as input data was able to enhance generalization performance of the neural network, and this has led to a better prediction of the measured data. Neural networks independently trained for each treatment tank and the combined analysis of the subsequent tank data allowed a successful prediction of the treatment system for at least 2 days.     

Hybrid neural network modeling of a full-scale industrial wastewater treatment process

In recent years, hybrid neural network approaches, which combine mechanistic and neural network models, have received considerable attention. These approaches are potentially very efficient for obtaining more accurate predictions of process dynamics by combining mechanistic and neural network models in such a way that the neural network model properly accounts for unknown and nonlinear parts of the mechanistic model. In this work, a full-scale coke-plant wastewater treatment process was chosen as a model system. Initially, a process data analysis was performed on the actual operational data by using principal component analysis. Next, a simplified mechanistic model and a neural network model were developed based on the specific process knowledge and the operational data of the coke-plant wastewater treatment process, respectively. Finally, the neural network was incorporated into the mechanistic model in both parallel and serial configurations. Simulation results showed that the parallel hybrid modeling approach achieved much more accurate predictions with good extrapolation properties as compared with the other modeling approaches even in the case of process upset caused by, for example, shock loading of toxic compounds. These results indicate that the parallel hybrid neural modeling approach is a useful tool for accurate and cost-effective modeling of biochemical processes, in the absence of other reasonably accurate process models.     

Effects of cell entrapment on growth rate and metabolic activity of pure cultures commonly found in biological wastewater treatment

The effects of cell entrapment on the growth rate and metabolic activity of major groups of bacteria (betaproteobacteria and gammaproteobacteria) in biological municipal wastewater treatment were investigated. Three different cell entrapment media (alginate, carrageenan and polyvinyl alcohol) and three cell-to-matrix ratios (0.1%, 0.2% and 0.6%, w v⁻¹) were examined. Representative species of betaproteobacteria were Alcaligenes faecalis and Comamonas testosteroni whereas Pseudomonas putida was a gammaproteobacteria species studied. Free (non-entrapped) cells were included in the study for comparative purpose. Results indicated that the entrapment, type of entrapment media, and cell-to-matrix ratio had significant effects on the growth and metabolic activity of major groups of bacteria in wastewater treatment. Polyvinyl alcohol entrapped cells had the highest specific growth and specific substrate utilization rates. Increase of cell-to-matrix ratio (from 0.1% to 0.2% or 0.6%) did not improve the specific growth and specific substrate utilization rates. The relative performances provided by different entrapment media of the three species studied were quite consistent. This study showed that the suitable choices of entrapment media and cell-to-matrix ratio are important but similar for major groups of bacteria in wastewater treatment.     

Extraction and optimization of exopolysaccharide from Lactobacillus sp. using response surface methodology and artificial neural networks

Abstract

The microbial polysaccharides secreted and produced from various microbes into their extracellular environment is known as exopolysaccharide. These polysaccharides can be secreted from the microbes either in a soluble or insoluble form.Lactobacillus sp. is one of the organisms that have been found to produce exopolysaccharide. Exo-polysaccharides (EPS) have various applications such as drug delivery, antimicrobial activity, surgical implants and many more in different fields. Medium composition is one of the major aspects for the production of EPS from Lactobacillus sp., optimization of medium components can help to enhance the synthesis of EPS . In the present work, the production of exopolysaccharide with different medium composition was optimized by response surface methodology (RSM) followed by tested for fitting with artificial neural networks (ANN). Three algorithms of ANN were compared to investigate the highest yeild of EPS. The highest yeild of EPS production in RSM was achieved by the medium composition that consists of (g/L) dextrose 15, sodium dihydrogen phosphate 3, potassium dihydrogen phosphate 2.5, triammonium citrate 1.5, and, magnesium sulfate 0.25. The output of 32 sets of RSM experiments were tested for fitting with ANN with three algorithms viz. Levenberg–Marquardt Algorithm (LMA), Bayesian Regularization Algorithm (BRA) and Scaled Conjugate Gradient Algorithm (SCGA) among them LMA found to have best fit with the experiments as compared to the SCGA and BRA.     

云锦杜鹃不同花期挥发性成分的HS-SPME-GC-MS检测与主成分分析

为了探究云锦杜鹃的挥发性成分,该研究采用顶空固相微萃取-气相色谱-质谱联用技术对云锦杜鹃不同花期中的挥发性成分进行定性定量分析,并通过主成分分析法分析其特征挥发性成分。结果表明:四个花期共检测出50种挥发性成分,共分为苯丙酸类/苯环型、萜烯类、醇类、醛类、烃类和其他类六大类组分。对29种主要挥发性物质进行主成分分析,提取了两个主成分,累计方差贡献率达到88.545%。分析发现,β-月桂烯、β-罗勒烯、可巴烯、异喇叭烯、桉树脑、衣兰烯、(+)-表二环倍半水芹烯、(3R-反式)-4-乙烯基-4-甲基-3-(1-甲基乙烯基)-1-(1-甲基乙基)-环己烯与第1主成分呈高度正相关,第2主成分的贡献率为31.455%,其中丁香酚的影响最大,呈高度负相关,这些物质是影响云锦杜鹃香气的关键性成分。在9种高度相关的物质中,萜烯类物质占了7种。综上认为,萜烯类物质是云锦杜鹃主要特征香气成分。     

Novel Leuconostoc citreum starter culture system for the fermentation of kimchi, a fermented cabbage product

To determine the dominant microorganisms involved in kimchi fermentation and to examine their effect on kimchi fermentation, we randomly isolated and characterized 120 lactic acid bacteria from kimchi during a 5-day fermentation at 15°C. Leuconostoc citreum was dominant during the early and mid-phases of kimchi fermentation whereas Lactobacillus sake/Lactobacillus curvatus or Lactobacillus brevis were found during later stages. Eighty-two out of 120 isolates (68%) were identified as Leuconostoc citreum by means of a polyphasic method, including 16S rDNA sequencing and DNA/DNA hybridization. A few Weissella confusa-like strains were also isolated during the mid-phase of the fermentation. Strain IH22, one of the Leuconostoc citreum isolates from kimchi, was used as an additive to evaluate growth and acid production in kimchi fermentation. This strain was consistently over 95% of the population in IH22-treated kimchi over a 5-day fermentation, while heterogeneous lactic acid bacteria were observed in the control kimchi. The pH in IH22-treated kimchi dropped rapidly but was stably maintained for 5 days, compared to its slow and prolonged decrease in the control kimchi. These results indicate that Leuconostoc citreum IH22 dominates over and retards the growth of other lactic acid bacteria in kimchi, suggesting it can be used as a bacterial starter culture to maintain the quality of kimchi for prolonged periods. This revised version was published online in June 2006 with corrections to the Cover Date.     

降解尿酸乳酸菌复合菌系的筛选与菌株组合提升降解效果

【背景】高尿酸症由血液中尿酸含量明显升高而导致,利用乳酸菌对人体的益生作用缓解高尿酸血症越来越受到关注。【目的】获得具有降解尿酸能力的乳酸菌复合菌系与纯培养菌株。【方法】以泡菜为样品来源,以尿酸为底物,采用MRS培养基筛选降解尿酸的乳酸菌复合菌系,通过高效液相色谱法测定复合菌系对尿酸的降解能力。【结果】得到一组乳酸菌复合菌系,当培养温度为37 °C、pH值为6.20、静置培养72 h后复合菌系对尿酸的降解率为12.08%;通过优化培养条件,当该菌系在以牛肉膏为单一氮源、初始pH值为5.00、温度为35 °C的条件下培养72 h,尿酸降解率上升至17.19%,降解率比优化前提高了42.3%;从该菌系中分离出两株具有尿酸降解能力的菌株UA-1与UA-2,它们的尿酸降解率分别为10.85%和8.65%;通过形态学观察和16S rRNA基因序列分析,经鉴定两株菌均为布氏乳杆菌(Lactobacillus buchneri)。将两株单菌组合降解尿酸试验发现,UA-1与UA-2比例为2:1的尿酸降解率为20.2%,比原复合菌系的降解能力提高了67.22%。【结论】研究证明了乳酸菌复合菌系对尿酸的降解能力优于单个菌株,为后续利用乳酸菌复合菌系应用提供了数据支持。     

基于神经网络简单集成的湖库富营养化综合评价模型

根据中国水利部推荐的地表水富营养化控制标准,以叶绿素a、总磷、总氮、化学需氧量和透明度为评价指标,采用线性插值方法生成均匀分布的训练样本,建立了用于湖泊、水库富营养化综合评价的神经网络简单集成模型,其个体网络采用反向传播网络。通过递增法分别确定个体网络隐含层节点数为3,集成规模为40。所有个体网络均采用弹性反传训练算法和带动量的梯度下降学习算法。将该模型应用于巢湖富营养化综合评价,结果表明该模型有效消除了单个反向传播神经网络对初始网络权重的敏感性,泛化能力得到显著的提高。该模型的评价结果与综合营养状态指数法差异极显著,而与插值评分法差异不显著;但相关性较高,相关系数分别为0．9406和0．8891。通过对比分析,表明该模型较好地归纳了评价标准中的潜在评价规则,评价结果客观、可靠。     

Online filtering of CO<Subscript>2</Subscript> signals from a bioreactor gas outflow using a committee of constructive neural networks

This work proposes a committee of Cascade Correlation neural networks as an online smoother for random measurement noise. The goals of this paper are twofold: first it intends to explore the possibilities of using a constructive neural network algorithm to learn how to filter typical noisy data from a bioreactor, CO₂ mol fractions of the effluent gas during the aerobic cultivation of Bacillus megaterium to produce the enzyme penicillin G acylase. Second, to propose a committee of such networks for achieving more realistic results, capturing the inherent trend of the process. In order to do that this paper discusses the advantages of using a constructive neural network algorithm, describes how the committee of NNs operates and evaluates its performance using real CO₂ online data obtained in laboratorial experiments. The paper also presents results obtained with classical filtering algorithms, for comparison.     

Validation of a radioimmunoassay for (+)-abscisic acid in extracts of apple and sweet-pepper tissue using high-pressure liquid chromatography and combined gas chromatography-mass spectrometry

A radioimmunoassay for (+)-abscisic acid (ABA) was developed and applied to the analysis of free ABA in extracts of apple (Malus pumila Mill.) and sweet pepper (Capsicum annuum L.) leaves at various stages during extract purification. Conjugates of ABA, were quantified after alkaline hydrolysis. The validity of the radioimmunoassay was tested by comparison of immunoassay estimates of ABA at different levels of extract purity with high-pressure liquid chromatography (HPLC) and combined gas chromatography-mass spectrometry. The antiserum, raised against (+)-ABA, was almost equally sensitive to (-)-ABA. Serum cross-reactivity with the methyl ester of ABA was 160% and with the glycosyl ester of ABA was 34%. Cross-reactivity with protein-ABA conjugates was very slight for C₄-conjugated keyholelimpet haemocyanin, but about 1000% for C₁-conjugated alkaline phosphatase. Other compounds tested showed extremely low or undetectable cross-reactivities. Further evidence for the specificity of the assay came from the agreement between the results using different assay methods for both apple and pepper extracts, and from the observation that the only zone of immunoreactivity on HPLC elution profiles corresponded with authentic (+)-ABA. The use of polyvinylpyrrolidone in the assay minimised interference by other substances in plant extracts. In pepper, free ABA levels increased rapidly during water stress and recovered to pre-stress levels within two days after rewatering. Levels of ABA conjugates were significantly lowr than free ABA in unstressed plants, and also increased rapidly with stress, although not to the same extent as free ABA, and did not recover as rapidly as did free ABA. In apple, levels of free ABA and of ABA conjugates both increased more than twofold over a two-week period of water stress. In contrast to pepper, however, immunoreactivity of the conjugate fraction was increased by hydrolysis, indicating that different ABA conjugates predominate in the two species.Abbreviations ABA abscisic acid - GC-MS combined gas chromatography-mass spectrometry - HPLC high-pressure liquid chromatography - Me-ABA methyl ester of ABA - PVP polyvinylpyrrolidone - RIA radioimmunoassay     

Nitrifying bacterial communities in an aquaculture wastewater treatment system using fluorescence in situ hybridization (FISH), 16S rRNA gene cloning, and phylogenetic analysis

Aquaculture, especially shrimp farming, has played a major role in the growth of Thailand's economy in recent years, as well as in many South East Asian countries. However, the nutrient discharges from these activities have caused adverse impacts on the quality of the receiving waterways. In particular nitrogenous compounds, which may accumulate in aquaculture ponds, can be toxic to aquatic animals and cause environmental problems such as eutrophication. The mineralization process is well known, but certain aspects of the microbial ecology of nitrifiers, the microorganisms that convert ammonia to nitrate, are poorly understood. A previously reported enrichment of nitrifying bacteria (ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB)) from a shrimp farm inoculated in a sequencing batch reactor (SBR) was studied by molecular methods. The initial identification and partial quantification of the nitrifying bacteria (AOB and NOB) were carried out by fluorescence in situ hybridization (FISH) using previously published 16S rRNA-targeting oligonucleotide probes. The two dominant bacterial groups detected by FISH were from the Cytophaga-Flavobacterium-Bacteroides and Proteobacteria (beta subdivision) phyla. Published FISH probes for Nitrobacter and Nitrospira did not hybridize to any of the bacterial cells. Therefore it is likely that new communities of NOBs, differing from previously reported ones, exist in the enrichments. Molecular genetic techniques (cloning, sequencing, and phylogenetic analysis) targeting the 16S rRNA genes from the nitrifying enrichments were performed to identify putative AOBs and NOBs.     

Assessment of NIR spectroscopy for nondestructive analysis of physical and chemical attributes of sulfamethazine bolus dosage forms

The goal of this study was to assess the utility of near infrared (NIR) spectroscopy for the determination of content uniformity, tablet crushing strength (tablet hardness), and dissolution rate in sulfamethazine veterinary bolus dosage forms. A formulation containing sulfamethazine, corn starch, and magnesium stearate was employed. The formulations were wet granulated with a 10% (wt/vol) starch paste in a high shear granulator and dried at 60°C in a convection tray dryer. The tablets were compressed on a Stokes B2 rotary tablet press running at 30 rpm. Each sample was scanned in reflectance mode in the wavelengths of the NIR region. Principal component analysis (PCA) of the NIR tablet spectra and the neat raw materials indicated that the scores of the first 2 principal components were highly correlated with the chemical and physical attributes. Based on the PCA model, the significant wavelengths for sulfamethazine are 1514, (1660–1694), 2000, 2050, 2150, 2175, 2225, and 2275 nm; for corn starch are 1974, 2100, and 2325 nm; and for magnesium stearate are 2325 and 2375 nm. In addition, the loadings show large negative peaks around the water band regions (≈1420 and 1940 nm), indicating that the partial least squares (PLS) models could be affected by product water content. A simple linear regression model was able to predict content uniformity with a correlation coefficient of 0.986 at 1656 nm; the use of a PLS regression model, with 3 factors, had anr ² of 0.9496 and a sandard error of calibration of 0.0316. The PLS validation set had anr ² of 0.9662 and a standard error of 0.0354. PLS calibration models, based on tablet absorbance data, could successfully predict tablet crushing strength and dissolution in spite of varying active pharmaceutical ingredient (API) levels. Prediction plots based on these PLS models yielded correlation coefficients of 0.84 and 0.92 on independent validation sets for crushing strength and Q₁₂₀ (percentage dissolved in 120 minutes), respectively. Published: September 20, 2005 The opinions expressed in this paper are of the authors' personal views. They do not necessarily reflect the views or policies of the FDA.     

乳酸乳球菌表达系统的发展现状与前景展望

乳酸乳球菌作为全球公认安全的微生物,具有多种益生作用,常被用作基因工程宿主菌.在过去的二十年中,乳酸乳球菌作为载体在递呈病毒、细菌抗原等方面得到了广泛的应用,并且在不同领域发挥着重要作用.本文以乳链菌肽控制的表达(nisin-controlled expression,NICE)系统为例,介绍了基于乳酸乳球菌的表达系统...     

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.     

黔中石漠化区衰老退化与正常生长顶坛花椒根区土壤质量特征

为了探讨顶坛花椒(Zanthoxylum planispimum var. dintanensis)衰老退化的原因,实现林分稳定与可持续性经营,该文以开黄花为典型标志的衰老退化植株和正常生长的植株为研究对象,采用土壤农业化学和环境矿物学的方法,分析不同植株根区土壤矿质元素含量,评价土壤综合质量。结果表明:(1)正常生长植株根区土壤的C、P、K、S、Se、Sr、Mo、氧化物等总体水平显著高于衰老退化植株,总氮、速效磷、速效氮、Cu、Pb、Zn、Cr无显著差异,其他元素的变化规律不明显。元素的有效态含量亦对植株生长产生影响。从植物营养需求角度看,大量、中量、微量元素均对土壤质量产生支配效应。(2)根区土壤质量指数表现为正常生长植株3最高、衰老退化植株1最低,说明土壤质量对顶坛花椒衰老退化具有影响。(3)顶坛花椒林分经营时,应培育良好的土壤结构,注重土壤养分的全面性和均衡性,关注矿质元素过低引起的亏缺效应。     

Quantification of rapid,transient increases in jasmonic acid in wounded plants using a monoclonal antibody

A monoclonal antibody (MAB JAH1-8-B4) for the analysis of 3R, 7R-jasmonic acid and its methyl ester is described. An IgG₁(kappa) immunoglobulin, MAB JAH1-8-B4, was used to set up a competitive enzymelinked immunoassay employing 3R, 7R-jasmonate coupled to alkaline phosphatase as tracer. The assay has a linearity range (logit/log) between 50 fmol and 50 pmol (approx. 10 pg-10 ng) of 3R, 7R-methyljasmonate, the assay standard. A procedure combining prepurification of plant extracts by solid-phase extraction, followed by high-performance liquid chromatography and quantitation has been worked out, which uses 4 g of fresh plant material and has a detection limit between 0.2 and 0.4 g of 3R, 7R-jasmonic acid (determined as its methyl ester) per kg of tissue, depending on the tissue. Internal standards of 3R, 7R-methyljasmonate, added to split samples during extraction as well as a second internal standard, 3R, 7R-methyljasmonate-[O-C³H₃], added to all samples prior to methylation, served to correct for workup losses and for the monitoring of Chromatographie separations. Using this assay, it was found that levels of jasmonic acid rise immediately and transiently in the tissues analyzed as a consequence of wounding. These data provide further and direct evidence for the hypothesis that wound-induction of the plant defense reactions is mediated by endogenous jasmonates.Abbreviations DHJA 9,10-dihydrojasmonic acid - ELISA enzyme-linked immunosorbent assay - GC-MS gas chromatography-mass spectrometry - HCy hemocyanin - HPLC high-performance liquid chromatography - JA jasmonic acid - MAB monoclonal antibody - ME methyl ester - PDA 12-oxo-phytodienoic acid This work was supported by the Deutsche Forschungsgemeinschaft, Bonn, FRG, by the Fonds der Chemischen Industrie, Frankfurt, FRG (literature provision), and by the Ministerium fü Wissenschaft und Forschung des Landes Sachsen-Anhalt, Magdeburg, FRG. We thank Drs. M. H. Zenk and Z.-Q. Xia, Pharmazeutische Biologie, Universität München, FRG, for gifts of reference compounds. We are especially grateful to Dr. M. J. Müller from the same institute for GC-MS analyses.     

ICP-MS法测定广西金樱根及炮制品中22种金属元素

为建立广西金樱根药材中22种金属元素的电感耦合等离子体质谱(ICP-MS)分析方法,该文对不同产地药材及其炮制品中金属元素含量进行比较分析,评价其药材的质量和优选适当的炮制方法.同时,采用ICP-MS法测定22种金属元素含量,绘制其特征金属元素柱状图谱,并采用系统聚类分析和主成分分析对金樱根药材不同产地、不同炮制品的4...     

Modeling of the pyruvate production with Escherichia coli: comparison of mechanistic and neural networks-based models

Three different models: the unstructured mechanistic black-box model, the input–output neural network-based model and the externally recurrent neural network model were used to describe the pyruvate production process from glucose and acetate using the genetically modified Escherichia coli YYC202 ldhA::Kan strain. The experimental data were used from the recently described batch and fed-batch experiments [ Zelić B, Study of the process development for Escherichia coli-based pyruvate production. PhD Thesis, University of Zagreb, Faculty of Chemical Engineering and Technology, Zagreb, Croatia, July 2003. (In English); Zelić et al. Bioproc Biosyst Eng 26:249–258 (2004); Zelić et al. Eng Life Sci 3:299–305 (2003); Zelić et al Biotechnol Bioeng 85:638–646 (2004)]. The neural networks were built out of the experimental data obtained in the fed-batch pyruvate production experiments with the constant glucose feed rate. The model validation was performed using the experimental results obtained from the batch and fed-batch pyruvate production experiments with the constant acetate feed rate. Dynamics of the substrate and product concentration changes was estimated using two neural network-based models for biomass and pyruvate. It was shown that neural networks could be used for the modeling of complex microbial fermentation processes, even in conditions in which mechanistic unstructured models cannot be applied.