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Database Containing (database + containing)
Selected AbstractsHigh bacterial diversity of a waste gas-degrading community in an industrial biofilter as shown by a 16S rDNA clone libraryENVIRONMENTAL MICROBIOLOGY, Issue 11 2002Udo Friedrich Summary The bacterial diversity of an industrial biofilter used for waste gas abatement in an animal-rendering plant was investigated. A 16S rDNA clone library was generated and 444 clones were screened using computer-aided amplified ribosomal DNA restriction analysis (ARDRA). Of the screened clones, 60.8% showed unique ARDRA patterns and the remaining 174 clones were clustered into 65 groups. Almost full-length 16S rDNA sequences of 106 clones were determined and 90.5% of the clones were affiliated with the two phyla Proteobacteria and Bacteroidetes. Alpha -, Beta -, and Gammaproteobacteria accounted for 22.1, 17.6 and 18.6% respectively. Minor portions were affiliated with the Actinobacteria (2.0%), Firmicutes and Verrucomicrobia (both 1.0%), and the Deltaproteobacteria and Thermomicrobia (each 0.5%). Only six out of the 106 16S rDNA sequences exhibited similarities of more than 97% to classified bacterial species indicating that a substantial fraction of the clone sequences were derived from unknown taxa. It was also evaluated whether a database containing 281 computer-simulated bacterial rDNA fragment patterns generated from published reference sequences can be used for identification purposes. The data analysis demonstrated that this was possible only for a small number of clones, which were closely related to described bacterial strains. Rarefaction analysis of ARDRA clusters demonstrated that the 444 clones screened are insufficient to describe the entire diversity of the clone library. [source] A simulation model for design and evaluation of micro-irrigation systemsIRRIGATION AND DRAINAGE, Issue 4 2001C.M.G. Pedras goutteurs; micro-aspersion; modèle AVALOC; analyse des performances Abstract The rational use and conservation of water resources require that irrigation performance, including emission uniformity, be as high as possible. Simulation models can help achieve these objectives. The AVALOC model has been developed for design and performance analysis of microirrigation systems, adopting the sector as the unit for analysis. The model works with the Windows operating system and is explored interactively through a simple dialogue structure consisting of a sequence of user-friendly interfaces. Model computations are supported by a database containing updated information on the emitters and pipes available on the market, and where the information relative to the sectors being designed or evaluated is stored. The databases allow easy introduction, visualization and correction of data through a user-friendly menu. In the design mode, the model provides for the selection of pipes and emitters that permit the attainment of the target performance, including emitter discharge uniformity. In the performance analysis mode, a hydraulics simulation is executed and several system performance parameters are then computed. The simulation can be performed using data created during design or data collected from field system evaluation. The present paper describes the main features of the model and shows a design example applied to an olive orchard. Copyright © 2001 John Wiley & Sons, Ltd. L'utilisation rationnelle des ressources en eau et la conservation des ressources naturelles requiert que les performances des systèmes d'irrigation soient les plus hautes que possible. L'utilisation des modèles de simulation pour le projet et l'évaluation des systèmes de microirrigation peut aider à atteindre un tel objectif. Ainsi, le modèle AVALOC a été développé pour le projet de ces systèmes et aussi pour leur analyse de performance. Le modèle utilise le language Visual Basic pour le système opératif Windows et il est exploré à travers d'un ensemble d'interfaces de dialogue avec l'utilisateur. L'unité d'analyse est le secteur d'irrigation. Les calculs font appel à une base de données contenant de l'information actualisée sur les caractéristiques des émisseurs , gouteurs et microasperseurs , et des conduites disponibles sur le marché et où l'on emmagasine l'information relative aux secteurs en cours de projet ou d'évaluation. Les bases de données sont accedées par une interface de dialogue qui permet l'introduction, visualisation ou correction des données. Le mode de projet permet la selection des emisseurs et tuyaux qui donnent satisfaction aux objectfs de performance fixés à priori. La simulation du fonctionnement hydraulique du secteur en projet permet le calcul de plusieurs indicateurs de performance et, donc, de vérifier si la solution considérée doit être retenue ou modifiée, le modèle étant utilisé de façon interactive. Le mode de simulation hydraulique est aussi utilisé avec des données de terrain pour évaluer la performance des systèmes en operation. Cet article décrit les caractéristiques principales du modèle en même temps qu'on présente un exemple d'application à un système de goutte à goutte pour un olivier. Copyright © 2001 John Wiley & Sons, Ltd. [source] Improving the prediction of liquid back-mixing in trickle-bed reactors using a neural network approachJOURNAL OF CHEMICAL TECHNOLOGY & BIOTECHNOLOGY, Issue 9 2002Simon Piché Abstract Current correlations aimed at estimating the extent of liquid back-mixing, via an axial dispersion coefficient, in trickle-bed reactors continue to draw doubts on their ability to conveniently represent this important macroscopic parameter. A comprehensive database containing 973 liquid axial dispersion coefficient measurements (DAX) for trickle-bed operation reported in 22 publications between 1958 and 2001 was thus used to assess the convenience of the few available correlations. It was shown that none of the literature correlations was efficient at providing satisfactory predictions of the liquid axial dispersion coefficients. In response, artificial neural network modeling is proposed to improve the broadness and accuracy in predicting the DAX, whether the Piston,Dispersion (PD), Piston,Dispersion,Exchange (PDE) or PDE with intra-particle diffusion model is employed to extract the DAX. A combination of six dimensionless groups and a discrimination code input representing the residence-time distribution models are used to predict the Bodenstein number. The inputs are the liquid Reynolds, Galileo and Eötvos numbers, the gas Galileo number, a wall factor and a mixed Reynolds number involving the gas flow rate effect. The correlation yields an absolute average error (AARE) of 22% for the whole database with a standard deviation on the AARE of 24% and remains in accordance with parametric influences reported in the literature. © 2002 Society of Chemical Industry [source] Comparing performances of logistic regression and neural networks for predicting melatonin excretion patterns in the rat exposed to ELF magnetic fieldsBIOELECTROMAGNETICS, Issue 2 2010Samad Jahandideh Abstract Various studies have been reported on the bioeffects of magnetic field exposure; however, no consensus or guideline is available for experimental designs relating to exposure conditions as yet. In this study, logistic regression (LR) and artificial neural networks (ANNs) were used in order to analyze and predict the melatonin excretion patterns in the rat exposed to extremely low frequency magnetic fields (ELF-MF). Subsequently, on a database containing 33 experiments, performances of LR and ANNs were compared through resubstitution and jackknife tests. Predictor variables were more effective parameters and included frequency, polarization, exposure duration, and strength of magnetic fields. Also, five performance measures including accuracy, sensitivity, specificity, Matthew's Correlation Coefficient (MCC) and normalized percentage, better than random (S) were used to evaluate the performance of models. The LR as a conventional model obtained poor prediction performance. Nonetheless, LR distinguished the duration of magnetic fields as a statistically significant parameter. Also, horizontal polarization of magnetic fields with the highest logit coefficient (or parameter estimate) with negative sign was found to be the strongest indicator for experimental designs relating to exposure conditions. This means that each experiment with horizontal polarization of magnetic fields has a higher probability to result in "not changed melatonin level" pattern. On the other hand, ANNs, a more powerful model which has not been introduced in predicting melatonin excretion patterns in the rat exposed to ELF-MF, showed high performance measure values and higher reliability, especially obtaining 0.55 value of MCC through jackknife tests. Obtained results showed that such predictor models are promising and may play a useful role in defining guidelines for experimental designs relating to exposure conditions. In conclusion, analysis of the bioelectromagnetic data could result in finding a relationship between electromagnetic fields and different biological processes. Bioelectromagnetics 31:164,171, 2010. © 2009 Wiley-Liss, Inc. [source] | ||||||||||