Fuzzy knowledge- and data-based models of damage to reeds by grazing of Greylag Geese

This paper describes a fuzzy and neuro-fuzzy approach to modelling feeding intensity of Greylag Geese on reed. As a consequence of the presence of some non-measurable or random factors and the heterogeneity of reed and goose behaviour, the relationships between the model variables are often not well known and the data collected have a high degree of uncertainty. A fuzzy approach was selected which can be applied with vague knowledge and data of high uncertainty. Fuzzy logic can be used to handle inexact reasoning in knowledge-based models with fuzzy rules and fuzzy sets to handle uncertainty in data. The neural network technique was applied to develop the fuzzy data-based models. For training, several dataset combinations of three lakes in North Germany were used. The generalisation capability of these models was checked for other lakes. The performance of these models was compared with the results of the fuzzy knowledge-based model developed in the next step. The knowledge base of this model contains the Mamdani-type rules formulated by a domain expert. All models were implemented using the Fuzzy Logic Toolbox of MATLAB^®.     

Mathematical modeling of phenol degradation system using fuzzy comprehensive evaluation

New mathematical model for phenol degradation is developed that uses fuzzy comprehensive evaluation. Biodegradation of phenol by Pseudomonas putida (NICM 2174), a potential biodegradent of phenol has been investigated for its degrading potential under different conditions. In the present work, results of batch study on P. putida (NICM 2174) and its degradation activity on phenolic compounds such as phenol, o-cresol, p-cresol, p-nitrophenol and resorcinol each of concentration 0.300 g/l are considered. In the present study, the effect of glucose, yeast extract, ammonium sulphate and NaCl each at 0.5, 1.5, 2.0, 3.0 and 4 g/l on degradation of aforementioned phenolic compounds have been investigated and a fuzzy control model has been developed to predict the extent of degradation. Main aspect of this study is to establish a fuzzy relation matrix R for objective evaluation of phenol degradation. A series of membership functions for the degradation are being evaluated after investigating the growth properties of bacteria at various levels of carbon and nitrogen sources. Important element is the factor vector A, which is deduced from a survey of panel of judges (n=25). A in conjunction with R generates a multifactorial equation which can be used to calculate the extent of phenol degradation. Biomass growth contributed significantly to phenol degradation rates especially when the degradation medium was supplemented with a utilizable carbon and nitrogen sources.     

Comparison of prediction performances between models obtained by the group method of data handling and neural networks for the alcoholic fermentation rate in enology

The modelling of winemaking processes, to predict as far ahead as possible the fermentation performance, is necessary for enhanced supervision and to enable appropriate corrective action to be taken to remedy incorrect fermentation before it is too late. In this paper, we briefly present two heuristic modelling methods—the Group Method of Data Handling (GMDH) and Neural Networks (NN)—which can be used to obtain unstructured models. The identification and prediction performances of the models obtained with these two methods are compared with respect to the alcoholic fermentation rate (dCO₂/dt) at five prediction horizons and for four fermentations. It is shown that predictive models obtained with neural network methodology are more accurate than those obtained with GMDH. On the other hand, GMDH models are more versatile when used for the prediction of the fermentation rate of a different fermentation than the one used in the learning process.     

Classification of antituberculosis herbs for remedial purposes by using fuzzy sets

Using fuzzy set theory, we created a system, that assesses a herb's usefulness for the treatment of tuberculosis, based on ethnobotanical data. We analysed two systems which contain different amount of inputs. The first system contains four inputs, the second one contains six inputs. We used the Takagi-Sugeno-Kanga model. Mamdani model is poor at representation as it needs more fuzzy rules than that of TSK to model a real world system where accuracy is demanded. It has been employed a fuzzy controller, and a fuzzy model, in successfully solving difficult control and modelling problems in practice. It is implemented in the Fuzzy Logic Toolbox in Matlab. The data for inputs are gathered in the database named SOPAT (selection of plants against tuberculosis), which is part of a project coordinated by the Oxford International Biomedical Centre. In this database there could be up to one million plant species. It would be cumbersome to select a remedy from one (or some) of these species looking at the data base one-by-one. By means of the fuzzy set theory this remedy can be chosen very quickly.     

Estimation of specific growth rate from cell density measurements

For modelling purposes it is of great importance to derive the specific growth rate as a function of time from biomass measurements. Traditional methods such as exponential or polynomial fitting do not give satisfactory results nor do these methods take the noise characteristics of the biomass measurements into account. Standard recursive techniques, such as Kalman filtering, use only the data up to the time under consideration and are dependent of a good initial estimation. This paper describes a technique based on combining subsequent backward and forward extended Kalman filtering to give a smoothing estimator for the specific growth rate. The estimator does not need an initial value and is shown to have a single tuning parameter. The applicability of the estimator is demonstrated on batch and fed-batch cultivations of two organisms: Bordetella pertussis and Neisseria meningitidis.     

Systems for the removal of a selection marker and their combination with a positive marker

Many systems have been developed for the removal of a selection marker in order to generate marker-free transgenic plants. These systems consist of (1) a site-specific recombination system (Cre/lox) or a phage-attachment region (attP) to remove the selectable marker gene and (2) a transposable element system (Ac) or a co-transformation system to segregate the gene of interest from the selectable marker gene. Overall, the process is more time-consuming than conventional transformation methods because two rounds of transformation - two steps of regeneration or sexual crossings - are required to obtain the desired transgenic plants. Recently, removal systems combined with a positive marker, denoted as MAT vectors, have been developed to save time and effort by generating marker-free transgenic plants through a single-step transformation. We summarize here the transformation procedures using these systems and discuss their feasibility for practical use.     

Modelling of continuous microbial cultivation taking into account the memory effects

The problem of chemostat dynamics modelling for the purpose of control is considered. The "memory" of the culture is explicitly taken into account. Two possibilities for improving the quality of the proposed modelling approaches are discussed. A general model that accounts for the culture `memory' by means of different `memory' functions in the expressions of the specific growth rate and of the specific consumption rate and a polynomial function of the substrate concentration for the yield factor is proposed. The case where the maintenance energy is taken into account is also discussed. Two modifications of the general model (w-type and S-type) are presented. A zero-order `memory' function and a i-function with delay are applied in order to describe the `memory' effects. Continuous growth of the strain Saccharomyces cerevisiae on a glucose limited medium is considered as a case study. Detailed investigations of the variety of models, derived from the general model by applying different `memory' functions and different assumptions are carried out. The results are compared with those previously reported for the same process. It is shown that a significant improvement in predicting the substrate dynamics (not accompanied by any decrease in the quality of the model with respect to the biomass concentration) could be achieved, involving a first- or second-order polynomial function for the yield factor. It is also shown that the quality of the model mainly depends on the way that `memory' function is incorporated. The detailed investigations give priority to the w-type models. In this case past values of both biomass and substrate variables are considered. The time delay models with pure (constant) delay and those which account for the culture `memory' by zero-order `memory' function (adaptability parameter) are compared with respect to their utilization for the purpose of model-based control.     

Calculating Partition Coefficients of Peptides by the Addition Method

A new addition method is described in this study for calculating the partition coefficients of peptides. LogP and logD values of peptides are calculated by summing the contributions of the component amino acids. The final models are derived from a multivariate linear regression analysis of 219 peptides with known experimental data. The standard errors in a leave-one-out cross-validation are 0.23 and 0.24 log units for the logP and logD values, respectively. The predictive ability of the model is tested by an extra set of ten peptides, and the self-consistency of the model is further demonstrated by a new validation procedure called the evolution test. The parameters obtained in regression could be used as hydrophobicity scales for amino acids. The application of such hydrophobicity scales has also been discussed.     

Design and implementation of a qualitative simulation model of lambda phage infection

MOTIVATION: Molecular biology databases hold a large number of empirical facts about many different aspects of biological entities. That data is static in the sense that one cannot ask a database 'What effect has protein A on gene B?' or 'Do gene A and gene B interact, and if so, how?'. Those questions require an explicit model of the target organism. Traditionally, biochemical systems are modelled using kinetics and differential equations in a quantitative simulator. For many biological processes however, detailed quantitative information is not available, only qualitative or fuzzy statements about the nature of interactions. RESULTS: We designed and implemented a qualitative simulation model of lambda phage growth control in Escherichia coli based on the existing simulation environment QSim. Qualitative reasoning can serve as the basis for automatic transformation of contents of genomic databases into interactive modelling systems that can reason about the relations and interactions of biological entities.      

Modelling habitat selection of the cryptic Hazel Grouse Bonasa bonasia in a montane forest

The Hazel Grouse Bonasa bonasia is strongly affected by forest dynamics, and populations in many areas within Europe are declining. As a result of the ‘wilding’ concept implemented in the National Park Bavarian Forest, this area is one of the refuges for the species in Germany. Even though the effects of prevailing processes make the situation there particularly interesting, no recent investigation about habitat selection in the rapidly changing environment of the national park has been undertaken. We modelled the species–habitat relationship to derive the important habitat features in the national park as well as factors and critical threshold for monitoring, and to evaluate the predictive power of models based on field surveys compared to an analysis of infrared aerial photographs. We conducted our surveys on 49 plots of 25 ha each where Hazel Grouse was recorded and on an equally sized set of plots with no grouse occurrence, and used this dataset to build a predictive habitat-suitability model using logistic regression with backward stepwise variable selection. Habitat heterogeneity, stand structure, presence of mountain ash and willow, root plates, forest aisles, and young broadleaf stands proved to be predictive habitat variables. After internal validation via bootstrapping, our model shows an AUC value of 0.91 and a correct classification rate of 87%. Considering the methodological difficulties attached to backward selection, we applied Bayesian model averaging as an alternative. This multi-model approach also yielded similar results. To derive simple thresholds for important predictors as a basis for management decisions, we alternatively ran tree-based modelling, which also leads to a very similar selection of predictors. Performance of our different survey approaches was assessed by comparing two independent models with a model including both data resources: one constructed only from field survey data, the other based on data derived from aerial photographs. Models based on field data seem to perform slightly better than those based on aerial photography, but models using both predictor datasets provided the highest predictive accuracy.     

Phenol utilization by filamentous yeast Trichosporon cutaneum

The investigated strain Trichosporon cutaneum shows well expressed capability for metabolizing high concentrations of phenol, up to 1 g/l, utilizing it as the sole carbon source for the growth and development of the population. The data reported, prove the good perspectives for its application in protecting the environment from phenol pollution. No data about modelling the process of cultivation of Trichosporon cutaneum in phenol media is available in scientific literature up to now. The mathematical model, reported here, consists of two nonlinear differential equations, describing cell growth and substrate consumption. The unknown parameters are estimated following the method of Hooke and Jeeves. A number of simulation investigations are carried out. They prove the adequacy of the model and its applicability in further studies on the processes of growth and phenol uptake of Trichosporon cutaneum.     

Incorporating habitat use in models of fauna fatalities on roads

Aim  To highlight the benefit of using habitat use to improve the accuracy of predictive road fatality models.
Location  The Snowy Mountains Highway in southern New South Wales, Australia.
Methods  A binary logistic regression model was constructed using wombat fatality presences and randomly generated absences. Species-specific habitat variables were included as predictors in the model selection process as well as two spatially explicit measures of wombat habitat use. Generalized additive models (GAMs) were constructed for each possible combination of predictors in R. The final model was selected by comparing all models subsets for the eight predictors and employing the one standard error rule to select the best model set.
Results  The final predictive model had high discriminatory power and incorporated both measures of species habitat use, greatly exceeding the variation explained by a previously published model for the same species and road.
Main Conclusions  Our findings highlight the importance of incorporating variables that describe habitat use by fauna for predictive modelling of animal-vehicle crashes. Reliance upon models that ignore landscape patterns are limited in their capacity to identify hotspots and inform managers of locations to engage in mitigation.     

The application of SSADM to modelling the logical structure of proteins

A logical design that describes the overall structure of proteins,together with a more detailed design describing secondary andsome supersecondary structures, has been constructed using thecomputer-aided software engineering (CASE) tool, Auto-mate.Auto-mate embodies the philosophy of the Structured SystemsAnalysis and Design Method (SSADM) which enables the logicaldesign of computer systems. Our design will facilitate the buildingof large information systems, such as databases and knowledgebasesin the field of protein structure, by the derivation of systemrequirements from our logical model prior to producing the finalphysical system. In addition, the study has highlighted theease of employing SSADM as a formalism in which to conduct thetransferral of concepts from an expert into a design for a knowledge-basedsystem that can be implemented on a computer (the knowledge-engineeringexercise). It has been demonstrated how SSADM techniques maybe extended for the purpose of modelling the constituted Prologrules. This facilitates the integration of the logical systemdesign model with the derived knowledge-based system. Received on March 20, 1991; accepted on April 2, 1991     

Evaluation of consensus methods in predictive species distribution modelling

Aim  Spatial modelling techniques are increasingly used in species distribution modelling. However, the implemented techniques differ in their modelling performance, and some consensus methods are needed to reduce the uncertainty of predictions. In this study, we tested the predictive accuracies of five consensus methods, namely Weighted Average (WA), Mean(All), Median(All), Median(PCA), and Best, for 28 threatened plant species.
Location  North-eastern Finland, Europe.
Methods  The spatial distributions of the plant species were forecasted using eight state-of-the-art single-modelling techniques providing an ensemble of predictions. The probability values of occurrence were then combined using five consensus algorithms. The predictive accuracies of the single-model and consensus methods were assessed by computing the area under the curve (AUC) of the receiver-operating characteristic plot.
Results  The mean AUC values varied between 0.697 (classification tree analysis) and 0.813 (random forest) for the single-models, and from 0.757 to 0.850 for the consensus methods. WA and Mean(All) consensus methods provided significantly more robust predictions than all the single-models and the other consensus methods.
Main conclusions  Consensus methods based on average function algorithms may increase significantly the accuracy of species distribution forecasts, and thus they show considerable promise for different conservation biological and biogeographical applications.     

Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments

Systems biology applies quantitative, mechanistic modelling to study genetic networks, signal transduction pathways and metabolic networks. Mathematical models of biochemical networks can look very different. An important reason is that the purpose and application of a model are essential for the selection of the best mathematical framework. Fundamental aspects of selecting an appropriate modelling framework and a strategy for model building are discussed. Concepts and methods from system and control theory provide a sound basis for the further development of improved and dedicated computational tools for systems biology. Identification of the network components and rate constants that are most critical to the output behaviour of the system is one of the major problems raised in systems biology. Current approaches and methods of parameter sensitivity analysis and parameter estimation are reviewed. It is shown how these methods can be applied in the design of model-based experiments which iteratively yield models that are decreasingly wrong and increasingly gain predictive power.     

A working guide to boosted regression trees

1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.     

CORAL: Building up QSAR models for the chromosome aberration test

A high level of chromosomal aberrations in peripheral blood lymphocytes may be an early marker of cancer risk, but data on risk of specific cancers and types of chromosomal aberrations are limited. Consequently, the development of predictive models for chromosomal aberrations test is important task. Majority of models for chromosomal aberrations test are so-called knowledge-based rules system. The CORAL software (http://www.insilico.eu/coral, abbreviation of “CORrelation And Logic”) is an alternative for knowledge-based rules system. In contrast to knowledge-based rules system, the CORAL software gives possibility to estimate the influence upon the predictive potential of a model of different molecular alerts as well as different splits into the training set and validation set. This possibility is not available for the approaches based on the knowledge-based rules system. Quantitative Structure–Activity Relationships (QSAR) for chromosome aberration test are established for five random splits into the training, calibration, and validation sets. The QSAR approach is based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) without data on physicochemical and/or biochemical parameters. In spite of this limitation, the statistical quality of these models is quite good.     

Susceptibility to oxidative stress in Adélie and emperor penguin

The antioxidant defences in aerobic organisms represent the detoxification pathway against toxicity of reactive oxygen species (ROS). These highly reactive molecules are normally produced during the 4-electrons reduction of molecular oxygen to water coupled with oxidative phosphorylation, and during the activity of several enzymatic systems which produce ROS as intermediates. However, the endogenous generation of oxyradicals may be influenced by different environmental and biological factors, and the basal efficiency of antioxidant systems generally reflects the normal prooxidant pressure to which organisms are exposed. If the antioxidant capacity is exceeded (i.e. as a consequence of enhanced intracellular formation of ROS), a pathological condition, generally termed oxidative stress, may arise. In this preliminary work, susceptibility to oxidative stress has been compared in plasma of Adélie penguin (Pygoscelis adeliae), emperor penguin (Aptenodytes forsteri), south polar skua (Catharacta maccormicki) and snow petrel (Pagodroma nivea). Within the framework of the Italian Research Program in Antarctica, blood samples were collected during the austral summer 1998-1999 and the Total Oxyradical Scavenging Capacity (TOSC) analysed. The TOSC assay, measuring the capability of biological samples to neutralise different oxyradicals, has been recently standardised to provide a quantifiable value of biological resistance to toxicity of ROS. Penguins exhibited higher scavenging capacity towards peroxyl radicals than south polar skua and snow petrel. The greater resistance to toxicity of oxyradicals might suggest that penguins are naturally exposed to a higher basal prooxidant pressure in comparison to other analysed Antarctic birds.