Spectrophotometric resolution of ternary mixtures of tryptophan, tyrosine, and histidine with the aid of principal component-artificial neural network models

A simple and sensitive spectrophotometric method to resolve ternary mixtures of tryptophan (Trp), tyrosine (Tyr), and histidine (His) in synthetic and water samples is described. It relies on the different kinetic rates of the analytes in their oxidative reaction with potassium ferricyanide (K(3)Fe(CN)(6)) in alkaline medium. The absorbance data were monitored on the analytical wavelength (420 nm) of K(3)Fe(CN)(6) spectrum. Synthetic mixtures of the three amino acids were analyzed, and the data obtained were processed by principal component-artificial neural network (PC-ANN) models. After reducing the number of spectral data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Tangent and sigmoidal transfer function were used in the hidden and output layers, respectively. The analytical performance of this method was characterized by relative standard error. The method allowed the determination of Trp, Tyr, and His at concentrations between 10 and 55, 10 and 60, and 10 and 40 microg ml(-1), respectively. The results show that the PC-ANN is an efficient method for prediction of the three analytes.     

A simultaneous procedure facilitates acquisition under an optimal interstimulus interval in artificial neural networks and rats

In a computer simulation, a neural network first received a simultaneous procedure, where the interstimulus interval (ISI) was 0 time-steps (ts). Output activations were near zero under this procedure. The network then received a forward-delay procedure where the ISI was 8 ts. Output activations increased to the near-maximum level faster than those of a control network that first received an explicitly unpaired procedure. Comparable results were obtained with rats that first received trials where a retractable lever was presented for 3s concurrently with access to water. Low-lever pressing was observed under this procedure. The rats then received trials where the lever was followed 15s after by water. Lever pressing appeared faster than a control group that received the 15-s ISI after an explicitly unpaired procedure. The model used in the simulation explains these results as connection-weight increments that promote little output activations in a simultaneous procedure, but facilitate acquisition in an optimal ISI.     

A reliable method for spectrophotometric determination of glycine betaine in cell suspension and other systems

Glycine betaine is a quaternary ammonium compound that accumulates in a large variety of species in response to different types of stress. Glycine betaine counteracts adverse effects caused by abiotic factors, preventing the denaturation and inactivation of proteins. Thus, its determination is important, particularly for scientists focused on relating structural, biochemical, physiological, and/or molecular responses to plant water status. In the current work, we optimized the periodide technique for the determination of glycine betaine levels. This modification permitted large numbers of samples taken from a chlorophyllic cell line of the grass Bouteloua gracilis to be analyzed. Growth kinetics were assessed using the chlorophyllic suspension to determine glycine betaine levels in control (no stress) cells and cells osmotically stressed with 14 or 21% polyethylene glycol 8000. After glycine extraction, different wavelengths and reading times were evaluated in a spectrophotometer to determine the optimal quantification conditions for this osmolyte. Optimal results were obtained when readings were taken at a wavelength of 290 nm at 48 h after dissolving glycine betaine crystals in dichloroethane. We expect this modification to provide a simple, rapid, reliable, and cheap method for glycine betaine determination in plant samples and cell suspension cultures.     

Some structural determinants of Pavlovian conditioning in artificial neural networks

This paper investigates the possible role of neuroanatomical features in Pavlovian conditioning, via computer simulations with layered, feedforward artificial neural networks. The networks’ structure and functioning are described by a strongly bottom-up model that takes into account the roles of hippocampal and dopaminergic systems in conditioning. Neuroanatomical features were simulated as generic structural or architectural features of neural networks. We focused on the number of units per hidden layer and connectivity. The effect of the number of units per hidden layer was investigated through simulations of resistance to extinction in fully connected networks. Large networks were more resistant to extinction than small networks, a stochastic effect of the asynchronous random procedure used in the simulator to update activations and weights. These networks did not simulate second-order conditioning because weight competition prevented conditioning to a stimulus after conditioning to another. Partially connected networks simulated second-order conditioning and devaluation of the second-order stimulus after extinction of a similar first-order stimulus. Similar stimuli were simulated as nonorthogonal input-vectors.     

The application of artificial neural networks to the problem of reservoir classification and land use determination on the basis of water sediment composition

Presented work reports on the use of artificial neural networks to recognize and classify water reservoir types (lakes, rivers) and the nature of their surroundings (forests, fields, meadows) based on the chemical composition of sediments. The quantitative content of a selection of elements (Ag, As, Ba Ca, Cd, Co, Cr, Cu, Fe, Hg, Mg, Mn, Ni, P, Pb, S, Sr, TOC – Total Organic Cabon, V and Zn) in the sediments of lakes and rivers in the Lublin Province (Poland) were taken and used as working data file. Statistical analysis suggested that both reservoir types and area usage differ in terms of the quantity of studied determinants (elements) and thus might be distinguished on their basis. Artificial neural networks were then examined with respect to their ability to recognize and classify the data. Multilayer perceptron was used as the statistical model. Constructed models were able to give correct answers in 74% of cases when classifying reservoir’s area usage and 100% for the type of body of water.     

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.     

Investigating the relationship between proteomic,compositional, and histologic biomarkers and cartilage biomechanics using artificial neural networks

A thorough understanding of the relationship between the biological and mechanical functions of articular cartilage is necessary to develop diagnostics and treatments for arthritic diseases. A key step in developing this understanding is the establishment of models which utilize large numbers of biomarkers to create comprehensive models of the interplay between cartilage biology and biomechanics, which will more accurately demonstrate the complex etiology and progression of tissue adaptation and degradation. It is the goal of this study to demonstrate the ability of artificial neural networks (ANNs) to utilize biomarkers to create predictive models of articular cartilage biomechanics, which will provide a basis for more sophisticated research in the future. Osteochondral plugs were collected from patients undergoing total knee arthroplasty, cultured, then analyzed to collect proteomic, compositional, and histologic biomarker data. Samples were subjected to stress relaxation testing as well as computational simulations using finite element analysis (FEA) modeling and optimization to determine key mechanical properties. The acquired data was fed into an ANN to generate a model which predicts the biomechanical properties of cartilage from given biomarkers. Using all significant inputs, the developed neural network predicted the ground substance modulus with a moderate degree of accuracy, but had difficulty predicting the collagen fiber modulus and cartilage permeability. Using only clinically attainable biomarkers, the best-performing model produced comparably accurate and more consistent predictions of all three mechanical properties. These models demonstrate the potential for ANNs to be included in clinical studies of articular cartilage.     

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.     

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.     

Prediction of amino acid profiles in feed ingredients:: Genetic algorithm calibration of artificial neural networks

Linear regression (LR) has been used to predict the amino acid (AA) profiles of feed ingredients, given proximate analysis (PA) input. Artificial neural networks (ANN) have also been trained to predict AA levels, generally with better results. Past projects have indicated that ANN more effectively identified the complex relationship between nutrients and feed ingredients than did LR. It was shown that the maximum R² value, a measurement of the amount of variability explained by the model, was highest when a general regression neural network (GRNN) with iterative calibration (GRNNIT) was used to train the ANN. This was in comparison to LR, Ward backpropagation (WBP) or 3-layer backpropagation (3BP) architectures. The current study investigated the potential of a new, advanced method of calibration using the genetic algorithm (GA) to optimize GRNN smoothing values. Calibration of an ANN allows the neural network to generalize well and therefore provide good results on new data. A GRNN architecture (NeuroShell 2^® Software) with GA calibration (GRNNGA) was used to train an ANN to predict AA levels in maize, soya bean meal (SBM), meat and bone meal, fish meal and wheat, based on proximate analysis input. Within the GRNNGA architecture, ANN were trained with either an Euclidean or City Block distance metric and a (0,1), (−1,1), (logistic) or (tanh) input scale. Predictive performance was judged on the basis of the maximum R² value. In general, maximum R² values were higher when the GA calibration was used in comparison to LR. For example, the highest methionine (MET) R² value for SBM was 0.54 (LR), 0.81 (3BP), 0.87 (WBP), 0.92 (GRNNIT) and 0.98 (GRNNGA). Genetic algorithm calibration of GRNN architecture led to further improvements in ANN performance for AA level predictions in most of the cases studied. Exceptions were the TSAA level in SBM (0.94 with GRNNIT vs. 0.90 with GRNNGA) and the TRY level in maize (0.88 with GRNNIT vs. 0.61 with GRNNGA).     

Simultaneous determination of glucose, fructose and lactose in food samples using a continuous-flow chemiluminescence method with the aid of artificial neural networks.

In this study, a simple continuous-flow chemiluminescence (CL) system was developed for simultaneous determination of glucose, fructose and lactose in ternary mixtures of reducing sugars without previous separation. This method was based on the different kinetics of the individual sugars in the oxidation reaction with potassium ferricyanide. The known luminol-K(3)Fe(CN)(6) CL system was used to measure the kinetic data of the system. The CL intensity was measured and recorded every second from 1 to 300 s. The data obtained were processed chemometrically using an artificial neural network. The relative standard errors of prediction for three analytes were <5%. The proposed method was successfully applied to the simultaneous determination of the three sugars in some food samples.     

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.     

Using artificial neural networks to determine the relative contribution of abiotic factors influencing the establishment of insect pest species

The use of multi-layer perceptrons (MLP) to determine the relative significance of climatic variables to the establishment of insect pest species is described. Results show that the MLP are able to learn to accurately predict the establishment of a pest species within a specific geographic region. Analysis of the MLP yielded insights into the contribution of the individual input variables and allowed for the identification of those variables that were most significant in either encouraging or inhibiting establishment.     

Chemometric-assisted kinetic-spectrophotometric method for simultaneous determination of ascorbic acid, uric acid, and dopamine

A chemometric-assisted kinetic spectrophotometric method has been developed for simultaneous determination of ascorbic acid (AA), uric acid (UA), and dopamine (DA). This method relies on the difference in the kinetic rates of the reactions of analytes with a common oxidizing agent, tris(1,10-phenanthroline) and iron(III) complex (ferritin, [Fe(phen)₃]³⁺) at pH 4.4. The changes in absorbance were monitored spectrophotometrically. The data obtained from the experiments were processed by chemometric methods of artificial neural network (ANN) and partial least squares (PLS). Acceptable techniques of prediction set, randomization t test, cross-validation, and Y randomization were applied for the selection of the best chemometric method. The results showed that feedforward artificial neural network (FFANN) is more efficient than the other chemometric methods. The parameters affecting the experimental conditions were optimized, and it was found that under optimal conditions Beer’s law is followed in the concentration ranges of 4.3–74.1, 4.3–78.3, and 2.0–33.0 μM for AA, UA, and DA, respectively. The proposed method was successfully applied to the determination of analytes in serum and urine samples.     

Automata networks as preprocessing technique of artificial neural network in estimating primary production and dominating phytoplankton levels in a reservoir: An experimental work

Artificial Neural Networks (ANN) is computational architectures that can be used for estimating primary production levels and dominating phytoplankton species in reservoirs. Automata Networks (AN) were applied as a pre-processing method with subsequent ANN model development for Demirdöven Dam Reservoir. The primary purpose of using pre-processing technique was to distinguish the suitable and appropriate constituents of the input parameters' matrix, to eliminate redundancy, to enhance prediction power and calculation efficiency. The data were collected monthly over two years. The applications have yielded following results: The correlation coefficients (r values) between predicted and observed counts were as high as 0.83, 0.87, 0.83 and 0.88 for Cyclotella ocellata, Sphaerocystis schroeteri, Staurastrum longiradiatum counts, and Chlorophyll-a (Chl-a) concentrations respectively with AN. The performance of AN based pre-processing technique was compared with the performance of a well-known pre-processing technique, namely Principle Component Analysis(PCA), experimentally. r values between the predicted and observed C. ocellata, S. schroeteri and S. longiradiatum counts, and (Chl-a) were as high as 0.80, 0.86, 0.81 and 0.86 respectively with PCA.