Anti-glycated activity prediction of polysaccharides from two guava fruits using artificial neural networks

High-efficiency ultrasonic treatment was used to extract the polysaccharides of Psidium guajava (PPG) and Psidium littorale (PPL). The aims of this study were to compare polysaccharide activities from these two guavas, as well as to investigate the relationship between ultrasonic conditions and anti-glycated activity. A mathematical model of anti-glycated activity was constructed with the artificial neural network (ANN) toolbox of MATLAB software. Response surface plots showed the correlation between ultrasonic conditions and bioactivity. The optimal ultrasonic conditions of PPL for the highest anti-glycated activity were predicted to be 256 W, 60 °C, and 12 min, and the predicted activity was 42.2%. The predicted highest anti-glycated activity of PPG was 27.2% under its optimal predicted ultrasonic condition. The experimental result showed that PPG and PPL possessed anti-glycated and antioxidant activities, and those of PPL were greater. The experimental data also indicated that ANN had good prediction and optimization capability.     

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.     

Trunk lateral cells are neural crest-like cells in the ascidian Ciona intestinalis: insights into the ancestry and evolution of the neural crest

Neural crest-like cells (NCLC) that express the HNK-1 antigen and form body pigment cells were previously identified in diverse ascidian species. Here we investigate the embryonic origin, migratory activity, and neural crest related gene expression patterns of NCLC in the ascidian Ciona intestinalis. HNK-1 expression first appeared at about the time of larval hatching in dorsal cells of the posterior trunk. In swimming tadpoles, HNK-1 positive cells began to migrate, and after metamorphosis they were localized in the oral and atrial siphons, branchial gill slits, endostyle, and gut. Cleavage arrest experiments showed that NCLC are derived from the A7.6 cells, the precursors of trunk lateral cells (TLC), one of the three types of migratory mesenchymal cells in ascidian embryos. In cleavage arrested embryos, HNK-1 positive TLC were present on the lateral margins of the neural plate and later became localized adjacent to the posterior sensory vesicle, a staging zone for their migration after larval hatching. The Ciona orthologues of seven of sixteen genes that function in the vertebrate neural crest gene regulatory network are expressed in the A7.6/TLC lineage. The vertebrate counterparts of these genes function downstream of neural plate border specification in the regulatory network leading to neural crest development. The results suggest that NCLC and neural crest cells may be homologous cell types originating in the common ancestor of tunicates and vertebrates and support the possibility that a putative regulatory network governing NCLC development was co-opted to produce neural crest cells during vertebrate evolution.     

Optimization of actinomycin V production by Streptomyces triostinicus using artificial neural network and genetic algorithm

Artificial neural network (ANN) and genetic algorithm (GA) were applied to optimize the medium components for the production of actinomycinV from a newly isolated strain of Streptomyces triostinicus which is not reported to produce this class of antibiotics. Experiments were conducted using the central composite design (CCD), and the data generated was used to build an artificial neural network model. The concentrations of five medium components (MgSO₄, NaCl, glucose, soybean meal and CaCO₃) served as inputs to the neural network model, and the antibiotic yield served as outputs of the model. Using the genetic algorithm, the input space of the neural network model was optimized to find out the optimum values for maximum antibiotic yield. Maximum antibiotic yield of 452.0 mg l⁻¹ was obtained at the GA-optimized concentrations of medium components (MgSO₄ 3.657; NaCl 1.9012; glucose 8.836; soybean meal 20.1976 and CaCO₃ 13.0842 gl⁻¹). The antibiotic yield obtained by the ANN/GA was 36.7% higher than the yield obtained with the response surface methodology (RSM).     

Biosorption of Acid Black 172 and Congo Red from aqueous solution by nonviable Penicillium YW 01: Kinetic study, equilibrium isotherm and artificial neural network modeling

The main objective of this work was to investigate the biosorption performance of nonviable Penicillium YW 01 biomass for removal of Acid Black 172 metal-complex dye (AB) and Congo Red (CR) in solutions. Maximum biosorption capacities of 225.38 and 411.53 mg g⁻¹ under initial dye concentration of 800 mg L⁻¹, pH 3.0 and 40 °C conditions were observed for AB and CR, respectively. Biosorption data were successfully described with Langmuir isotherm and the pseudo-second-order kinetic model. The Weber-Morris model analysis indicated that intraparticle diffusion was the limiting step for biosorption of AB and CR onto biosorbent. Analysis based on the artificial neural network and genetic algorithms hybrid model indicated that initial dye concentration and temperature appeared to be the most influential parameters for biosorption process of AB and CR onto biosorbent, respectively. Characterization of the biosorbent and possible dye-biosorbent interaction were confirmed by Fourier transform infrared spectroscopy and scanning electron microscopy.     

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.     

Application of artificial neural network model for the development of optimized complex medium for phenol degradation using <Emphasis Type="Italic">Pseudomonas pictorum</Emphasis> (NICM 2074)

Biodegradation of phenol using Pseudomonas pictorum (NICM 2074) a potential biodegradant of phenol was investigated for its degrading potential under different operating conditions. The neural network input parameter set consisted of the same set of four levels of maltose (0.025, 0.05, 0.075 g/l), phosphate (3, 12.5, 22 g/l), pH (7, 8, 9) and temperature (30°C, 32°C, 34°C) on phenol degradation was investigated and a Artificial Neural Network (ANN) model was developed to predict the extent of degradation. The learning, recall and generalization characteristic of neural networks was studied using phenol degradation system data. The efficiency of the model generated by the ANN, was tested and compared with the results obtained from an established second order polynomial multiple regression analysis (MRA). Further, the two models (ANN and MRA) were used to predict the percentage of degradation of phenol for blind test data. Performance of both the models were validated in the cases of training and test data, ANN was recommended based on the following higher coefficient of determination R ²; lower standard error of residuals and lower mean absolute percentage deviation.     

Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts

The biochemical mode-of-action (MOA) for herbicides and other bioactive compounds can be rapidly and simultaneously classified by automated pattern recognition of the metabonome that is embodied in the 1H NMR spectrum of a crude plant extract. The ca. 300 herbicides that are used in agriculture today affect less than 30 different biochemical pathways. In this report, 19 of the most interesting MOAs were automatically classified. Corn (Zea mays) plants were treated with various herbicides such as imazethapyr, glyphosate, sethoxydim, and diuron, which represent various biochemical modes-of-action such as inhibition of specific enzymes (acetohydroxy acid synthase [AHAS], protoporphyrin IX oxidase [PROTOX], 5-enolpyruvylshikimate-3-phosphate synthase [EPSPS], acetyl CoA carboxylase [ACC-ase], etc.), or protein complexes (photosystems I and II), or major biological process such as oxidative phosphorylation, auxin transport, microtubule growth, and mitosis. Crude isolates from the treated plants were subjected to 1H NMR spectroscopy, and the spectra were classified by artificial neural network analysis to discriminate the herbicide modes-of-action. We demonstrate the use and refinement of the method, and present cross-validated assignments for the metabolite NMR profiles of over 400 plant isolates. The MOA screen also recognizes when a new mode-of-action is present, which is considered extremely important for the herbicide discovery process, and can be used to study deviations in the metabolism of compounds from a chemical synthesis program. The combination of NMR metabolite profiling and neural network classification is expected to be similarly relevant to other metabonomic profiling applications, such as in drug discovery.     

Disruption of alpha-mannosidase processing induces non-canonical hybrid-type glycosylation

Golgi alpha-mannosidase II is essential for the efficient formation of complex-type glycosylation. Here, we demonstrate that the disruption of Golgi alpha-mannosidase II activity by swainsonine in human embryonic kidney cells is capable of inducing a novel class of hybrid-type glycosylation containing a partially processed mannose moiety. The discovery of 'Man(6)-based' hybrid-type glycans reveals a broader in vivo specificity of N-acetylglucosaminyltransferase I, further defines the arm-specific tolerance of core alpha1-6 fucosyltransferase to terminal alpha1-2 mannose residues, and suggests that disruption of Golgi alpha-mannosidase II activity is capable of inducing potentially 'non-self' structures.     

Modulation of endoplasmic reticulum chaperone GRP78 by high glucose in hippocampus of streptozotocin-induced diabetic mice and C6 astrocytic cells

Diabetes mellitus is known to increase the risk of neurodegeneration, and both diseases are reported to be linked to dysfunction of endoplasmic reticulum (ER). Astrocytes are important in the defense mechanism of central nervous system (CNS), with great ability of tolerating accumulation of toxic substances and sensitivity in Ca²⁺ homeostasis which are two key functions of ER. Here, we investigated the modulation of the glucose-regulated protein 78 (GRP78) in streptozotocin (STZ)-induced diabetic mice and C6 cells cultured in high glucose condition. Our results showed that more reactive astrocytes were presented in the hippocampus of STZ-induced diabetic mice. Simultaneously, decrease of GRP78 expression was found in the astrocytes of diabetic mice hippocampus.     

Critical amino acid residues of maurocalcine involved in pharmacology, lipid interaction and cell penetration

Maurocalcine (MCa) is a 33-amino acid residue peptide that was initially identified in the Tunisian scorpion Scorpio maurus palmatus. This peptide triggers interest for three main reasons. First, it helps unravelling the mechanistic basis of Ca²⁺ mobilization from the sarcoplasmic reticulum because of its sequence homology with a calcium channel domain involved in excitation-contraction coupling. Second, it shows potent pharmacological properties because of its ability to activate the ryanodine receptor. Finally, it is of technological value because of its ability to carry cell-impermeable compounds across the plasma membrane. Herein, we characterized the molecular determinants that underlie the pharmacological and cell-penetrating properties of maurocalcine. We identify several key amino acid residues of the peptide that will help the design of cell-penetrating analogues devoid of pharmacological activity and cell toxicity. Close examination of the determinants underlying cell penetration of maurocalcine reveals that basic amino acid residues are required for an interaction with negatively charged lipids of the plasma membrane. Maurocalcine analogues that penetrate better have also stronger interaction with negatively charged lipids. Conversely, less effective analogues present a diminished ability to interact with these lipids. These findings will also help the design of still more potent cell penetrating analogues of maurocalcine.