Pulp and paper from vine shoots: neural fuzzy modeling of ethylene glycol pulping

The influence of operational variables in the pulping of vine shoots by use of ethylene glycol [viz. temperature (155-185 degrees C), cooking time (30-90 min) and ethylene glycol concentration (50-70% v/v)] on the properties of the resulting pulp (viz. yield, kappa number and viscosity) and paper sheets (breaking length, stretch, burst index, tear index and brightness) was studied. A central composite factorial design was used in conjunction with the software ANFIS Edit Matlab 6.5 to develop fuzzy neural model that reproduced the experimental results of the dependent variables with errors less than 5%. The model is therefore effective with a view to simulating the ethylene glycol pulping process.     

Wavelet neural networks applied to pulping of oil palm fronds

In the organosolv pulping of the oil palm fronds, the influence of the operational variables of the pulping reactor (viz. cooking temperature and time, ethanol and NaOH concentration) on the properties of the resulting pulp (yield and kappa number) and paper sheets (tensile index and tear index) was investigated using a wavelet neural network model. The experimental results with error less than 0.0965 (in terms of MSE) were produced, and were then compared with those obtained from the response surface methodology. Performance assessment indicated that the neural network model possessed superior predictive ability than the polynomial model, since a very close agreement between the experimental and the predicted values was obtained.     

Neural fuzzy model applied to ethylene-glycol pulping of non-wood raw materials

We studied the influence of the operational variables (viz. ethylene-glycol concentrations of 50-70%, temperatures of 155-185 degrees C, times of 30-90 min and numbers of PFI beating revolutions of 500-1500) on pulp yield and various paper properties (breaking length, stretch, burst index, tear index and brightness) obtained in the ethylene-glycol pulping of vine shoots, cotton stalks, leucaena (Leucaena leucocephala) and tagasaste (Chamaecytisus proliferus). The fuzzy neural network models used reproduced the experimental results with errors less than 15% and smaller than those provided by second-order polynomial models in all cases. An ethylene-glycol concentration of 65% at 180 degrees C for 75 min and 1500 PFI beating revolutions were found to provide substantial savings in energy, chemicals and facility investments as a result of operating under milder conditions than the strongest ones studied in this work. Tagasaste was found to be the most suitable raw material among those tested as it provided the paper sheets with the highest breaking length (4644 m), stretch (2.87%), burst index (2.46 kN/g), tear index (0.33 m Nm(2)/g) and brightness (40.92%); its pulp yield was also high (62.88%), which reflects efficient use of this raw material.     

Three-state combinatorial switch models as applied to the binding of oxygen by human hemoglobin

We have generated a series of all 6561 unique, discrete three-state combinatorial switch models to describe the partitioning of the cooperative oxygen-binding free change among the 10 variously ligated forms of human hemoglobin tetramers. These models were inspired by the experimental observation of Smith and Ackers that the cooperative free energy of the intersubunit contact regions of the 10 possible ligated forms of human hemoglobin tetramers can be represented by a particular distribution of three distinct energy levels [Smith, F. R., & Ackers, G. K. (1985) Proc. Natl. Acad. Sci. U.S.A. 82, 5347-5351]. A statistical thermodynamic formulation accounting for both dimer-tetramer equilibria and ligand binding properties of hemoglobin solutions as a function of oxygen and protein concentrations was utilized to exhaustively test these thermodynamic models. In this series of models each of the 10 ligated forms of the hemoglobin tetramer can exist in one, and only one, of three possible energy levels; i.e., each ligated form was assumed to be associated with a discrete energy state. This series of models includes all possible ways that the 10 ligation states of hemoglobin can be distributed into three distinct cooperative energy levels. The mathematical models, as presented here, do not permit equilibria between energy states to exist for any of the 10 unique ligated forms of hemoglobin tetramers. These models were analyzed by nonlinear least-squares estimation of the free energy parameters characteristic of this statistical thermodynamic development.(ABSTRACT TRUNCATED AT 250 WORDS)     

Comparisons among biomass allocation and spatial distribution patterns of some vine,pteridophyte, and gymnosperm shoots

The interspecific allometry of leaf, stem, and reproductive biomass distal to stem diameter was determined for a total of 12 angiosperm vine, gymnosperm, and pteridophyte species to compare allocation patterns to vegetative and reproductive shoot organs. The allometry of stem diameter in terms of the distance from shoot apices also was determined to quantify the manner in which vines, gymnosperms, and pteridophyte stems tapered along their length. The stems of vine species were found to weigh more than those of arborescent gymnosperm species distal to any point of equivalent stem diameter. Vine species also distribute more of their stem mass to shoot length as opposed to girth than gymnosperm species. Vine stems also supported proportionally larger leaf and reproductive biomass in comparison to gymnosperm stems of equivalent diameter, yet partitioned their total shoot biomass more or less equally between leaf and stem biomass in the same manner as the gymnosperm species examined. The allometry of vine as well as gymnosperm leaf biomass with respect to stem biomass appeared to be slightly anisometric and negative, suggesting that more massive stems had proportionally less leaf biomass than their smaller, less massive counterparts. Vine stems could be approximated as very slender cones; the shape and geometry of gymnosperm stems complied with those of stubby, truncated cones whose top diameter (for those examined), on the average, equaled 28% of the basal diameter. In general terms, the interspecific allometry of vines was most similar to that of pteridophytes. Collectively, these data refute the commonly held notion that vine stems are simply more slender than those of species with self-supporting stems.     

Cooperative free energies for nested allosteric models as applied to human hemoglobin.

A model is developed for ligand binding to human hemoglobin that describes the detailed cooperative free-energies for each of the ten different ligated (cyanomet) species as observed by Smith and Ackers (Smith, F.R., and G.K. Ackers. 1985. Proc. Natl. Acad. Sci. USA.82:5347-5351). The approach taken here is an application of the general principle of hierarchical levels of allosteric control, or nesting, as suggested by Wyman (Wyman, J. 1972. Curr. Top. Cell. Reg. 6:207-223). The model is an extension of the simple two-state MWC model (Monod, J., J. Wyman, and J.P. Changeux. 1965. J. Mol. Biol. 12:88-118) using the idea of cooperative binding within the T (deoxy) form of the macromolecule, and has recently been described as a "cooperon" model (Di Cera, E. 1985. Ph.D. thesis). The T-state cooperative binding is described using simple interaction rules first devised by Pauling (Pauling, L. 1935. Proc. Natl. Acad. Sci. USA. 21:186-191). In this application three parameters suffice to describe the cooperative free-energies of the 10 ligated species of cyanomet hemoglobin. The redox process in the presence of cyanide, represented as a Hill plot, is simulated from Smith and Ackers' cooperative free-energies and is compared with available electrochemical binding measurements.     

Neural mass models as a tool to investigate neural dynamics during seizures

Epilepsy is one of the most common neurological disorders and is characterized by recurrent seizures. We use theoretical neuroscience tools to study brain dynamics during seizures. We derive and simulate a computational model of a network of hippocampal neuronal populations. Each population within the network is based on a model that has been shown to replicate the electrophysiological dynamics observed during seizures. The results provide insights into possible mechanisms for seizure spread. We observe that epileptiform activity remains localized to a pathological region when a global connectivity parameter is less than a critical value. After establishing the critical value for seizure spread, we explored how to correct the effect by altering particular synaptic gains. The spreading of seizures is quantified using numerical methods for seizure detection. The results from this study provide a new avenue of exploration for seizure control.     

Drifting Markov models with polynomial drift and applications to DNA sequences

In this article, we introduce the drifting Markov models (DMMs) which are inhomogeneous Markov models designed for modeling the heterogeneities of sequences (in our case DNA or protein sequences) in a more flexible way than homogeneous Markov chains or even hidden Markov models (HMMs). We focus here on the polynomial drift: the transition matrix varies in a polynomial way. To show the reliability of our models on DNA, we exhibit high similarities between the probability distributions of nucleotides obtained by our models and the frequencies of these nucleotides computed by using a sliding window. In a further step, these DMMs can be used as the states of an HMM: on each of its segments, the observed process can be modeled by a drifting Markov model. Search of rare words in DNA sequences remains possible with DMMs and according to the fits provided, DMMs turn out to be a powerful tool for this purpose. The software is available on request from the author. It will soon be integrated on seq++ library (http://stat.genopole.cnrs.fr/seqpp/).     

Influence of the metabolism pathway on lactic acid production from hemicellulosic trimming vine shoots hydrolyzates using Lactobacillus pentosus

Hemicellulosic hydrolyzates from trimming wastes of vine shoots were proposed as a carbon source for lactic acid production by Lactobacillus pentosus CECT-4023T (ATCC-8041). These hydrolyzates are composed mainly of glucose (12.0 g/L), xylose (17.5 g/L) and arabinose (4.3 g/L). Acetic acid, the main subproduct, started to be produced after all of the glucose was completely depleted, showing that the acetic acid coproduction came only from the xylose and arabinose consumption. In the absence of glucose, the L. pentosus pathway shifts from homo to heterofermentative. Thus, L. pentosus can be considered a facultative heterofermentative organism, degrading hexoses (glucose) via the Embden-Meyerhoff-Parnas pathway and pentoses (xylose and arabinose) via the phosphoketolase pathway. Hydrolyzates were vacuum evaporated to increase the initial sugars concentration up to 35.4 g/L of glucose, 52.3 g/L of xylose, and 13.0 g/L of arabinose. Under these conditions the lactic acid concentration reached 46.0 g/L (Q(P) = 0.933 g/L.h, Y(P/S) = 0.78 g/g; Y(P/S) theoretical = 91.7%) and a clear product inhibition was observed. Additional experiments with synthetic sugars, in the absence of inhibitory compounds, indicate that this inhibition must be attributed to the metabolic pathway but not to the inhibitory compounds present in the fermentation broth.     

A multi-dimensional coalescent process applied to multi-allelic selection models and migration models

For a sample of two genes from a population divided into an arbitrary number of allele classes, a general mathematical framework is developed to address the expectation and variance of the time of the most recent common ancestor. Depending on the meaning of allele classes and the manner in which genes can change among them, this framework can be applied to a diversity of population genetic models. By adoption of the infinite sites model, the effect on heterozygosity is modelled for balancing selection among allele classes, mutation between allele classes, migration among populations, and gene conversion between loci. Most results are described for a continuous time approximation to a discrete generation model. It is also shown how the discrete generation model can be used to study the hitch-hiking effect of favorable mutations.     

Characterization of vine shoots, cotton stalks, Leucaena leucocephala and Chamaecytisus proliferus, and of their ethyleneglycol pulps

We characterized vine shoots, cotton stalks, Leucaena leucocephala and Chamaecytisus proliferus as pulping raw materials and found C. proliferus and cotton stalks to be the best for the intended purpose on the grounds of their increased contents in holocellulose (79.73% and 72.86%) and alpha-cellulose (45.37% and 58.48%), and their decreased contents in ethanol-benzene extractables (2.64% and 1.42%), hot water solubles (2.79% and 3.33%) and 1% soda solubles (16.67% and 20.34%). These properties resulted in increased pulp yields and hence in efficient use of these two types of raw material. The previous raw materials were pulped by using an ethyleneglycol concentration of 65% at 180 degrees C for 75min, followed by beating at 1500 revolutions in a PFI refiner. The paper sheets obtained were characterized and those from C. proliferus found to be the best overall as they exhibited an increased breaking length (4644m), stretch (2.87%), burst index (2.46kN/g) tear index (0.33mNm(2)/g) and brightness (49.92% ISO); in addition C. proliferus pulp was obtained with a high-yield (62.88%). On the other hand, vine shoots provided the poorest results among the studied raw materials.     

Kinetic models of odor transduction implemented as artificial neural networks

We present a formal model of olfactory transduction corresponding to the biochemical reaction cascade found in chemosensory neurons. It assumes that odorants bind to receptor proteins which, in turn, activate transducer mechanisms corresponding to second messenger-mediated processes. The model is reformulated as a mathematically equivalent artificial neural network (ANN). To enable comparison of the computational power of our model, previously suggested models of chemosensory transduction are also presented in ANN versions. In ANNs, certain biological parameters, such as rate constants and affinities, are transformed into weights that can be fitted by training with a given experimental data set. After training, these weights do not necessarily equal the real biological parameters, but represent a set of values that is sufficient to simulate an experimental set of data. We used ANNs to simulate data recorded from bee subplacodes and compare the capacity of our model with ANN versions of other models. Receptor neurons of the nonpheromonal, general odor-processing subsystem of the honeybee are broadly tuned, have overlapping response spectra, and show highly nonlinear concentration dependencies and mixture interactions, i.e., synergistic and inhibitory effects. Our full model alone has the necessary complexity to simulate these complex response characteristics. To account for the complex response characteristics of honeybee receptor neurons, we suggest that several different receptor protein types and at least two second messenger systems are necessary that may interact at various levels of the transduction cascade and may eventually have opposing effects on receptor neuron excitability.     

The Krylov-Bogoliubov-Mitropolskii method applied to models of population dynamics

In this study the possibility of applying the asymptotic method of Krylov-Bogoliubov-Mitropolskii to problems of population dynamics is shown. Especially a general Volterra-Gause-Witt type model for prey-predator interaction is investigated. A discussion on the results obtained is given for the general model and for a particular case as well.     

From cognitive to neural models of working memory

Working memory refers to the temporary retention of information that was just experienced or just retrieved from long-term memory but no longer exists in the external environment. These internal representations are short-lived, but can be stored for longer periods of time through active maintenance or rehearsal strategies, and can be subjected to various operations that manipulate the information in such a way that makes it useful for goal-directed behaviour. Empirical studies of working memory using neuroscientific techniques, such as neuronal recordings in monkeys or functional neuroimaging in humans, have advanced our knowledge of the underlying neural mechanisms of working memory. This rich dataset can be reconciled with behavioural findings derived from investigating the cognitive mechanisms underlying working memory. In this paper, I review the progress that has been made towards this effort by illustrating how investigations of the neural mechanisms underlying working memory can be influenced by cognitive models and, in turn, how cognitive models can be shaped and modified by neuroscientific data. One conclusion that arises from this research is that working memory can be viewed as neither a unitary nor a dedicated system. A network of brain regions, including the prefrontal cortex (PFC), is critical for the active maintenance of internal representations that are necessary for goal-directed behaviour. Thus, working memory is not localized to a single brain region but probably is an emergent property of the functional interactions between the PFC and the rest of the brain.     

Rules extraction from neural networks applied to the prediction and recognition of prokaryotic promoters

Promoters are DNA sequences located upstream of the gene region and play a central role in gene expression. Computational techniques show good accuracy in gene prediction but are less successful in predicting promoters, primarily because of the high number of false positives that reflect characteristics of the promoter sequences. Many machine learning methods have been used to address this issue. Neural Networks (NN) have been successfully used in this field because of their ability to recognize imprecise and incomplete patterns characteristic of promoter sequences. In this paper, NN was used to predict and recognize promoter sequences in two data sets: (i) one based on nucleotide sequence information and (ii) another based on stability sequence information. The accuracy was approximately 80% for simulation (i) and 68% for simulation (ii). In the rules extracted, biological consensus motifs were important parts of the NN learning process in both simulations.     

Characterization and evaluation of golpata fronds as pulping raw materials

The chemical, morphological and anatomical studies of golpata fronds have been discussed. The lignin, pentosan and alpha-cellulose in golpata fronds were comparable to some common nonwood and hardwood raw materials. The alkali, water and dichloromethane solubility were higher than wood and common nonwood. The fiber length 1.73 mm was observed, which is little bit shorter than softwood and longer than hardwood but fiber diameter was very short. The pulpability of golpata fronds has also been studied. The pulp yield was very low and Kappa number was high. The strength properties were better than that of some common nonwood pulps. FT-IR spectrum and alkaline nitrobenzene oxidation products showed that golpata fronds lignin mainly consist of syringyl (S) and guaiacyl units (V).