Emulating cellular automata in chemical reaction–diffusion networks

Chemical reactions and diffusion can produce a wide variety of static or transient spatial patterns in the concentrations of chemical species. Little is known, however, about what dynamical patterns of concentrations can be reliably programmed into such reaction–diffusion systems. Here we show that given simple, periodic inputs, chemical reactions and diffusion can reliably emulate the dynamics of a deterministic cellular automaton, and can therefore be programmed to produce a wide range of complex, discrete dynamics. We describe a modular reaction–diffusion program that orchestrates each of the fundamental operations of a cellular automaton: storage of cell state, communication between neighboring cells, and calculation of cells’ subsequent states. Starting from a pattern that encodes an automaton’s initial state, the concentration of a “state” species evolves in space and time according to the automaton’s specified rules. To show that the reaction–diffusion program we describe produces the target dynamics, we simulate the reaction–diffusion network for two simple one-dimensional cellular automata using coupled partial differential equations. Reaction–diffusion based cellular automata could potentially be built in vitro using networks of DNA molecules that interact via branch migration processes and could in principle perform universal computation, storing their state as a pattern of molecular concentrations, or deliver spatiotemporal instructions encoded in concentrations to direct the behavior of intelligent materials.     

Robust stabilization of competing species in the chemostat

This work addresses the problem of robust stabilization of the concentrations of two different species of living organisms, which compete for a single limiting substrate in a bioreactor. This stabilization is performed using discontinuous feedback control laws that ensure the coexistence of all species. The control laws are designed considering bounded parametric uncertainties on the kinetic rates.     

On the use of a self organising map as feature compressor in the building of calibration models: Application to FTIR-spectrophotometry

Considerable attention has been given to strategies for variable selection in spectroscopic analysis. Here we introduce a different approach, the self organising map as a feature compressor, which also helps reducing the dimensionality of the problem. The method is straightforward and does not need previous knowledge about the regions of the spectra that contain relevant variables or information, so it applies generally. We coupled the method to multiple linear regression, partial component analysis and partial least squares and used it to quantitatively analyse 2-component liquid samples using FTIR spectroscopy. The predicted concentrations of the species within the mixture were extremely accurate (the correlation coefficients of estimated versus real concentrations were 0.997 and 0.995 for methanol and p-xylene, respectively). Furthermore, when applying the feature compression step, calibration models become more stable since they are able to better estimate a concentration not present in the training set.     

Determination of leaf pigment content in Calluna vulgaris shoots from spectral reflectance

Leaf pigment concentrations are indicative of a range of plant physiological properties and processes. The measurement of leaf spectral reflectance is a rapid, non-destructive method for determining pigment content, and a large number of spectral indices have been developed for the estimation of leaf pigment content. Despite their ‘applicability’ across many species types, some ecologically important species remain to be explored. The objective of this paper was to investigate a wide range of hyperspectral indices for determining the chlorophyll and carotenoid content in a microphyllous and sclerophyllous species, Calluna vulgaris. We carried out spectral measurements on individual heather shoots with a handheld GER-1500 spectroradiometer, and sampled each measured shoot for biochemical analysis using high-performance liquid chromatography (HPLC). We found that several previously published indices performed relatively poorly and yielded coefficients of determination (R²) for chlorophyll ranging from 0.34 to 0.66, with the first derivative of reflectance at the red edge yielding the highest correlation with chlorophyll content (R² = 0.66). Only one of the carotenoid indices we tested (the Photochemical Reflectance Index, PRI) provided a strong correlation with the de-epoxidation state of the xanthophyll cycle (R² = 0.78). The other previously published carotenoid indices performed poorly within our data set. We concluded that only a few of the so-called ‘widely applicable’ indices were applicable to use with this data set, which would present limitations when working with remotely sensed data at a larger scale where a mix of species, including Calluna vulgaris, is present.     

Matching samples of multiple views

Multi-view learning studies how several views, different feature representations, of the same objects could be best utilized in learning. In other words, multi-view learning is analysis of co-occurrence data, where the observations are co-occurrences of samples in the views. Standard multi-view learning such as joint density modeling cannot be done in the absence of co-occurrence, when the views are observed separately and the identities of objects are not known. As a practical example, joint analysis of mRNA and protein concentrations requires mapping between genes and proteins. We introduce a data-driven approach for learning the correspondence of the observations in the different views, in order to enable joint analysis also in the absence of known co-occurrence. The method finds a matching that maximizes statistical dependency between the views, which is particularly suitable for multi-view methods such as canonical correlation analysis which has the same objective. We apply the method to translational metabolomics, to identify differences and commonalities in metabolic processes in different species or tissues. The metabolite identities and roles in the different species are not generally known, and it is necessary to search for a matching. In this paper we show, using different metabolomics measurement batches as the views so that the ground truth is known, that the metabolite identities can be reliably matched by a consensus of several matching solutions.     

与Jurbanite固相平衡的酸性天然地表水中铝形态分布的计算机模拟

基于热力学平衡计算,用计算机模拟研究了与Jurbanite固相平衡的酸性天然地表水中各种无机铝形态、有机铝形态、聚合铝形态随水溶液pH变化的关系,用文献提供的实际水样数据作为输入数据,比较了与酸性天然地表水中可能存在的固相矿物如Jurbanite,basaluminite,alunite和gibbsite相平衡的几种土壤溶液中的铝形态变化规律,重点放在易受酸沉降影响的Jurbanite上。在矿相Jurbanite存在的条件下,由模型预知,SO4^2-对铝的形态分布和总铝浓度有显著影响,而在所研究的pH范围内有机铝络合物和氟络合物的浓度相对较小。模型同时被用于文献中与Jurbanite固相平衡的土壤溶液中铝形态的计算,预测其浓度大小依次为总可溶性铝、无机铝和有机铝。然而,模型结果表明,Jurbanite受酸沉降影响溶解之后将显著地影响土壤溶液Al化学。     

A comparison of hierarchical multi-output recognition approaches for anuran classification

In bioacoustic recognition approaches, a “flat” classifier is usually trained to recognize several species of anurans, where the number of classes is equal to the number of species. Consequently, the complexity of the classification function increases proportionally with the number of species. To avoid this issue, we propose a “hierarchical” approach that decomposes the problem into three taxonomic levels: the family, the genus, and the species. To accomplish this, we transform the original single-labelled problem into a multi-output problem (multi-label and multi-class) considering the biological taxonomy of the species. We then develop a top-down method using a set of classifiers organized as a hierarchical tree. We test and compare two hierarchical methods, using (1) one classifier per parent node and (2) one classifier per level, against a flat approach. Thus, we conclude that it is possible to predict the same set of species as a flat classifier, and additionally obtain new information about the samples and their taxonomic relationship. This helps us to better understand the problem and achieve additional conclusions by the inspection of the confusion matrices at the three classification levels. In addition, we propose a soft decision rule based on the joint probabilities of hierarchy pathways. With this we are able to identify and reject confusing cases. We carry out our experiments using cross-validation performed by individuals. This form of CV avoids mixing syllables that belong to the same specimens in the testing and training sets, preventing an overestimate of the accuracy and generalizing the predictive capabilities of the system. We tested our methods in a dataset with sixty individual frogs, from ten different species, eight genera, and four families, achieving a final Macro-Fscore of 80 and 70% with and without applying the rejection rule, respectively.     

A BASIC program for the numerical solution of the transient kinetics of complex biochemical models

A highly optimized software for the kinetic analysis of complex chemical models is presented. The program is applied to the analysis of a vectorial biochemical reaction, where many species are linked by multiple equilibria of any order. The reaction stimulates the Ca2(+)-transport-linked ATPase reaction taking place in a suspension of vesicular fragments of isolated sarcoplasmic reticulum membranes, as described in many experimental reports. The model includes 12 reactants and intermediate chemical species, 14 kinetic constants, compartmentalization, and thermodynamic adjustment. The concentrations of all the model components, at any time, starting from a known initial condition, are calculated. The transient concentrations of the species are obtained by numerical integration of the appropriate differential equations, using an optimized version of the Runge-Kutta-Gill algorithm, with the aid of a Digital PDP11/23 computer and a standard BASIC-11 software, which could be fast and easily fitted to work with any microcomputer and/or alternative language or faster working compiled BASIC version. The errors of the calculations are evaluated.     

Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data

Tree species identification is important for a variety of natural resource management and monitoring activities including riparian buffer characterization, wildfire risk assessment, biodiversity monitoring, and wildlife habitat assessment. Intensity data recorded for each laser point in a LIDAR system is related to the spectral reflectance of the target material and thus may be useful for differentiating materials and ultimately tree species. The aim of this study is to test if LIDAR intensity data can be used to differentiate tree species. Leaf-off and leaf-on LIDAR data were obtained in the Washington Park Arboretum, Seattle, Washington, USA. Field work was conducted to measure tree locations, tree species and heights, crown base heights, and crown diameters of individual trees for eight broadleaved species and seven coniferous species. LIDAR points from individual trees were identified using the field-measured tree location. Points from adjacent trees within a crown were excluded using a procedure to separate crown overlap. Mean intensity values of laser returns within individual tree crowns were compared between species. We found that the intensity values for different species were related not only to reflective properties of the vegetation, but also to a presence or absence of foliage and the arrangement of foliage and branches within individual tree crowns. The classification results for broadleaved and coniferous species using linear discriminant function with a cross validation suggests that the classification rate was higher using leaf-off data (83.4%) than using leaf-on data (73.1%), with highest (90.6%) when combining these two LIDAR data sets. The result also indicates that different ranges of intensity values between two LIDAR datasets didn't affect the result of discriminant functions. Overall results indicate that some species and species groups can be differentiated using LIDAR intensity data and implies the potential of combining two LIDAR datasets for one study.     

A hydrogel-actuated environmentally sensitive microvalve for active flow control

This paper reports on the fabrication and test of a hydrogel-actuated microvalve that responds to changes in the concentration of specific chemical species in an external liquid environment. The microvalve consists of a thin hydrogel, sandwiched between a stiff porous membrane and a flexible silicone rubber diaphragm. Swelling and deswelling of the hydrogel, which results from the diffusion of chemical species through the porous membrane is accompanied by the deflection of the diaphragm and hence closure and opening of the valve intake orifice. A phenylboronic-acid-based hydrogel was used to construct a smart microvalve that responds to the changes in the glucose and pH concentrations. The fastest response time (for a pH concentration cycle) achieved was 7 min using a 30-/spl mu/m-thick hydrogel and a 60-/spl mu/m-thick porous membrane with 0.1 /spl mu/m pore size and 40% porosity.     

Simple evolution of complex crystal species

Cairns-Smith has proposed that life began as structural patterns in clays that self-replicated during cycles of crystal growth and fragmentation. Complex, evolved crystal forms could then have catalyzed the formation of a more advanced genetic material. A crucial weakness of this theory is that it is unclear how complex crystals might arise through Darwinian evolution and selection. Here we investigate whether complex crystal patterns could evolve using a model system for crystal growth, DNA tile crystals, that is amenable to both theoretical and experimental inquiry. It was previously shown that in principle, the evolution of crystals assembled from a set of thousands of DNA tile types under very specific environmental conditions could produce arbitrarily complex patterns. Here we show that evolution driven only by the dearth of one monomer type could produce complex crystals from just 12 monomer types. When a monomer type is rare, crystals that use few of this monomer type are selected for. We use explicit enumeration to show that there are situations in which crystal species that use a particular monomer type less frequently will grow faster, yet to do so requires that the information contained in the crystal become more complex. We show that this feature of crystal organization could allow more complex crystal morphologies to be selected for in the right environment, using both analysis in a simple model of self-assembly and stochastic kinetic simulations of crystal growth. The proposed mechanism of evolution is simple enough to test experimentally and is sufficiently general that it may apply to other DNA tile crystals or even to natural crystals, suggesting that complex crystals could evolve from simple starting materials because of relative differences in concentrations of the materials needed for growth.     

Computational study of band-crossing reactions

A numerical study of band-crossing reactions is conducted using a quasi-one-dimensional (1-D) computational model that accounts for species bulk advection, electromigration velocities, diffusion, and chemical reaction. The model is used to simulate chemical reactions between two initially distinct sample zones, referred to as "bands," that cross each other due to differences in electromigration velocities. The reaction is described in terms of a single step, reversible mechanism involving two reactants and one product. A parametric study is first conducted of the behavior of the species profiles, and results are interpreted in terms of the Damko/spl uml/hler number and of the ratios of the electromigration velocities of the reactant and product. Computed results are then used to explore the possibility of extracting forward and backward reaction rates based on time resolved observation of integral moments of species concentrations. In particular, it is shown that in the case of fast reactions, robust estimates can be obtained for high forward rates, but that small reverse rates may not be accurately observed.     

Grid refinement tests of a least-squares finite element method for advective transport of reactive species

A time splitting least-squares finite element method (LSFEM) and the ‘stiff ODEs’ solver LSODE are used to simulate the advective transport of reactive species. Specifically, the rotating cone problem with chemical reactions serves as a model to test the algorithm. A non-linear filter is used to suppress spurious oscillations at each advective time step. All simulations are carried out by using linear and quadratic elements on two mesh systems. Results from the coarse mesh system suggest that the use of robust numerical methods alone will not be able to provide accurate results and point to the need of grid refinement. The grid refinement tests are evaluated by pollutant peak concentrations, pollutant concentration distributions, average relative errors, species mass conservation and distribution ratios and CPU times. Results from the fine mesh system are satisfactory and imply that accurate simulations of the transport of reactive species require adequate grid resolution and robust numerical methods for each individual advective and reactive step.     

Fractional differential equations in electrochemistry

Electrochemistry was one of the first sciences to benefit from the fractional calculus. Electrodes may be thought of as “transducers” of chemical fluxes into electricity. In a typical electrochemical cell, chemical species, such as ions or dissolved molecules, move towards the electrodes by diffusion. Likewise, other species are liberated into solution by the electrode reaction and diffuse away from the electrode into the bulk solution. It is demonstrated in this paper that the electric current is linearly related to the temporal semiderivative of the concentrations, at the electrode, of the species involved in the electrochemical reaction. More usefully, the semiintegral of the current provides immediate access information about concentrations.     

A reaction compiler for electrochemical kinetics

A computer code serving for an automatic translation of user-written source texts of electrochemical reaction mechanisms into corresponding target texts of mathematical equations that govern the kinetics of electrochemical systems under transient conditions is reported. The rules of the language enabling symbolic specification of the reaction mechanisms, the compiler options, and conventions regarding the target formulae are outlined and illustrated by examples. A considerable diversity of reaction mechanisms involving equilibrium, non-equilibrium reversible or irreversible reactions that can be electrochemical, heterogeneous non-electrochemical or homogeneous, is permitted. The reactions may involve bulk species (distributed in the electrolyte volume) and interfacial species (localized at the electrodes) of variable or constant concentrations, and electrons. The transient conditions may correspond to a number of electrochemical techniques, including potential-step method, linear potential scan voltammetry and chronopotentiometry. For kinetic problems in one-dimensional space geometry the generated governing equations take the general form of the reaction-advection-diffusion partial differential equations for the concentrations of bulk species (with initial and boundary conditions), optionally coupled with algebraic, ordinary differential or differential-algebraic equations for the concentrations of interfacial species. The governing equations can be obtained in the form of ELSIM problem definitions, enabling further solution by means of this simulation program.     

A mathematical model of laminar axisymmetrical natural gas flames

A computational procedure for determining the velocities, temperatures and species concentrations in a laminar Bunsen type flame is presented. The boundary layer form of the Navier-Stokes equations with coupled chemistry are solved for a compressible, viscous and axisymmetric flow. An implicit finite difference method is used in the solutions. Velocities, temperatures and stable species concentrations are compared with experimental data.     

Prediction of Particlulate Air Pollution using Neural Techniques

We have analysed the possibility of predicting hourly average concentrations of suspended atmospheric particulate matter with aerodynamic diameter less than 2.5 microns (PM2.5) several hours in advance using data obtained in downtown Santiago, Chile. By performing some standard tests used in the study of dynamical systems, we are able to extract some features of the time series of data. We use this information to estimate the amount of data on the past to be used as input to a neural network in order to predict future values of PM2.5 concentrations. We show that improvement of predictions is possible by using another neural network for noise reduction on the original series. The best results are obtained with a type of neural network which is equivalent to a linear regression. Up to six hours in advance, predictions generated in this way have significantly smaller errors than predictions based on the persistence of the long term average of the data.