Reaction-diffusion Method Research Articles

Extended-spectrum beta-lactamase (ESBL)- and non-ESBL producing Escherichia coli surveillance in surface water sources in Edo State, Nigeria: a public health concern

This research explores the antimicrobial resistance (AMR) profiles and prevalence of extended-spectrum beta-lactamase (ESBL) and non-ESBL-producing Escherichia coli in Ojerame Dam and Ovokoto Spring, Edo State, Nigeria. Over 12 months, water was systematically sampled to accommodate seasonal variations and analyzed by employing an ESBL-selective medium for bacterial species. Additionally, bacterial isolates underwent identification and characterization using polymerase chain reaction (PCR) and disk diffusion methods to evaluate their susceptibility to antimicrobials. Results indicated significant prevalence of ESBL-producing E. coli, which exhibited complete resistance to common antimicrobials like ceftriaxone, ceftazidime, cefotaxime, and ampicillin while demonstrating 100% sensitivity to ertapenem, imipenem, meropenem, and nitrofurantoin. Non-ESBL-producing E. coli were resistant to ampicillin but sensitive to other antimicrobials mentioned earlier. Furthermore, both ESBL and non-ESBL-producing E. coli displayed multidrug resistance to varying degrees. Specific ESBL genes, including blaTEM, blaCTX-M-1, and blaCTX-M-15, were identified, alongside resistance genes like tetA, tetM, sul1, sul2, sul3, qnrA, qnrB, and qnrS in E. coli. This study pioneers the documentation of ESBL-producing E. coli in surface water in the region. This signals impending health risks associated with water being a reservoir of resistant genes while emphasizing the urgency for further research and public awareness concerning the quality of surface water.

Controlled Growth of Highly Defected Zirconium-Metal-Organic Frameworks via a Reaction-Diffusion System for Water Remediation.

The relentless growth of metal-organic framework (MOF) chemistry is paralleled by the persistent urge to control the MOFs physical and chemical properties. While this control is mostly achieved by solvothermal syntheses, room temperature procedures stand out as more convenient and sustainable pathways for the production of MOF materials. Herein, a novel approach to control the crystal size and defect numbers of a dihydroxy-functionalized zirconium-based metal-organic framework (UiO-66(OH)2) at room temperature is reported. Through a reaction-diffusion method in a 1D system, zirconium salt was diffused into an agar gel matrix containing the organic linker to form nanocrystals of UiO-66(OH)2 with tailored structural features that include crystal size distribution, surface area, and defect number. By variation of the synthesis parameters of the system, hierarchical MOF nanocrystals with an average size ranging from 30 nm up to 270 nm and surface areas between 201 and 500 m2 g-1 were obtained in a one-pot synthetic route. To stress the importance of crystal size, morphology, and structural defects on the adsorption properties of UiO-66(OH)2, the adsorption capacity of the MOF toward methylene blue dye was tested with the largest and most defected crystals achieving the best performance of 202 mg/g. The distinctive structural characteristics including the hierarchical micromesoporous frameworks, the nanosized particles, and the highly defective crystals obtained by our synthesis procedure are deemed challenging through the conventional synthesis methods. This work paves the way for engineering MOF crystals with tunable physical and chemical properties, using a green synthesis procedure, for their advantageous use in many desirable applications.

A Dormant Reagent Reaction-Diffusion Method for the Generation of Co-Fe Prussian Blue Analogue Periodic Precipitate Particle Libraries.

Liesegang patterns that develop as a result of reaction-diffusion can simultaneously form products with slightly different sizes spatially separated in a single medium. We show here a reaction-diffusion method using a dormant reagent (citrate) for developing Liesegang patterns of cobalt hexacyanoferrate Prussian Blue analog (PBA) particle libraries. This method slows the precipitation reaction and produces different-sized particles in a gel medium at different locations. The gel-embedded particles are still catalytically active. Finally, the applicability of the new method to other PBAs and 2D systems is presented. The method proves promising for obtaining similar inorganic framework libraries with catalytic abilities.

Self-Powered Multifunction Ionic Skins Based on Gradient Polyelectrolyte Hydrogels.

Human skin is the largest organ, and it can transform multiple external stimuli into the biopotential signals by virtue of ions as information carriers. Ionic skins (i-skins) that can mimic human skin have been extensively explored; however, the limited sensing capacities as well as the need of an extra power supply significantly restrict their broad applications. Herein, we develop self-powered humanlike i-skins based on gradient polyelectrolyte membranes (GPMs) that can directly and accurately perceive multiple stimuli. Prepared by a hydrogel-assisted reaction-diffusion method, the GPMs exhibit gradient-distributed charged groups across polymer networks, enabling one to generate a thickness-dependent and thermoresponsive self-induced potential in a hydrated situation and in a humidity-sensitive self-induced potential in a dehydrated/dried situation, respectively. Consequently, the GPM-based i-skins can precisely detect pressure, temperature, and humidity in a self-powered manner. The coupling of mechano-electric and thermo-electric effects inherent in GPMs provides a general strategy for developing innovative self-powered ion-based perception systems.

An implicit lipid model for efficient reaction-diffusion simulations of protein binding to surfaces of arbitrary topology.

Localization of proteins to a membrane is an essential step in a broad range of biological processes such as signaling, virion formation, and clathrin-mediated endocytosis. The strength and specificity of proteins binding to a membrane depend on the lipid composition. Single-particle reaction-diffusion methods offer a powerful tool for capturing lipid-specific binding to membrane surfaces by treating lipids explicitly as individual diffusible binding sites. However, modeling lipid particle populations is expensive. Here, we present an algorithm for reversible binding of proteins to continuum surfaces with implicit lipids, providing dramatic speed-ups to many body simulations. Our algorithm can be readily integrated into most reaction-diffusion software packages. We characterize changes to kinetics that emerge from explicit vs implicit lipids as well as surface adsorption models, showing excellent agreement between our method and the full explicit lipid model. Compared to models of surface adsorption, which couple together binding affinity and lipid concentration, our implicit lipid model decouples them to provide more flexibility for controlling surface binding properties and lipid inhomogeneity, thus reproducing binding kinetics and equilibria. Crucially, we demonstrate our method's application to membranes of arbitrary curvature and topology, modeled via a subdivision limit surface, again showing excellent agreement with explicit lipid simulations. Unlike adsorption models, our method retains the ability to bind lipids after proteins are localized to the surface (through, e.g., a protein-protein interaction), which can greatly increase the stability of multiprotein complexes on the surface. Our method will enable efficient cell-scale simulations involving proteins localizing to realistic membrane models, which is a critical step for predictive modeling and quantification of in vitro and in vivo dynamics.

An Adaptive Stopping Active Contour Model for Image Segmentation

Active contour models (ACMs) are widely used in image segmentation applications. However, the selection of maximum iterations which controls the convergence of the ACMs is still a challenging problem. In this paper, an adaptive method for choosing the optimal number of iterations based on the local and global intensity fitting energy is proposed, which increases the automaticity of the active contour model. Moreover, the adoption of the reaction diffusion (RD) method instead of the distance regularization term can improve the accuracy and speed of segmentation effectively. Experimental results on synthetic and real images show that the proposed model outperforms other representative models in terms of accuracy and efficiency.

Modeling the Self-Assembly of Protein Complexes through a Rigid-Body Rotational Reaction-Diffusion Algorithm.

The reaction-diffusion equations provide a powerful framework for modeling nonequilibrium, cell-scale dynamics over the long time scales that are inaccessible by traditional molecular modeling approaches. Single-particle reaction-diffusion offers the highest resolution technique for tracking such dynamics, but it has not been applied to the study of protein self-assembly due to its treatment of reactive species as single-point particles. Here, we develop a relatively simple but accurate approach for building rigid structure and rotation into single-particle reaction-diffusion methods, providing a rate-based method for studying protein self-assembly. Our simplifying assumption is that reactive collisions can be evaluated purely on the basis of the separations between the sites, and not their orientations. The challenge of evaluating reaction probabilities can then be performed using well-known equations based on translational diffusion in both 3D and 2D, by employing an effective diffusion constant we derive here. We show how our approach reproduces both the kinetics of association, which is altered by rotational diffusion, and the equilibrium of reversible association, which is not. Importantly, the macroscopic kinetics of association can be predicted on the basis of the microscopic parameters of our structurally resolved model, allowing for critical comparisons with theory and other rate-based simulations. We demonstrate this method for efficient, rate-based simulations of self-assembly of clathrin trimers, highlighting how formation of regular lattices impacts the kinetics of association.

Whole heart modeling - Spatiotemporal dynamics of electrical wave conduction and propagation.

Cardiac electrical activities are varying in both space and time. Human heart consists of a fractal network of muscle cells, Purkinje fibers, arteries and veins. Whole-heart modeling of electrical wave conduction and propagation involves a greater level of complexity. Our previous work developed a computer model of the anatomically realistic heart and simulated the electrical conduction with the use of cellular automata. However, simplistic assumptions and rules limit its ability to provide an accurate approximation of real-world dynamics on the complex heart surface, due to sensitive dependence of nonlinear dynamical systems on initial conditions. In this paper, we propose new reaction-diffusion methods and pattern recognition tools to simulate and model spatiotemporal dynamics of electrical wave conduction and propagation on the complex heart surface, which include (i) whole heart model; (ii) 2D isometric graphing of 3D heart geometry; (iii) reaction-diffusion modeling of electrical waves in 2D graph, and (iv) spatiotemporal pattern recognition. Experimental results show that the proposed numerical solution has strong potentials to model the space-time dynamics of electrical wave conduction in the whole heart, thereby achieving a better understanding of disease-altered cardiac mechanisms.

The helical melamine–Keggin hybrid nanorods: syntheses and characterizations with two strategies, one-step solid-state chemical reaction and experimental solvent diffusion method

Two new Melamine-Keggin hybrid nanorods, MELH-PMo12 and MELH-PW12, for the first time are successfully synthesized by two chemical methods, one-step solid-state chemical reaction method and experimental solvent diffusion method at room temperature. One-step solid-state reactions involve grounded nanorod materials along with grinding of row material and with solvent diffusion method these nanorods synthesized in single crystals form. These compounds crystallized in trigonal space groups P-3. Elemental analysis, infrared spectroscopy, UV spectroscopy and XRD analysis results prove that the samples still possess Keggin type structure. Transmittance electron microscopy and single-crystal X-ray have shown that the samples have nanorod structures.

A novel level set model with automated initialization and controlling parameters for medical image segmentation

In this paper, a level set model without the need of generating initial contour and setting controlling parameters manually is proposed for medical image segmentation. The contribution of this paper is mainly manifested in three points. First, we propose a novel adaptive mean shift clustering method based on global image information to guide the evolution of level set. By simple threshold processing, the results of mean shift clustering can automatically and speedily generate an initial contour of level set evolution. Second, we devise several new functions to estimate the controlling parameters of the level set evolution based on the clustering results and image characteristics. Third, the reaction diffusion method is adopted to supersede the distance regularization term of RSF-level set model, which can improve the accuracy and speed of segmentation effectively with less manual intervention. Experimental results demonstrate the performance and efficiency of the proposed model for medical image segmentation.

Rotational Waves in an Oscillatory Electrochemical System

The existence of waves in distributed systems has been observed in chemical systems mainly through reaction-diffusion methods and such waves can be rotational when performed on a ring-shaped surface. In such ring systems, phase model analysis predicts several waves may co-rotate in an even distribution over the ring. In this contribution, an experimental setup composed of twenty nickel electrodes undergoing electrodissolution is constructed to investigate rotational waves. The electrodes are positioned into the ring topology by coupling. Only complete synchrony is attained when coupled. In order to arrive at a rotational state, the temporary application of topology alterations and global feedback is required. Four rotational solutions are found, distinguished by the rotational velocity and the number of waves in each rotation. The rotational velocity is defined as ν0/m where ν0 is a base velocity of all rotations and m is the integer winding number which is equal to the number of waves in the rotation. The non-rotational solution, m=0, is characterized by complete synchronization while the rotational solutions are winding numbers m=1,2,3,4 where a specific winding number is achieved by the application of appropriate topological tuning. Any further increase in winding number is shown to not be stable without external assistance. The results indicate rotational waves, predicted by phase model approximations, can be realized by the application of the experimental method.

Local average fitting active contour model with thresholding for noisy image segmentation

In this paper, an active contours model using neighborhood average fitting with thresholding is proposed for noisy images segmentation. Energy of the proposed model is formulated according to the difference between the local average and global region information. For images corrupted by noise, the neighborhood average method is capable of denoising at the expense of blurring images to some extent. However, problems that appear with average method can be settled by thresholding in this work. Minimization of the energy associated with the active contour model is then implemented in a variational level set framework. Moreover, to eliminate the need for costly re-initialization procedure, a reaction-diffusion method is adopted to regularize the level set function for stability. Experimental results on synthetic and real images validate the effectiveness of the proposed approach.

ANALYSIS OF ANATOMICAL REGIONS IN ALZHEIMER'S BRAIN MR IMAGES USING LEVEL SETS AND MINKOWSKI FUNCTIONALS

In this work, an attempt is made to analyze anatomical regions of Alzheimer's brain MR images using level set and Minkowski functionals (MFs). The T1 weighted sagittal view of normal and abnormal images considered in this work, are obtained from MIRIAD database. The ventricle along with the hippocampus is segmented using Reaction Diffusion (RD) level set method and is subjected to further analysis using MFs. The prominent feature derived from the segmented region which contains ventricle and hippocampus is correlated with the Mini-Mental State Examination (MMSE) score. Results show that RD method is able to delineate the exact boundary of the ventricle along with hippocampus. It is observed that the MF-area of segmented region provides better discrimination of normal and abnormal subjects (p < 0.001) compared to the MF-perimeter and MF-Euler number. The correlation of the MMSE with prominent MF-area for normal, mild, moderate and severe are found to be 0.71, 0.82, 0.84 and 0.89, respectively. This suggests that characterization of brain structures using MFs improve the discrimination capability of brain disorders. Hence MF-area feature could be used for the study of progression in Alzheimer's disease (AD) like neurodegenerative disorders.

Obtaining new composite biomaterials by means of mineralization of methacrylate hydrogels using the reaction–diffusion method

The present paper describes the synthesis and characterization of a new polymeric biomaterial mineralized with calcium phosphate using the reaction–diffusion method. The scaffold of this biomaterial was a hydrogel constituted by biocompatible polyethylene glycol methyl ether methacrylate (PEGMEM) and 2-(dimethylamino)ethyl methacrylate (DMAEM), which were cross-linked with N-N’-methylenebisacrylamide (BIS). The cross-linking content of the hydrogels was varied from 0.25% to 15% (w/w). The gels were used as matrix where two reactants (Na2HPO4 and CaCl2) diffused from both ends of the gel and upon encountering produced calcium phosphate crystals that precipitated within the polymer matrix forming bands. The shape of the crystals was tuned by modifying the matrix porosity in such a way that when the polymer matrix was slightly reticulated the diffusion reaction produced round calcium phosphate microcrystals, whilst when the polymer matrix was highly reticulated the reaction yielded flat calcium phosphate crystals. Selected area electron diffraction performed on the nanocrystals that constitute the microcrystals showed that they were formed by Brushite (CaHPO4.2H2O). This new composite material could be useful in medical and dentistry applications such as bone regeneration, bone repair or tissue engineering.

Editorial: Special Issue on Stochastic Modelling of Reaction–Diffusion Processes in Biology

Editorial: Special Issue on Stochastic Modelling of Reaction–Diffusion Processes in Biology

A Diffusion-Augmented Level Set Method with Efficient Two Step Implementation

The level set method was devised by Osher and Sethian [2] in as a simple and versatile method for computing and analyzing the motion of an interface Γ in two or three dimensions. Γ bounds a region Ω. The goal is to compute and analyze the subsequent motion of Γ under a velocity field v [1]. This velocity can depend on position, time, the geometry of the interface and the external physics. The interface is captured for later time as the zero level set of a smooth function ϕ(x, t), i.e., Γ (t) = {x|ϕ(x, t) = 0}. ϕ is positive inside Ω, negative outside Ω and is zero on Γ (t) [1]. This paper presents a reaction-diffusion method used to describe a physicochemical phenomenon that comprises two elements, namely chemical reactions and diffusion for implicit active contours[21][37][39][40], which is completely free of the costly re-initialization procedure in level set evolution (LSE). A diffusion term is introduced into LSE, resulting in a diffusion-augmented level set method with efficient two step implementation. First we iteratively solve the diffusion term and then iteratively solve the level set equation. By solving equation in two steps we can stabilize the level set function without re-initialization. This is also called two step splitting method for image segmentation.

Reinitialization-Free Level Set Evolution via Reaction Diffusion

This paper presents a novel reaction-diffusion (RD) method for implicit active contours that is completely free of the costly reinitialization procedure in level set evolution (LSE). A diffusion term is introduced into LSE, resulting in an RD-LSE equation, from which a piecewise constant solution can be derived. In order to obtain a stable numerical solution from the RD-based LSE, we propose a two-step splitting method to iteratively solve the RD-LSE equation, where we first iterate the LSE equation, then solve the diffusion equation. The second step regularizes the level set function obtained in the first step to ensure stability, and thus the complex and costly reinitialization procedure is completely eliminated from LSE. By successfully applying diffusion to LSE, the RD-LSE model is stable by means of the simple finite difference method, which is very easy to implement. The proposed RD method can be generalized to solve the LSE for both variational level set method and partial differential equation-based level set method. The RD-LSE method shows very good performance on boundary antileakage. The extensive and promising experimental results on synthetic and real images validate the effectiveness of the proposed RD-LSE approach.

A Computational Approach to Increase Time Scales in Brownian Dynamics–Based Reaction-Diffusion Modeling

Particle-based Brownian dynamics simulations offer the opportunity to not only simulate diffusion of particles but also the reactions between them. They therefore provide an opportunity to integrate varied biological data into spatially explicit models of biological processes, such as signal transduction or mitosis. However, particle based reaction-diffusion methods often are hampered by the relatively small time step needed for accurate description of the reaction-diffusion framework. Such small time steps often prevent simulation times that are relevant for biological processes. It is therefore of great importance to develop reaction-diffusion methods that tolerate larger time steps while maintaining relatively high accuracy. Here, we provide an algorithm, which detects potential particle collisions prior to a BD-based particle displacement and at the same time rigorously obeys the detailed balance rule of equilibrium reactions. We can show that for reaction-diffusion processes of particles mimicking proteins, the method can increase the typical BD time step by an order of magnitude while maintaining similar accuracy in the reaction diffusion modelling.

Exact on-lattice stochastic reaction-diffusion simulations using partial-propensity methods

Stochastic reaction-diffusion systems frequently exhibit behavior that is not predicted by deterministic simulation models. Stochastic simulation methods, however, are computationally expensive. We present a more efficient stochastic reaction-diffusion simulation algorithm that samples realizations from the exact solution of the reaction-diffusion master equation. The present algorithm, called partial-propensity stochastic reaction-diffusion (PSRD) method, uses an on-lattice discretization of the reaction-diffusion system and relies on partial-propensity methods for computational efficiency. We describe the algorithm in detail, provide a theoretical analysis of its computational cost, and demonstrate its computational performance in benchmarks. We then illustrate the application of PSRD to two- and three-dimensional pattern-forming Gray-Scott systems, highlighting the role of intrinsic noise in these systems.

Fabrication of ordered nanoporous anodic alumina prepatterned by mold-assisted chemical etching

In this article, a simple and cost-effective method to create patterned nanoindentations on Al surface via mold-assisted chemical etching process is demonstrated. This report shows the reaction-diffusion method which formed nanoscale shallow etch pits by the absorption/liberation behaviors of chemical etchant in poly(dimethylsiloxane) stamp. During subsequent anodization, it was possible to obtain the ordered nanopore arrays with 277 nm pitch that were guided by the prepatterned etch pits. The prepatterned etch pits obtained can guide the growth of AAO nanopores during anodization and facilitate the preparation of ordered nanopore arrays.