Function projective synchronization of memristor-based Cohen–Grossberg neural networks with time-varying delays

This paper deals with the problem of function projective synchronization for a class of memristor-based Cohen–Grossberg neural networks with time-varying delays. Based on the theory of differential equations with discontinuous right-hand side, some novel criteria are obtained to realize the function projective synchronization of addressed networks by combining open loop control and linear feedback control. As some special cases, several control strategies are given to ensure the realization of complete synchronization, anti-synchronization and the stabilization of the considered memristor-based Cohen–Grossberg neural network. Finally, a numerical example and its simulations are provided to demonstrate the effectiveness of the obtained results.     

The stability of impulsive stochastic Cohen–Grossberg neural networks with mixed delays and reaction–diffusion terms

The global asymptotic stability of impulsive stochastic Cohen–Grossberg neural networks with mixed delays and reaction–diffusion terms is investigated. Under some suitable assumptions and using Lyapunov–Krasovskii functional method, we apply the linear matrix inequality technique to propose some new sufficient conditions for the global asymptotic stability of the addressed model in the stochastic sense. The mixed time delays comprise both the time-varying and continuously distributed delays. The effectiveness of the theoretical result is illustrated by a numerical example.     

A new regime for highly robust gamma oscillation with co-exist of accurate and weak synchronization in excitatory–inhibitory networks

A great number of biological experiments show that gamma oscillation occurs in many brain areas after the presentation of stimulus. The neural systems in these brain areas are highly heterogeneous. Specifically, the neurons and synapses in these neural systems are diversified; the external inputs and parameters of these neurons and synapses are heterogeneous. How the gamma oscillation generated in such highly heterogeneous networks remains a challenging problem. Aiming at this problem, a highly heterogeneous complex network model that takes account of many aspects of real neural circuits was constructed. The network model consists of excitatory neurons and fast spiking interneurons, has three types of synapses (GABA_A, AMPA, and NMDA), and has highly heterogeneous external drive currents. We found a new regime for robust gamma oscillation, i.e. the oscillation in inhibitory neurons is rather accurate but the oscillation in excitatory neurons is weak, in such highly heterogeneous neural networks. We also found that the mechanism of the oscillation is a mixture of interneuron gamma (ING) and pyramidal-interneuron gamma (PING). We explained the mixture ING and PING mechanism in a consistent-way by a compound post-synaptic current, which has a slowly rising-excitatory stage and a sharp decreasing-inhibitory stage.     

H∞ filtering for discrete-time genetic regulatory networks with random delays

The H(∞) filtering problem is investigated in this paper for a class of discrete-time genetic regulatory networks (GRNs) with random delays. The addressed filtering problem is to estimate the concentrations of mRNA and protein, and the filtering error system is modeled as a Markovian switched system. By using a properly constructed Lyapunov function, a sufficient condition is derived in terms of linear matrix inequalities (LMIs), which can guarantee stochastic stabilization of the filtering error system. Then, an optimization problem with LMIs constraints is established to design an H(∞) filter which ensures an optimal H(∞) disturbance attenuation level. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed results.     

Seidel–Herzel model of human baroreflex in cardiorespiratory system with stochastic delays

The stochastic versus deterministic solution of the Seidel–Herzel model describing the baroreceptor control loop (which regulates the short-time heart rate) are compared with the aim of exploring the heart rate variability. The deterministic model solutions are known to bifurcate from the stable to sustained oscillatory solutions if time delays in transfer of signals by sympathetic nervous system to the heart and vasculature are changed. Oscillations in the heart rate and blood pressure are physiologically crucial since they are recognized as Mayer waves. We test the role of delays of the sympathetic stimulation in reconstruction of the known features of the heart rate. It appears that realistic histograms and return plots are attainable if sympathetic time delays are stochastically perturbed, namely, we consider a perturbation by a white noise. Moreover, in the case of stochastic model the bifurcation points vanish and Mayer oscillations in heart period and blood pressure are observed for whole considered space of sympathetic time delays.      

A predictor of transmembrane α-helix domains of proteins based on neural networks

Back-propagation, feed-forward neural networks are used to predict a-helical transmembrane segments of proteins. The networks are trained on the few membrane proteins whose transmembrane -helix domains are known to atomic or nearly atomic resolution. When testing is performed with a jackknife procedure on the proteins of the training set, the fraction of total correct assignments is as high as 0.87, with an average length for the transmembrane segments of 20 residues. The method correctly fails to predict any transmembrane domain for porin, whose transmembrane segments are -sheets. When tested on globular proteins, lower and upper limits of 1.6 and 3.5% for a total of 26826 residues are determined for the mispredicted cases, indicating that the predictor is highly specific for -helical domains of membrane proteins. The predictor is also tested on 37 membrane proteins whose transmembrane topology is partially known. The overall accuracy is 0.90, two percentage points higher than that obtained with statistical methods. The reliability of the prediction is 100% for 60% of the total 18242 predicted residues of membrane proteins. Our results show that the local directional information automatically extracted by the neural networks during the training phase plays a key role in determining the accuracy of the prediction. Correspondence to: R. Casadio     

Permanence of a predator–prey discrete system with Holling-IV functional response and distributed delays

A predator–prey discrete-time model with Holling-IV functional response and distributed delays is investigated in this paper. By using the comparison theorem of the difference equation and some analysis technique, some sufficient conditions are obtained for the permanence of the discrete predator–prey system. Two examples are given to illustrate the feasibility of the obtained result.     

The role of transmitter diffusion and flow versus extracellular vesicles in volume transmission in the brain neural–glial networks

Two major types of intercellular communication are found in the central nervous system (CNS), namely wiring transmission (point-to-point communication, the prototype being synaptic transmission with axons and terminals) and volume transmission (VT; communication in the extracellular fluid and in the cerebrospinal fluid (CSF)) involving large numbers of cells in the CNS. Volume and synaptic transmission become integrated inter alia through the ability of their chemical signals to activate different types of receptor protomers in heteroreceptor complexes located synaptically or extrasynaptically in the plasma membrane. The demonstration of extracellular dopamine (DA) and serotonin (5-HT) fluorescence around the DA and 5-HT nerve cell bodies with the Falck–Hillarp formaldehyde fluorescence method after treatment with amphetamine and chlorimipramine, respectively, gave the first indications of the existence of VT in the brain, at least at the soma level. There exist different forms of VT. Early studies on VT only involved spread including diffusion and flow of soluble biological signals, especially transmitters and modulators, a communication called extrasynaptic (short distance) and long distance (paraaxonal and paravascular and CSF pathways) VT. Also, the extracellular vesicle type of VT was demonstrated. The exosomes (endosome-derived vesicles) appear to be the major vesicular carriers for VT but the larger microvesicles also participate. Both mainly originate at the soma–dendritic level. They can transfer lipids and proteins, including receptors, Rab GTPases, tetraspanins, cholesterol, sphingolipids and ceramide. Within them there are also subsets of mRNAs and non-coding regulatory microRNAs. At the soma–dendritic membrane, sets of dynamic postsynaptic heteroreceptor complexes (built up of different types of physically interacting receptors and proteins) involving inter alia G protein-coupled receptors including autoreceptors, ion channel receptors and receptor tyrosine kinases are hypothesized to be the molecular basis for learning and memory. At nerve terminals, the presynaptic heteroreceptor complexes are postulated to undergo plastic changes to maintain the pattern of multiple transmitter release reflecting the firing pattern to be learned by the heteroreceptor complexes in the postsynaptic membrane.     

Universality in neural networks: the importance of the ‘mean firing rate’

We present a general analysis of highly connected recurrent neural networks which are able to learn and retrieve a finite number of static patterns. The arguments are based on spike trains and their interval distribution and require no specific model of a neuron. In particular, they apply to formal two-state neurons as well as to more refined models like the integrate-and-fire neuron or the Hodgkin-Huxley equations. We show that the mean firing rate defined as the inverse of the mean interval length is the only relevant parameter (apart from the synaptic weights) that determines the existence of retrieval solutions with a large overlap with one of the learnt patterns. The statistics of the spiking noise (Gaussian, Poisson or other) and hence the shape of the interval distribution does not matter. Thus our unifying approach explains why, and when, all the different associative networks which treat static patterns yield basically the same results, i.e., belong to the same universality class.     

Treatment of mixed domestic–industrial wastewater using cyanobacteria

Alexandria Sanitary Drainage Company (ASDCO), Alexandria, Egypt has two primary treatment plants, the eastern and the western wastewater treatment plants (EWTP and WWTP) that receive mixed domestic–industrial influents and discharge into L. Mariut. The lake is subjected therefore to severe levels of pollution and dominated by members of cyanobacteria that can cope with the high pollution load in the lake water. Isolation and utilization of the locally generated cyanobacterial biomass for remediation processes of highly toxic pollutants offers a very efficient and cheap tool for governmental or private industrial activities in Alexandria and will generate a source of revenue in Egyptian localities. The main objective of the present study was to investigate the biodegradation and biosorption capacity of some potential cyanobacterial species dominating the lake ecosystem toward organic and inorganic contaminants polluting the primary-treated effluents of the EWTP and WWTP. The primary effluents were subjected to biological treatment using three axenic cyanobacterial strains (Anabaena oryzae, Anabaena variabilis and Tolypothrix ceytonica) as batch system for 7 days. Removal efficiencies (RE) of the different contaminants were evaluated and compared. Results confirmed the high efficiencies of the investigated species for the removal of the target contaminants which were species and contaminant-dependent. BOD₅ and COD recorded 89.29 and 73.68% as maximum RE(s) achieved by Anabaena variabilis and Anabaena oryzae, respectively. The highest RE of the TSS recorded 64.37% achieved by Tolypothrix ceytonica, while 38.84% was recorded as the highest TSD RE achieved by Anabaena variabilis. Tolypothrix ceytonica also exhibited the highest RE for FOG recorded 93.75%. Concerning the contaminant metals, Tolypothrix ceytonica showed the highest biosorption capacity where 86.12 and 94.63% RE were achieved for Zn and Cu, respectively. In conclusion, results of the present study confirmed the advantageous potential of using the tested cyanobacterial species for the treatment of contaminated wastewater. Results also clearly showed the quality improvement of the discharged wastewater which in turn will eliminate or at least minimize the expected deterioration of the receiving environment.     

Robust stabilization and H(∞) controllers design for stochastic genetic regulatory networks with time-varying delays and structured uncertainties

This paper deals with the problem of stabilization design and H(∞) control for a class of genetic regulatory networks (GRNs) with both intrinsic perturbation and extrinsic perturbation. Some delay-dependent mean-square stabilization criteria are put forward for nominal systems and uncertain systems by using an improved free-weighting matrix approach. As a result, the corresponding stabilization controllers and H(∞) controllers of GRNs are constructed with time delays compensated and suboptimal solutions are obtained via exploiting an iterative procedure together with the linear matrix inequality (LMI) method and the cone complementarity liberalization (CCL) algorithm. Finally, three numerical examples are presented to illustrate the effectiveness of the proposed theoretical results.     

Bursting synchronization dynamics of pancreatic β-cells with electrical and chemical coupling

Based on bifurcation analysis, the synchronization behaviors of two identical pancreatic β-cells connected by electrical and chemical coupling are investigated, respectively. Various firing patterns are produced in coupled cells when a single cell exhibits tonic spiking or square-wave bursting individually, irrespectively of what the cells are connected by electrical or chemical coupling. On the one hand, cells can burst synchronously for both weak electrical and chemical coupling when an isolated cell exhibits tonic spiking itself. In particular, for electrically coupled cells, under the variation of the coupling strength there exist complex transition processes of synchronous firing patterns such as “fold/limit cycle” type of bursting, then anti-phase continuous spiking, followed by the “fold/torus” type of bursting, and finally in-phase tonic spiking. On the other hand, it is shown that when the individual cell exhibits square-wave bursting, suitable coupling strength can make the electrically coupled system generate “fold/Hopf” bursting via “fold/fold” hysteresis loop; whereas, the chemically coupled cells generate “fold/subHopf” bursting. Especially, chemically coupled bursters can exhibit inverse period-adding bursting sequence. Fast–slow dynamics analysis is applied to explore the generation mechanism of these bursting oscillations. The above analysis of bursting types and the transition may provide us with better insight into understanding the role of coupling in the dynamic behaviors of pancreatic β-cells.     

Reuse of structural domain–domain interactions in protein networks

Background  

Protein interactions are thought to be largely mediated by interactions between structural domains. Databases such as iPfam relate interactions in protein structures to known domain families. Here, we investigate how the domain interactions from the iPfam database are distributed in protein interactions taken from the HPRD, MPact, BioGRID, DIP and IntAct databases.     

Efficiency of neural networks for prediction of in vitro culture conditions and inoculum properties for optimum productivity

This study represents an ANN based computational scheming of physical, chemical and biological parameters at flask level for mass multiplication of plants through micropropagation using bioreactors of larger volumes. The optimal culture environment at small scale for Glycyrrhiza plant was predicted by using neural network approach in terms of pH and volume of growth medium per culture flask, incubation room temperature and month of inoculation along with inoculum properties in terms of inoculum size, fresh weight and number of explant per flask. This kind of study could be a model system in commercial propagation of various economically important plants in bioreactors using tissue culture technique. In present course of study the ANN was trained by implementing MATLAB neural network. A feed-forward back propagation type network was created for input vector (seven input elements), with single hidden layer (seven nodes) and one output unit in output layer. The ‘tansig’ and ‘purelin’ transfer functions were adapted for hidden and output layers respectively. The four training functions viz. traingda, trainrp, traincgf, traincgb were randomly selected to train four networks which further examined with available dataset. The efficiency of neural networks was concluded by the comparison of results obtained from this study with that of empirical data obtained from the detailed tissue culture experiments and designated as Target set (mean fresh weight biomass per culture flask after 40 days of in vitro culture duration). Efficiency of networks for better training initialization was judged on the basis of comparative analysis of ‘Mean Square Error at zero epoch’ for each network trained in which the least error at initial point was observed with trainrp followed by traincgb and traincgf. A comparative assessment between experimental target data range obtained from wet lab practice and all trained network output range for the efficiency of trained networks for least deviation from target range revealed the output range of network ‘trainrp’ was closest to the empirical target range while least comparison was worked out from network ‘traincgb’ which had output range more than the target decided and ultimately showed meaningless result.     

Rewiring of peatland plant–microbe networks outpaces species turnover

Enviro–climatic changes are thought to be causing alterations in ecosystem processes through shifts in plant and microbial communities; however, how links between plant and microbial communities change with enviro–climatic change is likely to be less straightforward but may be fundamental for many ecological processes. To address this, we assessed the composition of the plant community and the prokaryotic community – using amplicon-based sequencing – of three European peatlands that were distinct in enviro–climatic conditions. Bipartite networks were used to construct site-specific plant–prokaryote co-occurrence networks. Our data show that between sites, plant and prokaryotic communities differ and that turnover in interactions between the communities was complex. Essentially, turnover in plant–microbial interactions is much faster than turnover in the respective communities. Our findings suggest that network rewiring does largely result from novel or different interactions between species common to all realised networks. Hence, turnover in network composition is largely driven by the establishment of new interactions between a core community of plants and microorganisms that are shared among all sites. Taken together our results indicate that plant–microbe associations are context dependent, and that changes in enviro–climatic conditions will likely lead to network rewiring. Integrating turnover in plant–microbe interactions into studies that assess the impact of enviro–climatic change on peatland ecosystems is essential to understand ecosystem dynamics and must be combined with studies on the impact of these changes on ecosystem processes.