Emergence of stochastic resonance in a two-compartment hippocampal pyramidal neuron model.

In vitro studies have shown that hippocampal pyramidal neurons employ a mechanism similar to stochastic resonance (SR) to enhance the detection and transmission of weak stimuli generated at distal synapses. To support the experimental findings from the perspective of multicompartment model analysis, this paper aimed to elucidate the phenomenon of SR in a noisy two-compartment hippocampal pyramidal neuron model, which was a variant of the Pinsky-Rinzel neuron model with smooth activation functions and a hyperpolarization-activated cation current. With a bifurcation analysis of the model, we demonstrated the underlying dynamical structure responsible for the occurrence of SR. Furthermore, using a stochastically generated biphasic pulse train and broadband noise generated by the Orenstein-Uhlenbeck process as noise perturbation, both SR and suprathreshold SR were observed and quantified. Spectral analysis revealed that the distribution of spectral power under noise perturbations, in addition to inherent neurodynamics, is the main factor affecting SR behavior. The research results suggested that noise enhances the transmission of weak stimuli associated with elongated dendritic structures of hippocampal pyramidal neurons, thereby providing support for related laboratory findings.

Stochastic analysis of COVID-19 by a SEIR model with Lévy noise.

We propose a Lévy noise-driven susceptible-exposed-infected-recovered model incorporating media coverage to analyze the outbreak of COVID-19. We conduct a theoretical analysis of the stochastic model by the suitable Lyapunov function, including the existence and uniqueness of the positive solution, the dynamic properties around the disease-free equilibrium and the endemic equilibrium; we deduce a stochastic basic reproduction number R0 s for the extinction of disease, that is, if R0 s≤1, the disease will go to extinction. Particularly, we fit the data from Brazil to predict the trend of the epidemic. Our main findings include the following: (i) stochastic perturbation may affect the dynamic behavior of the disease, and larger noise will be more beneficial to control its spread; (ii) strengthening social isolation, increasing the cure rate and media coverage can effectively control the spread of disease. Our results support the feasible ways of containing the outbreak of the epidemic.

Emergence of Beta Oscillations of a Resonance Model for Parkinson's Disease.

In Parkinson's disease, the excess of beta oscillations in cortical-basal ganglia (BG) circuits has been correlated with normal movement suppression. In this paper, a physiologically based resonance model, generalizing an earlier model of the STN-GPe circuit, is employed to analyze critical dynamics of the occurrence of beta oscillations, which correspond to Hopf bifurcation. With the experimentally measured parameters, conditions for the occurrence of Hopf bifurcation with time delay are deduced by means of linear stability analysis, center manifold theorem, and normal form analysis. It is found that beta oscillations can be induced by increasing synaptic transmission delay. Furthermore, it is revealed that the oscillations originate from interaction among different synaptic connections. Our analytical results are consistent with the previous experimental and simulating findings, thus may provide a more systematic insight into the mechanisms underlying the transient beta bursts.

Modelling the effects of cutting off infected branches and replanting on fire-blight transmission using Filippov systems.

Fire blight is one of the most devastating plant diseases in the world. This paper proposes a Filippov fire-blight model incorporating cutting off infected branches and replanting susceptible trees. The Filippov-type model is formulated by considering that no control strategy is taken if the number of infected trees is less than an infected threshold level Ic; further, we cut off infected branches once the number of infected trees exceeds Ic; meanwhile, we replant trees if the number of susceptible trees is less than a susceptible threshold level Sc. The global dynamical behaviour of the Filippov system is investigated. It is shown that model solutions ultimately converge to the positive equilibrium that lies in the region above Ic, or below Ic, or on I=Ic, as we vary the susceptible and infected threshold values Sc and Ic. Our results indicate that proper combinations of the susceptible and infected threshold values based on the threshold policy can lead the number of infected trees to an acceptable level, when complete eradication is not economically desirable.

Cellular microRNA miR-10a-5p inhibits replication of porcine reproductive and respiratory syndrome virus by targeting the host factor signal recognition particle 14.

Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most economically important viruses affecting the swine industry worldwide. MicroRNAs have recently been demonstrated to play vital roles in virus-host interactions. Our previous research on small RNA deep sequencing showed that the expression level of miR-10a increased during the viral life cycle. The present study sought to determine the function of miR-10a and its molecular mechanism during PRRSV infection. In the current study, the result of PRRSV infection inducing miR-10a expression was validated by quantitative reverse transcriptase PCR. Overexpression of miR-10a-5p using its mimics markedly reduced the expression level of intracellular PRRSV ORF7 mRNA and N protein. Simultaneously, overexpression of miR-10a-5p also significantly decreased the expression level of extracellular viral RNA and virus titres in the supernatants. These results demonstrated that miR-10a-5p could suppress the replication of PRRSV. A direct interaction between miR-10a-5p and signal recognition particle 14 (SRP14) was confirmed using bioinformatic prediction and experimental verification. miR-10a-5p could directly target the 3'UTR of pig SRP14 mRNA in a sequence-specific manner and decrease SRP14 expression through translational repression but not mRNA degradation. Further, knockdown of SRP14 by small interfering RNA also inhibits the replication of PRRSV. Collectively, these results suggested that miR-10a-5p inhibits PRRSV replication through suppression of SRP14 expression, which not only provides new insights into virus-host interactions during PRRSV infection but also suggests potential new antiviral strategies against PRRSV infection.

Switches in a genetic regulatory system under multiplicative non-Gaussian noise.

The non-Gaussian noise is multiplicatively introduced to model the universal fluctuation in the gene regulation of the bacteriophage λ. To investigate the key effect of non-Gaussian noise on the genetic on/off switch dynamics from the viewpoint of quantitative analysis, we employ the high-order perturbation expansion to deduce the stationary probability density of repressor concentration and the mean first passage time from low concentration to high concentration and vice versa. The occupation probability of different concentration states can be estimated from the height and shape of the peaks of the stationary probability density, which could be used to determine the overall expression level. A further concern is the mean first passage time, also referred to as the mean switching time, which can be adopted as an important measure to characterize the adaptability of gene expression to the environmental variation. Through our investigation, it is observed that the non-Gaussian heavy-tailed noise can better induce the switches between distinct genetic expression states and additionally, it accelerates the switching process more evidently compared to the Gaussian noise and the bounded noise.

Genome and molecular characterization of a CSFV strain isolated from a CSF outbreak in South China.

In the present study, the full-length nucleotide sequences of the CSFV-GZ-2009 strain of classical swine fever virus (CSFV) isolated from a hog pen in Guangdong province in China was determined. Results demonstrated that the genome of CSFV-GZ-2009 is 12,298 nucleotides (nt) in length, is composed of a 373-nt 5'-untranslated region (UTR), has an 11,697-nt open reading frame encoding a polyprotein of 3,898 amino acids, and has a 228-nt 3'-UTR. Genome comparison of the CSFV-GZ-2009 isolate (GenBank accession No. HQ380231) with other CSFV strains was also analyzed. Gene regions from CSFV-GZ-2009 and other known strains were shown to share 92.7-96.7% identity at the nucleotide level and 94.7-99.2% identity at the amino acid level. Phylogenetic analysis of the full-length genome and the following regions E(rns), E2 and NS5B revealed that the CSFV-GZ-2009 isolate was classified within subgroup 1.1 of group I and closely related to the highly virulent strain JL1 (06), cF114, Shimen and SWH with pairwise distances of 0.0037, 0.0043, 0.0058 and 0.0107, respectively. Analysis of recombination with the SimPlot program demonstrated that strain CSFV-GZ-2009 was not a naturally homologous recombinant. Furthermore, the change of clinical signs of pigs after infection of CSFV-GZ-2009 isolates showed typical symptoms such as diarrhea, persistent fever, and mononuclear lymphocytopenia after CSFV infection. Based on phylogenetic analysis and an animal infection test, we could conclude that the CSFV-GZ-2009 isolate belonged to subgroup 1.1 of group I and was of high virulence.

Mutual information measure of visual perception based on noisy spiking neural networks.

Note that images of low-illumination are weak aperiodic signals, while mutual information can be used as an effective measure for the shared information between the input stimulus and the output response of nonlinear systems, thus it is possible to develop novel visual perception algorithm based on the principle of aperiodic stochastic resonance within the frame of information theory. To confirm this, we reveal this phenomenon using the integrate-and-fire neural networks of neurons with noisy binary random signal as input first. And then, we propose an improved visual perception algorithm with the image mutual information as assessment index. The numerical experiences show that the target image can be picked up with more easiness by the maximal mutual information than by the minimum of natural image quality evaluation (NIQE), which is one of the most frequently used indexes. Moreover, the advantage of choosing quantile as spike threshold has also been confirmed. The improvement of this research should provide large convenience for potential applications including video tracking in environments of low illumination.

Genetic diversity and positive selection analysis of classical swine fever virus isolates in south China.

Classical swine fever virus (CSFV) causes a highly contagious disease that leads to significant economic losses in the pig industry worldwide. However, there is a paucity of knowledge on the accurate genotyping of CSFV isolates in south China. This study genotyped the E2 gene of 14 CSFV strains isolated during 2008-2010 from domestic pigs in different districts of south China. Phylogenetic analyses revealed that all of the 14 CSFV isolates were clustered into genetic subgroup 1.1. This contrasts with most parts of China, where group 2 isolates are predominant. Furthermore, the positive selection pressures acting on the E(rns) and E2 envelope protein genes of CSFV were assessed and a site-by-site analysis of the dN/dS ratio was performed to identify specific codons that undergo diversification under positive selection. While no significant evidence for positive selection was observed in E(rns), two positively selected sites at amino acid residues 49 and 72 in the E2 encoding region were identified. Our results revealed that a predominance of subgroup 1.1 CSFV isolates is currently circulating in some districts of south China, which appear to be unrelated to the Chinese C-strain vaccine. Moreover, the envelope protein gene, E2, has undergone positive selection in 14 CSFV strains and two positively selected sites have been identified in this study. Understanding the molecular epidemiology and functional importance of these positively selected amino acid positions could help to predict possible changes in virulence, the development of vaccines and disease control.

Aperiodic stochastic resonance in neural information processing with Gaussian colored noise.

The aim of this paper is to explore the phenomenon of aperiodic stochastic resonance in neural systems with colored noise. For nonlinear dynamical systems driven by Gaussian colored noise, we prove that the stochastic sample trajectory can converge to the corresponding deterministic trajectory as noise intensity tends to zero in mean square, under global and local Lipschitz conditions, respectively. Then, following forbidden interval theorem we predict the phenomenon of aperiodic stochastic resonance in bistable and excitable neural systems. Two neuron models are further used to verify the theoretical prediction. Moreover, we disclose the phenomenon of aperiodic stochastic resonance induced by correlation time and this finding suggests that adjusting noise correlation might be a biologically more plausible mechanism in neural signal processing.

Moment dynamics for gene regulation with rational rate laws.

This aim of this paper is mainly to investigate the performance of two typical moment closure schemes in gene regulatory master equations of rational rate laws. When the reaction rate is polynomial, the error bounds between the authentic and approximate moments obtained by schemes of Gaussian moment closure and log-normal moment closure are explicitly given. When the reaction rate is not polynomial, it is shown that the two schemes both behave well in the absence of active-inactive state switch, but in the presence of active-inactive state switch the log-normal closure scheme is far superior to the Gaussian closure scheme in capturing the asymptotic ensemble statistics. Moreover, the accuracy of the log-normal closure method is further confirmed by steady-state analytic results and the conditional Gaussian closure method. It is also disclosed that optimal negative feedback exists in suppressing protein noise in the presence of the on-off switch control.

Stochastic Resonance Based Visual Perception Using Spiking Neural Networks.

Our aim is to propose an efficient algorithm for enhancing the contrast of dark images based on the principle of stochastic resonance in a global feedback spiking network of integrate-and-fire neurons. By linear approximation and direct simulation, we disclose the dependence of the peak signal-to-noise ratio on the spiking threshold and the feedback coupling strength. Based on this theoretical analysis, we then develop a dynamical system algorithm for enhancing dark images. In the new algorithm, an explicit formula is given on how to choose a suitable spiking threshold for the images to be enhanced, and a more effective quantifying index, the variance of image, is used to replace the commonly used measure. Numerical tests verify the efficiency of the new algorithm. The investigation provides a good example for the application of stochastic resonance, and it might be useful for explaining the biophysical mechanism behind visual perception.

Stochastic master equation for early protein aggregation in the transthyretin amyloid disease.

It is significant to understand the earliest molecular events occurring in the nucleation of the amyloid aggregation cascade for the prevention of amyloid related diseases such as transthyretin amyloid disease. We develop chemical master equation for the aggregation of monomers into oligomers using reaction rate law in chemical kinetics. For this stochastic model, lognormal moment closure method is applied to track the evolution of relevant statistical moments and its high accuracy is confirmed by the results obtained from Gillespie's stochastic simulation algorithm. Our results show that the formation of oligomers is highly dependent on the number of monomers. Furthermore, the misfolding rate also has an important impact on the process of oligomers formation. The quantitative investigation should be helpful for shedding more light on the mechanism of amyloid fibril nucleation.

Subcritical Hopf Bifurcation and Stochastic Resonance of Electrical Activities in Neuron under Electromagnetic Induction.

The FitzHugh-Nagumo model is improved to consider the effect of the electromagnetic induction on single neuron. On the basis of investigating the Hopf bifurcation behavior of the improved model, stochastic resonance in the stochastic version is captured near the bifurcation point. It is revealed that a weak harmonic oscillation in the electromagnetic disturbance can be amplified through stochastic resonance, and it is the cooperative effect of random transition between the resting state and the large amplitude oscillating state that results in the resonant phenomenon. Using the noise dependence of the mean of interburst intervals, we essentially suggest a biologically feasible clue for detecting weak signal by means of neuron model with subcritical Hopf bifurcation. These observations should be helpful in understanding the influence of the magnetic field to neural electrical activity.

Fractional Gaussian noise-enhanced information capacity of a nonlinear neuron model with binary signal input.

This paper reveals the effect of fractional Gaussian noise with Hurst exponent H∈(1/2,1) on the information capacity of a general nonlinear neuron model with binary signal input. The fGn and its corresponding fractional Brownian motion exhibit long-range, strong-dependent increments. It extends standard Brownian motion to many types of fractional processes found in nature, such as the synaptic noise. In the paper, for the subthreshold binary signal, sufficient conditions are given based on the "forbidden interval" theorem to guarantee the occurrence of stochastic resonance, while for the suprathreshold binary signal, the simulated results show that additive fGn with Hurst exponent H∈(1/2,1) could increase the mutual information or bits count. The investigation indicated that the synaptic noise with the characters of long-range dependence and self-similarity might be the driving factor for the efficient encoding and decoding of the nervous system.

Observing stochastic resonance in an underdamped bistable Duffing oscillator by the method of moments.

The method of moments is applied to an underdamped bistable oscillator driven by Gaussian white noise and a weak periodic force for the observations of stochastic resonance and the resulting resonant structures are compared with those from Langevin simulation. The physical mechanisms of the stochastic resonance are explained based on the evolution of the intrawell frequency peak and the above-barrier frequency peak via the noise intensity and the fluctuation-dissipation theorem, and the three possible sources of stochastic resonance in the system are confirmed. Additionally, with the noise intensity fixed, the stochastic resonant structures are also observed by adjusting the nonlinear parameter.

Parabolic bursting induced by veratridine in rat injured sciatic nerves.

A specific bursting, parabolic bursting induced by veratridine, has been observed in rat injured sciatic nerve. With the help of Plant model, the biophysical mechanism for such a phenomenon is revealed from the viewpoint of nonlinear dynamical theory. The slow sodium influx educed by veratridine and the calcium-dependent potassium outflux are regarded as the two slow variables, which are responsible for the parabolic bursting. Furthermore, the roles that veratridine plays in the emergence of the parabolic bursting, namely inhibiting the inactivation of sodium channels and eliciting the slow sodium influx, are clarified.

Size selectivity in the confined ternary colloidal mixtures: the depletion in the competition.

Based on classical density functional theory, we study the size selectivity for ternary colloidal mixtures in the presence of a Gauss barrier. The competition between the external potential and the depletion potential is also investigated. The effects of bulk fraction of each species, the size asymmetry, and the strength and width of the Gauss barrier on the selectivity of the big species are calculated and analyzed in detail. The results in different conditions of bulk fraction suggest that the larger the bulk fraction for the small species, the stronger selectivity of big particles. On the contrary, increase of bulk fraction for the big species leads to a reduction in selectivity. In addition, results under different conditions of size asymmetry suggest that the medium particles can also be selected by the Gauss barrier when they are sufficiently large in comparison to the small particles. We also demonstrate the effect of barrier geometry on the selectivity of the big species and the competition between the depletion and the external potential.

Classical swine fever virus suppresses maturation and modulates functions of monocyte-derived dendritic cells without activating nuclear factor kappa B.

Classical swine fever virus (CSFV) compromises the host immune system, causing the severe disease of pigs. Dendritic cells (DCs) are the most potent inducers of immune responses. In the present study, we investigated the functional properties of porcine monocyte-derived DCs (Mo-DCs) affected by CSFV. Results showed that the expression of surface markers of DCs such as major histocompatibility complex class II (MHC-II), CD80, CD83 and CD86 were unimpaired, but an obviously increased expression of CD172a in DCs was noticed 48 h after CSFV infection. The expression profiles of cytokines were detected in cultured Mo-DCs after various treatments for 48 h by Q-RT-PCR. The findings suggested that CSFV infection significantly increased the mRNA expression of IL-10 and TNF-α, and inhibited IL-12 expression, with little effect on IFN-α and IFN-γ expression. We further demonstrated that CSFV was incapable of activating the nuclear factor kappa B (NF-κB) in infected DCs, which was characterized by an unvaried DNA binding activity of NF-κB, the lack of translocation of p65/RelA from the cytoplasm to the nucleus and the stabilization of p65/RelA expression. Furthermore, Western blot analysis indicated that the inactivation of NF-κB was due to the failure of IκBα degradation. The data demonstrated that CSFV could be replicated in DCs and CSFV infection could modulate the secretion of crucial co-stimulatory molecules and cytokines which down-regulated maturation of DCs, without activating NF-κB in DCs. Thus, the results suggested a possible mechanism for CSFV evasion of innate host defenses, providing the basis for understanding molecular pathways in CSFV pathogenesis.

Suprathreshold stochastic resonance in neural processing tuned by correlation.

Suprathreshold stochastic resonance (SSR) is examined in the context of integrate-and-fire neurons, with an emphasis on the role of correlation in the neuronal firing. We employed a model based on a network of spiking neurons which received synaptic inputs modeled by Poisson processes stimulated by a stepped input signal. The smoothed ensemble firing rate provided an output signal, and the mutual information between this signal and the input was calculated for networks with different noise levels and different numbers of neurons. It was found that an SSR effect was present in this context. We then examined a more biophysically plausible scenario where the noise was not controlled directly, but instead was tuned by the correlation between the inputs. The SSR effect remained present in this scenario with nonzero noise providing improved information transmission, and it was found that negative correlation between the inputs was optimal. Finally, an examination of SSR in the context of this model revealed its connection with more traditional stochastic resonance and showed a trade-off between supratheshold and subthreshold components. We discuss these results in the context of existing empirical evidence concerning correlations in neuronal firing.