Criticality and partial synchronization analysis in Wilson-Cowan and Jansen-Rit neural mass models.

Synchronization is a phenomenon observed in neuronal networks involved in diverse brain activities. Neural mass models such as Wilson-Cowan (WC) and Jansen-Rit (JR) manifest synchronized states. Despite extensive research on these models over the past several decades, their potential of manifesting second-order phase transitions (SOPT) and criticality has not been sufficiently acknowledged. In this study, two networks of coupled WC and JR nodes with small-world topologies were constructed and Kuramoto order parameter (KOP) was used to quantify the amount of synchronization. In addition, we investigated the presence of SOPT using the synchronization coefficient of variation. Both networks reached high synchrony by changing the coupling weight between their nodes. Moreover, they exhibited abrupt changes in the synchronization at certain values of the control parameter not necessarily related to a phase transition. While SOPT was observed only in JR model, neither WC nor JR model showed power-law behavior. Our study further investigated the global synchronization phenomenon that is known to exist in pathological brain states, such as seizure. JR model showed global synchronization, while WC model seemed to be more suitable in producing partially synchronized patterns.

Omicron vs. the rest: Assessing the competitive dynamics and coinfection scenarios of COVID-19 strains on a social network.

The rapid spread and evolving nature of COVID-19 variants have raised concerns regarding their competitive dynamics and coinfection scenarios. In this study, we assess the competitive interactions between the Omicron variant and other prominent variants (Alpha, Beta and Delta) on a social network, considering both single infection and coinfection states. Using the SIRS model, we simulate the progression of these variants and analyze their impact on infection rates, mortality and overall disease burden. Our findings demonstrate that the Alpha and Beta strains exhibit comparable contagion levels, with the Alpha strain displaying higher infection and mortality rates. Moreover, the Delta strain emerges as the most prevalent and virulent strain, surpassing the other variants. When introduced alongside the less virulent Omicron strain, the Delta strain results in higher infection and mortality rates. However, the Omicron strain's dominance leads to an overall increase in disease statistics. Remarkably, our study highlights the efficacy of the Omicron variant in supplanting more virulent strains and its potential role in mitigating the spread of infectious diseases. The Omicron strain demonstrates a competitive advantage over the other variants, suggesting its potential to reduce the severity of the disease and alleviate the burden on healthcare systems. These findings underscore the importance of monitoring and understanding the dynamics of COVID-19 variants, as they can inform effective prevention and mitigation strategies, particularly with the emergence of variants that possess a relative advantage in controlling disease transmission.

DNA sequencing via molecular dynamics simulation with functionalized graphene nanopore.

Through this research, functionalized graphene nanopores are used to verify how effective such an apparatus for DNA sequencing is. The circular symmetric pores are functionalized with hydrogen and a hydroxyl group bonded with carbon atoms of the pore rim. Plus, two adenine bases are also put at the rim perimeter to verify whether such a combination would lead to base detection. A homopolymer of single-stranded DNA (ssDNA) is pulled through a nanopore using steered molecular dynamics (SMD) simulation. Pulling force profile, moving fashion of ssDNA in irreversible DNA pulling as well as the base orientation during translocation relative to the graphene plane, called beta angle, are assessed. Based on the studied parameters, SMD force, and base orientation, the hydrogenated and hydroxylated pores do not show a clear distinction between bases, while the adenine-functionalized pore can distinguish between adenine and cytosine. Therefore, there may be some hope for achieving single-base sequencing, while further research is needed.

Nasal airflow promotes default mode network activity.

BACKGROUND AND OBJECTIVES: Default mode network (DMN) is a principal network that is more active at the baseline functional state of consciousness and spontaneous brain activity. Nasal breathing beyond the oxygen supply, entrained brain oscillations in widespread brain regions. Consistent with the important role of nasal breathing on neural oscillation for brain function, here we aimed to evaluate respiration entrained DMN rhythms. MATERIALS AND METHODS: Using electroencephalography (EEG), we assessed the power spectral density and connectivity in DMN during the resting state among a group of sixteen healthy during three successive sessions. In addition to DMN, synchrony of the signal over the widespread cortical regions including somatosensory areas was investigated. Signal acquisition sessions consist of three times including nasal breathing, oral breathing, and nasal air-puff state that odorless air was puffed using a nasal cannula via an electrical valve (open duration of 630 ms) with a frequency of 0.2 Hz while subjects spontaneously breath orally. RESULTS: Our analyses demonstrated that nasal airflow, during both nasal breathing and nasal air-puff states, enhanced the power and connectivity of DMN regions specially at higher frequency bands, particularly gamma ranges. Enhancement in brain areas activity and connectivity including DMN and somatosensory due to the nasal airflow were not affected even in the condition that subjects were not attending to the nasal air-puff. CONCLUSIONS: Nasal airflow promotes brain oscillations, particularly at the range of gamma that is very essential for higher brain functions.

The trend of disruption in the functional brain network topology of Alzheimer's disease.

Alzheimer's disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain's functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer's disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.

Phase synchronization and measure of criticality in a network of neural mass models.

Synchronization has an important role in neural networks dynamics that is mostly accompanied by cognitive activities such as memory, learning, and perception. These activities arise from collective neural behaviors and are not totally understood yet. This paper aims to investigate a cortical model from this perspective. Historically, epilepsy has been regarded as a functional brain disorder associated with excessive synchronization of large neural populations. Epilepsy is believed to arise as a result of complex interactions between neural networks characterized by dynamic synchronization. In this paper, we investigated a network of neural populations in a way the dynamics of each node corresponded to the Jansen-Rit neural mass model. First, we study a one-column Jansen-Rit neural mass model for four different input levels. Then, we considered a Watts-Strogatz network of Jansen-Rit oscillators. We observed an epileptic activity in the weak input level. The network is considered to change various parameters. The detailed results including the mean time series, phase spaces, and power spectrum revealed a wide range of different behaviors such as epilepsy, healthy, and a transition between synchrony and asynchrony states. In some points of coupling coefficients, there is an abrupt change in the order parameters. Since the critical state is a dynamic candidate for healthy brains, we considered some measures of criticality and investigated them at these points. According to our study, some markers of criticality can occur at these points, while others may not. This occurrence is a result of the nature of the specific order parameter selected to observe these markers. In fact, The definition of a proper order parameter is key and must be defined properly. Our view is that the critical points exhibit clear characteristics and invariance of scale, instead of some types of markers. As a result, these phase transition points are not critical as they show no evidence of scaling invariance.

In-vitro, in-vivo, and in-silico assessment of radical scavenging and cytotoxic activities of Oliveria decumbens essential oil and its main components.

We aimed to explore and compare new insights on the pharmacological potential of Oliveria decumbence essential oil (OEO) and its main components highlighting their antioxidant activity in-vitro, in-vivo, and in-silico and also cytotoxic effects of OEO against A549 lung cancer cells. At first, based on GC-MS analysis, thymol, carvacrol, p-cymene, and γ-terpinene were introduced as basic ingredients of OEO and their in-vitro antioxidant capacity was considered by standard methods. Collectively, OEO exhibited strong antioxidant properties even more than its components. In LPS-stimulated macrophages treated with OEO, the reduction of ROS (Reactive-oxygen-species) and NO (nitric-oxide) and down-regulation of iNOS (inducible nitric-oxide-synthase) and NOX (NADPH-oxidase) mRNA expression was observed and compared with that of OEO components. According to the results, OEO, thymol, and carvacrol exhibited the highest radical scavenging potency compared to p-cymene, and γ-terpinene. In-silico Molecular-Docking and Molecular-Dynamics simulation indicated that thymol and carvacrol but no p-cymene and γ-terpinene may establish coordinative bonds in iNOS active site and thereby inhibit iNOS. However, they did not show any evidence for NOX inhibition. In the following, MTT assay showed that OEO induces cytotoxicity in A549 cancer cells despite having a limited effect on L929 normal cells. Apoptotic death and its dependence on caspase-3 activity and Bax/Bcl2 ratio in OEO-treated cells were established by fluorescence microscopy, flow cytometry, colorimetric assay, and western blot analysis. Additionally, flow cytometry studies demonstrated increased levels of ROS in OEO-treated cells. Therefore, OEO, despite showing antioxidant properties, induces apoptosis in cancer cells by increasing ROS levels. Collectively, our results provided new insight into the usage of OEO and main components, thymol, and carvacrol, into the development of novel antioxidant and anti-cancer agents.

The effects of beta-cell mass and function, intercellular coupling, and islet synchrony on [Formula: see text] dynamics.

Type 2 diabetes (T2D) is a challenging metabolic disorder characterized by a substantial loss of [Formula: see text]-cell mass and alteration of [Formula: see text]-cell function in the islets of Langerhans, disrupting insulin secretion and glucose homeostasis. The mechanisms for deficiency in [Formula: see text]-cell mass and function during the hyperglycemia development and T2D pathogenesis are complex. To study the relative contribution of [Formula: see text]-cell mass to [Formula: see text]-cell function in T2D, we make use of a comprehensive electrophysiological model of human [Formula: see text]-cell clusters. We find that defect in [Formula: see text]-cell mass causes a functional decline in single [Formula: see text]-cell, impairment in intra-islet synchrony, and changes in the form of oscillatory patterns of membrane potential and intracellular [Formula: see text] concentration, which can lead to changes in insulin secretion dynamics and in insulin levels. The model demonstrates a good correspondence between suppression of synchronizing electrical activity and published experimental measurements. We then compare the role of gap junction-mediated electrical coupling with both [Formula: see text]-cell synchronization and metabolic coupling in the behavior of [Formula: see text] concentration dynamics within human islets. Our results indicate that inter-[Formula: see text]-cellular electrical coupling depicts a more important factor in shaping the physiological regulation of islet function and in human T2D. We further predict that varying the whole-cell conductance of delayed rectifier [Formula: see text] channels modifies oscillatory activity patterns of [Formula: see text]-cell population lacking intercellular coupling, which significantly affect [Formula: see text] concentration and insulin secretion.

Mechanical properties of ester- and ether-DPhPC bilayers: A molecular dynamics study.

In addition to its biological importance, DPhPC lipid bilayers are widely used in droplet bilayers, study of integral membrane proteins, drug delivery systems as well as patch-clamp electrophysiology of ion channels, yet their mechanical properties are not fully measured. Herein, we examined the effect of the ether linkage on the mechanical properties of ester- and ether-DPhPC lipid bilayers using all-atom molecular dynamics simulation. The values of area per lipid, thickness, intrinsic lateral pressure profile, order parameter, and elasticity moduli were estimated using various computational frameworks and were compared with available experimental values. Overall, a good agreement was observed between the two. The global properties of the two lipid bilayers are vastly different, with ether bilayer being stiffer, less ordered, and thicker than ester bilayer. Moreover, ether linkage decreased the area per lipid in the ether lipid bilayer. Our computational framework and output demonstrate how ether modification changes the mechano-chemical properties of DPhPC bilayers.

The Concepts and Applications of Fractional Order Differential Calculus in Modeling of Viscoelastic Systems: A Primer.

Viscoelasticity and other related phenomena are of great importance in the study of mechanical properties of materials, especially biological materials. Certain materials demonstrate some complicated behavior under mechanical tests that cannot be described by a standard linear equation (SLE), mostly due to the shape memory effect during the deformation phase. Recently, researchers have been making use of fractional calculus (FC) in order to probe viscoelasticity of such materials accurately. FC is a powerful tool for modeling complicated phenomena. In this tutorial paper, it is sought to provide clear descriptions of this powerful tool and its techniques and implementation. It is endeavored to keep the details to a minimum while still conveying a good idea of what and how can be done with this powerful tool. The reader will be provided with the basic techniques that are used to solve the fractional equations analytically and/or numerically. More specifically, simulating the shape memory phenomena with this powerful tool will be studied from different perspectives, and some physical interpretations are made in this regard. This paper is also a review of fractional order models of viscoelastic phenomena that are widespread in bioengineering. Thus, in order to show the relationship between fractional models and SLEs, a new fractal system comprising spring and damper elements is considered and the constitutive equation is approximated with a fractional element. Finally, after a brief literature review, two fractional models are utilized to investigate the viscoelasticity of the cell and a comparison is made between the findings and the experimental data from the previous models. Verification results indicate that the fractional model not only matches well with the experimental data but also can be a good substitute for previously used models.

A proposed mechanism for mind-brain interaction using extended Bohmian quantum mechanics in Avicenna's monotheistic perspective.

In quantum approaches to consciousness, the authors try to propose a model and mechanism for the mind-brain interaction using modern physics and some quantum concepts which do not exist in the classical physics. The independent effect of mind on the brain has been one of the challenging issues in the history of science and philosophy. In some recent mind-brain interaction models, the direct influence of mind on matter is either not accepted (as in Stapp's model) or not clear, and there have not been any clear mechanism for it (as in Penrose-Hameroff's model or in Eccles's model). In this manuscript we propose a model and mechanism for mind's effect on the matter using an extended Bohmian quantum mechanics and Avicenna's ideas. We show that mind and mental states can affect brain's activity without any violation of physical laws. This is a mathematical and descriptive model which shows the possibility of providing a causal model for mind's effect on matter. It is shown that this model guarantees the realistic philosophical constraints and respects the laws of nature. In addition, it is shown that it is in agreement with the Libet style experimental results and parapsychological data. To propose this model, we obtained a modified (non-unitary) Schrödinger equation via second quantization method which affects the particle through a modified quantum potential and a new term in the continuity equation. At the second quantized level, which is equivalent to quantum field theory level (QFT), we can use the path integral formalism of Feynman. We show that there are three methods to extend Bohmian QM via path integral formalism, which has different interpretations. By numerical simulation of trajectories in the two-slits experiment, we show their differences and choose one of these methods for our mind-brain model which can be the basis for explaining some phenomena which are not possible to explain in the standard Bohmian QM.

Implicit solvent systematic coarse-graining of dioleoylphosphatidylethanolamine lipids: From the inverted hexagonal to the bilayer structure.

Lamellar and hexagonal lipid structures are of particular importance in the biological processes such as membrane fusion and budding. Atomistic simulations of formation of these phases and transitions between them are computationally prohibitive, hence development of coarse-grained models is an important part of the methodological development in this area. Here we apply systematic bottom-up coarse-graining to model different phase structures formed by 1,2-dioleoylphosphatidylethanolamine (DOPE) lipid molecules. We started from atomistic simulations of DOPE lipids in water carried out at two different water/lipid molar ratio corresponding to the lamellar Lα and inverted hexagonal HII structures at low and high lipid concentrations respectively. The atomistic trajectories were mapped to coarse-grained trajectories, in which each lipid was represented by 14 coarse-grained sites. Then the inverse Monte Carlo method was used to compute the effective coarse-grained potentials which for the coarse-grain model reproduce the same structural properties as the atomistic simulations. The potentials derived from the low concentration atomistic simulation were only able to form a bilayer structure, while both Lα and HII lipid phases were formed in simulations with potentials obtained at high concentration. The typical atomistic configurations of lipids at high concentration combine fragments of both lamellar and non-lamellar structures, that is reflected in the extracted coarse-grained potentials which become transferable and can form a wide range of structures including the inverted hexagonal, bilayer, tubule, vesicle and micellar structures.

Simulation of the effect of an external GHz electric field on the potential energy profile of Ca2+ ions in the selectivity filter of the CaV Ab channel.

CaV channels are transmembrane proteins that mediate and regulate ion fluxes across cell membranes, and they are activated in response to action potentials to allow Ca2+ influx. Since ion channels are composed of charge or polar groups, an external alternating electric field may affect the ion-selective membrane transport and the performance of the channel. In this article, we have investigated the effect of an external GHz electric field on the dynamics of calcium ions in the selectivity filter of the CaV Ab channel. Molecular dynamics (MD) simulations and the potential of mean force (PMF) calculations were carried out, via the umbrella sampling method, to determine the free energy profile of Ca2+ ions in the CaV Ab channels in presence and absence of an external field. Exposing CaV Ab channel to 1, 2, 3, 4, and 5 GHz electric fields increases the depth of the potential energy well and this may result in an increase in the affinity and strength of Ca2+ ions to binding sites in the selectivity filter the channel. This increase of strength of Ca2+ ions binding in the selectivity filter may interrupt the mechanism of Ca2+ ion conduction, and leads to a reduction of Ca2+ ion permeation through the CaV Ab channel.

On the Influence of Structural Connectivity on the Correlation Patterns and Network Synchronization.

Since brain structural connectivity is the foundation of its functionality, in order to understand brain abilities, studying the relation between structural and functional connectivity is essential. Several approaches have been applied to measure the role of the structural connectivity in the emergent correlation/synchronization patterns. In this study, we investigates the cross-correlation and synchronization sensitivity to coupling strength between neural regions for different topological networks. We model the neural populations by a neural mass model that express an oscillatory dynamic. The results highlight that coupling between neural ensembles leads to various cross-correlation patterns and local synchrony even on an ordered network. Moreover, as the network departs from an ordered organization to a small-world architecture, correlation patterns, and synchronization dynamics change. Interestingly, at a certain range of the synaptic strength, by fixing the structural conditions, different organized patterns are seen at the different input signals. This variety switches to a bifurcation region by increasing the synaptic strength. We show that topological variations is a major factor of synchronization behavior and lead to alterations in correlated local clusters. We found the coupling strength (between cortical areas) to be especially important at conversions of correlation and synchronization states. Since correlation patterns generate functional connections and transitions of functional connectivity have been related to cognitive operations, these diverse correlation patterns may be considered as different dynamical states corresponding to various cognitive tasks.

Modeling ion permeation through a bacterial voltage-gated calcium channel CaVAb using molecular dynamics simulations.

Activation of voltage-gated calcium channels by action potentials leads to the influx of Ca2+ ions. In this study, the ion permeation characteristics in bacterial voltage-gated calcium (CaVAb) channels were investigated using molecular dynamics simulations. Furthermore, the potential of mean force (PMF) calculations was evaluated to determine the free energy profile for the permeation of cations (Ca2+ and Na+) and anions (Cl-) in the CaVAb channel. The results showed that both Ca2+ and Na+ cations experienced a deep energy well, while the Cl- anion experienced a relatively high energy barrier at the center of the selectivity filter (site 2). Consistent with the experimental data, the results obtained from this study demonstrate that sites 2 and 3 displayed the highest and lowest affinities to Ca2+, respectively. These findings also indicate that Na+ can easily and quickly pass through the CaVAb channel in the absence of Ca2+, while Cl- ions lack this ability.

Structural and Functional Effect of an Oscillating Electric Field on the Dopamine-D3 Receptor: A Molecular Dynamics Simulation Study.

Dopamine as a neurotransmitter plays a critical role in the functioning of the central nervous system. The structure of D3 receptor as a member of class A G-protein coupled receptors (GPCRs) has been reported. We used MD simulation to investigate the effect of an oscillating electric field, with frequencies in the range 0.6-800 GHz applied along the z-direction, on the dopamine-D3R complex. The simulations showed that at some frequencies, the application of an external oscillating electric field along the z-direction has a considerable effect on the dopamine-D3R. However, there is no enough evidence for prediction of changes in specific frequency, implying that there is no order in changes. Computing the correlation coefficient parameter showed that increasing the field frequency can weaken the interaction between dopamine and D3R and may decrease the Arg128{3.50}-Glu324{6.30} distance. Because of high stability of α helices along the z-direction, applying an oscillating electric field in this direction with an amplitude 10-time higher did not have a considerable effect. However, applying the oscillating field at the frequency of 0.6 GHz along other directions, such as X-Y and Y-Z planes, could change the energy between the dopamine and the D3R, and the number of internal hydrogen bonds of the protein. This can be due to the effect of the direction of the electric field vis-à-vis the ligands orientation and the interaction of the oscillating electric field with the dipole moment of the protein.

Corrigendum: MWCOACH: Protein complex prediction in weighted PPI networks [Genes Genet. Syst. (2015) 90, p. 317-324].

"J-STAGE Advance published date: 15 January 2015" on p. 317 should be changed to "J-STAGE Advance published date: 15 January 2016".

The Combined Effect of Hydrophobic Mismatch and Bilayer Local Bending on the Regulation of Mechanosensitive Ion Channels.

The hydrophobic mismatch between the lipid bilayer and integral membrane proteins has well-defined effect on mechanosensitive (MS) ion channels. Also, membrane local bending is suggested to modulate MS channel activity. Although a number of studies have already shown the significance of each individual factor, the combined effect of these physical factors on MS channel activity have not been investigated. Here using finite element simulation, we study the combined effect of hydrophobic mismatch and local bending on the archetypal mechanosensitive channel MscL. First we show how the local curvature direction impacts on MS channel modulation. In the case of MscL, we show inward (cytoplasmic) bending can more effectively gate the channel compared to outward bending. Then we indicate that in response to a specific local curvature, MscL inserted in a bilayer with the same hydrophobic length is more expanded in the constriction pore region compared to when there is a protein-lipid hydrophobic mismatch. Interestingly in the presence of a negative mismatch (thicker lipids), MscL constriction pore is more expanded than in the presence of positive mismatch (thinner lipids) in response to an identical membrane curvature. These results were confirmed by a parametric energetic calculation provided for MscL gating. These findings have several biophysical consequences for understanding the function of MS channels in response to two major physical stimuli in mechanobiology, namely hydrophobic mismatch and local membrane curvature.

Influence of Global and Local Membrane Curvature on Mechanosensitive Ion Channels: A Finite Element Approach.

Mechanosensitive (MS) channels are ubiquitous molecular force sensors that respond to a number of different mechanical stimuli including tensile, compressive and shear stress. MS channels are also proposed to be molecular curvature sensors gating in response to bending in their local environment. One of the main mechanisms to functionally study these channels is the patch clamp technique. However, the patch of membrane surveyed using this methodology is far from physiological. Here we use continuum mechanics to probe the question of how curvature, in a standard patch clamp experiment, at different length scales (global and local) affects a model MS channel. Firstly, to increase the accuracy of the Laplace's equation in tension estimation in a patch membrane and to be able to more precisely describe the transient phenomena happening during patch clamping, we propose a modified Laplace's equation. Most importantly, we unambiguously show that the global curvature of a patch, which is visible under the microscope during patch clamp experiments, is of negligible energetic consequence for activation of an MS channel in a model membrane. However, the local curvature (RL < 50) and the direction of bending are able to cause considerable changes in the stress distribution through the thickness of the membrane. Not only does local bending, in the order of physiologically relevant curvatures, cause a substantial change in the pressure profile but it also significantly modifies the stress distribution in response to force application. Understanding these stress variations in regions of high local bending is essential for a complete understanding of the effects of curvature on MS channels.

WCOACH: Protein complex prediction in weighted PPI networks.

Protein complexes are aggregates of protein molecules that play important roles in biological processes. Detecting protein complexes from protein-protein interaction (PPI) networks is one of the most challenging problems in computational biology, and many computational methods have been developed to solve this problem. Generally, these methods yield high false positive rates. In this article, a semantic similarity measure between proteins, based on Gene Ontology (GO) structure, is applied to weigh PPI networks. Consequently, one of the well-known methods, COACH, has been improved to be compatible with weighted PPI networks for protein complex detection. The new method, WCOACH, is compared to the COACH, ClusterOne, IPCA, CORE, OH-PIN, HC-PIN and MCODE methods on several PPI networks such as DIP, Krogan, Gavin 2002 and MIPS. WCOACH can be applied as a fast and high-performance algorithm to predict protein complexes in weighted PPI networks. All data and programs are freely available at http://bioinformatics.aut.ac.ir/wcoach.