Enhancing model identification with SINDy via nullcline reconstruction.

Many dynamical systems exhibit oscillatory behavior that can be modeled with differential equations. Recently, these equations have increasingly been derived through data-driven methods, including the transparent technique known as Sparse Identification of Nonlinear Dynamics (SINDy). This paper illustrates the importance of accurately determining the system's limit cycle position in phase space for identifying sparse and effective models. We introduce a method for identifying the limit cycle position and the system's nullclines by applying SINDy to datasets adjusted with various offsets. This approach is evaluated using three criteria: model complexity, coefficient of determination, and generalization error. We applied this method to several models: the oscillatory FitzHugh-Nagumo model, a more complex model consisting of two coupled cubic differential equations with a single stable state, and a multistable model of glycolytic oscillations. Our results confirm that incorporating detailed information about the limit cycle in phase space enhances the accuracy of model identification in oscillatory systems.

Perceptual Integration Compensates for Attention Deficit in Elderly during Repetitive Auditory-Based Sensorimotor Task.

Sensorimotor integration (SI) brain functions that are vital for everyday life tend to decline in advanced age. At the same time, elderly people preserve a moderate level of neuroplasticity, which allows the brain's functionality to be maintained and slows down the process of neuronal degradation. Hence, it is important to understand which aspects of SI are modifiable in healthy old age. The current study focuses on an auditory-based SI task and explores: (i) if the repetition of such a task can modify neural activity associated with SI, and (ii) if this effect is different in young and healthy old age. A group of healthy older subjects and young controls underwent an assessment of the whole-brain electroencephalography (EEG) while repetitively executing a motor task cued by the auditory signal. Using EEG spectral power and functional connectivity analyses, we observed a differential age-related modulation of theta activity throughout the repetition of the SI task. Growth of the anterior stimulus-related theta oscillations accompanied by enhanced right-lateralized frontotemporal phase-locking was found in elderly adults. Their young counterparts demonstrated a progressive increase in prestimulus occipital theta power. Our results suggest that the short-term repetition of the auditory-based SI task modulates sensory processing in the elderly. Older participants most likely progressively improve perceptual integration rather than attention-driven processing compared to their younger counterparts.

Piperidine Derivatives: Recent Advances in Synthesis and Pharmacological Applications.

Piperidines are among the most important synthetic fragments for designing drugs and play a significant role in the pharmaceutical industry. Their derivatives are present in more than twenty classes of pharmaceuticals, as well as alkaloids. The current review summarizes recent scientific literature on intra- and intermolecular reactions leading to the formation of various piperidine derivatives: substituted piperidines, spiropiperidines, condensed piperidines, and piperidinones. Moreover, the pharmaceutical applications of synthetic and natural piperidines were covered, as well as the latest scientific advances in the discovery and biological evaluation of potential drugs containing piperidine moiety. This review is designed to help both novice researchers taking their first steps in this field and experienced scientists looking for suitable substrates for the synthesis of biologically active piperidines.

Specific Proton-Donor Properties of Glycine Betaine. Metric Parameters and Enthalpy of Noncovalent Interactions in its Dimer, Water Complexes and Crystalline Hydrate.

Trimethylglycine (glycine betaine, GB) is an important organic osmolyte that accumulates in various plant species in response to environmental stresses and has significant potential as a bioactive agent with low environmental impact. It is assumed that the hydration of GB is playing an important role in the protective mechanism. The hydration and aggregation properties of GB have not yet been studied in detail at the atomistic level. In this work, noncovalent interactions in the GB dimer and its complexes with water and crystalline monohydrate are studied. Depending on the object, periodic and non-periodic DFT calculations are used. Particular attention is paid to the metric parameters and enthalpies of intermolecular hydrogen bonds. The identification of noncovalent interactions is carried out by means of the Bader analysis of periodic or non-periodic electron density. The enthalpy of hydrogen bonds is estimated using the Rosenberg formula (PCCP 2 (2000) 2699). The specific proton donor properties of glycine betaine are due to its ability to form intermolecular C-H∙∙∙O bonds with the oxygen atom of a water molecule or the carboxylate group of a neighboring GB. The enthalpy of these bonds can be significantly greater than 10 kJ/mol. The water molecule that forms a hydrogen bond with the carboxylate group of GB also interacts with its CH groups through lone pairs of electrons. The C-H∙∙∙O bonds contribute up to 40% of the total entropy of the GB-water interaction, which is about 45 kJ/mol. The possibility of identifying C-H∙∙∙O bonds by the proton nuclear magnetic resonance method is discussed.

The Antimicrobial and Antibiofilm Potential of New Water-Soluble Tris-Quaternary Ammonium Compounds.

The invention and innovation of highly effective antimicrobials are always crucial tasks for medical and organic chemistry, especially at the current time, when there is a serious threat of shortages of effective antimicrobials following the pandemic. In the study presented in this article, we established a new approach to synthesizing three novel series of bioactive water-soluble tris-quaternary ammonium compounds using an optimized one-pot method, and we assessed their antimicrobial and antibiofilm potential. Five pathogenic microorganisms of the ESKAPE group, including highly resistant clinical isolates, were used as the test samples. Moreover, we highlighted the dependence of antibacterial activity from the hydrophilic-hydrophobic balance of the QACs and noted the significant performance of the desired products on biofilms with MBEC as low as 16 mg/L against bacteria and 8 mg/L against fungi. Particularly notable was the high activity against Pseudomonas aeruginosa and Acinetobacter baumannii, which are among the most resilient bacteria known. The presented work will provide useful insights for future research on the topic.

Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network.

Large-scale functional connectivity is an important indicator of the brain's normal functioning. The abnormalities in the connectivity pattern can be used as a diagnostic tool to detect various neurological disorders. The present paper describes the functional connectivity assessment based on artificial intelligence to reveal age-related changes in neural response in a simple motor execution task. Twenty subjects of two age groups performed repetitive motor tasks on command, while the whole-scalp EEG was recorded. We applied the model based on the feed-forward multilayer perceptron to detect functional relationships between five groups of sensors located over the frontal, parietal, left, right, and middle motor cortex. Functional dependence was evaluated with the predicted and original time series coefficient of determination. Then, we applied statistical analysis to highlight the significant features of the functional connectivity network assessed by our model. Our findings revealed the connectivity pattern is consistent with modern ideas of the healthy aging effect on neural activation. Elderly adults demonstrate a pronounced activation of the whole-brain theta-band network and decreased activation of frontal-parietal and motor areas of the mu-band. Between-subject analysis revealed a strengthening of inter-areal task-relevant links in elderly adults. These findings can be interpreted as an increased cognitive demand in elderly adults to perform simple motor tasks with the dominant hand, induced by age-related working memory decline.

Extreme synchronization events in a Kuramoto model: The interplay between resource constraints and explosive transitions.

Many living and artificial systems possess structural and dynamical properties of complex networks. One of the most exciting living networked systems is the brain, in which synchronization is an essential mechanism of its normal functioning. On the other hand, excessive synchronization in neural networks reflects undesired pathological activity, including various forms of epilepsy. In this context, network-theoretical approach and dynamical modeling may uncover deep insight into the origins of synchronization-related brain disorders. However, many models do not account for the resource consumption needed for the neural networks to synchronize. To fill this gap, we introduce a phenomenological Kuramoto model evolving under the excitability resource constraints. We demonstrate that the interplay between increased excitability and explosive synchronization induced by the hierarchical organization of the network forces the system to generate short-living extreme synchronization events, which are well-known signs of epileptic brain activity. Finally, we establish that the network units occupying the medium levels of hierarchy most strongly contribute to the birth of extreme events emphasizing the focal nature of their origin.

Machine learning evaluates changes in functional connectivity under a prolonged cognitive load.

One must be aware of the black-box problem by applying machine learning models to analyze high-dimensional neuroimaging data. It is due to a lack of understanding of the internal algorithms or the input features upon which most models make decisions despite outstanding performance in classification, pattern recognition, and prediction. Here, we approach the fundamentally high-dimensional problem of classifying cognitive brain states based on functional connectivity by selecting and interpreting the most relevant input features. Specifically, we consider the alterations in the cortical synchrony under a prolonged cognitive load. Our study highlights the advances of this machine learning method in building a robust classification model and percept-related prestimulus connectivity changes over the conventional trial-averaged statistical analysis.

Monitoring the Cortical Activity of Children and Adults during Cognitive Task Completion.

In this paper, we used an EEG system to monitor and analyze the cortical activity of children and adults at a sensor level during cognitive tasks in the form of a Schulte table. This complex cognitive task simultaneously involves several cognitive processes and systems: visual search, working memory, and mental arithmetic. We revealed that adults found numbers on average two times faster than children in the beginning. However, this difference diminished at the end of table completion to 1.8 times. In children, the EEG analysis revealed high parietal alpha-band power at the end of the task. This indicates the shift from procedural strategy to less demanding fact-retrieval. In adults, the frontal beta-band power increased at the end of the task. It reflects enhanced reliance on the top-down mechanisms, cognitive control, or attentional modulation rather than a change in arithmetic strategy. Finally, the alpha-band power of adults exceeded one of the children in the left hemisphere, providing potential evidence for the fact-retrieval strategy. Since the completion of the Schulte table involves a whole set of elementary cognitive functions, the obtained results were essential for developing passive brain-computer interfaces for monitoring and adjusting a human state in the process of learning and solving cognitive tasks of various types.

Quaternary Ammonium Compounds (QACs) and Ionic Liquids (ILs) as Biocides: From Simple Antiseptics to Tunable Antimicrobials.

Quaternary ammonium compounds (QACs) belong to a well-known class of cationic biocides with a broad spectrum of antimicrobial activity. They are used as essential components in surfactants, personal hygiene products, cosmetics, softeners, dyes, biological dyes, antiseptics, and disinfectants. Simple but varied in their structure, QACs are divided into several subclasses: Mono-, bis-, multi-, and poly-derivatives. Since the beginning of the 20th century, a significant amount of work has been dedicated to the advancement of this class of biocides. Thus, more than 700 articles on QACs were published only in 2020, according to the modern literature. The structural variability and diverse biological activity of ionic liquids (ILs) make them highly prospective for developing new types of biocides. QACs and ILs bear a common key element in the molecular structure-quaternary positively charged nitrogen atoms within a cyclic or acyclic structural framework. The state-of-the-art research level and paramount demand in modern society recall the rapid development of a new generation of tunable antimicrobials. This review focuses on the main QACs exhibiting antimicrobial and antifungal properties, commercial products based on QACs, and the latest discoveries in QACs and ILs connected with biocide development.

Revealing a multiplex brain network through the analysis of recurrences.

A multilayer approach has recently received particular attention in network neuroscience as a suitable model to describe brain dynamics by adjusting its activity in different frequency bands, time scales, modalities, or ages to different layers of a multiplex graph. In this paper, we demonstrate an approach to a frequency-based multilayer functional network constructed from nonstationary multivariate data by analyzing recurrences in application to electroencephalography. Using the recurrence-based index of synchronization, we construct intralayer (within-frequency) and interlayer (cross-frequency) graph edges to model the evolution of a whole-head functional connectivity network during a prolonged stimuli classification task. We demonstrate that the graph edges' weights increase during the experiment and negatively correlate with the response time. We also show that while high-frequency activity evolves toward synchronization of remote local areas, low-frequency connectivity tends to establish large-scale coupling between them.

Chimera-like behavior in a heterogeneous Kuramoto model: The interplay between attractive and repulsive coupling.

Interaction within an ensemble of coupled nonlinear oscillators induces a variety of collective behaviors. One of the most fascinating is a chimera state that manifests the coexistence of spatially distinct populations of coherent and incoherent elements. Understanding of the emergent chimera behavior in controlled experiments or real systems requires a focus on the consideration of heterogeneous network models. In this study, we explore the transitions in a heterogeneous Kuramoto model under the monotonical increase of the coupling strength and specifically find that this system exhibits a frequency-modulated chimera-like pattern during the explosive transition to synchronization. We demonstrate that this specific dynamical regime originates from the interplay between (the evolved) attractively and repulsively coupled subpopulations. We also show that the above-mentioned chimera-like state is induced under weakly non-local, small-world, and sparse scale-free coupling and suppressed in globally coupled, strongly rewired, and dense scale-free networks due to the emergence of the large-scale connections.

Motor execution reduces EEG signals complexity: Recurrence quantification analysis study.

The development of new approaches to detect motor-related brain activity is key in many aspects of science, especially in brain-computer interface applications. Even though some well-known features of motor-related electroencephalograms have been revealed using traditionally applied methods, they still lack a robust classification of motor-related patterns. Here, we introduce new features of motor-related brain activity and uncover hidden mechanisms of the underlying neuronal dynamics by considering event-related desynchronization (ERD) of µ-rhythm in the sensorimotor cortex, i.e., tracking the decrease of the power spectral density in the corresponding frequency band. We hypothesize that motor-related ERD is associated with the suppression of random fluctuations of µ-band neuronal activity. This is due to the lowering of the number of active neuronal populations involved in the corresponding oscillation mode. In this case, we expect more regular dynamics and a decrease in complexity of the EEG signal recorded over the sensorimotor cortex. In order to support this, we apply measures of signal complexity by means of recurrence quantification analysis (RQA). In particular, we demonstrate that certain RQA quantifiers are very useful to detect the moment of movement onset and, therefore, are able to classify the laterality of executed movements.

Age-Related Distinctions in EEG Signals during Execution of Motor Tasks Characterized in Terms of Long-Range Correlations.

The problem of revealing age-related distinctions in multichannel electroencephalograms (EEGs) during the execution of motor tasks in young and elderly adults is addressed herein. Based on the detrended fluctuation analysis (DFA), differences in long-range correlations are considered, emphasizing changes in the scaling exponent α. Stronger responses in elderly subjects are confirmed, including the range and rate of increase in α. Unlike elderly subjects, young adults demonstrated about 2.5 times more pronounced differences between motor task responses with the dominant and non-dominant hand. Knowledge of age-related changes in brain electrical activity is important for understanding consequences of healthy aging and distinguishing them from pathological changes associated with brain diseases. Besides diagnosing age-related effects, the potential of DFA can also be used in the field of brain-computer interfaces.

Feed-forward artificial neural network provides data-driven inference of functional connectivity.

We propose a new model-free method based on the feed-forward artificial neuronal network for detecting functional connectivity in coupled systems. The developed method which does not require large computational costs and which is able to work with short data trials can be used for analysis and reconstruction of connectivity in experimental multichannel data of different nature. We test this approach on the chaotic Rössler system and demonstrate good agreement with the previous well-known results. Then, we use our method to predict functional connectivity thalamo-cortical network of epileptic brain based on ECoG data set of WAG/Rij rats with genetic predisposition to absence epilepsy. We show the emergence of functional interdependence between cortical layers and thalamic nuclei after epileptic discharge onset.

Artificial neural network detects human uncertainty.

Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.

Amplification through chaotic synchronization in spatially extended beam-plasma systems.

In this paper, we have studied the relationship between chaotic synchronization and microwave signal amplification in coupled beam-plasma systems. We have considered a 1D particle-in-cell numerical model of unidirectionally coupled beam-plasma oscillatory media being in the regime of electron pattern formation. We have shown the significant gain of microwave oscillation power in coupled beam-plasma media being in the different regimes of generation. The discovered effect has a close connection with the chaotic synchronization phenomenon, so we have observed that amplification appears after the onset of the complete time scale synchronization regime in the analyzed coupled spatially extended systems. We have also provided the numerical study of physical processes in the chain of beam-plasma systems leading to the chaotic synchronization and the amplification of microwave oscillations power, respectively.

Self-organized bistability on globally coupled higher-order networks.

Self-organized bistability (SOB) stands as a critical behavior for the systems delicately adjusting themselves to the brink of bistability, characterized by a first-order transition. Its essence lies in the inherent ability of the system to undergo enduring shifts between the coexisting states, achieved through the self-regulation of a controlling parameter. Recently, SOB has been established in a scale-free network as a recurrent transition to a short-living state of global synchronization. Here, we embark on a theoretical exploration that extends the boundaries of the SOB concept on a higher-order network (implicitly embedded microscopically within a simplicial complex) while considering the limitations imposed by coupling constraints. By applying Ott-Antonsen dimensionality reduction in the thermodynamic limit to the higher-order network, we derive SOB requirements under coupling limits that are in good agreement with numerical simulations on systems of finite size. We use continuous synchronization diagrams and statistical data from spontaneous synchronized events to demonstrate the crucial role SOB plays in initiating and terminating temporary synchronized events. We show that under weak-coupling consumption, these spontaneous occurrences closely resemble the statistical traits of the epileptic brain functioning.

From Antibacterial to Antibiofilm Targeting: An Emerging Paradigm Shift in the Development of Quaternary Ammonium Compounds (QACs).

In a previous development stage, mostly individual antibacterial activity was a target in the optimization of biologically active compounds and antiseptic agents. Although this targeting is still valuable, a new trend has appeared since the discovery of superhigh resistance of bacterial cells upon their aggregation into groups. Indeed, it is now well established that the great majority of pathogenic germs are found in the environment as surface-associated microbial communities called biofilms. The protective properties of biofilms and microbial resistance, even to high concentrations of biocides, cause many chronic infections in medical settings and lead to serious economic losses in various areas. A paradigm shift from individual bacterial targeting to also affecting more complex cellular frameworks is taking place and involves multiple strategies for combating biofilms with compounds that are effective at different stages of microbiome formation. Quaternary ammonium compounds (QACs) play a key role in many of these treatments and prophylactic techniques on the basis of both the use of individual antibacterial agents and combination technologies. In this review, we summarize the literature data on the effectiveness of using commercially available and newly synthesized QACs, as well as synergistic treatment techniques based on them. As an important focus, techniques for developing and applying antimicrobial coatings that prevent the formation of biofilms on various surfaces over time are discussed. The information analyzed in this review will be useful to researchers and engineers working in many fields, including the development of a new generation of applied materials; understanding biofilm surface growth; and conducting research in medical, pharmaceutical, and materials sciences. Although regular studies of antibacterial activity are still widely conducted, a promising new trend is also to evaluate antibiofilm activity in a comprehensive study in order to meet the current requirements for the development of highly needed practical applications.