Recurrence microstates for machine learning classification.

Recurrence microstates are obtained from the cross recurrence of two sequences of values embedded in a time series, being the generalization of the concept of recurrence of a given state in phase space. The probability of occurrence of each microstate constitutes a recurrence quantifier. The set of probabilities of all microstates are capable of detecting even small changes in the data pattern. This creates an ideal tool for generating features in machine learning algorithms. Thanks to the sensitivity of the set of probabilities of occurrence of microstates, it can be used to feed a deep neural network, namely, a microstate multi-layer perceptron (MMLP) to classify parameters of chaotic systems. Additionally, we show that with more microstates, the accuracy of the MMLP increases, showing that the increasing size and number of microstates insert new and independent information into the analysis. We also explore potential applications of the proposed method when adapted to different contexts.

Factors associated with non-completion of TB preventive treatment in Brazil.

BACKGROUND: Among Brazilian initiatives to scale up TB preventive therapy (TPT) are the adoption of the 3HP regimen (12 weekly doses of rifapentine and isoniazid [INH]) in 2021 and the implementation in 2018 of the TPT surveillance information system. Since then, 63% of the 76,000 eligible individuals notified completed TPT. Recommended regimens in this period were 6H, 9H (6 or 9 months of INH) and 4R (4 months of rifampicin).OBJECTIVE: To analyse the factors associated with TPT non-completion.METHODS: We analysed the cohort of TPT notifications from 2018 to 2020. Robust variance Poisson regression model was used to verify the association of TPT non-completion with sociodemographic, clinical and epidemiological variables.RESULTS: Of the 39,973 TPT notified in the study period, 8,534 (21.5%) were non-completed, of which 7,858 (92.1%) were lost to follow-up. Age 15-60 years (relative risk [RR] 1.27, 95% confidence interval [95% CI] 1.20-1.35), TPT with isoniazid (RR 1.40, 95% CI 1.19-1.64) and Black/mixed race (RR 1.17, 95% CI 1.09-1.25) were associated with a higher risk of non-completion.CONCLUSION: Individuals in situations of social and financial vulnerability such as being Black/pardo race, younger and on longer TPT regimens were more likely to be associated with TPT incompletion.

Molecular and morphological alterations in uninjured skin of streptozotocin-induced diabetic mice.

Diabetes affects every tissue in the body, including the skin. The main skin problem is the increased risk of infections, which can lead to foot ulcers. Most studies evaluating the effects of diabetes on the skin are carried out in wound healing areas. There are fewer studies on uninjured skin, and some particularities of this tissue are yet to be elucidated. In general, cellular and molecular outcomes of diabetes are increased oxidative stress and lipid peroxidation. For our study, we used C57BL/6 mice that were divided into diabetic and non-diabetic groups. The diabetic group received low doses of streptozotocin on 5 consecutive days. To evaluate the effects of hyperglycemia on uninjured skin, we performed morphological analysis using hematoxylin/eosin staining, cellular analysis using Picrosirius red and Nissl staining, and immunostaining, and evaluated protein expression by polymerase chain reaction. We confirmed that mice were hyperglycemic, presenting all features related to this metabolic condition. Hyperglycemia caused a decrease in interleukin 6 (Il-6) and an increase in tumor necrosis factor alpha (Tnf-α), Il-10, F4/80, tumor growth factor beta (Tgf-ß), and insulin-like growth factor 1 (Igf-1). In addition, hyperglycemia led to a lower cellular density in the epidermis and dermis, a delay in the maturation of collagen fibers, and a decrease in the number of neurons. Furthermore, we showed a decrease in Bdnf expression and no changes in Ntrk2 expression in the skin of diabetic animals. In conclusion, chronic hyperglycemia in mice induced by streptozotocin caused disruption of homeostasis even before loss of skin continuity.

Molecular and morphological alterations in uninjured skin of streptozotocin‐induced diabetic mice

Diabetes affects every tissue in the body, including the skin. The main skin problem is the increased risk of infections, which can lead to foot ulcers. Most studies evaluating the effects of diabetes on the skin are carried out in wound healing areas. There are fewer studies on uninjured skin, and some particularities of this tissue are yet to be elucidated. In general, cellular and molecular outcomes of diabetes are increased oxidative stress and lipid peroxidation. For our study, we used C57BL/6 mice that were divided into diabetic and non-diabetic groups. The diabetic group received low doses of streptozotocin on 5 consecutive days. To evaluate the effects of hyperglycemia on uninjured skin, we performed morphological analysis using hematoxylin/eosin staining, cellular analysis using Picrosirius red and Nissl staining, and immunostaining, and evaluated protein expression by polymerase chain reaction. We confirmed that mice were hyperglycemic, presenting all features related to this metabolic condition. Hyperglycemia caused a decrease in interleukin 6 (Il-6) and an increase in tumor necrosis factor alpha (Tnf-α), Il-10, F4/80, tumor growth factor beta (Tgf-β), and insulin-like growth factor 1 (Igf-1). In addition, hyperglycemia led to a lower cellular density in the epidermis and dermis, a delay in the maturation of collagen fibers, and a decrease in the number of neurons. Furthermore, we showed a decrease in Bdnf expression and no changes in Ntrk2 expression in the skin of diabetic animals. In conclusion, chronic hyperglycemia in mice induced by streptozotocin caused disruption of homeostasis even before loss of skin continuity.

Phase-locking intermittency induced by dynamical heterogeneity in networks of thermosensitive neurons.

In this work, we study the phase synchronization of a neural network and explore how the heterogeneity in the neurons' dynamics can lead their phases to intermittently phase-lock and unlock. The neurons are connected through chemical excitatory connections in a sparse random topology, feel no noise or external inputs, and have identical parameters except for different in-degrees. They follow a modification of the Hodgkin-Huxley model, which adds details like temperature dependence, and can burst either periodically or chaotically when uncoupled. Coupling makes them chaotic in all cases but each individual mode leads to different transitions to phase synchronization in the networks due to increasing synaptic strength. In almost all cases, neurons' inter-burst intervals differ among themselves, which indicates their dynamical heterogeneity and leads to their intermittent phase-locking. We argue then that this behavior occurs here because of their chaotic dynamics and their differing initial conditions. We also investigate how this intermittency affects the formation of clusters of neurons in the network and show that the clusters' compositions change at a rate following the degree of intermittency. Finally, we discuss how these results relate to studies in the neuroscience literature, especially regarding metastability.

Bistability in the synchronization of identical neurons.

We investigate the role of bistability in the synchronization of a network of identical bursting neurons coupled through an generic electrical mean-field scheme. These neurons can exhibit distinct multistable states and, in particular, bistable behavior is observed when their sodium conductance is varied. With this, we consider three different initialization compositions: (i) the whole network is in the same periodic state; (ii) half of the network periodic, half chaotic; (iii) half periodic, and half in a different periodic state. We show that (i) and (ii) reach phase synchronization (PS) for all coupling strengths, while for (iii) small coupling regimes do not induce PS, and instead, there is a coexistence of different frequencies. For stronger coupling, case (iii) synchronizes, but after (i) and (ii). Since PS requires all neurons being in the same state (same frequencies), these different behaviors are governed by transitions between the states. We find that, during these transitions, (ii) and (iii) have transient chimera states and that (iii) has breathing chimeras. By studying the stability of each state, we explain the observed transitions. Therefore, bistability of neurons can play a major role in the synchronization of generic networks, with the simple initialization of the system being capable of drastically changing its asymptotic space.

Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networks.

Extracting relevant properties of empirical signals generated by nonlinear, stochastic, and high-dimensional systems is a challenge of complex systems research. Open questions are how to differentiate chaotic signals from stochastic ones, and how to quantify nonlinear and/or high-order temporal correlations. Here we propose a new technique to reliably address both problems. Our approach follows two steps: first, we train an artificial neural network (ANN) with flicker (colored) noise to predict the value of the parameter, [Formula: see text], that determines the strength of the correlation of the noise. To predict [Formula: see text] the ANN input features are a set of probabilities that are extracted from the time series by using symbolic ordinal analysis. Then, we input to the trained ANN the probabilities extracted from the time series of interest, and analyze the ANN output. We find that the [Formula: see text] value returned by the ANN is informative of the temporal correlations present in the time series. To distinguish between stochastic and chaotic signals, we exploit the fact that the difference between the permutation entropy (PE) of a given time series and the PE of flicker noise with the same [Formula: see text] parameter is small when the time series is stochastic, but it is large when the time series is chaotic. We validate our technique by analysing synthetic and empirical time series whose nature is well established. We also demonstrate the robustness of our approach with respect to the length of the time series and to the level of noise. We expect that our algorithm, which is freely available, will be very useful to the community.

The role of individual neuron ion conductances in the synchronization processes of neuron networks.

The partial phase synchronization (sometimes called cooperation) of neurons is fundamental for the understanding of the complex behavior of the brain. The lack or the excess of synchronization can generate brain disorders like Parkinson's disease and epilepsy. The phase synchronization phenomenon is strongly related to the regular or chaotic dynamics of individual neurons. The individual dynamics themselves are a function of the ion channel conductances, turning the conductances into important players in the process of neuron synchronized health depolarization/repolarization processes. It is well known that many diseases are related to alterations of the ion-channel conductance properties. To normalize their functioning, drugs are used to block or activate specific channels, changing their conductances. We investigate the synchronization process of a Hodgkin-Huxley-type neural network as a function of the values of the individual neuron conductances, showing the dynamics of the neurons must be taken into account in the synchronization process. Particular sets of conductances lead to non-chaotic individual neuron dynamics allowing synchronization states for very weak coupling and resulting in a non-monotonic transition to synchronized states, as the coupling strength among neurons is varied. On the other hand, a monotonic transition to synchronized states is observed for individual chaotic dynamics of the neurons. We conclude the analysis of the individual dynamics of isolated neurons allows the prediction of the synchronization process of the network. We provide alternative ways to achieve the desired network state (phase synchronized or desynchronized) without any changes in the synaptic current of neurons but making just small changes in the neuron ion-channel conductances. The mechanism behind the control is the close relation between ion-channel conductance and the regular or chaotic dynamics of neurons. Finally, we show that by changing at least two conductances simultaneously the control may be much more efficient since the second conductance makes the synchronization possible just by performing a small change in the first. The study presented here may have an impact on new drug development research.

Maximum entropy principle in recurrence plot analysis on stochastic and chaotic systems.

The recurrence analysis of dynamic systems has been studied since Poincaré's seminal work. Since then, several approaches have been developed to study recurrence properties in nonlinear dynamical systems. In this work, we study the recently developed entropy of recurrence microstates. We propose a new quantifier, the maximum entropy (Smax). The new concept uses the diversity of microstates of the recurrence plot and is able to set automatically the optimum recurrence neighborhood (ϵ-vicinity), turning the analysis free of the vicinity parameter. In addition, ϵ turns out to be a novel quantifier of dynamical properties itself. We apply Smax and the optimum ϵ to deterministic and stochastic systems. The Smax quantifier has a higher correlation with the Lyapunov exponent and, since it is a parameter-free measure, a more useful recurrence quantifier of time series.

Mechanism for explosive synchronization of neural networks.

Here we investigate the mechanism for explosive synchronization (ES) of a complex neural network composed of nonidentical neurons and coupled by Newman-Watts small-world matrices. We find a range of nonlocal connection probabilities for which the network displays an abrupt transition to phase synchronization, characterizing ES. The mechanism behind the ES is the following: As the coupling parameter is varied in a network of distinct neurons, ES is likely to occur due to a bistable regime, namely a chaotic nonsynchronized and a regular phase-synchronized state in the phase space. In this case, even small coupling changes make possible a transition between them. The onset of ES occurs via a saddle-node bifurcation of a periodic orbit that leads the network dynamics to display a locally stable phase-synchronized state. The presence of this regime is accompanied by a hysteresis loop on the network dynamics as the coupling parameter is adiabatically increased and decreased. The end of the hysteresis loop is marked by a frontier crisis of the chaotic attractor which also determines the end of the coupling strength interval where ES is possible.

Correlated Brownian motion and diffusion of defects in spatially extended chaotic systems.

One of the spatiotemporal patterns exhibited by coupled map lattices with nearest-neighbor coupling is the appearance of chaotic defects, which are spatially localized regions of chaotic dynamics with a particlelike behavior. Chaotic defects display random behavior and diffuse along the lattice with a Gaussian signature. In this note, we investigate some dynamical properties of chaotic defects in a lattice of coupled chaotic quadratic maps. Using a recurrence-based diagnostic, we found that the motion of chaotic defects is well-represented by a stochastic time series with a power-law spectrum 1/fσ with 2.3≤σ≤2.4, i.e., a correlated Brownian motion. The correlation exponent corresponds to a memory effect in the Brownian motion and increases with a system parameter as the diffusion coefficient of chaotic defects.

Erratum: Phase synchronization and intermittent behavior in healthy and Alzheimer-affected human-brain-based neural network [Phys. Rev. E 99, 022402 (2019)].

This corrects the article DOI: 10.1103/PhysRevE.99.022402.

Phase synchronization and intermittent behavior in healthy and Alzheimer-affected human-brain-based neural network.

We study the dynamical proprieties of phase synchronization and intermittent behavior of neural systems using a network of networks structure based on an experimentally obtained human connectome for healthy and Alzheimer-affected brains. We consider a network composed of 78 neural subareas (subnetworks) coupled with a mean-field potential scheme. Each subnetwork is characterized by a small-world topology, composed of 250 bursting neurons simulated through a Rulkov model. Using the Kuramoto order parameter we demonstrate that healthy and Alzheimer-affected brains display distinct phase synchronization and intermittence properties as a function of internal and external coupling strengths. In general, for the healthy case, each subnetwork develops a substantial level of internal synchronization before a global stable phase-synchronization state has been established. For the unhealthy case, despite the similar internal subnetwork synchronization levels, we identify higher levels of global phase synchronization occurring even for relatively small internal and external coupling. Using recurrence quantification analysis, namely the determinism of the mean-field potential, we identify regions where the healthy and unhealthy networks depict nonstationary behavior, but the results denounce the presence of a larger region or intermittent dynamics for the case of Alzheimer-affected networks. A possible theoretical explanation based on two locally stable but globally unstable states is discussed.

Ureterostomias cutânea e colônica em suínos: avaliação da exequibilidade das técnicas / Cutaneous ureterostomies in pigs: technical feasibility assessment

O aumento da expectativa de vida dos animais de companhia favorece a ocorrência de casos de câncer, como o de bexiga. O objetivo da presente pesquisa foi avaliar a exequibilidade da técnica de ureterostomia cutânea em suínos após a realização da cistectomia total, quando comparada à técnica de ureterostomia colônica, assim como detectar e caracterizar possíveis complicações transoperatórias. Foram utilizados 20 animais, distribuídos em dois grupos. Todos foram submetidos à cistectomia radical e, em seguida, à ureterostomia cutânea ou colônica. A exequibilidade das técnicas foi avaliada. Observou-se o tamanho da incisão, o tempo de diérese, de realização da derivação urinária e o tempo de síntese, estimou-se a perda sanguínea e o grau de dificuldade na realização dos procedimentos de preparo dos ureteres, o preparo do sítio de ureteroanastomose e a realização da ureteroanastomose em si. Os resultados indicam que a ureterostomia cutânea apresentou maior eficiência quando comparada à colônica nos quesitos perda de sangue, tamanho da incisão realizada e facilidade de realização da anastomose. Já a ureterocolostomia apresentou melhor resultado referente à execução do preparo ureteral. Assim, concluiu-se que ambas as técnicas podem ser indicadas como derivações urinárias viáveis e que a escolha dependerá de fatores intrínsecos ao cirurgião e ao paciente.(AU)

Increased life expectancy of pets favors the occurrence of cancer, such as the urinary bladder ones. The aim of this study was to evaluate the feasibility of cutaneous ureterostomy technique in pigs after the total cystectomy compared to the technique of colonic ureterostomy, as well as detect and characterize possible intraoperative complications. For that, 20 pigs were used, divided into two groups. All patients underwent radical cystectomy and then the cutaneous or colonic ureterostomies. The technical feasibility was assessed during the procedures. The procedures compared were: the incision size, dieresis time, realization of urinary diversion and the synthesis time. Also, the estimated blood loss and the degree of difficulty in performing the preparation procedures of the ureters, ureteroanastomose site preparation and ureteroanastomose itself were compared. The results indicate that performing cutaneous ureterostomy was better than the colonic ureterostomy in relation to blood loss, incision size, and anastomosis performance. In contrast, ureterocolonostomy showed better results on the implementation of ureteral preparation. Thus, it was concluded that both techniques can be indicated as viable urinary diversions and the choice will depend on factors intrinsic to the surgeon and the patient.(AU)

Synchronous patterns and intermittency in a network induced by the rewiring of connections and coupling.

The connection architecture plays an important role in the synchronization of networks, where the presence of local and nonlocal connection structures are found in many systems, such as the neural ones. Here, we consider a network composed of chaotic bursting oscillators coupled through a Watts-Strogatz-small-world topology. The influence of coupling strength and rewiring of connections is studied when the network topology is varied from regular to small-world to random. In this scenario, we show two distinct nonstationary transitions to phase synchronization: one induced by the increase in coupling strength and another resulting from the change from local connections to nonlocal ones. Besides this, there are regions in the parameter space where the network depicts a coexistence of different bursting frequencies where nonstationary zig-zag fronts are observed. Regarding the analyses, we consider two distinct methodological approaches: one based on the phase association to the bursting activity where the Kuramoto order parameter is used and another based on recurrence quantification analysis where just a time series of the network mean field is required.

Neuron dynamics variability and anomalous phase synchronization of neural networks.

Anomalous phase synchronization describes a synchronization phenomenon occurring even for the weakly coupled network and characterized by a non-monotonous dependence of the synchronization strength on the coupling strength. Its existence may support a theoretical framework to some neurological diseases, such as Parkinson's and some episodes of seizure behavior generated by epilepsy. Despite the success of controlling or suppressing the anomalous phase synchronization in neural networks applying external perturbations or inducing ambient changes, the origin of the anomalous phase synchronization as well as the mechanisms behind the suppression is not completely known. Here, we consider networks composed of N = 2000 coupled neurons in a small-world topology for two well known neuron models, namely, the Hodgkin-Huxley-like and the Hindmarsh-Rose models, both displaying the anomalous phase synchronization regime. We show that the anomalous phase synchronization may be related to the individual behavior of the coupled neurons; particularly, we identify a strong correlation between the behavior of the inter-bursting-intervals of the neurons, what we call neuron variability, to the ability of the network to depict anomalous phase synchronization. We corroborate the ideas showing that external perturbations or ambient parameter changes that eliminate anomalous phase synchronization and at the same time promote small changes in the individual dynamics of the neurons, such that an increasing individual variability of neurons implies a decrease of anomalous phase synchronization. Finally, we demonstrate that this effect can be quantified using a well known recurrence quantifier, the "determinism." Moreover, the results obtained by the determinism are based on only the mean field potential of the network, turning these measures more suitable to be used in experimental situations.

Enteric viruses' dissemination in a private reserve of natural heritage.

This study aimed to assess anthropogenic impact of surrounding population in the Private Reserve of Natural Heritage at Pantanal, the world's largest freshwater wetland ecosystem located in the centre of South America. Viral aetiological agents of acute gastroenteritis as rotavirus A (RVA), noroviruses, human adenoviruses, klassevirus and of hepatitis, as hepatitis A virus, were investigated in different aquatic matrices. Annual collection campaigns were carried out from 2009 to 2012, alternating dry and rainy seasons. Viral particles present in the samples were concentrated by the adsorption-elution method, with negatively charged membranes, and detected by qualitative and quantitative PCR. From a total of 43 samples at least one virus was detected in 65% (28) of them. Viruses were detected in all matrices with concentrations ranging from 2 × 102 to 8·3 × 104 genome copies per litre. A significant higher RVA frequency was observed in the dry season. Our data revealing dissemination of human enteric viruses in water matrices both inside and outside the reserve could be useful to trace faecal contamination in the environment and to minimize the risk of infection by exposure of susceptible individuals. SIGNIFICANCE AND IMPACT OF THE STUDY: This study is part of a collaborative project designed to investigate the environmental and health conditions of the Private Reserve of Natural Heritage at Pantanal, the largest seasonally flooded wetland in the world. The project aimed to promote health and quality of human and wildlife extending technical-scientific knowledge about pathogens present in the region. By assessing the occurrence of human enteric viruses in different water matrices we demonstrated the anthropogenic impact of surrounding population and pointed out the potential risk of infection by exposure of susceptible individuals.

Detection of nonstationary transition to synchronized states of a neural network using recurrence analyses.

We study the stability of asymptotic states displayed by a complex neural network. We focus on the loss of stability of a stationary state of networks using recurrence quantifiers as tools to diagnose local and global stabilities as well as the multistability of a coupled neural network. Numerical simulations of a neural network composed of 1024 neurons in a small-world connection scheme are performed using the model of Braun et al. [Int. J. Bifurcation Chaos 08, 881 (1998)IJBEE40218-127410.1142/S0218127498000681], which is a modified model from the Hodgkin-Huxley model [J. Phys. 117, 500 (1952)]. To validate the analyses, the results are compared with those produced by Kuramoto's order parameter [Chemical Oscillations, Waves, and Turbulence (Springer-Verlag, Berlin Heidelberg, 1984)]. We show that recurrence tools making use of just integrated signals provided by the networks, such as local field potential (LFP) (LFP signals) or mean field values bring new results on the understanding of neural behavior occurring before the synchronization states. In particular we show the occurrence of different stationary and nonstationarity asymptotic states.

Childhood tuberculosis and human immunodeficiency virus status in Brazil: a hierarchical analysis.

SETTING: Human immunodeficiency virus (HIV) infection may impact tuberculosis (TB) diagnosis, clinical presentation and treatment outcomes in children as the signs and symptoms of both diseases overlap. OBJECTIVE: To compare the sociodemographic and clinical profiles of childhood TB according to HIV status in Brazil. METHODS: This was a cross-sectional study of data on subjects aged <15 years retrieved from the Brazilian National Electronic Disease Registry (Sistema de Informação de Agravos de Notificação) database on TB to compare TB-HIV coinfected patients and patients with TB only registered between 2007 and 2011. A hierarchical logistic regression model was applied. RESULTS: Of 6091 cases analysed, 780 (12%) were TB-HIV patients, while 5311 (87%) presented with TB only. TB-HIV patients were more likely to be institutionalised (OR 2.22, 95%CI 1.43-3.46), to present with relapsed TB (OR 5.03, 95%CI 2.02-12.5) and be readmitted after treatment default (OR 16.7, 95%CI 4.34-64.46). They were also more likely to have unfavourable outcomes, including default (OR 2.85, 95%CI 1.81-4.49), death due to TB (OR 2.76, 95%CI 1.27-6.03) and death from other causes (OR 5.59, 95%CI 2.63-11.8). CONCLUSION: Our study highlights the challenges of using national registers for research into childhood TB.

Synchronization of bursting Hodgkin-Huxley-type neurons in clustered networks.

We considered a clustered network of bursting neurons described by the Huber-Braun model. In the upper level of the network we used the connectivity matrix of the cat cerebral cortex network, and in the lower level each cortex area (or cluster) is modelled as a small-world network. There are two different coupling strengths related to inter- and intracluster dynamics. Each bursting cycle is composed of a quiescent period followed by a rapid chaotic sequence of spikes, and we defined a geometric phase which enables us to investigate the onset of synchronized bursting, as the state in which the neuron start bursting at the same time, whereas their spikes may remain uncorrelated. The bursting synchronization of a clustered network has been investigated using an order parameter and the average field of the network in order to identify regimes in which each cluster may display synchronized behavior, whereas the overall network does not. We introduce quantifiers to evaluate the relative contribution of each cluster in the partial synchronized behavior of the whole network. Our main finding is that we typically observe in the clustered network not a complete phase synchronized regime but instead a complex pattern of partial phase synchronization in which different cortical areas may be internally synchronized at distinct phase values, hence they are not externally synchronized, unless the coupling strengths are too large.