Neuroprotective effects of platinum nanoparticle-based microreactors in bicuculline-induced seizures.

Epilepsy designates a group of chronic brain disorders, characterized by the recurrence of hypersynchronous, repetitive activity, of neuronal clusters. Epileptic seizures are the hallmark of epilepsy. The primary goal of epilepsy treatment is to eliminate seizures with minimal side effects. Nevertheless, approximately 30% of patients do not respond to the available drugs. An imbalance between excitatory/inhibitory neurotransmission, that leads to excitotoxicity, seizures, and cell death, has been proposed as an important mechanism regarding epileptogenesis. Recently, it has been shown that microreactors composed of platinum nanoparticles (Pt-NP) and glutamate dehydrogenase possess in vitro and in vivo activity against excitotoxicity. This study investigates the in vivo effects of these microreactors in an animal model of epilepsy induced by the administration of the GABAergic antagonist bicuculline. Male Wistar rats were administered intracerebroventricularly (i.c.v.) with the microreactors or saline and, five days later, injected with bicuculline or saline. Seizure severity was evaluated in an open field. Thirty min after behavioral measurements, animals were euthanized, and their brains processed for neurodegeneration evaluation and for neurogenesis. Treatment with the microreactors significantly increased the time taken for the onset of seizures and for the first tonic-clonic seizure, when compared to the bicuculline group that did not receive the microreactor. The administration of the microreactors also increased the time spent in total exploration and grooming. Treatment with the microreactors decreased bicuculline-induced neurodegeneration and increased neurogenesis in the dorsal and ventral hippocampus. These observations suggest that treatment with Pt-NP-based microreactors attenuates the behavioral and neurobiological consequences of epileptiform seizure activity.

Ising-like model replicating time-averaged spiking behaviour of in vitro neuronal networks.

We analyze time-averaged experimental data from in vitro activities of neuronal networks. Through a Pairwise Maximum-Entropy method, we identify through an inverse binary Ising-like model the local fields and interaction couplings which best reproduce the average activities of each neuron as well as the statistical correlations between the activities of each pair of neurons in the system. The specific information about the type of neurons is mainly stored in the local fields, while a symmetric distribution of interaction constants seems generic. Our findings demonstrate that, despite not being directly incorporated into the inference approach, the experimentally observed correlations among groups of three neurons are accurately captured by the derived Ising-like model. Within the context of the thermodynamic analogy inherent to the Ising-like models developed in this study, our findings additionally indicate that these models demonstrate characteristics of second-order phase transitions between ferromagnetic and paramagnetic states at temperatures above, but close to, unity. Considering that the operating temperature utilized in the Maximum-Entropy method is T o = 1 , this observation further expands the thermodynamic conceptual parallelism postulated in this work for the manifestation of criticality in neuronal network behavior.

Impact of CoronaVac on Covid-19 outcomes of elderly adults in a large and socially unequal Brazilian city: A target trial emulation study.

BACKGROUND: Although CoronaVac was the only Covid-19 vaccine adopted in the first months of the Brazilian vaccination campaign, randomized clinical trials to evaluate its efficacy in elderly adults were limited. In this study, we use routinely collected surveillance and SARS-CoV-2 vaccination and testing data comprising the population of the fifth largest city of Brazil to evaluate the effectiveness of CoronaVac in adults 60+ years old against severe outcomes. METHODS: Using large observational databases on vaccination and surveillance data from the city of Fortaleza, Brazil, we defined a retrospective cohort including 324,302 eligible adults aged ≥60 years to evaluate the effectiveness of the CoronaVac vaccine. The cohort included individuals vaccinated between January 21, 2021, and August 31, 2021, who were matched with unvaccinated persons at the time of rollout following a 1:1 ratio according to baseline covariates of age, sex, and Human Development Index of the neighborhood of residence. Only Covid-19-related severe outcomes were included in the analysis: hospitalization, ICU admission, and death. Vaccine effectiveness for each outcome was calculated by using the risk ratio between the two groups, with the risk obtained by the Kaplan-Meier estimator. RESULTS: We obtained 62,643 matched pairs for assessing the effectiveness of the two-dose regimen of CoronaVac. The demographic profile of the matched population was statistically representative of the population of Fortaleza. Using the cumulative incidence as the risk associated with each group, starting at day 14 since the receipt of the second dose, we found an 82.3 % (95 % CI 66.3-93.9) effectiveness against Covid-19-related death, 68.4 % (95 % CI 42.3-86.4) against ICU admission, and 55.8 % (95 % CI 42.7-68.3) against hospital admission. CONCLUSIONS: Our results show that, despite critical delays in vaccine delivery and limited evidence in efficacy trial estimates, CoronaVac contributed to preventing deaths and severe morbidity due to Covid-19 in elderly adults.

Unveiling the paths of COVID-19 in a large city based on public transportation data.

Human mobility plays a key role in the dissemination of infectious diseases around the world. However, the complexity introduced by commuting patterns in the daily life of cities makes such a role unclear, especially at the intracity scale. Here, we propose a multiplex network fed with 9 months of mobility data with more than 107 million public bus validations in order to understand the relation between urban mobility and the spreading of COVID-19 within a large city, namely, Fortaleza in the northeast of Brazil. Our results suggest that the shortest bus rides in Fortaleza, measured in the number of daily rides among all neighborhoods, decreased [Formula: see text]% more than the longest ones after an epidemic wave. Such a result is the opposite of what has been observed at the intercity scale. We also find that mobility changes among the neighborhoods are synchronous and geographically homogeneous. Furthermore, we find that the most central neighborhoods in mobility are the first targets for infectious disease outbreaks, which is quantified here in terms of the positive linear relation between the disease arrival time and the average of the closeness centrality ranking. These central neighborhoods are also the top neighborhoods in the number of reported cases at the end of an epidemic wave as indicated by the exponential decay behavior of the disease arrival time in relation to the number of accumulated reported cases with decay constant [Formula: see text] days. We believe that these results can help in the development of new strategies to impose restriction measures in the cities guiding decision-makers with smart actions in public health policies, as well as supporting future research on urban mobility and epidemiology.

Platinum nanoparticle-based microreactors protect against the behavioral and neurobiological consequences of chronic stress exposure.

Excitotoxicity is described as the exacerbated activation of glutamate AMPA and NMDA receptors that leads to neuronal damage, and ultimately to cell death. Astrocytes are responsible for the clearance of 80-90% of synaptically released glutamate, preventing excitotoxicity. Chronic stress renders neurons vulnerable to excitotoxicity and has been associated to neuropsychiatric disorders, i.e., anxiety. Microreactors containing platinum nanoparticles (Pt-NP) and glutamate dehydrogenase have shown in vitro activity against excitotoxicity. The purpose of the present study was to investigate the in vivo effects of these microreactors on the behavioral and neurobiological effects of chronic stress exposure. Rats were either unstressed or exposed for 2 weeks to an unpredictable chronic mild stress paradigm (UCMS), administered intra-ventral hippocampus with the microreactors (with or without the blockage of astrocyte functioning), and seven days later tested in the elevated T-maze (ETM; Experiment 1). The ETM allows the measurement of two defensive responses, avoidance and escape, in terms of psychopathology respectively related to generalized anxiety and panic disorder. Locomotor activity in an open field was also measured. Since previous evidence shows that stress inhibits adult neurogenesis, we evaluated the effects of the different treatments on the number of cells expressing the marker of migrating neuroblasts doublecortin (DCX) in the dorsal and ventral hippocampus (Experiment 2). Results showed that UCMS induces anxiogenic effects, increases locomotion, and decreases the number of DCX cells in the dorsal and ventral hippocampus, effects that were counteracted by microreactor administration. This is the first study to demonstrate the in vivo efficacy of Pt-NP against the behavioral and neurobiological effects of chronic stress exposure.

Digital contact tracing and network theory to stop the spread of COVID-19 using big-data on human mobility geolocalization.

The spread of COVID-19 caused by the SARS-CoV-2 virus has become a worldwide problem with devastating consequences. Here, we implement a comprehensive contact tracing and network analysis to find an optimized quarantine protocol to dismantle the chain of transmission of coronavirus with minimal disruptions to society. We track billions of anonymized GPS human mobility datapoints to monitor the evolution of the contact network of disease transmission before and after mass quarantines. As a consequence of the lockdowns, people's mobility decreases by 53%, which results in a drastic disintegration of the transmission network by 90%. However, this disintegration did not halt the spreading of the disease. Our analysis indicates that superspreading k-core structures persist in the transmission network to prolong the pandemic. Once the k-cores are identified, an optimized strategy to break the chain of transmission is to quarantine a minimal number of 'weak links' with high betweenness centrality connecting the large k-cores.

Fast algorithm to identify minimal patterns of synchrony through fibration symmetries in large directed networks.

Recent studies have revealed the interplay between the structure of network circuits with fibration symmetries and the functionality of biological networks within which they have been identified. The presence of these symmetries in complex networks predicts the phenomenon of cluster synchronization, which produces patterns of a synchronized group of nodes. Here, we present a fast, and memory efficient, algorithm to identify fibration symmetries in networks. The algorithm is particularly suitable for large networks since it has a runtime of complexity O(Mlog⁡N) and requires O(M+N) of memory resources, where N and M are the number of nodes and edges in the network, respectively. The algorithm is a modification of the so-called refinement paradigm to identify circuits that are symmetrical to information flow (i.e., fibers) by finding the coarsest refinement partition over the network. Finally, we show that the algorithm provides an optimal procedure for identifying fibers, overcoming current approaches used in the literature.

Self-similar but not conformally invariant traces obtained by modified Loewner forces.

The two-dimensional Loewner exploration process is generalized to the case where the random force is self-similar with positively correlated increments. We model this random force by a fractional Brownian motion with Hurst exponent H≥1/2≡H_{BM}, where H_{BM} stands for the one-dimensional Brownian motion. By manipulating the deterministic force, we design a scale-invariant equation describing self-similar traces which lack conformal invariance. The model is investigated in terms of the "input diffusivity parameter" κ, which coincides with the one of the ordinary Schramm-Loewner evolution (SLE) at H=H_{BM}. In our numerical investigation, we focus on the scaling properties of the traces generated for κ=2,3, κ=4, and κ=6,8 as the representatives, respectively, of the dilute phase, the transition point, and the dense phase of the ordinary SLE. The resulting traces are shown to be scale invariant. Using two equivalent schemes, we extract the fractal dimension, D_{f}(H), of the traces which decrease monotonically with increasing H, reaching D_{f}=1 at H=1 for all κ values. The left passage probability (LPP) test demonstrates that, for H values not far from the uncorrelated case (small ε_{H}≡H-H_{BM}/H_{BM}), the prediction of the ordinary SLE is applicable with an effective diffusivity parameter κ_{eff}. Not surprisingly, the κ_{eff}'s do not fulfill the prediction of SLE for the relation between D_{f}(H) and the diffusivity parameter.

Eye-tracking as a proxy for coherence and complexity of texts.

Reading is a complex cognitive process that involves primary oculomotor function and high-level activities like attention focus and language processing. When we read, our eyes move by primary physiological functions while responding to language-processing demands. In fact, the eyes perform discontinuous twofold movements, namely, successive long jumps (saccades) interposed by small steps (fixations) in which the gaze "scans" confined locations. It is only through the fixations that information is effectively captured for brain processing. Since individuals can express similar as well as entirely different opinions about a given text, it is therefore expected that the form, content and style of a text could induce different eye-movement patterns among people. A question that naturally arises is whether these individuals' behaviours are correlated, so that eye-tracking while reading can be used as a proxy for text subjective properties. Here we perform a set of eye-tracking experiments with a group of individuals reading different types of texts, including children stories, random word generated texts and excerpts from literature work. In parallel, an extensive Internet survey was conducted for categorizing these texts in terms of their complexity and coherence, considering a large number of individuals selected according to different ages, gender and levels of education. The computational analysis of the fixation maps obtained from the gaze trajectories of the subjects for a given text reveals that the average "magnetization" of the fixation configurations correlates strongly with their complexity observed in the survey. Moreover, we perform a thermodynamic analysis using the Maximum-Entropy Model and find that coherent texts were closer to their corresponding "critical points" than non-coherent ones, as computed from the Pairwise Maximum-Entropy method, suggesting that different texts may induce distinct cohesive reading activities.

Tracing contacts to evaluate the transmission of COVID-19 from highly exposed individuals in public transportation.

We investigate, through a data-driven contact tracing model, the transmission of COVID-19 inside buses during distinct phases of the pandemic in a large Brazilian city. From this microscopic approach, we recover the networks of close contacts within consecutive time windows. A longitudinal comparison is then performed by upscaling the traced contacts with the transmission computed from a mean-field compartmental model for the entire city. Our results show that the effective reproduction numbers inside the buses, [Formula: see text], and in the city, [Formula: see text], followed a compatible behavior during the first wave of the local outbreak. Moreover, by distinguishing the close contacts of healthcare workers in the buses, we discovered that their transmission, [Formula: see text], during the same period, was systematically higher than [Formula: see text]. This result reinforces the need for special public transportation policies for highly exposed groups of people.

Morphological Transition between Patterns Formed by Threads of Magnetic Beads.

Magnetic beads attract each other, forming chains. We push such chains into an inclined Hele-Shaw cell and discover that they spontaneously form self-similar patterns. Depending on the angle of inclination of the cell, two completely different situations emerge; namely, above the static friction angle the patterns resemble the stacking of a rope and below they look similar to a fortress from above. Moreover, locally the first pattern forms a square lattice, while the second pattern exhibits triangular symmetry. For both patterns, the size distributions of enclosed areas follow power laws. We characterize the morphological transition between the two patterns experimentally and numerically and explain the change in polarization as a competition between friction-induced buckling and gravity.

Predicting dengue outbreaks at neighbourhood level using human mobility in urban areas.

Dengue is a vector-borne disease transmitted by the Aedes genus mosquito. It causes financial burdens on public health systems and considerable morbidity and mortality. Tropical regions in the Americas and Asia are the areas most affected by the virus. Fortaleza is a city with approximately 2.6 million inhabitants in northeastern Brazil that, during the recent decades, has been suffering from endemic dengue transmission, interspersed with larger epidemics. The objective of this paper is to study the impact of human mobility in urban areas on the spread of the dengue virus, and to test whether human mobility data can be used to improve predictions of dengue virus transmission at the neighbourhood level. We present two distinct forecasting systems for dengue transmission in Fortaleza: the first using artificial neural network methods and the second developed using a mechanistic model of disease transmission. We then present enhanced versions of the two forecasting systems that incorporate bus transportation data cataloguing movement among 119 neighbourhoods in Fortaleza. Each forecasting system was used to perform retrospective forecasts for historical dengue outbreaks from 2007 to 2015. Results show that both artificial neural networks and mechanistic models can accurately forecast dengue cases, and that the inclusion of human mobility data substantially improves the performance of both forecasting systems. While the mechanistic models perform better in capturing seasons with large-scale outbreaks, the neural networks more accurately forecast outbreak peak timing, peak intensity and annual dengue time series. These results have two practical implications: they support the creation of public policies from the use of the models created here to combat the disease and help to understand the impact of urban mobility on the epidemic in large cities.

Circuits with broken fibration symmetries perform core logic computations in biological networks.

We show that logic computational circuits in gene regulatory networks arise from a fibration symmetry breaking in the network structure. From this idea we implement a constructive procedure that reveals a hierarchy of genetic circuits, ubiquitous across species, that are surprising analogues to the emblematic circuits of solid-state electronics: starting from the transistor and progressing to ring oscillators, current-mirror circuits to toggle switches and flip-flops. These canonical variants serve fundamental operations of synchronization and clocks (in their symmetric states) and memory storage (in their broken symmetry states). These conclusions introduce a theoretically principled strategy to search for computational building blocks in biological networks, and present a systematic route to design synthetic biological circuits.

Time evolution of the behaviour of Brazilian legislative Representatives using a complex network approach.

The follow up of Representative behavior after elections is imperative for a democratic Representative system, at the very least to punish betrayal with no re-election. Our goal was to show how to follow Representatives' and how to show behavior in real situations and observe trends in political crises including the onset of game changing political instabilities. We used correlation and correlation distance matrices of Brazilian Representative votes during four presidential terms. Re-ordering these matrices with Minimal Spanning Trees displays the dynamical formation of clusters for the sixteen year period, which includes one Presidential impeachment. The reordered matrices, colored by correlation strength and by the parties clearly show the origin of observed clusters and their evolution over time. When large clusters provide government support cluster breaks, political instability arises, which could lead to an impeachment, a trend we observed three years before the Brazilian President was impeached. We believe this method could be applied to foresee other political storms.

Fundraising and vote distribution: A non-equilibrium statistical approach.

The number of votes correlates strongly with the money spent in a campaign, but the relation between the two is not straightforward. Among other factors, the output of a ballot depends on the number of candidates, voters, and available resources. Here, we develop a conceptual framework based on Shannon entropy maximization and Superstatistics to establish a relation between the distributions of money spent by candidates and their votes. By establishing such a relation, we provide a tool to predict the outcome of a ballot and to alert for possible misconduct either in the report of fundraising and spending of campaigns or on vote counting. As an example, we consider real data from two proportional elections with more than 6000 candidates each, where a detailed data verification is virtually impossible, and show that the number of potential misconducting candidates to audit can be reduced to less than ten.

A universal approach for drainage basins.

Drainage basins are essential to Geohydrology and Biodiversity. Defining those regions in a simple, robust and efficient way is a constant challenge in Earth Science. Here, we introduce a model to delineate multiple drainage basins through an extension of the Invasion Percolation-Based Algorithm (IPBA). In order to prove the potential of our approach, we apply it to real and artificial datasets. We observe that the perimeter and area distributions of basins and anti-basins display long tails extending over several orders of magnitude and following approximately power-law behaviors. Moreover, the exponents of these power laws depend on spatial correlations and are invariant under the landscape orientation, not only for terrestrial, but lunar and martian landscapes. The terrestrial and martian results are statistically identical, which suggests that a hypothetical martian river would present similarity to the terrestrial rivers. Finally, we propose a theoretical value for the Hack's exponent based on the fractal dimension of watersheds, γ = D/2. We measure γ = 0.54 ± 0.01 for Earth, which is close to our estimation of γ ≈ 0.55. Our study suggests that Hack's law can have its origin purely in the maximum and minimum lines of the landscapes.

Anxiolytic and panicolytic-like effects of environmental enrichment seem to be modulated by serotonin neurons located in the dorsal subnucleus of the dorsal raphe.

In a previous study, we showed that exposure of rats to a one-week environmental enrichment (EE) protocol decreases elevated T-maze (ETM) avoidance responses, an anxiolytic-like effect, without altering escape reactions, in clinical terms related to panic disorder. These anxiolytic-like effects were followed by decreased delta FosB-immunoreactivity (delta FosB-ir) in the cingulate cortex, dorsolateral and intermediate lateral septum, hippocampus (cornus of Ammon), anterior and dorsomedial hypothalamus, medial and basolateral amygdala and ventral region of the dorsal raphe nucleus. The purpose of the present study was to further investigate behavioral and neurophysiological alterations induced by EE exposure. For that, in a first experiment we verified if increasing the time of exposure to the same EE protocol used in our previous study (from one to two weeks) altered male Wistar rats' ETM escape responses. All animals were tested in an open field, immediately after the ETM, for locomotor activity assessment. Since anxiety and panic-related reactions have been associated to the functioning of specific subnuclei of the dorsal raphe nucleus (DR), we also evaluated delta FosB-ir in serotonergic cells of DR regions. At last, we analyzed plasma corticosterone levels in animals submitted to EE and to standard housing. Results showed that a two-week exposure to EE decreases both ETM avoidance and escape reactions, inducing anxiolytic and panicolytic-like effects, respectively. There was also a significant decrease in the number of double staining neurons in the midrostral region of the dorsal subnucleus of the dorsal raphe. No changes in corticosterone levels, however, were observed. These results contribute to a better understanding of the effects of EE on anxiety and panic-related responses.

Correction: Dynamics in the Fitness-Income plane: Brazilian states vs World countries.

[This corrects the article DOI: 10.1371/journal.pone.0197616.].

The price of a vote: Diseconomy in proportional elections.

The increasing cost of electoral campaigns raises the need for effective campaign planning and a precise understanding of the return of such investment. Interestingly, despite the strong impact of elections on our daily lives, how this investment is translated into votes is still unknown. By performing data analysis and modeling, we show that top candidates spend more money per vote than the less successful and poorer candidates, a relation that discloses a diseconomy of scale. We demonstrate that such electoral diseconomy arises from the competition between candidates due to inefficient campaign expenditure. Our approach succeeds in two important tests. First, it reveals that the statistical pattern in the vote distribution of candidates can be explained in terms of the independently conceived, but similarly skewed distribution of money campaign. Second, using a heuristic argument, we are able to explain the observed turnout percentage for a given election of approximately 63% in average. This result is in good agreement with the average turnout rate obtained from real data. Due to its generality, we expect that our approach can be applied to a wide range of problems concerning the adoption process in marketing campaigns.