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Brain Complexity (BC) have successfully been applied to study the brain electroencephalographic signal (EEG) in health and disease. In this study, we employed recurrence entropy to quantify BC associated with the neurophysiology of movement by comparing BC in both resting state and cycling movement. We measured EEG in 24 healthy adults and placed the electrodes on occipital, parietal, temporal and frontal sites on both the right and left sides of the brain. We computed the recurrence entropy from EEG measurements during cycling and resting states. Entropy is higher in the resting state than in the cycling state for all brain regions analysed. This reduction in complexity is a result of the repetitive movements that occur during cycling. These movements lead to continuous sensorial feedback, resulting in reduced entropy and sensorimotor processing. [ABSTRACT FROM AUTHOR]

The brain is understood as an intricate biological system composed of numerous elements. It is susceptible to various physical and chemical influences, including temperature. The literature extensively explores the conditions that influence synapses in the context of cellular communication. However, the understanding of how the brain's global physical conditions can modulate ephaptic communication remains limited due to the poorly understood nature of ephapticity. This study proposes an adaptation of the Hodgkin and Huxley (HH) model to investigate the effects of ephaptic entrainment in response to thermal changes (HH-E). The analysis focuses on two distinct neuronal regimes: subthreshold and suprathreshold. In the subthreshold regime, circular statistics are used to demonstrate the dependence of phase differences with temperature. In the suprathreshold regime, the Inter-Spike Interval are employed to estimate phase preferences and changes in the spiking pattern. Temperature influences the model's ephaptic interactions and can modify its preferences for spiking frequency, with the direction of this change depending on specific model conditions and the temperature range under consideration. Furthermore, temperature enhance the anti-phase differences relationship between spikes and the external ephaptic signal. In the suprathreshold regime, ephaptic entrainment is also influenced by temperature, especially at low frequencies. This study reveals the susceptibility of ephaptic entrainment to temperature variations in both subthreshold and suprathreshold regimes and discusses the importance of ephaptic communication in the contexts where temperature may plays a significant role in neural physiology, such as inflammatory processes, fever, and epileptic seizures. [ABSTRACT FROM AUTHOR]

We show how the maximum of recurrence-microstates entropy configures a new way to properly compute appropriate recurrence vicinity parameter and time sampling to perform recurrence analysis of continuous data. For experimental data, we show the same procedure may be used to find the optimum sampling or to perform down-sampling of the data, preserving recurrence meaning and adjusting the ideal sampling. The new method retrieves results obtained using traditional methods with the advantage of being independent of any free parameter, since all parameter dependencies are automatically set. Our results are also less sensitive to noise when experimental data are used. Due to the automatized way to capture suitable recurrence parameters, the method is adequate for using in autonomous numerical algorithms, allowing the recovery of relevant recurrence information embedded in time series (including over-sampled data), rationalizing the process of data acquisition and allowing only relevant data to be collected. [ABSTRACT FROM AUTHOR]

The brain is commonly understood as a complex network system with a particular organization and topology that can result in specific electrophysiological patterns. Among all the dynamic elements resulting from the circuits of the brain's network, ephapticity is a cellular communication mechanism that has received little attention. To understand the network's properties of ephaptic entrainment, we start investigating the ephaptic effect on a single neuron. In this study, we used numerical simulations to examine the relationship between alterations in ephaptic neuronal entrainment and impaired electrophysiological properties of the neuronal membrane, which can occur via spike field coherence (SFC). This change in frequency band amplitude is observed in some neurodegenerative diseases, such as Parkinson's or Alzheimer's. To further investigate these phenomena, we proposed a damaged model based on the impairment of both the resistance of the ion channels and the capacitance of the lipid membrane. Therefore, we simulated ephaptic entrainment with the hybrid neural model quadratic integrate-and-fire ephaptic (QIF-E), which mimics an ephaptic entrainment generated by an LFP (simulate a neuronal group). Our results indicate a link between peak entrainment (ephapticity) preference and a shift in frequency band when damage occurs mainly in ion channels. Finally, we discuss possible relationships between ephaptic entrainment and neurodegenerative diseases associated with aging factors. [ABSTRACT FROM AUTHOR]

Data-centric inverse problems are a process of inferring physical attributes from indirect measurements. Full-waveform inversion (FWI) is a non-linear inverse problem that attempts to obtain a quantitative physical model by comparing the wave equation solution with observed data, optimizing an objective function. However, the FWI is strenuously dependent on a robust objective function, especially for dealing with cycle-skipping issues and non-Gaussian noises in the dataset. In this work, we present an objective function based on the Kaniadakis κ -Gaussian distribution and the optimal transport (OT) theory to mitigate non-Gaussian noise effects and phase ambiguity concerns that cause cycle skipping. We construct the κ -objective function using the probabilistic maximum likelihood procedure and include it within a well-posed version of the original OT formulation, known as the Kantorovich–Rubinstein metric. We represent the data in the graph space to satisfy the probability axioms required by the Kantorovich–Rubinstein framework. We call our proposal the κ -Graph-Space Optimal Transport FWI (κ -GSOT-FWI). The results suggest that the κ -GSOT-FWI is an effective procedure to circumvent the effects of non-Gaussian noise and cycle-skipping problems. They also show that the Kaniadakis κ -statistics significantly improve the FWI objective function convergence, resulting in higher-resolution models than classical techniques, especially when κ = 0.6 . [ABSTRACT FROM AUTHOR]

The conventional approach to data-driven inversion framework is based on Gaussian statistics that presents serious difficulties, especially in the presence of outliers in the measurements. In this work, we present maximum likelihood estimators associated with generalized Gaussian distributions in the context of Rényi, Tsallis and Kaniadakis statistics. In this regard, we analytically analyze the outlier-resistance of each proposal through the so-called influence function. In this way, we formulate inverse problems by constructing objective functions linked to the maximum likelihood estimators. To demonstrate the robustness of the generalized methodologies, we consider an important geophysical inverse problem with high noisy data with spikes. The results reveal that the best data inversion performance occurs when the entropic index from each generalized statistic is associated with objective functions proportional to the inverse of the error amplitude. We argue that in such a limit the three approaches are resistant to outliers and are also equivalent, which suggests a lower computational cost for the inversion process due to the reduction of numerical simulations to be performed and the fast convergence of the optimization process. [ABSTRACT FROM AUTHOR]

Inverse problems are challenging in several ways, and we cite the non-linearity and the presence of non-Gaussian noise. Least squared is the standard method to construct a equivalent functional for optimization, which is equivalent to the L2 norm of the misfit. Alternative norms in the optimization process are an useful strategy in the inverse problem solution. Generalized statistics can be present at two sides of the inverse problem: in the noise that pollutes the data and in the norm used in the optimization algorithm. With help of a seismic problem, we polluted the signal with a q-exponential noise (using an exponent q noise ) and subsequently inverted the problem using a norm associated to a q-exponential (with an exponent q inv ). The same procedure was also applied to the simpler problem of a linear fitting. We tested the hypothesis of a relation between the exponents q noise and q inv . The overall pattern observed is the following: inversion error are smaller for low q noise and high q inv . In contrast, the worst inversion is found for high polluting noise (far from Gaussian noise) and for inversion with low q inv (close to the Gaussian case). [ABSTRACT FROM AUTHOR]

In recent decades, there has been a growing interest in the impact of electric fields generated in the brain. Transmembrane ionic currents originate electric fields in the extracellular space and are capable of affecting nearby neurons, a phenomenon called ephaptic neuronal communication. In the present work, the Quadratic Integrated-and-Fire model (QIF-E) underwent an adjustment/improvement to include the ephaptic entrainment behavior between neurons and electric fields. Indeed, the aim of our study is to validate the QIF-E model, which is a model to estimate the influence of electric fields on neurons. For this purpose, we evaluated whether the main properties observed in an experiment by Anastassiou et al. (Nat Neurosci 14:217–223, 2011), which analyzed the effect of an electric field on cortical pyramidal neurons, are reproduced with the QIF-E model. In this way, the analysis tools are employed according to the neuronal activity regime: (i) for the subthreshold regime, the circular statistic is used to describe the phase differences between the input stimulus signal (electrode) and the modeled membrane response; (ii) in the suprathreshold regime, the Population Vector and the Spike Field Coherence are used to estimate phase preferences and the entrainment intensity between the input stimulus and Action Potentials. The results observed are (i) in the subthreshold regime the values of the phase differences change with distinct frequencies of the input stimulus; (ii) in the supra-threshold regime the preferential phase of Action Potentials changes for different frequencies. In addition, we explore other parameters of the model, such as noise and membrane characteristic-time, in order to understand different types of neurons and extracellular environment related to ephaptic communication. Such results are consistent with results observed in empirical experiments based on ephaptic phenomenon. In addition, the QIF-E model allows further studies on the physiological importance of ephaptic communication in the brain, and its simplicity may open a door to simulate the ephaptic response in neuronal networks and assess the impact of ephaptic communication in such scenarios. [ABSTRACT FROM AUTHOR]

Ants use chemical, visual, magnetic and/or solar cues during foraging activity. Orientation is important, since foragers need to return to the nest. In this study, we analyze the maze performance of the Dinoponera quadriceps using chemical and visual cues to study spatial orientation. We used a white maze with seventeen chambers and we allowed the ants to explore for ten minutes in each session. Six treatments performed by manipulating presence or absence of chemical and visual cues. Two treatments occurred in the presence and absence of odor, but without visual cues. In four treatments, we introduced visual cues, in two upper visual cues and in two frontal visual cues, both with and without odor. Our results showed that both chemical and visual cues improve maze performance during ant movement to the food source and to the nest. We suggest bimodal navigation in D. quadriceps. The association of multiple cues (chemical and visual) improves workers navigation performance, which probably enhances foraging rate and individual fitness. [ABSTRACT FROM AUTHOR]

In recent decades, there has been a growing interest in the impact of electric fields generated in the brain. Transmembrane ionic currents originate electric fields in the extracellular space and are capable of affecting nearby neurons, a phenomenon called ephaptic neuronal communication. In the present work, the Quadratic Integrated-and-Fire model (QIF-E) underwent an adjustment/improvement to include the ephaptic entrainment behavior between neurons and electric fields. Indeed, the aim of our study is to validate the QIF-E model, which is a model to estimate the influence of electric fields on neurons. For this purpose, we evaluated whether the main properties observed in an experiment by Anastassiou et al. (Nat Neurosci 14:217–223, 2011), which analyzed the effect of an electric field on cortical pyramidal neurons, are reproduced with the QIF-E model. In this way, the analysis tools are employed according to the neuronal activity regime: (i) for the subthreshold regime, the circular statistic is used to describe the phase differences between the input stimulus signal (electrode) and the modeled membrane response; (ii) in the suprathreshold regime, the Population Vector and the Spike Field Coherence are used to estimate phase preferences and the entrainment intensity between the input stimulus and Action Potentials. The results observed are (i) in the subthreshold regime the values of the phase differences change with distinct frequencies of the input stimulus; (ii) in the supra-threshold regime the preferential phase of Action Potentials changes for different frequencies. In addition, we explore other parameters of the model, such as noise and membrane characteristic-time, in order to understand different types of neurons and extracellular environment related to ephaptic communication. Such results are consistent with results observed in empirical experiments based on ephaptic phenomenon. In addition, the QIF-E model allows further studies on the physiological importance of ephaptic communication in the brain, and its simplicity may open a door to simulate the ephaptic response in neuronal networks and assess the impact of ephaptic communication in such scenarios. [ABSTRACT FROM AUTHOR]

Population abundance data are often used to define species' conservation status. Abundance of marine turtles is typically estimated using nesting beach monitoring data such as nest counts and clutch frequency (CF, i.e., the number of nests female turtles lay within a nesting season). However, studies have shown that CF determined solely from nesting beach monitoring data can be underestimated, leading to inaccurate abundance estimates. To obtain reliable estimates of CF for hawksbill turtles in northeastern Brazil (6.273356° S, 35.036271° W), the region with the highest nesting density in the South Atlantic, data from beach monitoring and satellite telemetry were combined from 2014 to 2019. Beach monitoring data indicated the date of first nesting event, while state-space modeling of satellite telemetry data indicated the departure date of turtles, allowing calculations of residence length at breeding site and CF estimates based on internesting intervals. Females were estimated to nest up to six times within the nesting season with CF estimates between 4.5 and 4.8 clutches per female. CF estimates were used to determine the number of nesting females at the study site based in two approaches: considering and not considering transient turtles. Our approach and findings highlight that transients heavily influence CF estimates and need for reconsideration of how this key parameter is commonly determined for marine turtle populations and the use of beach monitoring data and satellite telemetry for estimations of CF. [ABSTRACT FROM AUTHOR]

Full-waveform inversion (FWI) is a powerful methodology employed in estimating subsurface physical parameters. FWI is classically formulated as a data-fitting problem based on minimizing the squared l 2 -norm of the difference between the observed data and modeled data (i.e., the data residuals or errors). The FWI based on l 2 -norm (hereafter classical FWI) stems from the assumption that the errors follow a Gaussian probability distribution. However, since, in most cases the error is non-Gaussian for non-linear problems and therefore, the classical FWI tends to fail. In this work, we consider the Tsallis q-deformation of the exponential function, which is widely used in the field of statistical physics, to derive an alternative misfit function to mitigate the FWI sensitivity to spurious measurements (outliers). In this regard, we formulated the FWI from a q-generalization of the Laplace probability distribution (or q-Laplace distribution). Application on a typical Brazilian pre-salt velocity model with an acoustic synthetic noisy-data is presented, in which we compare the performance of our proposal against the classical FWI. In addition, we compare also our methodology with the robust misfit function based on l 1 -norm, which is based on the Laplace distribution. The results show that our proposal outperforms classical FWI, and also the robust FWI based on l 1 -norm. The results also reveal that our proposal accelerates the data inversion process when the data is polluted by outliers. [ABSTRACT FROM AUTHOR]

The inference of physical parameters from measured data is essential for describing and analysing several complex systems. In this regard, the inverse problem theory has been applied to solve this task based on the Boltzmann–Gibbs statistical mechanics by considering that the errors are Gaussian-like. However, in the non-Gaussian noise case, the classical inverse-problem approach estimates model parameters which may be inaccurate and grossly biased. In the case of extreme outliers in the data set, for example, the most common approach used is based on the l 1 -norm of the difference between modelled and observed data. This approach is famous for being robust because it is based on a long-tailed probability function: the Laplace distribution. However, l 1 -norm-based inverse problems equally weight noise and information, which limits its robustness to a large number of outliers in the data set. To mitigate the limitations of the l 1 -norm approach, we introduce in this work the inverse problem theory in the context of Tsallis and Kaniadakis generalised statistical mechanics, by generalising the Laplace distribution. Applications to a classical geophysical data-inversion problem demonstrate that inverse problems based on Tsallis q- and Kaniadakis κ -statistics outperform the classical and l 1 -norm-based frameworks. They also reveal that the best results are associated with the limit in which both generalised distributions are equivalent. [ABSTRACT FROM AUTHOR]

The problem of inferencing parameters of complex systems from measured data has been extensively studied based on the inverse problem theory. Classically, an inverse problem is formulated as an optimisation task in which the objective function is based on the maximisation of Boltzmann–Gibbs entropy under appropriate constraints. In this classical framework, errors are assumed to follow a Gaussian-behaviour. However, in many situations, the errors are non-Gaussian and therefore the classical approach is predestined to failure. To mitigate the inverse-problem sensitivity to non-Gaussian errors, we have considered in this study a q-generalised objective function within the context of nonextensive statistical mechanics. In this regard, the errors are assumed to follow the q-Gaussian distribution, which arises from the maximisation of the nonadditive Tsallis entropy. We study the robustness properties of the q-objective function, which is a generalisation of the classical objective function, through the so-called influence function. In this work, we analyse and discuss from an analytical and numerical perspective, the role of the entropic index (q) of Tsallis entropy on the effectiveness and robustness in the inference of physical parameters from with strongly noisy-data. In this perspective, we show that exists an optimum value for the entropic index at the limit q → 3 , which implies in δ q = 3 - q ≈ 0 in the q-objective function. To validate our proposal, we consider a classic geophysical inverse problem by employing a realistic geological model and a data set contaminated by spikes. [ABSTRACT FROM AUTHOR]

The physical analysis of earthquakes is essential for the exploration of the interior structure of a region. Nowadays, the statistics of earthquakes' occurrence are dominated by the Gutenberg–Richter (GR) law. Here, we report evidence of the similarity between earthquakes and marsquakes by using the generalized GR law in the context of Tsallis nonextensive statistical mechanics. We analyze the Martian quakes that were recorded by the InSight's seismometer in the Elysium Planitia region, Mars, from January 2019 to December 2019, which corresponds to the first year of geophysical observations by Nasa's InSight mission. The results show evidence for the similarity between the triggering mechanism of earthquakes and marsquakes. They also reveal the fractal nature of the Martian geological faults. [ABSTRACT FROM AUTHOR]

Full-waveform inversion (FWI) is a powerful technique to obtain high-resolution subsurface models, from seismic data. However, FWI is an ill-posed problem, which means that the solution is not unique, and therefore the expert use of the information is required to mitigate the FWI ill-posedness, especially when wide-aperture seismic acquisitions are considered. In this way, we investigate the multiscale frequency-domain FWI by using a weighting operator according to the distances between each source-receiver pair. In this work, we propose a weighting operator that acts on the data misfit as preconditioning of the objective function that depends on the source-receiver distance (offset) and the frequency used during the inversion. The proposed operator emphasizes information from long offsets, especially at low frequencies, and as a consequence improves the update of deep geological structures. To demonstrate the effectiveness of our proposal, we perform numerical simulations on 2D acoustic Marmousi2 case study, which is widely used in seismic imaging tests, considering three different scenarios. In the first two ones, we have used an acquisition geometry with a maximum offset of 4 and 8 km, respectively. In the last one, we have considered all-offsets. The results show that our proposal outperforms similar strategies, for all scenarios, providing more reliable quantitative subsurface models. In fact, our inversion result has the lowest error and the highest similarity to the true model than similar approaches. [ABSTRACT FROM AUTHOR]

Many complex systems, from earthquakes and financial markets to Barkhausen effect in ferromagnetic materials, respond with a noise consisting of discrete avalanche-like events with broad range of sizes and durations, separated by waiting times. Here we focus on the waiting-time statistics in magnetic systems. By investigating the Barkhausen noise in amorphous and polycrystalline ferromagnetic films having different thicknesses, we uncover the form of the waiting-time distribution in time series recorded from the irregular and irreversible motion of magnetic domain walls. Further, we address the question of if the waiting-time distribution evolves with the threshold level, as well as with the film thickness and structural character of the materials. Our results, besides informing on the temporal avalanche correlations, disclose the waiting-time statistics in magnetic systems also bring fingerprints of the universality classes of Barkhausen avalanches and a dimensional crossover in the domain wall dynamics. [ABSTRACT FROM AUTHOR]

Recordings were made in nests of Eretmochelys imbricata and 107 samples of 10-min recordings revealed 575 sounds that were classified manually into 4 categories. Our results show that hawksbill turtles vocalize within the nest, especially during and after eclosion, which suggests vocalizations are important for communication among hatchlings to synchronize emergence from the nest. [ABSTRACT FROM AUTHOR]

The behavior of sea turtle species can be influenced by the lunar cycle, possibly due to moonlight variability. We analyzed the relationship between nesting behavior and moon phase using nesting hawksbill turtle records for beaches in Northeast Brazil for the 2006–2007 to 2015–2016 seasons. The total number of records was 4807, while the total number with time point registration was 1031. The Eretmochelys imbricata inter-nesting period was approximately half the lunar cycle; we therefore expected nesting phase synchronization with lunar phases within each season. We computed the lunar angle for the hawksbill records, and the Kuiper test for uniformity indicated that the species shows some lunar phase preferences. We observed that oviposition at the first and last quarters of the moon is more frequent than at full moon or new moon phases. We also computed the lunar angle throughout several seasons for remigrant turtles and found an absence of preferential lunar phase across different seasons. This indicates that the hawksbill does not choose a lunar phase previously chosen in other nesting seasons. We analyzed the relationship between the presence of the moon in the sky and nesting turtles, and, in sequence, compared the records of false crawls and nest crawls; no relation was found between these variables. [ABSTRACT FROM AUTHOR]

A previous comparison of whistles using data sampled at 48 kHz suggested that certain frequency parameters vary along a latitudinal gradient. This geographical pattern may be biased because whistles sampled at higher frequencies could potentially have very different frequency contents. The current study compared the acoustic parameters of Guiana dolphin (Sotalia guianensis) whistles recorded at a higher sampling rate (96 kHz) and from groups occupying two never before sampled sites, Benevente Bay, Espírito Santo, Brazil, and Formosa Bay, Rio Grande do Norte, Brazil, with recordings of other populations in South America. By only considering data sampled at a rate of at least 96 kHz, we aimed to detect differences in whistles across locations. Contrary to previous findings, our analyses do not indicate any clear separation between northern and southern populations based on whistles, and do not corroborate the hypothesis of latitudinal acoustic variation in this species. The variation in Guiana dolphin whistle parameters found here appears to be influenced by latitude to some extent, but several other factors, including sampling method, environmental fluctuations, and social influence on vocal learning, may be confounding the detection of a geographic pattern in these whistle samples. [ABSTRACT FROM AUTHOR]

Bioengineering, which studies the principles and design of biological systems, is a field that has inspired the development of several technologies that are currently in use. In this work, we use concepts from the fish lateral line sensing mechanism and apply them to seismic imaging processing. The lateral line is a sensory system composed of an integrated array of mechanical sensors spanning along the fish body. We compare the array of sensors along body fish with the seismic acquisition, which employs an array of equally spaced identical mechanical sensors to image the Earth’s subsurface. In both situations, the mechanical sensors capture and process mechanical vibrations from the environment to produce useful information. We explore the strategy of using the low-pass and high-pass sensors schema of fish lateral line to improve the seismic technique. We use the full-wave inversion method to compare the conventional acquisition procedure of identical sensors with alternative sets of different sensors, which mimics the fish lateral line. Our results show that the alternate sensors arrangement surpasses the performance of the conventional acquisition method, using just half of the input information. The results point at an image processing technique that is computationally more efficient and economical than the usual seismic processing method. [ABSTRACT FROM AUTHOR]

Recurrence analysis and its quantifiers are strongly dependent on the evaluation of the vicinity threshold parameter, i.e., the threshold to regard two points close enough in phase space to be considered as just one. We develop a new way to optimize the evaluation of the vicinity threshold in order to assure a higher level of sensitivity to recurrence quantifiers to allow the detection of even small changes in the dynamics. It is used to promote recurrence analysis as a tool to detect nonstationary behavior of time signals or space profiles. We show that the ability to detect small changes provides information about the present status of the physical process responsible to generate the signal and offers mechanisms to predict future states. Here, a higher sensitive recurrence analysis is proposed as a precursor, a tool to predict near future states of a particular system, based on just (experimentally) obtained signals of some available variables of the system. Comparisons with traditional methods of recurrence analysis show that the optimization method developed here is more sensitive to small variations occurring in a signal. The method is applied to numerically generated time series as well as experimental data from physiology. [ABSTRACT FROM AUTHOR]

We conceive a new recurrence quantifier for time series based on the concept of information entropy, in which the probabilities are associated with the presence of microstates defined on the recurrence matrix as small binary submatrices. The new methodology to compute the entropy of a time series has advantages compared to the traditional entropies defined in the literature, namely, a good correlation with the maximum Lyapunov exponent of the system and a weak dependence on the vicinity threshold parameter. Furthermore, the new method works adequately even for small segments of data, bringing consistent results for short and long time series. In a case where long time series are available, the new methodology can be employed to obtain high precision results since it does not demand large computational times related to the analysis of the entire time series or recurrence matrices, as is the case of other traditional entropy quantifiers. The method is applied to discrete and continuous systems. [ABSTRACT FROM AUTHOR]

We construct a self-organized critical (SOC) model to explain spontaneous collective activity in animal tissue without the necessity of a muscular or a central control nervous system. Our prototype model is an epithelial cuboid tissue formed by a single layer of cells as the internal digestive cavity of primitive animals. The tissue is composed by cells that absorb nutrients and store energy, with probability , to participate in a collective tissue activity. Each cell can be in two states: at high energy and able to became active or at low metabolic energy and remain at rest. Any cell can spontaneously, with a very low probability, spark a collective activity across its neighbors that share a minimal energy. Cells participating in tissue activity consume all their energy. A power-law relation for the probability of having a collective activity with cells is observed. By construction this model is analogue to the forest fire SOC model. Our approach produces naturally a critical state for the activity in animal tissue, besides it explains self-sustained activity in a living animal tissue without feedback control. [ABSTRACT FROM AUTHOR]

Arousals can be roughly characterized by punctual intrusions of wakefulness into sleep. In a standard perspective, using human electroencephalography (EEG) data, arousals are associated to slow-wave rhythms and K-complex brain activity. The physiological mechanisms that give rise to arousals during sleep are not yet fully understood. Moreover, subtle body movement patterns, which may characterize arousals both in human and in animals, are usually not detectable by eye perception and are not in general present in sleep studies. In this paper, we focus attention on accelerometer records (AR) to characterize and predict arousal during slow wave sleep (SWS) stage of mice. Furthermore, we recorded the local field potentials (LFP) from the CA1 region in the hippocampus and paired with accelerometer data. The hippocampus signal was also used here to identify the SWS stage. We analyzed the AR dynamics of consecutive arousals using recurrence technique and the determinism (DET) quantifier. Recurrence is a fundamental property of dynamical systems, which can be exploited to characterize time series properties. The DET index evaluates how similar are the evolution of close trajectories: in this sense, it computes how accurate are predictions based on past trajectories. For all analyzed mice in this work, we observed, for the first time, the occurrence of a universal dynamic pattern a few seconds that precedes the arousals during SWS sleep stage based only on the AR signal. The predictability success of an arousal using DET from AR is nearly 90%, while similar analysis using LFP of hippocampus brain region reveal 88% of success. Noteworthy, our findings suggest an unique dynamical behavior pattern preceding an arousal of AR data during sleep. Thus, the employment of this technique applied to AR data may provide useful information about the dynamics of neuronal activities that control sleep-waking switch during SWS sleep period. We argue that the predictability of arousals observed through DET(AR) can be functionally explained by a respiratory-driven modification of neural states. Finally, we believe that the method associating AR data with other physiologic events such as neural rhythms can become an accurate, convenient and non-invasive way of studying the physiology and physiopathology of movement and respiratory processes during sleep. [ABSTRACT FROM AUTHOR]

The Small-World Network on a Sphere SWNS is a non-crossing network that has no hubs and presents the small-world property diam ~ log N with diam being the maximal distance between any two vertices and N being the number of vertices. The SWNS is constructed using a partition of the sphere and the parallels are regular sections of the sphere with constant latitude. The number of cells on the parallels, however, increases exponentially from the pole to the equator of the sphere. We analytically compute the distribution of connectivity, the clustering coefficient and the SWNS distances. The resilience of the model against selective attacks is also discussed. [ABSTRACT FROM AUTHOR]

We analyzed 410 nest locations from 150 individual nesting hawksbill turtles ( Eretmochelys imbricata) on the northeastern Brazilian coast during 8 nesting seasons from 2006 to 2014 to evaluate individual nesting preferences. We determined the consistency of nest site choice within and between nesting seasons for open sand and vegetation nest microhabitats and also for nest site distances from the current waterline, highest spring tide, vegetation line, and position along the beach. We found that behavioral consistency within seasons was more robust than between seasons. This suggests that a decrease in the consistency of nest site choice may be related to progressive landscape changes in the nesting environment, driving behavioral flexibility in nesting preferences. [ABSTRACT FROM AUTHOR]

Nestedness is a concept employed to describe a particular pattern of organization in species interaction networks and in site-by-species incidence matrices. Currently the most widely used nestedness index is the NODF (Nestedness metric based on Overlap and Decreasing Fill), initially presented for binary data and later extended to quantitative data, WNODF. In this manuscript we present a rigorous formulation of this index for both cases, NODF and WNODF. In addition, we characterize the matrices corresponding to the two extreme cases, (W)NODF=1 and (W)NODF=0, representing a perfectly nested pattern and the absence of nestedness respectively. After permutations of rows and columns if necessary, the perfectly nested pattern is a full triangular matrix, which must of course be square, with additional inequalities between the elements for WNODF. On the other hand there are many patterns characterized by the total absence of nestedness. Indeed, any binary matrix (whether square or rectangular) with uniform row and column sums (or marginals) satisfies this condition: the chessboard and a pattern reflecting an underlying annular ecological gradient, which we shall call gradient-like, are symmetrical or nearly symmetrical examples from this class. [ABSTRACT FROM AUTHOR]

We employ quantum biochemistry methods based on the Density Functional Theory (DFT) approach to unveil the detailed binding energy features of willardiines co-crystallized with the AMPA receptor. Our computational results demonstrate that the total binding energies of fluorine–willardiine (FW), hydrogen–willardiine (HW), bromine–willardiine (BrW) and iodine–willardiine (IW) to the iGluR2 ligand-pocket correlate with the agonist binding energies, whose experimental sequential data match our computational counterpart, excluding the HW case. We find that the main contributions to the total willardiine–iGluR2 binding energy are due to the amino acid residues in decreasing order Glu705 ＞ Arg485 ＞ Ser654 ＞ Tyr450 ＞ T655. Furthermore, Met708, which is positioned close to the 5-substituent, attracts HW and FW, but repels BrW and IW. Our results contribute significantly to an improved understanding of the willardiine–iGluR2 binding mechanisms. [ABSTRACT FROM AUTHOR]

Habitat split is a major force behind the worldwide decline of amphibian populations, causing community change in richness and species composition. In fragmented landscapes, natural remnants, the terrestrial habitat of the adults, are frequently separated from streams, the aquatic habitat of the larvae. An important question is how this landscape configuration affects population levels and if it can drive species to extinction locally. Here, we put forward the first theoretical model on habitat split which is particularly concerned on how split distance – the distance between the two required habitats – affects population size and persistence in isolated fragments. Our diffusive model shows that habitat split alone is able to generate extinction thresholds. Fragments occurring between the aquatic habitat and a given critical split distance are expected to hold viable populations, while fragments located farther away are expected to be unoccupied. Species with higher reproductive success and higher diffusion rate of post-metamorphic youngs are expected to have farther critical split distances. Furthermore, the model indicates that negative effects of habitat split are poorly compensated by positive effects of fragment size. The habitat split model improves our understanding about spatially structured populations and has relevant implications for landscape design for conservation. It puts on a firm theoretical basis the relation between habitat split and the decline of amphibian populations. [ABSTRACT FROM AUTHOR]

Female Hawksbill Turtles (Eretmochelys imbricata) nesting along the southeast coastline of the state of Rio Crande do Norte Brazil (6°13′40″S, 35°03′05″W) were captured and weighed during the 2006-07 and 2007-08 nesting seasons. The mean value for the first postoviposition mass was 79.6 kg. Individuals that were subsequently recaptured showed a mean mass change of 1.6 kg in the interval between two consecutive postovipositions (i.e., after one internidal interval). We plotted the mass of the individuals against the curvilinear carapace length. An analysis of residual mass above average body condition reveals that females with good body condition start nesting at the beginning of the season. Preoviposition mass was measured when the female aborted the nesting process. Cross mass change was 5.46 kg. Mean body mass recovery was 3.2 kg. Body mass recovery was always significantly lower than the change in gross mass. This is in agreement with the observed mass loss tendency throughout the breeding season for this species. Mass recovery was analyzed using allometric law, converting both loss in body mass and total egg mass to energy. Using mean turtle body mass, we performed three scenarios for the metabolic maintenance rate of the Hawksbill Turtle during the nesting period. The energy that the turtles expended in egg laying was estimated at 1,183 kJ • d-1. The daily net mass loss for the most realistic scenario converted into energy was 4,213 kJ • d-1. The total daily energy consumption (maintenance plus egg production) was similar to the daily energy from mass loss. This theoretical treatment suggests that, under this scenario, there is no reason for significant extra energy intake during the oviposition period. [ABSTRACT FROM AUTHOR]

Classical models of phytoplankton–zooplankton interaction show that with nutrient enrichment such systems may abruptly shift from limit cycles to stable phytoplankton domination due to zooplankton predation by planktivorous fish. Such models assume that planktivorous fish eat only zooplankton, but there are various species of filter-feeding fish that may also feed on phytoplankton. Here, we extend these classical models to systematically explore the effects of omnivory by planktivorous fish. Our analysis indicates that if fish forage on phytoplankton in addition to zooplankton, the alternative attractors predicted by the classical models disappear for all realistic parameter settings, even if omnivorous fish have a strong preference for zooplankton. Our model also shows that the level of fish biomass above which zooplankton collapse should be higher when fish are omnivorous than when fish are zooplanktivorous. We also used the model to explore the potential effects of the now increasingly common practice of stocking lakes with filter-feeding fish to control cyanobacteria. Because omnivorous filter-feeding fish forage on phytoplankton as well as on the main grazers of phytoplankton, the net effect of such fish on the phytoplankton biomass is not obvious. Our model suggests that there may be a unimodal relationship between the biomass of omnivorous filter-feeding fish and the biomass of phytoplankton. This implies that to manage for reductions in phytoplankton biomass, heavy stocking or strong reduction of such fish is best. [ABSTRACT FROM AUTHOR]

No abstract received. [ABSTRACT FROM AUTHOR]

Entropy-based indices are long-established measures of biological diversity, nowadays used to gauge partitioning of diversity at different spatial scales. Here, we tackle the measurement of diversity of interactions among two sets of organisms, such as plants and their pollinators. Actual interactions in ecological communities are depicted as bipartite networks or interaction matrices. Recent studies concentrate on distinctive structural patterns, such as nestedness or modularity, found in different modes of interaction. By contrast, we investigate mutual information as a general measure of structure in interactive networks. Mutual information (MI) measures the degree of reciprocal matching or specialization between interacting organisms. To ascertain its usefulness as a general measure, we explore (a) analytical solutions for different models; (b) the response of MI to network parameters, especially size and occupancy; (c) MI in nested, modular, and compound topologies. MI varies with fundamental matrix parameters: dimension and occupancy, for which it can be adjusted or normalized. Apparent differences among topologies are contingent on dimensions and occupancy, rather than on topological patterns themselves. As a general measure of interaction structure, MI is applicable to conceptually and empirically fruitful analyses, such as comparing similar ecological networks along geographical gradients or among interaction modalities in mutualistic or antagonistic networks. [ABSTRACT FROM AUTHOR]

The nonextensive statistical mechanics proposed by Tsallis have been successfully used to model and analyze many complex phenomena. Here, we study the role of the generalized Tsallis statistics on the inverse problem theory. Most inverse problems are formulated as an optimisation problem that aims to estimate the physical parameters of a system from indirect and partial observations. In the conventional approach, the misfit function that is to be minimized is based on the least-squares distance between the observed data and the modelled data (residuals or errors), in which the residuals are assumed to follow a Gaussian distribution. However, in many real situations, the error is typically non-Gaussian, and therefore this technique tends to fail. This problem has motivated us to study misfit functions based on non-Gaussian statistics. In this work, we derive a misfit function based on the q-Gaussian distribution associated with the maximum entropy principle in the Tsallis formalism. We tested our method in a typical geophysical data inverse problem, called post-stack inversion (PSI), in which the physical parameters to be estimated are the Earth's reflectivity. Our results show that the PSI based on Tsallis statistics outperforms the conventional PSI, especially in the non-Gaussian noisy-data case. [ABSTRACT FROM AUTHOR]

Nanomechanical resonators, of importance for signal filtering and transduction, are investigated within the limit of extreme nonlinear excitation. The Ruelle-Takens route establishes the transition of a system into chaos with n frequencies present. By providing a set of three sources, this transition from linear to nonlinear and finally to chaotic response can be traced, and it is found in our experiment. Knowledge of potentially chaotic behavior of nanomechanical systems is crucial for their application. Our resonator system has an overall length of 4 µm and a cross section of about 100 nm × 300 nm with natural frequencies of ∼50 MHz. [ABSTRACT FROM AUTHOR]

We present measurements on nanoelectromechanical systems fabricated in semiconductor materials suited for operating frequencies in the radio band. We demonstrate how to achieve parametric frequency tuning of nanoelectromechanical resonators required for radio frequency sensor and communication applications. © 2001 American Institute of Physics. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article on the electrical characterization of acupuncture points by A. C. Ahn and O. G. Martinsen in the previous issue of "The Journal of Alternative and Complementary Medicine."

A letter to the editor is presented in response to the article "Response to The statistics of skin impedance," by A. P. Colbert, S. Pearson, and J. McNames in the 2011 issue.