Interactive behavior among humans is governed by the dynamics of movement synchronization in a variety of repetitive tasks. This requires the interaction partners to perform for example rhythmic limb swinging or even goal-directed arm movements. Inspired by that essential feature of human interaction, we present a novel concept and design methodology to synthesize goal-directed synchronization behavior for robotic agents in repetitive joint action tasks. The agents’ tasks are described by closed movement trajectories and interpreted as limit cycles, for which instantaneous phase variables are derived based on oscillator theory. Events segmenting the trajectories into multiple primitives are introduced as anchoring points for enhanced synchronization modes. Utilizing both continuous phases and discrete events in a unifying view, we design a continuous dynamical process synchronizing the derived modes. Inverse to the derivation of phases, we also address the generation of goal-directed movements from the behavioral dynamics. The developed concept is implemented to an anthropomorphic robot. For evaluation of the concept an experiment is designed and conducted in which the robot performs a prototypical pick-and-place task jointly with human partners. The effectiveness of the designed behavior is successfully evidenced by objective measures of phase and event synchronization. Feedback gathered from the participants of our exploratory study suggests a subjectively pleasant sense of interaction created by the interactive behavior. The results highlight potential applications of the synchronization concept both in motor coordination among robotic agents and in enhanced social interaction between humanoid agents and humans. Synchronization is frequently observed across different modalities and situations. In particular, the synchronization of movements is found to play an essential role in the interactive behavior of humans. Due to its ubiquity in human life, interpersonal synchronization is experimentally investigated in various tasks that require jointly performed movements in a shared workspace: When walking in a group, humans tend to synchronize their gait joint action tasks. These tasks involve actions performed by two or more individuals in a common social setting, inducing action coordination in space and time Inspired by the appealing prospect to enrich the interaction repertoire of robots, this article addresses the challenge of designing interactive behavior for artificial agents engaging in repetitive One line of research on human synchronization behavior follows the dynamical systems approach. Patterns of coordination are considered to result from attractors of dynamical systems, that model interconnected perception-action loops Limit cycle systems creating rhythmic movement based on self-sustained oscillators are also called central pattern generators (CPGs) in robotics. Entrainment tasks, such as robot drumming However, the above works focus either on fundamental research of human synchronization behavior or, within human-machine interaction, on applications in purely rhythmic tasks. To the authors’ best knowledge, none of the existing works, except our previous In this article, we develop a concept of movement synchronization for repetitive joint action tasks. Those tasks are assumed to be described by closed movement trajectories that can be goal-directed and comprise multiple primitives. The modeling concept pursued in our analytical work on human synchronization behavior The remainder of this article is organized as follows. First, we clarify the required assumptions and definitions. Based on those, synchronization modes are analyzed and dynamical synchronization behavior is designed accordingly. Next, the required transformations between phase variables and movement trajectories are developed. After the design concept, we describe the human-robot experiment, its implementation and the applied measures. A detailed assessment and evaluation of the implemented synchronization behavior is presented. After a discussion of the results and insights, we sum up and draw the conclusions.a and b are used when variables belonging to agent ‘a’ and agent ‘b’ need to be distinguished. For clarity these superscripts are omitted otherwise.Bold characters are used for denoting vectors in this article. Superscripts This section provides the reader with the formal representations and definitions that are used in this article to characterize the joint action task as well as movement synchronization.t. The state vector can be composed by the configuration of the agent’s limbs, their hand (effector) position, or any other coordinates that describe the movements associated with the individual task.The notion of joint action Note. A certain set of states is considered suitable if the information conveyed through the chosen description allows to explain and model the synchronization behavior of the agents.cyclic, i.e. for any time t and finite time spans period. It follows that the state space representation of limit cycleThe concept of movement synchronization exploits the repetitive aspect of the individual task. Therefore, the state trajectory is required to be Note. For trajectories obtained from noisy measurements, condition (1) is relaxed by examining the return times to the Poincaré secant surface primitives. Each primitive events, see The limit cycle Note. The segmentation points are assumed to be such that any task-related constraints on the state can be satisfied, e.g. goal points or forbidden state regions.primitive durationThe times current primitive duration current period relative primitive durationRelating the The distribution The limit cycle phase and describes the motion on amplitudes that describe motions transverse to Through a coordinate transformation, the limit cycle is re-parameterized by the one-dimensional variable Among all possible transformations, we choose the phase obtained from the harmonic phase oscillator, which is one of the simplest oscillator models. Its unperturbed oscillations evolve at constant phase velocity Note. The above transformation can be understood as a decomposition of the task into the phase, which is the voluntary degree of freedom available for synchronization, and the amplitudes, which are the remaining degrees of freedom necessarily complying with the task goals.phase synchronizationWith the above definition of phase and under the assumption that the phase is originated from a self-sustained oscillating entity, a synchronization problem between a pair of phase oscillators is posed accounting for a dyad’s coordination in time. The oscillators are assumed to be mutually coupled through some coupling function and completely described by their phases event synchronization, if the events keep the temporal relationIn addition, the quasi-simultaneous appearance of event pairs is considered, known as event synchronization Note. The above notion of event synchronization implies phase synchronization, since the time lag and thus, the phase difference between the considered events is bounded. Event synchronization depending on the definition of relevant events provides a problem-specific characterization of the temporal organization of two limit cycles.Following the above definitions of phase and event synchronization and inspired by principles of human movement synchronization, in this Section we design synchronization behavior with application to repetitive joint action tasks. Accounting for the derived descriptions of possible synchronization modes, a unified synchronization process is developed.After analyzing the common modes of the synchronization between quasi-harmonic trajectories, which usually result from rhythmic movement tasks, we broaden the repertoire of potential synchronization modes between limit cycles featuring multiple primitives and events.in-phase and the anti-phase relation usually characterize the common modes of synchronization. When we calculate the relative phase differenceResearch on movement synchronization within human dyads has mainly focused on task paradigms requiring purely rhythmic movements such as finger tapping, leg or pendulum swinging. These tasks are usually described by one-dimensional motion trajectories, e.g. with the state These modes are equivalently described by event synchronization according to the above definition of synchronization. Evaluating the phase (4) at the event Thus, we obtain In repetitive joint action tasks, the limit cycles multiple event pairs requires an additional adjustment of The example in Note. Only a task-dependent subset of events might be synchronized, e.g. only those that are perceived by the interaction partner.dynamical systems approachSynchronization behavior is modeled in line with the In accordance to the definition of phase synchronization, the model structure is given by a pair of cross-coupled phase oscillatorsNote. Synchronization behavior is assumed to be voluntary and compliant with the task-related goals. We therefore require the coupling functions to be weak and Kuramoto modelIn the following, we review a realization of the coupling functions extended Kuramoto model. The natural frequencies model the individually preferred speed of task performance, whereas the sinusoidal coupling with the isotropic gain Note. The extended Kuramoto model implies equal attractor strengths, as both attractors were met nearly equally often in the experimental task.In-phase and anti-phase synchronization between harmonic limit cycles is now generalized to synchronization modes of single event pairs in arbitrary combinations. Again, stable modes of synchronization are mapped to stable equilibrium points For each event pair representing a synchronized mode, the vector field The phase plot is of oscillating shape, modeling an alternating sequence of attractors and repellors.The gradient and extrema in the vicinity of an equilibrium point In order to obtain relative synchronization, we require In contrast to the extended Kuramoto model and similar coordination models, symmetry Positive (negative) values Note. The attractor landscape of the phase dynamics becomes time-varying, if the relative primitive durations The coupled process (12), (13) accounts for synchronization modes that can be achieved by mutual entrainment of both periods and phase difference within certain domains of attraction. However, the simultaneous synchronization of multiple event pairs remains generally unexplained, as pointed out in the previous section. Therefore, the relative primitive durations Note. Normalization is preserved e.g. by adjusting the components of In the following, we outline a possible realization of the process (20), (21) featuring the mode illustrated in By (22), normalization is preserved. The gain The thresholds The synchronization process developed in the previous section governs the phase variables instantaneously, based on measurements of the movement trajectory. Besides the instantaneous phase The problem considered first is how to determine the partner’s phase Different methods have been applied to extract instantaneous phase variables from limit cycles that are known only by their observables, e.g. their cyclic movement trajectories. However, none of them fulfills our requirements entirely. First and foremost, only one-dimensional and narrow-band trajectories can be analyzed properly by the common methods. These methods are the analytic signal concept based on the Hilbert transform non-stationary, i.e. the period Movement variabilities due to interaction or other perturbations considered as noise will cause the limit cycles of human partners to be The variables prediction of the event The desired phase variable is required to instantaneously reflect changes of the period, while it is also required to comply with the definition (4) prescribing the unperturbed phase evolution. Given a Note. Given a stationary limit cycle and assuming perfect prediction Both the instantaneous phase (25) denoting the numerical solution of (24) and the relative primitive durations obtained from (2) and (3) require on-line predictions of the events Acquiring limit cycles: Reference limit cyclesClassifying limit cycles and predicting events: The current state We propose the following two-step technique to obtain predictions from experimental measurements:If the state Note. The quality of the event predictions depends on the number of reference limit cycles and their distribution of periods, i.e. how fine-grained the covered portion of the state space is sampled.Robotic agents implementing the synchronization behavior require the transformation inverse to the previous one as well. By means of a technique based on movement models, the process variables are transformed back to the cyclic movement trajectory representing the individual action task. After defining the required model properties, we develop an exemplary realization of this transformation through a model based on the minimum-jerk criterion The trajectory is again composed by a given number of The function fulfill the condition (1) for finite periods facilitate temporal scaling implemented by facilitate spatial scaling depending on Models complying with these properties are discussed in Note. The process variables voluntary behavior of movement synchronization. The movement model necessarily comply with the task-related segmentation points Human hand trajectories composed of point-to-point movements are known to be successfully reproduced by the minimum-jerk model formulated in a Cartesian frame The parameter If (34) fulfills the conditionNote. If The concept of movement synchronization is applied to render the interactive behavior of a robotic agent that performs a joint action task together with a human partner. Supporting information is provided in a and b are replaced by H and R when variables belonging to the human and the robotic agent need to be distinguished.In the following, superscripts The design of the experimental task is inspired by the dot-tapping paradigm studied in our previous work multiple primitives with closed trajectories (cycles). Multiple cycles performed consecutively allow to study synchronization effects.Both agents perform repetitive movements composed by sequences of synchronization events.Since we investigate different modes of synchronization, the cycles need to offer potentially relevant goal-directed, i.e. the agents’ effectors have to reach one or more goal points.The task is Overlapping workspaces provoke close interaction and constrain synchronization, since collision avoidance is required in certain workspace regions.sensory information about each others’ actions is allowed to let interaction emerge.Mutual pick up of Accordingly, the task paradigm depicted in Three synchronization modes are investigated in the above joint action task, see Procedures were approved by the ethics committee of the medical faculty of the TUM and conformed to the principles expressed in the Declaration of Helsinki. In total, 12 people  participated in this experiment. They were between 20 and 48 years old (M = 30.8). All were right handed and had normal or corrected-to-normal vision and were nave as to the purpose of the experiment. For participation, they were paid 8 EUR per hour. Prior to their inclusion in the present study, all participants gave written informed consent. The individual in this manuscript has given written informed consent (as outlined in PLOS consent form) to publish these case details.NOS: No SynchronizationThe robot performed at Two conditions manipulated the synchronization behavior of the robot:PES: Phase and Event Synchronization.The robot aimed to synchronize the three modes we designed above, applying the parameters from NOS and the coupling gains In both conditions, the effector trajectory of the robot was subject to collision avoidance as described in the The experimental procedure was as follows. The mobile platform of the robot was maneuvered to a target pose calibrated with respect to the table by means of markers, such that the goal points assigned to the robot were within the workspace of its right manipulator. Similarly, the participants were seated in a comfortable posture close to the table, cf. The following measures are deployed to assess the synchronization behavior observed in the experiment.The synchronization of events targeted by the behavioral model of the robot is assessed based on the measured Cartesian position trajectories of the human hand Note: The mode-related asynchrony quantifies the mean time lag between multiple event pairs measured in seconds. Only complete sets of event pairs corresponding to the defined modes are probed.active, and pursued by the robot in condition PES. According to the vector field design cf. At any time, one of three synchronization modes is considered to be Given the evolution of the active mode active mode. The resulting indexes SImode-related synchronization indexPhase synchronization is often quantified by means of the synchronization index, see e.g. Note: The As shown in our synchronization concept, the entrainment across the relative primitive durations external measure. Feedback gathered from a short questionnaire is reported as well. We also assess the synchronization behavior through measures relying on internal variables of the robot. Note, that the results presented in the following are based on a group of nine participants unless stated otherwise. The remaining group of three participants performed at movement speeds either far below or above the speed range the robot is capable of moving at, thus impeding movement synchronization in the experiment. Possible reasons are discussed later.The observable degree of event synchronization between the movements is evaluated as The following results allow to explain, how far the overall goal of our synchronization concept is reached objectively, i.e. if it fosters the entrainment of movements by synchronizing multiple event pairs. In addition, subjective feedback from the participants gives rise to discuss some perceived effects.After having completed the experiment, participants were asked whether or not they had the feeling that the robot reacted to them. In case of a positive answer, they were asked to state if they found that perceived reactiveness pleasant (yes/no) and to reason about this answer. Eleven out of twelve participants recognized reactiveness of the robot in response to their movements during parts of the experiment. Ten out of eleven participants who answered positively stated that they liked the perceived reactiveness, giving reasons such as:It makes the robot appear lively.Having the control over task speed is pleasant.Adjustment towards similar speed is pleasant.It fosters smoother interaction.Negotiation among partners is beneficial.It is a nice feeling, but a bit uncanny as well.The one who disliked the reactive behavior of the robot described the interaction as flurry and unsteady.condition  and start-off mode (1–3) reveals a clear decrease of asynchrony in each of the start-off modes, The evaluation of the objective measure of event synchronization, which is the mode-related asynchrony MASYN, is depicted in In the following, the behavioral dynamics is evaluated based on its internal representation, i.e. the internal variables of the robotic agent.To start, we explain the inner processes governing the synchronization behavior of the robot during an exemplary trial. The trajectories of relevant process variables are illustrated in The relative amount of time spent in the synchronization modes and the relative amount of mode switches are illustrated in This is also reflected by the relative share of mode switches. Results of a The preferred phase relations as a result of phase synchronization are reflected by histograms of the phase difference, see The convergence and performance of the dynamical process of synchronization is measured by means of the process variables, which are the phases or the phase difference The characteristic evolution of the period and phase estimation obtained from the human movements are illustrated by means of the sample trajectories depicted in The results gained from the human-robot synchronization experiment provide the proof of concept and evidence the potentials of synchronization behavior in human-robot joint action. In brief, the following novel insights beyond existing research on movement synchronization are identified: New synchronization modes are explored in the context of goal-directed joint action tasks. The mode-related asynchrony MASYN is successfully decreased by the proposed unified entrainment process of both phase and relative primitive durations. Therefore, the interactive behavior of the robot driven by the proposed concept under the condition PES significantly improves the overall degree of synchronization between the robot and human partners compared to the static behavior under NOS. Mixed discrete and continuous repetitive movement primitives are synchronized in the pick-and-place task. Hence, the novel phase estimation technique is evidently applicable to multi-primitive movement cycles, which cover a wide range of repetitive joint action tasks. Subjective feedback reveals that the synchronization attempts of the robot towards the designed modes lead to an enriched sense of interaction with the robot for most of the participants. This highlights the strong potential of this approach to advance the social interaction capabilities of robots that perform joint actions with humans. In the following, the synchronization concept and the experimental results are discussed in more detail, both in the light of human-robot joint action and the design of interactive behavior for artificial agents.Both the objective improvement of event synchronization achieved in our exploratory study and the summary of subjective feedback underpin the endeavor to investigate synchronization behavior evident in human-human interaction in the context of human-robot joint action. The behavioral dynamics pursues weak phase synchronization enforced by sinusoidal coupling of strength However, the objective results also suggest some implications and pitfalls that need to be addressed carefully in the design of synchronization behavior. Subjectively pleasant, mutual entrainment of movements appears to be rather sensitive to parameterizations of the behavioral dynamics, first and foremost their attractors and their associated strengths. If those do not match the individual entrainment behavior of the human counterpart within certain ranges, inter-agent entrainment may fail and even worse: artificial entrainment attempts may be misinterpreted and lead to a degraded sensation of interaction compared to non-reactive behavior.The appearance of the robotic partner and, strongly connected, its capabilities anticipated by the participants, is expected to also affect human interactive behavior Human interactive behavior may furthermore heavily depend on how the task context is conveyed and understood One key idea of the synchronization concept is the design of synchronization modes by means of a dynamical synchronization process unifying both phase synchronization and the entrainment of relative primitive durations. It has to be emphasized that both processes are usually cross-coupled: The entrained components of The presented design of synchronization behavior offers several interesting degrees of freedom which are not investigated in this article. The structure of the phase synchronization process is originated from the extended Kuramoto model Since the definition of the instantaneous phase purely depends on recurrent events within the period, the movement trajectory can be of arbitrary shape, as long as predictions of those events are provided. Instead of the presented technique based on minimum distance classification in the state space, the application of machine learning techniques such as programming by demonstration In this article, we propose a novel concept and design methodology to synthesize goal-directed synchronization behavior for robotic agents in repetitive joint action tasks. Those tasks are assumed to be performed by dyads of agents in a common interactive setting. We only require the tasks to be described by closed trajectories in state spaces, where the states capture the relevant movements. Based on oscillator theory, the closed state trajectories are interpreted as limit cycles, for which corresponding phase variables are derived. The sought phases reflect the expected non-stationarity in the limit cycles instantaneously, or in other words, they are defined on a within-period scope and determined on-line. Goal-directed repetitive movements are shown to contain much richer information concerning synchronization than purely their oscillating property captured by the phase variable. Through segmentation, we split complex movement trajectories into sequences of multiple primitives, which are separated by events, e.g. the occurrence of points with zero velocity. Beyond in-phase and anti-phase known from harmonic oscillations, enhanced synchronization modes within limit cycle pairs are synthesized. Their definition utilizes both continuous phases and discrete events as anchoring points for synchronization. The key idea of the synchronization concept is the design of interactive behavior synchronizing the synthesized modes by dynamical processes. In a unifying view, the entrainment of both phases and primitive durations is designed to happen simultaneously on a continuous time scale, as mutual state feedback is assumed to be continuously available to the agents. Inverse to the phase estimation problem, action taking of the robotic agent governed by the synchronization behavior is addressed as well. In the prototypical scenario of a repetitive pick-and-place task, we enable a full-sized, anthropomorphic robot driven by the synchronization concept to cooperate with a human partner. Both objective synchronization measures and subjective feedback evidence effectiveness of the synchronization behavior. Besides the proof of concept, the results gained from the exploratory study highlight the potential of the synchronization concept to enhance the social competence of robots interacting with humans.The continuous attractor dynamics of the synchronization behavior facilitates the intuitive and systematic design of goal-directed movement coordination. Therefore, the synchronization concept is considered as a promising enhancement to the approach of central pattern generators in the field of robotics. Applications ranging from intra- to inter-agent action coordination are worth looking at in this line of research. We expect the risk of mutual entrainment mismatch in human-robot interaction to diminish, if the behavioral rules of entrainment are derived from observations of human-human interaction. Furthermore, humanoid robots as interaction partners should be employed in realistic joint action scenarios, in order to ultimately disentangle the effects of robotic motor coordination on human-robot joint action.Video S1Details of the experimental scenario and the implementation.(MP4)Click here for additional data file.Appendix S1Experimental setup and implementation.(PDF)Click here for additional data file.


Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis. Cardiac diseases are a major cause of mortality in the world. Studies carried out in 2006 in Colombia establish that heart diseases produced circa 30875 deceases with an overall increase of 19.6% since 1999. Therefore, there has been great interest in the development of computational tools for prognosis and diagnosis. The main aim of these tools is to improve performance of cardiologists on prognostic and diagnostic tasks, i.e., reducing both the number of missed diagnoses or prognoses and the time taken to reach such decisions. Under these conditions, it is expected that detecting cardiac signs helps to decrease the mentioned decease rates. At the same time, the introduction of computational systems offers additional benefits, since the early identification of patients would help the specialists to deal efficiently with certain cardiac diseases. Moreover, as traditional risk stratifiers are commonly used for prognosis, their positive predictive value is not as high as the clinical practice demands.Heart rate variability (HRV) has been often related with the diagnosis and prognosis of certain cardiac diseases and, in fact, is a standard method for studying the autonomic nervous system (ANS) in heart control Cardiovascular risk in general terms is often related to low or excessive fluctuations of the NN intervals given by intrinsic and extrinsic factors Furthermore, during the last 25 years, the significance of HRV in assessing cardiac health has been recognized and various techniques have been developed in order to analyze the fluctuations of NN intervals. Time and frequency domain analyses are often referred to as classical analysis as they were the very first methods being used for the HRV processing In 1996, the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (ESC/NASPE) published standards on HRV analysis. They proposed several time and frequency domain parameters and their clinical uses, based on short-term (5 min) and long-term (24 h) HRV data Relatively recent findings have shown that frequency domain methods in HRV are related to hypertension Classical methods of analysis are not absolutely suitable for analysis of HRV Recently, new dynamic methods of HRV quantification have been used to uncover nonlinear fluctuations in heart rate that otherwise are not apparent. Several methods have been proposed: Return map (Poincaré plot) calculation In this work, HRV analysis methods and pattern recognition schemes  were used to discriminate between healthy control subjects and cardiovascular risk patients. Extensive experiments were carried out regarding the overall usefulness of the features with emphasis on the prognostic values associated to classical and non-linear analysis methods. We determined the potential application of such methods to clinical practice in order to increase the success rates of cardiovascular risk assessment. There is a strong consideration for breathing frequency as a relevant feature of the HRV analysis, given the respiratory sinus arrhythmia (RSA) phenomenon This study was approved by the Institutional Review Board of Universidad Autonoma de Occidente (UAO), Cali, Colombia. Each patient in this study was informed in detail about the procedure and signed an informed consent which guaranteed the transparence of the test and the records' future usage.Two distinct source materials were employed in this study: (a) A database of risk and non-risk individuals given by Coomeva IPS  experts and (b) the corresponding electrocardiographic data extracted by us using a medical expert protocol.The requirement for patient's clinical history and positive or negative cardiovascular risk verification was assessed by Coomeva experts as this verification concedes extreme importance on the validity and quality of the subsequent results of this work. The main purpose of this step was to measure the relationship between HRV indices and the subjects with risk factors. Diabetes Mellitus, high blood cholesterol and other lipids, high blood pressure, metabolic syndrome, overweight, obesity, physical inactivity, tobacco and drug consumption are common risk factors of cardiovascular heart diseases (CHD) and heart failure; nearly all of these risk factors are associated with HRV reduction or excessive fluctuations In order to get the ECG and respiratory signals, a PowerLab device -ref. ML865- (ADInstruments) and a piezoelectric band -ref. MLT1132/D- were used. PowerLab is a data acquisition system used in a variety of experiments and applications with humans' biopotentials. The unit can record more than 200000 samples per second and has individually selectable input sensibilities. Additionally, it has a bioamplifier  and an internal processor with low and high pass filters. The hardware of the PowerLab uses the software package Chart and Scope in order to record and analyze each acquired dataset.On the other hand, a non-invasive blood pressure (NIBP) measure was taken into account for the HRV evaluation as it is considered one of the main risk factors in the cardiovascular assessment Each register  was recorded following a medical protocol designed by the teamwork using the frontal-bipolar derivation (D2) of the electrocardiogram. The duration of each record was 5 minutes, following the international standards established by the Task Force of the ESC/NASPE Respiration has an important influence in HRV. This phenomenon is known as respiratory sinus arrhythmia (RSA), which is a rhythmic fluctuation of the heart beat intervals in a phase relation with the inspiration and the expiration. The autonomous nervous system (ANS) is the part of the nervous system which extrinsically controls the vital functions and organs such as the heart, lungs and glands. ANS is divided into two major subsystems: The Sympathetic Nervous System and the Parasympathetic Nervous System. These systems are antagonists and responsible for the tuning of some physiologic mechanisms. Intrinsic as well as extrinsic factors may affect such balance and sometimes provoke different nervous activity patterns (sympathetic or parasympathetic), which are often the cause of functional irregularities; those patterns are given by hyper/hypoactivity of such subsystems.When the respiration is being monitored in a controlled environment, the R-R intervals tend to be shorter during the inspiration and larger during the expiration. Various theories have been exposed about this phenomenon, according to many experiments on animals Obtaining the respiratory frequency has been used to evaluate the magnitude of the RSA, defined as the sum of the power spectral density estimations on the respiratory band. The variations induced by the respiratory rate introduce a significant change on the measurement of HRV; however, if the respiratory frequency is a constant  and the tidal volume is relatively constant for the maximum capacity, the error in the measurement caused by respiratory irregularities is eliminated and allows a better stability and an effective comparison of the RSA magnitude between patients 1 to 24 using the Haar wavelet The R peaks were extracted using the Pan-Tompkins algorithm and the wavelet transform by keeping the detail coefficients from 2In order to know the effectiveness of the wavelet decomposition levels, the performance of the algorithm was measured using the detail coefficients from the first decomposition level to the fourth decomposition level using on 6120 heart beats recorded with the implemented medical protocol. The results of this evaluation procedure are summarized in According to Past publications have shown that eliminating the ectopic HRV data is often better than interpolating them or doing any other cumbersome procedure For a specified limit j is the position of one given value of the sample. In this way, the critical values of the test are determined by specifying t-student statistical test where In this approach, a set of 7 well-known statistical indexes were calculated: First, the mean and the standard deviation (SDNN) of the NN intervals of each 5 min record; second, the square root of the mean of the sum of the square of differences between adjacent NN intervals (RMSSD); third, the so-called pNN50 was computed as the NN50 count value divided by the total of all NN intervals, where NN50 is the count of adjacent intervals differing by more than 50 ms in the entire HRV record; fourth, the interquartile margin of the NN intervals (MIRR) was also calculated, i.e., the first quartile subtracted from the third quartile of the NN series; in addition, the median of the absolute differences of the NN intervals (MDARR) and the standard deviation of the differences between adjacent NN intervals (SDSD) were calculated.Spectral or frequency-domain measures are based on the power spectral density (PSD) analysis of the R-R series. In this kind of analysis some processing techniques, such as interpolation and detrending, are necessary. The spectral measures have the advantage of relating the power of variation in different frequency bands to different physiological modulating effects For parametric spectral methods, the data can be modeled as the output of a discrete and causal filter whose input is white noise.The spectral power of an AR process is given by:p = 16 for the AR model was taken into account for this study where According to the literature consulted The wavelet packet analysis in HRV is used to separate the signal into multiple scales. This method allows us to analyze both frequency and spatial domains and removes polynomial non-stationarities of the signal Once the wavelet coefficients are known, it is possible to calculate the energy for each coefficient: Then, the total energy can be calculated as the mean value of the energy for each coefficient.On the other hand, the wavelet entropy can be calculated as the probability distribution of the coefficients or the wavelet energy into normalized values:In this way, using the definition of entropy given by Shannon, the entropy can be calculated as follows:In A problem with wavelet packets implementation is coping with redundant information coming from the wavelet transform computation for the approximation and detail coefficients HRV has been often evaluated using non-linear methods The most popular non-linear technique to assess the HRV is the so-called Poincaré map . The Poincaré map corresponds to the reconstruction of the attractor of the system based on the HRV experimental series In order to extract features of the Poincaré map, two methods were applied in this work: The ellipse fitting technique and the histogram technique A group of axes with orientation onto the identity line or principal diagonal is the main feature of the ellipse fitting technique. The axes of the plot are related to a new group of axes with a rotation of On the new reference system axes, the dispersion of the points through the On the other hand, the ellipse approximation is satisfactory in many cases; however, the shortening that occurs on short R-R intervals is not taken into account by this technique As the visual interpretation of the histogram can be useful to extract information about the heart, it is necessary to parameterize it. The computation of the width of the three histograms is a very strong feature for the prognosis of cardiovascular risk, as it gives the absolute statistical ranges of the NN intervals and its projections. These features are useful for assessing short term HRV, long term HRV and the distribution of NN intervals itself. These histogram widths were taken into account in this study.There are several approximations for estimating regularities of different kinds of signals. The most widely used complexity measures for short and noisy data series are approximate entropy (ApEn) and sample entropy (SmEn). These features assign a non-negative number to temporal series in order to quantify the regularity of its fluctuations. Given this fact, complexity measures have been highly useful for the analysis of HRV signals m and a window size r. ApEn measures the logarithmic similarity amongst neighboring input patterns (those with a separation radius less than r) for m contiguous observations. On the other hand, SmEn is an unbiased estimator introduced to avoid the self-couplings and to quantify the regularity of highly irregular temporal series. SmEn is equal to the negative of the natural logarithm of a conditional probability. It is the probability that sequences close to each other for m consecutive data points will also be close to each other when one more point is added to each sequence To calculate ApEn and SmEn from temporal series it is necessary to choose two parameters: A length k runs over 0.1 to 0.2 For both, ApEn and SmEn calculation, it is recommended to take Up to this moment, several statistical methods have been related to feature selection for training and testing classifiers and for improving their overall performance. As most of computational methods in pattern recognition require a feature selection step, the nature of such procedure depends largely on the structure of the data. As a result, many computational approaches use parametric statistics, e.g., the so-called multivariate analysis of variance (MANOVA), which often reports adequate results for such purposes. However, we cannot assume that all the data has resemblance to a standard normal distribution at high statistical significance. Therefore, we implemented two methods in order to test the normality of the data: First, we conducted the Pearson's chi-square test; and second, the one-sample KS-test. According to our results, the data , has a remarkably different distribution when compared to the normal distribution , Radial Basis Function (RBF) networks and several Support Vector Machines (SVM) were evaluated for the classification stage of this work. All classification schemes were trained to capture the difference between cardiovascular risk subjects and healthy ones.Multilayer Perceptrons (MLP) are frequently implemented for classification tasks, given their generalization capabilities. In this work, a standard three-layer network has been proposed.μ .We used the Levenberg-Marquardt backpropagation algorithm to train the MLP neural network, as it is the most popular and successful learning method for training MLPs. The algorithm employs iterative mean squared error minimization using least squares curve fitting The network consisted of three layers , with 5, 200 and 1 neurons, respectively.k-means.RBF network is a well-known classifier which combines supervised and unsupervised learning. A standard three-layer RBF network was implemented in this work. The hidden layer of the network is responsible for producing a non-linear expansion of the input space to a hidden space where the classes are linearly separable by unsupervised learning k), whose synaptic weights In this procedure, we establish a number of neurons , with 5, 3 and 1 neurons, respectively.In the most cumbersome case, the patters are not linearly separable. The main objective of the SVM schemes is to map the input data from the N-dimensional space to the M-dimensional space (M>N), where the classes are supposed to be linearly separable and can be classified by the calculation of a standard separating hyperplane In our work, polynomial and radial basis function (RBF) as well as linear SVM were used to classify the input data.In order to train the classifiers and perform the KS-tests, all samples were normalized using the MinMax normalization There are several ways to compute the performance of a recognition system. The pattern recognition schemes were evaluated using two different procedures: The calculation of several performance measures such as sensitivity (Se), specificity (Sp), positive predictive value (Pp), negative predictive value (Np) and accuracy (Ac), all in the interval . On the other hand, Receiver Operating Characteristic (ROC) curve was used in order to measure the accuracy of ANNs. The ROC curve is the plot of the true positive rate (Se) versus the false positive rate (1 – Sp) for different testing points in a diagnostic test. An ROC curve illustrates various aspects: First, it shows the tradeoff between the sensitivity and the specificity in the evaluation of a model; and second, it is a measure of the accuracy of the algorithm given by the area under the curve, i.e., the algorithm's probability of giving correct classifications when a new input pattern is presented For each subject statistical, spectral, multi-resolution and non-linear features were calculated using Matlab™ 7.6.0. The flow diagram of the whole system is depicted in The application works with 5-min electrocardiographic (ECG) signals; thereby, a preprocessing step is involved in the procedure, i.e., the R-peak extraction and NN intervals calculation. Therefore, there is an ectopic beat removing algorithm, i.e., Grubbs Test extended by Rosner outlier detector. The next stage contains the feature extraction, i.e., statistical, spectral, multi-resolution and non-linear features calculation. For spectral features calculation, the 4 Hz cubic interpolation and the smoothness priors The respiratory rate mean and standard deviation feature can be used to corroborate that respiratory rate is relatively constant over the whole ECG record and that is approximately equal to 12 breaths/min. If this condition is not fulfilled, the results would be invalid.th and 75th percentiles of the samples, respectively. The line in the middle of each box is the sample median; this illustrates the skewness of the samples. The dashed lines extending below and above each box are drawn from the ends of the interquartile ranges to the furthest observation within the dashed line length. Crosses are the outliers of the samples; they represent atypical data sufficiently distant from the limits of the box. Note that their elimination is not justified, provided that the objective of box diagrams is to give a complete knowledge of the shape of the data distribution.All the feature analysis results obtained in this work were reported using standard box diagrams, given their suitability for this statistical analysis and since their interpretation can be performed in a remarkably easy way, regardless of the fact that variables could present a great deviation from the normal distribution. The tops and bottoms of each box are the 25Statistical, spectral, multi-resolution and non-linear features were calculated from the recorded HRV database. The statistical analysis for the classical measures is shown in The KS-test showed that all statistical features  are statistically significant . These 26 groups of coefficients, according to the PCA, retain approximately 98% of the variance of the model; however, the features projections given by this transformation were not used to train the classifiers due to the decreasing statistical significance of the projected features. The main results of PCA for the two principal components are illustrated in A total of 27 wavelet packet-based features were selected. Statistical significance levels of the entropy measures showed that 15 wavelet entropy components were statistically significant withNon-linear analysis KS-test results are illustrated in ApEn and SmEn significance test results are contained in The total number of features extracted from spatial and frequency domains, multi-resolution and non-linear algorithms is equal to 52 . The total number of optimal features extracted by KS-tests is equal to 5 due to the statistically significant discrimination presented by them (the next section will illustrate this fact clearly). The resulting feature set combined 3 non-linear and 2 multi-resolution features. These features were used to train and test the ANN and SVM classifiers. Additional experiments were conducted in order to compare the performance of the four principal PCA feature projections to those chosen by KS-tests.N observations by choosing randomly the training and test observations. Each AI scheme was trained using approximately 66% of the observations  and tested using the remaining 33% of them . This division gave an optimal performance  for the ANNs as well as for SVMs.In order to evaluate each artificial intelligence (AI) scheme, the cross validation method was used. This method allows generating the indexes for the validation of the Experiments were performed using 5 (3 from non-linear domain and 2 from multi-resolution domain), 10  and 15 features  selected by KS-test. The results of MLP and RBF neural networks as well as of SVM classifications of HRV records from normal (N) and cardiovascular risk (R) subjects are illustrated in According to In addition, as part of the experiments, we evaluated the effect of using all the features from all the analysis methods shown in this paper. The results showed that for SVM classifiers, the three schemes evaluated  reached higher performances than ANN ones. The main results of these experiments are registered in A topic of remarkable discussion has been whether non-linear and multi-resolution features on HRV analysis perform better than the conventional and clinical-applied statistical and spectral analysis methods The proposed classifiers were trained using statistical and spectral features  and using the multi-resolution and non-linear features (novel features) separately, especially in order to know the overall suitability of each group of features with respect to the HRV analysis. The main results of our experiments are depicted in Due to the non-linearity and non-stationarity of the HRV signal, many authors prefer using non-linear and multi-resolution features rather than statistical or spectral ones in order to train classifiers In addition to the results reported above, several classification experiments regarding PCA features' projections were performed in our work. They were carried out using the cross validation method under the same setup reported above. These experiments were made on the basis of two main goals: First, in order to investigate whether the significance levels of the projected features remained similar to the original ones; and second, to classify the data using the 3, 4, 5 and 10 principal features' projections from such PCA analysis, as the variance retained by the 3, 4, 5 and 10 first components for both groups –normal healthy subjects and cardiovascular risk patients– was equal to 77.65%, 83.33%, 86.96% and 94.45%, respectively. Furthermore, the 99% of the variance of the model was retained by the first 21 components. According to the KS-test results, from the five first PCA projections, three of them remained to be statistically significant  and cardiovascular risk (R) subjects are depicted in The main aim of using these four different numbers of PCA projections was to compare the performance reached by the classifiers and to identify whether the data structure was suitable for the proposed classifiers architecture. Indeed, there exists more than one group of features that reported high success rates at classifying the HRV records. Amongst all the classifiers, the maximum overall performance was reached by the polynomial kernel SVM with overall sensitivity, specificity and accuracy of 90.91%, 93.18% and 92.05%, respectively.Heart rate variability has been related to numerous cardiac diseases. According to the literature, the short variations on statistical features of the NN intervals are related to: Complete heart block (CHB), left bundle branch block (LBBB) and ischemic cardiopaty. On the other hand, the long variations on statistical features of the NN intervals are related to: Premature ventricular contractions (PVCs), sinus syndrome (SSS) and atrial fibrillation (AF). There are several modifications on AR spectrums that can be noticed by the calculation of the power in frequency bands. Short variations of HRV segments usually lead to high VLF and LF bands power. Conversely, long variations of HRV segments, usually leads to higher HF band power. As cardiovascular risk is highly related to variations on statistical and spectral components, one of the major disadvantages of these methods is the linearity and consequently, their poor suitability for highly non-stationary HRV dynamics and, certainly, the NN intervals fluctuation by nervous mechanisms. The Fourier transform techniques (frequency domain methods such as Welch periodogram or AR spectrum as well) resolve the time domain signal into complex exponential functions, along with information about their phase shift measured with respect to a specific reference instant. Here the frequency components extend from The patterns in HRV are directly related to the Poincaré map patterns in visual assessment. In the case of short fluctuations, HRV segments are not much dispersed and torpedo shapes One of the main contributions of this work is the prognosis of cardiovascular risk in a general way, not only for specific cardiac diseases or the prediction of specific cardiac episodes like other publications have shown From the classification schemes, MLP provided the best classification rates for the prognosis of cardiovascular risk, with an area under the ROC curve equal to 0.9800. On the other hand, schemes such as the RBF network and SVMs showed relatively high classification performances too. Another remarkable finding is the improvement of the classification rates of SVM using all the extracted features (not only the features selected by KS significance tests) and PCA, which can be attributable to the computation of the decision surface and the apparent SVM bias towards the positive and negative cases. Furthermore, as expected, ANN schemes often presented overfitting using all those features. The comparison between the performances of all the implemented classifiers was limited to sensitivity, specificity, positive predictive value, negative predictive value and accuracy as a consequence of ROC curves limitations, i.e., usually its contributions become cumbersome when a comparison between different classifiers is needed. Besides, their transformation to objective values is usually limited to the calculation of the area under the curve According to findings reported in the literature Besides the strong considerations of the breathing rate and the breathing signal, our computational implementation allows working with the ECG and HRV signals for medical analysis. There are new possibilities of analysis that commercial and conventional HRV analysis software According to the results of this study, we strongly suggest working with multi-resolution and non-linear analysis in order to achieve more reliable cardiovascular risk prognosis, especially for classification schemes such as ANNs and SVMs. It is evident that a nonlinear deterministic approach is more appropriate to describe more complex phenomena, indicating that apparently erratic behavior can be generated even by a simple deterministic system with nonlinear structure. In general terms, the fluctuations of heartbeats during normal sinus rhythm could be partially attributed to deterministic chaos, and a decrease in this type of nonlinear variability could be observed in different cardiovascular diseases This work has been focused on 4 major aspects: (1) The HRV database conformation integrating the breathing signal; (2) integrating the breathing frequency to the analysis of HRV records; (3) the analysis of statistical, spectral, multi-resolution and non-linear features linked with classification schemes for the prognosis of cardiovascular risk as well as the confirmation of the properties of these features as they are reported in the literature; (4) a brief illustration of the software implementation of advanced HRV analysis integrating an automatic prognosis tool using a standard 5 min ECG record.This work has developed a method for the cardiovascular risk prognosis using statistical, spectral, multi-resolution and non-linear features extracted from HRV data. The database used in this work was recorded using a proper medical protocol and contains a total of 90 HRV and breathing signal records from normal and cardiovascular risk subjects. The suitability of each HRV analysis feature has been shown by using KS-tests and neural and support vector classifiers. Short-term HRV features are highly useful for the prognosis of cardiovascular risk; nonetheless, long-term features can be useful for increasing the positive predictive value of the proposed classifiers.In addition, it has become obvious that the performance reported by the different feature selection strategies depends largely on the pattern recognition scheme implemented to classify the data. For the short-term HRV features analyzed in this work, there were two main observed effects: First, the ANN schemes were more suitable for the KS test-based feature selection as well as for feature spaces of low dimension; and second, SVM schemes were more suitable for the PCA-based feature selection as well as to high dimensional feature spaces. Nonetheless, as the PCA projected features improved by far the performance of SVM classifiers, their statistical significance showed influential decrease that affected negatively the performance of the ANN classifiers.Breathing signal and breathing frequency were employed in this work for the analysis of HRV, given the findings about RSA phenomena on the modification of the fluctuations of NN intervals at different breathing frequencies Finally, HRV signal –as both a traditional and non-linear signal in nature– has been an important predictor of cardiovascular risk. Furthermore, this work represents an important step in comprehensively understanding such dynamics, and more importantly, on the prognosis of cardiovascular risk as a stated goal established by Chattipakorn et al. (2007) and a plethora of researchers and clinicians: “It is necessary to understand the mechanisms of HRV components and improve their sensitivity and specificity as a prognostic marker” Appendix S1Detailed description of complexity measures calculation.(DOC)Click here for additional data file.Appendix S2Detailed description of SVM learning algorithm.(DOC)Click here for additional data file.


A new coarse-grain model for molecular dynamics simulation of lipid membranes is presented. Following a simple and conventional approach, lipid molecules are modeled by spherical sites, each representing a group of several atoms. In contrast to common coarse-grain methods, two original (interdependent) features are here adopted. First, the main electrostatics are modeled explicitly by charges and dipoles, which interact realistically through a relative dielectric constant of unity ( Over the past decade, coarse-grain (CG) modeling has become an increasingly popular approach to the simulation of membrane systems In fact, in recent years, we have developed our own CG model, characterized by two interdependent features which are normally absent from alternative CG force fields Despite the encouraging results obtained, our original model also showed a number of limitations. In particular, we recently identified issues related to the Gay-Berne potential Our new CG force field has been named “ELBA”, an acronym for “electrostatics-based”; this highlights the most important and distinguishing feature of the model, which is its explicit incorporation of charges and dipoles to describe realistically the main electrostatics of the systems. In this paper, we first define the various components of the ELBA force field, and we report on its parametrization. We then test the model's ability to reproduce various experimental observables for a number of different membrane systems, including a gel phase. In particular, we study several physical parameters and processes that are difficult to access by experiment; for example, we investigate the distributions of pressure and electrostatic potential inside the membrane, and we simulate self-aggregation processes. Finally, we summarize the main findings, assess the methodology's limitations, and discuss future prospects.The ELBA water model is based on the general Stockmayer potential for polar fluids To simulate the model by molecular dynamics, forces and torques were obtained by differentiating the potentials; explicit expressions are reported in the The ELBA water force field comprises six independent parameters: The model was parametrized specifically for bulk water in the liquid phase. The dipole moment magnitude To rigorously validate the ELBA water model, simulations were carried out with systems comprising 4000 sites, simulated at c models .To further assess the model, it would be useful to study the dielectric constant The water phase behavior was also investigated, by simulating water systems at increasingly lower temperatures. Freezing was almost immediate at In this work, we present and validate CG models for the following lipid species: dioleoylphosphatidylcholine (DOPC), distearoylphosphatidylcholine (DSPC) and dioleoylphosphatidylethanolamine (DOPE). These three lipids, while being structurally similar, are characterized by very different behaviors from one another; in fact, each of them can be considered representative of a different category amongst the most relevant for biological membranes. DOPC is one of the most typical lipids found in real biomembranes A sketch of the ELBA coarse-grain model for DOPC is reported in Intralipid  interactions between any bonded pair The repulsion-dispersion interaction between lipid sites is modeled through the shifted-force Lennard-Jones potential  reported earlier in equation 2; here we specify the “mixing rules” used to assign the parameters Electrostatic interactions are treated using classical expressions, slightly modified to address cutoff-related issues as already discussed above. Pair interactions between a point charge Lennard-Jones and electrostatic interactions are not computed for pairs of sites which are directly bonded to each other  and of our previous lipid force field To optimize the lipid volume (and hence the system density), we could tune the Lennard-Jones parameters in a similar way as previously described for the water parametrization. The lipid area was found to be particularly sensitive to the Lennard-Jones scaling factors To reproduce the experimental spontaneous curvature The parameter set obtained for DOPC was subsequently extended to DSPC and DOPE. DSPC is structurally very similar to DOPC, the only difference being that while DOPC has a double-bond, or unsaturation, in the middle of each of the two tails, DSPC contains exclusively (saturated) single-bonds. The DSPC model could therefore be derived straightforwardly from the DOPC model by simply resetting the unsaturated reference angle of The final optimized parameter set, used for all the calculations presented in the remainder of this paper, is reported in All simulations described in this paper were conducted using the molecular dynamics software Brahms Several simulations were conducted, as described in the following sections; each of these runs was carried out on a single processor. Thanks to the availability of a sufficient number of processors, all simulations could be run concurrently. Specifically, calculations were run on a supercomputer Simulations of preassembled bilayers were set up, for each of the three species considered , at different sizes, hydration levels and temperature. Initial coordinates for hydrated bilayers were generated by adapting algorithms for standard cubic lattices To study the self-assembly process, we set up a number of simulations starting from “random” mixtures of lipids and water  bilayers at temperatures below To further confirm the phase states of the simulated systems, we also calculated the lipid lateral diffusion coefficients; results will be reported in the dedicated “Lipid diffusion” section below.The pressure distribution inside a membrane can be characterized through the “lateral pressure profile” We calculated the lateral pressure profiles from simulation following a standard methodology Unfortunately, no experimental data are available for a quantitative assessment of the profiles obtained with our model. In fact, the single experimental investigation of the DOPC pressure profile reported to date only provides qualitative data for part of the tail region Regarding DOPE, the calculated pressure profiles are displayed in The pressure profiles for DSPC, both in the gel and in the fluid phase, are reported in ase DOPC , as intuAccording to Helfrich's well-established theory Defining the lateral pressure profile as pivotal surface, defined as the surface at which there is no change in the molecular cross-sectional area upon bending From the first moment (equation 14) it is possible to calculate the spontaneous curvature as The curvature elastic parameters obtained for DOPC are collected in Considering a coordinate The intramembrane electrostatic potential is a very difficult property to study experimentally Regarding the full electrostatic potential profile Regarding We calculated the lateral diffusion coefficient By monitoring water particles that spontaneously cross the bilayer during the course of our simulations, it is possible to estimate the corresponding permeability coefficients Starting from “random” mixtures of DOPC lipids and water, we simulated the spontaneous self-assembly of stable defect-free bilayer structures; snapshots from a typical DOPC self-aggregation simulation are displayed in Regarding DOPE, the experimental phase diagram highlights a rather complex behavior The ELBA force field for coarse-grain molecular dynamics simulation of membranes has been presented. Specific models were validated for the DOPC, DOPE and DSPC lipid species. The force field also includes a new coarse-grain dipolar water model, which we developed by parametrizing the Stockmayer potential to represent individual water molecules.A notable and unique characteristic of the ELBA CG model is that the Lennard-Jones interactions between different site types are simply treated through the standard Lorentz-Berthelot mixing rules Single-component bilayers were simulated under fluid phase conditions; the force field proved capable of reproducing experimental measurements for several fundamental properties, including structural parameters, curvature elastic constants, the electrostatic potential distribution, the lipid diffusion coefficient, and the bilayer self-aggregation process. A realistic gel phase was also obtained by simulating DSPC at a temperature below its experimental transition temperature.A comparison of the lateral pressure profiles obtained for different lipids highlights large variations in the peak values , 8 and 9The simulation of bilayer systems of different sizes allowed us to highlight a size dependence in the calculation of the lipid area and generally of intramembrane profiles, which was explained in terms of membrane undulations not accounted for by the calculation procedures employed. The severity of the resulting artifacts was shown to correlate with the system size. Since the procedures that we employed are standard A notable and somewhat surprising aspect of the ELBA model is its ability to display a realistic dynamical behavior. This represents an improvement over alternative CG models, for which diffusion coefficients for lipids in the liquid phase have been reported to be four To quantify the computational efficiency of the ELBA model, we estimated the gain in sampling speed with respect to two standard atomistic approaches  by simulating membrane systems representing an equivalent number of lipid and water molecules. All details of these benchmarks are reported in the In terms of limitations of our methodology, two aspects should be discussed. Regarding the force field, we believe that the only major issue involves the self-assembly of DOPE systems, which did not reproduce the experimental inverse hexagonal phase; rather, the formation of a pseudo inverse cubic phase was observed. Future efforts will be devoted to reconcile the model's results with the experimental findings. In particular, longer simulations will have to be conducted (to assess the attainment of equilibrium) and larger systems will need to be considered (to prevent the geometry of the simulation box from biasing the system's evolution); moreover, a refinement of the relevant parameters may be required. Regarding the simulation framework, the ELBA force field is currently implemented only in our in-house software A potentially controversial issue involves the lack of long-range electrostatics in the ELBA force field. In fact, while we include electrostatic interactions explicitly up to the cutoff distance, the remaining contribution (beyond the cutoff) is neglected. We are aware that this approximation has been shown to introduce simulation artifacts In terms of future developments, the force field will be extended to include additional lipid species; in fact, preliminary studies are being conducted to model ceramide, which is a major component of the skin, and also a lipid capable of forming microdomains believed to play crucial roles in signaling processes Supporting Information S1Forces and torques for the ELBA potentials, dielectric constant of the ELBA water model, error analysis, additional membrane results, sensitivity to timestep size, benchmarks.(PDF)Click here for additional data file.


Radiative heat exchange at the nanoscale presents a challenge for several areas due to its scope and nature. Here, we provide a thermokinetic description of microscale radiative energy transfer including phonon-photon coupling manifested through a non-Debye relaxation behavior. We show that a lognormal-like distribution of modes of relaxation accounts for this non-Debye relaxation behavior leading to the thermal conductance. We also discuss the validity of the fluctuation-dissipation theorem. The general expression for the thermal conductance we obtain fits existing experimental results with remarkable accuracy. Accordingly, our approach offers an overall explanation of radiative energy transfer through micrometric gaps regardless of geometrical configurations and distances. After this, it seemed to be a well-established fact that the maximum power extracted from a hot body depended on the temperature as There is a general consensus that the search for clean sources of energy with no climatic and environmental impact constitutes a major strategic objective at present. In this sense, nanoscale thermal radiation conversion offers a source for intensive clean energy generation. Thermal radiation has always been an active field of study. Throughout the 19However, recently, thanks to modern technological advances, it has been shown that energy exchange through thermal radiation at nanometric distances breaks by several orders of magnitude the limits posed by Stefan-Boltzmann law for black body radiation. Moreover, near-field thermal radiation is approximately monochromatic and reveals itself coherent in space and time, which may lead to stationary interference phenomena in a micro-cavity. Therefore, this monochromaticity and coherence along with the overcoming of Stefan-Boltzmann limits, all of these distinguishing features together confer near-field radiation a great potential for future applications in nanotechnology and, as we have said at the beginning, energy conversion as well. Several reviews on this issue have been written recently, see Ref. An ever-increasing number of investigators has marked the recent history of the research on near-field radiation. Polder and van Hove Nonetheless, as two nanostructures thermalized at different temperatures come closer to each other, the distribution of charges and currents becomes asymmetric and therefore, defies description in terms of dipolar interactions. Hence, it becomes clear that one must bear in mind higher order effects beyond the dipole In this article, we will go deeper into these aspects by showing how phonon-photon coupling effects account for microscale radiative energy transfer by considering non-Debye relaxation due to the excess of modes in the low and high frequencies in the bulk material Our theoretical framework has been described in previous publications In general, entropy is produced due to irreversible processes, in such a manner that irreversible processes in nonequilibrium systems are described by means of currents, thermodynamic forces (affinities), and the entropy production rate, which is always positive. It is precisely this positive character of the entropy production that enables us to derive relaxation equations. Since the probability density is conserved the existence of a density gradient The main contribution of this article is to apply this formalism to the description of the radiative heat transfer between a nanosphere and a plate at different temperatures, nce  see . We assuIn the near-field regime, confinement of the electromagnetic waves in a micrometric gap separating neighboring nanostructures introduces peculiar effects in the spectrum of the thermal radiation. Here is where the collective modes (phonons) excited in the material by the impinging radiation come into play. To discern whether confinement effects are important or absent we shall assume a cut-off wavelength, the thermal wavelength of a photon In these circumstances, integrating by parts the resultant continuity equation gives usIn the stationary state, once the system has thermalized at temperatures At this point, it is worthwhile to make a short digression about the physical meaning of the time scale Now, let's return to the main topic after that brief digression. Note that according to For first order in the temperature difference elastic collisions, which is similar to Rayleigh scattering, it is known that the intensity of radiation is proportional to inelastic contribution to the near-field heat exchange, this is the analogue to the Raman scattering of light. In this case, the distribution of modes presents anomalies which result from states located at a lower energy region When the mechanism of heat exchange is through Hence, the excitation of a mode constitutes a photoinduced cooperative phenomenon. It is plausible to assume that the cumulative effect of incident photons ends perturbing the material, thus triggering collective oscillations. In addition, the lognormal accounts for an excess of ways of adsorbing energy by the system with respect to the ways obtained when merely the Debye squared-frequency law describes the relaxation. Therefore, this provides a reasonable description of the non-Debye law elastic and inelastic contributions mentioned aboveAccordingly, the heat transfer coefficient, The thermal conductance is obtained by integrating the heat transfer coefficient over the surface of a sphere of radius We have calculated numerically the surface average by adjusting the parameters mentioned above to the experimental results obtained in the Ref In summary, we have evaluated thermal conductance in the near-field, giving a thermokinetic description of some experiments involving heat radiation through a very narrow gap. Although near-field radiative transfer is a highly complex phenomenon, we have been able to provide a unified and highly accurate explanation of heat exchange processes at the nanoscale. Our theory covers all distances from the far-field up to contact. Since the experiments examined may involve a great variety of nanostructures, our theory possesses a wide scope of applications. The general methodology presented here may also be used in the study of other heat exchange processes such as those occurring in phonon systems and in the analysis of thermal contributions to Casimir forces, even in charge conduction problems in nanosystems.Appendix S1Supporting Information.(PDF)Click here for additional data file.


This paper proposed a novel bionic selective visual attention mechanism to quickly select regions that contain salient objects to reduce calculations. Firstly, lateral inhibition filtering, inspired by the limulus’ ommateum, is applied to filter low-frequency noises. After the filtering operation, we use Artificial Bee Colony (ABC) algorithm based selective visual attention mechanism to obtain the interested object to carry through the following recognition operation. In order to eliminate the camera motion influence, this paper adopted ABC algorithm, a new optimization method inspired by swarm intelligence, to calculate the motion salience map to integrate with conventional visual attention. To prove the feasibility and effectiveness of our method, several experiments were conducted. First the filtering results of lateral inhibition filter were shown to illustrate its noise reducing effect, then we applied the ABC algorithm to obtain the motion features of the image sequence. The ABC algorithm is proved to be more robust and effective through the comparison between ABC algorithm and popular Particle Swarm Optimization (PSO) algorithm. Except for the above results, we also compared the classic visual attention mechanism and our ABC algorithm based visual attention mechanism, and the experimental results of which further verified the effectiveness of our method. Small and dim target detection in complicated background is a key technology and has found wide applications in such areas as remote sensing, distant early warning, space surveillance, aerospace, and so on In order to solve this problem, many methods have been proposed so far, such as dual-band infrared image fusion for dim target detection Lateral inhibition mechanism is discovered and verified by Hartline and his research team when they carried out an electrophysiology experiment on the limulus’ vision Visual attention mechanism is what Primates use to detect, in real time, conspicuous objects in cluttered visual environments. By using the sensitivity of the features including intensity, chromatic, orientation and motion, it could extract the interest information and orientate rapidly towards salient objects in complicated visual scene, which can accelerate the object detection process. In 1985, Koch and Ullman proposed the first visual attention mechanism model Positions of targets change irregular with the influence of camera motion and noise. Motion vector can be obtained as an important measurement characteristic in saliency map. In order to solve the problem of obtaining motion vector globally, some metaheuristic search algorithms are more potential as optimization algorithms have been proved to be apt to deal with function optimization problem. As a new optimization method, which is based on swarm intelligence and motivated by the intelligent behavior of honey bees, ABC algorithm has been proved to possess a better performance in function optimization problem, compared with genetic algorithm(GA), differential evolution (DE) and particle swarm optimization (PSO) To quickly select regions that contain salient objects to reduce calculations, a novel bionic selective visual attention mechanism is proposed in this paper, which is based on lateral inhibition filtering and ABC optimized motion feature. We attempt to adopt the lateral inhibition filter to illustrate its noise reducing effect, then the ABC algorithm is applied to obtain the motion feature of the image sequence. Combining our new feature channel with the typical model of visual attention, our proposed visual attention mechanism outperform others, hence to prove the feasibility and effectiveness of our model.The remainder of this paper is organized as follows. Section 2 introduced the key principles and main procedure of our proposed method, and theoretical analysis is also conducted in this section. Series of experimental results and detailed contrast explanation are given in Section 3. Our concluding remarks and future work are contained also in the final section.In order to solve the small target detection problem, this paper proposed a novel bionic selective visual attention mechanism to quickly select regions that contain salient objects to reduce calculations. The lateral inhibition mechanism is discovered and verified by Hartline and his research team when they carried out an electrophysiology experiment on the limulus’ vision In retinal image, the intensively excited receptors in illuminatingly light area inhibit the receptors in illuminatingly dark area more strongly than the latter to the former. Therefore, the contrast and the distortion of sensory information are enhanced. In this way, the important characters of vision scene and the intensity gradient in retinal image, namely the image’s edges, are both strengthened After a lot of electrophysiological experiments, Hartlein and his colleagues advanced the classical lateral inhibition model:The function of the lateral inhibition enhancing the contrast of the image is verified in i, j) to the central pixel, In order to introduce this mechanism to image processing, the classical lateral inhibition model is modified in two-dimensional and gray form. Gray value of the pixel  in image is given by:U can be calculated with The size of receptive field chosen in this essay is 5×5.Then the competing coefficient of the lateral inhibition network is:U with Visual Attention is an important psychological adjustment mechanism of human vision. Bottom-up visual attention is the process by which primates quickly select regions of an image that contain salient or conspicuous objects. The most important function of selective visual attention is to direct our gaze rapidly towards objects of interest in our visual environment, which can help accelerate the target detection.The typical model of visual attention for static image was proposed by Koch and Ullman As the scenes we adopt are gray scales, the color characteristic in typical model of visual attention can not be used. Firstly, some features are extracted from the image. The features are one intensity feature.Secondly, the multi-scale approach is aimed at detecting conspicuous features of different sizes to produce a set of images using imaging pyramids operator. In this paper, we obtain 9 scales, from scale 0 to scale 8 in accordance with the profit of the searched food sources.In this paper, ABC algorithm is used to get the translation vector between images to obtain the movement characteristics.Ns as the total number of bees, Ne as the colony size of the employed bees and Nu as the size of unemployed bees, which satisfy the equationD is the dimension of individual solution vector, Define Randomly initialize a set of feasible solutions For an employed bee in the nth iterationXi(n), and has no relation with past location vectors as well as the iteration number n.Generally, this searching process is actually a random mapping from individual space to individual space, and this process can be denoted with Apply the greedy selection operator n.The greedy selection operator ensures that the population is able to retain the elite individual, and accordingly the evolution will not retreat. Obviously, the distribution ofEach unemployed bee selects an employed bee from the colony according to their fitness values. The probability distribution of the selection operator The unemployed bee searches in the neighborhood of the selected employed bee’s position to find new solutions. The updated best fitness value can be denoted withIf the searching times surrounding an employed bee Bas exceeds a certain threshold Limit, but still could not find better solutions, then the location vector can be re-initialized randomly according to:T>Tmax), output the optimal fitness value If the iteration value is larger than the maximum number of the iteration (that is, Step 6 is a most prominent aspect making ABC algorithm different from other algorithms, which is designed to enhance the diversity of the population to prevent the population from trapping into the local optimum. Obviously, this step can improve the probability of finding the best solution efficiently, and make the ABC algorithm perform much better.Generally, moving objects can always attract more attention than static objects, and the faster the object moves, the more attention it gets. Therefore, in our visual attention scheme, we presented movement characteristics including moving speed and direction to better detect and estimate the attention areas. In the image matching process, we usually adopted general matching algorithms, which include optical flow vectors, character matching methods and so on. However, these algorithms would be influenced by the complex content of imagery, especially the computation of optical flow vectors. In this work, we use ABC algorithm to obtain the translation vectors M*N is the size of the search window, and in this paper we define the size as 15*15. The translation vector, namely the motion vector is obtained when the SAD value finds its minimum.In this paper, we define the matching criteria as :Obtain the image, and convert it into grayscale format. For each pixel of the image, select an image block of fixed size surround the pixel.Ns, the number of employed bees Ne and the number of the unemployed bees Nu. A larger Ns will contribute to a larger possibility of finding the best solution of the problem, however, it also means an increased computing complexity of the algorithm. In general, we define Ne = Nu. Denote the largest searching times with Limit, current iterations with T, and the largest iterations with Bas = 0, and the starting iteration T = 1.Initialize the parameters of artificial bee colony optimization algorithm, such as the population of the bee colony According to the geometric parameters of the employed bees, calculate their similarity values respectively based on the defined similarity function. When the fitness function The employed bees search around their current positions to find new solutions, and update their positions if the new fitness value is lower than the original value.The unemployed bees apply the roulette selection method to choose the bee individual that possesses a relatively good fitness value as the leading bee, according to the calculated fitness results of employed bees.Bas re-initialized as 0, or else, keep searching around, and its Bas value plus one.Each recruited unemployed bee continues to search new solutions just around the leading bee’s solution space, and calculate their fitness values. If the value of the new solution is better than the original value, the unemployed bee converts into an employed bee, which means that update the positions of the employed bees, and continue exploring with Bas is larger than certain threshold Limit, the employed bee gives up the solution, and re-search the new food resources, which is realized by re-initializing the geometric parameters and calculating the fitness value.If the search times Store the best solution parameters and the best fitness value.T<Tmax, go to Step 4. Otherwise, output the optimal parameters and optimal fitness value.If Ms with the equation:After getting the translation vectors of each block, we calculate the movement distance of each pixel in the image, generating the movement speed image Except for this, we can also calculate Through the center-surround operator, we calculate the salience maps according to:S.Choose the movement orientation salience map, the direction of which has the largest contrast, to fuse with the velocity salience map, in order to get the final motion salience map In order to validate the performance of our proposed algorithm, experiments for small and dim target detection are performed on infrared images with sky-cloud.According to our Filtering designed in Section II, we conducted several experiments, the results of which are shown in To validate the performance of translation vector acquisition based on ABC algorithm, first we conduct an image matching test based on ABC algorithm and PSO algorithm. The images we used here are shown in We conduct this experiment for 10 times to get the average performance of the two algorithms, the results of which are shown in After applying ABC algorithm to get the motion features, we established our visual attention based on obtained motion features and conduct the experiment, the results of which are shown as follows. To further validate the performance and robustness of our proposed algorithm for small and dim target detection, we conduct several experiments in more challenging scenes, the results of which can be shown in This paper proposed a novel bionic selective visual attention mechanism to quickly select regions that contain salient objects to reduce calculations. First, lateral inhibition filter is applied to filter low-frequency noises. After the filtering operation, we use ABC algorithm based selective visual attention mechanism to obtain the interested object to carry through the following recognition operation. To prove the feasibility and effectiveness of our method, several experiments were conducted.The experimental results show that lateral inhibition filtering is effective to reduce the unneeded noises and highlight those of great significance, which can help detect target in cluttered background. The ABC algorithm is proved to be more robust and effective through the image matching comparison between ABC algorithm and popular PSO algorithm. And from the comparison between classic visual attention mechanism and our ABC algorithm based visual attention mechanism, our ABC algorithm based visual attention mechanism can extract reliable motion features form several adjacent frames effectively. Therefore our algorithm can put much more attention on the small and dim moving target and separate the moving targets from the background quickly for the following recognition process.