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This paper presents new results on a novel coupled-physics modality called magneto-acousto-electrical tomography (MAET). With static magnetic field, a beam of ultrasound is applied to the sample to be imaged; the ultrasonic vibration can lead to the separation of the charges due to the Lorentz force and produces the current distributed in the object. In this paper, simulations were conducted on biological tissue model, and the electrical properties were reconstructed, which could reflect the state of physiological or pathological. We also conducted experiments to detect the voltage signal of microvolt level by the electrodes placed around the sample. The series of experiments were conducted on low-conductivity (0.2 S/m) animal gelatin phantom and real biological tissue, successively placed in a 260 mT magnetic field and sonicated with a plane ultrasonic transducer of central frequency 2.25 MHz. In this paper, we obtained axis resolution of 1 mm, much more significant improvement than the previous reports. More importantly, the experimental result does also demonstrate the feasibility of MAET to image the electrical properties of biological tissue. [ABSTRACT FROM AUTHOR]

Periodicity is a frequently happening phenomenon for social interactions in temporal networks. Mining periodic communities are essential to understanding periodic group behaviors in temporal networks. Unfortunately, most previous studies for community mining in temporal networks ignore the periodic patterns of communities. In this paper, we study the problem of seeking periodic communities in a temporal network, where each edge is associated with a set of timestamps. We propose novel models, including $\sigma$ σ -periodic $k$ k -core and $\sigma$ σ -periodic $k$ k -clique, that represent periodic communities in temporal networks. Specifically, a $\sigma$ σ -periodic $k$ k -core (or $\sigma$ σ -periodic $k$ k -clique) is a $k$ k -core (or clique with size larger than $k$ k ) that appears at least $\sigma$ σ times periodically in the temporal graph. The problem of searching periodic core is efficient but the resulting communities may be not enough cohesive; the problem of enumerating all periodic cliques is not efficient (NP-hard) but the resulting communities are very cohesive. To compute all of them efficiently, we first develop two effective graph reduction techniques to significantly prune the temporal graph. Then, we transform the temporal graph into a static graph and prove that mining the periodic communities in the temporal graph equals mining communities in the transformed graph. Subsequently, we propose a decomposition algorithm to search maximal $\sigma$ σ -periodic $k$ k -core, a Bron-Kerbosch style algorithm to enumerate all maximal $\sigma$ σ -periodic $k$ k -cliques, and a branch-and-bound style algorithm to find the maximum $\sigma$ σ -periodic clique. The results of extensive experiments on five real-life datasets demonstrate the efficiency, scalability, and effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

The collective intelligent behaviors of insects or animal groups in nature have maintained the survival of the species for thousands of years. In this paper, a novel swarm intelligence algorithm called the social group entropy optimization (SGEO) algorithm is proposed for solving optimization tasks. The proposed algorithm is based on the social group model, the status optimization model, and the entropy model, which are the main contributions of this paper. First, the social group model and the feedback mechanism between Leaders and Followers are developed to reduce the probability of local optimum. Second, the status optimization model is described to reveal the changing rule about the population behavior states, to support the conversion between different social behaviors during evolution, to promote the algorithm to optimize quickly, and to avoid local optimization. Third, the entropy model is introduced to analyze the entropy of social groups, the change rule of difference entropy, and to set the information entropy as behavior’s criterion of state optimization. In addition, the mathematical model of the SGEO is deduced from the group theory, matter dynamics, and the information entropy theory. The convergence and parallelism of it have been analyzed and verified theoretically. Moreover, to test the effectiveness of the SGEO, it is used to solve benchmark functions’ problems that are commonly considered within the literature of evolutionary algorithms. Experimental results are compared with those of three other state-of-the-art algorithms. The superior performance of the SGEO validates its effectiveness and efficiency for the optimization problems, especially for the high-dimension problems. [ABSTRACT FROM PUBLISHER]

Detecting large animals on roadways using automated systems such as robots or vehicles is a vital task. This can be achieved using conventional tools such as ultrasonic sensors, or with innovative technology based on smart cameras. In this paper, we investigate a vision-based solution. We begin the paper by performing a comparative study between three detectors: 1) Haar-AdaBoost; 2) histogram of oriented gradient (HOG)-AdaBoost; and 3) local binary pattern (LBP)-AdaBoost, which were initially developed to detect humans and their faces. These detectors are implemented, evaluated, and compared to each other in terms of accuracy and processing time. Based on our evaluation and comparison results, we design a two-stage architecture which outperforms the aforementioned detectors. The proposed architecture detects candidate regions of interest using LBP-AdaBoost in the first stage, which offers robustness to false positives in real-time conditions. The second stage is based on support vector machine classifiers that were trained using HOG features. The training data are generated from our novel dataset called large animal dataset, which contains common and thermographic images of large road-animals. We emphasize that no such public dataset currently exists. [ABSTRACT FROM PUBLISHER]

In the field of smart agriculture health monitoring of livestock is an important field of research. Maintaining the good health of cows is very much essential for the steady growth of milk production. Unfortunately, in a large dairy cow farm, day-to-day monitoring of the health status of individual cows is a complex and time-consuming activity. This paper proposed LiveCare, an IoT-based framework that automatically monitors the health of cows in a large cow farm. It tracks the cow’s behavioral changes on a daily basis. This paper also proposed the Cow Disease Prediction (CDP) algorithm, which is an unsupervised multi-class classifier that serves as the LiveCare framework’s central component. The CDP algorithm can predict several cow diseases by analyzing the cows’ behavioral changes. In this framework, we have also tabulated a few common cow diseases, their measurable symptoms, and the various sensors used to record them. We compared the efficiency of the proposed CDP algorithm to that of other machine learning algorithms. [ABSTRACT FROM AUTHOR]

Monitoring physiological signs in animal testing is crucial for the development of new therapeutic strategies and better understanding of diseases. This paper exploits Doppler radar and nonlinear phase demodulation effect to achieve noncontact measurement of both displacements and frequencies of a laboratory rat’s cardiorespiratory activities. The implementation of displacement acquisition method relying on demodulation-generated harmonics is described, and this paper provides a guideline of implementing the acquisition method. Demodulation-generated harmonics are analyzed, and the effect of vibration-generated harmonics is studied. To demonstrate the effectiveness, a 60-GHz radar is used that shows good correlation with the instrument-recorded data. Average errors of respiration rate and heart rate (HR) are 0.057% and 0.33%, respectively, using three test subjects. In addition, two drug tests are performed to verify the monitoring function with controlled raised/dropped HR. By observing the time periods before, during, and after drug injection, results of rat’s physiological response to drugs reveal the drug efficacy and body reaction over time. The measured cardiorespiratory variations can provide useful information without using implant devices, and the implementation could be an improvement over current invasive methods. The result presented in this paper is believed to be the first reported simultaneous measurements of both frequencies and displacements of both respiration and heartbeat movements of a laboratory animal using microwave radar. [ABSTRACT FROM AUTHOR]

Currently, medical devices such as the wireless capsule endoscopy (WCE) are used for data transmission from inside to outside the body. However, for certain applications such as WCE, the data rates offered by the current medical frequency bands can be insufficient. Ultrawideband (UWB) frequency band has become an interesting solution for this. However, to date, there is no formal channel path loss (PL) model for the UWB frequency band in the gastrointestinal (GI) scenario due to the huge differences between the proposed studies. There are three main methodologies to characterize the propagation channel, software simulations, and experimental measurements either in phantom or in in vivo animals. Previous works do not compare all the methodologies or present some disagreements with the literature. In this paper, a dedicated study of the PL using the three aforementioned methodologies (simulations, phantoms, and in vivo measurements) and a comparison with previous studies in the literature is performed. Moreover, numerical values for a PL model which agrees with the three methodologies and the literature are proposed. This paper aims at being the starting point for a formal PL model in the UWB frequency band for wireless body area networks in the GI scenario. [ABSTRACT FROM AUTHOR]

This retrospective describes the overall research project that gave rise to the authors’ paper “Neuronlike adaptive elements that can solve difficult learning control problems” that was published in the 1983 Neural and Sensory Information Processing special issue of the IEEE Transactions on Systems, Man, and Cybernetics. This look back explains how this project came about, presents the ideas and previous publications that influenced it, and describes our most closely related subsequent research. It concludes by pointing out some noteworthy aspects of this article that have been eclipsed by its main contributions, followed by commenting on some of the directions and cautions that should inform future research. [ABSTRACT FROM AUTHOR]

In this paper, we present the initial experimental investigation of a two-coil receive/transmit design for small animals imaging at 7T MRI. The system uses a butterfly-type coil tuned to 300 MHz for scanning the 1H nuclei and a non-resonant loop antenna with a metamaterial-inspired resonator with the ability to tune over a wide frequency range for X-nuclei. 1H, 31P, 23Na and 13C, which are of particular interest in biomedical MRI, were selected as test nuclei in this work. Coil simulations show the two parts of the radiofrequency (RF) assembly to be decoupled and operating independently due to the orthogonality of the excited RF transverse magnetic fields. Simulations and phantom experimental imaging show sufficiently homogeneous transverse transmit RF fields and tuning capabilities for the pilot multiheteronuclear experiments. [ABSTRACT FROM AUTHOR]

Ultrasound therapy has a long history of novel applications in medicine. Compared to high-intensity ultrasound used for tissue heating, low-intensity ultrasound has drawn increasing attention recently due to its ability to induce therapeutic changes without biologically significant temperature increase. Low-intensity pulsed ultrasound (LIPUS) is a specific type of ultrasound that delivers at a low intensity and outputs in the mode of pulsed waves. It has minimal thermal effects while maintaining the transmission of acoustic energy to the target tissue, which is able to provide noninvasive physical stimulation for therapeutic applications. LIPUS has been demonstrated to accelerate the healing of fresh fracture, nonunion and delayed union in both animal and clinical studies. The effectiveness of LIPUS for the applications of soft-tissue regeneration and inhibiting inflammatory responses has also been investigated experimentally. Additionally, research has shown that LIPUS is a promising modality for neuromodulation. The purpose of this review is to provide an overview of the recent developments of LIPUS for therapeutic applications, based on the papers that report positive effects, and to present the findings on the understanding of its mechanism. Current available LIPUS devices are also briefly described in this paper. [ABSTRACT FROM AUTHOR]

Drug delivery to subcortical regions is susceptible to the blood–brain barrier (BBB) impeding the molecular exchange between the blood stream and the brain parenchyma. Focused ultrasound (FUS) coupled with the administration of microbubbles has been proved to open the BBB locally, transiently, and noninvasively both in rodents and in nonhuman-primates (NHPs). The development of this disruption technique independent of MRI monitoring is of primordial importance yet restrained to the targeting optimization. This paper establishes the linear relationship of the incidence angle with the volume of BBB opening ( V\mathrm{BBB} ) and the peak negative pressure when sonicating the caudate nucleus and the putamen region of five NHPs. In addition, the effect of central nervous system structures on the opening morphology is evaluated by identification of the gray-to-white-matter ratio at the opening site. Finally, the targeting accuracy is assessed through the estimation of the geometric and angle shift of the opening from the targeted region. Interestingly, results prove a monotonic increase of the opening volume with close to normal incidence angles. Moreover, 80.35% of the opening lies on gray-matter regions compared with only 19.41% attributed to the white matter. The opening was found to be shifted axially, toward the transducer, and laterally with an average angle shift of 4.5°. Finally, we were capable of showing reproducibility of targeting accuracy with the same stereotactic and ultrasonic parameters. This paper documents the a priori prediction of the opening volume through manipulation of the angle and pressure as well as establishing the predictability, accuracy, and safety of FUS-induced BBB opening in NHPs. [ABSTRACT FROM AUTHOR]

In this paper, distributed formation control for multi-agent systems is considered. The proposed control laws are designed to globally obtain and keep the desired formation in a two-dimensional space. It is assumed that the local frames of the agents are not aligned with each other and each agent does not know the orientation of others’ coordinate frames. Further, the agent measures only relative positions of neighbors with respect to the local reference frame. Therefore, the control of the system is completely decentralized and the global convergence is achieved without the global reference frame. The stability and convergence of the system are analyzed mathematically and the experiment using quadcopters is performed to verify the results of the theoretical analysis. In addition, the ambiguity problem and time-varying velocity case are also handled and discussed. [ABSTRACT FROM PUBLISHER]

Many neuroscience experiments with animal subjects require free motion of the animal within a metal cage environment. In such cage environments, wireless communication with implanted devices, e.g., neural recording and stimulation is particularly challenging because the metal cage walls form a reverberant cavity with dense multipath. In the case of backscatter communication with the implanted device, the multipath challenge is particularly acute because of the round-trip nature of the backscatter channel. This paper demonstrates the reverberant cavity effect via measurement of the channel transfer function inside a metal cage used for nonhuman primate research in the 902–928 MHz ultrahigh-frequency industrial, scientific, and medical band. A reduced-size ceramic patch antenna developed for the Neurochip neural recording and stimulation device was affixed to a saline tissue proxy, while a commercial air-dielectric patch antenna was affixed to the ceiling of the cage. A measured 3 dB channel bandwidth greater than 6.5 MHz with a port-to-port insertion loss between 14 and 37 dB was achieved at 126 surveyed locations within the cage volume. A 6.25 Mb/s backscatter data uplink using a differential quadrature phase shift keying constellation was successfully validated inside the cage, with effectively 0% packet error rate for all but two of the surveyed locations. The simulation and experimental results show good agreement and reveal that wideband backscatter communication systems can perform well despite the significant multipath inside the reverberant cage environment. [ABSTRACT FROM AUTHOR]

This paper extends previous research on using a Bayesian network model to investigate impacts of time (month) and weather (number of fair weather days in a week) on animal-related outages in distribution systems. Outage history (outages in the previous week) is included as an additional input to the model, and inputs and outputs are classified systematically to reduce errors in estimates of outputs. Conditional probability table obtained from the historical data are used to estimate weekly animal-related outages which is followed by a Monte Carlo simulation to find estimates of mean and confidence limits for monthly animal-related outages. Comparison of results obtained for four cities of different sizes in Kansas with those obtained using a hybrid wavelet/neural network model shows consistency between the two models. The methodology presented in this paper is simple to implement and useful for the utilities for year-end analysis of the outage data to identify specific reliability-related concerns. [ABSTRACT FROM PUBLISHER]

In the past years, significant progresses have been achieved in signpost-based indoor navigation and positioning systems. In this paper, inspired by recent bionic researches on long-distance animal geomagnetic navigation, we propose an autonomous local navigation and positioning system, based on an artificially established magnetic gradient for in-grid or desktop applications. Without using any magnetic-field strength information, the proposed navigation and positioning can be accomplished by matching a spatial angle included by the direction of the vector magnetic field and a direction defined in the navigation plane using an algorithm derived based on the Kalman filter. Simulations and experiments verified the effectiveness of the proposed approach. This method can be used to improve the positioning precision of signpost-based systems. It can also be used in new applications of autonomous in-grid or desktop navigation and positioning for automatic robot arms. [ABSTRACT FROM AUTHOR]

An action is something which is done by an agent. Most action recognition researchers merely focus on the actions to be recognized, and ignore the differences of agents. Philosophers and behaviorists discover that actions are common among many species, but are performed in different ways and with different levels of sophistication. In this paper, in order to bridge action recognition tasks between different agents, we introduce a new problem, cross-agent action recognition, i.e., recognizing action for one particular agent (target) while training from other agents (source). We model this problem under three different scenarios: single source and single target, multiple sources and single target, and multiple sources and multiple targets. To this end, corresponding methods based on transfer learning are proposed to address these problems. We further design three different strategies to model the situation when a partial labeled data is provided for the target. Experimental results show that the performances of the transfer method are generally better than those of the comparative method without transfer learning, especially when we have multiple sources. Particularly, the transfer method outperforms the others significantly when the source is a human adult. In addition, cross-agent method significantly improves the results when partially labeled data is provided for the target. These demonstrate that for action recognition, knowledge can be transferred across different agents. A straightforward application of this finding is to use human action (training data is abundant) data to enhance animal action recognition. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this work is to study whether the active state and species of biological tissues can influence changes in their dielectric properties. Methods: In this paper, the dielectric properties of liver, kidney and spleen tissues from human active, human inactive and animal tissues are measured in the frequency range of 10 Hz to 100 MHz. The four- and two-electrode methods are used to measure dielectric properties at different frequencies. Statistical analysis and the pattern recognition method are used to compare the dielectric properties of human active tissues, human inactive tissues, animal tissues and data provided by the IFAC database. Results: The results show that the dielectric properties of human active tissues are significantly different from those of human inactive tissues and animal tissues, resulting in a great difference between the dielectric properties provided by the IFAC database and those of human active tissues. The dielectric properties of human active tissues can be identified by the pattern recognition method based on principal component analysis, which further proves that the dielectric properties of human active tissues cannot be replaced. Conclusion: The dielectric properties of biological tissues are closely related to the activity and species of tissues. The dielectric properties of human active tissues cannot be replaced by those of human cadaver tissues or animal tissues. Significance: The significance of this study is suggesting that the IFAC database should be updated with the dielectric properties of human active tissues to provide accurate data for bioelectromagnetics research. [ABSTRACT FROM AUTHOR]

Objective: The invisibility of domestic oocyte nucleus in bright field currently forces operators to blindly aspirate nucleus out in oocyte enucleation, usually causing large cytoplasm losses and poor developmental competences of cloned embryos. Although fluorescent labeling of nucleus allows for nucleus localization, the involved photobleaching problems and barriers to the execution of enucleation process limit its online-application in oocyte enucleation. This paper reports a novel label-free oocyte enucleation method for precise removal of the nucleus with less cytoplasm loss. Methods: The relative positions between the injection pipette and nucleus for complete removal of nucleus with less cytoplasm loss were determined through a finite element modeling of nucleus aspiration. To position injection pipette to the above positions relative to nucleus, the appropriate oocyte orientation and trajectory of injection pipette inside oocyte were derived according to the offline-calibrated 3-D distribution of nucleus and the simulated dynamic drift of nucleus that occurs as injection pipette is maneuvered inside oocyte. Finally, a robotic label-free precise enucleation procedure was established. Results: The experimental results on more than 1000 porcine oocytes proved that this system is capable of reducing cytoplasm loss by 60% at the same level of enucleation success rate and almost doubling the cleavage rate of clone embryos in comparison to blind aspiration method. Conclusions: The results prove that our method significantly improves the developmental competence of cloned embryos in comparison to manual enucleation method. Significance: Our method is expected to improve the extremely low success rate of animal cloning in the future. [ABSTRACT FROM AUTHOR]

Electrical nerve fiber stimulation is a technique widely used in prosthetics and rehabilitation, and its study from a computational point of view can be a useful instrument to support experimental tests. In the last years, there was an increasing interest in computational modeling of neural cells and numerical simulations on nerve fibers stimulation because of its usefulness in forecasting the effect of electrical current stimuli delivered to tissues through implanted electrodes, in the design of optimal stimulus waveforms based on the specific application (i.e., inducing limb movements, sensory feedback or physiological function restoring), and in the evaluation of the current stimuli properties according to the characteristics of the nerves surrounding tissue. Therefore, a review study on the main modeling and computational frameworks adopted to investigate peripheral nerve stimulation is an important instrument to support and drive future research works. To this aim, this paper deals with mathematical models of neural cells with a detailed description of ion channels and numerical simulations using finite element methods to describe the dynamics of electrical stimulation by implanted electrodes in peripheral nerve fibers. In particular, we evaluate different nerve cell models considering different ion channels present in neurons and provide a guideline on multiscale numerical simulations of electrical nerve fibers stimulation. [ABSTRACT FROM AUTHOR]

Plenty of evidence has implied that animals like pigeons and sea turtles utilize geomagnetic information for long-distance migration and homing. In this paper, we conduct an ab initio investigation to imitate a migratory animal's geomagnetic homing across a long distance over the surface of the Earth. Based on a simple assumption that such animals have a natural ability to sense and compare the included angle between the vector geomagnetic field (GF) and a specific geographic direction such as the due north, we show that a long-distance geomagnetic navigation (GN) can be accomplished without any assistance of prestored geomagnetic and geographic information, as long as the navigation is inside a vast area where such a spatial angle can uniquely determine a geographic location. Without any requirement of measuring the strength of the GF, this GN will not be visibly affected by the recurrent fluctuation of the GF that severely hampers traditional GN. By introducing the extended-Kalman-filter algorithm, it is able to find a shortcut in the geomagnetic space, overcoming interferences of strong Gaussian and local-area geomagnetic anomaly. We believe that the proposed approach will find a wide range of potential applications in global, local, and indoor navigations based on magnetic field in the future. [ABSTRACT FROM PUBLISHER]

Magnetic resonance imaging of short relaxation time spin systems has been a widely discussed topic with serious clinical applications and led to the emergence of fast imaging ultra-short echo-time sequences. Nevertheless, these sequences suffer from image blurring, due to the related sampling point spread function and are highly prone to imaging artefacts arising from, e.g., chemical shifts or magnetic susceptibilities. In this paper, we present a concept of spherical quasi-random single-point imaging. The approach is highly accelerateable, due to intrinsic undersampling properties and capable of strong metal artefact suppression. Imaging acceleration is achieved by sampling of quasi-random points in $k$ -space, based on a low-discrepancy sequence, and a combination with non-linear optimization reconstruction techniques [compressed sensing (CS)]. The presented low-discrepancy trajectory shows ideal noise like undersampling properties for the combination with CS, leading to denoised images with excellent metal artefact reduction. Using eightfold undersampling, acquisition time of a few minutes can be achieved for volume acquisitions. [ABSTRACT FROM PUBLISHER]

The connectivity structure between neurons is useful for determining how groups of neurons perform tasks. Directed information is a measure that can be used to infer connectivity between neurons using their recorded time series. In this paper, we develop a method of calculating the directed information rate from one neuron to another neuron it is connected to, given a particular neuronal topology. We assume a leaky integrate-and-fire (LIF) neuron model with independent and identically distributed random spike train inputs, which governs how the membrane potential of the output neuron evolves. We use this neuron model to find the dynamics of the resulting output spike train from its membrane potential dynamics, both for when the past of the input neuron is observed and when it is not. We show that an action potential in the LIF model causes a conditional independence of the activity before and after it, and we capture this conditional independence via a Markov model. We use these spike train dynamics to then calculate the directed information between the spike train of the input neuron to the spike train generated by the LIF model. In addition, we show how changing the refractory period of the LIF model affects the directed information, and also how the spike train dynamics are affected by memory constraints, which are commonly imposed in estimators of directed information. [ABSTRACT FROM AUTHOR]

A unicycle is an inherently unstable system in three dimensions. As shown by a human riding a unicycle, the rider's complicated successive dynamic control actions are needed for the postural stability control of the unicycle, and the rider's actions for stability are quite nonlinear and jerky. By emulating the performance of a human riding unicycle, we can do tests of different classic and modern control methods used in 3-D inherently unstable systems, where a theoretical analytical methodology for the stability of this unstable system is expected to be developed. Because the longitudinal and lateral stability are coupled to each other in this system, and no actuator can be used directly for the control of unicycle's posture in 3-D (pitch, roll, and yaw angles), it is not easy for us to emulate the performance of a human riding a unicycle. In this paper, a unique unicycle robot is developed. The postural stability control of a human riding a unicycle in 3-D is realized by this robot. [ABSTRACT FROM PUBLISHER]

This article is a position paper about models and algorithms that are generally called “stream clustering.” Semantics and methods used in this field are often co-opted from static clustering, but they do not serve well for streaming data analysis. Most “state-of-the-art” methods, such as sequential k-means, Birch, CluStream, DenStream, etc., acknowledge that the data are seen but once in real streaming analysis (e.g., intrusion detection, voter fraud, etc.). Interpretation of their outputs generally overlooks the fact that when the data cannot be saved, batch clustering ideas, such as preclustering assessment, partitioning, and cluster validity are not relevant. But in the current literature, the data, or some subset of it, are often saved for hindsight evaluation (we call this fake stream clustering). Our position? Useful analysis of real streaming data is in its infancy. We do not argue that current approaches to streaming clustering are wrong: rather, we regard them as transitional methods which will eventually lead to a new and useful paradigm for this type of computation. We think that this class of models and algorithms are actually classifiers, but with a special added component, viz., continuously updated cluster footprints of the instream processing. We need to carefully define the objectives of streaming analysis, and then choose terminology and methods that suit this evolving paradigm. [ABSTRACT FROM AUTHOR]

The measurements of valve activity in a population of bivalves under natural environmental conditions (16 oysters in the Bay of Arcachon, France) are used for a physiological model identification. A nonlinear autoregressive exogenous (NARX) model is designed and tested. The method to design the model has two parts: 1) structure of the model: the model takes into account the influence of environmental conditions using the measurements of sunlight intensity, the moonlight, tide levels, precipitation, and water salinity levels. A possible influence of the internal circadian/circatidal clocks is also analyzed and 2) least square calculation of the model parameters. Through this paper, it is demonstrated that the developed dynamical model of the oyster valve movement can be used for estimating normal physiological rhythms of permanently immersed oysters and can be considered for detecting perturbations of these rhythms due to changes in the water quality, i.e., for ecological monitoring. [ABSTRACT FROM PUBLISHER]

In the case of direct or indirect contact with electrically energized parts, an electric current circulates through the body. When the magnitude and duration of the current through the heart exceed the ventricular fibrillation thresholds, the cardiac muscle starts uncoordinated contractions, greatly jeopardizing the life of the subject. Technical standards on electrical installations describe the protective measures against direct and indirect contact necessary to minimize the probability of inception of ventricular fibrillation. Safety considerations are based on experiments made in the past on animals, but the extrapolation of results to human beings is complex and rather questionable. The purpose of this study is to analyze the body factors that affect the distribution of currents passing through the human body with virtual anatomical models. A set of 16 models of individuals is used to simulate different electric contacts. The use of virtual models of a diverse population (i.e., eight males and eight females, ten adults and six children) provides a statistical support to the results. The obtained heart-current factors, are compared with values present in the literature. This paper provides a novel viewpoint on the problem, and supports the ongoing research activity and efforts to improve the electrical safety of persons. [ABSTRACT FROM PUBLISHER]

In this paper, we present Salamandra robotica II: an amphibious salamander robot that is able to walk and swim. The robot has four legs and an actuated spine that allow it to perform anguilliform swimming in water and walking on the ground. The paper first presents the new robot hardware design, which is an improved version of Salamandra robotica I. We then address several questions related to body–limb coordination in robots and animals that have a sprawling posture like salamanders and lizards, as opposed to the erect posture of mammals (e.g., in cats and dogs). In particular, we investigate how the speed of locomotion and curvature of turning motions depend on various gait parameters such as the body–limb coordination, the type of body undulation (offset, amplitude, and phase lag of body oscillations), and the frequency. Comparisons with animal data are presented, and our results show striking similarities with the gaits observed with real salamanders, in particular concerning the timing of the body’s and limbs’ movements and the relative speed of locomotion. [ABSTRACT FROM PUBLISHER]

Parkinson’s disease (PD) is a chronic neurodegenerative disease that affects the central nervous system. PD mainly affects the motor nervous system and may cause cognitive and behavioral problems. One of the best tools to investigate the pathogenesis of PD is animal models, among which the 6-OHDA-treated rat is a widely employed rodent model. In this research, three-dimensional motion capture technology was employed to obtain real-time three-dimensional coordinate information about sick and healthy rats freely moving in an open field. This research also proposes an end-to-end deep learning model of CNN-BGRU to extract spatiotemporal information from 3D coordinate information and perform classification. The experimental results show that the model proposed in this research can effectively distinguish sick rats from healthy rats with a classification accuracy of 98.73%, providing a new and effective method for the clinical detection of Parkinson’s syndrome. [ABSTRACT FROM AUTHOR]

Animals search for food in their environment with a decision strategy which keeps them fit. Optimal foraging theory models this foraging behavior to determine the optimal decision strategy followed by animals. This theory has been successfully applied for humans as they search for information and is termed as information foraging. When people visit a tourist location, they follow a similar strategy to move from one spot to another, and collect information by capturing photographs. This behavior has similarities with the foraging behavior of animals which has been widely studied by the researchers. In this paper, we propose to employ optimal foraging theory to help tourists explore a location and capture photographs in an optimal way. We determine a decision strategy for tourist which provides a list of interesting spots to visit in a tourist location along with corresponding stay time. Finally, we solve an optimization problem to find a path through these spots which can be followed by tourists. The experimental results on a public dataset demonstrate the effectiveness of the proposed method. 1 Code available at https://github.com/vyzuer/foraging_theory [ABSTRACT FROM AUTHOR]

Techniques of long-term in vivo electrophysiological recording play important roles in brain research and neural rehabilitation. To avoid interruption of experiment and risk of infection, use of wireless power transfer (WPT) technique has been suggested to eliminate cumbersome wires and batteries attached to the animals in rodent electrophysiological applications. This paper presents a holistic assessment of the relationships among the physical sizes of the transmitting and receiving coils, power transfer characteristics, and specific absorption rate (SAR) in animals of a simple WPT system using two rectangular coaxial transmitting coils. With given space for the animal and size of the receiving coil, a procedure for designing the minimum driving current, and the transmitting coil dimensions and separation to deliver sufficient power to the receiver, and interactions between the transmitting and receiving coils is derived. A π-capacitor network that can match the impedances of the receiving coil and the load to operate the receiver at the maximum power transfer condition is proposed. It is also optimized for the overall volume. A 100-mW prototype with an operating zone of 400 × 240 × 40 mm3 and a receiving coil with a diameter of 11.45 mm is built and studied. The SAR in the animal is evaluated and compared with the recommended restriction level. [ABSTRACT FROM AUTHOR]

Hyperpolarized 129Xe magnetic resonance imaging is a powerful modality capable of assessing lung structure and function. While it has shown promise as a clinical tool for the longitudinal assessment of lung function, its utility as an investigative tool for animal models of pulmonary diseases is limited by the necessity of invasive intubation and mechanical ventilation procedures. In this paper, we overcame this limitation by developing a gas delivery system and implementing a set of imaging schemes to acquire high-resolution gas- and dissolved-phase images in free-breathing mice. Gradient echo pulse sequences were used to acquire both high- and low-resolution gas-phase images, and regional fractional ventilation was quantified by comparing signal buildup among low-resolution gas-phase images acquired at two flip-angles. Dissolved-phase images were acquired using both ultra-short echo time and chemical shift imaging sequences with discrete sets of flip-angle/repetition time combinations to visualize gas uptake and distribution throughout the body. Spectral features distinct to various anatomical regions were identified in images acquired using the latter sequence and were used for the quantification of gas arrival times for respective compartments. [ABSTRACT FROM AUTHOR]

We present the first systematic study on concealed object detection (COD), which aims to identify objects that are visually embedded in their background. The high intrinsic similarities between the concealed objects and their background make COD far more challenging than traditional object detection/segmentation. To better understand this task, we collect a large-scale dataset, called COD10K, which consists of 10,000 images covering concealed objects in diverse real-world scenarios from 78 object categories. Further, we provide rich annotations including object categories, object boundaries, challenging attributes, object-level labels, and instance-level annotations. Our COD10K is the largest COD dataset to date, with the richest annotations, which enables comprehensive concealed object understanding and can even be used to help progress several other vision tasks, such as detection, segmentation, classification etc. Motivated by how animals hunt in the wild, we also design a simple but strong baseline for COD, termed the Search Identification Network (SINet). Without any bells and whistles, SINet outperforms twelve cutting-edge baselines on all datasets tested, making them robust, general architectures that could serve as catalysts for future research in COD. Finally, we provide some interesting findings, and highlight several potential applications and future directions. To spark research in this new field, our code, dataset, and online demo are available at our project page: http://mmcheng.net/cod. [ABSTRACT FROM AUTHOR]

In this paper, distributed formation control laws for the multi-agent systems are proposed when the leader is moving constantly with unknown velocity. The proposed control laws are based on the distance-based formation control; therefore, each distance between agents is controlled measuring the relative position. Because the control laws use only local information such as relative position with respect to their own local reference frame, the control laws are completely decentralized. The proposed control laws are designed to make a group of agents keep the desired formation and move with a constant reference velocity in two-dimensional space. The considered system consists of three types of agents: 1) leader; 2) first follower; and 3) follower. The leader knows the reference velocity value, whereas the other agents do not know the reference velocity. For this reason, the first follower and follower estimate the reference velocity by using the adaptive method. Using graph theory and nonlinear control theory, the control laws are verified mathematically. Additionally, the three-agent case is extended to $N$-agent case by an inductive way. The stability and convergence of the system are theoretically analyzed, and the validity of the proposed control laws is clarified by performing simulations and real experiments. [ABSTRACT FROM PUBLISHER]

Spinal cord stimulation (SCS) has enabled motor recovery in paraplegics with motor complete spinal cord injury (SCI). However, the physiological mechanisms underlying this recovery are unknown. This paper analyzes muscle synergies in two motor complete SCI patients under SCS during standing and compares them with muscle synergies in healthy subjects, in order to help elucidate the mechanisms that enable motor control through SCS. One challenge is that standard muscle synergy extraction algorithms, such as non-negative matrix factorization (NMF), fail when applied to SCI patients under SCS. We develop a new algorithm–rShiftNMF–to extract muscle synergies in these cases. We find muscle synergies extracted by rShiftNMF are significantly better at interpreting electromyography (EMG) activity, and resulting synergy features are more physiologically meaningful. By analyzing muscle synergies from SCI patients and healthy subjects, we find that: 1) SCI patients rely significantly on muscle synergy activation to generate motor activity; 2) interleaving SCS can selectively activate an additional muscle synergy that is critical to SCI standing; and 3) muscle synergies extracted from SCI patients under SCS differ substantially from those extracted from healthy subjects. We provide evidence that after spinal cord injury, SCS influences motor function through muscle synergy activation. [ABSTRACT FROM AUTHOR]

Overactive bladder (OAB) patients suffer from a frequent urge to urinate, which can lead to a poor quality of life. Current neurostimulation therapy uses open-loop electrical stimulation to alleviate symptoms. Continuous stimulation facilitates habituation of neural pathways and consumes battery power. Sensory feedback-based closed-loop stimulation may offer greater clinical benefit by driving bladder relaxation only when bladder contractions are detected, leading to increased bladder capacity. Effective delivery of such sensory feedback, particularly in real-time, is necessary to accomplish this goal. We implemented a Kalman filter-based model to estimate bladder pressure in real-time using unsorted neural recordings from sacral-level dorsal root ganglia, achieving a 0.88 ± 0.16 correlation coefficient fit across 35 normal and simulated OAB bladder fills in five experiments. We also demonstrated closed-loop neuromodulation using the estimated pressure to trigger pudendal nerve stimulation, which increased bladder capacity by 40% in two trials. An offline analysis indicated that unsorted neural signals had a similar stability over time as compared to sorted single units, which would require a higher computational load. We believe this paper demonstrates the utility of decoding bladder pressure from neural activity for closed-loop control; however, real-time validation during behavioral studies is necessary prior to clinical translation. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel and practical approach for benchmarking agility. We focus on terrestrial, multilegged locomotion in the field of bio-inspired robotics. We define agility as the ability to perform a set of different but specific tasks executed in a fast and efficient manner. This definition is inspired by the analysis of natural role models, such as dogs and horses as well as robotic systems. An evaluation of existing benchmarks in robotics is done and taken into account in our proposed benchmark. After the general definition, the actual normalized benchmarking values are defined, and measuring methods, as well as an online database for agility score collection and distribution, are presented. To provide a baseline for agile locomotion, various videos of dog-agility competitions were analyzed and agility scores calculated wherever applicable. Finally, validation and implementation of the benchmark are done with different robots directly available to the authors. In conclusion, our benchmark will enable researchers not only to compare existing robots and find out strengths and weaknesses in different design approaches, but also give a tool to define new fitness functions for optimization, learning processes, and future robots developments, intensifying the links between biology and technology even further. [ABSTRACT FROM AUTHOR]

Over the past three decades, passive acoustic telemetry has significantly helped marine scientist to study and understand the spatial ecology, migratory behaviors, and mortality rates of aquatic animals. A popular telemetry system consists of two components: an acoustic transmitter tag attached to an aquatic animal and powered by a small battery, and a stationary station that receives the acoustic signals from the tagged animal and determines its location. The added weight and increased size of the tag introduced by the battery limit the implementation of this system to relatively large animals. Moreover, these tags have a limited operational time determined by the lifetime of the battery in combination with the measurement frequency and data resolution and transfer rate. In this paper, a self-powered magneto-acoustic resonator for animal tracking is proposed. It is achieved by utilizing the low-frequency motions of the animals to excite high-frequency acoustic pulses. The measurement results show that the device is capable of producing an average acoustic sound of 55 dB sound pressure level at 1 m of distance with a resonant frequency of 15 kHz. [ABSTRACT FROM AUTHOR]

Epidural spinal cord stimulation (ESCS) combined with partial weight-bearing therapy (PWBT) has been shown to facilitate recovery of functional walking for individuals after spinal cord injury (SCI). The investigation of neural mechanisms of recovery from SCI under this treatment has been conducted broadly in rodent models, yet a suitable ESCS system is still unavailable. This paper describes a practical, programmable, and fully implantable stimulator for laboratory research on rats to explore fundamental neurophysiological principles for functional recovery after SCI. The ESCS system is composed of a personal digital assistant (PDA), an external controller, an implantable pulse generator (IPG), lead extension, and stimulating electrodes. The stimulation parameters can be programmed and adjusted through a graphical user interface on the PDA. The external controller is placed on the rat back and communicates with the PDA via radio-frequency (RF) telemetry. An RF carrier from the class-E power amplifier in the external controller provides both data and power for the IPG through an inductive link. The IPG is built around a microcontroller unit to generate voltage-regulated pulses delivered to the bipolar electrode for ESCS in rats. The encapsulated IPG measures 22 mm\,\times\,23 mm\,\times\,7 mm with a mass of \sim 3.78 g. This fully implantable batteryless stimulator provided a simplified and efficient method to carry out chronic experiments in untethered animals for medical electro-neurological research. [ABSTRACT FROM PUBLISHER]

Discovering trajectory patterns is shown to be very useful in learning interactions between moving objects. Many types of trajectory patterns have been proposed in the literature, but previous methods were developed for only a specific type of trajectory patterns. This limitation could make pattern discovery tedious and inefficient since users typically do not know which types of trajectory patterns are hidden in their data sets. Our main observation is that many trajectory patterns can be arranged according to the strength of temporal constraints. In this paper, we propose a unifying framework of mining trajectory patterns of various temporal tightness, which we call unifying trajectory patterns (UT-patterns). This framework consists of two phases: initial pattern discovery and granularity adjustment. A set of initial patterns are discovered in the first phase, and their granularities (i.e., levels of detail) are adjusted by split and merge to detect other types in the second phase. As a result, the structure called a pattern forest is constructed to show various patterns. Both phases are guided by an information-theoretic formula without user intervention. Experimental results demonstrate that our framework facilitates easy discovery of various patterns from real-world trajectory data. [ABSTRACT FROM AUTHOR]

Quantifying the similarity between two networks is critical in many applications. A number of algorithms have been proposed to compute graph similarity, mainly based on the properties of nodes and edges. Interestingly, most of these algorithms ignore the physical location of the nodes, which is a key factor in the context of brain networks involving spatially defined functional areas. In this paper, we present a novel algorithm called “SimiNet” for measuring similarity between two graphs whose nodes are defined a priori within a 3D coordinate system. SimiNet provides a quantified index (ranging from 0 to 1) that accounts for node, edge and spatiality features. Complex graphs were simulated to evaluate the performance of SimiNet that is compared with eight state-of-art methods. Results show that SimiNet is able to detect weak spatial variations in compared graphs in addition to computing similarity using both nodes and edges. SimiNet was also applied to real brain networks obtained during a visual recognition task. The algorithm shows high performance to detect spatial variation of brain networks obtained during a naming task of two categories of visual stimuli: animals and tools. A perspective to this work is a better understanding of object categorization in the human brain. [ABSTRACT FROM AUTHOR]

Remote sensing offers an objective and efficient way to monitor ecosystem properties including their spatial variability across different land cover types. Especially, the representation of gradients of biochemical and structural properties of ecosystems using continuous fields (CF) approaches bears advantages compared to discrete land cover classification schemes. This paper presents a concept to synergistically generate CF maps of an alpine ecosystem parameter, i.e., total surface water content, from imaging spectrometer (IS) data. Further, the potential of linking such maps to ecological patterns, i.e., the spatial distribution of large ungulates is being assessed. In vegetated areas, total surface water content is considered as a surrogate of plant physiological status. Water is, besides temperature, light, or nutrients, an important limiting growth factor determining biomass production and therefore potential animal forage quantity in alpine grasslands. Resource ecology interested in trophic interactions between large ungulates and their forage requires spatial and temporal information on ecosystem properties and processes. The study area is located in the upper Trupchun Valley (Val Trupchun) in the Swiss National Park (SNP). The valley is famous for its high densities of chamois (Rupicapra rupicapra L.), ibex (Capra ibex L.), and red deer (Cervus elaphus L.). CF maps of total surface water content were derived from Airborne Prism EXperiment (APEX) IS data collected over the SNP in June 2010 and 2011. Abundance maps of predominant land cover classes were derived from linear spectral mixture analysis (SMA). They were then combined with water content information of the respective land cover originating from either empirically or physically based approaches. The resulting CF maps depicted a spatially continuous representation of relative total surface water content. APEX IS data from two consecutive seasons revealed differences in total surface water content in June 2010 and 2011, predominantly related to an advanced phenological development in spring 2011 and to considerable differences in snow cover between the 2 years. Linking total surface water content of grasslands to observed ungulates spatial distributions did not reveal any statistically significant patterns of habitat use. We conclude that water availability in Val Trupchun may not be the dominant limiting factor for potential forage quantity (biomass), or that ungulates choose their grazing sites based on other criteria, i.e., high nutritious quality (P, N). Nevertheless, multitemporal CF maps derived from APEX IS data were found to provide spatially explicit and fine-scaled information for analyses of an ecosystem’s total surface water content. The combination of multitemporal CF maps of a wide range of ecosystem parameters and more accurate and extensive observations of animal habitat use will contribute to ongoing and future vegetation-ungulates research in the SNP. [ABSTRACT FROM PUBLISHER]

Demyelination in peripheral nerves causes dysfunction of slowing down and stoppage of nerve impulses causing many neurological diseases, such as chronic inflammatory demyelinating polyneuropathy, Guillain-Barre syndrome, etc. This paper aims to develop a recovery model having interaction of a demyelinated nerve with a normal myelinated nerve. We validated the model by coupling between peripheral nerve of toad (demyelinated with Naja kaouthia venom) and a normal nerve of toad. An increase in both nerve conduction velocity as well as compound action potential amplitude is observed in the repetition of the experiments indicating gradual recovery of the patients. The significance behind this work is to suppress the malfunctioning of the demyelinated nerve by the normal electro-physiological activity of the normal nerve for speedy recovery using coupling model. The recovery model will be used in the treatment of neurological disorders with the influence of normal neuro physiological properties of a normal adjacent nerve. [ABSTRACT FROM PUBLISHER]