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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]

Obtaining physiological data from animals in a non-obtrusive and continuous manner is important to veterinary science. This paper demonstrates the design and deployment of a miniaturized capsule-based system for subdermal injection to provide real-time and continuous heart-rate, movement, and core-body-temperature measurements. The presented device incorporates sensors for photoplethysmography, motion detection, and temperature measurements. A bluetooth-low-energy enabled microcontroller configures the sensors, digitizes the sensor information, and wirelessly connects with external devices. The device is powered by a CR425 battery for this paper, and various other battery solutions are available based upon the use case. The design uses only commercially available integrated circuits in order to reduce the development cost and be modular. The encapsulation is a combination of medical epoxy and poly(methyl methacrylate) that fits within a 6-gauge hypodermic needle. The preliminary evaluation of the device included an in vitro assessment of its thermal response and measurement accuracy, the impact of one-month implantation on surrounding tissue, the power consumption with duty cycling of various sensors, and a measurement of physiological signals in a rat and a chicken. Having a form factor and implantation method similar to existing devices for animals, this novel system is a useful platform for both scientists and veterinarians to better study a diverse range of animals. [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]

The small animal positron emission tomography (PET) is dedicated to small animal imaging, which requires high position and energy precision, as well as good flexibility and efficiency of the electronics. This paper presents the design of a digital signal processing logic for a marmoset brain PET system based on lutetium-yttrium oxyorthosilicate (LYSO) crystal arrays, SiPMs, and the resistive network readout method. We implement 32-channel signal processing in a single Xilinx Artix-7 Field-Programmable Gate Array (FPGA). The logic is designed to support four online modes which are regular data processing mode, flood map construction mode, energy spectrum construction mode, and raw data mode. Several functions are integrated, including 2-D) raw position calculation, crystal locating, events filtering, and synchronization detection. Furthermore, a series of online corrections is also integrated, such as photon peak correction to 511 keV and time measurement result correction with crystal granularity. A gigabit ethernet interface is utilized for data transfer, look-up tables (LUTs) configuration, and command issuing. The pipeline logic works at 125 MHz with a signal processing capability beyond the required data rate of 1 000 000 events/s/channel. A series of initial tests is conducted. The results indicate that the logic design meets the application requirement. [ABSTRACT FROM AUTHOR]

Cattle health monitoring on the feedlot is a crucial but nontrivial task. The conventional way of monitoring relies heavily on the cowboy’s visual surveillance, which makes the animal monitoring quality highly subjective and correlated with the obviousness of the observed traits. In order to achieve early detection of each individual animal’s illness, in this paper, a wireless sensor network system is developed to monitor the animal’s feeding and drinking behaviors. A directional antenna is used to allow one router to monitor multiple animals simultaneously, and an energy efficient mesh routing strategy is proposed to aggregate the monitoring data. The performance of the proposed system has been evaluated through numerical analysis and simulations. The contributions of this paper lie in the novelty and feasibility of using directional antenna and wireless sensor network technologies for feedlot animal health monitoring. [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]

The term “In Silico Trial” indicates the use of computer modelling and simulation to evaluate the safety and efficacy of a medical product, whether a drug, a medical device, a diagnostic product or an advanced therapy medicinal product. Predictive models are positioned as new methodologies for the development and the regulatory evaluation of medical products. New methodologies are qualified by regulators such as FDA and EMA through formal processes, where a first step is the definition of the Context of Use (CoU), which is a concise description of how the new methodology is intended to be used in the development and regulatory assessment process. As In Silico Trials are a disruptively innovative class of new methodologies, it is important to have a list of possible CoUs highlighting potential applications for the development of the relative regulatory science. This review paper presents the result of a consensus process that took place in the InSilicoWorld Community of Practice, an online forum for experts in in silico medicine. The experts involved identified 46 descriptions of possible CoUs which were organised into a candidate taxonomy of nine CoU categories. Examples of 31 CoUs were identified in the available literature; the remaining 15 should, for now, be considered speculative. [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]

In Silico Trials methodologies will play a growing and fundamental role in the development and de-risking of new medical devices in the future. While the regulatory pathway for Digital Patient and Personal Health Forecasting solutions is clear, it is more complex for In Silico Trials solutions, and therefore deserves a deeper analysis. In this position paper, we investigate the current state of the art towards the regulatory system for in silico trials applied to medical devices while exploring the European regulatory system toward this topic. We suggest that the European regulatory system should start a process of innovation: in principle to limit distorted quality by different internal processes within notified bodies, hence avoiding that the more innovative and competitive companies focus their attention on the needs of other large markets, like the USA, where the use of such radical innovations is already rapidly developing. [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]

Bioluminescence imaging (BLI), an optical preclinical imaging modality, is an invaluable imaging modality due to its low-cost, high throughput, fast acquisition times, and functional imaging capabilities. BLI is being extensively used in the field of cancer imaging, especially with the recent developments in genetic-engineering, stem cell, and gene therapy treatments. The purpose of this paper is to provide a comprehensive review of the principles, developments, and current status of BLI in cancer research. This paper covers the fundamental BLI concepts including BLI reporters and enzyme-substrate systems, data acquisition, and image characteristics. It reviews the studies discussing the use of BLI in cancer research such as imaging tumor-characteristic phenomena including tumorigenesis, metastasis, cancer metabolism, apoptosis, hypoxia, and angiogenesis, and response to cancer therapy treatments including chemotherapy, radiotherapy, immunotherapy, gene therapy, and stem cell therapy. The key advantages and disadvantages of BLI compared to other common imaging modalities are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, the metal organic framework (MOF) concept is contributed to rearrange the bone-seeking agent composed of carrier-free lutetium-177 (Lu-177), 1, 4, 7, 10-tetraazacyclododecane-1, 4, 7, 10-tetraaminomethylenephosphonate (DOTMP) and cupper (II) (Cu (II)) for preparation of a potential agent for treatment of bone metastases. The product was characterized (infra-red spectroscopy, IR, and X-ray diffraction analysis) and quality-controlled (radio-thin layer chromatography, (RTLC)). The stability and in vitro hydroxyapatite binding was checked up to 1.5 month at 37 °C in human serum. Radio-MOF crystals and radio-MOF particles that were obtained by varying the synthesizing conditions (including pH and temperature) showed similar IR patterns and similar elemental analysis results. The final product was synthesized at pH = 8, stirring at room temperature (yield >99%, RTLC, particle size: 90 ± 20 nm). Biodistribution study experiments showed interesting bone-seeking (%ID/g: 8.5%) affinity of the prepared formula with no significant liver or lung uptake. Also high accumulation of radio-complex in bone tissue was estimated by the modeling of the radiation dose delivery using radiation dose assessment resource software. This novel radio-MOF with promising therapeutic results is the first study of the usage of the MOF concept to provide a high payload of Lu-177 for treatment of bone metastases. As it was expected, the most important outcome of the paper was higher bone-uptake rates rather than conventional 177Lu-DOTMP. [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]

Some mobile sensor network applications require the sensor nodes to transfer their trajectories to a data sink. This paper proposes an adaptive trajectory (lossy) compression algorithm based on compressive sensing. The algorithm has two innovative elements. First, we propose a method to compute a deterministic projection matrix from a learnt dictionary. Second, we propose a method for the mobile nodes to adaptively predict the number of projections needed based on the speed of the mobile nodes. Extensive evaluation of the proposed algorithm using six data sets shows that our proposed algorithm can achieve submeter accuracy. In addition, our method of computing projection matrices outperforms two existing methods. Finally, comparison of our algorithm against a state-of-the-art trajectory compression algorithm shows that our algorithm can reduce the error by 10-60 cm for the same compression ratio. [ABSTRACT FROM PUBLISHER]

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]

In this paper, the application of a technique to evaluate in vivo biodistribution of magnetic nanoparticles (MNP) is addressed: the Multichannel AC Biosusceptometry System (MC-ACB). It allows real-time assessment of magnetic nanoparticles in both bloodstream clearance and liver accumulation, where a complex network of inter-related cells is responsible for MNP uptake. Based on the acquired MC-ACB images, we propose a mathematical model which helps to understand the distribution and accumulation pharmacokinetics of MNP. The MC-ACB showed a high time resolution to detect and monitor MNP, providing sequential images over the particle biodistribution. Utilizing the MC-ACB instrument, we assessed regions corresponding to the heart and liver, and we determined the MNP transfer rates between the bloodstream and the liver. The pharmacokinetic model resulted in having a strong correlation with the experimental data, suggesting that the MC-ACB is a valuable and accessible imaging device to assess in vivo and real-time pharmacokinetic features of MNP. [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 this paper, we investigate the feasibility of harvesting energy from cardiac valvular perturbations to self-power a wireless sonomicrometry sensor. Compared to the previous studies involving piezoelectric patches or encasings attached to the cardiac or aortic surface, the proposed study explores the use of piezoelectric sutures that can be implanted in proximity to the valvular regions, where non-linear valvular perturbations could be exploited for self-powering. Using an ovine animal model, the magnitude of valvular perturbations are first measured using an array of sonomicrometry crystals implanted around the tricuspid valve. These measurements were then used to estimate the levels of electrical energy that could be harvested using a simplified piezoelectric suture model. These results were revalidated across seven different animals, before and after valvular regurgitation was induced. Our study shows that power harvested from different annular planes of the tricuspid valve (before and after regurgitation) could range from nano-watts to milli-watts, with the maximum power harvested from the leaflet plane. We believe that these results could be useful for determining optimal surgical placement of wireless and self-powered sonomicrometry sensor, which in turn could be used for investigating the pathophysiology of ischemic regurgitation. [ABSTRACT FROM AUTHOR]

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]

The preclinical studies are conducted using small rodents for analysis on different functions of central neural system, cardiovascular functions, and neuropathological disorders. The implantable biomedical sensors used for sensing the behavior of test subject requires a continuous wireless power supply. Recently, many findings project that the inductive resonant power transfer method is a suitable choice for wireless power and data transfer to an implants. However, for the freely moving animal misalignment between the transmitting (Tx) and receiving (Rx) section reduce the power and data transfer efficiency. This paper proposes a new approach for the combined power and data transfer using 3-D Tx coils. The designed Tx coil itself is utilized for the data and power transfer at different orientation of the Rx unit. The mathematical modeling, circuit simulation, finite-element study, and experimental analysis are performed at different orientation of the Rx coil. The integration of the digital pressure sensor and the ESP-12E sensor node to the Rx and Tx coil section is examined. In addition, for the continuous data logging system and remote monitoring of test subject, an Internet of Things module is integrated with the demodulation unit. The obtained results from the measurement show that for the long-term continuous monitoring of the testing animal, the proposed 3-D wireless power and data transfer will be a better choice. [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]

This paper discusses the current state of underwater optical imaging in the context of physics, technology, biology, and history. The paper encompasses not only the history of human's ability to see underwater, but also the adaptations that various organisms living in oceans or lakes have developed. The continued development of underwater imaging systems at military, commercial, and consumer levels portends well for both increased visibility and accessibility by these various segments. However, the fundamental limits imposed by the environment, as currently understood, set the ultimate constraints. Physics, biology, computer modeling, processing, and the development of technology that ranges from simple cameras and lights to more advanced gated and modulated illumination are described. The future prospects for continuing advancements are also discussed. [ABSTRACT FROM PUBLISHER]

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]

Hibernation is a unique metabolic adaptation employed by several animal species for survival where its study would further enhance our understanding of metabolic disorders, such as diabetes and obesity. As a primate animal with close genetic ties to humans, the recent discovery of hibernation in dwarf lemurs of Madagascar has attracted the attention of researchers. Traditional recording systems require the physical tethering of the animals to the recording apparatus or the use of implantable devices. Scalp and needle electrodes interfere with the natural hibernation process and limit the continuity of the experiments, while invasive procedures are banned on endangered species. By integrating a full-wave rectifier, low-noise signal conditioning circuit, frequency modulation transmitter, and antenna in a single application specific integrated circuit (ASIC), we have developed an ultra-miniaturized wireless system that measures 34 \times 4 \times 2.6 mm3 in volume. It only requires three off-chip components (a coil wound around a ferrite rod and two external capacitors) to be powered wirelessly through a 1-MHz inductive link, such that it can be packaged inside a glass or polymer capsule and injected subcutaneously underneath the scalp or chest without requiring a surgery, thereby addressing the shortcomings of the traditional monitoring systems. Our recording device provides an input/output correlation coefficient greater than 80% for input amplitudes ranging from 60 to 260 \mu V_{\mathrm{ pp}} , with a wireless data transmission range of ~2.5 cm while operating near the 902–928 MHz ISM frequency band. This system would enable future studies of electroencephalography and electrocardiography in hibernating dwarf lemurs. The ASIC was fabricated using the ON Semiconductor 0.5- \mu \text{m}$ CMOS process with an active area of 2.5 $\times $ 1 mm2 and has a power consumption of 7.75 mW from a 3.1 V supply. In this paper, we demonstrate the in vitro functionality of the system using simulated physiological signals directly applied to the ASIC or through standard stainless steel electrodes immersed in saline solution. [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]

Radio frequency identification (RFID) technology is more and more adopted in a wide range of applicative scenarios. Nevertheless, in many applications, commercial and general-purpose solutions can be unsuitable as in the case of the tracking of small-size living animals for the behavior analysis. In such a case, the whole RFID hardware, as well as the control software, should be opportunely tailored for the particular application. In this paper, a novel RFID-based approach enabling an effective localization and tracking of small-sized laboratory animals is proposed. It is mainly based on a near-field (NF) RFID multiantenna system working in the UHF bandwidth, to be placed below the animal's cage, and able to rigorously identify the NF RFID tags implanted in laboratory animals. Once the requirements of the reader antenna have been individuated, an antenna system composed of a matrix of specifically designed segmented loops has been prototyped. Moreover, to improve the effectiveness of the whole tracking system, a properly algorithm based on the measured received signal strength indication has been developed and integrated. It aims at removing potential minor ambiguities of the data captured by the multiantenna system. The whole animal tracking system has been then largely tested on phantom mice to verify its ability to precisely localize each subject and to reconstruct its path. Additionally, a first test performed on living mice has been presented. The achieved and discussed results demonstrate the effectiveness of the proposed approach. [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]