Freshwater organisms potentially useful as biosensors and power-generation mediators in biohybrid robotics.

Facing the threat of rapidly worsening water quality, there is an urgent need to develop novel approaches of monitoring its global supplies and early detection of environmental fluctuations. Global warming, urban growth and other factors have threatened not only the freshwater supply but also the well-being of many species inhabiting it. Traditionally, laboratory-based studies can be both time and money consuming and so, the development of a real-time, continuous monitoring method has proven necessary. The use of autonomous, self-actualizing entities became an efficient way of monitoring the environment. The Microbial Fuel Cells (MFC) will be investigated as an alternative energy source to allow for these entities to self-actualize. This concept has been improved with the use of various lifeforms in the role of biosensors in a structure called "biohybrid" which we aim to develop further within the framework of project Robocoenosis relying on animal-robot interaction. We introduce a novel concept of a fully autonomous biohybrid agent with various lifeforms in the role of biosensors. Herein, we identify most promising organisms in the context of underwater robotics, among others Dreissena polymorpha, Anodonta cygnaea, Daphnia sp. and various algae. Special focus is placed on the "ecosystem hacking" based on their interaction with the electronic parts. This project uses Austrian lakes of various trophic levels (Millstättersee, Hallstättersee and Neusiedlersee) as case studies and as a "proof of concept".

Using a Support-Vector Machine Algorithm to Classify Lower Extremity EMG Signals During Running Shod/Unshod With Different Foot Strike Patterns.

The present study aimed to use a Support Vector Machine (SVM) algorithm to identify and classify shod and barefoot running as well as rearfoot and forefoot landings. Ten habitually shod runners ran at self-selected speed. Thigh and leg muscle surface electromyography (EMG) were recorded. Discrete Wavelet transformation (DWT) and Fast Fourier transformation (FFT) were used for the assembly of vectors for training and classification of a SVM. Using the FFT coefficients for the gastrocnemius and tibialis anterior muscles presented the best results for differentiating between rearfoot/forefoot running in the window before foot-floor contact possibly due to these muscles' critical role in determining which part of the foot will first touch the floor. The classification rate was 76% and 67% respectively, with a probability of being random of 0.5% and 4% respectively. For the same terms and conditions of classification, the DWT produced a reduction in the percentage of correctness of 60% and 53% with a probability of having reached these levels randomly of 15% and 35%. In conclusion, based on EMG signals, the use a FFT to train a SVM was a better option to differentiate running forefoot/rearfoot than to use the DWT. Shod/barefoot running could not be differentiated.

Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring.

Rapidly intensifying global warming and water pollution calls for more efficient and continuous environmental monitoring methods. Biohybrid systems connect mechatronic components to living organisms and this approach can be used to extract data from the organisms. Compared to conventional monitoring methods, they allow for a broader data collection over long periods, minimizing the need for sampling processes and human labour. We aim to develop a methodology for creating various bioinspired entities, here referred to as 'biohybrids', designed for long-term aquatic monitoring. Here, we test several aspects of the development of the biohybrid entity: autonomous power source, lifeform integration and partial biodegradability. An autonomous power source was supplied by microbial fuel cells which exploit electron flows from microbial metabolic processes in the sediments. Here, we show that by stacking multiple cells, sufficient power can be supplied. We integrated lifeforms into the developed bioinspired entity which includes organisms such as the zebra musselDreissena polymorphaand water fleaDaphniaspp. The setups developed allowed for observing their stress behaviours. Through this, we can monitor changes in the environment in a continuous manner. The further development of this approach will allow for extensive, long-term aquatic data collection and create an early-warning monitoring system.

Shape transition during nest digging in ants.

Nest building in social insects is among the collective processes that show highly conservative features such as basic modules (chambers and galleries) or homeostatic properties. Although ant nests share common characteristics, they exhibit a high structural variability, of which morphogenesis and underlying mechanisms remain largely unknown. We conducted two-dimensional nest-digging experiments under homogeneous laboratory conditions to investigate the shape diversity that emerges only from digging dynamics and without the influence of any environmental heterogeneity. These experiments revealed that, during the excavation, a morphological transition occurs because the primary circular cavity evolves into a ramified structure through a branching process. Such a transition is observed, whatever the number of ants involved, but occurs more frequently for a larger number of workers. A stochastic model highlights the central role of density effects in shape transition. These results indicate that nest digging shares similar properties with various physical, chemical, and biological systems. Moreover, our model of morphogenesis provides an explanatory framework for shape transitions in decentralized growing structures in group-living animals.

Macrophage Polarization in the Perivascular Fat Was Associated With Coronary Atherosclerosis.

Background Inflammation of the perivascular adipose tissue (PvAT) may be related to atherosclerosis; however, the association of polarized macrophages in the pericoronary PvAT with measurements of atherosclerosis components in humans has not been fully investigated. Methods and Results Coronary arteries were dissected with surrounding PvAT. We evaluated the percentage of arterial obstruction, intima-media thickness, fibrous cap thickness, plaque components, and the number of vasa vasorum. The number of proinflammatory (M1) and anti-inflammatory (M2) macrophages in the periplaque and control PvAT were evaluated using immunohistochemistry. Regression models adjusted for sociodemographic and clinical variables were used. In 319 segments from 82 individuals, we found a correlation of the M1/M2 macrophage density ratio with an increase in arterial obstruction (P=0.02) and lipid content (P=0.01), and a decrease in smooth muscle cells (P=0.02). M1 and the ratio of M1/M2 macrophages were associated with an increased risk of thrombosis (P=0.03). In plaques with thrombosis, M1 macrophages were correlated with a decrease in fibrous cap thickness (P=0.006), an increase in lipid content (P=0.008), and the number of vasa vasorum in the adventitia layer (P=0.001). M2 macrophages were correlated with increased arterial obstruction (P=0.01), calcification (P=0.02), necrosis (P=0.03) only in plaques without thrombosis, and decrease of the number of vasa vasorum in plaques with thrombosis (P=0.003). Conclusions M1 macrophages in the periplaque PvAT were associated with a higher risk of coronary thrombosis and were correlated with histological components of plaque progression and destabilization. M2 macrophages were correlated with plaque size, calcification, necrotic content, and a decrease in the number of vasa vasorum in the adventitia layer.

Improving social odometry robot networks with distributed reputation systems for collaborative purposes.

The improvement of odometry systems in collaborative robotics remains an important challenge for several applications. Social odometry is a social technique which confers the robots the possibility to learn from the others. This paper analyzes social odometry and proposes and follows a methodology to improve its behavior based on cooperative reputation systems. We also provide a reference implementation that allows us to compare the performance of the proposed solution in highly dynamic environments with the performance of standard social odometry techniques. Simulation results quantitatively show the benefits of this collaborative approach that allows us to achieve better performances than social odometry.

Artificial pheromone for path selection by a foraging swarm of robots.

Foraging robots involved in a search and retrieval task may create paths to navigate faster in their environment. In this context, a swarm of robots that has found several resources and created different paths may benefit strongly from path selection. Path selection enhances the foraging behavior by allowing the swarm to focus on the most profitable resource with the possibility for unused robots to stop participating in the path maintenance and to switch to another task. In order to achieve path selection, we implement virtual ants that lay artificial pheromone inside a network of robots. Virtual ants are local messages transmitted by robots; they travel along chains of robots and deposit artificial pheromone on the robots that are literally forming the chain and indicating the path. The concentration of artificial pheromone on the robots allows them to decide whether they are part of a selected path. We parameterize the mechanism with a mathematical model and provide an experimental validation using a swarm of 20 real robots. We show that our mechanism favors the selection of the closest resource is able to select a new path if a selected resource becomes unavailable and selects a newly detected and better resource when possible. As robots use very simple messages and behaviors, the system would be particularly well suited for swarms of microrobots with minimal abilities.

Evaluation of multi-class support-vector machines strategies and kernel adjustment levels in hand posture recognition by analyzing sEMG signals acquired from a wearable device.

One-vs-One (OVO) and One-vs-All (OVA) are decomposition methods for multi-class strategies used to allow binary Support-Vector Machines (SVM) to transform a given k-class problem into pairwise small problems. In this context, the present work proposes the analysis of these two decomposition methods applied to the hand posture recognition problem in which the sEMG data of eight participants were collected by means of an 8-channel armband bracelet located on the forearm. Linear, Polynomial and Radial Basis Function kernels functions and its adjustments level were implemented combined to the strategies OVO and OVA to compare the performance of the SVM when mapping posture data into the classification spaces spanned by the studied kernels. Acquired sEMG signals were segmented considering 0.16 s e 0.32 s time windows. Root Mean Square (RMS) feature was extracted from each time window of each posture and used for SVM training. The present work focused in investigating the relationship between the multi-class strategies combined to kernels adjustments levels and SVM classification performance. Promising results were observed using OVA strategy which presents a reduced number of binary SVM implementation achieved a mean accuracy of 97.63%.

An Open Localization and Local Communication Embodied Sensor.

In this paper we describe a localization and local communication system which allows situated agents to communicate locally, obtaining at the same time both the range and the bearing of the emitter without the need of any centralized control or any external reference. The system relies on infrared communications with frequency modulation and is composed of two interconnected modules for data and power measurement. Thanks to the open hardware license under which it is released, the research community can easily replicate the system at a low cost and/or adapt it for applications in sensor networks and in robotics.

An automated method for large-scale monitoring of seed dispersal by ants.

Myrmecochory is the process of seed dispersal by ants; however, it is highly challenging to study, mainly because of the small size of both partners and the comparatively large range of dispersal. The mutualistic interaction between ants and seeds involves the former retrieving diaspores, consuming their elaiosome (a nutrient-rich appendage), and the rejection of seeds from the nest. Here, we introduce a semi-automated method based on stitching high resolution images together, allowing the study of myrmecochory in a controlled environment over time. We validate the effectiveness of our method in detecting and discriminating seeds and ants. We show that the number of retrieved diaspores varies highly among colonies, and is independent of both their size and activity level, even though the dynamics of diaspore collection are correlated with the arrival of ants at the food source. We find that all retrieved seeds are rejected from the nest in a clustered pattern, and, surprisingly, they are also frequently redispersed within the arena afterwards, despite lacking elaiosome. This finding suggests that the dispersal pattern might be more complex and dynamic than expected. Our method unveils new insights on the mechanisms of myrmecochory, and could be usefully adapted to study other dispersal phenomena.

Effect of the land area elevation on the collective choice in ants.

Collective decisions regarding food source exploitation in social insects are influenced by a range of parameters, from source quality to individual preference and social information sharing. Those regarding the elevation of the physical trail towards a food source have been neglected. In this work, we investigated the effect of ascending and descending paths from the nest to a food source on collective choice in two ant species Lasius niger and Myrmica rubra. Our hypothesis that returning loaded with food from the high source is more energy efficient was validated by choice experiments: when the sources are simultaneously introduced the high food source is preferentially exploited by both species. The flexibility of colony response was then tested by introducing the preferred source (high) incidentally, after recruitment towards the down food source began. Despite the well-known lack of flexibility of L. niger, both species showed the ability to reallocate their foraging workforce towards the highest food source. The collective choice and the flexibility are based on the difference between the u-turn rates when foragers are facing the ascending or descending branch. We discuss these results in terms of species-specifics characteristics and ecological context.

Capacitive soft strain sensors via multicore-shell fiber printing.

A new method for fabricating textile integrable capacitive soft strain sensors is reported, based on multicore-shell fiber printing. The fiber sensors consist of four concentric, alternating layers of conductor and dielectric, respectively. These wearable sensors provide accurate and hysteresis-free strain measurements under both static and dynamic conditions.

Self-organized discrimination of resources.

When selecting a resource to exploit, an insect colony must take into account at least two constraints: the resource must be abundant enough to sustain the whole group, but not too large to limit exploitation costs, and risks of conflicts with other colonies. Following recent results on cockroaches and ants, we introduce here a behavioral mechanism that satisfies these two constraints. Individuals simply modulate their probability to switch to another resource as a function of the local density of conspecifics locally detected. As a result, the individuals gather at the smallest resource that can host the whole group, hence reducing competition and exploitation costs while fulfilling the overall group's needs. Our analysis reveals that the group becomes better at discriminating between similar resources as it grows in size. Also, the discrimination mechanism is flexible and the group readily switches to a better suited resource as it appears in the environment. The collective decision emerges through the self-organization of individuals, that is, in absence of any centralized control. It also requires a minimal individual cognitive investment, making the proposed mechanism likely to occur in other social species and suitable for the development of distributed decision making tools.

Analyzing fish movement as a persistent turning walker.

The trajectories of Kuhlia mugil fish swimming freely in a tank are analyzed in order to develop a model of spontaneous fish movement. The data show that K. mugil displacement is best described by turning speed and its auto-correlation. The continuous-time process governing this new kind of displacement is modelled by a stochastic differential equation of Ornstein-Uhlenbeck family: the persistent turning walker. The associated diffusive dynamics are compared to the standard persistent random walker model and we show that the resulting diffusion coefficient scales non-linearly with linear swimming speed. In order to illustrate how interactions with other fish or the environment can be added to this spontaneous movement model we quantify the effect of tank walls on the turning speed and adequately reproduce the characteristics of the observed fish trajectories.