Biomimetics, Volume 5, Issue 1 (March 2020) – 11 articles

Past published studies have already documented that, subsequent to the intravenous injection of colloidal lipid nanocarriers, apolipoprotein (apo)A-I is adsorbed from the blood onto the nanoparticle surface. The adsorbed apoA-I mediates the interaction of the nanoparticle with scavenger receptors on the blood–brain barrier (BBB), followed by receptor-mediated endocytosis and subsequent transcytosis across the BBB. By incorporating the appropriate drug(s) into biomimetic (lipid cubic phase) nanocarriers, one obtains a multitasking combination therapeutic which targets certain cell-surface scavenger receptors, mainly class B type I (i.e., SR-BI), and crosses the BBB. Documented similarities in lipid composition between naturally occurring high-density lipoproteins (HDL) and the artificial biomimetic (nanoemulsion) nanocarrier particles can partially simulate or mimic the known heterogeneity (i.e., subpopulations or subspecies) of HDL particles. Such biomedical application of colloidal drug-nanocarriers can potentially be extended to the treatment of complex medical disorders like dementia. The risk factors for dementia trigger widespread inflammation and oxidative stress; these two processes involve pathophysiological cascades which lead to neuronal Ca²⁺ increase, neurodegeneration, gradual cognitive/memory decline, and eventually (late-onset) dementia. In particular, more recent research indicates that chronic inflammatory stimulus in the gut may induce (e.g., via serum amyloid A (SAA)) the release of proinflammatory cytokines. Hence, an effective preventive and therapeutic strategy could be based upon drug targeting toward a major SAA receptor responsible for the SAA-mediated cell signaling events leading to cognitive decline and eventually Alzheimer’s disease or (late-onset) dementia. Full article

Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quantification of proximity to other fish. Here we examine the distribution of sensors on the surface of an artificial swimmer so that it can optimally identify a leading group of swimmers. We employ Bayesian experimental design coupled with numerical simulations of the two-dimensional Navier Stokes equations for multiple self-propelled swimmers. The follower tracks the school using information from its own surface pressure and shear stress. We demonstrate that the optimal sensor distribution of the follower is qualitatively similar to the distribution of neuromasts on fish. Our results show that it is possible to identify accurately the center of mass and the number of the leading swimmers using surface only information. Full article

Some anguilliform swimmers such as eels and lampreys swim near the ground, which has been hypothesized to have hydrodynamic benefits. To investigate whether swimming near ground has hydrodynamics benefits, two large-eddy simulations of a self-propelled anguilliform swimmer are carried out—one swimming far away from the ground (free swimming) and the other near the ground, that is, midline at $0.07$ of fish length (L) from the ground creating a gap of $0.04L$ . Simulations are carried out under similar conditions with both fish starting from rest in a quiescent flow and reaching steady swimming (constant average speed). The numerical results show that both swimmers have similar speed, power consumption, efficiency, and wake structure during steady swimming. This indicates that swimming near the ground with a gap larger than $0.04L$ does not improve the swimming performance of anguilliform swimmers when there is no incoming flow, that is, the interaction of the wake with the ground does not improve swimming performance. When there is incoming flow, however, swimming near the ground may help because the flow has lower velocities near the ground. Full article

The application of deep learning techniques may prove difficult when datasets are small. Recently, techniques such as one-shot learning, few-shot learning, and Siamese networks have been proposed to address this problem. In this paper, we propose the use a convolutional Siamese network (CSN) that learns a similarity metric that discriminates between plant species based on images of leaves. Once the CSN has learned the similarity function, its discriminatory power is generalized to classify not just new pictures of the species used during training but also entirely new species for which only a few images are available. This is achieved by exposing the network to pairs of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. We conducted experiments to study two different scenarios. In the first one, the CSN was trained and validated with datasets that comprise 5, 10, 15, 20, 25, and 30 pictures per species, extracted from the well-known Flavia dataset. Then, the trained model was tested with another dataset composed of 320 images (10 images per species) also from Flavia. The obtained accuracy was compared with the results of feeding the same training, validation, and testing datasets to a convolutional neural network (CNN) in order to determine if there is a threshold value t for dataset size that defines the intervals for which either the CSN or the CNN has better accuracy. In the second studied scenario, the accuracy of both the CSN and the CNN—both trained and validated with the same datasets extracted from Flavia—were compared when tested on a set of images of leaves of 20 Costa Rican tree species that are not represented in Flavia. Full article

Paddle boats like canoes and kayaks draw a sinusoidal path when a linear movement is intended. The reason for this behavior is that each paddle stroke induces a lateral movement of the boat. In this study, we sought to reduce the so-called yawing motion. We therefore replaced the stiff stern by a flexible stern, which is based on the Fin Ray Effect^®. We built down-scaled boat models and tested them in a water channel. The similarities between experimental and original setup were evaluated by means of a dimensional analysis. (Thermoplastic) elastomers with various flexibility were used for the stern construction. In the experiments conducted in the water channel, we determined the forces acting on the boat with different stern models. The results reveal that the flexible stern induced a torque counteracting the boat’s deflection, while the stiff stern caused a torque enhancing it. A paddle boat with a flexible stern could hence be a promising new method to reduce the boat’s yawing movement. Full article

The photonic structures of butterfly wing scales are widely known to cause angle-dependent colours by light interference with nanostructures present in the wing scales. Here, we quantify the relevance of the horizontal alignment of the butterfly wing scales on the wing. The orientation-dependent reflection was measured at four different azimuth angles, with a step size of 90°, for ten samples—two of different areas of the same species—of eight butterfly species of three subfamilies at constant angles of illumination and observation. For the observed species with varying optical structures, the wing typically exhibits higher orientation-dependent reflections than the individual scale. We find that the measured anisotropy is caused by the commonly observed grating structures that can be found on all butterfly wing scales, rather than the local photonic structures. Our results show that the technique employed here can be used to quickly evaluate the orientation-dependence of the reflection and hence provide important input for bio-inspired applications, e.g., to identify whether the respective structure is suitable as a template for nano-imprinting techniques. Full article

Thin shells are found across scales ranging from biological blood cells to engineered large-span roof structures. The engineering design of thin shells used as mechanisms has occasionally been inspired by biomimetic concept generators. The research goal of this paper is to establish the physical limits of scalability of shells. Sixty-four instances of shells across length scales have been organized into five categories: engineering stiff and compliant, plant compliant, avian egg stiff, and micro-scale compliant shells. Based on their thickness and characteristic dimensions, the mechanical behavior of these 64 shells can be characterized as 3D solids, thick or thin shells, or membranes. Two non-dimensional indicators, the Föppl–von Kármán number and a novel indicator, namely the gravity impact number, are adopted to establish the scalability limits of these five categories. The results show that these shells exhibit similar mechanical behavior across scales. As a result, micro-scale shell geometries found in biology, can be upscaled to engineered shell geometries. However, as the characteristic shell dimension increases, gravity (and its associated loading) becomes a hindrance to the adoption of thin shells as compliant mechanisms at the larger scales-the physical limit of compliance in the scaling of thin shells is found to be around 0.1 m. Full article

The lithium salt of the binuclear nitrido complex of ruthenium (IV) Li₃(Ru₂NCl₈·2H₂O) was synthesized. Using UV spectroscopy and voltammetry, we studied complex behavior in aqueous solutions. It was found that in dilute solutions of this compound, Cl⁻ ions are replaced by H₂O molecules, and the intra-sphere redox reaction between Ru (IV) and H₂O, as well as the oxidation of water with the formation of oxygen and the acidic dissociation of coordinated water molecules also have been taking place. It was established by IR spectroscopy and ESI mass spectrometric analysis that not only the binuclear structure of the complex is preserved in acidic solutions, but also its dimerization product into the tetra-ruthenium dinitrido cluster Ru₄N₂O₅⁺, which is a catalyst for the water oxidation reaction. The activity of the catalyst was TOF = 0.33 s⁻¹, TON = 304. Full article

The movement of plankton is often dictated by local flow patterns, particularly during storms and in environments with strong flows. Reefs, macrophyte beds, and other immersed structures can provide shelter against washout and drastically alter the distributions of plankton as these structures redirect and slow the flows through them. Advection–diffusion and agent-based models are often used to describe the movement of plankton within marine and fresh water environments and across multiple scales. Experimental validation of such models of plankton movement within complex flow environments is challenging because of the difference in both time and spatial scales. Organisms on the scale of 1 mm or less swim by beating their appendages on the order of 1 Hz and are advected meters to kilometers over days, weeks, and months. One approach to study this challenging multiscale problem is to insert actively moving agents within a background flow field. Open source tools to implement this sort of approach are, however, limited. In this paper, we combine experiments and computational fluid dynamics with a newly developed agent-based modeling platform to quantify plankton movement at the scale of tens of centimeters. We use Artemia spp., or brine shrimp, as a model organism given their availability and ease of culturing. The distribution of brine shrimp over time was recorded in a flow tank with simplified physical models of macrophytes. These simplified macrophyte models were 3D-printed arrays of cylinders of varying heights and densities. Artemia nauplii were injected within these arrays, and their distributions over time were recorded with video. The detailed three-dimensional flow fields were quantified using computational fluid dynamics and validated experimentally with particle image velocimetry. To better quantify plankton distributions, we developed an agent-based modeling framework, Planktos, to simulate the movement of plankton immersed within such flow fields. The spatially and temporally varying Artemia distributions were compared across models of varying heights and densities for both the experiments and the agent-based models. The results show that increasing the density of the macrophyte bed drastically increases the average time it takes the plankton to be swept downstream. The height of the macrophyte bed had less of an effect. These effects were easily observed in both experimental studies and in the agent-based simulations. Full article

Speech signals are degraded in real-life environments, as a product of background noise or other factors. The processing of such signals for voice recognition and voice analysis systems presents important challenges. One of the conditions that make adverse quality difficult to handle in those systems is reverberation, produced by sound wave reflections that travel from the source to the microphone in multiple directions. To enhance signals in such adverse conditions, several deep learning-based methods have been proposed and proven to be effective. Recently, recurrent neural networks, especially those with long short-term memory (LSTM), have presented surprising results in tasks related to time-dependent processing of signals, such as speech. One of the most challenging aspects of LSTM networks is the high computational cost of the training procedure, which has limited extended experimentation in several cases. In this work, we present a proposal to evaluate the hybrid models of neural networks to learn different reverberation conditions without any previous information. The results show that some combinations of LSTM and perceptron layers produce good results in comparison to those from pure LSTM networks, given a fixed number of layers. The evaluation was made based on quality measurements of the signal’s spectrum, the training time of the networks, and statistical validation of results. In total, 120 artificial neural networks of eight different types were trained and compared. The results help to affirm the fact that hybrid networks represent an important solution for speech signal enhancement, given that reduction in training time is on the order of 30%, in processes that can normally take several days or weeks, depending on the amount of data. The results also present advantages in efficiency, but without a significant drop in quality. Full article