Ratiometric Decoding of Pheromones for a Biomimetic Infochemical Communication System.

Biosynthetic infochemical communication is an emerging scientific field employing molecular compounds for information transmission, labelling, and biochemical interfacing; having potential application in diverse areas ranging from pest management to group coordination of swarming robots. Our communication system comprises a chemoemitter module that encodes information by producing volatile pheromone components and a chemoreceiver module that decodes the transmitted ratiometric information via polymer-coated piezoelectric Surface Acoustic Wave Resonator (SAWR) sensors. The inspiration for such a system is based on the pheromone-based communication between insects. Ten features are extracted from the SAWR sensor response and analysed using multi-variate classification techniques, i.e., Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), and Multilayer Perception Neural Network (MLPNN) methods, and an optimal feature subset is identified. A combination of steady state and transient features of the sensor signals showed superior performances with LDA and MLPNN. Although MLPNN gave excellent results reaching 100% recognition rate at 400 s, over all time stations PNN gave the best performance based on an expanded data-set with adjacent neighbours. In this case, 100% of the pheromone mixtures were successfully identified just 200 s after they were first injected into the wind tunnel. We believe that this approach can be used for future chemical communication employing simple mixtures of airborne molecules.

Growth of nanoparticles and microparticles by controlled reaction-diffusion processes.

The synthesis of different sizes of nanoparticles and microparticles is important in designing nanostructured materials with various properties. Wet synthesis methods lack the flexibility to create various sizes of particles (particle libraries) using fixed conditions without the repetition of the steps in the method with a new set of parameters. Here, we report a synthesis method based on nucleation and particle growth in the wake of a moving chemical front in a gel matrix. The process yields well-separated regions (bands) filled with nearly monodisperse nanoparticles and microparticles, with the size of the particles varying from band to band in a predictable way. The origin of the effect is due to an interplay of a precipitation reaction of the reagents and their diffusion that is controlled in space and time by the moving chemical front. The method represents a new approach and a promising tool for the fast and competitive synthesis of various sizes of colloidal particles.

Label-free in situ optical monitoring of the adsorption of oppositely charged metal nanoparticles.

The mechanism of alternating deposition of oppositely charged gold nanoparticles (AuNPs) was investigated by optical waveguide lightmode spectroscopy (OWLS). OWLS allows monitoring of the kinetics of layer-by-layer (LbL) adsorption of positively and negatively charged nanoparticles in real time without using any labels so that the dynamics of layer formation can be revealed. Positively charged NPs that are already deposited on a negatively charged glass substrate strongly facilitate the adsorption of the negatively charged particles. The morphology of the adsorbed layer was also investigated with atomic force microscopy (AFM). AFM revealed that the interaction between oppositely charged particles results in the formation of NP clusters with sizes varying between 100 and 6000 NPs. The cluster size distribution is found to be an exponentially decaying function, and we propose a simple theory to explain this finding.

Helices in the wake of precipitation fronts.

A theoretical study of the emergence of helices in the wake of precipitation fronts is presented. The precipitation dynamics is described by the Cahn-Hilliard equation and the fronts are obtained by quenching the system into a linearly unstable state. Confining the process onto the surface of a cylinder and using the pulled-front formalism, our analytical calculations show that there are front solutions that propagate into the unstable state and leave behind a helical structure. We find that helical patterns emerge only if the radius of the cylinder R is larger than a critical value R>R(c), in agreement with recent experiments.

Rapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex.

We present a biologically-constrained neuromorphic spiking model of the insect antennal lobe macroglomerular complex that encodes concentration ratios of chemical components existing within a blend, implemented using a set of programmable logic neuronal modeling cores. Depending upon the level of inhibition and symmetry in its inhibitory connections, the model exhibits two dynamical regimes: fixed point attractor (winner-takes-all type), and limit cycle attractor (winnerless competition type) dynamics. We show that, when driven by chemosensor input in real-time, the dynamical trajectories of the model's projection neuron population activity accurately encode the concentration ratios of binary odor mixtures in both dynamical regimes. By deploying spike timing-dependent plasticity in a subset of the synapses in the model, we demonstrate that a Hebbian-like associative learning rule is able to organize weights into a stable configuration after exposure to a randomized training set comprising a variety of input ratios. Examining the resulting local interneuron weights in the model shows that each inhibitory neuron competes to represent possible ratios across the population, forming a ratiometric representation via mutual inhibition. After training the resulting dynamical trajectories of the projection neuron population activity show amplification and better separation in their response to inputs of different ratios. Finally, we demonstrate that by using limit cycle attractor dynamics, it is possible to recover and classify blend ratio information from the early transient phases of chemosensor responses in real-time more rapidly and accurately compared to a nearest-neighbor classifier applied to the normalized chemosensor data. Our results demonstrate the potential of biologically-constrained neuromorphic spiking models in achieving rapid and efficient classification of early phase chemosensor array transients with execution times well beyond biological timescales.

Full counting statistics in a propagating quantum front and random matrix spectra.

One-dimensional free fermions are studied with emphasis on propagating fronts emerging from a step initial condition. The probability distribution of the number of particles at the edge of the front is determined exactly. It is found that the full counting statistics coincide with the eigenvalue statistics of the edge spectrum of matrices from the Gaussian unitary ensemble. The correspondence established between the random matrix eigenvalues and the particle positions yields the order statistics of the rightmost particles in the front and, furthermore, it implies their subdiffusive spreading.

Probability of the emergence of helical precipitation patterns in the wake of reaction-diffusion fronts.

Helical and helicoidal precipitation patterns emerging in the wake of reaction-diffusion fronts are studied. In our experiments, these chiral structures arise with well-defined probabilities P(H) controlled by conditions such as, e.g., the initial concentration of the reagents. We develop a model which describes the observed experimental trends. The results suggest that P(H) is determined by a delicate interplay among the time and length scales related to the front and to the unstable precipitation modes and, furthermore, that the noise amplitude also plays a quantifiable role.

[Guidelines for writing biomedical articles containing statistical figures and analysis]. / Szempontok a biometriai módszereket használó közlemények írásához.

Some advices are provided in order to make statistical figures and analyses more correct in biomedical articles. The sample size has an outstanding significance. Analysing different statistical relationships the requirements of biometry should be fulfilled (number and distribution of data etc.) and interpretation of statistical findings could be done only within the domain of minimal and maximal values of independent variable. Extrapolation is not recommended due to the specific nature of the biomedical system. In some cases laboratory data should be transformed mathematically and the results of subsequent statistical calculations should be retransformed back into the biomedical system.