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Atomic force microscopy (AFM)-based fishing is a promising method for the detection of low-abundant proteins. This method is based on the capturing of the target proteins from the analyzed solution onto a solid substrate, with subsequent counting of the captured protein molecules on the substrate surface by AFM. Protein adsorption onto the substrate surface represents one of the key factors determining the capturing efficiency. Accordingly, studying the factors influencing the protein adsorbability onto the substrate surface represents an actual direction in biomedical research. Herein, the influence of water motion in a flow-based system on the protein adsorbability and on its enzymatic activity has been studied with an example of horseradish peroxidase (HRP) enzyme by AFM, attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) and conventional spectrophotometry. In the experiments, HRP solution was incubated in a setup modeling the flow section of a biosensor communication. The measuring cell with the protein solution was placed near a coiled silicone pipe, through which water was pumped. The adsorbability of the protein onto the surface of the mica substrate has been studied by AFM. It has been demonstrated that incubation of the HRP solution near the coiled silicone pipe with flowing water leads to an increase in its adsorbability onto mica. This is accompanied by a change in the enzyme’s secondary structure, as has been revealed by ATR-FTIR. At the same time, its enzymatic activity remains unchanged. The results reported herein can be useful in the development of models describing the influence of liquid flow on the properties of enzymes and other proteins. The latter is particularly important for the development of biosensors for biomedical applications—particularly for serological analysis, which is intended for the early diagnosis of various types of cancer and infectious diseases. Our results should also be taken into account in studies of the effects of protein aggregation on hemodynamics, which plays a key role in human body functioning.

Application of micro-Raman spectroscopy for the monitoring of quality of nanowire sensor chips fabrication has been demonstrated. Nanowire chips have been fabricated on the basis of «silicon-on-insulator» (SOI) structures (SOI-NW chips). The fabrication of SOI-NW chips was performed by optical litography with gas-phase etching. The so-fabricated SOI-NW chips are intended for highly sensitive detection of brain cancer biomarkers in humans. In our present study, two series of experiments have been conducted. In the first experimental series, detection of a synthetic DNA oligonucleotide (oDNA) analogue of brain cancer-associated microRNA miRNA-363 in purified buffer solution has been performed in order to demonstrate the high detection sensitivity. The second experimental series has been performed in order to reveal miRNA-363 itself in real human plasma samples. To provide detection biospecificity, the SOI-NW chip surface was modified by covalent immobilization of probe oligonucleotides (oDNA probes) complementary to the target biomolecules. Using the SOI-NW sensor chips proposed herein, the concentration detection limit of the target biomolecules at the level of 3.3 × 10−17 M has been demonstrated. Thus, the approach employing the SOI-NW chips proposed herein represents an attractive tool in biomedical practice, aimed at the early revelation of oncological diseases in humans.

Our present study concerns the influence of the picosecond rise-time-pulsed electromagnetic field, and the impact of nanosecond pulsed pressure on the aggregation state of horseradish peroxidase (HRP) as a model enzyme. The influence of a 640 kV/m pulsed electromagnetic field with a pulse rise-time of ~200 ps on the activity and aggregation state of an enzyme is studied by the single-molecule atomic force microscopy (AFM) method. The influence of such a field is shown to lead to aggregation of the protein and to a decrease in its enzymatic activity. Moreover, the effect of a shock wave with a pressure front rise-time of 80 ns on the increase in the HRP aggregation is demonstrated. The results obtained herein can be of use in modeling the impact of electromagnetic and pressure pulses on enzymes and on whole living organisms. Our results are also important for taking into account the effect of pulsed fields on the body in the development of drugs, therapeutic procedures, and novel highly sensitive medical diagnosticums.

Elastomeric materials have great application potential in actuator design and soft robot development. The most common elastomers used for these purposes are polyurethanes, silicones, and acrylic elastomers due to their outstanding physical, mechanical, and electrical properties. Currently, these types of polymers are produced by traditional synthetic methods, which may be harmful to the environment and hazardous to human health. The development of new synthetic routes using green chemistry principles is an important step to reduce the ecological footprint and create more sustainable biocompatible materials. Another promising trend is the synthesis of other types of elastomers from renewable bioresources, such as terpenes, lignin, chitin, various bio-oils, etc. The aim of this review is to address existing approaches to the synthesis of elastomers using “green” chemistry methods, compare the properties of sustainable elastomers with the properties of materials produced by traditional methods, and analyze the feasibility of said sustainable elastomers for the development of actuators. Finally, the advantages and challenges of existing “green” methods of elastomer synthesis will be summarized, along with an estimation of future development prospects.

Currently, the preparation of actuators based on ionic electroactive polymers with a fast response is considered an urgent topic. In this article, a new approach to activate polyvinyl alcohol (PVA) hydrogels by applying an AC voltage is proposed. The suggested approach involves an activation mechanism in which the PVA hydrogel-based actuators undergo extension/contraction (swelling/shrinking) cycles due to the local vibration of the ions. The vibration does not cause movement towards the electrodes but results in hydrogel heating, transforming the water molecules into a gaseous state and causing the actuator to swell. Two types of linear actuators based on PVA hydrogels were prepared, using two types of reinforcement for the elastomeric shell (spiral weave and fabric woven braided mesh). The extension/contraction of the actuators, activation time, and efficiency were studied, considering the PVA content, applied voltage, frequency, and load. It was found that the overall extension of the spiral weave-reinforced actuators under a load of ~20 kPa can reach more than 60%, with an activation time of ~3 s by applying an AC voltage of 200 V and a frequency of 500 Hz. Conversely, the overall contraction of the actuators reinforced by fabric woven braided mesh under the same conditions can reach more than 20%, with an activation time of ~3 s. Moreover, the activation force (swelling load) of the PVA hydrogels can reach up to 297 kPa. The developed actuators have broad applications in medicine, soft robotics, the aerospace industry, and artificial muscles.

We propose a sparse computation method for optimizing the inference of neural networks in reinforcement learning (RL) tasks. Motivated by the processing abilities of the brain, this method combines simple neural network pruning with a delta-network algorithm to account for the input data correlations. The former mimics neuroplasticity by eliminating inefficient connections; the latter makes it possible to update neuron states only when their changes exceed a certain threshold. This combination significantly reduces the number of multiplications during the neural network inference for fast neuromorphic computing. We tested the approach in popular deep RL tasks, yielding up to a 100-fold reduction in the number of required multiplications without substantial performance loss (sometimes, the performance even improved).

Modern AI systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the brain. This article discusses such limitations and the ways they can be mitigated. Next, it presents an overview of currently available neuromorphic AI projects in which these limitations are overcome by bringing some brain features into the functioning and organization of computing systems (TrueNorth, Loihi, Tianjic, SpiNNaker, BrainScaleS, NeuronFlow, DYNAP, Akida). Also, the article presents the principle of classifying neuromorphic AI systems by the brain features they use (neural networks, parallelism and asynchrony, impulse nature of information transfer, local learning, sparsity, analog and in-memory computing). In addition to new architectural approaches used in neuromorphic devices based on existing silicon microelectronics technologies, the article also discusses the prospects of using a new memristor element base. Examples of recent advances in the use of memristors in neuromorphic applications are also given.

In real applications of Reinforcement Learning (RL), such as robotics, low latency, energy-efficient and high-throughput inference is very desired. The use of sparsity and pruning for optimizing Neural Network inference, and particularly to improve energy efficiency, latency and throughput, is a standard technique. In this work, we conduct a systematic investigation of the application of these optimization techniques with popular RL algorithms, specifically Deep Q-Network and Soft Actor Critic, in different RL environments, including MuJoCo and Atari, which yields up to a 400-fold reduction in the size of neural networks. This work presents a systematic study on the applicability limits of using pruning and quantization to optimize neural networks in RL tasks, with a perspective of deployment in hardware to reduce power consumption and latency, while increasing throughput.

The asteroid impact near the Russian city of Chelyabinsk on 15 February 2013 was the largest airburst on Earth since the 1908 Tunguska event, causing a natural disaster in an area with a population exceeding one million. Because it occurred in an era with modern consumer electronics, field sensors, and laboratory techniques, unprecedented measurements were made of the impact event and the meteoroid that caused it. Here, we document the account of what happened, as understood now, using comprehensive data obtained from astronomy, planetary science, geophysics, meteorology, meteoritics, and cosmochemistry and from social science surveys. A good understanding of the Chelyabinsk incident provides an opportunity to calibrate the event, with implications for the study of near-Earth objects and developing hazard mitigation strategies for planetary protection.

This article describes the one-pot microwave synthesis of silver nanoparticles (AgNPs) assisted with natural polyelectrolytes—humic substances (HS). The humic polyelectrolytes served both as chemical reductants for silver ions and as end-capping agents for AgNPs. Three commercially available sodium humates extracted from lignites and leonardite and one sodium fulvate isolated from natural brown water seeped through peat deposits were used in this study. The dynamics of the growth rate of AgNPs was characterised by UV–VIS spectroscopy by measuring the intensity of surface plasmon resonance at 420 nm. Transmission electron microscopy was used to characterise the size and morphology of AgNPs. Dynamic light scattering was used to determine size distributions of the synthesised AgNPs in the solutions. It was established that both conventional and microwave syntheses assisted with the coal humates produced small-size AgNPs in the range from 4 to 14 nm, with the maximum share of particles with sizes of (6 ± 2) nm by TEM estimates. The peat fulvate yielded much larger NPs with sizes from 10 to 50 nm by TEM estimates. DLS measurements revealed multimodal distributions of AgNPs stabilised with HS, which included both single NPs with the sizes from 5 to 15 nm, as well as their dominating aggregates with sizes from 20 to 200 nm and a smaller portion of extra-large aggregates up to 1000 nm. The given aggregates were loosely bound by humic polyelectrolyte, which prevented the coalescence of AgNPs into larger particles, as can be seen in the TEM images. The significant acceleration in the reaction time—a factor of 60 to 70—was achieved with the use of MW irradiation: from 240 min down to 210–240 s. The coal humate stabilised AgNPs showed antimicrobial properties in relation to S. aureus. A conclusion was made regarding the substantial advantages of microwave synthesis in the context of time and scaling up for the large-scale production of AgNP-HS preparations with antimicrobial properties suitable for external wound-healing applications.