Construction Of Circuits Research Articles

Shoot-root signal circuit: Phytoremediation of heavy metal contaminated soil.

High concentrations of heavy metals in the environment will cause serious harm to ecosystems and human health. It is urgent to develop effective methods to control soil heavy metal pollution. Phytoremediation has advantages and potential for soil heavy metal pollution control. However, the current hyperaccumulators have the disadvantages of poor environmental adaptability, single enrichment species and small biomass. Based on the concept of modularity, synthetic biology makes it possible to design a wide range of organisms. In this paper, a comprehensive strategy of "microbial biosensor detection - phytoremediation - heavy metal recovery" for soil heavy metal pollution control was proposed, and the required steps were modified by using synthetic biology methods. This paper summarizes the new experimental methods that promote the discovery of synthetic biological elements and the construction of circuits, and combs the methods of producing transgenic plants to facilitate the transformation of constructed synthetic biological vectors. Finally, the problems that should be paid more attention to in the remediation of soil heavy metal pollution based on synthetic biology were discussed.

Automated System of Competencies Indicators’ Formation Control during the Basic Boolean Algebra Studying

The effective study of Computer Science is impossible without the widespread use of information technology. Boolean algebra is one of the most fundamental sections of computer science. There is a list of disadvantages of this testing method for the evaluation of the master’s degree students’ knowledge of this topic. This results in development of other interactive tools. This article is devoted to the development and operation features of the automated system “Boolean algebra”, which was designed to control the formation of the competencies’ achievement indicators obtained during studying Boolean algebra. The developed system automatically creates task option and contains three types of tasks. The frst task includes construction of a truth table for three Boolean functions, the second requires Boolean circuit construction, and the third task is focused on Boolean expression simplifying. The system architecture consists of eightindependent modules which realize the new task option synthesis and its design, Boolean functions decomposition, truth tables construction, analysis, and evaluation of the results. The article describes the concept of each module. It describes in detail the synthesis of Boolean functions, as well as the analysis modules and their functioning algorithms. The automated system under operation proved to be effective since it allows one to objectively evaluate user’s theoretical knowledge and ability to apply these methods to the truth tables construction. There are also several advantages of the considered automated system: it requires little space on a hard disk, workes in a network and on local computer, has intuitive interface, generates a large variety of tasks and performs their autocheking.

On the long time behaviour of single stochastic Hodgkin-Huxley neurons with constant signal, and a construction of circuits of interacting neurons showing self-organized rhythmic oscillations

The stochastic Hodgkin-Huxley neurons considered in this paper replace time-constant deterministic input $a dt$ of the classical deterministic model by increments $\vartheta dt + dX_t$ of a stochastic process: $X$ is Ornstein-Uhlenbeck with volatility $\sigma>0$ and backdriving force $\tau>0$, and we call $\vartheta>0$ the signal. We have ergodicity and strong laws of large numbers for various functionals of the process, and characterize 'quiet behaviour' and 'regular spiking' as events whose probability depends on the parameters $(\tau,\sigma)$ and on the signal $\vartheta$. The notions of quiet behaviour and regular spiking allow for a construction of circuits of interacting stochastic Hodgkin-Huxley neurons, combining excitation with inhibition according to a bloc structure along the circuit, on which self-organized rhythmic oscillations can be observed.

Systematic Comparison and Rational Design of Theophylline Riboswitches for Effective Gene Repression.

Riboswitches are promising regulatory tools in synthetic biology. To date, 25 theophylline riboswitches have been developed for regulation of gene expression in bacteria. However, no one has systematically evaluated their regulatory effects. To promote efficient selection and application of theophylline riboswitches, we examined 25 theophylline riboswitches in Escherichia coli MG1655 and found that they varied widely in terms of activation/repression ratios and expression levels in the absence of theophylline. Of the 20 riboswitches that activate gene expression, only one exhibited a high activation ratio (63.6-fold) and low expression level without theophylline. Furthermore, none of the five riboswitches that repress gene expression were more than 2.0-fold efficient. To obtain an effective repression system, we rationally designed a novel theophylline riboswitch to control a downstream gene or genes by premature transcription termination. This riboswitch allowed theophylline-dependent downregulation of the TurboRFP reporter in a dose- and time-dependent manner. Its performance profile exceeded those of previously described repressive theophylline riboswitches. We then introduced as the second part a RepA tag (protein degradation tag) coding sequence fused at the 5'-terminal end of the turborfp gene, which further reduced protein level, while not reducing the repressive effect of the riboswitch. By combining two tandem theophylline riboswitches with a RepA tag, we constructed a regulatory cassette that represses the expression of the gene(s) of interest at both the transcriptional and posttranslational levels. This regulatory cassette can be used to repress the expression of any gene of interest and represents a crucial step toward harnessing theophylline riboswitches and expanding the synthetic biology toolbox. IMPORTANCE A variety of gene expression regulation tools with significant regulatory effects are essential for the construction of complex gene circuits in synthetic biology. Riboswitches have received wide attention due to their unique biochemical, structural, and genetic properties. Here, we have not only systematically and precisely characterized the regulatory properties of previously developed theophylline riboswitches but also engineered a novel repressive theophylline riboswitch acting at the transcriptional level. By introducing coding sequences of a tandem riboswitch and a RepA protein degradation tag at the 5' end of the reporter gene, we successfully constructed a simple and effective regulatory cassette for gene regulation. Our work provides useful biological components for the construction of synthetic biology gene circuits.

Chimeric MerR-Family Regulators and Logic Elements for the Design of Metal Sensitive Genetic Circuits in Bacillus subtilis.

Whole-cell biosensors are emerging as promising tools for monitoring environmental pollutants such as heavy metals. These sensors constitute a genetic circuit comprising a sensing module and an output module, such that a detectable signal is produced in the presence of the desired analyte. The MerR family of metal-responsive regulators offers great potential for the construction of metal sensing circuits, due to their high sensitivity, tight transcription control, and large diversity in metal-specificity. However, the sensing diversity is broadest in Gram-negative systems, while chassis organisms are often selected from Gram-positive species, particularly sporulating bacilli. This can be problematic, because Gram-negative biological parts, such as promoters, are frequently observed to be nonfunctional in Gram-positive hosts. Herein, we combined construction of synthetic genetic circuits and chimeric MerR regulators, supported by structure-guided design, to generate metal-sensitive biosensor modules that are functional in the biotechnological work-horse species Bacillus subtilis. These chimeras consist of a constant Gram-positive derived DNA-binding domain fused to variable metal binding domains of Gram-negative origins. To improve the specificity of the whole-cell biosensor, we developed a modular "AND gate" logic system based on the B.subtilis two-subunit σ-factor, SigO-RsoA, designed to maximize future use for synthetic biology applications in B.subtilis. This work provides insights into the use of modular regulators, such as the MerR family, in the design of synthetic circuits for the detection of heavy metals, with potentially wider applicability of the approach to other systems and genetic backgrounds.

Multifunctional Exo III-assisted scalability strategy for constructing DNA molecular logic circuits.

The construction of logic circuits is critical to DNA computing. Simple and effective scalability methods have been the focus of attention in various fields related to constructing logic circuits. We propose a double-stranded separation (DSS) strategy to facilitate the construction of complex circuits. The strategy combines toehold-mediated strand displacement with exonuclease III (Exo III), which is a multifunctional nuclease. Exo III can quickly recognize an apurinic/apyrimidinic (AP) site. DNA oligos with an AP site can generate an output signal by the strand displacement reaction. However, in contrast to traditional strand displacement reactions, the double-stranded waste from the strand displacement can be further hydrolysed by the endonuclease function of Exo III, thus generating an additional output signal. The DSS strategy allows for the effective scalability of molecular logic circuits, enabling multiple logic computing capabilities simultaneously. In addition, we succeeded in constructing a logic circuit with dual logic functions that provides foundations for more complex circuits in the future and has a broad scope for development in logic computing, biosensing, and nanomachines.

Detecting Entanglement-Generating Circuits in Cloud-Based Quantum Computing

Entanglement, which is a direct consequence of elementary quantum gates such as controlled-NOT and Toffoli, is a key resource that provides quantum advantages. In this work, we establish a framework for certifying entanglement generation in cloud-based quantum computing services and present the construction of quantum circuits that certify entanglement generation in a circuit-based quantum computing model. The framework relaxes the assumption of qubit allocation, which, in cloud services, relates the physical qubits in hardware to a circuit proposed by a user. Consequently, certification is valid in cloud computing regardless of the success or trustworthiness of qubit allocation. The certification of entanglement generation was demonstrated on 2 and 3 qubits in the IBMQ and IonQ services. Remarkably, entanglement generation was successfully certified in the IonQ service, which does not offer manual qubit allocation. The capabilities of entanglement generation in IBMQ and IonQ circuits were also quantified. We envisage the application of the proposed framework in cloud-based quantum computing services for practical computation and information tasks, with the results determining whether it is possible to achieve quantum advantages.

Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning

Prognostics and Health Management (PHM) concerns predicting machines’ behavior to support maintenance decisions through failure modes diagnosis and prognosis. Diagnosis is broadly applied in the context of rotating machines’ state classification using several traditional Machine Learning (ML) and Deep Learning (DL) methods. Recently, Quantum Computing (QC), a new and expanding research field, has contributed to different purposes and contexts, such as optimization, artificial intelligence, simulation, cybersecurity, pharmaceutics, and the energy sector. Despite the current limitations in terms of hardware, QC has been studied as an alternative for improving models’ speed and computational efficiency. Specifically, this paper proposes a Quantum Machine Learning (QML) approach to diagnose rolling bearings, which are essential components in rotating machinery, based on vibration signals. We apply hybrid models involving the encoding and construction of parameterized quantum circuits (PQC) connected to a classical neural network, the Multi-Layer Perceptron (MLP). We consider combinations of the Variational Quantum Eigensolver (VQE) framework with rotation gates and different entanglement (two-qubits) gates (CNOT, CZ and iSWAP). For each PQC configuration, we assess the impact of the number of layers (1, 5 and 10). We use two databases of different complexity levels not previously explored with QML, namely CWRU and JNU, with 10 and 12 failure modes, respectivel. For CWRU, all QML models presented higher accuracy than the classical MLP. For JNU, all QML models were superior to classical MLP as well. These results suggest that, despite the current limitations of quantum environments, QML models are promising tools to be further investigated in PHM.

Construction of DNA-based molecular circuits using normally open and normally closed switches driven by lambda exonuclease.

Building synthetic molecular circuits is an important way to realize ion detection, information processing, and molecular computing. However, it is still challenging to implement the NOT logic controlled by a single molecule input in synthetic molecular circuits wherein the presence or absence of the molecule represents the ON or OFF state of the input. Here, based on lambda exonuclease (λ exo), for the first time, we propose the normally open (NO) and normally closed (NC) switching strategy with a unified signal transmission mechanism to build molecular circuits. Specifically, the opposite logic can be output with or without a single signal, and the state of the switch can be adjusted by the addition order and time interval of the upstream signal and switch signal, which endows the switch with time-responsive characteristics. In addition, a time-delay relay with the function of delayed disconnection is developed to realize quantitative control of outputs, which has the potential to meet the automation control need of the system. Finally, digital square and square root circuits are constructed by cascading the NO and NC switches, which demonstrates the versatility of switches. Our design can be extended to time logic and complex digital computing circuits for use in information processing and nanomachines.

Energy Efficient Hyperparameter Tuned Deep Neural Network to Improve Accuracy of Near-Threshold Processor

When it comes to decreasing margins and increasing energy efficiency in near-threshold and sub-threshold processors, timing error resilience may be viewed as a potentially lucrative alternative to examine. On the other hand, the currently employed approaches have certain restrictions, including high levels of design complexity, severe time constraints on error consolidation and propagation, and uncontaminated architectural registers (ARs). The design of near-threshold circuits, often known as NT circuits, is becoming the approach of choice for the construction of energy-efficient digital circuits. As a result of the exponentially decreased driving current, there was a reduction in performance, which was one of the downsides. Numerous studies have advised the use of NT techniques to chip multiprocessors as a means to preserve outstanding energy efficiency while minimising performance loss. Over the past several years, there has been a clear growth in interest in the development of artificial intelligence hardware with low energy consumption (AI). This has resulted in both large corporations and start-ups producing items that compete on the basis of varying degrees of performance and energy use. This technology’s ultimate goal was to provide levels of efficiency and performance that could not be achieved with graphics processing units or general-purpose CPUs. To achieve this objective, the technology was created to integrate several processing units into a single chip. To accomplish this purpose, the hardware was designed with a number of unique properties. In this study, an Energy Efficient Hyperparameter Tuned Deep Neural Network (EEHPT-DNN) model for Variation-Tolerant Near-Threshold Processor was developed. In order to improve the energy efficiency of artificial intelligence (AI), the EEHPT-DNN model employs several AI techniques. The notion focuses mostly on the repercussions of embedded technologies positioned at the network’s edge. The presented model employs a deep stacked sparse autoencoder (DSSAE) model with the objective of creating a variation-tolerant NT processor. The time-consuming method of modifying hyperparameters through trial and error is substituted with the marine predators optimization algorithm (MPO). This method is utilised to modify the hyperparameters associated with the DSSAE model. To validate that the proposed EEHPT-DNN model has a higher degree of functionality, a full simulation study is conducted, and the results are analysed from a variety of perspectives. This was completed so that the enhanced performance could be evaluated and analysed. According to the results of the study that compared numerous DL models, the EEHPT-DNN model performed significantly better than the other models.

Implementation of low power N-bit hybrid parallel prefix adder using Xilinx-ISE

Recently, digital circuitry has demanded a decrease in space and power by decreasing time while simultaneously improving performance in speed. This has resulted in a need for more efficient use of the available space. Adders are fundamental components that are used in the construction of digital circuits. As a consequence of this, the performance of adders has to be improved in order to enhance the performance of integrated circuits that are used in the real world. The creation of a novel parallel prefix adder (PPA) architecture known as Hybrid PPA is the primary topic of this article. Hybrid PPA makes use of full carrier generation (FCG), full sum generation (FSG), half carry generation (HCG), and half sum generation (HSG) blocks. In addition to this, the N-bit Hybrid-PPA is constructed with features that may be reconfigured, and these features utilise square root additions through modified sum carry selection (MSCS). In addition, the implementation of multiplexer switching logic, which selects the whole sum bits and carry bits in a high-speed manner, reduces the amount of propagation time necessary for the generation of the sum and carry output. The results of the simulation show that using the proposed Hybrid PPA results in a reduction in area, latency, and power consumption when compared to using basic adders or approaches that are considered to be state of the art.

Guest Editorial: Selected papers from the 24th International Conference on Information Security and Cryptology (ICISC 2021)

Guest Editorial: Selected papers from the 24th International Conference on Information Security and Cryptology (ICISC 2021)

DESIGN AND ANALYSIS OF 1:8 RADIO FREQUENCY FEEDING NETWORK USING STEP-DOWN IMPEDANCE TRANSFORMER FOR C-BAND AIRBORNE SYNTHETIC APERTURE RADAR APPLICATIONS

In this research, we developed a unique power divider circuit construction that suppresses harmonic components while providing unequal power division ratios. In antenna arrays, a power divider is used to divide the power among the array's elements. Depending on the design, different power dividers have distinct properties. The purpose of this project was to create a design and examine a 1:8 unequal power divider for C-band applications that operates at 5.3 GHz. The prototype was built on a single board using a duroid RT5880 substrate with relative permittivity of 2.2 and thickness of 1.6 mm. A quarter-wave transformer was utilized to evenly distribute the entire incident power. This design involves adding an extra transmission line to the input side, which is known as a step-down transformer. The input impedance values were reduced and outstanding performance was achieved by using the step-down transformer. Advanced design system software was used in the simulations. The isolation network, which consisted of resisters between the transmission lines, was critical in implementing the radial layout styles and also improved the output port matching and port-to-port isolation. In the operating frequency range, the return loss parameters (S<sub>11</sub>–S<sub>99</sub>) and the isolation were greater than −17 and −37 dB, respectively. The intended structure is compact. The simulated results were in good agreement and demonstrated that all of the ports had matching impedance and the isolation matched the application's requirements.

The Development of Photolithographic Technology and Machines

Photolithography is the most complicated, accurate, expensive process in the manufacture of integrated circuits. The lithography machine is one of the most critical equipment in photolithographic process, which is used to duplicate the circuit construction onto the wafer. DUVL is the dominant photolithography technology at present for technology node among 714nm, while EUVL has been applied in the manufacture of semiconductor devices for the technology node beyond 7nm. The main components of EUVL are light source, objective lens system and countertop. This paper will introduce the function, main components, exposure method, light source and the future development of lithographic technology.

Toehold-Mediated Strand Displacement in Random Sequence Pools.

Toehold-mediated strand displacement (TMSD) has been used extensively for molecular sensing and computing in DNA-based molecular circuits. As these circuits grow in complexity, sequence similarity between components can lead to cross-talk, causing leak, altered kinetics, or even circuit failure. For small non-biological circuits, such unwanted interactions can be designed against. In environments containing a huge number of sequences, taking all possible interactions into account becomes infeasible. Therefore, a general understanding of the impact of sequence backgrounds on TMSD reactions is of great interest. Here, we investigate the impact of random DNA sequences on TMSD circuits. We begin by studying individual interfering strands and use the obtained data to build machine learning models that estimate kinetics. We then investigate the influence of pools of random strands and find that the kinetics are determined by only a small subpopulation of strongly interacting strands. Consequently, their behavior can be mimicked by a small collection of such strands. The equilibration of the circuit with the background sequences strongly influences this behavior, leading to up to 1 order of magnitude difference in reaction speed. Finally, we compare two established and one novel technique that speed up TMSD reactions in random sequence pools: a three-letter alphabet, protection of toeholds by intramolecular secondary structure, or by an additional blocking strand. While all of these techniques were useful, only the latter can be used without sequence constraints. We expect that our insights will be useful for the construction of TMSD circuits that are robust to molecular noise.

Self-Healing Electronics for Prognostic Monitoring of Methylated Circulating Tumor DNAs.

Methylated circulating DNAs (ctDNAs) have recently been reported as a promising biomarker for early cancer diagnostics, but limited tools are currently available for continuous and dynamic profiling of ctDNAs and their methylation levels, especially when such assays need to be conducted in point-of-care (POC) scenarios. Here, a self-healing bioelectronic patch (iMethy) is developed that combines transdermal interstitial fluid (ISF) extraction and field effect transistor-based (FET-based) biosensing for dynamic monitoring of methylated ctDNAs as a prognostic approach for cancer risk management. The projection micro-stereolithography-based 3D patterning of an Eutectic Gallium-Indium (EGaIn) circuit with an unprecedented 10µm resolution enables the construction of self-healing EGaIn microfluidic circuits that remain conductive under 100% strain and self-healing under severe destruction. In combination with continuous transdermal ISF sampling of methylated ctDNAs, iMethy can detect ctDNAs as low as 10-16 m in cellular models and is capable of phenotypic analysis of tumor growth in rodent animals. As the first demonstration of a wearable device for real-time in vivo analysis of disease-indicative biomarkers, this proof-of-concept study well demonstrated the potential of the iMethy platform for cancer risk management based on dynamic transdermal surveillance of methylated ctDNAs via a painless and self-administrable procedure.

Design of measuring circuit of motor speed based on Proteus

In this paper, 51 single chip microcomputer, photosensitive element and 1602 are used to design the motor speed measurement circuit based on grating method, and Proteus software is used to build the circuit and function verification. Finally the project achieves the motor speed measurement and adaptive content display under different precision, the project simulation verification results are well. At the same time, on this basis the construction of motor speed measurement circuit is discussed and promoted.

Rapid and Accurate Assembly of Large DNA Assisted by In Vitro Packaging of Bacteriophage.

Development of DNA assembly methods made it possible to construct large DNA. However, achieving a large DNA assembly easily, accurately, and at a low cost remains a challenge. This study shows that DNA assembled only by annealing of overlapping single-stranded DNA ends, which are generated by exonuclease treatment, without ligation can be packaged in phage particles and can also be transduced into bacterial cells. Based on this, I developed a simple method to construct long DNA of about 40-50 kb from five to ten PCR fragments using the bacteriophage in vitro packaging system. This method, namely, iPac (in vitro Packaging-assisted DNA assembly), allowed accurate and rapid construction of large plasmids and phage genomes. This simple method will accelerate research in molecular and synthetic biology, including the construction of gene circuits or the engineering of metabolic pathways.

Cbln1 regulates axon growth and guidance in multiple neural regions.

The accurate construction of neural circuits requires the precise control of axon growth and guidance, which is regulated by multiple growth and guidance cues during early nervous system development. It is generally thought that the growth and guidance cues that control the major steps of axon development have been defined. Here, we describe cerebellin-1 (Cbln1) as a novel cue that controls diverse aspects of axon growth and guidance throughout the central nervous system (CNS) by experiments using mouse and chick embryos. Cbln1 has previously been shown to function in late neural development to influence synapse organization. Here, we find that Cbln1 has an essential role in early neural development. Cbln1 is expressed on the axons and growth cones of developing commissural neurons and functions in an autocrine manner to promote axon growth. Cbln1 is also expressed in intermediate target tissues and functions as an attractive guidance cue. We find that these functions of Cbln1 are mediated by neurexin-2 (Nrxn2), which functions as the Cbln1 receptor for axon growth and guidance. In addition to the developing spinal cord, we further show that Cbln1 functions in diverse parts of the CNS with major roles in cerebellar parallel fiber growth and retinal ganglion cell axon guidance. Despite the prevailing role of Cbln1 as a synaptic organizer, our study discovers a new and unexpected function for Cbln1 as a general axon growth and guidance cue throughout the nervous system.

Toward the real-time evolution of gauge-invariant Z2 and U(1) quantum link models on noisy intermediate-scale quantum hardware with error mitigation

Practical quantum computing holds clear promise in addressing problems not generally tractable with classical simulation techniques, and some key physically interesting applications are those of real-time dynamics in strongly coupled lattice gauge theories. In this article, we benchmark the real-time dynamics of $\mathbb{Z}_2$ and $U(1)$ gauge invariant plaquette models using noisy intermediate scale quantum (NISQ) hardware, specifically the superconducting-qubit-based quantum IBM Q computers. We design quantum circuits for models of increasing complexity and measure physical observables such as the return probability to the initial state, and locally conserved charges. NISQ hardware suffers from significant decoherence and corresponding difficulty to interpret the results. We demonstrate the use of hardware-agnostic error mitigation techniques, such as circuit folding methods implemented via the Mitiq package, and show what they can achieve within the quantum volume restrictions for the hardware. Our study provides insight into the choice of Hamiltonians, construction of circuits, and the utility of error mitigation methods to devise large-scale quantum computation strategies for lattice gauge theories.