Artificial Signals Research Articles

Detection of Artificial ULF Signals at Staraya Pustyn Magnetic Station during the FENICS-2019 Experiment

Detection of Artificial ULF Signals at Staraya Pustyn Magnetic Station during the FENICS-2019 Experiment

Application of the Bispectral Wavelet Analysis for Searching Three-Wave Interactions in the Spectrum of Internal Waves

Purpose. The aim of the work is to test the bispectral wavelet analysis being applied as a tool for studying resonance interactions between the frequency components in the spectrum of internal waves (based on the example both of the model signals, the shape of which is similar to that of the solitons and boras, and the field observations data on temperature fluctuations resulted from the internal waves in the Gorlo Strait of the White Sea). Methods and Results. The paper represents a technique for detecting three-wave interactions in the internal waves’ spectrum. The method is based on the bispectral wavelet analysis. It permits to identify the interharmonic correlation and the magnitude of the quadratic phase relationship arising as a result of nonlinear interactions between the signal frequency components. In the first part of the paper, efficiency of the applied method was evaluated using the example of various artificial signals with quadratic nonlinearity in order to demonstrate the method features and advantages. In its second part, the method was used to analyze the temperature profiles obtained by scanning thermohaline sounding, in which the oscillations related to passing of the internal wave groups were recorded. It is shown that the waves with the 40 min period are generated due to quadratic nonlinearity. The auto-bicoherence function values confirm the fact that the higher harmonics are formed in the 60–120 min range as a result of the three-wave interactions. They change synchronously in time, and their amplitudes are proportional, that is typical of the initial stage of the waves’ nonlinear transformation. Absence of a periodic change in the biphase sign in the considered range indicates insignificant influence of the dispersion effects upon the short-period internal waves’ structure. Conclusions. The example of observations in the Gorlo Strait of the White Sea shows that the recorded asymmetric structure of the isotherm oscillations was formed being influenced by the three-wave interaction. Possibility of further application of the method for studying the processes of the internal waves’ nonlinear transformation and breaking is discussed.

Bioengineering of Genetically Encoded Gene Promoter Repressed by the Flavonoid Apigenin for Constructing Intracellular Sensor for Molecular Events.

In recent years, Synthetic Biology has emerged as a new discipline where functions that were traditionally performed by electronic devices are replaced by “cellular devices”; genetically encoded circuits constructed of DNA that are built from biological parts (aka bio-parts). The cellular devices can be used for sensing and responding to natural and artificial signals. However, a major challenge in the field is that the crosstalk between many cellular signaling pathways use the same signaling endogenous molecules that can result in undesired activation. To overcome this problem, we utilized a specific promoter that can activate genes with a natural, non-toxic ligand at a highly-induced transcription level with low background or undesirable off-target expression. Here we used the orphan aryl hydrocarbon receptor (AHR), a ligand-activated transcription factor that upon activation binds to specific AHR response elements (AHRE) of the Cytochrome P450, family 1, subfamily A, polypeptide 1 (CYP1A1) promoter. Flavonoids have been identified as AHR ligands. Data presented here show the successful creation of a synthetic gene “off” switch that can be monitored directly using an optical reporter gene. This is the first step towards bioengineering of a synthetic, nanoscale bio-part for constructing a sensor for molecular events.

Refined Composite Multi-Scale Reverse Weighted Permutation Entropy and Its Applications in Ship-Radiated Noise.

Ship-radiated noise is one of the important signal types under the complex ocean background, which can well reflect physical properties of ships. As one of the valid measures to characterize the complexity of ship-radiated noise, permutation entropy (PE) has the advantages of high efficiency and simple calculation. However, PE has the problems of missing amplitude information and single scale. To address the two drawbacks, refined composite multi-scale reverse weighted PE (RCMRWPE), as a novel measurement technology of describing the signal complexity, is put forward based on refined composite multi-scale processing (RCMP) and reverse weighted PE (RWPE). RCMP is an improved method of coarse-graining, which not only solves the problem of single scale, but also improves the stability of traditional coarse-graining; RWPE has been proposed more recently, and has better inter-class separability and robustness performance to noise than PE, weighted PE (WPE), and reverse PE (RPE). Additionally, a feature extraction scheme of ship-radiated noise is proposed based on RCMRWPE, furthermore, RCMRWPE is combined with discriminant analysis classifier (DAC) to form a new classification method. After that, a large number of comparative experiments of feature extraction schemes and classification methods with two artificial random signals and six ship-radiated noise are carried out, which show that the proposed feature extraction scheme has better performance in distinguishing ability and stability than the other three similar feature extraction schemes based on multi-scale PE (MPE), multi-scale WPE (MWPE), and multi-scale RPE (MRPE), and the proposed classification method also has the highest recognition rate.

Frequency response of EEG recordings from BIS and Entropy devices.

The BIS and Entropy systems are used as indicators of anaesthetic drug effect, and can also record EEGs in digital form. A number of studies have used such recordings for analysis, even though information about bandwidth and fidelity has not been provided by the manufacturers. In this study we consider these systemspurely as EEG recording devices, and evaluate their suitability for quantitative analysis. Using a calibrated electronic testing system, we played an artificial test signal of known amplitude with frequency varying from 0.01 to 400Hz into the BIS and Entropy EEG systems, and saved the recordingin each of their supported recording modes. The amplitude of the recording at each frequency was then used to derive the frequency response curves, as well as the 3dB and 0.5dB points for each mode. There are important differences between the various BIS and Entropy recordings. The BIS 256Hz and the Entropy 400Hz recordings are broadly comparable, although Entropy has a slightly reduced response above 50Hz and below 1Hz. The BIS 128Hz recording has a somewhat reduced bandwidth, due to its relatively low sampling rate. The Entropy 100Hz recording in the Datex-Ohmeda S/5 monitor has a flawed implementation, leading to aliasing of signals over 50Hzand potential distortion of the recording, while in the GE Carescape it has an uneven response and a narrowed bandwidth. Consequently, it is important to know which specific host monitor was used when an Entropy 100Hz recording was made. In summary, the choice of recording device and host monitormay affect the results of some quantitative EEG analysis, and some previously published studies may need to be re-evaluated.

Waveform Model to Characterize Time-Domain Pulses Resulting in EMI on Static Energy Meters

This article presents a time-domain waveform model developed to characterize pulsed, nonlinear, current waveforms resulting in electromagnetic interference on static energy meters. The waveform model is calculated by fitting the sampled waveform data into a linear piece-wise function through a process that involves applying algorithms of pulse extraction, change-point detection, and redundancy elimination. The model is applied to data from laboratory experiments that have indicated critical current waveforms resulting in electromagnetic interference problems with static meters. Afterwards, the parameters of the modeled waveforms are calculated in order to correlate them to metering errors. The most relevant parameters that are correlated to significant errors are the maximum slope, crest factor, pulse duration, and charge. The waveform model provides an accurate description of the complex nonlinear waveforms through simplified analytical expressions that reproduce the significant features of the interfering waveforms. This waveform modeling approach could be used to standardize the artificial test signals that are representative of realistic devices and scenarios.

Spiking Neural Network for Augmenting Electroencephalographic Data for Brain Computer Interfaces.

With the advent of advanced machine learning methods, the performance of brain–computer interfaces (BCIs) has improved unprecedentedly. However, electroencephalography (EEG), a commonly used brain imaging method for BCI, is characterized by a tedious experimental setup, frequent data loss due to artifacts, and is time consuming for bulk trial recordings to take advantage of the capabilities of deep learning classifiers. Some studies have tried to address this issue by generating artificial EEG signals. However, a few of these methods are limited in retaining the prominent features or biomarker of the signal. And, other deep learning-based generative methods require a huge number of samples for training, and a majority of these models can handle data augmentation of one category or class of data at any training session. Therefore, there exists a necessity for a generative model that can generate synthetic EEG samples with as few available trials as possible and generate multi-class while retaining the biomarker of the signal. Since EEG signal represents an accumulation of action potentials from neuronal populations beneath the scalp surface and as spiking neural network (SNN), a biologically closer artificial neural network, communicates via spiking behavior, we propose an SNN-based approach using surrogate-gradient descent learning to reconstruct and generate multi-class artificial EEG signals from just a few original samples. The network was employed for augmenting motor imagery (MI) and steady-state visually evoked potential (SSVEP) data. These artificial data are further validated through classification and correlation metrics to assess its resemblance with original data and in-turn enhanced the MI classification performance.

Primeneniye bispektral'nogo veyvlet-analiza dlya poiska trekhvolnovykh vzaimodeystviy v spektre vnutrennikh voln

Purpose. The aim of the work is to test the bispectral wavelet analysis being applied as a tool for studying resonance interactions between the frequency components in the spectrum of internal waves (based on the example both of the model signals, the shape of which is similar to that of the solitons and boras, and the field observations data on temperature fluctuations resulted from the internal waves in the Gorlo Strait of the White Sea). Methods and Results. The paper represents a technique for detecting three-wave interactions in the internal waves’ spectrum. The method is based on the bispectral wavelet analysis. It permits to identify the interharmonic correlation and the magnitude of the quadratic phase relationship arising as a result of nonlinear interactions between the signal frequency components. In the first part of the paper, efficiency of the applied method was evaluated using the example of various artificial signals with quadratic nonlinearity in order to demonstrate the method features and advantages. In the second part, the method was used to analyze the temperature profiles obtained by scanning thermohaline sounding, in which the oscillations related to passing of the internal wave groups were recorded. It is shown that the waves with the 40 min period are generated due to quadratic nonlinearity. The auto-bicoherence function values confirm the fact that the higher harmonics are formed in the 60–120 min range as a result of the three-wave interactions. They change synchronously in time, and their amplitudes are proportional, that is typical of the initial stage of the waves’ nonlinear transformation. Absence of a periodic change in the biphase sign in the considered range indicates insignificant influence of the dispersion effects upon the short-period internal waves’ structure. Conclusions. The example of observations in the Gorlo Strait of the White Sea shows that the recorded asymmetric structure of the isotherm oscillations was formed being influenced by the three-wave interaction. Possibility of further application of the method for studying the processes of the internal waves’ nonlinear transformation and breaking is discussed.

DNA walker-assisted aptasensor for highly sensitive determination of Ochratoxin A

Ochratoxin A (OTA), a toxic secondary metabolite produced via various fungus, poses a serious threat to the health of human beings and animals. In this paper, an aptasensor for OTA detection based on gold nanoparticles decorated molybdenum oxide (AuNPs-MoOx) nanocomposites, hybridization chain reaction (HCR) and a restriction endonuclease (Nb.BbvCI)-aided walker DNA machine was successfully constructed. In this electrochemical platform, the HCR was also used to embed more electrical signal molecules of methylene blue (MB) on silver nanoparticles (AgNPs) to achieve signal amplification. Under the optimum conditions, after adding OTA and Nb.BbvCI in turn and responding adequately under appropriate conditions, aptamer-DNA (6-DNA) carries the OTA away from the electrode surface, and walker DNA was hybridized autonomously with 5-DNA, releasing a large amount of 5′-DNA with the help of Nb.BBVCI. Finally, the electrochemical signal obtained by differential pulse voltammetry (DPV) was weakened. As an artificial and popular signal amplification technique, the DNA walking machine greatly improved the sensitivity. The proposed biosensor exhibited excellent analytical performance in the range of 0.01–10000 pg mL−1 with a detection limit as low as 3.3 fg mL−1. Furthermore, direct comparison with ultraperformance liquid chromatography (UPLC) indicates excellent agreement to actual samples such as apple juice, orange juice, red wine and serum.

Refined mapping of stripe rust resistance gene YrP10090 within a desirable haplotype for wheat improvement on chromosome 6A.

A large genomic region spanning over 300 Mb on chromosome 6A under intense artificial selection harbors multiple loci associated with favorable traits including stripe rust resistance in wheat. The development of resistance cultivars can be an optimal strategy for controlling wheat stripe rust disease. Although loci for stripe rust resistance have been identified on chromosome 6A in previous studies, it is unclear whether these loci span a common genetic interval, and few studies have attempted to analyze the haplotype changes that have accompanied wheat improvement over the period of modern breeding. In this study, we used F2:3 families and F6:7 recombinant inbred lines (RILs) derived from a cross between a resistant CIMMYT wheat accession P10090 and the susceptible landrace Mingxian 169 to improve the resolution of the QTL on chromosome 6A. The co-located QTL, designated as YrP10090, was flanked by SNP markers AX-94460938 and AX-110585473 with a genetic interval of 3.5cM, however, corresponding to a large physical distance of over 300Mb in RefSeq v.1.0 (positions 107.1-446.5Mb). More than 1,300 SNP markers in this genetic region were extracted for haplotype analysis in a panel of 1,461 worldwide common wheat accessions, and three major haplotypes (Hap1, Hap2, and Hap3) were identified. The favorable haplotype Hap1 associated with stripe rust resistance exhibited a large degree of linkage disequilibrium. Selective sweep analyses were performed between different haplotype groups, revealing specific genomic regions with strong artificial selection signals. These regions harbored multiple desirable traits associated with resilience to environmental stress, different yield components, and quality characteristics. P10090 and its derivatives that carry the desirable haplotype can provide a concrete foundation for bread wheat improvement including the genomic selection.

New inverse wavelet transform method with broad application in dynamics

Extracting multi-scale models from system identification of stationary or nonstationary measured signals (e.g., time series) is of great importance in engineering and the applied sciences. We propose a new computational method for harmonic analysis and decomposition of signals based on the inverse wavelet transform and demonstrate its efficacy in diverse areas in dynamics. The wavelet transform is a linear transformation of a signal measured in the temporal/spatial domain to the time–frequency/space-wavenumber domain and applies to stationary and nonstationary measurements. The new method is based on a numerical inverse wavelet transform and yields decomposition of the measured signal in terms of its dominant harmonic components. First, we formulate the analytical continuous inverse wavelet transform in a way that is suitable for computational implementation. Then, taking as example a general measured signal in the time domain, (i) we numerically compute its numerical wavelet transform spectrum, (ii) define a set of “harmonic regions” in the wavelet spectrum containing the dominant harmonics to be inverted and studied, and (iii) by numerically inverse wavelet transforming each of the harmonic regions separately, obtain the respective decomposed harmonics in the time domain. Note that, by construction, the superposition of all decomposed harmonics reconstructs the original signal. Next, we demonstrate the efficacy of the method with some examples. We start with an artificial signal with prescribed harmonic components to highlight the method and its accuracy. Then, we show applicability of the method to system identification, by applying it to the modal analysis of a system of linearly coupled oscillators with closely spaced modes. Lastly, we show how the new method enables quantification of the energy captured by each of the decomposed components (harmonics) in the response of a strongly nonlinear system. To this end, a single degree of freedom geometrically nonlinear oscillator is considered, and the method is used to quantify nonlinear energy “scattering” in its frequency domain. These examples hint at the broad applicability of the new method to diverse areas of signal processing and dynamics, including discrete and continuous dynamical systems with strongly (and even non-smooth) nonlinearities.

Preface

The 5th International Conference on Electronic Design (ICED) 2020 was successfully held on the 19th of August 2020. Due to the outbreak of Covid-19, this year’s conference, which was planned to be held in Penang, Malaysia, was organized as a fully virtual conference using the Google Meet platform.This conference is a continuation of the previous conferences successfully held in 2008, 2014, 2016, and 2018. The aim is to provide a platform to professionals, researchers, academicians, and industries to share and generate interest in cutting-edge applications, challenges, and issues in electronics and computer applications. The theme of the conference this year has been chosen given the current revolution and rapid developments in the field of electronics and communication design. The conference technical programme is divided into several tracks including Artificial intelligence, signal & image processing, IoT, Embedded systems, and System on Chip.This year, a total of 55 papers were accepted out of 81 papers that were received, with the presence of 3 keynote speakers and 3 invited speakers. Each keynote speaker was given an hour for the presentation and question and answer (Q&A) session. For the invited speakers, they were given 30 minutes which include Q&A session to present their work. As for the other authors/participants, they were each given 15 minutes to present their work and an additional 5 minutes for Q&A session.List of Committees and Peer Review Declaration Contact Details are available in this pdf.

INFLAMMATORY BOWEL DISEASE CLASSIFICATION USING THE GUT MICROBIOME: A BENCHMARK OF MICROBIAL DATA ANALYSIS METHODS

Abstract The prevalence of inflammatory bowel disease (IBD) is increasing throughout the developed world. For the newly diagnosed, the time between the appearance of symptoms and diagnosis can take months, involving invasive procedures. There is an urgent need to develop a simple, low cost, accurate and non-invasive diagnostic test. With decreasing costs of next-generation sequencing, many studies have compared IBD gut microbiomes to healthy controls, successfully identifying bacterial biomarkers for IBD. Unfortunately, a majority of these studies utilize machine learning and statistical methods on either single or low-sample size datasets. This results in the creation of disease classification models that have a high level of overfitting and therefore minimal clinical application to new patient cohorts. There are several data preprocessing methods available for data normalization and reduction of cohort specific signals (batch reduction) which can address this lack of cross-dataset performance. With an abundance of potential methods, there is a need to benchmark the performance and generalizability of various machine learning pipelines (combination of data preprocessing and model) for microbiome-based IBD diagnostic tools. We used a collection of 12 IBD-associated North American microbiome datasets (~4000 samples) to benchmark several machine learning pipelines. Raw sequencing data was processed, collapsed at the OTU or Genus level and merged using QIIME2. Datasets were then normalized using either sum-scaling or log based methods and batch reduction was performed using either zero-centering or Empirical Bayes’ approaches. Performance of pipelines was evaluated using binary accuracy, AUC, F1 metric and MCC score. Generalizability of pipelines was evaluated using leave one out cross validation, where data from one study was left out of the training set and tested upon. The best performing and most generalizable pipeline included a Random Forest model paired with centered log ratio based normalization and batch reduction via an Empirical Bayes’ based approach. This combination, along with others, showed equivalent or higher performance to that of more complex models involving deep neural networks (DNNs). In addition to benchmarking our pipelines, we also explore their limitations, such as the tendency of zero-centered batch reduction to rely on balanced data as input or the tendency of Empirical Bayes’ based methods to introduce artificial signals into data, evidencing certain methods as poor tools for clinical use. To our knowledge, this is the first comprehensive benchmark of data preprocessing and machine learning methods for microbiome-based disease classification of IBD. These findings will help improve the generalizability of machine learning models as we move towards non-invasive diagnostic and disease management tools for patients with IBD.

Vocal signals only impact speakers' own emotions when they are self-attributed.

Emotions are often accompanied by vocalizations whose acoustic features provide information about the physiological state of the speaker. Here, we ask if perceiving these affective signals in one's own voice has an impact on one's own emotional state, and if it is necessary to identify these signals as self-originated for the emotional effect to occur. Participants had to deliberate out loud about how they would feel in various familiar emotional scenarios, while we covertly manipulated their voices in order to make them sound happy or sad. Perceiving the artificial affective signals in their own voice altered participants' judgements about how they would feel in these situations. Crucially, this effect disappeared when participants detected the vocal manipulation, either explicitly or implicitly. The original valence of the scenarios also modulated the vocal feedback effect. These results highlight the role of the exteroception of self-attributed affective signals in the emergence of emotional feelings.

Artificial transmembrane signal transduction mediated by dynamic covalent chemistry.

Reversible formation of covalent adducts between a thiol and a membrane-anchored Michael acceptor has been used to control the activation of a caged enzyme encapsulated inside vesicles. A peptide substrate and papain, caged as the mixed disulfide with methane thiol, were encapsulated inside vesicles, which contained Michael acceptors embedded in the lipid bilayer. In the absence of the Michael acceptor, addition of thiols to the external aqueous solution did not activate the enzyme to any significant extent. In the presence of the Michael acceptor, addition of benzyl thiol led to uncaging of the enzyme and hydrolysis of the peptide substrate to generate a fluorescence output signal. A charged thiol used as the input signal did not activate the enzyme. A Michael acceptor with a polar head group that cannot cross the lipid bilayer was just as effective at delivering benzyl thiol to the inner compartment of the vesicles as a non-polar Michael acceptor that can diffuse across the bilayer. The concentration dependence of the output signal suggests that the mechanism of signal transduction is based on increasing the local concentration of thiol present in the vesicles by the formation of Michael adducts. An interesting feature of this system is that enzyme activation is transient, which means that sequential addition of aliquots of thiol can be used to repeatedly generate an output signal.

Redox switching of an artificial transmembrane signal transduction system.

Transmission of chemical signals across lipid bilayer membranes can be achieved using membrane-anchored molecules, where molecular motion across the bilayer is controlled by switching the polarity of two different head groups. An external redox signal delivered by ascorbic acid was used to trigger membrane translocation in a synthetic transduction system.

Employing the Empirical Mode Decomposition to Denoise the Random Telegraph Noise

Random Telegraph Noise (RTN) is a stochastic phenomenon which leads to characteristic variations in electronic devices. Finding features of this signal may result in its modeling and eventually removing the noise in the device. Measuring this signal is accompanied by some noise and therefore we require a method to improve the Signal to Noise Ratio (SNR). As a result, the extraction of an accurate RTN is a remarkable challenge. Empirical Mode Decomposition (EMD) as a fully adaptive and signal dependent method, with no dependency to the specific function, can be an appropriate solution. In this paper, we evaluate the most recent methods and compare them with our proposed approach for the artificial and actual RTN signals. The results show the higher accuracy and efficiency by about 54%, 61% and 39% improvement in SNR, Mean Square Error (MSE) and Percent Root mean square Difference (PRD) respectively for the optimized wited method. Finally, an indicator to evaluate the reliability in digital circuits is introduced.

Controlled dimerization of artificial membrane receptors for transmembrane signal transduction.

In biology, membrane-spanning proteins are responsible for the transmission of chemical signals across membranes, including the signal recognition-mediated conformational change of transmembrane receptors at the cell surface, and a trigger of an intracellular phosphorylation cascade. The ability to reproduce such biological processes in artificial systems has potential applications in smart sensing, drug delivery, and synthetic biology. Here, an artificial transmembrane receptors signaling system was designed and constructed based on modular DNA scaffolds. The artificial transmembrane receptors in this system are composed of three functional modules: signal recognition, lipophilic transmembrane linker, and signal output modules. Adenosine triphosphate (ATP) served as an external signal input to trigger the dimerization of two artificial receptors on membranes through a proximity effect. This effect induced the formation of a G-quadruplex, which served as a peroxidase-like enzyme to facilitate a signal output measured by either fluorescence or absorbance in the lipid bilayer vesicles. The broader utility of this modular method was further demonstrated using a lysozyme-binding aptamer instead of an ATP-binding aptamer. Therefore, this work provides a modular and generalizable method for the design of artificial transmembrane receptors. The flexibility of this synthetic methodology will allow researchers to incorporate different functional modules while retaining the same molecular framework for signal transduction.

Scattering effect of very low frequency transmitter signals on energetic electrons in Earth’s inner belt and slot region

Whistler mode very low frequency (VLF) waves from man-made ground-based transmitters in a frequency range of 10–30 kHz are mainly used for submarine communication, and they propagate primarily in the Earth-lower ionosphere waveguide and part of their energy can leak into the inner magnetosphere, leading the energetic electrons in inner radiation belt and slot region to precipitate into atmosphere and then affect the energetic electron dynamics in the near-Earth space. The scattering effects of artificial VLF signals from NWC, NAA and DHO38 transmitters on energetic electrons in Earth’s inner belt and slot region are investigated in detail in this work. Based on the quasi-linear theory and the Full Diffusion Code, we calculate the bounce-average pitch angle diffusion coefficients induced by NWC, NAA and DHO38 VLF transmitter signals, for which the resonance harmonics |<i>N</i>| ≤ 10 are considered, respectively. We further implement the one-dimensional Fokker-Planck diffusion simulations by using the available pitch angle diffusion rates to model the dynamic evolutions of energetic electrons caused by the scattering of the VLF transmitter signals in the inner belt and slot region in 200 d. The simulation results indicate that the NWC VLF transmitter signals are dominant in scattering ~100 keV electrons with pitch angles less than 60° at <i>L</i> ≤ 1.8, and the mainly scattered electron energy values increase with <i>L</i>-shell decreasing , from <i>L</i> = 1.8 to <i>L </i>= 1.5, the mainly scattered electron energy increases from 90–120 keV to 550–650 keV. The NAA and DHO38 VLF transmitter signals are important in scattering < 20 keV electrons with pitch angles less than 70° at higher <i>L</i>-shells (2.2 ≤ <i>L</i> ≤ 2.7), from <i>L</i> = 2.2 to <i>L</i> = 2.7, the mainly scattered electron energy decreases from 10–20 keV to several keV. The VLF transmitter signals are found to have a slight influence on the loss of energetic electrons with pitch angles larger than 80°.

Construction of protocell-based artificial signal transduction pathways.

The maintenance of an orderly and controllable multicellular society depends on the communication and signal regulation between various types of biological cells. How to replicate complicated signal transduction pathways in synthetic protocellular communities remains a key challenge in bottom-up synthetic biology. Herein, we review recent advances in the design and construction of interactive protocell communities, or protocell communities and biological communities, and explore the ways of designing and constructing artificial paracrine-like signaling pathways and juxtacrine-like signaling pathways. Key molecules involved in the signaling pathways that can be used to connect two or more spatially separated communities, and diverse signal outputs generated by the communication are summarized. We also propose the limitations, challenges and opportunities in this field.