Biological Signals Research Articles

The influence of mental calculations on brain regions and heart rates

Performing mathematical calculations is a cognitive activity that can affect biological signals. This study aims to examine the changes in electroencephalogram (EEG) and electrocardiogram (ECG) signals of 36 healthy subjects during the performance of arithmetic tasks. To process EEG signals in different frequency bands, the energy and entropy of entropy (EoE) were extracted from the power spectrum and phase spectrum, respectively. Statistical analysis was conducted to determine meaningful features. These features were sent into support vector machine (SVM) and multi-layer perception (MLP) classifiers to assess their capability in separating math and rest classes. Results indicated the highest classification accuracy of 98.4% for classifying good counters in math and rest state using the MLP method. Based on the majority of features selected for each EEG channel, discriminative brain areas were identified. Analyzing EEG signals proved that math calculation may have multiple influences on various parts of the brain. By comparing good counters’ brain activities to those in a resting state, prominent changes were observed in the F4, C4, T4, T5, P3, and O2 areas. However, O1 and O2 channels showed significant changes in the brain of bad counters compared to the resting state. Considering ECG signals also demonstrated that during math calculation the number of heart rates per minute surpasses the rest state. These alterations can occur due to cognitive abilities or emotional processes which were observed to be prominent in subjects who performed more accurate calculations.

Synthetic Materials Used for Cartilage Regeneration in Recent Decade

Cartilage injury is considered the major cause of joint pain, swelling and dysfunction. Due to the cartilage tissue’s limited repair capacity, it’s very urgent and crucial to develop and investigate biomaterials to improve effective cartilage regeneration. Over the past decade, researchers focused of the design and preparation of a variety of synthetic materials. There synthetic scaffolds are expected to provide a favorable cellular microenvironment which is beneficial for cartilage regeneration. This review comprehensively reviews the major synthetic materials that have been recently applied in cartilage regeneration engineering, including a variety of polymers and a number of 3D printed materials. These materials have unique physicochemical structures and properties that can provide biocompatible three-dimensional porous structures, mimic the extracellular matrix environment in vivo, and promotes cell adhesion, proliferation, and differentiation. These properties all guide the orderly regeneration of cartilage tissues. Especially through the composite with bioactive molecules, nanomaterials, etc., these materials can also realize the controlled release of biological signals and precisely regulate cell behavior. In addition, by compositing natural polymers with synthetic polymers gives the hybrid scaffolds good bioactivity, it will also enhance their mechanical properties as well as the scaffold elasticity. These techniques allow researchers to develop the most ideal scaffold that perfectly mimics the natural cartilage environment.

(Invited) Low-Frequency Noise in Flexible Carbon-Nanotube Analog Circuits

Since flexible devices can come into close contact with the human body, they are suitable for wearable sensor applications for long-term and non-invasive monitoring of biological and physiological signals in various fields such as medical/health care and sports, among others. In order to achieve a fully flexible sensor system, various electronic circuits need to be integrated on a flexible film, including the analog frontend (AFE) and radio-frequency (RF) frontend, as well as the sensors. Recently, we have demonstrated low-voltage CNT-based analog/digital mixed-signal circuits with excellent stability and robustness against variability, including the AFE. One remaining issue with CNT-based analog circuits is low-frequency noise, which directly impacts the sensitivity of the sensor system within the frequency range of physiological signals such as ECG and EEG.Here, we study the low-frequency noise of flexible carbon nanotube thin-film transistors and their influence on analog circuits. The low-frequency noise exhibits a typical 1/f spectrum. Inserting an interface layer between the CNT channel layer and gate insulator layer reduces Hooge's parameter of the 1/f noise. The results indicate that maintaining distance from the channel to traps in the gate oxide is important for reducing low-frequency noise.

Shirebi granules ameliorate acute gouty arthritis by inhibiting NETs-induced imbalance between immunity and inflammation

BackgroundAcute gouty arthritis (AGA) is classified as ‘arthritis’ in traditional Chinese medicine (TCM) theory. Shirebi granules (SGs), derived from the classic prescription SiMiaoWan, exerts satisfying therapeutic efficacy in ameliorating AGA clinically. However, the underlying mechanisms of SGs against AGA remain unclarified.MethodsAGA-related biological processes, signal pathways and biomarker genes were mined from the GEO database through bioinformatics. SGs components were systematically recognized using the UPLC-Q-TOF–MS/MS. A correlation network was established based on the biomarker genes and the chemical components, from which the signal pathway used for further study was selected. Finally, we established an AGA model using SD rats injected with monosodium urate (MSU) in the ankle joint for experimental validation. A combination of behavioral tests, H&E, safranin O- fast green, western blotting, and immunofluorescence were employed to reveal the mechanism of action of SGs on AGA.ResultsThe deterioration of AGA was significantly related to the imbalance between immunity and inflammation, neutrophil chemotaxis and inflammatory factor activation. HDAC5, PRKCB, NFκB1, MPO, PRKCA, PIK3CA were identified to be the candidate targets of SGs against AGA, associated with neutrophil extracellular traps (NETs) signal pathway. Animal experiments demonstrated that SGs effectively repaired cartilage damage, blocked TLR4 activation, and inhibited the expression of NETs indicators and inflammatory factors. In addition, SGs prominently alleviated joint redness and swelling, improved joint dysfunction, inhibited inflammatory infiltration of AGA rats.ConclusionOur data reveal that SGs may effectively alleviate the disease severity of AGA by suppressing NETs-promoted imbalance between immunity and inflammation.

Electrically Conductive Peptide Fibers as Direct Electron Transfer Scaffolds for Enzymatic Electrocatalysis

Biology provides many sources of inspiration for synthetic and multi-functional nanomaterials. Naturally evolved proteins possess a range of unique functions and self-assembly behavior. Some of these have electron transport functions as part of metabolic processes. Their flexible design and potential for stimuli responsiveness make proteins ideal building blocks of bioelectronic interfaces. In biosensor and electrocatalysis applications, bioelectronic materials are designed to interconvert biological signals or processes into electronic signals and vice versa. In this work, we designed a peptide fiber structure that exhibits stimuli-responsive self-assembly and the capacity to transport electrical current over micrometer long distances. Hierarchically ordered nanofibers of α-helical coiled-coil peptides were assembled using a pH responsive structure ordering motif comprising of two neighboring lysine residues. Cryo-EM structures of these assemblies reveal a novel organization of α-helical peptides in a cross coiled coil arrangement that exhibits electrical conductivity. Both solid-state and solution-based electrochemical characterization show that these fibers are conductive. Single-fiber conductive AFM determined that the solid-state electrical conductivity is on a similar order of magnitude with conductive cytochrome filaments from bacteria. Solution deposited fiber films approximately doubled the electroactive surface area of the electrode, confirming their conductivity in aqueous environments. Furthermore, these fibers can be used as scaffolds for redox enzyme immobilization for electrochemical biosensing. Progress towards immobilized enzymatic devices will be discussed as well. This work further expands the field of stimuli-responsive and multifunctional nanomaterials by using peptide assemblies as structural supports for future implementation in bioelectronic and bioelectrochemical systems.

Interpretable single-cell factor decomposition using sciRED.

Single-cell RNA sequencing (scRNA-seq) maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable Residual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation, and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data.

RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics

BackgroundEffective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals.ResultsWe present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh’s method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl.ConclusionsThe REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity.

Interpretable single-cell factor decomposition using sciRED.

Single-cell RNA sequencing (scRNA-seq) maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable Residual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation, and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data.

ScMaui: a widely applicable deep learning framework for single-cell multiomics integration in the presence of batch effects and missing data

The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.

Sublethal effects of nitenpyram on the development of silkworm

The utilization of nitenpyram for aphid and whitefly control may induce environmental contamination and negative repercussions on non-target organisms. Formerly, we found that nitenpyram would pollute the peripheral and sub-peripheral areas of the adjacent mulberry orchard. Under acute toxicity conditions, nitenpyram induced oxidative damage in silkworms, affected biological metabolism, synthesis, immunity, and signal transduction. Considering the impact of nitenpyram mist drift on mulberry leaves, we investigated the effects of low concentrations of nitenpyram on silkworms. The results showed that silkworms exposed to 0.17 mg/L, 0.35 mg/L and 0.70 mg/L of nitenpyram (1/40 LC50, 1/20 LC50 and 1/10 LC50) showed obvious poisoning symptoms. The cocoon weight and cocoon shell weight decreased gradually with increases in the concentration, and these decreases prolonged the growth and development time of silkworms and induced the detoxification enzymes carboxylesterase (CarE) and glutathione-S-transferase (GST) to cope with the stress damage caused by nitenpyram. Exposure to low concentrations of nitenpyram downregulates genes involved in the drug metabolism-other enzymes and peroxisome pathway in silkworms. Additionally, through injection of miRNA mimics and inhibitors, we discovered that detoxifying enzyme pathway genes are influenced by bmo-miR-3382-3P, bmo-miR-3213-5P and bmo-miR-133, regulating the immune response of silkworms. This study provides an overall view of the toxicity and detoxification metabolism of nitenpyram in silkworm, and provides a reference for environmental assessment.

A Standard-Cell-Based Neuro-Inspired Integrate-and-Fire Analog-to-Time Converter for Biological and Low-Frequency Signals - Comparison With Analog Version.

Continuous-time asynchronous data converters namely, analog-to-digital converters and analog-to-time converters, can be beneficial for certain types of applications, such as, processing of biological signals with sparse information. A particular case of these converters is the integrate-and-fire converter (IFC) that is inspired by the neural system. If it is possible to develop a standard-cell-based (SCB) IFC circuit to perform well in advanced technology nodes, it will benefit from the simplicity of SCB circuit designs and can be implemented in widely available field-programmable gate arrays (FPGAs). This way, this paper proposes two IFC circuits designed and prototyped in a 130 nm CMOS standard process. The first is a novel SCB open-loop dynamic IFC. The latter, is a closed-loop analog IFC with conventional blocks. This paper presents a through comparison between the two IFC circuits. They have a power dissipation of 59 μW and 53 μW, and an energy per pulse of 18 pJ and 1060 pJ, SCB and analog IFC, respectively. The SCB IFC has one of the lowest energy per pulse consumption reported for IFC circuits. The analog IFC, being fully differential, is to our knowledge the first of its kind. Moreover, they do not require an external clock. They can convert signals with a peak-to-peak amplitude from 1.6 mV to 28 mV and 0.6 mV to 2.4 mV, and a frequency range of 2 Hz to 42 kHz and 10 Hz to 4 kHz, SCB and analog IFC, respectively. Presenting low normalized RMS conversion plus reconstruction errors, below 5.2%. The maximum pulse density (average firing-rate) is 3300 kHz, for the SCB and 50 kHz, for the analog IFC.

Assessing the information-content of messy data to reconstruct population recovery dynamics for the world's rarest primate.

Understanding the dynamics of population recovery in threatened species requires robust longitudinal monitoring datasets. However, evidence-based decision-making is often impeded by variable data collection approaches, necessitating critical evaluation of restricted available baselines. The Hainan gibbon, the world's rarest primate, had possibly declined to only seven or eight individuals in 1978 at Bawangling National Nature Reserve but has experienced subsequent population growth. Past population estimates lack detailed reporting of survey effort, and multiple conflicting estimates are available, hindering assessment of gibbon recovery. We investigated all reported estimates of Bawangling gibbon population size from 1978 to 2022, to evaluate the biological signal of population trends and the extent to which noise associated with varying survey effort, reporting and estimation may mask or misrepresent any underlying signal. This longitudinal dataset demonstrates that the Bawangling population experienced a series of bottlenecks and recoveries, with three successive periods of growth interspersed by population crashes (1978-1989, 1989-2000 and 2000-2022). The rate of gibbon population recovery was progressively slower over time in each successive period of growth, and this potential decline in recovery rate following serial bottlenecks suggests that additional management strategies may be required alongside "nature-based solutions" for this species. However, population viability analysis suggests the 1978 founder population is unlikely to have been as low as seven individuals, raising concerns for interpreting reported historical population counts and understanding the dynamics of the species' recovery. We caution against overinterpreting potential signals within "messy" conservation datasets, and we emphasise the crucial importance of standardised replicable survey methods and transparent reporting of data and effort in all future surveys of Hainan gibbons and other highly threatened species.

An Algorithm for Initial Localization of Feature Waveforms Based on Differential Analysis Parameter Setting and Its Application in Clinical Electrocardiograms

In a biological signal analysis system, signals of the same type may exhibit significant variations in their feature waveforms. Biological signals are typically weak, which increases the complexity of their analysis. Furthermore, clinical biomedical signals are susceptible to various interferences from the human body itself, including muscle movements, respiration, and heartbeat. These interference factors further escalate the complexity and difficulty of signal analysis. Therefore, precise and targeted preprocessing is often required before analyzing these clinical biomedical signals to enhance the accuracy and reliability of subsequent feature extraction and classification. Here, we have established an effective and practical algorithm model that integrates preprocessing with the initial localization of target feature waveforms, achieving the following four objectives: 1. Determining the periodic positions of target feature waveforms. 2. Preserving the original amplitude and shape of target feature waveforms while eliminating negative interference. 3. Reducing or eliminating interference from other feature waveforms in the input signal. 4. Decreasing noise in the input signal, such as baseline drift, powerline interference, and muscle artifacts commonly found in biological signals. We have validated the algorithm on clinical electrocardiogram (ECG) data and the authoritative MIT-BIH open-source ECG database demonstrating its effectiveness and reliability.

An enzyme-free fluorescence sensing platform based on multiplex toehold-mediated strand displacement for point-of-care testing of single nucleotide polymorphisms

Single nucleotide polymorphisms (SNPs) are biomarkers for the prediction and diagnosis of biomedical diseases, as well as important reference markers for intelligent design and breeding of animals and plants. Therefore, the development of high-sensitivity, practicability, and convenient SNPs sensing strategies and detection platforms is of great significance. In this study, we pioneered an ultra-sensitive universal sensing strategy for single base mutation detection and an enzyme-free FRET fluorescence sensing point-of-care testing (POCT) platform for SNP detection. Based on the thermodynamically regulated strand replacement mechanism, the system rationally introduced extra base mismatches and combined multiple toehold-mediated strand displacement (TMSD) reactions to achieve enzyme-free recognition of the single base mutation and double amplification of the detection signal. Finally, the biological signal of the mutant target (MT) was recognized, amplified, and transduced into a fluorescence resonance energy transfer (FRET) signal between Cy3 and Cy5 for output. Overall, the proposed biosensing strategy demonstrated high sensitivity for MT, with a detection limit as low as 0.37 fM, and could achieve detection of targets with mutant abundances as low as 0.1 %. Moreover, a portable detection system was assembled in conjunction with this sensing strategy to form a POCT platform for SNP genotyping. This POCT platform was validated to accurately identify SNPs from human cancer cells and soybean leaf genome extracts. In summary, this study will provide new insights into the design of enzyme-free biosensors for SNP detection, and will provide important technical support tools for the prevention and diagnosis of major diseases, animal and plant breeding, etc.

CRISPR-Based Biosensors for Medical Diagnosis: Readout from Detector-Dependence Detection Toward Naked Eye Detection.

The detection of biomarkers (such as DNA, RNA, and protein) plays a vital role in medical diagnosis. The CRISPR-based biosensors utilize the CRISPR/Cas system for biometric recognition of targets and use biosensor strategy to read out biological signals without the employment of professional operations. Consequently, the CRISPR-based biosensors demonstrate great potential for the detection of biomarkers with high sensitivity and specificity. However, the signal readout still relies on specialized detectors, limiting its application in on-site detection for medical diagnosis. In this review, we summarize the principles and advances of the CRISPR-based biosensors with a focus on medical diagnosis. Then, we review the advantages and progress of CRISPR-based naked eye biosensors, which can realize diagnosis without additional detectors for signal readout. Finally, we discuss the challenges and further prospects for the development of CRISPR-based biosensors.

A Systematic Review on Machine Learning / Deep Learning Model-based Detection of Sleep Apnea Using Bio-Signals

Backgrounds: Sleep Apnea (SA) is a sleep-related breathing disorder diagnosed in clinical laboratories. The gold standard is Polysomnography (PSG), a multi-parameter evaluation of a sleep monitoring system that records the biological signals during overnight sleep. Apart from PSG recording, apnea events are recorded by various other bio-signals called Electrocardiogram (ECG), Electroencephalogram (EEG), Oxygen Saturation level (SpO2), etc. Further evaluation of the recorded bio-signals is tedious and time-consuming as experts perform it manually. Aiming to overcome the disadvantage without compromising accuracy, scientists focus on developing robust measurements of SA by using Machine Learning (ML) and Deep Learning (DL) models. Method: This study aimed to analyze the recent research findings in the field of sleep apnea classification and various machine learning and deep learning methods implemented in detecting SA. This study revealed the best-performing technique considering different types of bio-signals used for analysis and the respective ML or DL models used for automatic detection Result: The studies and patents included in this review underwent a precise screening process using PRISMA guidelines. The literature study is comprised of three different analysis tools to showcase the review process and provide evidence for the research findings obtained in the respective publications. The publications considered were limited to the last decade. Conclusion: This review delivers the key finding that ECG signals-based detection of sleep apnea using deep learning model-based deep neural network classifiers will provide more accurate and robust classification, which will pave the way for possible future research directions.

Cation‐Dependent Mixed Ionic‐Electronic Transport in a Perylenediimide Small‐Molecule Semiconductor

A rapidly growing interest in organic bioelectronic applications has spurred the development of a wide variety of organic mixed ionic‐electronic conductors. While these new mixed conductors have enabled the community to interface organic electronics with biological systems and efficiently transduce biological signals (ions) into electronic signals, the current materials selection does not offer sufficient selectivity towards specific ions of biological relevance without the use of auxiliary components such as ion‐selective membranes. Here, we present the molecular design of an n‐type (electron‐transporting) perylene diimide semiconductor material decorated with pendant oligoether groups to facilitate interactions with cations such as Na+ and K+. Using the cyclic 15‐crown‐5 oligoether motif, we find that the resulting mixed conductor PDI‐crown displays a strong dependence on the size of the electrolyte cation when tested in an organic electrochemical transistor configuration. In stark contrast to the low current response on the order of 1 µA observed with aqueous sodium chloride, a nearly 200‐fold increase in current is observed with aqueous potassium chloride. We ascribe the high selectivity to extended molecular aggregation and therefore efficient charge transport in the presence of K+ due to a favourable sandwich‐like structure between two adjacent 15‐crown‐5 motifs and the potassium ion.

Self-consistent signal transduction analysis for modeling context-specific signaling cascades and perturbations

Biological signal transduction networks are central to information processing and regulation of gene expression across all domains of life. Dysregulation is known to cause a wide array of diseases, including cancers. Here I introduce self-consistent signal transduction analysis, which utilizes genome-scale -omics data (specifically transcriptomics and/or proteomics) in order to predict the flow of information through these networks in an individualized manner. I apply the method to the study of endocrine therapy in breast cancer patients, and show that drugs that inhibit estrogen receptor α elicit a wide array of antitumoral effects, and that their most clinically-impactful ones are through the modulation of proliferative signals that control the genes GREB1, HK1, AKT1, MAPK1, AKT2, and NQO1. This method offers researchers a valuable tool in understanding how and why dysregulation occurs, and how perturbations to the network (such as targeted therapies) effect the network itself, and ultimately patient outcomes.

Interfacing Neuron-Motor Pathways with Stretchable and Biocompatible Electrode Arrays.

ConspectusIn the field of neuroscience, understanding the complex interactions within the intricate neuron-motor system depends crucially on the use of high-density, physiological multiple electrode arrays (MEAs). In the neuron-motor system, the transmission of biological signals primarily occurs through electrical and chemical signaling. Taking neurons for instance, when a neuron receives external stimuli, it generates an electrical signal known as the action potential. This action potential propagates along the neuron's axon and is transmitted to other neurons via synapses. At the synapse, chemical signals (neurotransmitters) are released, allowing the electrical signal to traverse the synaptic gap and influence the next neuron. MEAs can provide unparalleled insights into neural signal patterns when interfacing with the nerve systems through their excellent spatiotemporal resolution. However, the inherent differences in mechanical and chemical properties between these artificial devices and biological tissues can lead to serious complications after chronic implantation, such as body rejection, infection, tissue damage, or device malfunction. A promising strategy to enhance MEAs' biocompatibility involves minimizing their thickness, which aligns their bending stiffness with that of surrounding tissues, thereby minimizing damage over time. However, this solution has its limits; the resulting ultrathin devices, typically based on plastic films, lack the necessary stretchability, restricting their use to organs that neither stretch nor grow.For practical deployments, devices must exhibit certain levels of stretchability (ranging from 20 to 70%), tailored to the specific requirements of the target organs. In this Account, we outline recent advancements in developing stretchable MEAs that balance stretchability with sufficient electrical conductivity for effective use in physiological research, acting as sensors and stimulators. By concentrating on the neuron-motor pathways, we summarize how the stretchable MEAs meet various application needs and examine their effectiveness. We distinguish between on-skin and implantable uses, given their vastly different requirements. Within each application scope, we highlight cutting-edge technologies, evaluating their strengths and shortcomings. Recognizing that most current devices rely on elastic films with a Young's modulus value between ∼0.5 and 5 MPa, we delve into the potential for softer MEAs, particularly those using multifunctional hydrogels for an optimizing tissue-device interface and address the challenges in adapting such hydrogel-based MEAs for chronic implants. Additionally, transitioning soft MEAs from lab to fab involves connecting them to a rigid adapter and external machinery, highlighting a critical challenge at the soft-rigid interface due to strain concentration, especially in chronic studies subject to unforeseen mechanical strains. We discuss innovative solutions to this integration challenge, being optimistic that the development of durable, biocompatible, stretchable MEAs will significantly advance neuroscience and related fields.

Touch and slippage detection in robotic hands with spiking neural networks

Significant research efforts have focused on advancing upper limb prostheses, addressing issues such as dexterity, embodiment, weight, and resistance. These key features require enhancements to mitigate prosthesis abandonment. Sensory feedback is a vital feature for improving the dexterity and embodiment of prostheses for patients. Current techniques utilize electronic signals for feedback to control systems and to partially restore tactile sensations to amputees. However, it has already been demonstrated that, in order to elicit responses from biological neurons signals must be converted into neuromorphic tactile information. Most of those demonstrations relied on algorithmic implementations of mechanoreceptor and neuron models running on classic processor cores. However, integrating real-time encoding and control strategies into such architectures is challenging due to computational complexity and high power usage. A prospective solution is to increasingly incorporate neuromorphic technologies for prosthesis sensing and control. In this work, we demonstrate the feasibility of using spiking neural networks for performing two key functions in controlling upper limb prosthesis: touch and slippage detection. We found that a simple two-layered architecture with a few tens of neurons is sufficient to identify slip and touch with nearly 100% accuracy. The first layer consists of a heterogeneous population of neurons with their parameters adjusted to model slowly and fast-adapting type 1 mechanoreceptors. An output layer classifies touch or slip events. Furthermore, simulations showed that implementing the network in ultra-low-power neuromorphic hardware, such as Loihi, has the potential to reduce power consumption by up to 162 times, with respect to a von Neumann processor.