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Spike camera is a new type of bio-inspired vision sensor, each pixel of which perceives the brightness of the scene independently, and finally outputs 3-dimensional spatiotemporal spike streams. To bridge the spike camera and traditional frame-based vision, there is some works to reconstruct spike streams into regular images. However, the low spatial resolution (400×250) of the spike camera limits the quality of the reconstructed images. Thus, it is meaningful to explore a super-resolution reconstruction for spike streams. In this paper, we propose an end-to-end network to reconstruct high-resolution images from low-resolution spike streams. To utilize more spatiotemporal features of spike streams, our network adopts a multi-level features learning mechanism, including intra-stream feature extraction by spike encoder, inter-stream dependencies extraction based on optical flow module, and joint features learning via spike-based iterative projection. Experimental results demonstrate that our network is superior to the combination of state-of-the-art intensity image reconstruction methods and super-resolution networks on simulated and real datasets.

Ferroelectric field-effect transistor-based circuit implementation mimicking FitzHugh-Nagumo neuron is proposed in this work. The proposed circuit is shown to mimic biological neuron properties such as excitation block and anodal break excitation which are not mimicked by an integrate and fire neuron model. We also show a winner-take-all circuit that can be used with this proposed neuron implementation. The neuron implementation requires just one ferroelectric field-effect transistor, three baseline field-effect transistors, and one capacitor, making it area and energy-efficient. The neuron circuit, with minimum sized transistors, consumes approximately 10pJ per spike. The neuron’s energy consumption per spike can be reduced to as low as 100fJ by designing some of the transistors with aspect ratio less than one.

Low temperature in early spring impairs wheat growth and grain yield. However, little is known about the cytological and molecular mechanisms underlying low temperature regulation of wheat spike development. Microstructure observation and transcriptome sequencing of wheat spikes under low temperature were conducted. Low temperature slowed spike development, reduced the yield-component parameters of wheat spikes at the harvest stage, delayed the formation of lateral spikelets and tissue development, and induced the early differentiation of terminal spikelets. Low temperature increased the content of abscisic acid and caused the upregulation of genes in the abscisic acid signaling pathway, including those encoding PP2Cs, SnRK2s, and bZIP transcription factors. Low temperature also induced the upregulation of 33 cold-responsive genes involved in wheat response to low-temperature stress and regulation of abscisic acid biosynthesis and metabolism of other substances. The wheat spike adapted to cold conditions by changing the expression levels of genes involved in spike morphogenesis, including the transcription-factor genes MADS6, ERF4, ERF78, WOX6, and NAC48. These findings suggest that low temperature in early spring delays wheat spike development by increasing abscisic acid content or affecting the expression of genes involved in morphogenesis.

The biologically discovered intrinsic plasticity (IP) learning rule, which changes the intrinsic excitability of an individual neuron by adaptively turning the firing threshold, has been shown to be crucial for efficient information processing. However, this learning rule needs extra time for updating operations at each step, causing extra energy consumption and reducing the computational efficiency. The event-driven or spike-based coding strategy of spiking neural networks (SNNs), i.e., neurons will only be active if driven by continuous spiking trains, employs all-or-none pulses (spikes) to transmit information, contributing to sparseness in neuron activations. In this article, we propose two event-driven IP learning rules, namely, input-driven and self-driven IP, based on basic IP learning. Input-driven means that IP updating occurs only when the neuron receives spiking inputs from its presynaptic neurons, whereas self-driven means that IP updating only occurs when the neuron generates a spike. A spiking convolutional neural network (SCNN) is developed based on the ANN2SNN conversion method, i.e., converting a well-trained rate-based artificial neural network to an SNN via directly mapping the connection weights. By comparing the computational performance of SCNNs with different IP rules on the recognition of MNIST, FashionMNIST, Cifar10, and SVHN datasets, we demonstrate that the two event-based IP rules can remarkably reduce IP updating operations, contributing to sparse computations and accelerating the recognition process. This work may give insights into the modeling of brain-inspired SNNs for low-power applications.

Spiking Neural Networks (SNNs) use spatio-temporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. Motivated by the success of deep learning, the study of Deep Spiking Neural Networks (DeepSNNs) provides promising directions for artificial intelligence applications. However, training of DeepSNNs is not straightforward because the well-studied error back-propagation (BP) algorithm is not directly applicable. In this paper, we first establish an understanding as to why error back-propagation does not work well in DeepSNNs. To address this problem, we propose a simple yet efficient Rectified Linear Postsynaptic Potential function (ReL-PSP) for spiking neurons and propose a Spike-Timing-Dependent Back-Propagation (STDBP) learning algorithm for DeepSNNs. In STDBP algorithm, the timing of individual spikes is used to convey information (temporal coding), and learning (back-propagation) is performed based on spike timing in an event-driven manner. Our experimental results show that the proposed learning algorithm achieves state-of-the-art classification accuracy in single spike time based learning algorithms of DeepSNNs. Furthermore, by utilizing the trained model parameters obtained from the proposed STDBP learning algorithm, we demonstrate the ultra-low-power inference operations on a recently proposed neuromorphic inference accelerator. Experimental results show that the neuromorphic hardware consumes 0.751~mW of the total power consumption and achieves a low latency of 47.71~ms to classify an image from the MNIST dataset. Overall, this work investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems., This work has been submitted to the IEEE for possible publication. Copyrightmay be transferred without notice, after which this version may no longer beaccessible

Purpose To identify risk factors for surgical failure after gonioscopy-assisted transluminal trabeculotomy (GATT) in juvenile open angle glaucoma (JOAG). Design Prospective, interventional case series. Methods Seventy eyes of 70 patients with JOAG underwent GATT as the initial surgery. Surgical success was defined as a post-operative intraocular pressure (IOP) of ≤21 mmHg with at least a 20% reduction from pre-operative IOP with or without the use of anti-glaucoma medication (qualified and complete success, respectively) at each post-operative visit. IOP spike was defined as IOP >30 mmHg and an increase of at least 10 mmHg from IOP prior to the spike, and then reduced to ≤21 mmHg. Results The median age at the time of surgery was 19.3 (range: 4.9-37.5) years with a visual field mean deviation (MD) -17.4±10.6 dB. Mean IOP decreased from 31.3±9.5 mmHg pre-operatively to 15.8±2.7 at 12 months post-operatively. The complete and qualified success rates were 74.3% and 91.4%, respectively. Fifty-two (74%) eyes had an IOP spike with a median spike duration of 3.5 days (range 1-21). Longer duration of IOP spike (P=0.009) and older age at the time of surgery (P=0.025) were both associated with worse surgical outcomes. Advanced disease was associated with prolonged IOP spike (P=0.007). Conclusions GATT provided excellent outcomes in patients with severe JOAG. Older age and longer duration of post-operative IOP spike are risk factors for failure. Severe cases are more likely to have longer durations of IOP spike. Frequent IOP monitoring during the early post-operative period is needed to detect IOP spikes in these patients.

Key message A major QTL on chromosome arm 4BS was associated with reduced spike shattering and reduced plant height in coupling phase, and a second major QTL associated with reduced spike shattering was detected on chromosome arm 5AL in the same wheat variety Carberry. Abstract Spike shattering can cause severe grain yield loss in wheat. Development of cultivars with reduced shattering but having easy mechanical threshability is the target of wheat breeding programs. This study was conducted to determine quantitative trait loci (QTL) associated with shattering resistance, and epistasis among QTL in the populations Carberry/AC Cadillac and Carberry/Thatcher. Response of the populations to spike shattering was evaluated near Swift Current, SK, in four to five environments. Plant height data recorded in different locations and years were used to determine the relationship of the trait with spike shattering. Each population was genotyped and mapped with the wheat 90 K Illumina iSelect SNP array. Main effect QTL were analyzed by MapQTL 6, and epistatic interactions between main effect QTL were determined by QTLNetwork 2.0. Correlations between height and shattering ranged from 0.15 to 0.49. Carberry contributed two major QTL associated with spike shattering on chromosome arms 4BS and 5AL, detected in both populations. Carberry also contributed two minor QTL on 7AS and 7AL. AC Cadillac contributed five minor QTL on 1AL, 2DL, 3AL, 3DL and 7DS. Nine epistatic QTL interactions were identified, out of which the most consistent and synergistic interaction, that reduced the expression of shattering, occurred between 4BS and 5AL QTL. The 4BS QTL was consistently associated with reduced shattering and reduced plant height in the coupling phase. The present findings shed light on the inheritance of shattering resistance and provide genetic markers for manipulating the trait to develop wheat cultivars.

The tracking control problem for Flexible Joint Manipulator Control System (FJMCS) with unmeasurable states is addressed in this paper. Firstly, a High-Gain Observer (HGO) is constructed to estimate the unmeasurable states and the uncertainties. Then, a Dynamic Surface Control (DSC) scheme is developed by using the estimation of HGO. The newly proposed controller has two advantages over the existing methods: (A) a novel Spike Suppression Function (SSF) is developed to avoid the estimation spike problem in the existing HGO-based controllers. (B) Unlike the existing observer-based partial feedback control scheme that can only estimate the unmeasurable states, the proposed HGO can estimate both the unmeasurable states and uncertainties. The closed-loop system stability is proved by the Lyapunov theory. Simulation results demonstrate the effectiveness of the proposed controller.

This work explores an idea of modifying surface current distribution in a rectangular microstrip patch which contributes to reducing its H-plane cross-polarized (XP) radiations along with minimizing the backside radiation to some extent. A pair of grounded spikes has been strategically used and the physics behind its operation in weakening the XP generating TM02 mode has been thoroughly discussed. The vertical height of the spike is typically λ/4 and a further investigation demonstrates shortening of its effective height by folding the same by 90°. A set of prototypes has been experimentally studied to ensure the predicted characteristics viz. 12dB consistent suppression of XP level over the whole range of elevation in H-plane and an average of 5dB-7dB suppression in backside radiation. These achievements are also associated with more than 1dB improvement in peak gain. The design is straightforward, low-cost and commercially viable. The non-planar feature restricts its use only to those applications where antenna front is open or can accommodate the vertically extended or 90° bent spikes.

All available SARS-CoV-2 spike protein crystal and cryo-EM structures have shown missing electron densities for cytosolic C-terminal regions (CTR). Generally, the missing electron densities point towards the intrinsically disordered nature of the protein region (IDPR). This curiosity has led us to investigate the cytosolic CTR of the spike glycoprotein of SARS-CoV-2 in isolation. The spike CTR is supposed to be from 1235 to 1273 residues or 1242–1273 residues based on our used prediction. Therefore, we have demonstrated the structural conformation of cytosolic region and its dynamics through computer simulations up to microsecond timescale using OPLS and CHARMM forcefields. The simulations have revealed the unstructured conformation of cytosolic region. Further, we have validated our computational observations with circular dichroism (CD) spectroscopy-based experiments and found its signature spectra at 198 nm. We believe that our findings will surely help in understanding the structure-function relationship of the spike protein's cytosolic region., Graphical abstract Image 1

Spiking neural network (SNN) utilizes spike trains for information processing among neurons, which is more biologically plausible and widely regarded as the third-generation artificial neural network (ANN). It has the potential for effectively processing spatial-temporal information and has the characteristics of lower power consumption and smaller calculation load compared with conventional ANNs. In this work, we demonstrate the feasibility of applying SNN to classify tactile signals collected by a bionic artificial fingertip that touches a group of real-world metal surfaces with different roughness levels. A two-layer SNN is adopted and trained using an unsupervised learning method with spike-timing-dependent plasticity (STDP). Experiments show that the trained SNN can categorize the input tactile signals into different surface roughness of metal textures with more than 80% accuracy. This work lays the foundation of applying SNNs to more complex tactile signal processing in robotics, manufacturing, and other engineering fields.

A tool to probe SARS-CoV-2 biology To develop therapies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and emerging variants, it is important to understand the viral biology and the effect of mutations. However, this is challenging because live virus can only be studied in a few laboratories that meet stringent safety standards. Syed et al . describe a virus-like particle (VLP) that comprises the four SARS-CoV-2 structural proteins, but instead of packaging viral RNA, it packages messenger RNA (mRNA) that expresses a reporter protein (see the Perspective by Johnson and Menachery). The amount of reporter expressed in receiver cells depends on the efficiency of packaging and assembly in the producer cells and the efficiency of entry into receiver cells. Mutations in the nucleocapsid protein that are found in more transmissible variants increase mRNA packaging and expression. The VLPs provide a platform for studying the effect of mutations in the structural proteins and for screening therapeutics. —VV

Spiking Neural Networks (SNNs) have recently emerged as a new generation of low-power deep neural networks due to sparse, asynchronous, and binary event-driven processing. Most previous deep SNN optimization methods focus on static datasets (e.g., MNIST) from a conventional frame-based camera. On the other hand, optimization techniques for event data from Dynamic Vision Sensor (DVS) cameras are still at infancy. Most prior SNN techniques handling DVS data are limited to shallow networks and thus, show low performance. Generally, we observe that the integrate-and-fire behavior of spiking neurons diminishes spike activity in deeper layers. The sparse spike activity results in a sub-optimal solution during training (i.e., performance degradation). To address this limitation, we propose novel algorithmic and architectural advances to accelerate the training of very deep SNNs on DVS data. Specifically, we propose Spike Activation Lift Training (SALT) which increases spike activity across all layers by optimizing both weights and thresholds in convolutional layers. After applying SALT, we train the weights based on the cross-entropy loss. SALT helps the networks to convey ample information across all layers during training and therefore improves the performance. Furthermore, we propose a simple and effective architecture, called Switched-BN, which exploits Batch Normalization (BN). Previous methods show that the standard BN is incompatible with the temporal dynamics of SNNs. Therefore, in Switched-BN architecture, we apply BN to the last layer of an SNN after accumulating all the spikes from previous layer with a spike voltage accumulator (i.e., converting temporal spike information to float value). Even though we apply BN in just one layer of SNNs, our results demonstrate a considerable performance gain without any significant computational overhead. Through extensive experiments, we show the effectiveness of SALT and Switched-BN for training very deep SNNs from scratch on various benchmarks including, DVS-Cifar10, N-Caltech, DHP19, CIFAR10, and CIFAR100. To the best of our knowledge, this is the first work showing state-of-the-art performance with deep SNNs on DVS data.

The Internet of Things (IoT) is an interface with the physical world that usually operates in random-sparse-event (RSE) scenarios. This article discusses main challenges of IoT chips: power consumption, power supply, artificial intelligence (AI), small-signal acquisition, and evaluation criteria. To overcome these challenges, many works recently aimed at IoT system design have emerged. This work reviews the architecture and circuit innovations that have contributed to IoT developments. This paper does not cover security of IoT. Event-driven architectures and nonuniform sampling ADCs significantly reduce the long-term average power. Besides, embedding AI engines in IoT nodes (AIoT) is one critical trend. The computing-in-memory technique improves the energy efficiency of the AI engine. Asynchronous spike neural networks (ASNNs) AI engines show low power potential. In addition to data processing, small-signal acquisition is also critical. The charge-domain analog-front-end (AFE) techniques such as floating inverter-based amplifiers improve energy efficiency. In addition to the above low power and high energy efficiency technologies, energy harvesting can also enhance the lifetime of AIoT devices. This article discusses recent ambient RF and natural energy harvesting approaches and high-efficiency DC-DC with a wide load range. Finally, novel evaluation criteria are introduced to establish benchmark standards for AIoT chips.

Physical activity is commonly conceived of in terms of its human involvement – as a test of, and testament to, human ability. However, physical activity does not exist without the contributions of countless non-human agencies, such as equipment and environments, with which the athletes work closely and form relationships. As such, athletes have a unique understanding of non-human agency. In this article I analyse the power of non-human agency in skateboarding through the representations of the professional skateboarder Rodney Mullen and filmmaker Spike Jonze. I examine their lectures, interviews, and films to show the ways in which skateboarders experience, practice, and represent the principles of actor-network theory (ANT). Skateboarders utilise and manipulate the often-unanticipated potential of non-human tools and urban landscapes and translate them into a collaborative result. Skateboarding is a trial-and-error experiment of testing, innovating, and adapting possibilities and limitations set by a network of mediators including people and ‘things’. Mullen and Jonze commonly depict skateboarding as the product of networks rather than independent human action. Their representations reveal how skateboarders perceive and act out their role as humans within networks alongside non-human agencies such as skateboards and obstacles, and which combine to produce skateboarding.

We evaluate existing spike sorters and present a new one that resolves many sorting challenges. The new sorter, called “full binary pursuit” or FBP, comprises multiple steps. First, it thresholds and clusters to identify the waveforms of all unique neurons in the recording. Second, it uses greedy binary pursuit to optimally assign all the spike events in the original voltages to separable neurons. Third, it resolves spike events that are described more accurately as the superposition of spikes from two other neurons. Fourth, it resolves situations where the recorded neurons drift in amplitude or across electrode contacts during a long recording session. Comparison with other sorters on ground-truth data sets reveals many of the failure modes of spike sorting. We examine overall spike sorter performance in ground-truth data sets and suggest postsorting analyses that can improve the veracity of neural analyses by minimizing the intrusion of failure modes into analysis and interpretation of neural data. Our analysis reveals the tradeoff between the number of channels a sorter can process, speed of sorting, and some of the failure modes of spike sorting. FBP works best on data from 32 channels or fewer. It trades speed and number of channels for avoidance of specific failure modes that would be challenges for some use cases. We conclude that all spike sorting algorithms studied have advantages and shortcomings, and the appropriate use of a spike sorter requires a detailed assessment of the data being sorted and the experimental goals for analyses. NEW & NOTEWORTHY Electrophysiological recordings from multiple neurons across multiple channels pose great difficulty for spike sorting of single neurons. We propose methods that improve the ability to determine the number of individual neurons present in a recording and resolve near-simultaneous spike events from single neurons. We use ground-truth data sets to demonstrate the pros and cons of several current sorting algorithms and suggest strategies for determining the accuracy of spike sorting when ground-truth data are not available.

We present a new approach to compute and analyze the dynamical electro-geometric properties of proteins undergoing conformational changes. The molecular trajectory is obtained from Markov state models, and the electrostatic potential is calculated using the continuum Poisson-Boltzmann equation. The numerical electric potential is constructed using a parallel sharp numerical solver implemented on adaptive Octree grids. We introduce novel a posteriori error estimates to quantify the solution's accuracy on the molecular surface. To illustrate the approach, we consider the opening of the SARS-CoV-2 spike protein using the recent molecular trajectory simulated through the Folding@home initiative. We analyze our results, focusing on the characteristics of the receptor-binding domain and its vicinity. This work lays the foundation for a new class of hybrid computational approaches, producing high-fidelity dynamical computational measurements serving as a basis for protein bio-mechanism investigations. © 2021 The Author(s)

Background: By the end of 2019, the sudden outbreak of the novel coronavirus disease (COVID-19) has become a global threat. It is called COVID-19 because it was caused by the novel coronavirus (SARS-COV-2) in 2019. A total of 1.9 M deaths and 87.9 M cases have been reported all over the world, where 49M cases have recovered so far. Scientists are working hard to find chemotherapeutics and vaccines for COVID-19. Mutations in SARS-CoV-2 have been observed in a combination of several hazardous stresses, making them more resistant and beneficial. So to break down the viral system, the disease targets are examined. Objective: In today's review, a comprehensive study of spike protein explains the main purpose of the novel coronavirus and how to prevent the spread of the disease virus cross-transmission from infected to a healthy person. Methods: Covid-19 has already been declared a pandemic by the World Health Organization (WHO) due to its result in causing death and severe illness globally. SARS-CoV-2 is highly contagious; however, the intermediate host of the novel coronavirus is not clear. To explore the mechanisms of disease, one of the viral targets, such as the spike protein that binds to human cells and causes the disease by altering its genetic structure which is considered along with potential inhibitors. Results: It has been shown that the interaction of receptor-binding domain (RBD) protein of SARS- CoV-2 spike and the angiotensin-converting enzyme 2 (ACE2) host receptor and further replication of coronavirus spike protein causes its invasion in the host cell. The human Lymphocyte antigen 6 complex, Locus E (LY6E), inhibits the entry of CoV into host cells by interfering with the human gene, inducing spike protein-mediated membrane fusion. Some natural formulations have also been shown to prevent spike protein from binding to the host cell. Conclusion: With the development of the LY6E gene activator that can inhibit spike protein- ACE2-mediated membrane fusion, new opportunities for SARS-CoV-2 treatment may emerge. Existing antiviral fusion inhibitors and natural compounds targeting spike resistance can serve as a template for further SARS-CoV-2 drug formulation.

Spiking neural network (SNN) is one of the most successful methods to imitate biological brain behavior and learning potential. The information processing mechanism of SNN combines the concepts of time and space. To address the performance dropping problem during the conversion process from the artificial neural network (ANN) to SNN, this paper proposes a proportional attenuation leaky integrate-and-fire (RA-LIF) neuron model to solve the problem of membrane potential loss caused by leaky integrate-and-fire (LIF) neurons. The accuracy of the SNN network based on RA-LIF neurons on MNIST is 98.76%. This paper also proposes a weight normalization method to help adjust the network spiking rate to reduce the loss. We evaluate and analyze the performance of SNN networks based on LIF, RA-LIF, and AD-LIF. By analyzing the spike firing rate and convergence rate, the effects of spike frequency and neuron threshold on the performance of the network are discussed. So far, the SNN transformed by ANN can only achieve worse or similar performance compared with the original network. For the first time, the performance of the SNN transformed by the proposed RA-LIF neuron model and weight normalization method is better than the original network, which shows the test accuracy of 98.88% on MNIST. The SNN obtained by converting the LeNet network using the above method achieves a test accuracy of 98.91% on MNIST. In conclusion, the RA-LIF neuron model and normalization method proposed in this paper have promising applicability. Moreover, this method has the characteristics of high precision and fast calculation speed, which can provide a reference for the research of the SNN framework.

Graphical abstract Among the deadliest pandemics in history, coronavirus disease 2019 (COVID-19) has wreaked havoc on human lives, economies and public health systems worldwide. To temper its effects, diagnostic methods that are simple, rapid, inexpensive, accurate, selective and sensitive continue to be necessary. In our study, we developed an electrochemical biosensing platform based on gold clusters, mercaptoethanol, the spike protein of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) antigen and bovine serum albumin-modified glassy carbon electrode able to detect the SARS-CoV-2 spike antibody. Moreover, during the detection of the SARS-CoV-2 spike antibody in spiked-real samples, the anodic signal of the produced biosensor at 0.85 V decreased as the amount of the SARS-CoV-2 spike antibody increased. Meanwhile, the recovery and relative standard deviation values for saliva and oropharyngeal swab samples were 97.73% and 3.35% and 102.43% and 4.63%, respectively. In 35 min, the biosensing platform could detect 0.03 fg/mL of the SARS-CoV-2 spike antibody in synthetic media and spiked-saliva or -oropharyngeal swab samples. The method thus issues a linear response to the SARS-CoV-2 spike antibody from 0.1 fg/mL to 10 pg/mL. The cross-reactivity studies with spike antigens of Middle East respiratory syndrome-coronavirus and influenza A and the antigen of pneumonia confirmed the excellent selectivity of the proposed method. The developed method was compared with the lateral flow immunoassay method in terms of sensitivity and it was found to be approximately 109 times more sensitive. Biosensing mechanism of the platform to the SARS-CoV-2 spike antibody Supplementary Information The online version contains supplementary material available at 10.1007/s00216-021-03752-3.

Outbreak of coronavirus seems to have exacerbated across the globe, but drugs have not been discovered till now. Due to having the antiviral activity of D-glucopyranoside derivatives, this study was designed to examine as the inhibitor by in sillico study against the main protease (Mpro) and Spike protease (Spro) of SARS-CoV-2. First, these derivatives were optimised by Density Functional Theory (DFT). The observation of this study was monitored by molecular docking tools calculating the binding affinities. Afterwards, the ligand interaction with protein was accounted for selecting the how to bind of active sites of the protein. Next, the root means square deviation (RMSD) and root mean square fluctuation (RMSF) were illustrated for determining the stability of the docked complex. Finally, AMDET properties were calculated as well as the Lipisinki rule. All of the derivates showed a binding affinity more than -6.0 kcal/mol while derivatives 2, 3, and 9 were the best-bonded scoring inhibitor against Mpro and Spro. In addition, the chemical descriptors were more supportive tools as an inhibitor, and the Lipisinki rule was satisfied for maximum molecules as a drug. Besides, D-glucopyranoside derivatives may be predicted that they are non-carcinogenic and low toxic for both aquatic and non-aquatic species.

The paper presents a supercomputer parallel implementation of a brain inspired model combining a Python module simulating a layer of retina ganglion cells and NEST Simulator for a layer of spike timing neurons of Lateral geniculate nucleus (LGN) in the brain. Since the Python module appeared to be the bottleneck in the developed hierarchical model of human visual system, the proposed two parallel implementations of that module were combined with NEST Simulator in a single module. Simulations of developed module on different number of nodes and varying number of parallel processes were investigated and compared with respect to their time consumption.

As neuron is the fundamental unit of the nervous system, it is one of the main building blocks in the spiking neural networks hardware implementation. To implement hardware that consists of many thousands of neurons and accurately mimics the brain functions, an energy and area-efficient design for the neuron is essential. In this paper, we propose a VLSI circuit for the leaky integrate and fire (LIF) neuron model, in 130 nm CMOS technology. The proposed neuron has some important features: first, it consumes very low energy; second, it is simple and area-efficient. Third, it has the spike frequency adaptation capability that makes it more biologically plausible. The spike frequency adaptation mechanism is added to the model only by one additional transistor, and it is done just by one parameter. The energy per spike of the neuron circuit in the worst case is only 0.67 fJ/spike. The spike frequency is in the MHz range, which enables attractive hardware acceleration.

This paper is devoted to developing a biological-based algorithm to simulate the control of a human arm by means of a Spiking Neural Network (SNN) with a pre-set structure similar to those found in reflex arcs. The replication of the behavior of biological systems is a primary step to understand how the brain and the neural networks that comprise it work. The study of these systems began with the analysis of animals with low neural complexity as well as with small control neural networks such as those present in the reflex acts of the human body. However, the study of the brain, due to its intricate structure, still presents many unknowns and challenges to be solved. Meanwhile, one way to understand how biological systems work is to emulate their behavior through computer simulations. Artificial neural networks (ANNs) offered the opportunity to replicate neural structures to understand and reproduce their behavior and performance. Many types of ANNs are based on the use of activation functions. However, these ANNs are simplified models that do not replicate the behavior of complex biological neural systems accurately. For this reason, spike-based models have been developed to reproduce real biological systems more faithfully. This work proposes simulating the motor behavior of the central nervous system to control the position of an arm. To this end, a spiking neural network has been developed to emulate motion control using a fixed structure that reproduces reflex arcs. A channel-based synapse model and a control scheme based on the equilibrium point hypothesis have been proposed to improve biological similarity of the controller. Furthermore, the developed controller has learning capabilities thanks to the reproduction of the synapse plasticity process that takes place in real biological systems through a supervised Spike-Timing Dependent Plasticity (STDP) learning strategy. The performance of the proposed approach has been demonstrated by simulating the control of the movement of an arm using Hill’s Muscle Model. Results suggest that the proposed control algorithm resembles the response of biological systems adequately in terms of speed and temporal response.

Background Extracellular recording represents a crucial electrophysiological technique in neuroscience for studying the activity of single neurons and neuronal populations. The electrodes capture voltage traces that, with the help of analytical tools, reveal action potentials (‘spikes’) as well as local field potentials. The process of spike sorting is used for the extraction of action potentials generated by individual neurons. Until recently, spike sorting was performed with manual techniques, which are laborious and unreliable due to inherent operator bias. As neuroscientists add multiple electrodes to their probes, the high-density devices can record hundreds to thousands of neurons simultaneously, making the manual spike sorting process increasingly difficult. The advent of automated spike sorting software has offered a compelling solution to this issue and, in this study, we present a simple-to-execute framework for running an automated spike sorter. Methods Tetrode recordings of freely-moving mice are obtained from the CA1 region of the hippocampus as they navigate a linear track. Tetrode recordings are also acquired from the prelimbic cortex, a region of the medial prefrontal cortex, while the mice are tested in a T maze. All animals are implanted with custom-designed, 3D-printed microdrives that carry 16 electrodes, which are bundled in a 4-tetrode geometry. Results We provide an overview of a framework for analyzing single-unit data in which we have concatenated the acquisition system (Cheetah, Neuralynx) with analytical software (MATLAB) and an automated spike sorting pipeline (MountainSort). We give precise instructions on how to implement the different steps of the framework, as well as explanations of our design logic. We validate this framework by comparing manually-sorted spikes against automatically-sorted spikes, using neural recordings of the hippocampus and prelimbic cortex in freely-moving mice. Conclusions We have efficiently integrated the MountainSort spike sorter with Neuralynx-acquired neural recordings. Our framework is easy to implement and provides a high-throughput solution. We predict that within the broad field of bioelectronic medicine, those teams that incorporate high-density neural recording devices to their armamentarium might find our framework quite valuable as they expand their analytical footprint.

PurposeThe purpose of this paper is to suggest that student-led case studies are an important way to learn about mental health problems and to highlight this by presenting a case study of the comedic genius Spike Milligan.Design/methodology/approachCelebrities live their lives in the public eye. In recent years, many have talked about their struggles with mental health. This paper is based on a student-led case study of the celebrity Spike Milligan.FindingsThis case study suggests one previously under-emphasised issue and argues that Spike Milligan’s wartime experiences may have led to post-traumatic stress disorder. Second, that he may have developed neuro-inflammation, through contracting sandfly fever during the war. This could have been an additional trigger for bipolar disorder.Research limitations/implicationsWhile this is a single case study, it draws on a wide variety of research sources to back up the arguments advanced.Practical implicationsStudent-led case studies provide a way of engaging students more actively with mental health problems.Social implicationsMental illness is complex, if not more complex, than physical health problems. Case studies of celebrities like Spike Milligan can help develop a public understanding of mental illness, as they already have a working knowledge about the person.Originality/valueThe case study illustrates how Bipolar 1 disorder is a complex and unique condition and that every individual’s illness has different predisposing characteristics. It suggests that student-led case studies are a helpful learning tool.

Summary In recent years, a number of methods have been proposed to estimate the times at which a neuron spikes on the basis of calcium imaging data. However, quantifying the uncertainty associated with these estimated spikes remains an open problem. We consider a simple and well-studied model for calcium imaging data, which states that calcium decays exponentially in the absence of a spike, and instantaneously increases when a spike occurs. We wish to test the null hypothesis that the neuron did not spike—i.e., that there was no increase in calcium—at a particular timepoint at which a spike was estimated. In this setting, classical hypothesis tests lead to inflated Type I error, because the spike was estimated on the same data used for testing. To overcome this problem, we propose a selective inference approach. We describe an efficient algorithm to compute finite-sample $p$-values that control selective Type I error, and confidence intervals with correct selective coverage, for spikes estimated using a recent proposal from the literature. We apply our proposal in simulation and on calcium imaging data from the $\texttt{spikefinder}$ challenge.

SARS-CoV-2 is a pandemic virus that caused infections and deaths in many world countries, including the Middle East. The virus-infected human cells by binding via ACE-2 receptor through the Spike protein of the virus with Furin's help causing cell membrane fusion leading to Covid-19-cell entry. No registered drugs or vaccines are triggering this pandemic viral disease yet. Our present work is based on molecular docking and dynamics simulation that performed to spike protein-ACE-2 interface complex, ACE-2 receptor, Spike protein (RBD), and Furin as targets for new small molecules. These drugs target new potential therapies to show their probabilities toward the active sites of mentioned proteins, strongly causing inhibition and/or potential therapy for covid-19. All target proteins were estimated against new target compounds under clinical trials and repurposing drugs currently present. Possibilities of those molecules and potential therapeutics acting on a certain target were predicted. MD simulations over 200 ns with molecular mechanics-generalized Born surface area (MMGBSA) binding energy calculations were performed. The structural and energetic analyses demonstrated the stability of the ligands-MPros complex. Our present work will introduce new visions of some biologically active molecules for further studies in-vitro and in-vivo for Covid-19, repurposing of these molecules should be taking place under clinical works and offering different strategies for drugs repurposing against Covid-19 diseases. Communicated by Ramaswamy H. Sarma.

Studies of sensory-evoked neuronal responses often focus on mean spike rates, with fluctuations treated as internally-generated noise. However, fluctuations of spontaneous activity, often organized as traveling waves, shape stimulus-evoked responses and perceptual sensitivity. The mechanisms underlying these waves are unknown. Further, it is unclear whether waves are consistent with the low rate and weakly correlated “asynchronous-irregular” dynamics observed in cortical recordings. Here, we describe a large-scale computational model with topographically-organized connectivity and conduction delays relevant to biological scales. We find that spontaneous traveling waves are a general property of these networks. The traveling waves that occur in the model are sparse, with only a small fraction of neurons participating in any individual wave. Consequently, they do not induce measurable spike correlations and remain consistent with locally asynchronous irregular states. Further, by modulating local network state, they can shape responses to incoming inputs as observed in vivo., Spontaneous traveling cortical waves shape neural responses. Using a large-scale computational model, the authors show that transmission delays shape locally asynchronous spiking dynamics into traveling waves without inducing correlations and boost responses to external input, as observed in vivo.

The COVID-19 pandemic, and future pandemics, require diagnostic tools to track disease spread and guide the isolation of (a)symptomatic individuals. Lateral-flow diagnostics (LFDs) are rapid and of lower cost than molecular (genetic) tests, with current LFDs using antibodies as their recognition units. Herein, we develop a prototype flow-through device (related, but distinct to LFDs), utilizing N-acetyl neuraminic acid-functionalized, polymer-coated, gold nanoparticles as the detection/capture unit for SARS-COV-2, by targeting the sialic acid-binding site of the spike protein. The prototype device can give rapid results, with higher viral loads being faster than lower viral loads. The prototype’s effectiveness is demonstrated using spike protein, lentiviral models, and a panel of heat-inactivated primary patient nasal swabs. The device was also shown to retain detection capability toward recombinant spike proteins from several variants (mutants) of concern. This study provides the proof of principle that glyco-lateral-flow devices could be developed to be used in the tracking monitoring of infectious agents, to complement, or as alternatives to antibody-based systems.\ud \ud

The article raises the topic of “new sensuality” (“new sensualism”) as a phenomenon that revived the sentimentalism of the 1960s movie drama in the computer age and was embodied in a number of films of the last decade. In such films as Blade Runner 2049 (dir. Denis Villeneuve, 2017), Ex machina (dir. Alex Garland, 2015), I’m Here (dir. Spike Jonze, 2010) robots, replicant androids, and even laser projections act as men and women; and in the Spike Jonze film Her (2013), the main character, although a real man named Theodore, falls in love with the voice of the operating system Samantha, an affair with which becomes more real and valuable for him than anything he has previously experienced in relationships with real women. The plot of the film provides the basis for further practical and theoretical development of the film image of “akousmêtre”, a voice without a body. Digital technologies embodied in the film provide an opportunity to apply and rethink the concept of corporeality, which is relevant in phenomenological aesthetics, as well as to develop the meaning of synesthetic perception in the modern art space. At a new level of correlation between reality and virtuality, the ideas of dialogism that developed within the framework of philosophical anthropology in the twentieth century can be applied in the analysis of the film. Thus, the material of the presented films allows us to expand the art history discourse and raise a number of issues that are relevant not only for the screen arts of the 21st century, but also for people living in the world of digital technologies, but who have not lost the need for genuine human feelings.

Efficient learning of spikes plays a valuable role in training spiking neural networks (SNNs) to have desired responses to input stimuli. However, current learning rules are limited to a binary form of spikes. The seemingly ubiquitous phenomenon of burst in nervous systems suggests a new way to carry more information with spike bursts in addition to times. Based on this, we introduce an advanced form, the augmented spikes, where spike coefficients are used to carry additional information. How could neurons learn and benefit from augmented spikes remains unclear. In this paper, we propose two new efficient learning rules to process spatiotemporal patterns composed of augmented spikes. Moreover, we examine the learning abilities of our methods with a synthetic recognition task of augmented spike patterns and two practical ones for image classification. Experimental results demonstrate that our rules are capable of extracting information carried by both the timing and coefficient of spikes. Our proposed approaches achieve remarkable performance and good robustness under various noise conditions, as compared to benchmarks. The improved performance indicates the merits of augmented spikes and our learning rules, which could be beneficial and generalized to a broad range of spike-based platforms.

The spiking neural network (SNN)-based neuromorphic hardware has been extensively studied in dynamic information processing. However, there is still a lack of training algorithms to drive or support the implementation of compact spatio-temporal neuromorphic hardware; and the existing neuromorphic hardware uses excessive on-chip memory to store parameters, which limits its neuron and synapse scale. Here, we introduce a hardware-friendly weight binarized spiking neural network (BSNN) to efficiently recognize the spatio-temporal event-based data. The neuron of the spike response model (SRM) is used in BSNN due to its rich spatio-temporal characteristics. In the training process, a surrogate gradient method is used to replace the derivative of the spike train, and the weights are binarized. Moreover, combined with the spiking characteristics of SNN (i.e., the input/output of SNN and the communications of neurons in SNN are binary spikes), it is possible to replace the hardware-expensive matrix–vector multiplication (MVM) with the hardware-friendly “Signed AND” operation during inference, which is favored for constructing compact neuromorphic hardware. The trained BSNN has competitive recognition accuracies of 99.52% and 62.1%, 97.57%, and 90.35% on the dynamic images dataset N-MNIST and DVS-CIFAR10, dynamic gestures dataset DvsGesture, and dynamic audio dataset N-TIDIGITS18, respectively, which are related to human vision or hearing. The proposed compact SNN training method paves the way for real-time dynamic information processing oriented hardware-saving and power-efficient neuromorphic hardware.

The coronavirus disease 2019 (COVID-19) virus has been spreading rapidly, and scientists are endeavouring to discover drugs for its efficacious treatment. Chloroquine phosphate, an old drug for treatment of malaria, has shown to have apparent efficacy and acceptable safety against COVID-19. As a part of Drug Discovery Hackathon-2020, in this study, the authors have tried making the derivatives of CQ and HCQ using MarvinSketch by ChemAxon. Molecular docking studies of these ligands were performed using Glide by Schrodinger, and ADME profiles were obtained by using QikProp. The obtained results after data analysis demonstrated that ligands HCQ_imidazoll, choloroquine_3c, HCQ_pyrrolC had good binding affinity and complied with all the ADME parameters. The molecular dynamic simulation of these ligands in complex with the 2019-nCoV RBD/ACE-2-B0AT1 complex PDB ID: 6M17 were carried out, and the parameters like RMSD, RMSF, and radius of gyration were observed to understand the fluctuations and protein-ligand interaction.

Tactile sensing with spiking neural networks (SNNs) has attracted increasing attention in the past decades. In this article, a novel SNN framework is proposed for the tactile surface roughness categorization task. In contrast to supervised SNN methods such as ReSuMe and Tempotron that require prespecifying target spike trains, the presented method performs the classification through directly comparing the distance between multineuron spike trains. Unlike simple spike train fusion methods using average pairwise spike train distance or pooled spike train distance, the proposed method merges spike trains from different neurons with the multineuron spike train distance, which can capture the complex correlation of multiple spike trains. Specifically, the spike trains are generated via the Izhikevich neurons from tactile signals. The similarity of the multineuron spike trains is computed using the multineuron Victor–Purpura spike train distance, which can be efficiently implemented in an inductive manner. The classification can be performed by incorporating $k$ -nearest neighbors and the multineuron spike train distance as a similarity metric. The proposed framework is quite general, i.e., other multineuron spike train distances and spike train kernel-based methods can be readily incorporated. The effectiveness of the proposed method has been demonstrated on a tactile data set by comparing it with various feature- and spike-based methods. Note to Practitioners —In the soft neuromorphic implementation of biomimetic tactile sensing and the development of the tactile sensing capability in neurobotic systems, the processing and analysis of spike-like tactile signals are quite common. Inspired by human tactile perception, this article proposes a novel supervised spiking neural network method for tactile sensing tasks. The traditional methods have to prespecify target spike trains, which is still an open question. In addition, the current ways to fuse spike trains from multiple neurons are far from mature. This article tackles these two problems using spike train similarity comparison with multineuron spike train distance. The direct spike train similarity comparison avoids the need to prespecify target spike trains. The multineuron spike train distance can inherently fuse spike trains from different neurons. It is demonstrated that the proposed method is able to effectively perform classification in a tactile roughness discrimination task.

To monitor infection by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and successful vaccination against coronavirus disease 2019 (COVID‐19), the kinetics of neutralizing or blocking anti‐SARS‐CoV‐2 antibody titers need to be assessed. Here, we report the development of a quick and inexpensive surrogate SARS‐CoV‐2 blocking assay (SUBA) using immobilized recombinant human angiotensin‐converting enzyme 2 (hACE2) and human cells expressing the native form of surface SARS‐CoV‐2 spike protein. Spike protein‐expressing cells bound to hACE2 in the absence or presence of blocking antibodies were quantified by measuring the optical density of cell‐associated crystal violet in a spectrophotometer. The advantages are that SUBA is a fast and inexpensive assay, which does not require biosafety level 2‐ or 3‐approved laboratories. Most importantly, SUBA detects blocking antibodies against the native trimeric cell‐bound SARS‐CoV‐2 spike protein and can be rapidly adjusted to quickly pre‐screen already approved therapeutic antibodies or sera from vaccinated individuals for their ACE2 blocking activities against any emerging SARS‐CoV‐2 variants., A simple, inexpensive surrogate SARS‐CoV‐2 neutralization assay is introduced. Ectopic expression of the SARS‐CoV‐2 spike protein confers binding of Ramos‐Ig ‐ or HEK293 cells to plate‐bound recombinant human angiotensin‐converting‐enzyme‐2. This interaction can be blocked with reconvalescent or immune serum or with anti‐receptor‐binding domain antibodies. Bound spike‐expressing cells are quantitated with crystal violet.

The rapid outbreak of the new coronavirus SARS-COV-2 has created a major public health challenge. Immunoinformatics tools had a clear effect in tracking the genetic sequence of the virus and monitoring mutations and design vaccines that are effective enough to produce antibodies. In our study, we resorted to the emerging discipline of immunoinformatics in order to design a multi-epitope mRNA vaccine against the spike glycoprotein of SARS-CoV-2. We screened the B cell and T cell epitopes of the Spike glycoprotein. we used ABC pred server to predict B cell epitope in the spike glycoprotein sequence and we used NetMHC-4.1 server to predict the T-cell epitope. Then we selected the B cell and T cell epitopes that fulfilled the antigenicity, non-toxicity, non-allergenicity, induction of both IL4 and IFN gamma. Finally, we designed multi-epitope mRNA Vaccine construct by linking 6 B lymphocytes epitopes (BL) with 6 cytotoxic T lymphocytes epitopes (CTL) together with helper T lymphocyte (HTL) epitope up-streamed by 5’ cap and down-streamed by poly A tail. The vaccine was found to be antigenic, non-toxic, non-allergenic, capable of generating a robust immune response. Based on these parameters, this design can be considered a promising choice for a vaccine against SARS-CoV-2.

The food crisis that is currently being faced by the world is very vulnerable to the survival of mankind. A food crisis usually begins with a shock to either supply or demand for food and often involves a sudden spike in food prices. Food security is a flexible concept that is reflected in many attempts at every definition used in research and policy. Empowerment of farmers in the African agricultural sector is the key to realizing African food security. The strategy of privatizing agricultural inputs by prohibiting the circulation of traditional seeds indirectly forces small farmers to buy private agricultural inputs

Introduction: SARS-CoV-2 has become a worldwide pandemic that affects all aspects of life; therefore, numerous organizations and open exploration foundations focus their efforts on research for viable therapeutics. Given past experiences and involvement in SARS, the essential focus has been the Spike protein, considered as the perfect objective for COVID-19 immunotherapies. Most of the vaccines being developed target the spike proteins because this protein covers the virus and helps it invade human cells. Methods: Applications of deep neural network is a quickly expanding field now reaching many areas including proteomics. Results: To be precise, convolutional neural networks have been used for identifying the functional role of amino acid sequences, because of its ability to give nearly accurate results for multi-label classification problems. Here we present a modified convolutional deep learning model that can identify if a given amino acid sequence is a spike protein or not based on the length of the sequence and the function of the protein, that will be done with a short execution time and a relatively small error rate. Conclusion: CNN is an efficient tool at supervised multilabel classification problems

The present experiment was conducted during October 2011 – April 2012 in the Department of Horticulture, Sam Higginbottom Institute of Agriculture Technology and Science, Allahabad, (U.P.). The experiment of design was randomized block design consisting 11 treatment with 3 replication, with a view to find out the overall performance of different cultivars of Glodiolus viz. Panjab Morning (T1), Green Bay (T2), Noualux blue (T3), American Beauty (T4), Priscilla (T5),White Prosperity (T6) Red Majesty (T7), Summer Shunshine (T8) Delhi local (T9), Noualux (T10), Candyman (T11),. On the basis of different growth and yield parameters the maximum days for first floret durability (6.16). were recorded by White Prosperity (T6) followed by American Beauty (T4) (5.76). The maximum number of spike length (95.13cm) was obtained in White Prosperity (T6) followed by Red Majesty (T7) (94.16). The maximum weight of spike (64.40 g) was observed in White Prosperity (T6) followed by American Beauty (T4) (64.33g). The maximum floret diameter (12.63cm) was found in Summer Pearl (T8) followed by White Prosperity (T6) (12.53cm). The maximum number of floret per spike (18.00) was found in White Prosperity (T6) followed by American Beauty (T4) (17.73). The maximum number of spikes per plant (5.33) was observed in Delhi Local (T9) followed by White Prosperity (T6) (1.3). Which can be recommended for cultivation in Allahabad agro-climatic condition.

Emerging pathogens have been an eternal threat to mankind. In a series of pandemics caused by notorious coronaviruses, a newly emerged SARS-CoV2 virus is creating panic among the world population. The unavailability of reliable theranostics insists the exploration of antigenic determinants in spike glycoprotein of SARS-CoV2. The four novel inserts (‘70VSGTNGT76’, ‘150KSWM153’, 247SYLTPG252 and 674QTQTNSPRR682) in SARS-CoV2 spike protein were unraveled via multiple sequence alignment of spike proteins of SARS-CoV2, SARS-CoV, and MERS-CoV. The three-dimension (3D) modeling of the spike protein of the SARS-CoV2 and their interaction with the ACE2 receptor was delineated with the help of SWISS-MODEL and 3DLigandSite web servers. The predicted 3D model of SARS-CoV2 was further verified by SAVES, RAMPAGE, and ProSA-web tools. The potential B-cell immunogenic epitopes of SARS-CoV2 were predicted out by using various software viz. IEDB B-cell epitopes prediction tool, BepiPred linear epitope prediction tool, Emini Surface Accessibility Prediction tool, and Kolaskar-Tongaonkar antigenicity web tool. The five epitopes (i.e. ‘71SGTNGTKRFDN81, 247SYLTPG252, 634RVYST638, 675QTQTNSPRRARSV687, and 1054QSAPH1058) were selected as potent antigenic determinants. The quantum of information generated by this study will prove beneficial for the development of effective therapeutics, diagnostics, and multi-epitopic vaccines to combat this ongoing menace.

Spiking neural network (SNN), known as the third generation of neural networks, is attracting more and more researchers’ attention because of its high energy efficiency. However, due to the spatiotemporal characteristics, SNN involved a few complicated computations such as exponential function and logarithmic function, making it hard to implement on hardware. In this brief, we propose a SNN processor, which balances the trade-offs between cost and performance. In terms of cost, through software and hardware co-design, a high-robustness SNN model with 2-bit weights and a novel synapse delay management mechanism are adopted to reduce memory utilization. In terms of performance, a spike encoder and a VFA (Vote-For-All) decoder are used to reduce latency and improve inference accuracy respectively. We implement the design on the Xilinx ZCU102 FPGA board and apply it to the MNIST handwritten digit classification, which achieves 90.53% classification accuracy. Compared to a previous proposed SNN processor of a similar neuron scale, our design achieves a $156 \times $ inference speed-up and consumes $0,32 \times $ hardware resources.

With the ongoing global pandemic of coronavirus disease 2019 (COVID-19), there is an increasing quest for more accessible, easy-to-use, rapid, inexpensive, and high-accuracy diagnostic tools. Traditional disease diagnostic methods such as qRT-PCR (quantitative reverse transcription-PCR) and ELISA (enzyme-linked immunosorbent assay) require multiple steps, trained technicians, and long turnaround time that may worsen the disease surveillance and pandemic control. In sight of this situation, a rapid, one-step, easy-to-use, and high-accuracy diagnostic platform will be valuable for future epidemic control, especially for regions with scarce medical resources. Herein, we report a magnetic particle spectroscopy (MPS) platform for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) biomarkers: spike and nucleocapsid proteins. This technique monitors the dynamic magnetic responses of magnetic nanoparticles (MNPs) and uses their higher harmonics as a measure of the nanoparticles' binding states. By anchoring polyclonal antibodies (pAbs) onto MNP surfaces, these nanoparticles function as nanoprobes to specifically bind to target analytes (SARS-CoV-2 spike and nucleocapsid proteins in this work) and form nanoparticle clusters. This binding event causes detectable changes in higher harmonics and allows for quantitative and qualitative detection of target analytes in the liquid phase. We have achieved detection limits of 1.56 nM (equivalent to 125 fmole) and 12.5 nM (equivalent to 1 pmole) for detecting SARS-CoV-2 spike and nucleocapsid proteins, respectively. This MPS platform combined with the one-step, wash-free, nanoparticle clustering-based assay method is intrinsically versatile and allows for the detection of a variety of other disease biomarkers by simply changing the surface functional groups on MNPs.