spike sequences
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EBioMedicine ◽  
2022 ◽  
Vol 75 ◽  
pp. 103807
Author(s):  
Daniel Marrama ◽  
Jarjapu Mahita ◽  
Alessandro Sette ◽  
Bjoern Peters

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3237
Author(s):  
Alexander Sboev ◽  
Danila Vlasov ◽  
Roman Rybka ◽  
Yury Davydov ◽  
Alexey Serenko ◽  
...  

The problem with training spiking neural networks (SNNs) is relevant due to the ultra-low power consumption these networks could exhibit when implemented in neuromorphic hardware. The ongoing progress in the fabrication of memristors, a prospective basis for analogue synapses, gives relevance to studying the possibility of SNN learning on the base of synaptic plasticity models, obtained by fitting the experimental measurements of the memristor conductance change. The dynamics of memristor conductances is (necessarily) nonlinear, because conductance changes depend on the spike timings, which neurons emit in an all-or-none fashion. The ability to solve classification tasks was previously shown for spiking network models based on the bio-inspired local learning mechanism of spike-timing-dependent plasticity (STDP), as well as with the plasticity that models the conductance change of nanocomposite (NC) memristors. Input data were presented to the network encoded into the intensities of Poisson input spike sequences. This work considers another approach for encoding input data into input spike sequences presented to the network: temporal encoding, in which an input vector is transformed into relative timing of individual input spikes. Since temporal encoding uses fewer input spikes, the processing of each input vector by the network can be faster and more energy-efficient. The aim of the current work is to show the applicability of temporal encoding to training spiking networks with three synaptic plasticity models: STDP, NC memristor approximation, and PPX memristor approximation. We assess the accuracy of the proposed approach on several benchmark classification tasks: Fisher’s Iris, Wisconsin breast cancer, and the pole balancing task (CartPole). The accuracies achieved by SNN with memristor plasticity and conventional STDP are comparable and are on par with classic machine learning approaches.


2021 ◽  
Author(s):  
Yusuke Matsui ◽  
Lin Li ◽  
Mary Prahl ◽  
Arianna G. Cassidy ◽  
Nida Ozarslan ◽  
...  

AbstractPregnancy confers unique immune responses to infection and vaccination across gestation. To date, there is limited data comparing vaccine versus infection-induced nAb to COVID-19 variants in mothers during pregnancy. We analyzed paired maternal and cord plasma samples from 60 pregnant individuals. Thirty women vaccinated with mRNA vaccines were matched with 30 naturally infected women by gestational age of exposure. Neutralization activity against the five SARS-CoV-2 Spike sequences was measured by a SARS-CoV-2 pseudotyped Spike virion assay. Effective nAbs against SARS-CoV-2 were present in maternal and cord plasma after both infection and vaccination. Compared to wild type or Alpha variant Spike, these nAbs were less effective against the Kappa, Delta, and Mu Spike variants. Vaccination during the third trimester induced higher nAb levels at delivery than infection during the third trimester. In contrast, vaccine-induced nAb levels were lower at the time of delivery compared to infection during the first trimester. The transfer ratio (cord nAb level/maternal nAb level) was greatest in mothers vaccinated in the second trimester. SARS-CoV-2 vaccination or infection in pregnancy elicit effective nAbs with differing neutralization kinetics that is impacted by gestational time of exposure. Vaccine induced neutralizing activity was reduced against the Delta, Mu, and Kappa variants.Graphic abstract


Algorithms ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 348
Author(s):  
Zahra Tayebi ◽  
Sarwan Ali ◽  
Murray Patterson

The widespread availability of large amounts of genomic data on the SARS-CoV-2 virus, as a result of the COVID-19 pandemic, has created an opportunity for researchers to analyze the disease at a level of detail, unlike any virus before it. On the one hand, this will help biologists, policymakers, and other authorities to make timely and appropriate decisions to control the spread of the coronavirus. On the other hand, such studies will help to more effectively deal with any possible future pandemic. Since the SARS-CoV-2 virus contains different variants, each of them having different mutations, performing any analysis on such data becomes a difficult task, given the size of the data. It is well known that much of the variation in the SARS-CoV-2 genome happens disproportionately in the spike region of the genome sequence—the relatively short region which codes for the spike protein(s). In this paper, we propose a robust feature-vector representation of biological sequences that, when combined with the appropriate feature selection method, allows different downstream clustering approaches to perform well on a variety of different measures. We use such proposed approach with an array of clustering techniques to cluster spike protein sequences in order to study the behavior of different known variants that are increasing at a very high rate throughout the world. We use a k-mers based approach first to generate a fixed-length feature vector representation of the spike sequences. We then show that we can efficiently and effectively cluster the spike sequences based on the different variants with the appropriate feature selection. Using a publicly available set of SARS-CoV-2 spike sequences, we perform clustering of these sequences using both hard and soft clustering methods and show that, with our feature selection methods, we can achieve higher F1 scores for the clusters and also better clustering quality metrics compared to baselines.


2021 ◽  
Author(s):  
Patrick GUERIN ◽  
Nouara YAHI ◽  
Fodil AZZAZ ◽  
Henri CHAHINIAN ◽  
Jean-Marc SABATIER ◽  
...  

Abstract Objectives. The efficiency of Covid-19 vaccination is determined by cellular and humoral immune responses, and for the latter, by the balance between neutralizing and infection-enhancing antibodies. Here we analyzed the evolution of neutralizing and facilitating epitopes in the spike protein among SARS-CoV-2 variants. Methods. Amino acid alignments were performed on 929,203 spike sequences over the 4 last months. Molecular modeling studies of the N-terminal domain (NTD) and rod-like regions of the spike protein were performed on a representative panel of SARS-CoV-2 variants that were structurally compared with the original Wuhan strain. Results. D614, which belongs to an antibody-dependent-enhancement (ADE) epitope common to SARS-CoV-1 and SARS-CoV-2, has rapidly mutated to D614G in the first months of 2020, explaining why ADE has not been detected following mass vaccination. We show that this epitope is conformationally linked to the main ADE epitope of the SARS-CoV-2 NTD which is highly conserved among most variants. In contrast, the neutralizing epitope of the NTD showed extensive variations in SARS-CoV-2 variants. Conclusions. This molecular epidemiology study coupled with structural analysis of the spike protein indicates that the balance between facilitating and neutralizing antibodies in vaccinated people is in favor of neutralization for the Wuhan strain,alpha and beta variants, but not for gamma, delta, lambda and mu. The evolution of SARS-CoV-2 has dramatically affected the ADE/neutralization balance which is nowadays in favor of ADE. Future vaccines should consider these data to design new formulations adapted to SARS-CoV-2 variants and lacking ADE epitopes in the spike protein.


Author(s):  
Patrick Guérin ◽  
Nouara Yahi ◽  
Fodil Azzaz ◽  
Henri Chahinian ◽  
Jean-Marc Sabatier ◽  
...  

Objectives. The efficiency of Covid-19 vaccination is determined by cellular and humoral immune responses, and for the latter, by the balance between neutralizing and infection-enhancing antibodies. Here we analyzed the evolution of neutralizing and facilitating epitopes in the spike protein among SARS-CoV-2 variants. Methods. Amino acid alignments were performed on 929,203 spike sequences over the 4 last months. Molecular modeling studies of the N-terminal domain (NTD) and rod-like regions of the spike protein were performed on a representative panel of SARS-CoV-2 variants that were structurally compared with the original Wuhan strain. Results. D614, which belongs to an antibody-dependent-enhancement (ADE) epitope common to SARS-CoV-1 and SARS-CoV-2, has rapidly mutated to D614G in the first months of 2020, explaining why ADE has not been detected following mass vaccination. We show that this epitope is conformationally linked to the main ADE epitope of the SARS-CoV-2 NTD which is highly conserved among most variants. In contrast, the neutralizing epitope of the NTD showed extensive variations in SARS-CoV-2 variants. Conclusions. This molecular epidemiology study coupled with structural analysis of the spike protein indicates that the balance between facilitating and neutralizing antibodies in vaccinated people is in favor of neutralization for the Wuhan strain, alpha and beta variants, but not for gamma, delta, lambda and mu. The evolution of SARS-CoV-2 has dramatically affected the ADE/neutralization balance which is nowadays in favor of ADE. Future vaccines should consider these data to design new formulations adapted to SARS-CoV-2 variants and lacking ADE epitopes in the spike protein.


2021 ◽  
Vol 38 (3) ◽  
pp. 807-819
Author(s):  
Fatma Özcan ◽  
Ahmet Alkan

One of the goals of neural decoding in neuroscience is to create Brain-Computer Interfaces (BCI) that use nerve signals. In this context, we are interested in the activity of nerve cells. It is possible to classify nerve cells as excitatory or inhibitors by evaluating individual extra-cellular measurements taken from the frontal cortex of rats. Classification of neurons with only spike timing values has not been studied before, with deep learning, without knowing all of the wave properties and the intercellular interactions. In this study, inter-spike interval values of individual neuronal spike sequences were converted into recurrence plot images to analyze as point processing, image features were extracted using the pre-trained AlexNet with CNN deep learning method, and frontal cortex nerve cell type classification was made. Kernel classification, SVM, Naive Bayes, Ensemble, decision trees classification methods were used. The accuracy, sensitivity and specificity evaluate the proposed methods. A success of more than 81% has been achieved. Thus, the cell type is defined automatically. It has been observed that the ISI properties of spike trains can carry out information on cell type and thus neural network activity. Under these circumstances, these values are significant and important for neuroscientists.


2021 ◽  
Author(s):  
Kentaro Tao ◽  
Myung Chung ◽  
Akiyuki Watarai ◽  
Ziyan Huang ◽  
Mu-Yun Wang ◽  
...  

The ability to remember conspecifics is critical for adaptive cognitive functioning and social communication, and impairments of this ability are hallmarks of autism spectrum disorders (ASDs). Although hippocampal ventral CA1 (vCA1) neurons are known to store social memories, how their activities are coordinated remains unclear. Here we show that vCA1 social memory neurons, characterized by enhanced activity in response to memorized individuals, were preferentially reactivated during sharp-wave ripples (SPW-Rs). Spike sequences of these social replays reflected the temporal orders of neuronal activities within theta cycles during social experiences. In ASD model Shank3 knockout mice, the proportion of social memory neurons was reduced, and neuronal ensemble spike sequences during SPW-Rs were disrupted, which correlated with impaired discriminatory social behavior. These results suggest that SPW-R-mediated sequential reactivation of neuronal ensembles is a canonical mechanism for coordinating hippocampus-dependent social memories and its disruption underlies the pathophysiology of social memory defects associated with ASD.


2021 ◽  
Author(s):  
Toshitake Asabuki ◽  
Tomoki Fukai

The brain performs various cognitive functions by learning the spatiotemporal salient features of the environment. This learning likely requires unsupervised segmentation of hierarchically organized spike sequences, but the underlying neural mechanism is only poorly understood. Here, we show that a recurrent gated network of neurons with dendrites can context-dependently solve difficult segmentation tasks. Dendrites in this model learn to predict somatic responses in a self-supervising manner while recurrent connections learn a context-dependent gating of dendro-somatic current flows to minimize a prediction error. These connections select particular information suitable for the given context from input features redundantly learned by the dendrites. The model selectively learned salient segments in complex synthetic sequences. Furthermore, the model was also effective for detecting multiple cell assemblies repeating in large-scale calcium imaging data of more than 6,500 cortical neurons. Our results suggest that recurrent gating and dendrites are crucial for cortical learning of context-dependent segmentation tasks.


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