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Journal articles on the topic 'Spike-inference analysis'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Chen, Zhe. "An Overview of Bayesian Methods for Neural Spike Train Analysis." Computational Intelligence and Neuroscience 2013 (2013): 1–17. http://dx.doi.org/10.1155/2013/251905.

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Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian infere

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Harrison, Matthew T., Asohan Amarasingham, and Wilson Truccolo. "Spatiotemporal Conditional Inference and Hypothesis Tests for Neural Ensemble Spiking Precision." Neural Computation 27, no. 1 (2015): 104–50. http://dx.doi.org/10.1162/neco_a_00681.

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The collective dynamics of neural ensembles create complex spike patterns with many spatial and temporal scales. Understanding the statistical structure of these patterns can help resolve fundamental questions about neural computation and neural dynamics. Spatiotemporal conditional inference (STCI) is introduced here as a semiparametric statistical framework for investigating the nature of precise spiking patterns from collections of neurons that is robust to arbitrarily complex and nonstationary coarse spiking dynamics. The main idea is to focus statistical modeling and inference not on the f

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Zhang, Jingyue. "Signal processing and data analysis for GCaMP filtering and OASIS algorithm." Theoretical and Natural Science 73, no. 1 (2025): 172–76. https://doi.org/10.54254/2753-8818/2024.19391.

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This paper presents a comprehensive exploration of Calcium Imaging, a robust technique in neuroscience for monitoring neuronal activity. Utilizing GCaMP fluorescence indicators, the study focuses on the mouse primary visual cortex, aiming to decipher various cellular processes. The research highlights the significance of calcium ions in cellular processes and introduces the GCaMP indicator and elucidates the data preprocessing technique involving high-pass filtering and Fourier transformation, as well as the employment of the Online Active Set method to Infer Spike (OASIS) for spike inference.

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Ramírez-Mendoza, Abigail María Elena, Wen Yu, and Xiaoou Li. "A New Spike Membership Function for the Recognition and Processing of Spatiotemporal Spike Patterns: Syllable-Based Speech Recognition Application." Mathematics 11, no. 11 (2023): 2525. http://dx.doi.org/10.3390/math11112525.

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This paper introduces a new spike activation function (SPKAF) or spike membership function for fuzzy adaptive neurons (FAN), developed for decoding spatiotemporal information with spikes, optimizing digital signal processing. A solution with the adaptive network-based fuzzy inference system (ANFIS) method is proposed and compared with that of the FAN-SPKAF model, obtaining very precise simulation results. Stability analysis of systems models is presented. An application to voice recognition using solfeggio syllables in Spanish is performed experimentally, comparing the methods of FAN-step acti

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Du, Sizhen, Guojie Song, Lei Han, and Haikun Hong. "Temporal Causal Inference with Time Lag." Neural Computation 30, no. 1 (2018): 271–91. http://dx.doi.org/10.1162/neco_a_01028.

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Accurate causal inference among time series helps to better understand the interactive scheme behind the temporal variables. For time series analysis, an unavoidable issue is the existence of time lag among different temporal variables. That is, past evidence would take some time to cause a future effect instead of an immediate response. To model this process, existing approaches commonly adopt a prefixed time window to define the lag. However, in many real-world applications, this parameter may vary among different time series, and it is hard to be predefined with a fixed value. In this lette

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Qiu, Zhaomei, Fei Wang, Tingting Li, et al. "LGWheatNet: A Lightweight Wheat Spike Detection Model Based on Multi-Scale Information Fusion." Plants 14, no. 7 (2025): 1098. https://doi.org/10.3390/plants14071098.

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Wheat spike detection holds significant importance for agricultural production as it enhances the efficiency of crop management and the precision of operations. This study aims to improve the accuracy and efficiency of wheat spike detection, enabling efficient crop monitoring under resource-constrained conditions. To this end, a wheat spike dataset encompassing multiple growth stages was constructed, leveraging the advantages of MobileNet and ShuffleNet to design a novel network module, SeCUIB. Building on this foundation, a new wheat spike detection network, LGWheatNet, was proposed by integr

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Perez, Jean Claude, Valère Lounnas, and Montagnier Montagnier. "THE OMICRON VARIANT BREAKS THE EVOLUTIONARY LINEAGE OF SARS-COV2 VARIANTS." International Journal of Research -GRANTHAALAYAH 9, no. 12 (2021): 108–32. http://dx.doi.org/10.29121/granthaalayah.v9.i12.2021.4418.

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We analyze here 7 very first strains of OMICRON the SARS-CoV2 new variant from South Africa, the USA (California and Minesota), Canada and Belgium. We applied, at the scale of the whole genome and the spike gene, the biomathematics method of Fibonacci meta-structure fractal analysis applied to the UA / CG proportions. We have evidenced the RUPTURE of OMICRON with respect to ALL the previous variants: D614G, ALPHA, BETA, GAMMA, DELTA. Remarkably, it is observed that the density of OMICRON mutations in the SPIKE PRION region is more than 8 times that of the rest of the Spike protein. In pa

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Osborn, C. E., and M. D. Binder. "Correlation analysis of muscle receptor discharge during active contractions of the cat medial gastrocnemius muscle." Journal of Neurophysiology 57, no. 2 (1987): 343–56. http://dx.doi.org/10.1152/jn.1987.57.2.343.

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The spike trains of afferent fibers innervating muscle spindles and Golgi tendon organs in the medial gastrocnemius muscle were recorded during spontaneous contractions in either decerebrate cats or decapitate cats treated with L-dopa. For each afferent fiber, the approximate location of its receptor within the muscle was determined. Cross-correlation histograms were compiled from the simultaneously recorded spike trains of pairs of afferent fibers (Ia, Ib, spindle II) to determine if the degree of temporal correlation in their discharge was related to the mutual proximity of the receptors the

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Zhang, Qi, Yihui Zhang, and Yemao Xia. "Bayesian Feature Extraction for Two-Part Latent Variable Model with Polytomous Manifestations." Mathematics 12, no. 5 (2024): 783. http://dx.doi.org/10.3390/math12050783.

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Semi-continuous data are very common in social sciences and economics. In this paper, a Bayesian variable selection procedure is developed to assess the influence of observed and/or unobserved exogenous factors on semi-continuous data. Our formulation is based on a two-part latent variable model with polytomous responses. We consider two schemes for the penalties of regression coefficients and factor loadings: a Bayesian spike and slab bimodal prior and a Bayesian lasso prior. Within the Bayesian framework, we implement a Markov chain Monte Carlo sampling method to conduct posterior inference.

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Czanner, Gabriela, Uri T. Eden, Sylvia Wirth, Marianna Yanike, Wendy A. Suzuki, and Emery N. Brown. "Analysis of Between-Trial and Within-Trial Neural Spiking Dynamics." Journal of Neurophysiology 99, no. 5 (2008): 2672–93. http://dx.doi.org/10.1152/jn.00343.2007.

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Recording single-neuron activity from a specific brain region across multiple trials in response to the same stimulus or execution of the same behavioral task is a common neurophysiology protocol. The raster plots of the spike trains often show strong between-trial and within-trial dynamics, yet the standard analysis of these data with the peristimulus time histogram (PSTH) and ANOVA do not consider between-trial dynamics. By itself, the PSTH does not provide a framework for statistical inference. We present a state-space generalized linear model (SS-GLM) to formulate a point process represent

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Tu, Mengyu, Ruohe Zhao, Avital Adler, Wen-Biao Gan, and Zhe S. Chen. "Efficient Position Decoding Methods Based on Fluorescence Calcium Imaging in the Mouse Hippocampus." Neural Computation 32, no. 6 (2020): 1144–67. http://dx.doi.org/10.1162/neco_a_01281.

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Large-scale fluorescence calcium imaging methods have become widely adopted for studies of long-term hippocampal and cortical neuronal dynamics. Pyramidal neurons of the rodent hippocampus show spatial tuning in freely foraging or head-fixed navigation tasks. Development of efficient neural decoding methods for reconstructing the animal's position in real or virtual environments can provide a fast readout of spatial representations in closed-loop neuroscience experiments. Here, we develop an efficient strategy to extract features from fluorescence calcium imaging traces and further decode the

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Sheikhattar, Alireza, Sina Miran, Ji Liu, et al. "Extracting neuronal functional network dynamics via adaptive Granger causality analysis." Proceedings of the National Academy of Sciences 115, no. 17 (2018): E3869—E3878. http://dx.doi.org/10.1073/pnas.1718154115.

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Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying ada

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Boulila, Moncef. "Molecular Evolutionary Characteristics of the 2019 Novel Coronavirus (SARS-CoV-2) Contracted by Tunisian Citizens: Comparison and Relationship to Other Human and Animal Coronaviruses Based on Spike Glycoprotein-Coding Gene Sequences Analysis." International Journal of Coronaviruses 2, no. 4 (2021): 9–44. http://dx.doi.org/10.14302/issn.2692-1537.ijcv-21-3756.

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In contributing to the initiative to address the COVID-19 pandemic and in order to enhance the knowledge on driving forces shaping the evolution of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) (isolated from Tunisian patients), a comparison in relation to other coronaviruses infecting humans (SARS-CoV-1, MERS-CoV, HCoV/229E, HCoV/NL63, HCoV/OC43, and HCoV/HKU1) as well as animals (SARS-CoVs in tiger, bats, civet, pangolin, bovine, and MERS-CoV in dromedary/camel), was conducted. In-depth analysis was carried out involving 115 sequences of spike glycoprotein-coding gene extracte

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Kifouche, Abdessalam, and Abderrezak Guessoum. "Tracking times in temporal patterns embodied in intra-cortical data for controling neural prosthesis an animal simulation study." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 4721. http://dx.doi.org/10.11591/ijece.v10i5.pp4721-4737.

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Brain-machines capture brain signals in order to restore communication and movement to disabled people who suffer from brain palsy or motor disorders. In brain regions, the ensemble firing of populations of neurons represents spatio-temporal patterns that are transformed into outgoing spatio-temporal patterns which encode complex cognitive task. This transformation is dynamic, non-stationary (time-varying) and highly nonlinear. Hence, modeling such complex biological patterns requires specific model structures to uncover the underlying physiological mechanisms and their influences on system be

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Abdessalam, Kifouche, and Guessoum Abderrezak. "Tracking times in temporal patterns embodied in intra-cortical data for controlling neural prosthesis an animal simulation study." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 4721–37. https://doi.org/10.11591/ijece.v10i5.pp4721-4737.

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Brain-machines capture brain signals in order to restore communication and movement to disabled people who suffer from brain palsy or motor disorders. In brain regions, the ensemble firing of populations of neurons represents spatio-temporal patterns that are transformed into outgoing spatio-temporal patterns which encode complex cognitive task. This transformation is dynamic, non-stationary (time-varying) and highly nonlinear. Hence, modeling such complex biological patterns requires specific model structures to uncover the underlying physiological mechanisms and their influences on system be

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Yoon, Jong-Min, and Taesung Kim. "Comparative Study of Compensation Performance for Disturbance Observers Implemented Using Artificial Neural Network and Spiking Neural Network in Closed-Loop System." ECS Meeting Abstracts MA2025-01, no. 63 (2025): 3078. https://doi.org/10.1149/ma2025-01633078mtgabs.

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In this study, compensation performances of two Disturbance Observers (DOBs) implemented using an Artificial Neural Network (ANN) and a Spiking Neural Network (SNN) are compared through simulations to confirm feasibilities of adopting SNN structures to DOBs for closed-loop control systems. In most automatic control applications, a control loop is designed to make a target system follow given reference commands in real-time. A basic form of the loop contains a difference operator which generates errors between the reference commands and actual responses, and a controller which calculates contro

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Luo, Della Daiyi, Bapun Giri, Kamran Diba, and Caleb Kemere. "Extended Poisson Gaussian-Process Latent Variable Model for Unsupervised Neural Decoding." Neural Computation 36, no. 8 (2024): 1449–75. http://dx.doi.org/10.1162/neco_a_01685.

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Abstract Dimension reduction on neural activity paves a way for unsupervised neural decoding by dissociating the measurement of internal neural pattern reactivation from the measurement of external variable tuning. With assumptions only on the smoothness of latent dynamics and of internal tuning curves, the Poisson gaussian-process latent variable model (P-GPLVM; Wu et al., 2017) is a powerful tool to discover the low-dimensional latent structure for high-dimensional spike trains. However, when given novel neural data, the original model lacks a method to infer their latent trajectories in the

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Yan, Quanhui, Keke Wu, Weijun Zeng, et al. "Historical Evolutionary Dynamics and Phylogeography Analysis of Transmissible Gastroenteritis Virus and Porcine Deltacoronavirus: Findings from 59 Suspected Swine Viral Samples from China." International Journal of Molecular Sciences 23, no. 17 (2022): 9786. http://dx.doi.org/10.3390/ijms23179786.

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Since the beginning of the 21st century, humans have experienced three coronavirus pandemics, all of which were transmitted to humans via animals. Recent studies have found that porcine deltacoronavirus (PDCoV) can infect humans, so swine enteric coronavirus (SeCoV) may cause harm through cross-species transmission. Transmissible gastroenteritis virus (TGEV) and PDCoV have caused tremendous damage and loss to the pig industry around the world. Therefore, we analyzed the genome sequence data of these two SeCoVs by evolutionary dynamics and phylogeography, revealing the genetic diversity and spa

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Bulashevska, Svetlana, Colin Priest, Daniel Speicher, Jörg Zimmermann, Frank Westermann, and Armin B. Cremers. "SwitchFinder – a novel method and query facility for discovering dynamic gene expression patterns." BMC Bioinformatics 17, no. 1 (2016): 532. https://doi.org/10.1186/s12859-016-1391-0.

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<strong>Background: </strong>Biological systems and processes are highly dynamic. To gain insights into their functioning time-resolved measurements are necessary. Time-resolved gene expression data captures temporal behaviour of the genes genome-wide under various biological conditions: in response to stimuli, during cell cycle, differentiation or developmental programs. Dissecting dynamic gene expression patterns from this data may shed light on the functioning of the gene regulatory system. The present approach facilitates this discovery. The fundamental idea behind it is the following: the

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Yuan, Zixuan, Hao Liu, Renjun Hu, Denghui Zhang, and Hui Xiong. "Self-Supervised Prototype Representation Learning for Event-Based Corporate Profiling." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (2021): 4644–52. http://dx.doi.org/10.1609/aaai.v35i5.16594.

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Event-based corporate profiling aims to assess the evolving operational status of the corresponding corporate from its event sequence. Existing studies on corporate profiling have partially addressed the problem via (i) case-by-case empirical analysis by leveraging traditional financial methods, or (ii) the automatic profile inference by reformulating the problem into a supervised learning task. However, both approaches heavily rely on domain knowledge and are labor-intensive. More importantly, the task-specific nature of both approaches prevents the obtained corporate profiles from being appl

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Opazo, Miguel Angel Uribe, Gustavo Henrique Dalposso, Manuel Galea, et al. "Spatial variability of wheat yield using the gaussian spatial linear model." February 2023, no. 17(02):2023 (February 2, 2023): 179–89. http://dx.doi.org/10.21475/ajcs.23.17.02.p3742.

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Wheat production has grown over the years and is one of the most important grain food sources for humans. This work analyzed the yield of two varieties of wheat planted in a regular sampling grid in an experimental area in the south of Brazil, using some explanatory variables. For the study of the spatial variability of wheat yield of the COODETEC 101 (CD101) and COODETEC 103 (CD103) varieties, which were cultivated by the farmer in an area of 22.62 ha, 84 samples were defined considering a regular grid of 50 x 50 m. In the sampled sites, the following explanatory variables were collected: ave

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Sticker, Adriaan, Ludger Goeminne, Lennart Martens, and Lieven Clement. "Robust Summarization and Inference in Proteome-wide Label-free Quantification." Molecular & Cellular Proteomics 19, no. 7 (2020): 1209–19. http://dx.doi.org/10.1074/mcp.ra119.001624.

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Label-Free Quantitative mass spectrometry based workflows for differential expression (DE) analysis of proteins impose important challenges on the data analysis because of peptide-specific effects and context dependent missingness of peptide intensities. Peptide-based workflows, like MSqRob, test for DE directly from peptide intensities and outperform summarization methods which first aggregate MS1 peptide intensities to protein intensities before DE analysis. However, these methods are computationally expensive, often hard to understand for the non-specialized end-user, and do not provide pro

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Pownall, Grace, and Paul J. Simko. "The Information Intermediary Role of Short Sellers." Accounting Review 80, no. 3 (2005): 941–66. http://dx.doi.org/10.2308/accr.2005.80.3.941.

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This paper examines the conditions under which the market responds to disclosures of significant increases in short selling, and whether proxies for earnings expectations and alternative information sources help explain this response. Our sample is based on firms that experience abnormal short interest increases (“short spikes”) during 1989–1998. We find that the mean abnormal return around short spike announcements is significantly more negative for firms with low analyst following, consistent with short sellers providing perceived value when there are limited alternative sources of guidance

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Ortiz-Cuaran, Sandra, Lucas Michon, Marion Godefroy, et al. "Abstract 3478: Feasibility of single-cell transcriptomic profiling of pleural effusions from advanced-stage cancer patients." Cancer Research 82, no. 12_Supplement (2022): 3478. http://dx.doi.org/10.1158/1538-7445.am2022-3478.

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Abstract The pleura is a frequent metastatic site during the evolution of cancers, which can lead to a symptomatic accumulation of pleural fluid that contains tumor and immune cells. To improve patients’ comfort this fluid is removed via chest draining, and the recovered liquid is generally considered as biological waste. Here, we aim to develop an optimized protocol for single-cell sequencing of floating cells in pleural effusion from advanced-stage cancer patients. To date, pleural effusion samples have been obtained from treatment-naïve, advanced-stage cancer patients (lung n= 8, breast n=

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Jewell, Sean W., Toby Dylan Hocking, Paul Fearnhead, and Daniela M. Witten. "Fast nonconvex deconvolution of calcium imaging data." Biostatistics 21, no. 4 (2019): 709–26. http://dx.doi.org/10.1093/biostatistics/kxy083.

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Summary Calcium imaging data promises to transform the field of neuroscience by making it possible to record from large populations of neurons simultaneously. However, determining the exact moment in time at which a neuron spikes, from a calcium imaging data set, amounts to a non-trivial deconvolution problem which is of critical importance for downstream analyses. While a number of formulations have been proposed for this task in the recent literature, in this article, we focus on a formulation recently proposed in Jewell and Witten (2018. Exact spike train inference via $\ell_{0} $ optimizat

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Vanwalleghem, Gilles, Lena Constantin, and Ethan K. Scott. "Calcium Imaging and the Curse of Negativity." Frontiers in Neural Circuits 14 (January 6, 2021). http://dx.doi.org/10.3389/fncir.2020.607391.

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The imaging of neuronal activity using calcium indicators has become a staple of modern neuroscience. However, without ground truths, there is a real risk of missing a significant portion of the real responses. Here, we show that a common assumption, the non-negativity of the neuronal responses as detected by calcium indicators, biases all levels of the frequently used analytical methods for these data. From the extraction of meaningful fluorescence changes to spike inference and the analysis of inferred spikes, each step risks missing real responses because of the assumption of non-negativity

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van Haasteren, Rutger. "Use Model Averaging instead of Model Selection in Pulsar Timing." Monthly Notices of the Royal Astronomical Society: Letters, November 19, 2024. http://dx.doi.org/10.1093/mnrasl/slae108.

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Abstract Over the past decade and a half, adoption of Bayesian inference in pulsar timing analysis has led to increasingly sophisticated models. The recent announcement of evidence for a stochastic background of gravitational waves by various pulsar timing array projects highlighted Bayesian inference as a central tool for parameter estimation and model selection. Despite its success, Bayesian inference is occasionally misused in the pulsar timing community. A common workflow is that the data is analyzed in multiple steps: a first analysis of single pulsars individually, and a subsequent analy

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Frühwirth-Schnatter, Sylvia. "Generalized cumulative shrinkage process priors with applications to sparse Bayesian factor analysis." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 381, no. 2247 (2023). http://dx.doi.org/10.1098/rsta.2022.0148.

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The paper discusses shrinkage priors which impose increasing shrinkage in a sequence of parameters. We review the cumulative shrinkage process (CUSP) prior of Legramanti et al. (Legramanti et al . 2020 Biometrika 107 , 745–752. ( doi:10.1093/biomet/asaa008 )), which is a spike-and-slab shrinkage prior where the spike probability is stochastically increasing and constructed from the stick-breaking representation of a Dirichlet process prior. As a first contribution, this CUSP prior is extended by involving arbitrary stick-breaking representations arising from beta distributions. As a second con

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Peng, Hui, He Wang, Weijia Kong, Jinyan Li, and Wilson Wen Bin Goh. "Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference." Nature Communications 15, no. 1 (2024). http://dx.doi.org/10.1038/s41467-024-47899-w.

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AbstractIdentification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step makes it challenging to identify optimal workflows that maximize the identification of differentially expressed proteins. To identify optimal workflows and their common properties, we conduct an extensive study involving 34,576 combinatoric experiments on 24 gold standard spike-in datasets.

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Wang, Xin, Mingda Hu, Bo Liu, et al. "Evaluating the effect of SARS-CoV-2 spike mutations with a linear doubly robust learner." Frontiers in Cellular and Infection Microbiology 13 (April 19, 2023). http://dx.doi.org/10.3389/fcimb.2023.1161445.

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Driven by various mutations on the viral Spike protein, diverse variants of SARS-CoV-2 have emerged and prevailed repeatedly, significantly prolonging the pandemic. This phenomenon necessitates the identification of key Spike mutations for fitness enhancement. To address the need, this manuscript formulates a well-defined framework of causal inference methods for evaluating and identifying key Spike mutations to the viral fitness of SARS-CoV-2. In the context of large-scale genomes of SARS-CoV-2, it estimates the statistical contribution of mutations to viral fitness across lineages and theref

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"Classical Vs. Intelligence Controller in Resistance Spot Welding System." International Journal of Innovative Technology and Exploring Engineering 9, no. 3 (2020): 2583–88. http://dx.doi.org/10.35940/ijitee.f3631.019320.

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This paper is an attempt to accomplish a performance analysis of the classical (Proportional Integral) and intelligence(Artificial Neuro Fuzzy Inference System) control techniques on current spike reduction by means of magnetization level control in the primary winding on the medium recurrence transformer based DC spot welding framework. Cause for occurrence of spike in the primary winding of a transformer is unequal resistance between two secondary circuit’s of the transformer and different characteristics of rectifier diode. Which leads to the magnetic saturation in the form of spikes in the

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Barbier, Jean, Francesco Camilli, Marco Mondelli, and Manuel Sáenz. "Fundamental limits in structured principal component analysis and how to reach them." Proceedings of the National Academy of Sciences 120, no. 30 (2023). http://dx.doi.org/10.1073/pnas.2302028120.

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How do statistical dependencies in measurement noise influence high-dimensional inference? To answer this, we study the paradigmatic spiked matrix model of principal components analysis (PCA), where a rank-one matrix is corrupted by additive noise. We go beyond the usual independence assumption on the noise entries, by drawing the noise from a low-order polynomial orthogonal matrix ensemble. The resulting noise correlations make the setting relevant for applications but analytically challenging. We provide characterization of the Bayes optimal limits of inference in this model. If the spike is

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Loya, Hrushikesh, Georgios Kalantzis, Fergus Cooper, and Pier Francesco Palamara. "A scalable variational inference approach for increased mixed-model association power." Nature Genetics, January 9, 2025. https://doi.org/10.1038/s41588-024-02044-7.

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AbstractThe rapid growth of modern biobanks is creating new opportunities for large-scale genome-wide association studies (GWASs) and the analysis of complex traits. However, performing GWASs on millions of samples often leads to trade-offs between computational efficiency and statistical power, reducing the benefits of large-scale data collection efforts. We developed Quickdraws, a method that increases association power in quantitative and binary traits without sacrificing computational efficiency, leveraging a spike-and-slab prior on variant effects, stochastic variational inference and gra

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Festa, Dylan, Amir Aschner, Aida Davila, Adam Kohn, and Ruben Coen-Cagli. "Neuronal variability reflects probabilistic inference tuned to natural image statistics." Nature Communications 12, no. 1 (2021). http://dx.doi.org/10.1038/s41467-021-23838-x.

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AbstractNeuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences. In primary visual cortex (V1), modulation of variability by sensory and non-sensory factors supports this view. However, it is unknown whether V1 variability reflects the statistical structure of visual inputs, as would be required for inferences correctly tuned to the statistics of the natural environment. Her

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Yu, Changjo, Sungkyu Jung, and Jisu Kim. "Significance of modes in the torus by topological data analysis." Stat 12, no. 1 (2023). http://dx.doi.org/10.1002/sta4.636.

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AbstractThis paper addresses the problem of identifying modes or density bumps in multivariate angular or circular data, which have diverse applications in fields like medicine, biology and physics. We focus on the use of topological data analysis and persistent homology for this task. Specifically, we extend the methods for uncertainty quantification in the context of a torus sample space, where circular data lie. To achieve this, we employ two types of density estimators, namely, the von Mises kernel density estimator and the von Mises mixture model, to compute persistent homology, and propo

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Dang, Tung, Artem Lysenko, Keith A. Boroevich, and Tatsuhiko Tsunoda. "VBayesMM: variational Bayesian neural network to prioritize important relationships of high-dimensional microbiome multiomics data." Briefings in Bioinformatics 26, no. 4 (2025). https://doi.org/10.1093/bib/bbaf300.

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Abstract The analysis of high-dimensional microbiome multiomics datasets is crucial for understanding the complex interactions between microbial communities and host physiological states across health and disease conditions. Despite their importance, current methods, such as the microbe–metabolite vectors approach, often face challenges in predicting metabolite abundances from microbial data and identifying keystone species. This arises from the vast dimensionality of metagenomics data, which complicates the inference of significant relationships, particularly the estimation of co-occurrence p

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Andrea, Comella-Bolla, and Soriano Jordi. "Human Pluripotent Stem Cell-Derived Neurons Are Functionally Mature In Vitro and Integrate into the Mouse Striatum Following Transplantation." April 30, 2020. https://doi.org/10.1007/s12035-020-01907-4.

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Human pluripotent stem cells (hPSCs) are a powerful tool for modelling human development. In recent years, hPSCs have become central in cell-based therapies for neurodegenerative diseases given their potential to replace affected neurons. However, directing hPSCs into specific neuronal types is complex and requires an accurate protocol that mimics endogenous neuronal development. Here we describe step-by-step a fast feeder-free neuronal differentiation protocol to direct hPSCs to mature forebrain neurons in 37 days in vitro (DIV). The protocol is based upon a combination of specific morphogens

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Kazemi Naeini, Maryam, Mahdi Akbarzadeh, Iraj Kazemi, Doug Speed, and Sayed Mohsen Hosseini. "Using the Bayesian variational spike and slab model in a genome‐wide association study for finding associated loci with bipolar disorder." Annals of Human Genetics, December 31, 2023. http://dx.doi.org/10.1111/ahg.12538.

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AbstractObjectiveThe genome‐wide association studies (GWAS) analysis, the most successful technique for discovering disease‐related genetic variation, has some statistical concerns, including multiple testing, the correlation among variants (single‐nucleotide polymorphisms) based on linkage disequilibrium and omitting the important variants when fitting the model with just one variant. To eliminate these problems in a small sample‐size study, we used a sparse Bayesian learning model for finding bipolar disorder (BD) genetic variants.MethodsThis study used the Wellcome Trust Case Control Consor

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Kim, Youngeun, and Priyadarshini Panda. "Revisiting Batch Normalization for Training Low-Latency Deep Spiking Neural Networks From Scratch." Frontiers in Neuroscience 15 (December 9, 2021). http://dx.doi.org/10.3389/fnins.2021.773954.

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Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield huge energy efficiency benefits on neuromorphic hardware. However, SNNs convey temporally-varying spike activation through time that is likely to induce a large variation of forward activation and backward gradients, resulting in unstable training. To address this training issue in SNNs, we revisit Batch Normalization (BN) and propose a temporal Batch Normalization Through Time (BNTT) technique. Different from previo

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Zeng, Zijian, Meng Li, and Marina Vannucci. "Bayesian covariate-dependent graph learning with a dual group spike-and-slab prior." Biometrics 81, no. 2 (2025). https://doi.org/10.1093/biomtc/ujaf053.

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ABSTRACT Covariate-dependent graph learning has gained increasing interest in the graphical modeling literature for the analysis of heterogeneous data. This task, however, poses challenges to modeling, computational efficiency, and interpretability. The parameter of interest can be naturally represented as a 3-dimensional array with elements that can be grouped according to 2 directions, corresponding to node level and covariate level, respectively. In this article, we propose a novel dual group spike-and-slab prior that enables multi-level selection at covariate-level and node-level, as well

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Poli, Giovanni, Raffaele Argiento, Amedeo Amedei, and Francesco C. Stingo. "High‐Dimensional Bayesian Semiparametric Models for Small Samples: A Principled Approach to the Analysis of Cytokine Expression Data." Biometrical Journal 66, no. 8 (2024). http://dx.doi.org/10.1002/bimj.70000.

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ABSTRACTIn laboratory medicine, due to the lack of sample availability and resources, measurements of many quantities of interest are commonly collected over a few samples, making statistical inference particularly challenging. In this context, several hypotheses can be tested, and studies are not often powered accordingly. We present a semiparametric Bayesian approach to effectively test multiple hypotheses applied to an experiment that aims to identify cytokines involved in Crohn's disease (CD) infection that may be ongoing in multiple tissues. We assume that the positive correlation commonl

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Caduff, Lea, David Dreifuss, Tobias Schindler, et al. "Inferring transmission fitness advantage of SARS-CoV-2 variants of concern from wastewater samples using digital PCR, Switzerland, December 2020 through March 2021." Eurosurveillance 27, no. 10 (2022). http://dx.doi.org/10.2807/1560-7917.es.2022.27.10.2100806.

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Background Throughout the COVID-19 pandemic, SARS-CoV-2 genetic variants of concern (VOCs) have repeatedly and independently arisen. VOCs are characterised by increased transmissibility, increased virulence or reduced neutralisation by antibodies obtained from prior infection or vaccination. Tracking the introduction and transmission of VOCs relies on sequencing, typically whole genome sequencing of clinical samples. Wastewater surveillance is increasingly used to track the introduction and spread of SARS-CoV-2 variants through sequencing approaches. Aim Here, we adapt and apply a rapid, high-

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Tolentino, Jarel Elgin, Spyros Lytras, Jumpei Ito, and Kei Sato. "Recombination analysis on the receptor switching event of MERS-CoV and its close relatives: implications for the emergence of MERS-CoV." Virology Journal 21, no. 1 (2024). http://dx.doi.org/10.1186/s12985-024-02358-2.

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Abstract Background PlMERS-CoV is a coronavirus known to cause severe disease in humans, taxonomically classified under the subgenus Merbecovirus. Recent findings showed that the close relatives of MERS-CoV infecting vespertillionid bats (family Vespertillionidae), named NeoCoV and PDF-2180, use their hosts’ ACE2 as their entry receptor, unlike the DPP4 receptor usage of MERS-CoV. Previous research suggests that this difference in receptor usage between these related viruses is a result of recombination. However, the precise location of the recombination breakpoints and the details of the reco

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Fan, Kun, Yu Jiang, Shuangge Ma, Weiqun Wang, and Cen Wu. "Robust sparse Bayesian regression for longitudinal gene–environment interactions." Journal of the Royal Statistical Society Series C: Applied Statistics, April 8, 2025. https://doi.org/10.1093/jrsssc/qlaf027.

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Abstract In longitudinal studies, repeated measure analysis of variance (ANOVA) is a classical analysis where selecting important main and interaction effects for accurate estimation and prediction is among one of its central goals. With high-dimensional genetic factors, ANOVA leads to a sparse longitudinal gene–environment (G×E) interaction problem that has not been thoroughly investigated so far, partially due to the challenges to incorporate robustness against skewed phenotypic measurements, intra-cluster correlations among longitudinal observations, and structured sparsity arising from the

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Boileau, Etienne, Janine Altmüller, Isabel S. Naarmann-de Vries, and Christoph Dieterich. "A comparison of metabolic labeling and statistical methods to infer genome-wide dynamics of RNA turnover." Briefings in Bioinformatics, July 7, 2021. http://dx.doi.org/10.1093/bib/bbab219.

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Abstract Metabolic labeling of newly transcribed RNAs coupled with RNA-seq is being increasingly used for genome-wide analysis of RNA dynamics. Methods including standard biochemical enrichment and recent nucleotide conversion protocols each require special experimental and computational treatment. Despite their immediate relevance, these technologies have not yet been assessed and benchmarked, and no data are currently available to advance reproducible research and the development of better inference tools. Here, we present a systematic evaluation and comparison of four RNA labeling protocols

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Wu, Eric G., Andra Marija Rudzite, Martin O. Bohlen, et al. "Decomposition of retinal ganglion cell electrical images for cell type and functional inference." Journal of Neural Engineering, June 10, 2025. https://doi.org/10.1088/1741-2552/ade344.

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Abstract Objective: Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and for the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision. Approach: The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, con

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Sandoval, Eluzai Dinai Pinto, Agda Maria Bernegossi, Sonia Gallina, Rafael Reyna-Hurtado, and José Maurício Barbanti Duarte. "CYTOGENETIC, MOLECULAR AND MORPHOLOGICAL CHARACTERIZATION OF Odocoileus pandora (MERRIAM, 1901) (ARTIODACTYLA, CERVIDAE)." Canadian Journal of Zoology, June 9, 2023. http://dx.doi.org/10.1139/cjz-2022-0037.

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The Yucatan brown brocket deer was initially described as Mazama pandora Merriam, 1901. Phylogenetic analysis of mitochondrial DNA recovered the species as sister group of the genus Odocoileus and subsequently the species was repositioned to this genus naming Odocoileus pandora (Merriam, 1901). However, there is still a lack of taxonomic consensus that could assists in nomenclature definition of the species. We aim to clarify the taxonomy of O. pandora through an integrative assessment using morphological, cytogenetic, and molecular data from recently collected specimens. Morphological charact

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Antonello, Priscila C., Thomas F. Varley, John Beggs, Marimélia Porcionatto, Olaf Sporns, and Jean Faber. "Self-organization of in vitro neuronal assemblies drives to complex network topology." eLife 11 (June 16, 2022). http://dx.doi.org/10.7554/elife.74921.

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Activity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, and tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of spike trains recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the topology evolves during maturation. The meth

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Pavia, Grazia, Angela Quirino, Nadia Marascio, et al. "Persistence of SARS‐CoV‐2 infection and viral intra‐ and inter‐host evolution in COVID‐19 hospitalized patients." Journal of Medical Virology 96, no. 6 (2024). http://dx.doi.org/10.1002/jmv.29708.

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AbstractSevere acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) persistence in COVID‐19 patients could play a key role in the emergence of variants of concern. The rapid intra‐host evolution of SARS‐CoV‐2 may result in an increased transmissibility, immune and therapeutic escape which could be a direct consequence of COVID‐19 epidemic currents. In this context, a longitudinal retrospective study on eight consecutive COVID‐19 patients with persistent SARS‐CoV‐2 infection, from January 2022 to March 2023, was conducted. To characterize the intra‐ and inter‐host viral evolution, whole genome

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Knudsen, Berith E., Lasse Bergmark, Patrick Munk, et al. "Impact of Sample Type and DNA Isolation Procedure on Genomic Inference of Microbiome Composition." mSystems 1, no. 5 (2016). http://dx.doi.org/10.1128/msystems.00095-16.

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ABSTRACT Sequencing-based analyses of microbiomes may lead to a breakthrough in our understanding of the microbial worlds associated with humans, animals, and the environment. Such insight could further the development of innovative ecosystem management approaches for the protection of our natural resources and the design of more effective and sustainable solutions to prevent and control infectious diseases. Genome sequence information is an organism (pathogen)-independent language that can be used across sectors, space, and time. Harmonized standards, protocols, and workflows for sample proce

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