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opencc-zeroDec 2021View details →

Ripples reflect a spectrum of synchronous spiking activity in human anterior temporal lobe

Direct brain recordings have provided important insights into how high-frequency activity captured through intracranial EEG (iEEG) supports human memory retrieval. The extent to which such activity is comprised of transient fluctuations that reflect the dynamic coordination of underlying neurons, however, remains unclear. Here, we simultaneously record iEEG, local field potential (LFP), and single unit activity in the human temporal cortex. We demonstrate that fast oscillations within the previously identified 80–120 Hz ripple band contribute to broadband high-frequency activity in the human cortex. These ripple oscillations exhibit a spectrum of amplitudes and durations related to the amount of underlying neuronal spiking. Ripples in the macro-scale iEEG are related to the number and synchrony of ripples in the micro-scale LFP, which in turn are related to the synchrony of neuronal spiking. Our data suggest that neural activity in the human temporal lobe is organized into transient bouts of ripple oscillations that reflect underlying bursts of spiking activity.

opencc-by-4.0Mar 2022View details →

Approximate Entropy of Spiking Series Reveals Different Dynamical States in Cortical Assemblies

Self-organized criticality theory proved that information transmission and computational performances of neural networks are optimal in critical state. By using recordings of the spontaneous activity originated by dissociated neuronal assemblies coupled to Micro-Electrode Arrays (MEAs), we tested this hypothesis using Approximate Entropy (ApEn) as a measure of complexity and information transfer. We analysed 60 min of electrophysiological activity of three neuronal cultures exhibiting either sub-critical, critical or super-critical behaviour. The firing patterns on each electrode was studied in terms of the inter-spike interval (ISI), whose complexity was quantified using ApEn. We assessed that in critical state the local complexity (measured in terms of ApEn) is larger than in sub- and super-critical conditions. Our estimations were stable when considering epochs as short as 5 min. These preliminary results indicate that ApEn has the potential of being a reliable and stable index to monitor local information transmission in a neuronal network during maturation. Thus, ApEn applied on ISI time series appears to be potentially useful to reflect the overall complex behaviour of the neural network, even monitoring a single specific location.

opencc-by-4.0May 2022View details →

Basal Ganglia Neurons in Healthy and Parkinsonian Primates Generate Recurring Sequences of Spikes

These data are the source data for the manuscript entitled "Basal Ganglia Neurons in Healthy and Parkinsonian Primates Generate Recurring Sequences of Spikes", authored by Adriana Galvan and Thomas Wichmann (Emory University, Atlanta, GA).   The data are interspike interval measurements generated from electrophysiological recordings of spontaneous spiking activities of single neurons in the basal ganglia of awake monkeys, before and after rendering them parkinsonian by treatment with MPTP. THe animals were not engaged in any task or other structured activity. The data format is a one-column listing of successive interspike intervals, and are presented in milliseconds. The data are structured into different folders: GPe normal - data collected in the external pallidal segment, under normal conditions GPe MPTP - data collected in the external pallidal segment, under parkinsonian conditions STN normal - data collected in the subthalamic nucleus, under normal conditions STN MPTP - data collected in the subthalamic nucleus, under parkinsonian conditions GPi normal - data collected in the internal pallidal segment, under normal conditions GPi MPTP - data collected in the internal pallidal segment, under parkinsonian conditions A 'Read Me' file is included, containing these explanations. For questions, please contact the corresponding author, Dr. Thomas Wichmann (twichma@emory.edu).

opencc-zeroJun 2022View details →

Stimulus presentation can enhance spiking irregularity across subcortical and cortical regions

Stimulus presentation is believed to quench neural response variability as measured by fano-factor (FF). However, the relative contribution of within-trial spike irregularity and trial-to-trial rate variability to FF reduction has remained elusive. Here, we introduce a principled approach for accurate estimation of spiking irregularity and rate variability in time for doubly stochastic point processes. Consistent with previous evidence, analysis showed stimulus-induced reduction in rate variability across multiple cortical and subcortical areas. However, unlike what was previously thought, spiking irregularity, was not constant in time and could be enhanced partly due factors beyond local CV measures such as bursting abating the quench in the post-stimulus FF. Simulations confirmed plausibility of a time varying spiking irregularity arising from within and between pool correlations of excitatory and inhibitory neural inputs. By accurate parsing of neural variability, our approach reveals previously unnoticed changes in neural response variability and constrains candidate mechanisms that give rise to observed rate variability and spiking irregularity within brain regions.

opencc-by-4.0Aug 2022View details →

Source data for "Ca2+ channels couple spiking to mitochondrial metabolism in substantia nigra dopaminergic neurons"

Fig.1Aa-c.tif 2PLSM images (Fura-2 filled neuron) used for the reconstruction in Fig.1A   Fig1CDJ.xlsx Numerical data for the charts in Fig. 1C, Fig.1D, Fig.1J   Fig.1F_GCEPIA1er.tif Confocal image (green channel, G-CEPIA1er) for Fig. 1F   Fig.1F_TH.tif Confocal image (blue channel, anti-TH immunostaining) for Fig. 1F   Fig.1G_GCEPIA1er.tif Confocal image (green channel, G-CEPIA1er) for Fig. 1G   Fig.1G_CRT.tif Confocal image (magenta channel, anti-CRT immunostaining) for Fig. 1G   Fig.1H_GCEPIA1er.tif Confocal image (green channel, G-CEPIA1er) for Fig. 1H   Fig.1H_TH.tif Confocal image (blue channel, anti-TH immunostaining) for Fig. 1H   Fig. 2B_mitoGCaMP6.tif Confocal image (green channel, mito-GCaMP6) for Fig. 2B   Fig.2B_TH.tif Confocal image (blue channel, anti-TH immunostaining) for Fig. 2B   Fig. 2C_mitoGCaMP6.tif Confocal image (green channel, mito-GCaMP6) for Fig. 2C   Fig.2C_COXIV.tif Confocal image (magenta channel, anti-COXIV immunostaining) for Fig. 2C   Fig. 2D_mitoGCaMP6.tif Confocal image (green channel, mito-GCaMP6) for Fig. 2D   Fig.2D_TH.tif Confocal image (blue channel, anti-TH immunostaining) for Fig. 2D   Fig.2FGJL.xlsx Numerical data for the charts in Fig. 2F, Fig.2G, Fig.2J, Fig.2L   Fig.3BDE.xlsx Numerical data for the charts in Fig. 3B, Fig.3D, Fig.3E   Fig.3C_Alexa.tif MAX Projection of z-stack of 2PLSM images (magenta channel, Alexa 594 dye) used to generate Fig.3C top panel   Fig.3C_mitoGCaMP6.tif MAX Projection of z-stack of 2PLSM images (green channel, mito-GCaMP6) used to generate Fig.3C top panel   Fig.3C_inset_dendritic_mitoGCaMP6.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.3C bottom left panel   Fig.3C_inset_soma_mitoGCaMP6.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.3C bottom right panel   Fig. 4A_PercevalHR.tif Confocal image (green channel, PercevalHR) for Fig.4A   Fig.4A_TH.tif Confocal image (blue channel, anti-TH immunostaining) for Fig.4A   Fig. 4B_PercevalHR.tif Confocal image (green channel, PercevalHR) for Fig.4B   Fig.4B_TH.tif Confocal image (blue channel, anti-TH immunostaining) for Fig.4B   Fig.4G.xlsx Numerical data for the charts in Fig.4G   Fig.5BDFHI.xlsx Numerical data for the charts in Fig.5B, Fig.5D, Fig.5F, Fig.5H, Fig.5I   Fig.6ADEFJKLN.xlsx Numerical data for the charts in Fig.6A, Fig.6D, Fig.6E, Fig.6F, Fig.6J, Fig.6K, Fig.6L, Fig.6N   Fig.6H_mitoroGFP.tif 2PLSM image (green channel, mito-roGFP) for Fig.6H   Fig.6M_MCU-KO.tif Combined EM micrographs used to generate Fig. 6M right side   Fig.6M_wildtype.tif Combined EM micrographs used to generate Fig. 6M left side   Fig.7CEFG.xlsx Numerical data for the charts in Fig.7C, Fig.7E, Fig.7F, Fig.7G   Fig.8CEHJK.xlsx Numerical data for the charts in Fig.8C, Fig.8E, Fig.8H, Fig.8J, Fig.8K   Fig.S1A_bottom.tif 2PLSM image (green channel, G-CEPIA1er) for Fig.S1A bottom panel (low Ca2+)   Fig.S1A_top.tif 2PLSM image (green channel, G-CEPIA1er) for Fig.S1A top panel (high Ca2+)   Fig.S1B.xlsx Numerical data for the chart in Fig.S1B   Fig.S2A_baseline.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2A middle panel (baseline)   Fig.S2A_MAX.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2A top panel (high Ca2+)   Fig.S2A_min.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2A bottom panel (low Ca2+)   Fig.S2C_baseline.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2C middle panel (baseline)   Fig.S2C_MAX.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2C top panel (high Ca2+)   Fig.S2C_min.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2C bottom panel (low Ca2+)   Fig.S2E_baseline.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2E bottom left panel (baseline)   Fig.S2E_end.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2E bottom right panel   Fig.S2E_peak.tif 2PLSM image (green channel, mito-GCaMP6) for Fig.S2E bottom center panel   Fig.S2F.xlsx Numerical data for the chart in Fig.S2F   Fig.S3ABC.xlsx Numerical data for the charts in Fig.S3A, Fig.S3B, Fig.S3C   Fig.S4CD.xlsx Numerical data for the charts in Fig.S4C, Fig.S4D   Fig.S5ABC.xlsx Numerical data for the charts in Fig.S5A, Fig.S5B, Fig.S5C   Fig.S7A.tif 2PLSM image (green channel, GCaMP6) for Fig.S7A   Fig.S7CEFGH.xlsx Numerical data for the charts in Fig.S7C, Fig.S7E, Fig.S7F, Fig.S7G, Fig.S7H   Fig.S8ABCDEF.xlsx Numerical data for the charts in Fig.S8A, Fig.S8B, Fig.S8C, Fig.S8D, Fig.S8E, Fig.S8F   Fig.S9AHIJK.xlsx Numerical data for the charts in Fig.S9A, Fig.S9H, Fig.S9I, Fig.S9J, Fig.S9K   Fig.S9B_wildtype_DLStr.tif Confocal image of dorso-laterateral striatum in wildtype mouse (red channel, anti-TH immunostaining) for Fig.S9B   Fig.S9C_MCU-KO_DLStr.tif Confocal image of dorso-laterateral striatum in MCU-KO mouse (red channel, anti-TH immunostaining) for Fig.S9C   Fig.S9D_wildtype_SN.tif Confocal image of midbrain in wildtype mouse (red channel, anti-TH immunostaining) for Fig.S9D   Fig.S9E_MCU-KO_SN.tif Confocal image of midbrain in MCU-KO mouse (red channel, anti-TH immunostaining) for Fig.S9E   Fig.S9F_wildtype_openfield.png Open field path tracked for wildtype mouse for Fig.S9F   Fig.S9G_MCU-KO_openfield.png Open field path tracked for MCU-KO mouse for Fig.S9G   Fig.S10A.xlsx Numerical data for the charts in Fig.S10A

opencc-zeroSep 2022View details →

Data for: Immunogenicity of SARS-CoV-2 spike antigens derived from Beta & Delta variants of concern

Using our strongly immunogenic SmT1 SARS-CoV-2 spike antigen platform, we developed novel antigens based on the Beta & Delta variants of concern. These antigens elicited higher neutralizing antibody activity to the corresponding variant than comparable vaccine formulations based on the original reference strain, while a multivalent vaccine generated cross-neutralizing activity to all three variants. This suggests that while current vaccines may be effective at reducing severe disease to existing variants of concern, variant-specific antigens, whether in a mono- or multivalent vaccine, may be required to induce optimal immune responses and reduce infection against arising variants.

opencc-byJun 2021View details →

Hannes Kolehmainen's Spike Shoe

Hannes Kolehmainen's spike shoes play an interesting part in the history of Finnish sporting goods industry. In 1910 Arvo Hohenthal founded a company called Oy Urheilutarpeita – Sportartiklar Ab in Helsinki to sell sports implements, mainly javelins. Hannes Kolehmainen, who had already made a name for himself as runner, needed a new pair of shoes for the Stockholm Olympic Games of 1912. Hohenthal placed an order for him and travelled himself to London with exact measurements of the runner's foot. The shoes that arrived from London were too long and narrow, but it was too late to make any adjustments. Kolehmainen used the ill-fitting shoes in Stockholm and won three gold medals anyway. Hohenthal decided that Finnish athletes should never again need to suffer from sub-standard footwear. His company (later known as Karhu) started to manufacture sports shoes, including the ones Kolehmainen used to win Olympic gold in marathon in 1920. Source: Objaverse 1.0 / Sketchfab

opencc-by-4.0Oct 2017View details →

Blind Sparse Deconvolution for Spike Inference from Fluorescence: Larval Zebrafish fluorescence recordings

This repository contains the two fluorescence imaging datasets used in the publication "Blind Sparse Deconvolution for Spike Inference from Fluorescence Recordings". Each dataset is a T X N DF/F matrix, where T is the number of fluorescence measurement and N is the number of neurons. They were obtained from light-sheet fluorescence microscopy. Both recordings correspond to spontaneous activity. The first file "DFF_20Hz_GCaMP3.mat" was obtained by a 2D recording acquired at 20 frame/second for 20 minutes of a 5dpf-old zebrafish larva expressing the genetically encoded indicator GCaMP3 (elavl3:GCaMP3). The images were parsed into 8082 neural traces. The second file "DFF_1Hz_GCaMP5.mat" was obtained by a 3D, whole-brain recording acquired at 20 frame/second and 20 stacks, (1 measurement/voxel/s) of a 5dpf-old zebrafish larva expressing the genetically encoded indicator GCaMP5. After segmentation, 255463 fluorescence traces encompassing the brain volume are computed independently.

opencc-by-sa-4.0Jul 2018View details →

SRR606249 spiked with fungal and viral Illumina reads at 1x, 10X, and 100X

SRR606249 spiked with fungal and viral Illumina reads at 1x, 10X, and 100X using the ART simulator. See also: https://osf.io/dm938/ and https://dahak-metagenomics.github.io/ Fungal and viral genomes used for computational spike-ins. Kingdom    Organism    Accession    Link Fungus    Candida albicans SC5314    NC_032089.1    https://www.ncbi.nlm.nih.gov/genome/?term=Candida+albicans Fungus    Cryptococcus neoformans var. grubii H99    NC_026745.1    https://www.ncbi.nlm.nih.gov/genome/?term=Cryptococcus+neoformans Fungus    Aspergillus fumigatus Af293    NC_007194.1    https://www.ncbi.nlm.nih.gov/genome/?term=Aspergillus+fumigatus Fungus    Histoplasma capsulatum NAm1    NW_001813913.1    https://www.ncbi.nlm.nih.gov/genome/?term=Histoplasma+capsulatum Virus    Human immunodeficiency virus 1    NC_001802.1    https://www.ncbi.nlm.nih.gov/genome/10319 Virus    Influenza A virus (A/New York/392/2004(H3N2))    NC_007373.1    https://www.ncbi.nlm.nih.gov/genome/10290 Virus    Marburg virus - Musoke, Kenya, 1980    NC_001608.3    https://www.ncbi.nlm.nih.gov/genome/5314 Virus    Ebola virus - Mayinga, Zaire, 1976    NC_002549.1    https://www.ncbi.nlm.nih.gov/assembly/GCF_000848505.1/

opencc-by-4.0Dec 2023View details →

Intra-host single-genome sequences of SARS-CoV-2 spike from people without HIV and people living with HIV

Supporting data and code for the study "Rapid Intra-host Diversification and Evolution of SARS-CoV-2 in Advanced HIV Infection" (2024) by Ko & Radecki et al. See <a href="https://github.com/niaid/UMI-pacbio-pipeline/releases/tag/SC2-HIV-demo">the GitHub</a> for demo of SGS and haplotype generation using example data from the study. Raw sequencing data are deposited at <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1055920">PRJNA1055920</a>. READMEs are available within individual folders and releases.

Supplementary data for: Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C

Supplementary example data for the work presented in "Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signalling in a Model of Niemann-Pick Disease Type C".  Abstract:  Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing our understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavour. Here, we use specially designed graphene microelectrode arrays (G-MEAs), which are compatible with high spatial resolution imaging across various scales as well as permit high temporal resolution electrophysiological recordings to address these challenges. Furthermore, alongside G-MEAs, we have developed an easy-to-implement machine learning algorithm to efficiently process the large datasets collected from MEA recordings. We demonstrate that the combined use of G-MEAs, machine learning (ML) spike analysis, and four-dimensional (4D) structured illumination microscopy (SIM) enables monitoring the impact of disease progression on hippocampal neurons which have been treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC), and show that synaptic boutons, compared to untreated controls, significantly increase in size, leading to a loss in neuronal signalling capacity.

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 1 - Phycocyanin & Chlorophyll a

Accompanying data to publication: Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin

Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 2 - Phycocyanin & Chlorophyll a

opencc-by-4.0Sep 2024View details →

The 2023 global warming spike was driven by El Niño/Southern Oscillation (Data)

GFDL FLOR and CM2.1 surface temperature datasets used in: Raghuraman et al., 2024, Atmospheric Chemistry and Physics, "The 2023 global warming spike was driven by El Niño/Southern Oscillation" Point of Contact: Shiv Priyam Raghuraman, sraghur2@illinois.edu All other model data files used in paper can be downloaded from their respective websites (see paper's "Data availability" section).

opencc-by-4.0Nov 2024View details →

Docking data for "The evolution of the SARS-CoV-2 spike protein for differential usage of the host transmembrane serine proteases entry pathway"

The dataset includes predicted complexes of the SARS-CoV-2 Spike protein (specifically at the S2' cleavage site) with Hepsin and TMPRSS2 proteins. It contains data on three variants: Wuhan, Delta, and Omicron BA.1. Compressed folders: -357596-DeltaHepsin.tgz -357597-DeltaTMPRSS2.tgz -360039-WuhanHepsin.tgz -360042-TMPRSSWuhan.tgz -392981-TMPRSS-BA1_all.tgz -392982-Hepsin-BA-all.tgz Each compressed folder contains the following: -Initial structures in pdb format -Output complexes in pdb format -Clusters in pdb format -Protocols -Parameters -Scoring files   Protein-protein docking  Molecular docking between the SARS-CoV-2 S protein of Wuhan, Delta (PDB: 7W92, [DOI: 10.1038/s41467-022-28528-w]), and BA.1 (PDB: 7XO5, [DOI: 10.1038/s41422-022-00672-4]) and the human proteases TMPRSS2 (PDB: 8HD8, [DOI: 10.1038/s41467-023-42527-5]) and Hepsin (PDB: 1Z8G, [DOI: 10.1042/BJ20041955]) was performed using the HADDOCK v2.5-2024.03 webserver ([DOI: 10.1021/ja026939x], [DOI: 10.1016/j.jmb.2015.09.014]). Missing loops in the protein structures were reconstructed using Modeller v10.5 ([DOI: 10.1006/jmbi.1993.1626]). Every heteroatom was removed from the reference structures. The relaxed atomistic coordinates for each S protein variant were derived via all-atom molecular dynamics (MD) simulations. These simulations were performed using AMBER22 with the FF19SB force fields and the pmemd.cuda module for enhanced performance ([DOI: 10.1021/acs.jcim.3c01153], [DOI: 10.1021/jz501780a], [DOI:10.1021/ct400314y]). For the Wuhan variant the S protein was retrieved from our previous modeling study [DOI: 10.1039/D0NR03969A] where for Delta and BA.1, ecah S protein was placed in a dodecahedral box, extending 20 Å beyond the solute in every cartesian direction, and solvated with the four-site OPC water model ([DOI: 10.1021/jz501780a]). The systems were neutralized with counterions, specifically one Cl− ion for the Delta variant and three Cl- ions for the BA.1 variant. To remove local clashes, a geometric optimization was performed using the steepest descent algorithm for 5000 cycles. The MD equilibration process consisted of several stages. First, temperature equilibration in the NVT ensemble was performed by gradually increasing the temperature through steps of 150, 200, 250, 300, and finally 310 K, each lasting 200 ps. During this phase, position restraints were applied to the heavy atoms of the proteins, with progressively decreasing spring constants of 5.0, 4.0, 3.0, and 1.0 kcal mol−1 Å−2, facilitating gradual relaxation. This was followed by a 1 ns equilibration at 310 K in the NPT ensemble without restraints. For production MD, the simulations were run in the NPT ensemble with periodic boundary conditions and Particle Mesh Ewald (PME) method ([DOI: 10.1063/5.0040966], [DOI: 10.1021/ct9001015]) using a grid spacing of 1.0 Å for long-range electrostatics. Non-bonded interactions were modeled with a Lennard-Jones potential using a 9Å cutoff. Temperature control was maintained using Langevin dynamics ([DOI: 10.1021/ct800573m]) with a collision frequency of 4.0 ps−1, and pressure control was managed by the Monte Carlo barostat ([DOI: 10.1016/j.cplett.2003.12.039]) with a 2.0 ps relaxation time at 1 bar. Bond constraints on hydrogen atoms were applied using the SHAKE algorithm ([DOI: 10.1016/0021-9991(77)90098-5]), and the hydrogen mass repartitioning scheme was applied via ParmEd ([DOI: 10.1371/journal.pcbi.1005659]), enabling a 4 fs integration time step ([DOI: 10.1021/ct5010406]). Each protein complex was simulated for a total of 20 ns. For the Wuhan variant, the 3D coordinates were retrieved from [DOI: 10.5281/zenodo.3817446]. The active interaction region on the spike protein was defined as the cleavage site (residues P809-R815). For TMPRSS2 and Hepsin, the active sites were defined based on their catalytic residues: H296, D345, D435, S441, S460, and G462 for TMPRSS2, and H203, D257, D347, A348, and S353 for Hepsin. These specific regions were selected to guide the docking process and maximize biologically relevant interactions. Docking clusters were analyzed by selecting those with the lowest interaction energies for further structural analysis. To evaluate binding accuracy, native contacts between the S protein and proteases were computed using the contact map analysis based on the OV+rCSU method ([DOI: 10.12693/APhysPolA.145.S9, 10.1021/acs.jctc.6b00986]), which allows for a precise identification of critical stabilizing interactions, both specific and non-specifics. High-frequency contacts, defined as those appearing in over 70% of the generated models, were highlighted as key determinants of protein-protein recognition, providing insight into the most stable and consistent interactions across docking configurations.

opencc-zeroJun 2021View details →

The use of nanobodies in a sensitive ELISA test for SARS-CoV-2 Spike 1 protein

A rapid detection method for SARS-CoV-2 spike protein is essential for control of COVID19. We investigated various combinations of engineered nanobodies in a sandwich ELISA to detect the Spike protein of SARS-CoV-2. We have identified an optimal combination of nanobodies. These were selectively functionalised to further improve antigen capture. This dataset contains data from ELISA experiments described in the manuscript. Plate coating of nanobodies for ELISA by passive adsorption vs biotinylation was compared. A series of nanobody pairings (two cluster 2 ACE2-binding epitope and two cluster 1 CR3022 epitope) were screened for optimum sensitivity. The optimal pair were then tested against a series of SARS-COV-2 antigens: recombinant spike 1 protein; recombinant receceptor binding domain (RBD); pseudotyped HIV-1 and heat-empigen inactivated SARS-CoV-2 virus. X-ray irradiated SARS-CoV-2 was also tested. Sensitivity to these antigens was compared with nanobodies biotinylated a) site-selectively and b) in a non-specific stochastic manner. Batch-to-batch viral variation and effects of inactivating agents were investigated. Limit of detection was compared against delta and beta viral mutants. Combining optimal nanobody pairing and site-selective biotinylation, we observed a limit of detection of 147 pg/mL for Spike protein; 33 pg/mL for RBD; 16 TCID50/mL of pseudovirus and 15 ffu/mL of heat-Empigen inactivated SARS-CoV-2. The pairing also showed sensitivity towards delta variant. We have demonstrated the use and sensitivity of nanobodies in ELISA by detection of recombinant and viral SARS-CoV-2 antigens.

opencc-zeroAug 2021View details →

Data from: Cerebellar complex spikes multiplex complementary behavioral information

Purkinje cell (PC) discharge, the only output of cerebellar cortex, involves two types of action potentials, high-frequency simple spikes (SSs) and low-frequency complex spikes (CSs). While there is consensus that SSs convey information needed to optimize movement kinematics, the function of CSs, determined by the PC´s climbing fibre input, remains controversial. While initially thought to be specialized in reporting information on motor error for the subsequent amendment of behavior, CSs seem to contribute to other aspects of motor behavior as well. When faced with the bewildering diversity of findings and views unraveled by highly specific tasks one may wonder if there is just one true function with all the other attributions wrong? Or is the diversity of findings a reflection of distinct pools of PCs, each processing specific streams of information conveyed by climbing fibres? With these questions in mind, we recorded CSs from the monkey oculomotor vermis deploying a repetitive saccade task that entailed sizable motor errors as well as small amplitude saccades, correcting them. We demonstrate that in addition to carrying error-related information, CSs carry information on the metrics of both primary and small corrective saccades in a time-specific manner, with changes in CS firing probability coupled with changes in CS duration. Furthermore, we also found CS activity that seemed to predict the upcoming events. Hence PCs receive a multiplexed climbing fibre input that merges complementary streams of information on the behavior, separable by the recipient PC because they are staggered in time.

opencc-by-4.0Dec 2022View details →

Force-tuned Avidity of Spike Variant-ACE2 Interactions viewed on the Single-Molecule Level - MD simulations Dataset

Models of SARS-CoV-2 virus spike protein bound to 1-3 of ACE2 receptors embedded in lipid nanodisks. Systems include all files in GROMACS format needed to reproduce simulations performed in the "Force-tuned Avidity of Spike Variant-ACE2 Interactions viewed on the Single-Molecule Level" article.   <table> <caption>Details</caption> <thead> <tr> <th scope="col">system</th> <th scope="col">box size (x-y-z) [nm]</th> <th scope="col">Number of atoms</th> </tr> </thead> <tbody> <tr> <td>Spike + 1x ACE2, full length</td> <td>33.44834  28.96711  57.95573</td> <td>5,665,217</td> </tr> <tr> <td>Spike + 1x ACE2, truncated</td> <td>28.05757  24.29856  46.74417</td> <td>3,203,907</td> </tr> <tr> <td>Spike + 2x ACE2, truncated</td> <td>28.30864  21.23141  48.40873</td> <td>2,936,398</td> </tr> <tr> <td>Spike + 3x ACE2, truncated</td> <td>28.32733  21.24544  48.30436</td> <td>2,936,588</td> </tr> </tbody> </table>