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454 results for “Tau”
Boeken_Tau_SiMPull_Data
<p>This repository contains the data associated with the Tau SiMPull project, led by Dorothea Böken.</p> <p>The complete set of preprocessed data has been provided, as well as example images for use with the initial ComDet (diffraction-limited) and Picasso (super-resolution) steps. The datasets have been grouped by type of data (raw images vs preprocessed data), sample and imaging modality (super-resolution (SR) vs diffraction-limited (DL)).</p> <p> </p>
Cancer-specific association between Tau (MAPT) and cellular pathways, clinical outcome, and drug response
<p>To bring new evidence that Tau represents a key protein in cancer, we present an <em>in silico</em> pan-cancer analysis of <em>MAPT</em> transcriptomic profile in over 10000 clinical samples and over 1300 pre-clinical samples provided by the TCGA and the DEPMAP datasets respectively.</p>
Data for: ToadFishFinder classifier model v4: A catalog of oyster toadfish (Opsanus tau) calls for machine learning
<p>This data repository contains labeled passive underwater acoustic data used to train and test the machine-learning model of Bohnenstiehl (in prep – 2023), <span>Automated cataloging of oyster toadfish (<em>Opsanus</em> <em>tau</em>) calls using template matching and machine learning</span>. The software accompanying this paper is known as ToadFishFinder, and the classifier model presented in the paper is v4. It consists of more than 10000 labeled toadfish and 10000 labeled other signals. Labeled spectrogram images are provided, along with pressure-corrected waveforms (micro-Pascals) sampled at 24 kHz. Each waveform sample is 1350 ms long. The center 850 ms of these waveform segments represent the portion of the signal used in training and testing the classifier model. Waveform data are provided in multiple formats: 1) MATLAB (.mat) files containing the 'boatwhistle' and 'other' waveforms stored in column format, and 2) individual .wav files, each containing a labeled waveform example. Codes are provided to demonstrate how these .wav files can be read into MATLAB and PYTHON. These labeled data can be used to re-train the ToadFishFinder model or develop alternative classifiers. </p>
Data for: ToadFishFinder classifier model v4: A catalog of oyster toadfish (Opsanus tau) calls for machine learning
Open the record for dataset details and reuse information.
Single-synapse analyses of Alzheimer’s disease implicate pathologic tau, DJ1, CD47, and ApoE
Open the record for dataset details and reuse information.
Genetic variants beyond amyloid and tau associated cognitive decline: a cohort study
<p>Objective: To identify single nucleotide polymorphisms (SNPs) associated with cognitive decline independent of amyloid &[beta] (A&[beta]) and tau pathology in Alzheimer's disease (AD). Methods: Discovery and replication datasets consisting of 414 subjects (94 cognitively normal control [CN), 185 with mild cognitive impairment [MCI], and 135 AD) and 72 subjects (22 CN, 39 MCI, and 11 AD), respectively, were obtained from the Alzheimer's Disease Neuroimaging Initiative database. Genome-wide association analysis was conducted to identify SNPs associated with individual cognitive function (measured using the MMSE and ADAS-cog) while controlling for the level of A&[beta] and tau (measured as CSF p-tau/A&[beta]1-42). Gene ontology analysis was performed on SNP associated genes.</p> <p>Results: We identified one significant (rs55906536, &[beta]=-1.91,standard error 0.34, P =4.07×10<sup>-8</sup>) and four suggestive variants on chromosome 6, which were associated with poorer cognitive function. Congruent results were found in the replication data. A structural equation model showed that the identified SNP deteriorated cognitive function partially through cortical thinning of the brain in a region-specific manner. Furthermore, a bioinformatics analysis showed that the identified SNPs were associated with genes related to glutathione metabolism.</p> <p>Conclusions: In this study, we identified SNPs related to cognitive decline, in a manner which could not be explained by A&[beta] and tau levels. Our findings provide insight into the complexity of AD pathogenesis and support the growing literature on the role of glutathione in AD. This study suggests anti-oxidative agents may serve therapeutic for AD subjects with the identified SNPs.</p>
Data from: The presubiculum links incipient amyloid and tau pathology to memory function in older persons
Objective: To identify the hippocampal subregions linking initial amyloid and tau pathology to memory performance in clinically normal older individuals, reflecting preclinical Alzheimer's disease (AD). Methods: A total of 127 individuals from the Harvard Aging Brain Study (Mean age: 76.22 years ± 6.42, 68 females (53.5%)) with a Clinical Dementia Rating score of 0, a flortaucipir tau-PET scan, a Pittsburgh Compound B amyloid-PET scan, a structural MRI scan and cognitive testing were included. From these images, we calculated neocortical, hippocampal and entorhinal amyloid pathology, entorhinal and hippocampal tau pathology and the volumes of six hippocampal subregions and total hippocampal volume. Memory was assessed with the selective reminding test. Mediation and moderation analyses modeled associations between regional markers and memory. Analyses included covariates for age, sex and education. Results: Neocortical amyloid, entorhinal tau and presubiculum volume univariately associated with memory performance. The relationship between neocortical amyloid and memory was mediated by entorhinal tau and presubiculum volume, which was modified by hippocampal amyloid burden. With other biomarkers held constant, presubiculum volume was the only marker predicting memory performance in the total sample and in individuals with elevated hippocampal amyloid burden. Conclusions: The presubiculum captures unique AD-related biological variation that is not reflected in total hippocampal volume. Presubiculum volume may be a promising marker of imminent memory problems, and can contribute to understanding the interaction between incipient AD-related pathologies and memory performance. The modulation by hippocampal amyloid suggests that amyloid is a necessary process – but not sufficient – to drive neurodegeneration in memory-related regions.
Historic land use intensity (tau) development
<p>tau_xref_history_country.mz contains historic tau values on iso country level for total tau factor. This numbers were calculated by taking FAO yields and norming it to the 1995 tau values of the paper (faoyields*tau95/mean(faoyields[1995:2005]))</p>
Mechanical stimulation prevents impairment of axon growth and overcompensates microtubules destabilization in cellular models of Alzheimer's disease related Tau pathology
<p>Data and metadata associated to a publication 10.3389/fmed.2025.1519628</p>
Data for V1298 Tau TESS Transit Fits
<p>Light curve and Gaussian-process model data for the TESS observations of V1298 Tau. See our GitHub repository (<a href="https://github.com/afeinstein20/v1298tau_tess">https://github.com/afeinstein20/v1298tau_tess</a>) for Jupyter Notebooks on how to interact with these files.</p>
Data from "Disk Evolution Study Through Imaging of Nearby Young Stars (DESTINYS): A Panchromatic View of DO Tau's Complex Kilo-au Environment'
<p>Reduced data from Huang et al., 2022, "Disk Evolution Study Through Imaging of Nearby Young Stars (DESTINYS): A Panchromatic View of DO Tau's Complex Kilo-astronomical-unit Environment,' ApJ, 930, 171 (arXiv:2204.01758). </p> <p>See Table 1 of the article for the corresponding observing program codes and attributions for archival data (if applicable). </p> <p><strong>Images:</strong></p> <p>DOTau_12CO_automask.image.pbcor.fits: 12CO J=2-1 image cube<br> DOTau_12CO_automask.mom1.fits: 12CO J=2-1 moment 1 map<br> DOTau_12CO_automask.pbcor.2sigcut.mom0.fits: 12CO J=2-1 moment 0 map<br> DOTau_13CO_automask.image.pbcor.fits: 13CO J=2-1 image cube<br> DOTau_13CO_automask.mom1.fits: 13CO J=2-1 moment 1 map<br> DOTau_13CO_automask.pbcor.2sigcut.mom0.fits: 13CO J=2-1 moment 0 map<br> DOTau_C18O_automask.image.pbcor.fits: C18O J=2-1 image cube<br> DOTau_C18O_automask.pbcor.2sigcut.mom0.fits: C18O J=2-1 moment 0 map<br> DOTau_C18O_automask.pbcor.mom1.fits: C18O J=2-1 moment 1 map<br> DOTau_cADI_average.fits: SPHERE H-band cADI image (pixel scale: 0.01225 arcseconds)<br> DOTau_CS_automask.image.pbcor.fits: DO Tau CS J=5-4 image cube<br> DOTau_CS_automask.mom1.fits: DO Tau CS J=5-4 moment 1 map<br> DOTau_CS_automask.pbcor.2sigcut.mom0.fits: DO Tau CS J=5-4 moment 0 map<br> DOTau_DoLP.fits: DO Tau degree of linear polarization map (pixel scale: .0245 arcseconds)<br> DOTau_IDF-RDI.fits: SPHERE total intensity image produced with IDF-RDI (pixel scale: 0.01225 arcseconds)<br> DO-TAU_NICMOS_F110W_MRDILib-18_KL-2_Pixel.fits: HST NICMOS F110W image (pixel scale: 0.075 arcseconds)<br> DO-TAU_NICMOS_F160W_MRDILib-100_KL-1_Pixel.fits: HST NICMOS F160W image (pixel scale: 0.075 arcseconds)<br> DOTau_Qphi_average.fits: SPHERE H-band Qphi image (pixel scale: 0.01225 arcseconds)<br> DO_Tau_STIS_KlipWithin160pixel_counts_s_pixel.fits: HST STIS image (pixel scale: 0.0507 arcseconds)</p> <p><strong>Measurement sets:</strong></p> <p>DOTau_12CO.ms.contsub.tar: Self-calibrated, continuum-subtracted 12CO J=2-1 visibilities<br> DOTau_13CO.ms.contsub.tar: Self-calibrated, continuum-subtracted 13CO J=2-1 visibilities<br> DOTau_C18O.ms.contsub.tar: Self-calibrated, continuum-subtracted C18O J=2-1 visibilities<br> DOTau_CS.ms.contsub.tar: Self-calibrated, continuum-subtracted CS J=5-4 visibilities</p> <p><strong>Scripts:</strong></p> <p>DOTau_1.1mmreduction.py: CASA self-cal and imaging script for CS data <br> DOTau_1.3mmreduction.py: CASA self-cal and imaging script for CO data </p>
TAU Spatial Room Impulse Response Database (TAU-SRIR DB)
<p><strong>DESCRIPTION</strong></p> <p>The <strong>TAU Spatial Room Impulse Response Database (TAU-SRIR DB)</strong> database contains spatial room impulse responses (SRIRs) captured in various spaces of Tampere University (TAU), Finland, for a fixed receiver position and multiple source positions per room, along with separate recordings of spatial ambient noise captured at the same recording point. The dataset is intended for emulation of spatial multichannel recordings for evaluation and/or training of multichannel processing algorithms in realistic reverberant conditions and over multiple rooms. The major distinct properties of the database compared to other databases of room impulse responses are:</p> <ul> <li>Capturing in a high resolution multichannel format (32 channels) from which multiple more limited application-specific formats can be derived (e.g. tetrahedral array, circular array, first-order Ambisonics, higher-order Ambisonics, binaural).</li> <li>Extraction of densely spaced SRIRs along measurement trajectories, allowing emulation of moving source scenarios.</li> <li>Multiple source distances, azimuths, and elevations from the receiver per room, allowing emulation of complex configurations for multi-source methods.</li> <li>Multiple rooms, allowing evaluation of methods at various acoustic conditions, and training of methods with the aim of generalization on different rooms.</li> </ul> <p>The RIRs were collected by staff of TAU between 12/2017 - 06/2018, and between 11/2019 - 1/2020. The data collection received funding from the European Research Council, grant agreement 637422 <a href="https://cordis.europa.eu/project/id/637422">EVERYSOUND</a>.</p> <p><strong><em>NOTE</em></strong><em>: This database is a work-in-progress. We intend to publish additional rooms, additional formats, and potentially higher-fidelity versions of the captured responses in the near future, as new versions of the database in this repository.</em></p> <p> </p> <p><strong>REPORT AND REFERENCE</strong></p> <p>A compact description of the dataset, recording setup, recording procedure, and extraction can be found in:</p> <p>Politis., Archontis, Adavanne, Sharath, & Virtanen, Tuomas (2020). <strong>A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection</strong>. In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020)</em>, Tokyo, Japan.</p> <p>available <a href="https://dcase.community/documents/workshop2020/proceedings/DCASE2020Workshop_Politis_88.pdf">here</a>. A more detailed report specifically focusing on the dataset collection and properties will follow.</p> <p> </p> <p><strong>AIM</strong></p> <p>The dataset can be used for generating multichannel or monophonic mixtures for testing or training of methods under realistic reverberation conditions, related to e.g. multichannel speech enhancement, acoustic scene analysis, and machine listening, among others. It is especially suitable for the follow application scenarios:</p> <ul> <li>monophonic and multichannal reverberant single- or multi-source speech in multi-room reverberant conditions</li> <li>monophonic and multichannel polyphonic sound events in multi-room reverberant conditions </li> <li>single-source and multi-source localization in multi-room reverberant conditions, in static or dynamic scenarios</li> <li>single-source and multi-source tracking in multi-room reverberant conditions, in static or dynamic scenarios</li> <li>sound event localization and detection in multi-room reverberant conditions, in static or dynamic scenarios</li> </ul> <p> </p> <p><strong>SPECIFICATIONS</strong></p> <p>The SRIRs were captured using an [Eigenmike](https://mhacoustics.com/products) spherical microphone array. A [Genelec G Three loudspeaker](https://www.genelec.com/g-three) was used to playback a maximum length sequence (MLS) around the Eigenmike. The SRIRs were obtained in the STFT domain using a least-squares regression between the known measurement signal (MLS) and far-field recording independently at each frequency. In this version of the dataset the SRIRs and ambient noise are downsampled to 24kHz for compactness.</p> <p>The currently published SRIR set was recorded at nine different indoor locations inside the Tampere University campus at Hervanta, Finland. Additionally, 30 minutes of ambient noise recordings were collected at the same locations with the IR recording setup unchanged. SRIR directions and distances differ with the room. Possible azimuths span the whole range of $\phi\in[-180,180)$, while the elevations span approximately a range between $\theta\in[-45,45]$ degrees. The currently shared measured spaces are as follows:</p> <ol> <li>Large open space in underground bomb shelter, with plastic-coated floor and rock walls. Ventilation noise. Circular source trajectory.</li> <li>Large open gym space. Ambience of people using weights and gym equipment in adjacent rooms. Circular source trajectory.</li> <li>Small classroom (PB132) with group work tables and carpet flooring. Ventilation noise. Circular source trajectory.</li> <li>Meeting room (PC226) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.</li> <li>Lecture hall (SA203) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.</li> <li>Small classroom (SC203) with group work tables and carpet flooring. Ventilation noise. Linear source trajectory.</li> <li>Large classroom (SE203) with hard floor and rows of desks. Ventilation noise. Linear source trajectory.</li> <li>Lecture hall (TB103) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.</li> <li>Meeting room (TC352) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.</li> </ol> <p>The measurement trajectories were organised in groups, with each group being specified by a circular or linear trace at the floor at a certain distance from the z-axis of the microphone. For circular trajectories two ranges were measured, a <em>close</em> and a <em>far</em> one, except room TC352, where the same range was measured twice, but with different furniture configuration and open or closed doors. For linear trajectories also two ranges were measured, <em>close</em> and <em>far</em>, but with linear paths at either side of the array, resulting in 4 unique trajectory groups, with the exception of room SA203 where 3 ranges were measured resulting on 6 trajectory groups. Linear trajectory groups are always parallel to each other, in the same room.</p> <p>Each trajectory group had multiple measurement trajectories, following the same floor path, but with the source at different heights. </p> <p>The SRIRs are extracted from the noise recordings of the slowly moving source across those trajectories, at an angular spacing of approximately every 1 degree from the microphone. Instead of extracting SRIRs at equally spaced points along the path (e.g. every 20cm), this extraction scheme was found more practical for synthesis purposes, making emulation of moving sources at an approximately constant angular speed easier.</p> <p>More details on the trajectory geometries can be found in the <strong>README</strong> file and the <strong>measinfo.mat</strong> file.</p> <p> </p> <p><strong>RECORDING FORMATS</strong></p> <p>As with the DCASE2019-2021 datasets, currently the database is provided in two formats, first-order Ambisonics, and a tetrahedral microphone array - both derived from the Eigenmike 32-channel recordings. For more details on the format specifications, check the README. </p> <p>We intend to add additional formats of the database, of both higher resolution (e.g. higher-order Ambisonics), or lower resolution (e.g. binaural).</p> <p> </p> <p><strong>REFERENCE DOAs</strong></p> <p>For each extracted RIR across a measurement trajectory there is a direction-of-arrival (DOA) associated with it, which can be used as the reference direction for sound source spatialized using this RIR, for training or evaluation purposes. The DOAs were determined acoustically from the extracted RIRs, by windowing the direct sound part and applying a broadband version of the MUSIC localization algorithm on the windowed multichannel signal.</p> <p>The DOAs are provided as Cartesian components [x, y, z] of unit length vectors.</p> <p> </p> <p><strong>SCENE GENERATOR</strong></p> <p>A set of routines is shared, here termed <em>scene generator</em>, that can spatialize a bank of sound samples using the SRIRs and noise recordings of this library, to emulate scenes for the two target formats. The code is similar to the one used to generate the <a href="https://doi.org/10.5281/zenodo.5476980"><strong>TAU-NIGENS Spatial Sound Events 2021</strong></a> dataset, and has been ported to Python from the original version written in Matlab.</p> <p>The generator can be found [**<strong>here</strong>**](https://github.com/danielkrause/DCASE2022-data-generator), along with more details on its use. </p> <p>The generator at the moment is set to work with the <a href="https://zenodo.org/record/2535878">NIGENS</a> sound event sample database, and the <a href="https://zenodo.org/record/4060432">FSD50K</a> sound event database, but additional sample banks can be added with small modifications.</p> <p>The dataset together with the generator has been used by the authors in the following public challenges:</p> <p>- <a href="https://dcase.community/challenge2019/task-sound-event-localization-and-detection">DCASE 2019 Challenge Task 3</a>, to generate the <strong>TAU Spatial Sound Events 2019</strong> dataset (<a href="https://doi.org/10.5281/zenodo.2599196">development</a>/<a href="https://doi.org/10.5281/zenodo.3377088">evaluation</a>)</p> <p>- <a href="https://dcase.community/challenge2020/task-sound-event-localization-and-detection">DCASE 2020 Challenge Task 3</a>, to generate the <a href="https://doi.org/10.5281/zenodo.4064792"><strong>TAU-NIGENS Spatial Sound Events 2020</strong></a> dataset</p> <p>- <a href="https://dcase.community/challenge2021/task-sound-event-localization-and-detection">DCASE2021 Challenge Task 3</a>, to generate the <a href="https://doi.org/10.5281/zenodo.5476980"><strong>TAU-NIGENS Spatial Sound Events 2021</strong></a> dataset</p> <p>- <a href="https://dcase.community/challenge2022/task-sound-event-localization-and-detection">DCASE2022 Challenge Task 3</a>, to generate additional <a href="https://doi.org/10.5281/zenodo.6406873"><strong>SELD synthetic mixtures for training the task baseline</strong></a></p> <p><em><strong>NOTE</strong>: The current version of the generator is work-in-progress, with some code being quite "rough". If something does not work as intended or it is not clear what certain parts do, please contact us.</em></p> <p> </p> <p><strong>DATASET STRUCTURE</strong></p> <p>The dataset contains a folder of the SRIRs (<strong>TAU-SRIR_DB</strong>), with all the SRIRs per room in a single MAT file. The file <strong>rirdata.mat</strong> contains some general information such as sample rate, format specifications, and most importantly the DOAs of every extracted SRIR. The file <strong>measinfo.mat</strong> contains measurement and recording information in each room. Finally, the dataset contains a folder of spatial ambient noise recordings (<strong>TAU-SNoise_DB</strong>), with one subfolder per room having two audio recordings fo the spatial ambience, one for each format, FOA or MIC. For more information on how to SRIRs and DOAs are organized, check the README.</p> <p> </p> <p><strong>DOWNLOAD</strong></p> <p>The files <em>TAU-SRIR_DB.z01</em>, ..., <em>TAU-SRIR_DB.zip</em> contain the SRIRs and measurement info files.</p> <p>The files <em>TAU-SNoise_DB.z01</em>, ..., <em>TAU-SNoise_DB.zip</em> contain the ambient noise recordings.</p> <p>Download the zip files and use your preferred compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal:</p> <p>Combine the split archive to a single archive:</p> <pre><code>zip -s 0 split.zip --out single.zip</code></pre> <p>Extract the single archive using unzip:</p> <pre><code>unzip single.zip</code></pre> <p> </p> <p><strong>LICENSE</strong></p> <p>The database is published under a custom **<strong>open non-commercial with attribution</strong>** license. It can be found in the `LICENSE.txt` file that accompanies the data.</p>
Proximity labeling of tau interactions in primary neurons and mouse brain
<p><span>Microtubule-associated protein tau is a central factor in Alzheimer's disease and other tauopathies. However, physiological functions of tau are unclear. Here, we used proximity labelling proteomics to chart functional tau interactomes in primary neurons and mouse brai<span>n <span>in vivo</span></span><span>. Here, we use proximity labelling with the biotin ligase BioID2 to map interactomes of tau in neurons. Data sets relate to mass spectrometry and protein identification of biotinylated proteins in primary neurons and in mouse brain after delivery of BioID2-tau fusion protein or BioID2 control protein by adeno-associated virus (AAV). Mouse brain samples are either from P35 wild-type mice with intracranial AAV delivery at P0 or from tau knockout mice at P60 after hippocampal delivery of AAV. Details on BioID2 fusion protein expression, biotin supplementation and sample extraction can be obtained in the associated publication.<br></span></span></p>
Data from: Flortaucipir PET uncovers relationships between tau and β-amyloid in aging, primary age related tauopathy, and Alzheimer disease
<p>[<sup>18</sup>F]-Flortaucipir PET is considered a good biomarker of Alzheimer's disease. However, it is unknown how flortaucipir is associated with the distribution of tau across brain regions and how these associations are influenced by β-amyloid. It is also unclear whether flortaucipir can detect tau in definite primary age-related tauopathy (PART). We identified 248 individuals at Mayo Clinic that had undergone [<sup>18</sup>F]-flortaucipir PET during life, had died, and undergone an autopsy, 239 cases of which also had β-amyloid PET. We assessed nonlinear relationships between flortaucipir uptake in nine medial temporal and cortical regions, Braak tau stage and Thal β-amyloid phase using generalized additive models. We found that flortaucipir uptake was greater with increasing tau stage in all regions. Increased uptake at low tau stages in medial temporal regions was only observed in cases with high β-amyloid phase. Flortaucipir uptake linearly increased with β-amyloid phase in medial temporal and cortical regions. The highest flortaucipir uptake occurred with high Alzheimer's disease neuropathologic change (ADNC) scores, followed by low-intermediate ADNC scores, then PART, with entorhinal cortex providing the best differentiation between groups. Flortaucipir PET had limited ability to detect PART and imaging defined PART did not correspond with pathologically defined PART. In summary, spatial patterns of flortaucipir mirrored histopathological tau distribution, were influenced by β-amyloid phase, and were useful for distinguishing different ADNC scores and PART.</p>
Data for: Conformational Dependence of Chemical Shifts in the Proline Rich Region of TAU Protein
<div> <div> <div> <p>Nuclear magnetic resonance (NMR) is an important method for structure elucidation of proteins, as it is an easy accessible and well understood method. To characterize intrinsically disordered proteins (IDPs) using computational models it is often necessary to analyze and integrate calculated observables with measurements derived from solution NMR experiments.</p> <p>In this case study we investigate whether and which chemical shifts of the proline-rich region of Tau protein (residues 210-240) offer information about the conformational state to distinguish two different microscopic conformers. Using multiple computational methods, chemical shifts of those two conformationally distinct structures are calculated. The different methods are compared regarding their ability to compute chemical shifts that are sensitive to conformational change.</p> <p>The analysis of the data shows significant differences between the available methods and gives suggestions to an improved pathway for ensemble reweighting. Nevertheless, the variation in the chemical shifts which are predicted for configurations that are commonly considered to belong to the same conformation is such that this obscures a comparison between distinct conformations. Conformational sensitivity is found for up to ∼26% of calculated chemical shifts. It is found to be unrelated with atom element and had minor relation with the change of the corresponding φ dihedral angle.</p> </div> </div> </div>
TAU Spatial Sound Events 2019 - Ambisonic and Microphone Array, Evaluation Datasets
<p>This package consists of two evaluation datasets, <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong>. These datasets contain recordings from an identical scene, with <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> providing four-channel First-Order Ambisonic (FOA) recordings while <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> provides four-channel directional microphone recordings from a tetrahedral array configuration. Both formats are extracted from the same microphone array. The recordings in the two datasets consist of stationary point sources from multiple sound classes each associated with a temporal onset and offset time, and DOA coordinate represented using azimuth and elevation angle. These evaluation datasets are part of the <a href="https://github.com/sharathadavanne/seld-dcase2019">DCASE 2019 Sound Event Localization and Detection Task</a>. The corresponding development datasets can be downloaded <a href="https://doi.org/10.5281/zenodo.2599196">here</a>.</p> <p>The IRs were collected in Finland by Tampere University between 12/2017 - 06/2018. The data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND.</p> <ul> <li>The <strong>foa_eval.zip</strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> evaluation dataset.</li> <li>The <strong>mic_eval.zip</strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> evaluation dataset.</li> </ul> <p>-- Version 2 updates --</p> <p>The<a href="http://dcase.community/challenge2019/task-sound-event-localization-and-detection-results"> DCASE 2019 sound event localization and detection task has now ended</a>. Hence we are releasing the reference labels for the evaluation dataset in this version.</p> <ul> <li>The <strong><em>metadata_eval.zip</em></strong> is the common metadata for both <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> evaluation datasets. </li> <li>The <strong>short2longnames.txt</strong> file consists of the corresponding names for each recording in the dataset in the <a href="http://dcase.community/challenge2019/task-sound-event-localization-and-detection#development-dataset">development-set format</a>, i.e., including the information of the impulse response location and the maximum number of overlapping sound events in the recording.</li> </ul> <p>Download the zip files corresponding to the dataset of interest and use your favorite compression tool to unzip these split zip files.<br> </p> <p> </p> <p> </p>
Tau deposition is associated with imaging patterns of calcifications and immunhistochemical markers of osteogenesis in the P301L mouse model of human tauopathy
<p>Brain calcifications are associated with several neurodegenerative proteinopathies. Here, we report a new phenotype of imaging pattern of intracranial calcifications in transgenic P301L mice overexpressing 4 repeat human tau. P301L mice (Thy1.2) of 3, 5, 9 and 18-25 months-of-age and age-matched non-transgenic littermates (n = 4-11 per group) were assessed using <em>in vivo / ex vivo</em> magnetic resonance imaging with a gradient recalled echo sequence and micro computed tomography. Susceptibility weighted images computed from the gradient recalled echo data revealed regional hypointensities in the hippocampus, cortex, caudate nucleus and thalamus of P301L mice, in which corresponding phase images indicated diamagnetic lesions. In the hippocampus, occurrence of diamagnetic susceptibility calcifications increased with age. Concomitantly micro computed tomography detected hyperdense lesions. Immunochemical staining of brain sections revealed osteocalcin positive nodules, which is a marker of osteogenic maturation. Furthermore, we found vessel-associated and intra-neuronal osteocalcin positivity co-localizing with phosphorylated tau (AT8 and AT100) in the hippocampus. In contrast, osteocalcin containing nodules were vessel-associated, indicating ossified vessels, in the thalamus in absence of phosphorylated tau. In summary, we demonstrated imaging pattern of intracranial calcifications, early spheroid formation containing proteins associated with ossification along with phosphorylated tau in the P301L mouse model of human tauopathy.</p> <p> </p>
Microscopy images - " Particulate matter constituents trigger the formation of extracellular amyloid β and Tau -containing plaques and neurite shortening in vitro"
<p>This repository contains microscopy data from the manuscript "Particulate matter constituents trigger the formation of extracellular amyloid β and Tau -containing plaques and neurite shortening in vitro" by Aleksandar Sebastijanović, Laura Maria Azzurra Camassa, Vilhelm Malmborg, Slavko Kralj, Joakim Pagels, Ulla Vogel, Shan Zienolddiny-Narui, Iztok Urbančič, Tilen Koklič, and Janez Štrancar (published in Nanotoxicology, 18(4), 335–353. https://doi.org/10.1080/17435390.2024.2362367).</p> <p>Raw data are organized in folders named by image number, containing a subfolder with the date (year-day-month) of image acquisition. Followed by a subfolder named by a nanomaterial to which neurons were exposed. Each folder contains images from individual multi-channel, multi-position time-lapse experiments with different combinations of cells exposed to one nanomaterial. Files are named as: IMGxxxx_[ExperimentCode]_ROIxx_[Channel].tif, where each of the varying elements in [..] denotes the following:<br>• [ExperimentCode]: a short name of the experiment<br>• [Channel]: membrane (MEM), cytoplasm neuronal cells (NEU), nanomaterial (NANO), amyloid beta (AMY)</p>
Tau accelerates tubulin exchange in the microtubule lattice
<p>This dataset contains the data and source code for Figures 1-4 and and the source code for Supplementary Figures S5-S10 from the following publication: </p> <div> <p>Tau accelerates tubulin exchange in the microtubule lattice</p> </div> <div>by</div> <div> </div> <div>Subham Biswas, Rahul Grover, Cordula Reuther, Chetan S. Poojari, M. Reza Shaebani, Mona Grünewald, Amir Zablotsky, Jochen S. Hub, Stefan Diez, Karin John, Laura Schaedel</div> <div> </div> <div>doi: https://doi.org/10.1101/2024.10.05.616777</div>
FIG. 9 in Pigs and ritual-hunting among the highland Tau-Buhid in Mounts Iglit-Baco natural park, Philippines
FIG. 9. — Safong (circular burning) fully initiated. Photo credits: C. A. Rosales.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.