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forelimb is considered (c). Non-avian coelurosaurians are shown in grey, Oviraptorosauria in purple, Scansoriopterygidae in red, Deinonychosauria in green and Aves in blue. Silhouette of Archaeopteryx was from http:// phylopic.org/. Line drawings of the forelimbs of Deinocheirus, Microraptor, Ambopteryx and Dapingfangensis highlight the fact that, uniquely, scansoriopterygids have an elongate forelimb but short metacarpals. in A new Jurassic scansoriopterygid and the loss of membranous wings in theropod dinosaurs
forelimb is considered (c). Non-avian coelurosaurians are shown in grey, Oviraptorosauria in purple, Scansoriopterygidae in red, Deinonychosauria in green and Aves in blue. Silhouette of Archaeopteryx was from http:// phylopic.org/. Line drawings of the forelimbs of Deinocheirus, Microraptor, Ambopteryx and Dapingfangensis highlight the fact that, uniquely, scansoriopterygids have an elongate forelimb but short metacarpals.
vertebra; po, postacetabular process of ilium; pp, pubic peduncle of ilium; pr, preacetabular process of ilium; ra, radius; ti, tibia; ul, ulna; I1, manual phalange I-1; II1, II2 and II3, manual phalanges II-1, II-2 and II-3; III1, III2, III3 and III4, manual phalanges III-1, III-2, III-3 and III-4. Scale bars, 10 mm. in A new Jurassic scansoriopterygid and the loss of membranous wings in theropod dinosaurs
vertebra; po, postacetabular process of ilium; pp, pubic peduncle of ilium; pr, preacetabular process of ilium; ra, radius; ti, tibia; ul, ulna; I1, manual phalange I-1; II1, II2 and II3, manual phalanges II-1, II-2 and II-3; III1, III2, III3 and III4, manual phalanges III-1, III-2, III-3 and III-4. Scale bars, 10 mm.
Fig. 2 in A new Jurassic scansoriopterygid and the loss of membranous wings in theropod dinosaurs
Fig. 2 | Anatomy of Ambopteryx, IVPP V24192. a, Neck and pectoral girdle. b, Caudal vertebrae and pygostyle. c, Left forelimb and membranous soft tissues (blue arrowhead). d, Pelvis and left hindlimb. See Extended Data Fig. 3 for corresponding interpretative line drawings. co, coracoid; fe, femur; fi, fibula; hu, humerus; il, ilium; pc, posterior-most caudal vertebra; po, postacetabular process of ilium; pp, pubic peduncle of ilium; pr, preacetabular process of ilium; ra, radius; ti, tibia; ul, ulna; I1, manual phalange I-1; II1, II2 and II3, manual phalanges II-1, II-2 and II-3; III1, III2, III3 and III4, manual phalanges III-1, III-2, III-3 and III-4. Scale bars, 10 mm.
CHARMM36, NB-Fix approaches, without NBFIX, POPC membrane, Ca, Na ions,
<p>Different versions of NB-Fix from CHARMM-Gui and simulations without NB-Fix (called original charmm)</p> <p>200 POPC lipids, TIP3P - charrmm water model,</p> <p> </p>
Coarse-grained simulation of a dilute lipid membranes with 7 protein types
<pre>A simulation of a DPPC lipid membrane with one copy of seven protein types. This is an extension to the dataset [1] with an even more dilute membrane. Topologies and mdp files can be obtained from [1], and all the simulation parameters are equal to those described in [1] and in the related publication [2]. The trajectory does not contain the solvent and is stored every 10 ns. [1] https://doi.org/10.5281/zenodo.846428</pre> <p>[2] M. Javanainen, H. Martinez-Seara, R. Metzler, and I. Vattulainen; Diffusion of Integral Membrane Proteins in Protein-Rich Membranes. J. Phys. Chem. Lett., 2017, 8 (17), pp 4308–4313, DOI: 10.1021/acs.jpclett.7b01758<br> </p>
Thermodynamics of small-molecule insertion across membrane mixtures: Insight from the potential of mean force
<p>This repository contains input data referenced in supporting information of the paper titled "Thermodynamics of small-molecule insertion across membrane mixtures: Insight from the potential of mean force" by Alessia Centi, Arghya Dutta, Sapun H. Parekh, and Tristan Bereau.</p>
Simulations of mixed DPPC/cholesterol membranes Part 2/2
<p>Simulations of mixtures of DPPC and cholesterol performed with the Slipids force field. Files are named as DPPC_CC_TTT, where CC is the cholesterol concentration in mol-%, and TTT the temperature in K. This upload contains files related to systems with 15 or 20 mol-% of cholesterol, whereas systems with 10 mol-% are in a separate upload here: DOI: 10.5281/zenodo.3539263. The system containing DCHOL, as well as the simulations used for line tension calculations, are also included in this upload.</p> <p>Full details of simulations are available in the related manuscript, where they are also carefully analyzed:</p> <p>Nanoscale Membrane Domain Formation Driven by Cholesterol</p> <p>by Matti Javanainen, Hector Martinez-Seara, and Ilpo Vattulainen</p> <p>Scientific Reports 7: 1143 (2017)</p>
Simulations of mixed DPPC/cholesterol membranes Part 1/2
<p>Simulations of mixtures of DPPC and cholesterol performed with the Slipids force field. Files are named as DPPC_CC_TTT, where CC is the cholesterol concentration in mol-%, and TTT the temperature in K. This upload contains files related to systems with 10 mol-% of cholesterol, whereas systems with 15 and 20 mol-%, as well as systems with DCHOL and sytems used for line tension calculations are in a separate upload here: DOI: 10.5281/zenodo.3540513.</p> <p>Full details of simulations are available in the related manuscript, where they are also carefully analyzed:</p> <p>Nanoscale Membrane Domain Formation Driven by Cholesterol</p> <p>by Matti Javanainen, Hector Martinez-Seara, and Ilpo Vattulainen</p> <p>Scientific Reports 7: 1143 (2017)</p>
Mechanism for Vipp1 spiral formation, ring biogenesis and membrane repair
<p><span><span>Here</span><span>,</span><span> we collect </span><span>an image dat</span><span>aset</span><span> of Vipp1 polymerization dynamics on supported lipid bilayers </span><span>using atomic force microscopy (</span><span>AFM</span><span>)</span><span>. A JPK </span><span>NanoWizard</span> <span>Ultraspeed</span><span> AFM (Bruker and JPK </span><span>BioAFM</span><span>) equipped with USC-F0.3-k0.3-10 cantilevers with spring constant of 0.3N nm−1, resonance frequency of about 300 kHz (Nanoworld), was </span><span>employed </span><span>for image acquisition. The AFM was </span><span>operated</span><span> in tapping mode</span><span> with a</span><span> cantilever </span><span>oscil</span><span>l</span><span>ation </span><span>frequency </span><span>near </span><span>to 150kHz. </span><span>Both</span><span> topographic and phase images </span><span>were analysed </span><span>using</span><span> JPKSPM Data Processing, ImageJ, and </span><span>WSxM</span><span> software</span></span>.</p>
Metabolic profiles of IgA nephropathy, membranous nephropathy, and diabetic nephropathy
<p><span><strong>Supplementary Figure 1 </strong>Quality control analysis (A) Pearson correlation between quality control samples (positive mode) (B) principal component (PC) plot of five groups (positive mode, 2 dimensions) (C) PC plot of five groups (positive mode, 3 dimensions) (D) Pearson correlation between QC samples (negative mode) (E) PC plot of five groups (negative mode, 2 dimensions) (F) PC plot of five groups (negative mode, 3 dimensions). <strong>Supplementary Figure 2 </strong>Heatmaps of differential metabolites (A) IgA nephropathy (IgAN) vs controls pair (positive mode) (B) membranous nephropathy (MN) vs controls pair (positive mode) (C) diabetic nephropathy (DN) vs controls pair (positive mode) (D) IgAN vs controls pair (negative mode) (E) MN vs controls pair (negative mode) (F) DN vs controls pair (negative mode). <strong>Supplementary table 1 </strong>List of differential metabolites in three comparison pairs (positive mode). <strong>Supplementary table 2 </strong>List of differential metabolites in three comparison pairs (negative mode). <strong>Supplementary table 3 </strong>KEGG enrichment pathways</span></p>
EMI-Meshing: High-quality extracellular-membrane-intracellular meshes of the mouse visual cortex
<p>This repository features a family of tetrahedral meshes of a dense reconstruction of the mouse visual cortex at extreme resolution. Both the extracellular space (ECS) and the main cellular structures are explicitly represented and labelled.</p> <p>The dataset is based on the <a href="https://www.microns-explorer.org/cortical-mm3">Cortical MM^3 dataset</a> centred at position <a href="https://ngl.microns-explorer.org/#!%7B%22dimensions%22:%7B%22x%22:%5B4e-9%2C%22m%22%5D%2C%22y%22:%5B4e-9%2C%22m%22%5D%2C%22z%22:%5B4e-8%2C%22m%22%5D%7D%2C%22position%22:%5B225182.5%2C107314.5%2C22000.5%5D%2C%22crossSectionScale%22:11.406101410482504%2C%22projectionOrientation%22:%5B0.1528419554233551%2C0.49656152725219727%2C0.39075320959091187%2C0.7598538994789124%5D%2C%22projectionScale%22:40961.499900183306%2C%22layers%22:%5B%7B%22type%22:%22image%22%2C%22source%22:%7B%22url%22:%22precomputed://https://bossdb-open-data.s3.amazonaws.com/iarpa_microns/minnie/minnie65/em%22%2C%22subsources%22:%7B%22default%22:true%7D%2C%22enableDefaultSubsources%22:false%7D%2C%22tab%22:%22source%22%2C%22annotationColor%22:%22#7d7d7d%22%2C%22shaderControls%22:%7B%22normalized%22:%7B%22range%22:%5B86%2C172%5D%7D%7D%2C%22name%22:%22img%22%7D%2C%7B%22type%22:%22segmentation%22%2C%22source%22:%7B%22url%22:%22precomputed://gs://iarpa_microns/minnie/minnie65/seg%22%2C%22subsources%22:%7B%22default%22:true%2C%22mesh%22:true%7D%2C%22enableDefaultSubsources%22:false%7D%2C%22tab%22:%22segments%22%2C%22annotationColor%22:%22#949494%22%2C%22selectedAlpha%22:0.3%2C%22segments%22:%5B%22864691134947427836%22%2C%22864691135337771494%22%2C%22864691135393949941%22%2C%22864691135462270365%22%2C%22864691135474669888%22%2C%22864691135617729935%22%2C%22864691135718476593%22%2C%22864691136024102713%22%2C%22864691136390364287%22%2C%22864691136436690846%22%5D%2C%22segmentQuery%22:%22864691136194301772%2C%20864691136814938734%22%2C%22colorSeed%22:3728349837%2C%22name%22:%22seg%22%7D%5D%2C%22showAxisLines%22:false%2C%22showSlices%22:false%2C%22selectedLayer%22:%7B%22visible%22:true%2C%22layer%22:%22seg%22%7D%2C%22layout%22:%7B%22type%22:%224panel%22%2C%22orthographicProjection%22:true%7D%2C%22selection%22:%7B%22layers%22:%7B%22seg%22:%7B%22annotationId%22:%22data-bounds%22%2C%22annotationSource%22:0%2C%22annotationSubsource%22:%22bounds%22%7D%7D%7D%7D">225182-107314-22000</a> with a resolution of 32 x 32 x 40 nm^3. It contains a total of 20 meshes. The meshed domains are cubes with side lengths 5000, 10000, 20000 and 40000 nm and include the largest 5, 10, 50, 100 and 200 cells in the respective tissue volume, respectively. </p> <p><strong>Data</strong></p> <p>The dataset has the following content:</p> <ul> <li> <p>surface meshes: The surfaces of the extracted cells in `.ply` format, suitable for visualization with ParaView or usage in other meshing or simulation software</p> </li> <li> <p>volume meshes: The generated volumetric meshes in `.xdmf` format, containing labels for the extracellular space (label 1) and increasing integer values (2,..., N) for all cells. The file `facet.xdmf`contains facet marker, where the label *l* corresponds to the boundary between ECS and cell *l*. The outer boundaries are marked as `l + offset`, where `offset` is the next higher power of ten of the number of cells (`offset=int(10 ** np.ceil(np.log10(N_cells)))`).</p> </li> </ul> <p><strong>Usage</strong></p> <p>The meshes are intended for usage with FEniCS (see code below), but can equally be read and used with other Software.</p> <pre><code>from fenics import * import numpy as np mesh = Mesh() infile = XDMFFile("mesh.xdmf") infile.read(mesh) gdim = mesh.geometric_dimension() labels = MeshFunction("size_t", mesh, gdim) infile.read(labels, "label") infile.close() # get all local labels np.unique(labels.array()) # array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], dtype=uint64) infile = XDMFFile("facets.xdmf") infile.read(mesh) gdim = mesh.geometric_dimension() boundary_marker = MeshFunction("size_t", mesh, gdim - 1) infile.read(boundary_marker, "boundaries") infile.close() # get all local facet labels np.unique(boundary_marker.array()) # array([ 0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 101, 102, # 103, 104, 105, 106, 107, 108, 109, 110, 111], dtype=uint64)</code></pre> <p> </p>
Data from: "Glycerol-blended chitosan membranes with directional micro-grooves and reduced stiffness improve Schwann cell wound healing"
<h3>ABSTRACT</h3> <p>Regenerative medicine is continuously looking for new natural biocompatible and possibly biodegradable materials, but also mechanically compliant. Chitosan is emerging as a promising FDA-approved biopolymer for tissue engineering, however, its exploitation in regenerative devices is limited by its brittleness and can be further improved, for example, by blending it with other materials or by tuning its superficial microstructure. Here, we developed membranes made of chitosan and glycerol, by solvent casting and micropatterned them with directional geometries with different levels of axial symmetry. These membranes were characterized by light microscopy and atomic force microscopy (AFM), thermal, mechanical, and degradation assays, and also tested in vitro as scaffolds with Schwann cells. The glycerol-blended chitosan membranes are optimized in terms of mechanical properties, and present a physiological-grade Young's modulus (≈ 0.7 MPa). The directional topographies are effective in directing cell polarization and migration and in particular are highly performant substrates for collective cell migration. Here, we demonstrate that a combination of a soft compliant biomaterial and topographical micropatterning can improve the integration of these scaffolds with Schwann cells, which is a fundamental step in the peripheral nerve regeneration process.</p>
Si3N4 membrane breaks in ultrahigh vacuum during operando NEXAFS in solar hydrogen water splitting micro electrochemical cell
<p>This video was recorded 19 April 2012 during operando photoelectrochemical water splitting in a micro specroelectrochemical x-ray cell during NEXAFS beamtime at the Advanced Light Source in Berkeley. The window broke in the UHV chamber, when the potential was reversed with the potentiostat from 900 mV to lower potentials. Fortunately, we had already the light O1s x-ray absorption spectra in light on/off mode for the potentials up to 900 mV.</p>
Techno-economic comparison of power-to-Ammonia and biomass- to-Ammonia plants using electrolyzer, CO 2 capture and water-gas- shift membrane reactor
<p>A set of imput data used for the paper entitled: Techno-economic comparison of power-to-Ammonia and biomass-<br>to-Ammonia plants using electrolyzer, CO 2 capture and water-gas-shift membrane reactor </p>
Molecular Probes for Tracking Lipid Droplet Membrane Dynamics
<p><strong><span>Abstract</span></strong></p> <p><span>Lipid droplets (LDs) and their membrane proteins play crucial roles in lipid metabolism, signaling, and information transport within cells. LDs feature a unique monolayer lipid membrane that has not been extensively studied due to the lack of suitable molecular probes that are able to distinguish this membrane from the LD lipid core. In this work, we present a three-pronged molecular probe design strategy that combines lipophilicity-based organelle targeting with microenvironment-dependent activation. As a proof-of-concept, we designed an <u>LD</u> <u>m</u>embrane labeling pro-probe called<strong> LDM</strong>, which selectively localizes around LD membranes. Upon activation by the HClO/ClO</span><sup><span>−</span></sup><span> microenvironment that surrounds LDs, <strong>LDM</strong> pro-probe undergoes a color change and releases<strong> LDM-OH</strong> probe that binds to LD membrane proteins. This localizes the probe to the LD-as</span><span>sociated protein space which is restricted to the membrane thus enabling visualization of the ring-like LD membrane. By utilizing<strong> LDM</strong>, we identified the dynamic mechanism of LD membrane contacts and their protein accumulation parameters. Furthermore, using <strong>LDM</strong> in liver cancer cells allowed us to examine the changes in LD/mitochondrial protein accumulation caused by the state of starvation these cells encounter. This led to the discovery that liver cancer cells respond to energy stress during hunger by enhancing LD-mitochondria interactions. Taken together, <strong>LDM</strong> represents the first molecular probe for imaging LD membranes in live cells, and represents an attractive tool for further investigations into the specific regulatory mechanisms and drug discovery associa</span><span>ted with LD related metabolism diseases.</span></p> <p><span> </span></p> <p><em><span>Keywords:</span></em><span> Molecular Imaging, Cancer, Lipid droplets, Super-resolution Imaging</span></p>
Supplementary Dataset for Membrane lipid homeostasis dually regulates conformational transition of phosphoethanolamine transferase EptA
Open the record for dataset details and reuse information.
Molecular Dynamics Trajectories of Membrane-bound Influenza Hemagglutinin (A/duck/Alberta/35/76) in Complex with 0-3 Copies of FISW84 Fab Fragments
<p>MD simulation trajectories of influenza hemagglutinin (A/duck/Alberta/35/76) in a bilayer mimicking the viral membrane, with 0-3 copies of FISW84 antibody's Fab domains bound.</p> <p>Files are named as (copy number of Fab).(simulation replica ID).(file type extension). The systems were constructed in CHARMM format PSF files and the trajectories were recorded in DCD format.</p> <p>Simulations were performed with NAMD3. The trajectories were re-centered and re-wrapped about the periodic boundary from the raw trajectories, in order to keep the HA-Fab complex and the lipid bilayer appearing as a single continuous entity instead of isolated molecules at the opposite side of the periodic boundary. Water molecules in the simulation were removed in these trajectories due to file size limitations.</p>
Disorder predictions for all reviewed UniProt proteins with "membrane" subcellular localization
<h1>IDRs in membrane proteins</h1> <h3>About</h3> <p>The writeup below walks through how to answer the question "what fraction of membrane proteins have IDRs".</p> <p>The tl/dr: on average <strong>55% of membrane proteins [as defined here] have IDRs</strong>.</p> <h3>Data</h3> <p>This directory (WITH the associated sequences), which are not included on GitHub, are deposited on Zenodo.</p> <h2>Steps</h2> <h4>Install software</h4> <p>Install metapredict</p> <div> <pre><code>pip install metapredict</code></pre> </div> <p>Done.</p> <h4>Find the data</h4> <p>Using the "Advanced Search", I downloaded all reviewed proteins on <a href="https://uniprot.org/">UniProt</a> tagged as having a "membrane" subcellular localization.</p> <p>There are, of course, much more detailed ways one could get these data (e.g., requiring transmembrane helices, integral membrane protein filtering etc, etc), but I figured for purposes here, I'd just do this...</p> <h4>Download the data</h4> <p>From this, I downloaded all the FASTA sequences. This triggers the following file to be downloaded:</p> <div> <pre><code>uniprotkb_cc_scl_term_SL_0162_AND_revie_2024_11_09.fasta</code></pre> </div> <h4>Preprocessing the data</h4> <p>Next I wanted to fix any sequences with non-standard amino acids by converting those residues to "standard" amino acids. I did this using <code>pfasta</code>, a commandline tool that's part of <a href="https://protfasta.readthedocs.io/en/latest/">protfasta</a> which comes with metapredict.</p> <p>Did this simply with</p> <div> <pre><code>pfasta --invalid-sequence convert-all -o membrane_proteins.fasta uniprotkb_cc_scl_term_SL_0162_AND_revie_2024_11_08.fasta</code></pre> </div> <ul> <li>The <code>--invalid-sequence</code> flag tells pfasta what to with an invalid sequence, and <code>-o</code> tells it what the ouput file should be called.</li> </ul> <p>Running this (~5 seconds) generates the following output:</p> <div> <pre><code>........................ pfasta version 0.1.13+0.gc1786bd.dirty Please report bugs to: https://github.com/holehouse-lab/protfasta ........................ [INFO]: Reading in the file uniprotkb_cc_scl_term_SL_0162_AND_revie_2024_11_08.fasta [INFO]: Read in file with 1004330 lines [INFO]: Parsed file to recover 117691 sequences [INFO]: Converted 697 sequences to valid sequences [INFO]: Writing new sequence file [membrane_proteins.fasta]...</code></pre> </div> <h4>Predicting all IDRs</h4> <p>Run one of metapredict's command-line utilities to predict all IDRs in these proteins.</p> <div> <pre><code>metapredict-predict-idrs membrane_proteins.fasta -o membrane_protein_idrs.fasta</code></pre> </div> <p>This takes ~20 seconds on my desktop with an A4500 GPU or ~5 min with my Macbook Pro. We're then left with a file called <code>membrane_protein_idrs.fasta</code>. This is a FASTA file that the header format:</p> <div> <pre><code>><HEADER FOR ORIGINAL PROTEIN> IDR_START=<start position> IDR_END=<end_position></code></pre> </div> <p>e.g.</p> <div> <pre><code>>sp|B4R1D8|HH_DROSI Protein hedgehog OS=Drosophila simulans OX=7240 GN=hh PE=3 SV=1 IDR_START=0 IDR_END=94 MDNHSSVPWASAASVTCLSLDAKCHSSSSSCSSKSTASSISASPETQTMRHIAHTQRCLS RLTSLVALLLIVLPMMFSPAHSCGPGRGLGRHRA</code></pre> </div> <h3>Hacky way to get number of proteins with IDRs</h3> <p>Get the number of unique proteins in the main file:</p> <div> <pre><code>cat membrane_proteins.fasta | grep ">" | awk -F "|" {' print $2 '} | sort | uniq |wc</code></pre> </div> <p>Which returns</p> <div> <pre><code>117691 117691 829609</code></pre> </div> <p>So we have 117,691 unique proteins (NB: we could also just have gotten this without the <code>awk -F "|" {' print $2 '} | sort | uniq</code> part BUT we do need that for the number of unique proteins with IDRs so good to be consistent.</p> <div> <pre><code>cat membrane_protein_idrs.fasta | grep ">" | awk -F "|" {' print $2 '} | sort | uniq |wc</code></pre> </div> <p>Which returns</p> <div> <pre><code>64971 64971 458829 </code></pre> </div> <p>So on average <strong>55% of membrane proteins [as defined here] have IDRs</strong>.</p> <h3>Bonus!</h3> <p>In addition we can predict all disorder scores rather than discrete IDRs using</p> <div> <pre><code>metapredict-predict-disorder membrane_proteins.fasta -o membrane_protein_disorder_scores.csv</code></pre> </div> <p>This takes ~5 min on my Macbook Pro. The data for these are included here for completeness/convenience.</p>
Data from: Evaluation of novel large cut-off ultrafiltration membranes for adenovirus serotype 5 (Ad5) concentration.
The purification of virus particles and viral vectors for vaccine and gene therapy applications is gaining increasing importance in order to deliver a fast, efficient, and reliable production process. Ultrafiltration (UF) is a widely employed unit operation in bioprocessing and its use is present in several steps of the downstream purification train of biopharmaceuticals. However, to date few studies have thoroughly investigated the performance of several membrane materials and cut-offs for virus concentration/diafiltration. The present study aimed at developing a novel class of UF cassettes for virus concentration/diafiltration. A detailed study was conducted to evaluate the effects of (i) membrane materials, namely polyethersulfone (PES), regenerated cellulose (RC), and highly cross-linked RC (xRC), (ii) nominal cut-off, and (iii) UF device geometry at different production scales. The results indicate that the xRC cassettes with a cut-off of approximately 500 kDa are able to achieve a 10-fold concentration factor with 100% recovery of particles with a process time twice as fast as that of a commercially available hollow fiber. DNA and host cell protein clearances, as well as hydraulic permeability and fouling behavior, were also assessed.
Data from: A bizarre Jurassic maniraptoran theropod with preserved evidence of membranous wings
The wings of birds and their closest theropod relatives share a uniform fundamental architecture, with pinnate flight feathers as the key component. Here we report a new scansoriopterygid theropod, Yi qi gen. et sp. nov., based on a new specimen from the Middle–Upper Jurassic period Tiaojishan Formation of Hebei Province, China. Yi is nested phylogenetically among winged theropods but has large stiff filamentous feathers of an unusual type on both the forelimb and hindlimb. However, the filamentous feathers of Yi resemble pinnate feathers in bearing morphologically diverse melanosomes5. Most surprisingly, Yi has a long rod-like bone extending from each wrist, and patches of membranous tissue preserved between the rod-like bones and the manual digits. Analogous features are unknown in any dinosaur but occur in various flying and gliding tetrapods, suggesting the intriguing possibility that Yi had membranous aerodynamic surfaces totally different from the archetypal feathered wings of birds and their closest relatives. Documentation of the unique forelimbs of Yi greatly increases the morphological disparity known to exist among dinosaurs, and highlights the extraordinary breadth and richness of the evolutionary experimentation that took place close to the origin of birds.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.