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Figure S4 in Large-scale snake genome analyses provide insights into vertebrate development
Figure S4. Genetic bases of snake limb loss, body elongation, lung asymmetry, and eye structure simplification, related to Figures 3 and 4 (A) Multiple alignments of PTCH1. Fish, mammals, frogs, birds, crocodiles, turtles, lizards, and snakes are indicated in different colors. (B) CPLANE1 multiple alignments. The gene structure is outlined by the black line with yellow blocks representing CDSs. The position span is obtained based on the European glass lizard reference genome. The ''378–381'' and ''473'' show the positions of snake-specific missing amino acids. The ''His472Try'' and ''Gln474Glu'' indicate the two snake-specific amino acid mutations. (C) Relative body mass and relative body length of WT mice and PTCH1-mutated mice with snake deletion. Mean ± SD is shown by error bar. (D) Microstructural characteristics of the proximal shinbone (colored in red) in 1-week-old WT mice and PTCH1-mutated mice with snake deletion. The trabecular bone volume (BV) and bone volume per tissue volume (BV/TV) of WT type and PTCH1-mutated mice. Significantly smaller *p <0.05 (independent Student's t test). Mean ± SD is shown by error bar. (E) Statistics of phalanx relative length (absolute length divided by body length) in WT mice and PTCH1-mutated mice with snake deletion. Mutated mice had significantly shorter fourth forelimb phalanx (n = 8, *p <0.05, independent Student's t test) and third hindlimb phalanx (n = 8, *p <0.05, independent Student's t test). Mean ± SD is shown by error bar. (F) Expressions of genes related to limb development in stage E11.5 forelimb bud (left) and hindlimb (right) of WT mice and PTCH1-mutated mice with snake deletion. (G) The 17-bp deletion of snake ZRS enhancer in our newly sequenced snakes. (H) Expression levels of somite development-related genes (under positive selection) in P. dhumnades (40 days post oviposition). (I) AP3B1 multiple alignments of snakes and outgroup. The gene structure is outlined by the black line with orange sticks representing CDSs and the numbers indicating the position span according to the European glass lizard reference genome. The positions of snake lost amino acids residues are indicated by the interval number ''796–800.''and number "802". (J) Example of differentially expressed genes (DEGs) between embryonic left and right lungs (40 days post oviposition) of P. dhumnades. The significance is indicated as *p <0.05, **p <0.01, or ***p <0.001. Mean ± SD is shown by error bar. (K) The SD-CNE located at ~13 kb downstream of YAP1. The identities (compared with the 31 reptile species ancestor sequence) of the SD-CNE were decreasing along the evolution of snakes. (L) A SSSV inserted to the 5 kb upstream of the TITF1 transcriptional start site. The SSSV was predicted to have one binding site of HOXA5 transcription factor. Heatmap (left) displays the expressions (log10(quantiled FPKMs)) of HOXA5 and TITF1 in lung, liver, heart, and muscle of green anole (Acar), keeled slug snake (Pber), and many-banded krait (Bmul). TITF1 and HOXA5 were highly expressed in the lung of all species but the expressions of TITF1 were significantly higher in the two snakes than in green anole (Student's t test p values: 0.0048 [Pber] and 0.0077 [Bmul]). (M) Snake-specific amino acid residues loss of CC2D2A. The black line layouts the gene structure with blue sticks representing CDSs and the numbers indicating the position span based on the European glass lizard reference genome. The interval number ''41–74'' points out the sites of lost amino acid residues in CC2D2A based on European glass lizard sequence. The lost amino acid residues were indicated in red on 3D structure.
Data for Millennial-scale precipitation and chemical weathering variations since the last deglaciation recorded by the northwestern South China Sea sediments
<p>Magnetic, DRS and Geochemical data sets of core SCS-02</p>
Supplementary material 2 from: Mas H, Santoiemma G, Lencina JL, Gallego D, Pérez-Laorga E, Ruzzier E, Rassati D (2023) Investigating beetle communities in and around entry points can improve surveillance at national and international scale. NeoBiota 85: 145-165. https://doi.org/10.3897/neobiota.85.103904
List of beetle species trapped from 2017 to 2021
Replication data for "Large-scale stability and the Greater Horn of Africa long and short rains"
<p>Replication data and code for Schwarzwald et al., "Large-scale stability and the Greater Horn of Africa long and short rains". All code needed to replicate this study is available at <a href="http://doi.org/10.5281/zenodo.8092685">https://doi.org/10.5281/zenodo.8092685</a>. The latest version of replication code and instructions for use of this dataset can be found at <a href="https://github.com/ks905383/gha_stability">https://github.com/ks905383/gha_stability</a>.</p> <p>The data in this repository allows for the replication of main text figures and analysis. All other data is available per request.</p> <p> </p>
Supplement to the manuscript "Overcoming computational challenges to realize meter-to-submeter-scale resolution in cloud simulations using super-droplet method" (Matsushima et al., 2023)
<p>Supplemental codes, figures, movies, and datasets to the manuscript "Overcoming computational challenges to realize meter-to-submeter-scale resolution in cloud simulations using super-droplet method" (Matsushima et al., 2023)</p> <p>See README.md for more details.</p>
INFLAMeR: a machine learning algorithm based on large-scale perturbation screening identified new lncRNAs regulating differentiation and survival of leukaemia cells
<p><strong>Abstract</strong></p> <p>Long non-coding RNAs (lncRNAs) are a diverse group of transcripts with poorly understood<br> functionality. To address this gap, we developed INFLAMeR, an advanced machine learning<br> model trained on CRISPRi screening data, to predict functional lncRNAs using comprehensive<br> genetic features. We experimentally validated the predictions by assessing their impact on cell<br> proliferation and anticancer drug resistance. Among the selected lncRNAs, 85% showed<br> significant effects upon knockdown, while low-scoring lncRNAs had no discernible impact.<br> Notably, our study elucidated the functional role of SNHG6 in hematopoietic differentiation.<br> INFLAMeR greatly enhances the prediction of functional lncRNAs, providing valuable insights<br> into their regulatory landscape. By integrating INFLAMeR with experimental validation, we can<br> identify and characterize functional lncRNAs in a cell-type-specific manner, contributing to a<br> deeper understanding of their involvement in cellular processes. Our findings revealed insights<br> into lncRNA biology and a framework for improving the identification of functional lncRNAs.</p>
1D-1V Vlasov-Poisson Simulations of Mutual Impedance Experiments in the presence of small-scale and large-scale plasma inhomogeneities - PART 2
<p>PART 1 IS FOUND AT <strong>https://doi.org/10.5281/zenodo.8118023</strong></p> <p> </p> <p> </p> <p>=====================================================================================================<br> Author : L. Bucciantini<br> Date : 05/07/2023<br> Laboratory : CNRS-LPC2E, Orléans (France)<br> =====================================================================================================</p> <p>Dear reader, we thank you for your interest in our dataset. In this document, we describe what you will<br> find in it.</p> <p>In case you need help with using the dataset, or if you are interested in mutual impedance experiments,<br> do not hesitate to contact our team in Orléans (pierre.henri@cnrs-orleans.fr, pierre.henri@oca.eu)</p> <p>=====================================================================================================<br> Topic:<br> This dataset contains the outputs of numerical simulations performed to assess<br> the impact of plasma inhomogeneities on the diagnostic performance<br> of mutual impedance experiments.</p> <p><br> Numerical model:<br> The outputs are obtained from a numerical model based on the solution of the 1D-1V Vlasov-Poisson<br> system of equations. The scheme used to solve the model is the one developed<br> by Mangeney et al. (2002), A Numerical Scheme for the Integration of the Vlasov-Maxwell System of Equations. Journal of Computational Physics, (doi: https://doi.org/10.1006/jcph.2002.7071).<br> The 1D-1V Vlasov-Poisson version of this model is described in Henri, et al. (2010), Vlasov-Poisson<br> simulations of electrostatic parametric instability for localized Langmuir wave packets in the solar wind,<br> Journal of Geophysical Research (Space Physics), 115, 6106 (2010)<br> -----------------------------------------------------------------------------------------------------<br> What is inside the dataset:</p> <p>The dataset is composed of 7 different mutual impedance measurements, each corresponding to one<br> directory (see below). The measurements (i.e. directory) correspond to small-scale plasma inhomogeneities at different<br> positions with respect to the mutual impedance antennas, or to a large-scale plasma inhomogeneity.</p> <p>Each directory contains a number of folders. Each folder corresponds to the emission of a signal at<br> different frequency.<br> -----------------------------------------------------------------------------------------------------<br> List of directories:</p> <p>(Note : each directory corresponds to one mutual impedance measurement)</p> <p>L : These outputs correspond to one mutual impedance measurement in<br> small antenna emission amplitude, which corresponds to a linear<br> plasma response to the emission.</p> <p>s_xx : Each of these outputs corresponds to one mutual impedance measurement in correspondance of<br> a small-scale plasma inhomogeneity at xx Debye lengths of distance from the emitting antenna<br> </p> <p>-----------------------------------------------------------------------------------------------------<br> List of folders inside the directories:</p> <p>Folders begin with the name "000" and have increasing index. Inside the same directory, each folder<br> represents a different simulation used to build the same mutual impedance measurement.</p> <p>------------------------------------------------------------------------------------------------------<br> List of files inside the folders:</p> <p>density_e.npz : electron density inside the box, in function of time (tempo)</p> <p>density_p.npz : ion density inside the box, in function of time (tempo)</p> <p>E.npz : electric field in the box, in function of time (tempo)</p> <p>qrho.npz : electric potential in the box, in function of time (tempo)</p> <p>qrho_imposed.npz : electric charge imposed at the emitting antennas, in function of time (tempo)</p> <p>tempo.npz : time-vector for density, electric field, electric potential and charge vectors</p> <p>TEST_Luca.dat : parameters describing the characteristics of the simulated plasma box</p> <p><br> [<br> Note : the previous files can be opened as follows</p> <p>import numpy as np</p> <p>vector_file_name = np.load('file_name.npz') # Use these for the .npz files<br> characteristics_of_the_box = np.genfromtxt('TEST_Luca.dat',skip_header=1)</p> <p>]</p> <p><br> -------------------------------------------------------------------------------------------------------<br> Characteristics of the simulated plasma box (TEST_Luca.dat)</p> <p>nx : amount of spatial grid points</p> <p>xl : physical size of the spatial box, expressed in Debye length</p> <p>tt_w : time resolution for ion and electron density, electric field, electric potential and charge</p> <p>rap_m : ion-to-electron mass ratio</p> <p>R_p : ion-to-electron temperature ratio</p> <p>dt : time step used to evolve in time the numerical simulation</p> <p>emission : emission frequency</p> <p>power : amplitude of the electric charge imposed at the emitting antennas</p> <p><br> (Note : all parameters not listed here but present inside the TEST_Luca.dat file correspond to additional<br> functionalities of the model. For the use of this dataset, they can be discarded.)</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
A large-scale serological survey in pets from October 2020 through June 2021 in France shows significantly higher exposure to SARS-CoV-2 in cats
<p>Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can infect many animals, including pets such as dogs and cats. Many studies have documented infection in companion animals by bio-molecular and serological methods. However, only a few have compared seroprevalence in cats and dogs from the general population, and these studies were limited by small sample sizes and collections over short periods. Our goal was to obtain a more accurate evaluation of seroprevalence in companion animals in France and to determine whether cats and dogs differ in their exposure to SARS-CoV-2. For this purpose, we conducted an extensive SARS-CoV-2 serological survey of 2036 cats and 3577 dogs sampled by veterinarians during medical examinations in clinics throughout France. Sampling was carried out from October 2020 through June 2021, a period encompassing the second and third waves of SARS-CoV-2 infections in humans in the country. Using a microsphere immunoassay targeting receptor binding domain and trimeric spike protein, we found 7.1% seroprevalence in pets, in a subset of 308 seropositive samples, 26.3% had neutralizing antibodies. We found that cats were significantly more likely to test positive than dogs, with seropositivity rates of 9.3% and 5.9% in cats and dogs, respectively. Finally, data for both species showed that seroprevalence was lower in older animals and was not associated with the date of sampling or the sex of the animal. Our results show that cats are significantly more sensitive to SARS-CoV-2 than dogs, in line with experimental studies showing that cats are more susceptible than dogs. This study reinforces that pets are commonly infected or exposed to SARS-CoV-2, emphasizing the importance of a One-Health approach to the SARS-CoV-2 pandemic and raising the question of vaccination of companion animals in close contact with humans.</p>
Supplementary material 1 from: Shaffer JA, Parks D, Campbell K, Moragne A, Hueske B, Adams P, Bauman JM (2023) Coastal beaver, Chinook, coho, chum salmon and trout response to nearshore changes resulting from diking and large-scale dam removals: synergistic ecosystem engineering and restoration in the coastal zone. Nature Conservation 53: 61-83. https://doi.org/10.3897/natureconservation.53.85421
Mean woody plant abundance surveyed from the riparian and shrub-emergent marsh vegetation
Data Repository - Impact of competing energy scales on the shell-filling sequence in elliptic bilayer graphene quantum dots
<p>Data Repository for the Publication: Impact of competing energy scales on the shell-filling sequence in elliptic bilayer graphene quantum dots</p> <p>Abstract:</p> <p>We report on a detailed investigation of the shell-filling sequence in electrostatically defined elliptic bilayer graphene quantum dots (QDs) in the regime of low charge carrier occupation, N < 12, by means of magnetotransport spectroscopy and numerical calculations. We show the necessity of including both short-range electron-electron interaction and wavefunction-dependent valley g-factors for understanding the overall fourfold shell-filling sequence. These factors lead to an additional energy splitting at half-filling of each orbital state and different energy shifts in out-of-plane magnetic fields. Analysis of 31 different BLG QDs reveals that both valley g-factor and electron-electron interaction induced energy splitting increase with decreasing QD size, validating theory. However, we find that the electrostatic charging energy of such gate-defined QDs does not correlate consistently with their size, indicating complex electrostatics. These findings offer significant insights for future BLG QD devices and circuit designs.</p>
National-scale rainfall-triggered landslide susceptibility and exposure in Nepal
<p>This dataset is a 30 m resolution landslide susceptibility (hazard) model for Nepal. The dataset is derived from a fuzzy-logic model following the approach developed by Kritikos et al (2015), and is trained on a manually mapped landslide inventory (polygons of landslide locations and extents), originally produced by Durham University and NSET Nepal as part of the UKRI-DFID SHEAR program. The dataset shows the relative probability of a landslide occurring across the geographical extent of Nepal for rainfall triggered landslides only, on a scale of 1 (very highly anticipated) to 0 (very weakly anticipated). Further details and an online map viewer containing the data are available through the Bipad portal (https://bipadportal.gov.np/risk-info/#/hazard).</p> <p>The research relating to this landslide susceptibility model has been supported by the UKRI-DFID SHEAR program (201844-112), the EPSRC project ‘<em>Risk at the Margins</em>’ (EP/T024747/1), the EU ECHO HIP project ‘<em>Risk informed landslide management in Nepal’s hill areas</em>’ (ECHO/-XA/BUD/2020/91026), and by a grant from the Global Challenges Research Fund Multi-Hazard and Systemic Risk programme for the ‘Sajag-Nepal’ project (NE/T01038X/1).</p>
Preparatory Process in Advance of Runaway Fault Rupture through Fluid Injection observed in Laboratory Experiments using a Large Specimen of Sub-meter scale
<p>Fault slip is initiated by locally applied fluid pressure, and it can expand unstably over a wide area causing elastic waves having magnitudes that induce felt or destructive earthquakes. Thus, it is important to examine the unstable expansion of initial slips. However, it is hard to reproduce the process by general setup of laboratory experiment such as triaxial loading tests on cylindrical specimens with inclined faults. In this study, we prepared a cubic specimen of sub-meter scale, which was separated into two triangular prisms by a model fault. The specimen was subjected to bi-axial compressions with different magnitudes. A 2D array of strain gauges was embedded beneath the fault plane to measure the changes in shear strain with the fault slip driven by fluid injection. Based on the experimental results, we discussed the features of fault slips that lead to injection-induced earthquake. The dataset obtained by the experiments were presented in the current data repository.</p>
Wednesday 6 May: Large-scale studies using Netarkivet's HPC, Janne Nielsen, Aarhus University
<p>Wednesday 6 May: Large-scale studies using Netarkivet's HPC, Janne Nielsen, Aarhus University</p>
Wednesday 6 May: Deriving Specific Small-Scale Collections, Federico Nanni, Alan Turing Institute
<p>Wednesday 6 May: Deriving Specific Small-Scale Collections, Federico Nanni, Alan Turing Institute</p>
The structural landscape of the immunoglobulin fold by large-scale de novo design
<p>Dataset for the high-quality immunoglobulin designs.</p>
FiN-2: Larg-Scale Powerline Communication Dataset (Pt.4)
<p>See <a href="https://zenodo.org/record/8328113">https://zenodo.org/record/8328113</a> for more information.</p> <p> </p>
FiN-2: Larg-Scale Powerline Communication Dataset (Pt.2)
<p>See <a href="https://zenodo.org/record/8328113">https://zenodo.org/record/8328113</a> for more information.</p>
FiN-2: Larg-Scale Powerline Communication Dataset (Pt.3)
<p>See <a href="https://zenodo.org/record/8328113">https://zenodo.org/record/8328113</a> for more information.</p>
A chromosome-scale de novo genome assembly of the dwarf tomato variety Micro-Tom
<p>The cultivated tomato (<em>Solanum lycopersicum</em>) is an important crop and model species for genetics and plant molecular biology research. The dwarf tomato variety Micro-Tom is used extensively in research because it is rapid flowering, easy to grow in high volumes in minimal space, and is amenable to genetic transformation. Here we provide a de novo chromosome-scale genome assembly of Micro-Tom that was generated using PacBio HiFi reads and scaffolded using chromosome confirmation capture data. The HiFi data was assembled using the Hifiasm assembler and OmniC data was used for scaffolding using Salsa and several rounds of manual curation and validation.</p>
Figure 15 in The elasmoid scales of teleosts: from structure to bioinspired materials
Figure 15. – Cyprinus carpio. One step trichrome. Vertical section of the anterior margin of the scale. A thick membrane stained in green is observed between the epidermis and the dermis. Within the dermis, the peripheral scleroblasts form a rim around the margin of the scale (arrowheads). The elevation corresponds to the formation of the first circulus (arrow). Figure 16. – Carassius auratus. TEM. Section of the margin of the scale. A thin microfibrillar membrane (arrow) separates the epidermis (ep) from the dermis (d). Scale pocket cells (spc) form a rim around the scales and line the scleroblasts. The external layer, the first layer to be formed, is deposited between the episquamal scleroblasts (es) and the hyposquamal scleroblasts (hs). Figure 17. – Carassius auratus. Indirect immunofluorescence visualising microtubules in the episquamal scleroblasts. A: Phase constraste. B: Tyrtubulin Mts distribution. Mts radiate from the MTOCs (microtubule organising centre) in cells located between two circuli (c). Figure 18. – Carassius auratus. TEM. Episquamal scleroblats lining the external layer. Figure 19. – Margin of the scale. Elevation of the external layer forming a circulus (c). Figure 20. – Carassius auratus. TEM. Episquamal scleroblast (es) where cytoskeletal elements are coaligned with the thin collagen fibrils (cf). Figures 21-23. – Carassius auratus. TEM. Scales treated with rapid freezefixative procedures. Mineralization of the external layer. 21: Needle-shaped crystals are deposited in the interfibrillary spaces (arrow). 22: Clusters of crystals are randomly dispersed. 23: Mineralized external layer. Figure 24. – Carassius auratus. TEM. Section of a partially demineralized circulus. The external layer (el) is composed of network of randomly dispersed collagen fibrils. A thin layer of microfibrillar material is deposited on one side of the circulus. bp: basal plate; es: episquamal scleroblast. Figure 25. – Carassius auratus. TEM. Demineralized section. Detail of the organic material of the limiting layer (ll) made of a thin microfibrillar meshwork and the external layer (el) composed of thin collagen fibrils embedded in a matrix. Figure 26. – Cyprinus carpio. TEM. Demineralized section of the upper part of the scale. Bundles of collagen fibres forming Sharpey-fibre-like bundles (Sf) cross the external layer (el) and the limiting layer (ll).
ScienceDex guides
Understand access before you commit
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.