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FIG. 10 in Newly discovered fossils provide novel insights on the biology of the South American Miocene snake Colombophis Hoffstetter & Rage, 1977
FIG. 10. — Phylogenetic relationships of Colombophis Hoffstetter & Rage, 1977. Strict consensus tree from 79 most parsimonious trees, bootstrap percentages> 50% are shown below the branches. Major clades: A, Scolecophidia; B, Alethinophidia; C, Uropeltoidea; D, Caenophidia; E, Pythonoidea; F, Booidea.
FIG. 8 in Newly discovered fossils provide novel insights on the biology of the South American Miocene snake Colombophis Hoffstetter & Rage, 1977
FIG. 8. — Parazygantral foramina in Colombophis spp. vertebrae: A, B, C. portai Hoffstetter & Rage, 1977, MNHN VIV-6 (see also Appendix 7); C, D, C. portai, VPPLT-1740; E, F, C. spinosus Hsiou, Albino & Ferigolo, 2010, VPPLT-798; G, C. spinosus, VPPLT-1728; H, endocast of VPPLT-1728 showing the internal vascularization of the vertebra. Colors: blue, parazygantral foramina; green, zygantral foramina; red, lateral foramina. Views: 1, dorsal; 2, lateral; 3, posterior. Scale bars: A-F, 5 mm; G, H, 10 mm.
FIG. 5 in Newly discovered fossils provide novel insights on the biology of the South American Miocene snake Colombophis Hoffstetter & Rage, 1977
FIG. 5. — Colombophis portai Hoffstetter & Rage, 1977 VPPLT-1564 specimen: A, B, nine articulated precloacal vertebrae; C, D, several fragments of precloacal vertebrae and ribs embedded in the rock matrix; E-I, three articulated precloacal vertebrae in dorsal (E, F), anterior (G) and ventral (H, I) views; J-M, isolated vertebra in anterior (J, K) and posterior (L, M) views. Scale bars: A, B, 50 mm; C-N, 10 mm.
FIG. 1 in Newly discovered fossils provide novel insights on the biology of the South American Miocene snake Colombophis Hoffstetter & Rage, 1977
FIG. 1. — Location map of the different localities where Colombophis spp. fossils have been collected in: A, South America; B, The Tatacoa Desert area. Stratigraphic units and geological map of the Tatacoa Desert were modified from Montes et al. 2021: fig. 4.
FIG. 2 in Newly discovered fossils provide novel insights on the biology of the South American Miocene snake Colombophis Hoffstetter & Rage, 1977
FIG. 2. — Comparison of Colombophis Hoffstetter & Rage, 1977 mid-trunk vertebrae with extant fossorial squamates: A, Colombophis portai Hoffstetter & Rage, 1977, MNHN-VIV-6; B, Colombophis spinosus Hsiou, Albino & Ferigolo, 2010, UFAC-PV-2953, modified from Hsiou et al. (2010); C, Anilius scytale Linnaeus, 1758, UF-H-52001; D, Cylindrophis ruffus Laurenti, 1768 UF-H-52673; E, Uropeltis Cuvier, 1829, UF-H-11750; F, Rena dulcis Baird & Girard, 1853, UF-H- 11776; G, Amphisbaena alba Linnaeus, 1758, UR-uncatalogued specimen. Views: 1, anterior; 2, dorsal; 3, lateral; 4, ventral; 5, posterior views. Scale bars: A, B, G, 10 mm; C, D, E, 5 mm; F, 1 mm.
FIG. 3 in Newly discovered fossils provide novel insights on the biology of the South American Miocene snake Colombophis Hoffstetter & Rage, 1977
FIG. 3. — Colombophis portai Hoffstetter & Rage, 1977 specimens! A, B, VPPLT-1006; C, VPPLT-0067; D, VPPLT-0068; E, VPPLT-1739; F, VPPLT-1735. Views: 1, anterior; 2, dorsal; 3, lateral; 4, ventral, 5, posterior views. Scale bars: 10 mm.
FIG. 6 in Newly discovered fossils provide novel insights on the biology of the South American Miocene snake Colombophis Hoffstetter & Rage, 1977
FIG. 6. — Colombophis spinosus Hsiou, Albino & Ferigolo, 2010 from the Tatacoa Desert: A, B, VPPLT-1728; C, VPPLT-0798; D, VPPLT-1093; E, VPPLT-1741; F, VPPLT-1534. Views: 1, anterior; 2, dorsal; 3, lateral; 4, ventral, 5, posterior views. Scale bars: 10 mm.
FIG. 4 in Newly discovered fossils provide novel insights on the biology of the South American Miocene snake Colombophis Hoffstetter & Rage, 1977
FIG. 4. — Colombophis portai Hoffstetter & Rage, 1977 VPPLT-1740 specimen, which consists of seven associated vertebrae: A, B, mid-trunk vertebra; C, D, midtrunk vertebra; E, F, posterior-trunk vertebra. Views: 1, anterior; 2, dorsal; 3, lateral; 4, ventral; 5, posterior views. Scale bar: 10 mm.
APPENDIX 2 in Newly discovered fossils provide novel insights on the biology of the South American Miocene snake Colombophis Hoffstetter & Rage, 1977
APPENDIX 2. — Comparison of bone compactness in transverse section (Cts) between different ecomorphs (Aquatic, Subaquatic, Arboreal, Ground dweller and Fossorial) and with Colombophis Hoffstetter & Rage, 1977 values
PPENDIX 1 in Newly discovered fossils provide novel insights on the biology of the South American Miocene snake Colombophis Hoffstetter & Rage, 1977
PPENDIX 1. — Transverse sections of Colombophis spp. vertebrae: A, C. portai Hoffstetter & Rage, 1977, MNHN VIV-6 (see also Appendix 7); B, C. spinosus Hsiou, Albino & Ferigolo, 2010, VPPLT-798; C, C. portai, VPPLT-1564; D, E, C. portai, VPPLT-1740; F, C. portai, VPPLT-1006. Scale bar: 5 mm.
Protein sizing with 15-nm conical biological nanopore YaxAB
<p>This dataset belongs to the article: "Protein sizing with 15-nm conical biological nanopore YaxAB" and contains the raw electrophysiology data of protein capture by YaxAB, conductance distributions and reverse potential experiments. A Matlab script describing the analysis is also added. This dataset also includes the molecular dynamics simulations and analysis (see below)</p> <p>The Data_exp.zip contains experimental files; Data_MD.zip contains MD simulation files. </p> <p>For Data_exp.zip:</p> <p>- Each electrophysiology file contains measurement info as follows:<br> [Date of measurement]_[Buffer conditions]_[Pore type]_[added analyte(s)]_[operator initials]</p> <p>- Each electrophys trace is accompannied by Excel file with Clampfit analysis in Sheet1 (named [voltage], with the columns corresponding to: Trace; Search; Level; State; Event Start Time (ms); Event End Time (ms); Amplitude (pA); Amp S.D. (pA); Dwell Time (ms); Inst. Freq. (Hz); Interevent Interval (ms); [empy]; open pore current (pA)</p> <p>- This Excel file was analyzed with inhouse Matlab script (matlab_script_protein_capture_SAP); and event data (Ires, dwell time, Amp S.D.) was used in scatter plots. </p> <p> </p> <p><br> Data_exp_zip: Folder containing raw electrophysiology data and result after Clampfit analysis, with each folder containing the following:</p> <p>Figure 1: Current distributions of YaxAB and YaxA<sub>d40</sub>B</p> <p>Figure 2: I/V curves and reverse potential of YaxAB and YaxA<sub>d40</sub>B</p> <p>Figure 3: Protein capture by YaxA<sub>d40</sub>B</p> <p>Figure 4 and Figure S25-S26: Depleted serum capture and titrated CRP capture by YaxA<sub>d40</sub>B</p> <p>Figure S7: I/V curves of reverse potential experiments of YaxAB, YaxA<sub>d40</sub>B, YaxA<sub>d40</sub>B RRR, YaxA<sub>d40</sub>B NNN</p> <p>Figure S8: Current distributions of YaxA<sub>d40</sub>B RRR and YaxA<sub>d40</sub>B NNN</p> <p>FIgure S12: Protein capture by YaxAB</p> <p>FIgure S13: Protein capture by YaxA<sub>d40</sub>B</p> <p>Figure S14-S17, and S19-S21: Voltage-dependent protein capture by YaxA<sub>d40</sub>B</p> <p>Figure S18: Protein capture by YaxA<sub>d40</sub>B with different unitary conductance</p> <p>Figure S23: Concentration-dependent protein capture by YaxA<sub>d40</sub>B</p> <p>Figure S24: Mixed protein capture by YaxA<sub>d40</sub>B with different unitary conductance</p> <p>Figure S26: CRP capture in absence/presence of depleted serum by YaxA<sub>d40</sub>B</p> <p>matlab_script_protein_capture_SAP: Matlab script used to obtain event data in scatter plots </p> <p> </p> <p>Data_MD: Folder containing structures and<strong> </strong>MD analysis script and tools. Trajectories are not uploaded as they are more than 2Tb of data and can be easily reproduced using the provided files and described methods.</p> <p>Figure 1 (MD) - PDBs of the modeled didecameric YaxAB (<strong>yaxab-codine.pdb</strong>) and YaxA_{D40}B (<strong>yaxab-d40.pdb</strong>)</p> <p>Figure 2 (MD) - <strong>1)</strong> FORTRAN source code to obtain the axial-symmetric maps and resistance-per -length profile from the VMD Volmap density maps .dx (<strong>dx2radial.zip</strong>); <strong>2)</strong> VMD TCL script to compute the velocity map from the MD trajectories (.dcd) and FORTRAN code (<strong>get_vField.zip</strong>).</p> <p>Figure 3 (MD) - FORTRAN and bash codes to obtain the protein-pore resistance from the VMD Volmap occupancy 3D maps (<strong>Hindrance-Yax.zip</strong>)</p> <p>Figure S2 (MD) - VMD TCL script for MODELLER (<strong>Modella.tcl</strong>) and output PDB for the initial YaxAB nanopore model (<strong>yaxab-codine-init_MODELLER.pdb</strong>)</p> <p>Figure S4 (MD) - VMD TCL scripts to obtain the ionic fluxes, EOF and charge maps .dx (<strong>EFIELD_analysis.zip</strong>). The filtered map is obtained by the <strong>dx2radial </strong>FORTRAN<strong> </strong>program.</p> <p>Figure S5 (MD) - Nothing to upload<br> <br> Figure S6 (MD) - PDBs of mutated pores (<strong>yaxab-RRR.pdb</strong>, <strong>yaxab-NNN.pdb</strong>)</p> <p>Figure S22 (MD) - Analysis of the resistance were performed with <strong>dx2radial </strong>program; nothing additional to update.</p> <p> </p>
Repeatable randomness, invariant properties, and the design of biological signatures of identity
<p>What makes a perfect signature? Optimal signatures should be consistent within individuals and distinctive between individuals. In defence against avian brood parasitism, some host species have evolved 'signatures' of identity on their eggs, comprising inter-individual variation in colour and pattern. Tawny-flanked prinia (<em>Prinia subflava</em>) egg signatures facilitate recognition and rejection of parasitic cuckoo finch (<em>Anomalospiza imberbis</em>) eggs. Here, we show that consistency and distinctiveness of pattern are negatively correlated in prinia eggs, perhaps because non-random, repeatable pattern generation mechanisms increase consistency but limit distinctiveness. We hypothesise that pattern properties which are repeatable within individuals but random between individuals ('invariant properties') allow hosts to circumvent this trade-off. To find invariant properties, we develop a method to quantify entire egg phenotypes from images taken from different perspectives. We find that marking scale (a fine-grained measure of size), but not marking orientation or position, is an invariant property in prinias. Hosts should therefore use differences in marking scale in egg recognition, but instead field experiments show that these differences do not predict rejection of conspecific eggs by prinias. Overall, we show that invariant properties allow consistency and distinctiveness to coexist, yet receiver behaviour is not optimally tuned to make use of this information.</p>
Multi-Omics Visible Drug Activity Prediction with a Biologically Informed Neural Network Model
<p>Drug discovery is a challenging task, it takes several years for a drug to be introduced on the market, with most of<br> the studied drugs not even passing the first phase. The understanding of the mechanisms influencing response to drugs<br> can reduce failures and accelerate drug development. Virtual drug screening, based on Machine Learning models, is a<br> promising field for the prediction of the outcome of a treatment. However, the complex relationships between the features<br> learned by these models are still poorly understood and not easy to interpret.<br> We have designed a Neural Network model for drug sensitivity prediction that leverages a Visible Neural Network, an<br> easily interpretable model, due to its biologically informed nature. The trained model can be inspected to study which<br> biological processes were fundamental for the prediction and to identify the drug properties that affect sensitivity. It<br> combines multi-omics data from various types of tumor tissues and drug representations based on molecular descriptors.<br> The mechanisms learned from the network can also be exploited to find candidate drugs for synergy to predict the effect<br> of combined therapies. We consider the unbalanced nature of public drug screening datasets and show that our model<br> outperforms state-of-the-art visible machine learning models.</p>
Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images - dataset
<p>This dataset includes 4 files with segmentation results for 4 different ODT reconstructions of SH-SY5Y neuroblastoma cell. The segmentation results contain:</p> <ol> <li>3D binary masks of biological cells obtained through Cellpose [1] and <a href="https://github.com/biopto/ODT-SAS.git">ODT-SAS</a>;</li> <li>3D binary masks of organelles: nucleoli and lipid structures (LS) obtained through slice-by-slice manual segmentation and ODT-SAS.</li> </ol> <p>All files are .*mat files.</p> <p>The files <em>REC_SH-SY5Y_1.mat, REC_SH-SY5Y_2.mat</em> and<em> REC_SH-SY5Y_3.mat</em> consist of 7 variables:</p> <p>RECON – tomographic reconstruction of SH-SY5Y neuroblastoma cell;<br> n_imm – refractive index of object immersion medium;<br> dx – object space sample size in XY [<span class="math-tex">\(\mu m\)</span>];<br> rayXY – xy-coordinates of illumination vectors;</p> <p>maskManual – table with manually determined 3D binary masks of organelles;<br> maskCellpose – 3D binary mask of biological cell obtained through Cellpose;<br> maskODTSAS – table with 3D binary masks of biological cell and their organelles obtained through ODT-SAS.</p> <p>File <em>REC_SH-SY5Y_4.mat</em> includes masks for the ODT-SAS and Cellpose segmentation of three closely packed cells and consists of 5 variables: RECON, n_imm, dx, maskCellpose and maskODTSAS.<br> <br> Access a particular 3D binary mask from 'maskManual' and 'maskODTSAS' tables, using the following names: 'Cell', 'Nucleoli', 'LS'.<br> For example:</p> <pre><code>cellMask = maskODTSAS.Cell{1};</code></pre> <p><br> [1] Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. Nature methods, 18(1), 100-106.</p> <p> </p>
Supporting Information from The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)
<p>Supporting Information (Open Data) from The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)</p>
Annotation Data: Decoding the chromosome-scale genome of the nutrient-rich Agaricus subrufescens: A Resource for fungal biology and biotechnology
<p><strong>Decoding the chromosome-scale genome of the nutrient-rich Agaricus subrufescens: A Resource for fungal biology and biotechnology</strong></p> <p>Genome annotation data</p> <p><strong>Genome Browser:</strong> <a href="https://plantgenomics.ncc.unesp.br/gen.php?id=Asub">https://plantgenomics.ncc.unesp.br/gen.php?id=Asub</a></p>
Figure 3 in Bat fly (Diptera: Streblidae) and common vampire bat (Chiroptera: Phyllostomidae) association in Honduras: prevalence, mean intensity, infracommunities and influence of the biological characteristics of the host
Figure 3. Infracommunities of bat flies and relative frequency, collected on D. rotundus between May 2018 to November 2019 in Honduras.
Figure 2 in Bat fly (Diptera: Streblidae) and common vampire bat (Chiroptera: Phyllostomidae) association in Honduras: prevalence, mean intensity, infracommunities and influence of the biological characteristics of the host
Figure 2. Infestation intensity of Strebla wiedemanni, Trichobius joblingi, T. parasiticus and T. caecus, collected on Desmodus rotundus between May 2018 to November 2019 in Honduras.
Figure 1 in Bat fly (Diptera: Streblidae) and common vampire bat (Chiroptera: Phyllostomidae) association in Honduras: prevalence, mean intensity, infracommunities and influence of the biological characteristics of the host
Figure 1. Geographic location of the study sites in Honduras: 1) Joya Grande, 2) Río Arcagual, 3) Los Ochales, 4) Lainez, 5) El Hizopo, 6) Cuevas de Talgua, 7) UNAG, 8 Kurpha. See Table 1 for details.
Figures 7–10 in Description of the immature stages, biology and DNA-barcoding of Quichuana pogonosa (Diptera: Syrphidae) collected in Bromeliaceae in Paraná, Brazil
Figures 7–10. Quichuana pogonosa: (7) pupa, dorsal view (DZUP 690906); (8) pupa, ventral view (DZUP 690906); (9) operculum (DZUP 690914); (10) puparium, dorsal view (DZUP 690914). (a sp l) anterior spiracles larval; (a pr) abdominal prolegs; (ap) anal papillae; (ce) cephalopharyngeal skeleton; (op) operculum; (sp p) pupal spiracles. Scale bars: 2 mm.
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.