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10,561 results for “mechanics”
Human antimicrobial peptide inactivation mechanism of enveloped viruses
<p>This dataset provides the raw data supporting the paper: Human antimicrobial peptide inactivation mechanism of enveloped viruses. <a href="https://doi.org/10.1016/j.jcis.2023.11.055">https://doi.org/10.1016/j.jcis.2023.11.055</a></p><p>It comprises the infectivity data (Figure 1), DLS data (Figure 2 and Figures S1-5), cryo-TEM images (Figure 2), SAXS data (Figure 3), SANS data (Figure 3), and the Zeta-potential measurement (in text).</p><p>Setup and conditions for the experiments are described in the experimental section of the published (open access) manuscript.</p>
Data from: The mechanism of promoting rhizosphere nutrient turnover for arbuscular mycorrhizal fungi attribute to recruited functional bacterial assembly
<p>Symbiosis with arbuscular mycorrhizal (AM) fungi improves plant nutrient capture from the soil, yet there is limited knowledge about the diversity, structure, functioning, and assembly processes of AM fungi-related microbial communities. Here, 16S rRNA gene sequencing and metagenomic sequencing were used to detect bacteria in the rhizosphere of <em>Lotus japonicus</em> inoculated with and without AM fungi, and the <em>L. japonicus</em> mutant <em>ljcbx</em> (defective in symbiosis) inoculated with AM fungi in southern grassland soil. Our results show that AM symbiosis significantly increased bacterial diversity and promoted deterministic processes of bacterial community construction, suggesting that mycorrhizal symbiosis resulted in the directional enrichment of bacterial communities and established a stable rhizosphere bacterial community. AM fungi promoted the enrichment of nine bacteria, including <em>Ohtaekwangia</em>, <em>Niastella</em>, <em>Gemmatimonas</em>, <em>Devosia</em>, <em>Sphingomonas</em>, <em>Novosphingobium</em>, <em>Opitutus</em>, <em>Lysobacter</em>, <em>Brevundimonas</em>, which are positively correlated with NPK-related parameters. Through a functional identification experiment, we found that six of these genera, including <em>Brevundimonas</em>, <em>Lysobacter</em>, <em>Ohtaekwangia</em>, <em>Sphingomonas</em>, <em>Devosia</em>, and <em>Gemmatimonas</em>, demonstrated the ability to mineralize organophosphate and dissolve inorganic phosphorus, nitrogen, and potassium. Our study revealed that AM fungi can regulate rhizosphere bacterial community assembly and attract specific rhizosphere bacteria to promote soil nutrient turnover in southern grasslands.</p>
Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China"
<p>Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China".</p><p>Transformations among the parameters of the moment tensor model refer to the code package from Tape and Tape (https://github.com/carltape/mtbeach/; https://github.com/carltape/surfacevel2strain; Tape and Tape, 2009, 2012, 2013, 2015).</p><p>Tape, C., P. Muse, M. Simons, D. Dong, and F. Webb (2009). Multiscale estimation of GPS velocity fields, Geophys. J. Int. 179, no.2, 945-971, doi: 10.1111/j.1365-246X.2009.04337.x.</p><p>Tape, W., and C. Tape (2012). A geometric setting for moment tensors, Geophys. J. Int. 190, no. 1, 476–498, doi: 10.1111/j.1365-246X.2012.05491.x.</p><p>Tape, W., and C. Tape (2013). The classical model for moment tensors, Geophys. J. Int. 195, no. 3, 1701–1720, doi: 10.1093/gji/ggt302.</p><p>Tape, W., and C. Tape (2015). A uniform parametrization of moment tensors, Geophys. J. Int. 202, no. 3, 2074–2081, doi: 10.1093/gji/ggv262.</p>
Data from: Mechanical soil disturbance in a pine savanna has multi-year effects on plant species composition
<p>Data used in the manuscript Mechanical soil disturbance in a pine savanna has multi-year effects on plant species composition accepted for publication in Ecosphere. Data included are species lists for all plots and years, percent cover of species in each plot in 2021, and life-history characteristics (life span, dispersal mechanism, seed bank persistence) of all species. See manuscript for site description and data collection methodology. </p>
Data for Cost Optimization of Shedding Mechanisms as a Product Family
<p>This the data from the numerical examples presented in "Accelerating Cost Optimization of Existing Product Family Designs Using Sensitivity Analysis on Component Commonalities" article. It contains the data of the results for the cost optimization of a shedding mechanism as a product family from two types of optimization: (i) brute-force and (ii) guided optimization: proposed method which uses sensitivity analysis on component commonalities. Each datum (.mat file) contains design variables (length of the four-bar mechanisms and motor locations) of a specific commonality matrix.</p>
Elucidating molecular mechanisms of protoxin-2 state-specific binding to the human NaV1.7 channel
<p>Human voltage-gated sodium (hNaV) channels are responsible for initiating and propagating action potentials in excitable cells and mutations have been associated with numerous cardiac and neurological disorders. hNaV1.7 channels are expressed in peripheral neurons and are promising targets for pain therapy. The tarantula venom peptide protoxin-2 (PTx2) has high selectivity for hNaV1.7 and is a valuable scaffold for designing novel therapeutics to treat pain. Here, we used computational modeling to study the molecular mechanisms of the state-dependent binding of PTx2 to hNaV1.7 voltage-sensing domains (VSDs). Using Rosetta structural modeling methods, we constructed atomistic models of the hNaV1.7 VSD II and IV in the activated and deactivated states with docked PTx2. We then performed microsecond-long all-atom molecular dynamics (MD) simulations of the systems in hydrated lipid bilayers. Our simulations revealed that PTx2 binds most favorably to the deactivated VSD II and activated VSD IV. These state-specific interactions are mediated primarily by PTx2's residues R22, K26, K27, K28, and W30 with VSD and the surrounding membrane lipids. Our work revealed important protein-protein and protein-lipid contacts that contribute to high-affinity state-dependent toxin interaction with the channel. The workflow presented will prove useful for designing novel peptides with improved selectivity and potency for more effective and safe treatment of pain.</p>
Data for "Mechanically-Sensitive Fluorochromism by Molecular Domino Transformation in a Schiff Base Crystal"
<p>The dataset contains input and output files of computational chemistry by Quantum ESPRESSO and Gaussian softwares conducted on two polymorphic crystal structures of 4-nitro-N-salicylideneaniline.</p>
Preferential cannibalism as a key stabilizing mechanism of intraguild predation systems with trophic polymorphic predators
<p><span>Theory predicts intraguild predation (IGP) to be unstable despite its ubiquity in nature, prompting exploration of stabilizing mechanisms of IGP. One of the many ways IGP manifests is through inducible trophic polymorphisms in the intraguild (IG) predator, where a resource-eating predator morph competes with the intraguild (IG) prey for the shared resource while a top predator morph consumes the IG prey. Cannibalism is common in this type of system due to the top predator morph's specialization on the trophic level below it, which includes the resource-eating predator morph. Here, we explore the consequences of inducible trophic polymorphisms in cannibal predators for IGP stability using an IGP model with and without cannibalism. We employ linear stability analysis and identify regions of coexistence based on the top predator morph's preference for conspecifics vs. heterospecifics and the IG prey's competitive ability relative to the resource-eating morph. Our findings reveal preferential cannibalism (i.e. the preferential consumption of conspecifics) stabilizes the system when the IG prey and resource-eating morph have similar competitive abilities for the shared resource. Though original IGP theory finds the IG prey must be a superior resource competitor as a general criterion for coexistence, this is not typically the case when the predator has an inducible trophic polymorphism and the resource-eating morph is specialized in resource acquisition. Preferential cannibalism may therefore be a key stabilizing mechanism in IGP systems with a cannibalistic, trophic polymorphic IG predators, providing further insight into what general mechanisms stabilize the pervasive IGP interaction.</span></p>
Different components of cognitive-behavioural therapy affect specific cognitive mechanisms: supporting data
<p>This repository holds data associated with the manuscript "Different components of cognitive-behavioural therapy affect specific cognitive mechanisms" (<a href="https://osf.io/preprints/psyarxiv/ydct5">https://osf.io/preprints/psyarxiv/ydct5</a>).</p><ul><li>rew-eff-planningX .csv files represent data from experiments testing effects of a planning/goal-setting intervention on reward-effort decision-making (X=1, initial discovery sample, X=2, replication sample)</li><li>causal-attr-X .csv files represent data from experiments testing the effects of a cognitive restructuring intervention on causal attribution tendencies (X=1, initial discovery sample, X=2, replication sample)</li><li>crossover-study-X .csv files represent data from the crossover-design study (data saved separately for participants randomly allocated to complete the reward-effort vs causal attribution tasks).</li></ul><p>Data-generating task code and full analysis code can be found at <a href="https://github.com/agnesnorbury/cognitive-mechanisms-psychotherapy">https://github.com/agnesnorbury/cognitive-mechanisms-psychotherapy</a>.</p>
Unconventional mechanical and thermal behaviours of MOF CALF-20
<p>Data for reproducibility of the paper:</p> <p><a href="https://scholar.google.com/citations?user=fq_Q0rkAAAAJ&hl=en%5D(https://scholar.google.com/citations?hl=en&user=fq_Q0rkAAAAJ&view_op=list_works&sortby=pubdate)" rel="nofollow">D. Fan</a>, <a href="https://scholar.google.com/citations?hl=en&user=7qyxfhAAAAAJ&view_op=list_works&sortby=pubdate" rel="nofollow">S. Naskar</a>, and <a href="https://scholar.google.com/citations?hl=en&user=QNfwyjgAAAAJ&view_op=list_works&sortby=pubdate" rel="nofollow">G. Maurin</a>, Unconventional mechanical and thermal behaviours of MOF CALF-20 D Fan, <em>Nature Communications</em>, <strong>15</strong>, 3251 (2024)</p> <p>https://doi.org/10.1038/s41467-024-47695-6 </p> <p>Please also refer to the main dataset:</p> <p><em><a href="https://doi.org/10.5281/zenodo.10815341" rel="nofollow">https://doi.org/10.5281/zenodo.10815341</a></em></p> <p>The citation of the datasets should be :</p> <p>Fan, D., Naskar, S., & Maurin G. Unconventional mechanical and thermal behaviours of MOF CALF-20. Zenodo database, https://doi.org/10.5281/zenodo.10815341, (2024)</p>
Data from: The magnitude of Allee effects varies across Allee mechanisms, but not taxonomic groups
<p>The Allee effect is a density-dependent phenomenon in which individual fitness increases as population density increases at low population densities. Over the past few decades, a growing number of studies have identified Allee effects in populations using experimental approaches and statistical modelling techniques. These studies have investigated multiple Allee mechanisms (e.g., mate-finding, predation, resource limitation), across a range of systems and taxa (e.g., plants, vertebrates, invertebrates). This meta-analysis aims to synthesize studies that experimentally manipulated population density and measured either per capita population growth or fitness components, with the goal of determining whether the "magnitude" of the Allee effect (defined here as the correlation between population density and population growth or fitness) varies with Allee mechanism across taxonomic groups. A total of 2305 studies were screened, and 62 of these studies met our meta-analysis inclusion criteria. Within these 62 studies, 155 effect sizes encompassing 9 different Allee mechanisms were identified across 5 broad taxa. When grouped by Allee mechanism and taxa, the magnitude of the Allee effect differed across mechanisms, whereas taxonomic group was less useful at explaining variation in the magnitude of Allee effects. Of the nine Allee mechanisms identified, interspecific competition was associated with the strongest Allee effects, followed by fear, pollen limitation and mate limitation. These findings suggest that Allee effects may be more dependent on mechanism than taxa and may function similarly within different taxonomic groups. However, as the majority of experimental Allee effect studies included in this meta-analysis focused on plants and invertebrates, more research is needed on Allee effects in other taxonomic groups to confirm this conclusion. This first quantitative synthesis of Allee effect research in ecology offers novel insight into how Allee mechanisms affect the manifestation of Allee effects in populations, providing important information for ecologists and conservation managers.</p>
Datasets for polygenic mechanisms of hybrid incompatibility in butterflies
<p><strong>Version 1.2 includes data that are missing in the previous versions.</strong></p> <p> </p> <p>Note: relevant scripts can also be found at</p> <p>https://github.com/tzxiong/2022_Papilio_HybridIncompatibilityMapping</p> <p>======================================================<br>Description of source data and scripts for all figures<br>======================================================</p> <p>==== MAIN FIGURES ====</p> <p>Fig. 1</p> <p> - Panel A<br> * Schematic figure, no source data are provided</p> <p> - Panel B<br> * Schematic figure, no source data are provided</p> <p> - Panel C<br> * Source data folder(s):<br> SourceData/Fig1/Fig1C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 5</p> <p> - Panel D<br> * Source data folder(s):<br> SourceData/Fig1/Fig1D<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 5</p> <p> - Panel E<br> * Source data folder(s):<br> SourceData/Fig1/Fig1E<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 2</p> <p> - Panel F<br> * Source data folder(s):<br> SourceData/Fig1/Fig1F<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 2</p> <p>Fig. 2</p> <p> - Panels A-G<br> * Source data folder(s): <br> SourceData/Fig2+S1toS2 <br> * The "Raw" folder contains unedited images.<br> * Two edited images used in Fig2 is also included for each subfigure.</p> <p> - Panels H-L<br> * Source data folder(s): <br> SourceData/Fig2+S1toS2 <br> * The "Raw" folder (unzipped) contains unedited confocal data in .czi format.<br> * Edited images are included with both monochrome and merged versions.</p> <p>Fig. 3</p> <p> - Panel A<br> * Source data folder(s):<br> SourceData/Fig3+S8toS10/Fig3A_3B_3C_3D_3E<br> * Source code:<br> D(DB): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.1<br> B(BD): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 2.3</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10/Fig3A_3B_3C_3D_3E<br> * Source code:<br> D(DB): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.1<br> B(BD): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.2</p> <p> - Panel C<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10/Fig3A_3B_3C_3D_3E<br> * Source code:<br> D(DB): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.1<br> B(BD): SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.2</p> <p> - Panel D<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10/Fig3A_3B_3C_3D_3E<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Run all of Sections 4.1 and 4.2</p> <p> - Panel E<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10/Fig3A_3B_3C_3D_3E<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Run all of Sections 4.1 and 4.2</p> <p>Fig. 4</p> <p> - Note 1: For Heliconius analysis, all data are from SourceData/Fig4-Heliconius+S11C/dat.4.qtl.lumped.csv. This file contains Heliconius ovary dysgenesis data from https://doi.org/10.1111/mec.16272</p> <p> - Panel A (Heliconius)<br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.1<br> <br> - Panel A (Papilio) <br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.2</p> <p> - Panel B (Heliconius)<br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.1<br> <br> - Panel B (Papilio)<br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.2</p> <p> - Panel C (Heliconius) <br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.2<br> <br> - Panel C (Papilio) <br> * Source data folder(s):<br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.3</p> <p> - Panel D<br> * Schematic figure, no source data are provided<br> <br> - Panel E (Heliconius) <br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.3<br> <br> - Panel E (Papilio) <br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.3<br> <br> - Panel F (Heliconius)<br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.3<br> <br> - Panel F (Papilio) <br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.3<br> <br> <br> <br>==== SUPPLEMENTARY FIGURES ====</p> <p>Fig. S1-S2</p> <p> * Source data folder(s): <br> SourceData/Fig2+S1toS2 <br> * The "Raw" folder (unzipped) contains unedited confocal data in .czi format.<br> * Edited images are included with both monochrome and merged versions.</p> <p>Fig. S3</p> <p> * Source data folder(s): <br> SourceData/FigS3<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 1<br> * Note: Source data file 04.0_IBD.NgsRelate.zip contains results from the NGSRelate software.<br> </p> <p>Fig. S4</p> <p> * Source data folder(s): <br> SourceData/FigS4<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 2<br> * Note 1: Source data file CorrectedReferenceGenome.zip is the corrected reference genome used for all analyses. It is in .fasta format.<br> * Note 2: Source data file DenovoMarkerOrder_on_CorrectedRefGenome.zip contains all outputs from the LepMap3/OrderMarkers2 module that uses genotype likelihoods and pedigree information to generate a new marker order. Use script "OrderMarkers2_ReOrder.sh" from the script repo.<br> </p> <p>Fig. S5-S7</p> <p> * Source data folder(s): <br> SourceData/FigS5toS7<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 2<br> * Note: The source data file PedigreeAncestryInGrandparentalPhase.zip contains all outputs from the LepMap3/OrderMarkers2 module that uses genotype likelihoods and pedigree information to impute ancestry at each marker. Ancestry is phased according to the sex of grandparents. Use script "OrderMarkers2.sh" from the GitHub repo.</p> <p>Fig. S8</p> <p> - Panel A <br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.1</p> <p> - Panel B <br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 2.3</p> <p> - Panel C<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> See previous two panels</p> <p>Fig. S9</p> <p> - Panel A<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.1</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.2</p> <p>Fig. S10</p> <p> - Panel A<br> * Source data folder(s):<br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.1</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/Fig3+S8toS10<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 4.2</p> <p>Fig. S11</p> <p> - Panel A<br> * Source data folder(s): <br> SourceData/FigS11AB<br> * Source code:<br> Data in SpeciesAncestry.B0D1.zip can be directly visualized to get the figure</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/FigS11AB<br> * Source code:<br> Data in SpeciesAncestry.B0D1.zip can be directly visualized to get the figure<br> * Note: This file contains ancestry at each marker phased according to species (bianor=0, dehaanii=1). These data are directly transformed from files in SourceData/FigS5toS7/PedigreeAncestryInGrandparentalPhase.zip.</p> <p> - Panel C<br> * Source data folder(s): <br> SourceData/Fig4-Heliconius+S11C<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 6.4</p> <p>Fig. S12</p> <p> * Source data folder(s): <br> No source data are needed<br> * Source code:<br> SourceData/Code03_PolygenicGhostQTL.ipynb</p> <p>Fig. S13</p> <p> - Panel A<br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.3</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/Fig4-Papilio+S13<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 3.2</p> <p>Fig. S14</p> <p> * Source data folder(s): <br> No source data are needed<br> * Source code:<br> SourceData/Code03_PolygenicGhostQTL.ipynb</p> <p>Fig. S15</p> <p> - Panel A<br> * Source data folder(s): <br> SourceData/FigS15/FigS15A<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 2.1</p> <p> - Panel B<br> * Source data folder(s): <br> SourceData/FigS15/FigS15B<br> * Source code:<br> SourceData/Code.JupyterLab/Code02_Mapping.ipynb: Section 2.2</p> <p>Fig. S16</p> <p> * Source data folder(s): <br> SourceData/FigS16<br> * Source code:<br> SourceData/Code.JupyterLab/Code01_SequencingData.ipynb: Section 3<br> </p> <p>==== OTHER SOURCE DATA & SUMMARY OF SOURCE CODE FOLDERS====</p> <p>LepMap3-SourceData</p> <p> * LepMap3_SourceData-Family_Info_Finalized_withPseudoGrandParents_transposed.txt<br> <br> This file is the pedigree file ready-to-use in LepMap3. Note that it contains pseudo grandparents for families missing grandparents in sequencing. Pseudo grandparents are simply created from fixed SNPs in all existing grandparents and adding them to the original vcf files containing genotype likelihoods.</p> <p> * LepMap3_SourceData-vcf_files.zip<br> <br> The vcf files containing genotype likelihoods for LepMap3 to use. Note that it contains the aforementioned pseudo grandparents.</p> <p><br>Code.NGSRelate</p> <p> * Code used for inferring kinship from low-coverage sequencing data</p> <p>Code.LepMap3<br> <br> * Code used for all LepMap3 analysis</p> <p>Code.JupyterLab</p> <p> * Code used for all Julia and R analysis in .ipynb format</p> <p><br> </p>
Data from: Deep mutational scanning of HBV reveals a mechanism for cis preferential reverse transcription
<p>Hepatitis B virus (HBV) is a small double-stranded DNA virus that chronically infects 296 million people. Over half of its compact genome encodes protein in two overlapping reading frames, and during evolution, multiple selective pressures can act on shared nucleotides. This study combines an RNA-based HBV cell culture system with deep mutational scanning to uncouple <em>cis-</em> and <em>trans</em>-acting sequence requirements in the HBV genome. The results support a leaky ribosome scanning model for polymerase translation, provide a fitness map of the HBV polymerase at single nucleotide resolution, and identify conserved prolines adjacent to the HBV polymerase termination codon that stall ribosomes. Further experiments indicated that stalled ribosomes tether the nascent polymerase to its template RNA, ensuring <em>cis</em>-preferential RNA packaging and reverse transcription of the HBV genome.</p>
Study of the failure mechanism of a HDPE liner in a Type IV high-pressure storage tank [Dataset related to publication]
<p>Data type: SEM micrographs; ATR-FTIR spectra; XRD patterns, DSC profiles and analyses. </p> <p>Data format: *.tif; *.dat; *.asc; *.opj.</p> <p>Origin of the data: laboratory equipment from University of Udine (SEM, ATR-FTIR, XRD, DSC).</p> <p>Software needed to plot the data: folders need to be unzipped, Origin.</p>
Dataset for "Exploring Mechanism of Corrosion Inhibition of WE43 and AZ31 Alloys by Aqueous Molybdate in Hank's Solution by Multisine Impedimetric Monitoring"
<p>This dataset contains raw data of the publication “Exploring Mechanism of Corrosion Inhibition of WE43 and AZ31 Alloys by Aqueous Molybdate in Hank’s Solution by Multisine Impedimetric Monitoring”. Corrosion Science, Volume 231, 2024, 111979, ISSN 0010-938X, <a href="https://doi.org/10.1016/j.corsci.2024.111979">https://doi.org/10.1016/j.corsci.2024.111979</a>. The data was collected at Jerzy Haber Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences (Poland) and Gdansk University of Technology (Poland). All data are provided under CC0 license. The dataset includes Scanning electron and optical microscopy images (.tiff and ,jpg format); X-ray photoelectron spectroscopy spectra (.vms format); Electrochemical impedance spectra (.txt format). Dzmitry Kharytonau gratefully acknowledges the financial support of this study by the National Science Centre (Poland) under research Grant Sonatina no. 2021/40/C/ST5/00266. This work was also supported by Gdansk University of Technology under the Aurum Supporting International Research Team Building – ‘Excellence Initiative – Research University’ [grant number: 2/2021/IDUB/II.1.3].<br><br></p>
Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms
<p>RNA splicing is highly prevalent in the brain and has strong links to neuropsychiatric disorders, yet the role of cell-type-specific splicing or transcript-isoform diversity during human brain development has not been systematically investigated. Here, we leveraged single-molecule long-read sequencing to deeply profile the full-length transcriptome of the germinal zone (GZ) and cortical plate (CP) regions of the developing human neocortex at tissue and single-cell resolution. We identified 214,516 unique isoforms, of which 72.6% are novel (unannotated in Gencode-v33), and uncovered a substantial contribution of transcript-isoform diversity, regulated by RNA binding proteins, in defining cellular identity in the developing neocortex. We leveraged this comprehensive isoform-centric gene annotation to re-prioritize thousands of rare de novo risk variants and elucidate genetic risk mechanisms for neuropsychiatric disorders.</p>
KupferDigital mechanical testing datasets: Brinell hardness, Vickers hardness, and tensile tests
<p><span>The Kupfer<em>Digital</em> project aims to develop digital methods, tools, and data space infrastructures for digitalizing the entire life cycle of copper materials. The mechanical testing process is one of the main chains of such life cycles which generates lots of important testing data about the mechanical properties of the materials and their related materials and testing metadata. To train the digitalization of the mechanical testing process, different kinds of copper alloys were provided for this project, and their mechanical properties were measured by typical methods like Brinell and Vickers hardness and tensile testing. The primary raw testing data as well as the secondary datasets of these tests are provided. The detailed materials specifications, the utilized mechanical testing methods, and the provided datasets are described in the content file. The test data files of heterogeneous structures are processed by the Kupfer<em>Digital</em> digital tools to be converted to standardized machine-readable data files.</span></p>
Data from: Mechanisms underpinning the net removal rates of dissolved organic carbon in the global ocean
<p>With almost 700 Pg of carbon, marine dissolved organic carbon (DOC) stores more carbon than all living biomass on Earth combined. However, the environmental controls behind the persistence and the spatial patterns of DOC concentrations on basin scale remain largely unknown, precluding quantitative assessments of the fate of this large carbon pool in a changing climate. We present the first global dynamic DOC model in agreement with more than 40,000 DOC observations, in which a feedback between DOC and picoheterotrophs is explicitly included (model of MICrobial-DOC interactions, MICDOC). This dataset contains model output and related information for a global model simulation in which a colimitation of macronutrients and organic carbon on microbial DOC uptake is implemented and explains >70% of the global variation of observed DOC concentrations. It provides the model output, source code and meta data for the simulations performed for the publication (doi: 10.1029/2023GB007912) "Mechanisms Underpinning the Net Removal Rates of<br>Dissolved Organic Carbon in the Global Ocean" by Lennartz et al.</p>
Revealing the microscopic mechanism of vortex pinning in superconductors
<p>Vortex pinning is a crucial factor that determines the critical current of practical superconductors, but its microscopic mechanism has long been elusive. Here using high-resolution scanning tunneling microscopy, we studied the pinning of single vortex by point defect in high-Tc FeSe-based superconductors. We found the defect-vortex interaction drives low-energy vortex bound states away from EF, which effectively lowered the energy of vortex and caused the pinning. It indicates the local pairing near the pinned vortex core is actually enhanced, which is in sharp contrast to the traditional understanding that non-superconducting regions pin the vortex, but is well captured by our microscopic quantum model. Furthermore, we directly obtained the elementary pinning energy from tunneling spectrum and estimated the pinning force via the spatial gradient of pinning energy. The obtained value aligns with the bulk critical current measurement. Our study thus established a general microscopic mechanism of vortex pinning in superconductors.</p>
Mechanical properties of rubble pile asteroids (Dimorphos, Itokawa, Ryugu, and Bennu) through surface boulder morphological analysis
<p>This material constitutes the dataset used for the computation of the results of the paper Mechanical properties of rubble pile asteroids (Dimorphos, Itokawa, Ryugu, and Bennu) through surface boulder morphological analysis by Robin & al. </p> <p>The folders "Bennu", "Dimorphos", "Itokawa" and "Ryugu" contains the raw images from PDS used for this study as well as the images with the outlined boulders made with segmentanygrains (https://github.com/zsylvester/segmenteverygrain), and the associated morphological descriptor values. The folder "ExtremeCases" contains the raw and outlined images for extreme roundness cases.</p> <p>This material also contains the main figures of the paper as well as the code to remake most the figures.</p> <p>Last version of this manuscript has been submitted to Nature Communications on May 14th, 2024 and accepted on May 17th, 2024.</p> <p><strong>Abstract</strong>: </p> <table> <tbody> <tr> <td>Planetary defense efforts rely on estimates of the mechanical properties of asteroids, which are difficult to constrain accurately from Earth. The mechanical properties of asteroid material are also important in the interpretation of the Double Asteroid Redirection Test (DART) impact. Here we perform a detailed morphological analysis of the surface boulders on Dimorphos using images, the primary data set available from the DART mission. We estimate the bulk angle of internal friction of the boulders to be 32.7 ± 2.5° from our measurements of the roundness of the 34 best-resolved boulders ranging in size from 1.67 to 6.64 m. The elongated nature of the boulders around the DART impact site implies that they were likely formed through impact processing. Finally, we find striking similarities in the morphology of the boulders on Dimorphos with those on other rubble pile asteroids (Itokawa, Ryugu and Bennu). This leads to very similar internal friction angles across the four bodies and suggests that a common formation mechanism has shaped the boulders. Our results provide key inputs for understanding the DART impact and for improving our knowledge about the physical properties, the formation and the evolution of both near-Earth rubble-pile and binary asteroids.</td> </tr> <tr></tr> </tbody> </table>
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.