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zenodo40/100

FIGURE 3 in A typical enzyme activity for glutathione conjugation indicates exposure of pacu to pollutants

FIGURE 3 |GST specific activities in liver cytosol from Piaractus mesopotamicus injected with benzo[a]pyrene (15 mg kg-1). Assays were carried out with 1-chloro-2,4-dinitrobenzene (CDNB), ethacrynic acid (ETHA) or 2-dichloro-4-nitrobenzene (DCNB) 7 and 14 days after the injection. Bars represent the means ± S.E.M. of assays from nine fish injected with corn oil (clear) or benzo[a]pyrene (dark grey). Significant different from controls are indicated as ***(P <0.001).

opencc-by-4.0Oct 2022View details →

zenodo40/100

FIGURE 2 in A typical enzyme activity for glutathione conjugation indicates exposure of pacu to pollutants

FIGURE 2 |GST specific activities in kidney cytosol from Piaractus mesopotamicus injected with methyl parathion (8 mg kg-1). Assays were carried out with 1-chloro-2,4-dinitrobenzene (CDNB), ethacrynic acid (ETHA) or 2-dichloro-4-nitrobenzene (DCNB) at 24, 48 and 96 hours after the injection. Bars represent the means ± S.E.M. of assays from five fish injected with corn oil (clear) or methyl parathion (dark grey).

opencc-by-4.0Oct 2022View details →

zenodo40/100

FIGURE 1 in A typical enzyme activity for glutathione conjugation indicates exposure of pacu to pollutants

FIGURE 1 |GST specific activities in liver cytosol from Piaractus mesopotamicus injected with methyl parathion (8 mg kg-1). Assays were carried out with 1-chloro-2,4-dinitrobenzene (CDNB), ethacrynic acid (ETHA) or 2- dichloro-4-nitrobenzene (DCNB) at 24, 48 and 96 hours after the injection. Bars represent the means ± S.E.M. of assays from five fish injected with corn oil (clear) or methyl parathion (dark grey). Significant differences from controls are indicated as **(P <0.01).

opencc-by-4.0Oct 2022View details →

zenodo40/100

Enzymes, PLFA, NLFA and soil properties measured in Juniperus thurifera forest expansion gradient

<table> <tbody> <tr> <td>Columns </td> <td>Description</td> <td>Unit</td> <td>Comments</td> </tr> <tr> <td>Site</td> <td>Huertahernando, Ribarredonda, Maranchón</td> <td> </td> <td>3 levels</td> </tr> <tr> <td>Stage</td> <td>Stage of forest expansion gradient (Mature forest, transition zone and expanding front)</td> <td> </td> <td>3 levels</td> </tr> <tr> <td>Microhabitat</td> <td>Under canopy/Open areas</td> <td> </td> <td>2 levels</td> </tr> <tr> <td>Sample code</td> <td>Sample code corresponding to sampling point</td> <td> </td> <td> </td> </tr> <tr> <td>Plot</td> <td>18 plots</td> <td> </td> <td> </td> </tr> <tr> <td>From F to AG </td> <td>PLFAs peaks</td> <td>nmol g-1 soil</td> <td> </td> </tr> <tr> <td>Column AF</td> <td>NLFA peak</td> <td>nmol g-1 soil</td> <td> </td> </tr> <tr> <td>OM </td> <td>organic matter</td> <td>%</td> <td> </td> </tr> <tr> <td>pH</td> <td>pH</td> <td> </td> <td> </td> </tr> <tr> <td>From AK to AQ</td> <td>Enzimes</td> <td>pmol mg-1 min-1</td> <td> </td> </tr> </tbody> </table>

opencc-by-4.0Jul 2024View details →

zenodo40/100

Electrochemical data plotted in A. Fasano, C. Guendon, A. Jacq-Bailly, A. Kpebe, J. Wozniak, C. Baffert, M. del Barrio, V. Fourmond, M. Brugna, C. Léger , « A chimeric NiFe hydrogenase heterodimer to assess the role of the electron transfer chain in tuning the enzyme's catalytic bias and oxygen tolerance », J. Am. Chem. Soc. 145, 36, 20021–20030 (2023). doi: 10.1021/jacs.3c06895

Text file of all the electrochemical data shown in the following paper: A. Fasano, C. Guendon, A. Jacq-Bailly, A. Kpebe, J. Wozniak, C. Baffert, M. del Barrio, V. Fourmond, M. Brugna, C. Léger , « A chimeric NiFe hydrogenase heterodimer to assess the role of the electron transfer chain in tuning the enzyme's catalytic bias and oxygen tolerance », J. Am. Chem. Soc. 145, 36, 20021–20030 (2023). <a href="dx.doi.org/10.1021/jacs.3c06895" target="_blank" rel="noopener">doi: 10.1021/jacs.3c06895</a>

opencc-by-4.0Aug 2024View details →

zenodo40/100

Primary MS data and secondary data for: "Post-proline cleaving enzymes also show specificity to reduced cysteine"

raw LC-MS/MS data (Bruker Daltonics - timsTOF Pro and tims TOF SCP) and ESI-MS data (Bruker Daltonics - 15T ESI/MALDI FT-ICR MS) and related MASCOT generic files (.mgf) and search results (.csv) plus corresponding custom databases (*.fasta). Protein identifications were performed using MASCOT or PEAKS programs. Data were used to: <ul> <li>extract cleavage preferences of Clarity Ferm AnPEP, ProAlanase or Neprosin. Target samples were: protein mixture (BSA, cytC, bCA2, myoglobin, 14-3-3) or human serum from a healthy donor or HEK cell lysate or human insulin or oxidized bovine insulin beta chain</li> <li>profile protein content of Clarity Ferm AnPEP and quantify the proteins</li> </ul> Additional data are under Zenodo entry 10.5281/zenodo.13985598 Linked to the publication:  Postproline Cleaving Enzymes also Show Specificity to Reduced Cysteine. Kalaninová Z, Porta&scaron;iková JM, Jirečková B, Polák M, Nováková J, Kavan D, Novák P, Man P. Anal. Chem. 2024, 96, 48, 19084–19092 doi: 10.1021/acs.analchem.4c04277.

opencc-by-4.0Dec 2023View details →

zenodo40/100

Primary MS data and secondary data for: "Post-proline cleaving enzymes also show specificity to reduced cysteine" Part2

raw LC-MS/MS data (Bruker Daltonics - tims TOF SCP) and related PEAKS search results (*.csv) plus corresponding custom database (*.fasta). Protein identifications were performed using PEAKS programs. Data were used to: <ul> <li>extract cleavage preferences of Clarity Ferm AnPEP. The target sample was HEK cell lysate.</li> </ul> These data are related to https://doi.org/10.5281/zenodo.13938580.

opencc-by-4.0Oct 2024View details →

zenodo40/100

Water migration through enzyme tunnels is sensitive to the choice of explicit water model (DhaA)

This repository contains data for the haloalkane dehalogenase DhaA. Data underpinning analyses of alditol oxidase (AldO) and cytochrome P450 2D6 (CYP2D6) are available from the related repository: <a href="https://doi.org/10.5281/zenodo.11545455">https://doi.org/10.5281/zenodo.11545455</a>   Content: tt_conda.yml -> conda environment used for the calculations.          Usage :          conda env create -f tt_conda.yml          conda activate tt_conda.yml  01_MD_simulations.tar.gz -> the files to run simulation, out and restart files from simulation and simulation analysis results, organized by models and Tunnel Conformational Groups (TCGs). ├── 01_inputs │   ├── opc │   │   ├── TCG_d1.0_o1.1 │   │   ├── TCG_d1.4_o1.2 │   │   ├── TCG_d1.8_o1.4 │   │   ├── TCG_d2.5_o2.1 │   │   └── TCG_d3.0_o2.5 │   ├── scripts │   ├── tip3p │   │   ├── TCG_d1.0_o1.1 │   │   ├── TCG_d1.4_o1.2 │   │   ├── TCG_d1.8_o1.4 │   │   ├── TCG_d2.5_o2.1 │   │   └── TCG_d3.0_o2.5 │   └── tip4pew │       ├── TCG_d1.0_o1.1 │       ├── TCG_d1.4_o1.2 │       ├── TCG_d1.8_o1.4 │       ├── TCG_d2.5_o2.1 │       └── TCG_d3.0_o2.5 ├── 02_outputs │   ├── opc │   │   ├── TCG_d1.0_o1.1 │   │   ├── TCG_d1.4_o1.2 │   │   ├── TCG_d1.8_o1.4 │   │   ├── TCG_d2.5_o2.1 │   │   └── TCG_d3.0_o2.5 │   ├── tip3p │   │   ├── TCG_d1.0_o1.1 │   │   ├── TCG_d1.4_o1.2 │   │   ├── TCG_d1.8_o1.4 │   │   ├── TCG_d2.5_o2.1 │   │   └── TCG_d3.0_o2.5 │   └── tip4pew │       ├── TCG_d1.0_o1.1 │       ├── TCG_d1.4_o1.2 │       ├── TCG_d1.8_o1.4 │       ├── TCG_d2.5_o2.1 │       └── TCG_d3.0_o2.5 └── 03_analysis               02_caver.tar.gz -> results of CAVER calculations, organized by models and TCGs. ├── config_files ├── opc │   ├── TCG_d1.0_o1.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.4_o1.2 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.8_o1.4 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d2.5_o2.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── TCG_d3.0_o2.5 │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 ├── tip3p │   ├── TCG_d1.0_o1.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.4_o1.2 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.8_o1.4 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d2.5_o2.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── TCG_d3.0_o2.5 │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 └── tip4pew     ├── TCG_d1.0_o1.1     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d1.4_o1.2     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d1.8_o1.4     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d2.5_o2.1     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     └── TCG_d3.0_o2.5         ├── 1         ├── 2         ├── 3         ├── 4         └── 5 03_aquaduct.tar.gz -> results of AQUA-DUCT calculations, organized by models and TCGs.   ├── opc │   ├── TCG_d1.0_o1.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.4_o1.2 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.8_o1.4 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d2.5_o2.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── TCG_d3.0_o2.5 │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 ├── tip3p │   ├── TCG_d1.0_o1.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.4_o1.2 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d1.8_o1.4 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── TCG_d2.5_o2.1 │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── TCG_d3.0_o2.5 │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 └── tip4pew     ├── TCG_d1.0_o1.1     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d1.4_o1.2     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d1.8_o1.4     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     ├── TCG_d2.5_o2.1     │   ├── 1     │   ├── 2     │   ├── 3     │   ├── 4     │   └── 5     └── TCG_d3.0_o2.5         ├── 1         ├── 2         ├── 3         ├── 4         └── 5            04_transport_tools.tar.gz -> the results of TransportTools and analysis done from TransportTools results. ├── bottleneck_analyses │   ├── data │   │   └── super_clusters │   ├── statistics │   │   └── comparative_analysis │   └── visualization │       ├── comparative_analysis │       └── sources ├── overall_results │   ├── data │   │   ├── exact_matching_analysis │   │   └── super_clusters │   ├── statistics │   │   └── comparative_analysis │   └── visualization │       ├── comparative_analysis │       └── sources └── scripts     ├── bottleneck_residues     ├── presence_of_tunnels     └── water_transport_analysis         └── output 05_hbonds.tar.gz -> contains raw results of hydrogen bond analysis of transported waters for P1 tunnel of DhaA     06_control_NVE_calculations.tar.gz -> data obtained from NVE simulations of DhaA TCG o1.4d1.8 and files necessary to reproduce ├── 01_MD_simulations │   ├── 01_inputs │   │   ├── configs │   │   │   ├── NVE_equil.in │   │   │   ├── NVE_prod.in │   │   │   ├── NVT_cool.in │   │   │   └── NVT_equil.in │   │   ├── opc │   │   ├── tip3p │   │   └── tip4pew │   └── 02_outputs │       ├── opc │       ├── tip3p │       └── tip4pew ├── 02_caver │   ├── configs │   │   ├── calculate_tunnels.txt │   │   └── clustering.txt │   ├── opc │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── tip3p │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── tip4pew │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 ├── 03_aquaduct │   ├── opc │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   ├── tip3p │   │   ├── 1 │   │   ├── 2 │   │   ├── 3 │   │   ├── 4 │   │   └── 5 │   └── tip4pew │       ├── 1 │       ├── 2 │       ├── 3 │       ├── 4 │       └── 5 └── 04_transport_tools     ├── data     ├── statistics     ├── transport_tools.log     ├── tt_config.in     └── visualization

opencc-zeroOct 2024View details →

zenodo40/100

Computational Approach to Discovering Plastic Degradation Enzymes

With at least 150 million tons of plastic already in oceans and over 10 million tons of plastic entering oceans annually, plastic waste has become a major global problem. Current methods to address this problem such as incineration and landfills are unsustainable and environmentally harmful. More trending approaches such as the degradation of plastic using microbial enzymes are rarely efficient enough to be applied industrially. To fill this gap in our knowledge, we developed a computational method called IPDE (Identification of Plastic Degradation Enzymes) to systematically identify promising enzymes, enzyme combinations, and microbial species for effective plastic waste degradation. Using IPDE, we discovered 32 enzymes in ocean microbiomes, with at least 16 (50.0%) having a role in plastic degradation. Additionally, we identified 37 significant enzyme combinations, 8 (21.6%) of which contain enzymes that co-occur in the same metabolic pathways. Furthermore, we found 60 microbial species, 16 (26.6%) of which have been implied to be linked to plastic degradation in literature. The results from IPDE provide promising candidates for experimental validation, protein engineering, and industrial application to tackle this plastic waste problem. The IPDE tool is freely available at https://github.com/SophieL8/Plastic-degrading-enzymes.

opencc-by-4.0Nov 2024View details →

zenodo40/100

Diagnostic accuracy of a novel enzyme‑linked immunoassay for the detection of IgG and IgG4 against Strongyloides stercoralis based on the recombinant antigens NIE/SsIR.

Background: The diagnosis of strongyloidiasis is challenging. Serological tests are acknowledged to have high sensitivity, but issues due to cross-reactions with other parasites, native parasite antigen supply and intrinsic test variability do occur. Assays based on recombinant antigens could represent an improvement. The aim of this study was to assess the sensitivity and specificity of two novel immunoglobulin (Ig)G and IgG4 enzyme-linked immunosorbent assays (ELISAs) based on the recombinant antigens NIE/SsIR for the diagnosis of strongyloidiasis. Methods: This was a retrospective diagnostic accuracy study. We included serum samples collected from immigrants from strongyloidiasis endemic areas for whom there was a matched result for Strongyloides stercoralis on agar plate culture and/or PCR assay, or a positive microscopy for S. stercoralis larvae. For the included samples, results were also available from an in-house indirect fluorescent antibody test (IFAT) and a commercial (Bordier ELISA; Bordier Affinity Products SA) ELISA. We excluded: (i) samples with insufficient serum volume; (ii) samples from patients treated with ivermectin in the previous 6 months; and (iii) sera from patients for whom only routine coproparasitology was performed after formol–ether concentration, if negative for S. stercoralis larvae. The performance of the novel assays was assessed against: (i) a primary reference standard, with samples classified as negative/positive on the basis of the results of fecal tests; (ii) a composite reference standard (CRS), which also considered patients to be positive who had concordant positive results for the IFAT and Bordier ELISA or with a single “high titer” positive result for the IFAT or Bordier ELISA. Samples with a single positive test, either for the IFAT or Bordier ELISA, at low titer, were considered to be “indeterminate,” and analyses were carried out with and without their inclusion. Results: When assessed against the primary reference standard, the sensitivities of the IgG and IgG4 ELISAs were 92% (95% confidence interval [CI]: 88–97%) and 81% (95% CI: 74–87%), respectively, and the specificities were 91% (95% CI: 88–95%) and 94% (95% CI: 91–97%), respectively. When tested against the CRS, the IgG ELISA performed best, with 78% sensitivity (95% CI: 72–83%) and 98% specificity (95% CI: 96–100%), when a cut-off of 0.675 was applied and the indeterminate samples were excluded from the analysis. Conclusion: The NIE-SsIR IgG ELISA demonstrated better accuracy than the IgG4 assay and was deemed promising particularly for serosurveys in endemic areas.

opencc-by-4.0Dec 2020View details →

zenodo40/100

Accurate Prediction of Enzyme Thermostabilization with Rosetta using AlphaFold Ensembles

DTM vs DGf,mut values for scoring LovD, LipA, p-nitrobenzyl esterase, xylanase A and tryptophan 6-halogenase variants (DTM_vs_DDGf_mut.xlsx). AlphaFold predicted structures in PDB and Pymol sessions formats for top scoring LovD, LovD6, LovD9, LipA WT, LipA 6B, p-nitrobenzyl esterase WT, xylanase A WT and tryptophan 6-halogenase WT decoys (mAF-min_ensembles.zip). Rosetta energies for all calculations (Rosetta_scores.zip).

opencc-by-4.0Dec 2022View details →

dryad40/100

Annotation of genes encoding enzymes across marine phytoplankton genomes

Phytoplankton cells span a large size range, from picoplankton (<2µm), nanoplankton (2 to 20µm), microplankton (20 to 200µm) to macroplankton (200 to <2000µm). Cell size interacts with multiple selective pressures, including cellular metabolic rate, light absorption, nutrient uptake, cell nutrient quotas, trophic interactions and diffusional exchanges with the environment. Beyond simple size, cells of different shapes differ in surface area to volume ratio. For example, more elongated cells, such as pennate diatoms, have a larger surface area to volume ratio compared to more rounded cells, such as centric diatoms, of equivalent biovolume, which can in turn influence diffusional exchanges between cells and their environment. We assembled metadata on diverse marine phytoplankters, in parallel with genomic or transcriptomic data annotations to identify genes encoding enzymes, to facilitate analyses of genomic patterns of encoded enzymes across diverse taxa, sizes, growth forms and origins of strains.

opencc-zeroApr 2023View details →

zenodo40/100

Supplementary dataset for Enzyme promiscuous profiles for protein sequence and reaction annotation

The data and scripts used to produce, analyze, and visualize the results of the manuscript Enzyme promiscuous profiles for protein sequence and reaction annotation by Homa MohammadiPeyhani, Anastasia Sveshnikova, Ljubisa Miskovic, and Vassily Hatzimanikatis. The detailed description of the datafiles and scripts is provided in the accompanying README.rtf file.

opencc-by-4.0Aug 2023View details →

zenodo40/100

2,5-Furandicarboxaldehyde as a Bio-based Crosslinking Agent Replacing Glutaraldehyde for Covalent Enzyme Immobilization

In the quest for a bio-based and safer substitute for glutaraldehyde, we have investigated 2,5 diformylfuran (DFF) as bifunctional crosslinking agent for the covalent immobilization of glucoamylase on amino-functionalized methacrylic resins. Immobilization experiments and systematic comparison with glutaraldehyde at four different concentrations for the activation step showed that DFF leads to comparable enzymatic activities at all tested concentrations. Continuous flow experiment confirms a similar long term stability of the immobilized formulations obtained with the two crosslinkers. The NMR study of DFF in aqueous solution evidenced a much simpler behaviour as compared to glutaraldehyde, since no enolic forms can form and only a mono-hydrated form was observed. Unlike in the case of glutaraldehyde, DFF reacts covalently with the primary amino groups via imine bond formation only. Nevertheless, the stability of the covalent immobilization was confirmed also at acidic pH (4.5), most probably because of the higher stability of the imine bonds formed with the aromatic aldehydes. In terms of toxicity DFF has the advantage of being poorly soluble in water and, more importantly, poorly volatile as compared to glutaraldehyde, which displays severe respiratory toxicity. We have performed preliminary ecotoxicity assays using Aliivibrio fischeri, a marine bacterium, evidencing comparable behaviour (below the toxicity threshold) for both dialdehydes at the tested concentrations.

opencc-by-3.0Dec 2022View details →

ClinicalTrials.gov40/100

Study to Compare the Efficacy and Safety of Enzyme Replacement Therapies Avalglucosidase Alfa and Alglucosidase Alfa Administered Every Other Week in Patients With Late-onset Pompe Disease Who Have No

ClinicalTrials.gov study NCT02782741. IPD Sharing: YES. Countries: 25. Publications: 4.

controlledIPD-YESFeb 2026View details →

ClinicalTrials.gov40/100

A Study With [18F]MNI-1054 to Determine Lysine -Specific Demethylase 1A (LSD1) Brain Enzyme Occupancy of TAK-418 After Single-Dose Oral Administration in Healthy Participants

ClinicalTrials.gov study NCT04202497. IPD Sharing: YES. Countries: 1. Publications: 0.

controlledIPD-YESFeb 2026View details →

dryad40/100

Leak-resilient enzyme-free nucleic acid dynamical systems through shadow cancellation

Open the record for dataset details and reuse information.

publicApr 2024View details →

dryad40/100

Enzyme-like nanoparticle engineered-mesenchymal stem cell secreting HGF promotes visualized therapy for idiopathic pulmonary fibrosis in vivo

Open the record for dataset details and reuse information.

publicAug 2024View details →

dryad40/100

Annotation of genes encoding enzymes across marine phytoplankton genomes

Open the record for dataset details and reuse information.

publicApr 2023View details →

edi40/100

Cover crop application on dredged sediments increases corn yield through microorganism-associated enzyme-driven nutrient mineralization.

Common strategies to mitigate soil degradation of agricultural soils include cover crop application and soil amendment addition. Applying dredged sediments as a soil amendment is gaining popularity since they often provide benefits other amendments lack; however, their use with a cover crop is largely unexplored. To understand how cover crop use changes the restorative properties of dredged sediments, we assessed soil physical and chemical properties, enzymatic activities, and corn yield for plots of dredged sediments with and without a cover crop. Cover crop application on dredged sediments increased corn yields by ~24% when compared to dredged sediments alone. Increases in corn yield were driven by changes in nutrient mineralization, specifically within the nitrogen cycle. The physical and chemical properties of dredged sediments remained unchanged regardless of cover crop application. Our results suggest that when cover crops are applied to dredged sediments, crop yield increased through microorganism-driven nutrient mineralization. However, the physical and chemical environment remained optimal for corn growth within dredged sediments, regardless of cover crop application. This research is a vital step into understanding the use of dredged sediments in agricultural soil systems.

openCC (other)Apr 2023View details →

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record