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730 results for “biochemicals”
Data from: Biochemical, structural and dynamical characterizations of the lactate dehydrogenase from Selenomonas ruminantium provide information about an intermediate evolutionary step prior to complete allosteric regulation acquisition in the super family of lactate and malate dehydrogenases.
<p>This data accompanies the paper entitled <strong><em>Biochemical, structural and dynamical characterizations of the lactate dehydrogenase from Selenomonas ruminantium provide information about an intermediate evolutionary step prior to complete allosteric regulation acquisition in the super family of lactate and malate dehydrogenases.</em></strong></p> <p>The zip archive contains the results of molecular dynamics simulations of the 2 systems investigated in the paper: <em>S. rum</em> and <em>T. mar</em> LDHs. The systems have been simulated at 315 K for <em>S. rum </em>and 340 K for <em>T. mar</em>. Final configurations of the proteins after productions are provided for all the systems in GRO Gromos87 format. Trajectories with the positions of the proteins every 100 ps are provided for all the systems in XTC gromacs format.</p>
Molecular, biochemical and metabolomics analyses reveal constitutive and pathogen-induced defense responses of two sugarcane contrasting genotypes against leaf scald disease
<p>Leaf scald caused by the bacteria <em>Xanthomonas albilineans</em> is one of the major concerns to sugarcane production. To breed for resistance, mechanisms underlying plant-pathogen interaction need deeper investigations. Herein, we evaluated sugarcane defense responses against <em>X. albilineans</em> using molecular and biochemical approaches to assess pathogen-triggered ROS, phytohormones and metabolomics in two contrasting sugarcane genotypes from 0.5-144 h post-inoculation (hpi). In addition, the infection process was monitored using TaqMan-based quantification of <em>X. albilineans</em> and the disease symptoms were evaluated in both genotypes after 15 d post-inoculation (dpi) The susceptible genotype presented a response to the infection at 0.5 hpi, accumulating defense-related metabolites such as phenolics and flavonoids with no significant defense responses thereafter, resulting in typical symptoms of leaf scald at 15 dpi. The resistant genotype did not respond to the infection at 0.5 hpi but constitutively presented higher levels of salicylic acid and of the same metabolites induced by the infection in the susceptible genotype. Moreover, two subsequent pathogen-induced metabolic responses at 12 and 144 hpi were observed only in the resistant genotype in terms of amino acids, quinic acids, coumarins, polyamines, flavonoids, phenolics and phenylpropanoids together with an increase of hydrogen peroxide, ROS-related genes expression, indole-3-acetic-acid and salicylic acid. Multilevel approaches revealed that constitutive chemical composition and metabolic reprogramming hampers the development of leaf scald at 48 and 72 hpi, reducing the disease symptoms in the resistant genotype at 15 dpi. Phenylpropanoid pathway is suggested as a strong candidate marker for breeding sugarcane resistant to leaf scald.</p>
Proximate biochemical composition and Fatty acid profile of IMTA produced new and innovative products
<p>Proximate biochemical composition and Fatty acid profile of IMTA produced new and innovative products. Sea cucumbers (H. sanctori), Abalone (H. tuberculata), Scallops (Chlamys varia), Oysters (Ostrea edulis) and macroalgae Ulva spp, Gracilaria cornea, Alaria esculenta, Palmaria palmata, Saccharina latissima.</p>
68Ga-PSMA-R2 in Patients With Biochemical Relapse (BR) and Metastatic Prostate Cancer (mPCa)
ClinicalTrials.gov study NCT03490032. IPD Sharing: YES. Countries: 1. Publications: 0.
Kinetic modules in biochemical networks/ Upstream Algorithm
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Data from: Stomatal response to VPD in C4 plants with different biochemical sub-pathways
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Elemental and biochemical nutrient limitation of zooplankton: A meta-analysis
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Supplementary data for "Structural, biochemical, and computational characterization of sulfonamides as bimetallic peptidase inhibitors"
<p>Supplementary data for "Structural, biochemical, and computational characterization of sulfonamides as bimetallic peptidase inhibitors"</p> <p>Coordinates, schemes, NCI density visualization in VMD, and PyMOL sessions for the Figures in the publication are provided.</p>
Plate 1 in Reproduction Performance, Serum Biochemical and Growth Indices of Grower Rabbits (Oryctolagus cuniculus) fed Sheabutter (Vitellaria paradoxa C.F. Gaertn.) Nut Meal
Plate 1. Uterus of the dead doe showing four embryos
Biochemical fractionation of human α-Synuclein in a Drosophila model of synucleinopathies
<p>Synucleinopathies are a group of central nervous system pathologies that are characterized<br>by the intracellular accumulation of misfolded and aggregated α-synuclein in proteinaceous depositions<br>known as Lewy Bodies (LBs). The transition of α-synuclein from its physiological to pathological<br>form has been associated with several post-translational modifications such as phosphorylation and<br>an increasing degree of insolubility, which also correlate with disease progression in post-mortem<br>specimens from human patients. Neuronal expression of α-synuclein in model organisms, including<br>Drosophila melanogaster, has been a typical approach employed to study its physiological effects.<br>Biochemical analysis of α-synuclein solubility via high-speed ultracentrifugation with buffers of<br>increasing detergent strength offers a potent method for identification of α-synuclein biochemical<br>properties and the associated pathology stage. Unfortunately, the development of a robust and<br>reproducible method for the evaluation of human α-synuclein solubility isolated from Drosophila<br>tissues has remained elusive. Here, we tested different detergents for their ability to solubilize<br>human α-synuclein carrying the pathological mutation A53T from the brains of aged flies. We also<br>assessed the effect of sonication on the solubility of human α-synuclein and optimized a protocol<br>to discriminate the relative amounts of soluble/insoluble human α-synuclein from dopaminergic<br>neurons of the Drosophila brain. Our data established that, using a 5% SDS buffer, the three-step<br>protocol separates cytosolic soluble, detergent-soluble and insoluble proteins in three sequential<br>fractions according to their chemical properties. This protocol shows that sonication breaks down<br>α-synuclein insoluble complexes from the fly brain, making them soluble in the SDS buffer and thus<br>enriching the detergent-soluble fraction of the protocol.</p>
Supplementary material for paper "A fresh look at the celery collenchyma and parenchyma cell walls through a combination of biochemical, histochemical, and transcriptomic analyses"
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Raw data to: Biochemical Analyses of Cystatin-C Dimers and Cathepsin-B reveals a Trypsin-Driven Feedback Mechanism in Acute Pancreatitis
<p>This repository contains the initial structures, full conformational ensembles sampled using the TIGER2hPE replica-exchange MD simulation technique, and clusters resulting from subsequent ccPCA analysis, to extract major complex structures between proteins. Also attached are the initial structures of mCTSB and mCST3 predicted by AlphaFold2.</p> <table> <tbody> <tr> <td> <p><strong>Simulation Nr.</strong></p> </td> <td> <p><strong>Components simulated<br></strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p>CTSB</p> </td> </tr> <tr> <td> <p><strong>2</strong></p> </td> <td> <p>CTSB</p> </td> </tr> <tr> <td> <p><strong>3</strong></p> </td> <td> <p>CTSB + mCST3</p> </td> </tr> <tr> <td> <p><strong>4</strong></p> </td> <td> <p>CTSL + mCST3</p> </td> </tr> <tr> <td> <p><strong>5</strong></p> </td> <td> <p>CTSB + mCST3-R71</p> </td> </tr> <tr> <td> <p><strong>6</strong></p> </td> <td> <p>CTSB + mCST3-R45</p> </td> </tr> <tr> <td> <p><strong>7</strong></p> </td> <td> <p>CTSB + dCST3-R45</p> </td> </tr> <tr> <td> <p><strong>8</strong></p> </td> <td> <p>CTSB + dCST3-R28</p> </td> </tr> </tbody> </table>
Biochemical methane potential tests amended with graphene oxide and organic micropollutants: Methane production
<p>The spreadsheet comprises measurements of methane production of biochemical methane potential (BMP) assays amended with graphene oxide and organic micropollutants.</p> <p>A total of six sheets are present.</p> <p>“DOE (VS)” contains the design of the experiment with the initial set-up value for the different conditions tested.</p> <p>“Stock solution” where the concentrations of the added contaminants are calculated.</p> <p>“Inoculum-substrate” stores the characterization of the inoculum and the substrate used (i.e., microcrystalline cellulose).</p> <p>“Final_Character” contains the characterization measurements carried out at the end of the experiment.</p> <p>“Data” envelops the periodic (mostly daily) measurements used to calculate methane production via a manometric procedure.</p> <p>“Calculation” has the final calculation reporting the specific methane production (SMP) for the different conditions.</p>
Synergistic effect of salicylic acid and biochar on biochemical properties, yield and nutrient uptake of triticale under water stress
<p><span>The reduction of soil fertility and water sources in arid regions treats crop production. One of the practical solutions to overcome these problems is application of salicylic acid (SA) with biochar.</span><span> A pot experiment was conducted to consider the combination</span><span> of </span><span>SA with biochar</span><span> on biochemical and physiological parameters of triticale</span><span>.</span><span> Treatments consisted of irrigation regime (normal irrigation and irrigation according to 50% field capacity), salicylic acid application [without SA (SA0) and 3mM SA (SA3)] and fertilizer type including without fertilizer (control), application of 50 kg ha<sup>-1</sup> phosphorus (P), and application of wheat biochar (WB), cotton biochar (CB) and sesame biochar (SB) (2% w/w).</span><span> Under water stress, CB at SA0 and SA3 could improve the total chlorophyll by 119.4 and 70.6%, compare to control respectively.<span> Also, carotenoid content in SA3 treatments increased in the range of 75.8 to 34.6% compared to SA0</span>. </span><span>CB at SA3, created the highest catalase activity (11.4% increase) compared to SB.</span><span> At SA3, the highest RWC was observed in WB and CB by 26.7 and 18.1% increases compared to SA0, respectively. </span><span>At SA3, CB could enhance grain yield by 24.8% under water stress. </span><span>Under water stress, at SA3, remobilization efficiency from 63.2% in control was enhanced to 69.2, 74.3 and 68.1% in WB, CB and SB, respectively. CB and WB had better chemical properties in terms of EC, N, P, K and micronutrients compared to SB. These properties of BC and WB enhanced their ability to increase the nutrient availability, biochemical properties and consequently the grain yield enhancement, especially when applied with SA3.</span></p>
smFRET and biochemical data for the study of PPR binding to single-stranded RNA
<p>Dataset contains (predominantly) pre-processed data for single-molecule FRET (smFRET) and other biochemical data (e.g., SDS-PAGE gels, surface plasmon resonance experiments, FPLC chromatograms). For smFRET experiments, the conformation of individual pentatricopeptide repeat proteins (PPRs) was monitored in real-time in the presence of various single-stranded RNA target sequences (with data provided as .dat files). Also contained in this repository are data for three-colour FRET experiments, combining a FRET probe on the PPR protein and a fluorophore labelled RNA oligonucleotide (data provided as .ascii files). Data is published as a pre-print in BioRxiv (https://www.biorxiv.org/content/10.1101/2024.04.22.590477v1.abstract). </p> <p>Note: data reported in some supplementary figures are provided in Figure 2, 3 and 4 datasets.</p> <p> </p>
How can we biochemically validate protein function predictions with the Ras GTPase family? - Associated data
<p>This is the data that accompanies the pub "<a href="https://doi.org/10.57844/arcadia-74ad-345f">How can we biochemically validate ProteinCartography with the Ras GTPase family?</a>" It's part of a group of pubs focused on validating ProtienCartography that begins with "<a href="https://doi.org/10.57844/arcadia-cae9-96c4">A strategy to validate protein functions <em>in vitro</em></a><a href="https://doi.org/10.57844/arcadia-cae9-96c4">." </a></p> <p>For this repository, we ran ProteinCartography <a href="https://github.com/Arcadia-Science/ProteinCartography/releases/tag/v0.5.0">v0.5.0</a> using human HRas and KRas as our inputs for a single run (UniProt ID: <a href="https://www.uniprot.org/uniprotkb/P01112/entry">P01112</a> and <a href="https://www.uniprot.org/uniprotkb/P01116/entry">P01116</a>). We asked for 3,000 Foldseek hits and 7,000 BLAST hits for a total of 10,000 structures. The updated configuration file is in the zipped folder in this repository. Also included in the zipped folder are the inputs, structures of all hits, and all ProteinCartography results. </p> <p>Finally, we created a custom overlay for the protein map using this <a href="https://github.com/Arcadia-Science/2023-actin-embedding/blob/main/notebooks/3_plotting_overlays.ipynb">notebook</a> and the manually annotated TSV file in this repository, where we denoted which group of substrates a protein is predicted to act on based on its annotation from UniProt.</p>
How can we biochemically validate protein function predictions with the deoxycytidine kinase family? - Associated data
<p>This is the data that accompanies the pub "<a href="https://doi.org/10.57844/arcadia-1e5d-e272">How can we biochemically validate ProteinCartography with the deoxycytydine kinase family?</a>" It's part of a group of pubs focused on validating ProtienCartography that begins with "<a href="https://doi.org/10.57844/arcadia-cae9-96c4">A strategy to validate protein functions <em>in vitro</em></a><a href="https://doi.org/10.57844/arcadia-cae9-96c4">." </a></p> <p>For this repository, we ran ProteinCartography <a href="https://github.com/Arcadia-Science/ProteinCartography/releases/tag/v0.5.0">v0.5.0</a> on the deoxycytidine kinase (dCK) using human dCK as our input (UniProt ID: <a href="https://www.uniprot.org/uniprotkb/P27707/entry">P27707</a>). We asked for 3,000 Foldseek hits and 7,000 BLAST hits for a total of 10,000 structures. The updated configuration file is in the zipped folder in this repository. Also included in the zipped folder are the inputs, structures of all hits, and all ProteinCartography results. </p> <p>Finally, we created a custom overlay for the protein map using this <a href="https://github.com/Arcadia-Science/2023-actin-embedding/blob/main/notebooks/3_plotting_overlays.ipynb">notebook</a> and the manually annotated TSV file in this repository, where we denoted which group of substrates a protein is predicted to act on based on its annotation from UniProt.</p>
Biochemical and biophysical drivers of the hydrogen isotopic composition of carbohydrates and acetogenic lipids
<p>The hydrogen isotopic composition (δ2H) of plant compounds is increasingly used as a hydro-climatic proxy, however, the interpretation of δ2H values is hampered by potential co-effecting biochemical and biophysical processes. Here, we studied δ2H values of water and carbohydrates in leaves and roots, and of leaf n-alkanes, in two distinct tobacco (Nicotiana sylvestris) experiments. Large differences in plant performance and biochemistry resulted from (a) soil fertilization with varying nitrogen (N) species ratios and (b) knockout-induced starch deficiency. We observed a strong 2H-enrichment in sugars and starch with a decreasing performance induced by increasing NO3-/NH4+ ratios and starch deficiency, and from leaves to roots. However, δ2H values of cellulose and n-alkanes were less affected. We show that relative concentrations of sugars and starch, interlinked with leaf gas-exchange, shape δ2H values of carbohydrates. We thus provide novel insights into drivers of hydrogen isotopic composition of plant compounds and the mechanistic modelling of plant cellulose δ2H values.</p>
Hematological and biochemical profiles, infection and habitat quality in an urban rat population
<p>Dataset employed in the analysis of "Hematological and biochemical profiles, infection and habitat quality in an urban rat population". For details in sampling methodology, see article of same name.</p> <p>Composed of three dataset files, with codebooks associated:</p> <ul> <li>animal_data: morphological and parasitological data of captured individuals</li> <li>hematological_data: data on hematological parameters of captured individuals</li> <li>biochemical_data: data on biochemical markers of captured individuals</li> </ul> <p>All individuals are identified by an individual code (rat_ID), and can be cross-linked through the bases.</p> <p>Files are composed of two sheets</p> <p>-data sheet: original data, with regular labels</p> <p>-codebook: comprehensive codebook for the data sheet.</p>
Fig. 6 in Morphometric And Biochemical Variation And The Distribution Of The Genus Apodemus (Mammalia: Rodentia) In Turkey
Fig. 6. Genetic relationship of Apodemus species based on Nei genetic distance of 10 enzyme loci
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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.
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.