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1,542 results for “Degradation”
Effects of Soil Warming on Bacterial Degradation of Carbohydrates at Harvard Forest 2011
As Earth’s climate warms, soil carbon pools and the microbes that process them may change, altering the way in which carbon is recycled in soil. We used bacterial cultivation to evaluate the hypothesis that experimentally raising soil temperatures by 5°C for 20 years increased the potential for temperate forest soil microbial communities to degrade carbohydrates. A greater proportion of the 295 bacteria from 6 phyla (10 classes, 14 orders, and 34 families) isolated from heated plots in the 20-year experiment were able to depolymerize cellulose and xylan than bacterial isolates from control soils. These findings indicate that the enrichment of bacteria capable of degrading carbohydrates could be important for accelerated carbon cycling in a warmer world. Data for the isolates from the Harvard Forest culturing project is archived at https://osf.io/ahb2v/.
Dataset / Code: Targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy
<p><strong>Targeted protein degradation in mycobacteria uncovers antibacterial effects and potentiates antibiotic efficacy</strong></p> <p><strong> </strong></p> <p>Harim I. Won<sup>1,#</sup>, Samuel Zinga<sup>1,#</sup>, Olga Kandror<sup>1</sup>, Tatos Akopian<sup>1</sup>, Ian D. Wolf<sup>1</sup>, Jessica T.P. Schweber<sup>1</sup>, Ernst W. Schmid<sup>2</sup>, Michael C. Chao<sup>1</sup>, Maya Waldor<sup>1</sup>, Eric J. Rubin<sup>1,*</sup>, Junhao Zhu<sup>1,3,*</sup></p> <p><strong> </strong></p> <p><sup>1</sup>Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.</p> <p><sup>2</sup>Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Blavatnik Institute, Boston, Massachusetts 02115, USA.</p> <p><sup>3</sup>CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.</p> <p><sup>#</sup>These authors contributed equally to this work.</p> <p>*Corresponding authors: <a href="mailto:zhujh@im.ac.cn">zhujh@im.ac.cn</a> (J.Z.), <a href="mailto:erubin@hsph.harvard.edu">erubin@hsph.harvard.edu</a> (E. J. R.)</p> <p><strong> </strong></p> <p><strong>Abstract</strong></p> <p>Proteolysis-targeting chimeras (PROTACs) represent a new therapeutic modality involving selectively directing disease-causing proteins for degradation through proteolytic systems. Our ability to exploit targeted protein degradation (TPD) for antibiotic development remains nascent due to our limited understanding of which bacterial proteins are amenable to a TPD strategy. Here, we use a genetic system to model chemically-induced proximity and degradation to screen essential proteins in <em>Mycobacterium smegmatis </em>(<em>Msm</em>)<em>, </em>a model for the human pathogen <em>M. tuberculosis </em>(<em>Mtb</em>). By integrating experimental screening of 72 protein candidates and machine learning, we find that drug-induced proximity to the bacterial ClpC1P1P2 proteolytic complex leads to the degradation of many endogenous proteins, especially those with disordered termini. Additionally, TPD of essential <em>Msm </em>proteins inhibits bacterial growth and potentiates the effects of existing antimicrobial compounds. Together, our results provide biological principles to select and evaluate attractive targets for future <em>Mtb</em> PROTAC development, as both standalone antibiotics and potentiators of existing antibiotic efficacy.</p> <p> </p>
Rice straw degradation analysis with Kraken2/Bracken annotation
<p>Metadata and annotation of the reads obtained from the rice straw degradation process using Kraken2/Bracken.</p>
Microbial and soil moisture impacts of compost amendments and rainfall pulses in a degraded dryland soil, Arizona, 2021-2023
Compost, an organic soil amendment, has been proposed to increase soil carbon storage and water-holding capacity in drylands, and this management strategy may be particularly impactful in degraded drylands with low soil organic content. Compost additions and rainfall variability may interact to affect soil moisture, which is an important catalyst for soil microbial activity. This dataset is from a study that investigated how variable compost application amounts and simulated rainfall pulses affect soil moisture, microbial activity, and carbon content in a laboratory incubation study. Soils were amended with different amounts of compost (0, 0.35, and 0.70 g cm -2) and water pulses (5, 10, and 15 mm) in a full-factorial design. Each treatment received the same cumulative amount of water throughout the incubation, but pulses occurred at different frequencies (every 5, 10, and 15 days). Soil moisture content and microbial respiration were measured daily. Soil carbon content was measured at the end of the experiment.
SJR Dolphin SCA: Degradation Scores, OL Length and Identifications of Otoliths Collected from Bottlenose Dolphin Stomachs
Otoliths were collected from stomach contents of stranded bottlenose dolphins (Tursiops erebennus) in the St. Johns River in Jacksonville, Florida. Otoliths were analyzed by a panel of 3 reviewers to determine the level of otolith degradation that occurred during digestive processes. Otoliths with scores ≤ 3 were measured. Otolith length measurements were used to estimate the size of most species by applying standard regression equations developed from fish species collected from a nearby water system, the Indian River Lagoon, and for one species, equations developed from violet gobies collected from the St. Johns River. These equations enabled estimation of the mass of each prey species in each dolphin’s stomach, then the calculation of their relative proportions of reconstructed mass across all stomachs. For otolith identification purposes, a panel of 4 reviewers assigned each otolith with a family-level and species-level identification. Each identification was given a confidence code ranging from 1 (no confidence) to 4 (certainty). When the average code for all reviewers was < 3, the otolith was considered unidentified. If two of the reviewers agreed with the “weight” reviewer and all gave scores ≥ 3, the score of the outlying reviewer was discarded. Identification was assigned when the average confidence code was ≥ 3. The minimum number of species per dolphin stomach was determined by counting the left and right otoliths for each species separately, using the higher count as the minimum prey number. Unidentified species were counted, and half of their sum was considered the minimum prey number. The frequency of occurrence (%FO, or proportion of stomachs in which a species was detected) and numerical proportion (%N, or proportion of a given species pooled across all stomach samples) of each prey species were then calculated.
Vis-NIR Soil Spectral Library of the Hungarian Soil Degradation Observation System
<p>Since soil spectroscopy is considered to be a fast, simple, accurate and non-destructive analytical method, its application can be integrated with wet analysis as an alternative. Therefore, development of national-level soil spectral libraries containing information about all soil types represented in a country is continuously increasing to serve as a basis for calibrated predictive models capable of assessing physical and chemical parameters of soils at multiple spatial scales. In this article, we present a database containing laboratory and visible-near infrared spectral data of legacy soil samples from the Hungarian Soil Degradation Observation System (HSDOS). The published data set includes the following parameters measured in 5,490 soil samples: pH<sub>KCl</sub>, soil organic matter (SOM), calcium carbonate (CaCO<sub>3</sub>), total salt content (TSC), total nitrogen (N<sub>total</sub>), soluble phosphorus (P<sub>2</sub>O<sub>5</sub>-AL), soluble potassium (K<sub>2</sub>O-AL), plasticity index according to Hungarian standard (PLI), soil profile depth and reflectance data between 350 and 2,500 nm wavelength. The presented database can be a complement for further soil related research on continental, national or regional scales to support sustainable soil management.</p> <p>Uploaded CSV file contains variables for general information, soil parameters and reflectance data of spectral bands between 350-2,500 nm. Details about variables can be found in Table 1.</p> <table> <tbody> <tr> <td> <p>Column name</p> </td> <td> <p>Description</p> </td> <td> <p>Method</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>SAMPLE_TDR_ID</p> </td> <td> <p>Original TDR IDs</p> </td> <td> <p>-</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>SAMPLING_DATE</p> </td> <td> <p>Date of sampling</p> </td> <td> <p>-</p> </td> <td> <p>YYYY-MM-DD</p> </td> </tr> <tr> <td> <p>NORTHING_EOV</p> </td> <td> <p>Northing coordinate of sampling area centroids</p> </td> <td> <p>-</p> </td> <td> <p>m</p> </td> </tr> <tr> <td> <p>EASTING_EOV</p> </td> <td> <p>Easting coordinate of sampling area centroids</p> </td> <td> <p>-</p> </td> <td> <p>m</p> </td> </tr> <tr> <td> <p>LON_WGS84</p> </td> <td> <p>Longitude of sampling area centroids</p> </td> <td> <p>-</p> </td> <td> <p>°</p> </td> </tr> <tr> <td> <p>LAT_WGS84</p> </td> <td> <p>Latitude of sampling area centroids</p> </td> <td> <p>-</p> </td> <td> <p>°</p> </td> </tr> <tr> <td> <p>pH_KCl</p> </td> <td> <p>pH</p> </td> <td> <p>Potentiometer (MSZ–08 0206-2: 1978)<sup>40</sup></p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>SOM</p> </td> <td> <p>Soil organic matter</p> </td> <td> <p>E4/E6 ratio<sup>41</sup><sup>,</sup><sup>42</sup> (MSZ–08-0452:1980)<sup>43</sup></p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>CaCO3</p> </td> <td> <p>Calcium carbonate</p> </td> <td> <p>Scheibler type calcimeter (MSZ–08 0206-2:1978)<sup>40</sup></p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>TSC</p> </td> <td> <p>Total salt content</p> </td> <td> <p>EC-TDS electrode (MSZ–08-0206-2:1978)<sup>40</sup></p> </td> <td> <p>w/w %</p> </td> </tr> <tr> <td> <p>TN</p> </td> <td> <p>Total nitrogen</p> </td> <td> <p>Kjeldahl method<sup>44</sup> (ISO 11261:1995)<sup>45</sup></p> </td> <td> <p>mg kg<sup>-1</sup></p> </td> </tr> <tr> <td> <p>P2O5_AL</p> </td> <td> <p>Soluble phosphorus</p> </td> <td> <p>AL extract atomic adsorption spectrophotometry (MSZ 20135:1999)<sup>46</sup></p> </td> <td> <p>mg kg<sup>-1</sup></p> </td> </tr> <tr> <td> <p>K2O_AL</p> </td> <td> <p>Soluble potassium</p> </td> <td> <p>AL extract, flame photometer (MSZ 20135:1999)<sup>46</sup></p> </td> <td> <p>mg kg<sup>-1</sup></p> </td> </tr> <tr> <td> <p>PLI</p> </td> <td> <p>Plasticity index according to Hungarian standard</p> </td> <td> <p>Yarn test of Arany (MSZ–08 0205-2:1978)<sup>47</sup></p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>PROFILE_LEVEL</p> </td> <td> <p>Soil profile depth level</p> </td> <td> <p>-</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>SPC350:2500</p> </td> <td> <p>spectral reflectance in the range of 350 and 2500 nm</p> </td> <td> <p>ASD FieldSpec 4 spectroradiometer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>Table 1. Summary of included attributes and data set structure with laboratory test methods applied on the soil samples.</p> <p> </p> <p><strong>For more details / to cite this dataset please use:</strong></p> <p>Mészáros, J., Kovács, Zs., László, P., Vass-Meyndt, Sz., Koós, S., Pirkó, B., Szűcs-Vásárhelyi, N., Bakacsi, Zs., Laborczi, A., Balog, K., & Pásztor, L. (2024). Vis-NIR soil spectral library of the Hungarian Soil Degradation Observation System. <em>Sci Data</em> <strong>12</strong>, 363 (2025). https://doi.org/10.1038/s41597-025-04667-9</p>
CoAID dataset with multiple extracted features (both sparse and dense) and degraded by OCR
<p>This is the same datasets as:</p> <p>Guillaume Bernard. (2022). CoAID dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630405</p> <p>But with texts degraded by OCR as described in:</p> <p>Guillaume Bernard. (2022). CoAID dataset texts with OCR degradations (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630710</p>
Data from: Shift of bacterial and fungal communities upon soil amelioration is driven by carbon degradability of organic amendments
<p>Microbial communities of bacteria and fungi have been analyzed in soil. Agricultural soil was amended with different organic amendments including straw, compost, biogas residues, and biochar, and incubated in the lab. After 6 months, DNA extracted from soil samples was analyzed via Illumia MiSeq DNA sequencing (16S V3V4 for bacteria, ITS1 for fungi) to evaluate changes to the microbial community structure.</p> <p>For details, please see the respective publication (DOI: 10.1007/s44378-024-00012-5).</p>
Dataset: Insight in molecular degradation patterns and co-metabolism during rose waste co-composting
<p>This dataset and these scripts supports the article 'Insight in molecular degradation patterns and co-metabolism during rose waste co-composting' as published in Biogeochemistry. https://doi.org/10.1007/s10533-023-01092-1</p> <p>Roses are an important crop for the floricultural sector of Kenya and roses are a perennial crop and under continuous production for six to ten years. The cultivation produces large quantities of green waste, up to 50 kg per hectare per day. In this experiment we aimed to gain insight in the decomposition patterns underlying rose waste composting and to identify co-metabolisms of ligneous materials. Samples were taken during a six-month experiment and analyzed by pyrolysis-GC/MS (see manuscript for analytical details).</p>
Beyond the Four-Level Model: Dark and Hot States in Quantum Dots Degrade Photonic Entanglement
<p><strong>Dataset for "Beyond the four-level model: Dark and hot states in quantum dots degrade photonic entanglement"</strong></p> <p><em>Nano Lett.</em> 2023, 23, 4, 1409–1415<br> Publication Date: February 6, 2023<br> <a href="https://doi.org/10.1021/acs.nanolett.2c04734">https://doi.org/10.1021/acs.nanolett.2c04734</a></p> <p>A description of the dataset is found in the <strong>readme.md</strong> file (markdown markup language).</p> <p><strong>Data reuse</strong><br> Please cite B.U. Lehner et al., <em>Nano Lett.</em> 2023, 23, 4, 1409–1415 (2023) in publications that reuse this data and if possible inform the corresponding authors.</p>
Wildfires drive multi-year water quality degradation over the western U.S.
<p>Information on the 245 burned basins, 293 unburned basins, and 356 associated fires from across the U.S. West which were used in statistical analyses of post-wildfire water quality response. Included are physiographic characteristics, as well as ESRI Shapefile polygons representing delineations for each basin and fire. Additionally, daily carbon, nitrogen, phosphorus, sediment, and turbidity data sampled from the basins' outlets are provided from 1974-2022. R coding scripts used in data processing and modeling also included, as well as data directly used in generating manuscript and "Supplementary Information" plots.</p> <p>Water quality data used to create this dataset are from the Water Quality Portal and wildfire burn perimeters are from the Monitoring Trends in Burn Severity database.</p>
Data for a publication "Characterization of hFOB 1.19 cell line for studying Zn-based degradable metallic biomaterials"
<div> <p>These data are published as part of the paper: “Characterization of hFOB 1.19 cell line for studying Zn-based degradable metallic biomaterials” published in journal: “Materials”. </p> </div> <div> <p>This repository contains one folder, namely: “concentration ICP_MS” </p> </div> <div> <p>This folder contains further data relevant to the results published in the paper, which are described in a separate file inside. </p> <p> </p> <p><strong>Preprint evolution (versions).</strong></p> <p><strong>2024-01-31-V2</strong>; <a href="https://doi.org/10.20944/preprints202401.2053.v2" target="_blank" rel="noopener">(https://doi.org/10.20944/preprints202401.2053.v2</a>) - the acknowledgement was modified as well as the data availability mentioning the Zenodo repository with the dataset as well as the availability of the datasets generated during and/or analyzed during the current study on reasonable request from corresponding author.</p> </div> <div> <p> </p> </div>
Dataset for "Degradable and Printed Microstrip Line for Chipless Temperature and Humidity Sensing"
<p>This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 187223) in association with the recent publication entitled “Degradable and Printed Microstrip Line for Chipless Temperature and Humidity Sensing”. This work aims to study the humidity and temperature response of eco-friendly materials using a printed multi resonating microstrip line operating from 1.0 GHz to 2.6 GHz. The S12 signal of the resonator was measured using a vector network analyzer when varying the humidity inside a climatic chamber from 30% to 70% RH for temperatures of 15 °C, 25 °C and 35 °C. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.</p>
Radiofrequency ultrasound signals from bovine cartilage samples degraded with trypsin and collagenase
The folder Repository_RF_data contains the radiofrequency (RF) data acquired with an ArtUS EXT-1H system (Telemed, Italy) equipped with a 192 elements linear probe L15-7H40-A5 working in the frequency range 7.5-15 MHz, in the matlab format ".mat". Data were collected at 15 MHz, with a sampling rate of 40 MHz, adjusting the focus in the middle of the samples. The analysed samples were bovine cartilage samples, divided in three groups: - Control group: cartilage sample without any chemical treatment. - Trypsin group: cartilage samples immersed in a trypsin solution for 4h. - Collagenase group: cartilage samples immersed in a collagenase solution for 24h. The folder Repository_RF_data includes 2 matlab variables: - trypsin.mat = data acquired from 6 samples before and after the trypsin treatment - collagenase.mat = data acquired from 6 samples before and after the collagenase treatment Each variable is a TxN cell, where T is the time point of evaluation and N is the number of samples. The first row of each variable corresponds to the time zero of treatment, that is the control group; while the second row includes measurement at the final time point of treatment (4h for trypsin an 24h for collagenase). In particular, a single RF frame was acquired for all the analyses. Each recorded RF frame resulted in a matrix in which the columns (57) represented the number of RF scanning lines in a specific RF window, while the rows (727) constituted the number of samples in a single scanning line. For the details, see the articles published on Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Sorriento A, Cafarelli A, Valenza G, Ricotti L. Ex-vivo quantitative ultrasound assessment of cartilage degeneration. Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2976-2980. doi: 10.1109/EMBC46164.2021.9630198. PMID: 34891870.
Event Registry dataset with multiple extracted features (both sparse and dense) and degraded by OCR
<p>This is the same dataset as:</p> <p>Guillaume Bernard. (2022). Event Registry dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630367</p> <p>But with texts degraded by OCR as described in:</p> <p>Guillaume Bernard. (2022). Event Registry dataset texts with OCR degradations and synthesised segmentation (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6631305</p>
Event Registry titles dataset with multiple extracted features (both sparse and dense) and degraded by OCR
<p>This is the same dataset as:</p> <p>Guillaume Bernard. (2022). Event Registry titles only dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630447</p> <p>But with texts degraded by OCR as described in:</p> <p>Guillaume Bernard. (2022). Event Registry titles dataset texts with OCR degradations (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630828</p>
FibVid dataset with multiple extracted features (both sparse and dense) and degraded by OCR
<p>This is the same dataset as:</p> <p>Guillaume Bernard. (2022). Fibvid dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630409</p> <p>But with texts degraded by OCR as described in:</p> <p>Guillaume Bernard. (2022). FibVid dataset texts with OCR degradations (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630758</p>
Amoxicillin degradation pathways and mass spectra raw data (using LC-MS orbitrap)
<p>The link provides five documents namely:</p> <p>File No.1 (Proposed Chemical Structures-tabulated)</p> <p>File No.2 (MS and MS2 images) support for File no.1</p> <p>File No.3 Transformation Products Pathway</p> <p>File No.4 Explanation + Justification of proposed chemical structures</p> <p>Raw Data obtained from compound discoverer</p>
Dataset of "PEMFC performance decay during real-world automotive operation: evincing degradation mechanisms and heterogeneity of ageing"
<p>This is the underlying dataset of "PEMFC performance decay during real-world automotive operation: evincing degradation mechanisms and heterogeneity of ageing"</p>
On the Use of Artificially Degraded Manuscripts for Quality Assessment of Readability Enhancement Methods - Dataset & Code
<p>This object contains the dataset and python code used for the paper:</p> <p>S. Brenner and R. Sablatnig. On the Use of Artificially Degraded Manuscripts for Quality Assessment of Readability Enhancement Methods. Accepted for OAGM Workshop 2019<strong>, </strong>Steyr, Austria.</p> <p>The dataset is a modified subset of the UCL Multispectral Processed Images of Parchment Damage Dataset (<a href="http://dx.doi.org/10.14324/000.ds.1469099">10.14324/000.ds.1469099</a>). The accompanying code documents how the modified version was created and how the evaluations described in the paper were performed.</p>
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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.