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1,542 results for “Degradation”
A meta-analysis exploring associations between habitat degradation and Neotropical bat virus prevalence and seroprevalence
<p>Habitat degradation can increase zoonotic disease risks by altering infection dynamics in wildlife and increasing wildlife–human interactions. Bats are an important taxonomic group to consider these effects, because they harbour many relevant zoonotic viruses and have species- and context-dependent responses to degradation that could affect zoonotic virus dynamics. Yet our understanding of the associations between habitat degradation and bat virus prevalence and seroprevalence are limited to a small number of studies, which often differ in the bats or viruses sampled, the study region, and methodology. To develop a broad understanding of the associations between bat viruses and habitat degradation, we conducted an initial phylogenetic meta-analysis that combines published prevalence and seroprevalence ("(sero)prevalence") with remote-sensing habitat degradation data. Our dataset includes 588 unique records of (sero)prevalence across 16 studies, 64 bat species, and five virus families. We quantified the overall strength and direction of the relationship between habitat degradation and bat virus outcomes and tested how this relationship is moderated by the time between habitat degradation and bat sampling and by ecological traits of bat hosts while controlling for phylogenetic nonindependence among bat species. We found no effect of degradation on prevalence overall, although a weak effect may exist when forest loss occurs the year prior to bat sampling. In contrast, we detected a negative but weak association between degradation and seroprevalence overall that was strengthened when forest loss occurred the year prior to bat sampling. No bat traits that we investigated interacted with habitat degradation to impact virus outcomes, suggesting observed trends are independent of these traits. Biases in our initial dataset highlight opportunities for future work; prevalence was highly zero-inflated, and seroprevalence was dominated by <em>Desmodus rotundus</em> and rabies virus. These findings and subsequent analyses will improve our understanding of how global change affects host–pathogen dynamics.</p>
Data from: A cucumber protein, Phloem Phosphate Stress Repressed 1, rapidly degrades in response to a phosphate stress condition
<p>Under depleted external phosphate (Pi), many plant species adapt to this stress by initiating downstream signalling cascades. In plants, the vascular system delivers nutrients and signalling agents to control physiological and developmental processes. Currently, limited information is available regarding the direct role of phloem-borne long-distance signals in plant growth and development under Pi-stress conditions. Here, we report on the identification and characterization of a cucumber protein, <em>Cucumis sativus</em> Phloem Phosphate-Stress-Repressed 1 (CsPPSR1), whose level in the phloem translocation stream rapidly responds to imposed Pi-limiting conditions. CsPPSR1 degradation is mediated by the 26S proteasome; under Pi-sufficient conditions, CsPPSR1 is stabilized by its phosphorylation, within the sieve tube system, through the action of CsPPSR1 Kinase. Further, we discovered that CsPPSR1 Kinase was susceptible to Pi-starvation-induced degradation, in the sieve tube system. Our findings offer insight into a molecular mechanism underlying the response of phloem-borne proteins to Pi-limited stress conditions.</p>
Data from: Cryptic diversity of cellulose-degrading gut bacteria in industrialized humans
<p>Humans, like all mammals, depend on the gut microbiome for digestion of cellulose, the main component of plant fiber, but evidence for cellulose fermentation in the human gut is scarce. We have identified ruminococcal species in the gut microbiota of human populations that assemble functional multi-enzymatic cellulosome systems capable of degrading plant cell wall polysaccharides. One of these species, which is strongly associated with humans, likely originated in the ruminant gut and was subsequently transferred to the human gut potentially during domestication, where it underwent diversification and diet-related adaptation through the acquisition of genes from other gut microbes. Collectively, these species are abundant and widespread among ancient humans, hunter-gatherers, and rural populations, but are extremely rare in populations from industrialized societies, suggesting potential disappearance in response to the westernized lifestyle.</p>
Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled scRNAseq
<p><span>During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the relative contributions of mRNA transcription and degradation to shaping those profiles remains challenging, especially within embryos with diverse cellular identities.<span> Here, we </span>combine<span> </span>single-cell RNA-Seq and metabolic labeling to capture temporal cellular transcriptomes of zebrafish embryos where newly-transcribed (zygotic) and pre-existing (maternal) mRNA can be distinguished. We then introduce kinetic models to quantify mRNA transcription and degradation rates within individual cell types during their specification. These models reveal highly varied regulatory rates across thousands of genes, coordinated transcription and destruction rates for many transcripts, and link differences in degradation to specific sequence elements. They also identify cell-type-specific differences in degradation, namely selective retention of maternal transcripts within primordial germ cells and enveloping layer cells, two of the earliest specified cell-types. Our study provides a quantitative approach to study mRNA regulation during</span> a dynamic spatio-temporal response<span>.</span></p> <p> </p> <p>This repository contains the raw microscopy data that is analyzed in Figures 6F-I and Supplementary Figure S4 B-D.</p>
Data from: "Lithium-ion battery degradation: comprehensive cycle ageing data and analysis for commercial 21700 cells"
<h1><strong>Intro</strong></h1> <p>Dataset from the publication "Lithium-ion battery degradation: comprehensive cycle ageing data and analysis for commercial 21700 cells", DOI: https://doi.org/10.1016/j.jpowsour.2024.234185</p> <p>Full details of the study can be found in the publication, including thorough descriptions of the experimental methods and structure. A basic desciption of the experimental procedure and data structure is included here for ease of use.</p> <p>Commercial 21700 cylindrical cells (LG M50T, LG GBM50T2170) were cycle aged under 3 different temperatures [10, 25, 40] °C and 4 different SoC ranges [0-30, 70-85, 85-100, 0-100]%, as well as a further [0-100]% SoC range experiment which utilised a drive-cycle discharge instead of constant-current. The same C-rates (0.3C / 1 C, for charge / discharge) were used in all tests; multiple cells were tested under each condition. These are listed in the table below.</p> <table> <tbody> <tr> <td> <div> <p><strong>Experiment</strong></p> </div> </td> <td> <div> <p><strong>SOC Window</strong></p> </div> </td> <td> <div> <p><strong>Cycles per ageing set</strong></p> </div> </td> <td> <div> <p><strong>Current</strong></p> </div> </td> <td> <div> <p><strong>Temperature</strong></p> </div> </td> <td> <div> <p><strong>Number of Cells</strong></p> </div> </td> </tr> <tr> <td> <div> <p>1</p> </div> </td> <td> <div> <p>0-30%</p> </div> </td> <td> <div> <p>257</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>2,2</p> </div> </td> <td> <div> <p>70-85%</p> </div> </td> <td> <div> <p>515</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> <div> <p>3</p> </div> </td> <td> <div> <p>85-100%</p> </div> </td> <td> <div> <p>515</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>4</p> </div> </td> <td> <div> <p>0-100% (drive-cycle)</p> </div> </td> <td> <div> <p>78</p> </div> </td> <td> <div> <p>0.3C / noisy D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>5</p> </div> </td> <td> <div> <p>0-100%</p> </div> </td> <td> <div> <p>78</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> </tbody> </table> <p>Cells were base-cooled at set temperatures using bespoke test rigs (see our linked publications for details; the supporting information file contains detailed descriptions and photographs). Cells were subject to break-in cycles prior to beginning of life (BoL) performance tests using the ‘Reference Performance Test’ (RPT) procedures. They were then alternately subject to ageing sets and RPTs until the end of testing. Full details of each of these procedures are described in the linked publication.</p> <p>The data contained in this repository is then described in the Data section below. This includes a description of the folder structure and naming conventions, file formats, and data analysis methods used for the ‘Processed Data’ which has been calculated from the raw data.</p> <p>An 'experimental_metadata' .xlsx file is included to aid parsing of data. A jupyter notebook has also been included to demonstate how to access some of the data.</p> <h1>Data</h1> <p>Data are organised according to their parent ‘Experiment’, as defined above, with a folder for each. Within each Experiment folder, there are 3 subfolders: ‘Summary Data’, ‘Processed Timeseries Data’, and ‘Raw Data’.</p> <h2>Summary Data</h2> <p>This folder contains data which has been extracted by processing the raw data in the ‘Degradation Cycling’ and ‘Performance Checks’ folders. In most cases, the data you are looking for will be stored here.</p> <p>It contains: </p> <h3>Performance Summary</h3> <p>A summary file for each cell which details key ageing metrics such as number of ageing cycles, charge throughput, cell capacity, resistance, and degradation mode analysis results. Each row of data corresponds to a different SoH.</p> <p>Degradation Mode Analysis (DMA) was also performed on the C/10 discharge data at each RPT. This analysis uses an optimisation function to determine the capacities and offset of the positive and negative electrodes by calculating a full cell voltage vs capacity curve using 1/2 cell data and comparing against the experimentally measured voltage vs capacity data from the C/10 discharge. See our <a href="https://doi.org/10.1021/acsaem.2c02047">ACS publication</a> for more details.</p> <p>Data includes:</p> <p>· Ageing Set: numbered 0 (BoL) to x, where x is the number of ageing sets the cell has been subject to.</p> <p>· Ageing Cycles: number of ageing cycles the cell has been subject to. *this is not equivalent full cycles.</p> <p>· Ageing Set Start Date/ End date: The date that each ageing set began/ ended.</p> <p>· Days of degradation: Number of days between the date of the first ageing set beginning and the current ageing set ending.</p> <p>· Age set average temperature: average recorded surface temperature of the cell during cycle ageing. Temperature was recorded approximately 1/2 way up the length of the cell (i.e. between positive and negative caps).</p> <p>· Charge throughput: total accumulated charge recorded during all cycles during ageing (i.e. sum of charge and discharge). This is the cumulative total since BoL (not including RPTs, and not including break-in cycles).</p> <p>· Energy throughput: as with "charge throughput", but for energy.</p> <p>· C/10 Capacity: the capacity recorded during the C/10 discharge test of each RPT.</p> <p>· C/2 Capacity: the capacity recorded during the C/2 discharge test of each even-numbered RPT.</p> <p>· 0.1s Resistance: The resistance calculated from the 25-pulse GITT test of each even-numbered RPT. This value is taken from the 12th pulse of the procedure (which corresponds to ~52% SoC at BoL). The resistance is calculated by dividing the voltage drop by the current at a timecale of 0.1 seconds after the current pulse is applied (the fastest timescale possible under the 10 Hz recording condition).</p> <p>· Fitting parameters: output from the DMA optimisation function; 5 parameters which detail the upper/lower SoCs of each electrode, and the capacity fraction of graphite in the negative electrode.</p> <p>· Capacity and offset data: calculated based on the fitting parameters above alongside the measured C/10 discharge capacity.</p> <p>· DM data: Quantities of LLI, LAM-PE, LAM-NE, LAM-NE-Gr, and LAM-NE-Si calculated from the change in capacities/offset of each electrode since BoL.</p> <p>· RMSE data: the root mean squared error of the optimisation function calculated from the residual between the measured and simulated voltage vs capacity profiles.</p> <h3>Ageing Sets Summary</h3> <p>Data from the ageing cycles, summarised on an average per cycle and an average per ageing set basis. Metrics include mean/ max/ min temperatures, voltages etc.</p> <h2>Processed Timeseries data</h2> <p>Timeseries data (voltage, current, temperature, etc.) from each subtest (pOCV, GITT, etc.) of the RPTs, all grouped by subtest-type and by cell ID.</p> <p>Contains the same data as in the ‘Performance Checks’ subfolder of the 'Raw Data' folder, but has been processed to slice into relevant subtests from the RPT procedure and includes only limited variables (time, voltage, current, charge, temperature). These are all saved as .csv files. In general this data will be easier to access than the raw data, but perhaps not as rich.</p> <h2>Raw Data</h2> <p>These are the raw data from the performance checks and from the degradation cycles themselves. The data from here has already been processed by me to get values of ‘energy throughput’, ‘charge throughput’, ‘average ageing temperature’, etc., which are all saved in the ‘Summary Data’ folder as described in the relevant section above.</p> <p>The data in the ‘Degradation Cycling’ folder are organised by ageing set (where an ageing set is a defined number of ageing cycles, as described in the paper). In theory, each cell should have one datafile in each ageing set subfolder. However, due to experimental issues, tests can sometimes be interrupted midway though, requiring the test to be subsequently resumed. In this case, there may be multiple datafiles for each cell in a given ageing set; during analysis, these should be concatenated according to the descriptor in the filename (e.g., ‘cycling7’ + ‘cycling7 (part 2)').</p> <p>Similarly, the unprocessed raw data from the performance checks (i.e. RPTs) is stored in the 'Performance Checks' folder, and structured in the same way.</p> <p>The raw data are saved in the .mpr format produced by the Biologic battery cycler. This is a binary format which is storage-efficient but can be more difficult to process for analysis purposes. We have therefore also exported the data into .txt files (called .mpt) for the performance checks (RPTs) which make analysis easier. However, the exported .mpt files could not be included for the degradation cycling files due to their larger size. If you require access these degradation cycle data, the .mpr binary file can be parsed using the <a href="https://github.com/echemdata/galvani">Galvani</a> package in python, or you can use Biologic’s (proprietary) BT-Lab software to export the data into .txt files.</p> <h3>File Naming Convention</h3> <p>The raw datafiles are named with a standard format. This is:</p> <p> <em>NDK - LG M50 deg - exp 1 - rig 1 - 10degC - cell A - RPT1_01_MB_CB1</em></p> <p> {NDK - LG M50 deg} - {exp 1} – {rig 1} – {10degC} – {cell A} – {RPT1}_{01}_{MB}_{CB1}</p> <p>{Standard prefix} – {experiment number} – {ID of test rig} – {control temperature} – {Cell ID} – {RPT number <em>or</em> aging cycle number}_{step number for the characterisation procedure (see above)}_{experimental technique name (will always be “MB”)}_{battery cycler channel ID used (always the same for a particular cell/experiment)}</p> <p> </p>
Data for "A stochastic model of geomorphic risk due to episodic river aggradation and degradation"
<p>The code and the dataset can be read/run by using Matlab. The description as follows:<br>1. Dataset of riverbed measurement (long profile and water level gauge data), carbon dating data, and rainfall record in the Laonong River (Taiwan). The dataset are used for the model calibration and the model application. <br>2. The developed riverbed stochastic processing model and the maximum likelihood calibration model. </p> <p>Note: this new version includes the corrected Monte Carlo simulation code and a required Matlab function (fminsearchbnd.m) that was missing in the first version.</p>
Metagenomic analysis of gut microbiome illuminates the mechanisms and evolution of lignocellulose degradation in mangrove herbivorous crabs
<p><strong>Background:</strong></p> <p>Sesarmid crabs dominate mangrove habitat as the major primary consumers, which facilitates the trophic link and nutrient recycling in the ecosystem. Therefore, the adaptations and mechanisms of sesarmid crabs to herbivory is not only crucial to terrestrialization and its evolutionary success, but also to the healthy functioning of mangrove forest ecosystems. Although endogenous cellulases expressions were reported in crab species, it remains unknown if the endogenous enzymes alone can complete the whole lignocellulolytic pathway, or they also depend on the contribution from their intestinal microbiome. We attempt to investigate the role of gut symbiotic microbes of mangrove-feeding sesarmid crabs in plant digestion using a comparative metagenomic approach.</p> <p><strong>Results:</strong></p> <p>Metagenomics analyses on 43 crab gut samples from 23 species of mangrove crabs revealed a wide coverage of 127 CAZy families and nine KOs targeting lignocellulose and their derivatives in all species analyzed, including predominantly carnivorous species, suggesting the crab species gut microbiome have lignocellulolytic capacity regardless of dietary preference. Microbial cellulase, hemicellulase and pectinase genes in herbivorous and detritivorous crabs were differentially more abundant when compared to omnivorous and carnivorous crabs, indicating the importance of gut symbionts in lignocellulose degradation in mangrove crabs and the enrichment of lignocellulolytic microbes in response to diet with higher lignocellulose content. The herbivorous and detritivorous crabs showed highly similar CAZyme composition compared to dissimilarities observed in taxonomic profiles observed in both groups, suggesting a stronger selection force to gut microbiota by its functional capacity than by taxonomy. The gut microbiota in herbivorous sesarmid crabs were also enriched with nitrogen reduction and fixation genes, implying possible roles of the gut microbiota in supplementing nitrogen that is deficient in plant diet.</p> <p><strong>Conclusions:</strong></p> <p>Endosymbiotic cellulolytic microbes play an important role in lignocellulose degradation in most crab species but their abundance is strongly correlated with dietary preference, and they are highly enriched in herbivorous sesarmids, thus enhancing their capacity for digestion of mangrove leaves. Dietary preference is a stronger driver in determining the microbial CAZyme composition and taxonomic profile in mangrove crab microbiome, resulting in functional redundancy of endosymbiotic microbes. Our results showed that crabs implement a mixed mode of digestion utilizing both endogenous and microbial enzymes in lignocellulose degradation, as observed in most of the more advanced herbivorous invertebrate species.</p>
Comprehensive study of antimicrobial polycaprolactone/clay nanocomposite films: preparation, characterization, properties and degradation in simulated body fluid
<p>Even though the biodegradability of polycaprolactone (PCL) and its nanocomposites is lower compared to other biodegradable polyesters, this property and good biocompatibility are used for development of materials for drug delivery with a long-term effect. We prepared novel PCL/clay nanocomposite films with antimicrobials chlorhexidine (CH) or octenidine (OCT) combined with ZnO anchored on vermiculite (VER). The intercalation of CH and OCT into the interlayer of VER/ZnOVER was confirmed by XRD, FTIR and SEM. The organically modified nanofillers compared to VER (−46.0 mV) or ZnOVER (−34.9 mV) showed a positive ζ-potential (+30.7 mV (VER_CH), +21.9 mV (VER_OCT), +24.6 mV (ZnOVER_CH)) indicating a relatively stable materials, except ZnOVER_OCT (+8.6 mV), which strongly agglomerated.</p> <p>Thin PCL/clay films were prepared by solvent casting method and the effect of used nanofillers on structural, thermal, mechanical and antimicrobial properties followed by degradation under hydrolytic conditions was studied. The results showed that presence of ZnO significantly decreases thermal and mechanical stability. The nanofillers with the higher hydrophilic character are responsible for the fastest degradation of PCL matrix. Films possessed high antimicrobial efficiency in long time intervals, hence these nanocomposites open new avenues for the possible application of such materials for the drug delivery with a long-term effect.</p>
Project - Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis
<p>Here are the datasets for our publication entitled "<a href="https://www.nature.com/articles/s41467-024-48779-z">Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis</a>" published in Nature Communications. </p> <p>The object of this experiment is the 18650 nickel-cobalt-manganese (NCM) lithium-ion battery manufactured by "LISHEN". The chemical composition is LiNi<sub>0.5</sub>Co<sub>0.2</sub>Mn<sub>0.3</sub>O<sub>2</sub>. The nominal capacity of the battery is 2000 mAh, and the nominal voltage is 3.6 V. The charging cut-off voltage and discharging cut-off voltage are 4.2 V and 2.5 V, respectively. The whole experiment was conducted at room temperature. A total of 55 batteries were included in this experiment, conducted under 6 different charging and discharging strategies. The charging and discharging platform is ACTS-5V10A-GGS-D, and the sampling frequency for all data is 1Hz.</p> <p>Other details can be found in "Data Introduction.pdf" file.</p> <p>The <strong>Python Code</strong> for reading and preprocessing this dataset is available at: <a href="https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library">https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library</a></p> <p>Summary of articles using the this dataset: <a href="https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary">https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary</a></p> <p> </p> <p>If you find this data helpful, please consider citing our paper:</p> <p>Wang, F., Zhai, Z., Zhao, Z. <em>et al.</em> Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. <em>Nat Commun</em> <strong>15</strong>, 4332 (2024). https://doi.org/10.1038/s41467-024-48779-z</p>
Hyperspectral images of patches at different stages of degraded alpine meadows
<p>This data contains hyperspectral images of degraded alpine meadow patches in four stages, which are:active patches (Stage 0), inactive patches (Stage 1), recovering patches (Stage 2), and healthy alpine meadow (Stage 3).</p>
Chromosomal instability degrades developmental phenotypes essential for anti-GD2 immunotherapy outcomes in high-risk neuroblastoma
<p>Childhood Cancer Data Initiative (CCDI)<br>dbGaP Study Accession: phs002431</p>
Upconverting Nanoparticles in Aqueous Media: Not a Dead-End Road. Avoiding Degradation by Using Hydrophobic Polymer Shells
<p>Dataset of https://zenodo.org/record/5793193#.YcCAcWjMJPY</p>
A new approach to simulate peat accumulation, degradation and stability in a global land surface scheme (JULES vn5.8_accumulate_soil) for northern and temperate peatlands
<p>This is the data (model output from JULES and observational data) used in the paper "A new approach to simulate peat accumulation, degradation and stability in a global land surface scheme (JULES vn5.8_accumulate_soil) for northern and temperate peatlands" for the resubmitted version after review of the discussion paper in Geoscientific Model Development Discussions (2021) https://doi.org/10.5194/gmd-2021-263. R code is provided that will recreate all of the plots in the paper using the data provided. These data include outputs from the JULES model including developments to represent peat accumulation, and observational data of peat properties (most are taken from other sources: references provided therein).</p>
Synthesis, Herbicidal Activity, Crop Safety and Soil Degradation of Pyrimidine- and Triazine-Substituted Chlorsulfuron Derivatives Supplementary Information
<p>The content include SI1-Melting points, 1H-NMR, 13C-NMR and HRMS of W103-W111, SI2-Crystal data of compound W110(CCDC number 2142702), SI3-Biological assay and crop safety, SI4-Soil degradation assay, SI5-Report of soil analysis in Chinese, and SI6-Report of soil analysis in English.</p>
Dataset: Experimental carbon emissions from degraded Mediterranean seagrass (Posidonia oceanica) meadows under current and future summer temperatures.
<p> Experimental carbon emissions from degraded Mediterranean seagrass (<em>Posidonia oceanica</em>) meadows.</p> <p> </p> <p>Guillem Roca, Javier Palacios, Sergio Ruíz-Halpern, Núria Marbà</p> <p>Contact details: Guillem Roca, guillemrocac@gmail.com</p> <p>Issue date:</p> <p>Identifier:</p> <p> </p> <p>Citation: Roca, Guillem; Palacios, Javier; Ruíz-Halpern, Marbà, Núria;</p> <p>Experimental carbon emissions from degraded Mediterranean seagrass (<em>Posidonia oceanica</em>) meadows. [Dataset]</p> <p> </p> <p>Abstract: The dataset provides data on sediment C0<sub>2 </sub>efflux rates (μmol CO<sub>2 </sub>m<sup>-2 </sup>s<sup>-1</sup>), carbon emissions during the experiment (gm<sup>-2</sup>), % Organic Carbon, Organic Matter content (g m<sup>-2</sup>) of the <em>Posidonia oceanica</em> seagrass sediments collected in Pollença bay (North of Mallorca Island). Sediments were cultivated in 5 different seawater temperature treatments and two different agitation conditions.</p> <p> </p> <p>Keywords: C0<sub>2 </sub>efflux rates, C0<sub>2</sub> emissions, Sediment, Seagrass, <em>Posidonia Oceanica</em>, experiment, temperature treatment, Sediment suspension Blue carbon, Organic Carbon.</p> <p> </p> <p>Description: The dataset contains data on sediment C0<sub>2 </sub>efflux rates, carbon emissions during the experiment (gm<sup>-2</sup>), % Organic Carbon, Organic Matter content of the <em>Posidonia oceanica</em> seagrass sediments collected in Pollença bay (North of Mallorca Island). Sediments were cultivated in 5 different seawater temperature treatments and two different agitation conditions. Sediments used in the experiment were extracted in October 2017 from the <em>P. Oceanic</em>a meadow of Pollença in Mallorca Island at six-meter depth Figure (1). Sediments were sampled in October 2017 using sediment cores (9 cm ID and 30cm long) and directly transported to the laboratory. Only the top 10 cm of the sediment cores were used since this fraction is the most susceptible to erosion. Living seagrass tissues (roots, rhizomes, and leaves) were removed and sediment was mixed and homogenized. 40ml of sediments were poured into glass containers of 750ml with 500ml of seawater. Finally, each recipient contained a sediment layer of approximately 1.1cm in each container. Containers were placed at five different temperature baths (26,27.5, 29, 30.5, 32 ºC) simulating summer temperatures in the bay (Garcias-Bonet et al., 2019) at different agitation regimes (agitation/repose) to simulate exposed and sheltered conditions.10 containers were sampled right after the experiment started to provide initial sediment conditions. Five containers per temperature and agitation treatment were removed 7, 21, 43, 67, and 98 days from the experiment start, to analyse sediment organic matter and CaCO<sub>3</sub> content. CO<sub>2</sub> incubations were run 5, 14, 56, and 91 days from the experiment start. Sampling times were distributed considering that organic matter remineralisation was likely to follow an exponential trend, including a rapid phase of loss of the more labile material followed by a slower loss of more recalcitrant substrates (Arndt et al., 2013). The experiment was run in the dark to avoid photosynthesis in an isothermal chamber at 21ºC.</p> <p> </p> <p><strong>Organic Carbon analysis</strong></p> <p>In each sampling time, organic matter content in sediments (OM %DW) was estimated as the percentage weight loss of dry sediment sample after combustion at 550ºC for 4 hours. Organic carbon (Corg) was calculated from OM content using the relation described in (Mazarrasa et al., 2017b)</p> <p> </p> <p>y = 0.29x – 0.64; (R2=0.98, p< 0.0001, n=60)</p> <p> </p> <p>OM and POC stocks along the experiment (mg OM ml-1 and mg POC ml-1) were estimated by multiplying the OM and POC (%DW) by the sediment dry weight (mg) remaining in each experimental unit and standardized to the initial volume of sediment (40 ml) introduced in every glass container. Inorganic carbon was estimated as the percentage weight loss of already combusted sediment (550ºC) after combustion at 1000ºC.</p> <p> </p> <p><strong>Sediment CO<sub>2</sub> production</strong></p> <p>Container headspace CO<sub>2</sub> gas concentration was measured during 20 minutes continuum incubations (4 replicates) in each temperature and agitation treatment in all sampling times. CO<sub>2</sub> air concentration measures were carried out using an Infra Red Gas Analyser EGM4 from PPSystems. Concentration of dissolved CO<sub>2</sub> in seawater (in μmol CO<sub>2</sub> L<sup>−1</sup>) was calculated from the concentration of CO<sub>2</sub> (in ppm) measured in headspace air samples after equilibration as described in (Garcias-Bonet and Duarte, 2017; Wilson et al., 2012). Briefly, we calculate the dissolved CO<sub>2</sub> remaining in seawater after equilibration with the air phase ([CO<sub>2</sub>]SW−eq) by,</p> <p> </p> <p>[CO<sub>2</sub>]SW−eq = 10−6 β [C CO<sub>2</sub>]Air P</p> <p> </p> <p>where β is the Bunsen solubility coefficient of CO<sub>2</sub>, calculated according to Wiesenburg and Guinasso (1979), as a function of seawater temperature and salinity; [CO<sub>2</sub>]Air is the CO<sub>2</sub> concentration measured in containers headspace air (in ppm) and P is the atmospheric pressure (in atm) of dry air that was corrected by the effect of multiple sampling applying Boyle’s Law. Then, the initial CO<sub>2</sub> concentration in seawater before the equilibrium ([CO<sub>2]SW</sub>−before eq) was calculated (in ml CO<sub>2</sub> /ml H<sub>2</sub>O) by,</p> <p> </p> <p>[CO<sub>2</sub>]<sub>SW−before eq</sub> = ([CH<sub>4</sub>]<sub>SW−eq</sub> V<sub>Sw</sub> + 10−6 ([CO<sub>2</sub>]Air −[CO<sub>2</sub>]<sub>Air background</sub>) V<sub>Air</sub>)/V<sub>SW</sub></p> <p> </p> <p>Where V<sub>Sw</sub> is the volume of seawater in the core or in the seawater closed circuit, [CO<sub>2</sub>]<sub>Air background</sub> is the atmospheric CO<sub>2</sub> background level and V<sub>Air</sub> is the volume of the headspace or the closed air circuit. Finally, the initial CO<sub>2</sub> concentration was transformed to µmol CH<sub>4</sub> L<sup>−1</sup> by applying the ideal gas law.</p> <p>CO<sub>2</sub> efflux values were calculated from CO<sub>2</sub> variation per time unit. Then, we converted the rates to aerial (taking in account container surface) base, and thickness (in μmol m<sup>-2 </sup>s<sup>-1</sup>).</p> <p> </p> <p> </p> <p> </p> <p> </p>
A Tungsten Deep Neural-Network Potential for Simulating Mechanical Property Degradation Under Fusion Service Environment
<p>The DP-HYB and DP-SE2potential and the W training database.</p>
Data supporting: Environmental RNA degrades more rapidly than environmental DNA across a broad range of pH conditions
<p>Although the use and development of molecular biomonitoring tools based on environmental nucleic acids (eDNA and eRNA; collectively known as eNAs) have gained broad interest for the quantification of biodiversity in natural ecosystems, studies investigating the impact of site-specific physicochemical parameters on eNA-based detection methods (particularly eRNA) remain scarce. Here, we used a controlled laboratory microcosm experiment to comparatively assess the environmental degradation of eDNA and eRNA across an acid-base gradient following complete removal of the progenitor organism (<em>Daphnia pulex</em>). Using water samples collected over a 30-day period, eDNA and eRNA copy numbers were quantified using a droplet digital PCR (ddPCR) assay targeting the mitochondrial <em>cytochrome c oxidase</em> subunit I (COI) gene of <em>D. pulex</em>. We found that eRNA decayed more rapidly than eDNA at all pH conditions tested, with detectability—predicted by an exponential decay model—for up to 57 hours (eRNA; neutral pH) and 143 days (eDNA; acidic pH) post organismal removal. Decay rates for eDNA were significantly higher in neutral and alkaline conditions than in acidic conditions, while decay rates for eRNA did not differ significantly among pH levels. Collectively, our findings provide the basis for a predictive framework assessing the persistence and degradation dynamics of eRNA and eDNA across a range of ecologically relevant pH conditions, establish the potential for eRNA to be used in spatially and temporally sensitive biomonitoring studies (as it is detectable across a range of pH levels), and may be used to inform future sampling strategies in aquatic habitats.</p>
Data from: UV radiation doubles microbial degradation of standing litter in a subtropical forest
<p><span>UV radiation has been recognized as a direct driver of litter decomposition by photodegrading organic matter in dryland ecosystems. </span><span>However, the importance and mechanism of UV radiation on litter decomposition, especially on standing litter, in humid forest ecosystems remain unclear. </span></p> <p><span>We conducted a factorial experiment in a humid subtropical forest gap, manipulating the effects of UV radiation on the decomposition of standing litter under different microbial conditions. </span></p> <p><span>After 366 days of standing incubation, under normal conditions (UV pass with microorganisms), up to 40.63% of the litter mass was lost. However, under a UV pass without microorganisms, litter mass loss was only 16.30%. Under a UV block, the mass loss of litter with microorganisms was 27.68% and that of litter without microorganisms was 15.54%. Without microorganisms, UV radiation had no significant effect on the mass loss of litter carbon. However, UV radiation increased the DOC concentration of litter. And in the presence of microorganisms, UV radiation contributed to an increased mass loss of lignin by 16.72% and of cellulose by 14.75%. No negative effects of UV radiation on microorganisms were observed. These results suggest that UV radiation increased the net mass loss of litter by 106.67%,</span> <span>and this doubling promotion was achieved through microbial degradation. </span></p> <p><strong><em><span>Synthesis</span></em></strong><span>. The increase in microbial degradation under UV radiation may be linked to the increased degradability of lignin and cellulose caused by photodegradation. Our study indicates that direct photodegradation by UV radiation could be weak in subtropical forests, but UV photofacilitation generates rapid turnover of carbon in this system.</span></p>
Transcription feedback dynamics in the wake of cytoplasmic mRNA degradation shutdown
<p>In the last decade, multiple studies demonstrated that cells maintain a balance of mRNA production and degradation, but the mechanisms by which cells implement this balance remain unknown. Here, we monitored cells’ total and recently-transcribed mRNA profiles immediately following an acute depletion of Xrn1—the main 5′-3′ mRNA exonuclease—which was previously implicated in balancing mRNA levels. We captured the detailed dynamics of the adaptation to rapid degradation of Xrn1 and observed a significant accumulation of mRNA, followed by a delayed global reduction in transcription and a gradual return to baseline mRNA levels. We found that this transcriptional response is not unique to Xrn1 depletion; rather, it is induced earlier when upstream factors in the 5′-3′ degradation pathway are perturbed. Our data suggest that the mRNA feedback mechanism monitors the accumulation of inputs to the 5′-3′ exonucleolytic pathway rather than its outputs.</p>
The application of short and highly polymorphic microhaplotypes based on nonbinary-SNPs in kinship testing of extremely degraded samples
<p>Kinship testing becomes more difficult in extremely degraded samples. But commonly used genetic markers cannot completely solved this problem. Microhaplotype combining the advantages of STR and SNP may be a promising genetic marker for kinship testing in extremely degraded samples. Therefore, in this study, 36 short and highly polymorphism microhaplotype loci with length smaller than 100 bp and Ae greater than 3.0 were developed, of which 29 loci met the Hardy-Weinberg and linkage equilibrium. The CPD and CPE of these 29 loci were 0.99999999999999999999999997036 and 0.9999999945, respectively. Allele dropout of these loci was not observed in extremely degraded samples. Through simulated kinship analysis, the effectiveness of paternity testing reached 98.39% at threshold of 4/-4, and effectiveness of full-sibling testing reached 93.01% at threshold of 2/-2, which were greater than that of 15 STR loci. After combining with other 50 short and highly polymorphic microhaplotype loci, the effectiveness of half-sibling testing also reached 82.42% at the threshold of 2/-2. Our developed short and highly polymorphic microhaplotype loci may be useful for paternity testing and full-sibling testing in extremely degraded samples, and after combining with other short and highly polymorphic microhaplotype loci, may be helpful to analyze the more distant kinship relationship.</p>
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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.