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741 results for “Decay”
Dataset for the decay process of PM2.5 pollution episodes around Beijing
<p>PM2.5 concentrations in the 28 cities around Beijing (Apm); </p> <p>Time series of the decay phase days (LocaDate);</p> <p>Time series of the dry-day decay phase days (LocaDateNoPre)</p>
Methodology matters for comparing coarse wood and bark decay rates across tree species
<p>1. The importance of wood decay for the global carbon and nutrient cycles is widely recognized. However, relatively little is known about bark decay dynamics, even though bark represents up to 25% of stem dry mass. Moreover, bark presence versus absence can significantly alter wood decay rates. Therefore, it really matters for the fate of carbon whether variation in bark and wood decay rates is coordinated across tree species.</p> <p>2. Answering this question requires advances in methodology to measure both bark and wood mass loss accurately. Decay rates of large logs in the field are often quantified as loss in tissue density, in which case volume depletions of bark and wood can give large underestimations.</p> <p>3. To quantify the real decay rates, we assessed bark mass loss per stem surface area and wood mass loss based on volume-corrected density loss. We further defined the range of actual bark mass loss by considering bark cover loss. Then, we tested the correlation between bark and wood mass loss across 20 temperate tree species during 4 years of decomposition.</p> <p>4. The area-based method generally showed more than 3-fold higher bark mass loss than the density-based method (even higher if considering bark cover loss), and volume-corrected wood mass losses were 1.08-1.12 times higher than density-based mass loss. The deviation of bark mass loss between the two methods was higher for tree species with thicker inner bark. Bark generally decomposed twice as fast as wood across species, and faster decaying bark came with faster decaying wood (R2=0.26, P=0.006).</p> <p>5. We strongly suggest using corrected volume when assessing wood mass loss especially for the species with faster decomposable sapwood and all the wood at advanced decay stages. Further studies of coarse stem decomposition should consider trait "afterlife" effects of inner bark and estimate fraction of stem bark cover to obtain more accurate decay rates. 6. Our new method should benefit our understanding of the in situ dynamics of woody debris decay and monitoring research in different forest ecosystems worldwide, and should aid meta-analyses across diverse studies.</p>
Kappa matrices presented in "An EFT approach to baryon number violation: lower limits on the new physics scale and correlations between nucleon decay modes" arXiv:2312.13361
<p>In this archive we provide the kappa matrices introduced in Sec 2.4 of our paper <em>An EFT approach to baryon number violation: lower limits on the new physics scale and correlations between nucleon decay modes</em> <a href="https://arxiv.org/abs/2312.13361">2312.13361</a>, also <a href="https://link.springer.com/article/10.1007/JHEP07(2024)004">published in JHEP</a>. These are the numerical values that are presented as matrix plots in Appendix C. Here they are packaged into CSV files, whose first row is the row/column label.</p> <p>Please consult the README for further information on how to use these data.</p> <p><em>Version 3: Fixed error common to d=7 matrices.</em></p>
The Reactivation of Decayed Positive Leader with Sudden Channel Elongation in Laboratory Long Spark
<p>The data support the manuscript entitled "The Reactivation of Decayed Positive Leader with Sudden Channel Elongation in Laboratory Long Spark". The *.rar file contains all data used therein and can be decompressed and opened, where these file folders contain the data corresponding to their respective Figures, as they are named. The data can be used freely for scientific purposes with appropriate citation.</p>
Bryophytes enhance nitrogen content in decaying wood via biological interactions
<p>An increase in the nitrogen (N) content in coarse woody debris (CWD) facilitates its decomposition, affecting the cycling of N and other nutrients in forest ecosystems. Bryophytes may increase the N content by transferring the N in bryophyte tissues to the underlying CWD. This study examined whether and how bryophytes increase N content in the underlying CWD using N-stable isotope ratios (δ<sup>15</sup>N) as tracers for N sources. The N content and δ<sup>15</sup>N values in CWD with bryophytes were significantly higher than those in CWD without bryophytes. However, the δ<sup>15</sup>N values in CWD with bryophytes differed significantly from those in bryophytes on CWD, demonstrating that the N in bryophytes did not cause the increase in N content in CWD. The analyses using δ<sup>15</sup>N further indicated that the high N content in CWD with bryophytes may be attributed to increased N supply from wood-decomposing fungi and N-fixing bacteria. This increase in N content may result from the enhanced moisture content in CWD beneath bryophytes, which facilitates the activity of wood-decomposing fungi and N-fixing bacteria. Notably, the influence of bryophytes on the N content in CWD differed between bryophyte life forms: bryophytes that form dense mats increased the N content in CWD, whereas those with loose mats decreased the N content. This difference can be explained by the greater humidity experienced by CWD with bryophytes forming dense mats than that experienced by CWD with bryophytes forming loose mats. Given that the N content in CWD affects the decay processes, the results highlight the importance of biological interactions associated with bryophytes in forest ecosystems.</p>
Radiationless Decay Spectrum of O 1s Double Core Holes in Liquid Water - data
<p>Data set pertaining to the publication "Radiationless Decay Spectrum of O 1s Double Core Holes in Liquid Water", published in <a href="https://doi.org/10.1063/5.0205994"><em>J. Chem. Phys.</em> 160, 194503 (2024)</a>.</p> <p>The publication describes results on the Auger emission of double core hole states created by single photon photo-double-ionization, explored experimentally by liquid jet electron spectra and via simulations. Here we provide the underlying experimental data including metadata, ascii representations of the traces shown in the figures, and the coordinates used in the simulations.</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see<br>https://www.nexusformat.org/<br>https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br>NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br>* nexpy (distributed with python)<br>* https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)<br>Additionally, some properties of our liquid jet sample environment are described by extensions to standard NeXus explained in a notes-section in each file.</p> <p>In each NeXus file-entry, two types of spectra are shown:<br>1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data').<br>2. As-measured data ('raw').</p> <p>Files with extension .txt or with extension .xyz are tab-separated or space-separated ascii-files. Files with extension .asc are comma-separated ascii-files. Files with extension .zip are compressed multi-file archives, MIME-type application/zip.</p> <p>The following files are provided:</p> <p> </p> <table> <tbody> <tr> <td>Filename</td> <td>Content</td> </tr> <tr> <td> <div><a href="../api/records/10523681/draft/files/DataFig2_3_4.zip/content" target="_blank" rel="noopener noreferrer">DataFig2_3_4.zip</a></div> </td> <td>Ascii representation of the experimental data traces shown in Fig.s 3, 4 and 5</td> </tr> <tr> <td> <div><a href="../api/records/10523681/draft/files/dataFig5_6_7.zip/content" target="_blank" rel="noopener noreferrer">dataFig5_6_7.zip</a></div> </td> <td>Ascii representation of the simulated traces shown in Fig.s 6, 7 and 8</td> </tr> <tr> <td> <div><a href="../api/records/10523681/draft/files/DataSFig1-4.zip/content" target="_blank" rel="noopener noreferrer">DataSFig1-4.zip</a></div> </td> <td>Ascii representation of the experimental data traces shown in Supplementary Fig.s 1,3 and 4</td> </tr> <tr> <td> <div><a href="../api/records/10523681/draft/files/DataSFig5.zip/content" target="_blank" rel="noopener noreferrer">DataSFig5.zip</a></div> </td> <td>Ascii representation of the experimental data traces shown in Supplementary Fig. 5. Data <br>are shown before normalization to unity.</td> </tr> <tr> <td> <div><a href="../api/records/10523681/draft/files/DataSFig6-8.zip/content" target="_blank" rel="noopener noreferrer">DataSFig6-8.zip</a></div> </td> <td>Ascii representation of the experimental data traces shown in Supplementary Fig.s 6,7 and 8</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/dch-1812.h5/content" target="_blank" rel="noopener noreferrer">dch-1812.h5</a></td> <td>Experimental data, december 2018 campaign.</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/dch-1904s1.h5/content" target="_blank" rel="noopener noreferrer">dch-1904s1.h5</a></td> <td>Experimental data, april 2019 campaign, data set 1.</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/dch-1904s2.h5/content" target="_blank" rel="noopener noreferrer">dch-1904s2.h5</a></td> <td>Experimental data, april 2019 campaign, data set 2.</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/dch-1909h2o.h5/content" target="_blank" rel="noopener noreferrer">dch-1909h2o.h5</a></td> <td>Experimental data, september 2019 campaign.</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/dch-1909d2o.h5/content" target="_blank" rel="noopener noreferrer">dch-1909d2o.h5</a></td> <td>Experimental data, september 2019 campaign, deuterated water.</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/dch-2009h2o.h5/content" target="_blank" rel="noopener noreferrer">dch-2009h2o.h5</a></td> <td>Experimental data, september 2020 campaign.</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/dch-2009d2o.h5/content" target="_blank" rel="noopener noreferrer">dch-2009d2o.h5</a></td> <td>Experimental data, september 2020 campaign, deuterated water.</td> </tr> <tr> <td><a href="../api/records/10523681/draft/files/optimized_pentamer.xyz/content" target="_blank" rel="noopener noreferrer">optimized_pentamer.xyz</a></td> <td>Cartesian coordinates of the water pentamer used for the simulations, in Å.</td> </tr> </tbody> </table> <p> </p> <p>In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
Stem decomposition of temperate tree species is determined by stem traits and fungal community composition during early stem decay
<p>Dead trees are vital structural elements in forests playing key roles in the carbon and nutrient cycle. Stem traits and fungal community composition are both important drivers of stem decay, and thereby affect ecosystem functioning, but their relative importance for stem decomposition over time remains unclear.</p> <p>To address this issue, we used a common garden decomposition experiment in a Dutch larch forest hosting fresh logs from 13 common temperate tree species. In total 25 fresh wood and bark traits were measured as indicators of wood accessibility for decomposers, nutritional quality, and chemical or physical defense mechanisms. After one and four years of decay, we assessed the richness and composition of wood-inhabiting fungi using amplicon sequencing and determined the proportional wood density loss.</p> <p>Average proportional wood density loss for the first year was 18.5%, with further decomposition occurring at a rate of 4.3% yr<sup>-1</sup> for the subsequent three years across tree species. Proportional wood density loss varied widely across tree species in the first year (8.7-24.8% yr<sup>-1</sup>) and subsequent years (0-11.3% yr<sup>-</sup><sup>1</sup>). The variation was directly driven by initial wood traits during the first decay year, then later directly driven by bark traits and fungal community composition. Moreover, bark traits affected the composition of wood-inhabiting fungi and thereby indirectly affected decomposition rates. Specifically, traits promoting resource acquisition of the living tree, such as wide conduits that increase accessibility and high nutrient concentration, increased initial wood decomposition rates. Fungal community composition, but not fungal richness explained differences in wood decomposition after four years of exposure in the field, where fungal communities dominated by brown-rot and white-rot Basidiomycetes were linked to higher wood decomposition rate.</p> <p><em>Synthesis.</em> Understanding what drives deadwood decomposition through time is important to understand the dynamics of carbon stocks. Here, using a tailor-made experimental design in a temperate forest setting, we have shown that stem trait variation is key to understanding the roles of these drivers; Initially, wood traits explained decomposition rates while subsequently, bark traits and fungal decomposer composition drove decomposition rates. These findings inform forest management with a view to selecting tree species to promote carbon storage.</p>
Data from: Evaluating single-cell variability in proteasomal decay
<p>Gene expression is a stochastic process that leads to variability in mRNA and protein abundances even within an isogenic population of cells grown in the same environment. <br>This variation, often called gene-expression noise, has typically been attributed to transcriptional and translational processes while ignoring the contributions of protein decay variability across cells. <br>Here we estimate the single-cell protein decay rates of two degron GFPs in \textit{Saccharomyces cerevisiae} using time-lapse microscopy. <br>We find substantial cell-to-cell variability in the decay rates of the degron GFPs.<br>We evaluate cellular features that explain the variability in the proteasomal decay and find that the amount of 20s catalytic beta subunit of the proteasome marginally explains the observed variability in the degron GFP half-lives. <br>We propose alternate hypotheses that might explain the observed variability in the decay of the two degron GFPs.<br>Overall, our study highlights the importance of studying the kinetics of the decay process at single-cell resolution and that decay rates vary at the single-cell level, and that the decay process is stochastic. <br>A complex model of decay dynamics must be included when modeling stochastic gene expression to estimate gene expression noise.</p>
Data from: "Distance decay effects predominantly shape spider but not carabid community composition in crop fields in north-western Europe"
Open the record for dataset details and reuse information.
Supplementary material S19: The growth and decay rate of the Varroa jolting pulse.
<p>The growth and decay of the <em>Varroa </em>jolting pulse on each of the three substrates. Panel ‘a’ showcases the loudest jolting pulse waveform registered on honeycomb, and panel ‘b’ showcases the loudest jolting pulse waveform registered on petri-dish, both of which demonstrate an exponential growth and decay that is highlighted within the red envelope. The growth rate and decay constant were estimated visually (honeycomb growth rate = 0.05ms, honeycomb decay constant = 0.1ms, petri-dish growth rate = 0.09ms, petri-dish decay constant = 1.2ms). The growth rate is the only element of the waveform that is caused by the animal, the decay constant on both the honeycomb and petri-dish are likely the result of the response of the substrate. The brood-comb <em>Varroa </em>jolting pulse seen in panel ‘c’ is the result of the averaged accelerometer waveform for the 40 jolting pulses deemed to be loudest, and are shown in panel ‘d’ to have an envelope following a gaussian function. All peaks in the vibrational trace were forced to become positive values to demonstrate the gaussian function. The negligible exponential decay can be seen, beginning at approximately 3.9ms.</p>
Dataset of "Shock recovery with decaying compressive pulses: A shock effect in calcite (CaCO3) around the Hugoniot elastic limit"
<p>The text data supporting the figures on the manuscript. The names of variables are listed on the top column.</p>
Arthropod OTUs in fruit bodies of wood decay fungi
<p>Biological communities within living organisms are structured by their host's traits. How host traits affect biodiversity and community composition is poorly explored for some associations, such as arthropods within fungal fruit bodies. Using DNA metabarcoding, we revealed the arthropod communities in living fruit bodies of eleven wood-decay fungi from boreal forests and investigated how they were affected by different fungal traits. Arthropod diversity was higher in fruit bodies with a larger surface area-to-volume ratio, suggesting that colonisation is crucial to maintain arthropod populations. Diversity was not higher in long-lived fruit bodies, most likely because these fungi invest in physical or chemical defences against arthropods. Arthropod community composition was structured by all measured host traits, namely fruit body size, thickness, surface area, morphology and toughness. Notably, we identified a community gradient where soft and short-lived fruit bodies harboured more true flies, while tougher and long-lived fruit bodies had more oribatid mites and beetles, which might reflect different development times of the arthropods. Ultimately, close to 75% of the arthropods were specific to one or two fungal hosts. Besides revealing surprisingly diverse and host-specific arthropod communities within fungal fruit bodies, our study provided insight on how host traits structure communities.</p>
Decay by ectomycorrhizal fungi couples soil organic matter to nitrogen availability
<p>Interactions between soil nitrogen (N) availability, fungal community composition, and soil organic matter (SOM) regulate soil carbon (C) dynamics in many forest ecosystems, but context dependency in these relationships has precluded general predictive theory. We found that ectomycorrhizal (ECM) fungi with peroxidases decreased with increasing inorganic N availability across a natural inorganic N gradient in northern temperate forests, whereas ligninolytic fungal saprotrophs exhibited no response. Lignin-derived SOM and soil C were negatively correlated with ECM fungi with peroxidases and were positively correlated with inorganic N availability, suggesting decay of lignin-derived SOM by these ECM fungi reduced soil C storage. The correlations we observed link SOM decay in temperate forests to tradeoffs in tree N nutrition and ECM composition, and we propose SOM varies along a single continuum across temperate and boreal ecosystems depending upon how tree allocation to functionally distinct ECM taxa and environmental stress covary with soil N availability.</p>
Short-interval fires increasing in the Alaskan boreal forest as fire self-regulation decays across forest types: Code and data
<p>Code and data to reproduce results in the associated paper. Additional downloads of climate data and various R packages will be required for some analyses.</p> <p>Abstract: Climate drivers are increasingly creating conditions conducive to higher frequency fires. In the coniferous boreal forest, the world’s largest terrestrial biome, fires are historically common but relatively infrequent. Post-fire, regenerating forests are generally resistant to burning (strong fire self-regulation), favoring millennial coniferous resilience. However, short intervals between fires are associated with rapid, threshold-like losses of resilience and changes to broadleaf or shrub communities, impacting carbon content, habitat, and other ecosystem services.</p> <p>Fires burning the same location 2+ times comprise approximately 4% of all Alaskan boreal fire events since 1984, and the fraction of short-interval events (<20 years between fires) is increasing with time. While there is strong resistance to burning for the first decade after a fire, from 10-20 years post-fire resistance appears to decline. Reburning is biased towards coniferous forests and in areas with seasonally variable precipitation, and that proportion appears to be increasing with time, suggesting continued forest shifts as changing climatic drivers overwhelm the resistance of early postfire landscapes to reburning. As area burned in large fire years of ~15 years ago begin to mature, there is potential for more widespread shifts, which should be evaluated closely to understand finer grained patterns within this regional trend.</p>
Distance decay 2.0 – a global synthesis of taxonomic and functional decay in ecological communities
<p>Datasets used in the analysis of the manuscript by Graco-Roza, C., Aarnio, S., Abrego, N., Acosta, A. T., Alahuhta, J., Altman, J., ... & Soininen, J. (2022). Distance decay 2.0–a global synthesis of taxonomic and functional turnover in ecological communities.<em> Global Ecology and Biogeography.</em></p> <p> </p> <p><strong>raw_data.zip - </strong>Includes the raw datasets used in the analysis. </p> <p><strong>processed_data.xlsx - </strong>Includes the results from the distance decay analysis, specifically: </p> <p>- Dataset : dataset code (same as in raw_data)</p> <p>- Beta_type: The component of beta diversity (i.e., total similarity, replacement, richness differences)</p> <p>- Level : Taxonomic (TAX) or functional (FUN)</p> <p>- Based: Occurrence (occ) or Abundance (abund)</p> <p>- Organism: Code used to describe organisms (see Appendix S1 of the paper)</p> <p>- Realm: Aquatic, Terrestrial, or Freshwaters</p> <p>- Body_size </p> <p>- Dispersal_mode: Seeds, Passive or Active</p> <p>- Latitude: Mean latitude of the dataset (average of all data points)</p> <p>- Latitude_range Distance in kilometres between the two vertically most distant points.</p> <p>- Longitude_range: Distance in kilometres between the two horizontally most distant points.</p> <p>- spa_min: minimum distance between sites (in kilometres) </p> <p>- spa_mean: average distance between sites (in kilometres) </p> <p>- spa_max: maximum distance between sites (in kilometres)</p> <p>- ext: area in kilometres covered by all sites in the dataset</p> <p>- n_sites: Number of sites in each dataset</p> <p>- n_var: Number of environmental variables in each dataset</p> <p>- gamma_spe: Number of species observed in each dataset</p> <p>- gamma_trait: Volume of the hypervolume constructed using the traits in each dataset</p> <p>- n_traits: Number of traits in each dataset</p> <p>- Intercept_spa: Intercept of GLM including community similarity and spatial distances</p> <p>- Slope_Spa: Slope of GLM including community similarity and spatial distances</p> <p>- R2_spa: R² of GLM including community similarity and spatial distances</p> <p>- Intercept_env: Intercept of GLM including community similarity and environmental distances</p> <p>- Slope_env: Slope of GLM including community similarity and environmental distances</p> <p>- R2_env: R² of GLM including community similarity and environmental distances</p> <p>- Mantel_spa: Mantel statistics of community similarity and spatial distances</p> <p>- spa_signif: Significance of Mantel statistics considering community similarity and spatial distances</p> <p>- Mantel_env: Mantel statistics considering community similarity and environmental distances</p> <p>- env_signif: Significance of Mantel statistics considering community similarity and environmental distances</p> <p><strong>Null_models.zip - </strong>Includes the results from the null models for each dataset.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Age-specific differences in Asian Elephant defecation, dung decay, detection and their implication for dung count
<p><span>In vertebrate population estimation, converting faecal density into animal density requires information on faecal production rate, decay rate, and faecal density. Differences in the above factors for long-lived species across age classes were not evaluated. We have evaluated these factors associated with the dung count of Asian elephants </span><span>(<em>Elephas maximus</em>) </span><span>in the tropical forest of Southern India. </span></p> <p><span>The defecation rate of elephants was determined in semi-wild elephants at the Mudumalai elephant camp. The relationship between dung bolus diameter and age was determined to estimate the age of the elephant. Total and age-specific elephant density based on dung bolus diameter were estimated. A total of 24 transect lines of 2-4 km (125 km) were sampled in the study area. An experiment was conducted to assess detection probability across the age classes of dung piles. The dung decay rates across age classes and seasons were determined by marking fresh dung piles (n=1551). The dung-based age structure assessment and its limitations were evaluated. </span></p> <p><span>The mean defecation rate was 13.51</span><span>±</span><span>0.51 per day. The defecation rate was significantly lower for the younger age class and increased with the age of elephants. Defecation rates were significantly lower in the wet season than in the dry. </span><span>The dung-boli diameter positively increased with the age of elephants, and the growth curve can be used to predict the age and age structure of the elephant population. </span></p> <p><span>The disparity in the dung production rate results in the lower availability of younger age class (Juvenile and calf) dung in the transect for counting, that results in lower dung abundance. The detection probability of dung piles of younger age classes was low (0.58). The survival rates of dung piles of younger age classes were lower and increased with the age of elephants in the wet season. </span><span>Hence, demography assessment of the population based on dung needs to consider age-specific differences in dung production, decay, and detection probability. Through demography assessment using dung provides insight into population age structure, it has limitations </span><span>in predicting age structure for young elephants. </span></p>
Data from: Experimental analysis of organ decay and pH gradients within a carcass and the implications for phosphatization of soft tissues
<p>Replacement of soft-tissues by calcium phosphate yields spectacular fossils. Decay experiments have shown that pH is a major control on the precipitation of calcium phosphate and tissue replication: for this to occur pH must fall below the carbonic acid dissociation constant (pH 6.38). However, in the fossil record, phosphatisation is highly selective - some internal organs, such as muscles, stomachs, and intestines, appear to preferentially phosphatise while other organs seldomly phosphatise. The reasons for this are unclear but one hypothesis is that, during decay, organs create distinct chemical microenvironments and only some fall below the critical pH threshold for mineralization to occur. Here, we present a novel investigation using microelectrodes that records fluctuating dynamic spatial and temporal pH gradients inside of organs within a carcass in real time. Our experiments demonstrate that within a decaying carcass, organ-specific microenvironments are not generated. Rather, a pervasive pH environment forms within the body cavity (i.e. the coelom) which persists until integumentary failure. With no evidence to support the development of organ-specific microenvironments during decay other factors must control organ phosphatisation. We propose it is tissue histology that plays an important role in selective phosphatisation. Tissues with high phosphate content (and those rich in collagen) are most likely to phosphatise. Internal organs that have low tissue-bound phosphate, including the integuments of the stomach and intestine, only phosphatise when associated with ingested phosphate-rich organic matter. Identifying the driver behind selective phosphatisation may provide insights into other highly selective modes of soft-tissue preservation i.e. pyritization.</p>
Cross-kingdom interactions and functional patterns of active microbiota matter in governing deadwood decay
<p>Microbial community members are the primary microbial colonizers and active decomposers of deadwood. This study placed sterilized standardized beech and spruce sapwood specimens on the forest ground of 8 beech- and 8 spruce-dominated forest sites. After 370 days, specimens were assessed for mass loss, nitrogen (N) content and <sup><span>15</span></sup><span>N</span> isotopic signature, <span>hydrolytic and lignin-modifying enzyme activities. Each </span>specimen <span>was </span>incubated with bromodeoxyuridine (BrdU) to label <span>metabolically active fungal and bacterial community members, which were assessed using an amplicon sequencing. Fungal saprotrophs colonized the deadwood accompanied by a distinct bacterial community that was capable of cellulose degradation, aromatic depolymerisation, and N<sub>2</sub> fixation. The latter were governed by the genus <em>Sphingomonas</em>, which was co-present with the majority of saprotrophic fungi regardless of whether beech or spruce specimens were decayed. Moreover, the richness of the diazotrophic </span><span><em>Allorhizobium</em>-<em>Neorhizobium</em>-<em>Pararhizobium</em>-<em>Rhizobium</em></span><span> group were significantly correlated with mass loss, N content and <sup>15</sup>N isotopic signature. In contrast, presence of obligate predator <em>Bdellovibrio</em> spp. shifted bacterial community composition and were linked to decreased beech deadwood decay rates. Our study provides the first account of the composition and function of metabolically active wood-colonising bacterial and fungal communities, highlighting cross-kingdom interactions during the early and intermediate stages of wood decay.</span></p>
Systematic review and meta-analysis: water type and temperature affect environmental DNA decay metadata
<p>Environmental DNA (eDNA) has been used in a variety of ecological studies and management applications. The rate at which eDNA decays has been widely studied but at present it is difficult to disentangle study-specific effects from factors that universally affect eDNA degradation. To address this, a systematic review and meta-analysis was conducted on aquatic eDNA studies. Analysis revealed eDNA decayed faster at higher temperatures and in marine environments (as opposed to freshwater). DNA type (mitochondrial or nuclear) and fragment length did not affect eDNA decay rate, although a preference for < 200 bp sequences in the available literature means this relationship was not assessed with longer sequences (<em>e.g.</em> > 800 bp). At present, factors such as ultraviolet light, pH, and microbial load lacked sufficient studies to feature in the meta-analysis. Moving forward, we advocate researching these factors to further refine our understanding of eDNA decay in aquatic environments. This dryad entry contains the metadata which formed the basis for the meta-analysis.</p>
YB1 dephosphorylation attenuates atherosclerosis by promoting CCL2 mRNA decay
<p> Y-box binding protein 1 (YB1) is an RNA binding protein (RBP) that has been reported to play important roles in inflammation and atherosclerotic plaque formation. To explore the molecular mechanism of phosphorylated YB1 (pYB1) in atherosclerosis <em>in vitro</em>, we constructed YB1 phosphorylation site (Ser-100) mutant stable smooth muscle cell line (3ds-V5) and performed RNA profile screening through RNA-seq. We found inflammatory pathways and CCL2 was significantly decreased in the 3dS-V5 YB1 mutant compared with the control group (YB1-V5).</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
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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.