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50 results for “mass dynamics”
Consequences of non-random tree species loss on litter mass loss, nutrient dynamics, carbon cycling, and decomposer communities across a terrestrial-aquatic interface at Coweeta Hydrologic Lab, Otto, NC
Although litter decomposition is a fundamental ecological process, most of our understanding comes from studies of single-species decay. Recently, litter-mixing studies have tested whether monoculture data can be applied to mixed-litter systems. These studies have mainly attempted to detect non-additive effects of litter mixing, which address potential consequences of random species loss. The focus is not on which species are lost, but the decline in diversity per se. Under global change, species loss is likely to be non-random, with some species more vulnerable to extinction than others. Under such scenarios, the effects of individual species (additivity) as well as of species interactions (non-additivity) on decomposition rates are of interest. To examine potential impacts of non-random species loss on ecosystems, we studied additive and non-additive effects of litter mixing on decomposition. A full-factorial litterbag experiment was conducted using four deciduous leaf species, from which mass loss and nitrogen content were measured. Data were analysed using a statistical approach that first looks for additive identity effects based on the presence or absence of species and then significant species interactions occurring beyond those. It partitions non-additive effects into those caused by richness and or composition.
Eulerian and Lagrangian diagnostics of the dynamical properties of the water masses sampled during the Tara Pacific Expedition 2016-2018
<p>In order to provide a description of the dynamical properties of the water masses sampled, different Eulerian and Lagrangian diagnostics were calculated. </p> <p>For each of the 246 stations sampled, we proceeded as follows.</p> <p>We identified the water mass sampled at the given station. This was considered as a stadium shape with the two semi-circles centered on the starting and ending points of the transect, respectively. The radius of the stadium semi-circles was considered 0.1°, which is in accordance with previous studies25,29,30. The stadium was filled with virtual particles separated by 0.01°.</p> <p>For each virtual particle inside the stadium shape, we calculated an Eulerian or Lagrangian diagnostic (described above). The Eulerian diagnostics were extracted directly from the velocity field of the day of sampling. Concerning the Lagrangian diagnostics, these were obtained by advecting the virtual particle backward in time for an amount of time 𝞽 from the day of sampling day_S. For the Lagrangian betweenness, the advection was performed between day_S+𝞽/2 and day_S-𝞽/2, so that the advective time window was centered on the sampling day (details in25).</p> <p>For the Lagrangian diagnostics, we used the following advective times 𝞽: 5, 10, 15, 20, 30, and 60 days. The only exception is the retention time, which, by construction, was calculated only with the largest advective time, namely 𝞽=60 days.</p> <p>Once that, a given diagnostic (Eulerian or Lagrangian) was calculated for all the virtual particles filling the stadium shape, we calculated the mean value, and the 25, 50, and 75 percentiles. The percentiles were calculated in order to quantify the spatial variation of the diagnostic inside the stadium shape. Therefore, we associated each station with four values (mean, 25, 50, and 75 percentiles) of a given diagnostic.</p> <p> Furthermore, two different velocity fields were used, which are described as follows. </p> <p>Both the velocity fields were downloaded from E.U. Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). The first velocity field used was MULTIOBS_GLO_PHY_REP_015_004 [GlobEkmanDt]. This was produced by combining the altimetry derived geostrophic velocities and modeled Ekman surface currents. It had a spatial resolution of 0.25° and a temporal resolution of one day. The second velocity field was GLOBAL_REANALYSIS_PHY_001_030 [GloryS12]. It was obtained by a NEMO model assimilating altimetry and other observations. It had a spatial resolution of 1/12° and a temporal resolution of 1 day.</p> <p>The following Eulerian diagnostics were calculated:</p> <ul> <li> <p>Absolute velocity ([Uabs], m s-1): sqrt(u2+v2), where u and v are the zonal and meridional components of the horizontal velocity field used (described below)</p> </li> <li> <p>Kinetic energy ([Ekin], m2 .s-2): 0.5*(u2+v2)</p> </li> <li> <p>Divergence ([EulerDiverg], d-1): du/dx + dv/dy</p> </li> <li> <p>Vorticity ([Vorticity], d-1): dv/dx - du/dy</p> </li> <li> <p>Okubo-Weiss ([OW], d-2): s2-vorticity2, where s2 is (du/dx-dv/dy)2 + (dv/dx+du/dy)2. If negative, it indicates that the station sampled was inside an eddy.</p> </li> </ul> <p>The following Lagrangian diagnostics were calculated:</p> <ul> <li> <p>Finite-Time Lyapunov Exponents ([Ftle], d-1): it indicates the rate of horizontal stirring, and it is a means to quantify the intensity of turbulence in a given region. FTLE are commonly used to identify Lagrangian Coherent Structures, i.e. barriers to transport. In this case, a strong FTLE value indicates a region separating water masses which were far away backward in time.</p> </li> <li> <p>Lagrangian betweenness ([betw], adimensional): this diagnostic draws inspiration from Lagrangian Flow Network Theory26. It can identify regions which act as bottlenecks for the circulation, in that they receive waters coming from different origins, and that are then spread over several different destinations. These can represent possible hotspots driving biodiversity25.</p> </li> <li> <p>Lagrangian Divergence ([LagrDiverg], d-1). This diagnostic was calculated by integrating the Eulerian divergence along the backward trajectories. If positive, it indicates a water mass that, during the previous days, was subjected to a strong divergence, thus to a possible upwelling. If negative, it indicates a strong convergence, thus possible downwelling.</p> </li> <li> <p>Retention Time ([RetentionTime], d). This diagnostic indicates how many days a water mass has spent inside an eddy in the previous period. If the water mass is outside an eddy, then its retention time is set to zero.</p> </li> </ul>
Lipid center-of-mass trajectory: long-time dynamics
<p>This file contains the center-of-mass coordinates of the lipid molecules in a Molecular Dynamics simulation of a hydrated lipid bilayer. The simulated system consists of 2033 POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) molecules and 57952 water beads (equivalent to 231808 water molecules), using the coarse-grained representation of the MARTINI force field. Note that the mass of a POPC molecule is significantly higher (936 g/mol) in this representation than the mass of a real POPC molecule (760 g/mol). The simulation was performed in an NVT ensemble at T = 320 K.</p> <p>This trajectory stores the long-time dynamics of the lipid centers of masses, sampled at a time step of 18 ps up to a total length of 600 ns. The short-time dynamics are available at http://dx.doi.org/10.5281/zenodo.61742.</p> <p>For a detailed description of the simulation, see the thesis of Sławomir Stachura, available at http://www.theses.fr/2014PA066239</p> <p>Two analyses of this simulation have already been published:</p> <ul> <li>S. Stachura and G.R. Kneller Anomalous lateral diffusion in lipid bilayers observed by molecular dynamics simulations with atomistic and coarse-grained force fields Mol. Sim. 40, 245-250 (2014) (http://dx.doi.org/10.1080/08927022.2013.840902)</li> <li>S. Stachura and G.R. Kneller Probing anomalous diffusion in frequency space J. Chem. Phys. 143, 191103 (2015) (http://dx.doi.org/10.1063/1.4936129)</li> </ul> <p>This work was funded by the French Agence Nationale de la Recherche (Contract No. ANR- 2010-COSI-01-001).</p> <p><strong>Trajectory data</strong></p> <p>The trajectory is stored in an HDF5 file that uses the ActivePapers conventions (http://www.activepapers.org/). Any HDF5-compatible software can be used to read the trajectory data. The ActivePapers software is only required to re-use the included conversion script.</p> <p>The trajectory is contained in the group<br /> /data/POPC_martini_nvt</p> <p>It is stored in H5MD/MOSAIC format. The positions and time labels are contained in the following datasets:<br /> /data/POPC_martini_nvt/particles/universe/position/value<br /> /data/POPC_martini_nvt/particles/universe/position/time</p> <p>For a complete specification of the H5MD/MOSAIC format, see:<br /> http://nongnu.org/h5md/index.html<br /> http://mosaic-data-model.github.io/mosaic-specification/h5md_mosaic_module.html</p> <p><strong>Plots</strong></p> <p>Two 3D plots are provided to give an overview of the lipid motions:<br /> /documentation/all_lipids.pdf<br /> shows all the lipid center-of-mass positions once every 0.3 ps.<br /> /documentation/one_lipids.pdf<br /> shows a single lipid center of mass position every 10 ps.</p>
Lipid center-of-mass trajectory: short-time dynamics
<p>This file contains the center-of-mass coordinates of the lipid molecules in a Molecular Dynamics simulation of a hydrated lipid bilayer. The simulated system consists of 2033 POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) molecules and 57952 water beads (equivalent to 231808 water molecules), using the coarse-grained representation of the MARTINI force field. Note that the mass of a POPC molecule is significantly higher (936 g/mol) in this representation than the mass of a real POPC molecule (760 g/mol). The simulation was performed in an NVT ensemble at T = 320 K.</p> <p>This trajectory stores the short-time dynamics of the lipid centers of masses, sampled at a time step of 0.03 ps up to a total length of 300 ps. The long-time dynamics are available at http://dx.doi.org/10.5281/zenodo.61743.</p> <p>For a detailed description of the simulation, see the thesis of Sławomir Stachura, available at http://www.theses.fr/2014PA066239</p> <p>Two analyses of this simulation have already been published:</p> <ul> <li>S. Stachura and G.R. Kneller Anomalous lateral diffusion in lipid bilayers observed by molecular dynamics simulations with atomistic and coarse-grained force fields Mol. Sim. 40, 245-250 (2014) (http://dx.doi.org/10.1080/08927022.2013.840902)</li> <li>S. Stachura and G.R. Kneller Probing anomalous diffusion in frequency space J. Chem. Phys. 143, 191103 (2015) (http://dx.doi.org/10.1063/1.4936129)</li> </ul> <p>This work was funded by the French Agence Nationale de la Recherche (Contract No. ANR- 2010-COSI-01-001).</p> <p><strong>Trajectory data</strong></p> <p>The trajectory is stored in an HDF5 file that uses the ActivePapers conventions (http://www.activepapers.org/). Any HDF5-compatible software can be used to read the trajectory data. The ActivePapers software is only required to re-use the included conversion script.</p> <p>The trajectory is contained in the group<br /> /data/POPC_martini_nvt</p> <p>It is stored in H5MD/MOSAIC format. The positions and time labels are contained in the following datasets:<br /> /data/POPC_martini_nvt/particles/universe/position/value<br /> /data/POPC_martini_nvt/particles/universe/position/time</p> <p>For a complete specification of the H5MD/MOSAIC format, see:<br /> http://nongnu.org/h5md/index.html<br /> http://mosaic-data-model.github.io/mosaic-specification/h5md_mosaic_module.html</p> <p><strong>Plots</strong></p> <p>Two 3D plots are provided to give an overview of the lipid motions:<br /> /documentation/all_lipids.pdf<br /> shows all the lipid center-of-mass positions once every 3 ps.<br /> /documentation/one_lipids.pdf<br /> shows a single lipid center of mass position every 0.3 ps.</p>
Fig. 1 in The impact of mass-flowering crops on bee pathogen dynamics
Fig. 1. Chronological trend (1961–2019) of the global land area (ha) used for crops; red: almonds; ochre: rapeseed; green: seed cotton; light blue: soybeans; dark blue: sunflower; purple: the sum of 5 MFCs with biggest global land area used in 2019 (see Table 1); data from the Food and Agriculture Organization of the United Nations (2021). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Large mass hierarchies from strongly-coupled dynamics—Data release
<p>This release contains data associated with the publication <a href="https://arxiv.org/abs/1605.04258">Large mass hierarchies from strongly-coupled dynamics</a> (<a href="https://doi.org/10.1007/JHEP06(2016)114">JHEP 06 (2016) 114</a>)</p> <p>It comprises four files:</p> <ul> <li><code>README.md</code>: Containing this information, and a more detailed description of the dataset.</li> <li><code>Fig4.csv</code>: the data shown in Figure 4 of <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>. The mass $M$ of composite spin-0 and spin-2 states, and their excitations, computed for $c_1 = 0 = A_0$, as a function of $\Delta$, for $r_{UV} = 25$ and $r_{IR} = 10^{-6}$, normalised to the mass $M_0$ of the lightest scalar.</li> <li><code>Fig5.csv</code>: the data shown in Figure 5 of <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>. The mass $M$ of the composite spin-0 and spin-2 states, computed for $c_1 = 0 = A_0$, as a function of $-A(r_{IR}) = \log (\Lambda_0 / \Lambda_{IR})$, for $\Delta = 1.5$, normalised to the mass $M_T$ of the lightest tensor. $A(r_{UV}) - A(r_{IR})$ is fixed to 8.</li> <li><code>DataRelease.nb</code>: A Mathematica notebook that will take the above two files and generate plots similar to those shown in <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>.</li> </ul> <p> </p>
Data from: Mass extinctions alter extinction and origination dynamics with respect to body size
<p>Whether mass extinctions and their associated recoveries represent an intensification of background extinction and origination dynamics versus a separate macroevolutionary regime remains a central debate in evolutionary biology. Previous focus has been on extinction, but origination dynamics may be equally or more important for long-term evolutionary outcomes. The evolution of animal body size is an ideal process to test for differences in macroevolutionary regimes, as body size is easily determined, comparable across distantly related taxa, and scales with organismal traits. Here, we test for shifts in selectivity between background intervals and the "Big Five" mass extinction events using capture-mark-recapture models. Our body-size data cover 10,203 fossil marine animal genera spanning 10 Linnaean classes with occurrences ranging from Early Ordovician to Late Pleistocene (485–1 Mya). Most classes exhibit differences in both origination and extinction selectivity between background intervals and mass extinctions, with the direction of selectivity varying among classes and overall exhibiting stronger selectivity during origination after mass extinction than extinction during mass extinction. Thus, not only do mass extinction events shift the marine biosphere into a new macroevolutionary regime, the dynamics of recovery from mass extinction also appear to play an underappreciated role in shaping the biosphere in their aftermath.</p>
Large mass hierarchies from strongly-coupled dynamics---Recombined data release
<p>This release contains data associated with the publication <a href="https://arxiv.org/abs/1605.04258">Large mass hierarchies from strongly-coupled dynamics</a> (<a href="https://doi.org/10.1007/JHEP06(2016)114">JHEP 06 (2016) 114</a>).</p> <p>Compared to <a href="https://doi.org/10.5281/zenodo.13128485">the raw data release</a>, it makes the following changes:</p> <ul> <li>The two CSV files present in <a href="https://doi.org/10.5281/zenodo.13128485">the raw data release</a> are combined, with a common column schema. Since not all data were retained from all steps of the original computation, some of these data have been recomputed.</li> <li>The lattice data plotted in Figs. 2 and 3 of <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a> are also included, and plotted by the Mathematica notebook.</li> </ul> <p>It comprises five files:</p> <ul> <li><code>README.md</code>: containing this summary and further details of the contents of each file.</li> <li><code>su2_adjoint_lattice_data.csv</code>: the data for the points plotted in Figures 2 and 3 of <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>. Masses of the scalar and tensor glueball, extracted from Monte Carlo ensembles of SU(2) with adjoint fermions.</li> <li><code>sigmamodel.csv</code>: the data shown in Figures 4 and 5 of <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>. The mass of the composite spin-0 and spin-2 states, computed for , as a function of , normalised to the mass of the lightest scalar or tensor.</li> <li><code>DataRelease.nb</code>: A Mathematica notebook that will take the above three files and generate plots similar to thsoe shown in <a href="https://doi.org/10.1007/JHEP06(2016)114">the paper</a>. This has been tested using Mathematica 14.0.</li> <li><code>unify_lmh_2016.zip</code>: A Snakemake and Python workflow to take the data from <a href="https://doi.org/10.5281/zenodo.13128485">the raw data release</a>, some data released as part of <a href="https://doi.org/10.5281/zenodo.12802810">the analysis workflow for arXiv:2408.00171</a>, and some previously unpublished data included in the archive, and generate the two CSV files above. Includes a <code>README.md</code> file containing more information about how to run the workflow.</li> </ul>
Fig. 3 in Diversity dynamics of Early-Middle Jurassic brachiopods of Caucasus, and the Pliensbachian-Toarcian mass extinction
Fig. 3. Total species diversity changes, origination and extinction rates of NW Caucasus brachiopods in Early–Middle Jurassic.
Data from: Mass extinctions alter extinction and origination dynamics with respect to body size
Open the record for dataset details and reuse information.
Supplementary Movies: "Probing the dynamics of Streptococcus pyogenes Cas9 endonuclease bound to sgRNA complex using hydrogen-deuterium exchange mass spectrometry"
<p>Movies related to the article "Probing the dynamics of Streptococcus pyogenes Cas9 endonuclease bound to sgRNA complex using hydrogen-deuterium exchange mass spectrometry" in the International Journal of Molecular Science. "MD_Movie_Cas9_sgRNA_DNA" is the video of SpCas9-sgRNA-DNA complex behavior during 50 ns molecular dynamics simulation. In this movie, SpCas9 protein domains are shown in the following colors: REC lobe (green), CTD (blue), RuvC (pink), L-I-II (yellow), Arg (violet), and HNH (orange). RNA presented in cyan, and DNA -in dark blue.</p> <p>Movies "SpCas9_HeatMap" and "SpCas9-sgRNA_HeatMap" show the hydrogen exchange levels superimposed onto the protein structure obtained from MD trajectories. Relative uptake level presented at the time points of 10 s, 30 s, 1 min, 2 min, 5 min, 10 min, 30 min, 60 min, 120 min, 240 min, 360 min, and 480 min. The exchange scale is shown in a rainbow color scheme, where blue corresponds to the minimum uptake, while red corresponds to the highest observed uptake.</p>
Ocean Gateways and Ocean Circulation Dynamics: Unveiling the Deep Water-Mass properties in the Western Equatorial Pacific and Eastern Indian Ocean since the middle
<p>File contains a dataset related to census counts and stable isotopes that we used to write our manuscript.</p>
Effects of endurance flight on mitochondrial physiology, lean mass dynamics and flight muscle morphology in blackpoll warblers
Open the record for dataset details and reuse information.
Impacts of urbanization and nutrient fertilization on mass loss and nutrient dynamics during photodegradation of plant litter
To understand the potential impacts of N deposition on mass loss and chemical dynamics during aridland decomposition, we assessed N and phosphorus (P) dynamics of decomposing litter in a long-term N+P enrichment experiment in both urban (with N deposition) and outlying areas (without N deposition) of the Sonoran Desert. Litter was decomposed with and without UV radiation for 9 months, measuring mass loss, litter chemistry, and bacterial biomass. UV radiation significantly accelerated mass loss and altered N and P dynamics, and there was an impact of the urban environment and experimental fertilization on nutrient dynamics. Overall, these patterns suggest that the aridland urban environment, where rates of N deposition are elevated, alters nutrient dynamics during decomposition but not the fraction of litter lost to photodegradation.
Substrate mass, nitrogen, extracellular enzyme, and microbial biomass and stoichiometry dynamics: Nitrogen limitation in decomposition
Modern agriculture and fossil fuel combustion contribute to the transfer of N from largely inert pools (atmospheric N2, fossil fuel reserves) to biologically reactive forms that can be transported downwind from agricultural or industrial areas to ecosystems that historically may have experienced low levels of N inputs. Understanding how increased N inputs alter the cycling of another biologically important element, C, has been impeded by uncertainties about N effects on the process of decomposition. To date, ecologists remain unable to predict when, where, and in what forms N addition stimulates rates of decomposition. For example, recent work showed that in eight low-N sites in Central Minnesota, litter N was positively correlated with decomposition, suggesting N limitation of decomposition, yet addition of inorganic N fertilizer increased decomposition in only two of eight sites. These paradoxical results call into question the assumption that the often-observed correlation between substrate N concentration and decomposition arises because N limits decomposition. Research is addressing three interrelated questions:* (1) Why do litter N and externally supplied N have contrasting effects on decomposition in low-N ecosystems? (2) Do different forms of N (organic vs. inorganic; substrate vs. externally supplied) affect the activity, function and composition of the decomposer community differently, and, if so, what are the consequences for decomposition? (3) What are temporal dynamics of the activity, function, and composition of the decomposer community and do these dynamics depend upon the amount and forms of N supplied to the decomposer community?* These questions will be addressed using a 4-y decomposition experiment manipulating the quantity and form of N available to decomposers via use of substrates ranging in N concentrations and of inorganic (ammonium nitrate) and organic (amino acids) N fertilizers. The response of microbial biomass, stoichiometry, efficiency
Data from: Microbe biogeography tracks water masses in a dynamic oceanic frontal system
PLEASE NOTE, THESE DATA ARE ALSO REFERRED TO IN ANOTHER PUBLICATION. PLEASE SEE http://dx.doi.org/10.1098/rsos.160829. Dispersal limitation, not just environmental selection, plays an important role in microbial biogeography. The distance–decay relationship is thought to be weak in habitats where dispersal is high, such as in the pelagic environment, where ocean currents facilitate microbial dispersal. Most studies of microbial community composition to date have observed little geographical heterogeneity on a regional scale (100 km). We present a study of microbial communities across a dynamic frontal zone in the southwest Indian Ocean and investigate the spatial structure of the microbes with respect to the different water masses separated by these fronts. We collected 153 samples of free-living microorganisms from five seamounts located along a gradient from subtropical to subantarctic waters and across three depth layers: (i) the sub-surface chlorophyll maximum (approx. 40 m), (ii) the bottom of the euphotic zone (approx. 200 m), and (iii) the benthic boundary layer (300–2000 m). Diversity and abundance of microbial operational taxonomic units (OTUs) were assessed by amplification and sequencing of the 16S rRNA gene on an Illumina MiSeq platform. Multivariate analyses showed that microbial communities were structured more strongly by depth than by latitude, with similar phyla occurring within each depth stratum across seamounts. The deep layer was homogeneous across the entire survey area, corresponding to the spread of Antarctic intermediate water. However, within both the sub-surface layer and the intermediate depth stratum there was evidence for OTU turnover across fronts. The microbiome of these layers appears to be divided into three distinct biological regimes corresponding to the subantarctic surface water, the convergence zone and subtropical. We show that microbial biogeography across depth and latitudinal gradients is linked to the water masses the microbes persist in, resulting in regional patterns of microbial biogeography that correspond to the regional scale physical oceanography.
Dynamics of island mass effect: detection input & output data
<p>"dynamic_IMEdetection.zip" contains a dynamic island mass effect detection software package.</p> <p>To run:</p> <ul> <li>Unzip "dynamic_IMEdetection.zip"</li> <li>Download input data</li> <li>change path, region to process, and options in "main_dynamicIME_detection.m" to work with the location of the unzipped "dynamic_IME_input_output_data" directory.</li> <li>Run "main_dynamicIME_detection.m"</li> </ul> <p>"dynamic_IME_input_output_data.zip" contains the input data and ouput IME and BO masks of the "dynamic_IMEdetection" software for four case studies:</p> <ul> <li>I04 = Rapa Nui</li> <li>I07 = Society Islands</li> <li>I09 = Samoa</li> <li>I18 = Fiji-Tonga</li> </ul> <p>Input data are custom multi-satellite products computed using the custom-made satellite binning software found here: <a href="../doi/10.5281/zenodo.13376824">10.5281/zenodo.13376824</a>.</p> <p>Output IME and BO masks are computed using the dynamic IME detection software (i.e. "dynamic_IMEdetection") which works with the original naming (i.e. "I04", "I07", "I09", and "I18") and subdirectory path of of the share data "dynamic_IME_input_output_data".</p> <p>Output IME/BO mask, chlorophyll concentration, and modelled surface current timeseries in *.gif format:</p> <ul> <li>RapaNui_8Dbin_20160520-0712_20161205-2230_map-time-series.gif = Rapa Nui between 2016/05/20 and 2016/12/05</li> <li>SocietyIsl_8Dbin_20160622-1012_20170209-0945_map-time-series.gif = Society Islands between 2016/06/22 and 2017/02/09</li> <li>Samoa_8Dbin_20160810-1115_20170226-1030_map-time-series.gif = Samoa between 2016/08/10 and 2017/02/26</li> <li>FijiTonga_8Dbin_20170218-1240_20170906-1205_map-time-series.gif = Fiji - Tonga between 2017/02/18 and 2017/09/06</li> </ul>
SUPPORTING INFORMATION FOR: Stochastic dynamic mass spectrometric 3D structural analysis of caffeine metabolites
<p>Supporting information for the entitled contribution.</p><p>It contains:</p><p>Static quantum chemical and high accuracy molecular dynamics computational data on protomers, tautomers, zwitterions, and isotopomers of caffeine (CAFF), paraxanthine (PARAXAN), theobromine (THEOBR), theophylline (THEOPH), and guanine (GUA), uric acid (UA), and xantine (XAN), as well as their derivatives.</p><p>The content includes data on characteristic parent and product ions of the analytes in ion mobility spectrometric and mass spectrometric experimental conditions. Tautomers, charge transfer processes, and intramolecular rearrangement; if any, are accounted for considering. </p><p>Molecular mechanics/molecular dynamics data are shown as *.txt files. </p><p>High accuracy molecular dynamics includes adiabatic computations using Born-Oppenheimer approach.</p><p>High accuracy static ground state and transition state computations use M062X/SDD level of theory. </p><p>Figures in color, illustrating the entitled contribution shown as *.pdf files.</p><p>The experimental ion mobility spectrometry and mass spectrometry data are according to reference [1].</p><p>[1] H. Sepman, A. Kruve, S. Tshepelevitsh, H. Hupatz, Experimental IMS and MS/MS data of caffeine metabolites (2022). Zenodo, [https://doi.org/10.5281/zenodo.6637393][ https://zenodo.org/record/6637393] (Accessible for 04.04.2022.)</p><p>They have been used and processed via the following software:</p><p>[2] ProteoWizard 3.0.11565.0 (2017) [https://proteowizard.sourceforge.io/download.html];</p><p>[3] mMass 5.0.0 [http://www.mmass.org/download/old.php];</p><p>[4] AMDIS 2.71 (2012) software [https://chemdata.nist.gov/mass-spc/amdis/downloads/AMDIS_Installer-17.zip]; and</p><p>[5] NIST Search Software 2.0 [https://chemdata.nist.gov/dokuwiki/lib/exe/fetch.php?media=chemdata:nist17:nist17demo.zip], respectively.</p><p> </p><p> </p>
Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning
<p>In this repository we share processed data that were generated using the bioinformatics framework we developed in publication "Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning".</p> <p>These datasets accompany the source codes provided in our GitHub page https://github.com/dkleftogi/singleCellClassification. </p> <p>The datasets are as follows:</p> <ol> <li>cofactors_v2.RDa : antibody-specific co-factors used to harmonise fcs files from different batches</li> <li>ctrl_annotated.RDa : the annotated cohort of seven healthy donors</li> <li>data_umap.RDa : UMAP representation of the data used to generate the figures in our paper</li> <li>DREMI_feature_matrix.RDa : the DREMI feature matrix used for ML-based modelling presented in our paper</li> <li>median_feature_matrix.RDa : the baseline feature matrix based on medians used for ML-bases modelling in the paper</li> <li>patient_annotated.RDa : the annotated cohort of leukemia patients (n=43)</li> </ol> <p>We note that the raw files of the leukemia cohort can be found in http://flowrepository.org/id/RvFr0LLv9McDJ89jgK50G4lwnfDFRTrcMelxYgnSIcE2Cymrpf2qh2NaWybtWDNH</p> <p> </p>
Coping with seasonality: dynamics of adult body mass and survival in an alpine hibernator
<p><span>Alpine mammals are highly vulnerable to current and projected climate change because they are confined to a certain elevation range. Physiological and behavioural adaptations in burrowing species, such as finding shelter in burrows when the summer conditions are unfavorable and hibernating in winter during the stressful period of resource shortage, could partly buffer the negative impacts of these forecasted changes. We studied the links between environmental factors and annual variations in adult mass and survival over 14 years in hoary marmots. We hypothesized that annual variation in seasonal environmental factors determines individual mass and survival through direct effects on food quality and availability, expecting greater survival when marmots reach higher mass before hibernation. We found that harsh winters decreased mass at emergence from hibernation by 47% compared with mild winters. Nonetheless, adult marmots had a greater mass gain in summers following harsh winters and reached a similar mass at the end of the summer compared with summers following mild winters. This result suggests individuals can adopt a resource allocation strategy that allows maximizing summer mass gain to survive hibernation. Earlier springs also increased summer mass gain by 15 g/day, and tended to increase apparent adult survival by 23%, compared with late springs. While these findings suggest a warming climate could have positive effects on summer mass gain and survival, survival also tended to decrease by 24% in summers with more precipitation. This result suggests the forecasted changes in precipitation extremes could also trigger considerable</span><span> </span><span>negative effects on the demography of burrowing species in the long term. Our study shows</span><span> </span><span>that, although burrowing and hibernating behaviours could buffer responses to</span><span> </span><span>environmental changes, these behaviours are not an indefectible shield against climate</span><span> </span><span>change.</span></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.