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11,506 results for “Dynamics”
Tracking wildlife energy dynamics with unoccupied aircraft systems and 3-dimensional photogrammetry
<p>We present a novel application using unoccupied aircraft systems (UAS; drones) for structure-from-motion three-dimensional (3-D) photogrammetry of multiple, free-ranging animals simultaneously. Pinnipeds reliably haul-out on shore for pupping and breeding each year, accompanied by dramatic female-to-pup mass transfer over a short lactation period and males lose mass while defending mating territories. This provides a tractable study system for validating the use of UAS as a non-invasive tool for tracking energy dynamics in wild populations.</p> <p>UAS imagery of grey seals (<i>Halichoerus grypus</i>) was collected at Saddle Island, Nova Scotia. A multirotor UAS was piloted in 360-degree orbits around relatively dense animal aggregations and georeferenced images were used for construction of a 3-D point cloud, orthomosaic, and Digital Surface Model for animal volumetric measurements. Directly following UAS survey, a subset of adult females were hand-measured (morphometrics, blubber depth, n=21 handlings [15 were unique animals]) and female-pup pairs were weighed (adult females: n=32 [24]; pups: n=33 [23]) to validate that UAS 3-D photogrammetric models provided accurate animal volume and mass estimates.</p> <p>UAS two-dimensional body length measurements were sensitive to animal recumbency and posture. The new UAS 3-D photogrammetric method overcame these constraints, and aerial-derived body volume measurements were equivalent to those collected from the ground. UAS body volume measurements precisely predicted 'true' body mass (mean-absolute-error, adult female: 8 kg, 2.1% body mass; pup: 4.1 kg, 9.8%), and exhibited a stronger relationship with total body mass than with blubber volume.</p> <p>The method was applied to 673 free-ranging animals to characterize volume and mass dynamics across lactation and breeding for a much larger sample size than would be possible using traditional ground methods. Indeed, 1-46 animals (mean±SE: 9.2±1.2) were modeled concurrently within the focal area of a UAS flight. Application of the method also captured significant inter-annual variation in body volume/mass dynamics, and female-to-pup energy transfer efficiencies were lower when there was low sea-ice extent. The UAS 3-D photogrammetric method presented in this study is likely to be broadly applicable to other species, and the ability to measure whole groups of free-ranging animals at once makes strides towards 'weighing populations'.</p>
A non-native earthworm shifts the seed predation dynamics of a native weed
<p class="BodyAA"><span>Seed predators both consume and disperse seeds, with important consequences for the population dynamics of many plant species. The net effect of multiple seed predators depends on the relative proportion of the seed pool each predator obtains, and this proportion should reflect species-specific habitat preferences. We studied the effect of the non-native earthworm, <i>Lumbricus terrestris</i>, on seed loss dynamics in the native weed, <i>Ambrosia trifida</i> (giant ragweed)<i>. </i>Giant ragweed seeds are predated by mice, but <i>L. terrestris</i> may protect the seeds against rodent predation by caching them in its burrows. We investigated these interactions, as well as how environmental factors affected net seed losses by competing seed predators. </span></p> <p class="Default">A two-year field study was conducted in which we measured removal of experimentally dispersed giant ragweed seeds by earthworms and mice in habitats varying in plant cover. We analyzed the relative proportion of seeds taken by each species under the varying experimental conditions. </p> <p class="Default">Species-specific responses to abiotic conditions and plant cover drove variation in the share of seeds taken by earthworms versus mice, with earthworms gaining relatively more seeds under warmer, wetter conditions and low plant cover habitats, and mice obtaining more seeds under colder, drier conditions and high plant cover habitats. </p> <p class="Default">Plant cover and weather conditions also determined which predator species accessed seeds first, and this conferred a competitive advantage that was compounded over time.</p> <p class="Default">Earthworms cached some seeds under all experimental conditions, suggesting that <i>L. terrestris </i>can<i> </i>act mutualistically with giant ragweed by making seeds inaccessible to rodent seed predators. </p> <p class="Default"><i>Synthesis and applications. </i>Our results support the view that interactions among the environment and competing seed predators determine the fate of seed pools. The data also support the hypothesis that <i>L. terrestris</i> facilitates giant ragweed by competing with mice for giant ragweed seeds, likely contributing to its spread across the landscape and hindering effective weed management. <i>Lumbricus terrestris </i>is prevalent throughout temperate regions and may similarly affect seed predation dynamics of other large-seeded species, impacting plant communities across a range of habitats.</p>
SeisSol input files of the dynamic rupture scenarios of the 2004 Sumatra-Andaman earthquake published in Ulrich et al. (2021)
<p>This dataset contains the input files of the dynamic rupture scenarios of the 2004 Sumatra-Andaman earthquake presented in:</p> <p>Ulrich, T., Gabriel, A. A., Madden, E. H. (2021). Stress, rigidity and sediment strength control megathrust earthquake and tsunami dynamics. doi: 10.31223/osf.io/s9263.<br> </p> <p><strong>supermucNG_launch_script.sh</strong>: batch script for running a dynamic rupture earthquake scenario on Supermuc NG (LRZ).<br> <br> <strong>parameters_base_slab2.par, parameters_stronger_slab2.par, parameters_weaker_slab2.par</strong>: main parameter file for the base (resp. stronger, resp. weaker sediments) scenario.<br> <br> <strong>Sumatra_material_base_slab2.yaml, Sumatra_material_stronger_slab2.yaml, Sumatra_material_weaker_slab2.yaml</strong>: easi/yaml files describing the rock elastic and visco-plastic properties for each scenario.<br> It calls <strong>Sumatra_rhomulambda.yaml</strong> for the rock elastic properties and <strong>Sumatra_initial_stress_slab2.yaml</strong> for the stress tensor spatial variations.<br> <strong>Sumatra_fault_slab2.yaml</strong>: easi/yaml file describing the spatially variable on-fault parameters for the 3 main scenarios.<br> <strong>lithostaticStress_gamma.yaml</strong>: easi/yaml file describing the variations with depth of the lithostatic pressure, and specifying the pore fluid pressure ratio.<br> <br> <strong>Sumatra_fault_slab2_1d.yaml, Sumatra_initial_stress_slab2_1d.yaml, Sumatra_material_base_slab2_1d.yaml</strong>: easi/yaml files specific to the alternative scenario, which adopts a 1D PREM velocity structure.<br> <strong>Sumatra_fault_slab2_novar.yaml, Sumatra_initial_stress_slab2_novar.yaml, Sumatra_material_base_slab2_novar.yaml</strong>: easi/yaml files specific to the alternative dynamic rupture earthquake scenario in which no regional prestress variations are considered.<br> <br> <strong>Sumatra_slab2_layers_fixed.xdmf, Sumatra_slab2_layers_fixed</strong>: mesh file.</p>
Modeling cyanobacteria life cycle dynamics and historical nitrogen fixation in the Baltic Proper - Datasets
<p>Observational and model datasets used in: Modeling cyanobacteria life cycle dynamics and historical nitrogen fixation in the Baltic Proper.</p>
Seasonal dynamics of sinking organic matter in the Pacific Arctic Ocean revealed by nitrogen isotope ratios of amino acids
<p><span>The Pacific Arctic Ocean has experienced a rapidly changing climate, sea-ice retreat, and enhanced primary production over the past few decades. The export production generated by photoautotrophs and heterotrophs has been characterized in the Arctic Ocean, but their seasonal variations in relative proportion are largely unknown due to the limited access in the ice-covered season. We measured the concentration and nitrogen isotope ratio of individual amino acids from sinking particles in the northern east Siberian Sea (KAMS1), northern Chukchi Sea (KAMS2), and Northwind Ridge (KAMS4) from August 2017 to July 2019. </span><span>The average trophic position, based on differences in the nitrogen isotope ratios of glutamic acid and phenylalanine, can indicate the relative proportions of biogenic organic matters derived from photoautotrophs and heterotrophs in sinking particles. Decreasing values (close to 1.0) in summer at KAMS2 in 2018 suggest that primary producers are responsible for most of the downward flux of sinking particles. However, the average trophic position at KAMS1 in 2017 increased to > 1.5 in autumn and was maintained at approximately 1.7 during ice-covered winter periods, likely due to greater contributions from heterotrophic organisms. Exceptionally high average trophic positions (close to 2.0) of sinking particles in summer at KAMS1 in 2017 and KAMS4 in 2018 were likely due to small export of photoautotrophs due to the surface seawater stratification and limited pelagic production. </span><span>T</span><span>he average trophic position in sinking particles should reflect the spatiotemporal variation in export particle composition in the </span><span>Pacific Arctic Ocean</span><span>.</span></p>
Dynamic prostate cancer transcriptome analysis delineates the trajectory to disease progression.
<p>This file contains vst-normalized gene expression data along with annotations which can be used to reproduce our findings.</p>
Vascular KATP channel structural dynamics reveal regulatory mechanism by Mg-nucleotides
<p>MD simulation data for vascular KATP channel focusing on Kir6.1-pore and SUR2B in the presence and absence of MgADP. </p>
Model data repository of "The role of sediment accretion and buoyancy on subduction dynamics and geometry"
<p>This dataset contains the code and data used in Brizzi et al. (2021): The role of sediment accretion and buoyancy on subduction dynamics and geometry</p>
Drivers and Determinants of Strain Dynamics Following Faecal Microbiota Transplantation
<p>Faecal microbiota transplantation (FMT) is an efficacious therapeutic intervention, but its clinical mode of action and underlying microbiome dynamics remain poorly understood. Here, we analysed the metagenomes associated with 142 FMTs, in a time series-based meta-study across five disease indications. We quantified strain-level dynamics of 1,089 microbial species based on their pangenome, complemented with 47,548 newly constructed metagenome-assembled genomes. Using subsets of procedural-, host- and microbiome-based variables, LASSO-regularised regression models accurately predicted the colonisation and resilience of donor and recipient microbes, as well as turnover of individual species. Linking this to putative ecological mechanisms, we found these sets of variables to be informative of the underlying processes that shape the post-FMT gut microbiome. Recipient factors and complementarity of donor and recipient microbiomes, encompassing entire communities to individual strains, were the main determinants of individual strain population dynamics, and mostly independent of clinical outcomes. Recipient community state and the degree of residual strain depletion provided a neutral baseline for donor strain colonisation success, in addition to inhibitive priority effects between species and conspecific strains, as well as putatively adaptive processes. Our results suggest promising tunable parameters to enhance donor flora colonisation or recipient flora displacement in clinical practice, towards the development of more targeted and personalised therapies.</p>
Scripts to perform dynamic optimization of a model of invasive aspergillosis
<p>Matlab scripts to perform optimization and analyses of a dynamic optimization model of invasive aspergillosis:<br> "Dynamic optimization reveals alveolar epithelial cells as key mediators of host defense in invasive aspergillosis"<br> Jan Ewald, Flora Rivieccio, Lukáš Radosa, Stefan Schuster, Axel A. Brakhage, Christoph Kaleta<br> https://doi.org/10.1101/2021.05.12.443764</p> <p>File structure:</p> <p>--- Core optimization files ---<br> -- simulation.cpp --<br> Description of ODE model in 'C', used by solver to calculate the time course given the parameters, initial states and control variables</p> <p>-- simulation.mexa64 --<br> Compiled mex-File from simulation.cpp used as interface by MATLAB to 'C' files.</p> <p>-- optFun.m --<br> Definition of the objective function. Uses a control vector 'u' to calculate the, objective function value F, the time course of state variables 'x' and gradients used by the solver to determine the optimal control of 'u'.</p> <p>-- single_opt.m --<br> Function which performs an optimization run for a given parameter set and a randomized initial solution u0. Optimization can be conducted by SNOPT or in conjunction with IPOPT. Both solver are not provided here (proprietary).</p> <p><br> --- Additional files ---<br> -- solve_with_ipopt.m --<br> Configuration file and interface to call the solver IPOPT.</p> <p>-- lognorm_mu.m --<br> Function to provide log-normal distributed samples given the standard deviation, reference value (mode of distribution) and number of samples which should be calculated.</p> <p>-- mice_param_alv.m --<br> Reference parameter set of the model for mice as host organism and cell numbers normalized per alveolus.</p> <p>-- parameter_model.m --<br> Definition of parameter container to handle interface to optFun-call by solvers which do not allow parameter in function call.</p> <p>--- Optimization Runs ---<br> -- rand_params.m --<br> Performs optimization runs for multiple randomized parameter sets and several scenarios to determine parameter sensitivity.</p> <p>-- doseresponse.m --<br> Performs optimization runs for a sequnce of initial conidial dosages to determine the dose response of the system.</p> <p>--- Scripts for post-analysis ---<br> -- rand_analysis.m --<br> Loads the data created by the optimization runs (rand_params.m) and performs parameter sensitivity analysis as well as produces plots for analysis related to parameter influence.</p> <p>-- doseanalysis.m --<br> Loads the data created by the optimization runs (doseresponse.m ) and produces dose response curves as well as additional plots and analyses.</p> <p>--- Optimization output files ---<br> -- rand_param_out/ --<br> Folder contains optimization results as MATLAB session-file for randomized parameters used for parameter sensitivity analysis.</p> <p>-- dose_out/ --<br> Folder contains optimization results as MATLAB session-file for dose response curve calculation.</p>
Dataset (VII) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>MD simulation data of compound <strong>1</strong> in MSM <strong>2-<em>S</em><sub>3</sub></strong> conformations of the related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
Dataset (VIII) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>MD simulation data of compound <strong>1</strong> in MSM <strong>2-<em>S</em><sub>3</sub></strong> conformations of the related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
Dataset (V) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>MD simulation data of <strong>SB203580</strong> related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
Early queen infection shapes developmental dynamics and induces long-term disease protection in incipient ant colonies
<p>Infections early in life can have enduring effects on an organism's development and immunity. In this study, we show that this equally applies to developing "superorganisms" – incipient social insect colonies. When we exposed newly mated <i>Lasius niger</i> ant queens to a low pathogen dose, their colonies grew more slowly than controls before winter, but reached similar sizes afterwards. Independent of exposure, queen hibernation survival improved when the ratio of pupae to workers was small. Queens that reared fewer pupae before worker emergence exhibited lower pathogen levels, indicating that high brood rearing efforts interfere with the ability of the queen's immune system to suppress pathogen proliferation. Early-life queen pathogen-exposure also improved the immunocompetence of her worker offspring, as demonstrated by challenging the workers to the same pathogen a year later. Transgenerational transfer of the queen's pathogen experience to her workforce can hence durably reduce the disease susceptibility of the whole superorganism.</p>
Flight altitude dynamics of migrating European nightjars across regions and seasons
<p>Avian migrants may fly at a range of altitudes, but usually concentrate near strata where a combination of flight conditions is favourable. The aerial environment can have a large impact on the performance of the migrant and is usually highly dynamic, making it beneficial for the bird to regularly check the flight conditions at alternative altitudes. We recorded the migrations between northern Europe and sub-Saharan Africa of European nightjars <em>Caprimulgus europaeus</em> to explore their altitudinal space use during spring and autumn flights to test whether their climbs and descents were performed according to predictions from flight mechanical theory. The nightjars commonly operated at ascent rates below the theoretical maximum, and periods of descent were commonly undertaken by active flight, and rarely by energetically cheaper gliding flight, allowing the birds to sink at lower rates than possible during a best glide scenario. Spring migration across all regions was associated with more exploratory vertical flights involving major climbs, a higher degree of vertical displacement within flights, and less time spent in level flight, although flight altitude <i>per se</i> was only higher during the Sahara crossing. This study demonstrates a surprisingly frequent use of slow vertical displacements during migratory flights of a long-distance avian migrant and show how these behaviours vary between seasons and regions, presumably in response to different flight conditions. The results should inspire future studies on the potential costs associated with frequent altitude changes and their trade-offs against anticipated flight condition improvements for aerial migrants.</p>
Daily ranging and den usage patterns structure fission-fusion dynamics and social associations in spotted hyenas
<p>Environment structure often shapes social interactions. Spatial attractors that draw multiple individuals may play a particularly important role in dispersed groups, where individuals must first encounter one another to interact. We use GPS data recorded simultaneously from five spotted hyenas (<i>Crocuta crocuta</i>) within a single clan to investigate how communal dens and daily ranging patterns shape fission-fusion dynamics (subgroup splits and merges). We introduce a species-general framework for identifying and characterizing dyadic fission-fusion events and describe a taxonomy of ten possible configurations of these events. Applying this framework to the hyena data illuminates the spatiotemporal structure of social interactions within hyenas' daily routines. The most common types of fission-fusion events involve close approaches between individuals, do not involve co-travel together, and occur at the communal den. Comparison to permutation-based reference models suggests that den usage structures broad-scale patterns of social encounters, but that other factors influence how those encounters unfold. We discuss the dual role of communal dens in hyenas as physical and social resources, and suggest that dens are an example of a general "social piggybacking" process whereby environmental attractors take on social importance as reliable places to encounter conspecifics, causing social and spatial processes to become fundamentally intertwined.</p>
Spatio-temporal dynamics of attacks around deaths of wolves: A statistical assessment of lethal control efficiency in France
<h1>Description</h1> <p>This repository contains the supplementary materials (<em>Supplementary_information.pdf</em>, <em>Supplementary_figures.pdf</em>, <em>Supplementary_tables.pdf</em>) of the manuscript: "<strong>Spatio-temporal dynamics of attacks around deaths of wolves: A statistical assessment of lethal control efficiency in France</strong>". This repository also provides the R codes and datasets necessary to run the analyses described in the manuscript. </p> <h1>Datasets</h1> <p>We provide the spatially anonymized R datasets to respect confidentiality. Therefore, the preliminary preparation of the data is not provided in the public codes. These datasets, all geolocated and necessary to the analyses, are:</p> <ul> <li><em>Attack_sf.RData</em>: 19,302 analyzed wolf attacks on sheep <br> - ID: unique ID of the attack <br> - DATE: date of the attack <br> - PASTURE: the related pasture ID from "Pasture_sf" where the attack is located <br> - STATUS: column resulting from the preparation and the attribution of attacks to pastures (part 2.2.4 of the manuscript); not shown here to respect confidentiality </li> <li><em>Pasture_sf.RData</em>: 4987 analyzed pastures grazed by sheep <br> - ID: unique ID of the pasture <br> - CODE: Official code in the pastoral census <br> - FLOCK_SIZE: maximum annual number of sheep grazing in the pasture <br> - USED_MONTHS: months for which the pasture is grazed by sheep </li> <li><em>Removal_sf.RData</em>: 232 analyzed single or multiple wolf removals <br> - ID: unique ID of the removal <br> - OVERLAP: are they single removal ("single" in the manuscript => "NO" here), or not ("multiple" in the manuscrit, here "SIMULTANEOUS" for removals occurring during the exact same operation or "NON-SIMULTANEOUS" if not). <br> - DATE_MIN: date of the single removal or date of the first removal of a group <br> - DATE_MAX: date of the single removal or date of the last removal of a group <br> - CLASS: administrative type of the removal according to definitions from 2.1 part of the manuscript <br> - SEX: sex of the removed wolves if known <br> - AGE: class age of the removed wolves if known <br> - BREEDER: breeding status of the removed female wolves, "Yes" for female breeder, "No" for female non-breeder. Males are "No" by default, when necropsied; dead individuals with NA were not found. <br> - SEASON: season of the removal, as defined in part 2.3.4 of the manuscript <br> - MASSIF: mountain range attributed to the removal, as defined in part 2.3.4 of the manuscript </li> <li><em>Mountain_range_buff_sf.RData</em>: one row for each mountain range, corresponding to the buffered mountain ranges where removal control events could be sampled, as defined in part 2.3.3 of the manuscript </li> <li><em>Area_to_exclude_sf.RData</em>: one row for each mountain range, corresponding to the area too close from other mountain ranges, terrestrial and maritime limits, where removal control events could not be sampled, as defined in part 2.3.3 of the manuscript </li> <li><em>Overlapping_removal_sf.RData</em> corresponds to the spatial dataset necessary to run the supplementary figures about overlapping removals (S9) </li> </ul> <p>You can also find the object called <em>Subset_lt.RData</em>, which gives the ID of removals for each dataset (single/multiple removals) or subsets of single removals. </p> <p>The other RData resulted from the analysis. How to read their names: </p> <ul> <li>First part: <br> - <em>Buffer</em>: they link the attacks to removals or of control events. <br> - <em>Kernel</em>: they give the results of the kernel density estimation (z), according to the spatial (y) and temporal (x) coordinates, for each dataset.<br> - <em>Int</em>: they give, for each distance and time, the total amount of attack intensities before (INT_BEF) and after (INT_AFT) the combined locations and days of removals or control events. 1 unit of distance = 50 meters, 1 unit of time = 1 day.<br> - <em>Trend</em>: they give, for each distance and time, the trends in % of the attack intensities when comparing before and after, with their uncertainties and significance. Files with <em>spatshift</em> are designed for figures about spatial shift (unnested scales), contrary to files without <em>spatshift</em> (nested scales). </li> <li>Middle parts: (noted with an X after) <br> - <em>obs</em>: results from removals <br> - <em>jack</em>: results from jackknife samples of the removals <br> - <em>ctl</em>: results related to control sets <br> - <em>cor</em>: results corrected for livestock presence<br> - <em>raw</em>: results uncorrected for livestock presence<br> - <em>sim</em>: results for simulated attacks according to livestock presence </li> </ul> <p>Lengths of lists or sublists correspond to: </p> <p>- 20 elements: the two main datasets (single/multiple) and the 18 subsets of single removals. <br>- 100 elements: the 100 control sets. <br>- 1000 elements: the 1000 simulations of attacks (for the livestock presence correction). <br>- Other lengths and <em>jack</em> within the name: the jackknife samples. </p> <h1>Structure of the repository</h1> <h2><strong>Code 1</strong>: file <em>Buffer.R</em> </h2> <p>We keep only removals within geographic zones for the analysis (<em>Removal_analyzed_sf</em>), and sample their control events (100 simulations = control sets, <em>Removal_ctl_sf_lt</em>).</p> <p>We start by delimiting the spatio-temporal buffer for each row of the removal and control datasets. <br> - We identify the attacks from <em>Attack_sf.RData</em> within each buffer, thanks to the function <em>Buffer_fn</em>, giving the data frames <em>Buffer_X_df</em> (one row per attack) <br> - We select the pastures from <em>Pasture_sf.RData</em> within each buffer, thanks to the function <em>Buffer_pasture_fn</em>, giving the data frames <em>Buffer_X_sf</em> (one row per removal or control event) </p> <p>We calculate the spatial correction: <br> - We spatially slice each buffer into 200 rings with the function <em>Ring_fn</em>, giving the data frame <em>Ring_sf</em> (one row per ring) <br> - We add the total pastoral area of the ring of the attack ("SPATIAL_WEIGHT") with the function <em>Spatial_correction_fn</em>, for each attack of each buffer, within <em>Buffer_X_df</em> </p> <p>We calculate the pastoral correction: <br> - We create the pastoral matrix for each removal or control event with the function <em>Pastoral_matrix_fn</em>, giving a matrix of 200 rows (one for each ring) and 180 columns (one for each day, 90 days before the removal date and 90 day after the removal date), with the total pastoral area in use by sheep for each corresponding cell of the matrix (one element per removal, <em>Pastoral_X_mx_lt.RData</em>) <br> - We simulate, for each removal or control event, the random distribution of the attacks from <em>Buffer_X_df.RData</em> according to <em>Pastoral_X_mx_lt.RData</em> with the function <em>Buffer_sim_fn</em>. The process is done 1000 times (one element per simulation, <em>Buffer_X_sim_lt.RData</em>). </p> <h2>Code 2: file <em>Kernel.R</em> where we estimate the attack intensities </h2> <p>We classified the removals into 2 main datasets and 18 subsets, according to part 2.3.4 of the manuscript (<em>Subset_lt.RData</em>) (one element per set).<br>We compute the jackknife samples for each dataset or subset (<em>Removal_id_jack_lt</em>). <br>We perform the kernel estimations with the function <em>Kernel_fn</em> (<em>Kernel_X_lt</em>). <br>We sum the intensities of attacks before and after the removals or control events, with the function <em>Intensity_fn</em>, giving <em>Int_X_df_lt.RData</em>. </p> <h2>Code 3: file <em>Trend.R</em> where we calculate the trends of attack intensities after removals </h2> <p> We focus on the nested trends first: <br> - We calculate them (<em>Trend_X_df</em>) with function <em>Trend_fn </em><br> - We set the result significance test by observing the overlapping of confidence intervals of removals and control sets (<em>Trend_X_comparison_df</em>) </p> <p>We focus on the spatial shifts (trends for each specific distance):<br> - We calculate them (<em>Trend_X_spatshift_df</em>) with function <em>Trend_fn</em> <br> - We set the result significance test by observing the overlapping of confidence intervals of removals and control sets (<em>Trend_X_comparison_spatshift_df</em>) </p> <h2>- Code 4: file <em>Functions.R</em> where can be found all custom functions called in the other analysis codes </h2> <h2>- Code 5: file <em>Figures.R</em> produces part of the figures from the manuscript</h2> <p>Detailed comments are included in each code.</p> <h1>Support</h1> <p>If you have any question or request, do not hesitate to contact us at: oksana.grente@gmail.com</p> <h1>Authors and acknowledgment</h1> <p>Grente Oksana (CEFE, CNRS), Opitz Thomas (INRAE), Duchamp Christophe (OFB), Drouet-Hoguet Nolwenn (OFB), Chamaillé-Jammes Simon (CEFE, CNRS) and Gimenez Olivier (CEFE, CNRS). </p> <h1>License</h1> <p>GNU GENERAL PUBLIC LICENSE 3.0</p> <p> </p>
Pseudoproxies for the paper "A pseudoproxy assessment of data assimilation for reconstructing the atmosphere–ocean dynamics of hydroclimate extremes"
<p>Pseudoproxies for the paper “A pseudoproxy assessment of data assimilation for reconstructing the atmosphere–ocean dynamics of hydroclimate extremes” by Steiger and Smerdon 2017.</p> <p>If you have further questions, address them to the author Nathan J. Steiger.</p>
Molecular Dynamics Simulation on the Effect of Transition Metal Binding to the N-Terminal Fragment of Amyloid-β
<p>Molecular dynamics trajectories in PDB format for 3x Cu-Aβ, 3x Zn-Aβ and 9x Fe-Aβ<strong> </strong>simulations.</p>
Relative cerebral flow from dynamic PIB scans as an alternative for FDG scans in Alzheimer's disease PET studies
<p>In Alzheimer’s Disease (AD) dual-tracer positron emission tomography (PET) studies with 2-[<sup>18</sup>F]-fluoro-2-deoxy-D-glucose (FDG) and <sup>11</sup>C-labelled Pittsburgh Compound B (PIB) are used to assess metabolism and cerebral amyloid-β deposition, respectively. Regional cerebral metabolism and blood flow (rCBF) are closely coupled, both providing an index for neuronal function. The present study compared PIB-derived rCBF, estimated by the ratio of tracer influx in target regions relative to reference region (<em>R</em><sub>1</sub>) and early-stage PIB uptake (ePIB), to FDG scans. Fifteen PIB positive (+) patients and fifteen PIB negative (-) subjects underwent both FDG and PIB PET scans to assess the use of <em>R</em><sub>1 </sub>and ePIB as a surrogate for FDG. First, subjects were classified based on visual inspection of the PIB PET images. Then, discriminative performance (PIB+ versus PIB-) of rCBF methods were compared to normalized regional FDG uptake. Strong positive correlations were found between analyses, suggesting that PIB-derived rCBF provides information that is closely related to what can be seen on FDG scans. Yet group related differences between method’s distributions were seen as well. Also, a better correlation with FDG was found for <em>R</em><sub>1</sub> than for ePIB. Further studies are needed to validate the use of <em>R</em><sub>1</sub> as an alternative for FDG studies in clinical applications.</p> <p>The enclosed dataset refers to the work developed at the University Medical Center Groningen and consists of all the data retrieved from the PET images and from clinical assessment used in this work.</p>
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