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122 results for “structural stability”
MCR LTER: Coral Reef: Asynchrony in coral community structure contributes to reef‑scale community stability, data for Srednick et al., Nature 2023
These data were generated in support of the manuscript: Srednick G, Davis K, and Edmunds P, Nature To evaluate whether spatial insurance effects are important on coral reefs, we explored variation over 2006–2019 in coral community structure and environmental conditions in Moorea, French Polynesia. We studied coral community structure at a single site with fringing, back reef, and fore reef habitats, and used this system to explore associations among community asynchrony, asynchrony of environmental conditions, and community stability. The daily range in seawater temperature among habitats suggests it could be a factor contributing to the variation in coral community structure. Wave-forced seawater flow facilitated larval exchange among connected habitats, differing in strength among years, and accentuated periodic connectivity among habitats at 1-7 year intervals. At this site, connected habitats harboring taxonomically similar coral assemblages and exhibiting asynchronous population dynamics can provide insurance against extirpation and may promote community stability. If these effects apply at larger spatial scale, then among-habitat community asynchrony is likely to play an important role in determining reef-wide coral community resilience. This manuscript uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2023).
Mutual Induced Fit Transition Structure Stabilization of Corannulene's Bowl-to-Bowl Inversion in a Perylene Bisimide Cyclophane
<p>Additional data to report <a href="https://doi.org/10.1039/D3SC05341E">https://doi.org/10.1039/D3SC05341E</a>:<br><br>Corannulene is known to undergo a fast bowl-to-bowl inversion at r.t. <em>via</em> a planar transition structure (TS). Herein we present the catalysis of this process within a perylene bisimide (PBI) cyclophane composed of chirally twisted, non-planar chromophores, linked by <em>para</em>-xylylene spacers. Variable temperature NMR studies reveal that the bowl-to-bowl inversion is significantly accelerated within the cyclophane template despite the structural non-complementarity between the binding site of the host and the TS of the guest. The observed acceleration corresponds to a decrease in the bowl-to-bowl inversion barrier of 11.6 kJ mol<sup>−1</sup> compared to the uncatalyzed process. Comparative binding studies for corannulene (20 π-electrons) and other planar polycyclic aromatic hydrocarbons (PAHs) with 14 to 24 π-electrons were applied to rationalize this barrier reduction. They revealed high binding constants that reach, in tetrachloromethane as a solvent, the picomolar range for the largest guest coronene. Computational models corroborate these experimental results and suggest that both TS stabilization and ground state destabilization contribute to the observed catalytic effect. Hereby, we find a “mutual induced fit” between host and guest in the TS complex, such that mutual geometric adaptation of the energetically favored planar TS and curved π-systems of the host results in an unprecedented non-planar TS of corannulene. Concomitant partial planarization of the PBI units optimizes noncovalent TS stabilization by π–π stacking interactions. This observation of a “mutual induced fit” in the TS of a host–guest complex was further validated experimentally by single crystal X-ray analysis of a host–guest complex with coronene as a qualitative transition state analogue.</p>
Data for "Unfolding the structural stability of nanoalloys via symmetry-constrained genetic algorithm and neural network potential"
<p><strong>PtNi_alloy_eam.db</strong> is the dataset (ase.db object) consisting of 55982 intially sampled Pt-Ni alloy structures with EAM energies and forces.</p> <p><strong>PtNi_alloy_dft.db</strong> is the dataset (ase.db object) consisting of the final 6828 resampled Pt-Ni alloy structures with DFT energies and forces calculated by VASP. This is the training set for the NNP, and could be very useful for fitting other machine learning models.</p> <p><strong>PtNi_nanoalloy_vertices_nnp.db</strong> is the dataset (ase.db object) consisting of all the vertices (stable structures) on the convex hulls obtained from NNP-based SCGA runs on 36 Pt-Ni nanoalloy systems. The energies are given by the NNP. Additional information such as mixing energy, motif and symmetry axis are also saved in the dataset and can be queried by the 'data' keyword. An xyz format trajectory of these stable structures is also uploaded.</p> <p>All the input files and scripts for hybrid MC-MD simulations, QBC resampling, DFT calculations, NNP training, NNP-based SCGA runs and convex hull analysis are provided in <strong>inputs_and_scripts.zip</strong>.</p>
Structural build-up and stability of hybrid monoglyceride-triglyceride oleogels
<p>This dataset was used in the publication <em>"</em><em>Structural build-up and stability of hybrid monoglyceride-triglyceride oleogels</em><em>"</em>. An overview of files is given below:</p>
Spatial structure within root systems moderates stability of Arbuscular Mycorrhizal mutualism and plant-soil feedbacks
<p class="RealLife">The persistence of mutualisms is paradoxical, as there are fitness incentives for exploitation. This is particularly true for plant-microbe mutualisms like arbuscular mycorrhizae (AM), which are promiscuously, horizontally-transmitted. Preferential allocation by hosts to the best mutualist can stabilize horizontal mutualisms, however preferential allocation is imperfect, with its fidelity likely depending upon the spatial structure of symbionts in plant roots. In this study, we tested AM mutualisms' dependence on two dimensions of spatial structure: the initial dispersion of fungi and the ease of fungal dispersal, through three complementary experiments. We found that fitness of the beneficial AM fungus increased when fungi were initially separate, while initial spatial mixing benefited the fitness of the non-beneficial fungus. These effects were strongest when dispersal was limited, and hosts could discriminate. Additionally, we found that spatial structure moderated changes in AM fungal composition produced differential feedbacks on plant growth. Our results identify symbiont spatial structure within plant roots as an important modifier of plant preferential allocation and the dynamics of mycorrhizal mutualisms, with cascading effects on plant communities.</p>
Structure and stability constrained substitution models outperform traditional substitution models used for evolutionary inference
<p>The current knowledge about how protein structures influence sequence evolution is rarely incorporated into substitution models adopted for phylogenetic inference, which are commonly based on independent with the same substitution process and ignore the known variation of the evolutionary rates across sites with different structural properties. In previous works, we presented site-specific substitution models of protein evolution based on selection on the folding stability of the native state (Stab-CPE), which predict more realistically the evolutionary variability across protein sites. However, those Stab-CPE present qualitative differences from observed data, probably because they ignore changes in the native structure, despite empirical studies suggesting that conservation of the native structure is a strong selective force. Here we present novel structurally constrained substitution models (Str-CPE) based on Julián Echave's model of the structural change due to a mutation as the linear response of the protein to a perturbation and on the explicit model of the perturbation generated by a specific amino-acid mutation. Compared to our previous Stab-CPE models, the novel Str-CPE models are more stringent (they predict lower sequence entropy and substitution rate), provide higher likelihood to multiple sequence alignments (MSA) of the wild-type protein, and better predict the observed substitution rates. Next, we combine Str-CPE and Stab-CPE models to obtain structure and stability constrained substitution models (SSCPE) that fit the empirical MSAs even better. Importantly, these SSCPE models present a relevant improvement of the phylogenetic likelihood for all ten protein families that we analyzed with the program RAxML-NG. We implemented the SSCPE models in the program Prot evol, freely available at <a href="https://github.com/ugobas/Prot_evol">https://github.com/ugobas/Prot_evol</a>.</p>
Figure 2 -UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>In order to assign some basic features of di®erent speci¯c qualitative anal-<br> ysis, let us present in what follows some basic cases of the classic analysis of<br> the 3D mixing model associated to the vortex technology presented above [4].<br> Since in the classic analysis the target is the study of the e±ciencies e¸ and<br> e´ at successive moments, the analysis is a discrete one, which aims testing<br> the special events that could appear at various, random, values of the versors<br> Mi; Nj . Therefore, the factor (D : D)1=2 which in concrete cases has numerical<br> values, has not an important signi¯cance in the numeric analysis.</p>
Figure 1-UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>Starting from the importance of implementation of some optimized tech-<br> nologies for processing the polluted °uids, the bene¯ts of this technology are<br> both of scienti¯c and technologic type [6]. It concerns, one one-hand, ¯nd-<br> ing new physic- mathematical models for describing at optimal parameters<br> the turbulent mixing created by a vorticity structure, and from technologi-<br> cal standpoint, developing the vortex technology for handling the polluting<br> materials.</p>
Figure 9- UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>Thus, for the non-periodic °ow, it must be noticed that a little perturbation<br> has a consistent in°uence on the model, going into a far from equilibrium<br> model. If there is annexed the irrationality of the length / surface versors<br> values (an appliance used since the beginning of the mixing study [1,2,3,4]), a<br> globally panel is obtained, with random distributed events. This space-time<br> context consolidates the basic statement that the turbulent mixing °ows must<br> be approached as chaotic systems. This is in fact regaining the idea of a system<br> / model high sensitive to initial conditions.</p>
Figure 6-UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>At a ¯rst sight of the graphics, it is obvious that both in discrete and continuous case, the phenomena is not linear; there are relatively linear cases<br> { ¯g. 4,6,7 { but especially non-linear cases { the other pictures;</p>
Figure 4-UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>At a ¯rst sight of the graphics, it is obvious that both in discrete and<br> 154continuous case, the phenomena is not linear; there are relatively linear cases<br> { ¯g. 4,6,7 { but especially non-linear cases { the other pictures;</p>
Figure 8- UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>In fact it is about two types of phenomena for the same mixing model, and<br> this is extremely important: on one hand, in the phase-portrait analysis it is<br> very important to notice that when modifying the parameters the behavior is<br> going to be periodic { ¯g.3,5 { for the same time units, and on the other hand,<br> in the classical (discrete) analysis, when modifying the parameters there are<br> involved the so-called \rare events" [1,4], corresponding to the breakup of the<br> simulation { ¯g.8;</p>
Figure 7-UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>the phenomena is not linear; there are relatively linear cases<br> fig. 4,6,7 { but especially non-linear cases { the other pictures;</p>
Figure 3- UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>In fact it is about two types of phenomena for the same mixing model, and<br> this is extremely important: on one hand, in the phase-portrait analysis it is<br> very important to notice that when modifying the parameters the behavior is<br> going to be periodic { ¯g.3,5 { for the same time units, and on the other hand,<br> in the classical (discrete) analysis, when modifying the parameters there are<br> involved the so-called \rare events" [1,4], corresponding to the breakup of the<br> simulation { ¯g.8;</p>
Figure 5-UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>In fact it is about two types of phenomena for the same mixing model, and<br> this is extremely important: on one hand, in the phase-portrait analysis it is<br> very important to notice that when modifying the parameters the behavior is<br> going to be periodic { ¯g.3,5 { for the same time units, and on the other hand,<br> in the classical (discrete) analysis, when modifying the parameters there are<br> involved the so-called \rare events" [1,4], corresponding to the breakup of the<br> simulation { ¯g.8;</p>
Structure and Stability of [4Fe-4S]-Maquettes with Non-coded Amino Acids
<p>The dataset provides supporting information to a hypothesis of coded Cys being thermodynamically superior to non-coded homoCys and thioGly analogues. The results were first presented at the 30/80 Chemobrionics 2019 meeting in Granada, Spain (www.chemobrionics.eu) and the corresponding publication appears in the thematic issue Frontiers Interface RSC journal.</p> <p>The data set organized accordingly to the published figures and tables.<br> Folders starting with numbers correspond to figures in the paper. The force field key files are to be used in combination of the amber99sb.prm force field, as provided by the Tinker 8.7 (dasher.wustl.edu/tinker/).</p> <p>The full secondary structure analysis was carried out using our in-house code (10.5281/zenodo.1442864).</p> <p>The folder 'trajectories' contains the peptides without the waterbath, while the 'waterbath' folder has the NPT equilibrated 6 nm waterbath in truncated octahedron geometry.</p> <p>The files starting with thermodynamics in the top folder has the numerical results used for calculating the cluster stability and ligand exchange reaction energetics.</p> <p>The folders 'reference cubane' and 'maquette with short peptides' contains the spin-polarized, ferro and antiferro-magnetically coupled electronic structure for all [4Fe-4S] cluster complexes.</p> <p>The key for the file extensions is as follows:</p> <p>FCHK: formatted Gaussian16 checkpoint file with spin polarized wave function<br> KEY: Tinker control keyword file<br> LOG: output file generated by a computer code<br> LST: clear text file with the content of a compressed tar archive<br> MSV: Discovery Studio Viewer Pro 5.0 binary file<br> MD5: file checksum according to MD5 protocol<br> ODS: Open Office electronic spreadsheet file<br> PDW: PsiPlot data file<br> PDB: Protein Databank Files<br> PGW: PsiPlot graphics file<br> PRM: Tinker force field parameter file<br> TGZ: compressed tar archive<br> TXT: clear text file or raw data set<br> TXYZ: Tinker XYZ file with atomic, positions, atom types, and connectivity <br> XYZ: Xmol file with atomic positions in Cartesian coordinates<br> </p>
Data for "Species richness and food-web structure jointly drive community biomass and its temporal stability in fish communities"
<p>Data for the paper "Species richness and food-web structure jointly drive community biomass and its temporal stability in fish communities" which is in minor revision in Ecology Letters (manuscript id:ELE-00589-2021.R1). A doi will be provided upon publication.</p> <p>Current citation: Danet, A., Mouchet, M., Bonnaffé, W., Thébault, E., & Fontaine, C. (In revision) Species<br> richness and food-web structure jointly drive total biomass and its temporal stability in<br> fish communities Minor revision in Ecology Letters.</p> <p>The repository constains data describing fish community monitoring across stream sections in metropolitan France over the period 1995-2018 by the French Office of Water and Aquatic Ecosystems (ONEMA) using electrofishing.</p> <p>The repository contains:</p> <ul> <li> description of fishing: fishing_protocol.csv <ul> <li>surface: sampled surface</li> <li>opcod: fishing operation code, a unique identifier for each sampling event</li> <li>station: unique identifier for each site</li> <li>nb_sp, nb_ind: number of species, number of individuals</li> </ul> </li> <li>geographical information: station_basin.csv <ul> <li>X, Y: spatial coordinates of the station, expressed in metres in Lambert93 (epsg:2154)</li> <li>basin: name of the hydrographic basin</li> </ul> </li> <li>environment: environment.csv ( _mean: mean, _med: median, _cv: coefficient of variation) <ul> <li>alt: altitude</li> <li>d_source: distance to source</li> <li>strahler: strahler order</li> <li>BOD: Biological Oxygen Demand</li> <li>temperature: water temperature</li> <li>flow: water flow</li> </ul> </li> <li>community data: community_data.csv <ul> <li>species: three digits code corresponding to a given species (see Table S1, Danet et al. in revision)</li> <li>nind: number of individuals</li> <li>biomass: biomass in gram</li> </ul> </li> <li>Length of each fish individual: fish_length.csv <ul> <li>length: length of the fish in millimeter</li> </ul> </li> <li>Inferred food-web: class_network.rda <ul> <li>data: <ul> <li>class_id: size class of a fish individual</li> </ul> </li> <li>network: these data.frame can be handled by igraph::graph_from_data_frame() <ul> <li>from, to: "to" eats "from"</li> </ul> </li> <li>composition: <ul> <li>sp_class: concatenation of species and class_id columns</li> <li>bm_std: biomass reported to the sampled surface</li> </ul> </li> </ul> </li> </ul> <p> </p> <p> </p>
Fig. 1 in Stability and spatio-temporal structure in fish assemblages of two floodplain lagoons of the lower Orinoco River
Fig. 1. Locations of the two studied lagoons in the right bank of the lower Orinoco river, between the cities of Puerto Ordaz and Ciudad Bolívar, Bolívar State, Venezuela. The arrows in black indicate the lagoons.
Fig. 3 in Stability and spatio-temporal structure in fish assemblages of two floodplain lagoons of the lower Orinoco River
Fig. 3. Percentage abundance of total species (S) and number of species for orders in each habitats of the lagoons. The abbreviations of the habitats are explained in the Fig. 2.
Fig. 4 in Stability and spatio-temporal structure in fish assemblages of two floodplain lagoons of the lower Orinoco River
Fig. 4. Mean values (+ confidence interval) of abundance, biomass and richness by habitats and hydrological phases between lagoons. The abbreviations of the hydrological phases and habitats are explained in the Fig. 2.
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Allen Brain Atlas
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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.
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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
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