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126 results for “impact mechanism”
Data from: When spring ephemerals fail to meet pollinators: mechanism of phenological mismatch and its impact on plant reproduction
The flowering phenology of early-blooming plants is largely determined by snowmelt timing in high-latitude and high-altitude ecosystems. When the synchrony of flowering and pollinator emergence is disturbed by climate change, seed production may be restricted due to insufficient pollination success. We revealed the mechanism of phenological mismatch between a spring ephemeral (Corydalis ambigua) and its pollinator (overwintered bumble bees), and its impact on plant reproduction, based on 19 years of monitoring and a snow removal experiment in a cool-temperate forest in northern Japan. Early snowmelt increased the risk of phenological mismatch under natural conditions. Seed production was limited by pollination success over the three years of pollination experiment and decreased when flowering occurred prior to bee emergence. Similar trends were detected on modification of flowering phenology through snow removal. Following snowmelt, the length of the pre-flowering period strongly depended on the ambient surface temperature, ranging from 4 days (at >7ºC) to 26 days (at 2.5ºC). Flowering onset was explained with an accumulated surface degree-day model. Bumble bees emerged when soil temperature reached 6ºC, which was predictable by an accumulated soil degree-day model, although foraging activity after emergence might depend on air temperature. These results indicate that phenological mismatch tends to occur when snow melts early but subsequent soil warming progresses slowly. Thus, modification of the snowmelt regime could be a major driver disturbing spring phenology in northern ecosystems.
Dataset from Rummel et al.: "Spatially resolved salt intrusion mechanisms in a tidal estuary and the impact of channel deepening" - Part 1
<p>Model data from the numerical setup of the Weser River Estuary used in Rummel et al. (submitted to JGR:Oceans): "Spatially resolved salt intrusion mechanisms in a tidal estuary and the impact of channel deepening" - Part 1.</p> <p>The dates in the file names are connected to specific model runs and do not explain the modelled time period.</p> <p>Explanation of datasets:</p> <ul> <li>2D_elev* - 2D model output for the entire year 2016 for validation at one location each (associated station name included in file name), original topography.</li> <li>3D_stat* - 3D model output for the entire year 2016 for validation at one location each (associated station name included in file name), original topography.</li> <li>3D_cross_30_80* - 3D model output for one month of 2016 for the model domain from Weser km 30 to 80 including variables needed for the salt transport decomposition. <ul> <li>2024-05-23 - March 2016, original topography</li> <li>2024-06-07 - March 2016, dredged topography</li> <li>2024-06-06 - September 2016, original topography (different temporal resolution)</li> <li>2024-06-10 - September 2016, dredged topography</li> </ul> </li> <li>3D_channel* - 3D model output for the navigational channel in the entire model domain for the entire year 2016. <ul> <li>2024-04-02 - original topography</li> <li>2024-05-16 - dredged topography</li> </ul> </li> <li>3D_cross_55/65_2024-09-02* - 3D model output for September 2016, original topography for crosssections at Weser km 55 and 65 including variables needed for the salt transport decomposition.</li> </ul>
Dataset from Rummel et al.: "Spatially resolved salt intrusion mechanisms in a tidal estuary and the impact of channel deepening" - Part 2
<p>Model data from the numerical setup of the Weser River Estuary used in Rummel et al. (submitted to JGR: Oceans): "Spatially resolved salt intrusion mechanisms in a tidal estuary and the impact of channel deepening" - Part 2.</p> <p>The dates in the file names are connected to specific model runs and do not explain the modelled time period.</p> <p>This dataset contains daily averaged 3D model output for the entire year 2016 of the whole model domain with the original, not dredged topography.</p> <p> </p>
Dataset from Rummel et al.: "Spatially resolved salt intrusion mechanisms in a tidal estuary and the impact of channel deepening" - Part 3
<p>Model data from the numerical setup of the Weser River Estuary used in Rummel et al. (submitted to JGR: Oceans): "Spatially resolved salt intrusion mechanisms in a tidal estuary and the impact of channel deepening" - Part 3.</p> <p>The dates in the file names are connected to specific model runs and do not explain the modelled time period.</p> <p>This dataset contains daily averaged 3D model output for the entire year 2016 of the whole model domain with the dredged topography.</p>
Data from: Evolutionary impact of size-selective harvesting on shoaling behavior: Individual-level mechanisms and possible consequences for natural and fishing mortality
<p>Intensive and size-selective harvesting is an evolutionary driver of life-history as well as individual behavioral traits. Yet, whether and to what degree harvesting modifies the collective behavior of exploited species is largely unknown. We present a multi-generation harvest selection experiment with zebrafish (<em>Danio rerio)</em> as a model species to understand the effects of size-selective harvesting on shoaling behavior. The experimental system is based on a large-harvested (typical of most wild capture fisheries targeting larger size classes) and small-harvested (typical of specialized fisheries and gape-limited predators targeting smaller size classes) selection lines. By combining high resolution tracking of fish behavior with computational agent-based modeling we show that shoal cohesion changed in the direction expected by a trade-off between individual vigilance and the use of social cues. In particular, we document a decrease of individual vigilance in the small-harvested line, which was linked to an increase in the attention to social cues, favoring more cohesive shoals. Opposing outcomes were found for the large-harvested line, which formed less cohesive shoals. Using the agent-based model we outline possible consequences of changes in shoaling behavior for both fishing and natural mortality. The changes in shoaling induced by large size-selective harvesting may decrease fishing mortality, but increase mortality by natural predators. Our work suggests an insofar overlooked evolutionary mechanism by which size-selective harvesting can affect fishing and natural mortality of exploited fish.</p>
Impact of rapid thermal processing on bulk and surface recombination mechanisms in FZ silicon with fired passivating contacts
<p>Data underlying the article</p>
Data and code for Mechanisms of damage prevention, signalling, and repair impact the ability of Drosophila to tolerate enteric bacterial infection
<p>Data and code for</p> <p>Prakash, A., Monteith, K. M., & Vale, P. F. (2021). Mechanisms of damage prevention, signalling, and repair impact the ability of Drosophila to tolerate enteric bacterial infection. <em>bioRxiv</em>.</p> <p> </p> <p> </p>
Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study - dataset
<p>Abstract:<br>from [1]</p> <p>Polymer nanocomposites (PNCs) have shown great potential to meet the ever-growing requirements of modern engineering applications. Nowadays, molecular dynamics (MD) simulations are increasingly employed to complement experimental work and thereby gain a deeper understanding of the complex structure–property relations of PNCs. However, with respect to the thermoplastic’s mechanical behavior, the role of its average molar mass is rarely addressed, and many MD studies only consider uniform (monodispersed) polymers. Therefore, this contribution investigates the impact that and the dispersity Đ have on the stiffness and strength of PNCs through coarse-grained MD. To this end, we employed a Kremer–Grest bead–spring model and observed the expected increase in the mechanical performance of the neat polymer for larger . Our results indicated that the unimodal molar mass distribution does not impact the mechanical behavior in the investigated dispersity range Đ. For the PNC, we obtained the same -dependence and Đ-independence of the mechanical properties over a wide range of filler sizes and contents. This contribution proves that even simple MD models can reproduce the experimentally well researched effect of the molar mass. Hence, this work is an important step in understanding the complex structure–property relations of PNCs, which is essential to unlock their full potential.</p> <p>Contact:</p> <p>Maximilian Ries<br>Institute of Applied Mechanics<br>Friedrich-Alexander-Universität Erlangen-Nürnberg<br>Egerlandstr. 5<br>91058 Erlangen</p> <p>Software:</p> <p>All MD simulations were performed with LAMMPS [2,3], version: 23 Oct 2022 / 20220623</p> <p>Compiled with<br>Compiler: GNU C++ 11.2.0 with OpenMP not enabled<br>C++ standard: C++11</p> <p>Active compile time flags:<br>-DLAMMPS_GZIP<br>-DLAMMPS_SMALLBIG</p> <p>Installed packages:<br>CLASS2 DPD-BASIC EXTRA-DUMP INTEL KSPACE MANYBODY MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT PERI</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p>License:</p> <p>Creative Commons Attribution 4.0 International</p> <p>Context:</p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>Content:</p> <p>structure of data set:</p> <p> -01_neat <br> containing the neat polymer simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -02_distributed<br> containing samples with distributed chain lengths<br> -100-dist<br> samples with mean molar mass 100<br> -200-dist<br> samples with mean molar mass 200<br> -02_PNC<br> containing the polymer nanocomposite simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> -02_distributed<br> containing samples with distributed chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> </p> <p>naming convention for simulation folders</p> <p> - neat polymer simulations<br> example: GTP_UT_num_chains-80_num_beads_per_chain-500-8<br> * num_chains: number of polymer chains<br> * num_beads_per_chain: molar mass (chain length)<br> * distribution: standard deviation of gauss distribution govering dispersity<br> * "trailing number": batch number of sample<br> <br> - polymer nanocomposite simulations<br> example: GTP_rF-5_nF-10_chainlen-5_7-T_0.2<br> * rF: nanofiller radius<br> * nF: number of nanofillers<br> * chainlen: molar mass (chain length)</p> <p> </p> <p>Each simulation directory contains:</p> <p> lammps input file (*.in) of the specific simulation</p> <p> data file (*.data) containing the initial sample configuration</p> <p> input.prm: input parameters of the specific simulation (read by the input file)</p> <p> meta.info: meta data of the specific simulation run</p> <p> LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <p> thermo_out.Dat: raw output </p> <p> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> <p> thermo_out_STD.Dat: standard deviation of raw output</p> <p>Output quantities (columns of *.Dat files):<br>Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <p> Step: time step </p> <p> Time: time </p> <p> TotEng: total energy </p> <p> PotEng: potential energy</p> <p> KinEng: kinetic energy </p> <p> E_pair: pair energy </p> <p> E_bond: bond energy </p> <p> E_angle: angle energy </p> <p> E_dihed: dihedral energy </p> <p> Temp: temperature</p> <p> Press: hydrostatic pressure</p> <p> Pxx: xx component of pressure tensor </p> <p> Pyy: yy component of pressure tensor </p> <p> Pzz: zz component of pressure tensor </p> <p> Pxy: xy component of pressure tensor</p> <p> Pxz: xz component of pressure tensor</p> <p> Pyz: yz component of pressure tensor</p> <p> Volume: volume of simulation box </p> <p> Lx: box length in x direction </p> <p> Ly: box length in y direction </p> <p> Lz: box length in z direction </p> <p> Density: density </p> <p> c_RG: radius of gyration scalar </p> <p> c_RG[1]: squared radius of gyration tensor (xx component) </p> <p> c_RG[2]: squared radius of gyration tensor (yy component) </p> <p> c_RG[3]: squared radius of gyration tensor (zz component) </p> <p> c_RG[4]: squared radius of gyration tensor (xy component) </p> <p> c_RG[5]: squared radius of gyration tensor (xz component) </p> <p> c_RG[6]: squared radius of gyration tensor (yz component) </p> <p> c_bondave[1]: bond energy averaged over all atoms </p> <p> c_bondave[2]: bond distance averaged over all atoms </p> <p> c_bondave[3]: squared bond distance averaged over all atoms </p> <p> c_angleave[1]: angle energy averaged over all atoms </p> <p> c_angleave[2]: angle averaged over all atoms degree</p> <p> c_angleave[3]: cosine of angle </p> <p> c_angleave[4]: squared cosine of angle </p> <p> c_MSD[1]: mean squared displacement x-direction </p> <p> c_MSD[2]: mean squared displacement y-direction </p> <p> c_MSD[3]: mean squared displacement z-direction </p> <p> c_MSD[4]: total mean squared displacement </p> <p> c_COM[1]: x coordinate of center of mass </p> <p> c_COM[2]: y coordinate of center of mass </p> <p> c_COM[3]: z coordinate of center of mass </p> <p> v_strain_xx: xx component of engineering strain tensor </p> <p> v_strain_yy: yy component of engineering strain tensor </p> <p> v_strain_zz: zz component of engineering strain tensor </p> <p> v_vMisesequivstress: von Mises equivalent stress </p> <p> v_Cauchy_xx: xx component of stress tensor </p> <p> v_Cauchy_yy: yy component of stress tensor</p> <p> v_Cauchy_zz: zz component of stress tensor</p> <p> v_Cauchy_xy: xy component of stress tensor </p> <p> v_Cauchy_xz: xz component of stress tensor </p> <p> v_Cauchy_yz: yz component of stress tensor </p> <p> v_strain_xy: xy component of engineering strain tensor </p> <p> v_strain_xz: xz component of engineering strain tensor </p> <p> v_strain_yz: yz component of engineering strain tensor </p> <p>References:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Computer Physics Communications, vol. 271, p. 108171, 2022.</p> <p>[4] J. Roksvaag, M.Ries . “A fast self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, manuscript in preparation</p>
IMPACTS OF AGRICULTURE MECHANIZATION
<p>Agricultural mechanization, the application of machinery to agricultural tasks, has been a cornerstone of agricultural development for centuries. As populations grow and demand for food increases, the efficiency and productivity of agricultural practices become increasingly critical. This paper examines the benefits of agricultural mechanization, focusing on its potential to significantly increase production and reduce labor costs.</p>
Exploring the Impact of Physiological C-Terminal Truncation on α-Synuclein Conformations to Unveil Mechanisms Regulating Pathological Aggregation
<p><span>Emerging evidence suggests that <a name="_Hlk179101300"></a>physiological C-terminal truncation of α-synuclein (αS) plays a critical role in regulating <a name="_Hlk178755669"></a>liquid–liquid phase separation and promoting amyloid aggregation, processes implicated in neurodegenerative diseases such as Parkinson’s disease (PD). However, the molecular mechanisms through which C-terminal truncation influences αS conformation and modulates its aggregation remain poorly understood. In this study, we investigated the impact of C-terminal truncation on αS conformational dynamics by comparing full-length αS<sub>1-140</sub> with truncated αS<sub>1-103</sub> monomers using atomistic discrete molecular dynamics (DMD) simulations. Our findings revealed that both αS<sub>1-140</sub> and αS<sub>1-103</sub> primarily adopted helical conformations around residues 7–32, while residues 35-95, located in the second half of the N-terminal and NAC domains, predominantly formed a dynamic β-sheet core. The C-terminus of αS<sub>1-140</sub> was largely unstructured and dynamically wrapped around the β-sheet core. While residues 1-95 exhibited similar secondary structure propensities in both αS<sub>1-140</sub> and αS<sub>1-103</sub>, the dynamic capping by the C-terminus in αS<sub>1-140</sub> slightly enhanced β-sheet formation around residues 35-95. In contrast, key aggregation-driving regions (residues 2-9, 36-42, 45-57, and 68-78) were dynamically shielded by the C-terminus in αS<sub>1-140</sub>, reducing their exposure and potentially preventing inter-peptide interactions that drive aggregation. C-terminal truncation, on the other hand, increased the exposed surface area of these aggregation-prone regions, thereby enhancing inter-peptide interactions, phase separation, and amyloid aggregation. Overall, our simulations provide valuable insights into the conformational effects of C-terminal truncation on αS and its role in promoting pathological aggregation.</span></p>
A Study on the Biomechanical Mechanisms of Orthotic/Physical Training Correction of Hallux Valgus and Its Impact on the Lower Limbs
ClinicalTrials.gov study NCT07036120. IPD Sharing: NO. Countries: 1. Publications: 1.
Muscle-Related Side Effects of Statins: Functional Impact, Mechanisms, and Potential Relief With Vitamin D Supplementation
ClinicalTrials.gov study NCT01493648. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Impact of Lumboscopy Versus Laparoscopy on Ventilatory Mechanics
ClinicalTrials.gov study NCT06776068. IPD Sharing: NO. Countries: 1. Publications: 1.
The Impact of Hyaluronic Acid Injections on Osteoarthritic Knee Mechanics
ClinicalTrials.gov study NCT00778076. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Impact of Intravenous Iron Repletion On Mechanisms of Exercise InTolerance in HFpEF (IRONMET-HFpEF)
ClinicalTrials.gov study NCT04945707. IPD Sharing: NO. Countries: 1. Publications: 2.
The Impact of Mechanical Methods on the Postpartum Haemorrhage Prophylaxis During Caesarean Section
ClinicalTrials.gov study NCT05948436. IPD Sharing: YES. Countries: 1. Publications: 1.
Impact of the Depth of Neuromuscular Blockade on Respiratory Mechanics in Moderate to Severe ARDS Patients
ClinicalTrials.gov study NCT05697666. IPD Sharing: NO. Countries: 1. Publications: 4.
Black Impact: The Mechanisms Underlying Psychosocial Stress Reduction in a Cardiovascular Health Intervention
ClinicalTrials.gov study NCT06055036. IPD Sharing: NO. Countries: 1. Publications: 4.
Mitochondrial Dysfunction of Alveolar and Circulating Immune Cells During Acute Respiratory Distress Syndrome: Impact of Infectious Aggression and Alveolar Stretching as a Result of Mechanical Ventila
ClinicalTrials.gov study NCT03955887. IPD Sharing: Not stated. Countries: 1. Publications: 2.
The Impact of PEEP-guided Electrical Impedance Tomography on Oxygenation and Pulmonary Mechanics in Moderate-to-severe ARDS
ClinicalTrials.gov study NCT06733168. IPD Sharing: NO. Countries: 1. Publications: 4.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.