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30,334 results for “Responsiveness”
Extending Grime's CSR model to predict plant demographic responses across resource availability gradients: evidence from the Patagonian steppes
<p>Sexual reproduction, growth, and survival are crucial demographic strategies for plant population viability. Here, we propose a conceptual model predicting demographic responses of species based on their ecological strategy and the heterogeneity of environmental conditions within a biogeographical unit and then applied it to a case study from a 5-degree latitudinal gradient in the Patagonian steppes. We also aim to disentangle genetic from environmental effects on demographic responses. We performed <em>in-situ </em>and common garden experiments with two species from six local populations of the Occidental Phytogeographical District of the Patagonian steppes. Species differ in key ecological traits, and thus fit into Grime´s model for evolutionary strategies in plants: one as competitive species and the other as stress-tolerant species. We calculated population growth rate (λ) and performed elasticity analyses to compare the contribution of each demographic strategy to population fitness between species and among local populations distributed along 600 km latitudinal gradient with differences in mean annual precipitation (MAP). We highlight four results. First, the competitive species change from sexual reproduction to growth as MAP increases. Second, the stress-tolerant species relied on growth and survival along the MAP gradient. Third, interannual variation in resource availability modulated demographic responses for both strategies. Fourth, based on the comparison of the <em>in-situ</em> and common garden experiments, we submit that demographic responses were genetically driven. Our study shows that demographic responses can be roughly predicted by the ecological strategy across environmental gradients. We show that differences arise not only between species, but also were genetically driven differences within species among local populations. Scaling up plant-level responses to population-level dynamics allows for a process-based understanding of current and future biogeographical species organization. Furthermore, conservation and restoration efforts should be guided by demographic strategies underlying population viability.</p>
F I G U R E 3 in Effects of dietary hydrolysate supplementation on growth, body composition, hematological responses, and liver histology of juvenile giant trevally (Caranx ignobilis Forsskal, 1775)
F I G U R E 3 Proximate composition in the whole body of juvenile giant trevally fed tested diets for 8 weeks. ns, non-significant. Different subscript letters indicate differences among treatments.
FIGURE 2 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
FIGURE 2 (a) Principal component (PC) () C00, () C04, () C10, () C20, () C32, () E04, () E10, () E20, and () E32 and (b) linear discriminant (LD) scores for Salmo trutta treatment groups (C, control; E, exercise) across experimental weeks (i.e., age 00 (control sample before treatment initiation) to 32 (32 weeks of treatment); n = 6 individuals per group). PC1 and PC3, derived from a between-group PC analysis of Procrustes superimposed landmarks corrected for the arching artefact (PC2). LD1 and LD2, derived from a LD analysis on the corrected principal component scores. Ellipses demarcate 95% confidence intervals; O, group centroids. N.B. The change of direction for head size on LD1 resulting from a negative association with PC1 (see Table 2)
F I G U R E 4 in Effects of dietary hydrolysate supplementation on growth, body composition, hematological responses, and liver histology of juvenile giant trevally (Caranx ignobilis Forsskal, 1775)
F I G U R E 4 Hematological and serum biochemical parameters of giant trevally fed experimental diets for 8 weeks. ns, non-significant. Different subscript letters indicate differences among treatments.
FIGURE 4 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
FIGURE 4 Box plots showing median (), 25th–75th percentiles () and range () of Salmo trutta condition at length (KÞ for exercised () and control () Salmo trutta cohorts across the experimental period (i.e., age) weeks 4–32 after treatment initiation (n = 6 per group). *, significant differences of pairwise least-squares means between exercised and control cohorts; different lower-case letters (black, exercise; grey, control) denote significant differences of pairwise least-squares means within treatments across the experimental period
Fig. 1 in Protease inhibitors of fodder plants as a factor of immune response influencing the physiological state of the potato ladybird beetle Henosepilachna vigintioctomaculata (Coleoptera: Coccinellidae)
Fig. 1. Analysis of the population of the potato ladybird beetle with the species-specific PCR-markers of the gene COI mtDNA. А – species-specific marker for H. vigintioctopunctata, 400 b.p.; Б – species-specific marker for H. vigintioctomaculata, 406 b.p.; М – marker of the lengths of fragments 100 b.p. ladder; 1–3 – Primorsky krai: Chuguevsky district; 4–6 – Amurskaya oblast; 7–17 – Primorsky krai: Timiryazevsky.
Fig. 3 in Protease inhibitors of fodder plants as a factor of immune response influencing the physiological state of the potato ladybird beetle Henosepilachna vigintioctomaculata (Coleoptera: Coccinellidae)
Fig. 3. Sinergetic activity of the protainases of trypsin type (in an insect) and trypsin inhibitors (in a plant) in the course of feeding on different potato varieties.
Source code and simulation results: Efficient rational approximation of optical response functions with the AAA algorithm
<p>This publication provides data published in the article "Efficient rational approximation of optical response functions with the AAA algorithm" [1] in tabulated form along with the Matlab scripts that have been used to produce them. These scripts interface the finite element method solver JCMsuite [2,3]. The article presents rational approximations of optical response functions based on an extended version of the AAA algorithm [4] that allows to efficiently reconstruct sensitivty spectra and gives access to sensitivities of poles, residues, and zeros. Furthermore, the rational approximation of a scalar observalbe is used to construct solutions of the source free Maxwell's equation, i.e., a nonlinear eigenvalue problem. </p> <p><strong>The physical Structure</strong></p> <p>The example is based on the chiral metasurface introduced in [5]. For the sake of simplicity we added infinite layers of SiO\(_2\) to the top and the bottom of the structure. The original structure has a SiO\(_2\) substrate and a layer of PMMA polymethyl methacrylate (PMMA) deposited on top. PMMA can be modelled with the same refractive index of 1.45 as SiO\(_2\). Furthermore, our simulations include the 13 nm indium tin oxide (ITO) coating which drastically reduces the Q-factor as it is slightly absorbing. The accuracy of the discrete model is verified by assessing reflection, transmission, and absorption at 241 evenly spaced points within the specified range. Energy conservation requires that the discrepancy between their sum and the energy entering the system is zero. The numerical discretization is chosen such that the maximum relative error is less than \(3\times10^{−5}\).</p> <p><strong>Dispersion</strong></p> <p>Tabulated data for ITO has been taken from the <a href="https://refractiveindex.info/?shelf=other&book=In2O3-SnO2&page=Konig">refractiveindex.info</a> database (T. A. F. König et al., 2014, https://doi.org/10.1021/nn501601e) and the data for TiO2 was kindly provided the authors of [5]. The permittivity \(\varepsilon = (n+ik)^2\) is locally approximated as a rational function, i.e., only data in a vicinity of the frequency range of interest is considered. As we aim for a function with the symmetry \(f^\ast(\omega) = f(-\omega^\ast)\) we add the complex conjugated data at negative frequencies and enforce the symmetry in a second step. The partial fraction decomposition of the required function is of the form: \(\varepsilon(\omega) = \varepsilon_\infty + \sum_{j=1}^{4}a_j/(\omega-\omega_j) - a_j^\ast/(\omega+\omega_j^\ast)\) with the residues \(a_j\) and the poles \(\omega_j\). We expect 4 pairs of poles to sufficiently approximate the data within the range of interest (4 with positive and 4 with negative real parts).</p> <h4><strong>Requirements</strong></h4> <ul> <li>JCMsuite (at least 6.2.0)</li> <li>MATLAB (tested with version R2023b)</li> </ul> <p>In order to run the simulations with JCMsuite you must replace corresponding place holders with a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p> <p><strong>Usage</strong></p> <p>With the content of 'spectra.zip' you can reproduce results presented in the paper. Running the script 'plots.m' will not start any expensive simulation but use the provided data. With 'dispersion.m' the fits to the material data can be reproduced. Additionally, tabulated data is contained in 'data/ascii'. The archive 'eigenmodes.zip' must be extracted in the same directory as 'spectra.zip'.</p> <p><strong>References</strong></p> <p>[1] Fridtjof Betz, Martin Hammerschmidt, Lin Zschiedrich, Sven Burger, Felix Binkowski: Efficient rational approximation of optical response functions<br>with the AAA algorithm, https://doi.org/10.48550/arXiv.2403.19404.</p> <p>[2] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192.</p> <p>[3] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763.</p> <p>[4] Y. Nakatsukasa, O. Sète, and L. N. Trefethen, The AAA Algorithm for Rational Approximation, SIAM Journal on Scientific Computing <strong>40</strong>, A1494 (2018), http://dx.doi.org/10.1137/16M1106122.</p> <p>[5] X. Zhang, Y. Liu, J. Han, Y. Kivshar, and Q. Song, Chiral emission from resonant metasurfaces, Science <strong>377</strong>, 1215 (2022), http://dx.doi.org/%2010.1126/science.abq7870.</p>
AMIRIS demand response workflow input
<p>This upload contains the <strong>input data</strong> necessary to run a workflow applying the agent-based power market model <strong><a href="https://gitlab.com/dlr-ve/esy/amiris/amiris">AMIRIS</a> </strong>in order to study the impact of power tariffs design on <strong>demand response</strong> profitability.</p> <h2>Usage</h2> <p>The data has to be copied into the "./inputs/data/" folder of the <a href="https://github.com/jokochems/demand_response_analyses_workflow"><em>demand response analyses workflow</em></a> and unpacked there. See the description of the workflow on the dependencies how to execute the model.</p> <p>Important note: You will need a version of AMIRIS that is not yet open source and contains the demand response implementation. Feel free to contact the author of this data set in order to request it. Also, you will need a solver, such as Gurobi or CPLEX for instance.</p> <h2>Background</h2> <p>Data has been obtained from previous <a href="https://github.com/pommes-public/pommesinvest"><em>pommesinvest </em></a>model runs.</p> <p>It has been put together by executing a data <a href="https://github.com/pommes-public/pommesevaluation/blob/main/amiris_converter.ipynb">converter script</a> from the <a href="https://github.com/pommes-public/pommesevaluation/">pommesevaluation </a>repository that compiles the inputs into a format understood by AMIRIS, thus allowing for a soft model coupling (sequential execution) with full input harmonization.</p>
F I G U R E 2 in Movement and habitat shift responses of juvenile Atlantic Salmon (Salmo salar) to annually permanent stream flooding
F I G U R E 2 Mahers River study reach, Newfoundland, showing the habitat conditions before (riffle run) and after (pool) annual flooding.
F I G U R E 1 in Movement and habitat shift responses of juvenile Atlantic Salmon (Salmo salar) to annually permanent stream flooding
F I G U R E 1 Mahers River and North Arm River, Newfoundland, Canada. The seasonally created pool (Flooded Pool reach) is located within Mahers River. Two control reaches were located in Mahers River; one immediately upriver of the created pool habitat (Riffle Above Pool reach) and another approximate 3 km upstream (Mahers Control reach) and two additional control reaches were located in the nearby North Arm River (North Arm 1 and 2).
Fig. 1 in Birch Tree Response To Different Environmental And Meteorological Conditions In Lithuania
Fig. 1. Defoliation (%) of silver and downy birches growing in the sites of different humidity. Values are given as the mean±SE.
Dataset of "Switching Response in Organic Electrochemical Transistors by Ionic Diffusion and Electronic Transport"
<p>This dataset underpins the following article published in the Advanced Science:</p> <p>"Switching Response in Organic Electrochemical Transistors by Ionic Diffusion and Electronic Transport"</p> <p> </p> <p>Raw and processed data for the article "Switching Response in Organic Electrochemical Transistors by Ionic Diffusion and Electronic Transport". For further details see the readme.txt file.</p>
Responsiveness of the broad bean weevil Bruchus rufimanus Boh. to different Vicia faba L. genotypes
<p><span>The broad bean weevil <em>Bruchus rufimanus</em> Boh., also called bruchid beetle, is a major pest of Vicia faba L. given that larvae develop inside the seeds causing direct quality losses in agricultural products. Recurrent chemical applications are inappropriate because they represent a serious threat to pollinators in general. In addition, biological control approaches that have been attempted so far showed limited effectiveness, which makes studies of methods to control bruchid populations extremely important. Breeding resistant cultivars is the most adequate approach to achieve efficient levels of pest resistance and promote sustainable agriculture. To explore the mechanisms underlying bruchid resistance, we explored B. rufimanus behavior in different life stages and plant preference in feeding, oviposition and larval survival into adulthood both in greenhouse experiments and in the field. Our greenhouse results showed that females, compared to males, have preferences for flowers from some genotypes over the others although the choices made did not correspond with the laying preference. The egg laying preference of females was significantly affected by cultivars. This attraction could be explained by the number of seeds per pod produced by the genotypes, while in the field flowering time influenced bruchid infestation. Moreover, the survival of the larva into adulthood was greater in certain genotypes over the others. The results of our study lay the groundwork for further analyses to finely dissect V. faba resistance towards bruchids and pave the way for the development of methods to predict potential resistant genotypes in research and breeding programs.</span></p>
Tumor and Blood B Cell Abundance Outperforms Established ICB Response Prediction Signatures in Head and Neck Cancer
<div> <div> <div> <div> <p>This dataset contains processed flow cytometry data and clinical information for deidentified patients from Cohort 11, as well as deconvoluted cell abundances and clinical data for deidentified patients from Cohort 10, associated with the study titled <em>"Tumor and Blood B Cell Abundance Outperforms Established Immune Checkpoint Blockade Response Prediction Signatures in Head and Neck Cancer"</em> published in <strong>Annals of Oncology (2024)</strong>. <a href="https://doi.org/10.1016/j.annonc.2024.11.008" target="_new" rel="noopener">DOI: https://doi.org/10.1016/j.annonc.2024.11.008</a>.</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Response of Vegetation Canopy Growth to Climate Change in Northeast China
<p>Our study uniquely addresses gaps in existing research by investigating how vegetation canopy changes during various growth phases—development (April-June), maturation (July-August), and senescence (September-October)—and how these changes respond to preseason climatic factors. We highlight significant findings, such as the early advancement of the canopy maturation phase and the delayed senescence, particularly in forested areas. Moreover, we demonstrate that preseason air temperature exerts a considerable influence on canopy growth, with a transition from positive to negative correlations across different phases and vegetation types.The results contribute to understanding vegetation dynamics under climate change and provide actionable insights for sustainable agricultural, forestry, and animal husbandry management.</p>
Dataset for publication:Dataset for publication: "Application of shot peening to improve fatigue properties via enhancement of precipitation response in high-strength Al-Cu-Li alloys"
<p>The dataset contains a set of experimental data used in preparation for the manuscript "Application of shot peening to improve fatigue properties via enhancement of precipitation response in high-strength Al-Cu-Li alloys." The dataset contains results of fatigue tests, residual stress measurements, nanoindentation measurements, and surface roughness data.</p>
Output of simulations for "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis": Part 1
<p>This dataset contains the output of the simulations for the paper "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis" doi: [TO BE COMPLETED]. This is part 1. It contains data for the S1 simulation.</p>
Output of simulations for "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis": Part 2
<p>This dataset contains the output of the simulations for the paper "Atmosphere Response to an Oceanic Sub-mesoscale SST Front: A Coherent Structure Analysis" doi: [TO BE COMPLETED]. This is part 2. It contains the remaining of data for the S1 simulation, the data of the reference simulations RefC and RefW, and the data for the sensitivity analysis of the supplementary material.</p>
The impulse response of optic flow sensitive descending neurons to roll m-sequences
<p>When animals move through the world, their own movements generate widefield optic flow across their eyes. In insects, such widefield motion is encoded by optic lobe neurons. These lobula plate tangential cells (LPTCs) synapse with optic flow sensitive descending neurons, which in turn project to areas that control neck, wing and leg movements. As the descending neurons play a role in sensori-motor transformation, it is important to understand their spatio-temporal response properties. Recent work shows that a relatively fast and efficient way to quantify such response properties is to use m-sequences or other white noise techniques. We therefore here used m-sequences to quantify the impulse responses of optic flow sensitive descending neurons in male <i>Eristalis tenax </i>hoverflies. We focused on roll impulse responses as hoverflies perform exquisite head roll stabilizing reflexes, and the descending neurons respond particularly well to roll. We found that the roll impulse responses were fast, peaking after 16.5-18.0 ms. This is similar to the impulse response time-to-peak (18.3 ms) to widefield horizontal motion recorded in hoverfly LPTCs. We found that the roll impulse response amplitude scaled with the size of the stimulus impulse, and that its shape could be affected by the addition of constant velocity roll or lift. For example, the roll impulse response became faster and stronger with the addition of excitatory stimuli, and vice versa. We also found that the roll impulse response had a long return to baseline, which was significantly and substantially reduced by the addition of either roll or lift.</p>
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.