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3,339 results for “heterogeneities”
Exploring the influence of morphologic heterogeneity and discharge on downstream transport in streams with multiple logjams: 2. Insight from numerical models
<p>These are the input files to run HydroGeoSphere model in Little Beaver Creek, Colorado USA.</p>
The movement pattern of Père David's deer relative to habitat Heterogeneity
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Aligned Cross-modal Integration and Regulatory Heterogeneity Characterization of Single-Cell Multiomic Data with Deep Contrastive Learning
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Understanding tumor heterogeneity in melanoma brain metastasis using spatial transcriptomics and multi-regional bulk sequencing
<p>Melanoma brain metastasis (MBM) exhibits extensive inter- and intra-tumor heterogeneity, driven by a complex tumor microenvironment (TME). The aim with this study was to profile the MBMs by using a multi-omics approach, integrating spatial transcriptomics with bulk exome, proteome, and transcriptome profiling. We identified significant patient-specific variations in immune cell infiltration, particularly in B/plasma cells, myeloid cells, and cancer-associated fibroblasts (CAFs). Notably, immunotherapy-treated patients showed enrichedpathways related to EMT, IFN-γ signaling, oxidative phosphorylation, T-cell signaling, inflammation and DNA damage, which aligned with distinct cellular compositions observed in the spatial analysis. We also uncovered considerable intra-tumor heterogeneity, especially at the protein level, revealing differential expression patterns of key tumor and immune-related markers. The correlation between mRNA and protein data highlighted consistent enrichment of critical pathways across multi-omics layers. These findings provide a comprehensive view of MBM's molecular and cellular landscape, emphasizing the importance of addressing tumor heterogeneity in developing effective therapeutic strategies.</p>
Grain-Scale Stress Heterogeneity in Concrete from In-Situ X-Ray Measurements
<p>All data in this repository is described in Data-Description_Zenodo.docx.</p> <p> </p>
Versatile and Robust Reservoir Computing with PWM-Driven Heterogenous R-C Circuits
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Numerical simulation results for paper: Numerical Analysis of the Effect of Heterogeneity on CO2 Dissolution Enhanced by Gravity Driven Convection
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Data for paper 'Formation of motile cell clusters in heterogeneous model tumors: The role of cell-cell alignment' (PRE, 2024)
<p>The data provided in this repository is generated for the publication ‘Formation of motile cell clusters in heterogeneous model tumors: The role of cell-cell alignment’ by Quirine J.S. Braat, Cornelis Storm and Liesbeth M.C. Janssen and published in Physical Review E.</p> <p>The data is generated using the Cellular Potts Model in CompuCell3D [1] that can be retrieved from GitHub. The simulations contain a more detailed description of the data, and the data provided here can be reproduced using the appropriate simulation code and parameters. These can be found on GitHub via <a href="https://github.com/QBraat/Cluster-Formation-Alignment">https://github.com/QBraat/Cluster-Formation-Alignment</a>.</p> <p>The data in this repository has been divided into the following sets: </p> <ol> <li><strong>EmptyLayer_Random.zip</strong>. this data set belongs to section III.A and section III.C and contains detailed information about the cells’ positions, orientation as a function of time for the active cells in free space.</li> <li><strong>EmptyLater_Random_single.zip</strong>. this data set belongs to section III.A and contains processed data about the order parameter and cluster size as a function of time for the active cells in free space.</li> <li><strong>ConfluentLayer_Random_single.zip</strong>. this data set belongs to section III.B and contains the processed data about the order parameter and cluster size as a function of time for the active cells in a confluent layer. </li> <li><strong>ConfluentLayer_Random_FiniteSize.zip</strong>. this data set belongs to the data in the supplementary information.</li> <li><strong>ConfluentLayer_Random_InitialBlock.zip</strong>. this data set belongs to the data in the supplementary information.</li> </ol> <p>Other than these sets, there is an additional data set ‘<strong>ConfluentLayer_Random.zip</strong>’, which contains the detailed information about the cells’ positions, orientation etc. as a function of time for the confluent layer simulations in section III.B and section III.C. This data set has not been included as it contains a significantly large amount of data, but can be received upon request from the authors.</p> <h3>Detailed information about the data set</h3> <p>Each data point in the paper is generated by running 200 simulations using the simulation code for a set of parameters. For each combination of parameters (tau, gamma), we either got the full dynamic information (Full) or only the dynamic evolution of the mean cluster size and the steady state values for the mean cluster size and order parameter (Single).</p> <p><strong>Confluent layer (fraction = 0.25</strong><strong>)</strong></p> <table> <tbody> <tr> <td> <p><strong>tau / gamma</strong></p> </td> <td> <p><strong>1.0</strong></p> </td> <td> <p><strong>0.5</strong></p> </td> <td> <p><strong>0.2</strong></p> </td> <td> <p><strong>0.1</strong></p> </td> <td> <p><strong>0.05</strong></p> </td> <td> <p><strong>0.02</strong></p> </td> <td> <p><strong>0.01</strong></p> </td> <td> <p><strong>0.005</strong></p> </td> <td> <p><strong>0.001</strong></p> </td> <td> <p><strong>0.001</strong></p> </td> </tr> <tr> <td> <p>500 mcs</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>2500 mcs</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>4000 mcs</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> </tr> </tbody> </table> <p><strong> </strong></p> <p><strong>Empty layer (fraction = 0.25</strong><strong>) </strong></p> <table> <tbody> <tr> <td> <p><strong>tau / gamma</strong></p> </td> <td> <p><strong>1.0</strong></p> </td> <td> <p><strong>0.5</strong></p> </td> <td> <p><strong>0.2</strong></p> </td> <td> <p><strong>0.1</strong></p> </td> <td> <p><strong>0.05</strong></p> </td> <td> <p><strong>0.02</strong></p> </td> <td> <p><strong>0.01</strong></p> </td> <td> <p><strong>0.005</strong></p> </td> <td> <p><strong>0.001</strong></p> </td> <td> <p><strong>0.001</strong></p> </td> </tr> <tr> <td> <p>500 mcs</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>2500 mcs</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>4000 mcs</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Other simulations </strong></p> <p>Apart from the main results, we also ran simulations with different parameter settings to get more insights into the dynamic behavior.</p> <table> <tbody> <tr> <td> <p><strong>Simulations</strong></p> </td> <td> <p><strong>Settings</strong></p> </td> <td> <p><strong>Storage type</strong></p> </td> </tr> <tr> <td> <p>Different fraction active cells</p> </td> <td> <p>Confluent layer, fraction = 0.1, other parameters as before</p> </td> <td> <p>Single</p> </td> </tr> <tr> <td> <p>No alignment</p> </td> <td> <p>Confluent Layer + Empty Layer, fraction = 0.25, gamma = 0, tau = 2500 mcs</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>Initially aligned cluster</p> </td> <td> <p>Fully aligned cluster, Confluent Layer + Empty Layer, gamma = 1.0, 0.01, 0.001 and tau = 2500 mcs</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>Finite-size effects</p> </td> <td> <p>Confluent Layer, number of cells = 100, 400, 9000, 1600, 2500 cells</p> </td> <td> <p>Single</p> </td> </tr> </tbody> </table> <p> </p> <h2>Data format</h2> <p>When the simulations are run with full data export, the following files are generated: </p> <ul> <li>data_cells_<settings>.dat: <ul> <li>mcs: time stamp in Monte Carlo Steps (MCS) </li> <li>cell.id: number of the cell </li> <li>cell.type: type of the Cells (active = 2, passive = 1) </li> <li>xCOM: x-coordinate of the center of mass </li> <li>yCOM: y-coordinate of the center of mass </li> <li>zCOM: z-coordinate of the center of mass (always equal to 0 in 2D) </li> <li>pol_angle: angle with respect to the x-axis of the active force orientation. </li> <li>cluster.id: number of the cluster to which the cells belongs.</li> </ul> </li> <li>data_clusters_<settings>.dat: <ul> <li>mcs: time stamp in Monte Carlo Steps (MCS)</li> <li>cluster_id: number of the cluster (corresponding to the number cluster.id in data_cells_<settings>.dat </li> <li>cluster_size: number of cells in the given cluster </li> </ul> </li> <li>metadata_<settings>.dat: <ul> <li>Information about the full set of simulation parameters</li> </ul> </li> </ul> <p>When the simulations are run with single data export, the following files are generated: </p> <ul> <li>single_export_<settings>.dat: <ul> <li>steadyS: steady state value of the mean cluster size (mean calculated after 60000 mcs)</li> <li>steadyS_std: standard deviation of the steady state value of the mean cluster size</li> <li>steadyP: steady state value of the polarity order parameter </li> <li>steadyP_std: standard deviation of the steady state value of the polarity order parameter </li> </ul> </li> <li>St-single-export_<settings>.dat: <ul> <li>mcs: time stamp in Monte Carlo Steps (MCS)</li> <li>St: mean cluster size at given time stamp</li> <li>St_std: standard deviation of the mean cluster size at a given time stamp </li> </ul> </li> <li>S-max-single-export_<settings>.dat <ul> <li>mcs: time stamp in Monte Carlo Steps (MCS)</li> <li>Smax: largest cluster detected at a given time stamp</li> </ul> </li> <li>metadata_<settings>.dat: <ul> <li>Information about the full set of simulation parameters</li> </ul> </li> </ul>
Data from: Roles of pathogens on replacement of tree seedlings in heterogeneous light environments in a temperate forest: a reciprocal seed sowing experiment
In forest communities, the Janzen–Connell (J-C) hypothesis proposes that species diversity is maintained by non-competitive distance- and/or density-dependent seedling mortality caused by host-specific natural enemies. However, the effects of pathogen associations from nearby conspecifics versus heterospecifics remain unknown in spatially heterogeneous light environments. Seeds of hardwood species Cornus controversa (Cornus) and Prunus grayana (Prunus) were sown beneath 6–7 Cornus and Prunus adults in both the forest understory (FU) and in gaps (Gap) created by felling all woody vegetation near the focal adults. Seedling growth, mortality, killing agents (e.g. pathogens that cause damping-off and leaf diseases), and root infection by arbuscular mycorrhizal fungi (AMF) were investigated. We found strong habitat effects on the expression of soil fungi beneath both tree species. Seedling mortality caused by soil-borne damping-off pathogens was greater in FU than in Gap, and AMF infection, which enhanced relative seedling growth rate, was greater in Gap than in FU. Seedling mortality caused by damping-off pathogens did not differ between Cornus and Prunus seedlings beneath the adults of conspecific or heterospecific adults in both FU and Gap, suggesting little distance-dependence or host preference in the fungus. Beneath the adults of Cornus and Prunus, the most prevalent leaf diseases were zonate leaf blight and angular leaf spot caused by the airborne pathogenic fungi Haradamyces foliicola and Phaeoisariopsis pruni-grayanae, respectively. Although these pathogens attacked the seedlings of both species, conspecific seedlings (i.e. home) showed more severe leaf damage, earlier leaf shedding and/or less defensive behaviour (cell wall defence) relative to heterospecific seedlings (i.e. away), suggesting negative distance-dependent attack (i.e. host preference) for these leaf diseases. As a result, greater seedling mortality was observed for conspecific seedlings under both FU and Gap treatments. Synthesis. In the temperate forest, the J-C hypothesis is largely mediated through the strong negative influence of airborne leaf diseases rather than through soil-borne damping-off pathogens. We found that airborne diseases demonstrated distance-dependent host preferences, which led to greater conspecific seedling damage regardless of environmental light conditions.
Data from: Cis- and trans-acting genetic factors contribute to heterogeneity in the rate of crossing over between the Drosophila simulans clade species
In the genus Drosophila, variation in recombination rates has been found within and between species. Genetic variation for both cis- and trans-acting factors has been shown to affect recombination rates within species, but little is known about the genetic factors that affect differences between species. Here we estimate rates of crossing over for seven segments that tile across the euchromatic length of the X chromosome in the genetic backgrounds of three closely related Drosophila species. We first generated a set of Drosophila mauritiana lines each having two semi-dominant visible markers on the X chromosome and then introgressed these doubly marked segments into the genetic backgrounds of its sibling species, D. simulans and D. sechellia. Using these 21 lines (7 segments, 3 genetic backgrounds) we tested whether recombination rates within the doubly marked intervals differed depending on genetic background. We find significant heterogeneity among intervals and among species backgrounds. Our results suggest that a combination of both cis- and trans-acting factors have evolved among the three D. simulans clade species and interact to affect recombination rate.
Heterogeneous timing of asexual cycles in Plasmodium falciparum quantified by extended time-lapse microscopy
<p>Malarial fever arises from the synchronous bursting of human red blood cells by the <em>Plasmodium</em> parasite. The released parasites re-infect neighboring red blood cells and undergo another asexual cycle of differentiation and proliferation for 48 hours, before again bursting synchronously. The synchrony of bursting is lost during in vitro culturing of the parasite outside the human body, presumably because the asexual cycle is no longer entrained by host-specific circadian cues. Therefore, most in vitro malaria studies have relied on the artificial synchronization of the parasite population. However, much remains unknown about the degree of timing heterogeneity of asexual cycles and how artificial synchronization may affect this timing. Here, we combined time-lapse fluorescence microscopy and long-term culturing to follow single cells and directly measure the heterogeneous timing of in vitro asexual cycles. We first demonstrate that unsynchronized laboratory cultures are not fully asynchronous and the parasites exhibit a bimodal distribution in their first burst times. We then show that synchronized and unsynchronized cultures had similar asexual cycle periods, which indicates that artificial synchronization does not fundamentally perturb asexual cycle dynamics. Last, we demonstrate that sibling parasites descended from the same schizont exhibited significant variation in asexual cycle period, although smaller than the variation between non-siblings. The additional variance between non-siblings likely arises from the variable environments and/or developmental programs experienced in different host cells.</p>
Data from: An integrated assessment model of seabird population dynamics: can individual heterogeneity in susceptibility to fishing explain abundance trends in Crozet wandering albatross?
1. Seabirds have been incidentally caught in distant-water longline fleets operating in the Southern Ocean since at least the 1970s, and breeding numbers for some populations have shown marked trends of decline and recovery concomitant with longline fishing effort within their distributions. However, lacking is an understanding of how forms of among-individual heterogeneity may interact with fisheries bycatch and influence population dynamics. 2. We develop a model that uses comprehensive data on the spatial and temporal distributions of fishing effort and seabird foraging to estimate temporal overlaps, fishery catchability and consequent bycatch. We apply a population model that is structured by age, sex, life stage and spatially to Crozet Island wandering albatross and explore how heterogeneity in susceptibility to capture may have influenced the population's demography over time. 3. A model where some birds were assumed to be more susceptible to fisheries bycatch was able to successfully replicate the observed trend in breeding pairs. Considerably poorer fits were found without this assumption. Results suggested that the more susceptible birds may have been removed from the population by the 1990s. 4. The model was also able to highlight areas, times and fleets prone to increased bycatch. Knowledge of these factors should assist fisheries and conservation management bodies to quantify and reduce seabird bycatch through spatial management and fleet-specific mitigation efforts. 5. Synthesis and application. Many seabirds show complex life histories that make them highly susceptible to additional incidental mortality from fishing vessels. By applying a population model that integrates key aspects of seabird and fishery dynamics, we were able to explain the observed trends in the breeding population of Crozet wandering albatross and identify key areas and fleets where further mitigation may be required. In addition, the potential removal of a category of birds that shows increased susceptibility to capture has important implications for the conservation management of this population and other iconic species incidentally caught by large-scale commercial fisheries.
Data from: Ancestral state reconstruction, rate heterogeneity, and the evolution of reptile viviparity
Virtually all models for reconstructing ancestral states for discrete characters make the crucial assumption that the trait of interest evolves at a uniform rate across the entire tree. Although methods for identifying evolutionary rate shifts in continuous characters have attracted recent attention (e.g. Eastman et al., 2011, Stack et al., 2011), such methods for discrete characters have only very recently been developed (Beaulieu et al., 2013, Beaulieu and O'Meara 2014) and have yet to be widely used. However, ancestral state reconstructions of discrete characters are being performed on increasingly large phylogenies, where it is likely that evolutionary rates will vary greatly between different clades (Beaulieu and O'Meara 2014). Here, we show how failure to account for such variable evolutionary rates can cause highly anomalous (and likely incorrect) results, while three methods that accommodate rate variability yield the opposite, more plausible, and more robust reconstructions. The random local clock method, implemented in BEAST, estimates the position and magnitude of rate changes on the tree, split BiSSE estimates separate rate parameters for pre-specified clades, and the hidden rates model partitions each character state into a number of rate categories. The importance of accounting for rate heterogeneity in ancestral state reconstruction is highlighted empirically with a new analysis of the evolution of viviparity in squamate reptiles. Additionally, simulations show the inadequacy of traditional models when characters evolve with both asymmetry (different rates of change between states within a character) and heterotachy (different rates of character evolution across different clades).
Data from: Carrying capacity in a heterogeneous environment with habitat connectivity
A large body of theory predicts that populations diffusing in heterogeneous environments reach higher total size than if non-diffusing, and, paradoxically, higher size than in a corresponding homogeneous environment. However, this theory and its assumptions have not been rigorously tested. Here, we extended previous theory to include exploitable resources, proving qualitatively novel results, which we tested experimentally using spatially diffusing laboratory populations of yeast. Consistent with previous theory, we predicted and experimentally observed that spatial diffusion increased total equilibrium population abundance in heterogeneous environments, with the effect size depending on the relationship between r and K. Refuting previous theory, however, we discovered that homogeneously distributed resources support higher total carrying capacity than heterogeneously distributed resources, even with species diffusion. Our results provide rigorous experimental tests of new and old theory, demonstrating how the traditional notion of carrying capacity is ambiguous for populations diffusing in spatially heterogeneous environments.
Data from: Contrasting effects of spatial heterogeneity and environmental stochasticity on population dynamics of a perennial wildflower
Understanding how variation in growth, survival and reproduction affect population dynamics is a fundamental question in ecology. Although the effects of among-year variation (environmental stochasticity) are well understood, the effects of among-site variation (spatial heterogeneity) are less clearly defined. I evaluated the effects of spatial and temporal variation on the population dynamics of Pulsatilla patens, pasqueflower, a perennial prairie forb. I conducted a 10-year demographic monitoring study, and quantified vital rate variation among sites and years using generalized linear models. I incorporated vital rate functions using this variation into integral projection models for stochastic and spatially heterogeneous environments. I also explored the effects of temporal and spatial autocorrelation, by exploring model predictions over the range of possible values for temporal autocorrelation and local seed dispersal. Vital rates varied more among years than among sites. However, environmental stochasticity and spatial heterogeneity had similar magnitude effects on population dynamics. These effects were also qualitatively different: environmental stochasticity reduced population growth rates relative to the average, whereas spatial heterogeneity increased population growth rates. Spatial autocorrelation and negative temporal autocorrelation led to higher population growth rates, although environmental stochasticity still reduced growth rates for all autocorrelation values, and spatial heterogeneity increased growth rates for all autocorrelation values. Some form of autocorrelation would be necessary for model projections to match observed population trends. Synthesis. Spatial heterogeneity is as important as environmental stochasticity for population dynamics, but it is much less often incorporated into population projection models. This study points to a number of interesting avenues for future research into the roles of spatial heterogeneity and spatiotemporal variation for long-term population dynamics.
Data from: Rates of morphological evolution are heterogeneous in Early Cretaceous birds
The Early Cretaceous is a critical interval in the early history of birds. Exceptional fossils indicate that important evolutionary novelties such as a pygostyle and a keeled sternum had already arisen in Early Cretaceous taxa, bridging much of the morphological gap between Archaeopteryx and crown birds. However, detailed features of basal bird evolution remain obscure because of both the small sample of fossil taxa previously considered and a lack of quantitative studies assessing rates of morphological evolution. Here we apply a recently available phylogenetic method and associated sensitivity tests to a large data matrix of morphological characters to quantify rates of morphological evolution in Early Cretaceous birds. Our results reveal that although rates were highly heterogeneous between different Early Cretaceous avian lineages, consistent patterns of significantly high or low rates were harder to pinpoint. Nevertheless, evidence for accelerated evolutionary rates is strongest at the point when Ornithuromorpha (the clade comprises all extant birds and descendants from their most recent common ancestors) split from Enantiornithes (a diverse clade that went extinct at the end-Cretaceous), consistent with the hypothesis that this key split opened up new niches and ultimately led to greater diversity for these two dominant clades of Mesozoic birds.
Data from: Abiotic heterogeneity underlies trait-based competition and assembly
1. The fitness of individual species depends on their ability to persist and establish at low densities, just as the diversity of ecological communities depends on the establishment and persistence of low-density, 'invader' species. Theory predicts that abiotic conditions and the competitive make-up of resident communities jointly shape invader fitness, limiting the phenotypic identity of successful invaders. 2. We use an invasion experiment to ask how competitive traits of 20 introduced plant species alter their absolute fitness in fragments that differ in size, abiotic conditions and traits of the resident community. 3. We show that abiotic conditions interact with both invader traits and resident functional diversity to determine invader survival. Optimal invader traits depended on the soil characteristics, while greater resident trait diversity lowered invader fitness and had especially strong effects in low resource environments. Unlike other abiotic conditions, fragment size had consistent effects irrespective of invader identity, decreasing survival in larger fragments. 4. Synthesis. Our results illustrate how the abiotic environment mediates the effects of resident and invader traits on establishment, creating fitness landscapes that structure local diversity and the functional identities of successful species.
Data from: On the evolution of migration in heterogeneous environments
Populations often experience variable conditions, both in time and space. Here we develop a novel theoretical framework to study the evolution of migration under the influence of spatially and temporally variable selection and genetic drift. First we examine when polymorphism is maintained at a locus under heterogeneous selection, as a function of the pattern of spatial heterogeneity and the migration rate. In a second step, we study how levels of migration evolve under the joint action of kin competition and local adaptation at a polymorphic locus. This analysis reveals the existence of evolutionary bistability where a low or a high migration rate may evolve depending on the initial conditions. Last, we relax several assumptions regarding selection heterogeneity commonly made in previous studies and explore the consequences of more complex spatial and temporal patterns of variability in selection on the evolution of migration. We found that small modifications in the pattern of environmental heterogeneity may have dramatic effects on the evolution of migration. This work highlights the importance of considering more general scenarios of environmental heterogeneity when studying the evolution of life history traits in ecologically complex settings.
Data from: Spatial heterogeneity of tree diversity changes in montane forests under climate warming
<p>Many studies reported biotic change along a continental warming gradient. The temporal and spatial change of tree diversity and their sensitivity to climate warming might differ from region to region. however, understanding of the variation among studies with regard to the magnitude of such biotic changes is minimal, especially for montane ecosystems. To better understand spatial heterogeneity and temporal dynamics of mountain trees community change under climate warming over the past four decades. We re-surveyed and recorded all tree species from 107 long-term monitoring plots since 1974 in 2017 to study the changes of tree community composition of montane forests in the Giant Panda National Park. Our results showed that spatial differences were found in tree species diversity changes in response to climate warming over the past four decades. Tree species richness and abundance of montane forests increased over time in all our study area, except Liangshan (LS), especially in XiaoXiangLing with the highest warming rate. However, beta diversity underwent a significantly higher change rate at LS than in other mountains which indicated that plant species that do not belong to these four mountains entered LS in those year. Moreover, the beta diversities of tree between sample plots in the LS regions were homogenized. So, LS may become risk regions under continuing climatic warming, and should thus receive priority protection in the next conservation plan of the Giant Panda National Park (GPNP). We provide a explanation for the large variation among studies in warming-related biotic changes and recommend that the GPNP should implement a regional-specific conservation policy to strengthen conservation in at-risk regions (i.e., LS) under climate warming.</p>
Neural, functional, and aesthetic impacts of spatially heterogeneous flicker: a potential role of natural flicker
<p>Spatially heterogeneous flicker, characterized by probabilistic and locally independent luminance modulations, abounds in nature. It is generated by flames, water surfaces, rustling leaves, and so on, and it is pleasant to the senses. It affords spatiotemporal multistability that allows sensory activation conforming to the biases of the visual system, thereby generating the perception of spontaneous motion and likely facilitating the calibration of motion detectors. One may thus hypothesize that spatially heterogeneous flicker might potentially provide restoring stimuli to the visual system that engage fluent (requiring minimal top-down control) and self-calibrating processes. Here, we present some converging behavioral and electrophysiological evidence consistent with this idea. Spatially heterogeneous (multistable) flicker (relative to controls matched in temporal statistics) reduced posterior EEG (electroencephalography) beta power implicated in long-range neural interactions that impose top-down influences on sensory processing. Further, the degree of spatiotemporal multistability, the amount of posterior beta-power reduction, and the aesthetic responses to flicker were closely associated. These results are consistent with the idea that the pleasantness of natural flicker may derive from its spatiotemporal multistability that affords fluent and self-calibrating visual processing.</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.