Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
750
datasets available to search
ShareScore release 0.9.0
Dataset results
750 results for “heterogeneous data”
Data From: Investigating the Effect of GLU283 Protonation State on the Conformational Heterogeneity of CCR5 by Molecular Dynamics Simulations
<p>This dataset contains MD simulation results for CCR5 receptor in different states. There are three states this GPCR could be in: 1.apo state, i.e. not bound to any other protein or ligand 2.holo state, i.e. bound by maraviroc (MRV) 3.GP120 bound state, i.e. bounded to HIV envelope protein GP120 and human receptor CD4. </p> <p>For apo state simulations, three different starting structure were used and the simulation results for them are given in three different folders named after the PDB ID of initial structures. </p> <p>One critical residue of CCR5 receptor, GLU283, was considered in two different protonation state, hence there are two folders for each studied system, GLU283 and GLH283 for differently protonated systems. </p> <p>The MD trajectories of studied systems are in dcd format and due to size issues waters, ions and membrane atoms were removed. </p> <p>For each system, three replica MD simulations were performed, hence, there are three folders with names REPLICA1, REPLICA2, REPLICA3.</p> <p> </p>
Fig. 2 in Data storage and data re-use in taxonomy-the need for improved storage and accessibility of heterogeneous data
Fig. 2 Sch_m_ of a data packag_ in taxonomy c_nt_r_d around a sp_cim_n. This _xampl_ is from zoology, and data typ_s will obviously diff_r among taxa
Aligned Cross-modal Integration and Regulatory Heterogeneity Characterization of Single-Cell Multiomic Data with Deep Contrastive Learning
Open the record for dataset details and reuse information.
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.
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>
Data from: Reporting tumor molecular heterogeneity in histopathological diagnosis
Background: Detection of molecular tumor heterogeneity has become of paramount importance with the advent of targeted therapies. Analysis for detection should be comprehensive, timely and based on routinely available tumor samples. Aim: To evaluate the diagnostic potential of targeted multigene next-generation sequencing (TM-NGS) in characterizing gastrointestinal cancer molecular heterogeneity. Methods: 35 gastrointestinal tract tumors, five of each intestinal type gastric carcinomas, pancreatic ductal adenocarcinomas, pancreatic intraductal papillary mucinous neoplasms, ampulla of Vater carcinomas, hepatocellular carcinomas, cholangiocarcinomas, pancreatic solid pseudopapillary tumors were assessed for mutations in 46 cancer-associated genes, using Ion Torrent semiconductor-based TM-NGS. One ampulla of Vater carcinoma cell line and one hepatic carcinosarcoma served to assess assay sensitivity. TP53, PIK3CA, KRAS, and BRAF mutations were validated by conventional Sanger sequencing. Results: TM-NGS yielded overlapping results on matched fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissues, with a mutation detection limit of 1% for fresh-frozen high molecular weight DNA and 2% for FFPE partially degraded DNA. At least one somatic mutation was observed in all tumors tested; multiple alterations were detected in 20/35 (57%) tumors. Seven cancers displayed significant differences in allelic frequencies for distinct mutations, indicating the presence of intratumor molecular heterogeneity; this was confirmed on selected samples by immunohistochemistry of p53 and Smad4, showing concordance with mutational analysis. Conclusions: TM-NGS is able to detect and quantitate multiple gene alterations from limited amounts of DNA, moving one step closer to a next-generation histopathologic diagnosis that integrates morphologic, immunophenotypic, and multigene mutational analysis on routinely processed tissues, essential for personalized cancer therapy.
Data from: The genetics of adaptation to discrete heterogeneous environments: frequent mutation or large-effect alleles can allow range expansion
Range expansions are complex evolutionary and ecological processes. From an evolutionary standpoint, a populations' adaptive capacity can determine the success or failure of expansion. Using individual-based simulations, we model range expansion over a two-dimensional, approximately continuous landscape. We investigate the ability of populations to adapt across patchy environmental gradients and examine how the effect sizes of mutations influence the ability to adapt to novel environments during range expansion. We find that genetic architecture and landscape patchiness both have the ability to change the outcome of adaptation and expansion over the landscape. Adaptation to new environments succeeds via many mutations of small effect or few of large effect, but not via the intermediate between these cases. Higher genetic variance contributes to increased ability to adapt, but an alternative route of successful adaptation can proceed from low genetic variance scenarios with alleles of sufficiently large effect. Steeper environmental gradients can prevent adaptation and range expansion on both linear and patchy landscapes. When the landscape is partitioned into local patches with sharp changes in phenotypic optimum, the local magnitude of change between subsequent patches in the environment determines the success of adaptation to new patches during expansion.
Data from: Integrated molecular imaging reveals tissue heterogeneity driving host-pathogen interactions
All diseases are characterized by distinct changes in tissue molecular distribution. Molecular analysis of intact tissues traditionally requires pre-existing knowledge of, and reagents for, the targets of interest. Conversely, label-free discovery of disease-associated tissue analytes requires destructive processing for downstream identification platforms. Tissue-based analyses therefore sacrifice discovery to gain spatial distribution of known targets, or sacrifice tissue architecture for discovery of unknown targets. To overcome these obstacles, we developed a multi-modality imaging platform for discovery-based molecular histology. We apply this platform to a model of disseminated infection triggered by the important pathogen Staphylococcus aureus, leading to the discovery of infection-associated alterations in the distribution and abundance of proteins and elements in tissue. These data provide an unbiased, three-dimensional analysis of how disease impacts the molecular architecture of complex tissues, enable culture-free diagnosis of infection through imaging-based detection of bacterial and host analytes, and reveal molecular heterogeneity at the host-pathogen interface.
Data from: Grazing and nitrogen addition restructure the spatial heterogeneity of soil microbial community structure and enzymatic activities
<p>1. In grassland ecosystems, large herbivorous animal grazing activity and increasing nitrogen deposition strongly alters microbial community structure and function. Understanding the effects of grazing and nitrogen addition on the spatial heterogeneity in soil microbial community structure, enzymatic activities and the underlying mechanisms are crucial for making better predictions of soil organic matter dynamics and nutrient cycling. </p> <p>2. We examined the spatial heterogeneity of soil microbial community structure and enzymatic activity associated with changes in soil microclimate, soil characteristics, plant biomass and soil nutrient responses to grazing and nitrogen addition using a manipulative experiment with control (CK), grazing (G), nitrogen addition (N) and grazing plus nitrogen addition (NG) treatments in a <i>Leymus chinensis </i>meadow steppe, in northeastern China. </p> <p>3. The results demonstrated that soil microbial community structure and enzymatic activities showed a high level of spatial dependence [C/(C + C0)≥0.9] in the CK plot. G, N and NG treatments not only reduced the spatial variability ofsoil microbial community structure and enzymatic activities, but also reshaped the spatial links between enzymes activities and microbial community structure. Litter biomass, soil temperature and soil nutrients (soil dissolved inorganic nitrogen or soil dissolved organic carbon) explained 21-27% of the spatial variability of soil microbial community structure in the CK treatment and pH was the strongest driver for the spatial variability of soil enzymatic activities. Meanwhile, the homogenization in soil water content induced by the N addition treatment was a determinant of the reduction in spatial heterogeneity of the microbial community structure. The combination of soil physicochemical properties (bulk density, soil pH and soil dissolved inorganic nitrogen), soil temperature and root biomass explained 32-43% of the spatial variability of the microbial community structure in the G treatment, and N and G treatments had additive effects on the spatial heterogeneity of total PLFAs by homogenizing root biomass. Plant biomass and microbial community structure were the major drivers for the spatial heterogeneity of enzymatic activities under G, N and NG. In NG, the change in spatial variability of enzymatic activities was dominated by N addition. Regardless of grazing, N addition facilitated the spatial correlation between microbial community structure and enzyme activities. </p> <p>4. Overall, our results revealed the drivers of soil microbial community structure and enzymatic activities spatial pattern shift due to grazing and N addition, highlighting the role that spatial variability in soil microbial community structure and enzymatic activities has on the <i>L. chinensis</i> meadow steppe.</p>
Data from: Elements of metacommunity structure of diatoms and macroinvertebrates within stream networks differing in environmental heterogeneity
<p><strong>Aim:</strong> Idealized metacommunity structures (i.e. checkerboard, random, quasi-structures, nested, Clementsian, Gleasonian, and evenly spaced) have recently gained increasing attention, but their relationships with environmental heterogeneity and how they vary with organism groups remain poorly understood. Here we tested two main hypotheses: (1) gradient-driven patterns (Clementsian and Gleasonian) occur frequently in heterogeneous environments, and (2) small organisms (here, diatoms) are more likely to exhibit gradient-driven patterns than large organisms (here, macroinvertebrates).</p> <p><strong>Location:</strong> Streams in three regions in China.</p> <p><strong>Taxon:</strong> Diatoms and macroinvertebrates.</p> <p><strong>Methods:</strong> The stream diatom and macroinvertebrate data, as well as the environmental data collected from the same set of sites were used to examine the idealized metacommunity structures via the elements of the metacommunity structure (EMS; coherence, turnover, and boundary clumping) analysis in three regions. We extended the traditional EMS approach by ordering sites along known environmental gradients.</p> <p><strong>Results: </strong>We found that Clementsian structure with high degrees of coherence and turnover, and significantly positive clumping was typically observed in the high-heterogeneity regions, whereas randomness was prevalent in the low-heterogeneity region. Macroinvertebrates exhibited clearer Clementsian structures compared with diatoms, while diatoms showed more randomness compared with macroinvertebrates, indicating a stronger role of environmental filtering for macroinvertebrates than diatoms. In most cases, the results of the more novel EMS approach differed from the results of the traditional EMS technique.</p> <p><strong>Main Conclusions:</strong> Our results suggested that the occurrence of different metacommunity structures may be related with the degree of regional environmental heterogeneity. However, diatom metacommunities were more random than those of macroinvertebrate, and such an unexpected result may result from different dispersal abilities between the two organism groups. In addition, we found that the novel EMS approach increased power in discerning metacommunity structure in comparison to the traditional EMS technique.</p>
Data for: Continuous API Evolution in Heterogenous Enterprise Software Systems
<p>The ability to independently deploy parts of a software system is one of the cornerstones of modern software development, and allows for these parts to evolve independently and at different speeds.</p> <p>A major challenge of such independent deployment, however, is to ensure that despite their individual evolution, the interfaces between interacting parts remain compatible. This is especially important for enterprise software systems, which are often highly integrated and based on heterogenous IT infrastructures.</p> <p>Although several approaches for interface evolution have been proposed, many of these rely on the developer to adhere to certain rules, but provide little guidance for doing so. In this paper, we present an approach for interface evolution that is easy to use for developers, and also addresses typical challenges of heterogenous enterprise software, especially legacy system integration.</p> <p>This dataset contains the questions and results from the survey among developers to roughly assess the applicability of the approach in practice.</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.