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Gene Expression Transcriptomics Data for benchmarking Perturbation Models - Part 3
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Merged HLS2 and GEDI data for estimating canopy height with IBM's granite-geospatial-canopyheight model
<p>This dataset contains merged Harmonized Landsat-Sentinel 2 (HLS2) (L30 only) and Global Ecosystem Dynamics Investigation (GEDI) L2A data following CRS:4326. It has been assembled for estimating canopy height with a fine-tuned granite geospatial foundation model developed by IBM Research. Please see https://huggingface.co/ibm-granite/granite-geospatial-canopyheight for more information on data preparation and model use.</p> <p><strong>HLS2—</strong>Masek, J., J. Ju, J. Roger, S. Skakun, E. Vermote, M. Claverie, J. Dungan, Z. Yin, B. Freitag, C. Justice. HLS Sentinel-2 MSI Surface Reflectance Daily Global 30m v2.0. 2021, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/HLS/HLSS30.002 </p> <p><strong>GEDI L2A—</strong>Lee, J., S. Favrichon, S. Mauceri, Y. Yang, J. Armston, and S. Saatchi. 2023. Addressing underestimation in global forest structure mapping. <a href="https://doi.org/10.22541/essoar.167276451.10705079/v1">https://doi.org/10.22541/essoar.167276451.10705079/v1</a></p>
Support Data Operational Efficiency of Pharmaceutical Companies in China Based on Three Stage DEA with Undesirable Outputs Model.
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Research data - design patterns for platform revenue models
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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: A game-theoretical model of kleptoparasitic behaviour in an urban gull (Laridae) population.
Kleptoparasitism (food stealing) is a significant behaviour for animals that forage in social groups as it permits some individuals to obtain resources whilst avoiding the costs of searching for their own food. Evolutionary game theory has been used to model kleptoparasitism, with a series of differential equation based compartmental models providing significant theoretical insights into behaviour in kleptoparasitic populations. In this paper we apply this compartmental modelling approach to kleptoparasitic behaviour in a real foraging population of urban gulls (Laridae). Field data was collected on kleptoparasitism and a model developed that incorporated the same kleptoparasitic and defensive strategies available to the study population. Two analyses were conducted: 1. An assessment of whether the density of each behaviour in the population was at an equilibrium. 2. An investigation of whether individual foragers were using Evolutionarily Stable Strategies (ESS) in the correct environmental conditions. The results showed the density of different behaviours in the population could be at an equilibrium at plausible values for handling time and fight duration. Individual foragers used aggressive kleptoparasitic strategies effectively in the correct environmental conditions but some individuals in those same conditions failed to defend food items. This was attributed to the population being composed of three species that differed in competitive ability. These competitive differences influenced the strategies that individuals were able to use. Rather than gulls making poor behavioural decisions these results suggest a more complex three-species model is required to describe the behaviour of this population.
Data from: Mechanistic model of evolutionary rate variation en route to a nonphotosynthetic lifestyle in plants
Because novel environmental conditions alter the selection pressure on genes or entire subgenomes, adaptive and nonadaptive changes will leave a measurable signature in the genomes, shaping their molecular evolution. We present herein a model of the trajectory of plastid genome evolution under progressively relaxed functional constraints during the transition from autotrophy to a nonphotosynthetic parasitic lifestyle. We show that relaxed purifying selection in all plastid genes is linked to obligate parasitism, characterized by the parasite's dependence on a host to fulfill its life cycle, rather than the loss of photosynthesis. Evolutionary rates and selection pressure coevolve with macrostructural and microstructural changes, the extent of functional reduction, and the establishment of the obligate parasitic lifestyle. Inferred bursts of gene losses coincide with periods of relaxed selection, which are followed by phases of intensified selection and rate deceleration in the retained functional complexes. Our findings suggest that the transition to obligate parasitism relaxes functional constraints on plastid genes in a stepwise manner. During the functional reduction process, the elevation of evolutionary rates reaches several new rate equilibria, possibly relating to the modified protein turnover rates in heterotrophic plastids.
Data from: Using probability modelling and genetic parentage assignment to test the role of local mate availability in mating system variation.
The formal testing of mating system theories with empirical data is important for evaluating the relative importance of different processes in shaping mating systems in wild populations. Here we present a generally applicable probability modelling framework to test the role of local mate availability in determining a population's level of genetic monogamy. We provide a significance test for detecting departures in observed mating patterns from model expectations based on mate availability alone, allowing the presence and direction of behavioural effects to be inferred. The assessment of mate availability can be flexible and in this study it was based on population density, sex ratio and spatial arrangement. This approach provides a useful tool for (1) isolating the effect of mate availability in variable mating systems and (2) in combination with genetic parentage analyses, gaining insights into the nature of mating behaviours in elusive species. To illustrate this modelling approach, we have applied it to investigate the variable mating system of the mountain brushtail possum (Trichosurus cunninghami) and compared the model expectations with the outcomes of genetic parentage analysis over an 18 year study. The observed level of monogamy was higher than predicted under the model. Thus, behavioural traits, such as mate guarding or selective mate choice, may increase the population level of monogamy. We show that combining genetic parentage data with probability modelling can facilitate an improved understanding of the complex interactions between behavioural adaptations and demographic dynamics in driving mating system variation.
Data from: PaleoENM: applying ecological niche modeling to the fossil record
Ecological niche modeling (ENM) is a quantitative approach to predict species' abiotic requirements. It is a correlative technique, requiring geographically explicit information on species occurrences and the suites of environmental conditions experienced at each occurrence point. The output of these models is a set of environmental suitability rules that can be projected geographically and through time to test biogeographic, ecologic, and evolutionary hypotheses. Although developed by biologists and used extensively in the modern, ENM is in its early stages of application to the deep-time fossil record (hence PaleoENM). In part its limited use in the fossil record thus far reflects the methodological challenge of constructing paleoenvironmental layers needed for PaleoENM analysis, whereas in the modern these layers are available from large public databases (e.g., WorldClim). This paper provides a contextual and methodological framework for appropriately applying PaleoENM, including best practices for developing species occurrence and paleoenvironmental data sets for PaleoENM analyses.
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: Estimation of aboveground net primary productivity in secondary tropical dry forests using the Carnegie–Ames–Stanford approach (CASA) model
Although tropical dry forests (TDFs) cover roughly 42% of all tropical ecosystems, extensive deforestation and habitat fragmentation pose important limitations for their conservation and restoration worldwide. In order to develop conservation policies for this endangered ecosystem, it is necessary to quantify their provision of ecosystems services such as carbon sequestration and primary production. In this paper we explore the potential of the Carnegie–Ames–Stanford approach (CASA) for estimating aboveground net primary productivity (ANPP) in a secondary TDF located at the Santa Rosa National Park (SRNP), Costa Rica. We calculated ANPP using the CASA model (ANPPCASA) in three successional stages (early, intermediate, and late). Each stage has a stand age of 21 years, 32 years, and 50+ years, respectively, estimated as the age since land abandonment. Our results showed that the ANPPCASA for early, intermediate, and late successional stages were 3.22 Mg C ha−1 yr−1, 8.90 Mg C ha−1 yr−1, and 7.59 Mg C ha−1 yr−1, respectively, which are comparable with rates of carbon uptake in other TDFs. Our results indicate that key variables that influence ANPP in our dry forest site were stand age and precipitation seasonality. Incident photosynthetically active radiation and temperature were not dominant in the ANPPCASA. The results of this study highlight the potential of the use of remote sensing techniques and the importance of incorporating successional stage in accurate regional TDF ANPP estimation.
Data from: Protective effect of Galectin-9 in murine model of lung emphysema: involvement of neutrophil migration and MMP-9 production
Purpose: Chronic obstructive pulmonary disease (COPD) is characterized by irreversible airflow obstruction and pulmonary emphysema. Persistent inflammation and remodeling of the lungs and airways result in reduced lung function and a lower quality of life. Galectin (Gal)-9 plays a crucial role as an immune modulator in various diseases. However, its role in the pathogenesis of pulmonary emphysema is unknown. This study investigates whether Gal-9 is involved in pulmonary inflammation and changes in emphysema in a porcine pancreatic elastase (PPE)-induced emphysema model. Materials and Methods: Gal-9 was administered to mice subcutaneously once daily from 1 day before PPE instillation to day 5. During the development of emphysema, lung tissue and bronchoalveolar lavage fluid (BALF) were collected. Histological and cytological findings, concentrations of chemokines and matrix metalloproteinases (MMPs) in the BALF, and the influence of Gal-9 treatment on neutrophils were analyzed. Results: Gal-9 suppressed the pathological changes of PPE-induced emphysema. The mean linear intercept (Lm) of Gal-9-treated emphysema mice was significantly lower than that of PBS-treated emphysema mice (66.1 ± 3.3 µm vs. 118.8 ± 14.8 µm, respectively; p < 0.01). Gal-9 decreased the number of neutrophils and levels of MMP-9, MMP-2 and tissue inhibitor of metalloproteinases (TIMP)-1 in the BALF. The number of neutrophils in the BALF correlated significantly with MMPs levels. Interestingly, Gal-9 pretreatment in vitro inhibited the chemotactic activity of neutrophils and MMP-9 production from neutrophils. Furthermore, in Gal-9-deficient mice, PPE-induced emphysema progressed significantly compared with that in wild–type (WT) mice (108.7 ± 6.58 µm vs. 77.19 ± 6.97 µm, respectively; p < 0.01). Conclusions: These results suggest that Gal-9 protects PPE-induced inflammation and emphysema by inhibiting the infiltration of neutrophils and decreasing MMPs levels. Exogenous Gal-9 could be a potential therapeutic agent for COPD.
Data from: Hypothesised diprotomeric enzyme complex supported by stochastic modelling of Palytoxin-induced Na/K pump channels
The sodium-potassium pump (Na+/K+ pump) is crucial for cell physiology. Despite great advances in the understanding of this ionic pumping system, its mechanism is not completely understood. We propose the use of the Statistical Model Checker to investigate palytoxin-induced Na+/K+ pump channels. We modelled a system of reactions representing transitions between the conformational substates of the channel with parameters, concentrations of the substates, and reaction rates extracted from simulations reported in the literature, based on electrophysiological recordings in a whole-cell configuration. The model was implemented using the UPPAAL-SMC platform. Comparing simulations and probabilistic queries from stochastic system semantics with experimental data, it was possible to propose additional reactions to reproduce the single channel dynamic. The probabilistic analyses and simulations suggest that the palytoxin-induced Na+/K+ pump channel functions as a diprotomeric complex in which protein-protein interactions increase the affinity of the Na+/K+ pump affinity for palytoxin.
Data from: Comparing process-based and constraint-based approaches for modeling macroecological patterns
Ecological patterns arise from the interplay of many different processes, and yet the emergence of consistent phenomena across a diverse range of ecological systems suggests that many patterns may in part be determined by statistical or numerical constraints. Differentiating the extent to which patterns in a given system are determined statistically, and where it requires explicit ecological processes, has been difficult. We tackled this challenge by directly comparing models from a constraint-based theory, the Maximum Entropy Theory of Ecology (METE) and models from a process-based theory, the size-structured neutral theory (SSNT). Models from both theories were capable of characterizing the distribution of individuals among species and the distribution of body size among individuals across 76 forest communities. However, the SSNT models consistently yielded higher overall likelihood, as well as more realistic characterizations of the relationship between species abundance and average body size of conspecific individuals. This suggests that the details of the biological processes contain additional information for understanding community structure that are not fully captured by the METE constraints in these systems. Our approach provides a first step towards differentiating between process- and constraint-based models of ecological systems and a general methodology for comparing ecological models that make predictions for multiple patterns.
Data from: A simple behavioral model predicts the emergence of complex animal hierarchies
Social dominance hierarchies are widespread, but little is known about the mechanisms that produce non-linear structures. In addition to despotic hierarchies, where a single individual dominates, shared hierarchies exist where multiple individuals occupy a single rank. In vertebrates, these complex dominance relationships are thought to develop from interactions that require higher cognition, but similar cases of shared dominance have been found in social insects. Combining empirical observations with a modeling approach, we show that all three hierarchy structures-linear, despotic, and shared-can emerge from different combinations of simple interactions present in social insects. Our model shows that a linear hierarchy emerges when a typical winner-loser interaction (dominance biting) is present. A despotic hierarchy emerges when a policing interaction is added that results in the complete loss of dominance status for an attacked individual (physical policing). Finally, a shared hierarchy emerges with the addition of a "winner-winner" interaction that results in a positive outcome for both interactors (antennal dueling). Antennal dueling is an enigmatic ant behavior that has previously lacked a functional explanation. These results show how complex social traits can emerge from simple behaviors without requiring advanced cognition.
Data from: Model adequacy and microevolutionary explanations for stasis in the fossil record
Long-term phenotypic stasis is frequently observed in the fossil record, but not readily predicted from microevolutionary theory. To test competing explanations for stasis on macroevolutionary time scales we need reliably estimated parameters from appropriate evolutionary models that adequately describe the evolutionary trait dynamics. Here, we develop tests to assess the adequacy of the most commonly used stasis model in evolutionary biology and apply them to time series of phenotypic traits from fossil lineages. Of the 572 fossil time series we analyzed from the literature, 263 times series showed a better fit to the stasis model relative to alternative models, but only 172 of those fitted the stasis model both in relative and absolute terms. The estimated trait variances from these 172 time series do not correlate with rough proxies of effective population size. Our preliminary investigation of the fixed-optimum hypothesis hence fails to give empirical support to the idea that genetic drift around a constant trait optimum is an explanation for stasis in the fossil record. We argue that optima following stationary processes on the adaptive landscape is a viable hypothesis for stasis that needs further investigation. We end by discussing how investigations of model adequacy can be a valuable approach for increasing our understanding of the dynamics of the adaptive landscape on macroevolutionary time scales.
Data from: Model-based acceleration of Look-Locker T1 mapping
Mapping the longitudinal relaxation time T1 has widespread applications in clinical MRI as it promises a quantitative comparison of tissue properties across subjects and scanners. Due to the long scan times of conventional methods, however, the use of quantitative MRI in clinical routine is still very limited. In this work, an acceleration of Inversion-Recovery Look-Locker (IR-LL) T1 mapping is presented. A model-based algorithm is used to iteratively enforce an exponential relaxation model to a highly undersampled radially acquired IR-LL dataset obtained after the application of a single global inversion pulse. Using the proposed technique, a T1 map of a single slice with 1.6mm in-plane resolution and 4mm slice thickness can be reconstructed from data acquired in only 6s. A time-consuming segmented IR experiment was used as gold standard for T1 mapping in this work. In the subsequent validation study, the model-based reconstruction of a single-inversion IR-LL dataset exhibited a T1 difference of less than 2.6% compared to the segmented IR-LL reference in a phantom consisting of vials with T1 values between 200ms and 3000ms. In vivo, the T1 difference was smaller than 5.5% in WM and GM of seven healthy volunteers. Additionally, the T1 values are comparable to standard literature values. Despite the high acceleration, all model-based reconstructions were of a visual quality comparable to fully sampled references. Finally, the reproducibility of the T1 mapping method was demonstrated in repeated acquisitions. In conclusion, the presented approach represents a promising way for fast and accurate T1 mapping using radial IR-LL acquisitions without the need of any segmentation.
Data from: Accounting for uncertainty in dormant life stages in stochastic demographic models
Dormant life stages are often critical for population viability in stochastic environments, but accurate field data characterizing them are difficult to collect. Such limitations may translate into uncertainties in demographic parameters describing these stages, which then may propagate errors in the examination of population-level responses to environmental variation. Expanding on current methods, we 1) apply data-driven approaches to estimate parameter uncertainty in vital rates of dormant life stages and 2) test whether such estimates provide more robust inferences about population dynamics. We built integral projection models (IPMs) for a fire-adapted, carnivorous plant species using a Bayesian framework to estimate uncertainty in parameters of three vital rates of dormant seeds – seed-bank ingression, stasis and egression. We used stochastic population projections and elasticity analyses to quantify the relative sensitivity of the stochastic population growth rate (log λs) to changes in these vital rates at different fire return intervals. We then ran stochastic projections of log λs for 1000 posterior samples of the three seed-bank vital rates and assessed how strongly their parameter uncertainty propagated into uncertainty in estimates of log λs and the probability of quasi-extinction, Pq(t). Elasticity analyses indicated that changes in seed-bank stasis and egression had large effects on log λs across fire return intervals. In turn, uncertainty in the estimates of these two vital rates explained > 50% of the variation in log λs estimates at several fire-return intervals. Inferences about population viability became less certain as the time between fires widened, with estimates of Pq(t) potentially > 20% higher when considering parameter uncertainty. Our results suggest that, for species with dormant stages, where data is often limited, failing to account for parameter uncertainty in population models may result in incorrect interpretations of population viability.
Data from: Multiple regression modelling for estimating endocranial volume in extinct Mammalia
The profound evolutionary success of mammals has been linked to behavioral and life-history traits, many of which have been tied to brain size. However, studies of the evolution of this key trait have yet to explore the full potential of the fossil record, being limited by the difficulty of obtaining endocranial data from fossils. Using measurements of endocranial volume, length, height, and width of the braincase in 503 adult specimens from 199 extant species, representing 99 of 133 extant mammalian families, we expand upon a simple method of using multiple regression to develop a formula for estimating brain size from external skull measurements. We also examined non- mammalian synapsids to assess the phylogenetic limits of our model's application. Model-predicted volume correlates strongly with measured volume (R2 = 0.993) and prediction error is between 16% and 19%. Error decreases if models developed for well-sampled subclades such as primates or rodents are used, demonstrating that some differential evolution of the relationship between brain size and skull size has occurred. However, reanalysis using phylogenetically independent contrasts demonstrates weak phylogenetic dependency, indicating that our model is appropriate for estimating the endocranial volume of species of unknown phylogenetic affinity. Thus, the model represents a generally applicable, fast and cost-efficient way to dramatically expand the taxonomic and temporal scope of mammalian brain size data sets. Even endocranial volumes of taxa with highly derived crania, such as cetaceans and monotremes, can be estimated confidently. However, the model works best for generalized placental crania. Fundamental differences in cranial architecture suggest that the model cannot provide accurate estimates of endocranial volume in non-mammalian synapsids more basal than Morganucodon (ca. 200 Ma). Therefore, use of the model for taxa phylogenetically distant from the mammalian crown group is not warranted, but it might be used to establish relative brain sizes between closely related subgroups.
Data from: Comparison of non-Gaussian quantitative genetic models for migration and stabilizing selection
The balance between stabilizing selection and migration of maladapted individuals has formerly been modeled using a variety of quantitative genetic models of increasing complexity, including models based on a constant expressed genetic variance and models based on normality. The infinitesimal model can accommodate non-normality and a non-constant genetic variance as a result of linkage disequilibrium. It can be seen as a parsimonious one-parameter model which approximates the underlying genetic details well when a large number of loci are involved. Here, the performance of this model is compared to several more realistic explicit multilocus models, with either two, several or a large number of alleles per locus with unequal effect sizes. Predictions for the deviation of the population mean from the optimum are highly similar across the different models, so that the non-Gaussian infinitesimal model forms a good approximation. It does however generally estimate a higher genetic variance than the multilocus models, with the difference decreasing with an increasing number of loci. The difference between multilocus models depends more strongly on the effective number of loci, accounting for relative contributions of loci to the variance, than on the number of alleles per locus.
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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.