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544 results for “integrated models”
Structural insights into distinct mechanisms of RNA polymerase II and III recruitment to snRNA promoters - segmented EM density used in integrative modeling
<p>segmented cryoEM map and derived gaussian mixture model used in the integrative modeling of the human SNAPc complex </p>
Data for integrative modeling of the Nuclear Pore Complex from Schizosaccharomyces pombe
<p>Repository with input and output files utilized for integrative modeling of the Nuclear Pore Complex from Schizosaccharomyces pombe.</p>
Data from: Towards robust evolutionary inference with integral projection models
Integral projection models (IPMs) are extremely flexible tools for ecological and evolutionary inference. IPMs track the distribution of phenotype in populations through time, using functions describing phenotype-dependent development, inheritance, survival and fecundity. For evolutionary inference, two important features of any model are the ability to (i) characterize relationships among traits (including values of the same traits across ages) within individuals, and (ii) characterize similarity between individuals and their descendants. In IPM analyses, the former depends on regressions of observed trait values at each age on values at the previous age (development functions), and the latter on regressions of offspring values at birth on parent values as adults (inheritance functions). We show analytically that development functions, characterized this way, will typically underestimate covariances of trait values across ages, due to compounding of regression to the mean across projection steps. Similarly, we show that inheritance, characterized this way, is inconsistent with a modern understanding of inheritance, and underestimates the degree to which relatives are phenotypically similar. Additionally, we show that the use of a constant biometric inheritance function, particularly with a constant intercept, is incompatible with evolution. Consequently, current implementations of IPMs will predict little or no phenotypic evolution, purely as artefacts of their construction. We present alternative approaches to constructing development and inheritance functions, based on a quantitative genetic approach, and show analytically and through an empirical example on a population of bighorn sheep how they can potentially recover patterns that are critical to evolutionary inference.
Supplementary material for "Spatio-temporal modelling of abundance from multiple data sources in an integrated spatial distribution model"
<p><strong>Abstract</strong></p> <p><strong>Aim:</strong> In biodiversity monitoring, observational data are often collected in multiple, disparate schemes with greatly varying degrees of standardization and possibly at different spatial and temporal scales. Technical advances also change the type of data over time. The resulting heterogeneous data sets are often deemed to be incompatible. Consequently, many available data sets may be ignored in practical analyses. Here, we propose a more efficient use of disparate biodiversity data to assess species distributions and population trends.<br> <br> <strong>Location:</strong> Switzerland (Europe)<br> <br> <strong>Taxon:</strong> Birds</p> <p><strong>Methods: </strong>We developed an integrated, hierarchical species distribution model with a joint likelihood for all data sets using a shared state process (e.g., latent species abundance or occurrence), but distinct observation process for each data set. We show how the abundance submodel of a binomial N-mixture model can fuse four different data types (count, detection/non-detection, presence-only, and absence-only data) and enable improved inferences about spatio-temporal patterns in abundance. As case studies, we use data from multiple avian biodiversity monitoring schemes. In the first, the goal is estimating abundance-based species distribution maps. In the second, we infer trends in population abundance across time.</p> <p><strong>Results: </strong>Accuracy and precision of abundance estimates increased when combining data from different sources compared to using a single data source alone. This is particularly valuable when data from each single data source is too sparse for reliable parameter estimation.<br> Main conclusions: We show that exploiting the complementary nature of "cheap", but abundant, citizen-science data and less abundant, but more information-rich, data from structured monitoring programs might be ideal to estimate distribution and population trends more accurately, especially for rare species. Joint likelihoods allow to include a wide variety of different data sets to (1) combine all the available information and to (2) mitigate weaknesses of one by the strength of another.</p> <p> </p> <p> </p>
Integrating laboratory experiments and biogeographic modelling approaches to understand sensitivity to ocean warming in rare and common marine annelids
<p><span>Among ectotherms, rare species are expected to have a narrower thermal niche breadth and reduced acclimation capacity and thus be more vulnerable to global warming than their common relatives. To assess these hypotheses, we experimentally quantified the thermal sensitivity of seven common, uncommon, and rare species of temperate marine annelids of the genus <em>Ophryotrocha</em> to assess those species' vulnerability to ocean warming. We measured the upper and lower limits of physiological thermal tolerance, survival, and reproductive performance of each species along a temperature gradient (18, 24, and 30 °C). We then combined this information to produce curves of each species' fundamental thermal niche by including trait plasticity. Each thermal curve was then</span> <span>expressed as a habitat suitability index (HSI) and projected for the Mediterranean Sea and temperate Atlantic Ocean under a present day (1970-2000), mid- (2050-2059) and late- (2090-2099) 21st Century scenario for two climate change scenarios (RCP2.6 and RCP8.5). Rare and uncommon species showed a reduced upper thermal tolerance compared to common species, and the niche breadth and acclimation capacity were comparable among groups. The simulations predicted an overall increase in the HSI for all species and identified potential hotspots of HSI decline for uncommon and rare species</span> <span>along the warm boundaries of their potential distribution, though they failed to project the higher sensitivity of these species into a greater vulnerability to ocean warming. In the discussion, we provide elements and caveats on the implications of our results for conservation efforts.</span></p>
Modeling Sequences of Earthquakes and Aseismic Slip (SEAS) in Elasto-Plastic Fault Zones With a Hybrid Finite Element Spectral Boundary Integral Scheme
<p>This repository contains the results of 2D simulations of the earthquake cycles accounting for off-fault plasticity.</p> <p>Folder Case1 contains the slip rate and time history for all simulations with cohesion c = 47 MPa</p> <p>Folder Case2 contains the slip rate and time history for all simulations with cohesion c = 25 MPa</p> <p>the mat files contain equivalent plastic strain of each element stored at the start and end of each event with element connectivity and node coordinates.</p>
Development of a multivariable risk model integrating urinary peptide metabolites and Extracellular Vesicle RNA data to detect significant prostate cancer
<p>The aim of this study was to investigate whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples were analyzed by mass spectrometry and NanoString gene-expression analysis. As a result, four classifiers were generated: ‘MassSpec’ (CE-MS proteomics), ‘EV-RNA’, ‘SoC’ (standard of care) and ‘ExoSpec’. The best prediction for Gs³3+4 at initial biopsy (AUC=0.83, 95% CI:0.77-0.88) was achieved by applying ‘ExoSpec’ classifier and he outperformed other predictive classifiers. In addition, the results showed that the performance of ‘ExoSpec’ could reduce unnecessary biopsies by 30%.</p>
PrISM: Precision for Integrative Structural Models
<p>Datasets with input, output and scripts to reproduce results and figures from "PrISM: Precision for Integrative Structural Models". Manuscript at https://www.biorxiv.org/content/10.1101/2021.06.22.449385v4.</p>
EMRNA: Accurate RNA structure determination from cryo-EM maps by deep learning and integrated modeling
<p>EMRNA: Accurate RNA structure determination from cryo-EM maps by deep learning and integrated modeling.</p><p>Here stores the input files and output structures of EMRNA and the reproduction result of auto-DRRAFTER.</p>
Integrated Image-based Deep Learning and Language Models for Primary Diabetes Care
<h2><strong>Example data for the paper "Integrated Image-based Deep Learning and Language Models for Primary Diabetes Care"</strong></h2> <p><strong>Example data for the paper "Integrated Image-based Deep Learning and Language Models for Primary Diabetes Care"</strong></p>
Evaluating the effects of nest management on a recovering raptor using integrated population modeling
<p>Data and code from Cappello et al. 2024 Ecosphere</p> <p>Evaluating the effects of nest management on a recovering raptor using integrated population modeling.</p> <p>Abstract: Evaluating population responses to management is a crucial component of successful conservation programs. Models predicting population growth under different management scenarios can provide key insights into the efficacy of specific management actions both in reversing population decline and in maintaining recovered populations. Bald eagle (Haliaeetus leucocephalus) conservation in the United States has seen many successes over the last 50 years, yet the extent to which the bald eagle population has recovered in Arizona, an important population within the Southwest region, remains an area of debate. Estimates of the species’ population trend and an evaluation of ongoing nest-level management practices are needed to inform management decisions. We developed a Bayesian integrated population model (IPM) and population viability analysis (PVA) using a 36-year dataset to assess Arizona bald eagle population dynamics and their underlying demographic rates under current and possible future management practices. We estimated that the population grew from 77 females in 1993 to 180 females in 2022, an average yearly increase of 3%. Breeding sites that had trained personnel (i.e., nestwatchers) stationed at active nests to mitigate human disturbance had 28% higher reproductive output than nests without this protection. Uncertainty around population trends was high, but scenarios that continued the nestwatcher program were less likely to predict abundance declines than scenarios without nestwatchers. Here, the IPM-PVA framework provides a useful tool both for estimating the effectiveness of past management actions and for exploring the management needs of a delisted population, highlighting that continued management action may be necessary to maintain population viability even after meeting certain recovery criteria.</p>
A Unified Theoretical Framework for the Synergistic Integration of Transformers and Diffusion Models
<p>This paper introduces a novel, comprehensive theoretical framework for the synergistic integration of Transformer and Diffusion models, two paradigms that have independently revolutionized machine learning. We establish a fundamental correspondence between these models through a unified representation and a generalized dynamics equation, bridging the gap between their seemingly disparate architectures. Our key contributions include:</p> <p>(1) A unified mathematical formulation that encapsulates both Transformer and Diffusion processes;</p> <p>(2) A novel Diffusion-Enhanced Attention mechanism that incorporates Diffusion dynamics into Transformer attention;</p> <p>(3) Rigorous theoretical analyses including convergence guarantees, generalization bounds, and sample efficiency proofs for the integrated model.</p> <p>We provide detailed mathematical derivations and empirical validations across various tasks, demonstrating significant improvements over standalone models and existing hybrid approaches. This work lays the foundation for a new class of AI models that leverage the strengths of both paradigms, potentially leading to more powerful, efficient, and versatile AI systems. Our framework opens up new avenues for research in areas such as enhanced language modeling, advanced image generation, and multi-modal learning, paving the way for the next generation of AI technologies.</p>
Data for the manuscript: TOWARDS AN INTEGRATED SOIL-PLANT-ATMOSPHERE MODELING ENVIRONMENT: IMPLEMENTATION OF A DYNAMIC PLANT UPTAKE MODULE FOR THE HYDRUS MODEL
<p>Data used in the manuscript for the theoretical and experimental validation, and for the Global Sensitivity Analysis. For a thorough description, please refer to the manuscript.</p>
Integrating Bayesian groundwater mixing modeling with on-site helium analysis to identify unknown water sources
<p>Analyzing groundwater mixing ratios is crucial for many groundwater management tasks such as assessing sources of groundwater recharge and flow paths. However, estimating groundwater mixing ratios is affected by various uncertainties, which are related to analytical and measurement errors of tracers, the selection of end-members and finding the most suitable set of tracers. Although these uncertainties are well recognized, it is still not common practice to account for them. We address this issue by using a new set of tracers in combination with a Bayesian modeling approach, which explicitly considers the possibility of unknown end-members while fully accounting for tracer uncertainties. We apply the Bayesian model we developed to a tracer set which includes helium-4 analyzed on-site to determine mixing ratios in groundwater. Thereby, we identify an unknown end-member, that contributes up to 84% to the water mixture observed at our study site. For the helium-4 analysis, we use a newly developed Gas Equilibrium Membrane Inlet Mass Spectrometer (GE-MIMS), operated in the field. To test the reliability of on-site helium-4 analysis, we compare results obtained with the GE-MIMS to the conventional lab-based method, which is comparatively expensive and labor intensive. Our work demonstrates that (i) tracer-aided Bayesian mixing modeling can detect unknown water sources, thereby revealing valuable insights into the conceptual understanding of the groundwater system studied and ii) on-site helium-4 analysis with the GE-MIMS system is an accurate and reliable alternative to the lab-based analysis.</p>
Biomolecular and cellular probing of the interaction between the bitter peptide VAPFPEVF and TAS2R16 reveals a distinct binding studied by an integrated AFM and modeling approach
<p>Topology and initial coordinates files of the Molecular Dynamics (MD) simulations of TAS2R16 models in the complex with VAPFPEVF peptide. We used OpenMM (v8.0) as a molecular engine, CHARMM36 as force field. Three replicas of 1 µs (dcd files: filtered1.dcd, filtered2.dcd, filtered3.dcd) for each binding modes are reported. Water molecules, ions, and membrane atoms (POPC: phosphatidylcholine) atoms were removed from the original trajectories and topologies before the upload.</p>
Dataset accompanying "Integrated nowcasting of convective precipitation with Transformer-based models using multi-source data"
<p>Dataset accompanying the article <a href="https://arxiv.org/abs/2409.10367" target="_blank" rel="noopener">Integrated nowcasting of convective precipitation with Transformer-based models using multi-source data</a>. </p> <p>Contains almost 8000 events that are sampled from the summer months (where convective precipitation events are most likely to occur) of 2019-2023, centred over Austria.</p> <p>Each sample has a temporal span of 4 hours with a spatial extent of 400 x 700 km, with following data streams:</p> <ul> <li>4 MSG infrared channels with central wavelengths of 6.2, 7.3, 8.7, and 10.8 μm</li> <li>Rain rates mosaicked from ground-based radar observations</li> <li>Lightning data from ground-based observations</li> <li>INCA precipitation analysis</li> <li>INCA convective available potential energy (CAPE) estimates</li> </ul> <p>The dataset is accompanied by elevation and coordinate information. </p> <p>Please refer to the manuscript and the <a href="https://github.com/caglarkucuk/earthformer-multisource-to-inca">GitHub repository</a> for further information and helper code for reading the data files.</p>
Ren et al. (2024), Integrated Risk Management for Cascading Reservoirs Under Uncertainty using Networked Modelling
<p>Description of Research Data and Code<br>This repository contains the data and code associated with the research paper: Ren et al. (2024), Integrated Risk Management for Cascading Reservoirs Under Uncertainty using Networked Modelling, currently under review at Water Resources Research.</p> <p>Overview<br>To investigate the risk interdependencies arising from hydraulic interactions in cascading reservoir systems, we developed a risk propagation model using Bayesian networks (see file: Risk_propagation_model). Building on this model, we employed EMODPS to create a robust operational model for the reservoirs (see file: Robust_operation_model). Our goal was to minimize the joint risks of insufficient hydropower output and ecological water shortages while formulating robust operating policies to mitigate system performance degradation in the face of uncertain future runoffs (generated from our runoff simulations, see file: runoff simulation).</p> <p>Additionally, we analyzed the relationship between overall risk and risk at individual reservoir sites using a scenario discovery algorithm to pinpoint scenarios that reveal vulnerabilities (see file: python_project_scenariodiscovery).</p> <p>Acknowledgments<br>This project builds upon the code developed by Giuliani et al. (2016) M3O-Multi-Objective-Optimal-Operations (https://mxgiuliani00.github.io/M3O-Multi-Objective-Optimal-Operations/), Hadka and Reed (2013) BORG MOEA (http://borgmoea.org/), and Kevin Patrick Murphy et al. (2007) Bayesian Network Toolbox (https://www.ipcc.ch/report/ar6/wg1/#InteractiveAtlas). We are grateful to the original authors for their contributions.</p> <p>While we have made modifications and extensions to the original code, we have not altered its license. Users should refer to the original repositories for more details and ensure compliance with the terms of the original authors' licenses.</p>
Data for An Integrative Data-driven Model Simulating C. elegans Brain, Body and Environment Interactions
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Forest Carbon Modeling Improved through Hierarchical Integration of Pool-Based Measurements
<p>This folder contains data from </p> <ol> <li>measured carbon stocks (carbonpools) from forest inventories</li> <li>carbon stocks (modeled_C_stock_40) estimated by each HDA constraint</li> <li>posterior parameter sets (para_posterior) after burn-in</li> <li>carbon fluxes (modeled_C_HD) estimated by 500 randomly selected posterior parameter sets from each HDA step.</li> <li>carbon stocks (modeled_C_stock_40_DEF) estimated by the default model settings (uninformed)</li> </ol> <p>figures.R to reproduce the figures in the manuscript. </p>
Theory-based integrated modelling of tungsten transport in ITER plasmas
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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.