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79 results for “integrative framework”
Integrating venom peptide libraries into a phylogenetic and broader biological framework
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Data from: An integrated framework to identify wildlife populations under threat from climate change
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Data from: A framework for the integrated analysis of the magnitude, selectivity, and biotic effects of extinction and origination
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Data from: A spatially integrated framework for assessing socioecological drivers of carnivore decline
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A framework for mapping the distribution of seabirds by integrating tracking, demography and phenology
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Data from: Information use and resource competition: an integrative framework
Organisms may reduce uncertainty regarding how best to exploit their environment by collecting information about resource distribution. We develop a model to demonstrate how competition can facilitate or constrain an individual's ability to use information when acquiring resources. Since resource distribution underpins both selection on information use and the strength and nature of competition between individuals, we demonstrate interdependencies between the two that should be common in nature. Individuals in our model can search for resources either personally or by using social information. We explore selection on social information use across a comprehensive range of ecological conditions, generalising the producer-scrounger framework to a wide diversity of taxa and resources. We show that resource ecology – defined by scarcity, depletion rate, and monopolisability – determines patterns of individual differences in social information use. These differences suggest co-evolutionary processes linking dominance systems and social information use, with implications for the evolutionary demography of populations.
Data from: Integrated metabolic strategy: a framework for predicting the evolution of carbon-water tradeoffs within plant clades
1. The fundamental tradeoff between carbon gain and water loss has long been predicted as an evolutionary driver of plant strategies across environments. Nonetheless, challenges in measuring carbon gain and water loss in ways that integrate over leaf lifetime have limited our understanding of the variation in and mechanistic bases of this tradeoff. Furthermore, the microevolution of plant traits within species versus the macroevolution of strategies among closely related species may not be same, and accordingly, the latter must be addressed using comparative phylogenetic analyses. 2. Here we introduce the concept of 'integrated metabolic strategy' (IMS) to describe the ratio between carbon isotope composition (δ13C) and oxygen isotope composition above source water (Δ18O) of leaf cellulose. IMS is a measure of a leaf-level conditions that integrate several mechanisms contributing to carbon gain (δ13C) and water loss (Δ18O) over leaf lifespan, with larger values reflecting higher metabolic efficiency and hence less of a trade-off. We tested how IMS evolves among closely related yet ecologically diverse milkweed species, and subsequently addressed phenotypic plasticity in response to water availability in species with divergent IMS. 3. IMS varied strongly among 20 Asclepias species when grown under controlled conditions, and phylogenetic analyses demonstrate species-specific tradeoffs between carbon gain and water loss. Larger IMS values were associated with species from dry habitats, with larger carboxylation capacity, smaller stomatal conductance and smaller leaves; smaller IMS was associated with wet habitats, smaller carboxylation capacity, larger stomatal conductance and larger leaves. The evolution of IMS was dominated by changes in species' demand for carbon (δ13C) more so than water conservation (Δ18O). Although some individual physiological traits showed phylogenetic signal, IMS did not. 4. In response to experimental decreases in soil moisture, three species maintained similar IMS across levels of water availability because of proportional increases in δ13C and Δ18O (or little change in either), while one species increased IMS due to disproportional changes in δ13C relative to Δ18O. 5. Synthesis: IMS is a broadly applicable mechanistic tool; IMS variation among and within species may shed light on unresolved questions relating to evolution and ecology of plant ecophysiological strategies.
Data from: The EGA+GNM framework: an integrative approach to modelling behavioural syndromes
1. Behavioural syndromes refer to correlated suites of behavioural traits exhibiting consistent among-individual variation, i.e. personality. Factor analysis (FA) is currently the dominant method for modelling behavioural syndromes in humans and animals. Although FA is useful for inferring the latent causes underlying trait correlations, it does not account for pairwise behavioural interactions that also contribute to syndrome structure. Given that latent factors and pairwise interactions are likely ubiquitous causes of trait covariation, both should be modelled simultaneously. Currently, however, behavioural ecologists lack an integrative framework for describing and inferring such behavioural syndromes. 2. Generalized network modelling (GNM), representing an integration of FA and Gaussian graphical modelling (GGM), meets this challenge. We provide a theoretical introduction to GNM as well as a method for detecting latent factors in GGMs called exploratory graph analysis (EGA). We then propose the novel EGA+GNM framework for modelling multiple sources of trait correlations and ensuring more robust causal inferences. To empirically demonstrate the utility of this framework, we compare models derived from EGA+GNM and FA using observational measures of social and arousal behaviour in common marmosets (Callithrix jacchus). 3. Using information-theoretic model comparison, we find support for the EGA+GNM models compared to models generated by FA. Two EGA+GNM models suggest that while latent factors contribute to the emergence of clustered sociability and arousal behaviours, correlations among these traits may also be partially explained by pairwise interactions. Additionally, these behavioural clusters are hypothesized to be causally linked by a positive pairwise interaction between allogrooming and activity level. 4. These results support our claim that EGA+GNM provides a superior and integrative framework for describing behavioural syndromes. Consequently, by simultaneously modelling both latent factors and pairwise interactions, behavioural ecologists can better understand the evolutionary causes and consequences of animal personality. A formal overview of the EGA+GNM framework and a R tutorial demonstrating its application are provided in the electronic supplementary material.
Application_of_the_WHO-INTEGRATE_framework_Supplementary_File
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Data from: Delimiting species of marine gastropods (Turridae, Conoidea) using RAD-sequencing in an integrative taxonomy framework
Species delimitation in poorly-known and diverse taxa is usually performed based on monolocus, DNA barcoding-like approaches, while multilocus data are often used to test alternative species hypotheses in well-studied groups. We combined both approaches to delimit species in the Xenuroturris / Iotyrris complex, a group of venomous marine gastropods from the Indo-Pacific. First, COI sequences were analyzed using three methods of species delimitation, ABGD, PTP and GMYC to propose primary species hypotheses (PSH). Second, RAD-seq data were also obtained and an IQ-tree phylogenetic tree produced. We tested the impact of the level of missing data on the robustness of the phylogenetic tree obtained with the RAD-seq data. Alternative species partitions revealed with the COI dataset were also tested using the RAD-seq data and the BFD method. The congruence between the species hypotheses proposed with the mitochondrial gene and the clades in the RAD-seq tree, together with the morphological variability of the shell and the radula and the distribution pattern, was used to turn the PSH into secondary species hypotheses (SSH). Allopatric PSH defined with the COI gene were interpreted to correspond to intraspecific structure. Most of the species are found sympatrically in the Philippines, and only one is confidently identified as a new species and described as Iotyrris conotaxis n. sp. The results obtained demonstrate the efficiency of the combined monolocus/multilocus approach to delimit species.
Files for Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation
<p>This are the files needed for running the experiments of "Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation".</p> <ul> <li> <p>listening_events.tsv.bz2 : Dataset excerpt from <a href="http://www.cp.jku.at/datasets/LFM-2b/">LFM-2b</a>, before filtering (see submission for details)</p> </li> <li> <p>BPR_item_embeddings.tsv.bz2 : Item embeddings obtained from the pre-trained BPR instance</p> </li> <li> <p>user_split.tar.bz2 : csv file of the listening history of each user</p> </li> <li> <p>2023_recsys_actr_poster.pdf ; poster presented at RecSys 2023</p> </li> </ul> <p>The code for running the experiments is available on <a href="https://github.com/hcai-mms/actr">GitHub</a><br> <br> If you use these files, please cite<br> </p> <blockquote> <p>@inproceedings{10.1145/3604915.3608838,<br> author = {Moscati, Marta and Wallmann, Christian and Reiter-Haas, Markus and Kowald, Dominik and Lex, Elisabeth and Schedl, Markus},<br> title = {Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation},<br> year = {2023},<br> isbn = {9798400702419},<br> publisher = {Association for Computing Machinery},<br> address = {New York, NY, USA},<br> url = {https://doi.org/10.1145/3604915.3608838},<br> doi = {10.1145/3604915.3608838},<br> abstract = {Music listening sessions often consist of sequences including repeating tracks. Modeling such relistening behavior with models of human memory has been proven effective in predicting the next track of a session. However, these models intrinsically lack the capability of recommending novel tracks that the target user has not listened to in the past. Collaborative filtering strategies, on the contrary, provide novel recommendations by leveraging past collective behaviors but are often limited in their ability to provide explanations. To narrow this gap, we propose four hybrid algorithms that integrate collaborative filtering with the cognitive architecture ACT-R. We compare their performance in terms of accuracy, novelty, diversity, and popularity bias, to baselines of different types, including pure ACT-R, kNN-based, and neural-networks-based approaches. We show that the proposed algorithms are able to achieve the best performances in terms of novelty and diversity, and simultaneously achieve a higher accuracy of recommendation with respect to pure ACT-R models. Furthermore, we illustrate how the proposed models can provide explainable recommendations.},<br> booktitle = {Proceedings of the 17th ACM Conference on Recommender Systems},<br> pages = {840–847},<br> numpages = {8},<br> keywords = {Music Recommender Systems, Psychology-Informed Recommender Systems, Collaborative Filtering, Adaptive Control Thought-Rational (ACT-R), Sequential Recommendation, Explainability},<br> location = {Singapore, Singapore},<br> series = {RecSys '23}<br> }</p> </blockquote> <p>This research was funded in whole, or in part, by the Austrian Science Funds (FWF): P33526 and DFH-23, and by the State of Upper Austria and the Federal Ministry of Education, Science, and Research, through grant LIT-2020-9-SEE-113.</p>
Integrating the Fundamentals of Care Framework Into Italian Nursing Education
ClinicalTrials.gov study NCT05177627. IPD Sharing: NO. Countries: 0. Publications: 9.
Data from: Integrated metabolic strategy: a framework for predicting the evolution of carbon-water tradeoffs within plant clades
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Data from: The EGA+GNM framework: an integrative approach to modelling behavioural syndromes
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Data from: Delimiting species of marine gastropods (Turridae, Conoidea) using RAD-sequencing in an integrative taxonomy framework
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Data from: Information use and resource competition: an integrative framework
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An integrated transcription factor framework for Treg identity and diversity
GEO Series GSE298730. Mus musculus. 5 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
An integrative multiomics framework for identification of therapeutic targets in pulmonary fibrosis [TimeSeries]
GEO Series GSE213709. Mus musculus. 25 samples. Type: Expression profiling by high throughput sequencing.
Uniform optimal framework for integrative next-gen sequence analysis
GEO Series GSE34073. Mus musculus. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Reconstructing Epigenomic Dynamics through a Single-Cell Multi-Epigenome Data Integration Framework
GEO Series GSE266320. Mus musculus. 59 samples. Type: Expression profiling by high throughput sequencing; Other; Genome binding/occupancy profiling by high throughput sequencing.
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.