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32 results for “interaction partner”
Mass spectrometry analysis of MSI2 interacting partners in K562 cells
<p>Mass spectrometry analysis of MSI2 interacting partners in K562 cells for manuscript "<strong>Functional screen of MSI2 interactors identifies an essential role for SYNCRIP in myeloid leukemia stem cells"</strong> by Vu et al. 2017. </p>
Attempted expression and purification of full-length huntingtin Q23 with putative interaction partners from baculoviral expression system production in sf9 insect cells – 2018/07/19
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Rationale: </strong>To obtain monodisperse and conformationally constrained huntingtin protein samples for high resolution structural biology, interaction partners are required as highlighted by <a href="https://www.nature.com/articles/nature25502">Guo et al (2018)</a>.</p>
Comparative analysis of huntingtin interaction partner database searches with Dr. Maiuri's work - 2018/08/31
<p><strong>Project </strong>- Huntingtin structure-function open lab notebook. </p> <p><strong>Rationale</strong> - To compare the ROS-specific huntingtin interaction partners identified by Dr. Tamara Maiuri with those detailed in existing databases. </p> <p><strong>Overview</strong> - Previously, different online databases which detail protein interaction partners were searched for huntingtin protein interaction partners. Following completion of this initial analysis, Dr. Tamara Maiuri posted in her open notebook a detailed list of high and medium confidence ROS-specific huntingtin interacting proteins: <a href="https://zenodo.org/record/1319540">https://zenodo.org/record/1319540</a>. A comparison of these interactors with those identified in the previously mined databases is briefly detailed. </p>
Nickel and FLAG Pull-Down Results From HTT With Putative Interaction Partners
<p>Huntington’s disease (HD) is a progressive neurological disorder caused by a mutation in the huntingtin gene, which encodes the huntingtin protein. Last year, the first structure of this protein was published. With a global resolution of approximately 4 Angstroms, this was a fantastic leap forward in our understanding of this protein. This structure helped our knowledge of Huntingtin, but there is still much more to find out. The structure published by this group is the only representative of about 75% of the protein, the other 25% being too mobile to capture using many high-resolution mapping techniques. One such mobile area is Exon 1, the location of the triplet repeat expansion responsible for HD. This critical region of the protein requires more structural information to understand. To obtain higher resolution images of this region, we are looking for huntingtin interaction partners that bind this region. These interaction partners will hopefully stabilize the structure enough to image the area. Interaction partners will be verified by a Pull-Down assay. Utilizing the FLAG-Tag and His-Tag present on HuntingtinPolyQ54+HAP40(HTT) complex used in this assay, we will look to identify reliable interaction partners. </p>
Plant species with larger extrafloral nectaries produce better quality nectar only when needed and favour interactions with best ant partners
<p>Few studies investigated the phenotypic plasticity of extrafloral nectary (EFN) functioning associated with indirect plant defense across species. Here, we experimentally investigate in three sympatric legume species the role of EFNs, hypothesizing that plant species with larger EFNs have higher induced nectar secretion after herbivory events, greater control over secretion, and are more likely to interact with more protective ant partners. We targeted 30 individuals of each legume species and estimated EFN size and activity in the field. We conducted field experiments to evaluate the phenotypic plasticity of nectar production after leaf damage and censused ant species feeding on EFNs. Plant species increased nectar after leaf damage but in different ways. Supporting our hypothesis, <em>C. duckeana</em>, with the largest EFNs, increased all nectar descriptors, taking its place as the most productive and intense post-herbivory induced response, attracting more dominant ants than the other plant species. The higher control over reward production in plant species with larger-sized EFN reflects an induction mechanism under damage that reduces costs and increases the potential benefits of indirect biotic defences. Together, these plant traits shape the patterns of ant attendance and defence against herbivores, possibly favouring the maintenance of plant protection mutualisms widespread in nature.</p>
20230822-IKEM: Intrinsic exploration motivation (individual / social and direct / indirect curiosity, creativity and non-exploration), that influences which object and, if the agent is social, partner(s) are chosen for the interaction, is introduced.
<p>This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.<br> <br> Experiment Label: 20230822-IKEM<br> <br> Experiment design: Agents interact by playing games with objects and adapt their ontologies to agree on decision taking. Intrinsic exploration motivation (individual / social and direct / indirect curiosity, creativity and non-exploration), that influences which object and, if the agent is social, partner(s) are chosen for the interaction, is introduced.<br> <br> Experiment setting: Agents learn decision trees (transformed into ontologies); choose the objects (and partners) to interact with; adapt by splitting their leaf nodes<br> <br> Hypotheses: 1) Agents will be able to fulfil their motivation in terms of increased exploration (exploratory motivations) and decreased exploration (non-exploratory motivation). 2) Agents with exploratory motivation will be more complete, but less accurate than the baseline and non-exploration. 3) Curious agents will be more accurate and complete, but converge slower in comparison with creativity. 4) In more complex settings (higher number of agents and properties), exploration leads to more completeness, accuracy, distance and faster convergence than the baseline. 5) Agents with non-exploratory motivation will be less accurate and complete than the baseline. 6) The indirect learning models have a higher accuracy and completeness than the direct models. 7) Agents with socially oriented intrinsic motivation will have less diverse knowledge, but agree more and converge faster than agents with individual intrinsic motivation. 8) Heterogeneously motivated agents will have a higher accuracy and completeness, but lower diversity and converge slower than homogeneously motivated agents.<br> <br> Detailed information can be found in index.html or notebook.ipynb.<br> <br> [1] <a href="\">https://sake.re/20230822-IKEM</a><br> [2] <a href="\">https://gitlab.inria.fr/moex/lazylav/</a><br> </p>
Plant species with larger extrafloral nectaries produce better quality nectar only when needed and favour interactions with best ant partners
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Variance partitioning of nest provisioning rates in blue tits: individual repeatability, heritability and partner interactions
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Haruspex Analysis for TCP2 human_interaction_partners pdb entry 6nq1 emdb 0478
Haruspex (version 1.0 190116) analysis for TCP2 human_interaction_partners , pdb entry 6nq1 , emdb 0478. https://onlinelibrary.wiley.com/doi/10.1002/anie.202000421
Haruspex Analysis for TCP2 human_interaction_partners pdb entry 6j8g emdb 9781
Haruspex (version 1.0 190116) analysis for TCP2 human_interaction_partners , pdb entry 6j8g , emdb 9781. https://onlinelibrary.wiley.com/doi/10.1002/anie.202000421
Data from: Cannibalism as an interacting phenotype: pre-cannibalistic aggression is influenced by social partners in the endangered Socorro isopod (Thermosphaeroma thermophilum)
Models for the evolution of cannibalism highlight the importance of asymmetries between individuals in initiating cannibalistic attacks. Studies may include measures of body size but typically group individuals into size/age classes or compare populations. Such broad comparisons may obscure the details of interactions that ultimately determine how socially contingent characteristics evolve. We propose that understanding cannibalism is facilitated by using an interacting phenotypes perspective that includes the influences of the phenotype of a social partner on the behaviour of a focal individual and focuses on variation in individual pairwise interactions. We investigated how relative body size, a composite trait between a focal individual and its social partner, and the sex of the partners influenced precannibalistic aggression in the endangered Socorro isopod, Thermosphaeroma thermophilum. We also investigated whether differences in mating interest among males and females influenced cannibalism in mixed sex pairs. We studied these questions in three populations that differ markedly in range of body size and opportunities for interactions among individuals. We found that relative body size influences the probability of and latency to attack. We observed differences in the likelihood of and latency to attack based on both an individual's sex and the sex of its partner but found no evidence of sexual conflict. The instigation of precannibalistic aggression in these isopods is therefore a property of both an individual and its social partner. Our results suggest that interacting phenotype models would be improved by incorporating a new conditional ψ, which describes the strength of a social partner's influence on focal behaviour.
Data from: Cannibalism as an interacting phenotype: pre-cannibalistic aggression is influenced by social partners in the endangered Socorro isopod (Thermosphaeroma thermophilum)
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Data from: Double mutualism: Dual rewards or redundancy? Insights from the interactions between mistletoes and their avian partners in the tropical hotspot of Southwest China
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Data from: The evolution of cooperation: interacting phenotypes among social partners
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Data from: Partner diversity and identity impacts on plant productivity in Acacia-rhizobial interactions
1.Genetic variation for functionally important traits is ubiquitous in communities of nitrogen-fixing rhizobia, and while some studies have described significant effects of diversity on the functioning of plant-associated microbial communities, we lack a systematic test of how rhizobial diversity influences plant productivity. 2. The complexity of potential interactions among rhizobia and plants complicates the development of general predictions regarding causal relationships between rhizobial diversity and plant productivity. For example, while rhizobial complementarity may result in positive associations between symbiont diversity and plant productivity, antagonistic competition may reduce rhizobial community function. 3. Using two widespread native Australian Acacia species (A. salicina, A. stenophylla) and experimental rhizobial communities derived from 16 bacterial genotypes naturally associated with these hosts, we examined how the provision of mutualistic benefit varies with rhizobial identity, diversity and phylogenetic relatedness. 4. Analysis of plant performance in relation to rhizobial genotypic richness revealed that the presence of multiple rhizobial genotypes in the rhizosphere was associated with a general decrease in plant productivity compared to growth with single rhizobial genotypes. Importantly, these results appear to be robust in the face of variation in host identity and host diversity (i.e. one or two species mixtures). We also found that rhizobial genotypic identity and host species significantly influenced plant productivity in Acacia-rhizobia interactions, both in single and multi-strain inoculations. 5. Synthesis. Together, our data show that multiple rhizobia interacting with a single host species creates opportunities for emergent or higher-order effects that extend beyond those that could be simply predicted based upon outcomes of pairwise interactions, and that increased mutualist diversity does not necessarily translate into positive effects on plant growth.
Data from: Coevolution between positive reciprocity, punishment, and partner switching in repeated interactions
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Data from: Partner diversity and identity impacts on plant productivity in Acacia-rhizobial interactions
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FOXL2 interaction with different binding partners regulates the dynamics of granulosa cell differentiation across ovarian development [RNA-Seq]
GEO Series GSE236859. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
New partners in regulation of gene expression: the Enhancer of Trithorax and Polycomb Corto interacts with methylated Ribosomal Protein L12 via its chromodomain.
GEO Series GSE38435. Drosophila melanogaster. 4 samples. Type: Expression profiling by high throughput sequencing.
Regulation of Megakaryocytic differentiation in Cell Line Models by Dynamic Combinatorial Interactions of RUNX1 with Its Cooperating Partners
GEO Series GSE24777. Homo sapiens. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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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.