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1,112 results for “effector”
Distinct roles of the Chlamydia trachomatis effectors TarP and TmeA in the regulation of formin and Arp2/3 during entry
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Type III secretion system effector proteins are mechanically labile
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Data from: A rapid-response soft end effector inspired by the hummingbird beak
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Barley endosomal MONENSIN SENSITIVITY1 is a target of the powdery mildew effector CSEP0162 and plays a role in plant immunity
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Dynamin-dependent entry of Chlamydia trachomatis is sequentially regulated by the effectors TarP and TmeA
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Data from: Brucella NyxA and NyxB dimerization enhances effector function during infection
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Data from: Allospecific splenic Tr1 cells drive effector T cell exhaustion through upregulated Areg-EGFR signaling to promote transplant tolerance
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The effector secretome of the Irish potato famine pathogen Phytophthora infestans
<p>Phytophthora infestans is the most destructive pathogen of potato and a model organism for the oomycetes. Haas et al. (2009) reported the sequence of the 240 megabases (Mb) genome of P. infestans. In this dataset we provide an annotated list of candidate effectors that we identified in the proteome of P. infestans T30-4 (Haas et al. 2009). We have frozen this list since 2015 and we reference all the effectors with “PITG_xxxxx” numbers from this list. The proteins can be cross-referenced to the GenBank sequences using these PITG_xxxxx numbers. The matching GenBank accession for the genome assembly is GCA_000142945.1. We also provide here the assembly and gtf files.</p> <p>Reference:</p> <p><a href="https://kamounlab.dreamhosters.com/pdfs/Nature_09.pdf">Haas, B.J., Kamoun, S., Zody, M.C., Jiang, R.H.Y., Handsaker, R.E., Cano, L.M., Grabherr, M., Kodira, C.D., Raffaele, S., Torto-Alalibo, T., Bozkurt, T.O., Ah-Fong, A.M.V., Alvarado, L., Anderson, V.L., Armstrong, M.R., Avrova, A., Baxter, L., Beynon, J., Boevink, P.C., Bollmann, S.R., Bos, J.I.B., Bulone, V., Cai, G., Cakir, C., Carrington, J.C., Chawner, M., Conti, L., Costanzo, S., Ewan, R., Fahlgren, N., Fischbach, M.A., Fugelstad, J., Gilroy, E.M., Gnerre, S., Green, P.J., Grenville-Briggs, L.J., Griffith, J., Grunwald, N.J., Horn, K., Horner, N.R., Hu, C.-H., Huitema, E., Jeong, D.-H., Jones, A.M.E., Jones, J.D.G., Jones, R.W., Karlsson, E.K., Kunjeti, S.G., Lamour, K., Liu, Z., Ma, L., MacLean, D., Chibucos, M.C., McDonald, H., McWalters, J., Meijer, H.J.G., Morgan, W., Morris, P.F., Munro, C.A., O'Neill, K., Ospina-Giraldo, M., Pinzon, A., Pritchard, L., Ramsahoye, B., Ren, Q., Restrepo, S., Roy, S., Sadanandom, A., Savidor, A., Schornack, S., Schwartz, D.C., Schumann, U.D., Schwessinger, B., Seyer, L., Sharpe, T., Silvar, C., Song, J., Studholme, D.J., Sykes, S., Thines, M., van de Vondervoort, P.J.I., Phuntumart, V., Wawra, S., Weide, R., Win, J., Young, C., Zhou, S., Fry, W., Meyers, B.C., van West, P., Ristaino, J., Govers, F., Birch, P.R.J., Whisson, S.C., Judelson, H.S., and Nusbaum, C. 2009. Genome sequence and analysis of the Irish potato famine pathogen Phytophthora infestans. Nature 461:393-398.</a></p>
MATLAB results files of MS-based analysis and raw photometer data - Systematic identification of allosteric effectors in Escherichia coli metabolism
<p>MATLAB result tables from progress curve analysis for each of the 19 enzymes tested with 79 potential effectors metabolites in MS-based approach. Excel tables with labelled photometer data.</p>
Are some effector systems harder to switch to? In search of cost asymmetries when switching between manual, vocal, and oculomotor tasks
<p>Data of "Are some effector systems harder to switch to? In search of cost asymmetries when switching between manual, vocal, and oculomotor tasks ", Hoffmann, Koch, & Huestegge.</p> <p>Raw data of Experiment 1 and Experiment 2.</p>
Towards a Generic Grasp Planning Pipeline using End-Effector Specific Primitive Grasping Actions
<p>In the past few years, several robotic end-effectors based on diverse kinematics and actuation principles have been developed to provide grasping and manipulation functionalities. To ease the control and application of these wide-ranging end-effectors, the development of effective reusable tools that can facilitate the end-effector motion planning and control is necessary. In this work, we introduce a generic grasp planner that leverages on the concept of the primitive grasping actions. Given the specific characteristics of an end-effector, including its kinematic and actuation arrangements, a number of primitive grasping actions are extracted and employed by the proposed grasp planner to autonomously plan and synthesize more complex grasping behaviours. The grasp planner is validated through experimental trials involving the HERI II robotic hand, a four-fingers tendon-driven under-actuated hand. The results of these experiments demonstrate the efficacy of the proposed method to generate appropriate planning actions enabling to grasp objects of different shapes.</p>
Genome annotations for: Multi-omics approaches define novel aphid effector candidates associated with virulence and avirulence phenotypes
<div> <p><span><span>Peter Thorpe</span></span><span><span>1</span></span><span><span>, Simone Altmann</span></span><span><span>1</span></span><span><span>, Rosa Lopez-Cobollo</span></span><span><span>2</span></span><span><span>, Nadine Douglas</span></span><span><span>3</span></span><span><span>, Javaid Iqbal</span></span><span><span>2</span></span><span><span>, Sadia Kanvil</span></span><span><span>2</span></span><span><span>, </span><span>Jean-Christophe Simon</span></span><span><span>4</span></span><span><span>, </span><span>James </span><span>C. </span><span>Carolan</span></span><span><span>3</span></span><span><span>, Jorunn Bos</span></span><span><span>1</span><span>*</span></span><span><span>, Colin Turnbull</span></span><span><span>2</span><span>*</span></span><span> </span></p> </div> <div> <p><span><span>1</span></span><span><span>School of Life Sciences, University of Dundee, UK;</span> </span><span><span>2</span></span><span><span>Department of Life Sciences, Imperial College London, UK; </span></span><span><span>3</span></span><span><span>Department of Biology, Maynooth University, </span><span>Republic of Ireland</span><span>; </span></span><span><span>4</span></span> <span><span>INRAE , France</span><span>. *Authors for correspondenc</span><span>e: </span></span><a href="mailto:j.bos@dundee.ac.uk" target="_blank" rel="noreferrer noopener"><span><span>j.bos@dundee.ac.uk</span></span></a><span><span>, </span></span><a href="mailto:c.turnbull@imperial.ac.uk" target="_blank" rel="noreferrer noopener"><span><span>c.turnbull@imperial.ac.uk</span></span></a><span><span>. </span></span><span> </span></p> <p> </p> <p><span>This is a repository for the version3 gene predictions and annotation for the pea aphid used for the publication:</span></p> <p> </p> <p><strong><span><span><span>Multi-omics approaches define novel aphid effector candidates associated with </span><span>virulence and </span><span>avirulence</span> <span>phenotypes</span></span><span> </span></span></strong></p> <p> </p> <div> <p><span><span>ABSTRACT</span></span></p> </div> <div> <p><span><span>Background</span></span><span><span>. Compatibility between aphids and plant hosts is genetically </span><span>determined</span><span> by both interacting organisms. For example, plants may carry resistance (R) genes or deploy chemical defences. Aphid saliva </span><span>contains</span><span> many proteins that are secreted into host tissues. </span><span>S</span><span>ubset</span><span>s</span><span> of these proteins are predicted to act as effectors, either subverting or triggering host immunity. However, associating </span><span>particular effectors</span><span> with virulence or </span><span>avirulence</span><span> outcomes presents challenges due to the combinatorial complexity. Here we use defined aphid and host genetics to test for co-segregation of expressed aphid transcripts and proteins with virulent or avirulent phenotypes.</span></span><span> </span></p> </div> <div> <p><span><span>Results</span><span>. </span></span><span><span>We compared virulent and avirulent pea aphid parental genotypes, and their bulk segregant F</span></span><span><span>1</span></span><span><span> progeny on </span></span><span><span>Medicago </span><span>truncatula</span> </span><span><span>genotypes</span></span> <span><span>carrying or lacking the </span></span><span><span>RAP1 </span></span><span><span>resistance </span><span>quantitative trait locus</span><span>. </span><span>D</span><span>ifferential expression </span><span>analysis based on </span><span>RNA sequencing </span><span>of whole bod</span><span>y and head samples, </span><span>in combination with proteomics of saliva and salivary glands</span><span>,</span> <span>enabled us </span><span>to pinpoint proteins </span><span>associated</span><span> with virulence/</span><span>avirulence</span><span> phenotypes. </span></span><span><span><span>There was relatively </span><span>little impact</span><span> of </span></span></span><span><span><span>host genotype, </span></span></span><span><span><span>whereas</span><span> l</span></span></span><span><span>arge numbers of transcripts and proteins were differentially expressed between parental aphids, </span><span>likely a</span><span> reflection of their classification as divergent biotypes within the pea aphid species complex. Many fewer </span><span>transcripts</span> <span>intersected with the equivalent differential expression patterns in the bulked F</span></span><span><span>1</span></span><span><span> progeny, providing an effective filter for removing </span><span>genomic </span><span>background effects</span></span><span><span>. </span><span>Overall, t</span><span>here were more upregulated genes detected in the </span><span>F</span></span><span><span>1</span></span><span> <span>avirulent </span><span>dataset </span><span>compared with the virulent one. </span><span>Some of the</span><span> differentially expressed transcripts </span><span>were also found in the differentially expressed proteomes</span><span>, with a</span><span>minopeptidase N prot</span><span>eins </span><span>being </span><span>t</span><span>he most frequent</span><span> differentially expressed</span><span> family</span></span><span><span>. </span><span>In addition</span><span>, a </span><span>substantial</span> <span>proportion </span><span>(26%) </span><span>of salivary proteins lack annotations, suggesting that </span><span>many </span><span>novel functions </span><span>remain</span><span> to be discovered. </span></span><span> </span></p> </div> <div> <p><span><span>Conclusions.</span></span><span><span> Especially when combined with tightly controlled genetics of both insect and host, multi-</span><span>omic</span><span> approaches are powerful tools for revealing and filtering candidate lists down to plausible genes for further functional analysis as putative </span><span>aphid </span><span>effectors.</span></span><span> </span></p> </div> </div>
Validation of LEXO® End-Effector Robot-assisted Training in Patients with Gait Deficits after Central Nervous System Diseases. A Descriptive Cross-sectional Study.
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List of 844 candidate effectors in Blumeria graminis f.sp. tritici CHE_96224
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Ketterer et al. "The putative Type 4 secretion system effector BspD is involved in maintaining envelope integrity of the pathogen Brucella"
<p>Supplementary Data to Ketterer et al. "<span>The putative Type 4 secretion system effector BspD is involved in maintaining envelope integrity of the pathogen <em>Brucella"</em></span></p>
Dataset related to "Structure-guided secretome analysis of gall-forming microbes offers insights into effector diversity and evolution"
<p>Three .zip files contain the PDB files, mature protein sequences (without predicted signal peptides), and corresponding JSON files from AlphaFold2 containing pLDDT scores.</p> <p>Two .output files contain the all-vs-all sequence and structural similarity scores.</p>
Development of compact transcriptional effectors using high-throughput measurements in diverse contexts
<p>HT-recruit and CRISPR HT-recruit processed datasets </p> <p>Abstract: <span>Transcriptional effectors are protein domains known to activate or repress gene expression, however, a systematic understanding of which effector domains regulate transcription robustly across genomic, cell-type, and DNA-binding domain (DBD) contexts is lacking. Here, we develop dCas9-mediated high-throughput recruitment (HT-recruit), a pooled screening method for quantifying effector function at endogenous target genes, and test effector function for a library containing 5,092 nuclear protein Pfam domains across varied contexts. We also map context dependencies of effectors drawn from unannotated protein regions using a larger library containing 114,288 sequences tiling chromatin regulators and transcription factors. We find that many effectors depend on target and DBD contexts, such as HLH domains that can act as either activators or repressors. To enable efficient perturbations, we select context-robust domains, including ZNF705 KRAB, that improve CRISPRi tools to silence promoters and enhancers. We engineer a compact human activator NFZ by combining several domains, which enables efficient CRISPRa with better viral delivery, and inducible control of CAR T-cells. Together, this effector-by-context functional map reveals context-dependence across human effectors and guides effector selection for manipulating transcription.</span></p>
Source data for Incarbone et al (2021) - "Immunocapture of dsRNA-bound proteins provides insight into tobacco rattle virus replication complexes and reveals Arabidopsis DRB2 to be a wide-spectrum antiviral effector"
<p>Source data for Incarbone et al (2021) - "Immunocapture of dsRNA-bound proteins provides insight into tobacco rattle virus replication complexes and reveals Arabidopsis DRB2 to be a wide-spectrum antiviral effector"</p> <p>Includes full scans of blots mounted in figures and additional microscopy acquisitions, including brightfield channel</p>
Cooperative virulence via the collective action of secreted pathogen effectors
<p>Barcode sequence data. Unique barcode sequences were incorporated into the effector plasmids to confirm that all individual effector clones within the metaclone were present throughout infection and that the overall diversity of the metaclones remained unchanged <strong>(Extended Data Fig. 4). </strong></p>
Data and Code from "Structure-based prediction of Ras-effector binding affinities and design of 'branchegetic' interface mutations"
<p>Data, data generation and data analysis for manuscript "Structure-based prediction of Ras-effector binding affinities and design of ‘branchegetic’ interface mutations", currently available as a preprint <a href="https://doi.org/10.1101/2022.09.04.506480">here</a>.</p> <p>Contains the following directories:</p> <ul> <li>01_models: Contains all scripts for model generation and selection, as well as some of the generated and selected models. <ul> <li>01_inputs: The different inputs for the homology modelling pipeline. This includes AlphaFold single and complex templates, PDB templates and sequence alignments.</li> <li>02_validation: Model generation and initial selection for validation models, based on AF2 single models and PDB complex models.</li> <li>03_production1: Model generation and initial selection for Ras effector complexes, based on AF2 single models and PDB complex models.</li> <li>04_production2: Model generation and initial selection for Ras effector complexes, based on AF2 single models and AF2 complex models.</li> <li>05_selection_optics: Code and analysis for selection by unsupervised learning using OPTICS.</li> </ul> </li> <li>02_selected_models: The three representative models selected for each complex.</li> <li>03_affinity_prediction: Contains code and data for the prediction of binding affinities for Ras effector complexes.</li> <li>04_branch_pruning: Contains code and data for branch pruning analysis.</li> <li>05_systems_analysis: Contains code and data for the analysis of Ras effector systems based on affinities derived from affinity prediction and branch pruning analysis.</li> <li>06_visualization: Information on where in the raw data the panels for the figures in the manuscript can be found.</li> </ul>
ScienceDex guides
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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.