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Extending the executability of assembly task poses by robot through end-effectors
<p><strong>Extending the executability of assembly task poses by robots through end-effectors</strong></p> <p><br><em>Aline Kluge-Wilkes, Presley Demuner Reverdito</em></p> <p>The following data set was created during the validation of a proposed method to evaluate assembly station formations considering the executability of assembly tasks, including the effects of equipped end-effectors on robots.</p> <p>The underlying paper can be found at: https://doi.org/10.1007/978-3-031-34821-1_58 </p> <p>The underlying source code can be found at: https://git-ce.rwth-aachen.de/wzl-mq-public/iop/ws-b2.iv_formation-planning-of-mobile-robots/end-effector-dependent-executability</p> <p>In dependence on the geometries and degrees of freedom of the equipped end-effectors on the robot, a single task pose is transferred into an area of feasible task poses. Determining the executability of the task, the resulting representation of the feasible task poses is overlapped with the robot's workspace. If an overlap occurs, it can be assumed that there is a feasible robot configuration to execute the allocated assembly task. The proposed method is exemplified on a UR10 equipped with a screwdriver, and distributed task poses. The proposed method quantifies the end-effector's effect on the executability of assembly tasks and provides a means of determining the feasible base placement of robots in changeable assembly stations. Therefore, the method lays the foundation for automated formation planning in assembly stations.</p> <p><strong>Design of Experiments:</strong></p> <p>The published data here results from a conducted series of experiments structured as a full-factorial design of experiments. The following variables and expressions / data points of those variables were chosen:</p> <p>Tasks ("GoalPose") poses as [x, y, z, x-quaternion, y-quaternion, z-quaternion, w-quaternion]:</p> <ul> <li> Task 1 = (0.416m ,-0.399m, 0.764m, 0.071, 0.703, 0.134, 0.694)</li> <li> Task 2 = (0.438m, -0.647m, 0.816m, 0.005, 0.707, 0.068, 0.704)</li> <li> Task 3 = (0.448m, -0.755m, 0.945m, -0.243, 0.664 ,-0.183, 0.683)</li> </ul> <p>Dimension of the tools ("DimensionOfTool") as [x, y, z]: </p> <ul> <li> (0.1m, 0.2m, 0m)</li> <li> (0.2m, 0.1m, 0m)</li> </ul> <p>Robot model ("RobotModel"): </p> <ul> <li> UR10 (https://www.universal-robots.com/de/produkte/ur10-roboter/)</li> <li> UR5 (https://www.universal-robots.com/products/ur5-robot/)</li> </ul> <p>Resolution of reachability map ("ReachMap"): </p> <ul> <li> 0.05m</li> <li> 0.08m</li> <li> 0.1m</li> </ul> <p>IK solver ("IKSolver"): </p> <ul> <li>TRAC-IK <ul> <li>documented in: http://docs.ros.org/en/kinetic/api/moveit_tutorials/html/doc/trac_ik/trac_ik_tutorial.html </li> <li>source: https://bitbucket.org/traclabs/trac_ik/src/master/</li> </ul> </li> <li>KDL solver <ul> <li>https://docs.orocos.org/kdl/overview.html</li> </ul> </li> </ul> <p>Based on these definitions, a fully factorial design was implemented, resulting in 72 required tests (2³ × 3²). These tests were randomly organized into a single experimental block.</p> <p><strong>Test execution:</strong></p> <p>The function "[calcForAllTasks](https://git-ce.rwth-aachen.de/wzl-mq-public/iop/ws-b2.iv_formation-planning-of-mobile-robots/end-effector-dependent-executability/-/blob/main/code/main.py?ref_type=heads)" on the file main.py was created to generate 72 files, each containing 10 poses of the circle of possible poses for the robot with a calculated reachability map. These poses were manually tested using MoveIt! (https://moveit.ros.org/) and using two different IK solvers, KDL and TRAC-IK.</p> <p>There were now four packages: ur5_kdl, ur5_trac_ik, ur10_kdl, and ur10_trac_ik. Each package had its own group_name, which was the same as the package name. For each test, it was necessary to run the MoveIt! file of the robot with the correspondent IK solver, and the [move_group_python_interface.py](https://git-ce.rwth-aachen.de/wzl-mq-public/iop/ws-b2.iv_formation-planning-of-mobile-robots/end-effector-dependent-executability/-/blob/main/code/move_group_python_interface.py?ref_type=heads) with the desired pose. On the Python code, the goal pose was set, and using the MoveGroupPythonIntefaceTutorial class, Moveit! attempt to move the robot to the desired pose. If the pose is reachable, the reachability index was set to 1. Otherwise, it was set to 0, and the terminal output would show "ABORTED: No motion plan found. No execution attempted."</p> <p>After simulating all poses, the reachability average of each test was calculated, along with the sum of reachable results. The reachability index ranged from 0 to 100, and the reachable column ranged from 0 to 10. This result can be found in the file DoE-tests-and-results.xlsx.</p> <p> </p> <p>The file "DoE-tests-and-results.xlsx" provides an overview of all experiments in its first table. The creation order ("StdOrder") and the order in which the experiments were conducted are given ("RunOrder"). The following columns in the first table indicate the expressions of the variables as explained above (GoalPose, DimensionOfTool, RobotMode, ReachMap, IKSolver). Next, the results of the experiments are given: the average reachability index per tested goal pose and the indication of how many of the ten discrete robot flange poses per goal pose are reachable by the robot (Reachability Index, Reachable). </p> <p>The following 72 tables each provide the ten robot flange poses per goal pose per experiment as [x, y, z, x-quaternion, y-quaternion, z-quaternion, w-quaternion]. </p> <p>The 72 .csv files contain the same information as the 72 tables in the Excel file but contain the information as it was transferred during execution. </p> <p> </p> <p> </p> <p>After all simulations, it is possible to conclude that the reachability depends on the position of the task and the model of the robot. For example, UR5 could not reach task 3, while the UR10 had a high value of reachability for it due to the fact that the UR10 has a bigger workspace.</p> <p>-------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Acknowledgement:</p> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC-2023 Internet of Production - 390621612.<br> </p>
Real-world grasp data of a dual-arm Yumi robot with a parallel gripper and suction cup end-effectors
<p>The attached txt file contains indexes to a cleaner subset of the data issued in the first version.</p> <p>Note: <br>Version 1 contains samples with failure cases due to environment constraints, which work well for the platform used in GraspAgent 1.0 (https://doi.org/10.1109/LRA.2024.3502066). However, this can degrade the performance if used on another platform with different constraints. To solve this, version 2 reports a subset of the raw data, excluding the failure modes due to environmental causes. </p>
Datasets for "The Venturia inaequalis effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins "
<p>Datasets for preprint entitled "The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi"</p> <p><strong>1) ViAnnotation.gff3</strong><br> Gene annotation of <em>Venturia inaequalis</em> MNH120 (<a href="https://genome.jgi.doe.gov/Venin1/Venin1.home.html">https://genome.jgi.doe.gov/Venin1/Venin1.home.html</a>) generated as part of the study "The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi". </p> <p>Gene reannotation was performed to include genes that would have been missed in the previous annotation by Deng et al. (2017), especially those genes encoding putative effector proteins, which are difficult to predict. For this purpose, we used a three-step approach. In the first step, coding sequences (CDSs) from <em>V. inaequalis</em> isolate 05/172, which were predicted as part of a previous study by Passey et al. (2018) (<a href="https://journals.asm.org/doi/full/10.1128/MRA.01062-18">https://journals.asm.org/doi/full/10.1128/MRA.01062-18</a>), were downloaded from the National Center for Biotechnology Information (<a href="https://www.ncbi.nlm.nih.gov/nuccore/QFBF00000000.1/">https://www.ncbi.nlm.nih.gov/nuccore/QFBF00000000.1/</a>) and mapped to the MNH120 genome using GMAP v2021-02-22. In the second step, RNA-seq reads from one biological replicate representing each <em>in planta</em> time point of <em>Malus domestica</em> infection by <em>V. inaequalis </em>(12 hour post-inoculation [hpi], 24 hpi, 2 days post-inoculation [dpi], 3 dpi, 5 dpi, 7 dpi), as well as one time point representing growth of the fungus in culture, were mapped to the MNH120 genome using HISAT2 v2.2.1. Then, a genome-guided <em>de novo</em> transcriptome assembly was performed using Trinity v2.12.0 and likely CDSs were identified using Transdecoder v5.5.0 (<a href="https://github.com/TransDecoder/TransDecoder">https://github.com/TransDecoder/TransDecoder</a>) in conjunction with a minimum open frame (ORF) length of 50 amino acids. Finally, in the third step, all annotations were visualized in Geneious v9.05, together with the previous annotation from Deng et al. (2017), and a manual curation was performed to create a consensus prediction. Note: this reannotation was generated with the aim of identifying as many genes as possible, and as a result, it contains many spurious genes. </p> <p><strong>2) Protein_sequences_ViAnnotation.fasta</strong></p> <p><strong>3) ECs_Families_AlphaFold.zip</strong></p> <p>This dataset is made up of predicted protein tertiary structures representing the main member of each up-regulated <em>V. inaequalis</em> effector candidate family. Structures were predicted using Alphafold with the ColabFold server (<a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n">https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n</a>). In cases where the effector candidate had less than 30 proteins with amino acid sequence similarity in the NCBI database, a custom multiple sequence alignment (MSA) was generated and used as input for AlphaFold2. Here, mature protein sequences were used.</p> <p><strong>4) singletons_AlphaFold_OpenSourceCASP14.zip</strong></p> <p>This dataset set is made up of predicted protein tertiary structures representing up-regulated<em> V. inaequalis</em> singleton effector candidates. Structures were predicted using AlphaFold (<a href="https://github.com/deepmind/alphafold">https://github.com/deepmind/alphafold</a>) open source code v2.0.1 and v2.1.0, with pre-set casp14, max_template_date: 2020-05-14. Mature protein sequences were used as input. </p> <p><strong>5) ECs_Avrs_phytopathogens_AlphaFold.zip</strong></p> <p>Predicted tertiary structures of avirulence (Avr) proteins or candidate Avr proteins from other fungal pathogens included in the "The <em>Venturia inaequalis</em> effector repertoire is expressed in waves, and is dominated by expanded families with predicted structural similarity to avirulence proteins from other fungi" study. These structures were predicted using Alphafold with the ColabFold server (<a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n">https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold2_advanced.ipynb#scrollTo=rowN0bVYLe9n</a>). Mature protein sequences were used as input. </p> <p>If you have any questions about the datasets, please contact us.<br> Mercedes Rocafort: <a href="mailto:m.rocafort.ferrer@massey.ac.nz">m.rocafort.ferrer@massey.ac.nz</a><br> Carl Mesarich: <a href="mailto:c.mesarich@massey.ac.nz">c.mesarich@massey.ac.nz</a></p>
Accelerometer and Force/Torque Sensor Measurements for Parameter and State Estimation of an Unknown Robot End Effector
<h1>Introduction</h1> <p>This dataset was created as part of a study on the development of an estimator for the contact wrench (force and torque) of an unknown robot end effector. A conference paper from this study has been submitted and accepted for the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR 2024) [1]. </p> <p>A force/torque sensor (FTS) was attached to the robot wrist, and the unknown end effector was attached to the FTS. An inertial measurement unit (IMU) was in turn attached to the end effector. The FTS measurement can be decomposed into the (1) sensor bias, (2) contact wrench, and the effects from (3) gravity, (4) inertia, (5) vibrations, and (6) noise. Estimation of the contact wrench requires that the remaining effects are compensated for. The FTS and IMU sensor biases, as well as mass and mass center of the unknown end effector, were estimated as described by Vougioukas [2]. His method requires FTS and IMU samples from 24 specific orientations of the sensors. See his paper for a description of this calibration method.</p> <p>The hardware used to generate this dataset were:</p> <ul> <li>KUKA LBR Med 14 serial robot (KUKA AG, Germany)</li> <li>ATI Gamma FTS (ATI Industrial Automation, Inc., USA)</li> <li>ATI Netbox (ATI Industrial Automation, Inc., USA)</li> <li>MPU6886 IMU (M5Stack, China) </li> <li>Arduino Mega 2580 with a W5500 Ethernet Shield </li> </ul> <h1>Method</h1> <p>The robot was used to move the end effector, FTS, and IMU such that a trajectory could be replicated with high precision and accuracy. The trajectory was a simple rotation about the FTS y-axis. This trajectory and the resulting measurements were performed three times. The sensor signals were sampled during each iteration when:</p> <ol> <li>The robot moved freely without any kind of disturbance (<strong>basline</strong>).</li> <li>The robot moved freely with gentle taps to the robot body, using a rubber hammer (<strong>vibrations</strong>).</li> <li>The robot moved with gentle taps to the body using the hammer, and with a manual force exerted on the end effector (<strong>vibrations and contact</strong>).</li> </ol> <p>The IMU signal was obtained by the Arduino Mega using I2C, and the signal was sent from the Arduino to the external PC using the ethernet shield. This setup resulted in <strong>a phase of the IMU signal by 8416 μs</strong>. This was compensated for in the offline analysis of the study on the contact wrench estimator [1]. The sensor samplig rates were different for each sensor; they were approximately 100 Hz for the robot controller (FTS orientation measurements), 700 Hz for the FTS, and 254 Hz for the IMU. The frequency for each signal can be obtained through the timestamps in the dataset.</p> <h1>Dataset</h1> <p>Each CSV file has a row which serves as the header, which labels the columns of each file. The following nomenclature of the column labels were used:</p> <p><strong>t </strong> - Timestep in microseconds. Epoch time. <br><strong>fx,</strong> <strong>fy, fz</strong> - The force components as measured by the FTS.<br><strong>tx, ty, tz </strong>- The torque components as measured by the FTS.<br><strong>ax, ay, az </strong> - The acceleration components measured by the IMU.<br><strong>gx,gy,gz </strong>- The direction of the gravitational vector in the FTS frame.<br><strong>r11, r12, r13, r21, r22, r23, r31, r32, r33 </strong>- The components of the rotation matrix that represents the FTS orientation in the world frame. (R_wf)</p> <p>The measurements from the FTS and IMU signals from the 24 orientations (as required for the calibration method described by Vougioukas [2]), are stored in <strong>0-calibration_fts-accel.csv</strong>. Additionally, the files <strong>0-steady-state_wrench.csv </strong>and <strong>0-steady-state_accel.csv</strong> contains the continuous sensor signal from the FTS and IMU, respectively, while they were at rest; these two files can be used to calculate the sensor signal variances.</p> <p>After calibration, each sensor signal was recorded independently and stored in a separate file from the other sensors. The raw (biased) values were stored. Each test iteration produced three files:</p> <ol> <li>The end effector/FTS/IMU orientation in <strong>[test_iteration]_orientation.csv</strong></li> <li>The unbiased wrench as measured by the FTS in [<strong>test iteration]_wrench.csv</strong></li> <li>The unbiased acceleration as measured by the IMU in <strong>[test_iteration]_accel.csv</strong></li> </ol> <p>The test iteration prefix for these files are: <strong>1-baseline</strong>, <strong>2-vibrations, </strong>and <strong>3-vibrations-contact, </strong>as described in the previous section "Method". To obtain the relative time between samples across the test iteration files ([]<strong>_orientation</strong>, []<strong>_wrench</strong>, and []<strong>_accel.csv</strong>), load each dataset and determine which has the earliest timestamped sample on the first row. Then, subtract this initial timestamp value from all timestamps across the files for the respective test iteration.</p> <p>Note that the IMU frame does not align with the FTS frame (<strong>_accel.csv</strong> vs <strong>_wrench.csv</strong>), the following table describes the rotation matrix R_fa which can be used to transform the acceleration measurements from the IMU frame {a} to the FTS frame {f}. </p> <p>R_fa = </p> <table> <tbody> <tr> <td>0</td> <td>-1</td> <td>0</td> </tr> <tr> <td>0</td> <td>0</td> <td>1</td> </tr> <tr> <td>-1</td> <td>0</td> <td>0</td> </tr> </tbody> </table> <h1>References</h1> <p>[1] A. Skrede, "A Linear Discrete Kalman Filter to Estimate the Contact Wrench of an Unknown Robot End Effector", Accepted for the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR), Ålesund, Norway, June 2024 </p> <p>[2] S. Vougioukas, “Bias Estimation and Gravity Compensation For Force-Torque Sensors,” in Recent Advances in Simulation, Computational Methods and Soft Computing. WSEAS Press, 2001, pp. 82–85. </p>
"The pathway of hyaluronic acid (HA) and its receptors (CD44, RHAMM) in the regulation of Rho GTPases and their effectors in an in vitro colorectal cancer model" ("Szlak kwasu hialuronowego (HA) i jego receptorów (CD44, RHAMM) w regulacji GTPaz Rho i ich efektorów w modelu raka jelita grubego in vitro"); NCN Miniatura 2022/06/X/NZ3/00848
<p>Results from Screening for "The pathway of hyaluronic acid (HA) and its receptors (CD44, RHAMM) in the regulation of Rho GTPases and their effectors in an in vitro colorectal cancer model" the project <strong>Miniatura</strong> (<strong>2022/06/X/NZ3/00848</strong>) funded by Polish <strong>National Science Centre (NCN)</strong></p> <p>Wyniki skriningu w projekcie "Szlak kwasu hialuronowego (HA) i jego receptorów (CD44, RHAMM) w regulacji GTPaz Rho i ich efektorów w modelu raka jelita grubego in vitro", <strong>Miniatura</strong> (<strong>2022/06/X/NZ3/00848</strong>) finansowanym przez <strong>Narodowe Centrum Nauki (NCN)</strong></p>
Supplementary data files for exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins
<p>This dataset includes 20 supplementary data files for manuscript <em>Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins </em>by Jakub W. Wojciechowski, Emirhan Tekoglu, Marlena Gąsior-Głogowska, Virginie Coustou, Natalia Szulc, Monika Szefczyk, Marta Kopaczyńska, Sven J. Saupe, and Witold Dyrka (under revision). </p> <ul> <li>SF2. Profile HMMs of NLR effector domains. The file includes previously unpublished models.</li> <li>SF3. Multiple sequence alignments of N-termini clusters. </li> <li>SF4. Tabularized results of N-termini annotation.</li> <li>SF5. Structure prediction of HeLo-/Goodbye-/MLKL-like domains. Full AlphaFold2/ColabFold outputs.</li> <li>SF6. Structure prediction of previously unannotated domains. Full AlphaFold2/ColabFold outputs.</li> <li>SF7. PCFGs for BASS. The file includes previously unpublished grammars and a sample scanning configuration.</li> <li>SF8. Candidate short NLR N-termini with ASMs. The FASTA file includes sequences from clusters with high content of ASM-like sequences, according to the BASS PCFGs (SF7).</li> <li>SF9. Profile HMMs of ASMs found in short NLR N-termini.</li> <li>SF10. Profile HMM of HeLo-related HRAMs.</li> <li>SF11. Genomic neighbors of candidate short N-termini NLRs with ASMs The list includes accessions of proteins encoded by genes within the neighborhood of 20kbp of genes encoding the query proteins (SF8).</li> <li>SF12. Short C-termini of 200–400 aa long proteins genomically neighboring candidate short NLR N-termini with ASMs. The FASTA file concerns target proteins listed in SF11.</li> <li>SF13. Pairwise hits of the same ASMs in N-termini of NLRs and C-termini of genomically neighboring proteins. The table is based on SF8–9 and SF11–12. </li> <li>SF14. Lists of HMMER domain hits of effector domain profiles. The lists were obtained through iterative searches in NCBI “nr” starting from Pfam profiles of known NLR effector domains.</li> <li>SF15. Short C-termini of effector proteins. The FASTA file concerns target proteins listed in SF14.</li> <li>SF16. Short N-termini of Pfam NACHT and NB-ARC proteins. The FASTA file concerns proteins from NCBI “nr” associated with the two families in the Pfam database.</li> <li>SF17. Profile HMMs of ASMs found both in effector C-termini and NLR N-termini of genomically neighboring proteins.</li> <li>SF18. Genomic neighbors of candidate short N-termini Pfam NACHT and NB-ARC proteins. The list includes accessions of proteins encoded by genes within the neighborhood of 20kbp of genes encoding the query proteins (SF16).</li> <li>SF19. Pairwise hits of the same ASMs in N-termini of NACHT/NB-ARC NLRs and C-termini of genomically neighboring effector proteins. The table is based on SF15–18. </li> <li>SF20. Pairwise hits of the same ASMs in N-termini of NLRs and C-termini of genomically co-occurring effector proteins. The table is based on SF8–9 and SF15. </li> <li>SF21. BaMLKL homologs identified with hmmsearch in Basidiomycota. A FASTA file.</li> </ul>
The Vibrio Type III Secretion System 2 is not restricted to the Vibrionaceae and encodes differentially distributed repertoires of effector proteins
<p>Supplementary Dataset for the work entitled "The Vibrio Type III Secretion System 2 is not restricted to the Vibrionaceae and encodes differentially distributed repertoires of effector proteins".</p> <p>This dataset includes files for the T3SS2 reconstructed phylogenetic tree (Newick tree and fasta file), hierarchical clustering data analysis file from MORPHEUS, Table S1 with genome accession numbers, and all the data of the absence/presence of T3SS2-related components, Table S2 with the prediction of novel effector proteins.</p>
Supplementary material for "Surface frustration re-patterning underlies the structural landscape and evolvability of fungal orphan candidate effectors"
<p><strong>Tables</strong></p> <p>Table S1. List of fungal genomes analyzed in this work, associated references and properties.</p> <p>Table S2. List of all secreted proteins less than 300 amino-acids from the 20 fungal genomes. The table includes Signalp4.0 output, mature sequence, Espritz % disorder, pfam domains, AlphaFold top prediction pLDDT and the associated pdb file in Dataset S1.</p> <p>Table S3. Top Hits to pdb database for all OCE structures. 'network_node_name' corresponds to the portein identifier in the OCE structure similarity network provided in Dataset S3. 'Hidef_raw_community' corresponds to groups of structural OCE analogs identified by HiDEF community detection performed on the network provided in Dataset S3.</p> <p>Table S4. Table S4. List of the 62 major OCE folds with associated statistics. Columns I to AB provide the number of occurrences per species. Note that the actual number of members per species might be underestimated due to the stringent pipeline used for OCE identification (excluding proteins larger than 300 amino acids or containing PFAMs for instance).</p> <p> </p> <p>Table S5. Relative surface exposure, conformational flexibility and conservation data mapped on residues of members of the Alt-A1 and BoNT families. RMSD, root mean square deviation for all aligned atoms; Conservation, percentage conservation in multiple structure alignment.</p> <p>Table S6. Assignment of NCBI accessions to MMseqs clusters and assignment of MMseqs clusters to HMM matching-based super-clusters.</p> <p>Table S7. Co-mutation occurrences and associated p-values in two OCE clades from the Alt-A1 and KP6 families.</p> <p>Table S8. Amino acid properties inferred from mutation scans and frustration analyses in Alt-A1 cluster yellow1 and KP6 cluster 43. 'Number of aa variants' corresponds to the number of different amino acids found at each position (deletion counts as 1). 'Alanine scan ∆Z' and 'Deletion scan ∆Z' correspond to the difference between Z-score for the native protein agains itself and Z-score for the native protein against mutant at each position (either Alanine replacement or 5-aa deletion). 'Destabilization factor' is the average of column E and F. 'Stabilization factor' corresponds to the difference between expected structural variation due to destabilization factor and the observed structural variation in multiple mutants. 'netEffect' is difference between column G and H. 'Max co-mutation %' is the highest frequency of co-mutation observed with other residues in natural variants, with 'Min co-mutation p-value (Bonferroni corrected)' the associated p-value.Table S9. Sequence and delta Z of natural variants and mutants from AA1_cl25</p> <p>Table S9. List of natural variants and <em>in silico</em> mutants from the Alt-A1 cluster 25 analyzed in this work, including protein sequence and structure comparison scores (comparison with the reconstructed clade ancestor n0).</p> <p>Table S10. List of natural variants and in silico mutants from the KP6 cluster 43 analyzed in this work, including protein sequence and structure comparison scores (comparison with the reconstructed clade ancestor n0).</p> <p>Table S11. Summary statistics for the phylogenetic trees of 15 OCE clades analyzed for structure and frustration evolution.</p> <p>Table S12. Mapping of structural and frustration data onto phylogenetic trees for 15 OCE clades. The corresponding trees and protein structures are provided in Dataset S7.</p> <p><strong>Datasets</strong></p> <p>Dataset S1. AlphaFold rank1 models for 3 927 OCEs (.pdb format).</p> <p>Dataset S2. Pairwise structure comparison for 3 911 OCE. DALI matrix output containing pairwise Z-scores.</p> <p>Dataset S3. Network file including 2 561 OCEs with 3 or more vertices of Z-score weight 5.2 or more, in .sif and .xgmml formats.</p> <p>Dataset S4. Videos illustrating the mapping of relative surface exposure and structural variability in Alt-A1 and BoNT groups, amino-acids conservation, co-selected mutation patches and residue net stabilization effects on Alt-A1 clade 25 ancestor and KP6 cluster 43 ancestor. Color scales are as in Figure 2 and 3 respectively (.mp4 format).</p> <p>Dataset S5. Phylogenetic trees (.nwk), ancestral (.fasta) and modern variant (.faa) sequences, and AlphaFold best protein models (.pdb) for members of KP6 cluster 43 and Alt-A1 cluster 25. The archive includes 140 Alt-A1 protein structure and 128 KP6 protein structures.</p> <p>Dataset S6. Best predicted structures for 917 natural variants and mutants of AA1_cl25 and 801 natural variants and mutants of KP6_cl43 (.pdb format).</p> <p>Dataset S7. Phylogenetic trees (.nwk) and AlphaFold best protein models (.pdb) for 15 OCE clades. The file includes 2 598 protein structures distributed from clades AA1_s (139), AA1_t (135), AA1_y1 (140), AA1_y2 (90), AA1_y3 (128), BoNT_s (291), CIP_s (167), CIP_t (231), crystallin (233), GNK2 (189), KP6_cl3 (203), KP6_cl26 (111), KP6_cl43 (123), KP6_cl96 (231), KP6_cl242 (187).</p> <p><strong>Text and Figures</strong></p> <p>Text S1. Contains supplementary methods, results and figures S1 to S13.</p>
Raw data for "Host-interactor screens of Phytophthora infestans RXLR proteins reveal vesicle trafficking as a major effector-targeted process"
<p>This dataset contains raw and original images, phylogenetic tree files, sequence alignment files used for phylogenetic tree construction and unprocessed data for figures presented in the manuscript titled "Host-interactor screens of <em>Phytophthora infestans</em> RXLR proteins reveal vesicle trafficking as a major effector-targeted process". Each zip file contains raw data for each figure in the manuscript. A version of the manuscript is available on bioRxiv with doi.org/10.1101/2020.09.24.308585.</p>
Data + Analyses: "Gaze-dependent Coding of Somatosensory Reach Targets after Effector Movement: Testing the Impact of Online Information, Movement Timing, and Target Distance"
<p>This upload contains the experiment scripts (written in Presentation), data, and analyses (performed with MATLAB and SPSS) underlying the publication<strong> </strong>by Mueller & Fiehler (2017). <em>PloS one</em>. doi:<strong>10.1371/journal.pone.0180782</strong></p>
Dataset for "Computational prediction of structure, function and interaction of Myzus persicae (green peach aphid) salivary effector proteins "
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Pyricularia MAX effectors Web Site Archive
<p><span>Collection of validated MAX AlphaFold models</span></p>
Our predicted Crinkler (CRN) family effector proteins and the corresponding GFF3 files across 128 Phytophthora isolates
<p>These files contain predicted Crinkler (CRN) family effector proteins and the corresponding GFF3 files across 128 Phytophthora isolates.</p>
Dataset: eFFECTOR Therapeutics, Inc. (EFTRW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: eFFECTOR Therapeutics, Inc. (EFTR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Alphafold2 and AlphaFold-Multimer Predicted Interactions of Soybean Proteins with Macrophomina phaseolina Effectors reveals putative protease inhibitors and SUSS effectors.
<p> </p> <ul> <li> <p><strong>Kunitz Monomer Prediction</strong>:</p> <ul> <li><strong>Data</strong>: Analysis of soybean Kunitz proteins.</li> <li><strong>Details</strong>: Detected on the apoplast at 3 days post-infection with <em>Macrophomina phaseolina</em>.</li> <li><strong>File</strong>: <code>KUNITZ_monomers_outputdir.zip</code></li> </ul> </li> <li> <p><strong>Uncharacterized M. phaseolina Protein Monomer Prediction</strong>:</p> <ul> <li><strong>Data</strong>: Predictions for uncharacterized proteins.</li> <li><strong>Details</strong>: Detected on the apoplast at 3 days post-infection.</li> <li><strong>File</strong>: <code>uncharacterised_proteins_SUSS_effectoroutputdir.zip</code></li> </ul> </li> </ul> <ul> <li> <p><strong>Positive Validation Set</strong>:</p> <ul> <li><strong>Data</strong>: Experimental verification of protein-inhibitor pairs.</li> <li><strong>Details</strong>: Pairs include experimentally verified interactions, specifically proteins and inhibitors, but lack resolved crystal structures.</li> <li><strong>File</strong>: <code>existing_non_existinpairs_Validation_outputdir.zip</code></li> </ul> </li> </ul> <ul> <li> <p><strong>Soybean Serine Protease-Kunitz Interaction</strong>:</p> <ul> <li><strong>Data</strong>: Interactions between soybean serine proteases and Kunitz proteins.</li> <li><strong>Details</strong>: Analyzed in the apoplastic space at 3 days post-infection.</li> <li><strong>File</strong>: <code>glycine max_Serine protease_Vs_Gmaxkunitz_outputdir.zip</code></li> </ul> </li> </ul> <ul> <li> <p><strong>Cysteine Protease without Pro-domain-MoErs-like effector Interaction</strong>:</p> <ul> <li><strong>Data</strong>: Interactions involving cysteine proteases.</li> <li><strong>Details</strong>: Rice RD21 and soybean cysteine proteases with pro-domains removed interacting with <em>MoErs1</em> and <em>MoErs1</em>-like M.phaseolina effectors.</li> <li><strong>File</strong>: <code>AF2-Multimer_RD21&GmaxCproteases_MoERS1_screening_outputdir.zip</code></li> </ul> </li> </ul> <ul> <li> <p><strong>Fungal Serine Protease-Kunitz Interaction</strong>:</p> <ul> <li><strong>Data</strong>: Interactions between <em>Macrophomina phaseolina</em> serine proteases and soybean Kunitz proteins.</li> <li><strong>Details</strong>: Evaluated in the apoplastic space at 3 days post-infection.</li> <li><strong>File</strong>: <code>fungalSerineprotease_Vs_Gmax_kunitzoutputdir.zip</code></li> </ul> </li> <li> <p><strong>Negative Validation Set</strong>:</p> <ul> <li><strong>Data</strong>: Known non-interacting pairs.</li> <li><strong>Details</strong>: Non-interacting pairs of serine proteases-chitinases that are not resolved as crystal structures</li> <li><strong>File</strong>: <code>Gmax_Serineprotease_Vs_Gmaxchitinases_Validation_outputdir.zip</code></li> </ul> </li> </ul>
Extracellular vesicles from Fusarium graminearum contain protein effectors expressed during infection of corn
<p><em>Fusarium</em> <em>graminearum</em> (<em>Fgr</em>) is a devastating filamentous fungal pathogen that causes diseases in cereals, while producing mycotoxins that are toxic for humans and animals, and render grains unusable. Low efficiency in managing <em>Fgr</em> poses a constant need for identifying novel control mechanisms. Evidence that fungal extracellular vesicles (EVs) from pathogenic yeast have a role in human disease led us to question whether this is also true for fungal plant pathogens. We separated EVs from <em>Fgr</em> and performed a proteomic analysis to determine if EVs carry proteins with potential roles in pathogenesis. We revealed that protein effectors, which are crucial for fungal virulence, were detected in EV preparations and some of them did not contain predicted secretion signals. Furthermore, a transcriptomic analysis of corn (<em>Zea</em> <em>mays</em>) plants infected by <em>Fgr</em> revealed that the genes of some of the effectors were highly expressed in vivo, suggesting that the <em>Fgr</em> EVs are a mechanism for the unconventional secretion of effectors and virulence factors. Our results expand the knowledge on fungal EVs in plant pathogenesis and cross-kingdom communication, and may contribute to the discovery of new antifungals.</p> <p>The following are available online at www.mdpi.com/xxx/s1, Figure S1. Controls for the separation of EVs from <em>Fusarium</em> <em>graminearum</em> (<em>Fgr</em>) by SEC. Figure S2. The superoxide dismutase [Cu-Zn] (SOD1) from F. <em>graminearum</em> (<em>Fgr</em>) contains a diacidic amino acid motif implicated in unconventional secretion. Figure S3. Sequence alignment of the chitinase GH18 domain. Figure S4. Computational prediction of effector candidates detected in EV samples from <em>Fusarium</em> <em>graminearum</em> (<em>Fgr</em>). Table S1. List of proteins detected in EVs from <em>Fusarium</em> <em>graminearum</em> (<em>Fgr</em>). Table S2. List of proteins employed in the computational effector prediction analysis. Table S3. Proteins identified in the secretome from <em>Fusarium</em> <em>graminearum</em> (<em>Fgr</em>). Table S4. List of transcripts identified in corn (<em>Zea</em> <em>mays</em>) infected by <em>Fusarium</em> <em>graminearum</em> (<em>Fgr</em>). Table S5. Gene expression values per biological replicate.</p>
Damaged mitochondria recruit the effector NEMO to activate NF-κB signaling
<p>Failure to clear damaged mitochondria via mitophagy disrupts physiological function and may initiate damage signaling via inflammatory cascades, though how these pathways intersect remains unclear. We discovered that NF-κB essential regulator NEMO is recruited to damaged mitochondria in a Parkin-dependent manner in a time-course similar to recruitment of the structurally-related mitophagy adaptor, OPTN. Upon recruitment, NEMO partitions into phase-separated condensates distinct from OPTN, but colocalizing with p62/SQSTM1. NEMO recruitment in turn recruits the active catalytic IKK component phospho-IKKb, initiating NF-κB signaling and the upregulation of inflammatory cytokines. Consistent with a potential neuroinflammatory role, NEMO is recruited to mitochondria in primary astrocytes upon oxidative stress. These findings suggest that damaged, ubiquitinated mitochondria serve as an intracellular platform to initiate innate immune signaling, promoting the formation of activated IKK complexes sufficient to activate NF-kB signaling. We propose that mitophagy and NF-κB signaling are initiated as parallel pathways in response to mitochondrial stress. </p>
A bacterial effector manipulates host lysosomal protease activity-dependent plasticity in cell death modalities to facilitate infection
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tRNA anticodon cleavage by target-activated CRISPR-Cas13a effector
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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.