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97 results for “Interaction Method”
Method Classification of Open Access INTACT Molecular Interaction data.
<p>Simple classification data derived from open access papers indexed in the INTACT database (https://www.ebi.ac.uk/intact/downloads) based on PSI-MI25 codes for interaction detection methods or participant detection methods based on the subfigure caption text. <br> <br> intact_records_and_captions_complete.tsv - This file links available text of subfigure captions to PSI-MI25 codes for the interaction detection method and participant detection method. </p> <p>evidx_run_file.txt - This file provides execution codes for the 'EvidX' machine learning text classifier (https://github.com/SciKnowEngine/evidX/releases/tag/v0.1.0)</p> <p> </p> <p> </p> <p> </p>
Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods"
<p>Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods". </p> <p>This supplement includes the following files:</p> <p> </p> <p>1. Structures of the most stable Protein-Aptamer complexes predicted from HADDOCK</p> <p>Haddock_Cluster1.pdb <br> Haddock_Cluster2.pdb <br> Haddock_Cluster3.pdb </p> <p>2. Structure of the most stable conformation aligned with Protein-transferring complex PDB</p> <p>cluster1_aligned.pdb <br> transferrin_aligned.pdb </p> <p>3. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer</p> <p>aptamer_new_rep01_Decomp.dat <br> aptamer_new_rep02_Decomp.dat <br> aptamer_new_rep03_Decomp.dat </p> <p>4. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer-Conjugate<br> conjugate_new_rep01_Decomp.dat <br> conjugate_new_rep02_Decomp.dat <br> conjugate_new_rep03_Decomp.dat <br> </p> <p><br> <br> </p>
SURFBIO Training: "Analytical methods for the study of microbial cell-Surface and Surface-colloid interactions" (2021)
<p>2021. SURFBIO project training within WP1.</p><p>Content:</p><ul><li><strong>Webinar on Vertical scanning interferometry: a microscopic technique to analyze surface reactivity, </strong>by Dr. Cornelius Fischer (HZDR, Germany) </li><li><strong>Webinar on An introduction to radiolabelling as a versatile tool in colloid tracing, </strong>by Stefan Schymura (HZDR, Germany).</li><li><strong>Webinar on Development and construction of biocarriers and aggregates for potential industrial applications</strong>, by Dr. Andre Skirtach and Dr. Bogdan Parakhonskiy (GHENT University). </li><li><strong>Materials and fluidic design to study artificially functionalized microorganisms</strong>, by Dr. Andre Skirtach and Dr. Bogdan Parakhonskiy (GHENT University). </li></ul>
Predicting metal-protein interactions using cofolding methods: Status quo
<p>Metals play important roles for enzyme function and many therapeutically relevant proteins. Despite the fact that the first drugs developed via computer aided drug design were metalloprotein inhibitors, many computational pipelines still discard metalloproteins due to the difficulties of modelling them computationally. New "cofolding" methods such as AlphaFold3 (AF3) and RoseTTAfold-AllAtom (RFAA) promise to improve this issue by being able to dock small molecules in presence of multiple complex cofactors including metals or covalent modifications. Here, we analyze the current status for metal ion prediction using these methods. We find that currently only AF3 provides realistic predictions for metal ions, RFAA in contrast does perform worse than more specialized models such as AllMetal3D in predicting the location of metal ions accurately. We find that AF3 predictions are consistent with expected physico-chemical trends/intuition whereas RFAA often also predicts unrealistic metal ion locations.</p>
Data for 'A multi‐methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal'
<p>Title: Data for 'A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal'</p> <p>Recommended citation: Adhikari, B., Urbach, D., Chettri, N., Sharma, E., Breu, T., Geschke, J., Fischer, M. & Prescott, G. W. (2023) A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal. Sustainable Development https://doi.org/10.1002/sd.2582</p> <p>R code available at: https://github.com/biraj-ad/SDGIntearctions_Nepal_2023</p> <p>Principal Investigator: Graham W Prescott (graham.prescott.research@gmail.com)</p> <p>Authors:<br> Biraj Adhikari (biraj.adhikari@unibe.ch, ORCID: 0000-0002-4260-8706)<br> Davnah Urbach (davnah.payne@unibe.ch, ORCID: 0000-0001-9170-7834)<br> Nakul Chettri (nakul.chettri@icimod.org, ORCID: 0000-0002-3338-8879)<br> Eklabya Sharma (eklabya.sharma11@gmail.com, ORCID:0000-0003-3089-8838)<br> Thomas Breu (thomas.breu@unibe.ch, ORCID: 0000-0003-2348-504X)<br> Jonas Geschke (jonas.geschke@unibe.ch, ORCID: 0000-0002-5654-9313)<br> Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900)<br> Graham W Prescott (graham.prescott.research@gmail.com, ORCID:0000-0001-5123-514X)</p> <p>Date of data collection: August 2021 - June 2022<br> Location of data collection: Kathmandu<br> Date of final release:</p> <p>Data Overview:<br> There are two excel files. Each excel sheet is also uploaded as a separate .csv file.</p> <p>1. 'Alldata.xls': This excel file contains data collected for three independent methods used to develop the study. There are four sheets in this excel workbook. The first three sheet pertains to data collected for the seven-point SDG interactions score, correlation, and expert elicitation method. The last sheet is a derivative of the first three methods, which contains synthesized information of data.<br> Sheet overview:<br> method1: This sheet relates to the data collected through online expert survey using Kobo Toolbox. Description of columns:<br> institution -> institutional affiliation of the participant<br> inst_cat -> categorization of the institution into Intergovernmental Organization (IGO), Non-governmental Organization (NGO), Academia, and Government.<br> exp_years -> experience (in years) of the participant in the conservation sector of Nepal<br> sdgout and sdgin -> The SDG that the participant was randomly assigned to rate its outgoing and incoming interaction with SDG 15 respectively<br> outscore -> the outgoing interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible)<br> conf_out -> the degree of confidence of the participant in their answer to the outgoing interaction score<br> text_out -> (optional) a description of why the participant gave that outgoing interaction score<br> inscore -> the incoming interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible)<br> conf_in -> the degree of confidence of the participant in their answer to the incoming interaction score<br> text_in -> (optional) a description of why the participant gave that incoming interaction score</p> <p>method2: This sheet relates to the data collected for correlation analysis. This includes time-series data of SDG indicators for Nepal obtained from the Global SDG Indicators Database (https://unstats.un.org/sdgs/indicators/database/). Description of columns:<br> Year -> Year when the indicator was measured<br> forest_cover -> Forest cover as a percent of total area (%)<br> kba_freshwater -> Average proportion of Freshwater Key Biodiversity Areas (KBAs) covered by protected areas (%)<br> kba_terrestiral -> Average proportion of Terrestrial Key Biodiversity Areas (KBAs) covered by protected areas (%)<br> redlist -> Red List Index<br> undernourishment -> Prevalence of undernourishment (%)<br> food_insecurity -> Prevalence of moderate or severe food insecurity in the adult population (%)<br> death_chronic -> Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability)<br> suicide -> Suicide mortality rate (deaths per 100,000 population)<br> uhc_index -> Universal health coverage (UHC) service coverage index<br> f_parliament -> Proportion of seats held by women in national parliaments (% of total number of seats)<br> safewater -> Proportion of population using safely managed drinking water services (%)<br> electricity -> Proportion of population with access to electricity, by urban/rural (%)<br> cleanfuel -> Proportion of population with primary reliance on clean fuels and technology (%)<br> renewable -> Renewable energy share in the total final energy consumption (%)<br> gdp_capita -> Annual growth rate of real GDP per capita (%)<br> manu_value -> Manufacturing value added as a proportion of GDP (%)<br> consumption_gdp -> Domestic material consumption per unit of GDP (kilograms per constant 2010 United States dollars)<br> consumption_all -> Domestic material consumption (tonnes)<br> consumption_cap -> Domestic material consumption per capita (tonnes)<br> death_missing -> Number of deaths, missing persons and persons affected by disaster per 100,000 people<br> local_drr -> Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%)</p> <p>method3: This sheet relates to the data collected through key informant interviews. We obtained the count of interactions by coding interview responses as outgoing or incoming co-benefits or trade-offs in MaxQDA. The sheet summarizes the count for type of interaction (co-benefit or trade-off) for each SDG.</p> <p>allmethods: This sheet summarizes the proportion of co-benefits or trade-offs uncovered by each method for each SDG.</p> <p>2. 'ForSynthesistable.xlsx': This excel file synthesizes goal level interactions for the three methods in three separate sheets. We use this table to develop figure 6 of the Manuscript.</p> <p> </p>
Supporting data for "A convolution method to assess subgrid-scale interactions between flow and patchy vegetation in biogeomorphic models"
<p>Dataset necessary to reproduce the results and analyses presented in the paper:</p> <p>Gourgue, O., van Belzen, J., Schwarz, C., Bouma, T.J., van de Koppel, J. & Temmerman, S. (2020) A convolution method to assess subgrid-scale interactions between flow and patchy vegetation in biogeomorphic models, Journal of Advances in Modeling Earth Systems, submitted.</p> <p>The dataset contains:</p> <ul> <li>Process-based model simulations, including their input files and the Python scripts to generate them (pre-processing), as well as the output files and Python scripts to post-process them.</li> <li>Flume experiment data processed for the model calibration.</li> <li>Python scripts to generate the figures and tables of the manuscript.</li> </ul>
Data for GMD article: "Towards an improved treatment of cloud-radiation interaction in weather and climate models: exploring the potential of the Tripleclouds method for various cloud types using libRadtran 2.0.4"
<p>Dataset for the publication by Nina Črnivec and Bernhard Mayer: "Towards an improved treatment of cloud-radiation interaction in weather and climate models: exploring the potential of the Tripleclouds method for various cloud types using libRadtran 2.0.4" submitted to Geoscientific Model Development in 2020.</p> <p>The repository contains data for stratocumulus, cirrus and cumulonimbus cloud case studies. It also contains MYSTIC benchmark radiation data (including atmosphering heating rate and net surface flux) for the aforementioned cloud cases. See README for additional information and description of data files.</p>
ROS-specific Huntingtin Interactions: Comparing transfection methods for inducible expression of huntingtin-specific chromobody
<p>Optimization step in the development of an inducible system expressing YFP-tagged huntingtin-specific intrabodies for stable transfection in TruHD fibroblasts.</p>
Replication Kit for the Paper "Alternative Interaction Methods for VR Environments with Leap Motion" submitted to UIST 2019
<p>This file contains the replication kit for the paper "Alternative Interaction Methods for VR Environments with Leap Motion" submitted to the "32nd ACM User Interface Software and Technology Symposium (UIST 2019)". It consists of the Virtual Reality implemented for the case study of the paper as well as all introductory material for participants, measurements, and analysis scripts.</p>
Data Set "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions"
<p>This data set accompanies the publication "Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions" by Johannes Vornweg and Christoph R. Jacob (TU Braunschweig, Germany) </p> <p>It contains the following files:</p> <p><br>Directory 1_structures:</p> <p> PDB files of all structures used for the test calculations. <br> The PDB files correspond to the protonated structures obtained <br> as described in the main text.</p> <p><br>Directory 02_figure_scripts:</p> <p> Jupyter Notebook for generating all plots included in the manuscript, <br> including raw numerical data.</p> <p><br>Directory 03_input_scripts:</p> <p> - min_congrad.mdp: input file for partial optimization of protonated <br> protein--ligand complexes with Gromacs</p> <p> PyADF input scripts:</p> <p> - sp_single.pyadf: single-point calculations of separate protein and ligand<br> - sp_complex.pyadf: single-point calculation of protein-ligand complex<br> - mfccmbe3.pyadf: MFCC and MFCC-MBE(2) calculations of P-L interaction energy</p> <p> These scripts can be used with PyADF v1.5 (DOI: 10.5281/zenodo.13236550)</p>
Quasi-Newton methods for partitioned simulation of fluid-structure interaction reviewed in the generalized Broyden framework: code and data
<p>These files accompany the publication</p><p>N. Delaissé, T. Demeester, R. Haelterman and J. Degroote. Quasi-Newton methods for partitioned simulation of fluid-structure interaction reviewed in the generalized Broyden framework.<i> Archives of Computational Methods in Engineering</i>, Vol.<strong> </strong>30, 3271-3300, 2023. doi: <a href="https://doi.org/10.1007/s11831-023-09907-y">10.1007/s11831-023-09907-y</a></p><p>In this work, the performance of multiple quasi-Newton methods are compared in terms of memory requirements and computational time. The results are generated for the well-known flexible tube example case, using the open-source code <a href="http://github.com/pyfsi/coconut">CoCoNuT</a>. This code, developed at Ghent University, is Python-based and has the capability to couple existing solvers, both open-source and commercial solvers.</p><p>This archive consists of the following files.</p><ul><li><strong>coconut.tar.gz: </strong>the specific CoCoNuT version used (sep-2022), including the Python flow and structure solvers for the flexible tube and modifications for monitoring memory requirements</li><li><strong>compare_coupling_algorithms.tar.gz: </strong>the scripts to set up the cases and perform the calculations and post-processing</li><li><strong>results.tar.gz:</strong> the generated result data</li></ul><p>For requirements to run CoCoNuT, refer to the <a href="http://pyfsi.github.io/coconut/">documentation</a>. Additionally, the Python package guppy3 is required for monitoring the memory use. In this work the data were generated with Andaconda3-2022.05 and the package guppy3-3.1.2.</p><p>Before running the provided scripts, make sure the parent directory of the "coconut" folder is added to the PYTHONPATH. The calculations can be started with "python run.py". For the cases which names contain "_m" followed by a number, e.g. "_m100", the number refers to the number of discretization points on the interface. The cases with suffix "_c" are distinct from those without, as they don't perform the time consuming memory monitoring and are therefore used for measuring computational time.</p>
Accurate TCR-pMHC Interaction Prediction Using a BERT-based Transfer Learning Method
<p>The datasets used for the TABR-BERT. For the complete training and testing code of TABR-BERT, see <a href="https://github.com/Freshwind-Bioinformatics/TABR-BERT">Freshwind-Bioinformatics/TABR-BERT: TABR-BERT: an Accurate and Robust BERT-based Transfer Learning Model for TCR-pMHC Interaction Prediction (github.com)</a>.</p>
Data from: Plant-derived environmental DNA complements diversity estimates from traditional arthropod monitoring methods but outperforms them detecting plant-arthropod interactions
<p>Our limited knowledge about the ecological drivers of global arthropod decline highlights the urgent need for more effective biodiversity monitoring approaches. Monitoring of arthropods is commonly performed using passive trapping devices, which reliably recover diverse communities, but provide little ecological information on the sampled taxa. Especially the manifold interactions of arthropods with plants are barely understood. A promising strategy to overcome this shortfall is environmental DNA (eDNA) metabarcoding from plant material on which arthropods have left DNA traces through direct or indirect interactions. However, the accuracy of this approach has not been sufficiently tested. In four experiments, we exhaustively test the comparative performance of plant-derived eDNA from surface washes of plants and homogenized plant material against traditional monitoring approaches. We show that the recovered communities of plant-derived eDNA and traditional approaches only partly overlap, with eDNA recovering various additional taxa. This suggests eDNA as a useful complementary tool to traditional monitoring. Despite the differences in recovered taxa, estimates of community α- and β-diversity between both approaches are well correlated, highlighting the utility of eDNA as a broad scale tool for community monitoring. Last, eDNA outperforms traditional approaches in the recovery of plant-specific arthropod communities. Unlike traditional monitoring, eDNA revealed fine-scaled community differentiation between individual plants and even within plant compartments. Especially specialized herbivores are better recovered with eDNA. Our results highlight the value of plant derived eDNA analysis for large-scale biodiversity assessments that include information about community level interactions.</p>
Interactive Mobile Health Intervention to Improve Early Postpartum Modern Contraceptive Method Uptake
ClinicalTrials.gov study NCT05666037. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: Plant-derived environmental DNA complements diversity estimates from traditional arthropod monitoring methods but outperforms them detecting plant-arthropod interactions
Open the record for dataset details and reuse information.
Fig. 2. A B in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 2. A B-spline solid is a closed object whose shape can be adjusted by moving control points (dark points) that deforms the local portion of the object near the control point. The initial cylindrical shape in A is adjusted (B and C) by pulling out the points at the ends and drawing the points in the middle closer to the axis.
Fig. 1 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 1. Body segments can be created using mass objects of different density and shape. Mass objects can be collected into mass sets to calculate their combined inertial properties; the most inclusive Tyrannosaurus mass set (whole body) is outlined here, as well as the trunk segment and its embedded mass objects.
Fig. 1 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 1. Body segments can be created using mass objects of different density and shape. Mass objects can be collected into mass sets to calculate their combined inertial properties; the most inclusive Tyrannosaurus mass set (whole body) is outlined here, as well as the trunk segment and its embedded mass objects.
Data from: A phylogenetic comparative method for evaluating trait coevolution across two phylogenies for sets of interacting species
Evaluating trait correlations across species within a lineage via phylogenetic regression is fundamental to comparative evolutionary biology, but when traits of interest are derived from two sets of lineages that co-evolve with one another, methods for evaluating such patterns in a dual-phylogenetic context remain underdeveloped. Here we extend multivariate permutation-based phylogenetic regression to evaluate trait correlations in two sets of interacting species while accounting for their respective phylogenies. This extension is appropriate for both univariate and multivariate response data, and may utilize one or more independent variables, including environmental covariates. Imperfect correspondence between species in the interacting lineages can also be accommodated, such as when species in one lineage associate with multiple species in the other, or when there are unmatched taxa in one or both lineages. For both univariate and multivariate data, the method displays appropriate type I error, and statistical power increases with the strength of the trait covariation and the number of species in the phylogeny. These properties are retained even when there is not a 1:1 correspondence between lineages. Finally, we demonstrate the approach by evaluating the evolutionary correlation between traits in fig species and traits in their agaonid wasp pollinators. R computer code is provided.
Data from: Environmental conditions and biotic interactions acting together promote phylogenetic randomness in semi-arid plant communities: new methods help to avoid misleading conclusions
QUESTIONS: Molecular phylogenies are increasingly used to better understand the mechanisms structuring natural communities. The prevalent theory is that environmental factors and biotic interactions promote the phylogenetic clustering and over-dispersion of plant communities, respectively. However, both environmental filtering and biotic interactions are very likely to interact in most natural communities, jointly affecting community phylogenetic structure. How do environmental filters and biotic interactions jointly affect the phylogenetic structure of plant communities across environmental gradients? LOCATION: Eleven Stipa tenacissima L. grasslands located along an environmental gradient from central to southeast Spain, covering the core of the distribution area of this vegetation type in Europe. METHODS: We jointly evaluated the effects of environmental conditions and plant–plant interactions on the phylogenetic structure – measured with the mean phylogenetic distance index of the studied communities. As an indicator of environmental conditions, we used a PCA ordination including eight climatic variables. Different metrics were used to measure the following processes: (1) competition/facilitation shifts at the entire community level (species combination index), and (2) the effect of microclimatic amelioration provided by the two most important nurse plants on neighbour composition (similarity indices and comparison of the phylogenetic pattern between canopy patches and bare ground areas). RESULTS: Biotic interactions and, to a less extent, environmental conditions affected the phylogenetic pattern of the studied communities. While positive plant–plant interactions (both at community level and the scale of individual nurse plants) increased phylogenetic overdispersion, higher rainfall increased phylogenetic clustering. The opposing effects of environmental conditions and biotic interactions could be the main cause of the overall random phylogenetic structure found inmost of these communities. CONCLUSIONS: Our results illustrate, for the first time, how an overall random phylogenetic pattern may not only be promoted by the lack of influence of either environmental filtering or biotic interactions, but rather by their joint and opposing effects. They caution about making inferences on the underlying mechanisms shaping plant communities from the sole use of their phylogenetic pattern. We also provide a comprehensive set of easy-to-measure tools to avoid misleading conclusions when interpreting phylogenetic structure data obtained from observational studies.
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Allen Brain Atlas
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International Brain Laboratory public data
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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.