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293 results for “Phase Separation”
Dynamics of Phase Separation from Holography
<p>We use holography to develop a physical picture of the real-time evolution of the spinodal instability of a four-dimensional, strongly-coupled gauge theory with a first-order thermal phase transition. The implemented planar symmetry on the gravity side reduces the dynamics to $1+1$ dimensions in the gauge theory. In this dataset, we publish the boundary data of several simulations each in its respective archive. The simulations are all for the same theory as the first evolution of the inhomogeneous triple peak solution published in <strong><a href="http://arXiv.org/abs/arXiv:1703.02948">arXiv:1703.02948</a></strong>. They differ in their initial state (initial<a href="https://www.google.com/search?client=firefox-b&q=homogeneous&spell=1&sa=X&ved=0ahUKEwjbruvE9JfgAhUj2OAKHa2wCL4QkeECCC4oAA"><strong><em> </em></strong></a>homogeneous energy density or initial excitation) and longitudinal extent, but are all on a circle in that longitudinal direction due to the periodic boundary condition. Most evolution finish in the preferred universal final state with a single phase separated domain. Their detailed physical analysis can be found in the upcoming paper<strong> <a href="https://arxiv.org/abs/1905.12544">arXiv:1905.12544</a></strong>. 000_Readme.txt provides a quick explanation on the content of each archive. We also provide an optional Mathematica script to plot properties of the stress tensor.</p>
Amino Acids Modulate Liquid-Liquid Phase Separation in vitro and in vivo by Regulating Protein-Protein Interactions
<p>The metadata, plots and microscopy images for the manuscript "Amino Acids Modulate Liquid-Liquid Phase Separation in vitro and in vivo by Regulating Protein-Protein Interactions".</p>
Intrinsic disorder, phase separation and fibrillation by the Henipavirus V and W proteins | Talk- I PhasAGE International Conference
<p>The <strong>I PhasAGE international conference</strong> brought together members of the PhasAGE consortium as well as outstanding international speakers showcasing high impact achievements in the field of liquid-liquid phase separation in aging and late-onset diseases.</p> <p>For details on conference program please see: https://phasage.eu/phasage-conference-1/ </p>
Phase separation of hnRNP A1 upon specific RNA-binding observed by magnetic resonance
<p>Experimental data, <a href="https://mmmx.info">MMMx</a> restraint and ensemble analysis files (.mcx), restraint data, raw ensembles, and ensemble lists with populations (.ens) pertaining to the manuscript "Phase separation of hnRNP A1 upon specific RNA-binding observed by magnetic resonance" <a href="https://www.biorxiv.org/content/10.1101/2022.03.21.485092v1">available at bioRxiv</a> and submitted to a peer-reviewed journal.</p>
PhasAGE Training School 2 - Phase separations and transitions by viral proteins: from viral factories to interference with host cell functions- LECTURE
<p>The Training School 2 “Biomolecular condensates in cell function, aging and disease” is the <strong>second</strong> edition of a series of PhasAGE training activities.</p> <p> </p> <p>The main goal of this training school is to raise awareness and provide expertise on fundamental aspects of phase separation and formation of <strong>biomolecular condensates</strong>, specifically covering the importance of this process to cellular biology and its contribution to the aging process and age-related diseases.</p>
Tailoring PVDF Membranes Surface Topography and Hydrophobicity by a Sustainable Two-Steps Phase Separation Process: dataset
<p>This is the dataset related to the article published in ACS Sustainable Chemistry & Engineering “Tailoring PVDF Membranes Surface Topography and Hydrophobicity by a Sustainable Two-Steps Phase Separation Process” DOI: 10.1021/acssuschemeng.8b01407</p>
Data for "A promiscuous mechanism to phase separate eukaryotic carbon fixation in the green lineage"
<p>This repository contains all raw data associated with the manuscript:</p> <p>"<em><strong>A promiscuous mechanism to phase separate eukaryotic carbon fixation in the green lineage</strong></em>"</p> <p> </p> <p>The files are organised according to their appearance as figures in the manuscript.</p> <p>Within each of the zipped figure folders is a <strong>_readme.txt</strong> file that contains information about the raw data provided for each figure.</p>
PhasAGE Training School 1 - Computational prediction and databases of protein phase separation - PRACTICAL
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
PhasAGE Training School 1 - Phase separation in diseases - LECTURE
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
PhasAGE Training School 1 - Computational prediction and databases of protein phase separation Overview- LECTURE
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
PhasAGE Training School 1 - Phase separation in virus-host interactions- LECTURE
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
Data for 'Grain boundary segregation and phase separation in ceria-zirconia from atomistic simulation'
<p>Data for the article 'Grain boundary segregation and phase separation in ceria-zirconia from atomistic simulation', including input and output files for simulations, and scripts to perform data analysis and generate figures.</p>
Data for 'Graphene oxide aerogels for gas phase adsorption and selective separation of aromatic hydrocarbons and cycloalkanes'
<p>### Selected experimental data for the 'Graphene oxide aerogels for gas phase adsorption and selective separation of aromatic hydrocarbons and cycloalkanes ###</p> <p>Authors of the manuscript related to the uploaded data:<br>1. Maksymilian Plata-Gryl, Department of Process Engineering and Chemical Technology, Faculty of Chemistry, Gdansk University of Technologym, email: maksymilian.plata-gryl@pg.edu.pl<br>2. Roberto Castro-Muñoz, Department of Sanitary Engineering, Faculty of Civil and Environmental Engineering, Gdansk University of Technology, email: food.biotechnology88@gmail.com<br>3. Emilia Gontarek-Castro, Department of Environmental Technology, Faculty of Chemistry, University of Gdansk<br>4. Alan Miralrio, Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey<br>5. Grzegorz Boczkaj, Department of Sanitary Engineering, Faculty of Civil and Environmental Engineering, Gdansk University of Technology, email: grzegorz.boczkaj@pg.edu.pl</p> <p>Package contains following data:<br>1. Fourier-transform infrared spectra of rGOA, GO, and graphite samples, format: .csv, number of files: 5<br>2. Raman spectra of rGOA samples, format: .csv, number of files: 3<br>3. Low temperature nitrogen adsorption-desorption isotherms, format: .txt, number of files: 4<br>4. Raw chromatograms of test probes for rGOA samples, format .txt, number of files: 234 in 12 subfolders</p> <p>For more information about experimental conditions or data please see the manuscript/publication or contact author/s.</p>
DNA-stimulated liquid-liquid phase separation by eukaryotic topoisomerase II modulates catalytic function
<p>Type II topoisomerases modulate chromosome supercoiling, condensation, and catenation by moving one double-stranded DNA segment through a transient break in a second duplex. How DNA strands are chosen and selectively passed to yield appropriate topological outcomes – e.g., decatenation vs. catenation – is poorly understood. Here we show that at physiological enzyme concentrations, eukaryotic type IIA topoisomerases (topo IIs) readily coalesce into condensed bodies. DNA stimulates condensation and fluidizes these assemblies to impart liquid-like behavior. Condensation induces both budding yeast and human topo IIs to switch from DNA unlinking to active DNA catenation, and depends on an unstructured C-terminal region, the loss of which leads to high levels of knotting and reduced catenation. Our findings establish that local protein concentration and phase separation can regulate how topo II creates or dissolves DNA links, behaviors that can account for the varied roles of the enzyme in supporting transcription, replication, and chromosome compaction.</p>
Figure 1. DiagAgentExpertDM architecture (adapted from Ioniță, 2014) -Intelligent System for Diagnosis of a Three-Phase Separator
<p>For the current hybrid system</p> <p>(DiagAgentExpertDM)</p> <p>, data from different sources and in</p> <p>various forms are preprocessed, in order to represe</p> <p>nt them in a unified way to be able to upload</p> <p>them in a learning module. Simultaneously, inconsis</p> <p>tency tests are made to eliminate the</p> <p>measurement errors caused by improper calibration o</p> <p>f transducers etc.</p> <p>In condition of using a diagnosis method based on p</p> <p>rocess history, the next step in fault</p> <p>identification referring to the gas-oil separation</p> <p>process is to scan the historical data. Retrieved d</p> <p>ata</p> <p>will possess a label with a certain priority which</p> <p>will be used in the next retrieval process. The</p> <p>learning module is supplied with multiple preproces</p> <p>sed data samples, in order to extract knowledge</p> <p>from them. For each data samples, a data mining alg</p> <p>orithm will be applied (figure 1).</p> <p> </p>
Figure 2. Correct Classified Instances for different data sets-Intelligent System for Diagnosis of a Three-Phase Separator
<p>The data mining models may be considered a superior</p> <p>technique that may be successful</p> <p>applied in diagnosis and may be develop in the futu</p> <p>re on the base of more training data to increase</p> <p>the accuracy of results.</p> <p>Industrial processes are dynamic processes with ran</p> <p>dom behavior and whose evolution over</p> <p>time cannot be predicted unless it is well known th</p> <p>e process model and use advanced predictive</p> <p>techniques. Consequently, design and implement an a</p> <p>utomated online monitoring and diagnosis</p> <p>three-phase separator remains a future direction of</p> <p>research conducted so far.</p> <p>Conceptually, this system should have permanent acc</p> <p>ess to data collected from field</p> <p>transducers, to be able to identify the type of fau</p> <p>lt occurred, to locate the fault and provide</p> <p>recommendations to remedy abnormal operating condit</p> <p>ion. Also, updating the database defects with</p> <p>new types of defects occurred and the adequate solu</p> <p>tions adopted for eliminating errors in the</p> <p>operating mode is an important feature to be consid</p> <p>ered during the design of the online diagnosis</p> <p>system. This is possible if the system would have s</p> <p>elf-learning capabilities. To acquire this "skill",</p> <p>the automatic online diagnosis system may contain a</p> <p>diagnosis module based on artificial neural</p> <p>networks.</p>
Cdt1 inhibits CMG helicase in early S phase to separate origin licensing from DNA synthesis
A fundamental concept in eukaryotic DNA replication is the temporal separation of G1 origin licensing from S phase origin firing. Re-replication and genome instability ensue if licensing occurs after DNA synthesis has started. In humans and other vertebrates, the E3 ubiquitin ligase CRL4Cdt2 starts to degrade the licensing factor Cdt1 after origins fire, raising the question of how cells prevent re-replication in early S phase. Here, using quantitative microscopy, we show that Cdt1 inhibits DNA synthesis during an overlap period when cells fire origins while Cdt1 is still present. Cdt1 inhibits DNA synthesis by suppressing CMG helicase progression at replication forks through the MCM-binding domain of Cdt1, and DNA synthesis commences once Cdt1 is degraded. Thus, instead of separating licensing from firing to prevent re-replication in early S phase, cells separate licensing from DNA synthesis through Cdt1-mediated inhibition of CMG helicase after firing.
Binary Classification as a Phase Separation Process (data repository)
<p><strong>For version 0.0.2 (from 2021) see below:</strong></p> <p>This is a data repository for the paper "Binary classification as a phase separation process", by Rafael Monteiro.</p> <ul> <li>Website with description of this project: <a href="https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html">https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html</a></li> <li>Github: <a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation">https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation</a></li> </ul> <p>This is a second version, which I wrote using tensorflow. It is much smaller (5 Gb when decompressed), a remarkable improvement when compared to the more than 100 Gb of the previous version).</p> <p>The new files are </p> <ul> <li>PSBC_BCs.tar.gz</li> <li>PSBC_classifier_PCA.tar.gz</li> <li>PSBC_dataset.tar.gz</li> <li>PSBC_libs_grids_statistics.tar.gz</li> <li> PSBC_notebooks.tar.gz</li> </ul> <p>Their content is explained in the file README_v2.pdf</p> <p><strong>UPDATE: <a href="https://drive.google.com/drive/folders/18l_92HuHDWJDkZnvXRuyGedcyC_3YZ2M?usp=sharing">a Google Colab folder is also available</a>. You can also find all the data and libraries there, unpacked.</strong></p> <p>For usage, see the Git-hub. </p> <blockquote> <p><strong>NOTE)</strong> I will keep the content for the previous version available in my Github as well. It is still a "nice exercise" to do all that is done in this new version in numpy, as done there. <strong><em>(Or, I should say, they should be studied as a cautionary tale of what to avoid.)</em></strong></p> </blockquote> <p> </p> <p><strong>For version 0.0.1 (from 2020) see below:</strong></p> <p>This is a data repository for the paper "Binary classification as a phase separation process", by Rafael Monteiro.</p> <ul> <li>Website with description of this project: <a href="https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html">https://rafael-a-monteiro-math.github.io/Binary_classification_phase_separation/index.html</a></li> <li>Github: <a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation">https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation</a></li> </ul> <p>Therein you will find</p> <ul> <li>Examples</li> <li>1D toy model examples</li> <li>Computational statistics</li> <li>Several trained PSBC on MNIST dataset, with different parameter configurations</li> <li>Extra simulations, investigating normalization properties, low dimensional models that fail due to "too much" model compression, and comparison among ANNs, KNNs, and the PSBC in 1D</li> </ul> <p>If you want to know</p> <ol> <li>how to read the data</li> <li>how to access computational statistics, raw data, and examples</li> <li>how to use the data stored in this data repository</li> </ol> <p>see the guide README.pdf on GitHub page at <a href="https://github.com/rafael-a-monteiro-math/Binary_Classification_Phase_Separation">Binary_Classification_Phase_Separation</a>, where a script that downloads (and organizes) all this data is also available ("download_PSBC.sh).</p> <p>I did not include a copy of the train-test set (0-1dubset of the MNIST database) in every folder with simulations. But you can find a copy of the normalized dataset in the tar ball "PSBC_Examples.tar.gz" as</p> <p>data_test_normalized_MNIST.csv and data_train_normalized_MNIST.csv.</p> <p> </p>
Data from: Membrane phase separation drives responsive assembly of receptor signaling domains
<p>Plasma membrane heterogeneity has been tied to a litany of cellular functions and is often explained by analogy to membrane phase separation, yet models based on phase separation alone fall short of describing the rich organization available within cell membranes. We present comprehensive experimental evidence motivating an updated model of plasma membrane heterogeneity in which membrane domains assemble in response to protein scaffolds. Quantitative super-resolution nanoscopy measurements in live B lymphocytes detect membrane domains that emerge upon clustering B cell receptors (BCR). These domains enrich and retain membrane proteins based on their preference for the liquid-ordered phase. Unlike phase separated membranes that consist of binary phases with defined compositions, membrane composition at BCR clusters is modulated through the protein constituents in clusters or the composition of the membrane overall. This tunable domain structure is detected through the variable sorting of membrane probes and impacts the magnitude of BCR activation.</p>
Reconstitution of phase-separated signaling clusters and actin polymerization on supported lipid bilayers
<p>Liquid–liquid phase separation driven by weak interactions between multivalent molecules contributes to the cellular organization by promoting the formation of biomolecular condensates. At membranes, phase separation can promote the assembly of transmembrane proteins with their cytoplasmic binding partners into micron-sized membrane-associated condensates. For example, phase separation promotes clustering of nephrin, a transmembrane adhesion molecule, resulting in increased Arp2/3 complex-dependent actin polymerization. In vitro reconstitution is a powerful approach to understanding phase separation in biological systems. With a bottom-up approach, we can determine the molecules necessary and sufficient for phase separation, map the phase diagram by quantifying de-mixing over a range of molecular concentrations, assess the material properties of the condensed phase using fluorescence recovery after photobleaching (FRAP), and even determine how phase separation impacts downstream biochemical activity. Here, we describe a detailed protocol to reconstitute nephrin clusters on supported lipid bilayers with purified recombinant protein. We also describe how to measure Arp2/3 complex-dependent actin polymerization on bilayers using fluorescence microscopy. These different protocols can be performed independently or combined as needed. These general techniques can be applied to reconstitute and study phase-separated signaling clusters of many different receptors or to generally understand how actin polymerization is regulated at membranes.</p>
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