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20 results for “Crystallisation”
Study of the Crystallisation Reaction Behaviour to Obtain Struvite - Agronomic Potential of Struvite and Crystallisation results
<p>The potential of N and P recovering from digestate by means of its precipitation in the form of struvite is evident. However, it is necessary to optimise the process at a larger scale, to achieve results that can be extrapolated to evaluate the technical and economic feasibility of the process at an industrial scale. In this work, batch and pilot plant tests were carried out in order to consolidate, at a sufficiently relevant scale, the results obtained at lab scale. For this purpose, the parameters that have the greatest effect on the reaction yield in a fludised bed reactor were selected (Mg and P concentration, flow rate of the fluidising agent (air) and reaction time). Digestate produced in anaerobic digestion plant from pig manure was used as raw material. According to the results obtained, for the struvite crystallisation reaction, the great operational levels for the Mg/P, N/P, air flow rate and reaction time are 1.5, 4.0, 6.0 NL·min<sup>−1</sup> and 0.5 h, respectively. Finally, a study was carried out to establish the agronomic potential of the salt (struvite) as a biofertiliser in the turf crop, obtaining a similar behaviour of the struvite used in this work to that of commercial struvite.</p>
Deposition of ALK2 (ACVR1) co-crystallised with M4K2117 (PDB: 6SRH)
<p>Desposition of the structure of ALK2 co-crystallised with the compound M4K2117 in the PDB database with code 6SRH.</p> <p>You can read more about it over on the <a href="https://openlabnotebooks.org/?p=3575&preview=true">Open Lab Notebooks project.</a></p> <p> </p>
Early pyroxene crystallisation deep below mid-ocean ridges: Supplementary data of gabbro phase mapping
<p>The data repository contains part of the original microscopy imagery datasets from optical microscopy, electron microscopy backscattered electron (SEM-BSE), and Synchrotron X-ray fluorescence microscopy (XFM) experiments presented in <a href="https://doi.org/10.1016/j.epsl.2025.119423">Ubide et al. (2025)</a> <strong>'Early pyroxene crystallisation deep below mid-ocean ridges' by Teresa Ubide<sup>*</sup>, David T Murphy, Robert B Emo, Michael Jones, Marco Acevedo Zamora, and Balz S Kamber</strong></p> <p>Specifically, it includes the QuPath software (<a href="https://www.nature.com/articles/s41598-017-17204-5">Bankhead et al., 2017</a>) project including rock (mid-ocean ridge olivine gabbro) thin sections 81-R5w and 80-R6w. The project contains the semantic image segmentation outputs generated with the <a href="https://qupath.readthedocs.io/en/stable/docs/tutorials/pixel_classification.html">Pixel Classifier</a> and MatLab script described in <a href="https://www.mdpi.com/2075-163X/13/2/156">Acevedo Zamora et al. 2023</a> (see <a href="https://github.com/marcoaaz/Acevedo-Kamber/tree/main/QuPath_generatingMaps">code repository</a>).</p> <p>Sample 80-R6w was segmented using image annotations in QuPath and an input comprising a false-colour Cr-Ti-Ca XFM image, cross-polarised light maximum intensity (XPL-max), and plane-polarised light (PPL-0 degrees) photomicrographs.</p> <p>Similarly, Sample 81-R5w used a false-colour Cr-Ti-Ca XFM image, <a href="https://github.com/marcoaaz/AcevedoEtAl._2024b_autoencoder">deep sparse autoencoder</a> image representation of XFM (after <a href="https://www.sciencedirect.com/science/article/pii/S0009254124000779?dgcid=rss_sd_all">Acevedo Zamora et al., 2024</a>), cross-polarised light (XPL-0 degrees), plane-polarised light (PPL-0 degrees) photomicrographs, and recoloured SEM-BSE (after <a href="https://www.mdpi.com/2075-163X/13/2/156">Acevedo Zamora et al. 2023</a>). </p> <p>The segmentation of both samples provided a conservative estimate of the locations of relict clinopyroxene cores (~4% volume of cpx mask), mantles, and rims in a similar phase map colour scheme for better comparison.</p> <p>If there are questions regarding the utilisation of the data, contact Marco Acevedo (marco.acevedozamora@qut.edu.au ; maaz.geologia@gmail.com).</p>
Sustainable recovery of critical elements from seawater saltworks bitterns by integration of high selective sorbents and reactive precipitation and crystallisation: Developing the probe of concept with on-site produced chemicals and energy
<p>The availability of raw mineral resources containing elements included in the Critical Raw Materials (CRMs) list is a growing concern for the European Union. Sea mining has been identified as a promising secondary source. In particular, brines obtained in solar saltworks (bitterns) contain relevant amounts of valuable CRMs such as Mg(II), B(III), other alkaline/alkaline earth metals (Rb(I), Cs(I), Sr(II)) and transition/post-transition elements (Co(II), Ga(III), Ge(IV)). However, the low concentration of some of these elements (µg/L) requires an effort to develop recovery routes that are sustainable and economically feasible where the required chemicals and energy are produced on-site from the saltworks bitterns (i.e. HCl and NaOH). Even the conventional recovery processes such as ion exchange, sorption and precipitation, which have proved to be competitive for metals recovery, are challenged in the case of Trace Elements (TEs). This work studies the recovery of TEs included in the CRMs list from saltworks bitterns after ion exchange processes. First, batch crystallisation and reactive precipitation were tested for some target elements in single-component solutions: Sr(II), Co(II), Ga(III), Ge(IV) and B(III). Then, the experiments were carried out with multi-component synthetic solutions assuming different scenarios of bittern streams coming out a selective extraction stage using sorption and ion exchange processes. The targeted elements were recovered except for Ge(IV), where alternative routes need to be evaluated, as its precipitation involves the use of tannic acid or sulphide solutions that could not be produced from the bitterns. However, a further concentration step would be necessary to achieve element concentrations closer to the mineral phases saturation. Moreover, model simulations were performed using the PHREEQC program, which provided a good prediction of the experimental trends obtained in most cases.</p>
X-ray diffraction images for endothiapepsin co-crystallised with inhibitor H189 to 0.94 Angstrom resolution.
<p>X-ray diffraction images collected on 23rd May 2000 at the BW7B beamline of DESY (Hamburg). </p>
VMXi Classification Dataset: Micrographs of Protein Crystallisation Experiments with Labels of Experimental Outcomes
<p>The VMXi Classification Dataset consists of images of protein crystallisation experiments collected on a Rock Imager 1000 (Formulatrix, USA) automated microplate imager at the VMXi experimental facility at Diamond Light Source, UK. These images were used to train the CHiMP (Crystal Hits in My Plate) Classifier network that classifies images into categories of experimental outcome.</p> <ul> <li>The directory named "VMXi_Classification_Images", consists of 18,782 JPEG images with a resolution of 3376 × 2704 pixels. </li> <li>13,951 of these images are associated with a label describing the experimental outcome depicted in the images. The labels are Clear, Crystals, Precipitate or Other.</li> <li>The file "VMXi_Classification_Train.csv" contains filenames and labels for the 11,161 images in the training set used for the CHiMP Classifier network.</li> <li>The file "VMXi_Classification_Validation.csv" contains filenames and labels for the 2,790 images in the validation set used for the CHiMP Classifier network.</li> </ul> <p>In addition, an independent test set of images are included in the directory named "VMXI_Classification_Test_Dataset". Within this direcectory:</p> <ul> <li>The subdirectory named "VMXi_Classification_Test_Images" contains 1000 JPEG images with a resolution of 3376 × 2704 pixels.</li> <li>Each image is associated with a label describing the experimental outcome depicted in the images. The labels are Clear, Crystals, Precipitate or Other.</li> <li>The file "unambiguous_test_dataset.csv" contains filenames and labels for the 632 images in the test set where three experts independently agreed on a label.</li> <li>The file "mostly_unambiguous_test_dataset.csv" contains filenames and labels for the 949 images in the test set where at least two experts independently agreed on a label.</li> <li>The file "original_expert_labels.csv" contains filenames for all 1000 images and the labels given by three experts independently. The column headed "expert_1_1" refers to labels given by expert number 1 at a timepoint 6 months prior to categorising the images again, given in the column "expert_1_2". The columns "expert_2" and "expert_3" contain the labels given by experts 2 and 3 respectively.</li> <li>The <em>unambiguous</em> and <em>mostly unambiguous</em> test sets were created from the categories chosen by "expert_1_2", "expert_2" and "expert_3"</li> </ul>
CHiMP Detector Datasets: Images of Sitting Drop Protein Crystallisation Experiments with Associated Image Masks of Drops and Crystals
<p>The CHiMP Detector Datasets consist of images of protein crystallisation experiments along with corresponding zipped NumPy archive files (.npz). All images have had their histograms adjusted using the Contrast Limited Adaptive Histogram Equalization ((CLAHE) algorithm using the OpenCV library with grid size of 12 and are in JPEG format. The .npz files contain class labels and instance segmentation masks for both the experimental droplets and any crystals that an expert annotator has deemed to be interesting/mountable. To class labels and masks can be loaded in the following way:</p> <pre><code>import numpy as np # load in the mask and class label list from .npz file located at mask_path mask_file = np.load(mask_path) masks = list(mask_file["masks"].astype(int)) class_labels = list(mask_file["class_labels"])</code></pre> <p>There are two datasets within this archive:</p> <ol> <li><strong>The VMXi CHiMP Detector Dataset</strong>. This consists of 237 images of resolution 1688 × 1352 pixels with corresponding masks. These images were collected on a Rock Imager 1000 (Formulatrix, USA) automated microplate imager at the VMXi experimental facility at Diamond Light Source, UK. These images and masks were used to train the VMXi CHiMP (Crystal Hits in My Plate) Detector network that performs object detection and instance segmentation of crystals in experimental micrographs using a Mask-R-CNN architecture. The files "vmxi_detector_training.csv" and "vmxi_detector_validation.csv" provide the filenames of the members of the training and validation sets respectively.</li> <li><strong>The XChem CHiMP Detector Dataset.</strong> This consists of 350 images of resolution 1024 × 1224 pixels with corresponding masks. These images were collected on a Rock Imager 1000 (Formulatrix, USA) automated microplate imager at the Crystallisation Facility@Harwell, located in the Research Complex at Harwell (RCaH). In addition to the images in the VMXi CHiMP Detector, these images were used to train the XChem CHiMP (Crystal Hits in My Plate) Detector network that performs object detection and instance segmentation of masks and crystals in experimental micrographs using a Mask-R-CNN architecture. The files "xchem_detector_training.csv" and "xchem_detector_validation.csv" provide the filenames of the members of the training and validation sets respectively.</li> </ol>
Purification of ACVR1 for co-crystallisation with LDN-193189 and other compounds
<p>Purification of constitutively active ACVR1 for co-crystallisation with LDN-193189 and three other compounds.</p>
Construct design for the crystallisation of huntingtin fragments - 2018/06/11
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Objective </strong>- Use the cryo-EM structure of huntingtin in complex with HAP40 <a href="http://www.rcsb.org/structure/6EZ8">http://www.rcsb.org/structure/6EZ8</a> to guide construct design of discrete domains and fragments which could be amenable to successful expression and purification of monodisperse protein samples and subsequent structure solution by X-ray crystallography. </p> <p> </p>
Training and test data for: Not getting in too deep: A practical deep learning approach to routine crystallisation image classification
<p>These data were used to classify crystallisation experiments in Milne et al., (<a href="https://doi.org/10.1101/2022.09.28.509868">https://doi.org/10.1101/2022.09.28.509868</a>). Here, four of the most widely-used convolutional deep-learning network architectures that can be implemented without the need for extensive computational resources were compared. It was shown that the classifiers have different strengths that can be combined to provide an ensemble classifier achieving a classification accuracy comparable to that obtained by a large consortium initiative (Bruno et al. PLOS one, 13(6), 2018). Eight classes were used to rank the experimental outcomes, thereby providing detailed information that can be used with routine crystallography experiments to automatically identify crystal formation for drug discovery and pave the way for further exploration of the relationship between crystal formation and crystallisation conditions.</p>
Training and test data for: Not getting in too deep: A practical deep learning approach to routine crystallisation image classification
Open the record for dataset details and reuse information.
Time-Resolved X-ray Phase-Contrast Video Imaging of Continuous Anti-Solvent Crystallisation
<p>X-ray phase-contrast video showing early crystal growth in a continuous anti-solvent crystalliser. Data was collected on the Diamond Light source I13-2 beamline. For further information see the paper:</p> <p>@article{das_pallipurath_leng_wanelik_mcginty_miller_kathyola_chang_al-madhagi_marathae_et<br> al._2020,<br> place={Cambridge},<br> title={Time-Resolved X-ray Phase-Contrast Imaging (XPCI) of Nucleation and Crystal Growth in the Anti-Solvent Crystallization of Lovastatin},<br> DOI={10.26434/chemrxiv.12911168.v1},<br> journal={ChemRxiv},<br> publisher={Cambridge Open Engage},<br> author={Das, Gunjan and Pallipurath, Anuradha and Leng, Joanna and Wanelik, Kazimir<br> and McGinty, John and Miller, Russell and Kathyola, Thokozile and Chang,<br> Sin-Yuen and Al-Madhagi, Laila H. and Marathae, Shashidhara and et<br> al.},<br> year={2020},<br> note={This content is a preprint and has not been peer-reviewed.}<br> }</p>
Computer-aided solvent mixture design for the crystallisation and isolation of mefenamic acid
<p>The files contain the MINLP formulations presented in this publication.</p> <p>The MINLP problems are implemented and solved in GAMS version 28.2.0, using SBB, a local branch-&-bound MINLP solver.</p>
Computer-aided solvent mixture design for the crystallisation and isolation of mefenamic acid
<p>The files contain the MINLP formulations presented in this publication.</p> <p>The MINLP problems are implemented and solved in GAMS version 28.2.0, using SBB, a local branch-&-bound MINLP solver.</p>
The 1.1 Å Structure of the Periplasmic Phosphate-Binding Protein from Stenotrophomonas maltophilia - a crystallisation contaminant identified by molecular replacement using the entire protein database (X-ray diffraction images).
<p>During efforts to crystallise the enzyme 2,4-dihydroxyacetophenone dioxygenase (DAD) from <em>Alcaligenes</em> sp. 4HAP, a small number of strongly diffracting protein crystals were obtained after two years of crystal growth in one condition. The crystals diffracted synchrotron radiation to almost 1.0 Å resolution and were, until recently, assumed to be formed by the DAD protein. However, when another crystal form of this enzyme was eventually solved at lower resolution, molecular replacement using this structure as the search model did not give a convincing solution with the original atomic resolution dataset. Hence we considered that these crystals might be due to a protein impurity, although molecular replacement using the structures of common crystallisation contaminants as search models again failed. A script to perform molecular replacement using MOLREP (Vagin, A. & Teplyakov, A. (2010). Acta Crystallogr. D 66, 22-25.) in which the first chain of every structure in the PDB was used as a search model was run on a multi-core cluster. This identified a number of prokaryotic phosphate binding proteins as scoring highly in the MOLREP peak lists. Calculation of an electron density map at 1.1 Å resolution allowed most of the amino acids to be identified visually and built into the model. A BLAST search then indicated that the molecule was most probably a phosphate binding protein from <em>Stenotrophomonas maltophilia</em> (UniProt ID: B4SL31; gene ID: Smal_2208) and fitting of the corresponding sequence to the atomic resolution map fully corroborated this. Proteins in this family have been linked with the virulence of antibiotic resistant strains of pathogenic bacteria and with biofilm formation. The structure has been refined to an R-factor of 10.15 % and an R-free of 12.46 % at 1.1 Å resolution. The molecule adopts the type-II periplasmic binding protein fold with a number of extensively elaborated loop regions. A fully-dehydrated phosphate anion is bound tightly between the two domains of the protein and interacts with conserved residues and a number of helix dipoles. </p>
C3 weakly labelled protein crystallisation data
<p>Set of weakly labelled protein crystallisation images which were used to augment the MARCO dataset for training in <a href="https://www.biorxiv.org/content/10.1101/2022.09.28.509867">Moving beyond MARCO</a>.</p> <p>Archive has been split into max 1GB chunks. Need to be joined together before extracting</p> <pre><code class="language-bash">cat c3_local_images.tar.xz.part* > c3_local_images.tar.xz</code></pre> <p> </p>
2.2 Å resolution anomalous diffraction data of Vibrio alkaline phosphatase, crystallised in 1.0 M NaCl
<p>2.20 Å resolution anomalous diffraction dataset for <em>Vibrio</em> alkaline phosphatase, crystallised in 1.0 M NaCl. Data were collected with an X-ray energy of 6 keV at the P14 beamline at the DESY-PETRA III synchrotron in Hamburg, Germany. This dataset was used to estimate the location of chloride ions bound to the enzyme. "NaClAnon.hkl" is the final non-merged anomalous reflection file from data processing in XDS and XSCALE.</p>
1.29 Å remote diffraction data and processing files of VAP crystallised in 0.5 M NaCl
<p>1.29 Å X-ray diffraction dataset collected from a crystal of <em>Vibrio</em> alkaline phosphatase grown in 0.5 M NaCl. Diffraction data were collected at the P11 beamline (DESY, Hamburg, Germany), using an X-ray wavelength of 1.033 Å. Also included are processing files form XDS and XSCALE. "SiM59_05MNaCl_remote.hkl" is the final processed reflections file.</p>
2.60 Å resolution X-ray diffraction data of Vibrio alkaline phosphatase, crystallised in 1.0 M KBr
<p>2.60 Å anomalous X-ray diffraction data collected from a <em>Vibrio </em>alkaline phosphatase crystal grown in 1.0 M KBr. The data was collected at the P14 beamline (DESY, Hamburg) using an X-ray wavelength of 0.918 Å (13.5 keV). The data set includes the raw diffraction images ("AP-VAPKBr-D3_4_00001.zip"), processed unmerged reflections ("KBr_D6-3anom.hkl"), refined coordinates and electoron density ("VAPKBr_D3_refine_12.pdb" and "VAPKBr_D3_refine_12.mtz"), an anomalous CCP4 format map derived from the data ("VAPKBr_D3_map_coeffs_anom.ccp4") and XDS and XSCALE processing files.</p>
2.45 Å resolution anomalous diffraction data of Vibrio alkaline phosphatase, crystallised in 0.5 M NaCl
<p>Long wavelength (2.066 Å/6 keV) diffraction data collected from a <em>Vibrio</em> alkaline phosphatase crystal grown in 0.5 M NaCl. The data set includes the raw diffraction images ("SiM59anom_001_data_000001.zip"), the processed unmerged reflections ("SiM59anom_05NaClVAP.hkl"), a derived ccp4 anomalous map ("SiM59anom_map_coeffs_anom.ccp4") and the refined electron density and coordinates ("SiM59anom-coordinates.pdb" and "SiM59anom-reflections.mtz"). Also included are processing files from XDS and XSCALE.The diffraction data was collected at the P11 beamline (DESY, Hamburg) on the 20th of April 2020. </p>
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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.