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41 results for “Sample preparation”
Pitfalls in Sample Preparation of Metalloproteins for Low-Temperature EPR: The Example of Alkaline Myoglobin
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements (EPR and UV-vis), computer simulations and data analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DTA</strong>, <strong>m</strong>, <strong>mat</strong>, <strong>ods</strong>,<strong> tif</strong></li> <li>Information on <strong>origin of the data</strong>: <ul> <li>EPR spectroscopic measurements have filename extensions <strong>DSC</strong> and <strong>DTA</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong></li> <li>UV-vis spectroscopic measurements have filename extensions <strong>ods</strong></li> <li>Processed data ready for simulation/figure preparation have filename extension <strong>mat </strong>(both for EPR and UV-vis)</li> <li>High-quality figures published in main text and supplementary are provided as <strong>tif</strong> files</li> </ul> </li> <li>CW-EPR measurements were generated with a Bruker ELEXSYS E580 X-band spectrometer equipped either with an Oxford CF935 continuous flow cryostat and a Bruker ER4118 SPT-N1 resonator or with an Oxford ESR 900 continuous-flow cryostat and a Bruker ER 4122 SHQ resonator.</li> <li>Pulse EPR experiments were performed with an Oxford CF935 continuous flow cryostat and a Bruker ER4118 SPT-N1 resonator.</li> <li>Simulations of EPR spectra and fitting were performed with the Easyspin software (v. 6.0.0-dev.26) implemented in Matlab (MathWorks, R2020b)</li> <li><strong>The dataset</strong>Files with extension <strong>m</strong> normally recall files with extension <strong>mat</strong> which should be stored in the same working folder <ul> <li>Files in <strong>PARACAT_WP3_20210929_01_CW</strong> folder include subfolders organised by topic: <ul> <li>“Alternative cryoprotectants” contains: ready-to-plot / ready-to-simulate data in <strong>mat</strong> format; simulation scripts in <strong>m</strong> format.</li> <li>“Glycerol effects - different buffers” contains: ready-to-plot / ready-to-simulate data in <strong>mat</strong> format; simulation scripts in <strong>m</strong> format.</li> </ul> </li> <li>Files in <strong>PARACAT_WP3_20210929_02_PULSE</strong> folder include pulse EPR spectroscopic measurements; original data are in <strong>DSC</strong> and <strong>DTA</strong> formats; processed data and fitting are in <strong>m</strong> format. Files in <strong>m</strong> format recall original data files, therefore they should be stored in the same working folder.</li> <li>Files in <strong>PARACAT_WP3_20210929_03_UV-VIS</strong> folder include UV-vis spectroscopic measurements; original and basic processed data are in <strong>ods</strong> format; ready-to-plot data are in <strong>mat</strong> format; scripts for figure preparation are in <strong>m</strong> format.</li> <li>Files in <strong>PARACAT_WP3_20210929_04_FIGURES</strong> folder include high-quality figures published in main text and supplementary, provided as <strong>tif</strong> files</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Information on</strong>: <ul> <li><strong>Specialized abbreviations:</strong> <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong><em>T<sub>m</sub></em></strong> or <strong><em>T<sub>2</sub></em></strong>– phase memory time, <strong>CAPS</strong> - N-cyclohexyl-3-aminopropanesulfonic acid, <strong>CHES</strong> - N-Cyclohexyl-2-aminoethanesulfonic acid</li> <li><strong>Definitions of variables:</strong> magnetic field (<strong>mT</strong> - milliTesla), pH (pH units), UV-vis absorbance intensity (<strong>A.U.</strong> – arbitrary units), EPR intensity (<strong>A.U.</strong> – arbitrary units), Hahn Echo Intensity (<strong>A.U.</strong> – arbitrary units), <em>g</em>-values (adimensional)</li> <li><strong>Units of measurements:</strong> <ul> <li>Concentration: <strong>mM</strong> (millimolar), <strong>µM</strong> (micromolar), <strong>% v/v</strong> (percentage volume/volume)</li> <li>Volume: <strong>mL</strong> (milliliters), <strong>µL</strong> (microliters)</li> <li>Wavelength: <strong>nm</strong> (nanometers)</li> <li>Temperature: <strong>°C</strong> (Celsius degrees), <strong>K</strong> (Kelvin degrees)</li> <li>Time: <strong>ns</strong> (nanoseconds)</li> <li>Frequency: <strong>GHz</strong> (gigahertz)</li> <li>Power: <strong>mW</strong> (milliwatt)</li> </ul> </li> </ul> </li> </ul>
Subset of 300 out of 3000 Prepared Sentinel 2 Scenes for Transfer Learning and Super-sampling.
<p>This dataset contains a random subset of 300 out of 3000 Sentinel 2 scenes prepared for Transfer Learning and Super-sampling. It comes in the form of zipped NumPy arrays in the npz format. The files have are named in the following fashion:</p> <p>latutide+latitude_decimals_longitude+longitude_decimals_month_of_the_year_for_mosaic. </p> <p>Each file contains:</p> <p>bands.npy: The Sentinel 2 bands in 10m resolution uint10: B02, B03, B04, B08, B05, B06, B07, B8A, B11, B12. The 20m bands have been resampled using bilinear resampling.</p> <p>nir.npy: B08 Resampled to 20m using average resampling and then resampled to 10m using bilinear. Useful for training super-sampling models.</p> <p>scl.npy: The Sentinel 2 Scene Classification file. Contains information on cloud cover and land cover.</p> <p>sincos.npy: Contains the latitude, longitude, and time of capture for each pixel encoded to sine and cosine waves in the [0,1] interval. The is useful when training a model to predict where on the globe an image was captured.</p> <p>The images can be processed to patches using the buteo toolbox: </p> <p>`pip install buteo --upgrade</p> <p>`import buteo as beo`</p> <p>`beo.get_patches(beo.raster_to_array("path_to_bands"))`</p> <p> </p>
Figure 4: Spectro°uorimetric spectra of TiO2/CdS composites samples with a di®erent mass proportion-PREPARATION AND CHARACTERIZATION OF TIO2/CDS LAYERS AS POTENTIAL PHOTOELECTROCATALYTIC MATERIALS
<p>Fig. 4 illustrates the fluorescence emission spectra of TiO2/CdS nanostructured ¯lm at the exciting<br> wavelength of 340 nm. The curves for TC 1, TC 2, TC 3 and TC 4 exhibited<br> the clear photoemission peaks at about 375 nm, and compared with emission of<br> bulk CdS (520 nm), 145 nm of blue shift was observed. These features indicate<br> the quantum-con¯ned e®ect of the TiO2/CdS nanocomposite ¯lms [25].</p>
Figure 1: X-ray di®raction pattern for TiO2/CdS composites samples-PREPARATION AND CHARACTERIZATION OF TIO2/CDS LAYERS AS POTENTIAL PHOTOELECTROCATALYTIC MATERIALS
<p>Fig. 1 shows the X-ray di®raction pattern for TiO2/CdS<br> composites (sample TC 1, TC 2, TC 3 and TC 4). The di®raction pattern of<br> the TiO2/CdS composites exhibits the di®raction peaks owning to CdS phase<br> (hexagonal and cubic). TiO2 (rutile and anatase) phases were also detected.</p>
Figure 3: Absorption spectra of TiO2/CdS composites samples with a di®erent mass proportion-PREPARATION AND CHARACTERIZATION OF TIO2/CDS LAYERS AS POTENTIAL PHOTOELECTROCATALYTIC MATERIALS
<p>Optical properties of TiO2/CdS semi-<br> conductor composites have been investigated using optical absorption and pho-<br> toluminescence spectroscopy. UV-VIS absorption spectra of the as-prepared<br> photocatalysts were taken on a JASCO V-550 spectrometer. The °uorescence<br> spectra were obtained using an ABL&Jasco V 6500 spectro°uorimeter with<br> xenon lamp. The optical studies of the TiO2/CdS composites were performed<br> using the absorption spectra observed in the wavelength range 200-900 nm.<br> Fig. 3 shows the UV-Vis spectra of TiO2/CdS samples.</p>
CONCATENATING sample files to prepare for multiple sequence alignment in Galaxy
<p>These are a few sample files to practice the correct way to concatenate files with the reference strain at the top, in order to continue with the next step, which is doing a multiple sequence alignment.</p>
Thermal evaporation as sample preparation for silver‐assisted laser desorption/ionization mass spectrometry imaging of cholesterol in amyloid tissues
<p><strong>Thermal evaporation as sample preparation </strong><strong>for </strong><strong>silver‐assisted laser desorption/ionization mass spectrometry imaging of cholesterol in amyloid tissues</strong></p> <p>MSI datasets in SCiLS Lab SL File (*.sl) or as flexImaging sequence (*.mis)</p>
Preparation of dephosphorylated HTT samples for cryoEM analysis 2019/03/20
<p><strong>Project: </strong>High resolution structural analysis of purified HTT samples</p> <p><strong>Experiment: </strong>Preparation of dephosphorylated HTT samples for cryoEM analysis</p> <p><strong>Date completed:­ </strong>2019/03/20</p> <p><strong>Rationale: </strong>We now have a good understanding of the global structure of HTT when in complex with HAP40 and our MALS and SAXS data suggest that apo HTT is likely self-associating and heterogenous in nature, despite high levels of purity. Dephosphorylating HTT from Sf9 cell production could alter its global structure sufficiently to allow high resolution structure determination by cryoEM. </p>
Analysis of dephosphorylated HTT samples for cryoEM analysis and further sample preparation 2019/04/15
<p><strong>Project: </strong>High resolution structural analysis of purified HTT samples</p> <p><strong>Experiment: </strong>Analysis of dephosphorylated HTT samples for cryoEM analysis and further sample preparation</p> <p><strong>Date completed:­ </strong>2019/04/15</p> <p><strong>Rationale: </strong>We now have a good understanding of the global structure of HTT when in complex with HAP40 and our MALS and SAXS data suggest that apo HTT is likely self-associating and heterogenous in nature, despite high levels of purity. Dephosphorylating HTT from Sf9 cell production could alter its global structure sufficiently to allow high resolution structure determination by cryoEM. </p>
Fig. 1 in Testate Amoebae as Sea-level Indicators in Northwestern Norway: Developments in Sample Preparation and Analysis
Fig. 1. Location map of the field sites.
Fig. 2 in Testate Amoebae as Sea-level Indicators in Northwestern Norway: Developments in Sample Preparation and Analysis
Fig. 2. Schematics of Svinøyosen and Storosen showing vegetation zonation.
Tagsteady: a metabarcoding library preparation protocol to avoid false assignment of sequences to samples
Metabarcoding of environmental DNA (eDNA) and DNA extracted from bulk specimen samples is a powerful tool in studies of biodiversity, diet and ecological interactions as its inherent labelling of amplicons allows sequencing of taxonomically informative genetic markers from many samples in parallel. However, the occurrence of so-called 'tag-jumps' can cause incorrect assignment of sequences to samples and artificially inflate diversity. Two steps during library preparation of pools of 5' nucleotide-tagged amplicons have been suggested to cause tag-jumps; i) T4 DNA polymerase blunt-ending in the end-repair step and ii) post-ligation PCR amplification of amplicon libraries. The discovery of tag-jumps has led to recommendations to only carry out metabarcoding PCR amplifications with primers carrying twin-tags to ensure that tag-jumps cannot result in false assignments of sequences to samples. As this increases both cost and workload, a metabarcoding library preparation protocol which circumvents the two steps that causes tag-jumps is needed. Here, we demonstrate Tagsteady, a metabarcoding Illumina library preparation protocol for pools of nucleotide-tagged amplicons that enables efficient and cost-effective generation of metabarcoding data with virtually no tag-jumps. We use pools of twin-tagged amplicons to investigate the effect of T4 DNA polymerase blunt-ending and post-ligation PCR on the occurrence of tag-jumps. We demonstrate that both blunt-ending and post-ligation PCR, alone or together, can result in detrimental amounts of tag-jumps (here, up to ca. 49% of total sequences), while leaving both steps out (the Tagsteady protocol) results in amounts of sequences carrying new combinations of used tags (tag-jumps) comparable to background contamination.
Mica sample substrate preparation for AFM - AFMBioMed Summer school 2020
<p>A 7 min video tutorial to prepare mica substrates for AFM.</p> <p>The video presents:</p> <ul> <li>How to cut mica sheets</li> <li>How to glue mica to teflon coated metallic support</li> </ul>
Purification of full-length huntingtin (HTT) constructs Q23 and Q46 from insect sf9 cells and preparation of samples for electron microscopy (2017/03/30)
<p>Huntingtin structure-function open lab notebook project</p>
Preparation and grafix of huntingtin Q23 samples for electron microscopy (2017/03/30)
<p>Huntingtin structure-function open lab notebook project</p>
Purification and grafix of full-length huntingtin Q23 and Q46 samples for cryo grid preparation for electron microscopy (2017/04/21)
<p>Huntingtin structure-function open lab notebook project</p>
From Microorganisms to Biosignatures: Subcritical Water Extraction as a Sample Preparation Technique for Future Life Detection Missions
<p>Data for all figures included in "From Microorganisms to Biosignatures: Subcritical Water Extraction as a Sample Preparation Technique for Future Life Detection Missions". Amino acid concentrations, enantiomeric excesses, and relative distributions for E. coli cells and B. subtilis spores after subcritical water extraction for 30 min at 200 °C using water and dilute acid.</p>
Supplementary Figure Legend S1: An overview of DNA samples included in the NGS based chimerism validation process and AlloSeq HCT library preparation workflow.
<p><strong>Supplementary Figure </strong><strong>Legend S1: </strong>An overview of DNA samples included in the NGS based chimerism validation process and AlloSeq HCT library preparation workflow. <strong>S1a: </strong>An overview of clinical and artificial DNA included in the NGS based chimerism validation process (Clinical samples, Artificial construct samples and ASHI EMO Proficiency samples). <strong>S1b:</strong> Schematic presentation of time required for the NGS Chimerism assay. <strong>S1c.</strong> AlloSeq HCT library preparation workflow.</p>
Evaluation of See.d as an Automated Blood Sample Preparator for Multiple Liquid Biopsy Applications
ClinicalTrials.gov study NCT06097156. IPD Sharing: NO. Countries: 1. Publications: 6.
Micron-resolution fiber mapping in histology independent of sample preparation
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