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CCDC 2032030: Experimental Crystal Structure Determination
No full textExperimental Crystal Structure Determination (CSD) Entry ABOLOW: bis(μ-ethyl)-bis(N²,N⁴-bis[2,6-bis(propan-2-yl)phenyl]pentane-2,4-diiminato)-di-ytterbium toluene solvate. Chemical formula: C₆₄ H₉₆ N₄ Yb₂. Space Group: P 1‾ (2). Cell: a 8.9244(7)Å b 14.0012(7)Å c 14.5450(6)Å, α 92.393(4)° β 99.833(5)° γ 101.713(5)°.The methodology can be found in the associated paper
Dataset for "Rheological Modification of Partially Oxidised Cellulose Nanofibril Gels with Inorganic Clays"
No full textThis dataset contains the rheological and SAXS data for mixtures of either Laponite or montmorillonite with oxidised cellulose nanofibril dispersions in water.Rheological measurements of each sample were made using a stress-controlled Discovery Hybrid Rheometer, Model HR-3 (TA Instruments) with a sand-blasted 40 mm parallel plate geometry. The temperature was kept at 25 °C using a Peltier unit (±0.1 °C). Results were copied directly from the software to a tab delimited format.
SAXS data was collected on an Anton-Parr SAXSpoint 2.0, using an SDD of 556.9mm, and normalised to maximum instensity before exporting to .txt format.There are two .zip files: one containing rheological data, one containing SAXS data
Dataset supporting the paper: Mechanochemical Co-crystallization: Insights and Predictions
No full textThe entire data supports the research publication and can be divided in 3 parts:
i) Training data (Raw PXRD data and its description of the labelling contained in the Documentation.zip),
ii) Excel-files that are the input of the algorithm (Results.xlsx contains reaction outcomes/ Chem.xlsx contains all chemical descriptors for used molecules; both are in the Documentation.zip)
iii) Code (Python algorithm that uses the excel-files from the previous point to generate predictive capabilities)The methodology is described in the supporting paper
Dataset for "Bottom-Up Cubosome Synthesis Without Organic Solvents"
No full textThis dataset contains processed SAXS data for mixtures of phytantriol with different diluents, and also following bottom-up synthesis of cubosomes. This data was collected to demonstrate the phase formations of phytantriol under different conditions.SAXS data was collected on an Anton-Parr SAXSpoint 2.0.SAXS data was collected on an Anton-Parr SAXSpoint 2.0, using an SDD of 556.9mm. The instrument is known to have a single dead pixel which sometimes results in an anomolous single-point peak
Dataset for Hydrophobic poly(vinylidene fluoride) / siloxene nanofiltration membranes
Get PDFThis dataset contains all the data used in the manuscript "HYDROPHOBIC POLY(VINYLIDENE FLUORIDE) / SILOXENE NANOFILTRATION MEMBRANES".
The dataset includes:
- All materials characterisation data necessary to fully characterise the membranes produced.
- Individual data files for pure water permeance and dye and salt rejection tests, inclusive of mass balances.
- Calibration data.
The dataset integrates the quantitative information already provided in the manuscript and the online supplementary information.Materials Characterisation:
The nanosheet morphology with elemental mapping was investigated by high-resolution TEM (JEM-2100Plus, JEOL) with EDS detector (X-Max detector, Oxford Instruments) and SAED was also obtained. The average thickness of the nanosheets was measured by AFM (Asylum Research Jupiter XR, Oxford Instruments). FTIR analysis was performed on siloxene-embedded KBr pellets using a Frontier FTIR spectrometer (Perkin Elmer) and Raman spectra were recorded with a RM1000 Raman Microscope (Renishaw) at 532 nm. XRD (D8-Advance PXRD, Bruker) with Cu Kα1 radiation source was operated at 40 kV and 40 mA (0.015° step size) to examine the crystallinity and phase of the siloxene powders. XPS was performed using a K-alpha+ spectrometer (Thermo Fisher Scientific) with survey scans recorded at 150 eV (1 eV step size) and high-resolution scans at 40 eV (0.1 eV step size).
Hydrophilicity of the membranes was assessed using water contact angle goniometer (OCA15, Date Physics) in sessile mode at room temperature. 1 μL droplets of water were used and the values reported are the average of ten measurements at different positions.
The surface zeta potential of each membrane sample was measured using a Zetasizer Nano (ZS, Malvern Instruments Ltd.) with the surface ζ accessory at neutral pH = 7.0. A tracer solution was prepared by adding a low concentration of polystyrene in 10 mM NaCl solution. Each sample was measured at least three times and the reported values were the average of the measurements.
The surface roughness of the membrane samples was assessed by AFM (AFM Multimode IIIA, Bruker) in tapping mode over scan areas of 5 × 5 μm2.
ATR-FTIR (Frontier, Perkin Elmer) was employed to characterize the chemical bonds on the membrane surface. The spectra were collected in the wavenumber range of 4000 to 600 cm-1 by accumulating 10 scans at a resolution of 4 cm-1.
The distributions of siloxene on membrane surfaces were investigated by Raman mapping (RM1000 with inVia system, Renishaw) at 532 nm [25]. Areas of 100 × 100 μm2 were scanned on each membrane sample with the line mapping technique.
XRD (D8-Advance PXRD, Bruker) with Cu Kα1 radiation source (1.5406 Å) was operated at 40 kV and 40 mA (0.015° step size) to examine the compactness of the PVSi membrane samples. The obtained spectra were analyzed using CrystalDiffract software (CrystalMaker Software Ltd, UK). 2 theta values are reported in Table 3 with 4 significant figures for ease of readability, whereas the original values have 6.
The melting behavior of each membrane sample was characterized using differential scanning calorimetry (DSC Q20, TA Instruments). The samples were heated from room temperature (⁓ 20 °C) to 220 °C with a ramping rate of 10 °C min-1. The percentage crystallinity of PVDF in each sample was determined by
crystallinity (%)=(ΔH_m)/(∆H_m^0 )×100% (1)
where ΔHm is the enthalpy associated with membrane melting and ΔH0m is the theoretical melting enthalpy of 100% crystalline PVDF, which is 104.7 J g-1. The reported data were the average of three measurements taking from the same membrane sample.
The dynamic mechanical properties of the membrane samples were analyzed using dynamic thermo-mechanical analysis (DMA1, Mettler Toledo) in auto-tension mode. The samples were cut into 20 × 5 mm2 strips. The sample strips were heated from – 80 °C to 145 °C with ramping rate of 3 °C min-1 in air. The data recorded were the average of three measurements.
Membrane performance:
Pure water and hexane permeation tests were conducted using a dead-end filtration cell (Sterlitech Corporation) connected with a 5 L feed tank. The operating pressure was fixed at 2 bar with compressed air. All the samples were compacted for 3 h prior to sample collection. The permeance, K (L m-1 h-1 bar-1), of the membrane was calculated by using Equation 2:
K= V/∆t∆pA (2)
where K is the permeance, V is the permeate volume, A is the effective membrane area (i.e., 14.6 cm2), Δt is the time for permeate collection and Δp is the operating pressure (i.e., 2 bar). After the pure water or solvent test, the membrane sample was transferred into a cross-flow cell for the rejection tests of different dyes and salts. The concentrations of all the dye feed solutions were 0.01 g L-1, whereas the concentrations of salt solutions were 1 g L-1 except for NaCl, which was 2 g L-1. The concentrations of dyes and salts in the feed, permeate and retentate solutions were measured by UV-visible spectrophotometer (Cary 100, Agilent) and conductivity meter (Thermo Fisher), respectively. The rejection of the tracer was calculated using Equation 3:
R=(1-C_p/C_f )×100% (3)
where R is the rejection, Cp and Cf are the tracer concentrations in the permeate and feed solutions, respectively. The mass balance for each rejection test was also calculated according to
mass balance (%)=(C_p V_p+C_r V_r)/(C_f V_f )×100% (4)
where Cr is the tracer concentration in the retentate solution, Vp, Vr and Vf are the volume of permeate, retentate and feed solutions, respectively. For all the filtration/separation tests, at least three samples were tested for each membrane and the average value was recorded
Dataset for "On the optimisation of urban form design, energy consumption and outdoor thermal comfort using a parametric workflow in a hot arid zone"
No full textThis dataset supports the study on the investigation of urban form design parameters and their relationship to outdoor thermal comfort and energy consumption in Cairo, Egypt. The study utilises Grasshopper for Rhino3D to manage parametric combinations of the urban geometry (Documentation video "Typologies") as such it uses the Ladybug-tools plugins which link Grasshopper with EnergyPlus for energy simulations (Documentation video "Simulation"). The CSV data file includes the input geometrical parameters (prefixed "IN:") and the corresponding output thermal comfort and energy use (prefixed "OUT:").TT-Toolbox Colibri Iterator was used to run the simulation of each geometrical configuration consecutively. Upon each run, EnergyPlus calculated the total energy (cooling, lighting electricity and equipment) loads as well as the outside surface temperatures which in turn are used as part of the thermal comfort estimation. Upon each iteration, the Colibri plugin exported the results to the CSV file.Rhinoceros V.6 Service Release 29
Grasshopper Build 1.0.0007
Ladybug V.0.0.69 Honeybee V.0.0.6
Data from "The impact of long-term physical inactivity on adipose tissue immunometabolism"
No full textThis dataset provides all the raw data collected for a trial investigating the impact of long-term physical inactivity in the form of head-down bed rest on adipose tissue immunometabolism in young (20–45 yrs), healthy males. This project was conducted as part of a larger, international investigation conducted by the European Space Agency (AO-BR-13) in the MEDES Facility, Toulouse, France.
Participants were recruited by international advertisement. The trial was conducted in accordance with Guidelines for Conducting Bed Rest Studies (Heer et al, 2009). All participants were confined to the clinical facility for 14 days prior to commencing bed rest, which was then undertaken for 60 days, followed by a 14 day recovery period. Baseline Characteristics data at the moment of entry to the clinical facility is reported in Tab 1. The ESA medical staff at the MEDES facility undertook the day-to-day running of the study. Diet was formulated and produced in-house. Exact portion sizes and foods/fluids not consumed were recorded by weighed inventory, along with food types consumed at each meal on each day of the study. Diet data (macro and micronutrient) for days pertaining to CGMS analyses conducted here (BDC-8, -7 and HDT+53, +55) are reported in Tab 2. Bloods were taken on the mornings of the adipose biopsies, immediately upon awaking, in the fasted state, data are presented in Tab 8 for plasma protein analysis, and Tab 3 for PBMC analysis by flow cytometry. Following blood extraction, participants underwent and adipose tissue biopsy conducted by a surgeon. Adipose was extracted by needle aspiration from the abdominal subcutaneous adipose tissue, 5cm lateral to the umbilicus. Whole adipose tissue was partitioned as outlined in Figure B, below. Immunoblotting (Tab 10) and rtPCPR (Tab 9) was carried out on whole- adipose tissue; ex vivo culturing of whole adipose tissue (Tab 7) was performed for 3 h upon collection; flow cytometry was also conducted on digested adipose tissue (Tab 3). For 5 days pre- and at the end of bed rest continuous glucose monitoring probes were inserted into the back of the participants arm, with the probe inserted subcutaneously. These data are presented in Tab 5. Urine samples (2mL) were collected from each void across a 24 hour period before and at the end of bed rest and analysed for glucose concentrations (Tab 6. All biological data can be found in respective tabs.
References:
Heer, M., Liphardt, A., and Frings-Meuthen, P. (2009). Standardisation of bed rest study conditions. Hamburg: DLR Institute of Aerospace Medicine.Experimental design:
Twenty healthy, young (20–45 years old) males undertook 60 days of complete bed rest (24h-a-day) preceded by a 14-day ambulatory control period. The study was conducted in accordance with ESA Standardisation of Bed Rest Study Conditions guidelines (Heer et al. 2009) at the Médecine et de Physiologie Spatiales (MEDES) clinical institute in Toulouse, France. The study was sponsored by the ESA and French National Space Agency (CNES) and was conducted in two campaigns (n = 10 per campaign) from January–April 2017 and September–December 2017. The protocol was reviewed and approved by the local ethics committee (CPP Sud-Ouest et Outre-Mer I, France, RCB: 2016-A00401-50) and was registered on ClinicalTrials.gov (NCT03594799). All procedures conformed to the Declaration of Helsinki. Participants were recruited by online advertisements and press announcements.
Within each campaign, half of participants (n = 5 in each campaign) consumed an antioxidant/ anti-inflammatory nutritional cocktail during the bed rest period (Cocktail Group), and the other half were a Control Group. Group allocation was blinded from all researchers until the completion of each bed rest campaign. Six pills were consumed each day, two per meal. No placebo was administered to the Control group due to the inability to mask the fish oil odour of ω-3 supplementation. Our primary focus was on the impact of bed rest per se on adipose tissue and there was no evidence of a supplement effect (see results).
Body composition:
Body composition, Fat Mass Index (FMI) and central fat mass (fat mass between L1 and L4 vertebrae) was determined using dual-energy X-ray absorptiometry (DEXA; Discovery, Hologic; Bedford, UK) two days prior to the start of bed rest and following 58 days of bed rest.
Physical activity baseline standardisation:
During the 14-day pre-bed rest period in the clinical facility, participants undertook approximately 8,000 steps/ d measured with a wrist-mounted pedometer (Polar Loop; Polar; France). Participants also undertook bouts of supervised structured exercise during this standardisation period on a treadmill and cycling ergometer,.
Dietary control:
Diet was strictly controlled throughout the study period, participants were confined to the MEDES facility according to guidelines detailed in Heer, Liphardt, Frings-Meuthen (2009). Caloric intake was based on basal metabolic rate (BMR) measured by indirect calorimetry (22). During the pre-bed rest period 140 % of BMR was consumed, which was reduced to 110 % BMR during the bed rest period in order to keep fat mass stable. An additional 1000 IU of 25 (OH) vitamin D was supplemented daily by oral administration as specified by Heer, Liphardt, Frings-Meuthen (2009).
Blood sampling:
Fasted venous blood samples were collected from an antecubital vein at 07:00 six days prior to the start of bed rest and following 56 days of bed rest. Plasma samples were immediately centrifuged and stored at −80oC. Peripheral blood mononuclear cells (PBMCs) were isolated by density gradient separation (Ficoll®, Greiner Bio-One; Stonehouse, UK) in Leucosep® tubes (Greiner Bio One Inc.; Kremsmünster, Austria) for analysis on the day of collection.
Adipose tissue sampling:
Pre- and post-bed rest subcutaneous adipose tissue samples were obtained from ~5 cm lateral to the umbilicus with a 14G needle using the needle aspiration method under local anaesthesia (1 % Lidocaine hydrochloride containing 0.005 mg/ mL adrenaline; Xylocaine; Dublin, Ireland). Adipose tissue was taken following blood draws 6 days prior to the start of bed rest and following 56 days of bed rest. Adipose samples were cleared of visible connective tissue, blood, and vasculature prior to washing the remaining tissue with 0.9 % NaCl solution (B.Braun; Sheffield, UK) over a single-use sterile 100 µm gauze to minimise blood contamination. Samples were placed into unsupplemented endothelial cell basal medium (PromoCell; Heidelberg, Germany) at room temperature for transfer to another laboratory for processing. Snap frozen tissues were stored at −80 oC until analysis.
Ex vivo adipose tissue measures:
Between 20 and 25 mg of adipose tissue was cultured ex vivo at a final concentration of 50 mg of tissue per millilitre for 3 hours .
Adipose tissue digestion:
Between 200 and 500 mg of adipose tissue was digested using collagenase as previously described . Isolated adipocytes were recovered by flotation and the SVF cells were recovered following centrifugation at 300 x g for 5 minutes.
Adipose tissue RNA isolation:
Total RNA (including microRNAs) was extracted using miRNeasy Mini Kit (Qiagen; Crawley, UK) according to manufacturer instructions. Following RNA isolation, samples were DNase-treated and purified as previously described . Thirty microlitres of RNA at a set concentration of 2.1 µg/ 30 µL were used for transcriptomic analysis.
Quantitative polymerase chain reaction (qPCR):
Quantitative polymerase chain reaction analysis was performed on DNase-treated RNA from adipose tissue on a StepOne™ analyser (Applied Biosystems; Warrington, UK) using pre-designed TaqMan Assays from Applied Biosystems (Invitrogen; California, US) for the measurement of PDK4 (hs00176875_m1), SREBP1c (hs01088691_m1), AKT2 (hs01086099_m1), INSR (hs00961557_m1), GLUT4 (hs00168966_m1), IRS2 (hs00275843_s1), AMPK1/2 (hs01562315_m1 and hs00178903_m1), AS160 (hs00952765_m1), FAS (hs00188012_m1), HK2 (hs00606086_m1), IRS1 (hs00178563_m1), and PPARG (hs01115513_m1). Data were normalised to an internal calibrator (peptidylprolyl isomerase A [PPIA] ; hc04194521_s1), using the ΔΔ comparative threshold (Ct) method .
Transcriptomic and bioinformatics analyses:
RNA-sequencing was performed on polyA-enriched total RNA, on a HiSeq4000 (Illumina, Inc.; California, US) by the Oxford Genomics Centre (Wellcome Trust; Oxford, UK). In brief, total RNA was quantified using RiboGreen (Invitrogen; California, US) on the FLUOstar OPTIMA plate reader (BMG Labtech GmbH; Aylesbury, UK) and the size profile and integrity analysed on the 2200 or 4200 TapeStation (Agilent, RNA ScreenTape [Agilent Technologies; California, US]). RIN estimates for all samples were between 4 and 8.4. Input material was normalised to 200 ng prior to library preparation. Polyadenylated transcript enrichment and strand specific library preparation was completed using NEBNext Ultra II mRNA kit (New England Biolabs Inc.; Massachusetts, US) following manufacturer’s instructions. Libraries were amplified (11 cycles) on a Tetrad (Bio-Rad Laboratories; California, US) using in-house unique dual indexing primers. Individual libraries were normalised using Qubit, and the size profile was analysed on the 2200 or 4200 TapeStation. Individual libraries were normalised and pooled together accordingly. The pooled library was diluted to ~10 nM, denatured and further diluted prior to loading on the sequencer. Paired end sequencing was performed using a HiSeq4000 75bp platform (Illumina, HiSeq 3000/4000 PE Cluster Kit and 150 cycle SBS Kit),
FastQ sequencing files were processed as previously described , using the Galaxy web platform (usegalaxy.org). GRCh38/hg38 was used as the reference genome. P-values were adjusted for transcriptome-wide false discovery rate (FDR), with an adjusted significance threshold of q < 0.05. Functional annotation was performed in the database for annotation, visualisation, and integrated discovery (DAVID) 6.8 (2019 release) and Genesis 1.8.1 (35). Pathway analysis was performed using Kyoto encyclopaedia of genes and genomes (KEGG) and gene ontology (GO)-terms, using a modified Fisher exact test (EASE [expression analysis systematic explorer]) with a significance threshold of p ≤ 0.01.
Immunoblotting:
Organic phases were extracted from QIAzol-treated tissue samples and processed for immunoblot analysis as previously described . Protein content was determined by BCA protein assay (ThermoFisher Scientific™; Leicestershire, UK). Isolated primary adipocytes were thawed on ice and lysed in radioimmunoprecipitation assay (RIPA) buffer (50 mM Tris [pH 7.4], 150 mM NaCl; 0.5 % sodium deoxycholate; 0.1 % SDS; 0.1 % NP-40), supplemented with HALT™ protease inhibitor cocktail (ThermoFisher™; Leicestershire, UK) and PhosSTOP EASYpack phosphatase inhibitor (Roche AG; Basel, Switzerland).
Proteins were separated by SDS-PAGE and transferred to a nitrocellulose membrane for immunoblot analysis using the following antibodies: Akt2 (Cell Signalling Technology; London, UK [1 in 500 dilution]); Akt substrate of 160 kilo-Daltons (kDa) (AS160) (Millipore; Massachusettes, US [1 in 500 dilution]); glyceraldehyde 3-phosphate dehydrogenase (GAPDH [1 in 2000 dilution]) (Proteintech; Manchester, UK); glucose transporter 4 (GLUT4)(1 in 5000 dilution); and Insulin receptor β-chain (InsRβ) (Santa Cruz Biotechnology; Texas, US [1 in 500 dilution]). Images were acquired with EPI Chemi II darkroom (UVP) and bands were quantified using ImageStudio Lite (LI-COR Biosciences®; Nebraska, US).
Flow cytometry:
Cells from the SVF were divided into two tubes, and additionally 250,000 PBMCs were place into two additional tubes. For each, one tube contained a T cell panel and the other a monocyte/ macrophage panel. The T cell panels contained the following fluorophore-conjugated antibodies: cluster of differentiation (CD)3–V450 clone UCHT1, CD4–APC clone RPA–T4, CD8–PerCP Sk1, CD45RA–FITC clone HI100, CD27–PE clone MT271, CD45–BV510 clone HI30, and human leukocyte antigen–DR isotype (HLA–DR)–APC-Cy7 clone L243. The monocyte/ macrophage tubes comprised of the following fluorophore-conjugated antibodies; CD14–PE Vio770 clone TUK4, CD16–FITC clone 3G8, CD206–APC clone 19.2, HLA-DR–APC Cy7 clone L243, and CD45–BV510 clone HI30. Isotype control antibodies for CD206 (APC–IgG1, κ clone MOPC-21) and HLA-DR (APC Cy7 IgG2a, κ clone G155-178) were used with PBMCs and informed the gating strategy for both PBMCs and SVF. Endothelial/ progenitor cells were identified in the SVF using the following fluorophore-conjugated antibodies; CD31–V450 clone WM59, CD34–PerCP clone 8G12, and MSCA-1–PE clone W8B2. Gating strategies are detailed in Supplementary Figure 3–5. Absolute cell counts were obtained using counting beads (100 µL of Perfect-Count Microspheres; Cytognos, Spain), according to manufacturer instructions. Flow cytometry was performed on a FACS Canto II (Becton Dickenson; Oxford, UK) and results analysed using FlowJo v.10.
Biochemical analysis of plasma, serum, and ex vivo adipose tissue supernatants:
Fasted plasma insulin was measured by ELISA (Mercodia, Mercodia AB; Sweden). A further 32 biomarkers were measured in fasted plasma and adipose cell culture supernatant using multiplex assays (R-plex, U-plex and V-plex kits on a QuickPlex SQ120; Mesoscale Diagnostics, LLC; Maryland, US). Biomarkers included MCP-1, MIP-1α, MIP-1β, RANTES, MIP-3α, IP-10, GM-CSF, IFN-γ, TNF-α, IL-1β, IL-4, IL-6, IL-10, IL-13, IL-15, IL-17A, IL-17B, IL-17C, IL17-D, ICAM-1, VCAM-1, SAA, VEGF-A, VEGF-D, granzyme-A, FGF-21, leptin, adiponectin, resistin, adipsin, and osteopontin. Ex vivo Leptin release by adipose tissue was assessed using ELISA kit (Quantikine, Bio-Techne Ltd; France). Blood glucose was measured using fresh whole-blood samples at the MEDES clinic using an automated analyser (Architect C8000; Abbott, CA) four days prior to the start of bed rest and following 49 days of bed rest.
Statistical analysis:
The influence of the nutritional countermeasure on the effect of bed rest was analysed by two-way repeated measures ANOVA. Pre- to post-bed rest comparisons for the whole cohort (groups collapsed) were assessed using paired samples t-tests where data were normally distributed, and Wilcoxon signed rank tests where not normally distributed (Shapiro Wilks: p > 0.05). Linear regression analysis was performed using Pearson’s r. Descriptive data are presented as mean ± standard deviation (SD), unless otherwise stated. Statistical analysis was performed using GraphPad Prism v.8.0.0 for Windows (GraphPad Software; California, US) and SPSS v.22 (IBM Corp.; New York, US). Significance was set at p ≤ 0.05.Participants are labelled with their respective campaign codes, A-J and either 1 or 2 depending on whether they participated in campaign 1 or 2 of the study, respectively. Participants are also labelled as Control or Cocktail depending on whether they were part of the control or nutritional intervention group, respectively
Data from the parametric analysis of masonry pointed arches with limit analysis subjected to vertical self-weight plus a vertical concentrated live load
No full textFor each one of the simulations performed from the parametric analysis of masonry pointed arches with limit analysis, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry panel. Finally, the .png file presents the collapse mechanism obtained
Dataset for "Development of Methodology to Investigate the Surface SMALPome of Mammalian Cells"
No full textThe purpose behind obtaining this data was to determine whether there were any differences between biotinylated surface membrane proteins that were extracted using styrene maleic acid (SMA), compared to detergent buffer (SDS/Sodium deoxycholate/NP-40). Here, Excel spreadsheets which contain raw datasets from mass-spectroscopy proteomics are present. Subsequent datasets are also included, following filters described in "Development of methodology to investigate the surface SMALPome of mammalian cells".Samples were generated as described in "Development of methodology to investigate the surface SMALPome of mammalian cells." In summary, 3T3-L1 mouse fibroblasts were surface biotinylated prior to extraction with either styrene maleic acid (SMA) or detergent buffer control (SDS/Sodium deoxycholate/NP-40). Purification of biotinylated surface membrane proteins using NeutrAvidin beads was conducted, prior to three different wash protocols.
Samples underwent nano-LC MS/MS with further proteomic analysis to determine differences between proteins extracted using SMA and detergent control.
The raw data files were processed and quantified using Proteome Discoverer software v2.1 (Thermo Scientific) and searched against the UniProt Mouse database (downloaded February 2020; 83561 sequences) using the SEQUEST HT algorithm.
The outputs from the Proteome Discoverer were filtered to identify transmembrane proteins and proteins that have a signal sequence. Firstly, any non-mouse contaminants were removed (i.e. contaminants = TRUE) from the data sets. Next, proteins with ≤ 1 unique peptide were removed from the data sets. The filtered data sets were compared with mouse proteins (86521 proteins) in the Uniprot database.Uniprot lists for mouse protein searches;
To identify integral membrane proteins: Organism [OS], Mus musculus (mouse) AND Keyword [KW] Transmembrane helix (18359 proteins, downloaded December 2020).
To identify proteins with a signal sequence but not a transmembrane domain: Organism [OS] mus musculus (mouse) AND PTM Processing>molecule processing.signal peptide, NOT Keyword [KW] transmembrane helix (6683 proteins, downloaded April 2021).
To identify GPI anchor proteins associated with lipid rafts: keyword:"GPI-anchor [KW-0336]" AND organism:"Mus musculus (Mouse) [10090]" (226 proteins, downloaded July 2021).
To identify mitochondrial transmembrane proteins: keyword:"Transmembrane [KW-0812]" keyword:"Mitochondrion [KW-0496]" AND organism:"Mus musculus (Mouse) [10090]" (810 proteins, downloaded June 2021).Surface biotinylation following the protocol from the Pierce™ Cell Surface Protein Isolation Kit (#89881).
Analysis of peptides by nano-LC MSMS using an Ultimate 3000 nano-LC system in line with an Orbitrap Fusion Tribrid mass spectrometer (Thermo Scientific) controlled by Xcalibur 3.0 software (Thermo Scientific) and operated in data-dependent acquisition mode.Supplementary table 1:
Contains the raw datasets and subsequent filters, legends are supplied for each tab to describe the dataset.
Supplementary table 2:
Contains datasets referring to mitochondrial membrane protein comparisons. Legends are supplied to further describe the dataset.
Supplementary table 3:
Contains datasets referring to Glycosylphosphatidylinositol (GPI) anchour protein comparisons. Legends are supplied to further describe the dataset
Dataset for "Observing the suppression of superconductivity in RbEuFe4As4 by correlated magnetic fluctuations"
No full textThe data was acquired by scanning Hall microscopy (SHM) in the rapid 'flying' mode that makes a rapid 2D magnetic scan of the maximum field of view. The data files consist of individual SHM images collected during these scans along with the appropriate text files for their averages.
Transport data, performed at Bath, and magnetization data, performed at Argonne National Laboratory, for figure 1 are also included.Magnetic images were obtained by scanning Hall probe microscopy. This involves rastering a GaAs Hall probe located in close proximity to a scanning tunnelling microscope tip over the sample of interest via a piezoelectric tube. At each step the Hall voltage is recorded and converted into a magnetic field via a known Hall co-efficient, thus producing the magnetic map.
Electronic transport measurements were performed by attaching gold wires with silver epoxy in a standard 5-lead Hall bar configuration. The in-plane resistivity was then measured as a function of temperature in a Quantum Design MPMS-7 system at Argonne National Labs.The SHM image data in the archive are raw as-captured data without any post-processing.SHM image datasets are formatted as the magnetic induction in Gauss measured at each point on a 1 128× 128 array of pixel positions. At the measurement temperatures of 35K, 30K, 25K, 20K, 17.5K, 16.25K, 15K, 13.75K, 12.5K, 10K, 7.5K and 5K. The scan area changes between temperatures with the following fitting:
Area=(-1.037e-08*Temperature^4 + 5.547e-06*Temperature^3 + -0.0009229*Temperature^2 + 0.2264*Temperature + 6.483
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